A population laboratory for studing disease processes and mortality: the demographic surveillance system, Matlab, Comilla, Bangladesh

Abstract

The paper briefly discusses the Demographic Suveillance System (DSS) in Matlab as an invaluable tool for the study of disease processes and mortality. It focuses less on working details of the DSS than on its capacity to serve as a data base for indepth studies in the health area. Demographers, while fully conversant with problems in their own field-related to data collection, quality control etc. --tend to be less aware that a good population data base is indispensable for the study of health problems. Examples of applied research are provided which show how the Matlab DSS has made possible some important research findings in the understanding of diarrhoeal diseases as well as mortality and morbidity processes. The paper is not intended to be a historical covering of the DSS, and consequently indicated in the paper remains limited to areas directly illustrative of the utility of the DSS. Although the inverse correlation between the socio-economic status and mortality has been well documented, the process by which higher mortality rates occur in the lower socio-economic groups is noy well understood. One "path" of understanding would be consider the sequence of elements: low economic status---mainutrition---"disease-prone"---morality. Studies in the "path" have indicated that lowering of infant morality rates is not necessarily connected with increasing nutritional standards. More efficacious may be inputs of public health preventive measures. At the ICDDR,B studies are being undertaken to investigate both "path" referred to earlier. The DSS has utilised as an overall vital registration system for the study area in Matlab. Intervention and "control" areas have been set up without close study of individual village characteristics. With the arrival of the computer, it is now possible to investigate in detail the "structure" of the various elements forming the study population. Data can be tabulated by village, baris and families. Health monitoring at the village level through FVWs will be mire effective when sufficient account is taken of ther factors such as community organization, participation of the community in the health field, type of health services provided, water supply.