Residential Density Is Associated With BMI Trajectories in Children and Adolescents: Findings From the Moving to Health Study

Introduction

This study investigates the associations between built environment features and 3-year BMI trajectories in children and adolescents.

Methods

This retrospective cohort study utilized electronic health records of individuals aged 5-18 years living in King County, Washington, from 2005 to 2017. Built environment features such as residential density; counts of supermarkets, fast-food restaurants, and parks; and park area were measured using SmartMaps at 1,600-meter buffers. Linear mixed-effects models performed in 2022 tested whether built environment variables at baseline were associated with BMI change within age cohorts (5, 9, and 13 years), adjusting for sex, age, race/ethnicity, Medicaid, BMI, and residential property values (SES measure).

Results

At 3-year follow-up, higher residential density was associated with lower BMI increase for girls across all age cohorts and for boys in age cohorts of 5 and 13 years but not for the age cohort of 9 years. Presence of fast food was associated with higher BMI increase for boys in the age cohort of 5 years and for girls in the age cohort of 9 years. There were no significant associations between BMI change and counts of parks, and park area was only significantly associated with BMI change among boys in the age cohort of 5 years.

Conclusions

Higher residential density was associated with lower BMI increase in children and adolescents. The effect was small but may accumulate over the life course. Built environment factors have limited independent impact on 3-year BMI trajectories in children and adolescents.

Road Traffic Injuries and the Built Environment in Bogotá, Colombia, 2015-2019: A Cross-Sectional Analysis

Nine in 10 road traffic deaths occur in low- and middle-income countries (LMICs). Despite this disproportionate burden, few studies have examined built environment correlates of road traffic injury in these settings, including in Latin America. We examined road traffic collisions in Bogotá, Colombia, occurring between 2015 and 2019, and assessed the association between neighborhood-level built environment features and pedestrian injury and death. We used descriptive statistics to characterize all police-reported road traffic collisions that occurred in Bogotá between 2015 and 2019. Cluster detection was used to identify spatial clustering of pedestrian collisions. Adjusted multivariate Poisson regression models were fit to examine associations between several neighborhood-built environment features and rate of pedestrian road traffic injury and death. A total of 173,443 police-reported traffic collisions occurred in Bogotá between 2015 and 2019. Pedestrians made up about 25% of road traffic injuries and 50% of road traffic deaths in Bogotá between 2015 and 2019. Pedestrian collisions were spatially clustered in the southwestern region of Bogotá. Neighborhoods with more street trees (RR, 0.90; 95% CI, 0.82-0.98), traffic signals (0.89, 0.81-0.99), and bus stops (0.89, 0.82-0.97) were associated with lower pedestrian road traffic deaths. Neighborhoods with greater density of large roads were associated with higher pedestrian injury. Our findings highlight the potential for pedestrian-friendly infrastructure to promote safer interactions between pedestrians and motorists in Bogotá and in similar urban contexts globally.

Novel Methods of Identifying Individual and Neighborhood Risk Factors for Loss to Follow-Up After Ophthalmic Screening

PRCIS

Residence in a middle-class neighborhood correlated with lower follow-up compared with residence in more affluent neighborhoods. The most common explanations for not following up were the process of making an appointment and lack of symptoms.

PURPOSE

To explore which individual-level and neighborhood-level factors influence follow-up as recommended after positive ophthalmic and primary care screening in a vulnerable population using novel methodologies.

PARTICIPANTS AND METHODS

From 2017 to 2018, 957 participants were screened for ophthalmic disease and cardiovascular risk factors as part of the Real-Time Mobile Teleophthalmology study. Individuals who screened positive for either ophthalmic or cardiovascular risk factors were contacted to determine whether or not they followed up with a health care provider. Data from the Social Vulnerability Index, a novel virtual auditing system, and personal demographics were collected for each participant. A multivariate logistic regression was performed to determine which factors significantly differed between participants who followed up and those who did not.

RESULTS

As a whole, the study population was more socioeconomically vulnerable than the national average (mean summary Social Vulnerability Index score=0.81). Participants whose neighborhoods fell in the middle of the national per capita income distribution had a lower likelihood of follow-up compared with those who resided in the most affluent neighborhoods (relative risk ratio=0.21, P -value<0.01). Participants cited the complicated process of making an eye care appointment and lack of symptoms as the most common reasons for not following up as instructed within 4 months.

CONCLUSIONS

Residence in a middle-class neighborhood, difficulty accessing eye care appointments, and low health literacy may influence follow-up among vulnerable populations.

Acceptability and anticipated effectiveness of a safe supply of opioids, among people who inject opioids in King County, WA

BACKGROUND

Opioid overdose mortality in the US has exceeded one million deaths over the last two decades. A regulated opioid supply may help prevent future overdose deaths by reducing exposure to the unregulated opioid supply. We examined the acceptability, delivery model preference, and anticipated effectiveness of different regulated opioid models among people in the Seattle area who inject opioids.

METHODS

We enrolled people who inject drugs in the 2022 Seattle-area National HIV Behavior Surveillance (NHBS) survey. Participants were recruited between July and December 2022 using respondent-driven sampling. Participants who reported injecting opioids (N = 453) were asked whether regulated opioids would be acceptable, their preferred model of receiving regulated opioids, and the anticipated change in individual overdose risk from accessing a regulated opioid supply.

RESULTS

In total, 369 (81 %) participants who injected opioids reported that a regulated opioid supply would be acceptable to them. Of the 369 who found a regulated opioid supply to be acceptable, the plurality preferred a take-home model where drugs are prescribed (35 %), followed closely by a dispensary model that required no prescription (28 %), and a prescribed model where drugs need to be consumed on site (13 %), a model where no prescription is required and drugs can be accessed in a community setting with a one-time upfront payment was the least preferred model (5 %). Most participants (69 %) indicated that receiving a regulated opioid supply would be "a lot less risky" than their current supply, 20 % said, "a little less risky", 10 % said no difference, and 1 % said a little or a lot more risky.

CONCLUSION

A regulated opioid supply would be acceptable to most participants, and participants reported it would greatly reduce their risk of overdose. As overdose deaths continue to increase in Washington state pragmatic and effective solutions that reduce exposure to unregulated drugs are needed.

Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study

OBJECTIVE

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.

RESULTS

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).

CONCLUSIONS

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.

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US

INTRODUCTION

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.

METHODS

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).

RESULTS

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).

CONCLUSION

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.

Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach

BACKGROUND

Firearm suicide has been more prevalent among males, but age-adjusted female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the rate increase among males by about 8 percentage points, and female firearm suicide may have different contributing circumstances. In the United States, the National Violent Death Reporting System (NVDRS) is a comprehensive source of data on violent deaths and includes unstructured incident narrative reports from coroners or medical examiners and law enforcement. Conventional natural language processing approaches have been used to identify common circumstances preceding female firearm suicide deaths but failed to identify rarer circumstances due to insufficient training data.

OBJECTIVE

This study aimed to leverage a large language model approach to identify infrequent circumstances preceding female firearm suicide in the unstructured coroners or medical examiners and law enforcement narrative reports available in the NVDRS.

METHODS

We used the narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 to 2018. The reports were written in English. We coded 9 infrequent circumstances preceding female firearm suicides. We experimented with predicting those circumstances by leveraging a large language model approach in a yes/no question-answer format. We measured the prediction accuracy with F1-score (ranging from 0 to 1). F1-score is the harmonic mean of precision (positive predictive value) and recall (true positive rate or sensitivity).

RESULTS

Our large language model outperformed a conventional support vector machine-supervised machine learning approach by a wide margin. Compared to the support vector machine model, which had F1-scores less than 0.2 for most infrequent circumstances, our large language model approach achieved an F1-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances.

CONCLUSIONS

The use of a large language model approach shows promise. Researchers interested in using natural language processing to identify infrequent circumstances in narrative report data may benefit from large language models.

A flexible matching strategy for matched nested case-control studies

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Bicycle infrastructure and the incidence rate of crashes with cars: A case-control study with Strava data in Atlanta

Introduction

Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes.

Methods

In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 2018-08-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of high-resolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated.

Results

After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]).

Conclusions

Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

Pediatric emergency department visits for pedestrian injuries in relation to the enactment of Complete Streets policy

Introduction

This study aimed to evaluate the rate of pediatric emergency department (ED) visits for pedestrian injuries in relation to the enactment of the Complete Streets policy.

Methods

The National Complete Streets policies were codified by county and associated with each hospital's catchment area and date of enactment. Pedestrian injury-related ED visits were identified across 40 children's hospitals within the Pediatric Health Information System (PHIS) from 2004 to 2014. We calculated the proportion of the PHIS hospitals' catchment areas covered by any county policy. We used a generalized linear model to assess the impact of the proportion of the policy coverage on the rate of pedestrian injury-related ED visits.

Results

The proportion of the population covered by Complete Streets policies increased by 23.9%, and pedestrian injury rates at PHIS hospitals decreased by 29.8% during the study period. After controlling for years, pediatric ED visits for pedestrian injuries did not change with increases in the PHIS catchment population with enacted Complete Streets policies.

Conclusion

After accounting for time trends, Complete Streets policy enactment was not related to observed changes in ED visits for pedestrian injuries at PHIS hospitals.

Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity

Real-world data (RWD) like electronic health records (EHR) has great potential for secondary use by health systems and researchers. However, collected primarily for efficient health care, EHR data may not equitably represent local regions and populations, impacting the generalizability of insights learned from it. We assessed the geospatial representativeness of regions in a large health system EHR data using a spatial analysis workflow, which provides a data-driven way to quantify geospatial representation and identify adequately represented regions. We applied the workflow to investigate geospatial patterns of overweight/obesity and depression patients to find regional "hotspots" for potential targeted interventions. Our findings show the presence of geospatial bias in EHR and demonstrate the workflow to identify spatial clusters after adjusting for bias due to the geospatial representativeness. This work highlights the importance of evaluating geospatial representativeness in RWD to guide targeted deployment of limited healthcare resources and generate equitable real-world evidence.

Associations Between Insurance, Race and Ethnicity, and COVID-19 Hospitalization, Beyond Underlying Health Conditions: A Retrospective Cohort Study

Introduction

: People of lower socioeconomic position (SEP) and people of color (POC) experience higher risks of severe COVID-19, but understanding of these associations beyond the effect of underlying health conditions (UHCs) is limited. Moreover, few studies have focused on young adults, who have had the highest incidence of COVID-19 during much of the pandemic.

Methods

: We conducted a retrospective cohort study using electronic health record data from the University of Washington Medicine healthcare system. Our study population included individuals aged 18-39 years who tested positive for SARS-CoV-2 from February 2020 to March 2021. Using regression modeling, we estimated adjusted risk ratios (aRRs) and differences (aRDs) of COVID-19 hospitalization by SEP (using health insurance as a proxy) and race and ethnicity. We adjusted for any UHC to examine these associations beyond the effect of UHCs. Results: Among 3,101 individuals, the uninsured/publicly insured had a 1.9-fold higher risk of hospitalization (aRR [95% CI]=1.9 [1.0, 3.6]) and 9 additional hospitalizations per 1,000 SARS-CoV-2 positive persons (aRD [95% CI]=9 [-1, 20]) compared to the privately insured. Hispanic or Latine, non-Hispanic (NH) Asian, NH Black, and NH Native Hawaiian or Pacific Islander patients had a 1.5-, 2.7-, 1.4-, and 2.1-fold-higher risk of hospitalization (aRR [95% CI]=1.5 [0.7, 3.1]; 2.7 [1.1, 6.5]; 1.4 [0.6, 3.3]; 2.1 [0.5, 9.1]), respectively, compared to NH White patients. Conclusions: Though they should be interpreted with caution given low precision, our findings suggest the increased risk of COVID-19 hospitalization among young adults of lower SEP and young adults of color may be driven by forces other than UHCs, including social determinants of health.

Measuring changes in neighborhood disorder using Google Street View longitudinal imagery: a feasibility study

Few studies have used longitudinal imagery of Google Street View (GSV) despite its potential for measuring changes in urban streetscapes characteristics relevant to health, such as neighborhood disorder. Neighborhood disorder has been previously associated with health outcomes. We conducted a feasibility study exploring image availability over time in the Philadelphia metropolitan region and describing changes in neighborhood disorder in this region between 2009, 2014, and 2019. Our team audited Street View images from 192 street segments in the Philadelphia Metropolitan Region. On each segment, we measured the number of images available through time, and for locations where imagery from more than one time point was available, we collected 8 neighborhood disorder indicators at 3 different times (up to 2009, up to 2014, and up to 2019). More than 70% of streets segments had at least one image. Neighborhood disorder increased between 2009 and 2019. Future studies should study the determinants of change of neighborhood disorder using longitudinal GSV imagery.

Classifying Firearm Injury Intent in Electronic Hospital Records Using Natural Language Processing

Importance

International Classification of Diseases-coded hospital discharge data do not accurately reflect whether firearm injuries were caused by assault, unintentional injury, self-harm, legal intervention, or were of undetermined intent. Applying natural language processing (NLP) and machine learning (ML) techniques to electronic health record (EHR) narrative text could be associated with improved accuracy of firearm injury intent data.

Objective

To assess the accuracy with which an ML model identified firearm injury intent.

Design, Setting, and Participants

A cross-sectional retrospective EHR review was conducted at 3 level I trauma centers, 2 from health care institutions in Boston, Massachusetts, and 1 from Seattle, Washington, between January 1, 2000, and December 31, 2019; data analysis was performed from January 18, 2021, to August 22, 2022. A total of 1915 incident cases of firearm injury in patients presenting to emergency departments at the model development institution and 769 from the external validation institution with a firearm injury code assigned according to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM), in discharge data were included.

Exposures

Classification of firearm injury intent.

Main Outcomes and Measures

Intent classification accuracy by the NLP model was compared with ICD codes assigned by medical record coders in discharge data. The NLP model extracted intent-relevant features from narrative text that were then used by a gradient-boosting classifier to determine the intent of each firearm injury. Classification accuracy was evaluated against intent assigned by the research team. The model was further validated using an external data set.

Results

The NLP model was evaluated in 381 patients presenting with firearm injury at the model development site (mean [SD] age, 39.2 [13.0] years; 348 [91.3%] men) and 304 patients at the external development site (mean [SD] age, 31.8 [14.8] years; 263 [86.5%] men). The model proved more accurate than medical record coders in assigning intent to firearm injuries at the model development site (accident F-score, 0.78 vs 0.40; assault F-score, 0.90 vs 0.78). The model maintained this improvement on an external validation set from a second institution (accident F-score, 0.64 vs 0.58; assault F-score, 0.88 vs 0.81). While the model showed some degradation between institutions, retraining the model using data from the second institution further improved performance on that site's records (accident F-score, 0.75; assault F-score, 0.92).

Conclusions and Relevance

The findings of this study suggest that NLP ML can be used to improve the accuracy of firearm injury intent classification compared with ICD-coded discharge data, particularly for cases of accident and assault intents (the most prevalent and commonly misclassified intent types). Future research could refine this model using larger and more diverse data sets.

Variation in tracheostomy placement and outcomes following pediatric trauma among adult, pediatric, and combined trauma centers

BACKGROUND

Tracheostomy placement is much more common in adults than children following severe trauma. We evaluated whether tracheostomy rates and outcomes differ for pediatric patients treated at trauma centers that primarily care for children versus adults.

METHODS

We conducted a retrospective cohort study of patients younger than 18 years in the National Trauma Data Bank from 2007 to 2016 treated at a Level I/II pediatric, adult, or combined adult/pediatric trauma center, ventilated >24 hours, and who survived to discharge. We used multivariable logistic regression adjusted for age, insurance, injury mechanism and body region, and Injury Severity Score to estimate the association between the three trauma center types and tracheostomy. We used augmented inverse probability weighting to model the likelihood of tracheostomy based on the propensity for treatment at a pediatric, adult, or combined trauma center, and estimated associations between trauma center type with length of stay and postdischarge care.

RESULTS

Among 33,602 children, tracheostomies were performed in 4.2% of children in pediatric centers, 7.8% in combined centers (adjusted odds ratio [aOR], 1.47; 95% confidence interval [CI], 1.20-1.81), and 11.2% in adult centers (aOR, 1.81; 95% CI, 1.48-2.22). After propensity matching, the estimated average tracheostomy rate would be 62.9% higher (95% CI, 37.7-88.1%) at combined centers and 85.3% higher (56.6-113.9%) at adult centers relative to pediatric centers. Tracheostomy patients had longer hospital stay in pediatric centers than combined (-4.4 days, -7.4 to -1.3 days) or adult (-4.0 days, -7.2 to -0.9 days) centers, but fewer children required postdischarge inpatient care (70.1% pediatric vs. 81.3% combined [aOR, 2.11; 95% CI, 1.03-4.31] and 82.4% adult centers [aOR, 2.51; 95% CI, 1.31-4.83]).

CONCLUSION

Children treated at pediatric trauma centers have lower likelihood of tracheostomy than children treated at combined adult/pediatric or adult centers independent of patient or injury characteristics. Better understanding of optimal indications for tracheostomy is necessary to improve processes of care for children treated throughout the pediatric trauma system.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Association between any underlying health condition and COVID-19-associated hospitalization by age group, Washington State, 2020-2021: a retrospective cohort study

BACKGROUND

Presence of at least one underlying health condition (UHC) is positively associated with severe COVID-19, but there is limited research examining this association by age group, particularly among young adults.

METHODS

We examined age-stratified associations between any UHC and COVID-19-associated hospitalization using a retrospective cohort study of electronic health record data from the University of Washington Medicine healthcare system for adult patients with a positive SARS-CoV-2 test from February 29, 2020, to March 13, 2021. Any UHC was defined as documented diagnosis of at least one UHC identified by the CDC as a potential risk factor for severe COVID-19. Adjusting for sex, age, race and ethnicity, and health insurance, we estimated risk ratios (aRRs) and risk differences (aRDs), overall and by age group (18-39, 40-64, and 65 + years).

RESULTS

Among patients aged 18-39 (N = 3,249), 40-64 (N = 2,840), 65 + years (N = 1,363), and overall (N = 7,452), 57.5%, 79.4%, 89.4%, and 71.7% had at least one UHC, respectively. Overall, 4.4% of patients experienced COVID-19-associated hospitalization. For all age groups, the risk of COVID-19-associated hospitalization was greater for patients with any UHC vs. those without (18-39: 2.2% vs. 0.4%; 40-64: 5.6% vs. 0.3%; 65 + : 12.2% vs. 2.8%; overall: 5.9% vs. 0.6%). The aRR comparing patients with vs. those without UHCs was notably higher for patients aged 40-64 years (aRR [95% CI] for 18-39: 4.3 [1.8, 10.0]; 40-64: 12.9 [3.2, 52.5]; 65 + : 3.1 [1.2, 8.2]; overall: 5.3 [3.0, 9.6]). The aRDs increased across age groups (aRD [95% CI] per 1,000 SARS-CoV-2-positive persons for 18-39: 10 [2, 18]; 40-64: 43 [33, 54]; 65 + : 84 [51, 116]; overall: 28 [21, 35]).

CONCLUSIONS

Individuals with UHCs are at significantly increased risk of COVID-19-associated hospitalization regardless of age. Our findings support the prevention of severe COVID-19 in adults with UHCs in all age groups and in older adults aged 65 + years as ongoing local public health priorities.

Associations between observed neighborhood physical disorder and health behaviors, New Jersey behavioral risk factor Surveillance System 2011-2016

This study tested associations between observed neighborhood physical disorder and tobacco use, alcohol binging, and sugar-sweetened beverage consumption among a large population-based sample from an urban area of the United States. Individual-level data of this cross-sectional study were from adult respondents of the New Jersey Behavioral Risk Factor Surveillance System, 2011-2016 (n = 62,476). Zip code tabulation area-level observed neighborhood physical disorder were from virtual audits of 23,276 locations. Tobacco use (current cigarette smoking or chewing tobacco, snuff, or snus use), monthly binge drinking occasions (5+/4+ drinks per occasion among males/females), and monthly sugar-sweetened beverages consumed were self-reported. Logistic and negative binomial regression models were used to generate odds ratios, prevalence rate ratios (PRR), 95 % confidence intervals (CI) by levels of physical disorder. Compared to the lowest quartile, residence in the second (PRR: 1.16; 95 % CI: 1.03, 1.13), third (PRR: 1.24; 95 % CI: 1.10, 1.40), and fourth (highest) quartile of physical disorder (PRR: 1.24; 95 % CI: 1.10, 1.40) was associated with higher monthly sugar-sweetened beverage consumption. Associations involving tobacco use and alcohol binging were mixed. Observed neighborhood disorder might be associated with unhealthy behaviors, especially sugar-sweetened beverage consumption.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

Registry Data in Injury Research: Study Designs and Interpretation

Purpose of Review

Injury data is frequently captured in registries that form a census of 100% of known cases that meet specified inclusion criteria. These data are routinely used in injury research with a variety of study designs. We reviewed study designs commonly used with data extracted from injury registries and evaluated the advantages and disadvantages of each design type.

Recent Findings

Registry data are suited to 5 major design types: (1) Description, (2) Ecologic (with Ecologic Cohort as a particularly informative sub-type), (3) Case-control (with location-based and culpability studies as salient subtypes), (4) Case-only (including case-case and case-crossover subtypes), and (5) Outcomes.

Summary

Registries are an important resource for injury research. Investigators considering use of a registry should be aware of the advantages and disadvantages of available study designs.

Causal Inference with Case-Only Studies in Injury Epidemiology Research

Purpose of Review

We review the application and limitations of two implementations of the "case-only design" in injury epidemiology with example analyses of Fatality Analysis Reporting System data.

Recent Findings

The term "case-only design" covers a variety of epidemiologic designs; here, two implementations of the design are reviewed: (1) studies to uncover etiological heterogeneity and (2) studies to measure exposure effect modification. These two designs produce results that require different interpretations and rely upon different assumptions. The key assumption of case-only designs for exposure effect modification, the more commonly used of the two designs, does not commonly hold for injuries and so results from studies using this design cannot be interpreted. Case-only designs to identify etiological heterogeneity in injury risk are interpretable but only when the case-series is conceptualized as arising from an underlying cohort.

Summary

The results of studies using case-only designs are commonly misinterpreted in the injury literature.

Assessment of the Accuracy of Firearm Injury Intent Coding at 3 US Hospitals

IMPORTANCE

The absence of reliable hospital discharge data regarding the intent of firearm injuries (ie, whether caused by assault, accident, self-harm, legal intervention, or an act of unknown intent) has been characterized as a glaring gap in the US firearms data infrastructure.

OBJECTIVE

To use incident-level information to assess the accuracy of intent coding in hospital data used for firearm injury surveillance.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional retrospective medical review study was conducted using case-level data from 3 level I US trauma centers (for 2008-2019) for patients presenting to the emergency department with an incident firearm injury of any severity.

EXPOSURES

Classification of firearm injury intent.

MAIN OUTCOMES AND MEASURES

Researchers reviewed electronic health records for all firearm injuries and compared intent adjudicated by team members (the gold standard) with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification (ICD-9-CM and ICD-10-CM) codes for firearm injury intent assigned by medical records coders (in discharge data) and by trauma registrars. Accuracy was assessed using intent-specific sensitivity and positive predictive value (PPV).

RESULTS

Of the 1227 cases of firearm injury incidents seen during the ICD-10-CM study period (October 1, 2015, to December 31, 2019), the majority of patients (1090 [88.8%]) were male and 547 (44.6%) were White. The research team adjudicated 837 (68.2%) to be assaults. Of these assault incidents, 234 (28.0%) were ICD coded as unintentional injuries in hospital discharge data. These miscoded patient cases largely accounted for why discharge data had low sensitivity for assaults (66.3%) and low PPV for unintentional injuries (34.3%). Misclassification was substantial even for patient cases described explicitly as assaults in clinical notes (sensitivity of 74.3%), as well as in the ICD-9-CM study period (sensitivity of 77.0% for assaults and PPV of 38.0% for unintentional firearm injuries). By contrast, intent coded by trauma registrars differed minimally from researcher-adjudicated intent (eg, sensitivity for assault of 96.0% and PPV for unintentional firearm injury of 93.0%).

CONCLUSIONS AND RELEVANCE

The findings of this cross-sectional study underscore questions raised by prior work using aggregate count data regarding the accuracy of ICD-coded discharge data as a source of firearm injury intent. Based on our observations, researchers and policy makers should be aware that databases drawn from hospital discharge data (most notably, the Nationwide Emergency Department Sample) cannot be used to reliably count or characterize intent-specific firearm injuries.

Opportunities and challenges of using social media big data to assess mental health consequences of the COVID-19 crisis and future major events

The present commentary discusses how social media big data could be used in mental health research to assess the impact of major global crises such as the COVID-19 pandemic. We first provide a brief overview of the COVID-19 situation and the challenges associated with the assessment of its global impact on mental health using conventional methods. We then propose social media big data as a possible unconventional data source, provide illustrative examples of previous studies, and discuss the advantages and challenges associated with their use for mental health research. We conclude that social media big data represent a valuable resource for mental health research, however, several methodological limitations and ethical concerns need to be addressed to ensure safe use.

Cumulative Payments Through the Earned Income Tax Credit Program in Childhood and Criminal Conviction During Adolescence in the US

IMPORTANCE

Childhood poverty is associated with poor health and behavioral outcomes. The Earned Income Tax Credit (EITC), first implemented in 1975, is the largest cash transfer program for working families with low income in the US.

OBJECTIVE

To assess whether cumulative EITC payments received during childhood are associated with the risk of criminal conviction during adolescence.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, the analytic sample consisted of US children enrolled in the 1979 National Longitudinal Study of Youth. The children were born between 1979 and 1998 and were interviewed as adolescents (age 15-19 years) between 1994 and 2016. Data analyses were performed from May 2021 to September 2022.

EXPOSURE

Cumulative simulated EITC received by the individual's family from birth through age 14 years.

MAIN OUTCOMES AND MEASURES

The main outcome was dichotomous, self-reported conviction for a crime during adolescence (age 14-18 years). A cumulative, simulated measure of mean EITC benefits received by a child's family from birth through age 14 years was derived from federal, state, and family-size differences in EITC eligibility and payments during the study period to capture EITC benefit variation due to differences in policy parameters but not endogenous factors such as changes in household income. Logistic regression models with fixed effects for state and year and robust SEs clustered by mother estimated relative risk of adolescent conviction. Models were adjusted for state-, mother-, and child-level covariates.

RESULTS

The analytical sample consisted of 5492 adolescents born between 1979 and 1998; 2762 (50.3%) were male, 1648 (30.0%) were Black, 1125 (20.5%) were Hispanic, and 2719 (49.5%) were not Black or Hispanic. Each additional $1000 of EITC received during childhood was associated with an 11% lower risk of self-reported criminal conviction during adolescence (adjusted odds ratio, 0.89; 95% CI, 0.84-0.95). Adjusted risk differences were larger among boys (-14.2 self-reported convictions per 1000 population [95% CI, -22.0 to -6.3 per 1000 population]) than among girls (-6.2 per 1000 population [95% CI, -10.7 to -1.6 per 1000 population]).

CONCLUSIONS AND RELEVANCE

The findings suggest that income support from the EITC may be associated with reduced youth involvement with the criminal justice system in the US. Cost-benefit analyses of the EITC should consider these longer-term and indirect outcomes.

Earned Income Tax Credit and Youth Violence: Findings from the Youth Risk Behavior Surveillance System

Family- and neighborhood-level poverty are associated with youth violence. Economic policies may address this risk factor by reducing parental stress and increasing opportunities. The federal Earned Income Tax Credit (EITC) is the largest cash transfer program in the US providing support to low-income working families. Many states have additional EITCs that vary in structure and generosity. To estimate the association between state EITC and youth violence, we conducted a repeated cross-sectional analysis using the variation in state EITC generosity over time by state and self-reported data in the Youth Risk Behavior Surveillance System (YRBSS) from 2005 to 2019. We estimated the association for all youth and then stratified by sex and race and ethnicity. A 10-percentage point greater state EITC was significantly associated with 3.8% lower prevalence of physical fighting among youth, overall (PR: 0.96; 95% CI 0.94-0.99), and for male students, 149 fewer (95% CI: -243, -55) students per 10,000 experiencing physical fighting. A 10-percentage point greater state EITC was significantly associated with 118 fewer (95% CI: -184, -52) White students per 10,000 experiencing physical fighting in the past 12 months while reductions among Black students (75 fewer; 95% CI: -176, 26) and Hispanic/Latino students (14 fewer; 95% CI: -93, 65) were not statistically significant. State EITC generosity was not significantly associated with measures of violence at school. Economic policies that increase financial security and provide financial resources may reduce the burden of youth violence; further attention to their differential benefits among specific population subgroups is warranted.

A Clinician's Guide to Conducting Research on Causal Effects

Surgeons are uniquely poised to conduct research to improve patient care, yet a gap often exists between the clinician's desire to guide patient care with causal evidence and having adequate training necessary to produce causal evidence. This guide aims to address this gap by providing clinically relevant examples to illustrate necessary assumptions required for clinical research to produce causal estimates.

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

BACKGROUND

Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight.

METHODS

Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight.

RESULTS

In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]).

CONCLUSIONS

Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Micro-scale pedestrian streetscapes and physical activity in Hispanic/Latino adults: Results from HCHS/SOL

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.

Association of State-Level Earned Income Tax Credits With Rates of Reported Child Maltreatment, 2004-2017

Poverty is an important predictor of child maltreatment. Social policies that strengthen the economic security of low-income families, such as the Earned Income Tax Credit (EITC), may reduce child maltreatment by impeding the pathways through which poverty leads to it. We used variations in the presence and generosity of supplementary EITCs offered at the state level and administrative child maltreatment data from the National Child Abuse and Neglect Data System (NCANDS) to examine the effect of EITC policies on state-level rates of child maltreatment from 2004 through 2017. Two-way fixed effects models indicated that a 10-percentage point increase in the generosity of refundable state EITC benefits was associated with 241 fewer reports of neglect per 100,000 children (95% Confidence Interval [CI] [-449, -33]). An increase in EITC generosity was associated with fewer reports of neglect both among children ages 0-5 (-324 per 100,000; 95% CI [-582, -65]) and children ages 6-17 (-201 per 100,000; 95% CI [-387, -15]). Findings also suggested associations between the EITC and reductions in other types of maltreatment (physical abuse, emotional abuse); however, those did not gain statistical significance. Economic support policies may reduce the risk of child maltreatment, especially neglect, and improve child wellbeing.

Microscale pedestrian environment surrounding pedestrian injury sites in Washington state, 2015-2020

OBJECTIVE

While microscale pedestrian environment features such as sidewalks and crosswalks can affect pedestrian safety, it is challenging to assess microscale environment associated risk across locations or at scale. Addressing these challenges requires an efficient auditing protocol that can be used to assess frequencies of microscale environment features. For this reason, we developed an eight-item pedestrian environment virtual audit protocol and conducted a descriptive epidemiologic study of pedestrian injury in Washington State, USA.

METHODS

We used data from police reports at pedestrian-automotive collision sites where the pedestrian was seriously injured or died. At each collision site, high school students participating in an online summer internship program virtually audited Google Street View imagery to assess the presence of microscale pedestrian environment features such as crosswalks and streetlighting. We assessed inter-rater reliability using Cohen's kappa and explored prevalence of eight microscale environment features in relation to injury severity and municipal boundaries.

RESULTS

There were 2248 motor vehicle crashes eliciting police response and resulting in death or serious injury of a pedestrian in Washington State between January 1, 2015 and May 8, 2020. Of the crashes resulting in serious injury or death, 498 (22%) resulted in fatalities and 1840 (82%) occurred within municipal boundaries. Cohen's kappa scores for the eight pedestrian features that were audited ranged from 0.52 to 0.86. Audit results confirmed that features such as sidewalks and crosswalks were more common at collision sites within city limits.

CONCLUSIONS

High school student volunteers with minimal training can reliably audit microscale pedestrian environments using limited resources.

Short-Term Effects of Tax Credits on Rates of Child Maltreatment Reports in the United States

OBJECTIVES

Poverty and low income are associated with increased risk for child maltreatment. The Earned Income Tax Credit (EITC) and Child Tax Credit (CTC) are among the largest antipoverty programs in the United States. We estimated associations between income transfer payments via the EITC and CTC and child maltreatment reports in the period shortly after families receive payments from these programs.

METHODS

We linked weekly EITC and CTC refund data from the Internal Revenue Service to state-specific child maltreatment report data from 48 states and the District of Columbia during the 2015 through 2018 tax seasons (January - April). We leveraged the natural experiment of a legislated change in the timing of EITC and CTC transfer payments to low-income families and quasi-experimental methods to estimate the association between EITC and CTC payments and child maltreatment reports.

RESULTS

EITC and CTC payments were associated with lower state-level rates of child maltreatment reports. For each additional $1000 in per-child EITC and CTC tax refunds, state-level rates of reported child maltreatment declined in the week of and 4 weeks following refund payments by an overall estimated 5.0% (95% confidence interval = 2.3%-7.7%).

CONCLUSIONS

Federal income assistance programs are associated with immediate reductions in child maltreatment reporting. These results are particularly relevant at this time, as expansions to such programs continue to be discussed at the state and federal levels.

The Earned Income Tax Credit and Intimate Partner Violence

Intimate partner violence (IPV) is a serious public health problem in the United States with adverse consequences for affected individuals and families. Recent reviews of the literature suggest that economic policies should be further investigated as part of comprehensive strategies to address IPV. The Earned Income Tax Credit (EITC) is the nation's largest anti-poverty program for working parents, and especially benefits low-income women with children, who experience an elevated risk of IPV. The EITC may prevent IPV by offering financial resources; such resources may help individuals experiencing IPV leave abusive relationships or address IPV risk factors, thereby preventing entry into abusive relationships. However, the association between EITC generosity and IPV has not been previously examined. We used state-level and individual-level datasets to examine the association between EITC generosity and IPV. Our state-level data source was the nationally representative National Crime Victimization Survey (NCVS; N = ~ 95,000 households per year). For NCVS, we used a difference-in-difference approach to investigate the relationship between state EITC generosity and IPV rates. We also used individual-level longitudinal data from the Fragile Families and Child Well-being Study (n = 13,422 person-waves). Using this cohort of US families at higher risk for IPV, we evaluated associations between estimated EITC benefits based on the mother's state of residence and number of children and self-reported IPV. In both state- and individual-level analyses, no significant association between state EITC benefits and IPV was found. Factors that may account for these null findings include program ineligibility for individuals who separate from abusive spouses. Future research efforts should more closely examine EITC policy implementation processes and the lived experience of participating in anti-poverty programs for people experiencing IPV.

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

Purpose of review

Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health.

Recent findings

Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas.

Summary

In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

Examining the Built Environment for Healthy Living via Virtual Street Audits

During the fall 2019 and spring 2020 semesters, 156 MPH students enrolled in the Integrative Learning Experience at the University of Alabama at Birmingham School of Public Health explored concepts of the built environment and health by auditing 2500 street segments in 4 urban neighborhoods in Birmingham, Alabama. In teams of 4 to 5, in-class and online students worked collaboratively to assess 63 built environment variables related to transportation, land use, advertisement, and neighborhood physical disorder. This type of "community assessment" is the first stage of the Evidence-based Public Health Framework and consistent with the applied nature of an MPH degree. Authors conducted secondary data analysis of final team assignments to demonstrate how students translated observations and ratings into practical recommendations for neighborhood improvements to promote physical activity. Students recommended improvements in neighborhood infrastructure and services, specifically: creating exercise space, providing outdoor exercise equipment, improving neighborhood safety, and cultivating a culture of health. The Integrative Learning Experience course encouraged students to use their knowledge and skills to prioritize recommendations to improve neighborhood conditions. Variable ratings and observations increased student awareness of the built environment and its potential to impact individual and community health. Moreover, the project helped students make connections between proximal outcomes, such as improving neighborhood walkability, and distal outcomes, such as improved health outcomes among residents. Finally, this project modeled for students the use of evidence-based strategies for making data-informed decisions, which are essential skills for new and emerging public health professionals.

Wage theft and life expectancy inequities in the United States: A simulation study

Wage theft - employers not paying workers their legally entitled wages and benefits - costs workers billions of dollars annually. We tested whether preventing wage theft could increase U.S. life expectancy and decrease inequities therein. We obtained nationally representative estimates of the 2001-2014 association between income and expected age at death for 40-year-olds (40 plus life expectancy at age 40) compiled from tax and Social Security Administration records, and estimates of the burden of wage theft from several sources, including estimates regarding minimum-wage violations (not paying workers the minimum wage) developed from Current Population Survey data. After modeling the relationship between income and expected age at death, we simulated the effects of scenarios preventing wage theft on mean expected age at death, assuming a causal effect of income on expected age at death. We simulated several scenarios, including one using data suggesting minimum-wage violations constituted 38% of all wage theft and caused 58% of affected workers' losses. Among women in the lowest income decile, mean expected age at death was 0.17 years longer in the counterfactual scenario than observed (95% confidence interval [CI]: 0.11-0.22), corresponding to 528,685 (95% CI: 346,018-711,353) years extended in the total 2001-2014 age-40 population. Among men in the lowest decile, the estimates were 0.12 (95% CI: 0.07-0.17) and 380,502 (95% CI: 229,630-531,374). Moreover, among women, mean expected age at death in the counterfactual scenario increased 0.16 (95% CI: 0.06-0.27) years more among the lowest decile than among the highest decile; among men, the estimate was 0.12 (95% CI: 0.03-0.21).

State earned income tax credits and depression and alcohol misuse among women with children

About 30% of single mothers in the US live at or below the poverty line. Poverty is associated with higher risk of depression and substance use. We investigated associations between state earned income tax credit (EITC) policies and reported depressive symptoms and alcohol misuse among birthing parents who responded to Pregnancy Risk Assessment Monitoring Survey spanning 1990-2017. Nearly half of birthing parents reported no more than a high school education (45.4%; 95% CI: 45.3%-45.6%). An estimated 28.5% of birthing parents reported binge drinking in the three months prior to conception (95% CI: 28.3-28.8%). Among birthing parents, each 10 percentage-point increase in the generosity of state EITC relative to the federal EITC was associated with a lower prevalence of binge drinking (prevalence ratio = 0.96; 95% CI: 0.93-0.99) prior to conception. This association was more pronounced among birthing parents with no more than high school education (prevalence ratio = 0.92; 95% CI: 0.88-0.97). There was no association between state EITC and number of reported depressive symptoms prior to conception or after birth, except among those with lower educational attainment (prevalence ratio = 0.94; 95% CI: 0.89-0.99). Anti-poverty policies such as EITC may reduce the burden of alcohol misuse, especially among people with children.

Ethical issues of collecting, storing, and analyzing geo-referenced tweets for mental health research

Spatial approaches to epidemiological research with big social media data provide tremendous opportunities to study the relationship between the socio-ecological context where these data are generated and health indicators of interest. Such research poses a number of ethical challenges, particularly in relation to issues such as privacy, informed consent, data security, and storage. While these issues have received considerable attention by researchers in relation to research for physical health purposes in the past 10 years, there have been few efforts to consider the ethical challenges of conducting mental health research, particularly with geo-referenced social media data. The aim of this article is to identify strengths and limitations of current recommendations to address the specific ethical issues of geo-referenced tweets for mental health research. We contribute to the ongoing debate on the ethical implications of big data research and also provide recommendations to researchers and stakeholders alike on how to tackle them, with a specific focus on the use of geo-referenced data for mental health research purposes. With increasing awareness of data privacy and confidentiality issues (even for non-spatial social media data) it becomes crucial to establish professional standards of conduct so that compliance with ethical standards of conducting research with health-related social media data can be prioritized and easily assessed.

G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?

Agent-based modeling and g-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built 4 models: g-computation, an agent-based model (ABM) with no between-agent interactions, an ABM with violent-interaction dynamics, and an ABM with neighborhood dynamics. Three interventions were tested: 1) reducing violent victimization by 37.2% (real-world reduction); 2) reducing violent victimization by100%; and 3) supplementing the income of 20% of lower-income participants. The g-computation model estimated population-level PTSD risk reductions of 0.12% (95% confidence interval (CI): -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The ABM with no interactions replicated the findings from g-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income-supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income-supplement risk reduction increased to 1.58%). Compared with g-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

The association between three key social determinants of health and life dissatisfaction: A 2017 behavioral risk factor surveillance system analysis

Poor health outcomes disproportionately impact certain populations in the United States owing to the inequitable distribution of social determinants of health (SDOH). Using the 2017 Behavioral Risk Factor Surveillance System (BRFSS), we estimated the association of three adverse SDOH (housing insecurity, food insecurity, and financial instability) with life dissatisfaction. Participants were from Wisconsin, Minnesota, and Ohio, the only states that included the SDOH and Emotional Support and Life Satisfaction modules (n = 25,850). Six percent of respondents reported life dissatisfaction. Those who reported housing insecurity (Prevalence difference (PD) = 14.2 per 100, 95% CI [7.6, 20.7]), food insecurity (PD = 10.9 [7.1, 14.7]), and financial instability (PD = 5.6 [4.9, 6.3]) had higher prevalence of life dissatisfaction. The differences in prevalence of life dissatisfaction, comparing those with and without an adverse SDOH, decreased with increased emotional support (for housing insecurity, food insecurity, and financial instability, respectively: low support, PD = 30.2 [11.6, 48.8], 22.1 [11.6, 32.6], 16.4 [12.0, 20.8]; high support, PD = 4.8 [-2.9, 12.6], 4.8 [0.0, 9.7], 1.7 [1.1, 2.3]). Participants with frequent mental distress (FMD) had greater prevalence differences than those without FMD (for housing insecurity, food insecurity, and financial instability, respectively: with FMD, PD = 15.4 [7.5, 23.3], 10.7 [4.7, 16.7], 14.4 [9.6, 19.3]; without FMD, PD = 6.1 [-0.5, 12.5], 5.3 [1.6, 9.0], 2.5 [2.0, 3.0]). Social determinants may not only influence physical health but also have an impact on psychological well-being. This impact may be altered by levels of emotional support and FMD.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age

OBJECTIVE

To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults.

METHODS

Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values.

RESULTS

Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures.

CONCLUSION

The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

Pathways between objective and perceived neighborhood factors among Black breast cancer survivors

BACKGROUND

Mounting evidence supports associations between objective neighborhood disorder, perceived neighborhood disorder, and health, yet alternative explanations involving socioeconomic and neighborhood social cohesion have been understudied. We tested pathways between objective and perceived neighborhood disorder, perceived neighborhood social cohesion, and socioeconomic factors within a longitudinal cohort.

METHODS

Demographic and socioeconomic information before diagnosis was obtained at interviews conducted approximately 10 months post-diagnosis from participants in the Women's Circle of Health Follow-up Study - a cohort of breast cancer survivors self-identifying as African American or Black women (n = 310). Neighborhood perceptions were obtained during follow-up interviews conducted approximately 24 months after diagnosis. Objective neighborhood disorder was from 9 items audited across 23,276 locations using Google Street View and scored to estimate disorder values at each participant's residential address at diagnosis. Census tract socioeconomic and demographic composition covariates were from the 2010 U.S. Census and American Community Survey. Pathways to perceived neighborhood disorder were built using structural equation modelling. Model fit was assessed from the comparative fit index and root mean square error approximation and associations were reported as standardized coefficients and 95% confidence intervals.

RESULTS

Higher perceived neighborhood disorder was associated with higher objective neighborhood disorder (β = 0.20, 95% CI: 0.06, 0.33), lower neighborhood social cohesion, and lower individual-level socioeconomic factors (final model root mean square error approximation 0.043 (90% CI: 0.013, 0.068)). Perceived neighborhood social cohesion was associated with individual-level socioeconomic factors and objective neighborhood disorder (β = - 0.11, 95% CI: - 0.24, 0.02).

CONCLUSION

Objective neighborhood disorder might be related to perceived disorder directly and indirectly through perceptions of neighborhood social cohesion.

The geographic distribution of retail tobacco outlets in Yogyakarta, Indonesia

INTRODUCTION

Tobacco smoking prevalence in Indonesia is among the highest in the world. Research worldwide identifies that physical access to tobacco through retail outlets is related to increased tobacco smoking. Tobacco outlet density is very high in many Indonesian cities, so tobacco access may contribute to the high prevalence of tobacco use in that country. The aim of this study was to examine distributions of tobacco outlets in one Indonesian city, Yogyakarta, in relation to social and physical environmental conditions.

METHODS

For this cross-sectional ecological study, we virtually audited randomly selected street segments (n = 1099) using Google Street View. The outcome of interest was a count of tobacco advertising banners (indicating the presence of retail outlets). Exposures were physical environmental conditions (scales of main roads, physical decay, presence of schools, mosques, churches) and social conditions measured at the neighbourhood level (concentrated disadvantage, age composition, population density).

RESULTS

Tobacco banners were present on 36.4% of sampled street segments, including 55 (37%) of 147 streets with schools; a total of 1381 banners were identified. Multilevel negative binomial regression models for street segments nested within neighbourhoods found the prevalence of tobacco banners per 100 m was lower near schools (RR = 0.66, 95% CI 0.45, 0.97) and was not associated with other exposure measures.

DISCUSSION AND CONCLUSIONS

Retail tobacco outlets are ubiquitous in Yogyakarta. Although they are relatively less prevalent on streets with schools, the high absolute values and wide spatial distribution means all residents of Yogyakarta are exposed to tobacco outlets.

From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

OBJECTIVE

Health system data were assessed for how well they can estimate obesity prevalence in census tracts.

METHODS

Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.

RESULTS

The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.

CONCLUSIONS

Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.

Google street view image availability in the Bronx and San Diego, 2007-2020: Understanding potential biases in virtual audits of urban built environments

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.

Walkability measures to predict the likelihood of walking in a place: A classification and regression tree analysis

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.

Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types

Background

Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies.

Methods and findings

We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics.

Conclusions

Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.