2 4. Direction of Dias. The direction of the bias caused by having a nonrandom sample of children is not clear. There are several factors which may affect biases in the u of health clinics. Which of these predominates may vary from place to p] 3 and time to time. Children who go to clinics may be sick or sicklv. Since most childhood illnesses can cause or accentuate malnutrition, and since malnourished children are more susceptible to illness, sick or sickly children are more likely to be malnourished than a random sample from the general population. On the other hand, children who go to clinics for preventive care may be less likely to be sick or malnourished than other children. It may also be that which children go to clinics is determined not by their health, but by the availability of services. In most countries, clinics are less accessible to the poor who are more often malnourished, so that clinic-based data would underestimate malnutrition rates. On the other hand, the wealthier children may receive most of their health care from private physicians not tied into the nutrition reporting system. In that case, the wealthy children, who are unlikely to be malnourished, would be under-represented in government clinic data, and malnutrition rates would be overestimated by data from public clinics.

5. Constancy of Bias. Even if estimates of the malnutrition rate are biased, if the amount and direction of bias is constant, a series of clinic data could still serve to monitor whether nutritional status was improving or deteriorating. It would seem that, in the short run, the factors which would determine the bias would be about constant. In the long run, or in periods of crisis, some of the same factors which would affect nutrition status may also affect the balance of who goes to clinics, and therefore, the bias with which the malnutrition rate is measured. In a period of economic crisis, for example, wages and employment may fall and prices rise, which would stress families' abilities to feed themselves. At the same time, the government may reduce spending on health and nutrition programs, leading to fever staff or hours of operation at the clinics, and longer queues. That may change the number of people willing to use government clinics. Or even if people with sick children are willing to queue, parents may forgo preventive visits for their children. Those who previously could afford private care may shift to public care. These kinds of changes in patterns of health care use would affect the amount of bias in the malnutrition rate as measured from clinic data.

# Section **II:** The Comparative Literature

6. The discussion thus far has made it clear that clinic malnutrition data may be biased. There is not a clear presumption as to the direction, strength, or even constancy of the bias. These issues are best resolved empirically. Thero is, however, a surprisingly slim literature on the subject.

7. The authors are aware of three studies which compare anthropometric data from household surveys and clinics in order to examine the differences in estimates of malnutrition rates. For El Salvador, Trowbridge, at al., (1980) compared household survey data for five regions to the clinic data from two contiguo.s time periods. The clinic data estimated malnutrition to be considerably higher than the household survey data, from 70-180% higher. The regional distritation of malnutrition as estimated from both sources