Inpatient post-acute care utilization patterns and outcomes in Pennsylvania medicaid

The Medicaid expansion, one of the key provisions under the Affordable Care Act (ACA), has turned Medicaid into a larger player in the US healthcare system. The intent of the expansion was to increase access to essential healthcare services such as post-acute care (PAC) for low-income individuals. Studies have shown that variation in Medicare spending is attributed to the variation in post-acute care (PAC) utilization which includes care in home health, skilled nursing facilities (SNF), inpatient rehabilitation facilities (IRF), and long-term acute care hospitals (LTACH). However, very little is known about PAC utilization in the Medicaid population. For instance, Pennsylvania expanded Medicaid in January 2015, initially under a 1115 waiver and then under the original terms of the ACA. Many more individuals and families of low socioeconomic status were able to enroll in the Medicaid program as a result of the expansion. With studies pointing to PAC utilization and spending as the driver of variation in Medicare healthcare costs, the same may pertain to the Medicaid population. Chapter 1 provides the purpose, findings, and implications of the dissertation. Chapter 2 is a retrospective cohort study that determines the association between insurance type, either enrolled in Medicaid or commercial insurance, and the likelihood of being admitted to an inpatient PAC facility. The study found that hospitalized Medicaid beneficiaries were as likely as similar patients with commercial insurance to be admitted to any PAC facility but less likely to be admitted to an inpatient PAC facility (SNF, IRF, LTACH). This would inform policymakers that new Medicaid enrollees, which tend to be low-income and nondisabled adults, will certainly increase the cost of the Medicaid program. Chapter 3 is a retrospective cohort study that determines whether Medicaid managed care utilizes inpatient PAC differently than its FFS counterpart. The study found that Medicaid managed care beneficiaries were more likely to be admitted to an inpatient PAC than their FFS counterparts. This has significant cost implications since the majority of all Medicaid beneficiaries in the United States were enrolled in an MCO. Chapter 4 is a retrospective cohort study that determines the degree to which patient outcomes observed among Medicaid beneficiaries was mediated by variation in the intensity of PAC utilization. The study found that PAC utilization patterns for hospitalized Medicaid beneficiaries impacted readmissions and mortality to a degree. While we could not determine whether more PAC utilization would result in better quality of care, the effect of these patterns on outcomes should encourage states to standardize their approach to PAC and take necessary steps to improve patient management and care coordination among providers. Public Health Significance This dissertation addressed the three tenets of the healthcare iron triangle: access, cost, and quality. It will inform policymakers on how new Medicaid enrollees due to the expansion can potentially affect future cost to the program and impact the outcomes of Medicaid beneficiaries

Carbon dioxide reforming of methane over modified iron-cobalt alumina catalyst : Role of promoter

Cobalt-based catalysts are widely employed in methane dry reforming but tend to deactivate quickly due to coke deposits and metal sintering. To enhance the performance, iron, a cost-effective promoter, is added, improving cobalt's metal dispersibility, reducibility, and basicity on the support. This addition accelerates carbon gasification, effectively inhibiting coke deposition. Methods: A series of iron-doped cobalt alumina MFe-5Co/Al2O3 (M= 0, 0.4, 0.8, 1, 2 wt.%) were prepared via simple incipient-wetness impregnation. The catalysts were thoroughly characterized via modern techniques including BET, XRD, H2-TPR, CO2-TPD. Significant findings: The addition of iron had a minimal impact on the properties of γ-Al2O3, but it significantly affected the dispersibility of cobalt. At an optimal dosage of 0.8 wt.%, there was a notable decrease of 29.44% in Co3O4 particle size. However, excessive iron loading induced agglomeration of Co3O4, which was reversible. The presence of iron also resulted in a decrease in the reduction temperature of Co3O4. The material's basicity was primarily influenced by the loading of iron, reaching its highest value of 705.7 μmol CO2 g−1 in the 2Fe-5Co/Al2O3. The correlation between catalytic activity and the physicochemical properties of the material was established. The 0.8Fe-5Co/Al2O3 sample exhibited excellent performance due to the favorable dispersibility of cobalt, its reducibility, and its affordable basicity

Study of the doubly charmed tetraquark T+cc

Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D0D0π+ mass spectrum just below the D*+D0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalar T+cc tetraquark with a quark content of ccu⎯⎯⎯d⎯⎯⎯ and spin-parity quantum numbers JP = 1+. Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D*+ mesons is consistent with the observed D0π+ mass distribution. To analyse the mass of the resonance and its coupling to the D*D system, a dedicated model is developed under the assumption of an isoscalar axial-vector T+cc state decaying to the D*D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the T+cc state. In addition, an unexpected dependence of the production rate on track multiplicity is observed

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Total Synthesis of (±)-Monomorine

An efficient, two-operation synthesis of the trail ant pheromone (±)-monomorine is reported. The synthesis features an aqueous Claisen-Schmidt condensation followed by the stereocontrolled installation of the three resident stereocenters in a single operation

Choice Factors When Vietnamese High School Students Consider Universities: A Mixed Method Approach

Higher education around the world and especially in Vietnam is becoming increasingly competitive. Universities apply marketing strategies to student recruitment and get to know their students and prospective students more closely, just as businesses learn about consumer attitudes and behavior. Therefore, studying students’ behavior of choosing a university is necessary, but most research about this topic has been conducted by a single approach. In order to examine the choice factors such as characteristics of institutions and information sources students consider when selecting universities, this research applies a mixed method approach, including both quantitative and qualitative data. Data were collected from questionnaire surveys with 670 responses from final-year high school students, and from 20 interviews with freshmen university students. Findings indicate the rankings of characteristics of institutions and information sources and the qualitative analysis explained how students consider them during their decision-making process. The research results provide important findings to help universities understand more about the factors that students are interested in and search for during the decision-making process

Discharge destination of eligible patients, limited to patients transferred to a long-term acute hospital, by originating hospital type (n = 11,084).

<p>LTAC = long-term acute care hospital.</p

Unadjusted outcomes for the propensity-matched cohort of patients transferred to long-term acute care hospitals.

<p>ICU = intensive care unit; LTAC = long-term acute care hospital</p><p>Unadjusted outcomes for the propensity-matched cohort of patients transferred to long-term acute care hospitals.</p

Flow diagram of hospitals and patients.

<p>The LTAC-level analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139742#pone.0139742.t001" target="_blank">Table 1</a>) contains the 379 LTACs in the continental United States with at least 1 Medicare admission. The patient level analyses examining patient characteristics between LTAC types (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139742#pone.0139742.t002" target="_blank">Table 2</a>) also contains patients in these 379 LTACs. The outcomes analysis (Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139742#pone.0139742.t003" target="_blank">3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139742#pone.0139742.t004" target="_blank">4</a>) contains the 289 LTACs in the final analysis and 10,118 patients in the matched sample. LTAC = long-term acute care hospital; DRG = diagnosis related group; HCRIS = Healthcare Cost Reporting Information System.</p

A Comparison of Free-Standing versus Co-Located Long-Term Acute Care Hospitals

<div><p>Background</p><p>Long-term acute care hospitals (LTACs) provide specialized treatment for patients with chronic critical illness. Increasingly LTACs are co-located within traditional short-stay hospitals rather than operated as free-standing facilities, which may affect LTAC utilization patterns and outcomes.</p><p>Methods</p><p>We compared free-standing and co-located LTACs using 2005 data from the United States Centers for Medicare & Medicaid Services. We used bivariate analyses to examine patient characteristics and timing of LTAC transfer, and used propensity matching and multivariable regression to examine mortality, readmissions, and costs after transfer.</p><p>Results</p><p>Of 379 LTACs in our sample, 192 (50.7%) were free-standing and 187 (49.3%) were co-located in a short-stay hospital. Co-located LTACs were smaller (median bed size: 34 vs. 66, p <0.001) and more likely to be for-profit (72.2% v. 68.8%, p = 0.001) than freestanding LTACs. Co-located LTACs admitted patients later in their hospital course (average time prior to transfer: 15.5 days vs. 14.0 days) and were more likely to admit patients for ventilator weaning (15.9% vs. 12.4%). In the multivariate propensity-matched analysis, patients in co-located LTACs experienced higher 180-day mortality (adjusted relative risk: 1.05, 95% CI: 1.00–1.11, p = 0.04) but lower readmission rates (adjusted relative risk: 0.86, 95% CI: 0.75–0.98, p = 0.02). Costs were similar between the two hospital types (mean difference in costs within 180 days of transfer: -$3,580, 95$8,720 –$1,550, p = 0.17).</p><p>Conclusions</p><p>Compared to patients in free-standing LTACs, patients in co-located LTACs experience slightly higher mortality but lower readmission rates, with no change in overall resource use as measured by 180 day costs.</p></div