Disparities in receipt of medications for opioid use disorder among pregnant women.

Background: Medications for opioid use disorder (MOUD) improve outcomes for pregnant women and infants. Our primary aim was to examine disparities in maternal MOUD receipt by family sociodemographic characteristics. Methods: This retrospective cohort study included mother-infant dyads with Medicaid-covered deliveries in Tennessee from 2009 to 2016. First, we examined family sociodemographic characteristics - including race/ethnicity, rurality, mother's primary language and education level, and whether paternity was recorded in birth records - and newborn outcomes by type of maternal opioid use. Second, among pregnant women with OUD, we used logistic regression to measure disparities in receipt of MOUD by family sociodemographic characteristics including interactions between characteristics. Results: Our cohort from Medicaid-covered deliveries consisted of 314,965 mother-infant dyads, and 4.2 percent were exposed to opioids through maternal use. Among dyads with maternal OUD, MOUD receipt was associated with lower rates of preterm and very preterm birth. Logistic regression adjusted for family sociodemographic characteristics showed that pregnant women with OUD in rural versus urban areas (aOR: 0.66; 95% CI: 0.60-0.72) and who were aged ≥35 years versus ≤25 years (aOR: 0.75; 95% CI: 0.64-0.89) were less likely to have received MOUD. Families in which the mother's primary language was English (aOR: 2.47; 95% CI: 1.24-4.91) and paternity was recorded on the birth certificate (aOR: 1.30; 95% CI: 1.19-1.42) were more likely to have received MOUD. Regardless of high school degree attainment, non-Hispanic Black versus non-Hispanic White race was associated with lower likelihood of MOUD receipt. Hispanic race was associated with lower likelihood of MOUD receipt among women without a high school degree. Conclusions: Among a large cohort of pregnant women, we found disparities in receipt of MOUD among non-Hispanic Black, Hispanic, and rural pregnant women. As policymakers consider strategies to improve access to MOUD, they should consider targeted approaches to address these disparities.

Body mass index and all-cause readmissions following acute heart failure hospitalization.

BACKGROUND/OBJECTIVES: Obesity is associated with a lower mortality risk among patients with heart failure (HF). Whether this obesity paradox applies to all-cause hospitalizations is unknown. We aimed to investigate the association between body mass index (BMI) and 30-day all-cause readmissions following HF hospitalization. SUBJECTS/METHODS: We retrospectively evaluated 2252 HF hospital admissions of Centers of Medicare Services beneficiaries from an academic medical center. We classified obesity using established BMI categories. All 30-day postdischarge readmission to all hospitals and mortality events were documented. We evaluated 30-day postdischarge unplanned, all-cause readmission and death in the total cohort, propensity-matched cohort, and by ejection fraction (EF). RESULTS: An Overweight-Obese BMI (BMI ≥ 25 kg/m2) was paradoxically associated with a lower mortality rate than a Normal BMI (18.5-24.9 kg/m2) (5.0% vs 8.5%, p = 0.0018). In contrast, an Overweight-Obese BMI was associated with a 29% (95% CI: 1.03-1.63) increased relative risk of all-cause readmission compared with a Normal BMI (23.2% vs 18.9%, p = 0.0288), which was consistent across obesity severity subgroups. Among 966 matched admissions, an Overweight-Obese BMI retained higher readmission risk compared with a Normal BMI (25.1% vs 17.2%, p = 0.003). After matching, readmissions remained higher for Overweight-Obese vs Normal BMI in admissions with reduced EF (25.7% vs 17.8%, p = 0.032) and preserved EF (23.0% vs 15.0%, p = 0.048). No difference in the percentage of readmissions for HF (40%) or noncardiovascular causes (45%) existed between Overweight-Obese and Normal BMI groups. CONCLUSIONS: Despite a lower mortality risk, increased BMI is associated with increased all-cause hospital readmission rates in an elderly HF population which persists after propensity matching.

Validation of an automated electronic algorithm and "dashboard" to identify and characterize decompensated heart failure admissions across a medical center.

BACKGROUND: We aim to validate the diagnostic performance of the first fully automatic, electronic heart failure (HF) identification algorithm and evaluate the implementation of an HF Dashboard system with 2 components: real-time identification of decompensated HF admissions and accurate characterization of disease characteristics and medical therapy. METHODS: We constructed an HF identification algorithm requiring 3 of 4 identifiers: B-type natriuretic peptide >400 pg/mL; admitting HF diagnosis; history of HF International Classification of Disease, Ninth Revision, diagnosis codes; and intravenous diuretic administration. We validated the diagnostic accuracy of the components individually (n = 366) and combined in the HF algorithm (n = 150) compared with a blinded provider panel in 2 separate cohorts. We built an HF Dashboard within the electronic medical record characterizing the disease and medical therapies of HF admissions identified by the HF algorithm. We evaluated the HF Dashboard's performance over 26 months of clinical use. RESULTS: Individually, the algorithm components displayed variable sensitivity and specificity, respectively: B-type natriuretic peptide >400 pg/mL (89% and 87%); diuretic (80% and 92%); and International Classification of Disease, Ninth Revision, code (56% and 95%). The HF algorithm achieved a high specificity (95%), positive predictive value (82%), and negative predictive value (85%) but achieved limited sensitivity (56%) secondary to missing provider-generated identification data. The HF Dashboard identified and characterized 3147 HF admissions over 26 months. CONCLUSIONS: Automated identification and characterization systems can be developed and used with a substantial degree of specificity for the diagnosis of decompensated HF, although sensitivity is limited by clinical data input.

Trends in and Factors Contributing to the Slowdown in Medicare Spending Growth, 2007-2018.

Importance: After decades of rapid increase, Medicare per-beneficiary spending growth was historically low in the period leading up to the passage of the Affordable Care Act. In the years immediately following the legislation, Medicare expenditure growth slowed even further. Objective: To evaluate factors contributing to the slowdown in Medicare per-beneficiary spending growth. Design, Setting, and Participants: In this cross-sectional study, expected spending growth for 2012 to 2015 and 2016 to 2018 was predicted holding payment rates and population characteristics constant. By contrasting predicted and actual spending growth during these periods, the contribution of population vs payment factors to the Medicare spending slowdown was determined. Analyses included all Medicare fee-for-service beneficiaries aged 65 years and older, ranging from 30 to 35 million beneficiaries annually between 2007 and 2018. Data analyses were conducted from January 2018 to August 2018 and updated with new data in June 2021. Main Outcomes and Measures: The main outcome included annual growth in total per-beneficiary spending. The roles of payment rate changes and differences in the Medicare population over time were considered, including demographic characteristics and numbers of chronic conditions. Results: Between 2008 to 2011 and 2012 to 2015, the adjusted annual Medicare Parts A and B per-beneficiary spending growth rate declined from 3.3% to -0.1%. From 2016 to 2018, the mean annual Medicare spending growth rate rose relative to the previous period but remained lower than in the baseline period at 1.7% per year. This slowdown extended across all sectors within Parts A and B, except for physician-administered drugs offered under Part B. Changes in payment rates (including sequestration measures) and beneficiary characteristics explained 44% of the difference in overall per-beneficiary spending growth between 2007 to 2011 and 2012 to 2015, and 63% between 2007 to 2011 and 2016 to 2018. Conclusions and Relevance: In this cross-sectional study of trends in spending growth per Medicare beneficiary aged 65 years or older, results suggested that Medicare payment policy, including sector-specific payment rate changes and sequestration, will be a critical determinant of whether the Medicare spending growth slowdown persists.

Establishing Medicaid incentives for liberating nursing home patients from ventilators.

BACKGROUND: Chronic ventilator use in Tennessee nursing homes surged following 2010 increases in respiratory care payment rates. Tennessee's Medicaid program implemented multiple policies between 2014 and 2017 to promote ventilator liberation in 11 nursing homes, including quality reporting, on-site monitoring, and pay-for-performance incentives. METHODS: Using repeated cross-sectional analysis of Medicare and Medicaid nursing home claims (2011-2017), hospital discharge records (2010-2017), and nursing home quality reports (2015-2017), we examined how service use changed as Tennessee implemented policies designed to promote ventilator liberation in nursing homes. We measured the annual number of nursing home patients with ventilator-related service use; discharge destination of ventilated inpatients and percent of nursing home patients liberated from ventilators. RESULTS: Between 2011 and 2014, the number of Medicare SNF and Medicaid nursing home patients with ventilator use increased more than sixfold. Among inpatients with prolonged mechanical ventilation, discharges to home decreased as discharges to nursing homes increased. As Tennessee implemented policy changes, ventilator-related service use moderately declined in nursing homes from a peak of 198 ventilated Medicare SNF patients in 2014 to 125 in 2017 and from 182 Medicaid patients with chronic ventilator use in 2014 to 145 patients in 2017. Nursing home weaning rates peaked at 49%-52% in 2015 and 2016, but declined to 26% by late 2017. Median number of days from admission to wean declined from 81 to 37 days. CONCLUSIONS: This value-based approach demonstrates the importance of designing payment models that target key patient outcomes like ventilator liberation.

Medicare Two-Midnight Rule Accelerated Shift To Observation Stays.

During the past two decades several policies have attempted to replace inappropriate hospital inpatient stays with observation hospital stays, where patients receive hospital care but are classified as outpatients. The Two-Midnight rule, adopted in October 2013 by the Centers for Medicare and Medicaid Services, states that more highly reimbursed inpatient payment is appropriate if care is expected to last at least two midnights; otherwise, observation stays should be used. For hospitals, the administrative burden associated with making these status determinations is substantial. We found that after the Two-Midnight rule was implemented, potentially inappropriate short inpatient stays decreased immediately by 2.0 stays per 1,000 beneficiaries and potentially more appropriate short outpatient stays increased immediately by 1.8 stays per 1,000 beneficiaries, hastening a preexisting trend in this direction. However, after this initial improvement, the rate of change slowed to a new steady state. Given the steady state and ongoing administrative resources needed, it is time to reconsider the value of status determination required by the Two-Midnight rule.

Trends in medicare spending across strata of resource utilization among older individuals in the United States.

BACKGROUND: Health care spending is an increasing proportion of government expenditures in most Western countries. How this growth is distributed between individuals with minimal compared to high health care utilization is unknown. METHODS: We examined total and per-capita government expenditure in an observational cohort of fee-for-service U.S. Medicare enrollees aged ≥65 years from 2007 to 2018. We categorized patients into annual resource utilization strata. We examined annualized changes in adjusted spending across resource utilization strata and the distribution of spending within and across strata for a variety of health care settings. FINDINGS: Examining 314,593,489 beneficiary-years of coverage, the top 1% of beneficiaries accounted for 14.9% of all expenditures, the top 5% for 41.5%, the top 10% for 60.0%, the top 20% for 79.1%, and the top 50% for 95.7%. Annual expenditures remained relatively stable from 2007 to 2018, with annual mean change of 0.7% (standard deviation 1.1%; median 1.1%) and mean per capita change of 0.4% (standard deviation 1·6%; median 0·3%). Changes were similar across strata with mean increases <1% in all, save for the <50th percentile strata (mean annual growth=1·9%), a significant difference (p = 0.0002). The overall distribution of expenditures across health care settings remained consistent over time, with different distributions between expenditure strata. INTERPRETATION: In the U.S. from 2007 to 2018, Medicare spending has a Pareto distribution in which 80% of the costs are attributable to 20% of beneficiaries. Despite low overall Medicare spending growth from 2007 to 2018, growth has been greatest among those in the lowest spending group. FUNDING: The Commonwealth Fund (20,202,411).

Change in admission blood glucose from chronic glycemic status in acute heart failure hospitalization and 30-day outcomes: A retrospective analysis.

BACKGROUND: Admission blood glucose (BG) has demonstrated contradictory association with 30-day mortality in acute heart failure (AHF) hospitalization. To explore these contradictory findings, we aimed to determine if admission BG reflects an acute change from chronic glucose control and investigate the association between the admission and chronic BG change (ΔBG) with 30-day mortality in AHF. METHODS: We analyzed patients (n = 1045) age ≥ 65 with Centers of Medicare Services benefits and known 30-day all-cause mortality hospitalized with AHF at an academic medical center from 2009 to 2016. We included diabetic (n = 736) and non-diabetic (n = 309) patients with recent Hemoglobin A1c (HbA1c). We defined ΔBG as the difference in the admission and chronic BG, calculated from HbA1c. RESULTS: Admission BG was 126 (101, 167) mg/dl and was moderately elevated (≥170 mg/dl) in 25% of admissions. The median (IQR) ΔBG was -7 (-29, 26) mg/dl, with 74% of all admissions (66% diabetic, 92% non-diabetic) presenting within ±50 mg/dl of the chronic BG. Admission BG was not associated with mortality. ΔBG > +100 mg/dl displayed increased 30-day mortality (18.6% vs 6.9%, p < 0.001) compared to -26 to +25 mg/dl. When admission BG was >200 mg/dl (n = 166), a ΔBG > +100 mg/dl was present in 77% of those deceased vs 31% of those alive at 30 days (p = 0.003; positive likelihood ratio = 5.7). ΔBG > +100 mg/dl was strongly associated with 30-day mortality risk (OR 6.4, 95% CI 2.3-18.9; p = 0.0005) after multivariate adjustment. CONCLUSIONS: Admission BG predominantly reflects chronic glycemic status. Increased change in admission from chronic BG was associated with increased 30-day mortality.

Buprenorphine Out-of-Pocket Costs and Discontinuation in Privately Insured Adults With Opioid Use Disorder.

This cohort study examined the association between out-of-pocket costs for an initial buprenorphine prescription and its discontinuation among commercially insured US adults with opioid use disorder.

Customizing national models for a medical center's population to rapidly identify patients at high risk of 30-day all-cause hospital readmission following a heart failure hospitalization.

BACKGROUND: Nationally-derived models predicting 30-day readmissions following heart failure (HF) hospitalizations yield insufficient discrimination for institutional use. OBJECTIVE: Develop a customized readmission risk model from Medicare-employed and institutionally-customized risk factors and compare the performance against national models in a medical center. METHODS: Medicare patients age ≥ 65 years hospitalized for HF (n = 1,454) were studied in a derivation cohort and in a separate validation cohort (n = 243). All 30-day hospital readmissions were documented. The primary outcome was risk discrimination (c-statistic) compared to national models. RESULTS: A customized model demonstrated improved discrimination (c-statistic 0.72; 95% CI 0.69 - 0.74) compared to national models (c-statistics of 0.60 and 0.61) with a c-statistic of 0.63 in the validation cohort. Compared to national models, a customized model demonstrated superior readmission risk profiling by distinguishing a high-risk (38.3%) from a low-risk (9.4%) quartile. CONCLUSIONS: A customized model improved readmission risk discrimination from HF hospitalizations compared to national models.

Centers for Medicare and Medicaid Services' readmission reports inaccurately describe an institution's decompensated heart failure admissions.

Hospitals typically use Center for Medicare and Medicaid Services' (CMS) Hospital Readmission Reduction Program (HRRP) administrative reports as the standard of heart failure (HF) admission quantification. We aimed to evaluate the HF admission population identified by CMS HRRP definition of HF hospital admissions compared with a clinically based HF definition. We evaluated all hospital admissions at an academic medical center over 16 months in patients with Medicare fee-for service benefits and age ≥65 years. We compared the CMS HRRP HF definition against an electronic HF identification algorithm. Admissions identified solely by the CMS HF definition were manually reviewed by HF providers. Admissions confirmed with having decompensated HF as the primary problem by manual review or by the HF ID algorithm were deemed "HF positive," whereas those refuted were "HF negative." Of the 1672 all-cause admissions evaluated, 708 (42%) were HF positive. The CMS HF definition identified 440 admissions: sensitivity (54%), specificity (94%), positive predictive value (87%), negative predictive value (74%). The CMS HF definition missed 324 HF admissions because of inclusion/exclusion criteria (15%) and decompensated HF being a secondary diagnosis (85%). The CMS HF definition falsely identified 56 admissions as HF. The most common admission reasons in this cohort included elective pacemaker or defibrillator implantations (n = 13), noncardiac dyspnea (n = 9), left ventricular assist device complications (n = 8), and acute coronary syndrome (n = 6). The CMS HRRP HF report is a poor representation of an institution's HF admissions because of limitations in administrative coding and the HRRP HF report inclusion/exclusion criteria.