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American Journal of Epidemiology Vol. 138, No. 11: 994-1006
Copyright © 1993 by The Johns Hopkins University School of Hygiene and Public Health

research-article

Estimation of Design Effects and Diarrhea Clustering within Households and Villages

Joanne Katz1,2,, Vincent J. Carey2, Scott L. Zeger2,3 and Alfred Sommer3,4

1Dana Center for Preventive Ophthalmology, Wilmer Institute, Johns Hopkins Schools of Medicine and Hygiene and Public Health Baltimore, MD
2Department of Biostatistics, The Johns Hopkins School of Hygiene and Public Health Baltimore, MD
3Dean's Office, The Johns Hopkins School of Hygiene and Public Health Baltimore, MD
4Department of Epidemiology, The Johns Hopkins School of Hygiene and Public Health Baltimore, MD

Reprint requests to Dr. Joanne Katz, Dana Center for Preventive Ophthalmology, Wilmer 120, 600 N. Wolfe Street, Baltimore, MD 21287-9019

The degree to which diarrheal disease clustered within households and within villages among preschool age children was examined using data from four population-based prevalence surveys undertaken in Malawi, Zambia, Indonesia, and Nepal over the past decade. The design effect for each cluster survey was calculated using the diarrhea prevalence, the cluster sizes, and the magnitude of diarrhea clustering within the sampling unit (villages). A recently developed statistical method, alternating logistic regression, was used to estimate disease associations within households of up to nine preschool age children residing within villages of up to 589 such children. Pairwise odds ratios estimating diarrhea clustering within villages ranged from 1.03 (95% confidence interval (Cl) 1.01–1.07) in Zambia to 2.19 (95% Cl 1.73–2.78) in Indonesia. The design effects ranged from 2.07 (95% Cl 1.26–3.19) in Zambia to 7.93 (95% Cl 5.16–11.52) in Indonesia. Design effects were strongly dependent on cluster size. The design effects for clusters of size 50 would have ranged from 1.38 to 4.73. Pairwise odds ratios for diarrhea clustering within households ranged from 1.88 (95% Cl 1.61–2.19) in Nepal to 10.05 (95% Cl 8.46–11.94) in Indonesia. Household odds ratios were always larger than village odds ratios. The village and household pairwise odds ratios adjusted for age, the type of latrine used by the household, and presence of a market in the village were slightly higher than the unadjusted odds ratios. Alternating logistic regression provided useful estimates of disease clustering within villages and household while allowing for covariate adjustment.

diarrhea; epidemiologic methods; odds ratio


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