Large Country-Lot Quality Assurance Sampling : A New Method for Rapid Monitoring and Evaluation of Health, Nutrition and Population Programs at Sub-National Levels

Hedt, Bethany L. ; Olives, Casey ; Pagano, Marcello ; Valadez, Joseph J.

World Bank, Washington, DC

关键词: ACQUIRED IMMUNODEFICIENCY SYNDROME;    ADULT EDUCATION;    ALLOCATION OF RESOURCES;    ANOVA;    ANTENATAL CARE;   

RP-ID  :  83424

学科分类：社会科学、人文和艺术（综合）

来源: World Bank Open Knowledge Repository

【 摘 要 】

Sampling theory facilitates developmentof economical, effective and rapid measurement of apopulation. While national policy maker value survey resultsmeasuring indicators representative of a large area (acountry, state or province), measurement in smaller areasproduces information useful for managers at the local level.It is often not possible to disaggregate a national surveyto obtain local information if that was not the intent ofthe original survey design. Cluster sampling is typicallyused for national or large area surveys because sampling inclusters lowers the cost of a survey. Lot Quality AssuranceSampling (LQAS) is used to measure results at a local level,since it requires small random samples and produces resultsuseful to local managers. However, current LQAS methodologyrequires all local areas (strata) be included in the surveyin order to be aggregated to produce point estimates for thenation or state. In large countries it is not feasible tosample all strata for logistical and financial reasons. Thispaper resolves this problem by presenting Large Country(LC)-LQAS, a method with two concurrent objectives: 1)provide local managers with accurate local information toenable data driven decisions, and 2) provide central policymakers with the aggregate information they require. Theseare achieved by integrating cluster sampling with LQASmethodologies. Two examples of the implementation of LC-LQASare provided, in an HIV/AIDS program in Kenya and a MalariaBooster Project in Nigeria. Classifications of local healthunits into performance categories and aggregate estimates ofcoverage, with associated confidence intervals, are providedfor select indicators in order to demonstrate its use,analysis, and costs. This paper is written as a manual tosupport the use of LC-LQAS by others.