Temporal and seasonal variations of mortality burden associated with hourly temperature variability: A nationwide investigation in England and Wales

A study of temperature variability on admissions and deaths for cardiovascular diseases in Northwestern China

OBJECTIVE

To explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs).

METHOD

The admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated.

RESULTS

There was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013-2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season.

CONCLUSION

TV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.

Ambient temperature variability and hospital admissions for pneumonia: A nationwide study

Few investigations have assessed the impact of short-term ambient temperature change on pneumonia risk. We aimed to study the relation of temperature variability (TV) with daily hospitalizations for pneumonia in China. We conducted a time-series study in 184 major cities by extracting daily hospital data between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. TV was calculated as standard deviation of daily minimum and maximum temperatures over exposure days. We estimated associations of pneumonia admissions with TV for each city using over-dispersed generalized linear models controlling for weather conditions and ambient air pollution, and pooled city-specific estimates using random effects meta-analyses. We also investigated exposure-response relationship curve and potential effect modifiers. We identified 4.2 million pneumonia hospitalizations during the study period. TV was positively related to daily pneumonia admissions. At the national-average level, each 1-°C increase in TV at 0-6 days' exposure corresponded to a 0.65 % (95 % CI: 0.34 %-0.96 %) increase in pneumonia admissions. An approximately linear exposure-response curve for the relation of TV with pneumonia admission was noted. The relations were more evident in cities with larger average age (P = 0.038). As the first study in China to assess the impact of temperature change on pneumonia on a national scale, our results indicated that acute TV exposure was related to higher admissions for pneumonia. Our findings should provide new insight into the health impacts associated with climate change.

Fatty acid metabolism in liver and muscle is strongly modulated by photoperiod in Fischer 344 rats

Circadian and seasonal variations produce variations in physiological processes throughout the day and the year, respectively. In this sense, both the light and the moment of feeding are strong modulators of the central and peripheral clocks. However, little is known about its influence on certain metabolic parameters and on the composition of liver and muscle fatty acids (FA). In the present study, 24 Fischer 344 rats were exposed for 11 weeks to different photoperiods, L6, L12 and L18, with 6, 12 and 18 h of light/day, respectively. They were fed a standard diet. Serum metabolic parameters, gene expression of liver enzymes and gastrocnemius muscle involved in the synthesis, elongation, desaturation and β-oxidation of FA were analyzed. We have found that exposure to different hours of light has a clear effect on FA composition and gene expression in the liver. Mainly, the biosynthesis of unsaturated FA was altered in the L18 animals with respect to those exposed to L12, while the L6 did not show significant changes. At the muscle level, differences were observed in the concentration of mono and polyunsaturated FA. A multivariate analysis confirmed the differences between L12 and L18 in a significant way. We conclude that exposure to long days produces changes in the composition of liver and muscle FA, as well as changes in the gene expression of oxidative enzymes compared to exposure to L12, which could be a consequence of different seasonal eating patterns.

Role of Snowpack-Hydrometeorological Sensors for Hydrogeological System Comprehension inside an Alpine Closed-Basin

Groundwater resource assessment and forecasting in mountain areas requires the monitoring of two conditions, local meteorological conditions, and springs' groundwater parameters. The reliability of the monitoring data and conditions are linked to the technical instrumentation, multiparametric probes, and sensors. This paper presents a set of attractive tools and sensors for springs' groundwater resource monitoring and assessment in mountain basins. Data from the combination of weather station sensors with spring flow-rate instruments, installed in the alpine Mascognaz basin, can guarantee an entire understanding of how one set of parameters can affect other results, defining consequential cause-and-effect relationships. Since a large part of the Alpine groundwater bodies are exploited for drinking purposes, understanding the evolution of their rechange processes requires making the right economic and instrumental investments aimed at using them according to forecast predictions and sustainable development goals.

Is Cold Apparent Temperature Associated With the Hospitalizations for Osteoporotic Fractures in the Central Areas of Wuhan? A Time-Series Study

Osteoporosis is alarming problem due to aggravation of global aging, especially in China. Osteoporotic fracture (OF) is one of the most severe consequents of osteoporosis. Many previous studies found that environmental factors had adverse effects on human health. Cold temperature was associated with OF and bone metabolism in prior observational and experimental researches. However, few studies had been conducted on the acute effect of low temperature and OF. Data on daily meteorological factors and hospitalizations for OF were collected from Wuhan, China, between January 1, 2017 to December 24, 2019. Apparent temperature (AT), comprehensively considered a variety of environmental factors, was calculated by ambient temperature, relative humidity and wind speed. A generalized linear regression model combined with distributed lag non-linear regression model (DLNM) with quasi-Poisson link was used to explore the association between AT and the number of hospitalizations for OF. Subgroup analyses stratified by gender, age and the history of fracture were applied for detecting susceptible people. The exposure-response curve of AT and OF were generally U-shaped with lowest point at 25.8°C. The significant relationship of AT-OF existed only in cold effect (-2.0 vs. 25.8°C) while not in warm effect (37.0 vs. 25.8°C). Statistically significant risks of OF for cold effects were only found in females [RR = 1.12 (95%CI: 1.02, 1.24) at lag 2 day], aged <75 years old [RR = 1.18 (95%CI: 1.04, 1.33) and 1.17 (95%CI: 1.04, 1.33) at lag 2 and 3 days, respectively] and people with history of fracture [RR = 1.39 (95%CI: 1.02, 1.90) and 1.27 (95%CI: 1.05, 1.53) at lag 1 and 2 days, respectively]. The significant associations of AT on OF were only found in cold effect. The females, people aged <75 years and people with history of fracture possibly appeared to be more vulnerable. Public health departments should pay attention to the negative effect of cold AT and take measures in time.

Fluctuating temperature modifies heat-mortality association in the globe

Studies have investigated the effects of heat and temperature variability (TV) on mortality. However, few assessed whether TV modifies the heat-mortality association. Data on daily temperature and mortality in the warm season were collected from 717 locations across 36 countries. TV was calculated as the standard deviation of the average of the same and previous days’ minimum and maximum temperatures. We used location-specific quasi-Poisson regression models with an interaction term between the cross-basis term for mean temperature and quartiles of TV to obtain heat-mortality associations under each quartile of TV, and then pooled estimates at the country, regional, and global levels. Results show the increased risk in heat-related mortality with increments in TV, accounting for 0.70% (95% confidence interval [CI]: −0.33 to 1.69), 1.34% (95% CI: −0.14 to 2.73), 1.99% (95% CI: 0.29–3.57), and 2.73% (95% CI: 0.76–4.50) of total deaths for Q1–Q4 (first quartile–fourth quartile) of TV. The modification effects of TV varied geographically. Central Europe had the highest attributable fractions (AFs), corresponding to 7.68% (95% CI: 5.25–9.89) of total deaths for Q4 of TV, while the lowest AFs were observed in North America, with the values for Q4 of 1.74% (95% CI: −0.09 to 3.39). TV had a significant modification effect on the heat-mortality association, causing a higher heat-related mortality burden with increments of TV. Implementing targeted strategies against heat exposure and fluctuant temperatures simultaneously would benefit public health.

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Increased temperature variability (TV) poses a greater mortality risk due to heat

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TV has a more profound modification effect on extreme heat-mortality association

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Strategies against heat and TV simultaneously would benefit public health

Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities

Temperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days' daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TV_daily and TV_hourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited. We collected daily mortality and meteorological data in 45 US metropolises, 1987-2000. A three-stage analysis was adopted to investigate TV-mortality associations using TV_daily and TV_hourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationships, which were then pooled using random-effects meta-analysis with maximum likelihood estimation. We additionally calculated attributable fraction (AF) as a reflection of mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risks. There were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular causes and 1.2 million were from respiratory causes. Per 1 °C increase in TV_daily and TV_hourly was associated with an increase of 0.53% (95% confidence interval: 0.31-0.76%) and 0.52% (0.26-0.79%) in cardiovascular mortality risks, 0.62% (0.26-0.98%) and 0.53% (0.13-0.94%) in respiratory mortality risks. Estimates of cardiovascular AF for TV_daily and TV_hourly were 2.43% (1.42-3.43%) vs. 1.63% (0.82-2.43%), whereas estimates of respiratory AF were 3.07% (1.11-4.99%) vs. 1.89% (0.43-3.34%). Both daily and hourly TV indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TV_daily than those using TV_hourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality. In conclusion, both TV_daily and TV_hourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily and hourly TV metrics exhibited comparable effects of mortality risk, while greater mortality burden was estimated using TV_daily than TV_hourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effects.

Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone

BACKGROUND

In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective.

METHODS

Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region.

RESULTS

HTV calculated in a relatively long-time window like 18 d (HTV_0-17) could capture the impact of HTV adequately. Per 1 °C raise of HTV_0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone.

CONCLUSION

HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.

Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends

Temperature change between neighboring days (TCN) is an important trigger for cardiovascular diseases, but the modulated effects by seasonal temperature trends have been barely taken into account. A quantified comparison between impacts of positive TCNs (temperature rise) and negative situations (temperature drop) is also needed. We evaluated the associations of TCNs with emergency room (ER) visits for coronary heart disease (CHD) and cerebral infarction (CI) in Beijing, China, from 2008 to 2012. A year was divided into two segments dominated by opposite temperature trends, quasi-Poisson regression with distributed lag nonlinear models estimating TCN-morbidity relations were employed, separately for each period. High morbidities of CHD and CI both occurred in transitional seasons accompanied by large TCNs. Under warming backgrounds, positive TCNs increased CHD risk in patients younger than 65 years, and old people showed limited sensitivity. In the cooling periods, negative TCNs induced CHD risk in females and the elderly; the highest RR showed on lag 6 d. In particular, a same diurnal temperature decrease (e.g., - 2°C) induced greater RR (RR = 1.113, 95% CIs: 1.033-1.198) on old people during warming periods than cooling counterparts (RR = 1.055, 95% CIs: 1.011-1.100). Moreover, positive TCNs elevated CI risk regardless of background temperatures, and males were particularly vulnerable. Seasonal temperature trends modify TCN-cardiovascular morbidity associations significantly, which may provide new insights into the health impact of unstable weathers.

Increased susceptibility to temperature variation for non-accidental emergency ambulance dispatches in Shenzhen, China

Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.

Air temperature variability and high-sensitivity C reactive protein in a general population of China

BACKGROUND

Along with global climate change, the relationship between temperature variability (TV) and cardiovascular hospitalization and deaths have been well established. However, limited studies were conducted to reveal the underlying mechanism for TV-related cardiovascular diseases.

OBJECTIVES

In the current study, a novel TV calculation, taking account for both interday and intraday TV as well as lag effects, was used to investigate the effect of short-term TV on the level of high-sensitivity C reactive protein (hs-CRP), which is a crucial preclinical predictor for cardiovascular disease (CVD).

RESULTS

Among the 11,623 Chinese population (46.0% male; mean age 49.8 years), the average hs-CRP was 1.4 mg/ L (standard deviation 1.6 mg/L). Statistical significance between TV and hs-CRP was observed for different TV exposure days (TV01-TV07) in adjusted model, with highest effect for TV06. Specifically, per 1 °C increase in TV06 led to 2.241% (95%CI: 1.552%-2.935%) increase in hs-CRP. Female, obesity and elderly population were more susceptible to TV. The largest mediator for the association of TV and hs-CRP was lipoprotein(a), accounting for 8.68%, followed by smoking status (4.78%), alcohol use (3.95%) and systolic BP (3.20%).

CONCLUSION

Short-term TV will significantly increase the level of hs-CRP, suggesting hs-CRP to be the potential biologic mechanisms underlying the cardiovascular effects of TV. And more attention should be paid to unstable weather in the global climate change context. Further developing efficient public health policies on climate change may benefit for global heath.

Socioeconomic inequality in vulnerability to all-cause and cause-specific hospitalisation associated with temperature variability: a time-series study in 1814 Brazilian cities

BACKGROUND

Exposure to temperature variability has been associated with increased risk of mortality and morbidity. We aimed to evaluate whether the association between short-term temperature variability and hospitalisation was affected by local socioeconomic level in Brazil.

METHODS

In this time-series study, we collected city-level socioeconomic data, and daily hospitalisation and weather data from 1814 Brazilian cities between Jan 1, 2000, and Dec 31, 2015. All-cause and cause-specific hospitalisation data was from the Hospital Information System of the Unified Health System in Brazil. City-specific daily minimum and maximum temperatures came from a 0·25° × 0·25° Brazilian meteorological dataset. We represented city-specific socioeconomic level using literacy rate, urbanisation rate, average monthly household income per capita (using the 2000 and 2010 Brazilian census), and GDP per capita (using statistics from the Brazilian Institute of Geography and Statistics for 2000-15), and cities were categorised according to the 2015 World Bank standard. We used quasi-Poisson regression to do time-series analyses and obtain city-specific associations between temperature variability and hospitalisation. We pooled city-specific estimates according to different socioeconomic quartiles or levels using random-effect meta-analyses. Meta-regressions adjusting for demographic and climatic characteristics were used to evaluate the modification effect of city-level socioeconomic indicators on the association between temperature variability and hospitalisation.

FINDINGS

We included a total of 147 959 243 hospitalisations (59·0% female) during the study period. Overall, we estimated that the hospitalisation risk due to every 1°C increase in the temperature variability in the current and previous day (TV_0-1) increased by 0·52% (95% CI 0·50-0·55). For lower-middle-income cities, this risk was 0·63% (95% CI 0·58-0·69), for upper-middle-income cities it was 0·50% (0·47-0·53), and for high-income cities it was 0·39% (0·33-0·46). The socioeconomic inequality in vulnerability to TV_0-1 was especially evident for people aged 0-19 years (effect estimate 1·21% [1·11-1·31] for lower-middle income vs 0·52% [0·41-0·63] for high income) and people aged 60 years or older (0·60% [0·50-0·70] vs 0·43% [0·31-0·56]), and for hospitalisation due to infectious diseases (1·62% [1·46-1·78] vs 0·56% [0·30-0·82]), respiratory diseases (1·32% [1·20-1·44] vs 0·55% [0·37-0·74]), and endocrine diseases (1·21% [0·99-1·43] vs 0·32% [0·02-0·62]).

INTERPRETATION

People living in less developed cities in Brazil were more vulnerable to hospitalisation related to temperature variability. This disparity could exacerbate existing health and socioeconomic inequalities in Brazil, and it suggests that more attention should be paid to less developed areas to mitigate the adverse health effects of short-term temperature fluctuations.

FUNDING

None.

Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics

BACKGROUND

Although the effects of seasonal variations and ambient temperature on the incidence of tuberculosis (TB) have been well documented, it is still unknown whether ambient temperature change is an independent risk factor for TB. The aim of this study was to assess the association between ambient temperature change and the risk of TB admissions.

METHOD

A distributed lag non-linear model (DLNM) combined with Poisson generalized linear regression model was performed to assess the association between ambient temperature change and the risk of TB admissions from 2014 to 2018 in Hefei, China. Two temperature change metrics including temperature change between neighboring days (TCN) and diurnal temperature range (DTR) were used to assess the effects of temperature change exposure. Subgroup analyses were performed by gender, age and season. Besides, the attributable risk was calculated to evaluated the public health significance.

RESULTS

The overall exposure-response curves suggested that there were statistically significant associations between two temperature change metrics and the risk of TB admissions. The maximum lag-specific relative risk (RR) of TB admissions was 1.088 (95%CI: 1.012-1.171, lag 4 day) for exposing to large temperature drop (TCN= -4 °C) in winter. Besides, the overall cumulative risk of TB admissions increased continuously and peaked at a lag of 7 days (RR=1.350, 95%CI: 1.120-1.628). Subgroup analysis suggested that exposure to large temperature drop had an adverse effect on TB admissions among males, females and adults. Similarly, large level of DTR exposure (DTR=15 °C) in spring also increased the risk of TB admissions on lag 0 day (RR=1.039, 95%CI: 1.016-1.063), and the cumulative RRs peaked at a lag of 1 days (RR=1.029, 95%CI: 1.012-1.047). We also found that females and elderly people were more vulnerable to the large level of DTR exposure. Additionally, the assessment of attributable risk suggested that taking target measures for the upcoming large temperature drop (b-AF = 4.17%, 95% eCI: 1.24%, 7.22%, b-AN = 1195) may achieve great public health benefits for TB prevention.

CONCLUSION

This study suggests that ambient temperature change is associated with the risk of TB admissions. Besides, TCN may be a better predictor for the TB prevention and public health.

Temperature Variability and Hospital Admissions for Chronic Obstructive Pulmonary Disease: Analysis of Attributable Disease Burden and Vulnerable Subpopulation

Purpose

Chronic obstructive pulmonary disease (COPD) is a major cause of chronic diseases causing considerable social and economic burden globally. Despite substantial evidence on temperature-COPD association, few studies have investigated the acute effect of temperature variability (TV), a potential trigger of exacerbation of COPD disease, and it remains unknown what fraction of the disease burden of COPD is attributable to TV.

Patients and Methods

Based on 71,070 COPD hospitalizations during 2013–2015 in Guangzhou, China, we conducted a time-series analysis using quasi-Poisson regression to assess the association between TV and hospital admission for COPD after adjusting for daily mean temperature. Short-term TV was captured by the standard deviation of hourly or daily temperatures across various exposure days. We also provided the fraction (total number) of COPD attributable to TV. Stratified analyses by admission route, sex, age, occupation, marital status and season were performed to identify vulnerable subpopulations.

Results

We found a linear relationship between TV and COPD hospitalization, with a 1°C increase in hourly TV and daily TV associated with 4.3% (95%CI: 2.2–6.4) and 4.0% (2.3–5.8) increases in COPD, respectively. The greater relative risks of TV identified males, people aged 0–64 years, blue collar, and divorced/widowed people as vulnerable population. There were 12.0% (8500 cases) of COPD hospitalization attributable to hourly TV during the study period. Daily TV produced similar estimates of relative effects (relative risk) but grater estimates of absolute effects (attributable fraction) than hourly TV.

Conclusion

We concluded that TV was an independent risk factor of COPD morbidity, especially among the susceptible subgroups. These findings would be helpful to guide the development of targeted public intervention.

The effect of diurnal temperature range on blood pressure among 46,609 people in Northwestern China

BACKGROUND

A large number of studies have found a positive association between diurnal temperature range (DTR) and cardiovascular diseases (CVDs) incidence and mortality. Few studies regarding the effects of DTR on blood pressure (BP) are available.

OBJECTIVE

To investigate the effects of DTR on BP in Jinchang, northwestern China.

METHODS

Based on a prospective cohort research, a total of 46,609 baseline survey data were collected from 2011 to 2015. The meteorological observation data and environmental monitoring data were collected in the same period. The generalized additive model (GAM) was used to estimate the relationship between DTR and BP after adjusting for confounding variables.

RESULTS

Our study found that there was a positive linear correlation between DTR and systolic blood pressure (SBP) and plus pressure (PP), and a negative linear correlation between DTR and diastolic blood pressure (DBP). With a 1 °C increase of DTR, SBP and PP increased 0.058 mmHg (95%CI: 0.018-0.097) and 0.114 mmHg (95%CI: 0.059-0.168) respectively, and DBP decreased 0.039 mmHg (95%CI:-0.065 ~ -0.014). There was a significant interaction between season and DTR on SBP and PP. DTR had the greatest impact on SBP and PP in hot season. The association between DTR and BP varied significantly by education level.

CONCLUSION

There was a significant association between DTR and BP in Jinchang, an area with large temperature change at high altitudes in northwestern China. These results provide new evidence that DTR is an independent risk factor for BP changes among general population. Therefore, effective control and management of BP in the face of temperature changes can help prevent CVDs.

Impact of climate variability on length of stay in hospital for childhood pneumonia in rural Bangladesh

OBJECTIVES

Pneumonia is a significant contributor to mortality and morbidity in children aged <5 years, and it is also one of the leading causes of hospitalisation for children in this age group. This study assessed the association between climate variability, patient characteristics (i.e. age, sex, weight, parental education, socio-economic status) and length of stay (LOS) in hospital for childhood pneumonia and its economic impact on rural Bangladesh.

STUDY DESIGN

An ecological study design was used.

METHODS

Data on daily hospitalisation for pneumonia in children aged <5 years (including patient characteristics) and daily climate data (temperature and relative humidity) between 1st January 2012 and 31st December 2016 were obtained from the Matlab Hospital (the International Centre for Diarrhoeal Disease Research, Bangladesh) and the Bangladesh Meteorological Department, respectively. A generalised linear model with Poisson link was used to quantify the association between climate factors, patient characteristics and LOS in hospital.

RESULTS

The study showed that average temperature, temperature variation and humidity variation were positively associated with the LOS in hospital for pneumonia. A 1°C rise in average temperature and temperature variation during hospital stay increased the LOS in hospital by 1% (relative risk [RR]: 1.010, 95% confidence interval [CI]: 1.001-1.018) and 9.3% (RR: 1.093, 95% CI: 1.051-1.138), respectively. A 1% increase in humidity variation increased the LOS in hospital for pneumonia by 2.2% (RR: 1.022, 95% CI: 1.004-1.039). In terms of economic impact, for every 1° C temperature variation during the period of hospital stay, there is an addition of 0.81 USD/day/patient as a result of direct costs and 1.8 USD/day/patient for total costs. Annually, this results in an additional 443 USD for direct and 985 USD for total costs.

CONCLUSIONS

Climate variation appears to significantly contribute to the LOS in hospital for childhood pneumonia. These findings may help policymakers to develop effective disease management and prevention strategies.

Examining the diurnal temperature range enigma: why is human health related to the daily change in temperature?

An increasing number of epidemiological studies are finding statistical evidence that diurnal temperature range (DTR) is positively correlated to human morbidity and mortality despite the lack of clear clinical understanding. We examine a 14-year daily time series of emergency department (ED) admissions to the University of Virginia Medical Center in Charlottesville, Virginia, relative to long-term climate records from the Charlottesville/Albemarle County Airport weather station and the Spatial Synoptic Classification. DTR has a consistent strong positive correlation (r ~ 0.5) with maximum temperature in all months but only a weak, negative correlation (r ~- 0.1) with minimum temperature except in late summer (r ~- 0.4). Warm season DTR is highest on dry air mass days with low dew point temperatures. Cool season DTR is unrelated to morning temperature. Using a distributed lag non-linear model with an emphasis on DTR and its seasonal variation, after stratifying the models by season, we find that ED visits are linked to extreme cold events (cold days and nights) and high DTR in the cold season. In the warm season, ED visits are also linked to high DTR, but these are cool, dry, and pleasant days. The existing confusion regarding interpretation of DTR impacts on health might be rectified through a more careful analysis of the underlying physical factors that drive variations in DTR over the course of a year.

Mortality risk and burden associated with temperature variability in China, United Kingdom and United States: Comparative analysis of daily and hourly exposure metrics

BACKGROUND

Temperature variability (TV) is closely associated with climate change, but there is no unified TV definition worldwide. Two novel composite TV indexes were developed recently by calculating the standard deviations of several days' daily maximum and minimum temperatures (TV_daily), or hourly mean temperatures (TV_hourly).

OBJECTIVES

This study aimed to compare the mortality risks and burden associated with TV_daily and TV_hourly using large time-series datasets collected from multiple locations in China, United Kingdom and United States.

METHODS

We collected daily mortality and hourly temperature data through 1987 to 2012 from 63 locations in China (8 communities, 2006-2012), United Kingdom (10 regions, 1990-2012), and USA (45 cities, 1987-2000). TV-mortality associations were investigated using a three-stage analytic approach separately for China, UK, and USA. First, we applied a time-series regression for each location to derive location-specific TV-mortality curves. A second-stage meta-analysis was then performed to pool these estimated associations for each country. Finally, we calculated mortality fraction attributable to TV based on above-described location-specific and pooled estimates.

RESULTS

Our dataset totally consisted of 23, 089, 328 all-cause death cases, including 93, 750 from China, 7,573,716 from UK and 15, 421, 862 from USA, respectively. In despite of a relatively wide uncertainty in China, approximately linear relationships were consistently identified for TV_daily and TV_hourly. In the three countries, generally similar lag patterns of TV effects were consistently observed for TV_daily and TV_hourly. A 1 °C rise in TV_daily and TV_hourly at lag 0-7 days was associated with mortality increases of 0.93% (95% confidence interval [CI]: 0.12, 1.74) and 0.97% (0.18, 1.77) in China, 0.33% (0.15, 0.51) and 0.41% (0.21, 0.60) in UK, and 0.55% (0.41, 0.70) and 0.51% (0.35, 0.66) in USA, respectively. Larger attributable fractions were estimated using TV_daily than those using TV_hourly, with estimates at 0-10 days of 3.69% (0.51, 6.75) vs. 2.59% (0.10, 5.01) in China, 1.14% (0.54, 1.74) vs. 0.98% (0.55, 1.42) in UK, and 2.57% (1.97, 3.16) vs. 1.67% (1.15, 2.18) in USA, respectively. Our meta-regression analyses indicated higher vulnerability to TV-induced mortality risks in warmer locations.

CONCLUSIONS

Our study added multi-country evidence for increased mortality risk associated with short-term exposure to large temperature variability. Daily and hourly TV exposure metrics produced generally comparable risk effects, but the attributable mortality burden tended to be higher using TV_daily instead of TV_hourly.

Impacts of exposure to ambient temperature on burden of disease: a systematic review of epidemiological evidence

Ambient temperature is an important determinant of mortality and morbidity, making it necessary to assess temperature-related burden of disease (BD) for the planning of public health policies and adaptive responses. To systematically review existing epidemiological evidence on temperature-related BD, we searched three databases (PubMed, Web of Science, and Scopus) on 1 September 2018. We identified 97 studies from 56 counties for this review, of which 75 reported the fraction or number of health outcomes (include deaths and diseases) attributable to temperature, and 22 reported disability-adjusted life years (include years of life lost and years lost due to disability) related to temperature. Non-optimum temperatures (i.e., heat and cold) were responsible for > 2.5% of mortality in all included high-income countries/regions, and > 3.0% of mortality in all included middle-income countries. Cold was mostly reported to be the primary source of mortality burden from non-optimum temperatures, but the relative role of three different temperature exposures (i.e., heat, cold, and temperature variability) in affecting morbidity and mortality remains unclear so far. Under the warming climate scenario, almost all projections assuming no population adaptation suggested future increase in heat-related but decrease in cold-related BD. However, some studies emphasized the great uncertainty in future pattern of temperature-related BD, largely depending on the scenarios of climate, population adaptation, and demography. We also identified important discrepancies and limitations in current research methodologies employed to measure temperature exposures and model temperature-health relationship, and calculate the past and project future temperature-related BD. Overall, exposure to non-optimum ambient temperatures has become and will continue to be a considerable contributor to the global and national BD, but future research is still needed to develop a stronger methodological framework for assessing and comparing temperature-related BD across different settings.

Temperature variability and hospitalization for ischaemic heart disease in Brazil: A nationwide case-crossover study during 2000-2015

BACKGROUND

Previous studies have suggested a potential relationship between temperature variability (TV) and ischaemic heart disease (IHD) but the nature and strength differ between studies. We quantify the association between TV and risk of hospitalization for IHD across Brazilian regions and examine how the relationship varies across important population subgroups.

METHODS

Data on hospitalization for IHD and meteorological parameters were collected from 1814 cities during 2000-2015. TV was defined as the standard deviation of daily minimum and maximum temperatures during exposure days. City-specific estimates were quantified using a time-stratified case-crossover approach, and then pooled at the national level using a random-effect meta-analysis. Stratified analyses were performed by region, sex and three age-groups.

RESULTS

There were 2,864,904 IHD hospitalizations during 2000-2015. The estimate of TV effect was strongest on 0-1 days' exposure: odds ratio was 1.019 [95% confidence interval (CI): 1.013-1.025] per 5 °C increase in TV. The relationship was stronger in men [1.025 (95%CI: 1.017-1.033)] than in women [1.011 (95%CI: 1.002-1.019)] and in successively older age groups [1.034 (95%CI: 1.018-1.050)]. Regional differences existed, with the association only apparent in the most ageing parts of Brazil.

CONCLUSIONS

Exposure to TV is associated with increased risk of hospitalization for IHD, particularly in men and in older age groups. Our findings add to the growing evidence regarding the potential impact of climatic factors on important health outcomes.

Impact of summer heat on mortality and years of life lost: Application of a novel indicator of daily excess hourly heat

BACKGROUND

Previous studies have widely assessed heat-mortality relationships across global regions, while the epidemiological evidence regarding the heat effect on years of life lost (YLL) is relatively sparse. Current investigations using daily mean data cannot take hourly temperature variation into consideration and may underestimate heat effects. We developed a novel indicator, daily excess hourly heat (DEHH), to precisely evaluate the potential heat effects on mortality and YLL.

METHODS

Hourly data on temperature and daily information, including concentrations of air pollutants, relative humidity, and records of all registered deaths were obtained in Wuhan, China during the warm seasons (May-September) of 2009-2012. DEHH, developed in this study, is defined as daily total hourly temperatures that exceed a specific heat threshold. By performing time series regression analyses, we assessed the changes in daily mortality and YLL per interquartile range (IQR) increase in DEHH across different lag days.

RESULTS

The heat threshold evaluated by the Akaike Information Criterion for DEHH calculation is 30 °C (92th percentile of whole-year mean temperature distribution). Daily average DEHH was 13.9 °C, with an IQR of 19.9 °C. Linear exposure-response curves were found between DEHH and two health outcomes. Generally, heat effects lasted for 2-3 days and DEHH at lag 0-1 was most strongly associated with increased mortality and YLL. The effects were especially remarkable for stroke and ischemic heart disease mortality. Most intense effect on YLL was found in non-accidental deaths (20.11, 95% confidence interval: 8.90-31.33) at lag 0-1. More DEHH-related mortality and YLL from cardiovascular deaths were observed among males. People aged 0-74 years and males suffered more from YLL burden due to high temperatures.

CONCLUSIONS

Our study demonstrated that DEHH may be an alternative indicator to precisely measure heat effects on daily mortality and YLL. Further DEHH-based evidence from large scale investigations is needed so as to better understand heat-associated health burden and improve public response to extremely high temperatures.

Spatiotemporal or temporal index to assess the association between temperature variability and mortality in China?

Epidemiological studies increasingly provide evidence about the adverse health effects of temperature variability (TV), which reflects short-term intra- and inter-day temperature change. However, calculation of TV only considers the temporal variability and lacks spatial variability. This study intends to investigate whether the lack of spatial variability in TV calculations has biased the health effect estimates. We collected daily data from the fine-gridded hourly temperatures and more than 2 million all-cause mortality counts in Zhejiang province in China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures based on records from multiple sites. This new index could be compared to the two typical temporal TV indices that are calculated based on the hourly temperatures from one-site and area-average records. The three types of TV are compared using a three-stage analytical approach: district-specific time series Poisson regression, meta-analysis, and calculation of attributable mortality fraction. We observe that both spatiotemporal and temporal TVs produce very similar TV-mortality associations, attributable mortality fractions, and model fits at the district level. For example, the mortality increase associated for every increase of 1 °C during 0-7 exposure days is 1.53% (95% CI: 1.31, 1.73) in spatiotemporal TV, whereas it is 1.48% (95% CI: 1.27, 1.68) and 1.45% (95% CI: 1.24, 1.67) in the one-site and area-average temporal TV, respectively. Thus, time series models using temporal TV index are equally good at estimating the associations between TV and mortality as spatiotemporal TV at the district level in population-based epidemiological studies in China. Epidemiological studies using temperature from one site or the averages of multiple sites in TV calculation will not bias the effect estimates of TV. Our study could provide an important guidance method for future TV-related research in China and even in other countries.

Impacts of heat, cold, and temperature variability on mortality in Australia, 2000-2009

OBJECTIVES

Evidence is limited on the relative contribution of different temperature exposures (i.e., heat, cold and significant temperature variability) to mortality. This study aims to examine mortality risk and associated mortality burden from heat, cold, and temperature variability in Australia.

METHODS

We collected daily time-series data on all-cause deaths and weather variables for the five most populous Australian cities (Sydney, Melbourne, Brisbane, Adelaide, and Perth), from 2000 to 2009. Temperature variability was calculated from the standard deviation of hourly temperatures between two adjacent days. Three-stage analysis was used. We firstly used quasi-Poisson regression models to model the associations of mortality with heat (mean temperature) during the warm season, with cold (mean temperature) during the cold season, and with temperature variability all year round, while controlling for long-term trend and seasonality, day of week, and population change over time. We then estimated the effects of different non-optimum temperatures using the simplified log-linear regression model. Finally, we computed and compared the fraction (%) of deaths attributable to different non-optimum temperatures.

RESULTS

The greatest percentage increase in mortality was for cold (2.0%, 95% confidence interval (CI): 1.4%, 2.6%), followed by heat (1.2%, 95% CI: 0.7%, 1.7%), and temperature variability (0.5%, 95% CI: 0.3%, 0.7%). There was no clear temporal pattern in mortality risk associated with any temperature exposure in Australia. Heat, cold and temperature variability together resulted in 42,414 deaths during the study period, accounting for about 6.0% of all deaths. Most of attributable deaths were due to cold (61.4%), and noticeably, contribution from temperature variability (28.0%) was greater than that from heat (10.6%).

CONCLUSIONS

Exposure to either cold or heat or a large variation in temperature was associated with increased mortality risk in Australia, but population adaptation appeared to have not occurred in most cities studied. Most of the temperature-induced deaths were attributable to cold, and contributions from temperature variability were greater than that from heat. Our findings highlight that, in addition to heat and cold, temperature variability needs to be considered in assessing and projecting the health impacts of climate change.

Temperature variability and mortality in rural and urban areas in Zhejiang province, China: An application of a spatiotemporal index

BACKGROUND

Temperature variability (TV) is a potential trigger for death in urban areas, but there is little evidence of this in rural areas. In addition, a typical TV index only considers the temporal variability of temperature and ignores its spatial variability, which should be considered due to the effects of human mobility. Here this study aimed to 1) develop a novel spatiotemporal TV index accounting for human mobility; and 2) based on this index, explore the urban-rural differences in TV-mortality associations in China.

METHODS

We collected daily data on fine-gridded hourly temperatures and >2 million deaths that occurred in Zhejiang province, China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures from multi-site records over the course of several exposure days. A three-stage analysis was performed to estimate the mortality risks and mortality burdens of TV. Stratified analyses were performed by cause-specific mortality, urban/rural district, age and gender.

RESULTS

Significant associations were found between TV and all types of targeted diseases, age groups, and genders. Percentage increase in mortality associated with a 1 °C increase in TV at 0-7 exposure days were found to be higher for rural dwellers than urban dwellers in the warm season [for all-cause mortality, 2.07% (95% CI: 1.49%, 2.64%) vs. 1.16% (95%CI: 0.70%, 1.62%)]. An estimated all-cause mortality fraction of 5.33% was attributable to TV, with 4.99% in urban areas and 6.02% in rural areas. The elderly (aged 65+ years) and females were more sensitive to TV than young people and males, respectively.

CONCLUSIONS

A spatiotemporal TV index was developed, considering both the temporal and spatial variability of temperatures. TV is an independent health risk factor. In China, rural areas generally suffer greater TV-related mortality risks than urban areas in the warm season. Our findings have important implications for developing area-, cause-, and group-specific adaptation strategies and emergency planning to reduce TV-related mortality.