Coarse particulate air pollution and mortality in a multidrug-resistant tuberculosis cohort.

RATIONALE: The association between ambient coarse particulate matter (PM2.5-10) and mortality in multi-drug resistant tuberculosis (MDR-TB) patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown. OBJECTIVES: To evaluate the effects of long-term PM2.5-10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients. METHODS: A Chinese cohort of 3469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study's end, averaging 2567 days per patient. PM2.5-10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 µg/m3 (interquartile range, IQR) exposure to PM2.5-10 and all-cause mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines. RESULTS: Hazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5-10 exposures for the full cohort under both long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and surrounding environment measured by greenness and nighttime light levels. Nonlinear exposure-response curves suggestes a cumulative risk of PM2.5-10-related mortality starting from a low exposure concentration around 15 µg/m3. CONCLUSION: Long-term exposure to PM2.5-10 poses significant harm among MDR-TB patients, with effects modified by temperature and humidity. Immediate surveillance of PM2.5-10 is crucial to mitigate the progression of MDR-TB severity, particularly due to co-exposures to air pollution and adverse weather conditions.

Long-term exposure to ambient fine particulate matter (PM2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment.

INTRODUCTION: Long-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China. METHODS: PM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications. RESULTS: Over 2007-2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%). CONCLUSION: We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.

The Burden of Occupational Noise-Induced Hearing Loss From 1990 to 2019: An Analysis of Global Burden of Disease Data.

OBJECTIVES: The relationship between long-term exposure to occupational noise and hearing loss has been extensively documented. We aimed to assess spatial and temporal changes in the burden of occupational noise-induced hearing loss (ONIHL) in 204 countries and territories with varying socio-demographic indexes (SDI) from 1990 to 2019. DESIGN: Temporal and spatial trends in age-standardized disability-adjusted life year rates (ASDR) for ONIHL were estimated by sex, age, SDI level, country, and geographic region from 1990 to 2019. We used the Joinpoint model to calculate annual average percentage changes to assess such trends and projected trends in ASDR for ONIHL globally and across different income regions from 2020 to 2044 using an age-period-cohort model. We fitted the relationship between ASDR and SDI, ASDR and healthcare access and quality index, respectively. RESULTS: Overall, the global burden of ONIHL has decreased since 1990, especially in middle and lower SDI regions. In 2019, the global ASDR for ONIHL was 84.23 (95% confidence interval: 57.46 to 120.52) per 100,000 population. From 1990 to 2019, the global ASDR for ONIHL decreased by 1.72% (annual average percentage change = -0.05, 95% confidence interval: -0.07 to -0.03). Our projections showed a decreasing trend in the global ONIHL burden until 2044. ASDR and SDI (R = -0.8, p < 0.05), ASDR and healthcare access and quality index (R = -0.75, p < 0.05) showed significant negative correlations. CONCLUSIONS: The global ONIHL burden has decreased over the past three decades, especially in regions with middle and lower SDI levels. However, the global ONIHL burden still remained severe in 2019, notably among males, the middle-aged and elderly, and regions with lower SDI levels.

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020.

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Association of residential greenness with obstructive sleep apnea among Chinese old adults and the mediation role of PM2.5 and leisure-time physical activity.

Few studies have investigated the association of residential greenness with obstructive sleep apnea (OSA). This study was to comprehensively examine the association of residential greenness exposure with OSA and explore the mediating effect of leisure-time physical activity (LTPA) and PM2.5 on the association among Chinese old adults. A prospective cohort study that enrolled 2027 adults aged ≥65 was conducted between 1st July 2015 and 30th September 2019 in Southern China. OSA was ascertained by Berlin Questionnaire. Greenness exposure was measured by contemporaneous and cumulative average normalized difference vegetation index (NDVI) in the 1000 m radius around each participant's residential address. Hazard ratios (HRs) with 95 % confidence intervals (CIs) were calculated by Cox proportional hazards model to assess the impact of greenness exposure on the incidence of OSA after adjusting for confounders. LTPA and PM2.5 were examined as potential mediators in the aforementioned models. A total of 293, nearly 14.5 %, participants developed OSA within 59,251 person-months of follow-up. When comparing the highest with lowest tertiles, both contemporaneous NDVI (>0.351 vs. ≤0.325: HR = 0.20, 95 % CI = 0.13-0.31) and cumulative NDVI (> 0.346 vs. ≤ 0.317: HR = 0.32, 95 % CI = 0.21-0.47) were associated with a reduced risk of OSA after adjusting for confounders. LTPA and PM2.5 significantly mediated the association between greenness and OSA. In conclusion, this study indicated that exposure to higher residential greenness could decrease OSA risk, and this benefit may be achieved by promoting physical activity and decreasing PM2.5 concentration. The findings suggest to formulate targeted interventional strategies by expanding residential greenness to prevent OSA and reduce disease burden.

Impact of heat on emergency hospital admissions related to kidney diseases in Texas: Uncovering racial disparities.

BACKGROUND AND OBJECTIVE: While impact of heat exposure on human health is well-documented, limited research exists on its effect on kidney disease hospital admissions especially in Texas, a state with diverse demographics and a high heat-related death rate. We aimed to explore the link between high temperatures and emergency kidney disease hospital admissions across 12 Texas Metropolitan Statistical Areas (MSAs) from 2004 to 2013, considering causes, age groups, and ethnic populations. METHODS: To investigate the correlation between high temperatures and emergency hospital admissions, we utilized MSA-level hospital admission and weather data. We employed a Generalized Additive Model to calculate the association specific to each MSA, and then performed a random effects meta-analysis to estimate the overall correlation. Analyses were stratified by age groups, admission causes, and racial/ethnic disparities. Sensitivity analysis involved lag modifications and ozone inclusion in the model. RESULTS: Our analysis found that each 1 °C increase in temperature was associated with a 1.73 % (95 % CI [1.43, 2.03]) increase in hospital admissions related to all types of kidney diseases. Besides, the effect estimates varied across different age groups and specific types of kidney diseases. We observed statistically significant associations between high temperatures and emergency hospital admissions for Acute Kidney Injury (AKI) (3.34 % (95 % CI [2.86, 3.82])), Kidney Stone (1.76 % (95 % CI [0.94, 2.60])), and Urinary Tract Infections (UTI) (1.06 % (95 % CI [0.61, 1.51])). Our research findings indicate disparities in certain Metropolitan Statistical Areas (MSAs). In Austin, Houston, San Antonio, and Dallas metropolitan areas, the estimated effects are more pronounced for African Americans when compared to the White population. Additionally, in Dallas, Houston, El Paso, and San Antonio, the estimated effects are greater for the Hispanic group compared to the Non-Hispanic group. CONCLUSIONS: This study finds a strong link between higher temperatures and kidney disease-related hospital admissions in Texas, especially for AKI. Public health actions are necessary to address these temperature-related health risks, including targeted kidney health initiatives. More research is needed to understand the mechanisms and address health disparities among racial/ethnic groups.

Protocol for a pragmatic cluster randomised controlled trial to evaluate the effectiveness of digital health interventions in improving non-communicable disease management during the pandemic in rural Pakistan.

BACKGROUND: The COVID-19 pandemic has revealed gaps in global health systems, especially in the low- and middle-income countries (LMICs). Evidence shows that patients with non-communicable diseases (NCDs) are at higher risk of contracting COVID-19 and suffering direct and indirect health consequences. Considering the future challenges such as environmental disasters and pandemics to the LMICs health systems, digital health interventions (DHI) are well poised to strengthen health care resilience. This study aims to implement and evaluate a comprehensive package of DHIs of integrated COVID-NCD care to manage NCDs in primary care facilities in rural Pakistan. METHODS: The study is designed as a pragmatic, parallel two-arm, multi-centre, mix-methods cluster randomised controlled trial. We will randomise 30 primary care facilities in three districts of Punjab, where basic hypertension and diabetes diagnosis and treatment are provided, with a ratio of 1:1 between intervention and control. In each facility, we will recruit 50 patients who have uncontrolled hypertension. The intervention arm will receive training on an integrated COVID-NCD guideline, and will use a smartphone app-based telemedicine platform where patients can communicate with health providers and peer-supporters, along with a remote training and supervision system. Usual care will be provided in the control arm. Patients will be followed up for 10 months. Our primary indicator is systolic blood pressure measured at 10 months. A process evaluation guided by implementation science frameworks will be conducted to explore implementation questions. A cost-effectiveness evaluation will be conducted to inform future scale up in Pakistan and other LMICs. DISCUSSION: Our study is one of the first randomised controlled trials to evaluate the effectiveness of DHIs to manage NCDs to strengthen health system resilience in LMICs. We will also evaluate the implementation process and cost-effectiveness to inform future scale-up in similar resource constrained settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier-NCT05699369.

Migraine and air pollution: A systematic review.

OBJECTIVE: To systematically synthesize evidence from a broad range of studies on the association between air pollution and migraine. BACKGROUND: Air pollution is a ubiquitous exposure that may trigger migraine attacks. There has been no systematic review of this possible association. METHODS: We searched for empirical studies assessing outdoor air pollution and any quantified migraine outcomes. We included short- and long-term studies with quantified air pollution exposures. We excluded studies of indoor air pollution, perfume, or tobacco smoke. We assessed the risk of bias with the World Health Organization's bias assessment instrument for air quality guidelines. RESULTS: The final review included 12 studies with over 4,000,000 participants. Designs included case-crossover, case-control, time series, and non-randomized pre-post intervention. Outcomes included migraine-related diagnoses, diary records, medical visits, and prescriptions. Rather than pooling the wide variety of exposures and outcomes into a meta-analysis, we tabulated the results. Point estimates above 1.00 reflected associations of increased risk. In single-pollutant models, the percent of point estimates above 1.00 were carbon monoxide 5/5 (100%), nitrogen dioxide 10/13 (78%), ozone 7/8 (88%), PM2.5 13/15 (87%), PM10 2/2 (100%), black carbon 0/1 (0%), methane 4/6 (75%), sulfur dioxide 3/5 (60%), industrial toxic waste 1/1 (100%), and proximity to oil and gas wells 6/13 (46%). In two-pollutant models, 16/17 (94%) of associations with nitrogen dioxide were above 1.00; however, more than 75% of the confidence intervals included the null value. Most studies had low to moderate risks of bias. Where differences were observed, stronger quality articles generally reported weaker associations. CONCLUSIONS: Balancing the generally strong methodologies with the small number of studies, point estimates were mainly above 1.00 for associations of carbon monoxide, nitrogen dioxide, ozone, and particulate matter with migraine. These results were most consistent for nitrogen dioxide.

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020.

Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning.

Impact of heat on emergency hospital admission in Texas: geographic and racial/ethnic disparities.

BACKGROUND: Studies exploring the racial/ethnicity disparity of the impact of heat on hospital admission are notably limited, especially in Texas, a state with a diverse population and consistently ranking among the top ten U.S. states for heat-related deaths per capita from 2018 to 2020. OBJECTIVE: Our objective is to determine the correlation between elevated temperatures and emergency hospital admissions for various causes and age groups across 12 Metropolitan Statistical Areas(MSAs) in Texas. Additionally, we aim to investigate health inequalities in the five largest MSAs in Texas between 2004 and 2013. METHODS: We used MSA-level hospital admission and weather data to estimate the relationship between heat and emergency hospital admissions. We applied a Generalized Additive Model and random effects meta-analysis to calculate MSA-specific associations and overall correlation, repeating the analysis for age groups and specific causes of admission. We also investigated health disparities across racial and ethnic groups and performed a sensitivity analysis. RESULTS: The results showed that a 1 °C increase in temperature was associated with a 0.50% (95% CI [0.38%, 0.63%]) increase in all-cause emergency hospital admissions. Heat's impact on hospital admissions varied among age groups and causes, with children under 6 years showing the highest effect estimate (0.64% (95% CI [0.32%,0.96%])). Statistically significant associations were found for Cardiovascular Diseases (0.27% (95% CI [0.07%,0.47%])), Ischemic Heart Diseases (0.53% (95% CI [0.15%,0.92%])), Pneumonia (0.70% (95% CI [0.25%,1.16%])), and Respiratory Diseases (0.67% (95% CI [0.18%,1.17%])). Health disparities were found among racial and ethnic groups in the five largest MSAs. IMPACT STATEMENT: Studies exploring the impact of heat on hospital admission in Texas are notably limited. Our research provided a comprehensive examination of the connection between heat and emergency hospital admissions throughout Texas. Furthermore, we are the first to examine racial/ethnic disparities, identifying African American and Hispanic groups as disproportionately affected. These insights provide valuable insights for policymakers to allocate resources and implement strategies to mitigate the negative consequences of rising temperatures.

Effects of long-term ambient air pollution exposure on township-level pulmonary tuberculosis notification rates during 2005-2017 in Ningxia, China.

Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter <2·5 µm [PM2·5], nitrogen dioxide [NO2] ozone [O3]) were associated with TB notification rates (as a proxy for incidence). Air pollution effects on TB notification rates at township-level were estimated as incidence rate ratios (IRR), fitted using a generalised estimating equation (GEE) adjusted for covariates (age, sex, occupation, education, ethnicity, remoteness [urban or rural], household crowding and solid fuel use). A total of 38,942 TB notifications were reported in NHAR between 2005 and 2017. The mean annual TB notification rate was 67 (standard deviation [SD]; 7) per 100,000 people. Median concentrations of PM2·5, NO2, and O3 were 42 µg/m3 (interquartile range [IQR]; 38-48 µg/m3), 15 ppb (IQR; 12-16 ppb), and 56 ppb (IQR; 56-57 ppb), respectively. In single pollutant models, adjusted for covariates, an interquartile range (IQR) increase (10 µg/m3) in PM2·5 was significantly associated with higher TB notification rates (IRR: 1∙35; 95% CI: 1·25-1·48). Comparable effects on notifications of TB were observed for increases in NO2 exposure (IRR: 1·20 per IQR (4 ppb) increase; 95% CI: 1·08-1·31). Ground-level ozone was not associated with TB notification rate in any models. The observed effects were consistent over time, in multi-pollutant models, and appeared robust to additional adjustment for indicators of household crowding, solid fuel use and remoteness. More rigorous study designs are needed to understand if improving air quality has population-level benefits on TB disease incidence in endemic settings.

Effect modification of greenness on PM2.5 associated all-cause mortality in a multidrug-resistant tuberculosis cohort.

RATIONALE: Evidence for the association between fine particulate matter (PM2.5) and mortality among patients with tuberculosis (TB) is limited. Whether greenness protects air pollution-related mortality among patients with multidrug-resistant tuberculosis (MDR-TB) is completely unknown. METHODS: 2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient's residence, annual average PM2.5 and estimated oxidant capacity Ox were assigned to patients regarding their geocoded home addresses. Cox proportional hazards regression models were used to estimate HRs per 10 µg/m3 exposure to PM2.5 and all-cause mortality among the cohort and individuals across the three tertiles, adjusting for potential covariates. RESULTS: HRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PM2.5 associated with mortality for the full cohort and individuals with the greatest tertile of NDVI. Exposures to PM2.5 were stronger in association with mortality for younger patients (HR 2.434 (2.432 to 2.435)), female (2.209 (1.874 to 2.845)), patients in rural (1.780 (1.731 to 1.829)) and from Ningxia (1.221 (1.078 to 1.385)). Cumulative exposures increased the HRs of PM2.5-related mortality, while greater greenness flattened the risk with HRs reduced in 0.188-0.194 on average. CONCLUSIONS: Individuals with MDR-TB could benefit from greenness by having attenuated associations between PM2.5 and mortality. Improving greener space and air quality may contribute to lower the risk of mortality from TB/MDR-TB and other diseases.

Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China.

Existing national- or continental-scale models of nitrogen dioxide (NO2) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO2 sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO2 for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO2 measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO2, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R2 values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO2 in the relatively sparsely-populated areas in NHAR and neighbouring provinces.

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: A population-based cohort study.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has infected 1.9% of the world population by May 2, 2021. Since most previous studies that examined risk factors for mortality and severity were based on hospitalized individuals, population-based cohort studies are called for to provide evidence that can be extrapolated to the general population. Therefore, we aimed to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19 diagnosed in 2020 in Ontario, Canada. METHODS AND FINDINGS: We conducted a retrospective cohort study of all individuals with COVID-19 in Ontario, Canada diagnosed between January 15 and December 31, 2020. Cases were linked to health administrative databases maintained in the ICES which covers all residents in Ontario. The primary outcome is all-cause 30-day mortality after the first COVID-19 diagnosis, and the secondary outcome is a composite severity index containing death and hospitalization. To examine the risk factors for the outcomes, we employed Cox proportional hazards regression models and logistic regression models to adjust for demographic, socio-economic variables and comorbidities. Results were also stratified by age groups. A total of 167,500 individuals were diagnosed of COVID-19 in 2020 and included in the study. About half (43.8%, n = 73,378) had at least one comorbidity. The median follow-up period were 30 days. The most common comorbidities were hypertension (24%, n = 40,154), asthma (16%, n = 26,814), and diabetes (14.7%, n = 24,662). Individuals with comorbidity had higher risk of mortality compared to those without (HR = 2.80, 95%CI 2.35-3.34; p<0.001), and the risk substantially was elevated from 2.14 (95%CI 1.76-2.60) to 4.81 (95%CI 3.95-5.85) times as the number of comorbidities increased from one to five or more. Significant predictors for mortality included comorbidities such as solid organ transplant (HR = 3.06, 95%CI 2.03-4.63; p<0.001), dementia (HR = 1.46, 95%CI 1.35-1.58; p<0.001), chronic kidney disease (HR = 1.45, 95%CI 1.34-1.57; p<0.001), severe mental illness (HR = 1.42, 95%CI%, 1.12-1.80; p<0.001), cardiovascular disease (CVD) (HR = 1.22, 95%CI, 1.15-1.30), diabetes (HR = 1.19, 95%, 1.12-1.26; p<0.001), chronic obstructive pulmonary disease (COPD) (HR = 1.19, 95%CI 1.12-1.26; p<0.001), cancer (HR = 1.17, 95%CI, 1.09-1.27; p<0.001), hypertension (HR = 1.16, 95%CI, 1.07-1.26; p<0.001). Compared to their effect in older age groups, comorbidities were associated with higher risk of mortality and severity in individuals under 50 years old. Individuals with five or more comorbidities in the below 50 years age group had 395.44 (95%CI, 57.93-2699.44, p<0.001) times higher risk of mortality compared to those without. Limitations include that data were collected during 2020 when the new variants of concern were not predominant, and that the ICES databases do not contain detailed individual-level socioeconomic and racial variables. CONCLUSION: We found that solid organ transplant, dementia, chronic kidney disease, severe mental illness, CVD, hypertension, COPD, cancer, diabetes, rheumatoid arthritis, HIV, and asthma were associated with mortality or severity. Our study highlights that the number of comorbidities was a strong risk factor for deaths and severe outcomes among younger individuals with COVID-19. Our findings suggest that in addition of prioritizing by age, vaccination priority groups should also include younger population with multiple comorbidities.

Effects of high-frequency temperature variabilities on the morbidity of chronic obstructive pulmonary disease: Evidence in 21 cities of Guangdong, South China.

BACKGROUND: While temperature changes have been confirmed as one of the contributory factors affecting human health, the association between high-frequency temperature variability (HFTV, i.e., temperature variation at short time scales such as 1, 2, and 5 days) and the hospitalization of chronic obstructive pulmonary disease (COPD) was rarely reported. OBJECTIVES: To evaluate the associations between high-frequency temperature variabilities (i.e., at 1, 2, and 5-day scales) and daily COPD hospitalization. METHODS: We collected daily records of COPD hospitalization and meteorological variables from 2013 to 2017 in 21 cities of Guangdong Province, South China. A quasi-Poisson regression with a distributed lag nonlinear model was first employed to quantify the effects of two HFTV measures, i.e., the day-to-day (DTD) temperature change and the intraday-interday temperature variability (IITV), on COPD morbidity for each city. Second, we used multivariate meta-analysis to pool the city-specific estimates, and stratified analyses were performed by age and sex to identify vulnerable groups. Then, the meta-regression with city-level characteristics was employed to detect the potential sources of the differences among 21 cities. RESULTS: A monotonic increasing curve of the overall exposure-response association was observed, suggesting that positive HFTV (i.e., increased DTD and IITV) will significantly increase the risk of COPD admission. Negative DTD was associated with reduced COPD morbidity while positive DTD elevated the COPD risk. An interquartile range (IQR) increase in DTD was associated with a 24% (95% CI: 12-38%) increase in COPD admissions. An IQR increase in IITV0-1 was associated with 18% (95% CI: 7-27%) increase in COPD admissions. Males and people aged 0-64 years appeared to be more vulnerable to the DTD effect than others. Potential sources of the disparity among different cities include urbanization level, sex structure, industry structure, gross domestic product (GDP), health care services, and air quality. CONCLUSIONS: The increases of DTD and IITV have significant adverse impacts on COPD hospitalization. As climate change intensifies, precautions need to be taken to mitigate the impacts of high-frequency temperature changes.

Greenness exposure and all-cause mortality during multi-drug resistant tuberculosis treatment: A population-based cohort study.

BACKGROUND: Living closer to greenness were thought to benefit various health outcomes. We aimed to assess the association between residential greenness and mortality among patients undergoing multidrug resistant tuberculosis (MDR-TB) treatment. METHODS: We enrolled all local MDR-TB patients reported in Zhejiang, China from 2009 to 2017 and followed them throughout the treatment. We calculated the contemporaneous normalized difference vegetation index (NDVI) in the 250 and 500 m radius around patient's residence. Cox proportional hazards regression models with time-varying NDVI were used to assess the impact of greenness exposure on all-cause mortality during MDR-TB treatment, adjusting for potential individual and contextual covariates. RESULTS: We ascertained 1,621 active MDR-TB cases, which contributed 3036 person-years at risk with an average follow-up of 684 days (s.d. 149 days) per patient. Among them, there were 163 deaths during follow-up, representing a crude mortality rate of 537 deaths per 10,000 person-years. Patients exposed to the second quintile (Q2) of greenness within the 500 m buffer had around 64% reduced mortality risk over the lowest quintile of greenness with hazard ratio (HR) = 0.364 (95% CI: 0.109-1.22). In lower nighttime light (NTL) areas, the hazard ratios (HR) per quintile increase in NDVI within the 500 m buffer were Q2: 0.35 (95% CI: 0.10-1.18), Q3: 0.24 (95% CI: 0.09-0.66), Q4: 0.26 (95% CI: 0.10-0.69), and Q5: 0.26 (95% CI: 0.10-0.71) relevant to the lowest quintile Q1, with a trend of p-value ≤0.01. Patients who were female, younger (<60 years), resided in urban areas, or had high PM2.5 (i.e. particles with diagram ≤2.5 µm) exposure were more likely to benefit from greenness exposure. Associations were neither observed with NDVI in the 250 m buffer nor for patients living in higher NTL areas. There was a non-linear exposure-response relationship between greenness and deaths with p-value ≤0.05. CONCLUSION: Increasing greenness exposure along with medical treatment reduces all-cause mortality among patients living in lower NTL areas.

Geographical disparities in access to hospital care in Ontario, Canada: a spatial coverage modelling approach.

OBJECTIVES: Previous studies on geographical disparities in healthcare access have been limited by not accounting for the healthcare provider's capacity, a key determinant of supply and demand relationships. DESIGN: This study proposed a spatial coverage modelling approach to evaluate disparities in hospital care access using Canadian Institute for Health Information data in 2007. SETTING: This study focusses on accessibility of inpatient and emergency cares at both levels of individual hospital and the administrative regions of Local Health Integration Network (LHIN) levels. MEASURES: We integrated a set of traffic and geographical data to precisely estimate travel time as a measure of the level of accessibility to the nearest hospital by three scenarios: walking, driving and a combination of the both. We estimated population coverage rates, using hospital capacities and population in the catchments, as a measure of the level of the healthcare availability. Hospital capacities were calculated based on numbers of medical staff and beds, occupation rates and annual working hours of healthcare providers. RESULTS: We observed significant disparities in hospital capacity, travel time and population coverage rate across the LHINs. This study included 25 teaching and 148 community hospitals. The teaching hospitals had stronger capacities with 489 209 inpatient and 130 773 emergency patients served in the year, while the population served in community hospitals were 2.64 times higher. Compared with north Ontario, more locations in the south could reach to hospitals within 30 min irrespective of the travel mode. Additionally, Northern Ontario has higher population coverage rates, for example, with 42.6~46.9% for inpatient and 15.7~44% for emergency cares, compared with 2.4~34.7% and 0.35~14.6% in Southern Ontario, within a 30 min catchment by driving. CONCLUSION: Creating a comprehensive, flexible and integrated healthcare system should be considered as an effective approach to improve equity in access to care.

Contribution of urbanization to the changes in extreme climate events in urban agglomerations across China.

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.