| Summary: | This work examines patterns of under-nutrition prevalence, based on data gathered through a growth monitoring programme run as part of a health information system by the Zimbabwean government. Most of the thesis makes use of secondary data - collected by other investigators for different purposes - but use is also made of a questionnaire survey of households in the Buhera district of Zimbabwe. Patterns of attendance and bias are assessed initially through a comparison of health information system statistics with census and resulted in an independent, community-based anthropometric survey. Under-nutrition prevalence data from the growth monitoring scheme are examined using regression analysis to identify temporal and spatial patterns. The pre-harvest period between January and March is found to be the season when underweight prevalence is greatest. Levels of under-nutrition are also found to have declined during the information system's life-time, though this apparent trend may be related to changing patterns of attendance. Agricultural, meteorological, infra-structural, and socio-economic data are collated within a Geographic Information System and related to the observed patterns of under-nutrition prevalence. Spatially, the areas which suffer from persistent problems of under-nutrition are those where rural poverty is greatest. Yearly fluctuations in district-level under-nutrition are found to be related to changes in rainfall, whilst monthly fluctuations are correlated with reported diarrhoea cases among children. A spatial simulation model of growth monitoring attendance is constructed for Buhera district, using the household survey data. The model's performance is poor when compared to health facility statistics, but the methodology developed enables data from different sources and different scales to be integrated. It is concluded that the methodology developed for analysing growth monitoring data in the thesis may have application elsewhere in southern Africa.
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