Projections of heat-related excess mortality in China due to climate change,population and aging

Climate change is one of the biggest health threats of the 21st century.Although China is the biggest developing country,with a large population and different climate types,its projections of large-scale heat-related excess mortality remain understudied.In particular,the effects of climate change on aging populations have not been well studied,and may result in significantly underestimation of heat effects.In this study,we took four climate change scenarios of Tier-1 in CMIP6,which were combinations of Shared Socioeconomic Pathways(SSPs)and Representative Concentration Pathways(RCPs).We used the exposure-response functions derived from previous studies combined with baseline age-specific non-accidental mortality rates to project heat-related excess mortality.Then,we employed the Logarithmic Mean Divisia Index(LMDI)method to decompose the impacts of climate change,population growth,and aging on heat-related excess mortality.Finally,we multiplied the heat-related Years of Life Lost(YLL)with the Value of a Statistical Life Year(VSLY)to quantify the economic burden of premature mortality.We found that the heat-related excess mortality would be concentrated in central China and in the densely populated south-eastern coastal regions.When aging is considered,heat-related excess mortality will become 2.8–6.7 times than that without considering aging in 2081–2100 under different scenarios.The contribution analysis showed that the effect of aging on heat-related deaths would be much higher than that of climate change.Our findings highlighted that aging would lead to a severe increase of heat-related deaths and suggesting that regional-specific policies should be formulated in response to heat-related risks.

Excess mortality in elderly hip fracture patients:An Indian experience

Purpose:Hip fractures in elderly have a high mortality.However,there is limited literature on the excess mortality seen in hip fractures compared to the normal population.The purpose of this study was to compare the mortality of hip fractures with that of age and gender matched Indian population.Methods:There are 283 patients with hip fractures aged above 50 years admitted at single centre prospectively enrolled in this study.Patients were followed up for 1 year and the follow-up record was available for 279 patients.Mortality was assessed during the follow-up from chart review and/or by telephonic interview.One-year mortality of Indian population was obtained from public databases.Standardized mortality ratio(SMR)(observed mortality divided by expected mortality)was calculated.Kaplan-Meir analysis was used.Results:The overall 1-year mortality was 19.0%(53/279).Mortality increased with age(p<0.001)and the highest mortality was seen in those above 80 years(aged 50-59 years:5.0%,aged 60-69 years:19.7%,aged 70-79 years:15.8%,and aged over 80 years:33.3%).Expected mortality of Indian population of similar age and gender profile was 3.7%,giving a SMR of 5.5.SMR for different age quintiles were:3.9(aged 50-59 years),6.6(aged 60-69 years),2.2(aged 70-79 years);and 2.0(aged over 80 years).SMR in males and females were 5.7 and 5.3,respectively.Conclusions:Indian patients sustaining hip fractures were about 5 times more likely to die than the general population.Although mortality rates increased with age,the highest excess mortality was seen in relatively younger patients.Hip fracture mortality was even higher than that of myocardial infarction,breast cancer,and cervical cancer.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.

Analysis of the Influence of Heat Wave on Death among the Elderly in Nanjing City

To obtain the influence of heat waves on death in the elderly, the influence of the heat waves in Nanjing in the summers (from June to August) of 2005-2008 on death among the elderly was analyzed by using statistical methods including generalized additive models. The results showed that the death toll over these four summers in Nanjing tended to increase;on an average 10.76% more males died than females, and the mortality rate of old people aged ≥65 accounted for 73.21% of all deaths. The mortality rate of older people rose with increasing maximum temperature. Furthermore, the average excess mortality rate caused by heat wave weather processes was 15.91%, while it was less affected by the duration of the heat wave. The death toll of the elderly increased with the increase in humidity, dropping of atmospheric pressure, and decrease of wind speed for 1°C increase of maximum temperature. Under the same humidity condition, atmospheric pressure, and wind speed, the death toll during heat wave days was higher than that occurring on other days, and heat waves increased the risk of death among the elderly by 26.6% (95% CI: 1.100 - 1.154). Daily mortality was mainly affected by the daily maximum temperature 1, 4, or 6 days later, particularly 4 days later. Heat wave was one of the principal factors, which caused the rise in death tolls in summer, and the elderly were most affected.