Spatial analysis of paediatric swimming pool submersions by housing type.

OBJECTIVE: Drowning is a major cause of unintentional childhood death. The relationship between childhood swimming pool submersions, neighbourhood sociodemographics, housing type and swimming pool location was examined in Harris County, Texas. STUDY DESIGN AND SETTING: Childhood pool submersion incidents were examined for spatial clustering using the Nearest Neighbor Hierarchical Cluster (Nnh) algorithm. To relate submersions to predictive factors, an Markov Chain Monte Carlo (MCMC) Poisson-Lognormal-Conditional Autoregressive (CAR) spatial regression model was tested at the census tract level. RESULTS: There were 260 submersions; 49 were fatal. Forty-two per cent occurred at single-family residences and 36% at multifamily residential buildings. The risk of a submersion was 2.7 times higher for a child at a multifamily than a single-family residence and 28 times more likely in a multifamily swimming pool than a single family pool. However, multifamily submersions were clustered because of the concentration of such buildings with pools. Spatial clustering did not occur in single-family residences. At the tract level, submersions in single-family and multifamily residences were best predicted by the number of pools by housing type and the number of children aged 0-17 by housing type. CONCLUSIONS: Paediatric swimming pool submersions in multifamily buildings are spatially clustered. The likelihood of submersions is higher for children who live in multifamily buildings with pools than those who live in single-family homes with pools.

Socioeconomic disparities based on shooting intent in pediatric firearm injury.

BACKGROUND: Pediatric firearm injury is often associated with socioeconomically disadvantaged neighborhoods. Most studies only include fatal injuries and do not differentiate by shooting intent. We hypothesized that differences in neighborhood socioeconomic disadvantage would be observed among shooting intents of fatal and nonfatal cases. METHODS: A linked integrated database of pediatric fatal and nonfatal firearm injuries was developed from trauma center and medical examiner records in Harris County, Texas (2018-2020). Geospatial analysis was utilized to map victim residence locations, stratified by shooting intent. Area Deprivation Index (ADI), a composite measure of neighborhood socioeconomic disadvantage at the census tract level was linked to shooting intent. Differences in high ADI (more deprived) versus low ADI among the shooting intents were assessed. Unadjusted and adjusted regression models assessed differences in ADI scores across shooting intent, adjusted models controlled for age, gender, and race/ethnicity. RESULTS: Of 324 pediatric firearm injuries, 28% were fatal; 77% were classified as interpersonal violence, 15% unintentional, and 8% self-harm. Differences were noted among shooting intent across the ADI quartiles; with increases in ADI score, the odds of interpersonal violence injuries compared with self-harm injuries significantly increased by 5%; however, when adjusting for individual-level variables of age, gender, and race and ethnicity, no significant differences in ADI were noted. CONCLUSION: Our results suggest that children living in disadvantaged neighborhoods are more likely to be affected by interpersonal firearm violence compared with self-harm; however, when differences in race/ethnicity are considered, the differences attributable to neighborhood-level disadvantage disappeared. Resources should be dedicated to improving structural aspects of neighborhood disadvantage, which disproportionately impact racial/ethnic minoritized populations. Furthermore, firearm self-harm injuries occurred among children living in the less disadvantaged neighborhoods. Understanding the associations among individual and neighborhood-level factors are important for developing streamlined injury prevention interventions by shooting intent. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Malignant mixes: The overlap of motor vehicle crashes and crime in Stockholm, Sweden.

Places that concentrate both motor vehicle crashes and crime in Stockholm, Sweden were examined for common socio-economic, land use, and neighborhood characteristics. Using vehicle crash (N = 3,700) and non-traffic crime (N = 605,052) data from 2016 to 2018, hot spots of these two sets of events and their overlap were identified. Crash hot spots captured 14% of the crashes in only 0.5% of Stockholm's area while crime hot spots captured 27% of the recorded offences in less than 1% of the area. There was overlap in these hot spots for 7% of the crashes and 10% of the crimes. To model predictors, the events were allocated to roadway segments (N = 5511) and tested using a Poisson-Gamma-CAR spatial regression model. Both crashes and crimes exhibit a clear center-periphery pattern that varies over time and by type of crashes and crimes. Crashes tended to occur on roadways with higher average daily traffic (ADT) while crimes tend to occur on roadways with lower ADT with around half occurring on residential streets. Both types of incidents tended to be higher in lower income neighborhoods. Land uses common to both types of harm were the location of underground stations, ATM machines, and alcohol-serving businesses. These are places where people and cars converge at particular times. The effect of these events on police, emergency, and medical services is discussed.

Workplace Violence in Outpatient Physician Clinics: A Systematic Review.

Workplace violence (WPV) has been extensively studied in hospitals, yet little is known about WPV in outpatient physician clinics. These settings and work tasks may present different risk factors for WPV compared to hospitals, including the handling/exchange of cash, and being remotely located without security presence. We conducted a systematic literature review to describe what is currently known about WPV in outpatient physician clinics. Six literature databases were searched and reference lists from included articles published from 2000-2019. Thirteen quantitative and five qualitative manuscripts were included which all focused on patient/family-perpetrated violence in outpatient physician clinics. No studies examined other violence types (e.g., worker-on-worker; burglary). The overall prevalence of Type II violence ranged from 9.5% to 74.6%, with the most common form being verbal abuse (42.1-94.3%), followed by threat of assault (14.0-57.4%), bullying (2.5-5.7%), physical assault, (0.5-15.9%) and sexual harassment/assault (0.2-9.3%). Worker consequences included reduced work performance, anger, and depression. Most workers did not receive training on how to manage a violent patient. More work is needed to examine the prevalence and risk factors of WPV in outpatient physician clinics for purposes of informing prevention efforts in these settings.

The location of late night bars and alcohol-related crashes in Houston, Texas.

A study in the City of Houston, Texas, related the location of establishments primarily serving alcohol ("bars") after midnight to late night alcohol-related motor vehicle crashes. There were three data sets for 2007-09: 1) 764bars that were open after midnight; 2) 1660 alcohol-related crashes that occurred within the City of Houston between midnight and 6 am; and 3) 4689 modeling network road segments to which bars and alcohol-related crashes were assigned. Forty-five percent of the late night alcohol-related crashes were within a quarter mile of a late night bar. The bars were highly concentrated in 17 small bar clusters. Using the modeling network, Poisson-Gamma-CAR and Poisson-Lognormal-CAR spatial regression models showed a positive exponential relationship between late night alcohol-related crashes and the number of late nights bars and bar clusters, and a negative exponential relationship to distance to the nearest late night bar controlling for the type of road segment (freeway, principal arterial, minor arterial). A more general model dropped the bar cluster variable. Further, the Poisson-Gamma-CAR model appeared to produce a better representation than the Poisson-Lognormal-CAR model though the errors were different. The general Poisson-Gamma-CAR model showed that each late night bar increased the frequency of alcohol-related crashes on a segment by approximately 190%. For each mile closer a segment was to a late night bar, the likelihood increased by 42%.