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Recent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change. Global warming is expected to increase mortality due to heat stress in many regions. Here, the authors asses how mortality due to high temperatures changes in China changes for different demographic groups and show that heat-related excess mortality is increasing under climate change, a process that is strongly amplified by population ageing.

In this study, researchers describe the clinical characteristics of coronavirus disease 2019 in a selected cohort of 1099 patients with laboratory-confirmed disease throughout mainland China during the first 2 months of the current outbreak.

ObjectiveTo examine cardiovascular disease (CVD) mortality burden attributable to ambient temperature; to estimate effect modification of this burden by gender, age and education level.MethodsWe obtained daily data on temperature and CVD mortality from 15 Chinese megacities during 2007–2013, including 1 936 116 CVD deaths. A quasi-Poisson regression combined with a distributed lag non-linear model was used to estimate the temperature-mortality association for each city. Then, a multivariate meta-analysis was used to derive the overall effect estimates of temperature at the national level. Attributable fraction of deaths were calculated for cold and heat (ie, temperature below and above minimum-mortality temperatures, MMTs), respectively. The MMT was defined as the specific temperature associated to the lowest mortality risk.ResultsThe MMT varied from the 70th percentile to the 99th percentile of temperature in 15 cities, centring at 78 at the national level. In total, 17.1% (95% empirical CI 14.4% to 19.1%) of CVD mortality (330 352 deaths) was attributable to ambient temperature, with substantial differences among cities, from 10.1% in Shanghai to 23.7% in Guangzhou. Most of the attributable deaths were due to cold, with a fraction of 15.8% (13.1% to 17.9%) corresponding to 305 902 deaths, compared with 1.3% (1.0% to 1.6%) and 24 450 deaths for heat.ConclusionsThis study emphasises how cold weather is responsible for most part of the temperature-related CVD death burden. Our results may have important implications for the development of policies to reduce CVD mortality from extreme temperatures.

Cardiovascular disease is the most common cause of death globally. Examining the relationship between the extreme temperature events (e.g. heat and cold waves) and cardiovascular mortality has profound public significance. However, this evidence is scarce, particularly those from China. We collected daily data on cardiovascular mortality and meteorological conditions from 31 major Chinese cities during the maximum period of 2007-2013. A two-stage analysis was used to estimate the effects of heat and cold waves, and the potential effect modification of their characteristics (intensity, duration, and timing in season) on cardiovascular mortality. Firstly, a generalized quasi-Poisson regression combined with distributed lag nonlinear model was used to evaluate city-specific effects. Then, the meta-analysis was performed to pool effect estimates at the national scale. Overall, cardiovascular mortality risk increased by 19.03% (95%CI: 11.92%, 26.59%) during heat waves and 54.72% (95%CI: 21.20%, 97.51%) during cold waves. The effect estimates varied by the wave's characteristics. In heat wave days, the cardiovascular mortality risks increased by 3.28% (95%CI: −0.06%, 6.73%) for every 1 °C increase in intensity, 2.84% (95%CI: 0.92%, 4.80%) for every 1-d more in duration and −0.07% (95%CI: −0.38%, 0.24%) for every 1-d late in the staring of heat wave; the corresponding estimates for cold wave were 1.82% (95%CI: −0.04%, 3.72%), 1.52% (95%CI: 0.60%, 2.44%) and −0.26% (95%CI: −0.67%, 0.16%). Increased susceptibility to heat and cold waves was observed among patients with ischemic heart disease, females, the elderly, and those with lower education level. And consistent vulnerable populations were found for the effects of changes in cold and heat wave's characteristics. The findings have important implications for the development of early warning systems and plans in response to heat and cold waves, which may contribute to mitigating health threat to vulnerable populations.

Background Although many studies have documented health effects of ambient temperature, little evidence is available in subtropical or tropical regions, and effect modifiers remain uncertain. We examined the effects of daily mean temperature on mortality and effect modification in the subtropical city of Guangzhou, China. Methods A Poisson regression model combined with distributed lag non-linear model was applied to assess the non-linear and lag patterns of the association between daily mean temperature and mortality from 2003 to 2007 in Guangzhou. The case-only approach was used to determine whether the effect of temperature was modified by individual characteristics, including sex, age, educational attainment and occupation class. Results Hot effect was immediate and limited to the first 5 days, with an overall increase of 15.46% (95% confidence interval: 10.05% to 20.87%) in mortality risk comparing the 99th and the 90th percentile temperature. Cold effect persisted for approximately 12 days, with a 20.39% (11.78% to 29.01%) increase in risk comparing the first and the 10th percentile temperature. The effects were especially remarkable for cardiovascular and respiratory mortality. The effects of both hot and cold temperatures were greater among the elderly. Females suffered more from hot-associated mortality than males. We also found significant effect modification by educational attainment and occupation class. Conclusions There are significant mortality effects of hot and cold temperatures in Guangzhou. The elderly, females and subjects with low socioeconomic status have been identified as especially vulnerable to the effect of ambient temperatures.

The coronavirus disease 2019 (COVID-19) pandemic has imposed a substantial disease burden globally and has further exacerbated pre-existing health inequities. This study aimed to provide a comprehensive assessment of the burden and inequities associated with COVID-19 across diverse populations. Using data from the Global Burden of Disease 2021, we systematically analyzed deaths, incidence, disability-adjusted life years (DALYs), and years of life lost (YLLs) of COVID-19 stratified by sex, age, and region. The temporal trends pre- and post-2019 (i.e., 1990-2019 and 2019-2021) were measured using average annual percent change (AAPC). Additionally, the cross-country absolute and relative sociodemographic index (SDI)-related health inequities were assessed using the slope index and concentration index, respectively. The SDI is a composite development indicator that incorporates income, educational attainment, and fertility conditions. Trends in the global burden of all-cause mortality and DALYs exhibited significant declines (AAPC < 0) from 1990 to 2019 but underwent a marked reversal trend (AAPC > 0) following the COVID-19 pandemic. In 2021, COVID-19 resulted in 2.28 billion incident cases and 7.89 million deaths globally, with an age-standardized DALYs rate of 2,501 per 100,000 population. While incidence rates were relatively evenly distributed across populations, mortality was disproportionately higher among males and older adults. Substantial health inequities in the burden of COVID-19 were evident across 204 countries and territories, with absolute widening inequities notably in 2021 (e.g., the slope index of inequity for DALYs rose from 2,713 in 2020 to 4,044 in 2021). Greater inequities are disproportionately concentrated among males, middle-aged and older individuals, and regions with lower SDI levels. These findings highlight the substantial disease burden of COVID-19 and elucidate the multidimensional health inequities exacerbated by the pandemic, providing crucial evidence for targeted interventions to address inequities and strengthen resilience in future global health emergencies.

Cold spells have been associated with mortality from a few broad categories of diseases or specific diseases. However, there is a lack of data about the health effects of cold spells on mortality from a wide spectrum of plausible diseases which can reveal a more comprehensive contour of the mortality burden of cold spells. We collected daily mortality data in Guangzhou during 2010-2018 from the Guangzhou Center for Disease Control and Prevention. The quasi-Poisson generalized linear regression model mixed with the distributed lag non-linear model (DLNM) was conducted to examine the health impacts of cold spells for 11 broad causes of death groupings and from 35 subcategories in Guangzhou. Then, we examined the effect modification by age group (0-64 and 65+ years) and sex. Effects of cold spells on mortality generally delayed for 3-5 d and persisted up to 27 d. Cold spells were significantly responsible for increased mortality risk for most categories of deaths, with cumulative relative risk (RR) over 0-27 lagged days of 1.57 [95% confidence interval (CI): 1.48-1.67], 1.95 (1.49-2.55), 1.58 (1.39-1.79), 1.54 (1.26-1.88), 1.92 (1.15-3.22), 1.75, (1.14-2.68), 2.02 (0.78-5.22), 1.92 (1.49-2.48), 1.48 (1.18-1.85), and 1.18 (1.06-1.30) for non-accidental causes, cardiovascular diseases, respiratory diseases, digestive diseases, nervous system diseases, genitourinary diseases, mental diseases, endocrine diseases, external cause and neoplasms, respectively. The magnitudes of the effects of cold spells on mortality varied remarkably among the 35 subcategories, with the largest cumulative RR of 2.87 (1.72-4.79) estimated for pulmonary heart diseases. The elderly and females were at a higher risk of mortality for most diseases after being exposed to cold spells. Increased mortality from a wide range of diseases was significantly linked with cold spells. Our findings may have important implications for formulating effective preventive strategies and early warning response plans that mitigate the health burden of cold spells.

Background Count time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate. Methods We proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs. Results Both methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69–20.51%), 45.07% (95% CI, 25.86–59.30%) and 42.94% (95% CI, 9.56–64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively. Conclusions The proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs.

Background Socioeconomic status and lifestyle conferred an impact on the incidence or first re-admission of chronic obstructive pulmonary disease (COPD), but the impact on recurrent re-admissions for COPD remains unclear. We aimed to simultaneously study their impacts on first hospital admission and re-admissions for COPD in a large prospective cohort study. Methods We included 439,463 participants without COPD at baseline from the UK Biobank and identified subsequent COPD-related admissions through linkage with national hospital databases, defined by International Classification of Diseases codes (ICD-9: 491, 492, 496; ICD-10: J40–J44). We adopted the Prentice, Williams and Peterson calendar time model to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of first admission and re-admissions for COPD associated with socioeconomic status and lifestyle factors (e.g., smoking, healthy diet, and physical activity). Subgroup analyses were further conducted by age, gender, and ethnicity. Results During a median follow-up of 13.7 years, 13,291 (3.02%) participants were hospitalized for COPD, among whom 6,980 (52.52%) were re-admitted with a median number of two hospital re-admissions per person. The risk of COPD re-admissions is elevated with the increasing number of cumulative admissions. Higher Townsend deprivation index, smoking, unhealthy diet, and lower physical activity were associated with a significantly increased risk of both first admission and re-admissions. The HRs of first admission and re-admissions were 1.08 (95%CI, 1.07–1.08) and 1.01 (95%CI, 1.01–1.02) per unit increase in Townsend deprivation index, respectively. Compared with non-smokers, both current smokers and ex-smokers had higher risks of first admission and re-admissions. Moderate- and vigorous-intensity physical activity was associated with decreased risks of admissions. Moreover, these associations were significantly modified by age, gender, and ethnicity. Conclusions Socioeconomic status and lifestyle factors are independent modifiable factors of first admission and multiple re-admissions for COPD. The findings provide important evidence to guide the management of patients with COPD to prevent or delay hospital admission for exacerbations.

The first community transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) in Guangzhou, China occurred between May and June 2021. Herein, we describe the epidemiological characteristics of this outbreak and evaluate the implemented containment measures against this outbreak. Guangzhou Center for Disease Control and Prevention provided the data on SARS-CoV-2 infections reported between 21 May and 24 June 2021. We estimated the incubation period distribution by fitting a gamma distribution to the data, while the serial interval distribution was estimated by fitting a normal distribution. The instantaneous effective reproductive number (Rt) was estimated to reflect the transmissibility of SARS-CoV-2. Clinical severity was compared for cases with different vaccination statuses using an ordinal regression model after controlling for age. Of the reported local cases, 7/153 (4.6%) were asymptomatic. The median incubation period was 6.02 (95% confidence interval [CI]: 5.42-6.71) days and the means of serial intervals decreased from 5.19 (95% CI: 4.29-6.11) to 3.78 (95% CI: 2.74-4.81) days. The incubation period increased with age (P<0.001). A hierarchical prevention and control strategy against COVID-19 was implemented in Guangzhou, with Rt decreasing from 6.83 (95% credible interval [CrI]: 3.98-10.44) for the 7-day time window ending on 27 May 2021 to below 1 for the time window ending on 8 June and thereafter. Individuals with partial or full vaccination schedules with BBIBP-CorV or CoronaVac accounted for 15.3% of the COVID-19 cases. Clinical symptoms were milder in partially or fully vaccinated cases than in unvaccinated cases (odds ratio [OR] = 0.26 [95% CI: 0.07-0.94]). The hierarchical prevention and control strategy against COVID-19 in Guangzhou was timely and effective. Authorised inactivated vaccines are likely to contribute to reducing the probability of developing severe disease. Our findings have important implications for the containment of COVID-19.