Drought is expected to be more severe and frequent due to climate change. Drought exerts not only extensive impacts on economy and environment, but also direct or indirect impacts on human health. This review systematically collected studies exploring the association between drought and human mortality, and summarized the associations between drought and all-cause mortality, chronic non-communicable disease mortality, communicable disease mortality, and injury mortality. The results revealed that drought was significantly associated with human mortality, leading to an elevated mortality risk of cardiovascular diseases, respiratory diseases, cancers, diarrhea, and injuries; serious drought increased much more mortality risk than mild drought; males in rural areas, the elderly, and children were vulnerable populations to drought. However, in-depth studies on the association of drought with human mortality are limited, which calls for related studies in the future. This review summarized the current research status and existing problems in drought and population death, and pointed out the future research direction, which can provide reference for future related research.

Background In 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. Objective Based on the data of Guangdong Province, to investigate the effect of temperature on MS and its pathway. Methods A 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. Results The 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. Conclusion There 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.

Background It is projected that the frequency, density, and duration of compound hot extreme may increase in the 21st century in the context of global warming. Objective To explore the association between compound hot extreme and blood pressure, and identify sensitive populations. Methods This 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. Result A 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]. Conclusion Compound 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.

In the context of global warming, the frequency, intensity, and duration of heatwaves will further increase. In recent years, plenty of studies have suggested from diversiform perspectives that heatwaves may increase mortality risk. However, the focuses of current studies on heat wave and human health are scattered, and a systematic theoretical framework has not yet been formed. We therefore systematically reviewed the previous literature in terms of possible mechanism paths and vulnerability mechanism of heatwave increasing mortality risk, summarized the vulnerability factors that influence exposure opportunities, constrain adaptive capacity, as well as affect physiological responses and behaviors. We further attempted to propose a theoretical framework from the processes of "exposure degree-modification effect-physiological behavior-health outcome". This paper summarized the research directions on heatwaves and health, which can provide ideas for future in-depth health risk assessment and adaptation to climate change.

Background Stroke has become a main cause of death in China. With global warming, the studies on temperature and stroke have attracted much attention. Objective To analyze he relationships between heatwave and the years of life lost (YLL) by different subtypes of stroke by controlling temporal and spatial effects with Bayesian spatio-temporal model, and to study the modifiers of the health effect of heatwave. Methods The daily information of stroke deaths, meteorological data, and air pollutant data in 40 districts and counties of Guangdong Province were collected during the warm seasons (from May to October) in the years from 2014 to 2017. The individual YLL was first calculated by matching age and gender according to the life table, and then the daily YLL rate (person-years/100 000 people) was obtained by summarizing the daily YLL and correcting it with the population of each district or county. Bayesian spatio-temporal model was used to fit a proposed exposure-response relationship between heatwave and the YLL rates of different subtypes of stroke. Finally, stratified analyses were conducted by age (<65 years, ≥65 years), gender (male, female), and region (Pearl River Delta and non-Pearl River Delta regions) to identify the major modifiers for the association between heatwave and stroke mortality. Results During the warm seasons from 2014 to 2017, a total of 23 heatwave events occurred in the 40 districts or counties of Guangdong Province, cumulatively lasting for 145 d. A total of 30 852 stroke deaths were recorded in the same time periods. The average daily YLL rate of total stroke was (2.39±3.63) person-years/100 000 people, and those for hemorrhagic stroke and ischemic stroke were (1.54±2.99) person-years/100 000 people and (0.84±1.85) person-years/100 000 people, respectively. Heatwave was associated with increased YLL rate of stroke in residents, and it had a greater impact on ischemic stroke with a lag effect. The largest cumulative effect of heatwave was at lag 0-1 day, which was associated with an increased YLL rate of total stroke and ischemic stroke by 0.17 (95%CI: 0.03-0.29) person-years/100 000 people and 0.13 (95%CI: 0.06-0.20) person-years/100 000 people, respectively. The results of stratified analyses showed that heatwave had a larger effect on ischemic stroke in residents of aged 65 years or older, male, and non-Pearl River Delta regions, and the rates of YLL increased by 1.11 (95%CI: 0.58-1.55), 0.13 (95%CI: 0.03-0.23), and 0.20 (95%CI: 0.07-0.32) person-years/100 000 people, respectively; Heatwave only had an effect on hemorrhagic stroke in residents aged 65 years or older with an increased YLL rate of 0.79 (95%CI: 0.26-1.31) person-years/100 000 people. Conclusion Heatwave could elevate the level of years of life lost associated with stroke in Guangdong residents, with greater impacts on ischemic stroke of the aged, men, and residents in non-Pearl River Delta regions, and on hemorrhagic stroke in the elderly.

Climate change has led to an increasing frequency and intensity of extreme climate events such as heat and drought extremes with considerable global public health burden. This systematic review collected 87 domestic and international studies from 2000 to 2023, considering the impacts of heat extremes, drought extremes, and compound hot-dry events on infectious diseases attributable to various transmission pathways such as waterborne, foodborne, insect-borne, airborne, and contact-transmitted diseases. Our results showed that high temperature was associated with increased transmission risks of waterborne and foodborne diseases including infectious diarrheal diseases (cholera, dysentery, typhoid, and paratyphoid) and infectious gastroenteritis; vector-borne diseases including dengue fever, Zika virus (ZIKV) disease, chikungunya fever, malaria, West Nile fever, and Rift Valley fever; airborne diseases including influenza-like diseases, influenza A, measles, and mumps; and contact-transmitted diseases including HIV/AIDS, schistosomiasis, and leptospirosis. Additionally, drought conditions also amplified the transmission risks of waterborne and foodborne diseases including cholera, Escherichia coli infection, rotavirus infection, and hepatitis E; vector-borne diseases such as scrub typhus, schistosomiasis, hemorrhagic fever with renal syndrome, and West Nile fever; airborne diseases including meningococcal meningitis, pertussis, measles, and upper respiratory infections; and contact-transmitted diseases such as HIV/AIDS. Along with global warming, the frequency of compound high temperature and drought events shows a considerably increasing trend, causing more adverse health effects than heat or drought alone. However, there is limited research quantifying their effects on infectious diseases. These associations may be mediated through temperature and precipitation on infectious disease pathogens, transmission vectors, population susceptibility, public health services, and behaviors. In the context of climate change, the increasing occurrence of compound events of high temperatures and droughts raises health concerns, and further studies are needed to enhance our understanding of the impacts of climate change on infectious diseases and improve human adaption to climate change.

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 29 February, 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.

Background Air 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. Objective To 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. Methods Data 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, PM_2.5, 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. Results PM_2.5, 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 PM_2.5, 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 . Conclusion The 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.

Objective: To evaluate the exported risk of novel coronavirus pneumonia (NCP) from Hubei Province and the imported risk in various provinces across China. Methods: Data of reported NCP cases and Baidu Migration Indexin all provinces of the country as of February 14, 2020 were collected. The correlation analysis between cumulative number of reported cases and the migration index from Hubei was performed, and the imported risks from Hubei to different provinces across China were further evaluated. Results: A total of 49 970 confirmed cases were reported nationwide, of which 37 884 were in Hubei Province. The average daily migration index from Hubei to other provinces was 312.09, Wuhan and other cities in Hubei were 117.95 and 194.16, respectively. The cumulative NCP cases of provinces was positively correlated with the migration index derived from Hubei province, also in Wuhan and other cities in Hubei, with correlation coefficients of 0.84, 0.84, and 0.81. In linear model, population migration from Hubei Province, Wuhan and other cities in Hubei account for 71.2%, 70.1%, and 66.3% of the variation, respectively. The period of high exported risk from Hubei occurred before January 27, of which the risks before January 23 mainly came from Wuhan, and then mainly from other cities in Hubei. Hunan Province, Henan Province and Guangdong Province ranked the top three in terms of cumulative imported risk (the cumulative risk indices were 58.61, 54.75 and 49.62 respectively). Conclusion The epidemic in each province was mainly caused by the importation of Hubei Province. Taking measures such as restricting the migration of population in Hubei Province and strengthening quarantine measures for immigrants from Hubei Province may greatly reduce the risk of continued spread of the epidemic.

Background Dengue 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. Objective To 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. Methods Reported 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. Results From 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. Conclusion Temperature 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.