Persistent DNA Methylation Changes across the First Year of Life and Prenatal NO2 Exposure in a Canadian Prospective Birth Study.

BACKGROUND: Evidence suggests that prenatal air pollution exposure alters DNA methylation (DNAm), which could go on to affect long-term health. It remains unclear whether DNAm alterations present at birth persist through early life. Identifying persistent DNAm changes would provide greater insight into the molecular mechanisms contributing to the association of prenatal air pollution exposure with atopic diseases. OBJECTIVES: This study investigated DNAm differences associated with prenatal nitrogen dioxide (NO2) exposure (a surrogate measure of traffic-related air pollution) at birth and 1 y of age and examined their role in atopic disease. We focused on regions showing persistent DNAm differences from birth to 1 y of age and regions uniquely associated with postnatal NO2 exposure. METHODS: Microarrays measured DNAm at birth and at 1 y of age for an atopy-enriched subset of Canadian Health Infant Longitudinal Development (CHILD) study participants. Individual and regional DNAm differences associated with prenatal NO2 (n=128) were identified, and their persistence at age 1 y were investigated using linear mixed effects models (n=124). Postnatal-specific DNAm differences (n=125) were isolated, and their association with NO2 in the first year of life was examined. Causal mediation investigated whether DNAm differences mediated associations between NO2 and age 1 y atopy or wheeze. Analyses were repeated using biological sex-stratified data. RESULTS: At birth (n=128), 18 regions of DNAm were associated with NO2, with several annotated to HOX genes. Some of these regions were specifically identified in males (n=73), but not females (n=55). The effect of prenatal NO2 across CpGs within altered regions persisted at 1 y of age. No significant mediation effects were identified. Sex-stratified analyses identified postnatal-specific DNAm alterations. DISCUSSION: Regional cord blood DNAm differences associated with prenatal NO2 persisted through at least the first year of life in CHILD participants. Some differences may represent sex-specific alterations, but replication in larger cohorts is needed. The early postnatal period remained a sensitive window to DNAm perturbations. https://doi.org/10.1289/EHP13034.

HealthyPlan.City: A Web Tool to Support Urban Environmental Equity and Public Health in Canadian Communities.

Urban environmental factors such as air quality, heat islands, and access to greenspaces and community amenities impact public health. Some vulnerable populations such as low-income groups, children, older adults, new immigrants, and visible minorities live in areas with fewer beneficial conditions, and therefore, face greater health risks. Planning and advocating for equitable healthy urban environments requires systematic analysis of reliable spatial data to identify where vulnerable populations intersect with positive or negative urban/environmental characteristics. To facilitate this effort in Canada, we developed HealthyPlan.City ( https://healthyplan.city/ ), a freely available web mapping platform for users to visualize the spatial patterns of built environment indicators, vulnerable populations, and environmental inequity within over 125 Canadian cities. This tool helps users identify areas within Canadian cities where relatively higher proportions of vulnerable populations experience lower than average levels of beneficial environmental conditions, which we refer to as Equity priority areas. Using nationally standardized environmental data from satellite imagery and other large geospatial databases and demographic data from the Canadian Census, HealthyPlan.City provides a block-by-block snapshot of environmental inequities in Canadian cities. The tool aims to support urban planners, public health professionals, policy makers, and community organizers to identify neighborhoods where targeted investments and improvements to the local environment would simultaneously help communities address environmental inequities, promote public health, and adapt to climate change. In this paper, we report on the key considerations that informed our approach to developing this tool and describe the current web-based application.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States.

BACKGROUND: Ambient nitrogen dioxide (NO2) and fine particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. OBJECTIVES: Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO2- and PM2.5-attributable health burdens, considering NO2-attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants. METHODS: Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO2 and PM2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study. RESULTS: Despite overall decreases in the public health damages associated with NO2 and PM2.5, racial and ethnic relative disparities in NO2-attributable pediatric asthma and PM2.5-attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM2.5-attributable premature mortality and NO2-attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM2.5-attributable premature mortality have increased by 40% and NO2-attributable pediatric asthma by 10%. DISCUSSION: Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions.

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Long-Term Exposure to Low-Level PM2.5 and Mortality: Investigation of Heterogeneity by Harmonizing Analyses in Large Cohort Studies in Canada, United States, and Europe.

BACKGROUND: Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on PM2.5 effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible. METHODS: We harmonized the study populations to individuals 65+ years of age, applied the same satellite-derived PM2.5 exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF). RESULTS: More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5-µg/m3 increase in PM2.5 were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed PM2.5 levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. https://doi.org/10.1289/EHP12141.

Reversal of trends in global fine particulate matter air pollution.

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 µg/m3) to a peak in 2011 (38.9 µg/m3) and decreased steadily afterwards (34.7 µg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning.

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.

"In small places, close to home": Urban environmental impacts on child rights across four global cities.

Urban environments influence child behaviours, exposures and experiences and may affect health, development, achievement and realization of fundamental human rights. We examined the status of eleven UN Convention on the Rights of the Child articles, in a multi-case study across four global cities. Within all study cities, children experienced unequal exposure to urban environmental risks and amenities. Many violations of child rights are related to car-based transportation systems and further challenged by pressures on urban systems from rapid population increases in the context of climate change. A child rights framework provides principles for a collective, multi-sectoral re-imagination of urban environments that support the human rights of all citizens.

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region.

Fine particulate matter (PM2.5) exposure is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM2.5, and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM2.5-attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM2.5 (>95 µg/m3). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sectors in South Asia.

Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images.

Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00394-6.

Burden of disease attributable to unsafe drinking water, sanitation, and hygiene in domestic settings: a global analysis for selected adverse health outcomes.

BACKGROUND: Assessments of disease burden are important to inform national, regional, and global strategies and to guide investment. We aimed to estimate the drinking water, sanitation, and hygiene (WASH)-attributable burden of disease for diarrhoea, acute respiratory infections, undernutrition, and soil-transmitted helminthiasis, using the WASH service levels used to monitor the UN Sustainable Development Goals (SDGs) as counterfactual minimum risk-exposure levels. METHODS: We assessed the WASH-attributable disease burden of the four health outcomes overall and disaggregated by region, age, and sex for the year 2019. We calculated WASH-attributable fractions of diarrhoea and acute respiratory infections by country using modelled WASH exposures and exposure-response relationships from two updated meta-analyses. We used the WHO and UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene public database to estimate population exposure to different WASH service levels. WASH-attributable undernutrition was estimated by combining the population attributable fractions (PAF) of diarrhoea caused by unsafe WASH and the PAF of undernutrition caused by diarrhoea. Soil-transmitted helminthiasis was fully attributed to unsafe WASH. FINDINGS: We estimate that 1·4 (95% CI 1·3-1·5) million deaths and 74 (68-80) million disability-adjusted life-years (DALYs) could have been prevented by safe WASH in 2019 across the four designated outcomes, representing 2·5% of global deaths and 2·9% of global DALYs from all causes. The proportion of diarrhoea that is attributable to unsafe WASH is 0·69 (0·65-0·72), 0·14 (0·13-0·17) for acute respiratory infections, and 0·10 (0·09-0·10) for undernutrition, and we assume that the entire disease burden from soil-transmitted helminthiasis was attributable to unsafe WASH. INTERPRETATION: WASH-attributable burden of disease estimates based on the levels of service established under the SDG framework show that progress towards the internationally agreed goal of safely managed WASH services for all would yield major public-health returns. FUNDING: WHO and Foreign, Commonwealth & Development Office.

Household Air Pollution and Adult Lung Function Change, Respiratory Disease, and Mortality across Eleven Low- and Middle-Income Countries from the PURE Study.

BACKGROUND: Globally, household air pollution (HAP) is a major environmental hazard that affects respiratory health. However, few studies have examined associations between HAP and lung function decline and respiratory disease and mortality. METHODS: We used data from the Prospective Urban and Rural Epidemiology study and examined adults residing in 240 rural communities in 11 low- and middle-income countries where HAP from cooking with solid fuels is common. Spirometry was conducted for 28,574 individuals at baseline and 12,489 individuals during follow-up (mean of 8 y between spirometry measures). In cross-sectional analyses, we compared lung function measurements [forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio] in those who used solid fuels for cooking in comparison with clean fuels. Using repeated measurements of lung function, we examined the percent change in lung function measures per year, comparing individuals by baseline fuel type and individuals who used solid fuels at baseline but switched to clean fuels during follow-up. We also examined associations with prospective health events (any respiratory diseases, respiratory disease hospitalizations, and all-cause mortality). RESULTS: In adjusted cross-sectional models, use of solid fuel in comparison with clean fuels was associated with lower FEV1 of -17.5mL (95% CI: -32.7, -2.3) and FVC of -14.4mL (95% CI: -32.0, 3.2), but not FEV1/FVC. In longitudinal analyses, individuals who switched from solid fuels to clean cooking fuels during follow-up (n=3,901, 46% of those using solid fuel at baseline), showed no differences in the annual rate of change in FEV1 or FVC, but had small improvements in FEV1/FVC change (0.2% per year, 95% CI: 0.03, 0.3). Individuals who switched from solid to clean fuels had a decreased hazard ratio for respiratory events of 0.76 (95% CI: 0.57, 1.00) in comparison with persistent solid fuel users, which was not attenuated by lung function measures. CONCLUSION: We observed modest associations between HAP exposure and lung function, lung function change, and respiratory disease and mortality. https://doi.org/10.1289/EHP11179.

Effectiveness of handwashing with soap for preventing acute respiratory infections in low-income and middle-income countries: a systematic review and meta-analysis.

BACKGROUND: Acute respiratory infection (ARI) is a leading cause of morbidity and mortality globally, with 83% of ARI mortality occurring in low-income and middle-income countries (LMICs) before the COVID-19 pandemic. We aimed to estimate the effect of interventions promoting handwashing with soap on ARI in LMICs. METHODS: In our systematic review and meta-analysis, we searched MEDLINE, Embase, Web of Science, Scopus, Cochrane Library, Global Health, and Global Index Medicus for studies of handwashing with soap interventions in LMICs from inception to May 25, 2021. We included randomised and non-randomised controlled studies of interventions conducted in domestic, school, or childcare settings. Interventions promoting hand hygiene methods other than handwashing with soap were excluded, as were interventions in health-care facilities or the workplace. The primary outcome was ARI morbidity arising from any pathogen for participants of any age. Secondary outcomes were lower respiratory infection, upper respiratory infection, influenza confirmed by diagnostic test, COVID-19 confirmed by diagnostic test, and all-cause mortality. We extracted relative risks (RRs), using random-effects meta-analysis to analyse study results, and metaregression to evaluate heterogeneity. We assessed risk of bias in individual studies using an adapted Newcastle-Ottawa scale, and assessed the overall body of evidence using a Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. The study is registered with PROSPERO, CRD42021231414. FINDINGS: 26 studies with 161 659 participants met inclusion criteria, providing 27 comparisons (21 randomised). Interventions promoting handwashing with soap reduced any ARI compared with no handwashing intervention (RR 0·83 [95% CI 0·76-0·90], I2 88%; 27 comparisons). Interventions also reduced lower respiratory infections (0·78 [0·64-0·94], I2 64%; 12 comparisons) and upper respiratory infections (0·74 [0·59-0·93], I2 91%; seven comparisons), but not test-confirmed influenza (0·94 [0·42-2·11], I2 90%; three comparisons), test-confirmed COVID-19 (no comparisons), or all-cause mortality (prevalence ratio 0·95 [95% CI 0·71-1·27]; one comparison). For ARI, no heterogeneity covariates were significant at p<0·1 and the GRADE rating was moderate certainty evidence. INTERPRETATION: Interventions promoting handwashing with soap can reduce ARI in LMICs, and could help to prevent the large burden of respiratory disease. FUNDING: Bill & Melinda Gates Foundation, Reckitt Global Hygiene Institute, and UK FCDO.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana.

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 µg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Influence of Neighborhood Built Environments on the Outdoor Free Play of Young Children: a Systematic, Mixed-Studies Review and Thematic Synthesis.

Urban environments shape early childhood exposures, experiences, and health behaviors, including outdoor free play, influencing the physical, cognitive, social, and emotional development of young children. We examined evidence for urban or suburban built environment influences on outdoor free play in 0-6-year-olds, considering potential differences across gender, culture, and geography. We systematically searched seven literature databases for relevant qualitative, quantitative, and mixed methods studies: of 5740 unique studies, 53 met inclusion criteria. We assessed methodological quality and thematically synthesized findings from included studies. Three broad themes, features of spaces for play, routes, and social factors intersected to influence the availability, accessibility, and acceptability of neighborhoods for young children's outdoor free play across diverse cultural and geographic contexts. Proximity to formal or informal space for play, protection from traffic, pedestrian environment, green and natural environments, and opportunity for social connection supported outdoor free play. Family and community social context influenced perceptions of and use of space; however, we did not find consistent, gendered differences in built environment correlates of outdoor free play. Across diverse contexts, playable neighborhoods for young children provided nearby space for play, engaging routes protected from traffic and facilitated frequent interaction between people, nature, and structures.