|Budget Amount *help
¥2,100,000 (Direct Cost : ¥2,100,000)
Fiscal Year 1994 : ¥600,000 (Direct Cost : ¥600,000)
Fiscal Year 1993 : ¥1,500,000 (Direct Cost : ¥1,500,000)
We have developed a prediction model for the citizens' health level possibly caused by the city-environmental change.
1. We have collected 600 indicators for each municipal in Greater Tkyo area as well as other areas and have examined categories necessary to analyze health-environmental interaction. The followings are the health categories required : indicators relating to (1) the death feature, (2) inequity in death within municipalities, (3) active life expectancy, and (4) quality of life. The required environmental categories were : indicators relating to (1) demography, (2) housing conditions, (3) infrastructure, (4) education, (5) health-care and welfare servics, (6) working conditions, (7) local economics, (8) i health-promotion activities, and (9) natural and ecological conditions. 2. We have extracted the factors representing health-level indicators and environmental indicators by the weighted principal factor analysis. A prediction model could be constructed from 5-10 factors f
or the health-level indicators and 20-30 factors for the environmental indicators ; the number of required factors depended on the areas. 3. We have collected following indicators besides those made from the existing statistics : indicators relating to (1) life style and health behavior of the citizens by a questionnaire survey, (2) natural and ecological environments, land use, and meteorological conditions from satellite remote-sensing data, and (3) linked data of mesh data. 4. We have analyzed the relationship between the health levels and the city environments by classifying cities with regard to be the population density, educational level, income level, working status, etc. Not only the socioeconomic factors but also the city planning including land use and natural and ecological condition were found to contribute to promote citizens' health. 5. We have developed a health prediction model from city-environments. The model predicted the observed health level well and a potential to elucidate the environmental conditions necessary to promote citizens'health. Less