Using lot quality assurance sampling to assess measurements for growth monitoring in a developing country's primary health care system.

ABSTRACT

PIP: Nurses and rural health supervisors used the Lot Quality Assurance Sampling (LQAS) technique to assess the quality of growth monitoring and promotion (GMP) conducted by community health workers (CHWs) in 12 health areas in Costa Rica. Each supervisor made 10 routine household visits and spent 4-8 hours in each area. The study allowed no more than two performance errors per CHW. CHWs could correctly identify children in need of the nutritional services of the primary health care (PHC) system. Yet they were weak in their referral, education, and documentation skills. The supply system and the documentation system that support growth monitoring did not work well. Perhaps the inadequate support system may have contributed to the CHWs' inferior use of their skills. The finding that there were inadequate supplies and poor documentation of required GMP data suggest that CHWs did not regularly conduct growth monitoring, perhaps due to a lack of scales and growth charts. The PHC system did not follow children with nutritional deficiencies, suggesting that health facilities did not keep a register and refer these children systematically. This would explain why CHWs did not refer malnourished children to health facilities. CHWs had significant time constraints that influenced their ability to perform regular growth monitoring. The evaluation team required 4-8 hours to observe growth monitoring in 10 households. The PHC system expects each CHW to conduct about 10 complete household visits/day, which includes growth monitoring, vaccinations, pre- and post-natal care, oral rehydration therapy training, and monitoring blood pressure. With each subsequent supervision visit, the misclassification error of substandard CHW (i.e., the probability of identifying an inadequate performer) decreases. By the sixth visit, supervisors could identify almost all CHWs with a performance quality of 80% or less. These findings suggest that supervisors use LQAS methods to regularly identify GMP problems.