38 J Health Popul Nutr Mar 2007 munization coverage, by identifying low-performing ar- eas 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, com- pared 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 surveil- lance. Keeping the above in mind, the present study aimed at comparing the proportion of inadequately-performing areas identified by the LQAS method with those derived using data from the Health and Demographic Surveil- lance System (HDSS) of ICDDR,B in Matlab. The con- sistency between the LQAS method and HDSS-based classification of areas in terms of performance was also examined. The paper also documents the experience gained in implementing the tool by the supervisors of the health and family-planning programme at the lowest level of implementation. MATERIALS AND METHODS Description of LQAS In the LQAS method, a defective article is defined as one that fails to conform to specifications in one or more quality characteristics. A common procedure in the method is to consider each submitted lot of product separately and to base the decision on acceptance or re- jection of the lot on the evidence of one or more samples chosen at random from the lot (7). Application of the method requires specification of three numbers. One is the number of articles ‘N’ in the lot from which the sample is to be drawn. The second is the number of articles ‘n’ in the random sample drawn from the lot. The third is the acceptance number ‘d’. The acceptance number is the maximum allowable number of defective articles in the sample. More than ‘d’ defec- tives will cause the rejection of the lot. For instance, if we have a situation with N=50, n=5, and d=0, it implies that “Take a random sample of size 5 from a lot of 50. If the sample contains more than 0 defectives, reject the lot; otherwise, accept the lot.” The LQAS method uses the binomial probability to calculate the probability of accepting or rejecting a lot. To apply the above in the context of delivery of health services, for example, vaccination coverage, let us assume the coverage of DPT1 for a health area as p. In a health area with an infinitely large population, the probability P(a) of selecting a number a of vaccinated Bhuiya A et al. individuals in a sample size n is calculated as: P(a)= n! a!(n-a)! pa qn-a Where p=the proportion of children with DPT1 (cover- age) in the health area q=(1-p), the proportion not having DPT1 n=the sample size a=the number of individuals in the sample who received the service n-a=the number of individuals in the sample without the service, usually denoted by d. The LQAS method aids programme personnel in choos- ing the sample size and the permissible value of n–a and interpreting results. To use the LQAS method in the context of health programmes, for example EPI, the fol- lowing five initial decisions must be made (8-10): first, the health system manager must select the intervention to assess. In our example, it is the coverage of DPT1; second, select the work-area whose coverage is to be assessed; third, select the target community to receive the intervention (infants in the case of DPT1); fourth, a triage system must be defined for classifying the level of coverage as adequate, somewhat inadequate, and very inadequate. This needs to be decided by prog- ramme managers, policy-makers, or other stakeholders of the EPI; and fifth, levels of provider and consumer risks. [Provider risk—probability of wrongly classify- ing a work-area/provider as very unsatisfactory, which can put the reputation of the worker at risk; Consumer risk—probability of wrongly classifying a very inade- quately-performing health area/worker as adequate which can put the inhabitants in the area at health risk]. In most cases, it may be around 10-15%. Using information from the above five decisions, a series of operating characteristics curve, or their cor- responding probability tables can be constructed with the above binomial formula. [An operating character- istics curve depicts the probabilities of accepting a lot based on the proportion of non-conformance in the lot, the sample size, and the value of d, allowable non-con- formances. An operating characteristics curve enables decision-makers to examine the possible risks involved]. From the operating characteristics curves, one can select the sample size (i.e. n) and the number of un-immunized individuals allowed (i.e. d) in the LQAS sample for a given level of provider and consumer risk before decid- ing that a health area has a sub-standard coverage. Let us assume that a consensus has been reached among various stakeholders of the EPI that EPI centres