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Epidemiology 1, 2, 3: study and sample design.

M H Hussein1, M K El-Sayed, M Talaat

  • 1Department of Community Medicine, Faculty of Medicine, Cairo University, Egypt.

The American Journal of Tropical Medicine and Hygiene
|May 17, 2000
PubMed
Summary
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This study demonstrates that complex sampling theory can be applied to schistosomiasis epidemiology, overcoming challenges like focal disease distribution and selection bias. The Epidemiology 1, 2, 3 (EPI 1, 2, 3) design provides reliable samples for schistosomiasis research.

Area of Science:

  • Epidemiology
  • Parasitology
  • Biostatistics

Background:

  • Schistosomiasis studies often suffer from poor sample designs and selection bias.
  • The focal nature of schistosomiasis presents challenges for reliable sampling.
  • Previous research has been hampered by difficulties in obtaining representative samples.

Purpose of the Study:

  • To demonstrate the applicability of sampling theory to epidemiologic studies of schistosomiasis.
  • To present the Epidemiology 1, 2, 3 (EPI 1, 2, 3) sample design.
  • To achieve the smallest feasible standard errors for EPI 1, 2, 3 objectives within logistical constraints.

Main Methods:

  • A multi-stage sampling scheme was employed in 9 Egyptian governorates.
  • Villages (ezbas) stratified by size and households were systematically selected from census frames.

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  • Ezbas were stratified by size and randomly selected; sample sizes were calculated based on study objectives.

    • Main Results:

    • The EPI 1, 2, 3 sample design proved applicable despite challenges with sampling frames and population variation.
    • The age structure of the selected sample closely paralleled the 1986 census of respective rural populations.
    • A 20% subsample was drawn for clinical and ultrasonographic examinations.

    Conclusions:

    • Complex sampling theory is applicable to schistosomiasis epidemiology.
    • The EPI 1, 2, 3 design offers a robust method for reliable schistosomiasis sampling.
    • This approach can overcome issues of focal disease distribution and selection bias.