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Active and adaptive case finding to estimate therapeutic program coverage for severe acute malnutrition: a
Sheila Isanaka1,2, Bethany L Hedt-Gauthier3,4, Halidou Salou5
1Department of Nutrition and Global Health and Population at Harvard School of Public Health, Boston, USA. sisanaka@hsph.harvard.edu.
Active and adaptive case finding (AACF) for severe acute malnutrition (SAM) requires high sensitivity for accurate coverage estimation. Ensuring exhaustive case identification is crucial for valid public health program assessments.
Area of Science:
- Public Health
- Epidemiology
- Nutrition Science
Background:
- Coverage estimation is vital for public health program performance assessment.
- Active and adaptive case finding (AACF) is a method for identifying cases in severe acute malnutrition (SAM) programs.
- AACF may yield non-representative samples, necessitating exhaustive case identification for valid coverage estimation.
Purpose of the Study:
- To assess the sensitivity of AACF compared to a census method for coverage estimation in SAM treatment programs.
- To determine the required AACF sensitivity for reliable coverage estimates.
Main Methods:
- A capture-recapture design was employed in northwestern Nigeria.
- Sensitivity of AACF and a census method were assessed.
- Program coverage was estimated using both case finding procedures.
Main Results:
- AACF sensitivity was 69.5% compared to 91.9% with census case finding.
- Coverage estimates were 40.3% (AACF) versus 34.9% (census).
- AACF sensitivity of at least 70% was generally needed for coverage estimates within ±10% of the census estimate.
Conclusions:
- Incomplete case finding and low sensitivity significantly impact coverage estimation accuracy.
- Adequate resources and attention are essential to ensure exhaustive or near-exhaustive case finding in SAM programs.
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