The temporal variation in the effects of extreme temperature on respiratory mortality: Evidence from 136 cities in China, 2006-2019.

BACKGROUND: In the context of climate change and urbanization, the temporal variation of the adverse health effect of extreme temperature has attracted increasing attention. METHODS: The meteorological data and the daily death records of mortality from respiratory diseases of 136 Chinese cities were from 2006 to 2019. Heat wave and cold spell were selected as the indicator events of extreme high temperature and extreme low temperature, respectively. The generalized linear model and time-varying distributed lag model were used to perform a two-stage time-series analysis to evaluate the temporal variation of the mortality risk associated with extreme temperature in the total population, sub-populations (sex- and age- specific) and different regions (climatic zone and relative humidity level). RESULTS: During the study period, relative risk (RR) of respiratory mortality associated with heat wave decreased from 1.22 (95 %CI: 1.07-1.39) to 1.13 (95 %CI: 1.01-1.26) in the total population, and RR of respiratory mortality associated with cold spell decreased from 1.30 (95 %CI: 1.14-1.49) to 1.17 (95 %CI: 1.08-1.26). The impact of heat wave reduced in the males (P = 0.044) and in the females as with cold spell (P < 0.001). The respiratory mortality risk of people over 65 associated with cold spell decreased (P = 0.040 for people aged 65-74 and P < 0.001 for people over 75). The effect of cold spell reduced in cities from tropical or arid zone (P = 0.035). The effects of both heat wave and cold spell decreased in cities with the relative humidity in the first quartile (P = 0.046 and 0.010, respectively). CONCLUSION: The impact of heat wave on mortality of respiratory diseases decreased mainly in males and cities with the lowest relative humidity, while the impact of cold spell reduced in females, people over 65 and tropical and arid zone, suggesting adaptation to extreme temperature of Chinese residents to some extent.

Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran.

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

Estimation of PM2.5 using high-resolution satellite data and its mortality risk in an area of Iran.

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08-1.95 and RR 95th percentile:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran.

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Role of residential greenspace in the trajectory of major neurological disorders: A longitudinal study in UK Biobank.

BACKGROUND: Stroke and dementia are major neurological disorders that contribute significantly to disease burden and are interlinked in terms of risk. Nevertheless, there is currently no study investigating the influence of residential greenspace on the trajectory of these neurological disorders. METHODS: This longitudinal study utilized data from the UK Biobank. Exposure to residential greenspace was measured by the percentage of total greenspace coverage within a 300-meter buffer zone surrounding the participants' residences. A multistate model was employed to illustrate the trajectory of major neurological disorders, and a piecewise Cox regression model was applied to explore the impact of residential greenspace on different time courses of disease transitions. RESULTS: With 422,649 participants and a median follow-up period of 12.5 years, 8568 (2.0 %), 5648 (1.3 %), and 621 (0.1 %) individuals developed incident stroke, dementia, and comorbidity of both conditions, respectively. An increase in residential greenspace by one interquartile range was associated with reduced risks of transitions from baseline to stroke, dementia, and death, as well as from stroke to comorbidity. The corresponding hazard ratios (HRs) were 0.967 (95 % CI: 0.936, 0.998), 0.928 (0.892, 0.965), 0.925 (0.907, 0.942), and 0.799 (0.685, 0.933), respectively. Furthermore, the protective effect of residential greenspace on the transition from stroke or dementia to comorbidity was particularly pronounced within the first year and over 5 years after stroke and during the 2 to 3 years after dementia onset, with HRs of 0.692 (0.509, 0.941), 0.705 (0.542, 0.918), and 0.567 (0.339, 0.949), respectively. CONCLUSION: This study observed a protective role of residential greenspace in the trajectory of major neurological disorders and contributed to identifying critical progression windows. These findings underscore the significance of environment-health interactions in the prevention of neurological disorders.

Summer heat waves and their mortality risk over a 14-year period in a western region of Iran.

Compared to previous decade, impact of heat waves (HWs) on mortality in recent years needs to be discussed in Iran. We investigated temporal change in added impact of summer HWs on mortality in eight cities of Iran. The pooled length of HWs was compared between 2015-2022 and 2008-2014 using random and fixed-effects of meta-analysis regression model. The temporal change in impact of HWs was evaluated through interaction effect between crossbasis function of HW and year in a two-stage time varying model. In order to pool the reduced coefficients of each period, multivariate meta-regression model, including city-specific temperature and temperature range as heterogenicity factors, was used. In addition to relative risk (RR), attributable fraction (AF) of HW in the two periods was also estimated in each city. In the last years, the frequency of all HWs was higher and the weak HWs were significantly longer. The only significant RR was related to the lowest and low severe HWs which was observed in the second period. In terms of AF, compared to the strong HWs, all weak HWs caused a considerable excess mortality in all cities and second period. The subgroup analysis revealed that the significant impact in the second period was mainly related to females and elderlies. The increased risk and AF due to more frequent and longer HWs (weak HWs) in the last years highlights the need for mitigation strategies in the region. Because of uncertainty in the results of severe HWs, further elaborately investigation of the HWs is need.

Role of air pollution on seasonal distribution of Covid-19: a case study in the west of Iran.

The seasonal distribution of SARS-CoV-2 might be affected by air pollution. To test the hypothesis, epidemic determinants, namely, shape, timing (Peak and Trough) and size (Peak to Trough Ratio and Excess Risk) of seasonal distribution of the outbreak were compared before and after adjusting for air pollutants in a distributed lag nonlinear model. We controlled for one-lagged outcome and meteorological parameters in the model. We also evaluated interaction effect between air pollutants and season using stratification method. The epidemic determinants were changed after adjusting for PM2.5 and O3 in the model, suggesting the existence of their association with the seasonal distribution of the outbreak. The Excess Risk of season (i.e., the proportion of confirmed Covid-19 cases that were attributed to season; AF) was increased as %4 (%95 CI - 29, 38) after adjusting for PM2.5. Adjusting for O3 in the model resulted in % 1 (%95 CI - 36, 34) decrease in the index. NO, NO2 and SO2 had no association with the seasonal distribution, though the interaction analysis revealed that association of NO2 and SO2 with Covid-19 confirmed cases were significantly higher in fall than winter and spring, respectively. Totally, PM2.5 has negatively associated with the seasonal distribution of the outbreak while O3 has positively associated in the region under study. Although some reasons such as wearing mask and oxidative effect might go before the negative and positive associations, but our results suggests that any association and causation between air pollution and Covid-19 should be carefully interpreted.

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.

Ambient temperature and Covid-19 transmission: An evidence from a region of Iran based on weather station and satellite data.

BACKGROUND: The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran. METHOD: Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models' parameters, autocorrelation model and function of associations. RESULTS: The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed. CONCLUSION: However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.

Heat and cold-related morbidity risk in north-east of Iran: a time-stratified case crossover design.

This study aimed to estimate morbidity risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019. A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using 1-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity. The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme, and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively. This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.

Projection of mortality attributed to heat and cold; the impact of climate change in a dry region of Iran, Kerman.

BACKGROUND: Estimating the effects of climate change on human health can help health policy makers plan for the future. In Iran, there are few studies, about investigating the effects of climate change on mortality. This study aimed to project the effect of low (cold) and high (heat) temperature on mortality in a dry region of Iran, Kerman. METHODS: Mortality attributed to temperature was projected by estimating the temperature-mortality relation for the observed data, projection of future temperatures by the statistical downscaling model (SDSM), and quantifying the attributable fraction by applying the observed temperature-mortality relation on the projected temperature. Climate change projection was done by three climate scenarios base on Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Adaptation was considered by using different minimum mortality temperatures (MMT) and risk reduction approaches. The current decade (2010-19) was considered as the reference period. RESULTS: All three climate change scenarios, showed that the mean of temperature will rise about 1 °C, by 2050 in Kerman. The number of deaths attributed to heat were obviously higher than cold in all periods. Assuming no adaptation, over 3700 deaths attributed to temperature will happen in each decade (2020s, 2030s and 2040s) in the future, in which over 3000 deaths will be due to heat and over 450 due to cold. In the predictions, as Minimum Mortality Temperature (MMT) went up, the contribution of heat to mortality slightly decreased, and cold temperature played a more important role. By considering the risk reduction due to adaptation, the contribution of heat in mortality slightly and insignificantly decreased. CONCLUSION: The results showed that although low temperatures will contribute to temperature-related mortality in the future, but heat will be a stronger risk factor for mortality, especially if adaptation is low.

The impact of heat waves on mortality and years of life lost in a dry region of Iran (Kerman) during 2005-2017.

The present study was conducted to compare the impact of heat waves on mortality and years of life lost (YLL) in Kerman, Iran during the years 2005-2017. Daily mean temperature in a combination of intensity and duration were used in order to define heat waves (90, 95, and 98th percentile and ≥ 2, 3, and 4 consecutive days). YLL was calculated according to Iran's life table and by considering the discount rate. In order to investigate the impact of heat waves in different lags and its cumulative effect on mortality and YLL, Poisson and linear models within distributed lag nonlinear models were used respectively. A maximum lag of 14 days was considered. The best model was selected based on AIC (Akaike Information Criteria). The model was adjusted for air pollutants, public holidays, days of the week, and humidity. The average daily mortality and YLL were 10.54 ± 4.31 deaths and 175.58 ± 91.39 years respectively. They were higher in men and in heat waves matching a definition of above the 98th temperature percentile and ≥ 3 days, than others. Except heat waves defined as the 98th percentile and ≥ 4 days, the impact of heat waves on mortality and YLL were the highest at lag 0. The cumulative relative risk of total mortality was significantly higher in heat waves above the 95 and 98th percentiles. The cumulative effect of heat waves on total YLL was significantly higher only above the 98th percentile. Men over 65 years old were the most vulnerable and had the highest mortality and YLL. Heat waves with temperatures above the 98th percentile that lasted at least 2 or 3 consecutive days had a significant effect in increasing both total YLL and mortality in Kerman, Iran.

Attributable risk of mortality associated with heat and heat waves: A time-series study in Kerman, Iran during 2005-2017.

The association between heat or heat waves and mortality should often be reported in a way that makes it sensible by health policymakers. In this study we aimed to assess the effect of heat and heat waves on mortality using attributable risks during 2005-2017. Nine heat waves were defined using a combination of severity and duration of mean daily temperature. Heat wave effects were assessed using added and main effects. Added effects were assessed as a binary variable and main effects were assessed by comparing the median temperature (in heat wave days) to Minimum Mortality Temperature (MMT). The effects of heat, mild heat and extreme heat on mortality were also assessed. Distributed Lag Non-linear Models were used to assess the relations in a bi-dimensional perspective in which the quadratic b-spline was chosen as the basis function for the dimension of the exposure and the natural cubic b-spline was chosen for lag dimension. The backward perspective was used to estimate the attributable risks. The total mortality attributed to non-optimal temperatures for all days was 1.91% (CI 95%: -6.36, 8.47). The attributable risks (AR) were 2.23%, 2.02% and 0.25% for heat, mild heat and extreme heat days, respectively. AR was more for females and the above 65 years old groups than other groups in heat, mild heat and extreme heat days. While the stronger heat waves defined based on temperature above the 95 and 98th percentile had a significant attributable risk for total mortality in the added effects; the weaker heat waves (defined based on temperature above of the 90th percentile (HW1, HW2, HW3) had higher attributable risks, significant for HW1 and HW2, in the main effects. Apparently weaker heat waves show more immediate effects, while stronger heat waves increase mortality over several days.