Weather Variability and COPD: A Risk Estimation Identified a Vulnerable Sub-population in Hungary.

BACKGROUND/AIM: Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting over 200 million people worldwide in 2019. The prevalence of COPD has risen by 40% from 1990 to 2010 and continued to increase by 13% from 2010 to 2019, causing over 3 million deaths globally in 2019, ranking it as the third leading cause of death. This study explored how daily weather changes relate to the number of COPD-related emergency department (ED) visits. MATERIALS AND METHODS: We collected data on daily COPD-related ED visits in 2017 in Pécs along with corresponding meteorological data to analyze this connection. RESULTS: High diurnal temperature range (DTR) and day-to-day variability in dew point were linked to a 4.5% increased risk of more COPD-related ED visits. Notably, DTR had a stronger impact on males, contributing to a 6.3% increase, while dew point variability significantly affected males with an odds ratio (OR) of 1.083. (OR=1.083). Stratifying by age revealed heightened risks for those aged 30-39 (43.5% increase) and 50-59 (7.6% increase). Females aged 30-39 and 50-59 faced elevated risks of 42.7% and 9.1%, respectively, whereas males aged 60-69 showed a 9.8% increase. CONCLUSION: Our findings highlight the influence of weather variations on COPD-related ED visits, with nuanced effects based on age and sex.

Seasonal impact of diurnal temperature range on intracerebral hemorrhage in middle-aged and elderly people in central China.

Objectives: This study investigated the seasonal impact of diurnal temperature range (DTR) on hospitalization rates for intracerebral hemorrhage (ICH) in middle-aged and elderly adults. Methods: We collected data on the DTR and hospitalization records of ≥45-year-old patients with ICH in 2019 in Hunan Province, central China. Time-series analyses were performed using a distributed lag nonlinear model. Results: Overall, 54,690 hospitalizations for ICH were recorded. DTR showed a nonlinear relationship with ICH hospitalization in both middle-aged and elderly populations (45-59 and ≥60 years, respectively). During spring, a low DTR coupled with persistently low temperatures increased ICH risk in both age groups, while a high DTR was associated with an increased risk in the middle-aged group only (relative risk [RR], 1.24; 95% confidence interval [CI], 1.21 to 1.27). In the summer, a low DTR combined with persistently high temperatures was linked to a higher risk exclusively in the middle-aged group. A high DTR in the autumn was correlated with increased risk in both age groups. In winter, either a low DTR with a continuously low temperature or a high DTR elevated the risk solely in the elderly population (RR, 1.37; 95% CI, 1.00 to 1.69). In the elderly group, the impact of DTR on hospitalization risk manifested within a 5-day period. Conclusion: The impact of DTR on ICH hospitalization risk differed significantly across seasons and between age groups. Elderly individuals demonstrated greater sensitivity to the impact of DTR. Weather forecasting services should emphasize DTR values, and interventions targeting sensitive populations are needed.

Racial and ethnic minorities disproportionately exposed to extreme daily temperature variation in the United States.

In the history of Homo sapiens, well-populated habitats have featured relatively stable temperatures with generally small daily variations. As the global population is increasingly residing in highly disparate climates, a burgeoning literature has documented the adverse health effects of single-day and day-to-day variation in temperature, raising questions of inequality in exposure to this environmental health risk. Yet, we continue to lack understanding of inequality in exposure to daily temperature variation (DTV) in the highly unequal United States. Using nighttime and daytime land surface temperature data between 2000 and 2017, this study analyzes population exposure to long-term DTV by race and ethnicity, income, and age for the 50 states and the District of Columbia. The analysis is based on population-weighted exposure at the census-tract level. We find that, on average, non-White (especially Black and Hispanic) and low-income Americans are exposed disproportionately to larger DTV. Race-based inequalities in exposure to DTV are larger than income-based disparities, with inequalities heightened in the summer months. In May, for example, the DTV difference by race and ethnicity of 51 states is between 0.20 and 3.01 °C (up to 21.0%). We find that younger populations are, on average, exposed to larger DTV, though the difference is marginal.

The impact of short-term exposure to meteorological factors on the risk of death from hypertension and its major complications: a time series analysis based on Hefei, China.

OBJECTIVES: This study aimed to reveal the short-term impact of meteorological factors on the mortality risk in hypertensive patients, providing a scientific foundation for formulating pertinent prevention and control policies. METHODS: In this research, meteorological factor data and daily death data of hypertensive patients in Hefei City from 2015 to 2018 were integrated. Time series analysis was performed using distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Furthermore, we conducted stratified analysis based on gender and age. Relative risk (RR) combined with 95% confidence interval (95% CI) was used to represent the mortality risk of single day and cumulative day in hypertensive patients. RESULTS: Single-day lag results indicated that high daily mean temperature (T mean) (75th percentile, 24.9 °C) and low diurnal temperature range (DTR) (25th percentile, 4.20 °C) levels were identified as risk factors for death in hypertensive patients (maximum effective RR values were 1.144 and 1.122, respectively). Extremely high levels of relative humidity (RH) (95th percentile, 94.29%) reduced the risk of death (RR value was 0.893). The stratified results showed that the elderly and female populations are more susceptible to low DTR levels, whereas extremely high levels of RH have a more significant protective effect on both populations. CONCLUSION: Overall, we found that exposure to low DTR and high T mean environments increases the risk of death for hypertensive patients, while exposure to extremely high RH environments significantly reduces the risk of death for hypertensive patients. These findings contribute valuable insights for shaping targeted prevention and control strategies.

Effects of socioeconomic status and greenspace on respiratory emergency department visits under short-term temperature variations: An age-stratified case time-series study.

INTRODUCTION: Neighborhood socioeconomic status (SES) and greenspace can affect respiratory health. However, it is unclear whether effects of neighborhood SES and greenspace on respiratory health still exist regardless of temperature variations. METHODS: This paper conducted a two-stage, age-stratified case time-series study. The first goal is to examine the associations between two temperature metrics (daily mean temperature [DMT] and diurnal temperature range [DTR]) and respiratory emergency department (ED) visits among four age groups in New York City. The second goal is to evaluate whether neighborhood SES and greenspace would be determinants of respiratory ED visits independent from temperature varying factors. A distributed lag nonlinear model was applied on ED data from 135 zip codes (October 2016 - February 2020). RESULTS: Our first-stage analysis indicated that older adults aged 65+ had higher risk of ED visits (RR=2.78, 95% eCI: 2.41, 3.22; with 7 days of lag) on days with low DMT (-10°C), followed by adults aged 18-64 (RR=2.48, 95% eCI: 2.32, 2.65), children and youth aged 5-17 (RR=1.38, 95% eCI: 1.24, 1.53), and young children aged 0-4 (RR=1.04, 95% eCI: 0.96, 1.13). However, no excess respiratory ED visits were observed on days with high DMT (30°C). Higher DTR was associated with higher risk, with children and youth more susceptible when DTR was high (DTR 20°C; RR=5.70, 95% eCI: 3.42, 9.49; with 7 days of lag). The second-stage analysis indicated neighborhood SES and greenspace had significant associations with respiratory ED visits regardless of temperature variations. Specifically, Higher income and greenspace exposure were negatively associated with ED visits among all age groups. CONCLUSIONS: Neighborhood SES and greenspace could affect respiratory morbidity regardless of weather conditions. Daily temperature variations accelerated the short-term risk among population subgroups under different weather conditions (e.g., higher risk of days with low DMT among older adults, higher risk of days with high DTR among children and youth aged 5-17), which could create co-effects with neighborhood SES and greenspace on respiratory health.

Association between diurnal temperature range and sperm quality: Evidence from a prospective cohort in Central China.

Inter-day temperature variability has been reported to be associated with sperm quality in a city-level exposure assessment study. However, studies exploring the impact of temperature variability within a single day on sperm quality at individual level are still lacking. The present study aims to bridge this research gap by analyzing the linear and non-linear associations between diurnal temperature range (DTR) exposure and sperm quality, utilizing data from the Anhui Prospective Assisted Reproduction Cohort. The study included 15,112 males (totaling 28,267 tests) and assessed individual exposure to various environmental factors (residential greenness, ambient particulate matter, sulfur dioxide, relative humidity, ambient temperature, and DTR) during the 0-90 day period before semen analysis. A combination of a linear mixed model, natural cubic splines, and subgroup analysis was employed. Significant "U"-shaped non-linear associations were observed between DTR exposure and total motility, sperm concentration, sperm count, total motile sperm count, and progressive motile sperm count. Lower DTR levels negatively impacted these parameters, whereas higher DTR levels showed a positive effect. Notably, these associations were more pronounced at ambient temperatures below 16.5 °C, while absent in warmer conditions. Sperm quality demonstrates increased sensitivity to DTR exposure in cooler environments. Therefore, implementing effective individual temperature management strategies is crucial for mitigating decreased sperm quality associated with DTR exposure, highlighting the potential benefits of government policies aimed at achieving carbon neutrality to enhance overall sperm quality in the general population.

Association between diurnal temperature range and outpatient visits for urticaria disease in Lanzhou, China: a distributed lag nonlinear analysis.

BACKGROUND: A growing number of epidemiological studies have shown that daily temperatures are associated with urticaria. However, the relationship between daily changes in temperature and urticaria is unclear. OBJECTIVES: To assess the diurnal temperature difference (DTR) effects on urticaria outpatient visits in Lanzhou, China. METHODS: Urticaria outpatient visits data during 2011-2019 were collected from three major tertiary hospitals in Lanzhou. Daily temperature data from the official website of China Meteorological Administration. Assessment of the relationship between urticaria outpatient volume and DTR in Lanzhou City using a distributed lag nonlinear model. RESULTS: A total of 83,022 urticaria visits were enrolled. There was a nonlinear relationship between DTR and urticaria outpatient visits and a lagged effect of DTR impact. The effects of high DTR on urticaria visits were not seen in all populations but in the male population and in the 15-59 age group. High DTR (P95: 18.2 °C) was associated with a 27% (95% CI: 0.01, 60.53%) and 31% (95% CI: 1.60, 68.99%) increase in the number of urticaria visits in the 21-day lag effect for the male cohort and the 15-59 year old cohort, respectively, compared with 11.5 °C, respectively. CONCLUSIONS: Our study suggests that DTR is a potential risk factor for urticaria. The results of this study may provide a scientific basis for local governments to improve preventive measures in the health care system.

Ambient temperature variations and AIDS-related mortality: A time-stratified case-crossover study in 103 counties, China.

BACKGROUND: Climate change, characterized by the steady ascent of global temperatures and the escalating unpredictability of climate patterns, poses multifaceted challenges to public health worldwide. However, vulnerable groups, particularly the population affected by HIV/AIDS, have received little attention. OBJECTIVES: We aimed to examine the impacts of temperature variations on AIDS-related mortality. METHODS: Data on individuals with HIV/AIDS were collected from the HIV/AIDS Comprehensive Response Information Management System between 2013 and 2019. Temperature variation metrics were constructed by diurnal temperature range (DTR), temperature changes between neighboring days (TCN), and temperature variability (TV0-t). Time-stratified case-crossover design with conditional logistic regression models was used to investigate the associations between ambient temperature variations and AIDS-related mortality. RESULTS: Each 1 °C elevated in DTR was linked with a 5.28 % [95 % confidence intervals (CIs): 1.61, 9.08] increment in AIDS-related mortality at a lag of 0-6 days. Stronger associations between DTR and AIDS-related mortality were observed in the married than in single, with corresponding excess ORs (%) of 5.33 (95 % CIs: 0.29, 10.62) versus 4.79 (95 % CIs: -0.50, 10.36) for 1 °C increased in DTR at lag 0-6 days. Additionally, we noticed the impact of DTR was more pronounced in the warm season, leading to a 7.32 % (95 % CIs: 0.57, 14.51) elevation in the risks of AIDS-related mortality for 1 °C increase in DTR at lag 0-6 days, while the effect value decreased to 5.16 % (95 % CIs: 0.71, 9.81) in the cold season. CONCLUSIONS: Our findings indicated that DTR might be a significant risk factor for AIDS-related deaths among ambient temperature variation indicators, and underscored the importance of considering temperature variability in public health interventions aimed at mitigating this risk of AIDS-related mortality.

Climate change and daily outpatient visits for dermatomyositis in Hefei, China: a time-series study.

With the deepening of research on the correlation between meteorological factors and autoimmune diseases, the relationship between climate change and dermatomyositis (DM) has come to our attention. This study aimed to explore the short-term correlation between meteorological factors and DM outpatient visits. Daily records of hospital outpatient visits for DM, air pollutants, and meteorological factor data in Hefei from January 1, 2018 to December 31, 2021 were obtained. The mean temperature (MT), relative humidity (RH), diurnal temperature range (DTR), and temperature change between neighboring days (TCN) were used to quantify environmental temperature and humidity and their variations. And we performed a time series analysis using a generalized linear model (GLM) in combination with a distributed lag nonlinear model (DLNM). Furthermore, gender and age were further stratified for the analysis. The sensitivity analysis was also performed. A total of 4028 DM outpatient visits were recorded during this period. There were statistically significant associations of low temperature (5th, 1.5 °C), low RH (1st, 48.6%), high RH (99th, 99%), high DTR (75th, 12.6°c), and low TCN (10th, -2.7 °C) that were associated with risk of DM outpatient visits, with lag days of 30, 16, 16, 10, and 14, respectively. Moreover, women were more susceptible to high RH exposure and low TCN exposure, while the elderly were more susceptible to low temperature. This study concluded that exposure to low temperature, extreme RH, and temperature changes (especially high DTR and low TCN) was associated with an increased risk of DM outpatient visits.

The Seasonal Divergence in the Weakening Relationship between Interannual Temperature Changes and Northern Boreal Vegetation Activity.

The response of boreal vegetation to global warming has shown a weakening trend over the last three decades. However, in previous studies, models of vegetation activity responses to temperature change have often only considered changes in the mean daily temperature (Tmean), with the diurnal temperature range (DTR) being neglected. The goal of this study was to evaluate the temporal trends of the relationships between two temperature factors (Tmean and DTR) and the vegetation activity across the boreal regions on both annual and seasonal timescales, by simultaneously employing satellite and climate datasets. We found that the interannual partial correlation between the growing season (GS) NDVI and Tmean (RNDVI-Tmean) has shown a significant decreasing trend over the last 34 years. At the seasonal scale, the RNDVI-Tmean showed a significant upward trend in the spring, while in the summer and autumn, the RNDVI-Tmean exhibited a significant downward trend. The temporal trend characteristics of the partial correlation between the NDVI and DTR (RNDVI-DTR), at both the GS and seasonal scales, were fully consistent with the RNDVI-Tmean. The area with a significant decrease in the GS RNDVI-Tmean and RNDVI-DTR accounted for approximately 44.4% and 41.2% of the boreal region with the 17-year moving window, respectively. In stark contrast, the area exhibiting a significant increasing trend in the GS RNDVI-Tmean and RNDVI-DTR accounted for only approximately 22.3% and 25.8% of the boreal region with the 17-year moving window, respectively. With respect to the seasonal patterns of the RNDVI-Tmean and RNDVI-DTR, the area with a significant upward trend in the spring was greater than that with a significant downward trend. Nevertheless, more areas had a significant downward trend in the RNDVI-Tmean and RNDVI-DTR in summer and autumn than a significant upward trend. Overall, our research reveals a weakening trend in the impact of temperature on the vegetation activity in the boreal regions and contributes to a deeper understanding of the vegetation response to global warming.

Characterization of the Difference between Day and Night Temperatures on the Growth, Photosynthesis, and Metabolite Accumulation of Tea Seedlings.

Currently, the effects of the differences between day and night temperatures (DIFs) on tea plant are poorly understood. In order to investigate the influence of DIFs on the growth, photosynthesis, and metabolite accumulation of tea plants, the plants were cultivated under 5 °C (25/20 °C, light/dark), 10 °C (25/15 °C, light/dark), and 15 °C (25/10 °C, light/dark). The results showed that the growth rate of the new shoots decreased with an increase in the DIFs. There was a downward trend in the photosynthesis among the treatments, as evidenced by the lowest net photosynthetic rate and total chlorophyll at a DIF of 15 °C. In addition, the DIFs significantly affected the primary and secondary metabolites. In particular, the 10 °C DIF treatment contained the lowest levels of soluble sugars, tea polyphenols, and catechins but was abundant in caffeine and amino acids, along with high expression levels of theanine synthetase (TS3) and glutamate synthase (GOGAT). Furthermore, the transcriptome data revealed that the differentially expressed genes were enriched in valine, leucine, and isoleucine degradation, flavone/flavonol biosyntheses, flavonoid biosynthesis, etc. Therefore, we concluded that a DIF of 10 °C was suitable for the protected cultivation of tea plants in terms of the growth and the quality of a favorable flavor of tea, which provided a scientific basis for the protected cultivation of tea seedlings.

Diurnal temperature range and hospital admission due to cardiovascular and respiratory diseases in Dezful, a city with hot climate and high DTR fluctuation in Iran: an ecological time-series study.

The results of previous studies have indicated the effects of temperature changes on health status. The present study was conducted to investigate the effects of diurnal temperature range (DTR) and hospital admission on cardiovascular and respiratory diseases in Dezful, in Iran. In this ecological time-series study, data related to hospital admissions based on ICD-10, meteorological, and climatological data were gathered over a period of six years from 2014 to 2019. A distributed lag nonlinear model combined with a quasi-Poisson regression was then used to assess the impact of DTR on cardiovascular and respiratory hospital admissions. Potential confounders, including wind speed, air pollution, seasonality, time trend, weekends and holidays, days of week, and humidity were controlled. In extreme low DTRs, the cumulative effects of cardiovascular admissions significantly increased in total, and in warm and cold seasons (Lag0-21, P ≤ 0.05). In addition, in extreme high DTRs, the cumulative effects of cardiovascular significantly decreased in total (Lag0-13 and Lag0-21, P ≤ 0.05), and in warm (Lag0-21, P ≤ 0.05) and cold seasons (Lag0-21, P ≤ 0.05). Moreover, respiratory admissions significantly decreased in total (Lag0-21, P ≤ 0.05) and in warm season (Lag0-21, P ≤ 0.05).Our result indicates that extreme low DTRs could increase the risk of daily cardiovascular admissions, and extreme high DTRs may cause a protective effect on daily respiratory and cardiovascular admissions in some regions with high fluctuations in DTR.

Impact of diurnal temperature range on other infectious diarrhea in Tongcheng, China, 2010-2019: a distributed lag non-linear analysis.

Our study aimed to quantify the exposure-lag-response effects of the diurnal temperature range (DTR) on other infectious diarrhea (OID) in Tongcheng city and examine the vulnerable populations. Distributed lag non-linear model (DLNM) and generalized additive model (GAM) were applied jointly to quantify the association between DTR and the daily number of OID cases compared with the median DTR. Stratified analysis was performed by gender, age, and seasons of onset. There are a total of 8231 cases during this decade. We observed a j-shaped relationship between DTR and OID, with a peak point at the maximum DTR (RR: 2.651, 95% CI: 1.320-5.323) compared to the median DTR. As DTR increased from 8.2 to 10.9 °C, we found the RRs started to decrease and then rise from day 0, and the minimum value occurred on day 7 (RR:1.003, 95% CI: 0.996-1.010). From stratified analysis, we observed that females and adults are more likely to be affected by high DTR significantly. In addition, the influence of DTR was different in cold and warm seasons. High DTR in warm seasons affects the number of OID daily cases, but no statistical significance was identified in cold seasons. This study suggests a significant relationship between high DTR and the incidence risk of OID.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities.

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Diurnal temperature range impacts on outpatients department visits for allergic rhinitis in Lanzhou, China.

OBJECTIVE: This study aims to investigate the association between the diurnal temperature range (DTR) and allergic rhinitis (AR) outpatient visits in Lanzhou, China, utilizing more than 7 years of participant surveys. METHODS: Our study used the distributed lag non-linear model (DLNM) aimed to evaluate the association between DTR and AR outpatient visits. We also performed subgroup analyses in order to find susceptible populations by gender and age groups. RESULTS: In 2013-2019, DTR in Lanzhou demonstrates a non-linear correlation with outpatient visits for AR, which is S-shaped. In addition, when DTR was located in the 0.9-5.3 °C and 12-20 °C compared with 12 °C, the risk of outpatient visits for AR increased. Moreover, males appeared to be more vulnerable to the DTR effect than females, the risk of children visits exceeded both the adult and the elderly groups at the higher DTR. CONCLUSION: Our study adds to the evidence that DTR is a possible risk factor for outpatient visits for AR; therefore, the public health sector and medical staff should take DTR into account when it comes to preventing AR onset.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe.

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

Projecting the excess mortality related to diurnal temperature range: A nationwide analysis in China.

The projection of excess mortality due to diurnal temperature range (DTR) in future has not been evaluated yet in China. Based on daily cause-specific mortality data from 266 cities in China, this study aimed to examine the association between DTR and mortality, which help project the future mortality burden attributable to DTR by considering the modification effects of altitude and population migration. We first found that every 10 °C increase in the DTR would result in a 3.3 % (95 % confidence interval: 2.6 %-4.1 %) excess risk of non-accidental mortality. The unit risk of DTR-associated cause-specific mortality at moderate or high altitudes was significantly lower than at lower altitudes, especially for cardiovascular disease. Subsequently, DTR-associated excess mortality in 2017 in China was 233,154 deaths (with a population-weighted attributable fraction of 2.9 %). Furthermore, we projected DTR-attributable additional mortality in the future, with the associated mortalities to be 221,860 deaths in 2050-2059 (2050s) and 132,305 deaths in 2090-2099 (2090s), under the SSP1-2.6 scenario. Meanwhile, the regional inequalities were exacerbated by 18 % in 2050s and 13 % in 2090s when considering the modification effects of city altitude. Future population migration would increase excess mortality in most areas in central and southern China, and reduce the disease burden in most areas in eastern, western, and northern China. Our findings underpinned that regional strategies should be adopted to mitigate excess mortality attributable to global climate change.

Epigenome-wide association study of short-term temperature fluctuations based on within-sibship analyses in Australian females.

BACKGROUND: Temperature fluctuations can affect human health independent of the effect of mean temperature. However, no study has evaluated whether short-term temperature fluctuations could affect DNA methylation. METHODS: Peripheral blood DNA methylation for 479 female siblings of 130 families were analysed. Gridded daily temperatures data were obtained, linked to each participant's home address, and used to calculate nine different metrics of short-term temperature fluctuations: temperature variabilities (TVs) within the day of blood draw and preceding one to seven days (TV 0-1 to TV 0-7), diurnal temperature range (DTR), and temperature change between neighbouring days (TCN). Within-sibship design was used to perform epigenome-wide association analyses, adjusting for daily mean temperatures, and other important covariates (e.g., smoking, alcohol use, cell-type proportions). Differentially methylated regions (DMRs) were further identified. Multiple-testing comparisons with a significant threshold of 0.01 for cytosine-guanine dinucleotides (CpGs) and 0.05 for DMRs were applied. RESULTS: Among 479 participants (mean age ± SD, 56.4 ± 7.9 years), we identified significant changes in methylation levels in 14 CpGs and 70 DMRs associated with temperature fluctuations. Almost all identified CpGs were associated with exposure to temperature fluctuations within three days. Differentially methylated signals were mapped to 68 genes that were linked to human diseases such as cancer (e.g., colorectal carcinoma, breast carcinoma, and metastatic neoplasms) and mental disorder (e.g., schizophrenia, mental depression, and bipolar disorder). The top three most significantly enriched gene ontology terms were Response to bacterium (TV 0-3), followed by Hydrolase activity, acting on ester bonds (TCN), and Oxidoreductase activity (TV 0-3). CONCLUSIONS: Short-term temperature fluctuations were associated with differentially methylated signals across the human genome, which provides evidence on the potential biological mechanisms underlying the health impact of temperature fluctuations. Future studies are needed to further clarify the roles of DNA methylation in diseases associated with temperature fluctuations.

Effects of short-term and long-term exposure to ambient air pollution and temperature on long recovery duration in COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has spread rapidly around the world since December 8, 2019. However, the key factors affecting the duration of recovery from COVID-19 remain unclear. OBJECTIVE: To investigate the associations of long recovery duration of COVID-19 patients with ambient air pollution, temperature, and diurnal temperature range (DTR) exposure. METHODS: A total of 427 confirmed cases in Changsha during the first wave of the epidemic in January 2020 were selected. We used inverse distance weighting (IDW) method to estimate personal exposure to seven ambient air pollutants (PM2.5, PM2.5-10, PM10, SO2, NO2, CO, and O3) at each subject's home address. Meteorological conditions included temperature and DTR. Multiple logistic regression model was used to investigate the relationship of air pollution exposure during short-term (past week and past month) and long-term (past three months) with recovery duration among COVID-19 patients. RESULTS: We found that long recovery duration among COVID-19 patients was positively associated with short-term exposure to CO during past week with OR (95% CI) = 1.42 (1.01-2.00) and PM2.5, NO2, and CO during past month with ORs (95% CI) = 2.00 (1.30-3.07) and 1.95 (1.30-2.93), and was negatively related with short-term exposure to O3 during past week and past month with ORs (95% CI) = 0.68 (0.46-0.99) and 0.41 (0.27-0.62), respectively. No association was observed for long-term exposure to air pollution during past three months. Furthermore, increased temperature during past three months elevated risk of long recovery duration in VOCID-19 patients, while DTR exposure during past week and past month decreased the risk. Male and younger patients were more susceptible to the effect of air pollution on long recovery duration, while female and older patients were more affected by exposure to temperature and DTR. CONCLUSION: Our findings suggest that both TRAP exposure and temperature indicators play important roles in prolonged recovery among COVID-19 patients, especially for the sensitive populations, which provide potential strategies for effective reduction and early prevention of long recovery duration of COVID-19.