Nutritional surveillance.

The concept of nutritional surveillance is derived from disease surveillance, and means "to watch over nutrition, in order to make decisions that lead to improvements in nutrition in populations". Three distinct objectives have been defined for surveillance systems, primarily in relation to problems of malnutrition in developing countries: to aid long-term planning in health and development; to provide input for programme management and evaluation; and to give timely warning of the need for intervention to prevent critical deteriorations in food consumption. Decisions affecting nutrition are made at various administrative levels, and the uses of different types of nutritional surveillance information can be related to national policies, development programmes, public health and nutrition programmes, and timely warning and intervention programmes. The information should answer specific questions, for example concerning the nutritional status and trends of particular population groups.Defining the uses and users of the information is the first essential step in designing a system; this is illustrated with reference to agricultural and rural development planning, the health sector, and nutrition and social welfare programmes. The most usual data outputs are nutritional outcome indicators (e.g., prevalence of malnutrition among preschool children), disaggregated by descriptive or classifying variables, of which the commonest is simply administrative area. Often, additional "status" indicators, such as quality of housing or water supply, are presented at the same time. On the other hand, timely warning requires earlier indicators of the possibility of nutritional deterioration, and agricultural indicators are often the most appropriate.DATA COME FROM TWO MAIN TYPES OF SOURCE: administrative (e.g., clinics and schools) and household sample surveys. Each source has its own advantages and disadvantages: for example, administrative data often already exist, and can be disaggregated to village level, but are of unknown representativeness and often cannot be linked with other variables of interest; sample surveys provide integrated data of more or less known representativeness, but sample sizes usually do not allow disaggregation to, for example, specific villages. A combination of these sources, with a capability for ad hoc surveys (formal or informal) is often the best solution. Finally, much depends on adequate facilities for data analysis, even though simple, comprehensible data outputs are what is required. Intersectoral cooperation is needed to provide realistic options for the decision-making process.