Effect Measures in Prevalence Studies

By Pearce, Neil | Environmental Health Perspectives, July 2004 | Go to article overview

Effect Measures in Prevalence Studies


Pearce, Neil, Environmental Health Perspectives


There is still considerable confusion and debate about the appropriate methods for analyzing prevalence studies, and a number of recent papers have argued that prevalence ratios are the preferred method and that prevalence odds ratios should not be used. These arguments assert that the prevalence ratio is obviously the better measure and the odds ratio is "unintelligible." They have often been accompanied by demonstrations that when a disease is common the prevalence ratio and the prevalence odds ratio may differ substantially. However, this does not tell us which measure is the more valid to use. In fact, the prevalence odds ratio a) estimates the incidence rate ratio with fewer assumptions than are required for the prevalence ratio; b) can be estimated using the same methods as for the odds ratio in case-control studies, namely, the Mantel-Haenszel method and logistic regression; and c) provides practical, analytical, and theoretical consistency between analyses of a prevalence study and prevalence case--control analyses based on the same study population. For these reasons, the prevalence odds ratio will continue to be one of the standard methods for analyzing prevalence studies and prevalence case--control studies. Key words: epidemiology, methods, prevalence case--control studies, prevalence studies.

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Although the methods for analyzing incidence studies (and incidence case--control studies) are now well established, there is still considerable confusion and debate about the appropriate methods for analyzing prevalence studies (and prevalence case--control studies). In particular, it has been argued that prevalence ratios are the preferred method and that prevalence odds ratios (PORs) should not be used. In this article I argue that PORs should continue to be one of the standard methods for analyzing such studies. I briefly review the relationship between incidence and prevalence studies and then discuss the relative merits of using PORs and prevalence ratios.

Incidence Studies

Table 1 shows the findings of a hypothetical incidence study of 20,000 persons followed for 10 years (Pearce 2003). Three measures of disease incidence are commonly used in incidence studies (Pearce 1993): the person-time incidence rate, the incidence proportion, and the incidence odds. These all involve the same numerator: the number of incident cases of disease (b). They differ in whether their denominators represent person-years at risk ([Y.sub.0]), persons at risk ([N.sub.0]), or survivors (d).

The person-time incidence rate is a measure of the disease occurrence per unit population time and has the reciprocal of time as its dimension. In this example (Table 1), there were 952 cases of disease diagnosed in the nonexposed group during the 10 years of follow-up, which involved a total of 95,163 person-years, and the person-time incidence rate, b/[Y.sub.0] = [I.sub.0], was 952/95,163 = 0.0100 (or 1,000 per 100,000 person-years).

The incidence proportion, or average risk, is a second measure of disease occurrence and is the proportion of study subjects who experience the outcome of interest at any time during the follow-up period. In this instance, there were 952 incident cases among the 10,000 people in the nonexposed group, and the incidence proportion, b/[N.sub.0] = [R.sub.0], was therefore 952/10,000 = 0.0952 over the 10-year follow-up period. When the outcome of interest is rare over the follow-up period (e.g., an incidence proportion < 10%), then the incidence proportion is approximately equal to the incidence rate multiplied by the length of time that the population has been followed (in the example this product is 0.1000, whereas the incidence proportion is 0.0952).

A third possible measure of disease occurrence is the incidence odds (Greenland 1987), which is the ratio of the number of people who experience the outcome (b) to the number of people who do not experience the outcome (d). …

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