A child health production function estimated from longitudinal data. Cebu Study Team

Collaborating researchers used a multi equation model to analyze 3080 mother-infant pairs living on the island of Cebu in the central Philippines and to estimate a child health production function. The econometric methods used eliminated obstacles such as heterogeneity and endogeneity of significant explanatory factors. They also maximized the longitudinal quality of the data. The results showed that morbidity in 1 period reduced infant weight in following periods. For example, diarrhea and febrile respiratory infection reduced growth in the time period following the illnesses. Further effects of some contributing factors were great near birth not diminished with age. For example, breast feeding promoted growth and protected against infection, especially diarrhea and febrile respiratory infection, more substantially near birth than it did later. Further, in urban areas, preventive health care positively affected growth independently of its indirect influence on reducing respiratory infection. Yet it had little effect on diarrhea. Diarrhea had a strong adverse effect on growth. Exposure of the infant to enteric pathogens had the greatest influence on production of diarrhea. In urban areas, exposure included water supply contamination, exposure to feces, a more pathogenic diet, and community crowding. In rural areas, however, exposure included community crowding and increased rainfall which presumably washed feces into the water supply. Household crowding in both urban and rural areas and irritation from smoke in urban areas only tended to bring about febrile respiratory infection.     

The demographic impact of AIDS in sub-Saharan Africa: short- and long-term projections

"This paper describes the methodology used to incorporate AIDS mortality in recently revised World Bank population projections....The paper first reviews different approaches for projecting AIDS and its demographic consequences. This is followed by a summary of an epidemiological model that simulates the spread of HIV used in this analysis, and a demographic model that translates mortality from AIDS into population outcomes. These models are then used in a set of simulations, from which the effect of current HIV prevalence on projected future mortality is extracted. Finally, the extracted equations linking current HIV prevalence with future mortality indicators are applied to sub-Saharan countries with a measurable level of current HIV prevalence."