40 J Health Popul Nutr Mar 2007 50% respectively. To illustrate the calculation, let us take the example of n=12 and d=3. To calculate the probability of wrongly classifying a work area/provider as inadequate, we first have to calculate the probability of having three or less unimmunized children—of 12 children—in an area with 80% coverage. Finally, we have to subtract this probability from 1 to get the prob- ability of wrongly classifying a work-area/provider as inadequate even if they could well be not inadequate. The probability of 0 unimmunized (equals to all 12 immunized) children—of 12 children—with 80% cov- erage is= P(12)= 8012.2012-12=.8012=0.0687 12 ! 12!(12-11)! Similarly, probability of 1 unimmunized (equaling 11 immunized)= .8011.2012-11=12X.8011X.2012-11 =0.2062 P(11)= 12 ! 11!(12-11)! Probability of 2 unimmunized (equaling 10 immuni- zed)=0.2835 Probability of 3 unimmunized (equaling 9 immunized) =0.2363 Therefore, the probability of having three or fewer children unimmunized in an area with 80% coverage is=0.0687+0.2062+0.2835+0.2363=0.7946. This also implies that, with 80% coverage in the area, there is a chance of 0.2054 (1–0.7946) to have three or fewer unimmunized children. Thus, if one, on the basis of hav- ing three or more unimmunized children among 12 chil- dren, declares that the performance of the area/health provider as inadequate has a chance of misclassifying the area in 20.54% of time. This puts the provider at risk of being wrongly classified as inadequately performing. On the other hand, with 50% coverage, the probability of having three or fewer children unimmunized is=P(12 immunized)+P(11 immunized)+P(10 immunized)+P(9 immunized)=0.0002+0.0029+0.0161+0.0536=0.0729. This implies that, with 50% coverage, there is still a probability of 0.0729 of having three or fewer un-im- munized children. Thus, the decision that an area/health- care provider is performing adequately on the basis of having three or fewer unimmunized children—of 12 children—may, in fact, be wrong in 7.29% of the time. This puts the community members at risk for they may be considered adequately covered when they are not. Table 1 shows that, with a sample of 28 children having nine or fewer unimmunized infants in the Bhuiya A et al. sample, EPI centres can be classified as ‘adequately’- performing centres. Samples with more than nine unimmunized infants will be identified as ‘inadequately’- performing EPI centres. Using this rule, managers will identify areas correctly with 80% or above coverage more than 95% of the time. Similarly, they can also judge an area as inadequate if more than nine of 28 chil- dren are unimmunized in more than 95% of the time. Following these procedures, the optimum decision rules in terms of a feasible sample size and the number of un- covered allowable subjects at given levels of consumer (infants in the case of DPT1) and provider (EPI centres in the case of DPT1) risks can be formulated for various services using the binomial probabilities as was done in Table 1. The same calculation can also be done in terms of children immunized instead of unimmunized children. While the statistical reasoning behind the LQAS method and the calculation involved may seem some- what complicated, its application in the field is, in fact, very easy. Once the decision rules are made, ideally in consultation with policy-makers and programme managers, the task of collecting the required data and their use in deciding which of the health facilities/ service providers are performing adequately or not is very simple. The method can also be used for various other outcome variables. Study design The study had two components. The first component compared the performance of the LQAS method with that of the HDSS in identifying inadequately-perform- ing work-areas. This was done by carrying out a LQAS survey and applying the LQAS decision rules to iden- tify work-areas. The consistency between findings based on the LQAS method and the HDSS was also examined by calculating sensitivity and specificity. The second component included application of the methods by pro- gramme supervisors. This was done to assess the feasi- bility of training programme supervisors and the prob- lems encountered by them in the real-life situation. Hypotheses The proportion of inadequately-performing work- areas of the community health workers in the ICDDR,B- served area in Matlab as derived using the LQAS meth- od is similar to those derived using data from the Matlab HDSS. Ho: Ps=Pq (Null hypothesis) Ha: Ps≠Pq (Alternative hypothesis) Where, Ps is the proportion of work-areas having less