Studies of prevalence: how a basic epidemiology concept has gained recognition in the COVID-19 pandemic

Abstract

BackgroundPrevalence measures the occurrence of any health condition, exposure or other factors related to health. The experience of COVID-19, a new disease caused by SARS-CoV-2, has highlighted the importance of prevalence studies, for which issues of reporting and methodology have traditionally been neglected.ObjectiveThis communication highlights key issues about risks of bias in the design and conduct of prevalence studies and in reporting them, using examples about SARS-CoV-2 and COVID-19.SummaryThe two main domains of bias in prevalence studies are those related to the study population (selection bias) and the condition or risk factor being assessed (information bias). Sources of selection bias should be considered both at the time of the invitation to take part in a study and when assessing who participates and provides valid data (respondents and non-respondents). Information bias appears when there are systematic errors affecting the accuracy and reproducibility of the measurement of the condition or risk factor. Types of information bias include misclassification, observer and recall bias. When reporting prevalence studies, clear descriptions of the target population, study population, study setting and context, and clear definitions of the condition or risk factor and its measurement are essential. Without clear reporting, the risks of bias cannot be assessed properly. Bias in the findings of prevalence studies can, however, impact decision-making and the spread of disease. The concepts discussed here can be applied to the assessment of prevalence for many other conditions.ConclusionsEfforts to strengthen methodological research and improve assessment of the risk of bias and the quality of reporting of studies of prevalence in all fields of research should continue beyond this pandemic.

COVID-19 vaccine acceptance in the general population and under-resourced communities from high-income countries: realist review

“…Studies reporting prevalence served as important sources of evidence during the COVID-19 pandemic and helped researchers understand factors related to the disease and inform policies. However, prevalence estimates from individual studies and pooled prevalence estimates from our meta-analyses may have been affected by selection and reporting biases 17. Nevertheless, our inclusion criteria attempted to reduce such risks of bias, and we performed multiple sensitivity analyses that provided insights into possible sources of heterogeneity.…”

“…However, we did not use total scores in analyses. Instead, we grouped questions into categories according to the bias domain they addressed 17. We analysed risk of selection bias and risk of non-response bias as potential sources of heterogeneity among studies.…”

Tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH)

“…The design of prevalence studies differs substantially depending on the question they intend to answer; as a result, having a universal tool for all types of prevalence studies, like we have for RCTs and some observational studies,23 30 might not be realistic; instead, we need tools that can be tailored to specific research questions 31…”

A tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH)

“…The design of prevalence studies differs substantially depending on the question they intend to answer; as a result, having a universal tool for all types of prevalence studies, like we have for RCTs and some observational studies, (36)(37)(38) might not be realistic; instead, we need tools that can be tailored to specific research questions (39).…”