PREVALENCE RATIO AND PREVALENCE ODDS RATIO IN CROSSSECTIONAL STUDIES
 by Anton Pradana
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By: Syndi Siahaan, Nurul Hidayah, Hafsah Amalia, Adhella Menur
A crosssectional study is an observational study in which all data from each subject is collected at a single point in time. It is considered more affordable and feasible than longitudinal studies, as it does not require following patients over time. Traditionally, a crosssectional study has been used to determine the prevalence of a disease or condition, defined as the proportion of a population with a specific characteristic at a given time. This is why a crosssectional study is also referred to as a “prevalence study.” However, it can also analyze the association between two or more variables, providing an analytical approach. This makes a crosssectional study a valuable option for exploring associations, especially in preliminary investigations or when resources are limited. Of note, the interpretation of the analysis requires caution regarding the potential association of disease duration with exposure status (survival bias). The crosssectional analysis results are often presented as Prevalence Ratio (PR), which measures and compares disease prevalence between two groups. The Odds Ratio (OR), a result commonly presented in casecontrol studies, can also be applied in crosssectional studies, where it is referred to as the Prevalence Odds Ratio (POR). There has been a debate about whether the OR should be exclusively used for casecontrol studies, with some authors reporting that when disease prevalence is high, the POR tends to overestimate the PR. This article will summarize how PR and POR are applied in crosssectional studies.
What to Choose: CrossSectional or CaseControl Studies
Crosssectional and casecontrol studies are commonly used in analytical observational study designs. As mentioned before, in a crosssectional study, data on exposure and outcomes (disease or condition) are collected simultaneously from each subject at one point in time (Figure 1). The analysis compares outcomes prevalence between exposed and unexposed individuals or the exposure levels between those with and without the disease or condition. Although crosssectional studies are often more practical to conduct, they have several limitations. They are not suitable for conditions with low prevalence, as such studies require a large sample size. Additionally, the findings depend on the disease’s duration since data is collected only once. While crosssectional studies can identify associations, they cannot determine causal relationships because it is unclear whether the disease or the exposure occurred first.
When studying the development of a condition or disease with low prevalence, a casecontrol study is more commonly used. This design compares a case group (individuals with the disease) to a control group (individuals without the disease) (Figure 2). Data on past exposures for both groups are collected retrospectively through medical records or laboratory results.
Choosing between crosssectional and casecontrol studies depends on the research questions; therefore, developing a specific research question is essential. Table 1 lists several research question types with the appropriate study design.
Measuring Association in CrossSectional Studies: Prevalence Ratio and Prevalence Odds Ratio
Measures of association are utilized to compare the association between a specific exposure and the outcomes. Note that evidence of an association does not imply that the relationship is causal; the association may also be artifactual or noncausal. To measure the association, analysis of epidemiological studies is performed using a 2×2 table, as shown in Table 2.
Prevalence ratio (PR) is analogous to the risk ratio (RR) of cohort studies. PR is interpreted as “exposed individuals have a disease or condition XX times greater than unexposed individuals.” Based on the Table 2, PR can be calculated as follows:
From this formula, we can see that the two equations are not reciprocal to each other. The denominators for both equations are fixed populations. This differs from the Prevalence Odds Ratio (POR), where the equations are reciprocal using different outcomes. POR represents the odds that an outcome will occur given a particular exposure compared to the odds of the outcome occurring without that exposure. The formula is as follows:
A POR value equal to 1 means the exposure is not associated with the disease. A POR greater than 1.0 indicates a positive association, and a POR less than 1.0 indicates a negative, or protective, association. Authors sometimes misinterpret POR with statements like “exposed individuals have XX times higher probability or risk of disease or condition.” Such statements are incorrect because the odds are not a ratio of probabilities or risks, and crosssectional designs cannot evaluate risk. The correct language is “exposed individuals have XX times greater odds of disease or condition.”
The literature is rich with articles discussing the advantages and disadvantages of PR versus POR and debating the ‘appropriate’ measure of association. CvetkovicVega et al. introduced the concept that the measure of association in a crosssectional study can be either PR or POR, depending on the initial observation of the outcome prevalence. It is considered that when the outcome prevalence is greater than or equal to 10%, PR should be used as the appropriate measure of association in crosssectional studies. Using POR in these cases would overestimate the PR value. When the prevalence of the outcome is below 10%, POR and PR are closer to each other; hence POR may be used. However, some researchers argue that PR is more recommended for crosssectional studies with analytical purposes.
The potential causeeffect relationship between the variables may provide consideration for selecting between PR and POR. When there is a reasonable assumption about which variable is the exposure and which is the outcome, it is convenient to compare the prevalence of the effect between exposed and nonexposed individuals and calculate the PR. When the causal relationship between the variables is unclear, POR has the advantage of maintaining the same numerical value regardless of its position in the contingency (2×2) table. For acute disease studies, PR is the preferred measure of association. For chronic disease studies or studies of longlasting risk factors, POR is the preferred measure of association.
Case Example
In this case example, we cite research by Tamhane et al. (2016) on the association of race sex with hypertension control status. Descriptive characteristics are shown in Table 3.
Table 4 below shows the results of PR and POR from the study. Using POR results in an overestimation of the strength of the association. For instance, in the Whitefemale group, when ‘Hypertension control = Yes’ was modeled (‘No’ as the reference group), POR was 2.63, while PR was 1.48.
In this case, since the prevalence of the outcome (hypertension control) is ≥10% (54.4%, 380/699), reporting PR was deemed more appropriate than POR due to the considerable overestimation of the association’s strength by POR.
Conclusion
To conclude, choosing the appropriate study design depends on the research question. In crosssectional studies, measuring association can be done using either PR or POR based on the initial observation of the prevalence and characteristics of the outcomes (disease or condition). Employing proper statistical methods in the analysis is crucial to avoid inappropriate estimates and interpretations. While using PR is generally recommended, reporting POR in crosssectional studies is acceptable as long as authors interpret POR correctly as the ratio between odds or for conditions or diseases with low prevalence.
^{References}
 Alexander LK, et al. Crosssectional studies. ERIC Notebook second edition. UNCCH Department of Epidemiology. 2015.
 Barros AJ, Hirakata VN. Alternatives for logistic regression in crosssectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC medical research methodology. 2003.
 Celentano D, Szklo M. Gordis Epidemiology. 6th ed. Philadelphia: Elsevier; 2019.
 CvetkovicVega A, et al. Crosssectional studies. Revista de la Facultad de Medicina Humana. 2021.
 Martinez BAF, et al. Odds ratio or prevalence ratio? An overview of reported statistical methods and appropriateness of interpretations in crosssectional studies with dichotomous outcomes in veterinary medicine. Frontiers in veterinary science. 2017.
 NHS. NHS Fife Research Study Guide. How to devise a research question and choose a study design. 2022.
 Omair, Aamir. Selecting the appropriate study design: Casecontrol and cohort study designs. Journal of Health Specialties. 2016.
 Quantitative study designs [Internet]. Australia: Deakin University; 2024. Available from: https://deakin.libguides.com/quantitativestudydesigns/ Reichenheim ME, Coutinho ES. Measures and models for causal inference in crosssectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression. BMC medical research methodology. 2010.
 Szumilas M. Explaining odds ratios. Journal of the Canadian Academy of Child and Adolescent Psychiatry. 2010.
 Tamhane AR, et al. Prevalence odds ratio versus prevalence ratio: choice comes with consequences. Statistics in medicine. 2016.
 Wang X, Cheng Z. Crosssectional studies: strengths, weaknesses, and recommendations. Chest. 2020.
