data analysis in quantitative research article

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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75 Comments

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

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A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

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Research Article

Recent quantitative research on determinants of health in high income countries: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

• Vladimira Varbanova, 
• Philippe Beutels

• Published: September 17, 2020
• https://doi.org/10.1371/journal.pone.0239031
• Peer Review
• Reader Comments

Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.

We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013—July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.

Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.

Conclusions

We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants’ influence.

Citation: Varbanova V, Beutels P (2020) Recent quantitative research on determinants of health in high income countries: A scoping review. PLoS ONE 15(9): e0239031. https://doi.org/10.1371/journal.pone.0239031

Editor: Amir Radfar, University of Central Florida, UNITED STATES

Received: November 14, 2019; Accepted: August 28, 2020; Published: September 17, 2020

Copyright: © 2020 Varbanova, Beutels. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study (and VV) is funded by the Research Foundation Flanders ( https://www.fwo.be/en/ ), FWO project number G0D5917N, award obtained by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Identifying the key drivers of population health is a core subject in public health and health economics research. Between-country comparative research on the topic is challenging. In order to be relevant for policy, it requires disentangling different interrelated drivers of “good health”, each having different degrees of importance in different contexts.

“Good health”–physical and psychological, subjective and objective–can be defined and measured using a variety of approaches, depending on which aspect of health is the focus. A major distinction can be made between health measurements at the individual level or some aggregate level, such as a neighborhood, a region or a country. In view of this, a great diversity of specific research topics exists on the drivers of what constitutes individual or aggregate “good health”, including those focusing on health inequalities, the gender gap in longevity, and regional mortality and longevity differences.

The current scoping review focuses on determinants of population health. Stated as such, this topic is quite broad. Indeed, we are interested in the very general question of what methods have been used to make the most of increasingly available region or country-specific databases to understand the drivers of population health through inter-country comparisons. Existing reviews indicate that researchers thus far tend to adopt a narrower focus. Usually, attention is given to only one health outcome at a time, with further geographical and/or population [ 1 , 2 ] restrictions. In some cases, the impact of one or more interventions is at the core of the review [ 3 – 7 ], while in others it is the relationship between health and just one particular predictor, e.g., income inequality, access to healthcare, government mechanisms [ 8 – 13 ]. Some relatively recent reviews on the subject of social determinants of health [ 4 – 6 , 14 – 17 ] have considered a number of indicators potentially influencing health as opposed to a single one. One review defines “social determinants” as “the social, economic, and political conditions that influence the health of individuals and populations” [ 17 ] while another refers even more broadly to “the factors apart from medical care” [ 15 ].

In the present work, we aimed to be more inclusive, setting no limitations on the nature of possible health correlates, as well as making use of a multitude of commonly accepted measures of general population health. The goal of this scoping review was to document the state of the art in the recent published literature on determinants of population health, with a particular focus on the types of determinants selected and the methodology used. In doing so, we also report the main characteristics of the results these studies found. The materials collected in this review are intended to inform our (and potentially other researchers’) future analyses on this topic. Since the production of health is subject to the law of diminishing marginal returns, we focused our review on those studies that included countries where a high standard of wealth has been achieved for some time, i.e., high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD) or Europe. Adding similar reviews for other country income groups is of limited interest to the research we plan to do in this area.

In view of its focus on data and methods, rather than results, a formal protocol was not registered prior to undertaking this review, but the procedure followed the guidelines of the PRISMA statement for scoping reviews [ 18 ].

We focused on multi-country studies investigating the potential associations between any aggregate level (region/city/country) determinant and general measures of population health (e.g., life expectancy, mortality rate).

Within the query itself, we listed well-established population health indicators as well as the six world regions, as defined by the World Health Organization (WHO). We searched only in the publications’ titles in order to keep the number of hits manageable, and the ratio of broadly relevant abstracts over all abstracts in the order of magnitude of 10% (based on a series of time-focused trial runs). The search strategy was developed iteratively between the two authors and is presented in S1 Appendix . The search was performed by VV in PubMed and Web of Science on the 16 th of July, 2019, without any language restrictions, and with a start date set to the 1 st of January, 2013, as we were interested in the latest developments in this area of research.

Eligibility criteria

Records obtained via the search methods described above were screened independently by the two authors. Consistency between inclusion/exclusion decisions was approximately 90% and the 43 instances where uncertainty existed were judged through discussion. Articles were included subject to meeting the following requirements: (a) the paper was a full published report of an original empirical study investigating the impact of at least one aggregate level (city/region/country) factor on at least one health indicator (or self-reported health) of the general population (the only admissible “sub-populations” were those based on gender and/or age); (b) the study employed statistical techniques (calculating correlations, at the very least) and was not purely descriptive or theoretical in nature; (c) the analysis involved at least two countries or at least two regions or cities (or another aggregate level) in at least two different countries; (d) the health outcome was not differentiated according to some socio-economic factor and thus studied in terms of inequality (with the exception of gender and age differentiations); (e) mortality, in case it was one of the health indicators under investigation, was strictly “total” or “all-cause” (no cause-specific or determinant-attributable mortality).

Data extraction

The following pieces of information were extracted in an Excel table from the full text of each eligible study (primarily by VV, consulting with PB in case of doubt): health outcome(s), determinants, statistical methodology, level of analysis, results, type of data, data sources, time period, countries. The evidence is synthesized according to these extracted data (often directly reflected in the section headings), using a narrative form accompanied by a “summary-of-findings” table and a graph.

Search and selection

The initial yield contained 4583 records, reduced to 3686 after removal of duplicates ( Fig 1 ). Based on title and abstract screening, 3271 records were excluded because they focused on specific medical condition(s) or specific populations (based on morbidity or some other factor), dealt with intervention effectiveness, with theoretical or non-health related issues, or with animals or plants. Of the remaining 415 papers, roughly half were disqualified upon full-text consideration, mostly due to using an outcome not of interest to us (e.g., health inequality), measuring and analyzing determinants and outcomes exclusively at the individual level, performing analyses one country at a time, employing indices that are a mixture of both health indicators and health determinants, or not utilizing potential health determinants at all. After this second stage of the screening process, 202 papers were deemed eligible for inclusion. This group was further dichotomized according to level of economic development of the countries or regions under study, using membership of the OECD or Europe as a reference “cut-off” point. Sixty papers were judged to include high-income countries, and the remaining 142 included either low- or middle-income countries or a mix of both these levels of development. The rest of this report outlines findings in relation to high-income countries only, reflecting our own primary research interests. Nonetheless, we chose to report our search yield for the other income groups for two reasons. First, to gauge the relative interest in applied published research for these different income levels; and second, to enable other researchers with a focus on determinants of health in other countries to use the extraction we made here.

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https://doi.org/10.1371/journal.pone.0239031.g001

Health outcomes

The most frequent population health indicator, life expectancy (LE), was present in 24 of the 60 studies. Apart from “life expectancy at birth” (representing the average life-span a newborn is expected to have if current mortality rates remain constant), also called “period LE” by some [ 19 , 20 ], we encountered as well LE at 40 years of age [ 21 ], at 60 [ 22 ], and at 65 [ 21 , 23 , 24 ]. In two papers, the age-specificity of life expectancy (be it at birth or another age) was not stated [ 25 , 26 ].

Some studies considered male and female LE separately [ 21 , 24 , 25 , 27 – 33 ]. This consideration was also often observed with the second most commonly used health index [ 28 – 30 , 34 – 38 ]–termed “total”, or “overall”, or “all-cause”, mortality rate (MR)–included in 22 of the 60 studies. In addition to gender, this index was also sometimes broken down according to age group [ 30 , 39 , 40 ], as well as gender-age group [ 38 ].

While the majority of studies under review here focused on a single health indicator, 23 out of the 60 studies made use of multiple outcomes, although these outcomes were always considered one at a time, and sometimes not all of them fell within the scope of our review. An easily discernable group of indices that typically went together [ 25 , 37 , 41 ] was that of neonatal (deaths occurring within 28 days postpartum), perinatal (fetal or early neonatal / first-7-days deaths), and post-neonatal (deaths between the 29 th day and completion of one year of life) mortality. More often than not, these indices were also accompanied by “stand-alone” indicators, such as infant mortality (deaths within the first year of life; our third most common index found in 16 of the 60 studies), maternal mortality (deaths during pregnancy or within 42 days of termination of pregnancy), and child mortality rates. Child mortality has conventionally been defined as mortality within the first 5 years of life, thus often also called “under-5 mortality”. Nonetheless, Pritchard & Wallace used the term “child mortality” to denote deaths of children younger than 14 years [ 42 ].

As previously stated, inclusion criteria did allow for self-reported health status to be used as a general measure of population health. Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [ 25 , 43 – 48 ]. Additionally, the Health Human Development Index [ 49 ], healthy life expectancy [ 50 ], old-age survival [ 51 ], potential years of life lost [ 52 ], and disability-adjusted life expectancy [ 25 ] were also used.

We note that while in most cases the indicators mentioned above (and/or the covariates considered, see below) were taken in their absolute or logarithmic form, as a—typically annual—number, sometimes they were used in the form of differences, change rates, averages over a given time period, or even z-scores of rankings [ 19 , 22 , 40 , 42 , 44 , 53 – 57 ].

Regions, countries, and populations

Despite our decision to confine this review to high-income countries, some variation in the countries and regions studied was still present. Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations–based on the instances where these were also explicitly listed—were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [ 30 , 45 , 54 ] or inconsistency [ 20 , 58 ], Gross Domestic Product (GDP) too low [ 40 ], differences in economic development and political stability with the rest of the sampled countries [ 59 ], and national population too small [ 24 , 40 ]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [ 60 ], similar healthcare systems [ 23 ], and being randomly drawn from a social spending category [ 61 ]. Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [ 50 ], Central and Eastern Europe [ 48 , 60 ], the Visegrad (V4) group [ 62 ], or the Asia/Pacific area [ 32 ]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [ 31 , 51 , 56 , 62 – 66 ]. In two particular cases, a mix of countries and cities was used [ 35 , 57 ]. In another two [ 28 , 29 ], due to the long time periods under study, some of the included countries no longer exist. Finally, besides “European” and “OECD”, the terms “developed”, “Western”, and “industrialized” were also used to describe the group of selected nations [ 30 , 42 , 52 , 53 , 67 ].

As stated above, it was the health status of the general population that we were interested in, and during screening we made a concerted effort to exclude research using data based on a more narrowly defined group of individuals. All studies included in this review adhere to this general rule, albeit with two caveats. First, as cities (even neighborhoods) were the unit of analysis in three of the studies that made the selection [ 56 , 64 , 65 ], the populations under investigation there can be more accurately described as general urban , instead of just general. Second, oftentimes health indicators were stratified based on gender and/or age, therefore we also admitted one study that, due to its specific research question, focused on men and women of early retirement age [ 35 ] and another that considered adult males only [ 68 ].

Data types and sources

A great diversity of sources was utilized for data collection purposes. The accessible reference databases of the OECD ( https://www.oecd.org/ ), WHO ( https://www.who.int/ ), World Bank ( https://www.worldbank.org/ ), United Nations ( https://www.un.org/en/ ), and Eurostat ( https://ec.europa.eu/eurostat ) were among the top choices. The other international databases included Human Mortality [ 30 , 39 , 50 ], Transparency International [ 40 , 48 , 50 ], Quality of Government [ 28 , 69 ], World Income Inequality [ 30 ], International Labor Organization [ 41 ], International Monetary Fund [ 70 ]. A number of national databases were referred to as well, for example the US Bureau of Statistics [ 42 , 53 ], Korean Statistical Information Services [ 67 ], Statistics Canada [ 67 ], Australian Bureau of Statistics [ 67 ], and Health New Zealand Tobacco control and Health New Zealand Food and Nutrition [ 19 ]. Well-known surveys, such as the World Values Survey [ 25 , 55 ], the European Social Survey [ 25 , 39 , 44 ], the Eurobarometer [ 46 , 56 ], the European Value Survey [ 25 ], and the European Statistics of Income and Living Condition Survey [ 43 , 47 , 70 ] were used as data sources, too. Finally, in some cases [ 25 , 28 , 29 , 35 , 36 , 41 , 69 ], built-for-purpose datasets from previous studies were re-used.

In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [ 31 , 62 , 63 , 66 ], otherwise defined areas [ 51 ] or cities [ 56 ], and seven others that were multilevel designs and utilized both country- and region-level data [ 57 ], individual- and city- or country-level [ 35 ], individual- and country-level [ 44 , 45 , 48 ], individual- and neighborhood-level [ 64 ], and city-region- (NUTS3) and country-level data [ 65 ]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [ 25 , 33 , 43 , 45 – 48 , 50 , 62 , 67 , 68 , 71 , 72 ], albeit in four of those [ 43 , 48 , 68 , 72 ] two separate points in time were taken (thus resulting in a kind of “double cross-section”), while in another the averages across survey waves were used [ 56 ].

In studies using longitudinal data, the length of the covered time periods varied greatly. Although this was almost always less than 40 years, in one study it covered the entire 20 th century [ 29 ]. Longitudinal data, typically in the form of annual records, was sometimes transformed before usage. For example, some researchers considered data points at 5- [ 34 , 36 , 49 ] or 10-year [ 27 , 29 , 35 ] intervals instead of the traditional 1, or took averages over 3-year periods [ 42 , 53 , 73 ]. In one study concerned with the effect of the Great Recession all data were in a “recession minus expansion change in trends”-form [ 57 ]. Furthermore, there were a few instances where two different time periods were compared to each other [ 42 , 53 ] or when data was divided into 2 to 4 (possibly overlapping) periods which were then analyzed separately [ 24 , 26 , 28 , 29 , 31 , 65 ]. Lastly, owing to data availability issues, discrepancies between the time points or periods of data on the different variables were occasionally observed [ 22 , 35 , 42 , 53 – 55 , 63 ].

Health determinants

Together with other essential details, Table 1 lists the health correlates considered in the selected studies. Several general categories for these correlates can be discerned, including health care, political stability, socio-economics, demographics, psychology, environment, fertility, life-style, culture, labor. All of these, directly or implicitly, have been recognized as holding importance for population health by existing theoretical models of (social) determinants of health [ 74 – 77 ].

https://doi.org/10.1371/journal.pone.0239031.t001

It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [ 44 ], in another–welfare system typology [ 45 ], but also gender inequality [ 33 ], austerity level [ 70 , 78 ], and deprivation [ 51 ]. Most often though, attention went exclusively to GDP [ 27 , 29 , 46 , 57 , 65 , 71 ]. It was often the case that research had a more particular focus. Among others, minimum wages [ 79 ], hospital payment schemes [ 23 ], cigarette prices [ 63 ], social expenditure [ 20 ], residents’ dissatisfaction [ 56 ], income inequality [ 30 , 69 ], and work leave [ 41 , 58 ] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators.

We visualized the combinations in which the different determinants have been studied in Fig 2 , which was obtained via multidimensional scaling and a subsequent cluster analysis (details outlined in S2 Appendix ). It depicts the spatial positioning of each determinant relative to all others, based on the number of times the effects of each pair of determinants have been studied simultaneously. When interpreting Fig 2 , one should keep in mind that determinants marked with an asterisk represent, in fact, collectives of variables.

Groups of determinants are marked by asterisks (see S1 Table in S1 Appendix ). Diminishing color intensity reflects a decrease in the total number of “connections” for a given determinant. Noteworthy pairwise “connections” are emphasized via lines (solid-dashed-dotted indicates decreasing frequency). Grey contour lines encircle groups of variables that were identified via cluster analysis. Abbreviations: age = population age distribution, associations = membership in associations, AT-index = atherogenic-thrombogenic index, BR = birth rate, CAPB = Cyclically Adjusted Primary Balance, civilian-labor = civilian labor force, C-section = Cesarean delivery rate, credit-info = depth of credit information, dissatisf = residents’ dissatisfaction, distrib.orient = distributional orientation, EDU = education, eHealth = eHealth index at GP-level, exch.rate = exchange rate, fat = fat consumption, GDP = gross domestic product, GFCF = Gross Fixed Capital Formation/Creation, GH-gas = greenhouse gas, GII = gender inequality index, gov = governance index, gov.revenue = government revenues, HC-coverage = healthcare coverage, HE = health(care) expenditure, HHconsump = household consumption, hosp.beds = hospital beds, hosp.payment = hospital payment scheme, hosp.stay = length of hospital stay, IDI = ICT development index, inc.ineq = income inequality, industry-labor = industrial labor force, infant-sex = infant sex ratio, labor-product = labor production, LBW = low birth weight, leave = work leave, life-satisf = life satisfaction, M-age = maternal age, marginal-tax = marginal tax rate, MDs = physicians, mult.preg = multiple pregnancy, NHS = Nation Health System, NO = nitrous oxide emissions, PM10 = particulate matter (PM10) emissions, pop = population size, pop.density = population density, pre-term = pre-term birth rate, prison = prison population, researchE = research&development expenditure, school.ref = compulsory schooling reform, smoke-free = smoke-free places, SO = sulfur oxide emissions, soc.E = social expenditure, soc.workers = social workers, sugar = sugar consumption, terror = terrorism, union = union density, UR = unemployment rate, urban = urbanization, veg-fr = vegetable-and-fruit consumption, welfare = welfare regime, Wwater = wastewater treatment.

https://doi.org/10.1371/journal.pone.0239031.g002

Distances between determinants in Fig 2 are indicative of determinants’ “connectedness” with each other. While the statistical procedure called for higher dimensionality of the model, for demonstration purposes we show here a two-dimensional solution. This simplification unfortunately comes with a caveat. To use the factor smoking as an example, it would appear it stands at a much greater distance from GDP than it does from alcohol. In reality however, smoking was considered together with alcohol consumption [ 21 , 25 , 26 , 52 , 68 ] in just as many studies as it was with GDP [ 21 , 25 , 26 , 52 , 59 ], five. To aid with respect to this apparent shortcoming, we have emphasized the strongest pairwise links. Solid lines connect GDP with health expenditure (HE), unemployment rate (UR), and education (EDU), indicating that the effect of GDP on health, taking into account the effects of the other three determinants as well, was evaluated in between 12 to 16 studies of the 60 included in this review. Tracing the dashed lines, we can also tell that GDP appeared jointly with income inequality, and HE together with either EDU or UR, in anywhere between 8 to 10 of our selected studies. Finally, some weaker but still worth-mentioning “connections” between variables are displayed as well via the dotted lines.

The fact that all notable pairwise “connections” are concentrated within a relatively small region of the plot may be interpreted as low overall “connectedness” among the health indicators studied. GDP is the most widely investigated determinant in relation to general population health. Its total number of “connections” is disproportionately high (159) compared to its runner-up–HE (with 113 “connections”), and then subsequently EDU (with 90) and UR (with 86). In fact, all of these determinants could be thought of as outliers, given that none of the remaining factors have a total count of pairings above 52. This decrease in individual determinants’ overall “connectedness” can be tracked on the graph via the change of color intensity as we move outwards from the symbolic center of GDP and its closest “co-determinants”, to finally reach the other extreme of the ten indicators (welfare regime, household consumption, compulsory school reform, life satisfaction, government revenues, literacy, research expenditure, multiple pregnancy, Cyclically Adjusted Primary Balance, and residents’ dissatisfaction; in white) the effects on health of which were only studied in isolation.

Lastly, we point to the few small but stable clusters of covariates encircled by the grey bubbles on Fig 2 . These groups of determinants were identified as “close” by both statistical procedures used for the production of the graph (see details in S2 Appendix ).

Statistical methodology

There was great variation in the level of statistical detail reported. Some authors provided too vague a description of their analytical approach, necessitating some inference in this section.

The issue of missing data is a challenging reality in this field of research, but few of the studies under review (12/60) explain how they dealt with it. Among the ones that do, three general approaches to handling missingness can be identified, listed in increasing level of sophistication: case-wise deletion, i.e., removal of countries from the sample [ 20 , 45 , 48 , 58 , 59 ], (linear) interpolation [ 28 , 30 , 34 , 58 , 59 , 63 ], and multiple imputation [ 26 , 41 , 52 ].

Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [ 33 , 42 – 44 , 46 , 53 , 57 , 61 ]. Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, “unique” within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [ 39 ]; difference-in-difference (DiD) analysis was applied in one case [ 23 ]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [ 80 ]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [ 70 , 78 ]; hierarchical Bayesian spatial models [ 51 ] and special autoregressive regression [ 62 ] were also implemented.

Purely cross-sectional data analyses were performed in eight studies [ 25 , 45 , 47 , 50 , 55 , 56 , 67 , 71 ]. These consisted of linear regression (assumed ordinary least squares (OLS)), generalized least squares (GLS) regression, and multilevel analyses. However, six other studies that used longitudinal data in fact had a cross-sectional design, through which they applied regression at multiple time-points separately [ 27 , 29 , 36 , 48 , 68 , 72 ].

Apart from these “multi-point cross-sectional studies”, some other simplistic approaches to longitudinal data analysis were found, involving calculating and regressing 3-year averages of both the response and the predictor variables [ 54 ], taking the average of a few data-points (i.e., survey waves) [ 56 ] or using difference scores over 10-year [ 19 , 29 ] or unspecified time intervals [ 40 , 55 ].

Moving further in the direction of more sensible longitudinal data usage, we turn to the methods widely known among (health) economists as “panel data analysis” or “panel regression”. Most often seen were models with fixed effects for country/region and sometimes also time-point (occasionally including a country-specific trend as well), with robust standard errors for the parameter estimates to take into account correlations among clustered observations [ 20 , 21 , 24 , 28 , 30 , 32 , 34 , 37 , 38 , 41 , 52 , 59 , 60 , 63 , 66 , 69 , 73 , 79 , 81 , 82 ]. The Hausman test [ 83 ] was sometimes mentioned as the tool used to decide between fixed and random effects [ 26 , 49 , 63 , 66 , 73 , 82 ]. A few studies considered the latter more appropriate for their particular analyses, with some further specifying that (feasible) GLS estimation was employed [ 26 , 34 , 49 , 58 , 60 , 73 ]. Apart from these two types of models, the first differences method was encountered once as well [ 31 ]. Across all, the error terms were sometimes assumed to come from a first-order autoregressive process (AR(1)), i.e., they were allowed to be serially correlated [ 20 , 30 , 38 , 58 – 60 , 73 ], and lags of (typically) predictor variables were included in the model specification, too [ 20 , 21 , 37 , 38 , 48 , 69 , 81 ]. Lastly, a somewhat different approach to longitudinal data analysis was undertaken in four studies [ 22 , 35 , 48 , 65 ] in which multilevel–linear or Poisson–models were developed.

Regardless of the exact techniques used, most studies included in this review presented multiple model applications within their main analysis. None attempted to formally compare models in order to identify the “best”, even if goodness-of-fit statistics were occasionally reported. As indicated above, many studies investigated women’s and men’s health separately [ 19 , 21 , 22 , 27 – 29 , 31 , 33 , 35 , 36 , 38 , 39 , 45 , 50 , 51 , 64 , 65 , 69 , 82 ], and covariates were often tested one at a time, including other covariates only incrementally [ 20 , 25 , 28 , 36 , 40 , 50 , 55 , 67 , 73 ]. Furthermore, there were a few instances where analyses within countries were performed as well [ 32 , 39 , 51 ] or where the full time period of interest was divided into a few sub-periods [ 24 , 26 , 28 , 31 ]. There were also cases where different statistical techniques were applied in parallel [ 29 , 55 , 60 , 66 , 69 , 73 , 82 ], sometimes as a form of sensitivity analysis [ 24 , 26 , 30 , 58 , 73 ]. However, the most common approach to sensitivity analysis was to re-run models with somewhat different samples [ 39 , 50 , 59 , 67 , 69 , 80 , 82 ]. Other strategies included different categorization of variables or adding (more/other) controls [ 21 , 23 , 25 , 28 , 37 , 50 , 63 , 69 ], using an alternative main covariate measure [ 59 , 82 ], including lags for predictors or outcomes [ 28 , 30 , 58 , 63 , 65 , 79 ], using weights [ 24 , 67 ] or alternative data sources [ 37 , 69 ], or using non-imputed data [ 41 ].

As the methods and not the findings are the main focus of the current review, and because generic checklists cannot discern the underlying quality in this application field (see also below), we opted to pool all reported findings together, regardless of individual study characteristics or particular outcome(s) used, and speak generally of positive and negative effects on health. For this summary we have adopted the 0.05-significance level and only considered results from multivariate analyses. Strictly birth-related factors are omitted since these potentially only relate to the group of infant mortality indicators and not to any of the other general population health measures.

Starting with the determinants most often studied, higher GDP levels [ 21 , 26 , 27 , 29 , 30 , 32 , 43 , 48 , 52 , 58 , 60 , 66 , 67 , 73 , 79 , 81 , 82 ], higher health [ 21 , 37 , 47 , 49 , 52 , 58 , 59 , 68 , 72 , 82 ] and social [ 20 , 21 , 26 , 38 , 79 ] expenditures, higher education [ 26 , 39 , 52 , 62 , 72 , 73 ], lower unemployment [ 60 , 61 , 66 ], and lower income inequality [ 30 , 42 , 53 , 55 , 73 ] were found to be significantly associated with better population health on a number of occasions. In addition to that, there was also some evidence that democracy [ 36 ] and freedom [ 50 ], higher work compensation [ 43 , 79 ], distributional orientation [ 54 ], cigarette prices [ 63 ], gross national income [ 22 , 72 ], labor productivity [ 26 ], exchange rates [ 32 ], marginal tax rates [ 79 ], vaccination rates [ 52 ], total fertility [ 59 , 66 ], fruit and vegetable [ 68 ], fat [ 52 ] and sugar consumption [ 52 ], as well as bigger depth of credit information [ 22 ] and percentage of civilian labor force [ 79 ], longer work leaves [ 41 , 58 ], more physicians [ 37 , 52 , 72 ], nurses [ 72 ], and hospital beds [ 79 , 82 ], and also membership in associations, perceived corruption and societal trust [ 48 ] were beneficial to health. Higher nitrous oxide (NO) levels [ 52 ], longer average hospital stay [ 48 ], deprivation [ 51 ], dissatisfaction with healthcare and the social environment [ 56 ], corruption [ 40 , 50 ], smoking [ 19 , 26 , 52 , 68 ], alcohol consumption [ 26 , 52 , 68 ] and illegal drug use [ 68 ], poverty [ 64 ], higher percentage of industrial workers [ 26 ], Gross Fixed Capital creation [ 66 ] and older population [ 38 , 66 , 79 ], gender inequality [ 22 ], and fertility [ 26 , 66 ] were detrimental.

It is important to point out that the above-mentioned effects could not be considered stable either across or within studies. Very often, statistical significance of a given covariate fluctuated between the different model specifications tried out within the same study [ 20 , 49 , 59 , 66 , 68 , 69 , 73 , 80 , 82 ], testifying to the importance of control variables and multivariate research (i.e., analyzing multiple independent variables simultaneously) in general. Furthermore, conflicting results were observed even with regards to the “core” determinants given special attention, so to speak, throughout this text. Thus, some studies reported negative effects of health expenditure [ 32 , 82 ], social expenditure [ 58 ], GDP [ 49 , 66 ], and education [ 82 ], and positive effects of income inequality [ 82 ] and unemployment [ 24 , 31 , 32 , 52 , 66 , 68 ]. Interestingly, one study [ 34 ] differentiated between temporary and long-term effects of GDP and unemployment, alluding to possibly much greater complexity of the association with health. It is also worth noting that some gender differences were found, with determinants being more influential for males than for females, or only having statistically significant effects for male health [ 19 , 21 , 28 , 34 , 36 , 37 , 39 , 64 , 65 , 69 ].

The purpose of this scoping review was to examine recent quantitative work on the topic of multi-country analyses of determinants of population health in high-income countries.

Measuring population health via relatively simple mortality-based indicators still seems to be the state of the art. What is more, these indicators are routinely considered one at a time, instead of, for example, employing existing statistical procedures to devise a more general, composite, index of population health, or using some of the established indices, such as disability-adjusted life expectancy (DALE) or quality-adjusted life expectancy (QALE). Although strong arguments for their wider use were already voiced decades ago [ 84 ], such summary measures surface only rarely in this research field.

On a related note, the greater data availability and accessibility that we enjoy today does not automatically equate to data quality. Nonetheless, this is routinely assumed in aggregate level studies. We almost never encountered a discussion on the topic. The non-mundane issue of data missingness, too, goes largely underappreciated. With all recent methodological advancements in this area [ 85 – 88 ], there is no excuse for ignorance; and still, too few of the reviewed studies tackled the matter in any adequate fashion.

Much optimism can be gained considering the abundance of different determinants that have attracted researchers’ attention in relation to population health. We took on a visual approach with regards to these determinants and presented a graph that links spatial distances between determinants with frequencies of being studies together. To facilitate interpretation, we grouped some variables, which resulted in some loss of finer detail. Nevertheless, the graph is helpful in exemplifying how many effects continue to be studied in a very limited context, if any. Since in reality no factor acts in isolation, this oversimplification practice threatens to render the whole exercise meaningless from the outset. The importance of multivariate analysis cannot be stressed enough. While there is no “best method” to be recommended and appropriate techniques vary according to the specifics of the research question and the characteristics of the data at hand [ 89 – 93 ], in the future, in addition to abandoning simplistic univariate approaches, we hope to see a shift from the currently dominating fixed effects to the more flexible random/mixed effects models [ 94 ], as well as wider application of more sophisticated methods, such as principle component regression, partial least squares, covariance structure models (e.g., structural equations), canonical correlations, time-series, and generalized estimating equations.

Finally, there are some limitations of the current scoping review. We searched the two main databases for published research in medical and non-medical sciences (PubMed and Web of Science) since 2013, thus potentially excluding publications and reports that are not indexed in these databases, as well as older indexed publications. These choices were guided by our interest in the most recent (i.e., the current state-of-the-art) and arguably the highest-quality research (i.e., peer-reviewed articles, primarily in indexed non-predatory journals). Furthermore, despite holding a critical stance with regards to some aspects of how determinants-of-health research is currently conducted, we opted out of formally assessing the quality of the individual studies included. The reason for that is two-fold. On the one hand, we are unaware of the existence of a formal and standard tool for quality assessment of ecological designs. And on the other, we consider trying to score the quality of these diverse studies (in terms of regional setting, specific topic, outcome indices, and methodology) undesirable and misleading, particularly since we would sometimes have been rating the quality of only a (small) part of the original studies—the part that was relevant to our review’s goal.

Our aim was to investigate the current state of research on the very broad and general topic of population health, specifically, the way it has been examined in a multi-country context. We learned that data treatment and analytical approach were, in the majority of these recent studies, ill-equipped or insufficiently transparent to provide clarity regarding the underlying mechanisms of population health in high-income countries. Whether due to methodological shortcomings or the inherent complexity of the topic, research so far fails to provide any definitive answers. It is our sincere belief that with the application of more advanced analytical techniques this continuous quest could come to fruition sooner.

Supporting information

S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0239031.s001

S1 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s002

S2 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s003

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This article has a correction. Please see:

• Correction: How to appraise quantitative research - April 01, 2019

• Xabi Cathala 1 ,
• Calvin Moorley 2
• 1 Institute of Vocational Learning , School of Health and Social Care, London South Bank University , London , UK
• 2 Nursing Research and Diversity in Care , School of Health and Social Care, London South Bank University , London , UK
• Correspondence to Mr Xabi Cathala, Institute of Vocational Learning, School of Health and Social Care, London South Bank University London UK ; cathalax{at}lsbu.ac.uk and Dr Calvin Moorley, Nursing Research and Diversity in Care, School of Health and Social Care, London South Bank University, London SE1 0AA, UK; Moorleyc{at}lsbu.ac.uk

https://doi.org/10.1136/eb-2018-102996

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Introduction

Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety. 1  This article provides a step by step guide on how to critically appraise a quantitative paper.

Title, keywords and the authors

The authors’ names may not mean much, but knowing the following will be helpful:

Their position, for example, academic, researcher or healthcare practitioner.

Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).

This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?

The abstract is a resume of the article and should contain:

Introduction.

Research question/hypothesis.

Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).

Main findings.

Conclusion.

The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.

The introduction

Example: the effect of paracetamol on levels of pain.

My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.

My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.

My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.

Background/literature review

The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge. 5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.

Methodology

In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable. 6 A correlational study examines the link (correlation) between two variables 7  and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes 8  and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.

There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:

Overview and rationale for the methodology.

Participants or sample.

Data collection tools.

Methods of data analysis.

Ethical issues.

Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool. 9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population. 10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.

The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in  table 1 .

• View inline

Some basic guidance for understanding statistics

Quantitative studies examine the relationship between variables, and the p value illustrates this objectively.  11  If the p value is less than 0.05, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.05, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.05 means, the hypothesis is accepted and if it is more than 0.05 the hypothesis is rejected.

The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result. 12  The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.

Discussion, recommendations and conclusion

The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.

A few other areas you may find helpful are:

Limitations of the study.

Conflicts of interest.

Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.

Quantitative paper appraisal checklist

• 1. ↵ Nursing and Midwifery Council , 2015 . The code: standard of conduct, performance and ethics for nurses and midwives https://www.nmc.org.uk/globalassets/sitedocuments/nmc-publications/nmc-code.pdf ( accessed 21.8.18 ).
• Gerrish K ,
• Moorley C ,
• Tunariu A , et al
• Shorten A ,

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

Correction notice This article has been updated since its original publication to update p values from 0.5 to 0.05 throughout.

Linked Articles

• Miscellaneous Correction: How to appraise quantitative research BMJ Publishing Group Ltd and RCN Publishing Company Ltd Evidence-Based Nursing 2019; 22 62-62 Published Online First: 31 Jan 2019. doi: 10.1136/eb-2018-102996corr1

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Quantitative research: Understanding the approaches and key elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

• Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
• Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
• Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

• Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
• Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
• Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
• Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
• Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
• Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

Canvs AI: Unlock critical insights from unstructured feedback Related Categories: Research Industry, Data Analysis, Quantitative Research Research Industry, Data Analysis, Quantitative Research, Artificial Intelligence / AI, Text Analytics

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Experiences of UK clinical scientists (Physical Sciences modality) with their regulator, the Health and Care Professions Council: results of a 2022 survey

• Mark McJury 1  

BMC Health Services Research volume  24 , Article number:  635 ( 2024 ) Cite this article

980 Accesses

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In healthcare, regulation of professions is an important tool to protect the public. With increasing regulation however, professions find themselves under increasing scrutiny. Recently there has also been considerable concern with regulator performance, with high profile reports pointing to cases of inefficiency and bias. Whilst reports have often focused on large staff groups, such as doctors, in the literature there is a dearth of data on the experiences of smaller professional groups such Clinical Scientists with their regulator, the Health and Care Professions Council.

This article reports the findings of a survey from Clinical Scientists (Physical Sciences modality) about their experiences with their regulator, and their perception of the quality and safety of that regulation.

Between July–October 2022, a survey was conducted via the Medical Physics and Engineering mail-base, open to all medical physicists & engineers. Questions covered typical topics of registration, communication, audit and fitness to practice. The questionnaire consisted of open and closed questions. Likert scoring, and thematic analysis were used to assess the quantitative and qualitative data.

Of 146 responses recorded, analysis was based on 143 respondents. Overall survey sentiment was significantly more negative than positive, in terms of regulator performance (negative responses 159; positive 106; significant at p  < 0.001). Continuous Professional Development audit was rated median 4; other topics were rated as neutral (fitness to practice, policies & procedures); and some as poor (value).

Conclusions

The Clinical Scientist (Physical Sciences) professional registrants rated the performance of their regulator more negatively than other reported assessments (by the Professional Standards Authority). Survey respondents suggested a variety of performance aspects, such as communication and fitness to practice, would benefit from improvement. Indications from this small dataset, suggest a larger survey of HCPC registrants would be useful.

Peer Review reports

In Healthcare, protection of patients and the public is a core principle. Part the framework of protections, includes regulation of professions [ 1 ]. This aims to mitigate risks such as the risk from bogus practitioners – insufficiently trained people acting as fully-trained professional practitioners, see Fig.  1 .

Recent UK media report on a bogus healthcare practitioner [ 2 ]

Regulation of professions ensures that titles (e.g. Doctor, Dentist, Clinical Scientist) are protected in law. The protected title means someone may only use that title, if they are on the national register, managed by the regulator – the Health and Care Professions Council (HCPC). It is a criminal offence to use a protected title if you are not entitled to do so [ 3 ]. There are a large number of regulators in healthcare – see Table  1 . Most of the regulators manage a register for one profession, except the HCPC which regulates 15 professions.

To be included on the register, a candidate must meet the regulators criteria for knowledge and training, and a key element to remain, is to show evidence of continuous professional development (CPD). Being on the register ensures that a practitioner has met the appropriate level of competence and professional practice.

For many healthcare workers, being on the HCPC register is a compulsory requirement to be appointable to a post. They must pay the necessary annual fees, and abide by the policies drawn-up by the regulator, and generally professions have no choice of regulator – these are statutory bodies, setup by government.

Recently, there has been considerable public dissatisfaction with the activity & performance of some regulators, notably Ofwat [ 4 ], and Ofgem [ 5 ]. Healthcare workers should expect a high level of professionalism, efficiency, and integrity from a regulator, as the regulator’s performance directly affects staff and public safety.

In terms of the regulation of UK Clinical Scientists, there is a dearth of data regarding experiences with the HCPC and views on the quality of regulation provided.

Findings are reported here from a 2022 survey of Medical Physicists and Engineers (one of the 16 job roles or ‘modalities’ under the umbrella of Clinical Scientist). The research aim was to assess experiences, and the level of ‘satisfaction’ with the regulator. For the remainder of this report, the term Clinical Scientist will be taken to mean Clinical Scientist (Medical Physicist/Engineer). The survey was designed to gather & explore data about opinions and experiences regarding several key aspects of how the HCPC performs its role, and perception of the quality & safety of regulation delivered.

A short survey questionnaire was developed, with questions aimed to cover the main regulatory processes, including registration & renewal, CPD audit, and fitness-to-practice. There were also questions relating more generally to HCPC’s performance as an organisation, e.g. handling of personal data. Finally, participants were asked to rate the HCPC’s overall performance and what they felt was the ‘value’ of regulation. The survey questions are listed in the Supplementary file along with this article.

Questions were carefully worded and there was a balance of open and closed questions. A five-point Likert score was used to rate closed questions. The survey was anonymous, and the questions were not compulsory, allowing the responders to skip irrelevant or difficult questions. The survey also aimed to be as short & concise as possible, to be a minimal burden to busy clinical staff & hopefully maximise response rate. There were a small number of questions at the start of the survey, to collect basic demographics on the respondents (role, grade, UK nation etc.).

The survey was advertised on the online JISC-hosted UK Medical Physics and Engineering (UKMPE) mail-base. This offered convenient access for the majority of Clinical Scientists. The survey was advertised twice, to allow for potential work absence, holiday/illness etc. It was active from the end of July 2002 until October 2022, when responses appeared to saturate.

The data is a combination of quantitative rating scores, and qualitative text responses. This allows a mixed-methods approach to data analysis, combining quantitative assessment of the Likert scoring, and (recursive) thematic analysis of the free-text answers [ 6 ]. Thematic analysis is a standard tool, and has been reported as a useful & appropriate for assessing experiences, thoughts, or behaviours in a dataset [ 7 ]. The survey questions addressed the main themes, but further themes were identified using an inductive, data-driven approach. Qualitative data analysis (QDA) was performed using NVivo (QSR International).

Two survey questions attempted to obtain an overall perception of HCPC’s performance: the direct one (Q12), and a further question’Would you recommend HCPC as a regulator…?’. This latter question doesn’t perhaps add anything more, and in fact a few respondents suggested it was a slightly awkward question, given professions do not have a choice of regulator – so that has been excluded from the analysis.

Study conduct was performed in accordance with relevant guidelines and regulations [ 8 , 9 ]. Before conducting the survey of Clinical Scientists, the survey was sent to their professional body, the Institute of Physics and Engineering in Medicine (IPEM). The IPEM Professional Standards Committee reviewed the survey questions [ 10 ]. Written informed consent was obtained from participants.

Data analysis

Data was collected via an MS form, in a single excel sheet and stored on a secure network drive. The respondents were anonymised, and the data checked for errors. The data was then imported into NVivo v12.

Qualitative data was manually coded for themes, and auto-coded for sentiment. An inductive approach was used to develop themes.

The sample size of responses allowed the use of simple parametric tests to establish the level of statistical significance.

Survey demographics

A total of 146 responses were collected. Two respondents noted that they worked as an HCPC Partner (a paid role). They were excluded from the analysis due to potential conflict of interest. One respondent’s responses were all blank aside from the demographic data, so they were also excluded from further analysis.

Analysis is based on 143 responses, which represents ~ 6% of the UK profession [ 11 ]. It is arguable whether it is representative of the profession at this proportion of response – but these responses do offer the only sizeable pool of data currently available. The survey was aimed at those who are on the statutory register as they are most likely to have relevant interactions & experiences of the HCPC, but a small number of responses were also received from Clinical Technologists (Medical Technical Officers-MTOs) and Engineers (CEs) and these have been included in the analysis. Figure  2 shows the breakdown in respondents, by nation.

Proportion of respondents, by nation

Of the respondents, 91% are registered Clinical Scientists, and would therefore have a broad range of experience with HCPC and its processes. Mean time on the register was 12 yrs. Respondents show a large range in seniority, and their roles are shown in Fig.  3 (CS-Clinical Scientist; CE-Clinical Engineer; MTO-Medical Technical Officer/Technician; CS-P are those working in private healthcare settings, so not on Agenda for Change (AfC) pay bands).

Breakdown in respondents, by role and pay banding

These data can be compared with the most recent HCPC ‘snapshot’ of the CS registrants (find here: Registrants by profession snapshot—1967 to 2019 | ( https://www.hcpc-uk.org/resources/data/2019/registrant-snapshot/ )).

The perception of overall regulator performance, can be assessed in two ways – one interview question directly asked for a rating score, and the overall survey sentiment also offers additional insight.

The score for overall performance was a median of 3 (mean 2.7; response rate 90%) which suggests neutral satisfaction.

Respondents were not asked directly to explain this overall performance rating – themes were extracted from the questionnaire as a whole.

The auto-coded sentiment scores generated in the NVivo software are shown in Table  2 . There is a significantly stronger negative sentiment than positive for HCPC performance – moderate, strong and total sentiment scores are all higher for negative sentiment. The normal test for a single proportion (109), shows the negative and positive sentiment differences have statistical significance with p  < 0.001. Whilst the PSA assessment of HCPC performance in 2022–23 shows 100% performance for 4 out of 5 assessment areas, survey data here from regulated professionals suggests considerably less satisfaction with HCPC. This raises associated questions about the relevance and validity of PSA assessment.

A large number of respondents seem to question the value of regulation. Whilst many accepted the value for it in terms of protecting the safety of the public, many questioned its relevance & benefit to themselves. Many respondents also queried the payment model where although the main beneficiaries of regulation are the public & the employer, it is the registrants actually pay the fees for registration. There was very little mention in survey responses, of benefit in terms of protected-title. These issues were amalgamated into Theme 1— Value of regulation , with the two sub-themes Value in monetary terms (value-for-money) and Value in professional terms (benefit and relevance to the individual professional) (see Table  3 ).

In the survey, several aspects of HCPC organisational performance were scored – handling of personal data, registration and renewal, engagement with the profession, audit, and the quality and usefulness of HCPC policies. These formed Theme 2 and its sub-themes.

A third theme Registrant competence and vulnerability , was developed to focus on responses to questions related to the assessment of registrant competence and Fitness To Practice (FTP) processes.

Finally, the survey also directly asked respondents if they could suggest improvements which would have resulted in higher scoring for regulation quality and performance. These were grouped into Theme 4.

Theme 1 – Value of regulation

Value in monetary terms.

The Likert score for value-for-money was a median of 2 (mean 2.3; response rate 100%) which suggests dissatisfaction. This is one of the few survey questions to elicit a 100% response rate – a clear signal of its importance for registrants.

There was a high number of responses suggesting fees are too expensive (and a significantly smaller number suggesting good value). This ties in with some respondents explaining that the ‘benefit’ from registration is mainly for the employer (an assurance of high quality, well-trained staff). Several respondents point to little ‘tangible’ benefit for registrants and query whether the payment model is fair and if the employer should pay registrant fees.

“Expensive fees for what appears to be very little support.” Resp094

“It seems that I pay about £100 per year to have my name written on a list. It is unclear to me what the HCPC actually does in order to justify such a high fee.” Resp014

“I get, quite literally, nothing from it. It’s essentially a tax on work.” Resp008

Several respondents suggested that as registration was mandated by the employer, it was in essence an additional ‘tax’ on their employment, which was highlighted previously by Unison [ 12 ]. A comparator for payment model, are the checks preformed on potential staff who will be working with children and vulnerable adults. In general, these ‘disclosure’ checks are paid for by the employer, however the checks are not recurrent cost for each individual, but done once at recruitment.

Value in professional terms & relevance

This was not a direct question on the questionnaire, but emerged consistently in survey responses. Aside from value-for-money, the value of regulation can also refer to more general benefit and relevance for a professional, for example in protecting a professional title or emphasising the importance of a role. Many respondents commented, in relation to the ‘value’ of regulation, about the relevance of the HCPC to them and their job/role.

The largest number of responses highlighted the lack of clarity about HCPC’s role, and also to note its lack of relevance felt by a significant proportion of respondents.

“Not sure I have seen any value in my registration except that it is a requirement for my role” Resp017

“I really fail to understand what (sic) the benefits of registration.” Resp018

“They do not promote the profession. I see no evidence of supporting the profession. I pay to have the title and I am not aware of any other benefits.” Resp038

Theme 2 – HCPC performance

Communication & handling data.

The survey questionnaire did not have a specific question relating to communication, therefore no specific Likert scores are available. Rather, communication was a sub-theme which emerged in survey responses. The response numbers related to positive (1) and negative experiences (50) clearly suggest an overall experience of poor communication processes (and statistically significant at p  < 0.001 for a normal proportion test).

One respondent noted they had ‘given up’ trying to communicate with HCPC electronically. Several respondents also noted issues with conventional communication—letters from HCPC going to old addresses, or being very slow to arrive.

“…I have given up on contacting by electronic means.” Resp134

When trying to renew their registration, communication with HCPC was so difficult that two respondents noted they raised a formal complaint.

A number of respondents noted that when they eventually got through to the HCPC, staff were helpful, so the main communication issue may relate to insufficiently resourced lines of communication (phones & email) or the need for a more focussed first point of contact e.g. some form of helpdesk or triaging system.

“Recently long wait to get through to speak to someone… Once through staff very helpful.” Resp126

This topic overlaps with the next (Processing Registration & renewals) in that both involve online logins, website use etc.

Security & data handling was rated as neutral (median 3, mean 3.4; response rate 91%). Although responses were balanced in terms of satisfaction, a significant number noted a lack of knowledge about HCPC processes. There are almost equal proportions of respondents reporting no issues, some problems with handling of personal data, or insufficient knowledge to express an opinion.

Registration and renewal

The score for processing registrations & renewals, was a median of 4 (mean 3.5; response rate 92%) which suggests modest satisfaction.

The overall rating also suggests that the issues may have been experienced by a comparative minority of registrants and that for most, renewal was straightforward.

“They expected people to call their phone number, which then wasn’t picked up. They didn’t reply to emails except after repeated attempts and finally having to resort to raising a complaint.” Resp023

“Difficult to get a timely response. Difficult to discuss my situation with a human being…” Resp044

Although the Likert score is positive, the themes in responses explaining the rating, are more mixed. Many respondents mentioned either having or knowing others who had issues with registration renewal, and its online processes including payments. A few respondents mentioned that the process was unforgiving of small errors. One respondent, for example, missed ticking a box on the renewal form, was removed from the register and experienced significant difficulties (poor communication with HCPC) getting the issue resolved.

Some respondents noted issues related to a long absence from work (e.g. maternity/illness etc.) causing them to miss registration deadlines – for some, this seems to have resulted in additional fees to renew registration. It seems rather easy for small errors (on either side) to result in registrants being removed from the register. For registrants, this can have very serious consequences and it can then be difficult and slow to resolve this, sometimes whilst on no pay. There have also been other reported instances of renewal payment collection errors [ 13 ].

“I had been off work… and had missed their renewal emails…I was told that there would be no allowances for this situation, and I would have to pay an additional fee to re-register…” Resp139.

Some respondents raised the issue of exclusion – certain staff groups not being included on the register—such as Clinical Technologists and Clinical Engineers. This desire for inclusion, also points to a perception of value in being on the register. One respondent raised an issue of very difficult and slow processing of registration for a candidate from outside the UK.

“Staff member who qualified as medical physicist abroad…has had a dreadful, drawn out and fruitless experience.” Resp135

Overall, many respondents noted difficulties in renewing registration and issues with HCPC’s online processes. Some of these issues (e.g. website renewal problems) may have been temporary and are now resolved, but others (e.g. available routes for registration) remain to be resolved.

Audit process & policies

In the survey, 12% respondents reported having been audited by HCPC regarding their CPD (response rate 97%). This is well above the level of 2.5% of each profession, which HCPC aims to review at each renewal [ 14 ], and similar values reported by some professional bodies [ 15 ]. The participants seem representative, although two respondents mentioned their perception of low audit rates. Data on CPD audit is available here: https://www.hcpc-uk.org/about-us/insights-and-data/cpd/cpd-audit-reports/

Respondents rated the process of being audited as a median of 4 (mean 3.7), which is the joint highest score on the survey, pointing to satisfaction with the process. From the responses, the overall perception could be summed up as straight-forward, but time-consuming. Without regular record-keeping, unfortunately most audits will be time-consuming – the HCPC more so, as it is not an annual audit, but covers the two preceding years.

Some respondents did find the process not only straight-forward, but also useful (related to feedback received). However, responses regarding feedback were mixed, with comments on both good, and poor feedback from HCPC.

“Not difficult but quite long-winded” Resp008

“Very stressful and time consuming” Resp081

“While it was a lot of work the process seemed very thorough and well explained.” Resp114

The HCPC’s policies & procedures were rated as a median of 3 (mean 3.2; response rate 98%). This neutral score could suggest a mixture of confidence in HCPC practise. This score may also reflect the fact that the majority of respondents had either not read, or felt they had no need to read the policies, and so are largely unfamiliar with them.

The reasons for this lack of familiarity are also explained by some respondents – four commented that the policies & procedures are rather too generic/vague. Three respondents noted that they felt the policies were not sufficiently relevant to their clinical roles to be useful. This may be due to the policies being written at a level to be applicable to registrants from all 16 modalities – and perhaps a limitation of the nature of HCPC as a very large regulator. Familiarity seemed mainly to be restricted to policies around registration, and CPD. There were slightly lower response levels for positive sentiment (6), than negative sentiment (9).

“I’ve never had cause to read them.” Resp115

“Detached from the real clinical interface for our professions…” Resp083

HCPC split their policies into ‘corporate’- which relate to organisational issues (e.g. equality & diversity; find them here: Our policies and procedures | ( https://www.hcpc-uk.org/about-us/corporate-governance/freedom-of-information/policies/#:~:text=Our%20main%20policies%20and%20procedures%201%20Customer%20feedback,scheme%20...%207%20Freedom%20of%20Information%20Policy%20 )) and those more relevant to professions (e.g. relating to the register; find them here: Resources | ( https://www.hcpc-uk.org/resources/?Query=&Categories=76 )).

One respondent noted not only that the policies were ‘as you might expect’, but felt the policies were less demanding than those from other similar bodies such as the CQC ( https://www.cqc.org.uk/publications ).

“…Other regulatory bodies (such as the CQC for example) have policies and procedures that are a lot more challenging to comply with.” Resp022

Theme 3 – Registrant competence and vulnerability

In this survey, 3.5% (5/143) of respondents noted some involvement with the HCPC’s Fitness to Practice service. These interactions were rated at a median of 3 (mean 2.8) suggesting neutral sentiment.

Firstly, we can immediately see the level of interaction with the FTP team is very small. CS registrants represent approx. 2% of HCPC registrants, and the level of CS referrals to FTP in 2020–21 was 0.2% [ 16 ].

The data is a very small sample, but responses vary strongly, so it is worth digging a little further into the granularity of individual responses. Response scores were 1, 1, 2, 5, 5 – which are mainly at the extremes of the rating spectrum. The majority of respondents described poor experiences with the FTP team: errors, a process which was ‘extremely prolonged’, involved slow/poor communication, and processes which were ‘entirely opaque’.

“It is slow, the process was badly managed… and the system was entirely opaque,” Resp37

“They were hard to contact and I didn't feel they listened…no explanation, apology or assurance it would not happen again. It left my colleague disillusioned and me very angry on their behalf…” Resp044

Some respondents commented that the team were not only difficult to contact, but also didn’t seem to listen. At the end of a process which involved errors from HCPC, one respondent noted were ‘no explanation, apologies or assurance that it would not happen again’, leaving the registrant ‘disillusioned’. These experiences do not fit with the HCPC’s stated goal to be a compassionate regulator, see Fig.  4 . Arguably it is more difficult to change a culture of behaviour and beliefs, than to publish a corporate goal or statement of vision.

HCPC’s vision statement & purpose [ 17 ]

Some survey respondents have noted the necessity of regulation for our profession.

“Ultimately I am very grateful that I can register as a professional.” Resp024

Theme 4 – Suggestions for improved regulation

Following the question relating to overall performance, respondents were invited to suggest things which might improve their rating for HCPC’s performance and value. These suggestions were also combined with those which appeared in earlier survey responses.

Although we are in a current cost-of-living crisis, responses did not query simply high absolute cost of fees, but also queried the value/benefit of HCPC regulation for registrants. Many responses expressed doubt as to the added value & relevance of HCPC registration for them. They seem to point to a desire for more tangible benefit from their fees. Perhaps, given the costs and levels of scrutiny, registrants want some definite benefit to balance the scales .

“Cost less and do more for the people who are on the register.” Resp089

“Vastly reduced cost. Employer paying registrant fees.” Resp074

A significant number of responses pointed out that the main benefits of registration are for the public, and for employers – but that it is the registrants who pay for registration. Many queries why this should be, and whether there should be a different payment model, where for example employers pay.

Similarly, some respondents felt that the HCPC’s unusual position of regulating a large swathe of healthcare professions was not necessarily helpful for their profession or others.

Communication and response times are obviously an issue of concern for registrants, and improvements are needed based on the low satisfaction levels reported here. This is also linked to a wish for increased engagement with the CS profession.

“Engagement with the workforce, specialism specific development, reduced fees” Resp025

Some responses suggested they would be comforted by increased accountability / governance of HCPC including improved FTP efficiency.

“More accountability to registrants” Resp130

Finally, improvement in terms of additional registration routes for Engineers & Technical staff were also suggested. It may be damaging to work-place moral, if two professionals doing roles of a similar nature are not being governanced is the same way and if there is not parity of their gross salary due to mandatory professional fees & reductions.

Value-for-money : This will vary between individuals depending on many variables, such as upbringing & environment, salary, lifestyle priorities, political persuasion, and so on. However, many of these factors should balance in a large sample. In general, it can be suggestive of satisfaction (or lack of) with a service. The score here suggesting dissatisfaction, echoes with other reports on HCPC’s spending, and financial irregularities [ 18 , 19 ].

In the survey findings, respondents have voiced dissatisfaction with registration value for money. In fact, HCPC’s registration fees are not high when compared to the other healthcare professions regulators. Table 1 shows data from 2021–22 for regulator annual registration fees. However, the HCPC has risen from having the lowest regulator fees in 2014–5, to its current position (9 th of 13) slightly higher in the table. Perhaps more concerning than the absolute level of fees, are when large increases are proposed [ 12 , 20 , 21 , 22 ].

However, fees have regularly increased to current figure of £196.48 for a two-year cycle. During a consultation process in 2018, the Academy for Healthcare Clinical Scientists (AHCS) wrote an open letter to the HCPC, disputing what they felt was a disproportionate fee increase [ 23 ]. Further fee rises have also been well above the level of inflation at the time.

HCPC expenditure (which is linked to registration fees) has arguably been even more controversial than fee increases – noted by several respondents. A freedom of information (FOI) request in 2016 showed HCPC’s spending of £17,000 for their Christmas party [ 18 ] – which amounts to just over £76 per person. This cost was close to the annual registration fee (at that time) for registrants.

In 2019, regulation of social workers in England moved from HCPC, to Social Work England. This resulted in a loss of over 100,000 registrants, and a loss in registration fee income. HCPC raised fees to compensate, but a freedom of information (FoI) request in 2020 [ 18 ] showed that even though there was an associated lowering in workload associated with the loss of 100 k registrants, the HCPC had no redundancies, suggesting the loss of income was compensated mainly by the fees increase.

Inherent value & relevance

One of HCPC’s aims is to promote ‘the value of regulation’ [ 24 ]. However, not only is there dissatisfaction with value-for-money, the second highest response suggests a lack of inherent value (or benefit) from regulation to the individual registrant. In some ways, there is a lack of balance – registrants are under increasing scrutiny, but feel there is little direct benefit, to provide balance.

This also suggests that HCPC’s aim or message is not getting through to the CS profession. It’s not clear what the HCPC 2021–22 achieved milestone – ‘Embedded our registrant experiences research into employee learning and development and inductions’ has actually achieved.

A large number of responses pointed to the lack of clarity about HCPC’s role, and also to note its lack of relevance for respondents. Some of this is understandable – until recently, many CS registrants will have little interaction with HCPC. They would typically get one email reminder each year to renew their registration and pay those fees, and hear little else from the HCPC. That is beginning to change, and HCPC have recently begun to send more regular, direct emails/updates to registrants.

However, for many registrants, the HCPC appears not to be clearly communicating its role, or the relevance/importance of regulation. As mentioned above, this also links in to previous mentions of the lack of any tangible benefit for registrants. Some note little more relevance other than the mandatory aspects of regulation.

Finally, relevance is also queried in relation to the limited access for some professional groups to a professional register. The current situation of gaps in registration for some groups, results in two situations – firstly, for Clinical Scientists and Clinical Engineers/Technologists, one group has to compulsorily pay a fee to be allowed/approved to do their job and the other does not; also, the public are routinely helped and assisted by Clinical Scientists and Clinical Engineers/Technologists – but only one group is regulated to ensure public safety.

HCPC Communication

This was highlighted by respondents as often poor. Recently in the media, there has been a concern raised by The College of Paramedics (CoP) about communications issues with HCPC—changes to the HCPC policy on the use of social media [ 25 ]. They raised particular concerns about the use of social media content and ‘historical content’ in the context of investigations of fitness-to practice.

There have previously been some concerns raised on the UKMPE mail-base regarding handling of personal data, and lack of efficiency in addressing the issue [ 26 ]. Several messages detailed HCPC communicating unencrypted registrant passwords in emails and sending personal data to the incorrect registrant. Some on the forum noted that they had reported this problem over a period of several years to HCPC, suggesting HCPC’s response to these serious issues was extremely slow. Several responses noted these previous issues.

Registration processes

Although responses here show some satisfaction, there have been reports in the media of significant issues with registration (such as removing registrants from the register in error) with associated impact for patients and the public [ 27 , 28 ]. Similarly, there were reports on the UKMPE mail-base of significant issues with registration renewals being problematic [ 26 ]. In Scotland, NHS.net email accounts ceased to be supported in July-Sept 2020 and the associated lack of access to email accounts and messages used for HCPC communication and registration, caused a major issue in registration renewal. This coincided with COVID lockdowns and a period of unusually difficult communication with HCPC. If NHS staff lose registration (irrespective of the reason), respondents noted that some Human Resources (HR) departments were quick to suspend staff from work, and in some cases withhold pay. That spike in difficulties is likely the cause of the most common responses suggesting issues with a complicated process.

In safe-guarding public safety, a key task for a healthcare regulator is assessing the competence of registrants. This is done via a small set of related activities. Registrants must return regular evidence of CPD, and these are audited for 2.5% registrants. This process is simple and routine, and as seen in Theme 2 responses here suggest registrants are reasonably satisfied with this process.

More formal and in-depth competence assessment happens when a complaint is raised against a registrant, either by a work colleague/management, a member of the public or occasionally by the HCPC itself. The process is complex, lengthy and can end in a registrant attending a court hearing [ 29 ].

It is usual for registrants to continue in their normal job during FTP investigations – effectively the public remains at risk from a registrant if their competence is eventually proven to be below the regulators standards, so there is a need for investigations to be efficient both in timeliness, and outcome.

Obviously, being under investigation can be highly stressful, and has the potential for the registrant to be ‘struck off’ the register, and lose their job if registration is mandated (e.g. NHS posts). There are many reports of the process & experience either provoking or increasing underlying mental health challenges [ 30 , 31 , 32 ]. Along with efficiency, a regulator needs to behave compassionately. Investigations of highly-skilled professionals engaging in complex work activities, is also necessarily complex and requires a high degree of knowledge and experience from the regulator’s investigational panel.

The Professional Standards Authority (PSA) regulate the HCPC, and publish annual reviews of their performance ( https://www.professionalstandards.org.uk/publications/performance-reviews ) (see Table  4 ). HCPC performance as reported by PSA, seems to be generally higher than noted by survey respondents here. For 2022–23, aside from one area, the HCPC has scored 100% for performance, which seems at odds with these survey responses [ 33 ]. The FTP team is notable in repeatedly performing very poorly compared to most other sections of the HCPC (even though the majority of the HCPC budget goes to FTP activity, see Fig.  4 ). The HCPC Annual Report 2018–9 [ 34 ] highlighted the completion of the first phase of the Fitness-To-Practice Improvement Plan. This delivered “A root and branch review of this regulatory function… a restructure, tightened roles and processes and the introduction of a new Threshold Policy”, but this seems to have no impact on the performance reported by the PSA for the next few years shown in Table  4 . However, the most recent data does suggest improvement, and HCPC continues to develop FTP team practice [ 17 ].

HCPC expenditure for the year 2020–21 [ 17 ]

There are other reports of poor experiences with this team [ 35 , 36 ], and in one report the FTP team’s processes have been noted as being rather inhumane [ 35 ].

Regulation is an important part of public protection, but how effectively it is managed & enforced is also a concern, given it involves increased scrutiny of registrants. A topical comparator is the current dissatisfaction by a large section of the public about several other government regulators allowing seemingly poor performance to go unchecked [ 4 , 5 ].

It is arguable, that registrants remain on the register as long as the HCPC allows them. Several respondents in this survey noted being removed from the register through HCPC administrative error. Removal could also happen through poor judgement/decision-making – the FTP team handle large numbers of very complex investigational cases – 1603 concluded cases for the year 2021–22 and 1024 hearings [ 16 ]. Every justice system is subject to a level of error – guilty parties can be erroneously ‘cleared’, and vice-versa. It is essential therefore, that policies & procedures relating to FTP are fit for purpose—that the FTP team work effectively and humanely, and that there is genuine & effective governance of HCPC to ensure accountability. In this survey, some respondents seem to be saying that currently this seems not to be the case.

It might have been anticipated that the greatest concern is costs, especially in the current cost-of-living crisis. The recent HCPC consultation to increase fees [ 37 ] seems particularly tone-deaf and has caused concern across the professions [ 21 , 22 ].

Above findings show respondents are interested in lower fees, but also increased benefit for their fees. Some respondents pointed out that whilst registrants pay for registration, benefit is mainly for the public and employers. The HCPC is a statutory body, its funding model will have been designed/decided upon by government, and may be unlikely to change. However, there are a variety of potential regulation models [ 38 ], and so change is possible. A review of the financial model for regulation may be welcome.

Regulator size

Some aspects of HCPC performance, policies, and distribution of spending, is related to the nature of it being the largest and only multi-professional regulator in the healthcare sector. Data from the HCPC suggests (see Fig.  5 ) that the majority of spending relates to FTP activity. Data also points to Clinical Scientists having very low levels of FTP investigation compared to others in HCPC [ 16 ]. This suggests that a significant proportion of CS registrant fees are used to investigate other professions. It’s possible (perhaps simplistically) that if, like many other healthcare professions such as doctors & dentists who’s regulator is concerned only with that single profession, if CSs were regulated separately, their registrant fees may be reduced. This model of single-profession regulation may also mitigate against other disadvantages of the HCPC’s practice, such as the ‘generic’ policies aiming to apply to a pool of 15 professions.

Although there is a very low level of data for this topic, the concerned raised by registrants are serious in nature. There also seems to be issues in handling of complaints related to this service and advocacy for registrants. Certainly, there is a clear governance path via PSA, to the Health Secretary. However, this does not offer a route for individual complaints to be raised and addressed. Unlike complaints from the public in other areas, there is no recourse to an ombudsman for registrants. The only option for individual registrants, is the submission of a formal complaint to the HCPC itself, which is dealt with internally. Comments from survey respondents suggest this process does not guarantee satisfaction. Indeed, one of the respondents who mentioned submitting a complaint, made it clear they remained unhappy with HCPC’s response. Overall, there seems to be a lack of clear & effective advocacy for registrants.

“…the HCPC’s stance appeared to be guilty until proven innocent… At no point did I feel the HCPC cared that their (sic) was an individual involved....” Resp044.

FTP processes affect a comparatively small number of CS registrants, compared to other professions. However, it seems clear that the majority of those who have interacted with the FTP team have had poor experiences, and respondents have suggested improvements are needed. The reason for FTP investigations, is protection of staff and the public. If processes are slow, and investigations prolonged, or decisions flawed, the public may be exposed to increased levels of risk, as healthcare practitioners who may be lacking in competence continue to practice. The data in Table  4 shows concerning but improving trends in FTP performance levels.

Limitations

There are two main limitations to this work. Firstly, due to time constraints, there was no pilot work done when designing the survey questionnaire. This may have helped, as noted earlier, a few responses pointed to some awkwardness with one survey question. Although no pilot work was done, the questionnaire was reviewed by the IPEM Professional Standards Committee, as noted in the Acknowledgements section.

The other obvious limitation is the low response rate (~ 6% of UK Medical Physicists). Circulation of the survey was performed via the only online forum for the profession currently available. The survey was advertised multiple times to ensure visibility to staff who may have missed it initially due to leave etc. However, the forum does reach 100% of the profession, and some addressees may have filters set to send specific posts to junk folders etc. The professional body IPEM declined to offer support in circulating the survey (believing the issues involved would affect/be of interest only to a small minority of members.)

The low response rate also has a particular impact on the pool of responses relating to FTP issues, which inherently affect low numbers of registrants.

However, the importance of some of the findings here (e.g. expressed dissatisfaction with regulation in terms of value; the poor experience of some members with the Registration, Communication and FTP teams) and the low sample surveyed, both justify the need for a larger follow-on survey, across all of Clinical Science.

In Healthcare, regulation of professions is a key aspect of protecting the public. However, to be effective, regulation must be performed professionally, impartially, and associated concerns or complaints investigated efficiently and respectfully.

This report presents findings from a survey aimed at collecting a snap-shot of the experiences of Clinical Scientists with their regulator, and their perception of the quality and safety of that regulation performance.

Overall survey sentiment scores showed a significantly more negative responses than positive. Survey comments relate not only to current issues, but to previous problems and controversial issues [ 18 , 26 ]. It seems that some respondents have at some point lost confidence and trust in the HCPC, and survey responses suggest there has not been enough engagement and work done by HCPC to repair and rebuild this trust.

In the midst of a cost of living crisis, costs are a large concern for many. The HCPC fees are neither the highest not lowest amongst the healthcare regulators. Spending is transparent, and details can be found in any of the HCPC’s annual reports.

A repeating sub-theme in responses, was a lack of tangible value for the registrant, and that the employer should pay the costs of registration, where registration is mandated by the job.

Many respondents have suggested that they feel there should be more proactive engagement from HCPC with the profession. Most respondents were not familiar with or felt the HCPC policies are relevant/important to them.

Survey data showed moderate satisfaction with registration processes for the majority of respondents. Some respondents also noted a lack of registration route for engineering & technical healthcare staff. CPD processes also achieved a score indicating registrant satisfaction. This generated the highest ratings in the survey. Communication scored poorly and many respondents suggests there needs to be improved levels of communication in terms of response times and access to support.

The CS profession experiences low levels of interaction with the FTP service. However, those interactions which were recorded in the survey, show some poor experiences for registrants. There also seems to be a lack of advocacy/route for complaints about HCPC from individual registrants. There may need to be more engagement between registrants and their professional body regarding HCPC performance, and more proactivity from the stake-holder, IPEM.

Some of the findings reported here relate to important issues, but the survey data are based on a low response rate. A larger survey across all of Clinical Science is being planned.

Availability of data and materials

To protect confidentiality of survey respondents, the source data is not available publicly, but are available from the author on reasonable request.

Abbreviations

Agenda for Change

Academy for Healthcare Clinical Scientists

Continuous professional development

Clinical Engineer

Clinical Scientist

College of Paramedics

Clinical Technologist

Freedom of Information

Fitness-to-practice

Health and Care Professions Council

Human resources

Institute of Physics and Engineering in Medicine

Joint Information Systems Committee

Medical Technical Officer

Professional Standards Authority

Professional Standards Committee

Qualitative data analysis

UK Medical Physics and Engineering

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Acknowledgements

The author wishes to kindly acknowledge the input of Dr Carl Rowbottom (IPEM Professional Standards Committee), in reviewing the survey questions. Thanks also to Dr Nina Cockton for helpful advice on ethics and recruitment issues.

There were no sources of funding required for this work.

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As this study relates to low risk, survey data, formal ethics committee approval is not required (exemption obtained from NHSGGC REC04 REC Officer Dr Judith Godden [email protected]). As the survey responses were from members of a professional body (The Institute of Medical Physics and Engineering in Medicine (IPEM) it was consulted. Its Professional Standards Committee (PSC) reviewed the survey and raised no objections. The survey questions were assessed for bias and approved unchanged (acknowledged in the manuscript). Written informed consent was obtained from all participants in the study.

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Supplementary Information

Additional file 1..

The survey questionnaire has been provided as a supplementary file.

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McJury, M. Experiences of UK clinical scientists (Physical Sciences modality) with their regulator, the Health and Care Professions Council: results of a 2022 survey. BMC Health Serv Res 24 , 635 (2024). https://doi.org/10.1186/s12913-024-10956-7

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Quantitative analysis of th e effects of brushing, flossing, and mouthrinsing on supragingival and subgingival plaque microbiota: 12-week clinical trial

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• Published: 17 May 2024
• Volume 24 , article number  575 , ( 2024 )

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• Kyungrok Min 1 ,
• Mary Lynn Bosma 1 ,
• Gabriella John 1 ,
• James A. McGuire 1 ,
• Alicia DelSasso 1 ,
• Jeffery Milleman 2 &
• Kimberly R. Milleman 2  

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Translational microbiome research using next-generation DNA sequencing is challenging due to the semi-qualitative nature of relative abundance data. A novel method for quantitative analysis was applied in this 12-week clinical trial to understand the mechanical vs. chemotherapeutic actions of brushing, flossing, and mouthrinsing against the supragingival dental plaque microbiome. Enumeration of viable bacteria using vPCR was also applied on supragingival plaque for validation and on subgingival plaque to evaluate interventional effects below the gingival margin.

Subjects with gingivitis were enrolled in a single center, examiner-blind, virtually supervised, parallel group controlled clinical trial. Subjects with gingivitis were randomized into brushing only (B); brushing and flossing (BF); brushing and rinsing with Listerine® Cool Mint® Antiseptic (BA); brushing and rinsing with Listerine® Cool Mint® Zero (BZ); or brushing, flossing, and rinsing with Listerine® Cool Mint® Zero (BFZ). All subjects brushed twice daily for 1 min with a sodium monofluorophosphate toothpaste and a soft-bristled toothbrush. Subjects who flossed used unflavored waxed dental floss once daily. Subjects assigned to mouthrinses rinsed twice daily. Plaque specimens were collected at the baseline visit and after 4 and 12 weeks of intervention. Bacterial cell number quantification was achieved by adding reference amounts of DNA controls to plaque samples prior to DNA extraction, followed by shallow shotgun metagenome sequencing.

286 subjects completed the trial. The metagenomic data for supragingival plaque showed significant reductions in Shannon-Weaver diversity, species richness, and total and categorical bacterial abundances (commensal, gingivitis, and malodor) after 4 and 12 weeks for the BA, BZ, and BFZ groups compared to the B group, while no significant differences were observed between the B and BF groups. Supragingival plaque vPCR further validated these results, and subgingival plaque vPCR demonstrated significant efficacy for the BFZ intervention only.

Conclusions

This publication reports on a successful application of a quantitative method of microbiome analysis in a clinical trial demonstrating the sustained and superior efficacy of essential oil mouthrinses at controlling dental plaque compared to mechanical methods. The quantitative microbiological data in this trial also reinforce the safety and mechanism of action of EO mouthrinses against plaque microbial ecology and highlights the importance of elevating EO mouthrinsing as an integral part of an oral hygiene regimen.

Trial registration

The trial was registered on ClinicalTrials.gov on 31/10/2022. The registration number is NCT05600231.

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Bacterial community composition of chronic periodontitis and novel oral sampling sites for detecting disease indicators

Microbial differences between dental plaque and historic dental calculus are related to oral biofilm maturation stage

Changes in the structure of microbial communities within the dental plaque biofilm serve as a primary etiological factor in common oral diseases, such as caries and periodontitis [ 1 ]. In addition to toothbrushing, controlling the plaque biofilm relies on a variety of adjunctive methods that include mechanical flossing and chemotherapeutic mouthrinses.

Despite limited evidence of efficacy, flossing has been a long-standing recommendation [ 2 ] among dental professionals for the mechanical removal of interproximal plaque. In a systematic review and meta-analysis conducted by Worthington et al., there was “low-certainty evidence” to suggest “that flossing, in addition to toothbrushing, may reduce gingivitis (measured by gingival index (GI)) at one month (SMD -0.58, 95% confidence interval (CI) ‐1.12 to ‐0.04; 8 trials, 585 participants), three months or six months. The results for proportion of bleeding sites and plaque were [also] inconsistent (very low‐certainty evidence).” [ 3 ].

When used as an adjunct to daily mechanical oral hygiene, an alcohol-containing mouthrinse with a fixed combination of four essential oils (EOs) has a long history of demonstrated clinical reductions in plaque, gingivitis, and gingival bleeding [ 4 , 5 ] and has performed favorably when compared to flossing in two recent 3-month clinical trials [ 6 , 7 ]. An alcohol-free EO mouthrinse also performed similarly to an alcohol-containing mouthrinse in 6-month clinical trials [ 8 , 9 ]. The antimicrobial action of alcohol-containing EO mouthrinses has consistently demonstrated reductions of oral microbes in a variety of oral anatomic locations, including the tongue, cheek, and subgingival crevice [ 10 , 11 , 12 , 13 ]. These data were derived using well-established, although dated, methodologies, such as bacterial cell culture enumeration [ 14 , 15 ] and checkerboard DNA-DNA hybridization examining specific bacterial species [ 16 , 17 ].

More recently, advances in microbial profiling using high throughput DNA sequencing have revealed the presence of over 700 bacterial species in the human oral cavity [ 18 ]. These new methods enable highly detailed studies of the oral microbiome, which is essential to more fully understand the role of oral microbes in the pathogenesis of, and therefore the potential prevention of, a variety of oral diseases. Currently, however, there is only partial understanding of how certain mechanical and chemotherapeutic interventions impact the oral microbiome. There are limited quantifiable microbiome data describing time-resolved changes in absolute individual bacterial species abundances, spatiotemporal development of microbial communities, and their clinical relevance on various oral surfaces. This is particularly true of interproximal sites where plaque can remain relatively undisturbed and has a greater diversity of bacteria, including those associated with gingivitis, than more easily accessible areas of the mouth [ 19 , 20 ].

This clinical trial investigated how flossing and mouthrinses containing a fixed combination of EOs with and without alcohol impact plaque microbiota by generating absolute quantitative microbiome data using a new method of microbiome profiling analysis [ 21 ] and viable bacteria enumeration by vPCR. Plaque specimens were spiked with known amounts of exogenous control DNA to enable the quantification of bacterial cell numbers. Further, species identities were carefully annotated and categorized according to their clinical relevance using published literature evidence. The subjects recruited in this trial used floss once daily, mouthrinses twice daily, or a combination of both flossing and mouthrinsing for 12 weeks [ 22 ]. This mechanistic study is the first to provide a comprehensive quantification of oral care regimen impacts on the plaque microbiome using clinically relevant microbiological metrics.

Study design

This clinical trial was conducted between April 18, 2022 and July 21, 2022 at Salus Research, Inc. (Fort Wayne, Indiana, USA), an independent clinical research site qualified by the American Dental Association Seal of Acceptance Program. This examiner-blind, controlled, randomized, single-center, and parallel-group clinical trial was conducted in accordance with the principles of the International Council on Harmonization for Good Clinical Practice.

Periodontally healthy subjects and subjects with gingivitis were enrolled separately according to the inclusion and exclusion criteria. All subjects refrained from oral hygiene, food, beverages, and smoking for 8 to 18 h before oral examination of the hard and soft tissues, gingivitis, and plaque. Supragingival plaque was collected for microbiome analysis and subgingival plaque for viable bacteria count using PCR (vPCR) as secondary study endpoints before staining the whole mouth plaque with a disclosing dye. The periodontally healthy cohort participated only in one baseline visit, while subjects with gingivitis progressed through the trial after randomization into one of five intervention groups: [B] brushing only; [BF] brushing and flossing with Reach® Unflavored Waxed Dental Floss (Dr. Fresh LLC, Buena Park, California, USA); [BA] brushing and rinsing with Listerine® Cool Mint® Antiseptic (Johnson & Johnson Consumer Inc, New Jersey, USA); [BZ] brushing and rinsing with Listerine® Cool Mint® Zero Alcohol (Johnson & Johnson Consumer Inc, New Jersey, USA); and [BFZ] brushing, flossing, and rinsing with Listerine® Cool Mint® Zero Alcohol. Complete dental prophylaxis was administered to remove all accessible plaque and calculus. The subjects were given a fluoridated toothpaste (Colgate® Cavity Protection, Colgate-Palmolive Company, NY, USA) and brushed twice daily for 1 timed minute using a standard soft-bristled toothbrush (Colgate® Classic Toothbrush Full Head/Soft Bristles, Colgate-Palmolive Company, NY, USA). Subjects in the flossing groups rinsed their mouth with water after brushing and then flossed once daily. Subjects in the mouthrinse groups rinsed with 20 mL of their assigned mouthrinse for 30 timed seconds twice daily after brushing and flossing or brushing. Primary endpoints were based on clinical gingivitis and plaque assessments and secondary endpoints included supragingival and subgingival plaque microbiome assessments. Supragingival plaque microbiome assessments were completed at baseline before prophylaxis and after 4 and 12 weeks of product intervention, while subgingival plaque vPCR assessments were completed only after 12 weeks of intervention. To ensure compliance, all subjects received an initial training at the clinical site for the correct usage of their assigned products and were subsequently supervised virtually once daily during the weekdays through a video call. Subjects were unsupervised for their second daily use in the evening or on weekends, however, compliance for homecare regimen was monitored through individual diaries and by weighing their assigned toothpaste and mouthrinses at each visit.

Subject inclusion & exclusion

Healthy adults 18 years of age or older with a minimum of 20 natural teeth with scorable facial and lingual surfaces were included. Requirements for the periodontally healthy subjects were whole-mouth mean scores of: Modified Gingival Index (MGI) [ 23 ] ≤ 0.75, Expanded Bleeding Index (EBI) [ 24 ] < 3%, and no teeth with periodontal pocket depth (PPD) exceeding 3 mm [ 25 , 26 , 27 ]. Requirements for the randomized subjects with gingivitis were evidence of some gingivitis (mild to severe), minimum of 10% bleeding sites based on the EBI, no more than three sites having PPD of 5 mm or any sites exceeding 5 mm, and absence of significant oral soft tissue pathology, advanced periodontitis, and oral appliances, which may interfere with flossing.

Key exclusion criteria included the use of chemotherapeutic oral care products containing triclosan, EOs, cetylpyridinium chloride, stannous fluoride, or chlorhexidine; professional dental prophylaxis 4 weeks before the baseline; use of probiotics within 1 week before baseline or during the study, antibiotics, anti-inflammatory, or anticoagulant therapy within 1 month before baseline or during the study; use of intraoral devices; substance abuse (alcohol, drugs, or tobacco); history of significant adverse effects; allergies or sensitivity against oral hygiene products; pregnancy; significant medical conditions; and participation in any clinical trials within 30 days of the trial.

Sample size, randomization, and blinding

The sample size for this study was based on power to detect differences based on plaque and gingivitis endpoints. The planned sample size of 50 completed subjects per randomized intervention group provides 95% power to detect a difference between BA or BZ and BF means of 0.34 for Interproximal Mean MGI, given a standard deviation of 0.43, based on a two-sided test at the 2.5% significance level. This sample size also provides greater than 99% power to detect a difference between BA or BZ and BF means of 0.54 for Interproximal Mean Turesky Plaque Index (TPI) [ 28 ], given a standard deviation of 0.38. The standard deviation estimates were based on previous three-month studies using the examiners for the current study, and the differences between means are conservative estimates based on previous studies of this type. Sample sizes were estimated using PASS version 14.0.4 (NCSS, LLC, Kaysville, UT, USA). Assuming a 5% drop-out rate, the trial recruited 54 subjects per group or 270 subjects with gingivitis to ensure that the trial would be completed with at least 250 subjects in the randomized intervention groups. An additional 30 subjects, representing the non-randomized and periodontally healthy reference group, were recruited for a baseline assessment only.

The randomization schedule for subjects with moderate gingivitis was generated using a validated program created by the Biostatistics Department at Johnson & Johnson Consumer Inc. (Skillman, NJ, USA). The subjects with gingivitis were randomized in an equal allocation using a block size of ten and were assigned a unique randomization number that determined the sequential assignment of intervention products at the baseline visit. To minimize bias, the principal investigator and examiners were blinded to the administered intervention products, while the clinical personnel dispensing them were excluded from subject examinations.

Oral examination

All clinical assessments in this trial were performed by the same dental examiners. One examiner performed the oral hard and soft tissue assessments, MGI grading, and selection of teeth (as described below) to be sampled. Another examiner performed EBI and TPI grading. Both examiners were trained and calibrated with the visual assessment of gingival inflammation, supragingival plaque, and gingival bleeding as measured using the MGI, TPI, and EBI. All examinations were conducted in the following order: an oral hard and soft tissue assessment, MGI, supragingival and subgingival plaque sampling, EBI, and TPI.

Plaque sample collection

Plaque samples were collected by the same dental hygienist from the same four teeth selected at baseline, which met the inclusion and exclusion criteria for periodontally healthy subjects or subjects with gingivitis. The preferential teeth numbers were 3, 7, 18, and 23. Adjacent teeth that met the selection criteria were substituted for missing teeth.

Supragingival plaque for microbiome analysis was collected at all visits by moving a sterile curette five strokes supragingivally from the mesiobuccal line angle to follow the gingival margin to interproximal, from the distobuccal line angle to interproximal, and then repeating on the lingual side. Subgingival plaque for vPCR analysis was collected at week 12, during the last visit, using a sterile 204-sickle scaler to enter the interproximal subgingival space, removing plaque within one stroke, and repeating on all buccal and lingual interproximal surfaces. For each individual subject, the supragingival plaque and subgingival plaque samples were pooled, placed separately in 250 µL of sterile ultrapure grade phosphate-buffered saline with pH 7.2, and stored at -80 o C.

Shotgun metagenomic sequencing

Microbiome analysis of supragingival plaque was performed using next-generation DNA sequencing at CosmosID, Inc. (Germantown, Maryland, USA). DNA isolation, library preparation, and sequencing were carried out according to vendor-optimized protocol. Briefly, ZymoBIOMICS Spike-in Control II (Zymo Research, Irvine, CA) was added to plaque specimens to enable bacterial cell number quantification. To enhance cell lysis, plaque samples were incubated with MetaPolyzyme at 35 °C for 12 h, and DNA was extracted using ZymoBIOMICS DNA MicroPrep with bead-beating according to the manufacturer’s instructions. DNA concentrations were determined using the Qubit dsDNA HS assay and Qubit 4 fluorometer (ThermoFisher Scientific, Waltham, MA). DNA libraries were prepared using 1 ng of input genomic DNA that was fragmented, amplified, and indexed employing the Nextera XT DNA Library Preparation and Nextera Indexing Kit (Illumina, San Diego, CA). DNA libraries were purified using AMPure magnetic beads (Beckman Coulter, Brea, CA) and then normalized for equimolar pooling. Sequencing was performed using a HiSeq sequencer (Illumina), targeting a coverage of 3 − 4 million paired-end 2 × 150 bp reads.

Viability qPCR

Quantification of live bacteria from supragingival and subgingival plaque samples was performed at week 12 using vPCR at Azenta Life Sciences, Inc. (South Plainfield, NJ). Plaque samples were treated with PMAxx™ dye (Biotium, San Francisco, CA) soon after their collection to a final concentration of 100 µM, followed by photolysis with blue light for 15 min to inactivate dead bacterial cell DNA. Excess dye was neutralized using Tris-Cl buffer to a final concentration of 5 mM, followed by another cycle of photolysis. After standard DNA extraction, vPCR was performed using a vendor-optimized protocol based on SYBR GREEN chemistry. Target detection included total bacteria using the 16S rRNA universal primer pair 5’-GTGSTGCAYGGYTGTCGTCA-3’ and 5’-ACGTCRTCCMCACCTTCCTC-3’; Actinomyces oris , using the 16S rRNA primer pair 5’-TCGACCTGATGGACGTTTCGC-3’ and 5’-ACGGTTGGCATCGTCGTGTT-3’; Fusobacterium nucleatum , using the RpoB primer pair 5’-GGTTCAGAAGTAGGACCGGGAGA-3’ and 5’-ACTCCCTTAGAGCCATGAGGCAT-3’; and Porphyromonas gingivalis , using RpoB primer pair 5’-TTGCTGGTTCTGGATGAGTG-3’ and 5’-CAGGCACAGAATATCCCGTATTA-3’.

Microbiome computational analysis

Raw DNA sequence reads were processed and quality filtered by CosmosID. Bacterial diversity analyses were performed using R statistical programming language version 3.6.1 [ 29 ]. Alpha-diversity was assessed using the vegan package version 2.5.6 [ 30 ] and included observed richness and Shannon-Weaver diversity indices at the species taxonomic level. Statistical comparisons between the treatment groups were evaluated using mixed effects model for repeated measures with baseline covariate and terms for treatment, visit, treatment-by-visit, and baseline-by-visit, and unstructured within-subject covariance. Based on this model, pair-wise comparisons were tested, each at the 5% significance level, two-sided, between each mouthrinse containing group and floss containing group with B and between each mouthrinse containing group with BF. Statistical significance between the healthy and gingivitis cohorts was tested at the 5% significance level, two-sided, using two-sample t -test assuming unequal variance.

Beta-diversity analysis was performed using the phyloseq package version 1.28.0 [ 31 ] to calculate the phylogenetic distance matrix by weighted UniFrac [ 32 ] and ordination using principal coordinate analysis. The input phylogenetic tree was constructed using GenBank Common Tree based on the data taxonomy table. Significance testing of factors and interactions that affect bacterial compositions was performed with permutational multivariate analysis of variance (PERMANOVA) [ 33 ] using adonis in the vegan package [ 30 ].

For bacterial abundance quantification, standard calibration curves of reference control DNA were evaluated for individual samples [ 21 ]. The DNA amounts of bacterial species were calculated using the linear regression of added amounts of reference control DNA vs. output relative abundances and genome molecular weights specific for each bacterial species from GenBank [ 34 ]. The resulting bacterial abundances were expressed in units of calculated microbial units (CMUs) and represented in base 10 log where appropriate.

For the quantitative assessment of product intervention, bacterial species were classified into specific categories based on their association with oral conditions. These included commensal, malodor, gingivitis, and acidogenic bacterial groups. The classification was based on a review of the primary scientific literature, including journal research articles and clinical research reports as well as annotations from the Human Oral Microbiome Database [ 35 ]. The abundance of bacterial species associated with these different categories was log10-transformed and aggregated per sample basis, and the means of log10 values from all samples were reported.

Study group characteristics

A summary of subject recruitment and a list of baseline demographic and oral health parameters are presented in Fig.  1 ; Table  1 . This trial enrolled 300 generally healthy adults, of which 16 discontinued. For full data analysis, 288 subjects were evaluated including those that partially completed the study with primary and secondary evaluations performed at baseline and at least one post-baseline visit: 30 subjects were in good periodontal health, whereas 256 had gingivitis and were randomized into five treatment arms: 53 in the brushing only group (B); 50 in the brushing and flossing group (BF); 51 in the brushing and rinsing with Listerine® Cool Mint® Antiseptic group (BA); 52 in the brushing and rinsing with Listerine® Cool Mint® Zero Alcohol group (BZ); and 52 in the brushing, flossing, and rinsing with Listerine® Cool® Mint Zero Alcohol group (BFZ). All treatments in this trial were well tolerated. The mean (SD) ages of the healthy subjects and subjects with gingivitis were 52.0 (16.2) years and 43.5 (14.0) years, respectively, with the majority of study participants being females (78.6%), Caucasian (88.2%), and non-smokers (97.5%). The whole-mouth and interproximal baseline oral health parameters were significantly distinct between the healthy and gingivitis cohorts, as expected based on the subject inclusion criteria, with approximately 0.742 vs. 2.675 for the MGI, 2.592 vs. 3.107 for the TPI, 0.012 vs. 0.326 for the EBI, and 0.869 vs. 2.186 for the PPD ( p- values < 0.001).

Study design flow chart and subject recruitment

Bacterial profiling of supragingival plaque

Metagenomic sequencing of supragingival plaque identified a total of 574 unique taxa at the species level (Table  2 ). Extensive clinical and scientific literature reviews of species identities helped to classify these taxa with clinical relevance (Additional File 1). At the study level, 236 species were identified as belonging to the human oral cavity, 228 were identified as transient or extraoral, and the remaining 109 were unknown or unclassified. At the individual subject level, there were, on average, 155 distinct species, of which 120 were identified as oral residents, nine were found to be transient or extraoral, and 26 were unknown or unclassified. While certain oral bacterial species overlapped across different categories, approximately 91 were commensal, whereas 28 were associated with gingivitis, 16 with malodor, and six with acidogenesis. No statistically significant differences in the species classification were observed between the healthy and gingivitis cohorts (Table  2 , p - values > 0.512).

Healthy vs. gingivitis supragingival plaque microbiota

Despite significant differences in the mean demographic age ( p  = 0.012) and clinical oral health parameters between the periodontally healthy and gingivitis cohorts (Table  1 ), microbiome analysis of supragingival plaque at subject recruitment showed no statistically significant differences in α-diversity measures, such as the Shannon-Weaver Diversity Index (Fig.  2 b, p =  0.336) or observed species richness (Fig.  2 c, p =  0.147), as well as β-diversity using weighted UniFrac PCoA analysis (Fig.  3 Baseline Visit). This compositional similarity coincided with the baseline whole-mouth and interproximal mean TPI scores showing the least amount of differentiation (Table  1 , Δ = 0.5) compared to MGI or EBI (Table  1 , Δ = 2 or 3). Quantification of total plaque bacteria, however, showed that healthy subjects had significantly lower abundances compared to subjects with gingivitis (Fig.  2 a, p  = 0.012). A detailed low-level comparison of individual bacteria demonstrated that 36 species were significantly more abundant in subjects with gingivitis than in healthy subjects (Table  3 ).

Impact of the oral care regimen on supragingival plaque

Quantitative analysis of supragingival plaque collected from subjects with gingivitis revealed significant differences between the mechanical and chemotherapeutic actions of oral care regimen after 4 weeks and 12 weeks. Specifically, compared to B, BF had no effects on Shannon-Weaver Diversity, observed species richness, total bacteria abundance, and β-diversity assessed by weighted UniFrac, showing lack of antimicrobial control against supragingival plaque (Figs. 2 and 3, BF vs. B). Further detailed analyses demonstrated that BF had no effects against commensal, gingivitis, malodor, or acidogenic groups of bacteria (Fig.  4 , BF vs. B). Moreover, at the individual species level, there were no significant differences in bacterial abundances between the BF and B groups except for 11 commensal species, which increased in abundance after 12 weeks (Table  4 , BF vs. B). The clinical endpoint measures for plaque also showed no statistically significant differences between B and BF [ 22 ] using interproximal mean TPI at week 4 ( p =  0.696) and at week 12 ( p =  0.164) and whole-mouth mean TPI at week 4 ( p =  0.430) and at week 12 ( p =  0.229).

Microbiome assessment of supragingival plaque. The means of ( a ) total oral bacteria abundance in log10 CMU, ( b ) Shannon-Weaver diversity index, and ( c ) observed species richness are shown. Dots represent individual samples. ns = not significant, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Weighted UniFrac principal coordinate analysis demonstrating time-resolved changes in the beta-diversity of the supragingival plaque microbiome after 4 weeks and 12 weeks of oral care regimen

Impact of the oral care regimen on the supragingival plaque microbiome. The mean abundances of bacterial species that are ( a ) oral commensal, ( b ) associated with gingivitis, ( c ) producing volatile sulfur compounds, and ( d ) acidogenic are shown. Error bars represent the standard error of the mean. ns = not significant, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

In contrast, however, the mouthrinse containing BA, BZ, and BFZ groups had significant reductions in Shannon-Weaver Diversity, observed species richness, and total bacteria compared to the B or BF groups (Figs. 2 and 3, BA, BZ, BFZ). Complete eradication of the supragingival plaque microbiota was not observed, but the results showed attenuated α-diversity and bacterial abundances consistent with microbial ecology curtailed of biomass accumulation. Amongst the mouthrinsing groups, impact assessment against clinically relevant groups of bacteria revealed that the BA group had greater bacterial reductions than the BZ and BFZ groups, likely arising from differences in formulations (Fig.  4 ). Comparisons versus the BF group showed that, after 4 and 12 weeks, BA significantly reduced bacterial abundances by 82.0% and 75.4% for commensal species, 93.6% and 91.3% for gingivitis species, and 88.5% and 85.2% for malodor species, respectively. BZ, on the other hand, significantly reduced bacterial abundances by 58.2% and 46.6% for commensal species, 85.8% and 80.2% for gingivitis species, and 68.5% after 4 weeks for malodor species after 4 and 12 weeks, respectively. While there were no statistically significant differences between the BZ and BFZ groups, comparisons versus the BF group showed that BFZ significantly reduced bacterial abundances for commensal species by 52.6% after 4 weeks; 84.5% and 75.9% for gingivitis species after 4 weeks and 12 weeks, respectively; and 60.7% for malodor species after 4 weeks. A detailed list of the individual bacterial species significantly impacted by the oral care regimen is presented in Table  4 . No effects were observed against acidogenic bacteria (Fig.  4 d), which were poorly represented in the collected specimens (Table  4 , acidogenic species), likely owing to the trial exclusion of subjects with active caries or significant carious lesions. The clinical endpoint measures for plaque showed statistically significant reductions for the mouthrinse containing BA, BZ, BFZ groups after 4 weeks and 12 weeks when compared to B using interproximal mean and whole-mouth mean TPI scores ( p <  0.001) with BA showing the largest degree of reduction while BZ and BFZ showed similar reductions [ 22 ].

Enumeration of viable bacteria on supragingival and subgingival plaque

Live bacteria that remained in the plaque were quantified using vPCR targeting total bacteria and three indicator species for precise comparisons of the oral care regimen after 12 weeks (Fig.  5 ). While very low abundances of live P. gingivalis were detected throughout, the results showed marked differences in antimicrobial control based on the plaque location and mechanical and chemotherapeutic actions of the oral care regimen. In supragingival plaque, BF had no effects, while BA, BZ, and BFZ significantly reduced total bacteria and indicator species similarly to metagenome sequencing results (Fig.  5 a; Table  4 ). A synergistic effect of combining flossing and rinsing (BFZ) was observed against F. nucleatum and P. gingivalis (Fig.  5 a, BFZ). In subgingival plaque, flossing (BF) and mouthrinsing (BA, BZ) by themselves generally had no effects against total bacteria and indicator species, except for flossing (BF) against P. gingivalis (Fig.  5 b). However, synergy was observed for the combined flossing and rinsing regimen (BFZ) against total bacteria, F. nucleatum , and P. gingivalis (Fig.  5 b, BFZ). While the supragingival vPCR results provided support for the quantitative microbiome analysis, the subgingival vPCR results also showed the same trend in clinical endpoint measures for the whole-mouth mean and interproximal mean EBI and MGI scores [ 22 ]. The clinical scores showed BF and mouthrinse containing BA, BZ and BFZ groups significantly reduced bleeding and inflammation after 4 weeks ( p <  0.001) and 12 weeks ( p  < 0.001) with BFZ showing the largest degree of reduction reflecting the synergistic antimicrobial effect against F. nucleatum and P. gingivalis subgingivally.

Viability qPCR results demonstrating the impact of the oral care regimen on total oral bacteria and select indicator species. The means of log10 abundance from ( a ) supragingival plaque and ( b ) subgingival plaque are shown. The dots represent individual samples. ns = not significant, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

This 12-week clinical trial investigated the effects of brushing with a sodium monofluorophosphate toothpaste, plus virtually supervised flossing, and/or using EO-containing mouthrinse regimens [ 22 ] on the microbiota of supragingival and subgingival plaque. While clinical reports of superior plaque control by mouthrinses compared to flossing are on the rise [ 6 , 7 , 36 , 37 , 38 , 39 ], there is paucity of information on how plaque biofilms are affected by mechanical and chemotherapeutic means of intervention, including how constituent bacterial species and their microbial ecology respond over time.

In this trial, subjects with mild gingivitis used specific oral care regimens for 4 weeks and 12 weeks and returned to the clinic for oral and microbiome evaluations 8–18 h after the last intervention. Subjects in good periodontal and general health were also included at the baseline visit in an observational capacity to determine if different signatures of supragingival plaque microbiome exist compared to the mild gingivitis cohort. While large differences were noted in the whole-mouth and interproximal mean clinical scores for MGI and EBI, TPI showed the least amount of differentiation (Table  1 ) between these cohorts at recruitment and no significant high-level differences were noted in their microbiome compositions using the α- and β- diversity results (Fig.  2 b, c and 3 baseline visit). Total bacterial abundance results, however, showed the mild gingivitis subjects significantly had 44% higher overall abundance compared to the healthy cohort (Fig.  2 A baseline, Δ = 0.25, p  = 0.012) with detailed low-level comparisons showing the presence of 36 species that were more abundant in gingivitis subjects (Table  3 ). There were no clearly differentiated microbial clusters of health vs. disease recognizable of Socransky’s subgingival plaque microbial complexes [ 17 ] or Kolenbrander’s coaggregation-based ecological succession [ 40 ] observed in this study population. However, these results demonstrate the importance of biomass accumulation in mild gingivitis subjects which is seldom investigated using relative abundance analysis offered by conventional next-generation DNA sequencing-based approach and point to the presence of different grades of periodontally healthy and early gingivitis states that show large degree of similarity in qualitative microbial diversity assessments.

The plaque microbiota represented in this mild gingivitis population exhibited both long-term accumulated product intervention effects and a short period of bacterial regrowth and recolonization. The quantitative results of supragingival plaque confirmed that daily brushing and flossing alone were insufficient to effectively manage plaque above the gingival margin (Figs. 2, 3 and 5 a and a). These supragingival plaque microbiome results closely mirrored the clinical endpoint measures of interproximal mean and whole-mouth mean TPI scores [ 22 ]. Notably, the mechanical removal of supragingival plaque by brushing or flossing is likely unable to achieve sustained plaque reductions due to the rapid recolonization of plaque bacteria [ 41 ] seeded from unaffected areas of the mouth. The results of the current trial, which showed a lack of significant differences in the microbiome diversity, species richness, and total and individual bacterial abundances between brushing only and brushing and flossing regimens, support this hypothesis (B vs. BF in Figs. 2, 3 and 5 a and a; Table  4 ).

Alcohol and non-alcohol EO-containing mouthrinses demonstrated effective and sustained chemotherapeutic means of managing supragingival plaque by maintaining reduced levels of microbiome diversity and bacterial abundances (BA, BZ, BFZ in Figs. 2, 3 and 5 a and a; Table  4 ). This result is consistent with historically published randomized controlled trials with clinical endpoints of plaque and gingivitis efficacy [ 4 , 5 , 38 , 42 ]. Given the results observed in this trial and the evidence base in the literature to date, we propose the following hypothesis regarding a sequence of three distinct mechanistic actions taking place against the supragingival plaque microbiome. First, 99.9% of plaque bacteria are killed within 30 s of contact [ 43 , 44 , 45 ], as EOs are able to penetrate thick layers of biofilms [ 46 ]. This bactericidal effect, however, is not permanent, since no complete eradication of plaque microbiota is achieved, consistent with total bacteria abundance results from the present trial and the published body of bacterial colony counting data from past clinical studies [ 5 , 11 , 13 , 38 ]. Second, given the different antimicrobial properties of EOs compared to cationic antimicrobials that have substantivity, such as chlorhexidine gluconate or cetylpyridinium chloride [ 47 , 48 , 49 , 50 ], there is an attenuated level of bacterial re-seeding taking place from other areas of the mouth that facilitates plaque recolonization within a few hours. This nascent plaque is enriched with commensal bacteria, while pathogenic species associated with gingivitis or malodor are impeded due to their slow growth rates [ 51 , 52 ]. The late-colonizing pathogenic species have specific requirements for metabolic and structural support from secondary and tertiary coaggregating partner species during dental plaque biofilm development [ 17 , 53 , 54 , 55 ]. Our study results corroborate a large presence of commensal bacteria compared to gingivitis or malodor associated bacteria after mouthrinsing regimens (Fig.  4 , cca. 0.3–1.1 × 10 8 commensal versus cca. 1.5–3.8 × 10 6 for gingivitis or malodor associated bacteria). Third, repeated twice-daily usage of EO mouthrinses helps to continually curtail plaque build-up, which prevents the maturation of biofilm and proliferation of pathogenic species associated with gingivitis and malodor, and lowers the total bacterial bioburden contributing to the maintenance of a health-associated stable oral microbial community or eubiosis (Table  4 ; Fig.  2 a).

The analysis of subgingival plaque in this study indicated a potentially important contribution of mechanical flossing in oral health maintenance. Viable bacteria enumeration by vPCR showed that flossing can act synergistically with mouthrinsing to reduce total bacteria and F. nucleatum below the gingival margin (Fig.  5 b, BFZ) and can selectively exert significant control against P. gingivalis (Fig.  5 b, BF). Interestingly, these subgingival plaque vPCR results were also observed in the clinical endpoint measures of bleeding and inflammation as assessed using the interproximal and whole-mouth mean EBI and MGI scores [ 22 ] which provides support for the importance of mechanical flossing controlling subgingival plaque in synergy with mouthrinsing. This finding also supports other previous studies that demonstrated clinical improvements in gingival inflammation and bleeding scores despite poor plaque reduction by flossing [ 6 , 7 , 36 , 37 , 38 , 39 ] and sheds light on how specific oral care regimens differentially affect distinct communities of the oral microbiome. Further quantitative research is required to understand the ability of different oral care regimens and products to reach not only subgingival plaque but also other oral surfaces, such as the gingiva, cheeks, tongue, oropharynx, and saliva. In addition, immunological evaluation of pro- and anti-inflammatory cytokines with respect to the microbial community clusters that exist during the progression of different gradation of periodontal health and disease are important considerations for future studies to better understand the dynamic nature of microbial recolonization. Such a detailed assessment of microbial ecology is of significant interest for public health, as many oral bacterial species are implicated in various systemic health or disease conditions.

The results of this 12-week randomized clinical trial provide numerical details of how mechanical and chemotherapeutic oral care regimens affect supragingival and subgingival microbiota. Brushing with a sodium monofluorophosphate toothpaste and flossing with a non-antimicrobial waxed dental floss alone do not appear to provide adequate control of plaque above and below the gingival margin, as constituent bacteria were unaffected, and there were no significant differences in bacterial abundances compared to the brushing control (Figs.  2 , 4 and 5 ; Table  4 , BF vs. B). However, alcohol and non-alcohol EO mouthrinses effectively managed supragingival plaque via a quick chemotherapeutic bactericidal mechanism of action, which appeared to be short-termed and allowed attenuated plaque regrowth enriched with commensal species (Figures, 2, 4, 5a, Table  4 , BA, BZ). Furthermore, analysis of subgingival plaque when flossing is used in combination with mouthrinsing seemed to implicate a role for mechanical flossing in enabling the antimicrobial effectiveness of EO mouthrinses below the gingival margin (Fig.  5 b, BFZ). In conclusion, this trial highlights the superior efficacy of EO mouthrinses at controlling plaque without adversely affecting its microbial ecology and elevates the role of alcohol and non-alcohol EO-containing mouthrinses beyond flossing, in conjunction with toothbrushing.

Data availability

Shotgun metagenomic sequence data and sample metadata information are available in the NCBI BioProject database under accession number PRJNA984617.

Abbreviations

Brushing only

Brushing and flossing

Brushing and rinsing with Listerine® Cool Mint® Antiseptic

Brushing, flossing, and rinsing with Listerine® Cool Mint® Zero Alcohol

Brushing and rinsing with Listerine® Cool Mint® Zero Alcohol

Expanded bleeding index

Essential oil

Modified gingival index

Not significant

Principal coordinate analysis

• Propidium monoazide

Periodontal pocket depth

Turesky Plaque Index

Viability polymerase chain reaction

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Acknowledgements

The authors gratefully acknowledge Michael Lynch and Marsha Tharakan for manuscript writing support and review, Kathleen Boyle for manuscript submission, and Kaylie Wills, BSDH for clinical trial coordination.

This trial was funded by Johnson & Johnson Consumer, Inc. (JJCI; Skillman, NJ, USA).

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MLB, GJ, KM, and JAM contributed to the study conception and design. JM and KRM executed the clinical trial. ADS contributed to clinical protocol writing, trial management, and supervision. KM carried out bioinformatic processing of microbiome data. KM and JAM performed data analysis and interpretation. JAM performed statistical analysis. KM wrote the manuscript. All co-authors reviewed the manuscript.

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This trial was sponsored by Johnson & Johnson Consumer Inc., (JJCI; Skillman, NJ, USA). KM, MLB, GJ, JAM, and AD contributed to the study while employed by JJCI. JM and KRM are directors at Salus Research, Inc. (Fort Wayne, IN, USA), an independent research site approved by the American Dental Association. JM and KRM received grants from JJCI and conducted the trial on behalf of JJCI. JM and KRM declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval and consent to participate

The study protocol, informed consent documents, and study materials were reviewed and approved by Veritas IRB, Inc. (Quebec, Canada), an independent third-party research ethics committee, on April 04, 2022, reference number 2022-3010-10278-1. Written informed consent was obtained from all subjects. The CONSORT statement was followed for the reporting of this randomized clinical trial.

JM and KRM are directors at Salus Research, Inc. (Fort Wayne, IN, USA), an independent research site approved by the American Dental Association. JM and KRM received grants from JJCI and conducted the trial on behalf of JJCI. JM and KRM declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Min, K., Bosma, M.L., John, G. et al. Quantitative analysis of th e effects of brushing, flossing, and mouthrinsing on supragingival and subgingival plaque microbiota: 12-week clinical trial. BMC Oral Health 24 , 575 (2024). https://doi.org/10.1186/s12903-024-04362-y

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Public and patient involvement in quantitative health research: A statistical perspective

Ailish hannigan.

1 Public and Patient Involvement Research Unit, Graduate Entry Medical School, University of Limerick, Limerick, Ireland

2 Health Research Institute, University of Limerick, Limerick, Ireland

The majority of studies included in recent reviews of impact for public and patient involvement (PPI) in health research had a qualitative design. PPI in solely quantitative designs is underexplored, particularly its impact on statistical analysis. Statisticians in practice have a long history of working in both consultative (indirect) and collaborative (direct) roles in health research, yet their perspective on PPI in quantitative health research has never been explicitly examined.

To explore the potential and challenges of PPI from a statistical perspective at distinct stages of quantitative research, that is sampling, measurement and statistical analysis, distinguishing between indirect and direct PPI.

Conclusions

Statistical analysis is underpinned by having a representative sample, and a collaborative or direct approach to PPI may help achieve that by supporting access to and increasing participation of under‐represented groups in the population. Acknowledging and valuing the role of lay knowledge of the context in statistical analysis and in deciding what variables to measure may support collective learning and advance scientific understanding, as evidenced by the use of participatory modelling in other disciplines. A recurring issue for quantitative researchers, which reflects quantitative sampling methods, is the selection and required number of PPI contributors, and this requires further methodological development. Direct approaches to PPI in quantitative health research may potentially increase its impact, but the facilitation and partnership skills required may require further training for all stakeholders, including statisticians.

1. BACKGROUND

Public and patient involvement (PPI) in health research has been defined as research being carried out “with” or “by” members of the public rather than “to,” “about” or “for” them. 1 PPI covers a diverse range of approaches from “one off” information gathering to sustained partnerships. Tritter's conceptual framework for PPI distinguished between indirect involvement where information is gathered from patients and the public, but they do not have the power to make final decisions and direct involvement where patients and the public take part in the decision‐making. 2

A bibliometric review of the literature reported strong growth in the number of published empirical health research studies with public involvement. 3 In a systematic review of the impact of PPI on health and social care research, Brett et al 4 reported positive impacts at all stages of research from planning and undertaking the study to analysis, dissemination and implementation. The design of the majority of empirical research studies included in both reviews was qualitative (70% of studies in Brett. et al 4 and 73% in Boote et al 3 ). More significant tensions have been reported in community‐academic partnerships that use quantitative methods rather than solely qualitative methods, for example tensions with the community about having and recruiting to a “no intervention” comparison group. 5 Particular challenges for PPI have been reported in the most structured and regulated of quantitative designs, that is a randomized controlled trial (RCT), where there is little opportunity for flexibility once the trial has started 6 and Boote et al 3 concluded that researchers may find it easier to involve the public in qualitative rather than quantitative research.

If the full potential of PPI for health research is to be realized, its potential and challenges in quantitative research require more exploration, particularly the features of quantitative research which are different from qualitative research, for example, sampling, measurement and statistical analysis. Statisticians in practice have a long history of working with a variety of stakeholders in health research and have examined the difference between an indirect or consulting role for the statistician and a more direct, collaborative role, 7 yet their perspective has never been explicitly explored in health research with PPI. The objective of this study therefore was to critically reflect on the potential and challenges for PPI at distinct stages of quantitative research from a statistical perspective, distinguishing between direct and indirect approaches to PPI. 2

2. SAMPLE SIZE AND SELECTION

Quantitative research usually aims to provide precise, unbiased estimates of parameters of interest for the entire population which requires a large, randomly selected sample. Brett et al 4 reported a positive impact of PPI on recruitment in studies, but the representativeness of the sample is as important in quantitative research as sample size. Studies have shown that even when accrual targets have been met, the sample may not be fully representative of the population of interest. In cancer clinical trials, for example, those with health insurance and from higher socio‐economic backgrounds can be over‐represented, while older patients, ethnic minorities and so‐called hard‐to‐reach groups (often with higher cancer mortality rates) are under‐represented. 8 This limits the ability to generalize the results of the trials to all those with cancer. There is evidence that a direct approach to PPI with sustained partnerships between community leaders, primary care providers and clinical trial researchers can be effective in increasing awareness and participation of under‐represented groups in cancer clinical trials 9 , 10 and therefore help to achieve the goal of a population‐representative sample.

Collecting representative health data for some groups in the population may only be possible with their involvement. Marin et al 11 reports on the challenges of identifying an appropriate sampling frame for a health survey of Aboriginal adults in Southern Australia. Access to information identifying Aboriginal dwellings was not publically available, making it difficult to randomly select participants for large population household surveys. Trying to overcome this challenge involved reaching agreement on the process of research for Aboriginal adults with their local communities. An 8‐month consultation process was undertaken with representatives from multiple locations including Aboriginal owned lands in one region; however, it was ultimately agreed that it was culturally inappropriate for the research team to survey this region. The study demonstrated the opportunities for PPI in quantitative research with a representative sample of randomly chosen Aboriginal adults (excluding those resident in one region) ultimately achieved but also the challenges for PPI. The direct approach to involvement in this study, after a lengthy consultation process, resulted in a decision not to carry out the planned sampling and data collection in one region with implications for generalization of results and overall sample size.

Of course, given the importance of representativeness in quantitative research, there may be particular challenges for statisticians and quantitative researchers in accepting the term patient or public representative with some suggesting PPI contributor as a more appropriate term. 6 PPI representative may suggest to a quantitative researcher that an individual patient or member of the public is typical of an often diverse population, yet there is evidence that the opportunities and capacity to be involved as PPI contributors vary by level of education, income, cognitive skills and cultural background. 12 Dudley et al carried out a qualitative study of the impact of PPI in RCTs with patients and researchers from a cohort of RCTs. 6 The types of roles of PPI contributors described by researchers involved in the RCTs were grouped into oversight, managerial and responsive roles. Responsive PPI was described as informal and impromptu with researchers approaching multiple “responsive” PPI contributors as difficulties arose, for example advising on patient information sheets and follow‐up of patients. It was reported that contributions from responsive roles may carry more weight with the researchers in RCTs because it allowed access to a more diverse range of contributors who researchers saw as more “representative” of the target population.

3. MEASUREMENT

Measurement of quantitative data involves decisions about what to measure, how to measure it and how often to measure it with these decisions typically made by the research team. Without the involvement of patients and the public, however, important outcomes for people living with a condition have been missed or overlooked, for example fatigue for people with rheumatoid arthritis 13 or the long‐term effects of therapy for children with asthma. 14

Core outcome sets (COS) are a minimum set of agreed important outcomes to be measured in research on particular illnesses, conditions or treatments to ensure important outcomes are consistently reported and allow the results from multiple studies to be easily combined and compared. Young reported on workshops to explore what principles, methods and strategies that COS developers may need to consider when seeking patient input into the development of a COS. 15 The importance of distinguishing between an indirect role for patients in COS development where patients respond to a consensus survey or a direct role where patients are partners in planning, running and disseminating a COS study was highlighted by delegates in the workshops. While all delegates agreed that participation by patients should be meaningful and on an equal footing with other stakeholders, there was considerable uncertainty on how to achieve this, for example how many patients are needed in the COS development process or what proportion of patients relative to other stakeholders should be included. This raises the issue again of the number and selection of PPI contributors for quantitative researchers, and it was concluded that methodological work was needed to understand the COS development process from the perspective of patients and how the process may be improved for them.

Important considerations in longitudinal research are the number and timing of repeated measurements. From a statistical perspective, measurements on the same subject at different times are almost always correlated, with measurements taken close together in time being more highly correlated than measurements taken far apart in time. Unequal spacing of observation times may be more computationally challenging in statistical analysis of repeated measurements and missing data within subjects over time can be particularly challenging depending on the amount, cause and pattern of missing data. 16 There are therefore important statistical considerations to be taken into account in the design of longitudinal studies but these have to be balanced with input from PPI contributors on appropriate timing and frequency of data collection for potential participants.

Lucas et al reported on how European birth cohorts are engaging and consulting with young birth cohort members. 17 Of the 84 individual cohorts identified, only eight had a mechanism for consulting with parents and three a mechanism for consulting with young people themselves (usually “one off” consultations). Very varied follow‐up rates were reported from 13% to 84% more than 10 years after enrolment for individual data rounds of the birth cohorts. 17 Being motivated to continue to participate may be influenced by whether a participant believes the study is interesting, important, or relevant to them. 18 One of the key strategies for retention in the Australian Aboriginal Birth Cohort study was partnerships with community members with local knowledge who were involved in all phases of the follow‐up. 19 Retention rates of 86% at 11‐year follow‐up and 72% at 18‐year follow‐up were reported which demonstrates the potential of a direct approach to PPI. Ethical approval for the study involved an Aboriginal Ethical Sub‐committee which had the power of veto and a staged consent was used where participants had the right to refuse individual procedures at each wave. As with all missing data, this has implications for the statistical analysis yet only 10% of participants in this study chose to opt out of different assessments at follow‐up.

3.1. Statistical analysis

A report on the impact of PPI found that it had a positive impact at all stages of qualitative research including data analysis but that there was little evidence of its impact on quantitative data analysis. 20 It was concluded this lack of evidence may reflect a lack of involvement rather than an evidence gap. Booth et al 3 also suggested that the public may be more comfortable with interpreting interview and focus group data compared with numeric data. Low levels of numerical and statistical literacy in the general population may contribute to this.

Statistical analysis involves describing the data using appropriate graphical and numerical summaries (descriptive statistics) and using more advanced statistical methods to draw inferences about the population using the data from a sample (statistical inference). Choosing appropriate methods for statistical inference, testing the underlying assumptions and checking the adequacy of the models produced requires advanced statistical training and implementing them typically involves the use of statistical software or programming. Statisticians bring this expertise to quantitative health research and while it is important that the chosen methods are adequately communicated to all stakeholders, replicating this type of expertise in PPI contributors seems like an inefficient use of resources for PPI.

Quantitative data are, however, “not just numbers, they are numbers with a context” 21 and most practising statisticians agree that knowledge of the context is needed to carry out even a purely technical role effectively. 22 While many associate statistical analysis with objectivity, in practice, statisticians routinely use “subjective” external information to guide, for example the decision on what is a meaningful effect size; whether an outlier is an error in data entry or represents an unusual but meaningful observation; and potential issues with measurement of variables and confounding. 23 Gelman and Hennin argue that we should move beyond the discussion of objectivity and subjectivity in statistics and “replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence.” 23 This debate within statistics is relevant for PPI where the perceived objectivity and standardization of statistical analysis can be used as a reason for lack of involvement.

External information and context are particularly important in statistical modelling where statisticians are often faced with many potential predictors of an outcome. The “best” way of selecting a multivariable model is still unresolved from a statistical perspective, and it is generally agreed that subject matter knowledge, when available, should guide model building. 24 Even when the potential predictors are known, understanding the causal pathways of exposure on an outcome is challenging where the effect of a variable on the outcome can be direct or indirect. Christiaens et al 25 used a causal diagram to visualize the relationship between pain acceptance and personal control of women in labour and the use of pain medication during labour. Their analysis accounted for the maternal care context of the country where the women were giving birth and other characteristics such as age of the woman and duration of labour. The choice of these characteristics was underpinned by a literature review but women who have given birth also have expert knowledge on why they use pain relief and how other variables such as their personal beliefs and social context might influence that decision. 26

Collaborative or participatory modelling is an approach to scientific modelling in areas such as natural resource management which involves all stakeholders in the model building process. Participants can suggest characteristics for inclusion in the model and how they may impact on the outcome. Causal diagrams are then used to create a shared view across stakeholders. 27 Rockman et al 28 concluded, in the context of marine policy, that “participatory modelling has the potential to facilitate and structure discussions between scientists and stakeholders about uncertainties and the quality of the knowledge base. It can also contribute to collective learning, increase legitimacy and advance scientific understanding.”

There is emerging evidence that the importance of PPI in the development and application of modelling in health research is being recognized. Van Voorn 29 discussed the benefits and risks of PPI in health economic modelling of cost‐effectiveness of new drugs and treatment strategies, with public and patients described as the missing stakeholder group in the modelling process. The potential benefits included the expertise that patients could bring to the process, a greater understanding and possible acceptance by patients of the results of the models and improved model validation. The risks included potential patient bias and the increased resources required for training. The number and selection of patients to contribute to the process was also discussed with a suggestion to include patients “who were able to take a neutral view” and “at least five patients that differ significantly in their background,” again highlighting the focus of quantitative researchers on bias and sample size. The role for this type of participatory modelling in informing debate on public health problems is increasingly being recognized, drawing on the experience of its use in other areas where optimal use of limited resources is required to address complex problems in society. 30

4. CONCLUSIONS

Statistical analysis of quantitative data is underpinned by having a representative sample, and there is evidence that a direct approach to PPI can help achieve that by supporting access to and increasing participation of under‐represented groups in the population. The direct approach has also demonstrated its potential in the retention of those recruited over time, thus reducing bias caused by missing data in longitudinal studies. At all stages of statistical analysis, a statistician continuously refers back to the context of the data collected. 22 Lay knowledge of PPI contributors has an important role in providing this context, and there is evidence from other disciplines of the benefits of including this knowledge in analysis to support collective learning and advance scientific understanding.

The direct approach to PPI where patients and the public have the power to make decisions also brings challenges and the statistician needs to be able to clearly communicate the impact of each decision on the scientific rigour and validity of sampling, measurement and analysis to all stakeholders. Decisions made on participation impact on generalizability. Participatory modelling requires facilitation and partnership skills which may require further training for all stakeholders, including statisticians.

The direct and indirect role for PPI contributors mirrors what happens for statisticians in practice. Statisticians can have a consultative role, that is answering a specific statistical question or a collaborative role where a statistician works with others as equal partners to create new knowledge, with professional organizations for statisticians providing guidance and mentorship on moving from consulting to collaboration to leadership roles. 7 , 31 Statisticians therefore bring very relevant experience and understanding for PPI contributors on the ladder of participation in health research. Further exploration is required on the impact of direct compared to indirect involvement in quantitative research, drawing on the evidence base for community‐based participatory research in quantitative designs 9 and the framework for participatory health research and epidemiology. 32 , 33

CONFLICT OF INTERESTS

No conflict of interests.

ACKNOWLEDGEMENTS

Prof. Anne MacFarlane, Public and Patient Involvement Research Unit, University of Limerick, for discussion of ideas and comments on drafts.

Hannigan A. Public and patient involvement in quantitative health research: A statistical perspective . Health Expect . 2018; 21 :939–943. 10.1111/hex.12800 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Approximating R1 and R2: a quantitative approach to clinical weighted MRI

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Weighted MRI images are widely used in clinical as well as open-source neuroimaging databases. Weighted images such as T1-weighted, T2-weighted, and proton density-weighted (T1w, T2w, and PDw, respectively) are used for evaluating the brain’s macrostructure; however, their values cannot be used for microstructural analysis, since they lack physical meaning. Quantitative MRI (qMRI) relaxation rate parameters (e.g., R1 and R2), and related relaxivity coefficients, do contain microstructural physical meaning.

Nevertheless, qMRI is rarely done in large-scale clinical databases.

Currently, the weighted images ratio T1w/T2w is used as a quantifier to approximate the brain’s microstructure. In this paper, we propose three additional quantifiers that approximate quantitative maps, which can help bring quantitative MRI to the clinic for easy use.

Following the signal equations and using simple mathematical operations, we combine the T1w, T2w, and PDw images to estimate the R1 and R2.

We find that two of these quantifiers (T1w/PDw and T1w/ln(T2w)) can serve as a semi-quantitative proxy for R1, and that (ln(T2w/PDw)) can approximate R2.

We find that this approach also can be applied to T2w scans taken from widely available DTI datasets. We tested these quantifiers on both in vitro phantom and in vivo human datasets. We found that the quantifiers accurately represent the quantitative parameters across datasets. Finally, we tested the quantifiers within a clinical context, and found that they retain tissue information across datasets. Our work provides a simple pipeline to enhance the usability and quantitative accuracy of MRI weighted images.

Competing Interest Statement

The authors have declared no competing interest.

Clinical Protocols

https://github.com/MezerLab/semiQ

Funding Statement

This study did not receive external funding

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All study procedures were approved by the Helsinki Ethics Committee from Hadassah Hospital, Jerusalem, Israel

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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