Effect of ambient ozone pollution on disease burden globally: A systematic analysis for the global burden of disease study 2019.

BACKGROUND: Exposure to ambient ozone pollution causes health loss and even death, and both are the main risk factors for the disease burden worldwide. We comprehensively evaluated the ozone pollution-related disease burden. METHODS: First, numbers and age-standardized rates of deaths and disability-adjusted life years (DALYs) were assessed globally and by sub-types in 2019. Furthermore, the temporal trend of the disease burden was explored by the linear regression model from 1990 to 2019. The cluster analysis was used to evaluate the changing pattern of related disease burden across Global Burden of Disease Study (GBD) regions. Finally, the age-period-cohort (APC) model and the Bayesian age-period-cohort (BAPC) model were used to predict the future disease burden in the next 25 years. RESULT: Exposure to ozone pollution contributed to 365,222 deaths and 6,210,145 DALYs globally in 2019, which accounted for 0.65 % of deaths globally and 0.24 % of DALYs globally. The disease burden was consistently increasing with age. Males were high-risk populations and low-middle socio-demographic index (SDI) regions were high-risk areas. The disease burden of ozone pollution varied considerably across the GBD regions and the countries. In 2019, the number of deaths and DALYs cases increased by 76.11 % and 56.37 %, respectively compared to those in 1990. The predicted results showed that the number of deaths cases and DALYs cases for both genders would still increase from 2020 to 2044. CONCLUSION: In conclusion, ambient ozone pollution has threatened public health globally. More proactive and effective strategic measures should be developed after considering global-specific circumstances.

Exploring the relationship between air quality index and lung cancer mortality in India: predictive modeling and impact assessment.

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India.

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China.

Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015-2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001-1.007) and 1.009 (1.005-1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

Time-Trends in Air Pollution Impact on Health in Italy, 1990-2019: An Analysis From the Global Burden of Disease Study 2019.

Objectives: We explored temporal variations in disease burden of ambient particulate matter 2.5 µm or less in diameter (PM2.5) and ozone in Italy using estimates from the Global Burden of Disease Study 2019. Methods: We compared temporal changes and percent variations (95% Uncertainty Intervals [95% UI]) in rates of disability adjusted life years (DALYs), years of life lost, years lived with disability and mortality from 1990 to 2019, and variations in pollutant-attributable burden with those in the overall burden of each PM2.5- and ozone-related disease. Results: In 2019, 467,000 DALYs (95% UI: 371,000, 570,000) were attributable to PM2.5 and 39,600 (95% UI: 18,300, 61,500) to ozone. The crude DALY rate attributable to PM2.5 decreased by 47.9% (95% UI: 10.3, 65.4) from 1990 to 2019. For ozone, it declined by 37.0% (95% UI: 28.9, 44.5) during 1990-2010, but it increased by 44.8% (95% UI: 35.5, 56.3) during 2010-2019. Age-standardized rates declined more than crude ones. Conclusion: In Italy, the burden of ambient PM2.5 (but not of ozone) significantly decreased, even in concurrence with population ageing. Results suggest a positive impact of air quality regulations, fostering further regulatory efforts.

Identifying the causal effects of long-term exposure to PM2.5 and ground surface ozone on individual medical costs in China-evidence from a representative longitudinal nationwide cohort.

BACKGROUND: There is little evidence on whether PM2.5 and ground surface ozone have consistent effects on increased individual medical costs, and there is a lack of evidence on causality in developing countries. METHODS: This study utilized balanced panel data from 2014, 2016, and 2018 waves of the Chinese Family Panel Study. The Tobit model was developed within a counterfactual causal inference framework, combined with a correlated random effects and control function approach (Tobit-CRE-CF), to explore the causal relationship between long-term exposure to air pollution and medical costs. We also explored whether different air pollutants exhibit comparable effects. RESULTS: This study encompassed 8928 participants and assessed various benchmark models, highlighting the potential biases from failing to account for air pollution endogeneity or overlooking respondents without medical costs. Using the Tobit-CRE-CF model, significant effects of air pollutants on increased individual medical costs were identified. Specifically, margin effects for PM2.5 and ground-level ozone signifying that a unit increase in PM2.5 and ground-level ozone results in increased total medical costs of 199.144 and 75.145 RMB for individuals who incurred fees in the previous year, respectively. CONCLUSIONS: The results imply that long-term exposure to air pollutants contributes to increased medical costs for individuals, offering valuable insights for policymakers aiming to mitigate air pollution's consequences.

Association of Long-term Exposure to Air Pollution With Late-Life Depression in Older Adults in the US.

Importance: Emerging evidence has suggested harmful associations of air pollutants with neurodegenerative diseases among older adults. However, little is known about outcomes regarding late-life mental disorders, such as geriatric depression. Objective: To investigate if long-term exposure to air pollution is associated with increased risk of late-life depression diagnosis among older adults in the US. Design, Setting, and Participants: This population-based longitudinal cohort study consisted of US Medicare enrollees older than 64 years. Data were obtained from the US Centers for Medicare and Medicaid Services Chronic Conditions Warehouse. The participants were continuously enrolled in the Fee-for-Service program and both Medicare Part A and Part B. After the 5-year washout period at entry, a total of 8 907 422 unique individuals were covered over the study period of 2005 to 2016, who contributed to 1 526 690 late-onset depression diagnoses. Data analyses were performed between March 2022 and November 2022. Exposures: The exposures consisted of residential long-term exposure to fine particulate matter (PM2.5), measured in micrograms per cubic meter; nitrogen dioxide (NO2), measured in parts per billion; and ozone (O3), measured in parts per billion. Main Outcomes and Measures: Late-life depression diagnoses were identified via information from all available Medicare claims (ie, hospital inpatient, skilled nursing facility, home health agency, hospital outpatient, and physician visits). Date of the first occurrence was obtained. Hazard ratios and percentage change in risk were estimated via stratified Cox proportional hazards models accounting for climate coexposures, neighborhood greenness, socioeconomic conditions, health care access, and urbanicity level. Results: A total of 8 907 422 Medicare enrollees were included in this study with 56.8% being female individuals and 90.2% being White individuals. The mean (SD) age at entry (after washout period) was 73.7 (4.8) years. Each 5-unit increase in long-term mean exposure to PM2.5, NO2, and O3 was associated with an adjusted percentage increase in depression risk of 0.91% (95% CI, 0.02%-1.81%), 0.61% (95% CI, 0.31%- 0.92%), and 2.13% (95% CI, 1.63%-2.64%), respectively, based on a tripollutant model. Effect size heterogeneity was found among subpopulations by comorbidity condition and neighborhood contextual backgrounds. Conclusions and Relevance: In this cohort study among US Medicare enrollees, harmful associations were observed between long-term exposure to air pollution and increased risk of late-life depression diagnosis.

Estimating the burden of disease attributable to ambient air pollution (ambient PM2.5 and ambient ozone) in South Africa for 2000, 2006 and 2012.

Background: Globally, a growing body of research has shown that ambient air pollution is one of the most critical environmental issues, especially in relation to human health. Exposure to ambient air pollution leads to serious health conditions such as lower respiratory infections, cancers, diabetes mellitus type 2, ischaemic heart disease, stroke and chronic obstructive pulmonary disease. Objectives: To estimate the burden of disease attributable to ambient air pollution in South Africa (SA) for the years 2000, 2006 and 2012. Methods: Comparative risk assessment method was used to determine the burden of disease due to two pollutants (particulate matter (PM2.5) and ambient ozone). Regionally optimised fully coupled climate chemistry models and surface air pollution observations were used to generate concentrations of PM2.5 and ozone for each SA Census Small Area Level, for the year 2012. For 2000 and 2006, population-weighted PM2.5and ozone were estimated, based on the 2012 results. Following the identification of disease outcomes associated with particulate matter with aerodynamic diameter <2.5 µm (PM2.5) and ozone exposure, the attributable burden of disease was estimated for 2000, 2006 and 2012. Furthermore, for the year 2012, the burden of disease attributable to ambient air pollution exposure was computed at provincial levels. Results: In 2012, approximately 97.6% of people in SA were exposed to PM2.5 at levels above the 2005 World Health Organization guideline: 10 µg/m3 annual mean. From 2000 to 2012, population-weighted annual average PM2.5 increased from 26.6 µg/m3 to 29.7 µg/m3, and ozone 6-month high 8-hour daily maximum increased from 64.4 parts per billion (ppb) to 72.1 ppb. At a national scale, in the year 2000, 15 619 (95% uncertainty interval (UI) 8 958 - 21 849) deaths were attributed to PM2.5 exposure, while 1 326 (95% UI 534 - 1 885) deaths were attributed to ozone. In 2006, an estimated 19 672 deaths (95% UI 11 526 - 27 086) were attributed to PM2.5, and a further 1 591 deaths (95% UI 651 - 2 236) to ozone exposure. In 2012, deaths attributed to PM2.5 were 19 507 (95% UI 11 318 - 27 111), and to ozone 1 734 (95% UI 727 - 2 399). Additionally, population-weighted provincial scale analysis showed that Gauteng Province had the highest number of attributable deaths due to both PM2.5 and ozone in 2012. Conclusion: The study showed that ambient air pollution exposure is an important health risk in SA, requiring both short- and long-term intervention. In the short term, the SA Ambient Air Quality Standards and industrial minimum emissions standards need to be enforced. In the longer term, to reduce air pollution and the associated disease burden, the combustion of fossil fuels as a source of energy for power generation and transportation, as well as industrial and domestic uses, needs to be replaced with clean renewable energy sources. In addition to local measures, when the southern African prevalent anticyclonic air dynamics that transport regionally emitted pollutants into SA (especially from biomass burning) are considered, it is also advisable to establish long-term regional co-operation in reducing air pollution.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project.Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses.Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 µg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure.For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-µg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 µg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]).Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged.We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 µg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards.There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty.The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb.institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred.A list of abbreviations and other terms appears at the end of this volume.

Short-term exposure to ozone and economic burden of premature mortality in Italy: A nationwide observation study.

Italy is among the countries with the highest ozone concentration in Europe. However, the mortality burden of ozone and related economic loss has not been fully characterized. This study aimed to estimate the ozone-mortality association in Italy and evaluate attributable mortality burden and related economic loss in 2015-2019. We collected daily all-cause mortality data stratified by age and sex from 2015 to 2019 in 107 provinces of Italy. A two-stage time-series framework was applied to estimate the association between daily maximum eight-hour average ozone and mortality as well as economic loss. An overall increase in the risk of mortality (RR=1.0043, 95% CI: 1.0029, 1.0057) was associated with every 10 µg/m3 increase in ozone. Generally, a total of 70,060 deaths and $65 billion economic loss were attributed to ozone exposure, corresponding to 3.11% of mortality and about 0.5% of the national GDP during the study period, respectively. The highest ozone-related mortality burden (30,910 deaths) and economic loss ($29.24 billion) were observed in the hot season. This nationwide study suggested considerable mortality burden and economic loss were associated with exposure to ozone. More actions and policies should be proposed to reduce ozone levels and help the public protect their health.

Air Pollutants and Asthma Hospitalization in the Medicaid Population.

Rationale: Risk of asthma hospitalization and its disparities associated with air pollutant exposures are less clear within socioeconomically disadvantaged populations, particularly at low degrees of exposure. Objectives: To assess effects of short-term exposures to fine particulate matter (particulate matter with an aerodynamic diameter of ⩽2.5 µm [PM2.5]), warm-season ozone (O3), and nitrogen dioxide (NO2) on risk of asthma hospitalization among national Medicaid beneficiaries, the most disadvantaged population in the United States, and to test whether any subpopulations were at higher risk. Methods: We constructed a time-stratified case-crossover dataset among 1,627,002 hospitalizations during 2000-2012 and estimated risk of asthma hospitalization associated with short-term PM2.5, O3, and NO2 exposures. We then restricted the analysis to hospitalizations with degrees of exposure below increasingly stringent thresholds. Furthermore, we tested effect modifications by individual- and community-level characteristics. Measurements and Main Results: Each 1-µg/m3 increase in PM2.5, 1-ppb increase in O3, and 1-ppb increase in NO2 was associated with 0.31% (95% confidence interval [CI], 0.24-0.37%), 0.10% (95% CI, 0.05 - 0.15%), and 0.28% (95% CI, 0.24 - 0.32%) increase in risk of asthma hospitalization, respectively. Low-level PM2.5 and NO2 exposures were associated with higher risk. Furthermore, beneficiaries with only one asthma hospitalization during the study period or in communities with lower population density, higher average body mass index, longer distance to the nearest hospital, or greater neighborhood deprivation experienced higher risk. Conclusions: Short-term air pollutant exposures increased risk of asthma hospitalization among Medicaid beneficiaries, even at concentrations well below national standards. The subgroup differences suggested individual and contextual factors contributed to asthma disparities under effects of air pollutant exposures.

Emulating causal dose-response relations between air pollutants and mortality in the Medicare population.

BACKGROUND: Fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are major air pollutants that pose considerable threats to human health. However, what has been mostly missing in air pollution epidemiology is causal dose-response (D-R) relations between those exposures and mortality. Such causal D-R relations can provide profound implications in predicting health impact at a target level of air pollution concentration. METHODS: Using national Medicare cohort during 2000-2016, we simultaneously emulated causal D-R relations between chronic exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and all-cause mortality. To relax the contentious assumptions of inverse probability weighting for continuous exposures, including distributional form of the exposure and heteroscedasticity, we proposed a decile binning approach which divided each exposure into ten equal-sized groups by deciles, treated the lowest decile group as reference, and estimated the effects for the other groups. Binning continuous exposures also makes the inverse probability weights robust against outliers. RESULTS: Assuming the causal framework was valid, we found that higher levels of PM2.5, O3, and NO2 were causally associated with greater risk of mortality and that PM2.5 posed the greatest risk. For PM2.5, the relative risk (RR) of mortality monotonically increased from the 2nd (RR, 1.022; 95% confidence interval [CI], 1.018-1.025) to the 10th decile group (RR, 1.207; 95% CI, 1.203-1.210); for O3, the RR increased from the 2nd (RR, 1.050; 95% CI, 1.047-1.053) to the 9th decile group (RR, 1.107; 95% CI, 1.104-1.110); for NO2, the DR curve wiggled at low levels and started rising from the 6th (RR, 1.005; 95% CI, 1.002-1.018) till the highest decile group (RR, 1.024; 95% CI, 1.021-1.027). CONCLUSIONS: This study provided more robust evidence of the causal relations between air pollution exposures and mortality. The emulated causal D-R relations provided significant implications for reviewing the national air quality standards, as they inferred the number of potential early deaths prevented if air pollutants were reduced to specific levels; for example, lowering each air pollutant concentration from the 70th to 60th percentiles would prevent 65,935 early deaths per year.

Assessing additive effects of air pollutants on mortality rate in Massachusetts.

BACKGROUND: We previously found additive effects of long- and short-term exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on all-cause mortality rate using a generalized propensity score (GPS) adjustment approach. The study addressed an important question of how many early deaths were caused by each exposure. However, the study was computationally expensive, did not capture possible interactions and high-order nonlinearities, and omitted potential confounders. METHODS: We proposed two new methods and reconducted the analysis using the same cohort of Medicare beneficiaries in Massachusetts during 2000-2012, which consisted of 1.5 million individuals with 3.8 billion person-days of follow-up. The first method, weighted least squares (WLS), leveraged large volume of data by aggregating person-days, which gave equivalent results to the linear probability model (LPM) method in the previous analysis but significantly reduced computational burden. The second method, m-out-of-n random forests (moonRF), implemented scaling random forests that captured all possible interactions and nonlinearities in the GPS model. To minimize confounding bias, we additionally controlled relative humidity and health care utilizations that were not included previously. Further, we performed low-level analysis by restricting to person-days with exposure levels below increasingly stringent thresholds. RESULTS: We found consistent results between LPM/WLS and moonRF: all exposures were positively associated with mortality rate, even at low levels. For long-term PM2.5 and O3, the effect estimates became larger at lower levels. Long-term exposure to PM2.5 posed the highest risk: 1 µg/m3 increase in long-term PM2.5 was associated with 1053 (95% confidence interval [CI]: 984, 1122; based on LPM/WLS methods) or 1058 (95% CI: 988, 1127; based on moonRF method) early deaths each year among the Medicare population in Massachusetts. CONCLUSIONS: This study provides more rigorous causal evidence between PM2.5, O3, and NO2 exposures and mortality, even at low levels. The largest effect estimate for long-term PM2.5 suggests that reducing PM2.5 could gain the most substantial benefits. The consistency between LPM/WLS and moonRF suggests that there were not many interactions and high-order nonlinearities. In the big data context, the proposed methods will be useful for future scientific work in estimating causality on an additive scale.

Impact of Environmental Degradation on Human Health: An Assessment Using Multicriteria Decision Making.

Air pollution has emerged as a major global concern in recent decades as a result of rapid urbanization and industrialization, leading to a variety of adverse health outcomes. This research aims to investigate the influence of exposure to ambient and household particulate matter pollution (PM2.5), and ground-level ozone (O3) pollution on respiratory and cardiac mortality in Pakistan. We used grey incidence analysis (GIA) methodology to estimate the degree of proximity among selected variables and rank them based on mortality. Hurwicz's criterion is then adopted for further optimization by prioritizing the selected factors with the greatest influence on respiratory and cardiac mortality. The GIA findings revealed that asthma mortality is considerably impacted by exposure to ambient and household PM2.5 concentration while ischemic heart disease (IHD) mortality is potentially influenced by ground-level ozone exposure. Furthermore, results based on Hurwicz's analysis demonstrated that exposure to ambient PM2.5 concentration appeared as the most intensified factor of respiratory and cardiac mortality. This corroboration adds to the growing body of research demonstrating that exposure to ambient PM2.5 adversely leads to respiratory and cardiac risks, emphasizing the demand for further improvement of air quality in Pakistan. Besides, the suggested methodologies provide a valuable tool and additional practical knowledge for policymakers and decision-makers in drawing rational decisions.

Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019.

BACKGROUND: The association of air pollution with multiple adverse health outcomes is becoming well established, but its negative economic impact is less well appreciated. It is important to elucidate this impact for the states of India. METHODS: We estimated exposure to ambient particulate matter pollution, household air pollution, and ambient ozone pollution, and their attributable deaths and disability-adjusted life-years in every state of India as part of the Global Burden of Disease Study (GBD) 2019. We estimated the economic impact of air pollution as the cost of lost output due to premature deaths and morbidity attributable to air pollution for every state of India, using the cost-of-illness method. FINDINGS: 1·67 million (95% uncertainty interval 1·42-1·92) deaths were attributable to air pollution in India in 2019, accounting for 17·8% (15·8-19·5) of the total deaths in the country. The majority of these deaths were from ambient particulate matter pollution (0·98 million [0·77-1·19]) and household air pollution (0·61 million [0·39-0·86]). The death rate due to household air pollution decreased by 64·2% (52·2-74·2) from 1990 to 2019, while that due to ambient particulate matter pollution increased by 115·3% (28·3-344·4) and that due to ambient ozone pollution increased by 139·2% (96·5-195·8). Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28·8 billion (21·4-37·4) and $8·0 billion (5·9-10·3), respectively, in India in 2019. This total loss of $36·8 billion (27·4-47·7) was 1·36% of India's gross domestic product (GDP). The economic loss as a proportion of the state GDP varied 3·2 times between the states, ranging from 0·67% (0·47-0·91) to 2·15% (1·60-2·77), and was highest in the low per-capita GDP states of Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh, and Chhattisgarh. Delhi had the highest per-capita economic loss due to air pollution, followed by Haryana in 2019, with 5·4 times variation across all states. INTERPRETATION: The high burden of death and disease due to air pollution and its associated substantial adverse economic impact from loss of output could impede India's aspiration to be a $5 trillion economy by 2024. Successful reduction of air pollution in India through state-specific strategies would lead to substantial benefits for both the health of the population and the economy. FUNDING: UN Environment Programme; Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Continuous exposure to ambient air pollution and chronic diseases: prevalence, burden, and economic costs.

Studies that assess the connection between the prevalence of chronic diseases and continuous exposure to air pollution are scarce in developing countries, mainly due to data limitations. Largely overcoming data limitations, this study aimed to investigate the association between the likelihood of reporting a set of chronic diseases (diabetes, cancer, stroke and myocardial infarction, asthma, and hypertension) and continuous exposure to carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and coarse particulate matter (PM10). Using the estimated associations, the disease burden and economic costs of continuous exposure to air pollutants were also approximated. A 2011 Health Equity Assessment and Response Tool survey from Tehran, Iran, was used in the main analyses. A sample of 67,049 individuals who had not changed their place of residence for at least 2 years before the survey and reported all relevant socioeconomic information was selected. The individuals were assigned with the average monthly air pollutant levels of the nearest of 16 air quality monitors during the 2 years leading to the survey. Both single- and multi-pollutant analyses were conducted. The country's annual household surveys from 2002 to 2011 were used to calculate the associated economic losses. The single-pollutant analysis showed that a one-unit increase in monthly CO (ppm), NO2 (ppb), O3 (ppb), and PM10 (µg/m3) during the 2 years was associated with 751 [confidence interval (CI): 512-990], 18 (CI: 12-24), 46 (CI: -27-120), and 24 (CI: 13-35) more reported chronic diseases in 100,000, respectively. The disease-specific analyses showed that a unit change in average monthly CO was associated with 329, 321, 232, and 129 more reported cases of diabetes, hypertension, stroke and myocardial infarction, and asthma in 100,000, respectively. The measured associations were greater in samples with older individuals. Also, a unit change in average monthly O3 was associated with 21 (in 100,000) more reported cases of asthma. The multi-pollutant analyses confirmed the results from single-pollutant analyses. The supplementary analyses showed that a one-unit decrease in monthly CO level could have been associated with about 208 (CI: 147-275) years of life gained or 15.195 (CI: 10.296-20.094) thousand US dollars (USD) in life-time labor market income gained per 100,000 30-plus-year-old Tehranis.

Violent crime and socioeconomic deprivation in shaping asthma-related pollution susceptibility: a case-crossover design.

BACKGROUND: The objective of this study was to quantify and compare the relative influence of community violent crime and socioeconomic deprivation in modifying associations between ozone and emergency department (ED) visits for asthma among children. METHODS: We used a spatiotemporal case-crossover analysis for all New York City EDs for the months May-September from 2005 to 2011 from a statewide administrative ED dataset. The data included 11 719 asthmatic children aged 5-18 years, and the main outcome measure was percentage of excess risk of asthma ED visit based on Cox regression analysis. RESULTS: Stronger ozone-asthma associations were observed for both elevated crime and deprivation (eg, on lag day 2, we found 20.0% (95% CI 10.2% to 30.6 %) and 21.0% (10.5% to 32.5%) increased risk per 10 ppb ozone, for communities in the highest vs lowest quartiles of violent crime and deprivation, respectively). However, in varied models accounting for both modifiers, only violence retained significance. CONCLUSIONS: The results suggest stronger spatiotemporal ozone-asthma associations in communities of higher violent crime or deprivation. Notably, violence was the more consistent and significant modifier, potentially mediating a substantial portion of socioeconomic position-related susceptibility.

Comparison of health and economic impacts of PM2.5 and ozone pollution in China.

Many studies have reported associations between air pollution and health impacts, but few studies have explicitly differentiated the economic effects of PM2.5 and ozone at China's regional level. This study compares the PM2.5 and ozone pollution-related health impacts based on an integrated approach. The research framework combines an air pollutant emission projection model (GAINS), an air quality model (GEOS-Chem), a health model using the latest exposure-response functions, medical prices and value of statistical life (VSL), and a general equilibrium model (CGE). Results show that eastern provinces in China encounter severer loss from PM2.5 and more benefit from mitigation policy, whereas the lower income western provinces encounter severer health impacts and economic burdens due to ozone pollution, and the impact in southern and central provinces is relatively lower. In 2030, without control policies, PM 2.5 pollution could lead to losses of 2.0% in Gross Domestic Production (GDP), 210 billion Chinese Yuan (CNY) in health expenditure and a life loss of around 10,000 billion, while ozone pollution could contribute to GDP loss by 0.09% (equivalent to 78 billion CNY), 310 billion CNY in health expenditure, and a life loss of 2300 billion CNY (equivalent to 2.7% of GDP). By contrast, with control policies, the GDP and VSLs loss in 2030 attributable to ambient air pollution could be reduced significantly. We also find that the health and economic impacts of ozone pollution are significantly lower than PM2.5, but are much more difficult to mitigate. The Chinese government should promote air pollution control policies that could jointly reduce PM2.5 and ozone pollution.

A national burden assessment of estimated pediatric asthma emergency department visits that may be attributed to elevated ozone levels associated with the presence of smoke.

Asthma is the most common pediatric disease in the USA. It has been consistently demonstrated that asthma symptoms are exacerbated by exposure to ozone. Ozone (O3) is a secondary pollutant produced when volatile organic compounds (VOCs) are oxidized in the atmosphere in the presence of nitrogen oxides (NOx). At ground level, elevated ozone is typically formed as a result of human activities. However, wildfires represent an additional source of ozone precursors. Recent evidence suggests that smoke can increase ozone concentrations. We estimated the number of excess asthma-related emergency department (ED) visits in children with asthma that may be attributed to elevated ozone associated with smoke (EOAS) in the USA. We conducted a quantitative burden assessment (BA) using a Monte Carlo approach to estimate the median number of excess pediatric asthma ED visits that may be attributed to EOAS among children with asthma in the continental USA between 2005 and 2014, as well as 95% confidence bounds (95% CB). We estimated that a median of 2403 (95% CB 235-5382) pediatric asthma ED visits could be attributed to EOAS exposure between 2005 and 2014 in the continental USA. Furthermore, the impact of EOAS on estimated asthma ED visits was greatest in the eastern half of the continental USA. We found a significant increase in pediatric asthma ED visits that may be attributed to exposure to EOAS. EOAS may have a measurable negative impact on children with asthma in the USA.

Longitudinal Effect of Ambient Air Pollution and Pollen Exposure on Asthma Control: The Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Asthma Study.

OBJECTIVE: Although exposure to air pollution and pollen is associated with asthma exacerbation and increased health care use, longitudinal effects of fine particulate matter 2.5 (PM2.5), ozone (O3), and pollen exposure on asthma control status in pediatric patients are understudied. This study investigated effects of exposure to PM2.5, O3, and pollen on asthma control status among pediatric patients with asthma. METHODS: A total of 229 dyads of pediatric patients with asthma and their parents were followed for 15 months. The Asthma Control and Communication Instrument was used to measure asthma control, which was reported weekly by parents during a 26-week period. PM2.5 and O3 data were collected from the US Environmental Protection Agency Air Quality System. Pollen data were obtained from Intercontinental Marketing Services Health. Mean air pollutant and pollen exposures within 7 days before the reporting of asthma control were used to estimate weekly exposures for each participant. Linear mixed-effects models were performed to test associations of PM2.5, O3, and pollen exposure with asthma control status. Sensitivity analyses were performed to evaluate the robustness of findings by different exposure monitoring days per week and distances between monitoring sites and participants' residences. RESULTS: Elevated PM2.5 concentration and pollen severity were associated with poorer asthma control status (P < .05), yet elevated O3 concentration was marginally associated with better asthma control (P < .1). CONCLUSIONS: Poorer asthma control status was associated with elevated PM2.5 and pollen severity. Reducing harmful outdoor environmental ambient exposure may improve asthma outcomes in children and adolescents.

Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China.

There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM10, PM2.5, SO2, O3, NO2, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O3 and NO2 have the highest health impact, followed by PM10 and PM2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RRTotal than that between AQI and RRTotal endorsed our efforts.