本文選題：熱浪 + 死亡��； 參考：《暨南大學(xué)》2013年碩士論文

【摘要】：目的評(píng)估廣東省南雄、廣州、珠海和臺(tái)山市熱浪對(duì)居民死亡的影響，探討熱浪的特點(diǎn)對(duì)熱浪與死亡關(guān)系的修飾作用大小，為制定政策降低熱浪的健康風(fēng)險(xiǎn)提供科學(xué)依據(jù)。 方法收集2006~2010年四地區(qū)逐日氣象和居民死亡數(shù)據(jù)，建立分布滯后非線性模型（DLNM），將熱浪對(duì)死亡的效應(yīng)分成主效應(yīng)（由高溫引起）和附加效應(yīng)（由高溫持續(xù)時(shí)間引起），分析不同滯后期各地區(qū)熱浪的累積主效應(yīng)與附加效應(yīng)大小，并用Meta分析合并四地區(qū)效應(yīng)值。然后研究熱浪對(duì)不同疾病死亡別、年齡別、性別人群死亡效應(yīng)大小。最后研究熱浪的持續(xù)時(shí)間、強(qiáng)度和發(fā)生時(shí)間對(duì)熱浪與人群死亡關(guān)系的修飾作用大小。 結(jié)果在熱浪發(fā)生當(dāng)天時(shí)，四地區(qū)熱浪的主效應(yīng)（RR=1.082，95%CI：1.034~1.132）大于附加效應(yīng)（RR=1.000，，95%CI：0.962~1.04）。熱浪當(dāng)天在兩個(gè)縣級(jí)市南雄（RR=1.154，95%CI：1.029~1.294）、臺(tái)山（RR=1.125，95%CI：1.064~1.19）所造成的死亡效應(yīng)比兩個(gè)經(jīng)濟(jì)發(fā)達(dá)城市廣州（RR=1.048，95%CI：1.006~1.091）、珠海（RR=1.046，95%CI：0.973~1.124）大。南雄、珠海和臺(tái)山累積主效應(yīng)均在滯后2日，廣州在滯后4日時(shí)達(dá)到最大，隨著滯后日增加逐漸減小，在一周左右降至最低。熱浪的主效應(yīng)對(duì)呼吸系統(tǒng)疾病死亡風(fēng)險(xiǎn)（RR=1.345，95%CI：1.174~1.542）和循環(huán)系統(tǒng)疾病死亡風(fēng)險(xiǎn)（RR=1.193，95%CI：1.095~1.301）均大于總死亡風(fēng)險(xiǎn)（RR=1.131，95%CI：1.071~1.195）。熱浪的主效應(yīng)中75歲以上年齡和女性人群死亡風(fēng)險(xiǎn)最大，效應(yīng)值分別為1.209（95%CI：1.081~1.351）、1.166（95%CI：1.062~1.279）。熱浪的持續(xù)時(shí)間每增加1天，人群的死亡風(fēng)險(xiǎn)增加1.4%（95%CI：-0.6%~3.4%）；熱浪期間溫度每增加1℃，人群的死亡風(fēng)險(xiǎn)增加1.2%（95%CI：-5.6%~8.5%）；熱浪的發(fā)生時(shí)間每推遲10天，人群的死亡風(fēng)險(xiǎn)減少0.1%（95%CI：-1.2%~1.0%），均無(wú)統(tǒng)計(jì)學(xué)意義。 結(jié)論熱浪的效應(yīng)主要?dú)w因于溫度升高的作用，熱浪的主效應(yīng)存在滯后作用，但持續(xù)時(shí)間較短。經(jīng)濟(jì)欠發(fā)達(dá)、貧困地區(qū)為熱浪的脆弱地區(qū)。循環(huán)系統(tǒng)疾病和呼吸系統(tǒng)疾病患者、75歲以上、女性是熱浪的脆弱人群。本研究沒(méi)有發(fā)現(xiàn)廣東省四地區(qū)熱浪的持續(xù)時(shí)間、強(qiáng)度和發(fā)生時(shí)間對(duì)熱浪與死亡關(guān)系有修飾作用。
[Abstract]:Objective to evaluate the effect of heat wave on the death of residents in Nanxiong, Guangzhou, Zhuhai and Taishan City, Guangdong Province, and to explore the effect of heat wave characteristics on the relationship between heat wave and death, so as to provide scientific basis for making policies to reduce the health risk of heat wave. Methods the daily meteorological data and death data of residents in the four regions from 2006 to 2010 were collected. A distributed lag nonlinear model (DLNM) was established to divide the heat wave effect on death into main effect (caused by high temperature) and additional effect (caused by high temperature duration). Meta analysis was used to analyze the effect values of the four regions. Then the effect of heat wave on the death of different diseases, age and sex was studied. Finally, the effect of heat wave duration, intensity and occurrence time on the relationship between heat wave and death was studied. Results on the day of the heat wave, the principal effect of heat wave in the four regions (RRN 1.082 / 95) was greater than the additional effect (RRN 1.00095% CI: 0.962U 1.04). On the same day, the heatwave in two county-level cities, Nanxiong (RRX 1.154) and Taishan (RRX 1.125 + 95 CI: 1.064 1.19), caused a greater death effect than in two economically developed cities, Guangzhou (RRRN 1.048) and Zhuhai (RRN 1.046 95 CI: 0.973cn 1.124), and in two county-level cities, Nanxiong (RRX 1.154), Taishan (RRX 1.125), and 95 CI: 1.064: 1.19, the two economically developed cities, Guangzhou (RRX 1.048) and Zhuhai (RRN 1.046 95 CI: 0.973 / 1.124). The cumulative main effects of Nanxiong, Zhuhai and Taishan were all 2 days behind, Guangzhou reached its maximum at 4 days, gradually decreased with the increase of lag day, and decreased to the lowest in about one week. The main effects of heat wave on the risk of respiratory disease death (RRN 1.34595 CI: 1.1741.542) and circulatory disease death risk (RRRN 1.19395 95 CI: 1.0951.301) were higher than the total death risk (RRRN 1.13195 CIW 1.0711.195). The main effect of heat wave was that 75 years old and over and female population had the highest risk of death, the effect values were 1.209 (95% CI: 1.081 1.351) and 1.166 (95% CI: 1.062 2 / 1.279). For every day that the heat wave lasts, the risk of death of the crowd increases by 1.4% (95% CI: -0.6%); for every 1 鈩

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