Racial and ethnic disparities in emergency department transfers to public hospitals.

OBJECTIVE: To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences. DATA SOURCES AND STUDY SETTING: ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018). STUDY DESIGN: Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means. DATA COLLECTION/EXTRACTION METHODS: We examined 1,265,588 adult ED patients transferred from 187 hospitals. PRINCIPAL FINDINGS: Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer. CONCLUSIONS: Large population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.

Background: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective: We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods: We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results: We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions: We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

Association of Racial Residential Segregation With Long-Term Outcomes and Readmissions After Out-of-Hospital Cardiac Arrest Among Medicare Beneficiaries.

Background The national impact of racial residential segregation on out-of-hospital cardiac arrest outcomes after initial resuscitation remains poorly understood. We sought to characterize the association between measures of racial and economic residential segregation at the ZIP code level and long-term survival and readmissions after out-of-hospital cardiac arrest among Medicare beneficiaries. Methods and Results In this retrospective cohort study, using Medicare claims data, our primary predictor was the index of concentration at the extremes, a measure of racial and economic segregation. The primary outcomes were death up to 3 years and readmissions. We estimated hazard ratios (HRs) across all 3 types of index of concentration at the extremes measures for each outcome while adjusting for beneficiary demographics, treating hospital characteristics, and index hospital procedures. In fully adjusted models for long-term survival, we found a decreased hazard of death and risk of readmission for beneficiaries residing in the more segregated White communities  and higher-income ZIP codes compared with the more segregated Black communities and lower-income ZIP codes across all 3 indices of concentration at the extremes measures (race: HR, 0.87 [95% CI, 0.81-0.93]; income: HR, 0.75 [95% CI, 0.69-0.78]; and race+income: HR, 0.77 [95% CI, 0.72-0.82]). Conclusions We found a decreased hazard of death and risk for readmission for those residing in the more segregated White communities  and higher-income ZIP codes compared with the more segregated Black communities and lower-income ZIP codes when using validated measures of racial and economic segregation. Although causal pathways and mechanisms remain unclear, disparities in outcomes after out-of-hospital cardiac arrest are associated with the structural components of race and wealth and persist up to 3 years after discharge.

Association of Transport Time, Proximity, and Emergency Department Pediatric Readiness With Pediatric Survival at US Trauma Centers.

Importance: Emergency department (ED) pediatric readiness is associated with improved survival among children. However, the association between geographic access to high-readiness EDs in US trauma centers and mortality is unclear. Objective: To evaluate the association between the proximity of injury location to receiving trauma centers, including the level of ED pediatric readiness, and mortality among injured children. Design, Setting, and Participants: This retrospective cohort study used a standardized risk-adjustment model to evaluate the association between trauma center proximity, ED pediatric readiness, and in-hospital survival. There were 765 trauma centers (level I-V, adult and pediatric) that contributed data to the National Trauma Data Bank (January 1, 2012, through December 31, 2017) and completed the 2013 National Pediatric Readiness Assessment (conducted from January 1 through August 31, 2013). The study comprised children aged younger than 18 years who were transported by ground to the included trauma centers. Data analysis was performed between January 1 and March 31, 2022. Exposures: Trauma center proximity within 30 minutes by ground transport and ED pediatric readiness, as measured by weighted pediatric readiness score (wPRS; range, 0-100; quartiles 1 [low readiness] to 4 [high readiness]). Main Outcomes and Measures: In-hospital mortality. We used a patient-level mixed-effects logistic regression model to evaluate the association of transport time, proximity, and ED pediatric readiness on mortality. Results: This study included 212 689 injured children seen at 765 trauma centers. The median patient age was 10 (IQR, 4-15) years, 136 538 (64.2%) were male, and 127 885 (60.1%) were White. A total of 4156 children (2.0%) died during their hospital stay. The median wPRS at these hospitals was 79.1 (IQR, 62.9-92.7). A total of 105 871 children (49.8%) were transported to trauma centers with high-readiness EDs (wPRS quartile 4) and another 36 330 children (33.7%) were injured within 30 minutes of a quartile 4 ED. After adjustment for confounders, proximity, and transport time, high ED pediatric readiness was associated with lower mortality (highest-readiness vs lowest-readiness EDs by wPRS quartiles: adjusted odds ratio, 0.65 [95% CI, 0.47-0.89]). The survival benefit of high-readiness EDs persisted for transport times up to 45 minutes. The findings suggest that matching children to trauma centers with high-readiness EDs within 30 minutes of the injury location may have potentially saved 468 lives (95% CI, 460-476 lives), but increasing all trauma centers to high ED pediatric readiness may have potentially saved 1655 lives (95% CI, 1647-1664 lives). Conclusions and Relevance: These findings suggest that trauma centers with high ED pediatric readiness had lower mortality after considering transport time and proximity. Improving ED pediatric readiness among all trauma centers, rather than selective transport to trauma centers with high ED readiness, had the largest association with pediatric survival. Thus, increased pediatric readiness at all US trauma centers may substantially improve patient outcomes after trauma.

Using medicare claims to estimate risk-adjusted performance of Pennsylvania trauma centers.

Trauma centers use registry data to benchmark performance using a standardized risk adjustment model. Our objective was to utilize national claims to develop a risk adjustment model applicable across all hospitals, regardless of designation or registry participation. Patients from 2013-14 Pennsylvania Trauma Outcomes Study (PTOS) registry data were probabilistically matched to Medicare claims using demographic and injury characteristics. Pairwise comparisons established facility linkages and matching was then repeated within facilities to link records. Registry models were estimated using GLM and compared with five claims-based LASSO models: demographics, clinical characteristics, diagnosis codes, procedures codes, and combined demographics/clinical characteristics. Area under the curve and correlation with registry model probability of death were calculated for each linked and out-of-sample cohort. From 29 facilities, a cohort comprising 16,418 patients were linked between datasets. Patients were similarly distributed: median age 82 (PTOS IQR: 74-87 vs. Medicare IQR: 75-88); non-white 6.2% (PTOS) vs. 5.8% (Medicare). The registry model AUC was 0.86 (0.84-0.87). Diagnosis and procedure codes models performed poorest. The demographics/clinical characteristics model achieved an AUC = 0.84 (0.83-0.86) and Spearman = 0.62 with registry data. Claims data can be leveraged to create models that accurately measure the performance of hospitals that treat trauma patients.

The Cost Shifting Economics of United States Emergency Department Professional Services (2016-2019).

STUDY OBJECTIVE: We estimate the economics of US emergency department (ED) professional services, which is increasingly under strain given the longstanding effect of unreimbursed care, and falling Medicare and commercial payments. METHODS: We used data from the Nationwide Emergency Department Sample (NEDS), Medicare, Medicaid, Health Care Cost Institute, and surveys to estimate national ED clinician revenue and costs from 2016 to 2019. We compare annual revenue and cost for each payor and calculate foregone revenue, the amount clinicians may have collected had uninsured patients had either Medicaid or commercial insurance. RESULTS: In 576.5 million ED visits (2016 to 2019), 12% were uninsured, 24% were Medicare-insured, 32% Medicaid-insured, 28% were commercially insured, and 4% had another insurance source. Annual ED clinician revenue averaged $23.5 billion versus costs of $22.5 billion. In 2019, ED visits covered by commercial insurance generated $14.3 billion in revenues and cost $6.5 billion. Medicare visits generated $5.3 billion and cost $5.7 billion; Medicaid visits generated $3.3 billion and cost $7 billion. Uninsured ED visits generated $0.5 billion and cost $2.9 billion. The average annual foregone revenue for ED clinicians to treat the uninsured was $2.7 billion. CONCLUSION: Large cost-shifting from commercial insurance cross-subsidizes ED professional services for other patients. This includes the Medicaid-insured, Medicare-insured, and uninsured, all of whom incur ED professional service costs that substantially exceed their revenue. Foregone revenue for treating the uninsured relative to what may have been collected if patients had health insurance is substantial.

A Conceptual Framework for Optimizing the Equity of Hospital-Based Emergency Care: The Structure of Hospital Transfer Networks.

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism. CONTEXT: Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we proposea conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers. METHODS: To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research. FINDINGS: The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity. CONCLUSIONS: The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.

A Geospatial Evaluation of 9-1-1 Ambulance Transports for Children and Emergency Department Pediatric Readiness.

OBJECTIVE: Whether ambulance transport patterns are optimized to match children to high-readiness emergency departments (EDs) and the resulting effect on survival are unknown. We quantified the number of children transported by 9-1-1 emergency medical services (EMS) to high-readiness EDs, additional children within 30 minutes of a high-readiness ED, and the estimated effect on survival. METHODS: This was a cross-sectional study using data from the National EMS Information System for 5,461 EMS agencies in 28 states from 1/1/2012 through 12/31/2019, matched to the 2013 National Pediatric Readiness Project assessment of ED pediatric readiness. We performed a geospatial analysis of children 0 to 17 years requiring 9-1-1 EMS transport to acute care hospitals, including day-, time-, and traffic-adjusted estimates for driving times to all EDs within 30 minutes of the scene. We categorized receiving hospitals by quartile of ED pediatric readiness using the weighted Pediatric Readiness Score (wPRS, range 0-100) and defined a high-risk subgroup of children as a proxy for admission. We used published estimates for the survival benefit of high readiness EDs to estimate the number of lives saved. RESULTS: There were 808,536 children transported by EMS, of whom 253,541 (31.4%) were high-risk. Among the 2,261 receiving hospitals, the median wPRS was 70 (IQR 57-85, range 26-100) and the median number of receiving hospitals within 30 minutes was 4 per child (IQR 2-11, range 1 to 53). Among all children, 411,685 (50.9%) were taken to EDs in the highest quartile of pediatric readiness, and 180,547 (22.3%) children transported to lower readiness EDs were within 30 minutes of a high readiness ED. Findings were similar among high-risk children. Based on high-risk children, we estimated that 3,050 pediatric lives were saved by transport to high-readiness EDs and an additional 1,719 lives could have been saved by shifting transports to high readiness EDs within 30 minutes. CONCLUSIONS: Approximately half of children transported by EMS were taken to high-readiness EDs and an additional one quarter could have been transported to such an ED, with measurable effect on survival.

Survival After Out-of-Hospital Cardiac Arrest: The Role of Racial Residential Segregation.

Racial and racialized economic residential segregation has been empirically associated with outcomes across multiple health conditions but not yet explored in relation to out-of-hospital cardiac arrest (OHCA). We sought to examine if measures of racial and economic residential segregation are associated with differences in survival to discharge after OHCA for Black and White Medicare beneficiaries. Utilizing age-eligible Medicare fee-for-service claims data from 2013 to 2015, we identified OHCA claims and determined survival to discharge. The primary predictor, residential segregation, was calculated using the index of concentration at the extremes (ICE) for the beneficiary residential ZIP code. Multilevel modified Poisson regression models were used to determine the association of OHCA outcomes and ZIP code level ICE measures. In total, 194,263 OHCA cases were identified among beneficiaries residing in 75% of US ZIP codes. Black beneficiaries exhibited 12.1% survival to discharge, compared with 12.5% of White beneficiaries. In fully adjusted models of the three ICE measures accounting for differences in treating hospital characteristics, there was as high as a 28% (RR 1.28, CI 1.23-1.26) higher relative likelihood of survival to discharge in the most segregated White ZIP codes (Q5) as compared to the most segregated Black ZIP codes (Q1). Racial residential segregation is independently associated with disparities in OHCA outcomes; among Medicare beneficiaries who generated a claim after suffering an OHCA, ICE measures of racial segregation are associated with a lower likelihood of survival to discharge for those living in the most segregated Black and lower income quintiles compared to higher quintiles.

Advanced Triage Protocol: The Role of an Automated Lactate Order in Expediting Rapid Identification of Patients at Risk of Sepsis in the Emergency Department.

We undertook a process improvement initiative to expedite rapid identification of potential sepsis patients based on triage chief complaint, vital signs, and initial lactate level. DESIGN: Prospective cohort study. SETTING: Seven hundred-bed tertiary care hospital with ≅65,000 patient visits/yr. PATIENTS: Patients presenting to emergency department (ED) triage who met the following criteria: greater than or equal to two of the three systemic inflammatory response syndrome criteria assessable in triage, a chief complaint suggestive of infection, emergency severity index 2 or 3, and ambulatory to ED. INTERVENTIONS: A computer-generated lactate order was created, staff education and resources increased, and point-of-care lactate testing was introduced. MEASUREMENTS AND MAIN RESULTS: Primary endpoints include the following: percent of patients having a lactate level drawn, percent of lactate samples resulting before room placement, and time intervals from triage to lactate blood draw and to lactate result. Secondary endpoints were percentage of patients admitted to the hospital, percentage admitted to the ICU, and in-hospital mortality. Six thousand nine hundred six patients were included: 226 historic controls (HCs) and 6,680 intervention group patients. The mean serum lactate level was 1.77 ± 1.18 mmol/L. The percentage of patients having a lactate resulted increased from 27.4% in the HC period to 79.6%. The percentage of these lactate results available while the patient was still in the waiting room increased from 0.4% during the HC period to 33.7% during Phase 5 (p < 0.0001). In the intervention period, time from triage to lactate result decreased (78.1-63.4 min; p < 0.0001) and time to treatment room decreased (59.3-39.6 min; p < 0.0001). CONCLUSIONS: Implementation of a computerized lactate order using readily available data obtained during ED triage, combined with point-of-care lactate testing, improves time to lactate blood draw and lactate result in patients at risk for severe sepsis. Initial lactate levels correlated with admission to the hospital, admission to the ICU, and in-hospital mortality.

Interstate Licensure: Has The Time Come?

In the midst of the unprecedented public health emergency of COVID-19, many states developed programs to recognize out-of-state practitioners' licenses for in-state practice. This rapid expansion had a profound impact on care during periods of surge and offers lessons for future disasters as well as usual operating periods. As technology improves and care delivery evolves, interstate licensure offers immense opportunities, but there are also risks that must be mitigated.

Emergency departments in the United States treating high proportions of patients with ambulatory care sensitive conditions: a retrospective cross-sectional analysis.

BACKGROUND: One in nine emergency department (ED) visits by Medicare beneficiaries are for ambulatory care sensitive conditions (ACSCs). This study aimed to examine the association between ACSC ED visits to hospitals with the highest proportion of ACSC visits ("high ACSC hospitals) and safety-net status. METHODS: This was a cross-sectional study of ED visits by Medicare fee-for-service beneficiaries ≥ 65 years using 2013-14 claims data, Area Health Resources File data, and County Health Rankings. Logistic regression estimated the association between an ACSC ED visit to high ACSC hospitals, accounting for individual, hospital, and community factors, including whether the visit was to a safety-net hospital. Safety net status was measured by Disproportionate Share Hospital (DSH) index patient percentage; public hospital status; and proportion of dual-eligible beneficiaries. Hospital-level correlation was calculated between ACSC visits, DSH index, and dual-eligible patients. We stratified by type of ACSC visit: acute or chronic. RESULTS: Among 5,192,729 ACSC ED visits, the odds of visiting a high ACSC hospital were higher for patients who were Black (1.37), dual-eligible (1.18), and with the highest comorbidity burden (1.26, p < 0.001 for all). ACSC visits had increased odds of being to high ACSC hospitals if the hospitals were high DSH (1.43), served the highest proportion of dual-eligible beneficiaries (2.23), and were for-profit (relative to non-profit) (1.38), and lower odds were associated with public hospitals (0.64) (p < 0.001 for all). This relationship was similar for visits to high chronic ACSC hospitals (high DSH: 1.59, high dual-eligibility: 2.60, for-profit: 1.41, public: 0.63, all p < 0.001) and to a lesser extent, high acute ACSC hospitals (high DSH: 1.02; high dual-eligibility: 1.48, for-profit: 1.17, public: 0.94, p < 0.001). The proportion of ACSC visits at all hospitals was weakly correlated with DSH proportion (0.2) and the proportion of dual-eligible patients (0.29), and this relationship was also seen for both chronic and acute ACSC visits, though stronger for the chronic ACSC visits. CONCLUSION: Visits to hospitals with a high proportion of acute ACSC ED visits may be less likely to be to hospitals classified as safety net hospitals than those with a high proportion of chronic ACSC visits.

The Evolution of the National Special Pathogen System of Care.

Infectious disease outbreaks and pandemics have repeatedly threatened public health and have severely strained healthcare delivery systems throughout the past century. Pathogens causing respiratory illness, such as influenza viruses and coronaviruses, as well as the highly communicable viral hemorrhagic fevers, pose a large threat to the healthcare delivery system in the United States and worldwide. Through the Hospital Preparedness Program, within the US Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response, a nationwide Regional Ebola Treatment Network (RETN) was developed, building upon a state- and jurisdiction-based tiered hospital approach. This network, spearheaded by the National Emerging Special Pathogens Training and Education Center, developed a conceptual framework and plan for the evolution of the RETN into the National Special Pathogen System of Care (NSPS). Building the NSPS strategy involved reviewing the literature and the initial framework used in forming the RETN and conducting an extensive stakeholder engagement process to identify gaps and develop solutions. From this, the NSPS strategy and implementation plan were formed. The resulting NSPS strategy is an ambitious but critical effort that will have impacts on the mitigation efforts of special pathogen threats for years to come.

Developing a measure of overall intensity of injury care: A latent class analysis.

BACKGROUND: While injury is a leading cause of death and debility in older adults, the relationship between intensity of care and trauma remains unknown. The focus of this analysis is to measure the overall intensity of care delivered to injured older adults during hospitalization. METHODS: We used Centers for Medicare and Medicaid Services Medicare fee-for-service claims data (2013-2014), to identify emergency department-based claims for moderate and severe blunt trauma in age-eligible beneficiaries. Medical procedures associated with care intensity were identified using a modified Delphi method. A latent class model was estimated using the identified procedures, intensive care unit length of stay, demographics, and injury characteristics. Clinical phenotypes for each class were explored. RESULTS: A total of 683,398 cases were classified as low- (73%), moderate- (23%), and high-intensity care (4%). Greater age and reduced injury severity were indicators of lower intensity, while males, non-Whites, and nonfall mechanisms were more common with high intensity. Intubation/mechanical ventilation was an indicator of high intensity and often occurred with at least one other procedure or an extended intensive care unit stay. CONCLUSION: This work demonstrates that, although heterogeneous, care for blunt trauma can be evaluated using a single novel measure. LEVEL OF EVIDENCE: For prognostic/epidemiological studies, level III.

Patient Markers of Successful Diabetes Management.

PURPOSE: For individuals with diabetes, diabetes health status may not align with A1C targets. Patients may use nonclinical targets when assessing their diabetes management success. Identifying these targets is important in developing patient-centered management plans. The purpose of this study was to identify patient markers of successful diabetes management among patients in an urban academic health system. METHODS: A secondary analysis of semistructured interviews was completed with 89 adults with type 1 or type 2 diabetes. Participants had a recent diabetes-related emergency department (ED) visits or hospitalization or were primary care patients with an A1C >7.5%. Interviews were conducted to saturation. Demographic data were collected via self-report and electronic medical records. Interviews were analyzed using conventional content analysis. This analysis focused on patient perceptions of successful management coded to "measuring management success." RESULTS: Although most participants cited A1C or blood glucose as a marker of successful diabetes management, they had varied understanding of these metrics. Most used a combination of targets from the following categories: 1) A1C, blood glucose, and numbers; 2) engagement in medical care; 3) taking medication and medication types; 4) symptoms; 5) diet, exercise, and weight; and 6) stress management and social support. CONCLUSION: Individuals not meeting glycemic goals and/or with recent diabetes-related ED visits or hospitalizations had varied understanding of A1C and blood glucose targets. They use multiple additional markers of successful management and had a desire for management discussions that incorporate these markers. These measures should be incorporated into their care plans along with clinical targets.

The Short and the Long of it: Timing of Mortality for Older Adults in a State Trauma System.

BACKGROUND: The impact of injury extends beyond the hospital stay, but trauma center performance metrics typically focus on in-hospital mortality. We compared risk adjusted rates of in-hospital and long-term mortality among Pennsylvania trauma centers. We hypothesized that centers with low rates of in-hospital mortality would also have low rates of long-term mortality. METHODS: We identified injured patients (age ≥ 65) admitted to Pennsylvania trauma centers in 2013 and 2014 using the Pennsylvania Trauma Outcomes Study, a robust, state-wide trauma registry. We matched trauma registry records to Medicare claims from the y 2013 to 2015. Matching variables included admission date and patient demographics including date of birth, zip, sex, and race and/or ethnicity. Outcomes examined were inpatient, 30-day, and 1-y mortality. Multivariable logistic regression models including presenting physiology, comorbidities, injury characteristics, and demographics were developed to calculate expected mortality rates for each trauma center at each time point. Trauma center performance was assessed using observed-to-expected ratios and ranking for in-hospital, 30-day, and 1-y mortality. RESULTS: Of the 15,451 patients treated at 28 centers, 8.1% died before discharge or were discharged to hospice. Another 3.4% died within 30 d, and another 14.7% died within 1 y of injury. Of patients who survived hospitalization but died within 30 d, 92.5% were injured due to fall, and 75.0% sustained head injuries. Survival at 1 y was higher in patients discharged home (88.4%), compared to those discharged to a skilled nursing facility or long-term acute care hospital (72.7% and 52.6%, respectively). Three centers were identified as outliers (two low and one high) for in-hospital mortality, none of which were outliers when the horizon was stretched to 30 d from injury. At 30 d, two different low and two different high outliers were found. CONCLUSION: Nearly one-in-three injured older adults who die within 30 d of injury dies after hospital discharge. Hospital rankings for in-hospital mortality correlate poorly with long-term outcomes. These findings underscore the importance of looking beyond survival to discharge for quality improvement and benchmarking.

A Spatiotemporal Tool to Project Hospital Critical Care Capacity and Mortality From COVID-19 in US Counties.

Objectives. To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses.Methods. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible‒exposed‒infectious‒recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction-0%, 20%, 30%, and 40%-and 4 surge response scenarios-very low, low, medium, and high.Results. In areas with increased demand, surge response measures could avert 104 120 additional deaths-55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time.Conclusions. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response.

Patient-important outcomes to inform shared decision making and goal setting for diabetes treatment.

OBJECTIVE: Despite well-established treatment guidelines, diabetes is difficult to manage for many individuals. The importance of using shared decision making to optimize diabetes treatment is recognized, yet what matters most to individuals with diabetes is not well established. Our goal was to identify patients' goals and priorities for diabetes management. METHODS: We engaged 141 participants through interviews and group concept mapping to identify patient-important outcomes (PIOs) for diabetes care. We generated a master list of PIOs by aggregating interview data coded to "goals" and ideas brainstormed during concept mapping, and then a patient advisory board sorted the PIOs into higher-level domains. RESULTS: We identified 41 PIOs sorted into 7 broad domains: optimize daily self-care, optimize long term health, learn about diabetes, achieve measurable goals, manage medications, manage diet and best utilize medical / professional services. CONCLUSIONS: Most (4/7) of PIO domains focused on personal and life goals, not medically-oriented goals. Use of these PIOs and domains may facilitate more effective SDM discussions for patients with diabetes. PRACTICE IMPLICATIONS: Use of PIOs from this work can enable the empowerment of patients to voice their priorities during SDM conversations, thus facilitating development of truly individualized diabetes treatment plans.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study.

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723-126,312) COVID-19 infections and 7978 (5692-10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9017 (6446-11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7540-13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality.