Uncompensated care provided by for-profit, not-for-profit, and government owned hospitals

<p>Abstract</p> <p>Background</p> <p>There is growing concern certain not-for-profit hospitals are not providing enough uncompensated care to justify their tax exempt status. Our objective was to compare the amount of uncompensated care provided by not-for-profit (NFP), for-profit (FP) and government owned hospitals.</p> <p>Methods</p> <p>We used 2005 state inpatient data (SID) for 10 states to identify patients hospitalized for three common conditions: acute myocardial infarction (AMI), coronary artery bypass grafting (CABG), or childbirth. Uncompensated care was measured as the proportion of each hospital's total admissions for each condition that were classified as being uninsured. Hospitals were categorized as NFP, FP, or government owned based upon data obtained from the American Hospital Association. We used bivariate methods to compare the proportion of uninsured patients admitted to NFP, FP and government hospitals for each diagnosis. We then used generalized linear mixed models to compare the percentage of uninsured in each category of hospital after adjusting for the socioeconomic status of the markets each hospital served.</p> <p>Results</p> <p>Our cohort consisted of 188,117 patients (1,054 hospitals) hospitalized for AMI, 82,261 patients (245 hospitals) for CABG, and 1,091,220 patients for childbirth (793 hospitals). The percentage of admissions classified as uninsured was lower in NFP hospitals than in FP or government hospitals for AMI (4.6% NFP; 6.0% FP; 9.5% government; P < .001), CABG (2.6% NFP; 3.3% FP; 7.0% government; P < .001), and childbirth (3.1% NFP; 4.2% FP; 11.8% government; P < .001). In adjusted analyses, the mean percentage of AMI patients classified as uninsured was similar in NFP and FP hospitals (4.4% vs. 4.3%; P = 0.71), and higher for government hospitals (6.0%; P < .001 for NFP vs. government). Likewise, results demonstrated similar proportions of uninsured patients in NFP and FP hospitals and higher levels of uninsured in government hospitals for both CABG and childbirth.</p> <p>Conclusions</p> <p>For the three conditions studied NFP and FP hospitals appear to provide a similar amount of uncompensated care while government hospitals provide significantly more. Concerns about the amount of uncompensated care provided by NFP hospitals appear warranted.</p

A high-risk study of bipolar disorder. Childhood clinical phenotypes as precursors of major mood disorders

CONTEXT: The childhood precursors of adult bipolar disorder (BP) are still a matter of controversy. OBJECTIVE: To report the lifetime prevalence and early clinical predictors of psychiatric disorders in offspring from families of probands with DSM-IV BP compared with offspring of control subjects. DESIGN: A longitudinal, prospective study of individuals at risk for BP and related disorders. We report initial (cross-sectional and retrospective) diagnostic and clinical characteristics following best-estimate procedures. SETTING: Assessment was performed at 4 university medical centers in the United States between June 1, 2006, and September 30, 2009. PARTICIPANTS: Offspring aged 12 to 21 years in families with a proband with BP (n = 141, designated as cases) and similarly aged offspring of control parents (n = 91). MAIN OUTCOME MEASURE: Lifetime DSM-IV diagnosis of a major affective disorder (BP type I; schizoaffective disorder, bipolar type; BP type II; or major depression). RESULTS: At a mean age of 17 years, cases showed a 23.4% lifetime prevalence of major affective disorders compared with 4.4% in controls (P = .002, adjusting for age, sex, ethnicity, and correlation between siblings). The prevalence of BP in cases was 8.5% vs 0% in controls (adjusted P = .007). No significant difference was seen in the prevalence of other affective, anxiety, disruptive behavior, or substance use disorders. Among case subjects manifesting major affective disorders (n = 33), there was an increased risk of anxiety and externalizing disorders compared with cases without mood disorder. In cases but not controls, a childhood diagnosis of an anxiety disorder (relative risk = 2.6; 95% CI, 1.1-6.3; P = .04) or an externalizing disorder (3.6; 1.4-9.0; P = .007) was predictive of later onset of major affective disorders. CONCLUSIONS: Childhood anxiety and externalizing diagnoses predict major affective illness in adolescent offspring in families with probands with BP

Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10−13), ALDH2/MYL2 (rs671, P = 3.40 × 10−11; rs12229654, P = 4.56 × 10−9), ITIH4 (rs2535633, P = 1.77 × 10−10) and NT5C2 (rs11191580, P = 3.83 × 10−8) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10−8) and an additional 14 at P < 1.0 × 10−3 with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity

Smooth ROC curve estimation via Bernstein polynomials.

The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a diagnostic test for classifying observations into two groups. We propose two novel tuning parameters for estimating the ROC curve via Bernstein polynomial smoothing of the empirical ROC curve. The new estimator is very easy to implement with the naturally selected tuning parameter, as illustrated by analyzing both real and simulated data sets. Empirical performance is investigated through extensive simulation studies with a variety of scenarios where the two groups are both from a single family of distributions (symmetric or right skewed) or one from a symmetric and the other from a right skewed distribution. The new estimator is uniformly more efficient than the empirical ROC estimator, and very competitive to eleven other existing smooth ROC estimators in terms of mean integrated square errors

Social media ratings of nursing homes associated with experience of care and “Nursing Home Compare” quality measures

Abstract Background Social media platforms offer unique opportunities for patients and families to provide real-time feedback on their healthcare experiences. Consumer-generated social media ratings of hospitals tend to reflect the more subjective aspects of inpatient care experiences; however, evidence on nursing home care is extremely limited. Methods We collected consumer-reported 5-star ratings of Maryland nursing homes posted from July 2015 to July 2017 on 4 popular social media or online review sites (Facebook, Yelp, Google Consumer Reviews, and Caring.com). We determined if the average score of social media ratings was associated with experience-of-care ratings derived from survey of family members or other responsible parties of nursing home residents, and with “Nursing Home Compare” (NHC) 5-star ratings and individual quality measures. Results One hundred ninety-six out of 206 nursing homes in Maryland were reviewed on at least one site and thus had one or more star ratings posted. The overall ratings were 3.11 on average on these sites and 3.03 on the NHC website, with a Pearson correlation of 0.41 (p < 0.001) between the 2 sets of ratings. The correlations between the social media rating and survey-based experience-of-care ratings ranged from 0.40 to 0.60, and the correlations between the social media rating and individual NHC quality measures of citations, nurse staffing, and complaints were about 0.35 (in absolute values). The social media rating also predicted well NHC and experience-of-care measures after adjusting for nursing home covariates and market competition. Conclusions The 5-star ratings collected from 4 social networking sites was correlated with and predictive of the NHC and survey-based experience-of-care measures for Maryland nursing homes

Are AMI Patients with Comorbid Mental Illness More Likely to be Admitted to Hospitals with Lower Quality of AMI Care?

<div><p>Objective</p><p>Older patients with comorbid mental illness are shown to receive less appropriate care for their medical conditions. This study analyzed Medicare patients hospitalized for acute myocardial infarction (AMI) and determined whether those with comorbid mental illness were more likely to present to hospitals with lower quality of AMI care.</p> <p>Methods</p><p>Retrospective analyses of Medicare claims in 2008. Hospital quality was measured using the five “Hospital Compare” process indicators (aspirin at admission/discharge, beta-blocker at admission/discharge, and angiotension-converting enzyme inhibitor or angiotension receptor blocker for left ventricular dysfunction). Multinomial logit model determined the association of mental illness with admission to low-quality hospitals (rank of the composite process score <10^th percentile) or high-quality hospitals (rank>90^th percentile), compared to admissions to other hospitals with medium quality. Multivariate analyses further determined the effects of hospital type and mental diagnosis on outcomes.</p> <p>Results</p><p>Among all AMI admissions to 2,845 hospitals, 41,044 out of 287,881 patients were diagnosed with mental illness. Mental illness predicted a higher likelihood of admission to low-quality hospitals (unadjusted rate 2.9% vs. 2.0%; adjusted odds ratio [OR]1.25, 95% confidence interval [CI] 1.17–1.34, p<0.01), and an equal likelihood to high-quality hospitals (unadjusted rate 9.8% vs. 10.3%; adjusted OR 0.97, 95% CI 0.93–1.01, p = 0.11). Both lower hospital quality and mental diagnosis predicted higher rates of 30-day readmission, 30-day mortality, and 1-year mortality.</p> <p>Conclusions</p><p>Among Medicare myocardial infarction patients, comorbid mental illness was associated with an increased risk for admission to lower-quality hospitals. Both lower hospital quality and mental illness predicted worse post-AMI outcomes.</p> </div

Characteristics of Medicare AMI patients, by mental illness.

<p>AMI = acute myocardial infarction; SD = standard deviation.</p>*<p>P<0.01 for comparisons across mental illness groups based on χ² tests or analyses of variance.</p

Admission to hospitals with low and high composite quality scores by Medicare acute myocardial infarction patients.

*<p>Defined as hospitals in the bottom (low quality) or top (high quality) 10% rankings of the composite quality score.</p>**<p>Multivariate multinomial logistic models adjusted for patient age, gender, race, median household income, high school graduation rate, tobacco use, distances to the admitting hospital and to the nearest hospital, and individual medical comorbidities (congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, hypertension, paralysis, other neurological disorders, chronic pulmonary disease, diabetes, hypothyroidism, renal failure, liver disease, peptic ulcer disease excluding bleeding, lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, and deficiency anemia).</p

Outcomes of acute myocardial infarction patients admitted to different hospitals.

<p>OR = odds ratio; CI = confidence interval; HR = hazard ratio.</p><p>Note: The analyses of length of stay and 30-day readmissions excluded patients who died in hospital or were transferred to another acute care hospital after admission. The analyses of 30-day readmissions also excluded readmissions for rehabilitations and were limited to patients admitted before November 30, 2008.</p>*<p>Multivariate generalized linear (for length of stay), logistic (for readmissions and 30-day mortality) and Cox proportional hazard (for 1-year mortality) models adjusted for patient age, gender, race, median household income, high school graduation rate, tobacco use, individual medical comorbidities (congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, hypertension, paralysis, other neurological disorders, chronic pulmonary disease, diabetes, hypothyroidism, renal failure, liver disease, peptic ulcer disease excluding bleeding, lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, and deficiency anemia), and hospital characteristics (including number of beds, profit status, rural vs. urban location, teaching status, and nurse staffing ratio).</p>**<p>Low-quality hospitals were defined as those in the bottom 10% rankings of the composite quality score, medium-quality hospitals in the middle 80%, and high-quality hospitals in the top 10% rankings of the composite quality score.</p