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Since 2005, female firearm suicide rates increased by 34%, outpacing the rise in male firearm suicide rates over the same period. The objective of this study was to develop and evaluate a natural language processing pipeline to identify a select set of common and important circumstances preceding female firearm suicide from coroner/medical examiner and law enforcement narratives. Unstructured information from coroner/medical examiner and law enforcement narratives were manually coded for 1,462 randomly selected cases from the National Violent Death Reporting System. Decedents were included from 40 states and Puerto Rico from 2014 to 2018. Naive Bayes, Random Forest, Support Vector Machine, and Gradient Boosting classifier models were tuned using 5-fold cross-validation. Model performance was assessed using sensitivity, specificity, positive predictive value, F1, and other metrics. Analyses were conducted from February to November 2022. The natural language processing pipeline performed well in identifying recent interpersonal disputes, problems with intimate partners, acute/chronic pain, and intimate partners and immediate family at the scene. For example, the Support Vector Machine model had a mean of 98.1% specificity and 90.5% positive predictive value in classifying a recent interpersonal dispute before suicide. The Gradient Boosting model had a mean of 98.7% specificity and 93.2% positive predictive value in classifying a recent interpersonal dispute before suicide. This study developed a natural language processing pipeline to classify 5 female firearm suicide antecedents using narrative reports from the National Violent Death Reporting System, which may improve the examination of these circumstances. Practitioners and researchers should weigh the efficiency of natural language processing pipeline development against conventional text mining and manual review. [ABSTRACT FROM AUTHOR]

Introduction: This study sought to determine the association between changes in state-level beer excise tax and firearm homicide rates among individuals aged 15-34years.Methods: A time series analysis with synthetic controls was conducted for the years 2003-2015. Exposed states changed the beer excise tax during the study period. Synthetic controls were weighted mimics that combined portions of unexposed states using state-year specific demographic and firearm covariates. Average annual incidence rate differences were calculated between each exposed state and its synthetic control. Alcohol taxes were available through the National Institute of Alcohol Abuse and Alcoholism and firearm homicide rates were obtained from theCenters for Disease Control and Prevention. States that changed the beer excise tax but forwhich <2years of pre-exposure data were available were excluded. Data were collected in 2017 and analyzedin 2018.Results: Five states met inclusion criteria, and all raised the beer excise tax: Illinois (2009), New York (2009), North Carolina (2009), Connecticut (2011), and Rhode Island (2013). The percentage increase in beer excise tax ranged from 10% to 27%. Differences in pre-exposure firearm homicide rates between exposed states and synthetic controls were minimal. The increase in beer excise tax was associated with a lower average annual firearm homicide rate in all states except Illinois (Rhode Island: incidence rate difference= -2.48, Connecticut: incidence rate difference= -2.57, New York: incidence rate difference= -1.45, North Carolina: incidence rate difference= -0.45, and Illinois: incidence rate difference=1.54 per 100,000 population).Conclusions: Among individuals aged 15-34years, price-sensitive consumption of beer may representone feasible tool for policymakers seeking to reduce rates of firearm homicide. [ABSTRACT FROM AUTHOR]

Abstract Background Bike sharing systems have potential to substantially boost active transportation levels (and consequent physical and mental health) in urban populations. We explored equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations. Methods Starting in July 2017, Seattle, Washington piloted a dockless bike share system that made 10,000 bikes available. We merged data on resident sociodemographic and economic characteristics from the American Community Survey about 93 defined neighborhoods with data about bike locations, bike idle time, and which neighborhoods operators rebalanced bikes to. We used mapping and descriptive statistics to compare access between neighborhoods along sociodemographic and economic lines. Results With many bikes available, no neighborhood was consistently excluded from access. However, the average availability ranged from 3 bikes per day to 341 per day. Neighborhoods with more bikes had more college-educated residents (median 75% college-educated vs. 65%) and local community resources (median opportunity index score of 24 vs. 19), and higher incomes (median 83,202 vs. 71,296). Rebalancing destinations were strongly correlated with neighborhood demand (r = 0.61). Conclusions The overall scale of the dockless system ensured there was baseline access throughout Seattle. We observed modest inequities in access along sociodemographic lines, similar to prior findings in studies of docked bike share systems. Dockless bike share systems hold promise for offering equitable spatial access to bike sharing. Highlights • Seattle's dockless bikeshare pilot provided bikes to all neighborhoods in the city. • Neighborhoods with more educated residents had modestly more bikes. • Most bikes were rebalanced to neighborhoods with low bike idle times. • Seattle's dockless systems has promising spatial equity characteristics. [ABSTRACT FROM AUTHOR]

Individual matching in case-control studies improves statistical efficiency over random selection of controls but can lead to selection bias if cases are excluded due to the lack of appropriate controls or residual confounding with less strict matching criteria. We introduce flex matching, an algorithm using multiple rounds of control selection with successively relaxed matching criteria to select controls for cases. We simulated exposure-disease relationships in multiple cohort data sets with a range of confounding scenarios and conducted 16,800,000 nested case-control studies, comparing random selection of controls, strict matching, and flex matching. We computed average bias and statistical efficiency in estimates of exposure-disease relationships under each matching strategy. On average, flex matching produced the least biased estimates of exposure-disease associations with the smallest standard errors. Strict matching algorithms that excluded cases for whom matched controls could not be identified produced biased estimates with larger standard errors. Estimates from studies with random assignment of controls were relatively unbiased, but the standard errors were larger than from studies using flex matching. Flex matching should be considered for case-control designs, especially for biomarker studies where matching on technical artifacts is necessary and maximizing efficiency is a priority. [ABSTRACT FROM AUTHOR]

We explored links between food environments, dietary intake biomarkers, and sudden cardiac arrest in a population-based longitudinal study using cases and controls accruing between 1990 and 2010 in King County, WA. Surprisingly, presence of more unhealthy food sources near home was associated with a lower 18:1 trans-fatty acid concentration (-0.05% per standard deviation higher count of unhealthy food sources, 95% Confidence Interval [CI]: 0.01, 0.09). However, presence of more unhealthy food sources was associated with higher odds of cardiac arrest (Odds Ratio [OR]: 2.29, 95% CI: 1.19, 4.41 per standard deviation in unhealthy food outlets). While unhealthy food outlets were associated with higher cardiac arrest risk, circulating 18:1 trans fats did not explain the association. [ABSTRACT FROM AUTHOR]

The pathways through which neighborhood poverty can affect resident depression are still unknown. We investigated mechanisms through which neighborhood poverty may influence depression among older adults. Participants were drawn from the New York City Neighborhood and Mental Health in the Elderly Study II, a 3-wave study of adults aged 65-75 (n=3,497) at baseline. Neighborhood poverty and homicide were associated with depressive symptoms at follow-up waves (RR:1.20, 95%CI: 1.05, 1.36; RR: 1.09, 95%CI: 1.02, 1.17, respectively). Homicide accounted for 30% of the effect of neighborhood poverty on depressive symptoms. Neighborhood exposure to violence may be a key mechanism through which neighborhood poverty influences depression among older adults. [ABSTRACT FROM AUTHOR]

Background: Obesity and depression are comorbid more often than chance predicts. However, depression among the obese is more common in settings where obesity is less common. This suggests that body habitus norms and social stigmatization may play a role in the etiology of depression among the obese. Methods: We developed an agent-based social network model to explore mechanisms by which deviance from normative body habitus may contribute to social isolation in the obese. At each of 240 simulated months (20 years), each agent updated its body mass index based on environmental, peer influence, and stochastic factors. At each month, each agent was subject to social ostracization and consequent depression if its body mass index deviated from that of its peers and the network-wide mean. We compared risk of depression as a function of obesity and obesity norms through simulations of a high-obesity context simulating the US state of Mississippi and a low-obesity context simulating the US state of Colorado, then explored the relationship between global obesogenic forces and agent-specific resistance to the forces. Results: Over 1000 simulations in each context, 25 percent of obese agents in simulated Colorado were ever-depressed as compared to 21 percent in simulated Mississippi, although 10 percent overall were ever-depressed in both settings. High and low levels of resistance to obesogeneity prevented the most depression, whereas medium resistance levels were more depressogenic. Conclusions: Social stigma and ostracization that occur as a consequence of deviance from body habitus norms may be a plausible mechanism by which weight stigma may influence depression in the obese. Public health interventions targeting individuals rather than obesogenic environments may modify body habitus norms with the unintended consequence of increasing stigma-based social isolation among those who remain obese. [ABSTRACT FROM AUTHOR]

Public health research has shown that neighborhood conditions are associated with health behaviors and outcomes. Systematic neighborhood audits have helped researchers measure neighborhood conditions that they deem theoretically relevant but not available in existing administrative data. Systematic audits, however, are expensive to conduct and rarely comparable across geographic regions. We describe the development of an online application, the Computer Assisted Neighborhood Visual Assessment System (CANVAS), that uses Google Street View to conduct virtual audits of neighborhood environments. We use this system to assess the inter-rater reliability of 187 items related to walkability and physical disorder on a national sample of 150 street segments in the United States. We find that many items are reliably measured across auditors using CANVAS and that agreement between auditors appears to be uncorrelated with neighborhood demographic characteristics. Based on our results we conclude that Google Street View and CANVAS offer opportunities to develop greater comparability across neighborhood audit studies. [ABSTRACT FROM AUTHOR]

• Virtual audit of observed neighborhood physical disorder via Google street view. • Spatio-temporal regression Kriging to predict physical disorder. • Model accuracy and concurrent validity with space-time lagged perceived disorder. • Greater validity nearest spaces and times of perceived disorder responses. • Using spatio-temporal model depends on space-time precision of epidemiologic data. This study tested spatio-temporal model prediction accuracy and concurrent validity of observed neighborhood physical disorder collected from virtual audits of Google Street View streetscapes. We predicted physical disorder from spatio-temporal regression Kriging models based on measures at three dates per each of 256 streestscapes (n = 768 data points) across an urban area. We assessed model internal validity through cross validation and external validity through Pearson correlations with respondent-reported perceptions of physical disorder from a breast cancer survivor cohort. We compared validity among full models (both large- and small-scale spatio-temporal trends) versus large-scale only. Full models yielded lower prediction error compared to large-scale only models. Physical disorder predictions were lagged at uniform distances and dates away from the respondent-reported perceptions of physical disorder. Correlations between perceived and observed physical disorder predicted from the full model were higher compared to that of the large-scale only model, but only at locations and times closest to the respondent's exact residential address and questionnaire date. A spatio-temporal Kriging model of observed physical disorder is valid. [ABSTRACT FROM AUTHOR]

Walkability is a popular and ubiquitous term at the intersection of urban planning and public health. As the number of potential walkability measures grows in the literature, there is a need to compare their relative importance for specific research objectives. This study demonstrates a classification and regression tree (CART) model to compare five familiar measures of walkability from the literature for their relative ability to predict whether or not walking occurs in a dataset of objectively measured locations. When analyzed together, the measures had moderate-to-high accuracy (87.8% agreement: 65.6% of true walking GPS-measured points classified as walking and 93.4% of non-walking points as non-walking). On its own, the most well-known composite measure, Walk Score, performed only slightly better than measures of the built environment composed of a single variable (transit ridership, employment density, and residential density).Thus there may be contexts where transparent and longitudinally available measures of urban form are worth a marginal tradeoff in prediction accuracy. This comparison of walkability measures using CART highlights the importance for public health and urban design researchers to think carefully about how and why particular walkability measures are used. [ABSTRACT FROM AUTHOR]

Many distinct characteristics of the social, natural, and built neighborhood environment have been included in walkability measures, and it is unclear which measures best describe the features of a place that support walking. We developed the Automatic Context Measurement Tool, which measures neighborhood environment characteristics from public data for any point location in the United States. We explored these characteristics in home neighborhood environments in relation to walking identified from integrated GPS, accelerometer, and travel log data from 681 residents of King Country, WA. Of 146 neighborhood characteristics, 92 (63%) were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery. The strongest built environment predictor of walking bout count was housing unit count. Models using data-driven and a priori defined walkability measures exhibited similar fit statistics. Walkability measures consisting of different neighborhood characteristic measurements may capture the same underlying variation in neighborhood conditions. [ABSTRACT FROM AUTHOR]

This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities. [ABSTRACT FROM AUTHOR]

Purpose: To outline a method for obtaining more accurate estimates of drug use in the United States (US) general population by correcting survey data for underreported and unknown drug use.Methods: We simulated a population (n = 100,000) reflecting the demographics of the US adult population per the 2018 American Community Survey. Within this population, we simulated the "true" and self-reported prevalence of past-month cannabis and cocaine use by using available estimates of underreporting. We applied our algorithm to samples of the simulated population to correct self-reported estimates and recover the "true" population prevalence, validating our approach. We applied this same method to 2018 National Survey on Drug Use and Health (NSDUH) data to produce a range of underreporting-corrected estimates.Results: Simulated self-report sensitivities varied by drug and sampling method (cannabis: 77.6%-78.5%, cocaine: 14.3%-22.1%). Across repeated samples, mean corrected prevalences (calculated by dividing self-reported prevalence by estimated sensitivity) closely approximated simulated "true" prevalences. Applying our algorithm substantially increased 2018 NSDUH estimates (self-report: cannabis = 10.5%, cocaine = 0.8%; corrected: cannabis = 15.6%-16.6%, cocaine = 2.7%-5.5%).Conclusions: National drug use prevalence estimates can be corrected for underreporting using a simple method. However, valid application of this method requires accurate data on the extent and correlates of misclassification in the general US population. [ABSTRACT FROM AUTHOR]

To examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes. Research Design and Methods: We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable. Patients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (−0.42, −0.43, and −0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = −0.37 at 1- and 3-years and −0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05). Lower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance. • Among people with type 2 diabetes, those in the lowest residential density areas had better glycemic control over 5-years. • People with type 2 diabetes who had a supermarket within 1600 m of home had better glycemic control over 3-years. • Associations were small indicating that other social environmental factors may have larger impacts on glycemic control [ABSTRACT FROM AUTHOR]

Introduction Little research to date has explored typologies of physical activity among older adults. An understanding of physical activity patterns may help to both determine the health benefits of different types of activity and target interventions to increase activity levels in older adults. This analysis, conducted in 2014, used a latent class analysis approach to characterize patterns of physical activity in a cohort of older adults. Methods A total of 3,497 men and women aged 65–75 years living in New York City completed the Physical Activity Scale for the Elderly (PASE) in 2011. PASE scale items were used to classify subjects into latent classes. Multinomial regression was then used to relate individual and neighborhood characteristics to class membership. Results Five latent classes were identified: “least active,” “walkers,” “domestic/gardening,” “athletic,” and “domestic/gardening athletic.” Individual-level predictors, including more education, higher income, and better self-reported health, were associated with membership in the more-active classes, particularly the athletic classes. Residential characteristics, including living in single-family housing and living in the lower-density boroughs of New York City, were predictive of membership in one of the domestic/gardening classes. Class membership was associated with BMI even after controlling for total PASE score. Conclusions This study suggests that individual and neighborhood characteristics are associated with distinct physical activity patterns in a group of older urban adults. These patterns are associated with body habitus independent of overall activity. [ABSTRACT FROM AUTHOR]

Summary: Problem: The use of body mass index (BMI) to assess obesity and health risks has been criticized in scientific and lay publications because of its failure to account for body shape and inability to distinguish fat mass from lean mass. We sought to determine whether other anthropometric measures (waist circumference (WC), waist-to-height ratio (WtH), percent body fat (%BF), fat mass index (FMI), or fat-free mass index (FFMI)) were consistently better predictors of components of the metabolic syndrome than BMI is. Methods: Cross-sectional measurements of height, weight, waist circumference and percent body fat were obtained from 12,294 adults who took part in annual physical exams provided by EHE International, Inc. Blood pressure was measured during the exam and HDL, LDL, and fasting glucose were measured from blood samples. Pearson correlations, linear regression, and adjusted Receiver Operator Characteristic (ROC) curves were used to relate each anthropometric measure to each metabolic risk factor. Results: None of the measures was consistently the strongest predictor. BMI was the strongest predictor of blood pressure, measures related to central adiposity (WC and WtH) performed better at predicting fasting glucose, and all measures were roughly comparable at predicting cholesterol levels. In all, differences in areas under ROC curves were 0.03 or less for all measure/outcome pairs that performed better than BMI. Conclusion: Body mass index is an adequate measure of adiposity for clinical purposes. In the context of lay press critiques of BMI and recommendations for alternative body-size measures, these data support clinicians making recommendations to patients based on BMI measurements. [Copyright &y& Elsevier]

Google Street View's 'Time Machine' feature holds promise for longitudinal street audits of built and natural environments for urban health research. As images are only available when Google collected data, differential image availability over time and place could bias audit data quality. We assessed image availability at 2000 randomly selected locations within the Bronx and San Diego from which Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants were recruited. In the Bronx, a mean of 7.4 images (95% CI: 7.2,7.5) were available at each location, and 63% of those locations had imagery in 2007 and 2019. In San Diego, fewer images were available (mean 5.4, 95% CI: 5.2,5.6) especially on minor streets (mean 4.4, 95% CI: 4.1,4.6). Image availability was more spatially clustered in San Diego (Moran's I 0.14) than the Bronx (Moran's I 0.04). Differential image availability may affect precision of neighborhood change estimates assessed by longitudinal virtual audit. [ABSTRACT FROM AUTHOR]

Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US. From tract-level 2015–2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA). The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001). LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US. • LPA characterized all census tracts in the contiguous U.S into distinct profiles. • 6-profile model was chosen, separated by demographic, social, commuting, and built characteristics. • Profiles displayed geographic heterogeneity. • Differences in population health were observed across the derived typology. [ABSTRACT FROM AUTHOR]

Introduction: Various built environment factors might influence certain health behaviors and outcomes. Reliable, resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger, more robust studies. This paper reports the item response prevalence, reliability, and rating time of a new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment of walkability and physical disorder characteristics across large geographic areas.Methods: Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning 360° around a point location, was developed using a modified version of the virtual audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately 8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors. Using a standardized protocol, 32 built environment items per a location within Google Street View were audited. Test-retest and inter-rater κ statistics were from a 5% subsample of locations. Data were collected in 2017-2018 and analyzed in 2018.Results: Roughly 70% of Google Street View scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of Google Street View scenes. Maximum test-retest reliability indicated substantial agreement (κ ≥0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds.Conclusions: Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing is quicker and similarly reliable for assessing built environment characteristics. Assessment of large geographies may be more feasible using drop-and-spin virtual auditing. [ABSTRACT FROM AUTHOR]

We examined associations of micro-scale environment attributes (e.g., sidewalks, street crossings) with three physical activity (PA) measures among Hispanic/Latino adults (n = 1776) living in San Diego County, CA. Systematic observation was used to quantify micro-scale environment attributes near each participant's home. Total PA was assessed with accelerometers, and PA for transportation and recreation were assessed by validated self-report. Although several statistically significant interactions between individual and neighborhood characteristics were identified, there was little evidence micro-scale attributes were related to PA. An important limitation was restricted environmental variability for this sample which lived in a small area of a single county. [ABSTRACT FROM AUTHOR]

This study aimed to understand socio-spatial inequalities in the placement of unhealthy commodity advertisements at transportation stops within the Central Belt of Scotland and to measure advertisement exposure using children's individual-level mobility data. We found that children who resided within more deprived areas had greater contact with the transport network and also greater exposure to unhealthy food and drink product advertising, compared to those living in less deprived areas. Individual-level mobility data provide evidence that city- or country-wide restrictions to advertising on the transport network might be required to reduce inequalities in children's exposure to unhealthy commodity advertising. [ABSTRACT FROM AUTHOR]

Disparities in availability of food retailers in the residential environment may help explain racial/ethnic and socio-economic differences in obesity risk. Research is needed that describes whether food environment dynamics may contribute to equalizing conditions across neighborhoods or to amplifying existing inequalities over time. This study improves the understanding of how the BMI-unhealthy food environment has evolved over time in New York City. We use longitudinal census tract-level data from the National Establishment Time-Series (NETS) for New York City in the period 1990–2010 and implement latent class growth analysis (LCGA) to (1) examine trajectories of change in the number of unhealthy food outlets (characterized as selling calorie-dense foods such as pizza and pastries) at the census tract-level, and (2) examine how trajectories are related to socio-demographic characteristics of the census tract. Overall, the number of BMI-unhealthy food outlets increased between 1990 and 2010. We summarized trajectories of evolutions with a 5-class model that indicates a pattern of fanning out, such that census tracts with a higher initial number of BMI-unhealthy food outlets in 1990 experienced a more rapid increase over time. Finally, fully adjusted logistic regression models reveal a greater increase in BMI-unhealthy food outlets in census tracts with: higher baseline population size, lower baseline income, and lower proportion of Black residents. Greater BMI-unhealthy food outlet increases were also noted in the context of census tracts change suggestive of urbanization (increasing population density) or increasing purchasing power (increasing income). • Exposure to the unhealthy food environment increased between 1990 and 2010. • We observed divergent trajectories of changes in the unhealthy food environment. • Greater increases in exposure occurred in poorer and non-Black neighborhoods. • Greater exposure was related to urbanization and increasing purchasing power. [ABSTRACT FROM AUTHOR]