Background:Living near greenspace is associated with decreased cardiovascular disease (CVD). Greenspace estimates, however, typically represent all types of vegetation using top-down satellite images, which incorporate exposure misclassification and limit policy relevance. Objective:We studied the association between street-view greenspace measures with incident CVD using a large, long-term prospective US cohort of female nurses. Methods:We estimated the percentage of streetscapes composed of visible trees, grass, and other green (plants/flowers/fields) from 350 million street-view images using deep learning models. Estimates were applied to Nurses' Health Study participants (N = 88,788) within 500 m of their residential addresses. We used Cox models to estimate associations from 2000 to 2018 between street-view greenspace measures and risk of incident CVD, assessed through self-report, medical record review, or death certificates, and adjusted for individual- and area-level factors. Results:In adjusted models, higher percentages of visible trees were associated with lower CVD incidence (hazard ratio [HR] per interquartile range [IQR] 0.96 (95% confidence interval 0.93, 1.00]), while higher percentages of visible grass (HR 1.06 [1.02, 1.11]) and other green space types (HR 1.03 [1.01, 1.04]) were associated with higher CVD incidence. We did not observe evidence of effect modification by population density, Census region, air pollution, satellite-based vegetation, or neighborhood socioeconomic status. Findings were robust to adjustment for other spatial and behavioral factors and persisted even after adjustment for traditional satellite-based vegetation indices. Discussion:Specific greenspace types may be protective or harmful for CVD. Aggregating greenspace into a single exposure category limits epidemiological research and potential interventions to increase health-promoting greenspace.

INTRODUCTION:Environmental exposures have been associated with type 2 diabetes (DM-2) prevalence, but associations for incidence remain inconclusive. This study examined the associations between environmental exposures (e.g. air pollution, greenness and traffic noise) and DM-2 incidence in a population where associations of these exposures with DM-2 prevalence have previously been established. METHODS:Data of 249,495 Dutch individuals who participated in the Public Health Monitor in 2012 were used, enriched with data on air pollution, greenness, road traffic noise, and obesogenic environment. The incidence of DM-2 was based on medication prescriptions from 2013 to 2022. Cox regression models were performed to investigate the association between the exposures and DM-2 incidence. Potential effect modification by age (<40 years, 40-64 years, 65-79 years and ≥80 years) and sex was examined. RESULTS:No significant associations were observed between the exposures and DM-2 incidence in the total population. No significant associations were also observed across age groups and men and women, except that greenness was statistically significantly associated with a lower DM-2 incidence (Hazard Ratio per interquartile range (HRIQR) = 0.96, 95% confidence interval (CI) = [0.92; 1.00]) in individuals aged 40-64 years. CONCLUSION:In conclusion, a wide range of environmental exposures were not associated with DM-2 incidence. Therefore, our study on DM-2 incidence does not support the previously identified associations between environmental exposures and DM-2 prevalence.

BACKGROUND:Sudden cardiac death (SCD) is an important contributor to the global burden of cardiovascular disease. Previous studies have reported an association with extremes in temperature in the hours and days leading up to SCD, but there has been little investigation focusing on the months and year before an SCD event. Therefore, the objective of this study was to investigate the relationship between short-, medium-, and long-term temperature and SCD. METHODS:Temperature predictions using 800-m Parameter-Elevation Regressions on Independent Slopes Model data were used to estimate monthly moving average temperature. Data on temperature were appended to each Nurses' Health Study participant's geocoded residential addresses. SCD cases were determined on the basis of medical record review and next-of-kin reports. Time-varying Cox proportional hazards models were used to assess the association between exposure 1- and 12-month moving averages of temperature and SCD. Individual risk factors, socioeconomic status, and cardiovascular comorbidities were considered as confounders in the final models. RESULTS:Over the follow-up period, there was a total of 428 definite and 36 probable SCD cases among the 118 636 participants. One- and 12-month moving average temperatures were inversely associated with SCD in the basic, risk factor, and fully adjusted models. For example, in the parsimonious individual risk factor model with definite cases only, per interquartile range (15.27 °C for the 1-month moving average temperature, 4.72 °C 12-month moving average temperature) increase in temperature, the hazard ratios for definite SCD were 0.58 (95% CI, 0.44-0.77) for the 1-month and 0.76 (95% CI, 0.66-0.88) for the 12-month moving average. CONCLUSIONS:After controlling for risk factors and cardiovascular morbidities, lower temperatures were associated with SCD. This study demonstrates that short-, medium-, and long-term cold temperature also play an important role in SCD.

Background:To mitigate the health impact of high temperatures, heat plans (HPs) have become widespread around the world. Our aim was to evaluate the temperature-mortality associations and estimate the temperature-related deaths in the Netherlands in the years before (2000-2009) and after (2010-2019) the first activation of the national HP. Methods:We obtained data about daily all-cause mortality (2000-2019) for the entire Dutch population, and by age, sex, neighborhood socioeconomic status, and urbanization. We linked the daily maximum temperature based on 23 monitoring stations across the Netherlands. Time-series Poisson regression models with a distributed lag nonlinear model, adjusted for long-term and seasonal trends and day of the week, were used to assess relative risks (RRs, 95% confidence intervals [CIs]) in the warm months (May-September). Temperature-attributable mortality fractions for high-temperature exposures and potential HP days were calculated. Results:We observed positive associations between daily maximum temperature and mortality in 2000-2009 and in 2010-2019. Associations of high temperatures (28.9 °C-95th percentile) were weaker in 2010-2019 (RR: 1.07, 95% CI: 1.05, 1.09) than in 2000-2009 (RR: 1.17, 95% CI: 1.15, 1.20). The attenuation in temperature-mortality risk was strongest for the elderly, women, and individuals living in low-socioeconomic status neighborhoods. The estimated mortality attributable fractions of high temperatures (≥28.9 °C) were lower in 2010-2019 (0.72, 95% CI: 0.60, 0.84) than in 2000-2009 (1.21%, 95% CI: 1.07, 1.33). Conclusion:The impact of high temperatures on mortality attenuated in the Netherlands. This might be due to the implementation of the national HP, but other factors may have played a role as well.

Background:There is considerable heterogeneity in fine particulate matter (PM2.5)-mortality associations between studies, potentially due to differences in exposure assessment methods. Our aim was to evaluate associations of PM2.5 predicted from different models with nonaccidental and cause-specific mortality.Methods:We followed 107,906 participants of the Nurses' Health Study cohort from 2001 to 2016. PM2.5 concentrations were estimated from spatiotemporal models developed by researchers at the University of Washington (UW), Pennsylvania State University (PSU), and Harvard TH Chan School of Public Health (HSPH). We calculated 12-month moving average concentrations and we used time-varying Cox proportional hazard ratios (HRs).Results:There were 30,242 nonaccidental deaths in 1,435,098 person-years. We observed high correlations and similar temporal trends between the PM2.5 predictions. We found no associations of UW, PSU, or HSPH PM2.5 with nonaccidental mortality, but suggestive positive associations with cancer, cardiovascular, and respiratory disease mortality. There were small differences in HRs between the PM2.5 predictions. All three predictions showed the strongest associations with cancer mortality: HRs (95% confidence interval, expressed per 5 mu g/m3 increase) were 1.06 (1.01, 1.12) for UW, 1.08 (1.03, 1.13) for PSU, and 1.05 (1.00, 1.10) for HSPH. In a subset restricted to participants who were always exposed to PM2.5 below 12 mu g/m3, we observed positive associations with nonaccidental mortality.Conclusion:We found that differences between PM2.5 exposure assessment methods could lead to minor differences in strengths of associations between PM2.5 and cause-specific mortality in a population of US female nurses.

BACKGROUND:Accurately capturing individuals' experiences with greenspace at ground-level can provide valuable insights into their impact on children's health. However, most previous research has relied on coarse satellite-based measurements. METHODS:We utilized CVH and residential address data from Project Viva, a US-based pre-birth cohort, tracking participants from mid-childhood to late adolescence (2007-21). A deep learning segmentation algorithm was applied to street-view images across the US to estimate % street-view trees, grass, and other greenspace (flowers, field, and plants). Exposure estimates were derived by linking street-view greenspace metrics to 500m of participants' residences during mid-childhood, early and late adolescence. CVH scores (range 0-100; higher indicate better CVH) were calculated using the American Heart Association's Life's Essential 8 algorithm at these three time points, incorporating four biomedical components (body weight, blood lipids, blood glucose, blood pressure) and four behavioral components (diet, physical activity, nicotine exposure, sleep). Linear regression models were used to examine cross-sectional and cumulative associations between street-view greenspace metrics and CVH scores. Generalized estimating equations models were used to examine associations between street-view greenspace metrics and changes in CVH scores across three timepoints. All models were adjusted for individual and neighborhood-level confounders. RESULTS:Adjusting for confounders, a one-SD increase in street-view trees within 500m of residence was cross-sectionally associated with a 1.92-point (95%CI: 0.50, 3.35) higher CVH score in late adolescence, but not mid-childhood or early adolescence. Longitudinally, street-view greenspace metrics at baseline (either mid-childhood or early adolescence) were not associated with changes in CVH scores at the same and all subsequent time points. Cumulative street-view greenspace metrics across the three time points were also not associated with CVH scores in late adolescence. CONCLUSION AND RELEVANCE:In this US cohort of children, we observed few evidence of associations between street-level greenspace children's CVH, though the impact may vary with children's growth stage.

BACKGROUND:Climate change is one of the greatest health threats facing humanity. Multiple studies have documented the impact of short-term temperature exposure on human health. However, long-term temperature exposures are far less studied. OBJECTIVES:We examined whether exposures to higher or lower summer and winter average temperatures compared to long-term average temperatures were associated with cardiovascular disease (CVD) incidence in three US-based cohorts. METHODS:We followed 276,618 participants from the Nurses' Health Study (NHS) (1991-2018), the Nurses' Health Study II (NHSII) (1994-2017), and the Health Professionals' Follow-Up Study (1991-2015). We used data (1986-2018) from PRISM Spatial Climate Datasets (800-×800-m spatial resolution) to calculate differences between the summer (June-August) and winter (December-February) average temperatures and the previous 5-year summer and winter average temperatures at residential addresses of each participant. CVD incidence was defined as first nonfatal or fatal myocardial infarction (MI) or nonfatal or fatal stroke. Cox proportional hazard models were used to examine associations with between average temperatures and CVD incidence. Hazard ratios (HRs) and 95% confidence intervals (95% CI) were pooled using random effect meta-analysis. We also examined associations in the populations <65 and 65+ years of age. RESULTS:After pooling HRs, we found no association of summer average temperatures higher than the previous 5-year average temperature, with CVD incidence. A winter average temperature lower than the previous 5-year average was associated with CVD incidence (HR=0.95 per 2.7°C increase; 95% CI: 0.89, 1.01). Among persons <65 years of age, we observed increased CVD risks with higher summer average temperatures (pooled HR=1.03 per 1.3°C increase; 95% CI: 1.00, 1.07) and lower winter average temperatures (pooled HR=0.91 per 2.7°C increase; 95% CI: 0.87, 0.95) compared to the previous 5-year average temperature. DISCUSSION:Exposure to a winter average temperature lower than the previous 5-year average was suggestively associated with an increased CVD risk. Exposure to a summer average temperature higher than the previous 5-year average was associated with CVD incidence in the population <65 years of age but not in the full population. https://doi.org/10.1289/EHP14677.

BACKGROUND AND AIMS:Inflammation is an established cardiovascular disease risk factor, but its role in the link between food environments and cardiovascular risk remains unexplored. We aimed to study longitudinal associations between residential fast food outlets (FFOs) and inflammatory markers in US females from the Nurses' Health Study II with stored blood and residential addresses. METHODS AND RESULTS:We counted FFOs within 1500-m buffers around each address in 1998 and 2010. In samples collected at two time points (1999, 2011), we measured C-reactive protein (CRP, N = 1350), Interleukin-6 (IL-6, N = 809), and adiponectin (N = 836). We performed multivariable linear regression with repeated measures to study changes in FFOs and inflammatory markers and multivariable linear regression analyses to study FFOs count in 1998 and changes in inflammatory markers between 1999 and 2011. Models were adjusted for age, race/ethnicity, partners' education, smoking, neighborhood socioeconomic status (nSES), and population density. We explored effect modification by nSES and population density. No associations were observed in linear mixed models (e.g., CRP (β: 0.00, 95 %CI: 0.01,0.01) or in linear models including changes in inflammatory outcomes (e.g., CRP (β:0.00, 95 %CI: 0.01, 0.02). We also observed no effect modification for nSES or population density. CONCLUSION:In conclusion, we found no evidence for longitudinal associations between FFOs count and inflammatory markers in this study.

BACKGROUND:Increasing evidence associates air pollution with dementia, but some pollutants and susceptible groups are understudied. METHODS:We followed all Danish residents aged ≥60 years as of 1-1-2000, without prior dementia, until 12-31-2018 for dementia incidence identified via hospital contact or prescription. We assessed annual mean levels of fine particulate matter (PM2·5), nitrogen dioxide (NO2), and black carbon (BC) in 2010 utilizing European-wide hybrid land-use regression models, at baseline (2000) residential addresses. We examined the associations between air pollution exposure and dementia incidence with Cox proportional hazard models, accounting for individual- and area-level socio-demographic covariates and whether the effects were modified by age, sex, income level, education attainment, employment status, and the presence of comorbid conditions, including cardio-metabolic, respiratory diseases, and depression. FINDINGS:Among 934,792 individuals, 81,731 developed dementia over a mean follow-up of 11·6 years. Mean levels of PM2·5 and NO2, and BC were 12·5 and 20·6 µg/m3, and 1·0 × 10-5/m respectively. We detected strong associations between these pollutants and dementia incidence, with hazard ratios (HR) [95 % confidence intervals (CIs)] of 1·14 (1·12, 1·16), 1·25 (1·22, 1·28), and 1·23 (1·20, 1·26) per interquartile range increase of 1·9 μg/m3 for PM2·5, 10·2 μg/m3 for NO2, and 0·5 × 10-5/m for BC, respectively. Stronger associations were observed in elderly (≥75 years), those with stroke, the unemployed, and those with lower income or education levels than corresponding groups. DISCUSSION:Even low levels of air pollution in Denmark were associated with dementia development, especially among certain susceptible groups, emphasizing the need for targeted intervention strategies.

BACKGROUND:Little is known about the impact of environmental exposures on mortality risk after a myocardial infarction (MI). OBJECTIVE:The goal of this study was to evaluate associations of long-term temperature, air pollution and greenness exposures with mortality among survivors of an MI. METHODS:We used data from the US-based Nurses' Health Study to construct an open cohort of survivors of a nonfatal MI 1990-2017. Participants entered the cohort when they had a nonfatal MI, and were followed until death, loss to follow-up, end of follow-up, or they reached 80 years old, whichever came earliest. We assessed residential 12-month moving average fine particulate matter (PM2.5) and nitrogen dioxide (NO2), satellite-based annual average greenness (in a circular 1230 m buffer), summer average temperature and winter average temperature. We used Cox proportional hazard models adjusted for potential confounders to assess hazard ratios (HR and 95% confidence intervals). We also assessed potential effect modification. RESULTS:Among 2262 survivors of a nonfatal MI, we observed 892 deaths during 19,216 person years of follow-up. In single-exposure models, we observed a HR (95%CI) of 1.20 (1.04, 1.37) per 10 ppb NO2 increase and suggestive positive associations were observed for PM2.5, lower greenness, warmer summer average temperature and colder winter average temperature. In multi-exposure models, associations of summer and winter average temperature remained stable, while associations of NO2, PM2.5 and greenness attenuated. The strength of some associations was modified by other exposures. For example, associations of greenness (HR = 0.88 (0.78, 0.98) per 0.1) were more pronounced for participants in areas with a lower winter average temperature. CONCLUSION:We observed associations of air pollution, greenness and temperature with mortality among MI survivors. Some associations were confounded or modified by other exposures, indicating that it is important to explore the combined impact of environmental exposures.

Objective: To examine the associations between characteristics of daily rainfall (intensity, duration, and frequency) and all cause, cardiovascular, and respiratory mortality. Design: Two stage time series analysis. Setting: 645 locations across 34 countries or regions. Population: Daily mortality data, comprising a total of 109 954 744 all cause, 31 164 161 cardiovascular, and 11 817 278 respiratory deaths from 1980 to 2020. Main outcome measure: Association between daily mortality and rainfall events with return periods (the expected average time between occurrences of an extreme event of a certain magnitude) of one year, two years, and five years, with a 14 day lag period. A continuous relative intensity index was used to generate intensity-response curves to estimate mortality risks at a global scale. Results: During the study period, a total of 50 913 rainfall events with a one year return period, 8362 events with a two year return period, and 3301 events with a five year return period were identified. A day of extreme rainfall with a five year return period was significantly associated with increased daily all cause, cardiovascular, and respiratory mortality, with cumulative relative risks across 0-14 lag days of 1.08 (95% confidence interval 1.05 to 1.11), 1.05 (1.02 to 1.08), and 1.29 (1.19 to 1.39), respectively. Rainfall events with a two year return period were associated with respiratory mortality only, whereas no significant associations were found for events with a one year return period. Non-linear analysis revealed protective effects (relative risk <1) with moderate-heavy rainfall events, shifting to adverse effects (relative risk >1) with extreme intensities. Additionally, mortality risks from extreme rainfall events appeared to be modified by climate type, baseline variability in rainfall, and vegetation coverage, whereas the moderating effects of population density and income level were not significant. Locations with lower variability of baseline rainfall or scarce vegetation coverage showed higher risks. Conclusion: Daily rainfall intensity is associated with varying health effects, with extreme events linked to an increasing relative risk for all cause, cardiovascular, and respiratory mortality. The observed associations varied with local climate and urban infrastructure.

Background: Access to fast food outlets (FFO) has increased in recent years and may decrease diet quality and increase cardiovascular disease risk. Inflammation is a well-established cardiovascular disease risk factor, but it remains unclear whether inflammation is one of the pathways explaining associations between the food environment and inflammatory markers. Methods: We included 6,609 participants from the US-based Nurses’ Health Study II (NHS-II), a prospective cohort of registered female nurses. Counts of FFO were obtained from the Infogroup US Historical Business Data within 1500-meter circular buffers and assigned to participants’ residential addresses from 1998 to 2011. Biomarkers included C-reactive protein (CRP), Interleukin-6 (IL-6), Tumor necrosis factor α receptor 2 (TNFαR2, in a subset), and adiponectin. We evaluated cross-sectional associations between counts of FFO (1998) and inflammatory markers (1999) in linear regression models and evaluated longitudinal associations by the change in counts of FFO between 1998 and 2010 and the change in inflammatory markers between 1999 and 2011 using linear mixed models. Models were adjusted for age, race/ethnicity, partners’ education level, smoking, neighborhood socioeconomic status, and population density. Interaction terms were included to explore effect modification by baseline neighborhood socioeconomic status and population density. Results: Our study comprised women with a mean age of 45.3 (+/- 4.4) years at 1998. A one-unit higher count of FFO within a 1500m buffer was not cross-sectionally associated with CRP (β: 0.004, 95%CI: -0.004, 0.010), IL-6 (β: 0.001, 95%CI: -0.001, 0.004), TNFR2 (β: 0.051, 95%CI: -1.495, 1.597), or adiponectin (β: -6.054, 95%CI: -14.576, 2.468). No associations were found in longitudinal models (CRP (β: 0.000, 95%CI: -0.007, 0.008), IL-6 (β: 0.001, 95%CI: -0.002, 0.003), or adiponectin (β: -1.127, 95%CI: -11.889, 14.3152)). No significant effect modification was found between count of FFO and neighborhood socioeconomic status, or population density (P-for-interaction >0.1). Conclusions: While several previous studies have found an association between frequency of consumption of food at fast food restaurants and biomarkers of cardiovascular risk, at the community level, we did not find associations between the count of FFO and several inflammatory markers in a population of US female nurses.

BACKGROUND:PM2.5 has been positively associated with cardiovascular disease (CVD) incidence. Most evidence has come from cohorts and administrative databases. Cohorts typically have extensive information on potential confounders and residential-level exposures. Administrative databases are usually more representative but typically lack information on potential confounders and often only have exposures at coarser geographies (e.g., ZIP code). The weaknesses in both types of studies have been criticized for potentially jeopardizing the validity of their findings for regulatory purposes. METHODS:We followed 101,870 participants from the US-based Nurses' Health Study (2000-2016) and linked residential-level PM2.5 and individual-level confounders, and ZIP code-level PM2.5 and confounders. We used time-varying Cox proportional hazards models to examine associations with CVD incidence. We specified basic models (adjusted for individual-level age, race and calendar year), individual-level confounder models, and ZIP code-level confounder models. RESULTS:Residential- and ZIP code-level PM2.5 were strongly correlated (Pearson r = 0.88). For residential-level PM2.5, the hazard ratio (HR, 95 % confidence interval) per 5 μg/m3 increase was 1.06 (1.01, 1.11) in the basic and 1.04 (0.99, 1.10) in the individual-level confounder model. For ZIP code-level PM2.5, the HR per 5 μg/m3 was 1.04 (0.99, 1.08) in the basic and 1.02 (0.97, 1.08) in the ZIP code-level confounder model. CONCLUSION:We observed suggestive positive, but not statistically significant, associations between long-term PM2.5 and CVD incidence, regardless of the exposure or confounding model. Although differences were small, associations from models with individual-level confounders and residential-level PM2.5 were slightly stronger than associations from models with ZIP code-level confounders and PM2.5.

INTRODUCTION:Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). METHODS:We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. RESULTS:There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. CONCLUSION:Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Background Few studies have investigated the relationship between the food and physical activity environment and odds of gestational diabetes mellitus (GDM). This study quantifies fi es the association between densities of several types of food establishments and fi tness centers with the odds of having GDM. Methods The density of supermarkets, fast-food restaurants, full-service restaurants, convenience stores and fi tness centers at 500, 1000 and 1500 m (m) buffers was counted at residential addresses of 68,779 pregnant individuals from Eastern Massachusetts during 2000-2016. - 2016. The ' healthy food index' ' assessed the relative availability of healthy (supermarkets) vs unhealthy (fast-food restaurants, convenience stores) food retailers. Multivariable logistic regression quantified fi ed the cross-sectional association between exposure variables and the odds of having GDM, adjusting for individual and area-level characteristics. Effect modification fi cation by area-level socioeconomic status (SES) was assessed. Findings In fully adjusted models, pregnant individuals living in the highest density tertile of fast-food restaurants had higher GDM odds compared to those living in the lowest density tertile (500 m: odds ratio (OR):1.17 95% CI: [1.04, 1.31]; 1000 m: 1.33 95% CI: [1.15, 1.53]); 1500 m: 1.18 95% CI: [1.01, 1.38]). Greater residential density of supermarkets was associated with lower odds of GDM (1000 m: 0.86 95% CI: [0.74, 0.99]; 1500 m: 0.86 95% CI: [0.72, 1.01]). Similarly, living in the highest fi tness center density tertile was associated with decreased GDM odds (500 m:0.87 95% CI: [0.76, 0.99]; 1500 m: 0.89 95% CI: [0.79, 1.01]). There was no evidence of effect modification fi cation by SES and no association found between the healthy food index and GDM odds. Interpretation In Eastern Massachusetts, living near a greater density of fast-food establishments was associated with higher GDM odds. Greater residential access to supermarkets and fi tness centers was associated with lower the odds of having GDM.

BACKGROUND:Landscape fire-sourced (LFS) air pollution is an increasing public health concern in the context of climate change. However, little is known about the attributable global, regional, and national mortality burden related to LFS air pollution. METHODS:We calculated country-specific population-weighted average daily and annual LFS fine particulate matter (PM2·5) and surface ozone (O3) during 2000-19 from a validated dataset. We obtained the relative risks (RRs) for both short-term and long-term impact of LFS PM2·5 and O3 on all-cause, cardiovascular, and respiratory mortality. The short-term RRs were pooled from community-specific standard time-series regressions in 2267 communities across 59 countries or territories. The long-term RRs were obtained from published meta-analyses of cohort studies on all-source PM2·5 and O3. Annual mortality, population, and socio-demographic data for each country or territory were extracted from the Global Burden of Diseases Study 2019. These data were used to estimate country-specific annual deaths attributable to LFS air pollution using standard algorithms. FINDINGS:Globally, 1·53 million all-cause deaths per year (95% empirical confidence interval [eCI] 1·24-1·82) were attributable to LFS air pollution during 2000-19, including 0·45 million (0·32-0·57) cardiovascular deaths and 0·22 million respiratory deaths (0·08-0·35). LFS PM2·5 and O3 contributed to 77·6% and 22·4% of the total attributable deaths, respectively. Over 90% of all attributable deaths were in low-income and middle-income countries, particularly in sub-Saharan Africa (606 769 deaths per year), southeast Asia (206 817 deaths), south Asia (170 762 deaths), and east Asia (147 291 deaths). The global cardiovascular attributable deaths saw an average 1·67% increase per year (ptrend <0·001), although the trends for all-cause and respiratory attributable deaths were not statistically significant. The five countries with the largest all-cause attributable deaths were China, the Democratic Republic of the Congo, India, Indonesia, and Nigeria, although the order changed in the second decade. The leading countries with the greatest attributable mortality rates (AMRs) were all in sub-Saharan Africa, despite decreasing trends from 2000 to 2019. North and central America, and countries surrounding the Mediterranean, showed increasing trends of all-cause, cardiovascular, and respiratory AMRs. Increasing cardiovascular AMR was also observed in southeast Asia, south Asia, and east Asia. In 2019, the AMRs in low-income countries remained four times those in high-income countries, though this had reduced from nine times in 2000. AMRs negatively correlated with a country-specific socio-demographic index (Spearman correlation coefficients r around -0·60). INTERPRETATION:LFS air pollution induced a substantial global mortality burden, with notable geographical and socioeconomic disparities. Urgent actions are required to address such substantial health impact and the associated environmental injustice in a warming climate. FUNDING:Australian Research Council, Australian National Health and Medical Research Council.