Linking emergency care and police department data to strengthen timely information on violence-related paediatric injuries.

BACKGROUND: Combined ED and police department (PD) data have improved violence surveillance in the UK, enabling significantly improved prevention. We sought to determine if the addition of emergency medical service (EMS) data to ED data would contribute meaningful information on violence-related paediatric injuries beyond PD record data in a US city. METHODS: Cross-sectional data on self-reported violence-related injuries of youth treated in the ED between January 2015 and September 2016 were combined with incidents classified by EMS as intentional interpersonal violence and incidents in which the PD responded to a youth injury from a simple or aggravated assault, robbery or sexual offence. Nearest neighbour hierarchical spatial clustering detected areas in which 10 or more incidents occurred during this period (hotspots), with the radii of the area being 1000, 1500, 2000 and 3000 ft. Overlap of PD incidents within ED&EMS hotspots (and vice versa) was calculated and Spearman's r tested statistical associations between the data sets, or ED&EMS contribution to PD violence information. RESULTS: There were 935 unique ED&EMS records (ED=381; EMS=554). Of these, 877 (94%) were not in PD records. In large hotspots >2000 ft, ED&EMS records identified one additional incident for every three in the PD database. ED and EMS provided significant numbers of incidents not reported to PD. Significant correlations of ED&EMS incidents in PD hotspots imply that the ED&EMS incidents are as pervasive across the city as that reported by PD. In addition, ED and EMS provided unique violence information, as ED&EMS hotspots never included a majority (>50%) of PD records. Most (676/877; 77%) incidents unique to ED&EMS records were within 1000 ft of a school or park. CONCLUSIONS: Many violence locations in ED and EMS data were not present in PD records. A combined PD, ED and EMS database resulted in new knowledge of the geospatial distribution of violence-related paediatric injuries and can be used for data-informed and targeted prevention of violence in which children are injured-especially in and around schools and parks.

Trapped in Place? Segmented Resilience to Hurricanes in the Gulf Coast, 1970-2005.

Hurricanes pose a continuing hazard to populations in coastal regions. This study estimates the impact of hurricanes on population change in the years 1970-2005 in the U.S. Gulf Coast region. Geophysical models are used to construct a unique data set that simulates the spatial extent and intensity of wind damage and storm surge from the 32 hurricanes that struck the region in this period. Multivariate spatial time-series models are used to estimate the impacts of hurricanes on population change. Population growth is found to be reduced significantly for up to three successive years after counties experience wind damage, particularly at higher levels of damage. Storm surge is associated with reduced population growth in the year after the hurricane. Model extensions show that change in the white and young adult population is more immediately and strongly affected than is change for blacks and elderly residents. Negative effects on population are stronger in counties with lower poverty rates. The differentiated impact of hurricanes on different population groups is interpreted as segmented withdrawal-a form of segmented resilience in which advantaged population groups are more likely to move out of or avoid moving into harm's way while socially vulnerable groups have fewer choices.

VULNERABILITY TO HURRICANE DAMAGE ON THE U.S. GULF COAST SINCE 1950.

We study hurricane risk on the U.S. Gulf Coast during 1950-2005, estimating the wind damage and storm surge from every hurricane in this extended period. Wind damage is estimated from the known path and wind speeds of individual storms and calibrated to fit actual damage reports for a sample of Gulf Coast storms. Storm surge is estimated using the SLOSH model developed by NOAA. These models provide the first comprehensive overview of the hurricane storm hazard as it has been experienced over a fifty-six-year period. We link the estimated damage with information on the population and specific socio-demographic components of the population (by age, race, and poverty status). Results show that white, young adult, and nonpoor populations have shifted over time away from zones with higher risk of wind damage, while more vulnerable population groups-the elderly, African Americans, and poor-have moved in the opposite direction. All groups have moved away from areas with high risk of storm surge since 1970. But in this case, perhaps because living near the water is still perceived as an amenity, those at highest risk are whites, elderly, and nonpoor households. Here exposure represents a trade-off between the risk and the amenity.

Effects of Social Vulnerability and Spatial Accessibility on COVID-19 Vaccination Coverage: A Census-Tract Level Study in Milwaukee County, USA.

COVID-19 vaccination coverage was studied by race/ethnicity, up-to-date doses, and by how it was affected by social vulnerability and spatial accessibility at the census-tract level in Milwaukee County, WI, USA. Social vulnerability was quantified at the census-tract level by an aggregate index and its sub-components calculated using the principal components analysis method. The spatial accessibility was assessed by clinic-to-population ratio and travel impedance. Ordinary least squares (OLS) and spatial regression models were employed to examine how social vulnerability and spatial accessibility relate to the vaccination rates of different doses. We found great disparities in vaccination rates by race and between areas of low and high social vulnerability. Comparing to non-Hispanic Blacks, the vaccination rate of non-Hispanic Whites in the county is 23% higher (60% vs. 37%) in overall rate (one or more doses), and 20% higher (29% vs. 9%) in booster rate (three or more doses). We also found that the overall social-vulnerability index does not show a statistically significant relationship with the overall vaccination rate when it is defined as the rate of people who have received one or more doses of vaccines. However, after the vaccination rate is stratified by up-to-date doses, social vulnerability has positive effects on one-dose and two-dose rates, but negative effects on booster rate, and the effects of social vulnerability become increasingly stronger and turn to negative for multi-dose vaccination rates, indicating the increasing challenges of high social vulnerability areas to multi-dose vaccination. The large negative effects of socio-economic status on the booster rate suggests the importance of improving general socio-economic conditions to promote multi-dose vaccination rates.

The spatial prevalence and associated factors of opioid overdose mortality in Milwaukee County, Wisconsin (2003-2018).

Mortality from opioid overdose has become the leading cause of non-natural death in Milwaukee County, Wisconsin in recent years. In order to better understand the opioid epidemic and formulate pro-active responses to the crisis at the local level, this study examines the spatial prevalence and associated factors of opioid overdoses that end in mortality in Milwaukee, WI using the spatial econometrics model. The social determinants of health framework is used to identify the potential related socioeconomic factors associated with opioid use and misuse. Using principal component analysis, 6 primary components are identified from the chosen social determinants and used as explanatory variables in the spatial econometric analysis. The age-adjusted standardized mortality rate is calculated for each census tract as the dependent variable in the analysis. Overall low socioeconomic status, labor-intensive occupations, income inequality, and the 20-34-year-old age group are identified as variables with a significant contribution to high overdose mortality rates, both directly and indirectly. A significant global spillover effect is also identified at the census tract level, indicating the severity of the opioid epidemic in Milwaukee County. This study reveals the overall contribution that socioeconomic factors have on the opioid epidemic and their associated feedback effects, providing targeted information on the opioid epidemic.

Validating Population Estimates for Harmonized Census Tract Data, 2000-2010.

Social scientists regularly rely on population estimates when studying change in small areas over time. Census tract data in the United States are a prime example, since there are substantial shifts in tract boundaries from decade to decade. This study compares alternative estimates of the 2000 population living within 2010 tract boundaries to the Census Bureau's own re-tabulation. All methods of estimation are subject to error; this is the first study to directly quantify the error in alternative interpolation methods for U.S. census tracts. A simple areal weighting method closely approximates the estimates provided by one standard source (the Neighborhood Change Data Base or NCDB), with some improvement provided by considering only area not covered by water. More information is used by the Longitudinal Tract Data Base (LTDB), which relies on a combination of areal and population interpolation as well as ancillary data about water-covered areas. Another set of estimates provided by NHGIS uses data about land cover in 2001 and the current road network and distribution of population and housing units at the block level. Areal weighting alone results in a large error in a substantial share of tracts that were divided in complex ways. The LTDB and NHGIS perform much better in all situations, but are subject to some error when boundaries of both tracts and their component blocks are redrawn. Users of harmonized tract data should be watchful for potential problems in either of these data sources.

Interpolating U.S. Decennial Census Tract Data from as Early as 1970 to 2010: A Longtitudinal Tract Database.

Differences in the reporting units of data from diverse sources and changes in units over time are common obstacles to analysis of areal data. We compare common approaches to this problem in the context of changes over time in the boundaries of U.S. census tracts. In every decennial census many tracts are split, consolidated, or changed in other ways from the previous boundaries to reflect population growth or decline. We examine two interpolation methods to create a bridge between years, one that relies only on areal weighting and another that also introduces population weights. Results demonstrate that these approaches produce substantially different estimates for variables that involve population counts, but they have a high degree of convergence for variables defined as rates or averages. Finally the paper describes the Longitudinal Tract Data Base (LTDB), through which we are making available public-use tools to implement these methods to create estimates within 2010 tract boundaries for any tract-level data (from the census or other sources) that are available for prior years as early as 1970.