|Estimating Disease Duration in Cross-sectional Surveys|
||Schmidt, Wolf-Peter; Boisson, Sophie, and Kenward, Michael G.
||Epidemiology, 26(6): 839-845; DOI: 10.1097/EDE.0000000000000364
More than one region
In some common episodic conditions, such as diarrhea, respiratory infections, or fever, episode duration can reflect disease severity. The mean episode duration in a population can be estimated if both the incidence and prevalence of the condition are known. In this article, we discuss how an estimator of the average episode duration may be obtained based on prevalence alone if data are collected for two consecutive units of time (usually days) in the same person.
We derive a maximum likelihood estimator of episode duration, explore its behavior through a simulation study, and illustrate its use through a real example.
We show that for two consecutive days, the estimator of the mean episode duration in a population equals one plus twice the ratio of the number of subjects with the condition on both days to the number of subjects with only 1 day ill. The estimator can be extended to account for 3 or 4 consecutive days. The estimator assumes nonoverlapping episodes and a time-constant incidence rate and is more precise for shorter than for longer average episode durations.
The proposed method allows estimating the mean duration of disease episodes in cross-sectional studies and is applicable to large demographic and health surveys in low-income settings that routinely collect data on diarrhea and respiratory illness. The method may further be used for the calculation of the duration of infectiousness if test results are available for two consecutive days, such as paired throat swabs for influenza.