Distributed Lag Non-linear Model Research Articles

Association of ambient temperature with intentional self-harm and suicide death in Seoul: a case-crossover design with a distributed lag nonlinear model.

Previous epidemiological studies have reported a short-term association between ambient temperature and suicide risk. To gain a clearer understanding of this association, it is essential to differentiate the risk factors for intentional self-harm (ISH) from those specifically associated with suicide deaths. Therefore, this study aims to examine whether the association between daily temperature and ISH or suicide deaths differs by age and sex. Between 2014 and 2019, cases of emergency room visits related to ISH and suicide deaths in Seoul were identified. A time-stratified case-crossover design was used to adjust for temporal trends and seasonal variation. A distributed lag nonlinear model was used to analyze the nonlinear and time-delayed effect of ambient temperature on ISH and suicide deaths. Positive associations were observed between temperature and both ISH and suicide deaths. For ISH, the relative risk (RR) was high at 1.17 (95% confidence interval (CI): 1.03, 1.34) for a temperature of 25.7°C compared with 14.8°C. The RR for suicide death was higher than those for ISH, at 1.43 (95% CI: 1.03, 2.00) for a temperature of 33.7°C. These associations varied by age and sex, with males and females aged 35-64 years showing increased susceptibility to suicide deaths. This study provides detailed evidence that unusually high temperatures, both anomalous and out of season, may trigger suicidal behaviors, including both ISH and suicide deaths.

Short-term Effects of Ambient Temperature on Pediatric Incident Seizure: A Case-crossover Analysis Using Distributed Lag Non-linear Models

Short-term Effects of Ambient Temperature on Pediatric Incident Seizure: A Case-crossover Analysis Using Distributed Lag Non-linear Models

Assessing the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease hospitalizations in residents of Panzhihua City: a multi-districts study using a distributed lag non-linear model

BackgroundTemperature fluctuations can impact the occurrence and progression of respiratory system diseases. However, the current understanding of the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remains limited. Therefore, our study aims to investigate the relationship between daily mean temperature (DMT) and the risk of AECOPD hospitalizations within Panzhihua City.MethodsWe systematically collected data on AECOPD hospitalizations at Panzhihua Central Hospital from 2015 to 2020 and meteorological factors across Panzhihua City’s districts. A two-stage analysis method was used to establish a distributed lag non-linear model to elucidate the influence of DMT on the frequency of admissions for AECOPD. Subgroup analyses were conducted by gender and age to identify populations potentially susceptible to the impact of DMT.ResultsA total of 5299 AECOPD hospitalizations cases were included. The DMT and the risk of AECOPD hospitalization showed a non-linear exposure–response pattern, with low temperatures exacerbating the risk of hospitalizations. The lag effects of low temperature and relatively low temperature peaked at 2th day, with the lag effects disappearing at 16–17 days. Females and elders aged ≥ 65 years were more sensitive to effects of low and relatively low temperature at lag 0–4 days, while male AECOPD patients exhibited longer lasting lag effects.ConclusionsLow temperatures are associated with an increased risk of AECOPD hospitalizations. Females or elders aged ≥ 65 years with chronic obstructive pulmonary disease should pay more attention to taking protective measures in cold environments. These findings are crucial for the formulation of public health policies, as they will help significantly alleviate the burden of AECOPD and improve respiratory health in the face of climate challenges.

Mortality risk and burden of sudden cardiac arrest associated with hot nights, heatwaves, cold spells, and non-optimum temperatures in 0.88 million patients: An individual-level case-crossover study

Sudden cardiac arrest (SCA) is a global health concern, imposing a substantial mortality burden. However, the understanding of the impact of various extreme temperature events, when accounting for the effect of daily average temperature on SCA, remains incomplete. Additionally, the assessment of SCA mortality burden associated with temperatures from an individual-level design is limited. This nationwide case-crossover study collected individual SCA death records across all (2844) county-level administrative units in the Chinese Mainland from 2013 to 2019. Four definitions for hot nights and ten for both cold spells and heatwaves were established using various temperature thresholds and durations. Conditional logistic regression models combined with distributed lag nonlinear models were employed to estimate the cumulative exposure-response relationships. Based on 887,662 SCA decedents, this analysis found that both hot nights [odds ratio (OR): 1.28; attributable fraction (AF): 1.32 %] and heatwaves (OR: 1.40; AF: 1.29 %) exhibited significant added effects on SCA mortality independent of daily average temperatures, while cold spells were not associated with an elevated SCA risk after accounting for effects of temperatures. Cold temperatures [below the minimum mortality temperature (MMT)] accounted for a larger mortality burden than high temperatures (above the MMT) [AF: 12.2 % vs. 1.5 %]. Higher temperature-related mortality risks and burdens were observed in patients who experienced out-of-hospital cardiac arrest compared to those with in-hospital cardiac arrest. This nationwide study presents the most compelling and comprehensive evidence of the elevated mortality risk and burden of SCA associated with extreme temperature events and ambient temperatures amid global warming.

Association between air temperature and self-perceived health status in Southern Germany: Results from KORA FIT study

BackgroundShort-term exposure to low and high air temperatures can cause serious harmful effects on human health. Existing literature has mostly focused on associations of ambient air temperature with mortality and the need for health care in population-level studies. Studies that have considered self-perceived health status as an outcome when examining the effects of air temperature on health are scarce. In this study, we explored the short-term association of daily mean air temperature with various measures of self-perceived health status. MethodsThis cross-sectional analysis is based on the Cooperative Health Research in the Region of Augsburg (KORA) FIT study conducted in 2018/2019 and included participants from the Augsburg region of Southern Germany. Health-related quality of life (HRQOL) was evaluated by using the 5-level EuroQol Five Dimension (EQ-5D-5L) questionnaire, including the EuroQol visual analog scale (EQ-VAS). Self-rated health (SRH) and comparative self-rated health (CSRH) were each assessed using a single question. Daily mean air temperature data was estimated using a spatiotemporal model and assigned to participants' home addresses at a resolution of 1 × 1 km. Regression models with a Distributed Lag Non-linear Modeling (DLNM) approach were used to investigate the associations between daily mean air temperature and self-perceived health measures. ResultsWe found no association of heat or cold with the HRQOL, SRH or CSRH. Nevertheless, there was a significant protective association of low air temperature with the EQ-5D-5L dimension “usual activities.” ConclusionThere was no evidence of daily mean air temperature adversely affecting participants' self-perceived health status.

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 13-16µg/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.

Short-term exposures to temperature and risk of sudden cardiac death in women: A case-crossover analysis in the Nurses' Health Study.

Sudden cardiac death (SCD) is a major source of mortality and is the first manifestation of heart disease for most cases. Thus, there is a definite need to identify risk factors for SCD that can be modified on the population level. Short-term exposures to temperature have been implicated as a potential risk factor. Our objective was to determine if short-term temperature exposures were associated with increased risk of SCD in a US-based time-stratified case-crossover study. A total of 465 cases of SCD were identified among participants of the prospective Nurses' Health Study (NHS). Control days were selected from all other matching days of the week within the same month as the case day. Average ambient temperature on the current day (Lag0) and preceding 27 days (Lags1-27) was determined at the residence level using 800-m resolution estimates. Conditional logistic distributed lag nonlinear models (DLNMs) were used to assess the relative risk (RR) of the full range of temperature exposures over the lag period. Warmer exposures in the days before event and colder temperatures 21-28 days prior were associated with increased risks of SCD. These results were driven by associations in regions other than the Northeast and among married women. Both warm and cold ambient temperatures are suggestively associated with risks of SCD among middle-aged and older women living across the United States.

Impact of temperature and relative humidity variability on children’s allergic diseases and critical time window identification

BackgroundThe effects of temperature and relative humidity on different types of children’s allergic diseases have not been comprehensively evaluated so far. This study aims to assess the impact of temperature and relative humidity variability on children’s allergic diseases and to identify the critical time window.MethodsWe collected outpatient data on allergen testing in children between July 2020 and January 2022 from the Affiliated Children’s Hospital of Nanjing Medical University. We defined the 1st, 10th, 90th, and 99th percentiles as extreme cold, moderate cold, moderate hot, and extreme hot for temperature, and as low, moderate high, and extreme high for relative humidity, respectively. A distributed lag nonlinear model (DLNM) combined with a binomial regression model was used to assess the possible nonlinear relationship at different periods. Subgroup analysis by gender and age was conducted.ResultsWe found that extreme and moderate cold temperatures were positively associated with skin allergies and total allergies (28 days: OR = 4.69, 95% CI: 2.88, 7.63; OR = 3.36, 95% CI: 2.39, 4.73) and (28 days: OR = 3.76, CI: 2.43, 5.81; OR = 2.71, 95% CI: 2.00, 3.68), respectively. Moderate and extreme hot temperatures were negatively associated with food allergies (28 days: OR = 0.13, 95% CI: 0.04, 0.41 and OR = 0.04; 95% CI: 0.01, 0.27). Low relative humidity was negatively associated with respiratory allergies, skin allergies, and total allergic diseases (28 days: OR = 0.26, 95% CI: 0.10, 0.71; OR = 0.29, 95% CI: 0.15, 0.55; and OR = 0.42, 95% CI: 0.26, 0.68). Meanwhile, extreme high relative humidity was negatively associated with respiratory allergies, and positively associated with skin allergies, food allergies, and total allergies (28 days: OR = 0.16, 95%CI: 0.07, 0.37; OR = 3.60, 95% CI: 2.52, 5.14; OR = 15.61, 95% CI: 3.23, 75.56; and OR = 2.33, 95% CI: 1.73, 3.15). A stronger relationship between temperature, relative humidity, and allergic diseases was observed in children under 5 years, specifically girls.ConclusionsOur study provides evidence that temperature and relative humidity variability may be associated with allergic diseases, however, the directionality of the relationship differs by allergic type.

Impact of surface ultraviolet radiation intensity on hospital admissions for ischemic and hemorrhagic stroke: A large-scale database study using distributed lag nonlinear analysis, 2015-2022, in Harbin, China

ObjectivesOur aim is to evaluate the impact of surface ultraviolet radiation intensity on hospital admissions for stroke and to compare the correlation and differences among different subtypes of strokes. Materials and methodsWe collected daily data on surface ultraviolet radiation intensity, temperature, air pollution, and hospital admissions for stroke in Harbin from 2015 to 2022. Using a distributed lag non-linear model, we determined the correlation between daily surface ultraviolet radiation intensity and the stroke admission rate. Relative risks (RR) with 95% confidence intervals (CI) and attributable fractions (AF) with 95% CI were calculated based on stroke subtypes, gender, and age groups. ResultsA total of 132,952 hospitalized stroke cases (including hemorrhagic and ischemic strokes) were included in the study. We assessed the non-linear effects of ultraviolet intensity on hospitalized patients with ischemic and hemorrhagic strokes. Compared to the maximum morbidity benchmark ultraviolet intensity (19.2 × 10^5 for ischemic stroke and 20.25 for hemorrhagic stroke), over the 0-10 day lag period, the RR for extreme low radiation (1st percentile) was 0.86 (95% CI: 0.77, 0.96), and the RR for extreme high radiation (99th percentile) was 0.86 (95% CI: 0.77, 0.96). In summary, -4.842% (95% CI: -7.721%, -2.167%) and -1.668% (95% CI: -3.061%, -0.33%) of ischemic strokes were attributed to extreme low radiation intensity with a lag of 0 to 10 days and extreme high radiation intensity with a lag of 0 to 5 days, respectively. The reduction in stroke hospitalization rates due to low or high ultraviolet intensity was more pronounced in females and younger individuals compared to males and older individuals. None of the mentioned ultraviolet intensity intensities and lag days had a statistically significant impact on hemorrhagic stroke. ConclusionsOur study fundamentally suggests that both lower and higher levels of surface ultraviolet radiation intensity in Harbin, China, contribute to a reduced incidence of ischemic stroke, with this effect lasting approximately 10 days. This finding holds significant potential for public health and clinical relevance.

Association between prenatal air pollutant exposure and autism spectrum disorders in young children: A matched case-control study in Canada

The direction and magnitude of association between maternal exposure to ambient air pollutants across gestational windows and offspring risk of autism spectrum disorders (ASD) remains unclear. We sought to evaluate the time-varying effects of prenatal air pollutant exposure on ASD. We conducted a matched case-control study of singleton term children born in Ontario, Canada from 1-Apr-2012 to 31-Dec-2016. Provincial birth registry data were linked with applied behavioural analysis services and ambient air pollutant datasets to ascertain prenatal exposure to nitrogen dioxide (NO2), ground-level ozone (O3), fine particulate matter (PM2.5), and ASD diagnoses. Covariate balance between cases and controls was established using coarsened exact matching. Conditional logistic regression was used to assess the association between prenatal air pollutant exposure and ASD. Distributed lag non-linear models (DLNM) were used to examine the effects of single-pollutant exposure by prenatal week. Sensitivity analyses were conducted to assess the impact of exposure period on the observed findings. The final sample included 1589 ASD cases and 7563 controls. Compared to controls, cases were more likely to be born to mothers living in urban areas, delivered by Caesarean section, and assigned male sex at birth. NO2 was a consistent and significant contributor to ASD risk after accounting for co-exposure to O3, PM2.5 and covariates. The odds ratio per interquartile range increase was 2.1 (95%CI 1.8–2.3) pre-conception, 2.2 (2.0–2.5) for the 1st trimester, 2.2 (1.9–2.5) for the 2nd trimester, and 2.1 (1.9–2.4) for the 3rd trimester. In contrast, findings for O3 and PM2.5 with ASD were inconsistent. Findings from DLNM and sensitivity analyses were similar. Exposure to NO2 before and during pregnancy was significantly associated with ASD in offspring. The relationship between prenatal O3 and PM2.5 exposure and ASD remains unclear. Further investigation into the combined effects of multi-pollutant exposure on child neurodevelopment is warranted.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure–response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were − 0.428 (95% CI − 0.637, − 0.218), -− 0.227 (95% CI − 0.293, − 0.161), and − 0.119 (95% CI − 0.159, − 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 μg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

Hourly effect of atmospheric reactive nitrogen species on the onset of acute ischemic stroke: Insight from the Shanghai Stroke Service System Database

Acute ischemic stroke (AIS) is one of the most predominant causes of mortality and disability in China. Significant uncertainties in stroke diagnosis and time of onset have resulted in inconsistent evidence on the association between ambient air pollution and the risk of AIS. The present study aimed to evaluate the impact of air pollution on AIS onset based on high time-resolution air pollution data and a stroke-specific registry across the past five years. Hourly concentrations of PM2.5, PM10, O3, SO2, CO, NO2 and nitrous acid (HONO) were monitored from 2017 to 2021, with which a distributed lag non-linear model and conditional logistic regression models coupled with a time-stratified case-crossover design were applied to 106,623 AIS cases recorded in the Shanghai Stroke Service (4S) database during the study period. Results from the conditional logistic regression models indicate that acute exposure to PM2.5, PM10, SO2, NO2 and HONO was found to be associated with AIS onset, respectively. The corresponding cumulative excessive risks of AIS onset were 0.8 %, 1 %, 2.4 %, 2.1 % and 1.8 % for each interquartile range increase in the respective concentration. The longest lag-effect (up to 13 h) was observed for reactive nitrogen species (RNS), such as NO2 and HONO, which remained robust in two-pollutant models. Similar important role of RNS in AIS onset were confirmed by the distributed lag non-linear model. By demonstrating the transient effect of ambient air pollution on AIS, especially the relationships between RNS and AIS for the first time, our study provides stringent evidence for future mitigation strategies for pollution emission and public health.

Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention. The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm. The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted. The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and zhongfu, among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities.

Nonlinear effects of agricultural drought on vegetation productivity in the Yellow River Basin, China

Agricultural drought (AD) is the main environmental factor affecting vegetation productivity (VP) in the Yellow River Basin (YRB). In recent years, the nonlinear effects of AD on VP in the YRB have attracted much attention. However, it is still unclear whether fluctuating AD will have complex nonlinear effects on VP in the YRB, and there are scant previous studies at large scale on whether there is a threshold for nonlinear effects of AD on VP in the YRB. Therefore, this study used a newly developed agricultural drought index to explore nonlinear effects on VP revealing the nonlinear effects of AD on VP in the YRB. First, we developed a kernel temperature vegetation drought index (kTVDI) based on kernel normalized difference vegetation index (kNDVI) and land surface temperature data to study the spatiotemporal variation of AD in the YRB. Second, we used GPP data from solar-induced chlorophyll fluorescence inversion as an indicator to explore the spatiotemporal variation of VP in the YRB. Finally, we used several statistical indicators and a distributed lag nonlinear model (DLNM) to analyze the nonlinear effect of AD on VP in the YRB. The results showed that AD decreased significantly during 2000–2020, mainly in the southeast of the Loess Plateau, while GPP increased significantly in 80.93 % of the YRB. Meanwhile, moderate and severe AD stress limited VP growth, with the negative effects gradually decreasing, while mild AD had an increasingly positive promoting effect on VP. AD stress resulted in a VP decrease of 69.78 %, and severe AD stress resulted in a VP decrease of 65.52 %, mainly distributed in the northern Loess and Ordos Plateau. AD had significant nonlinear effects on VP. The effects of moderate and severe AD on the sustained nonlinear lag of vegetation were more obvious, and those of moderate and severe AD on the nonlinear lag of VP were the largest when the lag was approximately 1 month and 7 months. The effect of AD on the nonlinear hysteresis of VP in YRB was significantly different under different vegetation types, and forests were more able to withstand longer and more severe droughts than grasslands and croplands. The results of the study provide a theoretical basis for evaluating AD and analyzing the nonlinear impact of AD on VP. This will provide scientific basis for studying the mechanism of drought effect on vegetation in other regions.

Mortality risk and burden of aortic aneurysm and dissection attributable to low temperatures: A nationwide case-crossover analysis in Brazil, a predominantly tropical country

BackgroundLow temperatures are adverse contributors to cardiovascular diseases, but the associations between short-term exposure to cold and the risk of death from aortic dissection and aneurysm remain unclear, particularly in tropical regions. ObjectiveThis study was conducted based on 123,951 records of deaths caused by aortic dissection and aneurysms extracted from the national Mortality Information System in Brazil between 2000 and 2019. MethodsRelative risks and 95 % confidence intervals (CI) for the aortic-related deaths associated with low ambient temperatures were estimated using the conditional logistic model combined with the distributed lag nonlinear model. Subgroup analyses were performed by age group, sex, race, education level, and residential region. Furthermore, this study calculated the number and fraction of aortic-related deaths attributed to temperatures below the temperature threshold to quantify the cold-related mortality burden of aortic diseases. ResultsDuring the study period, aortic-related deaths and mortality rates in Brazil exhibited a steady increase, rising from 4419 (2.66/100,000) in 2000 to 8152 (3.88/100,000) in 2019. Under the identified temperature threshold (26 °C), per 1 °C decrease in daily mean temperature was associated with a 4.77 % (95 % CI: 4.35, 5.19) increase in mortality risk of aortic-related diseases over lag 0–3 days. Females, individuals aged 50 years or older, Asian and Black race, and northern residents were more susceptible to low temperatures. Low temperatures were responsible for 19.10 % (95 % CI: 17.71, 20.45) of aortic-related deaths in Brazil. ConclusionThis study highlights that low temperatures were associated with an increased risk of aortic-related deaths, with a remarkable burden even in this predominantly tropical country.

Effects of apparent temperature on hospitalization rates for cardiovascular diseases: a case study of different rural areas in Northwest China

ABSTRACT Apparent temperature (AT) is a composite index that combines ambient temperature, humidity, wind speed and other meteorological factors, and reflects heat perception more accurately than raw temperature. This is the first study to investigate the association between AT and CVD in rural areas of Jiuquan and Longnan, Gansu Province, China. In this study, the distributed lag nonlinear model (DLNM) was used to examine the exposure-response relationship between AT and the 21 days relative risk (RR) of CVD admission. The results showed that the exposure risk of the gender group in Jiuquan was opposite to that of Longnan under the influence of cold effect. Under the influence of heat effect, it has a protective effect on all groups in Jiuquan area, which is harmful to males and adults in Longnan area. The results of this study can help local governments to formulate public policies.

Short-Term Exposure to Wildfire-Specific PM2.5 and Hospitalization for Diabetes Morbidity: A Study in Multiple Countries and Territories.

To evaluate associations of wildfire fine particulate matter (PM2.5) with diabetes across multiple countries and territories. We collected data on 3,612,135 diabetes hospitalizations from 1,008 locations in Australia, Brazil, Canada, Chile, New Zealand, Thailand, and Taiwan during 2000-2019. Daily wildfire-specific PM2.5 levels were estimated through chemical transport models and machine-learning calibration. Quasi-Poisson regression with distributed lag nonlinear models and random-effects meta-analysis were applied to estimate associations between wildfire-specific PM2.5 and diabetes hospitalization. Subgroup analyses were by age, sex, location income level, and country or territory. Diabetes hospitalizations attributable to wildfire-specific PM2.5 and nonwildfire PM2.5 were compared. Each 10 µg/m3 increase in wildfire-specific PM2.5 levels over the current day and previous 3 days was associated with relative risks (95% CI) of 1.017 (1.011-1.022), 1.023 (1.011-1.035), 1.023 (1.015-1.032), 0.962 (0.823-1.032), 1.033 (1.001-1.066), and 1.013 (1.004-1.022) for all-cause, type 1, type 2, malnutrition-related, other specified, and unspecified diabetes hospitalization, respectively. Stronger associations were observed for all-cause, type 1, and type 2 diabetes in Thailand, Australia, and Brazil; unspecified diabetes in New Zealand; and type 2 diabetes in high-income locations. Relative risks (95% CI) of 0.67% (0.16-1.18%) and 1.02% (0.20-1.81%) for all cause and type 2 diabetes hospitalizations were attributable to wildfire-specific PM2.5. Compared with nonwildfire PM2.5, wildfire-specific PM2.5 posed greater risks of all-cause, type 1, and type 2 diabetes and were responsible for 38.7% of PM2.5-related diabetes hospitalizations. We show the relatively underappreciated links between diabetes and wildfire air pollution, which can lead to a nonnegligible proportion of PM2.5-related diabetes hospitalizations. Precision prevention and mitigation should be developed for those in advantaged communities and in Thailand, Australia, and Brazil.

Effects of major air pollutants on angina hospitalizations: a correlation study

BackgroundAngina is a crucial risk signal for cardiovascular disease. However, few studies have evaluated the effects of ambient air pollution exposure on angina.ObjectiveWe aimed to explore the short-term effects of air pollution on hospitalization for angina and its lag effects.MethodsWe collected data on air pollutant concentrations and angina hospitalizations from 2013 to 2020. Distributed lag nonlinear model (DLNM) was used to evaluate the short-term effects of air pollutants on angina hospitalization under different lag structures. Stratified analysis by sex, age and season was obtained.ResultsA total of 39,110 cases of angina hospitalization were included in the study. The results showed a significant positive correlation between PM2.5, SO2, NO2, and CO and angina hospitalization. Their maximum harmful effects were observed at lag0-7 (RR = 1.042; 95% CI: 1.017, 1.068), lag0-3 (RR = 1.067; 95% CI: 1.005, 1.133), lag0-6 (RR = 1.078; 95% CI: 1.041, 1.117), and lag0-6 (RR = 1.244; 95% CI: 1.109, 1.397), respectively. PM10 did not have an overall risk effect on angina hospitalization, but it did have a risk effect on women and the elderly. O3 was significantly negatively correlated with angina hospitalization, with the most pronounced effect observed at lag0-6 (RR = 0.960; 95% CI: 0.940, 0.982). Stratified analysis results showed that women and the elderly were more susceptible to pollutants, and the adverse effects of pollutants were stronger in the cold season.ConclusionShort-term exposure to PM2.5, SO2, NO2, and CO increases the risk of hospitalization for angina.

Association of ambient temperature on acute ischemic stroke in Yancheng, China

BackgroundAcute ischemic stroke (AIS) is a major global public health issue. There is limited research on the relationship between ambient temperature and AIS hospital admissions, and the results are controversial. Our objective is to assess the short-term impact of ambient temperature on the risk of AIS hospital admissions in Yancheng, China.MethodsWe collected data on daily AIS hospital admissions, meteorological factors, and air quality in Yancheng from 2014 to 2019. We used Poisson regression to fit generalized linear models and distributed lag non-linear models to explore the association between ambient temperature and AIS hospital admissions. The effects of these associations were evaluated by stratified analysis by sex and age.ResultsFrom 2014 to 2019, we identified a total of 13,391 AIS hospital admissions. We observed that the influence of extreme cold and heat on admissions for AIS manifests immediately on the day of exposure and continues for a duration of 3–5 days. Compared to the optimal temperature (24.4 °C), the cumulative relative risk under extreme cold temperature (-1.3 °C) conditions with a lag of 0–5 days was 1.88 (95%CI: 1.28, 2.78), and under extreme heat temperature (30.5 °C) conditions with a lag of 0–5 days was 1.48 (95%CI: 1.26, 1.73).ConclusionsThere is a non-linear association between ambient temperature and AIS hospital admission risk in Yancheng, China. Women and older patients are more vulnerable to non-optimal temperatures. Our findings may reveal the potential impact of climate change on the risk of AIS hospital admissions.

Association between ambient temperature and cause-specific mortality: An individual-level case-crossover study in Suzhou, China

The changing climate poses a growing challenge to the population health. The objective of this study was to assess the association between ambient temperature and cause-specific mortality in Suzhou. Based on the non-accidental mortality data collected during 2008–2022 in Suzhou, China, this study utilized an individual-level case-crossover design to evaluate the associations of temperature with cause-specific mortality. We applied a distributed lag nonlinear model with a maximum lag of 14 days to account for lag effects. Mortality risk due to extreme cold (<2.5th percentile) and extreme heat (>97.5th percentile) was analyzed. A total of 634,530 non-accidental deaths were analyzed in this study. An inverse J-shaped exposure-response relationship was observed between ambient temperature and non-accidental mortality, with the minimum mortality temperature (MMT) at 29.1℃. The relative risk (RR) of mortality associated with extreme cold (2.5th percentile) was 1.37 [95 % confidence interval (CI): 1.30, 1.44], higher than estimate of 1.09 (95 %CI: 1.07, 1.11) for extreme heat (97.5th percentile) relative to the MMT. Heat effect lasted for 2–3 days, while cold effect could persist for almost 14 days. Higher mortality risk estimates were observed for cardiorespiratory deaths compared to total deaths, with statistically significant between-group differences. Consequently, this study provides first-hand evidence on the associations between ambient temperatures and mortality risks from various causes, which could help local government and policy-makers in designing targeted strategies and public health measures against the menace of climate change.