David N. Karp

Do hospital service areas and hospital referral regions define discrete health care populations

Background:Effective measurement of health care quality, access, and cost for populations requires an accountable geographic unit. Although Hospital Service Areas (HSAs) and Hospital Referral Regions (HRRs) have been extensively used in health services research, it is unknown whether these units accurately describe patterns of hospital use for patients living within them. Objectives:To evaluate the ability of HSAs, HRRs, and counties to define discrete health care populations. Research Design:Cross-sectional geographic analysis of hospital admissions. Subjects:All hospital admissions during the year 2011 in Washington, Arizona, and Florida. Measures:The main outcomes of interest were 3 metrics that describe patient movement across HSA, HRR, and county boundaries: localization index, market share index, and net patient flow. Regression models tested the association of these metrics with different HSA characteristics. Results:For 45% of HSAs, fewer than half of the patients were admitted to hospitals located in their HSA of residence. For 16% of HSAs, more than half of the treated patients lived elsewhere. There was an equivalent degree of movement across county boundaries but less movement across HRR boundaries. Patients living in populous, urban HSAs with multiple, large, and teaching hospitals tended to remain for inpatient care. Patients admitted through the emergency department tended to receive care at local hospitals relative to other patients. Conclusions:HSAs and HRRs are geographic units commonly used in health services research yet vary in their ability to describe where patients receive hospital care. Geographic models may need to account for differences between emergent and nonemergent care.

Reassessing the Stroke Belt: Using Small Area Spatial Statistics to Identify Clusters of High Stroke Mortality in the United States.

Background and Purpose— The stroke belt is described as an 8-state region with high stroke mortality across the southeastern United States. Using spatial statistics, we identified clusters of high stroke mortality (hot spots) and adjacent areas of low stroke mortality (cool spots) for US counties and evaluated for regional differences in county-level risk factors. Methods— A cross-sectional study of stroke mortality was conducted using Multiple Cause of Death data (Centers for Disease Control and Prevention) to compute age-adjusted adult stroke mortality rates for US counties. Local indicators of spatial association statistics were used for hot-spot mapping. County-level variables were compared between hot and cool spots. Results— Between 2008 and 2010, there were 393 121 stroke-related deaths. Median age-adjusted adult stroke mortality was 61.7 per 100 000 persons (interquartile range=51.4–74.7). We identified 705 hot-spot counties (22.4%) and 234 cool-spot counties (7.5%); 44.5% of hot-spot counties were located outside of the stroke belt. Hot spots had greater proportions of black residents, higher rates of unemployment, chronic disease, and healthcare utilization, and lower median income and educational attainment. Conclusions— Clusters of high stroke mortality exist beyond the 8-state stroke belt, and variation exists within the stroke belt. Reconsideration of the stroke belt definition and increased attention to local determinants of health underlying small area regional variability could inform targeted healthcare interventions.

National Differences in Regional Emergency Department Boarding Times: Are US Emergency Departments Prepared for a Public Health Emergency?

OBJECTIVES Boarding admitted patients decreases emergency department (ED) capacity to accommodate daily patient surge. Boarding in regional hospitals may decrease the ability to meet community needs during a public health emergency. This study examined differences in regional patient boarding times across the United States and in regions at risk for public health emergencies. METHODS A retrospective cross-sectional analysis was performed by using 2012 ED visit data from the American Hospital Association (AHA) database and 2012 hospital ED boarding data from the Centers for Medicare and Medicaid Services Hospital Compare database. Hospitals were grouped into hospital referral regions (HRRs). The primary outcome was mean ED boarding time per HRR. Spatial hot spot analysis examined boarding time spatial clustering. RESULTS A total of 3317 of 4671 (71%) hospitals were included in the study cohort. A total of 45 high-boarding-time HRRs clustered along the East/West coasts and 67 low-boarding-time HRRs clustered in the Midwest/Northern Plains regions. A total of 86% of HRRs at risk for a terrorist event had high boarding times and 36% of HRRs with frequent natural disasters had high boarding times. CONCLUSIONS Urban, coastal areas have the longest boarding times and are clustered with other high-boarding-time HRRs. Longer boarding times suggest a heightened level of vulnerability and a need to enhance surge capacity because these regions have difficulty meeting daily emergency care demands and are at increased risk for disasters. (Disaster Med Public Health Preparedness. 2016;10:576-582).

US Emergency Department Encounters for Law Enforcement–Associated Injury, 2006-2012

US Emergency Department Encounters for Law Enforcement–Associated Injury, 2006-2012 Deaths of civilians in contact with police have recently gained national public and policy attention. While journalists track police-involved deaths,1 epidemiologic data are incomplete,2,3 and trends in nonfatal injuries, which far outnumber deaths, are poorly understood. The International Classification of Diseases, Ninth Revision, Clinical Modification, includes external cause-of-injury codes identifying injuries owing to contact with law enforcement (E970-E978). Using these codes, prior studies have identified 715 118 nonfatal injuries, 3958 hospitalizations, and 3156 deaths between 2003 and 2011 from US Centers for Disease Control and Prevention data and the Nationwide Inpatient Sample,4 and 55 400 fatal and nonfatal injuries in 2012 from the Vital Statistics mortality census, Nationwide Inpatient and Emergency Department Samples, and journalists’ reports.5 In this study, we used a nationally representative database to determine whether the incidence of emergency department (ED) visits for injures by law enforcement increased relative to total ED visits from 2006 to 2012. We assessed demographic and clinical characteristics of visits for law enforcement–associated injury.

Which transfers can we avoid: Multi-state analysis of factors associated with discharge home without procedure after ED to ED transfer for traumatic injury

Objective: Among injured patients transferred from one emergency department (ED) to another, we determined factors associated with being discharged from the second ED without procedures, or admission or observation. Methods: We analyzed all patients with injury diagnosis codes transferred between two EDs in the 2011 Healthcare Utilization Project State Emergency Department and State Inpatient Databases for 6 states. Multivariable hierarchical logistic regression evaluated the association between patient (demographics and clinical characteristics) and hospital factors, and discharge from the second ED without coded procedures. Results: In 2011, there were a total of 48,160 ED‐to‐ED injury transfers, half of which (49%) were transferred to non‐trauma centers, including 23% with major trauma. A total of 22,011 transfers went to a higher level of care, of which 36% were discharged from the ED without procedures. Relative to torso injuries, discharge without procedures was more likely for patients with soft tissue (OR 6.8, 95%CI 5.6–8.2), head (OR 3.7, 95%CI 3.1–4.6), facial (OR 3.8, 95%CI 3.1–4.7), or hand (OR 3.1, 95%CI 2.6–3.8) injuries. Other factors included Medicaid (OR 1.3, 95%CI 1.2–1.5) or uninsured (OR 1.3, 95%CI 1.2–1.5) status. Treatment at the receiving ED added an additional

Quality Through Coopetition: An Empiric Approach to Measure Population Outcomes for Emergency Care–Sensitive Conditions

2859 on average (95% CI

Shift in U.S. payer responsibility for the acute care of violent injuries after the Affordable Care Act: Implications for prevention

2750–

Measuring Emergency Care Survival: The Implications of Risk-Adjusting for Race and Poverty.

2968) per discharged patient to the total charges for injury care, not including the costs of ambulance transport between facilities. Conclusion: Over a third of patients transferred to another ED for traumatic injury are discharged from the second ED without admission, observation, or procedures. Telemedicine consultation with sub‐specialists might reduce some of these transfers.

154 Alternatives to gun policy?: a bayesian analysis of county-level firearm mortality

Study objective: We develop a novel approach for measuring regional outcomes for emergency care–sensitive conditions. Methods: We used statewide inpatient hospital discharge data from the Pennsylvania Healthcare Cost Containment Council. This cross‐sectional, retrospective, population‐based analysis used International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes to identify admissions for emergency care–sensitive conditions (ischemic stroke, ST‐segment elevation myocardial infarction, out‐of‐hospital cardiac arrest, severe sepsis, and trauma). We analyzed the origin and destination patterns of patients, grouped hospitals with a hierarchical cluster analysis, and defined boundary shapefiles for emergency care service regions. Results: Optimal clustering configurations determined 10 emergency care service regions for Pennsylvania. Conclusion: We used cluster analysis to empirically identify regional use patterns for emergency conditions requiring a communitywide system response. This method of attribution allows regional performance to be benchmarked and could be used to develop population‐based outcome measures after life‐threatening illness and injury.

Geography, Not Health System Affiliations, Determines Patients’ Revisits to the Emergency Department

Background: Investment in violence prevention programs is hampered by lack of clearly identifiable stakeholders with a financial stake in prevention. We determined the total annual charges for the acute care of injuries from interpersonal violence and the shift in financial responsibility for these charges after the Medicaid expansion from the Affordable Care Act in 2014. Methods: We analyzed all emergency department (ED) visits from 2009 to 2014 with diagnosis codes for violent injury in the Nationwide Emergency Department Sample (NEDS). We used sample weights to estimate total charges with adjusted generalized linear models to estimate charges for the 15% of ED visits with missing charge data. We then calculated the share attributable by payer and determined the difference in proportion by payer from 2013 to 2014. Results: Between 2009 and 2013, the uninsured accounted for 28.2–31.3% of annual charges for the acute care of violent injury, while Medicaid was responsible for a similar amount (29.0–31.0%). In 2014, there were