Performance of the lot quality assurance sampling method 45 Table 8. Consistency between HDSS and LQAS method using the scheme in Table 7 to classify 57 work-areas LQAS HDSS Adequate Inadequate Predictability of LQAS (%) DPT Adequate 55 2 Sensitivity=100.0 LQAS (n=28) Inadequate 0 0 Specificity=0.0 Measles Adequate 53 1 Sensitivity=94.6 LQAS (n=28) Inadequate 3 0 Specificity=0.0 BCG Adequate 57 0 Sensitivity=100.0 LQAS (n=28) Inadequate 0 0 Specificity=Indeterminate CPR Adequate 54 0 Sensitivity=94.7 LQAS (n=28) Inadequate 3 0 Specificity=Indeterminate CPR=Contraceptive prevalence rate; HDSS=Health and demographic surveillance system; LQAS=Lot quality assurance sampling One of the real challenges is to adopt the LQAS method in the regular monitoring system at the lowest level of the programme. It is clear that the theoretical ba- sis of the LQAS method will be too complicated for su- pervisors. A practical difficulty in its use by programme supervisors was the computation of the sample size cor- responding to the different threshold levels of coverage and their associated level of error. One way of simplify- ing the task is to seek for a fixed sample size, as small as possible, which is reasonably good in terms of level of error for different threshold levels. If one examines the total classification error in Table 1, it is apparent that the sample sizes with the lowest level of error were 28 and 19. This points to the possibility of whether 19 could be used as a sample size for all levels of threshold points and what implications it would have on the magnitude of misclassification. Table 9 presents the maximum number of non-users allowed—of 19—with associated magnitude of errors under various levels of thresholds. It can be seen from Table 9 that if one is ready to ac- cept the level of consumer and provider risk less than 10% each than a sample size of 19 will serve the purpose for varied levels of threshold points. In such circumstances, the degree of misclassification of a work-area will be slightly higher, implying that, in the long run, one will misclassify 20 work-areas instead of 10 per 100. But to bring down the error level to less than 10%, the sample size may need to be increased up to 28 from 19, and in real-life resources needed to survey 28, instead of 19, are much higher. The trade- off is to be happy with a slightly higher level of error and reduced costs for the survey. Thus, for many prac- tical purposes, a sample size of 19 should serve the purpose for programme managers. At the field level, lack of a up-to-date sampling frame can pose a challenge to applying the method. In such circumstances, emphasis should be placed on up- dating the list as regularly as practical which not only makes the method easily adoptable but also may serve other programmatic purposes. If there is no sampling frame, it may be appropriate to adopt other methods for the selection of a simple random sample, such as going to the centre of a village, then spinning a bottle, or a similar object to select a direction to move to select the required number of respondents in a manner by ensur- ing spread over the village in that direction. The LQAS method assumed that the samples are selected at ran- dom from a list of respondents; so, the use of cluster- sampling scheme in selecting samples will require an upward adjustment of the sample size; the magnitude of adjustment will, however, depend on the size of intra- class correlation within clusters. Table 9. Magnitude of classification error with various upper and lower threshold levels with sample size of 19 in the LQAS method Parameter Maximum no. of non-users allowed Consumer risk Provider risk Total risk Threshold levels (lower-upper) 10-40 15-45 20-50 25-55 30-60 35-65 40-70 45-75 50-80 55-85 60-90 65-95 14 0.070 0.035 0.105 13 0.078 0.054 0.132 12 0.084 0.068 0.152 11 0.087 0.077 0.164 10 0.088 0.084 0.172 9 0.087 0.087 0.174 8 0.084 0.088 0.172 7 0.077 0.087 0.164 6 0.068 0.084 0.152 5 0.054 0.078 0.132 4 0.035 0.070 0.105 3 0.013 0.059 0.072