Influenza epidemics, seasonality, and the effects of cold weather on cardiac mortality

Rising trend and regional disparities of the global burden of disease attributable to ambient low temperature, 1990-2019: An analysis of data from the Global Burden of Disease 2019 study

Background

Previous studies on the effect of global warming on the global burden of disease have mainly focussed on the impact of high temperatures, thereby providing limited evidence of the effect of lower temperatures.

Methods

We adopted a three-stage analysis approach using data from the Global Burden of Disease 2019 study. First, we explored the global burden of disease attributable to low temperatures, examining variations by gender, age, cause, region, and country. Second, we analysed temporal trends in low-temperature-related disease burdens from 1990 to 2019 by meta-regression. Finally, we fitted a mixed-effects meta-regression model to explore the effect modification of country-level characteristics.

Results

In 2019, low temperatures were responsible for 2.92% of global deaths and 1.03% of disability-adjusted life years (DALYs), corresponding to a death rate of 21.36 (95% uncertainty interval (UI) = 18.26, 24.73) and a DALY rate of 335 (95% UI = 280, 399) per 100 000 population. Most of the deaths (85.12%) and DALYs (94.38%) attributable to low temperatures were associated with ischaemic heart disease, stroke, and chronic obstructive pulmonary disease. In the last three decades, we observed an upward trend for the annual number of attributable deaths (P < 0.001) and a downward trend for the rates of death (P < 0.001) and DALYs (P < 0.001). The disease burden associated with low temperatures varied considerably among regions and countries, with higher burdens observed in regions with middle or high-middle socio-demographic indices, as well as countries with higher gross domestic product per capita and a larger proportion of ageing population.

Conclusions

Our findings emphasise the significance of raising public awareness and prioritising policies to protect global population health from the adverse effects of low temperatures, even in the face of global warming. Particular efforts should be targeted towards individuals with underlying diseases (e.g. cardiovascular diseases) and vulnerable countries or regions (e.g. Central Asia and central Europe).

Health impacts of a cold wave and its economic loss assessment in China's high-altitude city, Xining

OBJECTIVE

Amidst climate change, extensive research has centered on the health impacts of heatwaves, yet the consequences of cold spells, particularly in cooler, higher-altitude regions, remain under-explored.

METHODS

Analyzing climatic data and non-accidental mortality in Xining, China's second-highest provincial capital, from 2016 to 2020, this study defines cold spells as daily mean temperatures below the 10th, 7.5th, or 5th percentiles for 2-4 consecutive days. A time-stratified case-crossover approach and distributional lag nonlinear modeling were used to assess the link between cold spells and mortality, calculating attributable fractions (AFs) and numbers (ANs) of deaths. The study also examined the impact of cold spells over different periods and analyzed the value of a statistical life (VSL) loss in 2018, a year with frequent cold spells. Stratified analyses by sex, age, and education level were conducted.

RESULTS

A significant association was found between cold spells and non-accidental mortality, with a relative risk of 1.548 (95% CI: 1.300, 1.845). The AF was 33.48%, with an AN of 9,196 deaths during the study's cold period. A declining trend in mortality risk was observed from 2019-2020. The 2018 VSL was approximately 2.875 billion CNY, about 1.75% of Xining's GDP. Higher risks were noted among males, individuals aged ≥ 65, and those with lower education levels.

CONCLUSION

The findings underscore the vulnerability and economic losses of high-altitude cities to cold spells. Implementing interventions such as improved heating, educational programs, and community support is vital for mitigating these adverse health effects.

Excess winter mortality in Finland, 1971-2019: a register-based study on long-term trends and effect modification by sociodemographic characteristics and pre-existing health conditions

OBJECTIVES

Excess winter mortality is a well-established phenomenon across the developed world. However, whether individual-level factors increase vulnerability to the effects of winter remains inadequately examined. Our aim was to assess long-term trends in excess winter mortality in Finland and estimate the modifying effect of sociodemographic and health characteristics on the risk of winter death.

DESIGN

Nationwide register study.

SETTING

Finland.

PARTICIPANTS

Population aged 60 years and over, resident in Finland, 1971-2019.

OUTCOME MEASURES

Age-adjusted winter and non-winter death rates, and winter-to-non-winter rate ratios and relative risks (multiplicative interaction effects between winter and modifying characteristics).

RESULTS

We found a decreasing trend in the relative winter excess mortality over five decades and a drop in the series around 2000. During 2000-2019, winter mortality rates for men and women were 11% and 14% higher than expected based on non-winter rates. The relative risk of winter death increased with age but did not vary by income. Compared with those living with at least one other person, individuals in institutions had a higher relative risk (1.07, 95% CI 1.05 to 1.08). Most pre-existing health conditions did not predict winter death, but persons with dementia emerged at greater relative risk (1.06, 95% CI 1.04 to 1.07).

CONCLUSIONS

Although winter mortality seems to affect frail people more strongly-those of advanced age, living in institutions and with dementia-there is an increased risk even beyond the more vulnerable groups. Protection of high-risk groups should be complemented with population-level preventive measures.

[Effect of Short-Term Exposure to Air Pollutants on Hospital Admissions for End-Stage Renal Disease Patients Undergoing Hemodialysis]

Objective

To evaluate the association between short-term exposure to air pollutants of end-stage renal disease (ESRD) patients on maintenance hemodialysis and the number of daily hospital admissions.

Methods

The data on hospitalizations were obtained from the database of the municipal Urban Employees' Basic Medical Insurance and Urban Residents' Basic Medical Insurance of a city in Southwest China. Single and multiple pollutant generalized additive models were utilized to estimate the effect of air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2) on patient admissions after the lag time of different numbers of days. In addition, subgroup analyses stratified by sex, age, PM2.5 and PM10 concentration thresholds, seasonality, and comorbidity status for cardiovascular diseases and hypertension were conducted.

Results

In the single pollutant models, the pollutants significantly associated with patient admissions and the corresponding lag time of the strongest association were as follows, every time CO increased by 0.1 mg/m3, there was a 2.39% increase (95% confidence interval [CI]: 0.96%-3.83%) in patient admissions after 7 days of lag time; every time NO2, O3, PM2.5, PM10, and SO2 increased by 10 μg/m3, patient admissions increased by 4.02% (95% CI: 1.21%-6.91%) after 7 days of lag time, 3.57% (95% CI: 0.78%-6.44%) after 0-4 days of lag time, 2.00% (95% CI: 1.07%-2.93%) after 6 days of lag time, 1.19% (95% CI: 0.51%-1.88%) after 7 days of lag time, and 8.37% (95% CI: 3.08%-13.93%) after 7 days of lag time, respectively. In the multiple pollutant model, every time O3 and PM2.5 increased by 10 μg/m3, there was an increase of 3.18% (95% CI: 0.34%-6.09%) in daily patient admissions after 0-4 days of lag time and an increase of 1.85% (95% CI: 0.44%-3.28%) after 7 days of lag time. Furthermore, subgroup analyses showed that seasonality, the severity of air pollution, and patients' comorbidities might be the effect modifiers for the association between ambient air pollution and hospital admissions in ESRD patients receiving maintenance hemodialysis.

Conclusion

Air pollution is closely associated with hospital admissions in ESRD patients undergoing maintenance hemodialysis and the strength of this association varies according to seasonality, the severity of air pollution, and patients' status of comorbidities.

Low ambient temperature and hospitalization for cardiorespiratory diseases in Brazil

Studies have shown that larger temperature-related health impacts may be associated with cold rather than with hot temperatures. Although it remains unclear the cold-related health burden in warmer regions, in particular at the national level in Brazil. We address this gap by examining the association between low ambient temperature and daily hospital admissions for cardiovascular and respiratory diseases in Brazil between 2008 and 2018. We first applied a case time series design in combination with distributed lag non-linear modeling (DLNM) framework to assess the association of low ambient temperature with daily hospital admissions by Brazilian region. Here, we also stratified the analyses by sex, age group (15-45, 46-65, and >65 years), and cause (respiratory and cardiovascular hospital admissions). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our sample included more than 23 million hospitalizations for cardiovascular and respiratory diseases nationwide between 2008 and 2018, of which 53% were admissions for respiratory diseases and 47% for cardiovascular diseases. Our findings suggest that low temperatures are associated with a relative risk of 1.17 (95% CI: 1.07; 1.27) and 1.07 (95% CI: 1.01; 1.14) for cardiovascular and respiratory admissions in Brazil, respectively. The pooled national results indicate robust positive associations for cardiovascular and respiratory hospital admissions in most of the subgroup analyses. In particular, for cardiovascular hospital admissions, men and older adults (>65 years old) were slightly more impacted by cold exposure. For respiratory admissions, the results did not indicate differences among the population groups by sex and age. This study can help decision-makers to create adaptive measures to protect public health from the effects of cold temperature.

Exploring vulnerability to heat and cold across urban and rural populations in Switzerland

Heat- and cold-related mortality risks are highly variable across different geographies, suggesting a differential distribution of vulnerability factors between and within countries, which could partly be driven by urban-to-rural disparities. Identifying these drivers of risk is crucial to characterize local vulnerability and design tailored public health interventions to improve adaptation of populations to climate change. We aimed to assess how heat- and cold-mortality risks change across urban, peri-urban and rural areas in Switzerland and to identify and compare the factors associated with increased vulnerability within and between different area typologies. We estimated the heat- and cold-related mortality association using the case time-series design and distributed lag non-linear models over daily mean temperature and all-cause mortality series between 1990-2017 in each municipality in Switzerland. Then, through multivariate meta-regression, we derived pooled heat and cold-mortality associations by typology (i.e. urban/rural/peri-urban) and assessed potential vulnerability factors among a wealth of demographic, socioeconomic, topographic, climatic, land use and other environmental data. Urban clusters reported larger pooled heat-related mortality risk (at 99th percentile, vs. temperature of minimum mortality (MMT)) (relative risk=1.17(95%CI:1.10;1.24, vs peri-urban 1.03(1.00;1.06), and rural 1.03 (0.99;1.08)), but similar cold-mortality risk (at 1st percentile, vs. MMT) (1.35(1.28;1.43), vs rural 1.28(1.14;1.44) and peri-urban 1.39 (1.27-1.53)) clusters. We found different sets of vulnerability factors explaining the differential risk patterns across typologies. In urban clusters, mainly environmental factors (i.e. PM2.5) drove differences in heat-mortality association, while for peri-urban/rural clusters socio-economic variables were also important. For cold, socio-economic variables drove changes in vulnerability across all typologies, while environmental factors and ageing were other important drivers of larger vulnerability in peri-urban/rural clusters, with heterogeneity in the direction of the association. Our findings suggest that urban populations in Switzerland may be more vulnerable to heat, compared to rural locations, and different sets of vulnerability factors may drive these associations in each typology. Thus, future public health adaptation strategies should consider local and more tailored interventions rather than a one-size fits all approach. size fits all approach.

Nationwide Analysis of the Heat- and Cold-Related Mortality Trends in Switzerland between 1969 and 2017: The Role of Population Aging

BACKGROUND

Because older adults are particularly vulnerable to nonoptimal temperatures, it is expected that the progressive population aging will amplify the health burden attributable to heat and cold due to climate change in future decades. However, limited evidence exists on the contribution of population aging on historical temperature-mortality trends.

OBJECTIVES

We aimed to a) assess trends in heat- and cold-related mortality in Switzerland between 1969 and 2017 and b) to quantify the contribution of population aging to the observed patterns.

METHODS

We collected daily time series of all-cause mortality by age group (<65, 65-79, and 80 y and older) and mean temperature for each Swiss municipality (1969-2017). We performed a two-stage time-series analysis with distributed lag nonlinear models and multivariate longitudinal meta-regression to obtain temperature-mortality associations by canton, decade, and age group. We then calculated the corresponding excess mortality attributable to nonoptimal temperatures and compared it to the estimates obtained in a hypothetical scenario of no population aging.

RESULTS

Between 1969 and 2017, heat- and cold-related mortality represented 0.28% [95% confidence interval (CI): 0.18, 0.37] and 8.91% (95% CI: 7.46, 10.21) of total mortality, which corresponded to 2.4 and 77 deaths per 100,000 people annually, respectively. Although mortality rates for heat slightly increased over time, annual number of deaths substantially raised up from 74 (12;125) to 181 (39;307) between 1969-78 and 2009-17, mostly driven by the ≥80-y-old age group. Cold-related mortality rates decreased across all ages, but annual cold-related deaths still increased among the ≥80, due to the increase in the population at risk. We estimated that heat- and cold-related deaths would have been 52.7% and 44.6% lower, respectively, in the most recent decade in the absence of population aging.

DISCUSSION

Our findings suggest that a substantial proportion of historical temperature-related impacts can be attributed to population aging. We found that population aging has attenuated the decrease in cold-related mortality and amplified heat-related mortality. https://doi.org/10.1289/EHP9835.

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study

BACKGROUND

Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature.

METHODS

We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models.

RESULTS

Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00-1.11] in the tropical zone and 1.23 (95% CI: 1.20-1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95-1.09) and 1.10 (95% CI: 1.07-1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature.

CONCLUSIONS

This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature.

Associations between Temperature and Influenza Activity: A National Time Series Study in China

Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%–3.72%). The risk of influenza cases reached a peak at −5.35 °C with RRs of 2.14 (95%CI: 1.38–3.33) and the monthly AT in the range of −5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%–9.96%). The risk of A/H1N1 reached a peak at −3.14 °C with RRs of 4.88 (95%CI: 1.01–23.75) and the weekly AT in the range of −3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.

Role of temperature, influenza and other local characteristics in seasonality of mortality: a population-based time-series study in Japan

OBJECTIVES

To investigate the extent to which temperature and influenza explained seasonality of mortality in Japan and to examine the association of the seasonality with prefecture-specific characteristics.

DESIGN

We conducted time-series analysis to estimate the seasonal amplitude before and after adjusting for temperature and/or influenza-like illness (ILI). Next, we applied linear mixed effect models to investigate the association of seasonal amplitudes with each indicator on prefecture-specific characteristics on climate, demographic and socioeconomic factors and adaptations.

SETTING

47 prefectures in Japan PARTICIPANTS: Deaths for all-cause, circulatory, and respiratory disease between 1999 and 2015.

OUTCOME MEASURES

Peak-to-trough ratio (PTR, a measure of seasonal amplitude).

RESULTS

The nationwide unadjusted-PTRs for all-cause, circulatory and respiratory mortality were 1.29 (95% CIs: 1.28 to 1.31), 1.55 (95% CI: 1.52 to 1.57) and 1.45 (95% CI: 1.43 to 1.48), respectively. These PTRs reduced substantially after adjusting for temperature but very little after a separate adjustment for ILI. Furthermore, seasonal amplitudes varied between prefectures. However, there was no strong evidence for the associations of PTR with the indicators on prefecture-specific characteristics.

CONCLUSIONS

Seasonality of mortality is primarily driven by temperature in Japan. The spatial variation in seasonal amplitudes was not associated with prefecture-specific characteristics. Although further investigations are required to confirm our findings, this study can help us gain a better understanding of the mechanisms underlying seasonality of mortality.

Weather temperature and the incidence of hospitalization for cardiovascular diseases in an aging society

Weather temperatures affect the incidence of cardiovascular diseases (CVD), but there is limited information on whether CVD hospitalizations are affected by changes in weather temperatures in a super-aging society. We aimed to examine the association of diurnal weather temperature changes with CVD hospitalizations. We included 1,067,171 consecutive patients who were admitted to acute-care hospitals in Japan between April 1, 2012 and March 31, 2015. The primary outcome was the number of CVD hospitalizations per day. The diurnal weather temperature range (DTR) was defined as the minimum weather temperature subtracted from the maximum weather temperature on the day before hospitalization. Multilevel mixed-effects linear regression models were used to estimate the association of DTR with cardiovascular hospitalizations after adjusting for weather, hospital, and patient demographics. An increased DTR was associated with a higher number of CVD hospitalizations (coefficient, 4.540 [4.310–4.765]/°C change, p < 0.001), with greater effects in those aged 75–89 (p < 0.001) and ≥ 90 years (p = 0.006) than among those aged ≤ 64 years; however, there were no sex-related differences (p = 0.166). Greater intraday weather temperature changes are associated with an increased number of CVD hospitalizations in the super-aging society of Japan, with a greater effect in older individuals.

A Comparative Analysis of the Temperature-Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions

New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature-mortality assessments across regions of different orography, climate, and size. Here we explored the performance of population-weighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high-resolution GCDs (HadUK-grid UKPOC-9 and MeteoSwiss-grid-product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi-Poisson time series regression with distributed lag nonlinear models to obtain the GCD- and region-specific temperature-mortality associations and calculated the corresponding cold- and heat-related excess mortality. Although the five exposure datasets yielded different average area-level temperature estimates, these deviations did not result in substantial variations in the temperature-mortality association or impacts. Moreover, local population-weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data.

Association among Weather Conditions, Ambient Air Temperature, and Sedentary Time in Chinese Adults

This study is aimed to quantify the association among weather conditions, ambient air temperature, and sedentary time in Chinese adults. The participants were 3,270 Chinese users of a wrist-worn activity tracker. Their daily activity data were collected using an algorithm based on raw data to determine the sedentary time. The data of ambient air temperature and weather were collected from the meteorological data released by China Central Meteorological Observatory. Two-level linear regression analyses showed that weather conditions had a significant influence on sedentary time in Chinese adults after adjustments for some covariates were made. When the weather condition changed from rainy days to sunny and cloudy days, sedentary time might decrease by about 6.89 and 5.60 min, respectively. In conclusion, weather conditions were independently associated with sedentary time in Chinese adults. The daily sedentary time was shorter on sunny and cloudy days than on rainy days.

Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities

OBJECTIVES

To examine the association between temperature and cause specific mortality, and to quantify the corresponding disease burden attributable to non-optimum ambient temperatures.

DESIGN

Time series analysis.

SETTING

272 main cities in China.

POPULATION

Non-accidental deaths in 272 cities covered by the Disease Surveillance Point System of China, from January 2013 to December 2015.

MAIN OUTCOMES AND MEASURES

Daily numbers of deaths from all non-accidental causes and main cardiorespiratory diseases. Potential effect modifiers included demographic, climatic, geographical, and socioeconomic characteristics. The analysis used distributed lag non-linear models to estimate city specific associations, and multivariate meta-regression analysis to obtain the effect estimates at national and regional levels.

RESULTS

1 826 186 non-accidental deaths from total causes were recorded in the study period. Temperature and mortality consistently showed inversely J shaped associations. At the national average level, relative to the minimum mortality temperature (22.8°C, 79.1st centile), the mortality risk of extreme cold temperature (at -1.4°C, the 2.5th centile) lasted for more than 14 days, whereas the risk of extreme hot temperature (at 29.0°C, the 97.5th centile) appeared immediately and lasted for two to three days. 14.33% of non-accidental total mortality was attributable to non-optimum temperatures, of which moderate cold (ranging from -1.4 to 22.8°C), moderate heat (22.8 to 29.0°C), extreme cold (-6.4 to -1.4°C), and extreme heat (29.0 to 31.6°C) temperatures corresponded to attributable fractions of 10.49%, 2.08%, 1.14%, and 0.63%, respectively. The attributable fractions were 17.48% for overall cardiovascular disease, 18.76% for coronary heart disease, 16.11% for overall stroke, 14.09% for ischaemic stroke, 18.10% for haemorrhagic stroke, 10.57% for overall respiratory disease, and 12.57% for chronic obstructive pulmonary diseases. The mortality risk and burden were more prominent in the temperate monsoon and subtropical monsoon climatic zones, in specific subgroups (female sex, age ≥75 years, and ≤9 years spent in education), and in cities characterised by higher urbanisations rates and shorter durations of central heating.

CONCLUSIONS

This nationwide study provides a comprehensive picture of the non-linear associations between ambient temperature and mortality from all natural causes and main cardiorespiratory diseases, as well as the corresponding disease burden that is mainly attributable to moderate cold temperatures in China. The findings on vulnerability characteristics can help improve clinical and public health practices to reduce disease burden associated with current and future abnormal weather.

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.

Cold exposure induced oxidative stress and apoptosis in the myocardium by inhibiting the Nrf2-Keap1 signaling pathway

BACKGROUND

Exposure to cold weather is associated with infaust cardiovascular responses, including myocardial infarction and arrhythmias. However, the exact mechanisms of these adverse changes in the myocardium under cold stress are unknown. This study was designed to investigate the mechanisms of cardiac injury induced by cold stress in mice.

METHODS

The mice were randomly divided into three groups, normal control (no handling), 1-week cold stress and 2-week cold stress. We observed physiological changes of the mice and morphological changes of myocardium tissues, and we measured the changes of 3'-nitrotyrosine and 4-hydroxynonenal, the expression levels of superoxide dismutase-1, superoxide dismutase-2, Bax, Bad, Bcl-2, Nuclear factor erythroid-derived 2-like 2 (Nrf2) and Kelch like-ECH-associated protein 1 (Keap1) in myocardium by western blot. Besides, we detected mRNA of superoxide dismutase-1, superoxide dismutase-2, Bax, Bad, Bcl-2, Nrf2 and Keap1 by real-time PCR. One-way analysis of variance, followed by LSD-t test, was used to compare each variable for differences among the groups.

RESULTS

Echocardiography analyses demonstrated left ventricle dysfunction in the groups receiving cold stress. Histological analyses witnessed inflammation, vacuolar and eosinophilic degeneration occurred in left ventricle tissues. Western blotting results showed increased 3'-nitrotyrosine and 4-hydroxynonenal and decreased antioxidant enzymes (superoxide dismutase-1 and superoxide dismutase-2) in the myocardium. Expression of Nrf2 and Keap1 followed a downward trend under cold exposure, as indicated by western blotting and real-time PCR. Expression of anti-apoptotic protein Bcl-2 also showed the same trend. In contrast, expression of pro-apoptotic proteins Bax and Bad followed an upward trend under cold exposure. The results of real-time PCR were consistent with those of western blotting.

CONCLUSIONS

These findings were very significant, showing that cold exposure induced cardiac injury by inhibiting the Nrf2-Keap1 signaling pathway.

A multi-country analysis on potential adaptive mechanisms to cold and heat in a changing climate

BACKGROUND

Temporal variation of temperature-health associations depends on the combination of two pathways: pure adaptation to increasingly warmer temperatures due to climate change, and other attenuation mechanisms due to non-climate factors such as infrastructural changes and improved health care. Disentangling these pathways is critical for assessing climate change impacts and for planning public health and climate policies. We present evidence on this topic by assessing temporal trends in cold- and heat-attributable mortality risks in a multi-country investigation.

METHODS

Trends in country-specific attributable mortality fractions (AFs) for cold and heat (defined as below/above minimum mortality temperature, respectively) in 305 locations within 10 countries (1985-2012) were estimated using a two-stage time-series design with time-varying distributed lag non-linear models. To separate the contribution of pure adaptation to increasing temperatures and active changes in susceptibility (non-climate driven mechanisms) to heat and cold, we compared observed yearly-AFs with those predicted in two counterfactual scenarios: trends driven by either (1) changes in exposure-response function (assuming a constant temperature distribution), (2) or changes in temperature distribution (assuming constant exposure-response relationships). This comparison provides insights about the potential mechanisms and pace of adaptation in each population.

RESULTS

Heat-related AFs decreased in all countries (ranging from 0.45-1.66% to 0.15-0.93%, in the first and last 5-year periods, respectively) except in Australia, Ireland and UK. Different patterns were found for cold (where AFs ranged from 5.57-15.43% to 2.16-8.91%), showing either decreasing (Brazil, Japan, Spain, Australia and Ireland), increasing (USA), or stable trends (Canada, South Korea and UK). Heat-AF trends were mostly driven by changes in exposure-response associations due to modified susceptibility to temperature, whereas no clear patterns were observed for cold.

CONCLUSIONS

Our findings suggest a decrease in heat-mortality impacts over the past decades, well beyond those expected from a pure adaptation to changes in temperature due to the observed warming. This indicates that there is scope for the development of public health strategies to mitigate heat-related climate change impacts. In contrast, no clear conclusions were found for cold. Further investigations should focus on identification of factors defining these changes in susceptibility.

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing

Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in a better fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly.

What drives cold-related excess mortality in a south Asian tropical monsoon climate-season vs. temperatures and diurnal temperature changes

Despite the tropical climate which is characterized by generally high temperatures and persistent mild temperatures during the winter season, Bangladesh, along with many other tropical countries, experiences strong winter and cold-related excess mortality. The objective of this paper was to analyse the nature of these cold effects and understand the role of season vs. temperature and diurnal changes in temperature. For approaching these questions, we applied different Poisson regression models. Temperature as well as diurnal temperature range (DTR) were considered as predictor variables. Different approaches to seasonality adjustment were evaluated and special consideration was given to seasonal differences in atmospheric effects. Our findings show that while seasonality adjustment affected the magnitude of cold effects, cold-related mortality persisted regardless the adjustment approach. Strongest effects of low temperatures were observed at the same day (lag 1) with an increase of 1.7% (95% CI = 0.86-2.54%) per 1 °C decrease in temperature during the winter season. Diurnal temperature affected mortality with increasing levels at higher ranges. Mortality increased with 0.97% (95% CI = 0.17-1.75%) when looking at the entire season, but effects of DTR were not significant during winter when running a seasonal model. Different from effects observed in the mid-latitudes, cold effects in Bangladesh occurred on a very short time scale highlighting the role of temperature versus season. Insufficient adaptation with regard to housing and clothing might lead to such cold-related increases in mortality despite rather moderate temperature values. Although the study did not demonstrate an effect of DTR during the cold season, the strong correlation with (minimum) temperature might cause a multicollinearity problem and effects are difficult to attribute to one driver.

Time series models of environmental exposures: Good predictions or good understanding

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease.

The effect of extreme cold temperatures on the risk of death in the two major Portuguese cities

It is well known that meteorological conditions influence the comfort and human health. Southern European countries, including Portugal, show the highest mortality rates during winter, but the effects of extreme cold temperatures in Portugal have never been estimated. The objective of this study was the estimation of the effect of extreme cold temperatures on the risk of death in Lisbon and Oporto, aiming the production of scientific evidence for the development of a real-time health warning system. Poisson regression models combined with distributed lag non-linear models were applied to assess the exposure-response relation and lag patterns of the association between minimum temperature and all-causes mortality and between minimum temperature and circulatory and respiratory system diseases mortality from 1992 to 2012, stratified by age, for the period from November to March. The analysis was adjusted for over dispersion and population size, for the confounding effect of influenza epidemics and controlled for long-term trend, seasonality and day of the week. Results showed that the effect of cold temperatures in mortality was not immediate, presenting a 1-2-day delay, reaching maximum increased risk of death after 6-7 days and lasting up to 20-28 days. The overall effect was generally higher and more persistent in Lisbon than in Oporto, particularly for circulatory and respiratory mortality and for the elderly. Exposure to cold temperatures is an important public health problem for a relevant part of the Portuguese population, in particular in Lisbon.

Hospitalizations from Hypertensive Diseases, Diabetes, and Arrhythmia in Relation to Low and High Temperatures: Population-Based Study

Little is known about the extent to which ambient temperatures contribute to the burden of hospitalizations from hypertensive diseases, diabetes, and arrhythmia. To fill this knowledge gap, we conducted a time-series study comprising entire population of Ontario, Canada during 1996–2013. A distributed lag non-linear model was developed to estimate the cumulative effect of temperatures over a 21-day lag period. We computed the burden of hospitalizations attributable to cold and heat. Furthermore, we separated the burden into components related to mild and extreme temperatures. Compared to the temperature with minimum risk of morbidity, cold temperatures (1^st percentile) were associated with a 37% (95% confidence interval: 5%, 78%) increase in hypertension-related hospitalizations whereas no significant association with hot temperatures (99^th percentile) was observed. Cold and hot temperatures were also associated with a 12% (1%, 24%) and a 30% (6%, 58%) increase in diabetes-related hospitalizations, respectively. Arrhythmia was not linked to temperatures. These estimates translate into ~10% of hypertension-related hospitalizations attributable to total cold, and ~9% from mild cold. Similarly, ~11% of diabetes-related hospitalizations were due to total heat, virtually all of which were from mild heat. In conclusion, ambient temperatures, especially in moderate ranges, contribute to excess hospitalizations from hypertension and diabetes.

The Role of Influenza in the Delay between Low Temperature and Ischemic Heart Disease: Evidence from Simulation and Mortality Data from Japan

Many studies have found that cardiovascular deaths mostly occur within a few days of exposure to heat, whereas cold-related deaths can occur up to 30 days after exposure. We investigated whether influenza infection could explain the delayed cold effects on ischemic heart diseases (IHD) as they can trigger IHD. We hypothesized two pathways between cold exposure and IHD: a direct pathway and an indirect pathway through influenza infection. We created a multi-state model of the pathways and simulated incidence data to examine the observed delayed patterns in cases. We conducted cross-correlation and time series analysis with Japanese daily pneumonia and influenza (P&I) mortality data to help validate our model. Simulations showed the IHD incidence through the direct pathway occurred mostly within 10 days, while IHD through influenza infection peaked at 4–6 days, followed by delayed incidences of up to 20–30 days. In the mortality data from Japan, P&I lagged IHD in cross-correlations. Time series analysis showed strong delayed cold effects in the older population. There was also a strong delay on intense days of influenza which was more noticeable in the older population. Influenza can therefore be a plausible explanation for the delayed association between cold exposure and cardiovascular mortality.

Assessment of the effect of cold and hot temperatures on mortality in Ontario, Canada: a population-based study

BACKGROUND

Ambient high temperature is associated with death; however, heat-related risk of death has not been quantified systematically in Ontario, the most populous province in Canada. Less is known about cold-related risk in this population. Our objective was to quantify the health impact from cold and hot temperatures in Ontario.

METHODS

The study population consisted of all residents of Ontario who died between Jan. 1, 1996, and Dec. 31, 2010, from any nonaccidental cause. A case-crossover analysis was applied to assess the relation between daily temperature fluctuation and deaths from nonaccidental and selected causes in cold (December-February) and warm (June-August) seasons, respectively, adjusting for various potential confounders. Risk estimates were obtained for each census division, then pooled across Ontario. We examined potential effect modification for selected comorbidities and sociodemographic characteristics.

RESULTS

In warm seasons, each 5°C increase in daily mean temperature was associated with a 2.5% increase in nonaccidental deaths (95% confidence interval [CI] = 1.3% to 3.8%) on the day of exposure (lag 0). In cold seasons, each 5°C decrease in daily temperature was associated with a 3.0% (95% CI 1.8% to 4.2%) increase in nonaccidental deaths, which persisted over 7 days (lag 0-6). The cold-related effects (lag 0-6) were stronger for cardiovascular-related deaths (any cardiovascular death: 4.1%, 95% CI 2.3% to 5.9%; ischemic heart disease: 5.8%, 95% CI 3.6% to 8.1%), especially among people less than 65 years of age (8.0%, 95% CI 3.0% to 13.0%). Conversely, heat most strongly increased respiratory-related deaths during admission to hospital (26.0%, 95% CI 0% to 61.4%). Across Ontario, each 5°C change in daily temperature was estimated to induce 7 excess deaths per day in cold seasons and 4 excess deaths in warm seasons.

INTERPRETATION

Heat contributed to excess deaths in Ontario, although the effect of cold weather appeared to be greater. Further work is required to better define high-risk subgroups, which might include the homeless and people with inadequately heated housing.

Winter Season Mortality: Will Climate Warming Bring Benefits?

Extreme heat events are associated with spikes in mortality, yet death rates are on average highest during the coldest months of the year. Under the assumption that most winter excess mortality is due to cold temperature, many previous studies have concluded that winter mortality will substantially decline in a warming climate. We analyzed whether and to what extent cold temperatures are associated with excess winter mortality across multiple cities and over multiple years within individual cities, using daily temperature and mortality data from 36 US cities (1985-2006) and 3 French cities (1971-2007). Comparing across cities, we found that excess winter mortality did not depend on seasonal temperature range, and was no lower in warmer vs. colder cities, suggesting that temperature is not a key driver of winter excess mortality. Using regression models within monthly strata, we found that variability in daily mortality within cities was not strongly influenced by winter temperature. Finally we found that inadequate control for seasonality in analyses of the effects of cold temperatures led to spuriously large assumed cold effects, and erroneous attribution of winter mortality to cold temperatures. Our findings suggest that reductions in cold-related mortality under warming climate may be much smaller than some have assumed. This should be of interest to researchers and policy makers concerned with projecting future health effects of climate change and developing relevant adaptation strategies.

Unusually cold and dry winters increase mortality in Australia

Seasonal patterns in mortality have been recognised for decades, with a marked excess of deaths in winter, yet our understanding of the causes of this phenomenon is not yet complete. Research has shown that low and high temperatures are associated with increased mortality independently of season; however, the impact of unseasonal weather on mortality has been less studied. In this study, we aimed to determine if unseasonal patterns in weather were associated with unseasonal patterns in mortality. We obtained daily temperature, humidity and mortality data from 1988 to 2009 for five major Australian cities with a range of climates. We split the seasonal patterns in temperature, humidity and mortality into their stationary and non-stationary parts. A stationary seasonal pattern is consistent from year-to-year, and a non-stationary pattern varies from year-to-year. We used Poisson regression to investigate associations between unseasonal weather and an unusual number of deaths. We found that deaths rates in Australia were 20-30% higher in winter than summer. The seasonal pattern of mortality was non-stationary, with much larger peaks in some winters. Winters that were colder or drier than a typical winter had significantly increased death risks in most cities. Conversely summers that were warmer or more humid than average showed no increase in death risks. Better understanding the occurrence and cause of seasonal variations in mortality will help with disease prevention and save lives.

Seasonal variation of overall and cardiovascular mortality: a study in 19 countries from different geographic locations

Background

Cardiovascular diseases (CVD) mortality has been shown to follow a seasonal pattern. Several studies suggested several possible determinants of this pattern, including misclassification of causes of deaths. We aimed at assessing seasonality in overall, CVD, cancer and non-CVD/non-cancer mortality using data from 19 countries from different latitudes.

Methods and Findings

Monthly mortality data were compiled from 19 countries, amounting to over 54 million deaths. We calculated ratios of the observed to the expected numbers of deaths in the absence of a seasonal pattern. Seasonal variation (peak to nadir difference) for overall and cause-specific (CVD, cancer or non-CVD/non-cancer) mortality was analyzed using the cosinor function model. Mortality from overall, CVD and non-CVD/non-cancer showed a consistent seasonal pattern. In both hemispheres, the number of deaths was higher than expected in winter. In countries close to the Equator the seasonal pattern was considerably lower for mortality from any cause. For CVD mortality, the peak to nadir differences ranged from 0.185 to 0.466 in the Northern Hemisphere, from 0.087 to 0.108 near the Equator, and from 0.219 to 0.409 in the Southern Hemisphere. For cancer mortality, the seasonal variation was nonexistent in most countries.

Conclusions

In countries with seasonal variation, mortality from overall, CVD and non-CVD/non-cancer show a seasonal pattern with mortality being higher in winter than in summer. Conversely, cancer mortality shows no substantial seasonality.

Are influenza-associated morbidity and mortality estimates for those ≥ 65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?

PURPOSES

To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.

PRINCIPAL RESULTS

Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.

EVIDENCE FOR VACCINE EFFECTIVENESS WAS SOUGHT FROM

(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.

MAJOR CONCLUSIONS

Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.

Cold spells and health: prevention and warning

OBJECTIVES

The aim of this paper is to point out the kinds of measures that should be implemented to protect the population from the health effects of cold and when to put them into action, thanks to meteorological thresholds.

STUDY DESIGN

The authors used pertinence criteria to determine if an alert system would be relevant to trigger preventive measures.

METHODS

The pertinence criteria included ability to prevent health impact through specific measures, simplicity, reactivity, adaptability, and the possibility to find indicators able to predict a health impact of cold. This was investigated in two pilot cities, using time-series models to identify mortality-relevant thresholds, if any.

RESULTS

Short-term measures are mainly directed at homeless people while actions focussing on the general population are mostly limited to providing advice on how to protect oneself from exposure to cold. The main long-term measures are housing insulation and heating. Combined minimum and maximum temperatures are the best indicators to predict the health impact of cold temperatures on mortality. Associated optimal thresholds for action in Paris were -9 °C and -2 °C for minimum and maximum temperatures respectively while thresholds in Marseille were -3 °C and +4 °C. When both thresholds are reached in a given city, the risk of excess mortality is greater than 15%.

CONCLUSION

Simple meteorological indicators could be used to improve the detection of dangerous cold episodes and promote communication. Nevertheless, long-term housing improvement and financial aids for home heating remain the best means to prevent the adverse effects of cold weather on community health.

The impact of the February 2012 cold spell on health in Italy using surveillance data

In February 2012 Italy was hit by an exceptional cold spell with extremely low temperatures and heavy snowfall. The aim of this work is to estimate the impact of the cold spell on health in the Italian cities using data from the rapid surveillance systems. In Italy, a national mortality surveillance system has been operational since 2004 in 34 cities for the rapid monitoring of daily mortality. Data from this system were used to evaluate the impact of the February 2012 cold spell on mortality shortly after the occurrence of the event. Furthermore, a cause-specific analysis was conducted in Roma using the Regional Mortality Registry and the emergency visits (ER) surveillance system. Cold spell episodes were defined as days when mean temperatures were below the 10^th percentile of February distribution for more than three days. To estimate the impact of the cold spell, excess mortality was calculated as the difference between observed and daily expected values. An overall 1578 (+25%) excess deaths among the 75+ age group was recorded in the 14 cities that registered a cold spell in February 2012. A statistically significant excess in mortality was observed in several cities ranging from +22% in Bologna to +58% in Torino. Cause-specific analysis conducted in Roma showed a statistically significant excess in mortality among the 75+ age group for respiratory disease (+64%), COPD (+57%), cardiovascular disease +20% ischemic heart disease (14%) and other heart disease (+33%). Similar results were observed for ER visits. Surveillance systems need to become are a key component of prevention plans as they can help improve public health response and are a valid data source to rapidly quantify the impact on health. Cold-related mortality is still an important issue and should not be underestimated by public health Authorities.