The science of prevalence: development of an evidence-based tool to assess the risk of bias in prevalence studies.

Buitrago Garcia, Diana (2024). The science of prevalence: development of an evidence-based tool to assess the risk of bias in prevalence studies. (Unpublished). (Dissertation, University of Bern, Faculty of Medicine and Faculty of Human Sciences)

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Prevalence is a core concept in epidemiology, defined as the proportion of people with specific
characteristics (diseases, risk factors, or protective factors) at a certain point or during a certain
period. The importance of prevalence might have been undervalued because calculating it or
designing prevalence studies are perceived to be straightforward. Furthermore, research on the
methodological aspects on prevalence estimates has not been exhaustive, compared to other
study designs such as randomised controlled trials. The same potential underestimation and
lack of research apply to systematic reviews of prevalence, despite their increase in recent years.
During the COVID-19 pandemic, prevalence gained relevance because prevalence data were
essential for many research questions related to topics such as seroprevalence or the
prevalence of specific symptoms and comorbidities of people with SARS-CoV-2 in different
settings.

The work presented in this thesis aims to provide knowledge about the epidemiology of
prevalence studies, and biases that affect them. The thesis contains seven chapters: an
introduction; four chapters that present the projects conducted during my doctoral studies,
which, at the time of this writing, either have been published or are currently being prepared for
submission; and finally, a discussion of the main findings and implications of the doctoral project.
This thesis also includes two additional projects that contributed to broadening my skills as an
epidemiologist, even though they did not constitute the core of my doctoral project.

In Chapter 1, I comprehensively introduce prevalence and relevant uses of prevalence studies.
Afterwards, I present systematic reviews, and the relevance of risk of bias assessment followed
by systematic reviews of observational studies and systematic reviews of prevalence. Then, I
present the current guidance available to conduct systematic reviews of prevalence.

Chapter 2 is a communication published in the BMJ Open. This publication approaches the basic
concepts of prevalence and the biases that affect prevalence estimates. Each scenario is
illustrated with an example of a study conducted on COVID-19. This communication highlights
how prevalence became more relevant during the pandemic.

Chapter 3 presents a meta-epidemiological study of systematic reviews of prevalence conducted
on adult populations from 2010 to 2020, where I included 1172 publications. I describe their
characteristics and compliance with the PRISMA 2009 checklist and assessed the relationship
between compliance with the PRISMA checklist and study level variables in a linear regression
analysis. Compliance with the PRISMA checklist is still suboptimal for some items, specifically in
the methods section. The year of publication, the number of authors who published the review,
publishing in an open-access journal and reporting the use of a guideline were associated with
completeness of reporting to the PRISMA 2009. This manuscript will be submitted soon.

Chapter 4 presents two iterations of a living systematic review about the proportion of people
with asymptomatic SARS-CoV-2. The first included 80 studies, and it found that the pooled
proportion of people with a truly asymptomatic infection was 20%, 95% CI (17-25), with a
prediction interval of 3% to 67%. In the next version, the eligibility criteria became more strict,
and were met by 146 studies. A pooled proportion of asymptomatic people with SARS-CoV-2 was
not presented, due to the high heterogeneity of the studies. A meta-regression analysis did not
identify the characteristics that were associated with heterogeneity.

Chapter 5 presents the development of a tool to assess bias in the prevalence of mental health
studies conducted during the first wave of the COVID-19 pandemic. The proposed tool
approached selection and information bias with three questions: one about the sample's
representativeness regarding the target population, the second about the representativeness of
the respondents, and the third one dealt with the measurement of the condition. Agreement for
the three items was 83%, 90% and 93%, respectively. The weighted kappa scores were 0.63,
95% CI (0.54 - 0.73), 0.71, 95% CI (0.67 - 0.85), and 0.32, 95% CI (–0.04 - 0.63).

Chapter 6 discusses the findings of the studies, the current stage of an evidence-based tool to
assess the risk of bias for any prevalence study, the study’s strengths and limitations, and future
questions.

This thesis provides a conceptual framework that portrays the bias affecting prevalence studies.
I show how systematic reviews of prevalence have been conducted, which aspects need
improvement, and the need for new methodologies. Furthermore, I present a living systematic
review that used the conceptual framework to answer questions relevant to the COVID-19
pandemic. I conclude with a risk of bias tool that assesses the bias of prevalence studies on
mental health. This tool forms the basis for future development of a domain-based tool to assess
the biases in prevalence studies.

Item Type:	Thesis (Dissertation)
Division/Institute:	04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
Graduate School:	Graduate School for Health Sciences (GHS)
UniBE Contributor:	Buitrago Garcia, Diana Carolina, Low, Nicola, Salanti, Georgia
Subjects:	600 Technology > 610 Medicine & health 300 Social sciences, sociology & anthropology > 360 Social problems & social services
Language:	English
Submitter:	Doris Kopp Heim
Date Deposited:	01 Feb 2024 13:01
Last Modified:	01 Feb 2024 13:01
Additional Information:	PhD in Health Sciences (Epidemiology)
URI:	https://boris.unibe.ch/id/eprint/192334