l0 reported. This dipped in ;9S5 as the bias peaked. Changes in the percent of children weighed do not, however, explain the absence of bias in the fourth quarter of 1989. As could be expected, .s the number of children with weights reported increases, biases in the estimation of malnutrition rates fall. It is important to note that the number of children with reported weights will be affected both by health care use - i.e. tho number of children who reach the public health care system, and by the rigor with which their weights are recorded and reported -- i.e. tne resources available for health administration.

31. Theoretically, with adequate data it should be possible to construct a correction factor based on the percentage of children weighed, changes in the use of health care, availability of resources to the health care sys.em, and the prevalence of malnutrition. The construction of accurate correction factors would, howrever, require many more points of comparison than a time series provides. Cross-sectional data from different regions could more easily gen3rate the needed sample size for a reasonable econometric model. The sample size for the survey would, however, have to be quite large to ensure that the sample be representative for relatively small geogrephically areas. Very special studies would be needed to attempt such an exercise.

32. Finally it ts interesting to compare the bias in clinic-based estimates of the malnutrition rates with that observed in Botswana, Swaziland and El Salvador. In Jamaica and Botswana the aggregate clinic-based estimates were lower than the surviy-based estimates. In Swazil **nd** they were about the same, and in El Salvador the aggregate clinic-based estimate was much higher than the survey-based estimate. In Jamaica the bias changed over time. In Botswana and Swaziland it varied by region, less so in El Salvador. In Swaziland t1le bias was constant with age. These comparisons strengthen the concl%Lsion that the biases inherent in clinic data are variable and difficult to acdust for.

33. Conclusion. In Jamaica, the clinic-based data underestimated malnutrition rates in all four cases of comparison, seriously so in three. The degree of underestimate was varisble, and the most abrupt change occurred within a single year. The degree of underestimate does not follow smoothly with either changes in children's use of public health care or with the resources devoted to public health care. There did appear to be an inverse relation between the percent of children weighed erd the degree to which clinic data underestimated malnutrition.

34. Although convenient, clinic-based estimates of malnutrition rates are biased and unreliable. They predict neither the level nor the trend of malnutrition accurately, even with such well-developed public health services as those Jamaica has. Reliable estimates of malnutrition rates require household surveys, an unfortunate conclusion, given their relative expense and infrequency. If clinic-based estimates of malnutrition are to continue to be the mainstay of policy work, policy-makers should be aware of the problems in the estimates and make allowances in their interpretation for measurable changes in factors which may affect the extent of bias. Nutrition analysts should explore in many more countries the direction, size and constancy of bias in clinic-based estimates of malnutrition and its causes.