The Excess Winter Deaths Measure: Why Its Use Is Misleading for Public Health Understanding of Cold-related Health Impacts

Applying Machine Learning Techniques to Implementation Science

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

The rise and fall of excess winter mortality in New Zealand from 1876 to 2020

Excess winter mortality (EWM) has been used as a measure of how well populations and policy moderate the health effects of cold weather. We aimed to investigate long-term changes in the EWM of Aotearoa New Zealand (NZ), and potential drivers of change, and to test for structural breaks in trends. We calculated NZ EWM indices from 1876 (4,698 deaths) to 2020 (33,310 deaths), total and by age-group and sex, comparing deaths from June to September (the coldest months) to deaths from February to May and October to January. The mean age and sex-standardised EWM Index (EWMI) for the full study period, excluding 1918, was 1.22. However, mean EWMI increased from 1.20 for 1886 to 1917, to 1.34 for the 1920s, then reduced over time to 1.14 in the 2010s, with excess winter deaths averaging 4.5% of annual deaths (1,450 deaths per year) in the 2010s, compared to 7.9% in the 1920s. Children under 5 years transitioned from a summer to winter excess between 1886 and 1911. Otherwise, the EWMI age-distribution was J-shaped in all time periods. Structural break testing showed the 1918 influenza pandemic strain had a significant impact on trends in winter and non-winter mortality and winter excess for subsequent decades. It was not possible to attribute the post-1918 reduction in EWM to any single factor among improved living standards, reduced severe respiratory infections, or climate change.

Assessing the impact of energy and fuel poverty on health: a European scoping review

BACKGROUND

The burden of energy and fuel poverty (EFP) in Europe is increasing in the face of the cost-of-living crisis, the Russian invasion of Ukraine, the coronavirus disease 2019 (COVID-19) pandemic and the climate emergency. While the health impacts of EFP are often the driving reason for addressing it, EFP's association with health is poorly delineated. This review aims to scope the evidence of EFP's association with health in Europe.

METHODS

A scoping review based on Arksey and O'Malley's framework was conducted using search terms relevant to EFP, health and Europe. Five databases were searched, in addition to hand searching. Review selection was performed by two independent reviewers, and articles were thematically analyzed.

RESULTS

Thirty-five articles published between January 2000 and March 2022 were included. The literature varied in definitions and measurements of EFP and in the health indicators examined. The review revealed a negative association between EFP and health, specifically, general unspecified poor health (9 articles), excess winter mortality (3 articles), communicable diseases (3 articles), non-communicable diseases (11 articles), mental health (15 articles) and well-being (12 articles). While women were reported to be at a higher risk of EFP than men, children and older adults were identified as particularly vulnerable to EFP's adverse health repercussions.

CONCLUSIONS

This scoping review illustrates a significant and complex association between EFP and various domains of health. Though heterogeneity across research makes it difficult to compare findings, our review supports the use of health as a justification to address EFP and urges public health to be more involved in EFP mitigation.

Estimating effects of health policy interventions using interrupted time-series analyses: a simulation study

BACKGROUND

A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions.

METHODS

Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models.

RESULTS

When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series.

CONCLUSION

Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).

Suicide and Associations with Air Pollution and Ambient Temperature: A Systematic Review and Meta-Analysis

Given health threats of climate change, a comprehensive review of the impacts of ambient temperature and ar pollution on suicide is needed. We performed systematic literature review and meta-analysis of suicide risks associated with short-term exposure to ambient temperature and air pollution. Pubmed, Scopus, and Web of Science were searched for English-language publications using relevant keywords. Observational studies assessing risks of daily suicide and suicide attempts associated with temperature, particulate matter with aerodynamic diameter ≤10 μm (PM₁₀) and ≤2.5 mm (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) were included. Data extraction was independently performed in duplicate. Random-effect meta-analysis was applied to pool risk ratios (RRs) for increases in daily suicide per interquartile range (IQR) increase in exposure. Meta-regression analysis was applied to examine effect modification by income level based on gross national income (GNI) per capita, national suicide rates, and average level of exposure factors. In total 2274 articles were screened, with 18 studies meeting inclusion criteria for air pollution and 32 studies for temperature. RRs of suicide per 7.1 °C temperature was 1.09 (95% CI: 1.06, 1.13). RRs of suicide per IQR increase in PM_2.5, PM₁₀, and NO₂ were 1.02 (95% CI: 1.00, 1.05), 1.01 (95% CI: 1.00, 1.03), and 1.03 (95% CI: 1.00, 1.07). O₃, SO₂, and CO were not associated with suicide. RR of suicide was significantly higher in higher-income than lower-income countries (1.09, 95% CI: 1.07, 1.11 and 1.20, 95% CI: 1.14, 1.26 per 7.1 °C increased temperature, respectively). Suicide risks associated with air pollution did not significantly differ by income level, national suicide rates, or average exposure levels. Research gaps were found for interactions between air pollution and temperature on suicide risks.

A Statistical Analysis of Death Rates in Italy for the Years 2015-2020 and a Comparison with the Casualties Reported from the COVID-19 Pandemic

We analyze the data about casualties in Italy in the period 1 January 2015 to 30 September 2020 released by the Italian National Institute of Statistics (ISTAT). The aim of this article was the description of a statistically robust methodology to extract quantitative values for the seasonal excesses of deaths featured by the data, accompanying them with correct estimates of the relative uncertainties. We will describe the advantages of the method adopted with respect to others listed in literature. The data exhibit a clear sinusoidal behavior, whose fit allows for a robust subtraction of the baseline trend of casualties in Italy, with a surplus of mortality in correspondence to the flu epidemics in winter and to the hottest periods in summer. The overall quality of the fit to the data turns out to be very good, an indication of the validity of the chosen model. We discuss the trend of casualties in Italy by different classes of ages and for the different genders. We finally compare the data-subtracted casualties, as reported by ISTAT, with those reported by the Italian Department for Civil Protection (DPC) relative to the deaths directly attributed to the Coronavirus Disease 2019 caused by the SARS-CoV-2 virus (COVID-19), and we point out the differences in the two samples, collected under different assumptions.

Associations between indoor temperature, self-rated health and socioeconomic position in a cross-sectional study of adults in England

OBJECTIVE

Excess winter deaths are a major public health concern in England and Wales, with an average of 20 000 deaths per year since 2010. Feeling cold at home during winter is associated with reporting poor general health; cold and damp homes have greater prevalence in lower socioeconomic groups. Overheating in the summer also has adverse health consequences. This study evaluates the association between indoor temperature and general health and the extent to which this is affected by socioeconomic and household factors.

DESIGN

Cross-sectional study.

SETTING

England.

PARTICIPANTS

Secondary data of 74 736 individuals living in England that took part in the Health Survey for England (HSE) between 2003 and 2014. The HSE is an annual household survey which uses multilevel stratification to select a new, nationally representative sample each year. The study sample comprised adults who had a nurse visit; the analytical sample was adults who had observations for indoor temperature and self-rated health.

RESULTS

Using both logistic and linear regression models to examine indoor temperature and health status, adjusting for socioeconomic and housing factors, the study found an association between poor health and higher indoor temperatures. Each one degree increase in indoor temperature was associated with a 1.4% (95% CI 0.5% to 2.3%) increase in the odds of poor health. After adjusting for income, education, employment type, household size and home ownership, the OR of poor health for each degree temperature rise increased by 19%, to a 1.7% (95% CI 0.7% to 2.6%) increase in odds of poor health with each degree temperature rise.

CONCLUSION

People with worse self-reported health had higher indoor temperatures after adjusting for household factors. People with worse health may have chosen to maintain warmer environments or been advised to. However, other latent factors, such as housing type and energy performance could have an effect.

Intensified impacts on mortality due to compound winter extremes in the Czech Republic

Although impacts of extremely cold temperatures on human health have been widely studied, adverse effects of other extreme weather phenomena have so far received much less attention. We employed a high-quality long-term mortality time series (1982-2017) to evaluate impacts of extreme winter weather in the Czech Republic. We aimed to clarify whether compound events of extreme weather cause larger impacts on mortality than do each type of extreme if evaluated individually. Using daily data from the E-OBS and ERA5 datasets, we analyzed 9 types of extreme events: extreme wind gust, precipitation, snowfall, and sudden temperature and pressure changes. Relative mortality deviations from the adjusted baseline were used to estimate the immediate effect of the selected extreme events on excess mortality. The impact was adjusted for the effect of extreme cold. Extreme events associated with sudden rise of minimum temperature and pressure drops had generally significant impact on excess mortality (3.7% and 1.4% increase). The impacts were even more pronounced if these events occurred simultaneously or were compounded with other types of extremes, such as heavy precipitation, snowfall, maximum temperature rise, and their combinations (increase as great as 14.4%). Effects of some compound events were significant even for combinations of extremes having no significant impact on mortality when evaluated separately. On the other hand, a "protective" effect of pressure increases reduced the risk for its compound events. Meteorological patterns during extreme events linked to excess mortality indicate passage of a low-pressure system northerly from the study domain. We identified extreme winter weather events other than cold temperatures with significant impact on excess mortality. Our results suggest that occurrence of compound extreme events strengthen the impacts on mortality and therefore analysis of multiple meteorological parameters is a useful approach in defining adverse weather conditions.

Heatwave-Related Mortality Risk and the Risk-Based Definition of Heat Wave in South Korea: A Nationwide Time-Series Study for 2011-2017

Studies on the pattern of heatwave mortality using nationwide data that include rural areas are limited. This study aimed to assess the risk of heatwave-related mortality and evaluate the health risk-based definition of heatwave. We collected data on daily temperature and mortality from 229 districts in South Korea in 2011–2017. District-specific heatwave-related mortality risks were calculated using a distributed lag model. The estimates were pooled in the total areas and for each urban and rural area using meta-regression. In the total areas, the threshold point of heatwave mortality risk was estimated at the 93rd percentile of temperature, and it was lower in urban areas than in rural areas (92nd percentile vs. 95th percentile). The maximum risk of heatwave-related mortality in the total area was 1.11 (95% CI: 1.01–1.22), and it was slightly greater in rural areas than in the urban areas (RR: 1.23, 95% CI: 0.99–1.53 vs. RR: 1.10, 95% CI: 1.01–1.20). The results differ by age- and cause-specific deaths. In conclusion, the patterns of heatwave-related mortality risk vary by area and sub-population in Korea. Thus, more target-specific heatwave definitions and action plans should be established according to different areas and populations.

Mortality attributable to seasonal influenza in Greece, 2013 to 2017: variation by type/subtype and age, and a possible harvesting effect

IntroductionEstimating the contribution of influenza to excess mortality in the population presents substantial methodological challenges.AimIn a modelling study we combined environmental, epidemiological and laboratory surveillance data to estimate influenza-attributable mortality in Greece, over four seasons (2013/14 to 2016/17), specifically addressing the lag dimension and the confounding effect of temperature.MethodsAssociations of influenza type/subtype-specific incidence proxies and of daily mean temperature with mortality were estimated with a distributed-lag nonlinear model with 30 days of maximum lag, separately by age group (all ages, 15-64 and ≥ 65 years old). Total and weekly deaths attributable to influenza and cold temperatures were calculated.ResultsOverall influenza-attributable mortality was 23.6 deaths per 100,000 population per year (95% confidence interval (CI): 17.8 to 29.2), and varied greatly between seasons, by influenza type/subtype and by age group, with the vast majority occurring in persons aged ≥ 65 years. Most deaths were attributable to A(H3N2), followed by influenza B. During periods of A(H1N1)pdm09 circulation, weekly attributable mortality to this subtype among people ≥ 65 years old increased rapidly at first, but then fell to zero and even negative, suggesting a mortality displacement (harvesting) effect. Mortality attributable to cold temperatures was much higher than that attributable to influenza.ConclusionsStudies of influenza-attributable mortality need to consider distributed-lag effects, stratify by age group and adjust both for circulating influenza virus types/subtypes and daily mean temperatures, in order to produce reliable estimates. Our approach addresses these issues, is readily applicable in the context of influenza surveillance, and can be useful for other countries.

Observational evidence of the seasonal and demographic variation in experienced temperature from 77 743 UK Biobank participants

BACKGROUND

Exposure to cold is known to be associated with severe health impacts. The primary epidemiological evidence for this is the seasonal variation in mortality. However, there is a paucity of directly measured data for personal cold temperature exposure. This paper develops the concept of experienced temperature, and reports how it varies with season, demographics and housing factors.

METHODS

This study uses data from 77 743 UK Biobank participants. A novel method to directly measure participant's exposure to low temperatures using a thermistor in a wrist-worn activity monitor is described. These readings are combined with demographic and housing factor variables in a multiple regression model to understand underlying relationships.

RESULTS

The study reveals a significant difference in experienced temperature of ~1.8°C between the periods of coldest and hottest external temperature. A number of demographic differences were also observed-such as people of Chinese ethnic background experiencing 0.65°C lower temperatures than other groups.

CONCLUSIONS

This paper presents primary evidence for a seasonal variation in experienced temperature. This variation likely contributes to cold related mortality and morbidity. It is hypothesized that this relationship would be less strong in countries which suffer fewer impacts of cold winter temperatures.

Assessment of Risk Hospitalization due to Acute Respiratory Incidents Related to Ozone Exposure in Silesian Voivodeship (Poland)

The main aim of this work is the estimation of health risks arising from exposure to ozone or other air pollutants by different statistical models taking into account delayed health effects. This paper presents the risk of hospitalization due to bronchitis and asthma exacerbation in adult inhabitants of Silesian Voivodeship from 1 January 2016 to 31 August 2017. Data were obtained from the daily register of hospitalizations for acute bronchitis (code J20-J21, International Classification of Diseases, Tenth Revision - ICD-10) and asthma (J45-J46) which is governed by the National Health Fund. Meteorological data and data on tropospheric ozone concentrations were obtained from the regional environmental monitoring database of the Provincial Inspector of Environmental Protection in Katowice. The paper includes descriptive and analytical statistical methods used in the estimation of health risk with a delayed effect: Almon Distributed Lag Model, the Poisson Distributed Lag Model, and Distributed Lag Non-Linear Model (DLNM). A significant relationship has only been confirmed by DLNM for bronchitis and a relatively short period (1-3 days) from exposure above the limit value (120 µg/m³). The relative risk value was RR = 1.15 (95% CI 1.03-1.28) for a 2-day lag. However, conclusive findings require the continuation of the study over longer observation periods.

Do hospital admission rates increase in colder winters? A decadal analysis from an eastern county in England

Background

The aim of the study was to measure the effect of colder winters compared to warmer winters on hospital admission rates in Suffolk County.

Methods

The setting of this study was Suffolk County in eastern England. The period of the study was financial years 2003/04-2012/13. The study was an analytic ecological study. Analysis involved calculation of rate ratios of hospital admission rates in colder winters compared to warmer winters, in all persons and the elderly.

Results

The main finding of the study was that all rate ratios for hospital admission rates in colder winters compared to warmer winters were significantly raised with effects of 2-5%. Rate ratios for all admissions in persons of all ages and persons aged 65 years and over were, respectively, 1.02 (99% confidence interval (CI): 1.01, 1.03; P < 0.001) and 1.02 (99% CI: 1.01, 1.04; P < 0.001). Rate ratios for emergency admissions in persons of all ages and persons aged 65 years and over were, respectively, 1.05 (99% CI: 1.03, 1.06; P < 0.001) and 1.04 (99% CI: 1.01, 1.06; P < 0.001).

Conclusion

In Suffolk County, hospital admission rates are significantly raised in colder winters compared to warmer winters. This evidence may be useful in planning hospital services.

The effect of the Winter Fuel Payment on household temperature and health: a regression discontinuity design study

Background

The Winter Fuel Payment (WFP) is a non-NHS population-level policy intervention that aims to reduce cold exposure and enhance the health and well-being of older adults. Labelling this cash transfer as ‘winter fuel’ has been shown to lead to increased household energy expenditure, but it is not known if this expenditure produces warmer homes or health benefits.

Objectives

First, the association between indoor temperature and health was established to identify the outcome measures most likely to be affected by the WFP. Then, whether or not receiving the WFP is associated with raised household temperature levels and/or improved health was assessed.

Design

Random and fixed effects regression models were used to estimate the link between ambient indoor temperature and health. A regression discontinuity (RD) design analysis exploiting the sharp eligibility criteria for the WFP was employed to estimate the potential impact of the payment.

Setting

The sample was drawn from the English Longitudinal Study of Ageing (ELSA), an observational study of community-dwelling individuals aged ≥ 50 years in England.

Participants

Analyses examining the association between household temperature and health had a maximum sample of 12,210 adults aged 50–90 years. The RD analyses drew on a maximum of 5902 observations.

Intervention

The WFP provides households with a member who is aged > 60 years (up to 2010, from which point the minimum age increased) in the qualifying week with a lump sum annual payment, typically in November or December.

Main outcome measures

Differences in indoor temperature were examined, and, following an extensive literature review of relevant participant-reported health indicators and objectively recorded biomarkers likely to be affected by indoor temperature, a series of key measures were selected: blood pressure, inflammation, lung function, the presence of chest infections, subjective health and depressive symptom ratings.

Data sources

The first six waves of the ELSA were drawn from, accessible through the UK Data Service (SN:5050 English Longitudinal Study of Ageing: Waves 0–7, 1998–2015).

Results

Results from both random and fixed-effects multilevel regression models showed that low levels of indoor temperature were associated with raised systolic and diastolic blood pressure levels and raised fibrinogen levels. However, across the RD models, no evidence was found that the WFP was consistently associated with differences in either household temperature or the health of qualifying (vs. non-qualifying) households.

Limitations

The presence of small effects cannot be ruled out, not detectable because of the sample size in the current study.

Conclusions

This study capitalised on the sharp assignment rules regarding WFP eligibility to estimate the potential effect of the WFP on household temperature and health in a national sample of English adults. The RD design employed did not identify evidence linking the WFP to warmer homes or potential health and well-being effects.

Future work

Further research should utilise larger samples of participants close to the WFP eligibility cut-off point examined during particularly cold weather in order to identify whether or not the WFP is linked to health benefits not detected in the current study, which may have implications for population health and the evaluation of the effectiveness of the WFP.

Funding

The National Institute for Health Research Public Health Research programme.

Assessing Effect Modification of Excess Winter Death by Causes of Death and Individual Characteristics in Zhejiang Province, China: A Multi-Community Case-Only Analysis

Mortality in many parts of the world has a seasonal pattern, with a marked excess of deaths during winter. To date, however, there is very little published evidence on the nature of this wintertime excess in low- and middle-income countries. In this study, we aimed to quantify the extent of the death peak in winter and to assess effect modification on excess winter death (EWD) by individual characteristics and cause of deaths in China. We used a Cosinor model to examine seasonal patterns for specific causes of deaths and a case-only analysis of deaths in winter compared with other seasons to assess effect modification by individual characteristics. A total of 398,529 deaths were investigated between January 2010 and December 2013 in Zhejiang Province, China. Deaths peaked in winter, and overall mortality was around 30% higher in winter than in summer. Although diseases of the respiratory and circulatory systems were highly seasonal, surprisingly we observed that deaths from mental and behavioral disorders exhibited greater fluctuation. Males, the elderly and illiterate individuals suffered high EWD. EWDs were also particularly common in emergency rooms, at home, on the way to hospitals, and in nursing homes/family wards. This study highlighted the high EWD in some previously unreported groups, indicating new information to facilitate the targeting of necessary preventive measures to those at greatest risk in order to mitigate wintertime death burdens.

Temporal changes in morality attributed to heat extremes for 57 cities in Northeast Asia

Recent studies have reported that heat-related mortality decreased by adaptation during decades. However, since the frequency of extreme heat events is increasing, it is difficult to conclude with certainty that the heat mortality burden is decreasing. To examine temporal changes in mortality attributed to heat extremes in Northeast Asia, we collected temperature and mortality data covering the years 1972-2012 from 57 cities of 3 countries (Taiwan, Korea, and Japan) in Northeast Asia. Poisson regression curves were fitted to the data from each city. The temporal changes in heat-mortality association were estimated with a time-varying distributed lag non-linear model. Heat extremes were defined as temperatures greater than the 97.5th percentiles of city-specific average temperatures. Attributable deaths were calculated considering temporal variations in exposure and relative risk. The estimates were then pooled through meta-analysis. The results show that the mortality risk on extreme heat days declined during the study period in all countries. However, as summer temperatures in Japan have shown more heat extremes over time, the mortality risk attributed to heat increased during 2003-2012 (0.32%) compared with 1972-1981 (0.19%). Thus, to assess the total health burden due to heat extremes related to climate change, public health strategies should focus on the temporal variation in heat-mortality association as well as changes in the distribution of heat extremes overtime.

Health effects of milder winters: a review of evidence from the United Kingdom

Cold-related mortality and morbidity remains an important public health problem in the UK and elsewhere. Health burdens have often reported to be higher in the UK compared to other countries with colder climates, however such assessments are usually based on comparison of excess winter mortality indices, which are subject to biases. Daily time-series regression or case-crossover studies provide the best evidence of the acute effects of cold exposure. Such studies report a 6% increase in all-cause deaths in England & Wales for every 1 °C fall in daily mean temperature within the top 5% of the coldest days. In major Scottish cities, a 1 °C reduction in mean temperature below 11 °C was associated with an increase in mortality of 2.9%, 3.4%, 4.8% and 1.7% from all-causes, cardiovascular, respiratory, and non-cardio-respiratory causes respectively. In Northern Ireland, a 1 °C fall during winter months led to increases of 4.5%, 3.9% and 11.2% for all-cause, cardiovascular and respiratory deaths respectively among adults. Raised risks are also observed with morbidity outcomes. Hip fractures among the elderly are only weakly associated with snow and ice conditions in the UK, with the majority of cases occurring indoors. A person's susceptibility to cold weather is affected by both individual- and contextual-level risk factors. Variations in the distributions of health, demographic, socio-economic and built-environment characteristics are likely to explain most differences in cold risk observed between UK regions. Although cold-related health impacts reduced throughout much of the previous century in UK populations, there is little evidence on the contribution that milder winters due to climate change may have made to reductions in more recent decades. Intervention measures designed to minimise cold exposure and reduce fuel poverty will likely play a key role in determining current and future health burdens associated with cold weather.

Green Space and Deaths Attributable to the Urban Heat Island Effect in Ho Chi Minh City

OBJECTIVES

To quantify heat-related deaths in Ho Chi Minh City, Vietnam, caused by the urban heat island (UHI) and explore factors that may alleviate the impact of UHIs.

METHODS

We estimated district-specific meteorological conditions from 2010 to 2013 using the dynamic downscaling model and calculated the attributable fraction and number of mortalities resulting from the total, extreme, and mild heat in each district. The difference in attributable fraction of total heat between the central and outer districts was classified as the attributable fraction resulting from the UHI. The association among attributable fraction, attributable number with a green space, population density, and budget revenue of each district was then explored.

RESULTS

The temperature-mortality relationship between the central and outer areas was almost identical. The attributable fraction resulting from the UHI was 0.42%, which was contributed by the difference in temperature distribution between the 2 areas. Every 1-square-kilometer increase in green space per 1000 people can prevent 7.4 deaths caused by heat.

CONCLUSIONS

Green space can alleviate the impacts of UHIs, although future studies conducting a heath economic evaluation of tree planting are warranted.

Seasonal variations of temperature-related mortality burden from cardiovascular disease and myocardial infarction in China

Incidence rate of cardiovascular disease (CVD) has significant seasonal trend, being higher in winter. However, the extent to which the seasonal variation of CVD deaths was caused by temperature remains unclear. We obtained daily data on temperature and CVD and myocardial infarction (MI) mortality from nine Chinese mega-cities during 2007-2013. Distributed lag non-linear models were applied to assess the city-specific temperature-related daily excess deaths across lag 0-21 days, using the minimum-mortality temperature as reference. Then, estimates of excess deaths in four seasons were separately aggregated from the daily series, and its ratio to the corresponding total deaths produced seasonal attributable fraction (AF). In total, 1,079,622 CVD and 201,897 MI deaths were recorded in the nine Chinese cities. Significant and non-linear associations between temperature and mortality were observed, with a total of 195,516 CVD and 50,658 MI deaths attributable to non-optimum temperatures. 103,439 (95% empirical CI: 54,475-141,537) CVD and 24,613 (5891-36,279) MI deaths related to non-optimum temperature occurred in winter, compared with 15,923 (1436-28,853) and 4946 (-325-9016) in summer. Temperature-related AFs were higher among MI than CVD, with AFs of 42% (9-62%) and 35% (19-48%) in winter, and 13% (-1-23%) and 8% (1-14%) in summer, respectively. This study may have important implications for developing effective targeted intervention measures on CVD events.