Epidemiological methods in diarrhoea studies--an update

Abstract

Background: Diarrhoea remains a leading cause of morbidity and mortality but is difficult to measure in epidemiological studies. Challenges include the diagnosis based on self-reported symptoms, the logistical burden of intensive surveillance and the variability of diarrhoea in space, time and person.

Methods: We review current practices in sampling procedures to measure diarrhoea, and provide guidance for diarrhoea measurement across a range of study goals. Using 14 available data sets, we estimated typical design effects for clustering at household and village/ neighbourhood level, and measured the impact of adjusting for baseline variables on the precision of intervention effect estimates.

Results: Incidence is the preferred outcome measure in aetiological studies, health services research and vaccine trials. Repeated prevalence measurements (longitudinal prevalence) are appropriate in high-mortality settings where malnutrition is common, although many repeat measures are rarely useful. Period prevalence is an inadequate outcome if an intervention affects illness duration. Adjusting point estimates for age or diarrhoea at baseline in randomized trials has little effect on the precision of estimates. Design effects in trials randomized at household level are usually <2 (range 1.0–3.2). Design effects for larger clusters (e.g. villages or neighbourhoods) vary greatly among different settings and study designs (range 0.1–25.8).

Conclusions: Using appropriate sampling strategies and outcome measures can improve the efficiency, validity and comparability of diarrhoea studies. Allocating large clusters in cluster randomized trials is compromized by unpredictable design effects and should be carried out only if the research question requires it.

Figure 1

Village-level diarrhoea incidence during a 12-month follow-up period in 11 control villages that participated in an intervention trial of solar water disinfection. Vertical lines mark bootstrapped 95% confidence intervals. The follow-up incidence is plotted against baseline incidence measured over a 6-week period (A), and against the village rank in baseline incidence over that same period (B)