Abstract
Rapid collection of data is of utmost importance in monitoring and evaluation of activities of public health importance. Among others techniques, 30 by 7 cluster sampling and Lot quality assurance sampling(LQAS) methods have been described in literature for this purpose. However, LQAS is often sparingly used in most settings, undermining its importance as a effective epidemiological tool in public health practice. To some extent LQAS is inadequately understood and even less emphasized method, especially in the postgraduate teaching and training. In this paper we aim to explain the use, method and application of LQAS in public health settings as well as discuss common pitfalls to avoid while planning and drawing inferences based on data collected through LQAS.
Highlights
Sampling strategy is one of the core considerations while planning any epidemiological study
Lot Quality Assurance Sampling (LQAS) has attracted attention of program managers for the purpose of monitoring and evaluation of large campaigns/ programs including national programs [4], it is imperative that LQAS as a survey method be given its due academic importance
This paper is primarily addressed to the post graduates in the departments of community medicine, community and family medicine, preventive and social medicine, social and preventive medicine, those pursuing master’s in public health, program managers, monitoring and evaluation professionals working in health care sector and the faculties of these departments and schools of public health across the country and thereafter
Summary
Sampling strategy is one of the core considerations while planning any epidemiological study. 100%, but for the purpose of study the researcher needs to Quality of LQAS depends upon randomness of the samples select lower and upper cut off values. If the upper cut off is taken as 80% and randomly selected (using random number table or any other the lowest acceptable cut-off is 70%, the decision cut off appropriate random number generator) This is usually a very number for the individual PHC to count as success will be 116 and the total sample size needed will be 156. There are few modifications of this method the null hypothesis is rejected One of such variants is met) the prudent inference is that the lot (PHC) has ‘Multiple Category-LQAS’, where the traditional LQAS vaccination coverage of at least 50%. In a study comparing LQAS to surveillance, it was not found inferior to surveillance [22]
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