A prediction model for the health level of citizens in relation to environmental change in enlarged dense cities

建立了城市环境变化可能引起的居民健康水平的预测模型.我们收集了大Tkyo地区每个城市以及其他地区的600个指标，并检查了分析健康-环境相互作用所需的类别。以下是所需的健康类别：与（1）死亡特征、（2）城市内死亡不平等、（3）积极预期寿命和（4）生活质量有关的指标。所需的环境类别是：与以下方面有关的指标：（1）人口统计;（2）住房条件;（3）基础设施;（4）教育;（5）保健和福利服务;（6）工作条件;（7）地方经济;（8）促进健康的活动;（9）自然和生态条件。2.通过加权主因子分析，提取了代表健康水平指标和环境指标的因子。预测模型可由5-10个因子组成， ...更多信息 20-30个环境指标因子;所需因子的数量视地区而定。3.除现有统计数据外，还收集了（1）通过问卷调查收集的居民生活方式和健康行为指标，（2）通过卫星遥感数据收集的自然生态环境、土地利用和气象条件指标，（3）网格数据的关联数据。4.我们通过对城市的人口密度、教育水平、收入水平、工作状况等进行分类，分析了健康水平与城市环境之间的关系，发现不仅社会经济因素，而且包括土地利用、自然和生态条件在内的城市规划也有助于促进市民的健康。5.我们开发了一个城市环境的健康预测模型。该模型很好地预测了观察到的健康水平，并有可能阐明促进公民健康所需的环境条件。少

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 … More 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