Daily ambient temperature and mortality in Thailand: Estimated effects, attributable risks, and effect modifications by greenness

Effects of Coexposure to Air Pollution from Vegetation Fires and Extreme Heat on Mortality in Upper Northern Thailand

Background: understanding the effects of coexposure to compound extreme events, such as air pollution and extreme heat, is important for reducing current and future health burdens. This study investigated the independent and synergistic effects of exposure to air pollution from vegetation fires and extreme heat on all-cause mortality in Upper Northern Thailand. Methods: we used a time-stratified case-crossover study design with a conditional quasi-Poisson model to examine the association between mortality and coexposure to air pollution due to vegetation fire events (fire-PM2.5) and extreme heat. Extreme heat days were defined using the 90th and 99th percentile thresholds for daily maximum temperature. Results: we observed a significant positive excess risk of mortality due to independent exposure to fire-PM2.5 and extreme heat, but not an interactive effect. All-cause mortality risk increased by 0.9% (95% confidence interval (CI): 0.1, 1.8) for each 10 μg/m3 increase in fire-PM2.5 on the same day and by 12.8% (95% CI: 10.5, 15.1) on extreme heat days (90th percentile) relative to nonextreme heat days. Conclusion: this study showed that exposure to PM2.5 from vegetation fires and extreme heat independently increased all-cause mortality risk in UNT. However, there was no evidence of a synergistic effect of these events.

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BACKGROUND

Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.

METHODS

Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation.

RESULTS

Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions.

CONCLUSION

Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

Associations between cold spells of different time types and coronary heart disease severity

Limited evidence showed the association between cold spells and the severity of coronary heart disease (CHD). This study was to investigate the association between cold spells with their different time types and CHD severity. We collected data on CHD patients admitted to the Zhongnan Hospital, Wuhan, China from 2016 to 2021. CHD severity was quantified using the SYNTAX score and transformed into a binomial variable. Daily mean, maximum and minimum temperature were collected during the study period. We first used daily mean temperature to find the optimum definition among multiple thresholds and durations. The daily maximum and minimum temperatures were used to define different types of cold spells (daytime, nighttime and compound) based on the optimum definition. Annual cold spell days were included to assess individual exposure to cold spells. Logistic regression models were performed to fit the association between cold spell days and CHD severity stratified by different tertiles of PM2.5 and NDVI. In this study, 1937 CHD patients were included. The cold spell defined as at least four consecutive days with daily mean temperature below the 5th percentile exhibited the optimum model. We found that a 4-day increase in cold spell days was associated with more severe CHD (OR = 1.170, 95% CI: 1.074, 1.282). Such an association was more pronounced under higher levels of PM2.5 by OR = 1.270 (1.086, 1.494) and lower levels of greenness by OR = 1.240 (1.044, 1.476). Compared with daytime and compound cold spells, nighttime cold spells showed the strongest association with CHD severity by OR = 1.141 (1.026, 1.269). This study showed that exposure to cold spells was positively associated with CHD severity, especially the nighttime cold spells. The association between cold spells and CHD severity was more significant in high levels of PM2.5 and low levels of greenness.

Association between temperature and mortality: a multi-city time series study in Sichuan Basin, southwest China

BACKGROUND

There are few multi-city studies on the association between temperature and mortality in basin climates. This study was based on the Sichuan Basin in southwest China to assess the association of basin temperature with non-accidental mortality in the population and with the temperature-related mortality burden.

METHODS

Daily mortality data, meteorological and air pollution data were collected for four cities in the Sichuan Basin of southwest China. We used a two-stage time-series analysis to quantify the association between temperature and non-accidental mortality in each city, and a multivariate meta-analysis was performed to obtain the overall cumulative risk. The attributable fractions (AFs) were calculated to access the mortality burden attributable to non-optimal temperature. Additionally, we performed a stratified analyses by gender, age group, education level, and marital status.

RESULTS

A total of 751,930 non-accidental deaths were collected in our study. Overall, 10.16% of non-accidental deaths could be attributed to non-optimal temperatures. A majority of temperature-related non-accidental deaths were caused by low temperature, accounting for 9.10% (95% eCI: 5.50%, 12.19%), and heat effects accounted for only 1.06% (95% eCI: 0.76%, 1.33%). The mortality burden attributable to non-optimal temperatures was higher among those under 65 years old, females, those with a low education level, and those with an alternative marriage status.

CONCLUSIONS

Our study suggested that a significant association between non-optimal temperature and non-accidental mortality. Those under 65 years old, females, and those with a low educational level or alternative marriage status had the highest attributable burden.

Effects of apparent temperature on daily outpatient and inpatient visits for cause-specific respiratory diseases in Ganzhou, China: a time series study

BACKGROUND

Non-optimum temperatures are associated with increased risk of respiratory diseases, but the effects of apparent temperature (AT) on respiratory diseases remain to be investigated.

METHODS

Using daily data from 2016 to 2020 in Ganzhou, a large city in southern China, we analyzed the impact of AT on outpatient and inpatient visits for respiratory diseases. We considered total respiratory diseases and five subtypes (influenza and pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma and chronic obstructive pulmonary disease [COPD]). Our analysis employed a distributed lag nonlinear model (DLNM) combined with a generalized additive model (GAM).

RESULTS

We recorded 94,952 outpatients and 72,410 inpatients for respiratory diseases. We found AT significantly non-linearly associated with daily outpatient and inpatient visits for total respiratory diseases, influenza and pneumonia, and URTI, primarily during comfortable AT levels, while it was exclusively related with daily inpatient visits for LRTI and COPD. Moderate heat (32.1 °C, the 75.0th centile) was observed with a significant effect on both daily outpatient and inpatient visits for total respiratory diseases at a relative risk of 1.561 (1.161, 2.098) and 1.276 (1.027, 1.585), respectively (both P < 0.05), while the results of inpatients became insignificant with the adjustment for CO and O3. The attributable fractions in outpatients and inpatients were as follows: total respiratory diseases (24.43% and 18.69%), influenza and pneumonia (31.54% and 17.33%), URTI (23.03% and 32.91%), LRTI (37.49% and 30.00%), asthma (9.83% and 3.39%), and COPD (30.67% and 10.65%). Stratified analyses showed that children ≤5 years old were more susceptible to moderate heat than older participants.

CONCLUSIONS

In conclusion, our results indicated moderate heat increase the risk of daily outpatient and inpatient visits for respiratory diseases, especially among children under the age of 5.

Association between ambient temperature exposure and pregnancy outcomes in patients undergoing in vitro fertilization in Shanghai, China: a retrospective cohort study

STUDY QUESTION

Does ambient temperature exposure affect outcomes including clinical pregnancy and live birth in women undergoing IVF?

SUMMARY ANSWER

Both extreme cold and hot ambient temperatures were significantly associated with adverse pregnancy outcomes of IVF cycles.

WHAT IS KNOWN ALREADY

Heat exposure has been linked to adverse pregnancy outcomes worldwide. However, the effect of ambient temperature on infertile women undergoing IVF treatment is unclear.

STUDY DESIGN, SIZE, DURATION

A retrospective cohort study was conducted from a database of 3452 infertile women who underwent their first fresh or frozen embryo transfer in the Shanghai First Maternity and Infant Hospital from April 2016 to December 2020.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Daily mean ambient temperature exposure for each patient was obtained based on their residential address. Temperature-stratified multiple logistic regression analysis was performed to investigate associations between temperature exposure and pregnancy outcomes after controlling for confounders. Vulnerable sub-groups were identified using forest plots.

MAIN RESULTS AND THE ROLE OF CHANCE

The clinical pregnancy rate and live birth rate were 45.7% and 37.1%, respectively. Regarding clinical pregnancy, a higher temperature during cold weather was significantly associated with a higher pregnancy rate in the period about 11 weeks before ovarian stimulation (adjusted odds ratio (aOR) = 1.102, 95% CI: 1.012-1.201). Regarding live birth, an increased temperature during cold weather was significantly related to a higher live birth rate in the period after confirmation of clinical pregnancy or biochemical pregnancy, with the aORs of 6.299 (95% CI: 3.949-10.047) or 10.486 (95% CI: 5.609-19.620), respectively. However, a higher temperature during hot weather was negatively associated with the live birth rate in the periods after confirmation of clinical pregnancy or biochemical pregnancy, with the aORs at 0.186 (95% CI: 0.121-0.285) or 0.302 (95% CI: 0.224-0.406), respectively. Moreover, the decline in live birth rates during cold and hot weather was accompanied by increased rates of early miscarriage (P < 0.05). Stratified analyses identified susceptibility characteristics among the participants.

LIMITATIONS, REASONS FOR CAUTION

Climate monitoring data were used to represent individual temperature exposure levels according to the patient's residential address in the study. We were not able to obtain information of personal outdoor activity and use of indoor air conditioners in this retrospective study, which may affect actual temperature exposure.

WIDER IMPLICATIONS OF THE FINDINGS

This study highlights that the ambient temperature exposure should be taken into account during IVF treatment and afterwards. There is a need to be alert to extremes in cold and hot ambient temperatures, especially during the period of follicle development and pregnancy. With this knowledge, clinicians can scientifically determine the timing of IVF treatment and reinforce patients' awareness of self-protection to minimize adverse pregnancy outcomes associated with extreme temperatures.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by a grant from the Clinical Research Plan of Shanghai Hospital Development Center [SHDC2020CR4080], a grant from the Science and Technology Commission of Shanghai Municipality [19411960500], and two grants from the National Natural Science Foundation of China [81871213, 81671468]. B.W.M. is supported by a NHMRC Investigator grant (GNT1176437). B.W.M. reports consultancy for ObsEva, and research grants from Merck KGaA, Ferring and Guerbet. The other authors have no conflict of interest to declare.

TRIAL REGISTRATION NUMBER

N/A.

Associations between residential greenness and air pollution and the incident metabolic syndrome in a Thai worker cohort

Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM10, CO, SO2, NO2, and O3) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM10 (9.5 μg/m3), SO2 (0.9 ppb), and CO (0.3 ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM10 at concentrations more than 50 μg/m3. In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes.

Temperature extremes and infectious diarrhea in China: attributable risks and effect modification of urban characteristics

Studies about the role of urban characteristics in modifying the health effect of temperature extremes are still unclear. This study is aimed at quantifying the morbidity risk of infectious diarrhea attributable to temperature extremes and the modified effect of a range of city-specific indicators. Distributed lag non-linear model and multivariate meta-regression were applied to estimate fractions of infectious diarrhea morbidity attributable to temperature extremes and to explore the effect modification of city-level characteristics. Extreme heat- and extreme cold-related infectious diarrhea amounted to 0.99% (95% CI: 0.57-1.29) and 1.05% (95% CI: 0.64-1.24) of the total cases, respectively. The attributable fraction of temperature extremes on infectious diarrhea varied between southern and northern China. Several city characteristics modified the association of extreme cold with infectious diarrhea, with a higher morbidity impact related to increased water consumption per capita and decreased latitude. Regions with higher levels of latitude or GDP per capita appeared to be more sensitive to extreme hot. In conclusion, exposure to temperature extremes was associated with increased risks of infectious diarrhea and the effect can be modified by urban characteristics. This finding can inform public health interventions to decrease the adverse effects of temperature extremes on infectious diarrhea.

Greenness and excess deaths from heat in 323 Latin American cities: Do associations vary according to climate zone or green space configuration?

Green vegetation may protect against heat-related death by improving thermal comfort. Few studies have investigated associations of green vegetation with heat-related mortality in Latin America or whether associations are modified by the spatial configuration of green vegetation. We used data from 323 Latin American cities and meta-regression models to estimate associations between city-level greenness, quantified using population-weighted normalized difference vegetation index values and modeled as three-level categorical terms, and excess deaths from heat (heat excess death fractions [heat EDFs]). Models were adjusted for city-level fine particulate matter concentration (PM2.5), social environment, and country group. In addition to estimating overall associations, we derived estimates of association stratified by green space clustering by including an interaction term between a green space clustering measure (dichotomized at the median of the distribution) and the three-level greenness variable. We stratified analyses by climate zone (arid vs. temperate and tropical combined). Among the 79 arid climate zone cities, those with moderate and high greenness levels had modestly lower heat EDFs compared to cities with the lowest greenness, although protective associations were more substantial in cities with moderate versus high greenness levels and confidence intervals (CI) crossed the null (Beta: -0.41, 95% CI: -1.06, 0.25; Beta -0.23, 95% CI: -0.95, 0.49, respectively). In 244 non-arid climate zone cities, associations were approximately null. We did not observe evidence of effect modification by green space clustering. Our results suggest that greenness may offer modest protection against heat-related mortality in arid climate zone Latin American cities.

Association of greenness with the disease burden of lower respiratory infections and mediation effects of air pollution and heat: a global ecological study

Exposure to greenness is increasingly linked to beneficial health outcomes, but the associations between greenness and the disease burden of lower respiratory infections (LRIs) are unclear. We used the normalized difference vegetation index (NDVI) and the leaf area index (LAI) to measure greenness and incidence, death, and disability-adjusted life years (DALYs) due to LRIs to represent the disease burden of LRIs. We applied a generalized linear mixed model to evaluate the association between greenness and LRI disease burden and performed a stratified analysis, after adjusting for covariates. Additionally, we assessed the potential mediating effects of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and heat on the association between greenness and the disease burden of LRIs. In the adjusted model, one 0.1 unit increase of NDVI and 0.5 increase in LAI were significantly inversely associated with incidence, death, and DALYs due to LRIs, respectively. Greenness was negatively correlated with the disease burden of LRIs across 15-65 age group, both sexes, and low SDI groups. PM2.5, O3, and heat mediated the effects of greenness on the disease burden of LRIs. Greenness was significantly negatively associated with the disease burden of LRIs, possibly by reducing exposure to air pollution and heat.

Greenness exposure: beneficial but multidimensional

Many studies have shown that greenness has beneficial health effects, particularly on psychological and cardiovascular outcomes. In this narrative review, we provide a synthesis of knowledge regarding greenness exposure and respiratory health. The following outcomes were reviewed: respiratory mortality, lung cancer mortality, lung cancer incidence, respiratory hospitalisations, lung function, COPD, and asthma. We identified 174 articles through a literature search in PubMed, of which 42 were eligible for inclusion in this review. The most common marker for greenness exposure was the normalised difference vegetation index (NDVI), which was used in 29 out of 42 papers. Other markers used were tree canopy cover, landcover/land-use, plant diversity, density of tall trees and subjectively perceived greenness. We found beneficial effects of greenness in most studies regarding respiratory mortality, lung cancer incidence, respiratory hospitalisations and lung function. For lung cancer mortality, asthma and COPD, the effects of greenness were less clear cut. While many aspects of greenness are beneficial, some aspects may be harmful, and greenness may have different health effects in different population subgroups. Future studies of greenness and respiratory diseases should focus on asthma and COPD, on effects in different population subgroups and on disentangling the health effects of the various greenness dimensions.

Association between ambient temperature and cause-specific cardiovascular disease admissions in Japan: A nationwide study

BACKGROUND

Substantial evidence suggests that non-optimal temperatures can increase the risk of cardiovascular disease (CVD) mortality and morbidity; however, limited studies have reported inconsistent results for hospital admissions depending on study locations, which also lack national-level investigations on cause-specific CVDs.

METHODS

We performed a two-stage meta-regression analysis to examine the short-term associations between temperature and acute CVD hospital admissions by specific categories [i.e., ischemic heart disease (IHD), heart failure (HF), and stroke] in 47 prefectures of Japan from 2011 to 2018. First, we estimated the prefecture-specific associations using a time-stratified case-crossover design with a distributed lag nonlinear model. We then used a multivariate meta-regression model to obtain national average associations.

RESULTS

During the study period, a total of 4,611,984 CVD admissions were reported. We found cold temperatures significantly increased the risk of total CVD admissions and cause-specific categories. Compared with the minimum hospitalization temperature (MHT) at the 98^th percentile of temperature (29.9 °C), the cumulative relative risks (RRs) for cold (5^th percentile, 1.7 °C) and heat (99^th percentile, 30.5 °C) on total CVD were 1.226 [95% confidence interval (CI): 1.195, 1.258] and 1.000 (95% CI: 0.998, 1.002), respectively. The RR for cold on HF [RR = 1.571 (95% CI: 1.487, 1.660)] was higher than those of IHD [RR = 1.119 (95% CI: 1.040, 1.204)] and stroke [RR = 1.107 (95% CI: 1.062, 1.155)], comparing to their cause-specific MHTs. We also observed that extreme heat increased the risk of HF with RR of 1.030 (95% CI: 1.007, 1.054). Subgroup analysis showed that the age group ≥85 years was more vulnerable to these non-optimal temperature risks.

CONCLUSIONS

This study indicated that cold and heat exposure could increase the risk of hospital admissions for CVD, varying depending on the cause-specific categories, which may provide new evidence to reduce the burden of CVD.

Residential greenness and kidney function: A cohort study of Thai employees

Higher residential greenness is associated with a lower risk of chronic kidney disease, but evidence on the association between greenness exposure and kidney function has not been conducted. Using cohort data from Electricity Generating Authority of Thailand (EGAT) employees, we investigated the association between long-term exposure to greenness and kidney function using estimated glomerular filtration rate (eGFR) in Bangkok Metropolitan Region (BMR), Thailand. We analyzed data from 2022 EGAT workers (aged 25-55 years at baseline) from 2009 to 2019. The level of greenness was calculated using the satellite-derived Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). From 2008 to 2019, the average concentration of each air pollutant (PM₁₀, O₃, NO₂, SO₂, and CO) at the sub-district level in BMR was generated using the Kriging method. Long-term exposure for each participant was defined as the 1-year average concentrations before the date of the physical examination in 2009, 2014, and 2019. We employed linear mixed effects models to evaluate associations of NDVI and EVI with eGFR. The robustness of the results was also tested by including air pollutants in the models. After relevant confounders were controlled, the interquartile range increase in NDVI was associated with higher eGFR [1.03% (95%CI: 0.33, 1.74)]. After PM₁₀ and SO₂ were included in the models, the associations between NDVI and eGFR became weaker. The additions of O₃, NO₂, and CO strengthened the associations between them. In contrast, we did not find any association between EVI and eGFR. In conclusion, there was a positive association between NDVI and eGFR, but not for EVI. Air pollutants had a significant impact on the relationship between NDVI and eGFR. Additional research is needed to duplicate this result in various settings and populations to confirm our findings.

Association between greenness and cardiovascular risk factors: Results from a large cohort study in Thailand

BACKGROUND

The risk of cardiovascular diseases may be reduced by residing in green environments. However, there are relatively few longitudinal cohort studies, especially in Southeast Asia, that focused on the health benefits of long-term greenness exposure in young adults. The present study examined the association between long-term exposure to residential greenness and self-reported morbidities in participants of the Thai Cohort Study (TCS) in Thailand from 2005 to 2013.

METHODS

The self-reported outcomes, including high blood pressure, high blood cholesterol, and diabetes, were reported in 2005, 2009, and 2013, where the study participants provided the exact year of disease occurrence. Greenness was assessed by the satellite-based Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), with a spatial resolution of 250 m. Long-term exposure to NDVI and EVI of each participant's sub-district was averaged over the period of person-time. We used Cox proportional hazards models to examine the association between greenness and health outcomes. Associations with self-reported morbidity were measured using hazard ratios (HRs) per interquartile range (IQR) increase in NDVI and EVI.

RESULTS

After adjusting for potential confounders, we observed that an IQR increase in NDVI was associated with lower incidence of high blood pressure (HR = 0.92, 95% CI: 0.89, 0.97) and high blood cholesterol (HR = 0.89, 95%CI: 0.87, 0.92), but not significantly associated with diabetes (HR = 0.93, 95%CI: 0.85, 1.01). EVI was also inversely associated with self-reported high blood pressure (HR = 0.92, 95%CI: 0.88, 0.96), high blood cholesterol (HR = 0.89, 95%CI: 0.87, 0.91), and diabetes (HR = 0.92, 95%CI: 0.85, 0.99).

CONCLUSIONS

Long-term exposure to residential greenness was inversely associated with self-reported high blood pressure, high blood cholesterol, and diabetes in participants of TCS. Our study provides evidence that greenness exposure may reduce cardiovascular disease risk factors in adult population.

Daily temperature effects on under-five mortality in a tropical climate country and the role of local characteristics

BACKGROUND

Climate change and its subsequent effects on temperature have raised global public health concerns. Although numerous epidemiological studies have shown the adverse health effects of temperature, the association remains unclear for children aged below five years old and those in tropical climate regions.

METHODS

We conducted a two-stage time-stratified case-crossover study to examine the association between temperature and under-five mortality, spanning the period from 2014 to 2018 across all six regions in Malaysia. In the first stage, we estimated region-specific temperature-mortality associations using a conditional Poisson regression and distributed lag nonlinear models. We used a multivariate meta-regression model to pool the region-specific estimates and examine the potential role of local characteristics in the association, which includes geographical information, demographics, socioeconomic status, long-term temperature metrics, and healthcare access by region.

RESULTS

Temperature in Malaysia ranged from 22 °C to 31 °C, with a mean of 27.6 °C. No clear seasonality was observed in under-five mortality. We found no strong evidence of the association between temperature and under-five mortality, with an "M-" shaped exposure-response curve. The minimum mortality temperature (MMT) was identified at 27.1 °C. Among several local characteristics, only education level and hospital bed rates reduced the residual heterogeneity in the association. However, effect modification by these variables were not significant.

CONCLUSION

This study suggests a null association between temperature and under-five mortality in Malaysia, which has a tropical climate. The "M-" shaped pattern suggests that under-fives may be vulnerable to temperature changes, even with a small temperature change in reference to the MMT. However, the weak risks with a large uncertainty at extreme temperatures remained inconclusive. Potential roles of education level and hospital bed rate were statistically inconclusive.

Effects of ambient temperature on mortality among elderly residents of Chengdu city in Southwest China, 2016-2020: a distributed-lag non-linear time series analysis

BACKGROUND

With complex changes in the global climate, it is critical to understand how ambient temperature affects health, especially in China. We aimed to assess the effects of temperature on daily mortality, including total non-accidental, cardiovascular disease (CVD), respiratory disease, cerebrovascular disease, and ischemic heart disease (IHD) mortality between 2016 and 2020 in Chengdu, China.

METHODS

We obtained daily temperature and mortality data for the period 2016-2020. A Poisson regression model combined with a distributed-lag nonlinear model was used to examine the association between temperature and daily mortality. We investigated the effects of individual characteristics by sex, age, education level, and marital status.

RESULTS

We found significant non-linear effects of temperature on total non-accidental, CVD, respiratory, cerebrovascular, and IHD mortality. Heat effects were immediate and lasted for 0-3 days, whereas cold effects persisted for 7-10 days. The relative risks associated with extreme high temperatures (99th percentile of temperature, 28 °C) over lags of 0-3 days were 1.22 (95% confidence interval [CI]: 1.17, 1.28) for total non-accidental mortality, 1.40 (95% CI: 1.30, 1.50) for CVD morality, 1.34 (95% CI: 1.24, 1.46) for respiratory morality, 1.33 (95% CI: 1.20, 1.47) for cerebrovascular mortality, and 1.38 (95% CI: 1.20, 1.58) for IHD mortality. The relative risks associated with extreme cold temperature (1st percentile of temperature, 3.0 °C) over lags of 0-14 days were 1.32 (95% CI: 1.19, 1.46) for total mortality, 1.45 (95% CI: 1.24, 1.68) for CVD morality, 1.28 (95% CI: 1.09, 1.50) for respiratory morality, 1.36 (95% CI: 1.09, 1.70) for cerebrovascular mortality, and 1.26 (95% CI: 0.95, 1.68) for IHD morality. We found that hot and cold affects were greater in those over 85 years of age, and that women, individuals with low education levels, and those who were widowed, divorced, or never married, were more vulnerable.

CONCLUSIONS

This study showed that exposure to hot and cold temperatures in Chengdu was associated with increased mortality, with people over 85 years old, women, those with low education levels, and unmarried individuals being more affected by hot and cold temperatures.

Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China

BACKGROUND

Evidence is scant on the relative and attributable contributions of ambient temperature on stroke subtypes mortality. Few studies have examined modification effects of multiple greenness indicators on such contributions, especially in China. We quantified the associations between ambient temperature and overall, ischemic, and hemorrhagic stroke mortality; further examined whether the associations were modified by greenness.

METHODS

We conducted a multicenter time-series analysis from January 1, 2013 to December 31, 2019. we adopted a distributed lag non-linear model to evaluate county-specific temperature-stroke mortality associations. We then applied a random-effects meta-analysis to pool county-specific effects. Attributable mortality was calculated for cold and heat, defined as temperatures below and above the minimum mortality temperature (MMT). Finally, We conducted a multivariate meta-regression to determine associations between greenness and stroke mortality risks for cold and heat, using normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) as quantitative indicators of greenness exposure.

RESULTS

In the study period, 138,749 deaths from total stroke were reported: 86,873 ischemic and 51,876 hemorrhagic stroke. We observed significant W-shaped relationships between temperature and stroke mortality, with substantial differences among counties and regions. With MMT as the temperature threshold, 17.16 % (95 % empirical CI, 13.38 %-19.75 %) of overall, 20.05 % (95 % eCI, 16.46 %-22.70 %) of ischemic, and 12.55 % (95 % eCI, 5.59 %-16.24 %) of hemorrhagic stroke mortality were attributable to non-optimum temperature (combining cold and heat), more mortality was caused by cold (14.94 %; 95 % eCI, 11.57 %-17.34 %) than by heat (2.22 %; 95 % eCI, 1.54 %-2.72 %). Higher levels of NDVI, SAVI and EVI were related to mitigated effects of non-optimum temperatures-especially heat.

CONCLUSIONS

Exposure to non-optimum temperatures aggravated stroke mortality risks; increasing greenness could alleviate that risks. This evidence has important implications for local communities in developing adaptive strategies to minimize the health consequences of adverse temperatures.

Environmental variable importance for under-five mortality in Malaysia: A random forest approach

BACKGROUND

Environmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach.

METHOD

We applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation.

RESULT

Some heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality.

CONCLUSION

Heat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.

Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM_2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM_2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM_2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM_2.5 was 22.37 (± 12.55) µg/m³ and the mean (standard deviation) of PM_2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m³, 33.60 (± 14.48) µg/m³, and 15.30 (± 4.78) µg/m³, respectively. The cross-validation yielded R ² of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM_2.5 concentration of 22.25 (± 9.97) µg/m³, 21.68 (± 9.14) µg/m³, 29.43 (± 9.45) µg/m³, and 15.74 (± 5.68) µg/m³ for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO₂ and O₃ into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM_2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM_2.5 concentration.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-022-01238-4.

Effects of face masks and ventilation on the risk of SARS-CoV-2 respiratory transmission in public toilets: a quantitative microbial risk assessment

Public toilets may increase the risk of COVID-19 infection via airborne transmission; however, related research is limited. We aimed to estimate SARS-CoV-2 infection risk through respiratory transmission using a quantitative microbial risk assessment framework by retrieving SARS-CoV-2 concentrations from the swab tests of 251 Thai patients. Three virus-generating scenarios were investigated: an infector breathing, breathing with a cough, and breathing with a sneeze. The infection risk (95th percentile) was as high as 10^-1 with breathing and increased to 1 with a cough or a sneeze. No significant gender differences for toilet users (receptors) were noted. The highest risk scenario, namely breathing with a sneeze, was further evaluated for risk mitigation measures. Mitigation to a lower risk under 10^-3 succeeded only when the infector and the receptor both wore N95 respirators or surgical masks. Ventilation of up to 20 air changes per hour (ACH) did not decrease the risk. However, an extended waiting time of 10 min between an infector and a receptor resulted in approximately 1.0-log₁₀ further risk reduction when both wore masks with the WHO-recommended 12 ACH. The volume of expelled droplets, virus concentrations, and receptor dwell time were identified as the main contributors to transmission risk.