The ACA Hospital Readmissions Reduction Program applies penalties for high readmission rates. Among Medicare beneficiaries, rates declined after the ACA went into effect. There was no significant ...association between changes in observation stays and readmissions.
Hospital readmissions within 30 days after discharge have drawn national policy attention because they are very costly, accounting for more than $17 billion in avoidable Medicare expenditures,
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and are associated with poor outcomes. In response to these concerns, the Affordable Care Act (ACA), which was passed in March 2010, created the Hospital Readmissions Reduction Program. Since October 2012, the start of fiscal year (FY) 2013, the program has penalized hospitals with higher-than-expected 30-day readmission rates for selected clinical conditions. In FY 2013 and 2014, these conditions were acute myocardial infarction, heart failure, and pneumonia. Total hip or knee replacement and . . .
Arkansas was among several states that implemented Medicaid work requirements. Results from surveys of low-income adults suggest that work requirements were associated with a decrease in the number ...of people in Arkansas with Medicaid coverage and an increase in the number without health insurance.
IMPORTANCE: Vitamin D deficiency has been associated with poor physical performance. OBJECTIVE: To determine the effectiveness of high-dose vitamin D in lowering the risk of functional decline. ...DESIGN, SETTING, AND PARTICIPANTS: One-year, double-blind, randomized clinical trial conducted in Zurich, Switzerland. The screening phase was December 1, 2009, to May 31, 2010, and the last study visit was in May 2011. The dates of our analysis were June 15, 2012, to October 10, 2015. Participants were 200 community-dwelling men and women 70 years and older with a prior fall. INTERVENTIONS: Three study groups with monthly treatments, including a low-dose control group receiving 24 000 IU of vitamin D3 (24 000 IU group), a group receiving 60 000 IU of vitamin D3 (60 000 IU group), and a group receiving 24 000 IU of vitamin D3 plus 300 μg of calcifediol (24 000 IU plus calcifediol group). MAIN OUTCOMES AND MEASURES: The primary end point was improving lower extremity function (on the Short Physical Performance Battery) and achieving 25-hydroxyvitamin D levels of at least 30 ng/mL at 6 and 12 months. A secondary end point was monthly reported falls. Analyses were adjusted for age, sex, and body mass index. RESULTS: The study cohort comprised 200 participants (men and women ≥70 years with a prior fall). Their mean age was 78 years, 67.0% (134 of 200) were female, and 58.0% (116 of 200) were vitamin D deficient (<20 ng/mL) at baseline. Intent-to-treat analyses showed that, while 60 000 IU and 24 000 IU plus calcifediol were more likely than 24 000 IU to result in 25-hydroxyvitamin D levels of at least 30 ng/mL (P = .001), they were not more effective in improving lower extremity function, which did not differ among the treatment groups (P = .26). However, over the 12-month follow-up, the incidence of falls differed significantly among the treatment groups, with higher incidences in the 60 000 IU group (66.9%; 95% CI, 54.4% to 77.5%) and the 24 000 IU plus calcifediol group (66.1%; 95% CI, 53.5%-76.8%) group compared with the 24 000 IU group (47.9%; 95% CI, 35.8%-60.3%) (P = .048). Consistent with the incidence of falls, the mean number of falls differed marginally by treatment group. The 60 000 IU group (mean, 1.47) and the 24 000 IU plus calcifediol group (mean, 1.24) had higher mean numbers of falls compared with the 24 000 IU group (mean, 0.94) (P = .09). CONCLUSIONS AND RELEVANCE: Although higher monthly doses of vitamin D were effective in reaching a threshold of at least 30 ng/mL of 25-hydroxyvitamin D, they had no benefit on lower extremity function and were associated with increased risk of falls compared with 24 000 IU. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01017354
IMPORTANCE: Under the Affordable Care Act (ACA), more than 30 states have expanded Medicaid, with some states choosing to expand private insurance instead (the “private option”). In addition, while ...coverage gains from the ACA’s Medicaid expansion are well documented, impacts on utilization and health are unclear. OBJECTIVE: To assess changes in access to care, utilization, and self-reported health among low-income adults in 3 states taking alternative approaches to the ACA. DESIGN, SETTING, AND PARTICIPANTS: Differences-in-differences analysis of survey data from November 2013 through December 2015 of US citizens ages 19 to 64 years with incomes below 138% of the federal poverty level in Kentucky, Arkansas, and Texas (n = 8676). Data analysis was conducted between January and May 2016. EXPOSURES: Medicaid expansion in Kentucky and use of Medicaid funds to purchase private insurance for low-income adults in Arkansas (private option), compared with no expansion in Texas. MAIN OUTCOMES AND MEASURES: Self-reported access to primary care, specialty care, and medications; affordability of care; outpatient, inpatient, and emergency utilization; receiving glucose and cholesterol testing, annual check-up, and care for chronic conditions; quality of care, depression score, and overall health. RESULTS: Among the 3 states included in the study, Arkansas (n=2890), Kentucky (n=2898, and Texas (n=2888), there were no differences in sex, income, or marital status. Respondents from Texas were younger, more urban, and disproportionately Latino compared with those in Arkansas and Kentucky. Significant changes in coverage and access were more apparent in 2015 than in 2014. By 2015, expansion was associated with a 22.7 percentage-point reduction in the uninsured rate compared with nonexpansion (P < .001). Expansion was associated with significantly increased access to primary care (12.1 percentage points; P < .001), fewer skipped medications due to cost (−11.6 percentage points; P < .001), reduced out-of-pocket spending (−29.5%; P = .02), reduced likelihood of emergency department visits (−6.0 percentage points, P = .04), and increased outpatient visits (0.69 visits per year; P = .04). Screening for diabetes (6.3 percentage points; P = .05), glucose testing among patients with diabetes (10.7 percentage points; P = .03), and regular care for chronic conditions (12.0 percentage points; P = .008) all increased significantly after expansion. Quality of care ratings improved significantly (−7.1 percentage points with “fair/poor quality of care”; P = .03), as did the share of adults reporting excellent health (4.8 percentage points; P = .04). Comparisons of Arkansas vs Kentucky showed increased private coverage in the former (21.7 percentage points; P < .001), increased Medicaid in the latter (21.3 percentage points; P < .001), and higher diabetic glucose testing rates in Kentucky (11.6 percentage points; P = .04), but no other statistically significant differences. CONCLUSIONS AND RELEVANCE: In the second year of expansion, Kentucky’s Medicaid program and Arkansas’s private option were associated with significant increases in outpatient utilization, preventive care, and improved health care quality; reductions in emergency department use; and improved self-reported health. Aside from the type of coverage obtained, outcomes were similar for nearly all other outcomes between the 2 states using alternative approaches to expansion.
Hospital participation in the Bundled Payments for Care Improvement initiative for five common medical conditions was not associated with changes in Medicare payments, clinical complexity, length of ...stay, emergency department use, hospital readmissions, or mortality.
IMPORTANCE: Studies have found differences in practice patterns between male and female physicians, with female physicians more likely to adhere to clinical guidelines and evidence-based practice. ...However, whether patient outcomes differ between male and female physicians is largely unknown. OBJECTIVE: To determine whether mortality and readmission rates differ between patients treated by male or female physicians. DESIGN, SETTING, AND PARTICIPANTS: We analyzed a 20% random sample of Medicare fee-for-service beneficiaries 65 years or older hospitalized with a medical condition and treated by general internists from January 1, 2011, to December 31, 2014. We examined the association between physician sex and 30-day mortality and readmission rates, adjusted for patient and physician characteristics and hospital fixed effects (effectively comparing female and male physicians within the same hospital). As a sensitivity analysis, we examined only physicians focusing on hospital care (hospitalists), among whom patients are plausibly quasi-randomized to physicians based on the physician’s specific work schedules. We also investigated whether differences in patient outcomes varied by specific condition or by underlying severity of illness. MAIN OUTCOMES AND MEASURES: Patients’ 30-day mortality and readmission rates. RESULTS: A total of 1 583 028 hospitalizations were used for analyses of 30-day mortality (mean SD patient age, 80.2 8.5 years; 621 412 men and 961 616 women) and 1 540 797 were used for analyses of readmission (mean SD patient age, 80.1 8.5 years; 602 115 men and 938 682 women). Patients treated by female physicians had lower 30-day mortality (adjusted mortality, 11.07% vs 11.49%; adjusted risk difference, –0.43%; 95% CI, –0.57% to –0.28%; P < .001; number needed to treat to prevent 1 death, 233) and lower 30-day readmissions (adjusted readmissions, 15.02% vs 15.57%; adjusted risk difference, –0.55%; 95% CI, –0.71% to –0.39%; P < .001; number needed to treat to prevent 1 readmission, 182) than patients cared for by male physicians, after accounting for potential confounders. Our findings were unaffected when restricting analyses to patients treated by hospitalists. Differences persisted across 8 common medical conditions and across patients’ severity of illness. CONCLUSIONS AND RELEVANCE: Elderly hospitalized patients treated by female internists have lower mortality and readmissions compared with those cared for by male internists. These findings suggest that the differences in practice patterns between male and female physicians, as suggested in previous studies, may have important clinical implications for patient outcomes.
This analysis of Medicare data suggests that 13% of patients are readmitted to the hospital within 30 days after major surgery. Readmission rates vary across hospitals and correlate with surgical ...volume and surgical mortality, two measures of surgical quality.
Reducing hospital-readmission rates is a priority for both policymakers and clinical leaders. The focus on readmissions has been driven by a belief that reducing the frequency with which patients return to the hospital can both improve care and lower costs. To date, much of the focus has been on readmissions after hospitalization for medical conditions, in which discharge planning and care coordination are often suboptimal.
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The Centers for Medicare and Medicaid Services (CMS) plans to include surgical procedures as it expands its readmissions penalty program.
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Using medical-readmission rates as a measure of hospital quality is controversial. Although hospitals vary . . .
In 2016, Medicare started mandatory bundled payment for joint-replacement surgery in randomly selected areas. Hospitals receive bonuses or pay penalties based on spending through 90 days after ...discharge. In the first 2 years, there was a slight reduction in spending.
IMPORTANCE: Few studies have analyzed contemporary data on outcomes at US teaching hospitals vs nonteaching hospitals. OBJECTIVE: To examine risk-adjusted outcomes for patients admitted to teaching ...vs nonteaching hospitals across a broad range of medical and surgical conditions. DESIGN, SETTING, AND PARTICIPANTS: Use of national Medicare data to compare mortality rates in US teaching and nonteaching hospitals for all hospitalizations and for common medical and surgical conditions among Medicare beneficiaries 65 years and older. EXPOSURES: Hospital teaching status: major teaching hospitals (members of the Council of Teaching Hospitals), minor teaching hospitals (other hospitals with medical school affiliation), and nonteaching hospitals (remaining hospitals). MAIN OUTCOMES AND MEASURES: Primary outcome was 30-day mortality rate for all hospitalizations and for 15 common medical and 6 surgical conditions. Secondary outcomes included 30-day mortality stratified by hospital size and 7-day mortality and 90-day mortality for all hospitalizations as well as for individual medical and surgical conditions. RESULTS: The sample consisted of 21 451 824 total hospitalizations at 4483 hospitals, of which 250 (5.6%) were major teaching, 894 (19.9%) were minor teaching, and 3339 (74.3%) were nonteaching hospitals. Unadjusted 30-day mortality was 8.1% at major teaching hospitals, 9.2% at minor teaching hospitals, and 9.6% at nonteaching hospitals, with a 1.5% (95% CI, 1.3%-1.7%; P < .001) mortality difference between major teaching hospitals and nonteaching hospitals. After adjusting for patient and hospital characteristics, the same pattern persisted (8.3% mortality at major teaching vs 9.2% at minor teaching and 9.5% at nonteaching), but the difference in mortality between major and nonteaching hospitals was smaller (1.2% 95% CI, 1.0%-1.4%; P < .001). After stratifying by hospital size, 187 large (≥400 beds) major teaching hospitals had lower adjusted overall 30-day mortality relative to 76 large nonteaching hospitals (8.1% vs 9.4%; 1.2% difference 95% CI, 0.9%-1.5%; P < .001). This same pattern of lower overall 30-day mortality at teaching hospitals was observed for medium-sized (100-399 beds) hospitals (8.6% vs 9.3% and 9.4%; 0.8% difference between 61 major and 1207 nonteaching hospitals 95% CI, 0.4%-1.3%; P = .003). Among small (≤99 beds) hospitals, 187 minor teaching hospitals had lower overall 30-day mortality relative to 2056 nonteaching hospitals (9.5% vs 9.9%; 0.4% difference 95% CI, 0.1%-0.7%; P = .01). CONCLUSIONS AND RELEVANCE: Among hospitalizations for US Medicare beneficiaries, major teaching hospital status was associated with lower mortality rates for common conditions compared with nonteaching hospitals. Further study is needed to understand the reasons for these differences.
AbstractObjectiveTo investigate the association of predicted lean body mass, fat mass, and body mass index (BMI) with all cause and cause specific mortality in men.DesignProspective cohort ...study.SettingHealth professionals in the United StatesParticipants38 006 men (aged 40-75 years) from the Health Professionals Follow-up Study, followed up for death (1987-2012).Main outcome measuresAll cause and cause specific mortality.ResultsUsing validated anthropometric prediction equations previously developed from the National Health and Nutrition Examination Survey, lean body mass and fat mass were estimated for all participants. During a mean of 21.4 years of follow-up, 12 356 deaths were identified. A J shaped association was consistently observed between BMI and all cause mortality. Multivariable adjusted Cox models including predicted fat mass and lean body mass showed a strong positive monotonic association between predicted fat mass and all cause mortality. Compared with those in the lowest fifth of predicted fat mass, men in the highest fifth had a hazard ratio of 1.35 (95% confidence interval 1.26 to 1.46) for mortality from all causes. In contrast, a U shaped association was found between predicted lean body mass and all cause mortality. Compared with those in the lowest fifth of predicted lean body mass, men in the second to fourth fifths had 8-10% lower risk of mortality from all causes. In the restricted cubic spline models, the risk of all cause mortality was relatively flat until 21 kg of predicted fat mass and increased rapidly afterwards, with a hazard ratio of 1.22 (1.18 to 1.26) per standard deviation. For predicted lean body mass, a large reduction of the risk was seen within the lower range until 56 kg, with a hazard ratio of 0.87 (0.82 to 0.92) per standard deviation, which increased thereafter (P for non-linearity <0.001). For cause specific mortality, men in the highest fifth of predicted fat mass had hazard ratios of 1.67 (1.47 to 1.89) for cardiovascular disease, 1.24 (1.09 to 1.43) for cancer, and 1.26 (0.97 to 1.64) for respiratory disease. On the other hand, a U shaped association was found between predicted lean body mass and mortality from cardiovascular disease and cancer. However, a strong inverse association existed between predicted lean body mass and mortality from respiratory disease (P for trend <0.001).ConclusionsThe shape of the association between BMI and mortality was determined by the relation between two body components (lean body mass and fat mass) and mortality. This finding suggests that the “obesity paradox” controversy may be largely explained by low lean body mass, rather than low fat mass, in the lower range of BMI.
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