A Brief Overview of Lot Quality Assurance Sampling (LQAS) and its application in the Health Sector Joseph Valadez, PhD, ScD, MPH Liverpool School of Tropical Medicine January 2010

A B C D E F G Good Below Average or Established Benchmark

Good Below Average or Established Benchmark Identify the reasons for program problems Develop targeted solutions Maintain the program at the current level Identify Supervisors and Health Workers that can help other Health Workers improve their performance

What is LQAS? An analysis method that can be used locally to –identify health administrative units that fail to reach an established performance benchmark for an indicator –support local management decision making by sharing information across different health administrative units. Estimate coverage at an aggregate level (e.g., district or state or nation) Catchment Area – Suitable for Reporting Purposes LQAS uses small samples –Most frequently used size = <20 per one admin unit

Examples of countries using LQAS M&E Data NigeriaEritreaKenyaUganda

Advantages of LQAS for Local Program Management  Can be used at a local level as it requires only modest supervision  Identifies where the successes and challenges are located using rigorous sampling theory  Produces information that can be rapidly interpreted by local managers  Paper/pencil analyses rather than requiring computer analyses  Data can be used for local management as well as for national reporting

Quality Benchmark : At least 65% (10 out of 19) Infants sampled must have slept under an ITN in the night preceding survey in each health administration unit Children 0-11 Months Slept Under ITN Last Night Anseba, Eritrea 2004 AreaYesNoTotal Total Insecticide Treated Bednets

Distribution of Project States by estimated coverage for ITN use by children and IPT by Pregnant Women

The small sample and pen & paper analysis make LQAS accessible to local public health practitioners: NGOs and public sector

12 Afghanistan: Tracking Progress

Recap: LQAS is a M&E tool based on robust sampling. Generally simple to administer once sampling frame is available and tools are standardized. Can help to identify local health administrative units failing to reach pre-determined performance benchmarks and learning from those which achieved the benchmarks Pooled data from different units can also be used to estimate coverage levels at sub national and national levels