Sampling strategies to measure the prevalence of common recurrent infections in longitudinal studiesReport as inadecuate




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Emerging Themes in Epidemiology

, 7:5

First Online: 03 August 2010Received: 17 October 2009Accepted: 03 August 2010

Abstract

BackgroundMeasuring recurrent infections such as diarrhoea or respiratory infections in epidemiological studies is a methodological challenge. Problems in measuring the incidence of recurrent infections include the episode definition, recall error, and the logistics of close follow up. Longitudinal prevalence LP, the proportion-of-time-ill estimated by repeated prevalence measurements, is an alternative measure to incidence of recurrent infections. In contrast to incidence which usually requires continuous sampling, LP can be measured at intervals. This study explored how many more participants are needed for infrequent sampling to achieve the same study power as frequent sampling.

MethodsWe developed a set of four empirical simulation models representing low and high risk settings with short or long episode durations. The model was used to evaluate different sampling strategies with different assumptions on recall period and recall error.

ResultsThe model identified three major factors that influence sampling strategies: 1 the clustering of episodes in individuals; 2 the duration of episodes; 3 the positive correlation between an individual-s disease incidence and episode duration. Intermittent sampling e.g. 12 times per year often requires only a slightly larger sample size compared to continuous sampling, especially in cluster-randomized trials. The collection of period prevalence data can lead to highly biased effect estimates if the exposure variable is associated with episode duration. To maximize study power, recall periods of 3 to 7 days may be preferable over shorter periods, even if this leads to inaccuracy in the prevalence estimates.

ConclusionChoosing the optimal approach to measure recurrent infections in epidemiological studies depends on the setting, the study objectives, study design and budget constraints. Sampling at intervals can contribute to making epidemiological studies and trials more efficient, valid and cost-effective.

Electronic supplementary materialThe online version of this article doi:10.1186-1742-7622-7-5 contains supplementary material, which is available to authorized users.

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Author: Wolf-Peter Schmidt - Bernd Genser - Mauricio L Barreto - Thomas Clasen - Stephen P Luby - Sandy Cairncross - Zaid Chalabi

Source: https://link.springer.com/







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