背景　目前对高血压患者住院费用开展的研究较多，但少有学者基于大数据开展高血压患者可避免住院的相关研究。目的　了解广东省老年高血压患者可避免住院现状，为我省紧密型医共体整合优化医疗卫生资源提供参考。方法　通过广东省基层医疗卫生信息管理系统、广东省全民健康信息平台、住院病案首页数据等集成获取全省65岁及以上老年人健康信息及2022年住院信息，人均GDP、全科医生、在职职工等数据来自2022广东省卫生健康统计年鉴、广东统计年鉴2022年，以地市为单位将统计年鉴的数据匹配到个案中。采用Logistic回归分析探讨老年高血压患者可避免住院的影响因素。结果　广东省老年高血压患者可避免住院率为8.76%，老年女性高血压患者可避免住院发生的可能性较男性更大［OR（95%CI）=1.231（1.217，1.246）］，65~69岁、70~74岁、75~79岁、80~84岁的高血压患者可避免住院的可能性分别是90岁老年高血压患者的2.044倍［OR（95%CI）=2.044（1.981，2.109）］、1.640倍［OR（95%CI）=1.640（1.590，1.693）］、1.288倍［OR（95%CI）=1.288（1.248，1.329）］、1.110倍［OR（95%CI）=1.110（1.073，1.147）］，人均GDP在10万元及以上的高血压患者可避免住院发生的可能性是人均GDP低于10万元的1.314倍［OR（95%CI）=1.314（1.278，1.350）］，全科医生数≥4人/万人口时高血压患者可避免住院的可能性是低于4人/万人口的1.039倍［OR（95%CI）=1.039（1.105，1.063）］；二三级医院拥有量≥0.20/万人口时，高血压患者可避免住院发生的可能性将降低40.60%［OR（95%CI）=0.594（0.570，0.619）］；二三级医院就诊人次数≥3时，高血压患者可避免住院的可能性是二三级医院人均就诊人次数﹤3的1.047倍［OR（95%CI）=1.047（1.021，1.074）］；当基层机构人均就诊人次数≥3人次时，高血压患者可避免住院的可能性是人均就诊人次数﹤3人次的1.229倍［OR（95%CI）=1.229（1.191，1.268）］。结论　性别、年龄等为老年高血压患者可避免住院的影响因素。人均GDP水平越高、就诊次数越多、全科医生数量越多，可避免住院发生的可能性增大。二三级医院数量的增加，未增加可避免住院发生的风险。

BackgroundAir pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages.ObjectiveTo construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution.MethodsData on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO2, NO2, CO, PM2.5, PM10, and maximum 8 h O3 concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model.ResultsPM2.5, NO2, SO2, and O3 were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM2.5, NO2, SO2 and O3 were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI .ConclusionThe J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.

BackgroundIt is projected that the frequency, density, and duration of compound hot extreme may increase in the 21st century in the context of global warming.ObjectiveTo explore the association between compound hot extreme and blood pressure, and identify sensitive populations.MethodsThis was a cross-sectional study. The study subjects were from six Guangdong Province Chronic Disease and Nutrition Surveys during 2002 through 2015. A questionnaire was administered to the participants with questions about demographic information, drinking and smoking status, and measurements on their height, weight, and blood pressure were also collected. We chose the data of May, September, and October to explore the association between compound hot extreme and blood pressure. Compound hot extreme means a hot day with a proceeding hot night. Daily meteorological data were obtained from China Meteorological Data Service Centre. We employed inverse distance weighting to interpolate the temperature and relative humidity values for each participant. A distributed lag non-linear model was used to estimate the association between compound hot extreme and blood pressure. Stratified analyses by sex, age, area, body mass index (BMI), smoking status, and drinking status were also performed to identify sensitive populations. A sensitivity analysis was conducted by adjusting the degrees of freedom for lag spline and removing relative humidity.ResultA total of 10967 participants without history of hypertension were included in this study. The average systolic blood pressure (SBP) was 120.8 mmHg and the average diastolic blood pressure (DBP) was 74.5 mmHg. The proportion of participants who experienced hot day, hot night, or compound hot extreme were 9.34%, 17.95% and 2.90%, respectively. Compared to hot day, hot night and compound hot extreme were related with decreased blood pressure, and the effect of compound hot extreme was stronger: the changes and 95%CI for SBP was −6.2 (−10.3-−2.1) mmHg, and for DBP was −2.7 (−5.2-−0.2) mmHg. Compound hot extreme induced decreased SBP among male, population ≥ 65 years, and those whose BMI < 24 kg·m-2, and their ORs (95%CIs) were −6.2 (−10.7-−1.6). −19.1 (−33.0-−5.1), and −6.7 (−11.8~−1.6) mmHg, respectively, and also decreased DBP among population ≥ 65 years, and its OR (95%CI) was −8.4 (−15.6-−1.1) mmHg. During compound hot extremes, participants living in rural areas showed decreased SBP and DBP, and the ORs (95%CIs) were −10.5 (−16.6-−4.5) and −4.4 (−7.7-−1.1) mmHg respectively, while those living in urban areas showed increased SBP, and the OR (95%CI) was 9.7 (2.9-16.5) mmHg. A significant decrease in blood pressure [OR (95%CI)] was also found in non-smokers [DBP, −3.7 (−6.6-−0.8) mmHg] and non-drinkers [SBP, −4.8 (−9.4-−0.2) mmHg; DBP, −3.4 (−6.0-−0.9) mmHg].ConclusionCompound hot extreme is negatively associated with SBP, and being male, aged 65 years and over, and having BMI < 24 kg·m−2 may be more sensitive to compound hot extreme.

BackgroundDengue fever is a mosquito-borne disease transmitted by Aedes aegypti and Aedes albopictus. Under the background of climate change, there are great challenges in the prevention and control of dengue fever, posing a serious health risk to the population.ObjectiveTo analyze the mechanism of temperature on dengue fever transmission and estimate the risk of dengue fever under different climate change scenarios by establishing a coupled human-mosquito dynamics model using Guangzhou as a research site, and to provide reference for adaptation to climate change.MethodsReported dengue fever cases and meteorological data from January 1, 2015 to December 31, 2019 in Guangzhou were collected from Guangdong Provincial Center for Disease Control and Prevention and China Meteorological Data Service Centre, respectively. The temperature data under three Representative Concentration Pahtyway (RCP2.6, RCP4.5, and RCP8.5) scenarios in 2030s (2031–2040), 2060s (2061–2070), and 2090s (2091–2099) were calculated by five general circulation models (GCMs) provided by the fifth phase of the Coupled Model Intercomparison Project. A dengue fever transmission dynamics (ELPSEI-SEIR) model was constructed to analyze the mechanism of temperature affecting dengue fever transmission by fitting the dengue fever epidemic trend from 2015–2019, and then the daily mean temperature under selected RCP scenarios for 2030s, 2060s, and 2090s was incorporated into the established dynamics model to predict the risk of dengue fever under different climate change scenarios in the future.ResultsFrom January 1, 2015 to December 31, 2019, a total of 4 234 cases of dengue fever were reported in Guangzhou, including 3741 local cases and 493 imported cases. The regression results showed that the model well fitted the dengue fever cases in Guangzhou from 2015 to 2019, and the coefficient of determination R2 to evaluate goodness of fit and the root mean squared error were 0.82 and 1.96, respectively. A U-shaped or inverted U-shaped relationship between temperature and mosquito habits could directly affect the number of mosquitoes and the transmission of dengue fever. We also found that temperature increase in most future scenarios could promote the transmission of dengue fever, and the epidemic period was significantly wider than the baseline stage. The epidemic of dengue fever would peak in the 2060s under the scenarios of RCP2.6 and RCP4.5. The estimated incidence of dengue fever was predicated to be highest in the 2030s and then decrease in the following years under RCP8.5, and in the 2090s, the incidence would decrease significantly, but the incidence peak would be earlier in each year, mainly from May to July.ConclusionTemperature can directly affect mosquito population and dengue fever transmission by affecting mosquito habits. The cases of dengue fever will increase under most climate scenarios in the future. However, the epidemic risk of dengue fever may be suppressed, and the epidemic season may be advanced under RCP8.5.

BackgroundIn recent years, the incidence of metabolic syndrome (MS) is increasing significantly in China. Some studies have found that temperature is related to single metabolic index, but there is a lack of research on associated mechanism and identifying path of the influence of temperature on MS.ObjectiveBased on the data of Guangdong Province, to investigate the effect of temperature on MS and its pathway.MethodsA total of 8524 residents were enrolled by multi-stage random sampling from October 2015 to January 2016 in Guangdong. Basic characteristics, behavioral characteristics, health status, and physical activity level were obtained through questionnaires and physical examinations, and meteorological data were obtained from meteorological monitoring sites. We matched individual data both with the temperature data of the physical examination day and of a lag of 14 d. A generalized additive model was used to explore the exposure-effect relationship between temperature and MS and its indexes, calculate effect values, and explore the effects of single-day lag temperature. Based on the literature and the results of generalized additive model analysis, a path analysis was conducted to explore the pathways of temperature influencing MS.ResultsThe association between daily average temperature on the current day or lag 14 day and MS risk was not statistically significant. When daily average temperature increased by 1 ℃, the change values of fasting blood-glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), and high density lipoprotein cholesterol (HDL-C) were −0.033 (95%CI: −0.040-−0.026) mmol·L−1, −0.662 (95%CI: −0.741-−0.583) mmHg, −0.277 (95%CI: −0.323-−0.230) mmHg, and −0.005 (95%CI: −0.007-−0.004) mmol·L−1 respectively. The effects of average daily temperature on FBG, blood pressure, HDL-C, and waist circumference lasted until lag 14 day. The effects of daily average temperature on SBP and DBP were the largest on the current day. Daily average temperature of current day had direct and indirect effects on FBG and SBP. Temperature had an indirect effect on TG, and the intermediate variables were waist circumference and FBG, with an indirect effect value of −0.011 (95%CI: −0.020-−0.002). The indirect effects of daily average temperature on SBP, FBG, and TG were weak.ConclusionThere is no significant correlation between temperature and risk of MS, and daily average temperature of current day could significantly affected blood pressure and FBG with a lag effect. Daily average temperature of current day has indirect effects on FBG and TG.

[背景]以往研究发现血清尿酸呈现季节性变化,高温作业环境增加职业人群高尿酸血症的患病风险,但缺乏气温对普通人群血清尿酸水平急性效应的研究.[目的]探讨气温与人群血清尿酸水平的关系,为开展相关的防控工作提供科学依据.[方法]2015年10月—2016年2月采用多阶段整群抽样方法,在"广东省居民慢性病与营养监测调查"项目中抽取研究对象,并调查其社会经济学特征、健康状况、饮食情况、体格和实验室检查信息.从中国气象科学数据共享服务平台获取同期全国698个气象监测站点气象资料(日均气温和日相对湿度),使用澳大利亚国立大学薄板样条函数软件插值获取全国的气象资料的栅格数据,尺度为0.01°×0.01°,并从中提取广东省气象栅格数据,根据参与者居住地址匹配气象栅格数据.采用分布滞后非线性模型分析滞后0～14 d日均气温和居民血清尿酸水平的关系,按照性别、年龄(<65岁和≥65岁)、体重指数(<24 kg·m-2和≥24 kg·m-2)、是否患高血压和是否患高血脂进行分层分析,控制日均气温及滞后天数的自由度及PM2.5、PM10、NO2、O3、水产品摄入量,对模型进行敏感性分析.[结果]本研究共纳入6670人.总人群血清尿酸浓度为(335.7±92.4)μmol·L-1,日均气温中位数为17.3(P5～P95:8.5～25.0)℃.日均气温和血清尿酸累积14 d的暴露-反应关系是非线性的,以日均气温最低点(2℃)为参考点,当日均气温上升至25℃(P95),血清尿酸水平累积增加了113.8(95％CI:71.1～156.6)μmol·L-1.日均气温P95(25℃)对人群血清尿酸影响的效应在第1天开始出现,约第4天达最低值后开始上升,约10 d达最高值后开始下降.日均气温P95(25℃)对女性、年龄<65岁、超重肥胖、高血压、高血脂人群尿酸水平的累积效应分别大于男性、年龄≥65岁、正常体重、血压正常和血脂正常人群,但组间差异没有统计学意义.敏感性分析显示,调整变量后,模型的结果均较稳定.[结论]气温对人群血清尿酸水平的影响具有一定的滞后效应,且气温上升可能会增加人群血清尿酸水平.

目的 调查新冠肺炎疫情期间公众口罩使用和认知情况及疫情基本防护意识,为今后疫情健康教育工作提供依据.方法 采用网络调查法,在“广东痰控”微信公众号发布问卷,以自行设计的调查表做自填式问卷调查.收集基本信息、口罩使用情况及防护信息,采用描述性方法和Logistic回归分别对公众口罩使用情况及新冠疫情防护意识强度的影响因素进行分析.结果 共回收有效问卷2 213份,97.0％调查对象在本次疫情期间佩戴口罩,96.5％调查对象在公共场所使用口罩,89.8％调查对象使用一次性医用普通或外科口罩,94.5％调查对象1～3d更换1次口罩;80.6％调查对象认同口罩的防护作用,58.5％调查对象有较强防护意识,女性(OR=1.792)和≥60岁年龄组(OR=4.245)的调查对象防护意识较强.结论 疫情期间公众的口罩使用率和基本防护意识较高,性别和年龄是防护意识的影响因素.

目的 了解广东省东莞、佛山、广州和深圳市气温对流行性腮腺炎(简称“流腮”)发病的影响.方法 收集4个城市2005-2018年流腮发病与气象数据,利用分布滞后非线性模型,在控制时间趋势、星期几效应、相对湿度和气压等混杂因素下,研究不同城市气温与流腮发病的关系.最后分别比较低温(日均气温的第5百分位数)和高温(日均气温的第95百分位数)对不同年龄别和性别人群流腮发病的冷热效应.结果 4个城市2005-2018年共报告流腮病例212 109例,日均气温中位数为23℃～25℃.4个城市气温与流腮日发病例数的总体效应关系呈倒“S”形的非线性关系.男性的冷效应(RR=1.131,95％CI: 1.018 ～ 1.256)略高于女性(RR=1.093,95％CI:0.955～1.251),女性的热效应(RR=1.014,95％CI:1.001～1.026)略高于男性(RR=1.009,95％CI:0.997～1.022),但差异均无统计学意义(P＞0.05);6～17岁青少年的冷效应(RR=1.476,95％CI: 1.300～1.677)和热效应(RR=1.020,95％CI:1.006～1.034)最高,差异有统计学意义(P＜0.05).结论 日均气温与流腮的发病呈非线性关系,可能是流腮发病的重要影响因素.6～17岁青少年是气温较为敏感的人群,应重点关注这些人群.

目的 估算中国不同气温带气温健康预警的阈值,为发展基于死亡风险的气温健康预警系统提供科学依据.方法 收集2006-2017年全国364个县、区作为研究点的死亡与气象数据,利用分布滞后非线性模型(distribution lag non-linear model,DLNM)和多变量Meta方法分析气温与死亡的暴露反应关系,划分气温预警阈值.结果 研究期间日平均气温16.0℃,日平均相对湿度73.0％,日均死亡人数为8.3例.不同气温带的气温-死亡的暴露反应关系总体上呈倒"J"型.中温带、暖温带+北亚热带、中亚热带以及南亚热带冷效应低风险气温范围分别9.1～13.8℃、0.1～19.3℃、8.8～24.3℃以及9.9～25.3℃,中风险分别为 1.8～9.1℃、-6.1～0.1℃、1.5～8.8℃以及4.8～9.9℃,高风险分别为＜1.8℃、＜～6.1℃、＜1.5℃以及＜4.8℃;热效应低风险气温范围分别为23.4～24.8℃、28.6～29.3℃、27.2～29.5℃以及28.2～28.6℃,中风险分别为24.8～26.1℃、29.3～30.1℃、29.5～31.0℃以及28.6～29.0℃,高风险分别为＞26.1℃、＞30.1℃、＞31.0℃以及＞29.0℃.所有气温带在高温端的日 均死亡人数均随着风险等级增加而升高,而除了暖温带+北亚热带外,其他气温带在低温端的日均死亡人数随着风险等级增加而升高.结论 基于死亡风险可以确定气温预警的阈值并进行预警等级划分,预警效果较好.

目的 探索登革热发病的影响因素,为制定登革热防控措施提供参考依据.方法 采用成组匹配的病例对照研究设计,对调查对象进行问卷调查及实验室检测.采用随机森林模型、单因素分析和多因素Logistic回归等方法 分析登革热发病的影响因素.结果 病例组病例294例(男158例,女136例),平均年龄(37.0±13.8)岁;对照组691例(男329例,女362例),平均年龄(36.6±13.2)岁.将随机森林模型筛选出的前20个潜在影响登革热发病的重要变量引入Logistic回归模型,结果 显示:外出采取防蚊措施(OR = 0.29,95％CI:0.20～0.43)、知道登革热传播途径(OR = 0.40,95％CI:0.25～0.64)、蚊虫孳生地认知得分高(OR = 0.67,95％CI:0.50～0.89)、夏天家中常使用空调(OR = 0.60,95％CI:0.38～0.90)、家中安装纱门(OR = 0.52,95％CI:0.34～0.79)、小区或家周围及时清理垃圾(OR =0.43,95％CI:0.23～0.79)、工作场所室内有空调(OR = 0.46,95％CI:0.30～0.72)能够降低登革热发病风险;家人罹患登革热(OR = 6.94,95％CI:2.91～16.56)、朋友或同事罹患登革热(OR = 2.71,95％CI:1.56～4.72)人群发病风险高.结论 登革热的发病与个人防病知识、防蚊措施、居住环境以及工作环境密切相关,针对高危因素采取防控措施对于减少登革热的发生至关重要.

Objective: To identify the threshold of a health warning system based on the association of apparent temperature and years of life lost (YLL). Methods: Daily mortality records and meteorological data were collected from 364 Chinese counties for 2006-2017. Distributed lag nonlinear model and multivariate Meta-analyses were applied to estimate the association between the apparent temperature and YLL rate. A regression tree model was employed to estimate the warning thresholds of the apparent temperature. Stratified analyses were further conducted by age and cause of death. Results: The daily YLL rate was 23.6/105. The mean daily apparent temperature was 15.7 ℃. U-shaped nonlinear associations were observed between apparent temperature and YLL rate. The actual temperature-caused YLL rate for the elderly was higher than the young population. The daily excess deaths rate increased with the higher effect levels. Conclusions: Regression tree model was employed to define the warning threshold for meteorological health risk. The present study provides theoretical support for the weather-related health warning system.

目的 研究空气中PM2.5对人群急性咳嗽的短期效应.方法 2017年11月至2018年2月,在广东省珠三角地区7个社区进行居民环境与健康问卷调查,询问调查对象过去2周急性咳嗽症状的发生情况,同时收集各社区对应时期每日空气污染和气象资料.采用二水平logistic回归分析方法分析7个社区居民急性咳嗽症状的发生与近期PM2.5暴露水平的关系.结果 共调查7 151人,其中223例(发生率为3.12％)在调查日过去2周出现急性咳嗽症状.PM2.5暴露对社区人群急性咳嗽发生具有统计学意义的短期效应,效应持续时间为7d.其中PM2.5滞后1d的健康效应最大,PM2.5每升高10 μg/m3对应的OR值为1.21(95％ CI: 1.06～1.37);PM2.5滑动平均0～4d的效应最大(OR=1.19,95％ CI: 1.05～1.37).人群分层结果显示,女性(OR=1.25, 95％CI: 1.04～1.50)发病风险高于男性(OR=1.10, 95％CI:0.92～1.33)差异有统计学意义(P＜0.05).结论 PM2.5可短期内增加当地居民急性咳嗽的风险,而且女性发生急性咳嗽的风险更高.

[背景]大量研究表明气温是影响人群健康的重要因素,而气温变异,尤其隔日气温变异对人群健康影响的研究较少.[目的]比较隔日温差(TCN,隔日平均气温之差)、气温变异(TV,隔日最高气温和最低气温的标准差)以及本研究新提出的根据隔日气温变异的方向和效应大小计算得到的隔日温度总变异(TTV)这三个隔日气温变异指标与居民寿命损失年(YLL)的暴露-反应关系,探索能更好反映隔日气温变异对居民死亡影响的指标.[方法]收集2013—2017年广东省40个区(县)气象数据以及死亡登记资料.采用分布滞后非线性模型(DLNM)和多变量meta分析的两阶段分析方法,分别拟合日夜温差和夜日温差与YLL率(每10万人口YLL值)的暴露-反应关系,提取日夜温差和夜日温差的归因YLL率作为各自权重计算TTV.计算Pearson相关系数,分析三个隔日气象变异指标间的相关性.采用DLNM和多变量meta分析两阶段分析方法,分别分析TCN、TV和TTV与居民YLL率的暴露-反应关系,比较不同隔日气温变异指标对人群死亡影响的差异.[结果]研究期间内广东省40个区(县)日均YLL率为22.3/10万.经计算,TCN平均值为(0.0±1.8)℃,TV为4.6±1.5,TTV平均值为(8.1±2.7)℃,三个指标均趋近正态分布.TCN与TV和TTV相关性较弱(r=0.0979,r=0.0880),而TV与TTV相关性较强(r=0.8891).在控制平均气温的滞后效应后,TCN与YLL率的暴露-反应关系无统计学意义,而TV和TTV与YLL率的暴露-反应关系有统计学意义.TV-YLL和TTV-YLL的暴露-反应关系曲线相似,均呈类似"U"型关系,过低或过高的TV和TTV均会增加人群的YLL率.极端低(第5百分位数)的TV(TV=2.2)和TTV(TTV=2.8℃)的归因YLL率及其95％CI依次为1.0/10万(0.1/10万～1.9/10万)和2.1/10万(0.2/10万～4.0/10万),极端高(第95百分位数)的TV(TV=7.2)和TTV(TTV=12.1℃)的归因YLL率效应值及其95％CI依次为3.1/10万(1.2/10万～5.1/10万)和4.1/10万(2.3/10万～5.8/10万),在极端低和极端高节点上,TTV的YLL率效应值均大于TV,而在中等低和中等高节点上,两个指标的效应相近.[结论]TCN、TV、TTV与YLL的暴露-反应关系存在差异,其中TTV综合考虑了气温变异的程度、方向以及健康效应,更加全面地反映了短时气温变异对人群健康的影响.

在环境与健康研究领域,经常需要用时间序列方法分析环境暴露水平与人群健康结局之间的关系.R语言作为一种开源免费的软件,具有强大的数据处理、统计和图形绘制功能等诸多优点.本文以公开的美国纽约市1987-2000年大气污染物与死亡数据为例,就广义线性模型在时间序列分析中的应用与R语言实现进行介绍.

Objective:To compare the epidemiological characteristics of COVID-19 in Guangzhou and Wenzhou, and evaluate the effectiveness of their prevention and control measures.Methods:Data of COVID-19 cases reported in Guangzhou and Wenzhou as of February 29, 2020 were collected. The incidence curves of COVID-19 in two cities were constructed. The real time reproduction number ( R t) of COVID-19 in two cities was calculated respectively. Results:A total of 346 and 465 confirmed COVID-19 cases were analysed in Guangzhou and Wenzhou, respectively. In two cities, most cases were aged 30-59 years (Guangzhou: 54.9%; Wenzhou: 70.3%). The incidence curve peaked on 27 January, 2020 in Guangzhou and on 26 January, 2020 in Wenzhou, then began to decline in both cities. The peaks of imported COVID-19 cases from Hubei occurred earlier than the peak of COVID-19 incidences in two cities, and the peak of imported cases from Hubei occurred earlier in Wenzhou than in Guangzhou. In early epidemic phase, imported cases were predominant in both cities, then the number of local cases increased and gradually took the dominance in Wenzhou. In Guangzhou, the imported cases was still predominant. Despite the different epidemic pattern, the R t and the number of COVID-19 cases declined after strict prevention and control measures were taken in Guangzhou and in Wenzhou. Conclusion:The time and scale specific differences of imported COVID-19 resulted in different epidemic patterns in two cities, but the spread of the disease were effectively controlled after taking strict prevention and control measures.

R是一种应用广泛的用于统计计算和绘图的语言和环境,是一种基于S语言环境而开发的免费开源的统计工具[1],S语言是由著名的贝尔实验室的John Chambers等于1976年共同开发的一种进行数据探索、统计分析、作图的解释型语言[2].一方面,R可以提供各种统计工具,具有良好的统计分析环境;另一方面,R保留了一定的灵活性,用户可以自行编写新的统计工具[3].由于R是一个免费开源的软件,而且可以兼容Windows、Mac、Linux等多个主流操作系统,用户数量庞大.R具有顶尖水准的制图功能,不仅可以实现复杂数据的可视化,而且操作灵活性强,制图美观,能够满足大部分用户的绘图需求.

R语言在公共卫生领域应用十分广泛,尤其是在统计分析、可视化和交互式等方面具有明显优势.一般情况下,原始数据并不十分规范,难以直接进行分析和利用.因此,数据管理是分析工作的基础,规范的数据将会使得R语言的应用更加高效和便捷.本文对R语言数据的基本处理方法[1-3],尤其是公共卫生领域的常用方法进行归纳总结.

目的 探讨广东省广州、佛山及珠海3个城市的大气污染二氧化氮(NO2)对居民每日死亡效应的影响.方法 收集2013-2016年广州、佛山及珠海3个城市的每日大气污染物浓度、气象资料数据和居民的每日总死亡数据,对数据基本特征进行统计描述,并通过Spearman分析其相关关系,最后利用广义相加模型(GAM)分别对3个城市的NO2日均浓度及每日总死亡数据进行分析.结果 2013-2016年广州、佛山及珠海市的大气污染物NO2日均浓度分别为46.4、48.4、33.1 μg/m3,均符合国家二级标准(80μg/m3).广州市大气中NO2日均浓度对当天、滞后1、2d的每日总死亡人数、循环系统疾病死亡人数的影响有统计学意义(均P＜ 0.05),佛山市滞后1、2d的NO2日均浓度对居民每日总死亡人数及循环系统疾病每日死亡人数的影响有统计学意义(均P＜ 0.05),广州和佛山市均表现出滞后1d时效应最大.滞后2d的NO2日均浓度对广州市居民的呼吸系统疾病每日死亡人数有影响(ER=1.38).结论 大气污染物NO2浓度的上升会引起居民死亡风险的增加,应引起重视.

随着全球气候变化,气温升高及极端气象事件的强度及频率增加严重着威胁人类的生命和健康.本文根据近年国内外的研究进展,综述了气象因素和极端气象事件对人群死亡和发病的影响,并对气象因素健康影响的未来研究提出建议,为相关学者开展研究提供参考.

目的 了解珠江三角洲地区城市大气细颗粒物(PM2.5)与臭氧(O3)对循环系统疾病就诊情况的影响,并进一步探讨PM2.5和O3的交互作用.方法 采用时间序列研究方法,选择广州、佛山和珠海市为研究点,从广东省疾病预防控制中心获得2015-2017年3个城市3家三甲医院的每日循环系统疾病门诊就诊数据,从广东省环境监测中心获得每日大气PM2.5和O3浓度数据,从广东省气象局获得每日气象数据.采用广义相加模型(GAM)分别分析PM25和O3平均浓度上升10μg/m3引起的超额风险(ER)及PM25和O3的交互作用,并采用Meta分析对多城市的结果进行合并.结果 2015-2017年,广州、佛山和珠海市大气PM2.5浓度每增加10 μg/m3引起循环系统疾病门诊就诊风险的ER分别为2.45％、0.64％和0.95％;3个城市大气PM2.5和O3浓度每增加10 μg/m3引起的合并ER分别为1.34％(95％ CI:0.25％～2.43％)和-0.17％ (95％ CI:-0.47％～0.14％).O3对PM2.5与循环系统疾病门诊就诊风险存在修饰效应,其中在O3浓度低时PM2.5导致的循环系统疾病门诊就诊风险最高,ER为4.19％(95％CI:1.82％～6.56％);而PM2.5对O3与循环系统疾病门诊就诊风险的修饰效应没有统计学意义.结论 珠江三角洲地区城市大气PM2.5可增加居民的循环系统疾病门诊就诊风险,O3对其存在修饰效应.