Performance of the Lot Quality Assurance Sampling Method Compared to Surveillance for Identifying Inadequately-Performing Areas in Matlab, Bangladesh (Report‪)‬

INTRODUCTION Health-programme personnel always aim to increase the level of use of the services they provide. Despite all-out efforts, the use of services often remains lower than expected and/or the level of use is not same for all areas or for all the service components. Focusing attention on deficient areas can help improve the situation. A prerequisite for focusing attention is the identification of deficient areas with a known degree of reliability. Data from surveillance and cross-sectional surveys are commonly used for assessing the level of use of services. Surveillance being very resource-intensive is not practical for this purpose. The cross-sectional survey, especially the 30 cluster-sampling scheme with 210 respondents, is considered to be reasonably practical and has been in use for monitoring the coverage of services under the Expanded Programme for Immunization (EPI) for quite some time (1). In fact, the task of covering 210 respondents per estimation area, if one looks carefully, is not so small a task when the performance of areas covered by the lowest-level workers--quite often large in numbers--is to be assessed. In addition, the task of data analysis and their interpretation also become technical, requiring expertise beyond the domain of programme management. In this respect, the lot quality assurance sampling (LQAS) method is simpler, resource- and time-efficient for it is based on a much smaller sample size and easy to apply (2,3). The LQAS method is also effective in improving public-health services, like immunization coverage, by identifying low-performing areas in some settings (4,5). Although the LQAS method has been used for identifying inadequately-performing areas in many settings (4) and, in some instances, compared to cross-sectional surveys (6,3), its performance in the field has not been compared with those based on more rigorous data-collection methods, such as surveillance.