Impact of Temperature on Mortality in Three Major Chinese Cities

Objective To study the relation between temperature and mortality by estimating the temperature-related mortality in Beijing, Shanghai, and Guangzhou. Methods Data of daily mortality, weather and air pollution in the three cities were collected. A distributed lag nonlinear model was established and used in analyzing the effects of temperature on mortality. Current and future net temperature-related mortality was estimated. Results The association between temperature and mortality was J-shaped, with an increased death risk of both hot and cold temperature in these cities. The effects of cold temperature on health lasted longer than those of hot temperature. The projected temperature-related mortality increased with the decreased cold-related mortality. The mortality was higher in Guangzhou than in Beijing and Shanghai. Conclusion The impact of temperature on health varies in the 3 cities of China, which may have implications for climate policy making in China.

Assessing Heat-related Mortality Risks in Beijing,China

Objective To obtain the exposure-response relationship for temperature and mortality, and assess the risk of heat-related premature death. Methods A statistical model was developed using a Poisson generalized linear regression model with Beijing mortality and temperature data from October 1st, 2006 to September 30th, 2008. We calculated the exposure-response relationship for temperature and mortality in the central city, and inner suburban and outer suburban regions. Based on this relationship, a health risk model was used to assess the risk of heat-related premature death in the summer （June to August） of 2009. Results The population in the outer suburbs had the highest temperature-related mortality risk. People in the central city had a mid-range risk, while people in the inner suburbs had the lowest risk. Risk assessment predicted that the number of heat-related premature deaths in the summer of 2009 was 1581. The city areas of Chaoyang and Haidian districts had the highest number of premature deaths. The number of premature deaths in the southern areas of Beijing （Fangshan, Fengtai, Daxing, and Tongzhou districts） was in the mid-range. Conclusion Ambient temperature significantly affects human mortality in Beijing. People in the city and outer suburban area have a higher temperature-related mortality risk than people in the inner suburban area. This may be explained by a temperature-related vulnerability. Key words： Temperature; Mortality; Premature death; Health risk; Generalized linear regression model; Climate change

Willingness to Pay for Measures of Managing the Health Effects of Heat Wave in Beijing,China:a Cross-sectional Survey

Objective There are evidences that heat wave events cause deaths and emergency cases. This article used the contingent valuation method to find the willingness to pay for the protective measures and investigated the factors that influence the willingness to pay. Methods A cross-sectional face-to-face household survey was completed by 637 urban long-term residents and 591 rural long-term residents aged 15-79 in Beijing, China. Binary logistic regression was used to identify factors that influenced the payment rate or payment amount for the protective measures, including independent variables for district, gender, age, education, income, air conditioner ownership, heat wave experience, and chronic non-communicable disease. Results The payment rate was 41.1% for protective measures provided by the government and 39.5% by measures provided by the market. Most of the respondents were willing to pay 40 CNY per capita annually for measures provided by the government or the market. The factors influencing willingness to pay were district, gender, income, air conditioner ownership, heat wave experience, and chronic non-communicable disease. Conclusion Protective measures for heat waves need to be provided immediately. More attention should be paid to the situation of vulnerable groups, such as people who live in urban areas, those without air conditioning, and those who have experienced a heat wave in the past.

Projections of Temperature-related Non-accidental Mortality in Nanjing, China

The health effects of climatic changes constitute an important research area, yet few researchers have reported city-or region-specific projections of temperature-related deaths based on assumptions about mitigation and adaptation. Herein, we provide quantitative projections for the number of additional deaths expected in the future.

广东省妇幼保健院孕妇学校不同课程模式效果及影响因素分析

目的 比较广东省妇幼保健院孕妇学校不同课程模式的效果,并分析影响因素,为个体化宣教提供依据。方法 从该院孕妇学校健康教育管理系统获取15 350名注册用户的数据,对不同课程模式的参与率、课后答题正确率、教学满意度以及不同课程模式的参与率、课后答题正确率的影响因素进行分析。结果 直播课参与率(66.4%)高于录播课(27.9%)及线下课(4.0%)(χ^(2)=4 564.320,P <0.001;χ^(2)=13 093.243,P <0.001)。参与直播课用户答题正确率(40.7%)高于录播课(29.9%)和线下课(22.8%)(χ^(2)=149.929,P <0.001;χ^(2)=78.323,P <0.001)。直播课的教学满意度得分[(90.96±8.39)分]高于线下课[(30.30±13.85)分](t=-23.19,P <0.001)。直播课参与率的回归分析中,相对已分娩用户,孕晚期用户的OR值为3.927。录播课参与率的回归分析中,相对已分娩用户,备孕、孕早中期、孕晚期用户的OR值为0.311、0.217、0.488。线下课参与率的回归分析中,相对已分娩用户,备孕、孕早中期、孕晚期用户的OR值为0.043、0.199、0.083;相比高龄用户,非高龄用户线下课参与率的OR值为0.778。课程答题正确率的回归分析中,相对已分娩用户,备孕、孕早中期及孕晚期用户的OR值为1.301、2.078及4.798,相对参与直播课的用户,不参与直播课用户的OR值为0.052;相对参与录播课的用户,不参与录播课用户的OR值为1.116;相对参与线下课的用户,不参与线下课用户的OR值为1.287。结论 孕妇学校直播课程优势明显。不同孕产状况用户不同课程模式参与率不同,建议妇幼保健机构可着重加强对孕晚期和非高龄用户开设线上课程(直播+录播),并继续重点为高龄用户开设线下课。导致不同孕产状况用户的课题答题正确率差异原因有待进一步研究。