The Association of Racial Residential Segregation and Survival After Out-of-Hospital Cardiac Arrest in the United States.

Background: Residential segregation has been identified as drivers of disparities in health outcomes, but further work is needed to understand this association with clinical outcomes for out-of-hospital cardiac arrest (OHCA). We utilized Cardiac Arrest Registry to Enhance Survival (CARES) dataset to examine if there are differences in survival to discharge and survival with good neurological outcome, as well as likelihood of bystander CPR, using validated measures of racial, ethnic, and economic segregation. Methods: We conducted a retrospective observational study using data from the Cardiac Arrest Registry to Enhance Survival (CARES) dataset to examine associations among adult OHCA patients. The primary predictor was the Index of Concentration at the Extremes (ICE), a validated measure that includes race, ethnicity, and income across three measures at the census tract level. The primary outcomes were survival to discharge and survival with good neurological status. A multivariable modified Poisson regression modeling approach with random effects at the EMS agency and hospital level was utilized. Results: We identified 626,264 OHCA patients during the study period. The mean age was 62 years old (SD 17.2 years), and 35.7% (n =223,839) of the patients were female. In multivariable models, we observed an increased likelihood of survival to discharge and survival with good neurological outcome for those patients residing in predominately White population census tracts and higher income census tracts as compared to lower income Black and Hispanic/Latinx population census tracts (RR 1.24, CI 1.20-1.28) and a 32% increased likelihood of receiving bystander CPR in higher income census tracts as compared to reference (RR 1.32, CI 1.30-1.34). Conclusions: In this study examining the association of measures of residential segregation and OHCA outcomes, there was an increased likelihood of survival to discharge, survival with good neurological status, and likelihood of receiving B-CPR for those patients residing in predominately White population and higher income census tracts when compared to predominately Black and/or Hispanic Latinx populations and lower income census tracts. This research suggests that areas impacted by residential and economic segregation are important targets for both public policy interventions as well as addressing disparities in care across the chain of survival for OHCA.

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.

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.

Civilian Medical Responder Perspectives to a Federal Military Medical Deployment in New York City during the COVID-19 Pandemic.

OBJECTIVES: This study aims to describe the civilian experience and perceptions of the patient coordination and management at the interface of the New York City (NYC) civilian and the military health systems during a large-scale public health emergency. METHODS: A qualitative study design was implemented with interviews conducted using a basic descriptive approach. Purposive sampling was used to recruit participants with experience working during the first wave of COVID-19 in NYC. Inclusion criteria were civilians who worked at the Javits Center, the USNS Comfort, or NYC hospitals, who interfaced with patient transfer and military personnel during the city-wide medical response to COVID-19. Semi-structured video interviews were conducted between July 20, 2021 and March 11, 2022. RESULTS: Civilian responders working in the clinical and transfer operations of patients to military facilities in NYC during March and April of 2020 described initial confusion, as well as logistical (patient selection, transfer logistics, patient tracking), communication, and leadership challenges. While the military deployment was felt to be necessary to address the surge capacity in hospitals, the lack of clarity about military medical resources and frameworks for response resulted in confusion about what was being offered by the military deployment. This was balanced by the positive impression of working with military members and the resources that they brought to the response more generally. The need for future trainings and exercises were highlighted. CONCLUSIONS: Initial challenges with civilian-military roles and responsibilities, regional needs assessment, patient selection, and logistics were ultimately resolved through adaptation of civilian and military leadership. Improvements in patient tracking, medical records, and standard hospital admission and discharge functions for patients in military alternative care facilities were identified as areas for improvement. Civilian government, health care, and military leaders should consider these ideas when planning for future military deployments in support of a domestic medical response.

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.

Clofarabine and Busulfan Myeloablative Conditioning in Allogeneic Hematopoietic Cell Transplantation for Patients With Active Myeloid Malignancies.

Patients with refractory or relapsed and refractory myeloid malignancies have a poor prognosis. Allogeneic hematopoietic cell transplantation (HCT) with myeloablative conditioning (MAC) in patients with active, chemotherapy-refractory myeloid disease is historically associated with high rates of relapse and nonrelapse mortality (NRM). A MAC regimen combining clofarabine with busulfan (Clo/Bu4) has been reported to exhibit antileukemic activity with acceptable toxicity in patients age ≤70 years. Here we describe the clinical outcomes of a real-world population of patients with active myeloid malignancies undergoing allogeneic HCT with Clo/Bu4 MAC. In a single-center retrospective descriptive analysis, we identified patients who underwent HCT for myeloid malignancies not in remission using Clo/Bu4 MAC between 2012 and 2020. We report event-free survival (EFS) and overall survival (OS), cumulative incidences of relapse and NRM, and the incidence and severity of acute and chronic graft-versus-host disease (GVHD). We identified 69 patients with a median age of 60 years (range, 22 to 70 years). Most patients had relapsed/refractory or primary refractory acute myelogenous leukemia (AML; n = 55) or refractory myelodysplastic syndrome (MDS; n = 12); 1 patient had chronic myelogenous leukemia, and 1 patient had a blastic plasmacytoid dendritic cell neoplasm. Fifty patients (72.5%) had complete remission at day 100 post-transplantation. Two-year EFS and OS were 30% (95% confidence interval [CI], 20% to 44%) and 40% (95% CI, 29% to 54%), respectively. Patients with AML had a 2-year EFS and OS of 28% (95% CI, 18% to 44%) and 38% (95% CI, 27% to 54%), respectively; those with MDS had a 2-year EFS and OS of 47% (95% CI, 25% to 88%) and 56% (95% CI, 33% to 94%), respectively. The cumulative incidence of relapse at 2 years was 39% (95% CI, 27% to 51%) for all patients, including 45% (95% CI, 31% to 58%) in the patients with AML and 18% (95% CI, 2% to 45%) in those with MDS. NRM at 2 years was 31% (95% CI, 20% to 42%), including 27% (95% CI, 15% to 39%) in patients with AML and 35% (95% CI, 10% to 63%) in those with MDS. The total incidence of acute GVHD (aGVHD) of any severity was 80%, and the incidence of grade III-IV aGVHD was 22%. In patients who achieved remission, those who required systemic immunosuppression for aGVHD (58%) had poorer 2-year EFS (29% versus 54%; P = .05) and 2-year OS (39% versus 70%; P = .04) compared to those who did not. The 2-year cumulative incidence of chronic GVHD was 44% (95% CI, 28% to 58%). Clo/Bu4 MAC followed by allogeneic HCT for patients with active myeloid malignancies is an effective transplantation strategy for patients up to age 70, particularly those with advanced MDS. The high incidence of and poor outcomes associated with aGVHD highlight the importance of optimizing preventative strategies.

Military Response to Medical Crises-Consensus Recommendations for Military-Civilian Transitions of Care.

OBJECTIVE: The threat that New York faced in 2020, as the COVID-19 pandemic unfolded, prompted an unprecedented response. The US military deployed active-duty medical professionals and equipment to NYC in a first of its kind response to a "medical" domestic disaster. Transitions of care for patients surfaced as a key challenge. Uniformed Services University and the Icahn School of Medicine at Mount Sinai hosted a consensus conference of civilian and military healthcare professionals to identify care transition best practices for future military-civilian responses. METHODS: We performed individual interviews followed by a modified Delphi technique during a two-day virtual conference. Patient transitions of care emerged as a key theme from pre-conference interviews. Twelve participants attended the two-day virtual conference and generated best practice recommendations from an iterative process. RESULTS: Participants identified 19 recommendations in 10 "sub-themes" related to patient transitions of care: needs assessment and capability analysis; unified command; equipment; patient handoffs; role of in-person facilitation; dynamic updates; patient selection; patient tracking; daily operations; and resource typing. CONCLUSIONS: The COVID-19 pandemic resulted in an unprecedented military response. This study created 19 consensus recommendations for care transitions between military and civilian healthcare assets that may be useful in future military-civilian medical engagements.

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.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population. Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient-boosting machine learning model to identify in-hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held-out data from the fifth hospital. Results: A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in-hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden's index, the model had a sensitivity of 0.77 (95% CI: 0.67-0.86) with a false positive rate of 1:9. Conclusion: A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

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.

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.

Synergistic effect of hypoalbuminaemia and hypotension in predicting in-hospital mortality and intensive care admission: a retrospective cohort study.

OBJECTIVE: Hypoalbuminaemia is an important prognostic factor. It may be associated with poor nutritional states, chronic heart and kidney disease, long-standing infection and cancer. Hypotension is a hallmark of circulatory failure. We evaluated hypoalbuminaemia and hypotension synergism as predictor of in-hospital mortality and intensive care unit (ICU) admission. DESIGN: We retrospectively analysed emergency department (ED) visits from January 2011 to December 2019. SETTING: Data were retrieved from five Mount Sinai health system hospitals, New York. PARTICIPANTS: We included consecutive ED patients ≥18 years with albumin measurements. PRIMARY AND SECONDARY OUTCOME MEASURES: Clinical outcomes were in-hospital mortality and ICU admission. The rates of these outcomes were stratified by systolic blood pressure (SBP) (<90 vs ≥90 mm Hg) and albumin levels. Variables included demographics, presenting vital signs, comorbidities (measured as ICD codes) and other common blood tests. Multivariable logistic regression models analysed the adjusted OR of different levels of albumin and SBP for predicting ICU admission and in-hospital mortality. The models were adjusted for demographics, vital signs, comorbidities and common laboratory results. Patients with albumin 3.5-4.5 g/dL and SBP ≥90 mm Hg were used as reference. RESULTS: The cohort included 402 123 ED arrivals (27.9% of total adult ED visits). The rates of in-hospital mortality, ICU admission and overall admission were 1.7%, 8.4% and 47.1%, respectively. For SBP <90 mm Hg and albumin <2.5 g/dL, mortality and ICU admission rates were 34.0% and 40.6%, respectively; for SBP <90 mm Hg and albumin ≥2.5 g/dL 8.2% and 24.1%, respectively; for SBP ≥90 mm Hg and albumin <2.5 g/dL 11.4% and 18.6%, respectively; for SBP ≥90 mm Hg and albumin 3.5-4.5 g/dL 0.5% and 6.4%, respectively. Multivariable analysis showed that in patients with hypotension and albumin <2.5 g/dL the adjusted OR for in-hospital mortality was 37.1 (95% CI 32.3 to 42.6), and for ICU admission was 5.4 (95% CI 4.8 to 6.1). CONCLUSION: Co-occurrence of hypotension and hypoalbuminaemia is associated with poor hospital outcomes.

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.

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.

Inpatient hospital performance is associated with post-discharge sepsis mortality.

BACKGROUND: Post-discharge deaths are common in patients hospitalized for sepsis, but the drivers of post-discharge deaths are unclear. The objective of this study was to test the hypothesis that hospitals with high risk-adjusted inpatient sepsis mortality also have high post-discharge mortality, readmissions, and discharge to nursing homes. METHODS: Retrospective cohort study of age-qualifying Medicare beneficiaries with sepsis hospitalization between January 2013 and December 2014. Hospital survivors were followed for 180-days post-discharge, and mortality, readmissions, and new admission to skilled nursing facility were measured. Inpatient hospital-specific sepsis risk-adjusted mortality ratio (observed: expected) was the primary exposure. RESULTS: A total of 830,721 patients in the cohort were hospitalized for sepsis, with inpatient mortality of 20% and 90-day mortality of 48%. Higher hospital-specific sepsis risk-adjusted mortality was associated with increased 90-day post-discharge mortality (aOR 1.03 per each 0.1 increase in hospital inpatient O:E ratio, 95% CI 1.03-1.04). Higher inpatient risk adjusted mortality was also associated with increased probability of being discharged to a nursing facility (aOR 1.03, 95% CI 1.02-1.03) and 90-day readmissions (aOR 1.03, 95% CI 1.02-1.03). CONCLUSIONS: Hospitals with the highest risk-adjusted sepsis inpatient mortality also have higher post-discharge mortality and increased readmissions, suggesting that post-discharge complications are a modifiable risk that may be affected during inpatient care. Future work will seek to elucidate inpatient and healthcare practices that can reduce sepsis post-discharge complications.

Modifiable Failures in the Colorectal Cancer Screening Process and Their Association With Risk of Death.

BACKGROUND & AIMS: Colorectal cancer (CRC) deaths occur when patients do not receive screening or have inadequate follow-up of abnormal results or when the screening test fails. We have few data on the contribution of each to CRC-associated deaths or factors associated with these events. METHODS: We performed a retrospective cohort study of patients in the Kaiser Permanente Northern and Southern California systems (55-90 years old) who died of CRC from 2006 through 2012 and had ≥5 years of enrollment before diagnosis. We compared data from patients with those from a matched cohort of cancer-free patients in the same system. Receipt, results, indications, and follow-up of CRC tests in the 10-year period before diagnosis were obtained from electronic databases and chart audits. RESULTS: Of 1750 CRC deaths, 75.9% (n = 1328) occurred in patients who were not up to date in screening and 24.1% (n = 422) occurred in patients who were up to date. Failure to screen was associated with fewer visits to primary care physicians. Of 3486 cancer-free patients, 44.6% were up to date in their screening. Patients who were up to date in their screening had a lower risk of CRC death (odds ratio, 0.38; 95% confidence interval, 0.33-0.44). Failure to screen, or failure to screen at appropriate intervals, occurred in a 67.8% of patients who died of CRC vs 53.2% of cancer-free patients; failure to follow-up on abnormal results occurred in 8.1% of patients who died of CRC vs 2.2% of cancer-free patients. CRC death was associated with higher odds of failure to screen or failure to screen at appropriate intervals (odds ratio, 2.40; 95% confidence interval, 2.07-2.77) and failure to follow-up on abnormal results (odds ratio, 7.26; 95% confidence interval, 5.26-10.03). CONCLUSIONS: Being up to date on screening substantially decreases the risk of CRC death. In 2 health care systems with high rates of screening, most people who died of CRC had failures in the screening process that could be rectified, such as failure to follow-up on abnormal findings; these significantly increased the risk for CRC death.

Qualitative study of system-level factors related to genomic implementation.

PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

Genetic Biomarker Prevalence Is Similar in Fecal Immunochemical Test Positive and Negative Colorectal Cancer Tissue.

BACKGROUND: Fecal immunochemical test (FIT) screening detects most asymptomatic colorectal cancers. Combining FIT screening with stool-based genetic biomarkers increases sensitivity for cancer, but whether DNA biomarkers (biomarkers) differ for cancers detected versus missed by FIT screening has not been evaluated in a community-based population. AIMS: To evaluate tissue biomarkers among Kaiser Permanente Northern California patients diagnosed with colorectal cancer within 2 years after FIT screening. METHODS: FIT-negative and FIT-positive colorectal cancer patients 50-77 years of age were matched on age, sex, and cancer stage. Adequate DNA was isolated from paraffin-embedded specimens in 210 FIT-negative and 211 FIT-positive patients. Quantitative allele-specific real-time target and signal amplification assays were performed for 7 K-ras mutations and 10 aberrantly methylated DNA biomarkers (NDRG4, BMP3, SFMBT2_895, SFMBT2_896, SFMBT2_897, CHST2_7890, PDGFD, VAV3, DTX1, CHST2_7889). RESULTS: One or more biomarkers were found in 414 of 421 CRCs (98.3%). Biomarker expression was not associated with FIT status, with the exception of higher SFMBT2_897 expression in FIT-negative (194 of 210; 92.4%) than in FIT-positive cancers (180 of 211; 85.3%; p = 0.02). There were no consistent differences in biomarker expression by FIT status within age, sex, stage, and cancer location subgroups. CONCLUSIONS: The biomarkers of a currently in-use multi-target stool DNA test (K-ras, NDRG4, and BMP3) and eight newly characterized methylated biomarkers were commonly expressed in tumor tissue specimens, independent of FIT result. Additional study using stool-based testing with these new biomarkers will allow assessment of sensitivity, specificity, and clinical utility.