2012/04/27 by Lindsay P. Galway, Nathaniel Bell, Al Shatari SAE +6 · 1 citation
Health Professions · Mathematics · Medicine · #Cluster sampling #Computer science #Context (archaeology) #Data mining #Data-Driven Disease Surveillance #Environmental health #Geographic information system #Geography #Global Maternal and Child Health #Health and Conflict Studies #Mathematics #Medicine #Population #Remote sensing #Sampling (signal processing) #Sampling design #Statistics
paper · pdf · doi:10.1186/1476-072x-11-12
openalex publication_date 2012/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
BACKGROUND: Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. RESULTS: We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. CONCLUSION: Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings.