Evaluation of the Safer Use of Antipsychotics in Youth Study on Population Level Antipsychotic Initiation: An Interrupted Time Series Analysis.

Background: Antipsychotics carry a higher-risk profile than other psychotropic medications and may be prescribed for youth with conditions in which other first-line treatments are more appropriate. This study aimed to evaluate the population-level effect of the Safer Use of Antipsychotics in Youth (SUAY) trial, which aimed to reduce person-days of antipsychotic use among participants. Methods: We conducted an interrupted time series analysis using segmented regression to measure changes in prescribing trends of antipsychotic initiation rates pre-SUAY and post-SUAY trial at four U.S. health systems between 2013 and 2020. Results: In our overall model, adjusted for age and insurance type, antipsychotic initiation rates decreased by 0.73 (95% confidence interval [CI]: 0.30, 1.16, p = 0.002) prescriptions per 10,000 person-months before the SUAY trial. In the first quarter following the start of the trial, there was an immediate decrease in the rate of antipsychotic initiations of 6.57 (95% CI: 0.99, 12.15) prescriptions per 10,000 person-months. When comparing the posttrial period to the pretrial period, there was an increase of 1.09 (95% CI: 0.32, 1.85) prescriptions per 10,000 person-months, but the increasing rate in the posttrial period alone was not statistically significant (0.36 prescriptions per 10,000 person-months, 95% CI: -0.27, 0.99). Conclusion: The declining trend of antipsychotic initiation seen between 2013 and 2018 (pre-SUAY trial) may have naturally reached a level at which prescribing was clinically warranted and appropriate, resulting in a floor effect. The COVID-19 pandemic, which began in the final three quarters of the posttrial period, may also be related to increased antipsychotic medication initiation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.