case study vs cross sectional study

Chapter 8. Case-control and cross sectional studies

Case-control studies

Selection of cases, selection of controls, ascertainment of exposure, cross sectional studies.

Chapter 1. What is epidemiology?
Chapter 2. Quantifying disease in populations
Chapter 3. Comparing disease rates
Chapter 4. Measurement error and bias
Chapter 5. Planning and conducting a survey
Chapter 6. Ecological studies
Chapter 7. Longitudinal studies
Chapter 9. Experimental studies
Chapter 10. Screening
Chapter 11. Outbreaks of disease
Chapter 12. Reading epidemiological reports
Chapter 13. Further reading

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

Describes specific characteristics in a population of interest
The most common forms are case reports and case series
In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes. On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

This design is transverse where we take a specific sample at a specific time without any follow-up
It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
This design is easy to conduct
For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
Prospective : we follow the individuals in the future to know who will develop the disease
Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
This design is the strongest among the observational studies
For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
This design is always retrospective
We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
Relatively easy to conduct
For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

Also known as interventional studies
Can involve animals and humans
Pre-clinical trials involve animals
Clinical trials are experimental studies involving humans
In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I: We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

A visual diagram describing the following. Two types of epidemiological studies are descriptive and analytical. Types of descriptive studies are case reports, case series, descriptive surveys. Types of analytical studies are observational or experimental. Observational studies can be cross-sectional, case-control or cohort studies. Types of experimental studies can be lab trials or field trials.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Cluster Randomized Trials: Concepts

This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.

Expertise-based Randomized Controlled Trials

This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.

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Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare. Cohort studies are used to study incidence, causes, and prognosis. Because they measure events in chronological order they can be used to distinguish between cause and effect. Cross sectional studies are used to determine prevalence. They are relatively quick and easy but do not permit distinction between cause and effect. Case controlled studies compare groups retrospectively. They seek to identify possible predictors of outcome and are useful for studying rare diseases or outcomes. They are often used to generate hypotheses that can then be studied via prospective cohort or other studies.

research methods
cohort study
case-control study
cross sectional study

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Cross-Sectional Study: Definition, Designs & Examples

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Cross-Sectional Study	Purpose	Example
	To describe the characteristics of a population.	Examining the dietary habits of high school students.
	To investigate associations between variables.	Studying the correlation between smoking and lung disease in adults.
	To gather information on a population or a subset.	Conducting a survey on the use of public transportation in a city.
	To determine the proportion of a population with a specific characteristic, condition, or disease.	Assessing the prevalence of obesity in a country.
	To examine the effects of certain occupational or environmental exposures.	Studying the impact of air pollution on respiratory health in industrial workers.
	To generate hypotheses for future research.	Investigating relationships between various lifestyle factors and mental health conditions.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
This is the most common type of cross-sectional study.
Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
Comparing grades of elementary school students whose parents come from different income levels
Determining the association between gender and HIV status (Setia, 2016)
Investigating suicide rates among individuals who have at least one parent with chronic depression
Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas cohort studies follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

Frequently asked questions

What’s the difference between a case-control study and a cross-sectional study.

A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease.

On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal here is to describe the characteristics of the population, such as their age, gender identity, or health status, and understand the distribution and relationships of these characteristics.

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Cohort vs Cross-Sectional Study: Similarities and Differences

In a cohort study , the researcher selects a group of exposed and another group of unexposed individuals and follows them over time to determine whether or not a particular outcome of interest will occur.

The objective is to find out which group is more likely to develop the outcome (eg. disease) by comparing its incidence (i.e. the number of individuals who developed this disease) in both groups over that period of time.

In a cross-sectional study , the researcher collects data simultaneously on both exposure and outcome at one given point in time.

The objective is to find out if the exposure is related to the outcome by comparing the prevalence of the outcome (i.e. the proportion of people who have the disease) in exposed and unexposed individuals.

graphical representation of the cross-sectional study design

Similarities between cohort and cross-sectional designs

1. both are observational studies.

In experiments (a.k.a. Randomized Controlled Trials), the investigator actively determines (in general via random allocation) who gets exposed to the risk factor (or treatment) and who doesn’t.

In observational studies, the investigator is an observer and does not intervene. So the participants are naturally divided into 2 groups: the exposed and the unexposed.

2. Both designs aim to study the association between an exposure and an outcome

So before conducting a cross-sectional or a cohort study, we need to have at least 1 hypothesis on which exposure or risk factor we think may cause the outcome.

We can of course examine multiple hypotheses by testing the association of more than 1 exposure with the outcome.

However, we should keep the number of statistical tests at minimum, as with multiple testing we will be at risk of p-hacking — in simple terms, by doing multiple tests, some associations will appear statistically significant just by chance.

3. Both are subject to information bias

Both cohort and cross-sectional studies are subject to bias in collection of information, errors in measurement of exposure and outcome, misclassification of participants, and bias in data analysis.

4. Both are subject to selection bias

People may refuse to participate in any type of study. The problem is when those who refuse to participate are not a random group people, but instead have higher or lower chance of being exposed (or having the disease) therefore biasing the study results.

5. Both are subject to confounding

Confounding happens when some variable or factor confuses the association between exposure and outcome, tricking us into believing, for example, that there is a statistically significant association between exposure and outcome, when, in reality there isn’t.

What this means is that, if we find an association between exposure and outcome in a cohort or a cross-sectional study, we cannot be 100% sure that it is causal in nature.

Differences between cohort and cross-sectional designs

Where a cohort design is better, 1. a cohort is better for assessing causality.

When trying to determine whether an exposure causes a particular outcome, it is very important that at least the exposure precedes the outcome.

In a cohort design, because we start with exposed and unexposed participants and follow them in time, we can be sure that the exposure occurred before the disease.

In a cross-sectional study, the exposure and the outcome are measured at the same time, so it is harder to determine which comes first.

2. Unlike a cross-sectional study, a cohort is not prone to survival bias

Suppose we have a risk factor that shortens the life of people who are exposed to it.

So if take a snapshot of people who are alive at a certain point in time (i.e. conduct a cross-sectional study), then we are by definition measuring the survivors excluding those who died of the disease caused by the exposure.

This will bias the study toward falsely concluding that the exposure is not related to the disease.

Where a cross-sectional design is better

1. in general, a cross-sectional study is less expensive and less time-consuming.

In a cohort study we need to wait for the outcome to occur. In case of rare outcomes, the follow-up period may be very long (sometimes we will be waiting years for the outcome to develop in enough numbers so that the exposed and unexposed groups can be compared).

A cross-sectional design will be, in general, cheaper and faster to execute.

Here’s an example of how this translates in practice:

Suppose we have a new hypothesis about a causative association between 2 variables. A smart decision might be to start with a cross-sectional design (as it is faster and cheaper), then if the results are positive, replicate the results using a cohort or a randomized controlled trial if possible.

Because it is fast and cheap, a cross-sectional study is useful for assessing the disease burden in society — it is good for examining a change in the prevalence of a disease or an exposure, for instance, for studying the trend in cancer, heart disease, etc.

2. In a cross-sectional study you won’t have to deal with participants follow-up

A cohort design requires following people over a period of time, so participants may be lost to follow-up. This may happen for a variety of reasons, but the problem occurs when loss to follow-up does not happen at random.

For instance, if participants who are more (or less) likely than others to develop the outcome are lost to follow-up the study will be biased.

A cross-sectional study does not suffer from such bias as it does not follow participants in time.

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Lesson 7 - other types of study designs: cross-sectional, ecologic, experimental, lesson 7 objectives.

Compare advantages/ disadvantages of cross-sectional and ecological studies
Describe ecological fallacy
Describe the main difference between observational and experimental studies
Identify design considerations unique to intervention studies including equipoise, randomization, and masking

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The seroprevalence of SARS-CoV-2-specific antibodies in Australian children: A cross-sectional study

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations National Centre for Immunisation Research and Surveillance, Westmead, NSW, Australia, Faculty of Medicine and Health, University of Sydney’s Hospital Westmead Clinical School, Westmead, NSW, Australia, Department of Infectious Diseases, Nepean Hospital, Kingswood, NSW, Australia

Roles Formal analysis, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Faculty of Medicine and Health, University of Sydney’s Hospital Westmead Clinical School, Westmead, NSW, Australia

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Affiliations Faculty of Medicine and Health, University of Sydney’s Hospital Westmead Clinical School, Westmead, NSW, Australia, Department of Infectious Diseases and Microbiology, The Children’s Hospital Westmead, Westmead, NSW, Australia

Roles Data curation, Formal analysis, Software, Writing – review & editing

Affiliations Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Parkville, VIC, Australia, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia

Roles Investigation, Project administration, Writing – review & editing

Affiliation National Centre for Immunisation Research and Surveillance, Westmead, NSW, Australia

Roles Investigation, Resources, Writing – review & editing

Affiliation Infectious Diseases Serology, Victorian Infectious Diseases Reference Laboratory, The Royal Melbourne Hospital at The Doherty Institute, Melbourne, VIC, Australia

Roles Data curation, Formal analysis, Software, Writing – original draft, Writing – review & editing

Affiliation Institute of Clinical Pathology and Medical Research, New South Wales Pathology, Westmead, Australia

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Roles Investigation

Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Affiliation Infection, Immunity & Global Health, Murdoch Children’s Research Institute, Parkville, VIC, Australia

Affiliations Wesfarmers Centre of Vaccine and Infectious Diseases, Telethon Kids Institute, Nedlands, WA, Australia, Centre for Child Health Research, The University of Western Australia, Crawley, WA, Australia

Affiliation Faculty of Health and Medical Sciences, The University of Adelaide, Adelaid, SA, Australia

Affiliations Infection Management, Children’s Health Queensland, Brisbane, QLD, Australia, School of Clinical Medicine, University of Queensland, Herston, QLD, Australia

Affiliations Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Tiwi, NT, Australia, Department of Paediatrics, Royal Darwin Hospital, Tiwi, NT, Australia

Roles Writing – review & editing

Affiliations Department of Infection and Immunity, Monash Children’s Hospital Melbourne, Clayton, VIC, Australia, Department of Paediatrics, Monash University, Clayton, VIC, Australia

Roles Investigation, Supervision, Writing – review & editing

Affiliation Department of Immunology and Infectious Diseases, Sydney Children’s Hospital, Randwick, NSW, Australia

Affiliation Department of Anaesthesia, The Children’s Hospital at Westmead, Westmead, NSW, Australia

Roles Investigation, Resources

Affiliation Melbourne Children’s Trials Centre, Murdoch Children’s Research Institute, Parkville, VIC, Australia

Affiliations Department of Anaesthesia and Pain Medicine, Perth Children’s Hospital, Nedland, WA, Australia, Institute for Paediatric Perioperative Excellence, The University of Western Australia, Perth, WA, Australia

Affiliations Department of Anaesthesia, Queensland Children’s Hospital, South Brisbane, QLD, Australia, Faculty of Medicine, University of Queensland, Herston, QLD, Australia

Affiliation Department of Children’s Anaesthesia, Women’s and Children’s Hospital, North Adelaide, SA, Australia

Affiliation Department of Anaesthesia and Perioperative Medicine, Royal Darwin Hospital, NT, Australia

Affiliation Serology and Virology Division (SAViD), Department of Microbiology, NSW Health Pathology East, Prince of Wales Hospital, Randwick, NSW, Australia

Roles Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Surveillance of Adverse Events Following Vaccination In the Community (SAFEVIC), Murdoch Children’s Research Institute, Parkville, Victoria, Australia

Affiliation Department of Infection and Immunity, Monash Children’s Hospital Melbourne, Clayton, VIC, Australia

Affiliation University Department of Paediatrics, Women’s and Children’s Hospital, North Adelaide, SA, Australia

Affiliation Infectious Disease Epidemiology, Telethon Kids Institute, Nedlands, WA, Australia

Affiliation Department of Paediatrics, Royal Darwin Hospital, Tiwi, NT, Australia

Affiliation Infectious Diseases Research, Children’s Health Queensland, South Brisbane, QLD, Australia

Affiliation Wesfarmers Centre of Vaccine and Infectious Diseases, Telethon Kids Institute, Nedlands, WA, Australia

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

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Archana Koirala,
Jocelynne McRae,
Philip N. Britton,
Marnie Downes,
Shayal A. Prasad,
Suellen Nicholson,
Noni E. Winkler,
Matthew V. N. O’Sullivan,
Fatima Gondalwala,

Published: September 18, 2024
https://doi.org/10.1371/journal.pone.0300555
Reader Comments

Following reduction of public health and social measures concurrent with SARS-CoV-2 Omicron emergence in late 2021 in Australia, COVID-19 case notification rates rose rapidly. As rates of direct viral testing and reporting dropped, true infection rates were most likely to be underestimated.

To better understand infection rates and immunity in this population, we aimed to estimate SARS-CoV-2 seroprevalence in Australians aged 0–19 years.

We conducted a national cross sectional serosurvey from June 1, 2022, to August 31, 2022, in children aged 0–19 years undergoing an anesthetic procedure at eight tertiary pediatric hospitals. Participant questionnaires were administered, and blood samples tested using the Roche Elecsys Anti-SARS-CoV-2 total spike and nucleocapsid antibody assays. Spike and nucleocapsid seroprevalence adjusted for geographic and socioeconomic imbalances in the participant sample compared to the Australian population was estimated using multilevel regression and poststratification within a Bayesian framework.

Blood was collected from 2,046 participants (median age: 6.6 years). The overall adjusted seroprevalence of spike-antibody was 92.1% (95% credible interval (CrI) 91.0–93.3%) and nucleocapsid-antibody was 67.0% (95% CrI 64.6–69.3). In unvaccinated children spike and nucleocapsid antibody seroprevalences were 84.2% (95% CrI 81.9–86.5) and 67.1% (95%CrI 64.0–69.8), respectively. Seroprevalence was similar across geographic remoteness index and socioeconomic quintiles. Nucleocapsid antibody seroprevalence increased with age while the point seroprevalence of the spike antibody seroprevalence decreased in the first year of life and then increased to 97.8 (95% Crl 96.1–99.2) by 12–15 years of age.

Most Australian children and adolescents aged 0–19 years, across all jurisdictions were infected with SARS-CoV-2 by August 2022, suggesting rapid and uniform spread across the population in a very short time period. High seropositivity in unvaccinated children informed COVID-19 vaccine recommendations in Australia.

Citation: Koirala A, McRae J, Britton PN, Downes M, Prasad SA, Nicholson S, et al. (2024) The seroprevalence of SARS-CoV-2-specific antibodies in Australian children: A cross-sectional study. PLoS ONE 19(9): e0300555. https://doi.org/10.1371/journal.pone.0300555

Editor: Caroline Watts, The University of Sydney, AUSTRALIA

Received: February 29, 2024; Accepted: September 1, 2024; Published: September 18, 2024

Copyright: © 2024 Koirala et al. 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. Individual line-listed data cannot be shared publicly because of the identification of Aboriginal and Torres Strait Islander and non-Indigenous children and adolescents in regional Australian settings, in particular participants from remote communities and re-identification of children with rare medical conditions. Data are available from the Sydney Children's Hospitals Network Human Research Ethics Committee (contact via [email protected] ) for researchers who meet the criteria for access to confidential data.

Funding: The study was wholly funded by the Australian Government Department of Health and Aged Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: All authors have no financial interests/personal relationships that may be considered as potential competing interests with this study. Some authors have received institutional research grants and travel funding from the Australian Government, non-government or private organisations for other bodies of work. Dr Archana Koirala, Professor Kristine Macartney and Professor Nicholas Wood declare funding support from the Australian Government Department of Health and Aged Care (DoHAC) to the institution National Centre for Immunisation Research and Surveillance (NCIRS). Dr Archana Koirala has received a travel and accommodation grant to present and attend the World Society of Paediatric Infectious Diseases scientific meeting in 2023. Professor Kristine Macartney received payment as an expert witness for Australian Health Departments against proceedings against COVID-19 health regulations in 2021-2022. Professor Kristine Macartney declares institutional directed funding support from the WHO and GAVI the Vaccine Alliance, Welcome Trust and the Australian NHMR, to NCIRS and the University of Sydney; payment as an expert witness for Australian health departments against proceedings against COVID-19 health regulations in 2021-2022, payment less than USD $5,000 for international travel for expert speaking engagements, non-pharmaceutical company sources. Professor Nicholas Wood, Professor Britta S von Ungern-Sternberg and Dr Brendan McMullan declare funding from the National Health and Medical Research Council (NHMRC) grants. Professor Britta S von Ungern-Sternberg declared funding support from Stan Perron Charitable Foundation. Professor Helen S Marshall declares a research grant from Pfizer to Women’s and Children’s Health Network (WCHN), no personal remuneration, for meningococcal research. Professor Peter Richmond declares research grants from Merck Sharpe & Dohme, GlaxoSmithKline (GSK) directed at the Telethon Kids Institute (TKI), no personal remuneration, on RSV, pneumococcal disease, varicella, meningococcal research and a COVID-19 vaccine study to assess immunogenicity and safety of homologous or heterologous vaccine schedules. Professor Peter Richmond, Professor Helen S Marshall and Professor Nicholas Wood declare a research grant from the Australian Government Department of Health and Aged Care to WCHN, TKI and University of Sydney for a COVID-19 DNA vaccine clinical trial Ms Alissa McMinn declares funding support from Pfizer provided support for flights/accommodation to attend the Public Health Association Australia Communicable Diseases & Immunisation Conference in 2023. Dr Ushma Wadia declares funding from Pfizer for flights and accommodation to attend Meningococcal Disease Vaccine Education in 2023. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

Understanding Severe Acquired Respiratory Syndrome Coronavirus (SARS-CoV-2) population-level infection rates is important in order to inform infection related risk and contextualize severe outcome rates, such as hospitalization and death. Surveillance for SARS-CoV-2 based on case reporting and hospital notification data underestimates the true number of SARS-CoV-2 infections [ 1 ]. Children in particular are subject to underreporting of infections due to higher rates of asymptomatic or mild infections [ 2 – 5 ] and difficulties in testing [ 6 ] but provide a unique population to understand spread, given lower vaccination rates in children compared to adults, and infants < 2 years being born since the 1 st Omicron wave. Serosurveillance, which measures serum antibodies in individuals within a population, can give insights into the cumulative prevalence of infection and/or vaccine uptake over time across a population.

Methods for obtaining blood samples can include residual sera from diagnostic pathology laboratories, blood donors or antenatal collections. However, such approaches result in children being underrepresented due to the lack of routine blood testing, lack of adequate volumes of residual sera and/or reluctance to submit children to painful procedures such as venipuncture, without clear necessity. Obtaining child population representative blood sampling is difficult and costly, resulting in small non representative cohorts. Of 4,160 serosurveys logged on SeroTracker globally: only 323 serosurveys have examined children [ 2 ]. In 2020, a serosurveillance study conducted in New South Wales, the most populous state in Australia, using residual diagnostic sera for testing found that children 0–9 years were the only age group in which recruitment targets were unable to be met [ 6 ].

An Australian pediatric hospital sentinel surveillance program, the Paediatric Active Enhanced Disease Surveillance network (PAEDS), has been operating since 2007. The network consists of eight tertiary pediatric hospitals, across five states and one territory that assess emergency department presentations and hospitalizations of select key communicable diseases [ 1 , 7 , 8 ] and associated syndromes, including COVID-19 [ 9 , 10 ], and Multisystem inflammatory Syndrome in children (or Pediatric inflammatory multisystem syndrome–temporally associated with SARS-CoV-2) [ 11 , 12 ]. Using this network, we previously conducted a SARS-CoV-2 serosurvey in children undergoing an elective anesthetic procedure within PAEDS hospitals, between November 2020 to March 2021. From a sample of 1685 children aged 0–19 years, the national seroprevalence of SARS-CoV-2 spike antibody (S-antibody) was estimated to be <0.6% [ 13 ], consistent with evidence of limited transmission of SARS-CoV-2 in Australia until June 2021 [ 14 ]. This survey [ 13 ] sampled children from all socioeconomic groups, rural and regional areas, and was broadly representative of the Australian population.

Following the emergence of the Omicron variant and easing of social and public health restrictions, very high SARS-CoV-2 case notifications occurred across all age groups in Australia from December 2021 [ 15 ]. Case notification data were not a reliable reflection of infection rates in the population, as tests were costly and required infected individuals or carers to self-notify their positive results.

Vaccines for SARS-CoV-2 became available for adults (including pregnant women) from February 1, 2021, in adolescents 12–15 years from September 13, 2021 and for children aged 5–11 years from January 20, 2022 [ 16 ]. Vaccines used were mRNA based: Pfizer Cominarty (≥ 5 years) and Moderna Spikevax (≥ 6 years); the adenoviral-vectored vaccine: AstraZeneca Vaxzevria (adults ≥ 18 years), and the protein subunit vaccine, Novavax Nuxavoid (≥ 12 years). All available vaccines induced a S-antibody response, but no nucleocapsid antibody (N-antibody) response. As SARS-CoV-2 infection induced a S- and N-antibody response, presence of N-antibody was a marker of infection in those vaccinated. Presence of S- and N-antibody in infants could represent infection or maternal antibody transferred to the fetus during pregnancy. Sequential serosurveys in Australian adult blood donors reported S-antibody positivity rates of > 90% and N-antibody positivity rates that rose from 17% in February to 46% by June 2022 [ 17 ], but no data were available in children. We aimed to estimate SARS-CoV-2 S- and N-antibody seropositivity to calculate a more accurate estimate population infection and hospitalization rates in Australian children and young adults aged 0–19 years after the emergence of the Omicron variant, vaccine rollout and opening of internal borders and easing of public health restrictions in Australia. We also aim to describe S and N-antibody levels in infected children who completed a primary 2-dose vaccination schedule versus unvaccinated children and describe features of COVID-19 hospitalization in the serosurvey participants.

Material and methods

Participants, study setting and recruitment.

Individuals aged 0–19 years were recruited prior to undergoing an elective surgical procedure requiring general anesthesia from June 1, 2022, to August 31, 2022 at one of eight pediatric tertiary referral hospitals across six of eight Australian jurisdictions: Queensland Children’s Hospital, Brisbane, Queensland; Sydney Children’s Hospital Randwick and the Children’s Hospital at Westmead, Sydney, New South Wales; The Royal Children’s Hospital and Monash Medical Centre, Melbourne, Victoria; Women’s and Children’s Hospital, Adelaide, South Australia; and Perth Children’s Hospital, Perth, Western Australia: and Royal Darwin Hospital, Darwin, Northern Territory. Collectively, these states and territory include 96.1% of the Australian pediatric population [ 18 ]. Children who were immunosuppressed or receiving intravenous immunoglobulin were excluded from the study as they may not mount a representative antibody response to infection or because immunoglobulin use could influence detection of SARS-CoV-2-specific antibodies. Participants were preferentially recruited from day stay lists of patients undergoing minor procedures to maximize recruitment of children without complex medical conditions. Written consent, on paper or the Research Electronic Data Capture (REDCap®) online database, was provided by parents/guardians of children aged <18 years and by those aged 18–19 years themselves. In addition, written assent was provided by adolescents aged ≥12 years. Blood collection occurred during intravenous cannulation following anesthetic induction. A questionnaire was administered to obtain demographic information (age, sex, Indigenous status, postcode of residence), history and timing of known SARS-CoV-2 infection, date of COVID-19 vaccination dose 1 and 2 and underlying medical conditions. Indigenous status was self-determined as being of Aboriginal and/or Torres Strait Islander background. Report of past infection was through self-report only, but the dose 1 and 2 vaccination dates were cross checked on the Australian Immunisation Register. An additional questionnaire was administered if infants were <1 year of age to ask about history of maternal SARS-CoV-2 infection and vaccination prior to delivery.

States and territories had differing public health measures and patterns of notified cases. We sought to obtain seroprevalence estimates for each jurisdiction separately, as well as a national estimate. A planned sample size of 385 samples in each jurisdiction was calculated based on a desired maximum 95% confidence interval (CI) width of +/- 5%. Greater precision with a maximum 95% CI width of +/- 3.6% would be achieved if the true population prevalence in the relevant subgroup was 85% or higher.

Sample processing and testing

Blood samples were centrifuged at each site and sera were separated and extracted. The serum samples were coded and de-identified before sending to Victoria Infectious Diseases Reference Laboratory, Melbourne, Victoria for testing. Antibody testing was performed using the Roche Elecsys Anti-SARS-CoV-2 S-antibody and N-antibody electro-chemiluminescence immunoassays which detect antibodies of all immunoglobulin classes against the receptor-binding domain of the S-protein and the N-protein, respectively. Specimens were deemed positive for the Anti-SARS-CoV-2 N-assay if the semiquantitative cut-off index (COI) was ≥1.0, and for the Anti-SARS-CoV-2 S-assay if the quantitative result was ≥0.8 U/ml, as per the manufacturer’s instructions for use for each test. These assays have been used in pediatric serosurveys in England [ 19 , 20 ] and Texas, USA [ 21 ]. Anti-SARS-CoV-2 S-antibody results >250 IU/ml, underwent retesting after a 1:10 dilution to determine the quantitative result.

The manufacturer’s reported anti-S assay sensitivity was 98.8% (95% CI: 98.1–99.3) in individuals who were infected ≥14 days with the ancestral strain and specificity of 100% (95% CI: 99.7–100). The anti-N assay sensitivity was 98.8% (95% CI 98.1–99.3) in individuals who were infected 2–35 weeks prior with the SARS-CoV-2 ancestral strain and specificity was 99.8% (95% CI 99.69–99.88) [ 22 ]. Local validation studies using 252 samples from vaccinated 97 Australian health care workers found 89.7% (95% CI 85.3–92.9) seropositivity for N-antibody in samples collected between collected between 14 and 216 days post illness (unpublished). Assay specificity was assessed as 100% for both S-antibody and N-antibody when tested against a panel of pre-pandemic serum samples from Australian adults (unpublished). Similar high sensitivity and specificity have been elicited in several studies, in both adults and children [ 23 – 26 ].

Data storage and analysis

Information on participants was entered into the REDCap ® database, accessible by the respective PAEDS site in each state. Data linkage was performed to link serosurvey participants to PAEDS SARS-CoV-2 hospitalization data. Serological results were reported via email from the study team to the parents/carers of the participants.

De-identified information from the study database was extracted for analysis using Microsoft Excel, STATA, and R 4.3.1.

As well as calculating crude seroprevalence estimates of anti-SARS-CoV-2 antibodies, we applied multilevel regression and poststratification within a Bayesian framework [ 27 , 28 ] to: (i) separately model the variation in SARS-CoV-2 S- and N- antibodies by the measured demographic covariates of age (0–5 months, 6–11 months, 1–4 years, 5–11 years, 12–15 years, 16–19 years), vaccination status (0, 1, ≥2-doses), socioeconomic quintile (as measured by ABS Socio-Economic Indexes for Areas 2016, Index of Relative Socio-economic Disadvantage), state or territory of residence and residential area of remoteness (major cities, regional, remote); and (ii) obtain prevalence estimates adjusted for imbalances with respect to these covariates in the participant sample relative to the national population aged 0–19 years. This was achieved by weighting model-based prevalence estimates for all possible combinations of these covariates by the corresponding covariate distribution in the population. Estimation assumed a uniform prior distribution for seroprevalence. We summarized seroprevalence estimates for the overall national population aged 0–19 years and various subgroups of interest using the median and 95% credible interval (CrI) of the corresponding posterior probability distribution. Adjusted estimates were not calculated for sex, Indigenous status, presence of co-morbidities, reported history of past infection as population data on these characteristics in combination with other covariates were not available.

Daily cumulative COVID-19 vaccination coverage (%) of doses 1 and 2 between January 1, 2021 and December 31, 2022 were estimated nationally by using the number of vaccinated people in each age group (5–11 years, 12–15 years and ≥16 years from the Australian Immunisation Register at April 2, 2023) as the numerator and the Australian Bureau of Statistics Estimated Resident Population (ABS-ERP) as of June 30, 2021 for each age group as the denominator. Infection fatality rate and infection hospital rate were calculated using data collected through the National Notifiable Diseases Surveillance System (NNDSS), population count from the ABS-ERP June 30, 2021 and adjusted S-antibody seroprevalence in unvaccinated children, determined through this serosurvey.

The S-antibody titers and N-antibody COI for both unvaccinated and 2-dose vaccinated participants were plotted by time from last vaccination or infection (whichever occurred more recently) to time of specimen collection. A locally weighted regression (loess) line was applied, and the overall median was calculated using R. The comparison between these medians was conducted using the Wilcoxon rank-sum test. Participants with reported infection who had received only 1 or >2 vaccines excluded due to low numbers.

Ethics approval for this national study was provided by the Sydney Children’s Hospital Network Human Research Ethics Committee (HREC 18/SCHN/72).

We obtained 2046 samples from 2314 consented participants ( Fig 1 ). Of all samples 2045 were tested for S-antibody and N-antibody; 1 sample was tested solely for N-antibody due to insufficient volume. Blood collection occurred between June 1 and August 31, 2022, at a time of 73.5% and 38.4% 2-dose COVID-19 vaccine coverage in children aged 12–15 years and 5–11 years respectively ( Fig 2 ). The demographic of those tested are described in Table 1 : 872 (42.6%) participants were female and 177 (8.7%) were Aboriginal or Torres Strait Islander peoples. The majority (1315/2043, 64.4%) of children had no underlying medical conditions. Among those with an underlying medical condition, airway/chest disease (29.2%; 213/728), neurological/neuromuscular disease (14.7%; 107/728), cardiac disease (13.9%; 101/728), and gastrointestinal disease (13.3%; 97/728) were most common ( S1 Table ). Of those with reported infection status, past SARS-CoV-2 infection was reported in 49.9% (1014/2033) of participants. Of those with reported vaccination status, 57.8% (1179/2040). were unvaccinated. Participants came from all socioeconomic quintiles and most resided in metropolitan (73.1%; 1495/2046) regions, compared to regional (24.4%; 499/2046) and remote regions (2.5%, 52/2046) ( Table 1 ). A small number of participants resided in the two other small jurisdictions not targeted in the study: Australian Capital Territory (n = 21) and the state of Tasmania (n = 14). Fig 3 shows the postcode of participants compared to Fig 4 , population geographic distribution of Australia.

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a The dots represent the centromere of the postcodes and do not indicate the exact location of the participant’s residence. In the NT, to limit identification of participants in remote communities, participants residing within remote communities (n = 16) have been removed from the map. ACT: Australian Capital Territory, NSW: New South Wales, NT: Northern Territory, QLD: Queensland, SA: South Australia, TAS: Tasmania, VIC: Victoria, WA: Western Australia.

https://doi.org/10.1371/journal.pone.0300555.g003

Crude seroprevalence of S-antibody was 1833/2045 (89.6%) and N-antibody 1309/2046 (64.0%). After adjustment for age, vaccination status, socioeconomic quintile and remoteness index, seroprevalence for S- antibody was 92.1% (95% CrI 91.0–93.3) and N-antibody 67.0% (95% CrI 64.6–69.3).

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Seroprevalence of both S-antibody and N-antibody was similar across jurisdictions and socioeconomic quintiles. N-antibody seroprevalence increased by age but S-antibody point seroprevalence decreased between 0–12 months and increased until 12 years age. ( Fig 5 ). S-antibody was present in all children who received 1 or ≥2 doses of the COVID-19 vaccine and an adjusted estimate of 84.2% (95% CrI 81.9–86.5) of unvaccinated participants. N-antibody was detected at similar levels among ≥2-dose vaccinated (adjusted estimate of 66.5%; 95% CrI 62.9–70.4), 1-dose vaccinated (adjusted estimate of 70.7%; 95% CrI 63.7–77.7) and unvaccinated individuals (adjusted estimate of 67.1%; 95% CrI 64.0–69.8).

Footnote Fig 5: ACT: Australian Capital Territory, NSW: New South Wales, NT: Northern Territory, QLD: Queensland, SA: South Australia, TAS: Tasmania, VIC: Victoria, WA: Western Australia; SEIFA quintile: measured using the Index of Relative Socio-economic Disadvantage; Q1 –highest quintile, and Q5 –lowest quintile.

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In participants who were unvaccinated and reported a past infection, the crude S-antibody positivity was 96.2% (533/554) and crude N-antibody positivity was 88.3% (489/554). In participants who were vaccinated and reported infection, the crude S-antibody positivity was 100% (455/455) and the crude N-antibody positivity was 92.3% (420/455) ( Table 2 ). S and N-antibody positivity among children with 1 or more underlying medical conditions were 88.6% (644/727) and 59.1% (430/728) respectively. A breakdown of seroprevalence by medical conditions can be found in S1 Table .

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In infants aged 0–11 months, S-antibody seroprevalence was high: adjusted estimates in those aged 0–5 months was 89.0% (95% CrI 82.4–95.2) and among those aged 6–11 months it was 83.9% (95% CrI 76.7–89.8). The adjusted N-antibody seroprevalence estimate increased from 36.8% (95% CrI 25.0–48.7) at 0–5 months to 53.5% (95% CrI 43.5–63.9) at 6–11 months ( Table 2 ). Data on past maternal infection and vaccination were available in 107/142 (75.4%) infants aged 0–11 months. S-antibody was detected universally (48/48) in infants aged 0–5 months in mothers who had been vaccinated, regardless of history of prior infant infection. In infants aged 6–11 months, S-antibody was detected in 15/15 infants who had reported prior infection but only in 15/17 infants with no reported prior infection. Seroprevalence of S-and N-antibody within this population is shown on Table 3 .

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Participants who reported a past infection and had received 2-doses of a COVID-19 vaccine had a median S-antibody titer of 12318 U/mL which was significantly (349 U/mL-fold, p<0.001) higher than the median S-antibody titer of 35.3 u/L of unvaccinated participants who reported a past infection. No significant difference was detected in the median N-antibody COI (19.0 in 2-dose vaccinated, 16.3 in the unvaccinated, difference 1.16; p = 0.55) between the two groups. Fig 6 shows there was minimal waning of S-antibody within 300 days in both groups. N-antibody showed the natural rise in COI for the first 14 days and also remained stable within 300 days.

a Locally weight regression (loess) was utilized to create a line of best fit on R.

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In the Australian National Notifiable Diseases Surveillance System dataset, between January 25, 2020 and August 31, 2022, there were 2,236,528 COVID-19 case notifications among children and adolescents aged 0–19 years. Of these 16,358 (0.7%) were hospitalized and or admitted to an Emergency Department with SARS-CoV-2 infection and 26 were reported to have died with COVID-19 [ 30 ]; using Australian Bureau of Statistics (ABS) quarterly population estimate for June 2022 [ 31 ] this equated to a crude case fatality rate of 0.01 per 1000). Alternatively, using cumulative adjusted S-antibody seroprevalence rates for unvaccinated children calculated through this serosurvey as the infection rate, the SARS-CoV-2 infection hospitalization rate (including emergency department encounters) was 307 per 100,000 (95% CrI 299–316 per 100,000 infections) and the infection fatality rate was 0.0049 per 1,000 infections (95% CrI 0.0048–0.0050 per 1,000) in those aged 0–19 years.

Only 23/2046 serosurvey participants (1.1%) had been admitted at one of the PAEDS sentinel hospitals with SARS-CoV-2 infection prior to participating in the serosurvey. Most had underlying medical conditions (19/23; 82.6%). The median age was 1.9 years (age range 2 days to 15.6 years). Twelve were admitted for fever and respiratory distress (COVID-19), 2 were admitted for unrelated bone fractures, 3 for seizures, 1 for management of chronic constipation and 1 for new diagnosis of Type 1 diabetes mellitus. Four children required intensive care admission– 3 for acute SARS-CoV-2 pneumonitis, of which 2 required invasive ventilation and 1 for diabetic ketoacidosis. All 3 children admitted to ICU with COVID pneumonitis had either complex genetic conditions and/ or underlying cardiorespiratory disease.

In Australia by August 2022, the estimated seroprevalence in children and adolescents of SARS-CoV-2 Spike antibody was 92.1% (95% CI 91.0–93.3), and N-antibody was 67.0% (95% CI 64.6–69.3). Thereby indicating the majority of those aged 0–19 years had been infected with SARS-CoV-2. Based on prevalence of S-antibody in unvaccinated children (indicative of infection) the true infection rate may be as high as 84.1%, estimating that over 5 million Australian children have been infected by August 31, 2022. This represents a large increase from August 2021 when <1% of the pediatric population had been infected [ 13 ]. Infection rates were across states and territories, metropolitan, regional and rural geographic regions and across socioeconomic quintiles, suggesting that Omicron variant SARS-CoV-2 infection spread rapidly and uniformly across Australia in a very short period of time. There was a higher rate of S-antibody and N-antibody positivity with increasing age and found that adolescents had similar rates to adult blood donors serosurveys performed between August 23, to September 2022 (N-antibody seroprevalence of 65.2% [95& CI 63.9–66.5]) and November 29 to December 13, 2022 (N-antibody seroprevalence of 70.8% [95% CI 69.5–72.0]) in adult blood donors [ 32 ]. This is likely reflective of higher vaccination coverage and increased infection related to social mixing patterns in adolescents compared to primary school age children.

Despite high infection rates in children, using national notification data, we estimated the infection fatality and hospitalization rates to be very low at 0.0049 per 1,000 (95% CrI 0.0048–0.0050 per 1,000) and 307 per 100,000 (95% CrI 299–316 per 100,000). The rate of hospitalizations caused by COVID-19 may also be much lower as studies including our study have found many children are hospitalized for alternate reasons, whilst having concurrent SARS-CoV-2 infection [ 10 , 33 ]. Our study also shows that majority of children under 5 years, who were ineligible for vaccination at the time of sera collection already had evidence of prior infection. However, S-antibody levels were higher in children who had been infected and vaccinated (hybrid immunity) compared to those only infected. In adults, hybrid immunity has been shown to protect against subsequent symptomatic infection and severe disease [ 34 ]. The clinical benefit of immunization in children with low population infection fatality and infection hospitalization rates warrants longitudinal follow up surveillance and studies to better understand who is most at risk (e.g. due to underlying medical conditions) and optimal vaccine schedules. Additionally, there is a need for better characterization of immunity, including non-humoral immunity overtime with repeated infection, in this population [ 35 ].

In infants < 6 months of age, our finding of higher S-antibody positivity (89%; 95% CrI 82.4–95.2) compared to 6–11 months in infants (83.9; 95% CrI 76.7–89.8) not reported to have had infection, acknowledging the statistical overlap, suggests that a proportion of detected S-antibody in <6 months was maternal in origin, alongside increased exposure to infection in older infants (as suggested by increasing N-antibody). Longitudinal studies following mother and infant pairs have shown high rates of detection of SARS-CoV-2 antibodies in infants of vaccinated mothers, with rapid decline in maternally-derived antibodies from 6 months of age [ 36 , 37 ] not dissimilar to antibody waning in infants whose mothers have been vaccinated in pregnancy for influenza [ 38 , 39 ]. Although overall rates in infants are lower compared to adults, they face the highest rates of hospitalization within the pediatric age group [ 40 – 42 ]. Transplacental transfer of antibody to the fetus in pregnancy may prevent severe disease in young infants, emphasizing the importance of vaccinating pregnant women [ 37 , 42 ]. Protection of infants < 6 months of age will be of ongoing interest with any new SARS CoV2 variants of concern, with seroprevalence studies helping to support the recommendations for vaccination of pregnant women.

We found using N-antibody alone may underestimate infection rates. In unvaccinated 1–19-year-olds in our cohort who reported past infection based on virus detection, crude S-antibody positivity was 96.2% (533/554) compared to crude N-antibody positivity of 88.3% (489/554). Compared to N-antibodies, S-antibodies are more persistent [ 43 ] but lack the ability to differentiate between infection and vaccination. In those who are vaccinated, S antibodies display a reduced magnitude of response and a quicker decline compared to their unvaccinated counterparts [ 44 ]. Notably, we found children who had past infection and 2-dose vaccination had a greater S-antibody response than past infection alone. The N-antibody COI response was similar in both groups, up to 8 months after infection. However, this observation comes from a cross-sectional study, limiting the ability to track individual variations over time post-vaccination or infection, thus preventing conclusive inferences about antibody levels and durability. A longitudinal school study of 184 infected participants (46 students and 138 staff members) in England, demonstrated that N-antibodies seroreversion occurred in 58.4% compared to S-antibody seroreversion of 20.9% by 24 weeks [ 45 ] and a study of 38 health care workers the geometry means of N-antibody COI declined from 77 (56.4–105) at 2 months post infection to 22.2 (13.1–37.9) at 18 months [ 46 ].

The strengths of this study were obtaining sera from children across Australia’s vast geographic range and all socioeconomic quintiles. The samples were broadly representative of the Australian pediatric population [ 47 ]. We were able to obtain some insights into S and N-antibody kinetics in vaccinated and unvaccinated children due to our large cohort of children and detailed data on infection and vaccination. Nevertheless, our study was necessarily pragmatic and cross-sectional in design and therefore had several limitations. These included opportunistic sampling which resulted a greater preponderance of males, however, sex is not a known risk factor for infection in children and thus unlikely to have biased seroprevalence levels. Reporting of past infection was based on recall of a positive laboratory test for SARS-CoV-2 and may be subject to biases related to recall and selective/under-testing in context of mild or no symptoms and more. The proportion of participants with an underlying medical condition in our survey was 35.6% (728/2043) which was lower than the ABS National Health Survey 2022 estimate of proportions of Australians aged 0–17 years with 1 or more current long-term health conditions (49.8% 95% CI 47.8%-52%) [ 48 ]. Nevertheless, we were able to show that most children with underlying medical conditions have been previously infected, without severe disease. We had low numbers of older adolescents aged 16–19 years which was related to patterns of care as most in this age group receive care/procedures in adult hospitals. We also did not test the samples for neutralizing antibodies against circulating variants and so were unable to further characterize the antibody to determine the SARS-CoV-2 Omicron subvariant responsible for infection; future studies are being undertaken in this regard.

In summary, by August 2022 most Australian children, spanning all geographic regions and socioeconomic quintiles had been infected with SARS-CoV-2. This indicates a swift and consistent spread of the virus throughout Australia within a brief timeframe to inform adaptive public health measures and vaccination strategies nationwide. Future data on seroprevalence should be complemented with studies of immune responses and correlated with disease outcomes to better understand infection in children and adolescents.

Supporting information

S1 table. crude sars-cov-2 spike and nucleocapsid antibody seropositivity with select underlying medical conditions in comparison to those without underlying medical conditions..

https://doi.org/10.1371/journal.pone.0300555.s001

Acknowledgments

Heather Gidding, Bette Liu, Mehyar Khair Baik, Deepika Jindal, Hayley Giuliano, Chelsea Bartel, Audrey Rattray, Jaimee Craft, Jill Nguyen, Klara Glavacevic, Mark Mayo, Vanessa Rigas, Celeste Woerle, Gemma Chamberlain, Margaret Lyon, Kelly McCrory, Karen Bellamy, Megan Wieringa, Joseph Speekman, Janine Maloney, Hilde Wegter, Annalisa Shine, Madison Bellamy, Melitta Allen, Michelle (Jia Xi) Li, Mai Khuu, Emma Leighton, Carolyn Pardo Vaccine Immunology Group, MCRI (Nadia Mazarakis, Rachel Higgins, Zheng Quan Toh.

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Observational research methods. Research design II: cohort, cross sectional, and case-control studies

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1 Department of Accident and Emergency Medicine, Taunton and Somerset Hospital, Taunton, Somerset, UK. [email protected]
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DOI: 10.1136/emj.20.1.54

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Open access
Published: 13 September 2024

Contraceptive risk events among family planning specialists: a cross sectional study

Taylor N. Weckstein 1 ,
Rebecca G. Simmons 2 ,
Jami Baayd 2 &
Kathryn E. Fay 1 , 3

Reproductive Health volume 21 , Article number: 133 ( 2024 ) Cite this article

Metrics details

Proponents of abortion restriction cite advancements in contraceptive technology as a reason against the need for abortion care today, most recently through oral arguments in the Supreme Court of the United States case, Dobbs v. Jackson Women’s Health. However, consistent and correct use of contraception requires reproductive health literacy. Our objectives were to quantify contraceptive risk events and assess contraceptive history and preferences among a population well-equipped to evade contraceptive risks, family planning specialists following initiation of their medical training. “Risk events” are defined as reported episodes of contraceptive failure, emergency contraception use and/or unprotected or underprotected intercourse.

This was a cross-sectional study among current members of a professional organization of family planning specialists. Inclusion criteria included: status as a current or retired clinician, consensual penile-vaginal intercourse and personal or partner capacity to become pregnant since the start of medical training. Descriptive statistics were performed. This study was IRB exempt.

Among 229 respondents, 157 (69%) reported experiencing a contraceptive risk event since training. Twenty-nine (13%) respondents reported an occurrence within the last year. By category, 47% (108/229; 3 reported unknown) reported under- or unprotected intercourse, 35% (81/229) reported emergency contraception use, and 52% of participants (117/227; 2 unknown) reported known or suspected contraceptive failure. The mean number of contraceptive methods used was 3.7 (SD 1.7) out of the 13 methods listed. Almost all (97%) participants reported at least one method was not an acceptable option, with a mean of 5.6 (SD 2.7) of the 13 listed methods.

Conclusions

The majority of family planning specialists have experienced contraceptive risk events during times of active pregnancy prevention since their medical training. Contraceptive method change is common and most respondents were limited in the number of methods that were personally acceptable to them. Dialogue idealizing the role of contraception in minimizing or eliminating abortion need is simplistic and inaccurately represents the lived realities of pregnancy-capable individuals and their partners, including among those with exceptional contraceptive literacy and access.

Antecedentes

Los que apoyan la restricción del aborto citan los avances en la tecnología anticonceptiva como una razón en contra de la necesidad de la atención del aborto hoy en día, más recientemente a través de los argumentos orales en el caso de la Corte Suprema de los Estados Unidos, Dobbs v. Jackson Women's Health . Sin embargo, el uso sistemático y indicado de los anticonceptivos requiere unos conocimientos sobre salud reproductive. Nuestros objetivos eran cuantificar los eventos de riesgo anticonceptivo y evaluar los antecedentes y las preferencias entre una población bien equipada para eludir los riesgos anticonceptivos, los especialistas en planificación familiar tras el inicio de su formación médica. Los "eventos de riesgo" se definen como episodios reportados de fallo anticonceptivo, uso de anticoncepción de emergencia y/o relaciones sexuales sin protección o con protección insuficiente.

Este fue un estudio transversal entre miembros actuales de una organización profesional de especialistas en planificación familiar. Los criterios de inclusión incluyeron: condición de clínico/a en activo/a o jubilado/a, relaciones sexuales consentidas pene-vagina desde el inicio de la formación médica y capacidad personal o de la pareja para quedarse embarazada. Se realizaron estadísticas descriptivas. Este estudio estaba exento de IRB.

De las 229 encuestadas, 157 (69%) declararon haber sufrido un evento de riesgo anticonceptivo desde la formación. Veintinueve (13%) encuestadas declararon haberlo sufrido un incidente en el último año. Por categoría, el 47% (108/229; 3 informaron de forma desconocida) informaron de relaciones sexuales sin protección o con poca protección, el 35% (81/229) informaron del uso de anticonceptivos de emergencia y el 52% de los participantes (117/227; 2 informaron de forma desconocida) informaron de un fallo anticonceptivo conocido o sospechado. El promedio de métodos anticonceptivos utilizados fue 3,7 (DE 1,7) de los 13 métodos enumerados. Casi todas las participantes (97%) informaron de que al menos un método no era una opción aceptable, con un promedio de 5,6 (DE 2,7) de los 13 métodos enumerados.

Conclusiones

La mayoría de los especialistas en planificación familiar han experimentado eventos de riesgo anticonceptivo en momentos de prevención activa del embarazo desde su formación médica. El cambio de método anticonceptivo es frecuente y la mayoría de los encuestados tenían un número limitado de métodos que les resultaban personalmente aceptables. El diálogo que idealiza el papel de la planificación familiar a la hora de minimizar o eliminar la necesidad de abortar es simplista y representa de forma inexacta las realidades vividas por las personas con capacidad de embarazo y sus parejas, incluso entre aquellas con conocimientos y acceso excepcionales a la anticoncepción.

Plain English Summary

Since Dobbs v. Jackson, the landmark Supreme Court of the United States case overturning the right to abortion, it is very important to better understand current birth control use and the risk of unintended pregnancy. While birth control helps people to avoid unintended pregnancy, current methods are not perfect. This study examined the limitations of current birth control, even when used by expert clinicians with special knowledge and access. We provided an online survey to doctors and advanced practice clinicians who specialize in birth control. We measured risk of unintended pregnancy by asking about experiences with birth control failure, emergency contraceptive use (such as plan B), and unprotected sex since the start of medical training. We also asked about reasons for changing or avoiding certain birth control methods. Among 229 expert clinicians, we found that nearly 70% had experienced a risk of unintended pregnancy since beginning their medical training. Birth control method change was common, and many reported that several options were unacceptable. Participants shared that they found methods difficult, unreliable, unpleasant, or had health conditions that limited the number of safe options available. Our findings suggest that, even among experts, everyone remains at risk of unintended pregnancy. The study highlights the need for improved birth control options as well as access to safe and legal abortion.

Peer Review reports

Major advancements in contraceptive technology since the 1960s have been cited as a reason against the need for abortion care today, recently in the pivotal Supreme Court case, Dobbs v. Jackson Women’s Health [ 1 ] . Specifically, oral arguments in the Dobbs case contended that “contraception is more accessible and affordable and available than it was at the time of Roe or Casey . It serves the same goal of allowing women to decide if, when, and how many children to have.”[ 2 ] Justice Barrett also remarked that safe-haven laws further mitigate concerns regarding unwanted parenthood. Thus, authoritative sources have made a simplified conclusion that between contraception and adoption placement, the role of abortion is not as relevant as it was at the time of Roe’s passage.

Consistent and correct use of contraception requires access, health literacy, tolerance of side effects, and for some methods, a willing partner. Even among recent medical school graduates, however, contraceptive knowledge is low [ 3 ]. Further, despite rigorous medical training, physicians report high rates of unprotected intercourse when not seeking conception and when partner sexually transmitted infection status is unknown [ 4 ]. It follows that abortion is not uncommon (11.5%) among physicians measured over the life course [ 5 ]. Thus, even among trained healthcare experts, let alone the general public, contraception does not eliminate the possibility of unintended pregnancy.

There are few populations more knowledgeable about contraceptive use and fertility than clinicians specializing in family planning. The purpose of this study was to assess the contracepting behaviors among this highly specialized group of individuals during their professional practice to assess whether their expertise was sufficient to nullify the risk of unintended pregnancy. Whether the lived experience of contemporary contraception use fully delivers on the point of deciding if, when, and how a pregnancy occurs, must be measured, starting with a population well-equipped to evade contraceptive risks.

This was a cross-sectional study exploring contraceptive practices and risk of unintended pregnancy among reproductive health experts during their professional careers. Participants were members of the Society of Family Planning, a professional reproductive health organization, including physicians, physician assistants, certified midwives, and nurse practitioners. Individuals were invited to participate through an email communication in the Society of Family Planning email listserv and a posting on an online research message board, available exclusively to members of the Society of Family Planning. There were two reminders to participate. Recruitment occurred between June 2022 to December 2022. Surveys were self-administered with data collection and management using REDCap electronic data capture tools hosted by Mass General Brigham Research Computing, Enterprise Research Infrastructure & Services (ERIS) group. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies [ 6 ]. The first page of the survey included a consent fact sheet; consent was implied by survey continuation. Inclusion criteria were (1) report of penile-vaginal intercourse since starting medical training; (2) personal or partner capacity to become pregnant since starting medical training; and (3) status as a current or retired clinician. This study consulted the CHERRIES (The Checklist for Reporting Results of Internet E-Surveys) reporting guidelines [ 7 ].

Our primary outcome measured contraceptive risk events: times when participants or their partners were at potential risk of pregnancy when not seeking conception. We defined this measure through three questions: (1) How many different times have you or a sexual partner used emergency contraception, including oral medications and IUDs? (2) Have you had consensual penile-vaginal intercourse without using contraception (other than emergency contraception) or partial penile-vaginal intercourse (partial meaning starting intercourse without a condom or other contraceptive method, but using one before ejaculation, using the pullout method, etc.) when you or a sexual partner wanted to prevent pregnancy? and (3) Have you had consensual penile-vaginal intercourse and thought the contraception may have failed? We defined underprotected and unprotected intercourse using adapted items from Aiken and Trussell [ 4 ]. Specifically, we measured underprotected intercourse to document the common practice of beginning intercourse without a form of contraception, given the risk of sperm exposure in pre-ejaculatory fluid, while also including withdrawal as a method of contraception. Participants were prompted to respond with events that occurred since the start of participants medical training.

We also included survey questions about participants’ contraceptive history. Participants reported all methods (n = 13) used personally or by their partner since the start of medical training and their reasons for discontinuing each method not currently being used. Participants were also asked about any method they would not want to use and their reasons for avoidance. The survey included write-in options for participants who indicated “other” as a reason for contraceptive method discontinuation and avoidance. These items were developed as adaptations from the National Survey for Family Growth, The Henry J. Kaiser Family Foundation, and Nelson et al . [ 8 , 9 , 10 ] The survey concluded with demographic items using items adapted from Kaplowitz and Laroche [ 11 ]. Item display order was not randomized or alternated; however, conditional display was utilized to supply additional questions only to participants who answered affirmatively to contraceptive risk events (to determine recency of event), method discontinuation items and method avoidance items (to identify reasons for discontinuation or avoidance for only and each method selected). No survey items required a response beyond the three initial screening items. Survey items were pilot testing among five medical professionals. At the end of the web-based survey, participants were invited to enter a drawing for a gift card via a separate survey link to preserve anonymity. Data storage was protected behind an institutional firewall.

We conducted descriptive statistical analyses to illustrate the prevalence of contraceptive risk events, method use, and reasons for method discontinuation or avoidance. This was a convenience sample; the sampling frame was determined by active registration as clinician with the Society of Family Planning. Qualitative responses to the write-in questions were thematically coded and compiled. All data analyses were conducted in STATA (StatCorp, 2019, College Station, TX). The study was reviewed by the Mass General Brigham Institutional Review Board and deemed exempt (2022P001454).

Of 711 currently registered clinicians, 253 (36% click rate) opened the survey invitation, and 229 (91% completion; 32% total sample frame) completed all three screening questions, met the inclusion criteria, and answered at least one of the three primary outcome items (Fig. 1 ). Participants trained across 35 states and Washington, D.C. The majority of respondents identified as women (95%), and the majority of respondents reported completing an MD degree (85%). Nearly half of participants (49%) responded that it had been one to two decades since they completed their training. A full summary of demographic data is included in Table 1 .

Study Flow. Sample size based on response rate, inclusion criteria, and completion of primary outcome items

Overall, 69% (n = 157) of respondents reported at least one contraceptive risk event during a time when pregnancy was undesired. Four individuals reported they did not know if a risk event had occurred. Thirteen percent (29/229) of all participants reported that the risk event occurred within the past year; all but one of these individuals reported being more than six years into their career. By risk event, 47% (108/229; 3 reported unknown) reported under- or unprotected intercourse, 35% (81/229) reported emergency contraception use, and 52% of participants (117/227; 2 reported unknown) reported known or suspected contraceptive failure since their training. Among emergency contraception users, seventeen individuals reported using emergency contraception more than three times, with some individuals (3/81) reporting use more than ten times.

The most common contraceptive methods participants reported using since beginning their medical training were hormonal IUDs, condoms and oral contraception; for each method, more than 70% of respondents reported personal or partner use since the start of their medical training. Contraceptive injection, spermicide, arm implant, contraceptive patch, and diaphragm were uncommon methods among family planning clinicians, with less than 10% of respondents reporting prior use per method (Fig. 2 ).

1 Methods used. Percentage of respondents reporting personal or partner use of a contraceptive method since the start of their training. 1 While emergency contraception (EC) can be an individual’s primary method for pregnancy prevention, we used EC as an indicator of risk and omitted it from this Figure. That is, we conceptualized EC use as a behavior in response to a contraceptive risk event

The mean number of contraceptive methods used since medical training was 3.7 (SD 1.7), including emergency contraception. Almost all (215/222, 97%) participants reported they would personally avoid at least one type of contraceptive method. On average, participants reported that 5.6 (SD 2.7) of the 13 methods available would be unacceptable to them for personal use. Figures 3 and 4 show reasons participants reported deciding to discontinue or avoid various methods. [Additional file 1 ] displays write-in “other” reasons respondents chose to discontinue or avoid particular methods.

Reasons for method discontinuation. Contributing factors for method discontinuation among prior methods used by respondents and/or their sexual partner

Reasons for method avoidance. Contributing factors for method avoidance by respondents and/or their sexual partner. Respondents checked all that applied

Approximately 7 in 10 family planning specialists reported a contraceptive risk event during their professional careers when pregnancy prevention was desired. While most participants were over a decade into their careers, 29 (13%) reported a risk event within the past year. These data show that even in the context of significant knowledge and high uptake of the most effective methods, risk of unintended pregnancy persists, underscoring the need for robust abortion access.

Our findings parallel metrics of similar contraceptive risks events in the general public. In an analysis of a national population of reproductive-aged women in 2015, 23% reported prior emergency contraception use, less than in our sample (35%) [ 12 ], which may be explained by improved access to, knowledge of, and comfort with reporting use of this contraceptive method among family planning specialists. This reported higher use among our study population may also be impacted by the measure of ever use since training (including older individuals, not just reproductiveaged) and ongoing increases in use since 2015 facilitated by lower costs and easier acquisition of emergency contraception. Participants’ report of under- or unprotected intercourse was similar to findings from a survey administered in 2014 to family planning specialists using the same definition: 76% lifetime risk and 7% past-year risk. Our ever-risk is likely lower because our query was limited to time since training commenced; our past-year risk of 13% may be accounted for by omission of withdrawal from the comparative study’s figure [ 4 ]. Regardless, among the family planning clinician population, contraceptive risks have been, and continue to be, part of the lived experience after the initiation of medical training. We measured perceived failure rather than pregnancy incidence. In a population of users highly trained to identify failure like incorrect or inconsistent use or device expulsion, capturing the potential for pregnancy may better address our research question than the overestimated performance deduced from clinically recognizable pregnancy used to calculate Pearl indices. Consequently, we refrain from situating our final metric of contraceptive failure in the context of the general typical use effectiveness measuring pregnancy incidence.

The most common methods used among both participants and the US population include oral contraception, external condoms, and intrauterine devices (IUDs), with a higher rate of IUD use among our participants compared to the general US population [ 13 ]. The hormonal IUD was the most common method still being used with the highest rate of discontinuation for planned conception. Individuals remain at risk of pregnancy, unsurprisingly, even with perfect use of contraception; multiple participants described experiences of IUD failures. However, it is unrealistic and unforgiving to expect that anyone—including reproductive health experts—will have perfect contraceptive use at each sexual encounter for a multitude of reasons, including the shortcomings of currently available contraceptive methods. Problems with access and adverse effects were infrequently reported reasons for discontinuation of a method in this cohort. Similarly, in the general population, side effects among oral contraception users have been reported as absent or mild, with minimal method discontinuation attributable to side effects [ 14 ]. However, side effects were a common reason for method avoidance in our study, particularly for the injection, nonhormonal IUD and implant. Prior study has found that among first time contraceptive users, nearly half were worried about side effects before starting contraception [ 9 ]; however, the degree to which these concerns have contributed to method avoidance among the general public is not clear. Given the unique expertise of family planning clinicians, extensive knowledge around potential side effects across methods likely contributed to informed decision-making and method avoidance. Participants’ report of side effects had overlap with other studies including bleeding and interference with sexual pleasure; although, based on write-in responses, weight and mood concerns were underrepresented in this population [ 9 ].

Participants also reported development of contraindications. In other studies, up to one third of individuals using combined oral contraceptives reported a relative or absolute contraindication to use, due to medical comorbidities [ 15 , 16 ]. The high prevalence of these comorbidities may limit the number of contraceptive options safely available to many pregnancy-capable individuals. Notably, participants echoed the sentiments of many other contraceptive users in emphasizing the importance of control over the method – rather than reliance on a partner for use or a clinician for initiation or discontinuation [ 17 , 18 , 19 ]. As is the case in all populations, there are a diverse set of factors contributing to the (un)desirability of a contraceptive method, again highlighting that effectiveness is not the only metric influencing contraceptive decision-making. This is consistent with other work demonstrating that the contraceptive decision-making process is often a dynamic and nuanced process that changes over the course of decades [ 20 ]. Contraceptive decision-making changes with changing bodies, belief systems, environments and relationships [ 21 ].

In examining a population with a unique knowledge base and likely excellent access to contraception, including long-acting reversible methods, contraceptive risk events are common over the course of individuals’ professional lives, as is method discontinuation (for reasons other than conception) and method avoidance. These findings normalize contraceptive risk behaviors, emphasize that “typical use” describes use among all contraceptive users, and highlights the narrow range of contraceptive choice when accounting for method contraindications, performance features, and evolving user preferences. These findings work to dismantle the idea of an ideal contraceptive method or contraceptive user in an era characterized by intolerance of undesired pregnancy and loss of abortion access. Such considerations factor into clinical care, by, for example, reducing “otherization” in contraceptive counseling, building empathy for contraceptive dissatisfaction, and expanding the image of potential abortion beneficiaries to everyone. More tangibly, this translates to provision of universal guidance and access to emergency contraception, counseling on the reality of contraceptive switching and discontinuation for many users, and consideration of the inclusion of abortion counseling with contraceptive counseling [ 22 ].

These data have implications for the contemporary social and environmental factors affecting sexual and reproductive health by highlighting contraceptive shortcomings and events representing potential abortion need. Further exploration of contraceptive dissatisfaction may facilitate public understanding of the limitations of contraceptive technology and the demands put on pregnancy-capable people in navigating method use. These findings also emphasize the need for expansion of contraceptive options with critical research focused on development of novel agents and delivery systems, including male hormonal contraceptive methods [ 23 ].

The strengths of this study include its unique insight into contraceptive risk behavior and contraceptive choices among family planning specialists using quantitative input. These are salient data for generating a response to current questions around the role of contraception, particularly as it pertains to abortion need. Our study is limited by a design that did not allow for a comparison between contraceptive risk event and method at the time of event. However, the focus of this study was on the prevalence of risk in a population with access to and knowledge about all contraceptive options; the relevance of method data was intentionally focused on exploring imperfections of current technology. Our survey did not fully explore the adverse impacts of each individual method. Finally, our response rate, while consistent with or better than most online surveys, may be subject to non-response bias, including the possibility of preferential response among those with a specific interest in sharing their contraceptive risk histories [ 24 ]. While demographics of the Society of Family Planning membership are not publicly available data, the geographic diversity of this sample is similar to those in the member directory providing support of generalizability along one dimension.

Family planning specialists report contraceptive risk events while actively avoiding pregnancy; thus, optimization of the role of contraception with education and access does not generate immunity to abortion need. Advances in contraceptive method diversity and technology should be celebrated, as should contraceptive uptake that meets the needs of its user. However, dialogue focusing on the role of contraception in minimizing or eliminating abortion need perpetuates stigma around abortion and does not accurately represent individuals’ lived experiences, including those with significant educational and social privilege like family planning specialists [ 25 ]. Further, like all contraceptive users, those with specialized knowledge of contraception also use individualized algebra to determine method goodness of fit, not limited to considerations of efficacy. Contraceptive preferences and method avoidance are driven by practical and important concerns, like side effects and ease of use, that greatly reduces the menu of options available to the contemporary contraceptive user. Contraception has not and never will eliminate the need for abortion, even among individuals with considerable personal interest and professional training in contraception. In the wake of significant losses in abortion protection, the expansion of contraception options and abortion access, together, should be celebrated in the effort to support reproductive liberty.

Availability of data and materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Abbreviations

Institutional review board

Research electronic data capture

Enterprise research infrastructure & services

The checklist for reporting results of internet E-surveys

Intrauterine device

Standard deviation

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Acknowledgements

We would like to thank Alexandra Gero, MPH, University of Utah School of Medicine, who greatly improved the survey through her review and editing of the survey items. We would also like to thank Maddy Mullholand, MPA, University of Utah School of Medicine, who masterfully envisioned data presentation and figure production.

KF receives support from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (K12HD103096). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Department of Obstetrics, Gynecology, and Reproductive Biology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA

Taylor N. Weckstein & Kathryn E. Fay

Department of Obstetrics and Gynecology, University of Utah School of Medicine, 30 North 1900 E, Room 2B200, Salt Lake City, UT, 84132, USA

Rebecca G. Simmons & Jami Baayd

Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

Kathryn E. Fay

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Contributions

TW was responsible for data analysis, interpretation of results, draft manuscript preparation and final manuscript preparation. JB and RS were responsible for study design, interpretation of results, and manuscript revision. KF was responsible for study conception and design, data collection, analysis and interpretation of results, and draft manuscript preparation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kathryn E. Fay .

Ethics declarations

Ethics approval and consent to participate.

The study was reviewed by the Mass General Brigham Institutional Review Board and deemed exempt (2022P001454). The first page of our online survey contained a consent fact sheet, and consent was implied by completing the survey.

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Not applicable.

Competing interests

KF is a consultant for Medicines360.

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Weckstein, T.N., Simmons, R.G., Baayd, J. et al. Contraceptive risk events among family planning specialists: a cross sectional study. Reprod Health 21 , 133 (2024). https://doi.org/10.1186/s12978-024-01870-6

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Observational and interventional study design types; an overview

The appropriate choice in study design is essential for the successful execution of biomedical and public health research. There are many study designs to choose from within two broad categories of observational and interventional studies. Each design has its own strengths and weaknesses, and the need to understand these limitations is necessary to arrive at correct study conclusions.

Observational study designs, also called epidemiologic study designs, are often retrospective and are used to assess potential causation in exposure-outcome relationships and therefore influence preventive methods. Observational study designs include ecological designs, cross sectional, case-control, case-crossover, retrospective and prospective cohorts. An important subset of observational studies is diagnostic study designs, which evaluate the accuracy of diagnostic procedures and tests as compared to other diagnostic measures. These include diagnostic accuracy designs, diagnostic cohort designs, and diagnostic randomized controlled trials.

Interventional studies are often prospective and are specifically tailored to evaluate direct impacts of treatment or preventive measures on disease. Each study design has specific outcome measures that rely on the type and quality of data utilized. Additionally, each study design has potential limitations that are more severe and need to be addressed in the design phase of the study. This manuscript is meant to provide an overview of study design types, strengths and weaknesses of common observational and interventional study designs.

Introduction

Study design plays an important role in the quality, execution, and interpretation of biomedical and public health research ( 1 – 12 ). Each study design has their own inherent strengths and weaknesses, and there can be a general hierarchy in study designs, however, any hierarchy cannot be applied uniformly across study design types ( 3 , 5 , 6 , 9 ). Epidemiological and interventional research studies include three elements; 1) definition and measure of exposure in two or more groups, 2) measure of health outcome(s) in these same groups, and 3) statistical comparison made between groups to assess potential relationships between the exposure and outcome, all of which are defined by the researcher ( 1 – 4 , 8 , 13 ). The measure of exposure in epidemiologic studies may be tobacco use (“Yes” vs . “No”) to define the two groups and may be the treatment (Active drug vs . placebo) in interventional studies. Health outcome(s) can be the development of a disease or symptom (e.g. lung cancer) or curing a disease or symptom (e.g. reduction of pain). Descriptive studies, which are not epidemiological or interventional, lack one or more of these elements and have limited application. High quality epidemiological and interventional studies contain detailed information on the design, execution and interpretation of results, with methodology clearly written and able to be reproduced by other researchers.

Research is generally considered as primary or secondary research. Primary research relies upon data gathered from original research expressly for that purpose ( 1 , 3 , 5 ). Secondary research focuses on single or multiple data sources that are not collected for a single research purpose ( 14 , 15 ). Secondary research includes meta-analyses and best practice guidelines for treatments. This paper will focus on the study designs and their strengths, weaknesses, and common statistical outcomes of primary research.

The choice of a study design hinges on many factors, including prior research, availability of study participants, funding, and time constraints. One common decision point is the desire to suggest causation. The most common causation criteria are proposed by Hill ( 16 ). Of these, demonstrating temporality is the only mandatory criterion for suggesting temporality. Therefore, prospective studies that follow study participants forward through time, including prospective cohort studies and interventional studies, are best suited for suggesting causation. Causal conclusions cannot be proven from an observational study. Additionally, causation between an exposure and an outcome cannot be proven by one study alone; multiple studies across different populations should be considered when making causation assessments ( 17 ).

Primary research has been categorized in different ways. Common categorization schema include temporal nature of the study design (retrospective or prospective), usability of the study results (basic or applied), investigative purpose (descriptive or analytical), purpose (prevention, diagnosis or treatment), or role of the investigator (observational or interventional). This manuscript categorizes study designs by observational and interventional criteria, however, other categorization methods are described as well.

Observational and interventional studies

Within primary research there are observational studies and interventional studies. Observational studies, also called epidemiological studies, are those where the investigator is not acting upon study participants, but instead observing natural relationships between factors and outcomes. Diagnostic studies are classified as observational studies, but are a unique category and will be discussed independently. Interventional studies, also called experimental studies, are those where the researcher intercedes as part of the study design. Additionally, study designs may be classified by the role that time plays in the data collection, either retrospective or prospective. Retrospective studies are those where data are collected from the past, either through records created at that time or by asking participants to remember their exposures or outcomes. Retrospective studies cannot demonstrate temporality as easily and are more prone to different biases, particularly recall bias. Prospective studies follow participants forward through time, collecting data in the process. Prospective studies are less prone to some types of bias and can more easily demonstrate that the exposure preceded the disease, thereby more strongly suggesting causation. Table 1 describes the broad categories of observational studies: the disease measures applicable to each, the appropriate measures of risk, and temporality of each study design. Epidemiologic measures include point prevalence, the proportion of participants with disease at a given point in time, period prevalence, the proportion of participants with disease within a specified time frame, and incidence, the accumulation of new cases over time. Measures of risk are generally categorized into two categories: those that only demonstrate an association, such as an odds ratio (and some other measures), and those that demonstrate temporality and therefore suggest causation, such as hazard ratio. Table 2 outlines the strengths and weaknesses of each observational study design.

Observational study design measures of disease, measures of risk, and temporality.


Prevalence (rough estimate)	Prevalence ratio	Retrospective
Proportional mortality Standardized mortality	Proportional mortality ratio Standardized mortality ratio	Retrospective
None	Odds ratio	Retrospective
Point prevalence Period prevalence	Odds ratio Prevalence odds ratio Prevalence ratio Prevalence difference	Retrospective
None	Odds ratio	Retrospective
Point prevalence Period prevalence Incidence	Odds ratio Prevalence odds ratio Prevalence ratio Prevalence difference Attributable risk Incidence rate ratio Relative risk Risk ratio Hazard ratio	Retrospective only Both retrospective and prospective Prospective only

Observational study design strengths and weaknesses.


	Very inexpensive Fast Easy to assign exposure levels	Inaccuracy of data Inability to control for confounders Difficulty identifying or quantifying denominator No demonstrated temporality
	Very inexpensive Fast Outcome (death) well captured	Utilize deaths only Inaccuracy of data (death certificates) Inability to control for confounders
	Reduces some types of bias Good for acute health outcomes with a defined exposure Cases act as their own control	Selection of comparison time point difficult Challenging to execute Prone to recall bias No demonstrated temporality
	Inexpensive Timely Individualized data Ability to control for multiple confounders Can assess multiple outcomes	No temporality Not good for rare diseases Poor for diseases of short duration No demonstrated temporality
	Inexpensive Timely Individualized data Ability to control for multiple confounders Good for rare diseases Can assess multiple exposures	Cannot calculate prevalence Can only assess one outcome Poor selection of controls can introduce bias May be difficult to identify enough cases Prone to recall bias No demonstrated temporality
	Temporality demonstrated Individualized data Ability to control for multiple confounders Can assess multiple exposures Can assess multiple outcomes	Expensive Time intensive Not good for rare diseases

Observational studies

Ecological study design.

The most basic observational study is an ecological study. This study design compares clusters of people, usually grouped based on their geographical location or temporal associations ( 1 , 2 , 6 , 9 ). Ecological studies assign one exposure level for each distinct group and can provide a rough estimation of prevalence of disease within a population. Ecological studies are generally retrospective. An example of an ecological study is the comparison of the prevalence of obesity in the United States and France. The geographic area is considered the exposure and the outcome is obesity. There are inherent potential weaknesses with this approach, including loss of data resolution and potential misclassification ( 10 , 11 , 13 , 18 , 19 ). This type of study design also has additional weaknesses. Typically these studies derive their data from large databases that are created for purposes other than research, which may introduce error or misclassification ( 10 , 11 ). Quantification of both the number of cases and the total population can be difficult, leading to error or bias. Lastly, due to the limited amount of data available, it is difficult to control for other factors that may mask or falsely suggest a relationship between the exposure and the outcome. However, ecological studies are generally very cost effective and are a starting point for hypothesis generation.

Proportional mortality ratio study design

Proportional mortality ratio studies (PMR) utilize the defined well recorded outcome of death and subsequent records that are maintained regarding the decedent ( 1 , 6 , 8 , 20 ). By using records, this study design is able to identify potential relationships between exposures, such as geographic location, occupation, or age and cause of death. The epidemiological outcomes of this study design are proportional mortality ratio and standardized mortality ratio. In general these are the ratio of the proportion of cause-specific deaths out of all deaths between exposure categories ( 20 ). As an example, these studies can address questions about higher proportion of cardiovascular deaths among different ethnic and racial groups ( 21 ). A significant drawback to the PMR study design is that these studies are limited to death as an outcome ( 3 , 5 , 22 ). Additionally, the reliance on death records makes it difficult to control for individual confounding factors, variables that either conceal or falsely demonstrate associations between the exposure and outcome. An example of a confounder is tobacco use confounding the relationship between coffee intake and cardiovascular disease. Historically people often smoked and drank coffee while on coffee breaks. If researchers ignore smoking they would inaccurately find a strong relationship between coffee use and cardiovascular disease, where some of the risk is actually due to smoking. There are also concerns regarding the accuracy of death certificate data. Strengths of the study design include the well-defined outcome of death, the relative ease and low cost of obtaining data, and the uniformity of collection of these data across different geographical areas.

Cross-sectional study design

Cross-sectional studies are also called prevalence studies because one of the main measures available is study population prevalence ( 1 – 12 ). These studies consist of assessing a population, as represented by the study sample, at a single point in time. A common cross-sectional study type is the diagnostic accuracy study, which is discussed later. Cross-sectional study samples are selected based on their exposure status, without regard for their outcome status. Outcome status is obtained after participants are enrolled. Ideally, a wider distribution of exposure will allow for a higher likelihood of finding an association between the exposure and outcome if one exists ( 1 – 3 , 5 , 8 ). Cross-sectional studies are retrospective in nature. An example of a cross-sectional study would be enrolling participants who are either current smokers or never smokers, and assessing whether or not they have respiratory deficiencies. Random sampling of the population being assessed is more important in cross-sectional studies as compared to other observational study designs. Selection bias from non-random sampling may result in flawed measure of prevalence and calculation of risk. The study sample is assessed for both exposure and outcome at a single point in time. Because both exposure and outcome are assessed at the same time, temporality cannot be demonstrated, i.e. it cannot be demonstrated that the exposure preceded the disease ( 1 – 3 , 5 , 8 ). Point prevalence and period prevalence can be calculated in cross-sectional studies. Measures of risk for the exposure-outcome relationship that can be calculated in cross-sectional study design are odds ratio, prevalence odds ratio, prevalence ratio, and prevalence difference. Cross-sectional studies are relatively inexpensive and have data collected on an individual which allows for more complete control for confounding. Additionally, cross-sectional studies allow for multiple outcomes to be assessed simultaneously.

Case-control study design

Case-control studies were traditionally referred to as retrospective studies, due to the nature of the study design and execution ( 1 – 12 , 23 , 24 ). In this study design, researchers identify study participants based on their case status, i.e. diseased or not diseased. Quantification of the number of individuals among the cases and the controls who are exposed allow for statistical associations between exposure and outcomes to be established ( 1 – 3 , 5 , 8 ). An example of a case control study is analysing the relationship between obesity and knee replacement surgery. Cases are participants who have had knee surgery, and controls are a random sampling of those who have not, and the comparison is the relative odds of being obese if you have knee surgery as compared to those that do not. Matching on one or more potential confounders allows for minimization of those factors as potential confounders in the exposure-outcome relationship ( 1 – 3 , 5 , 8 ). Additionally, case-control studies are at increased risk for bias, particularly recall bias, due to the known case status of study participants ( 1 – 3 , 5 , 8 ). Other points of consideration that have specific weight in case-control studies include the appropriate selection of controls that balance generalizability and minimize bias, the minimization of survivor bias, and the potential for length time bias ( 25 ). The largest strength of case-control studies is that this study design is the most efficient study design for rare diseases. Additional strengths include low cost, relatively fast execution compared to cohort studies, the ability to collect individual participant specific data, the ability to control for multiple confounders, and the ability to assess multiple exposures of interest. The measure of risk that is calculated in case-control studies is the odds ratio, which are the odds of having the exposure if you have the disease. Other measures of risk are not applicable to case-control studies. Any measure of prevalence and associated measures, such as prevalence odds ratio, in a case-control study is artificial because the researcher arbitrarily sets the proportion of cases to non-cases in this study design. Temporality can be suggested, however, it is rarely definitively demonstrated because it is unknown if the development of the disease truly preceded the exposure. It should be noted that for certain outcomes, particularly death, the criteria for demonstrating temporality in that specific exposure-outcome relationship are met and the use of relative risk as a measure of risk may be justified.

Case-crossover study design

A case-crossover study relies upon an individual to act as their own control for comparison issues, thereby minimizing some potential confounders ( 1 , 5 , 12 ). This study design should not be confused with a crossover study design which is an interventional study type and is described below. For case-crossover studies, cases are assessed for their exposure status immediately prior to the time they became a case, and then compared to their own exposure at a prior point where they didn’t become a case. The selection of the prior point for comparison issues is often chosen at random or relies upon a mean measure of exposure over time. Case-crossover studies are always retrospective. An example of a case-crossover study would be evaluating the exposure of talking on a cell phone and being involved in an automobile crash. Cases are drivers involved in a crash and the comparison is that same driver at a random timeframe where they were not involved in a crash. These types of studies are particularly good for exposure-outcome relationships where the outcome is acute and well defined, e.g. electrocutions, lacerations, automobile crashes, etc. ( 1 , 5 ). Exposure-outcome relationships that are assessed using case-crossover designs should have health outcomes that do not have a subclinical or undiagnosed period prior to becoming a “case” in the study ( 12 ). The exposure is cell phone use during the exposure periods, both before the crash and during the control period. Additionally, the reliance upon prior exposure time requires that the exposure not have an additive or cumulative effect over time ( 1 , 5 ). Case-crossover study designs are at higher risk for having recall bias as compared with other study designs ( 12 ). Study participants are more likely to remember an exposure prior to becoming a case, as compared to not becoming a case.

Retrospective and prospective cohort study design

Cohort studies involve identifying study participants based on their exposure status and either following them through time to identify which participants develop the outcome(s) of interest, or look back at data that were created in the past, prior to the development of the outcome. Prospective cohort studies are considered the gold standard of observational research ( 1 – 3 , 5 , 8 , 10 , 11 ). These studies begin with a cross-sectional study to categorize exposure and identify cases at baseline. Disease-free participants are then followed and cases are measured as they develop. Retrospective cohort studies also begin with a cross-sectional study to categorize exposure and identify cases. Exposures are then measured based on records created at that time. Additionally, in an ideal retrospective cohort, case status is also tracked using historical data that were created at that point in time. Occupational groups, particularly those that have regular surveillance or certifications such as Commercial Truck Drivers, are particularly well positioned for retrospective cohort studies because records of both exposure and outcome are created as part of commercial and regulatory purposes ( 8 ). These types of studies have the ability to demonstrate temporality and therefore identify true risk factors, not associated factors, as can be done in other types of studies.

Cohort studies are the only observational study that can calculate incidence, both cumulative incidence and an incidence rate ( 1 , 3 , 5 , 6 , 10 , 11 ). Also, because the inception of a cohort study is identical to a cross-sectional study, both point prevalence and period prevalence can be calculated. There are many measures of risk that can be calculated from cohort study data. Again, the measures of risk for the exposure-outcome relationship that can be calculated in cross-sectional study design of odds ratio, prevalence odds ratio, prevalence ratio, and prevalence difference can be calculated in cohort studies as well. Measures of risk that leverage a cohort study’s ability to calculate incidence include incidence rate ratio, relative risk, risk ratio, and hazard ratio. These measures that demonstrate temporality are considered stronger measures for demonstrating causation and identification of risk factors.

Diagnostic testing and evaluation study designs

A specific study design is the diagnostic accuracy study, which is often used as part of the clinical decision making process. Diagnostic accuracy study designs are those that compare a new diagnostic method with the current “gold standard” diagnostic procedure in a cross-section of both diseased and healthy study participants. Gold standard diagnostic procedures are the current best-practice for diagnosing a disease. An example is comparing a new rapid test for a cancer with the gold standard method of biopsy. There are many intricacies to diagnostic testing study designs that should be considered. The proper selection of the gold standard evaluation is important for defining the true measures of accuracy for the new diagnostic procedure. Evaluations of diagnostic test results should be blinded to the case status of the participant. Similar to the intention-to-treat concept discussed later in interventional studies, diagnostic tests have a procedure of analyses called intention to diagnose (ITD), where participants are analysed in the diagnostic category they were assigned, regardless of the process in which a diagnosis was obtained. Performing analyses according to an a priori defined protocol, called per protocol analyses (PP or PPA), is another potential strength to diagnostic study testing. Many measures of the new diagnostic procedure, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio can be calculated. These measures of the diagnostic test allow for comparison with other diagnostic tests and aid the clinician in determining which test to utilize.

Interventional study designs

Interventional study designs, also called experimental study designs, are those where the researcher intervenes at some point throughout the study. The most common and strongest interventional study design is a randomized controlled trial, however, there are other interventional study designs, including pre-post study design, non-randomized controlled trials, and quasi-experiments ( 1 , 5 , 13 ). Experimental studies are used to evaluate study questions related to either therapeutic agents or prevention. Therapeutic agents can include prophylactic agents, treatments, surgical approaches, or diagnostic tests. Prevention can include changes to protective equipment, engineering controls, management, policy or any element that should be evaluated as to a potential cause of disease or injury.

Pre-post study design

A pre-post study measures the occurrence of an outcome before and again after a particular intervention is implemented. A good example is comparing deaths from motor vehicle crashes before and after the enforcement of a seat-belt law. Pre-post studies may be single arm, one group measured before the intervention and again after the intervention, or multiple arms, where there is a comparison between groups. Often there is an arm where there is no intervention. The no-intervention arm acts as the control group in a multi-arm pre-post study. These studies have the strength of temporality to be able to suggest that the outcome is impacted by the intervention, however, pre-post studies do not have control over other elements that are also changing at the same time as the intervention is implemented. Therefore, changes in disease occurrence during the study period cannot be fully attributed to the specific intervention. Outcomes measured for pre-post intervention studies may be binary health outcomes such as incidence or prevalence, or mean values of a continuous outcome such as systolic blood pressure may also be used. The analytic methods of pre-post studies depend on the outcome being measured. If there are multiple treatment arms, it is also likely that the difference from beginning to end within each treatment arm are analysed.

Non-randomized trial study design

Non-randomized trials are interventional study designs that compare a group where an intervention was performed with a group where there was no intervention. These are convenient study designs that are most often performed prospectively and can suggest possible relationships between the intervention and the outcome. However, these study designs are often subject to many types of bias and error and are not considered a strong study design.

Randomized controlled trial study design

Randomized controlled trials (RCTs) are the most common type of interventional study, and can have many modifications ( 26 – 28 ). These trials take a homogenous group of study participants and randomly divide them into two separate groups. If the randomization is successful then these two groups should be the same in all respects, both measured confounders and unmeasured factors. The intervention is then implemented in one group and not the other and comparisons of intervention efficacy between the two groups are analysed. Theoretically, the only difference between the two groups through the entire study is the intervention. An excellent example is the intervention of a new medication to treat a specific disease among a group of patients. This randomization process is arguably the largest strength of an RCT ( 26 – 28 ). Additional methodological elements are utilized among RCTs to further strengthen the causal implication of the intervention’s impact. These include allocation concealment, blinding, measuring compliance, controlling for co-interventions, measuring dropout, analysing results by intention to treat, and assessing each treatment arm at the same time point in the same manner.

Crossover randomized controlled trial study design

A crossover RCT is a type of interventional study design where study participants intentionally “crossover” to the other treatment arm. This should not be confused with the observational case-crossover design. A crossover RCT begins the same as a traditional RCT, however, after the end of the first treatment phase, each participant is re-allocated to the other treatment arm. There is often a wash-out period in between treatment periods. This design has many strengths, including demonstrating reversibility, compensating for unsuccessful randomization, and improving study efficiency by not using time to recruit subjects.

Allocation concealment theoretically guarantees that the implementation of the randomization is free from bias. This is done by ensuring that the randomization scheme is concealed from all individuals involved ( 26 – 30 ). A third party who is not involved in the treatment or assessment of the trial creates the randomization schema and study participants are randomized according to that schema. By concealing the schema, there is a minimization of potential deviation from that randomization, either consciously or otherwise by the participant, researcher, provider, or assessor. The traditional method of allocation concealment relies upon sequentially numbered opaque envelopes with the treatment allocation inside. These envelopes are generated before the study begins using the selected randomization scheme. Participants are then allocated to the specific intervention arm in the pre-determined order dictated by the schema. If allocation concealment is not utilized, there is the possibility of selective enrolment into an intervention arm, potentially with the outcome of biased results.

Blinding in an RCT is withholding the treatment arm from individuals involved in the study. This can be done through use of placebo pills, deactivated treatment modalities, or sham therapy. Sham therapy is a comparison procedure or treatment which is identical to the investigational intervention except it omits a key therapeutic element, thus rendering the treatment ineffective. An example is a sham cortisone injection, where saline solution of the same volume is injected instead of cortisone. This helps ensure that patients do not know if they are receiving the active or control treatment. The process of blinding is utilized to help ensure equal treatment of the different groups, therefore continuing to isolate the difference in outcome between groups to only the intervention being administered ( 28 – 31 ). Blinding within an RCT includes patient blinding, provider blinding, or assessor blinding. In some situations it is difficult or impossible to blind one or more of the parties involved, but an ideal study would have all parties blinded until the end of the study ( 26 – 28 , 31 , 32 ).

Compliance is the degree of how well study participants adhere to the prescribed intervention. Compliance or non-compliance to the intervention can have a significant impact on the results of the study ( 26 – 29 ). If there is a differentiation in the compliance between intervention arms, that differential can mask true differences, or erroneously conclude that there are differences between the groups when one does not exist. The measurement of compliance in studies addresses the potential for differences observed in intervention arms due to intervention adherence, and can allow for partial control of differences either through post hoc stratification or statistical adjustment.

Co-interventions, interventions that impact the outcome other than the primary intervention of the study, can also allow for erroneous conclusions in clinical trials ( 26 – 28 ). If there are differences between treatment arms in the amount or type of additional therapeutic elements then the study conclusions may be incorrect ( 29 ). For example, if a placebo treatment arm utilizes more over-the-counter medication than the experimental treatment arm, both treatment arms may have the same therapeutic improvement and show no effect of the experimental treatment. However, the placebo arm improvement is due to the over-the-counter medication and if that was prohibited, there may be a therapeutic difference between the two treatment arms. The exclusion or tracking and statistical adjustment of co-interventions serves to strengthen an RCT by minimizing this potential effect.

Participants drop out of a study for multiple reasons, but if there are differential dropout rates between intervention arms or high overall dropout rates, there may be biased data or erroneous study conclusions ( 26 – 28 ). A commonly accepted dropout rate is 20% however, studies with dropout rates below 20% may have erroneous conclusions ( 29 ). Common methods for minimizing dropout include incentivizing study participation or short study duration, however, these may also lead to lack of generalizability or validity.

Intention-to-treat (ITT) analysis is a method of analysis that quantitatively addresses deviations from random allocation ( 26 – 28 ). This method analyses individuals based on their allocated intervention, regardless of whether or not that intervention was actually received due to protocol deviations, compliance concerns or subsequent withdrawal. By maintaining individuals in their allocated intervention for analyses, the benefits of randomization will be captured ( 18 , 26 – 29 ). If analysis of actual treatment is solely relied upon, then some of the theoretical benefits of randomization may be lost. This analysis method relies on complete data. There are different approaches regarding the handling of missing data and no consensus has been put forth in the literature. Common approaches are imputation or carrying forward the last observed data from individuals to address issues of missing data ( 18 , 19 ).

Assessment timing can play an important role in the impact of interventions, particularly if intervention effects are acute and short lived ( 26 – 29 , 33 ). The specific timing of assessments are unique to each intervention, however, studies that allow for meaningfully different timing of assessments are subject to erroneous results. For example, if assessments occur differentially after an injection of a particularly fast acting, short-lived medication the difference observed between intervention arms may be due to a higher proportion of participants in one intervention arm being assessed hours after the intervention instead of minutes. By tracking differences in assessment times, researchers can address the potential scope of this problem, and try to address it using statistical or other methods ( 26 – 28 , 33 ).

Randomized controlled trials are the principle method for improving treatment of disease, and there are some standardized methods for grading RCTs, and subsequently creating best practice guidelines ( 29 , 34 – 36 ). Much of the current practice of medicine lacks moderate or high quality RCTs to address what treatment methods have demonstrated efficacy and much of the best practice guidelines remains based on consensus from experts ( 28 , 37 ). The reliance on high quality methodology in all types of studies will allow for continued improvement in the assessment of causal factors for health outcomes and the treatment of diseases.

Standards of research and reporting

There are many published standards for the design, execution and reporting of biomedical research, which can be found in Table 3 . The purpose and content of these standards and guidelines are to improve the quality of biomedical research which will result in providing sound conclusions to base medical decision making upon. There are published standards for categories of study designs such as observational studies (e.g. STROBE), interventional studies (e.g. CONSORT), diagnostic studies (e.g. STARD, QUADAS), systematic reviews and meta-analyses (e.g. PRISMA ), as well as others. The aim of these standards and guideline are to systematize and elevate the quality of biomedical research design, execution, and reporting.

Published standard for study design and reporting.


Consolidated Standards Of Reporting Trials	CONSORT
Strengthening the Reporting of Observational studies in Epidemiology	STROBE
Standards for Reporting Studies of Diagnostic Accuracy	STARD
Quality assessment of diagnostic accuracy studies	QUADAS
Preferred Reporting Items for Systematic Reviews and Meta-Analyses	PRISMA
Consolidated criteria for reporting qualitative research	COREQ
Statistical Analyses and Methods in the Published Literature	SAMPL
Consensus-based Clinical Case Reporting Guideline Development	CARE
Standards for Quality Improvement Reporting Excellence	SQUIRE
Consolidated Health Economic Evaluation Reporting Standards	CHEERS
Enhancing transparency in reporting the synthesis of qualitative research	ENTREQ

Consolidated Standards Of Reporting Trials (CONSORT, www.consort-statement.org ) are interventional study standards, a 25 item checklist and flowchart specifically designed for RCTs to standardize reporting of key elements including design, analysis and interpretation of the RCT.
Strengthening the Reporting of Observational studies in Epidemiology (STROBE, www.strobe-statement.org ) is a collection of guidelines specifically for standardization and improvement of the reporting of observational epidemiological research. There are specific subsets of the STROBE statement including molecular epidemiology (STROBE-ME), infectious diseases (STROBE-ID) and genetic association studies (STREGA).
Standards for Reporting Studies of Diagnos tic Accuracy (STARD, www.stard-statement.org ) is a 25 element checklist and flow diagram specifically designed for the reporting of diagnostic accuracy studies.
Quality assessment of diagnostic accuracy studies (QUADAS, www.bris.ac.uk/quadas ) is a quality assessment of diagnostic accuracy studies.
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, www.prisma-statement.org ) is a 27 element checklist and multiphase flow diagram to improve quality of reporting systematic reviews and meta-analyses. It replaces the QUOROM statement.
Consolidated criteria for reporting qualitative research (COREQ) is a 32 element checklist designed for reporting of qualitative data from interviews and focus groups.
Statistical Analyses and Methods in the Published Literature (SAMPL) is a guideline for statistical methods and analyses of all types of biomedical research.
Consensus-based Clinical Case Reporting Guideline Development (CARE, www.carestatement.org ) is a checklist comprised of 13 elements and is designed only for case reports.
Standards for Quality Improvement Reporting Excellence (SQUIRE, www.squire-statement.org ) are publication guidelines comprised of 19 elements, for authors aimed at quality improvement in health care reporting.
Consolidated Health Economic Evaluation Reporting Standards (CHEERS) is a 24 element checklist of reporting practices for economic evaluations of interventional studies.
Enhancing transparency in reporting the synthesis of qualitative research (ENTREQ) is a guideline specifically for standardizing and improving the reporting of qualitative biomedical research.

When designing or evaluating a study it may be helpful to review the applicable standards prior to executing and publishing the study. All published standards and guidelines are available on the web, and are updated based on current best practices as biomedical research evolves. Additionally, there is a network called “Enhancing the quality and transparency of health research” (EQUATOR, www.equator-network.org ) , which has guidelines and checklists for all standards reported in Table 3 and is continually updated with new study design or specialty specific standards.

The appropriate selection of a study design is only one element in successful research. The selection of a study design should incorporate consideration of costs, access to cases, identification of the exposure, the epidemiologic measures that are required, and the level of evidence that is currently published regarding the specific exposure-outcome relationship that is being assessed. Reviewing appropriate published standards when designing a study can substantially strengthen the execution and interpretation of study results.

Potential conflict of interest

None declared.

COMMENTS

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A case report describes the medical case of 1 particular patient. A cross-sectional study is a snapshot in time of a sample of participants chosen from the population. Goal. To report an interesting or unusual case of a patient. To describe the association between an exposure and an outcome.
Chapter 8. Case-control and cross sectional studies
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A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study. Cross-sectional vs longitudinal example. You want to study the impact that a low-carb diet has on diabetes. You first conduct a cross-sectional study with a sample of diabetes patients ...
Overview: Cross-Sectional Studies
The cross-sectional design is an appropriate method to determine the prevalence of a disease, attribute, or phenomena in a study sample. The design provides a 'snapshot" of the sample, and investigators can describe their study sample and review associations between the collected variables (independent and dependent).
Study designs: Part 2
Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available. For cross-sectional studies, these can be easily collected (usually in one encounter). Thus, these study designs are often inexpensive, quick and do not need too much effort.
How to choose your study design
First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient ...
Introduction to Epidemiological Studies
The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure ...
What Is a Case-Control Study?
A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease. On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal ...
Observational research methods. Research design II: cohort, cross
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare. ...
Differences between cross-sectional, case-control, and cohort study
Amber S Gordon. Beverly Claire Walters. Case-control (case-control, case-controlled) studies are beginning to appear more frequently in the neurosurgical literature. They can be more robust, if ...
Observational research methods—Cohort studies, cross sectional studies
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Observational studies are often the only practicable method of answering questions of aetiology, the natural history and treatment of rare conditions and instances where a randomised controlled trial might be unethical.
Case-control and Cohort studies: A brief overview
Introduction. Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence. These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as ...
An introduction to different types of study design
We may approach this study by 2 longitudinal designs: Prospective: we follow the individuals in the future to know who will develop the disease. Retrospective: we look to the past to know who developed the disease (e.g. using medical records) This design is the strongest among the observational studies. For example - to find out the relative ...
Observational research methods. Research design II: cohort, cross
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the condition to be studied is rare. Cohort studies are used to study incidence, causes, and prognosis.
Cross-Sectional Study: Definition, Designs & Examples
Cross-Sectional vs. Longitudinal. A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time. This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of ...
Methodology Series Module 3: Cross-sectional Studies
Abstract. Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case-control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the ...
What's the difference between a case-control study and a cross
A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease. On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal ...
Cohort vs Cross-Sectional Study: Similarities and Differences
Where a cross-sectional design is better. 1. In general, a cross-sectional study is less expensive and less time-consuming. In a cohort study we need to wait for the outcome to occur. In case of rare outcomes, the follow-up period may be very long (sometimes we will be waiting years for the outcome to develop in enough numbers so that the ...
Lesson 7
Lesson 7 Objectives. Upon completion of this lesson, you should be able to: Compare advantages/ disadvantages of cross-sectional and ecological studies. Describe ecological fallacy. Describe the main difference between observational and experimental studies. Identify design considerations unique to intervention studies including equipoise ...
Observational Studies: Cohort and Case-Control Studies
Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e. prospective or retrospective study design). Cross-sectional studies, also known as prevalence studies, examine the data on disease and exposure at one particular time point (Figure ...
The seroprevalence of SARS-CoV-2-specific antibodies in Australian
The N-antibody COI response was similar in both groups, up to 8 months after infection. However, this observation comes from a cross-sectional study, limiting the ability to track individual variations over time post-vaccination or infection, thus preventing conclusive inferences about antibody levels and durability.
Observational research methods. Research design II: cohort, cross
Cohort, cross sectional, and case-control studies are collectively referred to as observational studies. Often these studies are the only practicable method of studying various problems, for example, studies of aetiology, instances where a randomised controlled trial might be unethical, or if the co …
In brief: What types of studies are there?
The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies. An official website of the United States government. ... Cross-sectional studies. Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group ...
Contraceptive risk events among family planning specialists: a cross
This was a cross-sectional study among current members of a professional organization of family planning specialists. Inclusion criteria included: status as a current or retired clinician, consensual penile-vaginal intercourse and personal or partner capacity to become pregnant since the start of medical training.
Observational and interventional study design types; an overview
Observational study designs, also called epidemiologic study designs, are often retrospective and are used to assess potential causation in exposure-outcome relationships and therefore influence preventive methods. Observational study designs include ecological designs, cross sectional, case-control, case-crossover, retrospective and ...

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An introduction to different types of study design

Descriptive studies

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Cross-sectional study:

Cohort study:

Case-Control Study:

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Cohort studies: prospective and retrospective designs

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Cross-Sectional Study: Definition, Designs & Examples

Analytical Studies

Descriptive Studies

Simple and Inexpensive

Minimal room for error

Multiple variables and outcomes can be researched and compared at once

The data can be a starting point for future research

Limitations

Report bias is probable

The timing of the snapshot is not always representative

It cannot be used to analyze behavior over a period of time

Cross-Sectional vs. Longitudinal

Further Information

1. Are cross-sectional studies qualitative or quantitative?

2. What’s the difference between cross-sectional and cohort studies?

3. What’s the difference between cross-sectional and case-control studies?

4. Does a cross-sectional study have a control group?

5. Is a cross-sectional study prospective or retrospective?

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Cohort vs Cross-Sectional Study: Similarities and Differences

Similarities between cohort and cross-sectional designs

2. Both designs aim to study the association between an exposure and an outcome

3. Both are subject to information bias

4. Both are subject to selection bias

5. Both are subject to confounding

Differences between cohort and cross-sectional designs

2. Unlike a cross-sectional study, a cohort is not prone to survival bias

Where a cross-sectional design is better

2. In a cross-sectional study you won’t have to deal with participants follow-up

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The seroprevalence of SARS-CoV-2-specific antibodies in Australian children: A cross-sectional study

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Contraceptive risk events among family planning specialists: a cross sectional study

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