Reimagining Long-Term Services and Supports in a Post-Pandemic World

Prior to the global pandemic, the United States struggled to coordinate, deliver, and finance quality, person-centered long-term services and supports (LTSS) through the default primary payer, Medicaid. The pandemic highlights the challenges of not having a LTSS system. LTSS workers are underpaid, overworked, and turning over at alarming rates. Families face mounting pressures of caring for a growing number of loved ones, some with very complex care. Costs continue to climb, and quality indicators are not improving. While our approach to LTSS has improved, costs and quality challenges still dominate the landscape. We are at juncture when we need to reimagine the LTSS system, one that genuinely puts the care recipients and their caregivers at the heart of the system. The pandemic has provided some lessons about how to think differently about what long-term services can look like. Now is the time to embrace innovative opportunities building on this adversity.

Acute Myeloid Leukemia (AML) Hospitalizations: Racial Disparity in Outcomes of Patients with Comorbidities

Background: Acute Myeloid Leukemia (AML) outcomes are dependent on leukemia-specific factors, such as cytogenetics and patient-specific factors, such as age and performance status. Racial and socioeconomic disparities have become apparent with self-reported African American race associated with poor survival in AML pts < 60 years (y) of age. We analyzed in-hospital death among AML-related hospitalizations and evaluated differences in sociodemographic characteristics, focusing on the effect of age and race. Methods: We conducted a retrospective cohort study using the Nationwide Inpatient Sample (HCUP-NIS), the largest all-payer database of hospital admissions in the United States, from January 1 st, 2009 through December 31 st, 2018. The study sample consisted of AML-associated hospitalizations of patients aged 18 years of age and older, identified on the basis of the presence of any ICD-9 and 10 codes indicative of AML We categorized patients' ages in groups of <60 y and ≥ 60 y. Ethnicity was initially stratified by reported ethnicity (Hispanic, Non-Hispanic), and the Non-Hispanic group was subdivided into White, Black, or other. The primary payer for the hospitalization was classified. As a proxy for socioeconomic status, the HCUP-NIS provides zip-code-level estimates of median household income, grouped into quartiles based on the patient's residence. Hospital factors included census region, bed size, and hospital type. Patients' comorbidity status was captured using Elixhauser Comorbidity Index (0, 1-4, 5+). Outcome of interest was in-hospital death. Among different age groups and ethnic groups of patients with AML, we used survey logistic regression to generate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) that measured the independent associations between various patient hospitalization characteristics and inpatient death. We adopted a 5% type I error rate for the calculation of CIs, and used appropriate survey weighting to generate national prevalence estimates considering the complex sampling design of the NIS. Statistical analyses were performed using R (version 3∙6∙1) and RStudio (Version 1∙2∙5001). Results: Of 662,417 AML-related hospitalizations, 57.6% were in patients ≥60 y. Of AML-related hospitalizations in patients <60 y, 61.6% were in NH-White patients, 11.2% were in NH Blacks, and 11.7% were in Hispanics. Of AML-related hospitalizations in patients ≥60 y, 74.6% were in NH-Whites, 7.3% in NH-Blacks and 5.4% in Hispanics. Analysis of in-hospital death among AML-related hospitalizations, stratified by race/ethnicity revealed increased in-hospital death among male NH-Black AML patients ≥ 60 y as compared to NH-Black females in the same age group (OR 1.24; CI 1.04-1.47) and an increased in-hospital death among Hispanic patients ≥ 60 years with comorbidities relative to their counterparts in the same age group without comorbidities (OR 17.8; CI 11.32-29.37 for Elixhauser Comorbidity Index 1-4 and OR 2.69; CI 1.09-5.26 for Elixhauser Comorbidity Index ≥5). Differences in income, primary payer, hospital region, size, location or teaching status were not associated with in-hospital death among AML patients stratified by race/ethnicity. See Table 1. Conclusions: We found a significant increase in in-hospital death among Hispanic patients ≥ 60 y with comorbidities relative to their counterparts in the same age group without comorbidities. Yet, comorbidities did not appear to have a statistically significant impact in mortality for patients of other race/ethnicity or those <60 y. It is plausible that the relationship between comorbidities and re-hospitalizations might be impacting our data. Previous work not specific to acute myeloid leukemia has shown that higher comorbidity is associated with an increased risk of readmission. Since our data was based on AML-related hospitalizations rather than patients with AML, it is possible that we are dealing with a false negative error because of patients with comorbidities being readmitted more. The lack of cytogenetic data in NIS is another notable limitation to our work. Nonetheless, we have found striking differences in the outcomes of elderly Hispanic comorbid patients and further evaluations are needed for correlation and to identify areas of optimization in their care. Figure 1 Figure 1. Disclosures Mims: Incyte: Research Funding; AVEO: Research Funding; Pfizer: Research Funding; IDEC: Current holder of individual stocks in a privately-held company; Celgene: Research Funding; Biogen: Current holder of individual stocks in a privately-held company.

Examining patient specific factors associated with inpatient hospital length of stay for solid tumor oncology patients from low socioeconomic communities in Milwaukee, WI.

232 Background: In a retrospective analysis at the Medical College of Wisconsin’s Cancer Center, we identified longer inpatient length of stay (LOS) for patients residing in low-socioeconomic (SES) ZIP Codes in Milwaukee County compared to their high-SES counterparts in 2020 (7.3 days compared to 7.1 days). Under the auspices of American Society of Clinical Oncology’ Quality Training Program (QTP) initiative, this study examined specific factors related to prolonged LOS for solid tumor oncology patients. Methods: This analysis includes initial CY20 LOS medical record data for select patient service areas. Supplemental data includes disease registry data, diagnostic data, and SES data determined by patient ZIP Code. We identified patients 18 years and older with a diagnosis of common oncologic malignancies from 1/1/2020-12/31/2020 (breast, gastrointestinal (GI), genitourinary (GU), gynecologic (GYN), head and neck (H&N), and lung cancers). Poisson regression models with robust standard errors were used to compare the LOS index (LOSi) between groups of patients based on race, SES group, primary payer, and BMI. Results: A total of 1,637 patients with solid tumor diagnosis admitted to hematology and oncology units were identified. The LOSi did not vary significantly by race (range 0.95 – 1.07, p = 0.40) or primary payer (range 0.99 – 1.04, p = 0.59), but lower SES groups tended to have longer LOSi, with LOSi ratio above 1 compared to high SES (low SES: 1.16, p = 0.2; medium-low SES: 1.24, p = 0.06). Among patients with breast cancer diagnosis, Black (LOSi = 1.24, p = 0.01), medium-low SES (LOSi = 1.46, p = 0.02), Medicaid (LOSi = 1.40, p = 0.00), underweight (LOSi = 1.66, p = 0.00), and overweight (LOSi = 1.23, p = 0.01) patients had slightly longer LOSi, with LOSi ratio above 1. Among patients with H&N cancer diagnosis, Black patients (LOSi = 0.77, p = 0.02) had slightly shorter LOSi, with LOSi ratio below 1. The LOSi did not vary significantly by other factors for patients with H&N cancer diagnosis or the other common oncologic malignancies evaluated. Conclusions: This study shows how patient-specific factors such as race, SES, primary payer, and BMI contribute to inpatient LOS. Healthcare systems may benefit by addressing patient-specific barriers and factors such as body mass index, SES and SDH, to reduce hospital LOS.

Risk of 30-day Readmission after Knee or Hip Replacement in Rheumatoid Arthritis and Osteoarthritis by non-Medicare and Medicare Payer Status

Objective To determine the indication and risk of 30-day rehospitalization after hip or knee replacement among rheumatoid arthritis (RA) and osteoarthritis (OA) by Medicare and non-Medicare status. Methods Using the Nationwide Readmission Database (2010-2014), we defined an Index hospitalization as an elective hospitalization with a principal procedure of total hip or knee replacement among adults aged ≥18 years. Primary payer was categorized as Medicare or non- Medicare. Survey logistic regression provided the odds of 30-day rehospitalization in RA relative to OA. We calculated the rates for principal diagnoses leading to rehospitalization. Results Overall, 3.53% of 2,190,745 index hospitalization had a 30-day rehospitalization. Patients with RA had a higher adjusted risk of rehospitalization after TKR (Odds Ratio [OR], 1.11; 95% Confidence Interval [CI], 1.02 to 1.21) and THR (OR, 1.39; 95% CI, 1.19 to 1.62). Persons with RA and OA did not differ with respect to rates of infections, cardiac events, or postoperative complications leading to the rehospitalization. After TKR, RA patients with Medicare had a lower VTE risk (OR, 0.58;95% CI, 0.58 to 0.88) while post-THR those with RA had a greater VTE risk (OR, 2.41;95% CI, 1.04 to 5.57). Conclusion RA patients had a higher 30-day rehospitalization than OA after TKR and THR regardless of payer type. While infections, postoperative complications, cardiac did not differ, there was a significant difference in venous thromboembolism as the rehospitalization's principal diagnosis.

Abstract 01: Thirty-day Readmission Rate For Hypertensive Crisis: A Nationwide Analysis

Objective: Hypertensive crisis is a life-threatening condition that leads to poor patient outcomes if not treated urgently. We evaluate the all-cause 30-day readmission rate, resource utilization, predictors of readmission, and the most common causes for readmission in patients admitted with hypertensive crisis. Methods: Using the National Readmission Database 2018, patients ≥18 years of age admitted with the principal diagnosis of the hypertensive crisis were included in the study. The primary outcome was the all-cause 30-day readmission rate in these patients. Results: We identified 129,239 patients as index admissions. The mean age of index patients was 61±0.14 years, and females were 56.6%. The 30-day readmission rate was 10.6 % (13,768± 355). The in-hospital mortality rate was 0.2 % for index admissions and 1.5 % for readmissions. The cumulative length of stay (LOS) for all readmitted patients was 62,046± 1,802 days. The cumulative total cost of hospital services in readmitted patients was 155 ± 4.6 million USD. Age<65, lower-income, increased LOS, increased comorbidity burden, primary payer, and disposition were important predictors of readmission (Table). The most common causes of readmission were hypertensive crisis (19 %), CHF (12.4%), acute renal failure (4.4%), sepsis (2.6%), ESRD (2%), NSTEMI (2%), and stroke (1.7%) Conclusion: In conclusion, the 30-day readmission rate in patients with hypertensive crisis is high (10.6%), with hypertensive crisis and CHF as the most common causes. Resource utilization is also high, with many potential avoidable days and costs. Future strategies should address underlying factors, especially socioeconomic factors.

Impact of socioeconomic disparities on diagnosis and overall survival in small cell lung cancer: A National Cancer Database analysis.

8515 Background: Small cell lung cancer (SCLC) accounts for approximately 13% of all lung cancer diagnoses in the United States. The demographics of this disease have evolved over time; in the 1970s 28% of patients with SCLC were female, while in the early 2000s, 50% were female. Remarkable differences in incidence, mortality rates, and trends by race and geographic location have also been noted. There has been a paucity of data regarding changes in epidemiology and patient demographics in SCLC since the early 2000s. Given recent treatment advances, the impact these factors have on patient outcomes for SCLC requires further evaluation. Methods: We identified all patients with SCLC in the NCDB from 2004 to 2016. Differences in demographic, disease, and treatment characteristics were assessed by year of diagnosis using Chi-square test. The effect of age, race, insurance status, income, distance to treatment center, and education level on overall survival (OS) was assessed by log-rank test. Results: There were 137,253 cases of SCLC diagnosed in the NCDB between 2004-2010 and 124,796 cases between 2011-2016. Patients diagnosed after 2010 were significantly older, had more comorbidities, had more stage IV disease, were more frequently treated at academic centers, more commonly had government primary payer insurance, and lived significantly further away from their treatment center. There were significant differences in gender, race/ethnicity groups, education level, and residence area, with more females, more African Americans, more patients without a high school diploma, and more rural patients diagnosed after 2010. OS in general improved between the two time periods, with median OS of 8.41 months (95% CI: 8.34-8.48%) and 5-year OS rate of 6.8% (95% CI: 6.6-6.9%) in patients diagnosed between 2004-2010 and median OS of 8.61 months (95% CI: 8.54-8.67%) and 5-year OS rate of 8.7% (95% CI: 8.5-8.9%) in patients diagnosed after 2010, despite an increase in stage IV disease in the latter group. Some of the differences in demographics were associated with changes in OS. Older patients, male patients, Caucasian patients, patients with stage IV disease, patients with government primary payer insurance, and rural patients all had significantly worse OS. Patients without comorbidities and patients treated at an academic center had significantly better OS. OS was found to significantly increase as both income and education level increase. Conclusions: SCLC continues to be a frequent cancer diagnosis. Despite improvement in overall survival during the time frame studied, there were significant disparities noted in key demographics that negatively affect access to healthcare resources, including rural communities, distance to an academic center, income, insurer, and education level. Collective efforts to impact these disparities will likely lead to improved outcomes for patients with SCLC.

Abstract MP26: Medicaid Expansion Did Not Reduce Stroke Readmissions After One Year

Introduction: Up to 21% of stroke survivors are re-hospitalized within 30 days. Health insurance promotes access to follow-up care that can mitigate the risk of readmission, but 12 states do not participate in the Affordable Care Act’s Medicaid expansion. Hypothesis: The probability of 30-day hospital readmission after acute ischemic stroke was lower in Medicaid expansion states than in non-expansion states. Methods: A retrospective, quasi-experimental study using six inpatient databases from AHRQ’s Healthcare Cost and Utilization Project: four from expansion states (AR, MD, NM and WA) and two non-expansion (FL and GA). The sample comprised all patients hospitalized in 2012-14 with a principal diagnosis of ischemic stroke (ICD-9-CM 433.x1, 434.x1 or 436) who were aged 19-64; resided in the state where admitted; had a primary payer of Medicaid, self-pay or no charge; and were discharged alive ( N =18,766). Mixed effects logit models with a time-by-treatment interaction were built to test if the probability of readmission changed differentially between expansion and non-expansion states from 2012-13 (before expansion) to 2014. Any in-state hospitalization within 30 days of discharge (except for rehabilitation, psychiatry, or cancer treatment) was considered a readmission. A secondary analysis of unplanned, potentially preventable readmissions (adapted from the AHRQ Prevention Quality Indicators) was also conducted. Models included race, sex, age, number of diagnoses, median household income quartile of patient ZIP code, and metropolitan residence as fixed effects, with random intercepts for hospital and state. Results: In 2012-13, 8.9% of the expansion state patients were readmitted compared to 9.0% in non-expansion states; in 2014, 11.1% were readmitted in expansion states versus 10.5% in non-expansion states. In multivariable models, the time-by-treatment interaction was not statistically significant: β=0.072, p= .541, for all readmissions, β=0.168, p =.683, for unplanned, potentially preventable readmissions. Conclusions: Medicaid expansion did not reduce 30-day readmissions after stroke in the first year of implementation in four diverse states. Stroke readmissions among non-elderly adults require more targeted interventions.

Influence of primary payer status on the management and outcomes of ST-segment elevation myocardial infarction in the United States

Background There are limited contemporary data on the influence of primary payer status on the management and outcomes of ST-segment elevation myocardial infarction (STEMI). Objective To assess the influence of insurance status on STEMI outcomes. Methods Adult (>18 years) STEMI admissions were identified using the National Inpatient Sample database (2000–2017). Expected primary payer was classified into Medicare, Medicaid, private, uninsured and others. Outcomes of interest included in-hospital mortality, use of coronary angiography and percutaneous coronary intervention (PCI), hospitalization costs, hospital length of stay and discharge disposition. Results Of the 4,310,703 STEMI admissions, Medicare, Medicaid, private, uninsured and other insurances were noted in 49.0%, 6.3%, 34.4%, 7.2% and 3.1%, respectively. Compared to the others, the Medicare cohort was older (75 vs. 53–57 years), more often female (46% vs. 20–36%), of white race, and with higher comorbidity (all p<0.001). The Medicare and Medicaid population had higher rates of cardiogenic shock and cardiac arrest. The Medicare cohort had higher in-hospital mortality (14.2%) compared to the other groups (4.1–6.7%), p<0.001. In a multivariable analysis (Medicare referent), in-hospital mortality was higher in uninsured (adjusted odds ratio (aOR) 1.14 [95% confidence interval {CI} 1.11–1.16]), and lower in Medicaid (aOR 0.96 [95% CI 0.94–0.99]; p = 0.002), privately insured (aOR 0.73 [95% CI 0.72–0.75]) and other insurance (aOR 0.91 [95% CI 0.88–0.94]); all p<0.001. Coronary angiography (60% vs. 77–82%) and PCI (45% vs. 63–70%) were used less frequently in the Medicare population compared to others. The Medicare and Medicaid populations had longer lengths of hospital stay, and the Medicare population had the lowest hospitalization costs and fewer discharges to home. Conclusions Compared to other types of primary payers, STEMI admissions with Medicare insurance had lower use of coronary angiography and PCI, and higher in-hospital mortality.