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An assessment of Lot Quality Assurance Sampling to evaluate malaria outcome indicators: extending malaria indicator surveys

  1. Caitlin Biedron1,
  2. Marcello Pagano2,*,
  3. Bethany L Hedt2,
  4. Albert Kilian3,
  5. Amy Ratcliffe4,
  6. Samuel Mabunda5 and
  7. Joseph J Valadez6
  1. 1Department of Global Health and Population, Harvard School of Public Health, 2Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA, 3Malaria Consortium, London, UK, 4Malaria Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA, 5National Malaria Control Program, Ministry of Health, Maputo, Mozambique, and 6International Health Group, Liverpool School of Tropical Medicine, Liverpool, UK.
  1. *Corresponding author. Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. E-mail: pagano{at}hsph.harvard.edu
  • Accepted November 10, 2009.

Abstract

Background Large investments and increased global prioritization of malaria prevention and treatment have resulted in greater emphasis on programme monitoring and evaluation (M&E) in many countries. Many countries currently use large multistage cluster sample surveys to monitor malaria outcome indicators on a regional and national level. However, these surveys often mask local-level variability important to programme management. Lot Quality Assurance Sampling (LQAS) has played a valuable role for local-level programme M&E. If incorporated into these larger surveys, it would provide a comprehensive M&E plan at little, if any, extra cost.

Methods The Mozambique Ministry of Health conducted a Malaria Indicator Survey (MIS) in June and July 2007. We applied LQAS classification rules to the 345 sampled enumeration areas to demonstrate identifying high- and low-performing areas with respect to two malaria program indicators—‘household possession of any bednet’ and ‘household possession of any insecticide-treated bednet (ITN)’.

Results As shown by the MIS, no province in Mozambique achieved the 70% coverage target for household possession of bednets or ITNs. By applying LQAS classification rules to the data, we identify 266 of the 345 enumeration areas as having bednet coverage severely below the 70% target. An additional 73 were identified with low ITN coverage.

Conclusions This article demonstrates the feasibility of integrating LQAS into multistage cluster sampling surveys and using these results to support a comprehensive national, regional and local programme M&E system. Furthermore, in the recommendations we outlined how to integrate the Large Country-LQAS design into macro-surveys while still obtaining results available through current sampling practices.

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