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Context: The Affordable Care Act resulted in unprecedented reductions in the uninsured population through subsidized private insurance and an expansion of Medicaid. Early estimates from the beginning of 2014 showed that the Medicaid expansion decreased uninsured discharges and increased Medicaid discharges with no change in total discharges. Objective: To provide new estimates of the effect of the ACA on discharges for specific conditions. Design, setting, and participants: We compared outcomes between states that did and did not expand Medicaid using state-level all-capture discharge data from 2009–2014 for 42 states from the Healthcare Costs and Utilization Project’s FastStats database; for a subset of states we used data through 2015. We stratified the analysis by baseline uninsured rates and used difference-in-differences and synthetic control methods to select comparison states with similar baseline characteristics that did not expand Medicaid. Main outcome: Our main outcomes were total and condition-specific hospital discharges per 1,000 population and the share of total discharges by payer. Conditions reported separately in FastStats included maternal, surgical, mental health, injury, and diabetes. Results: The share of uninsured discharges fell in Medicaid expansion states with below (-4.39 percentage points (p.p.), -6.04 –-2.73) or above (-7.66 p.p., -9.07 –-6.24) median baseline uninsured rates. The share of Medicaid discharges increased in both small (6.42 p.p. 4.22–6.62) and large (10.5 p.p., 8.48–12.5) expansion states. Total and most condition-specific discharges per 1,000 residents did not change in Medicaid expansion states with high or low baseline uninsured rates relative to non-expansion states (0.418, p = 0.225), with one exception: diabetes. Discharges for that condition per 1,000 fell in states with high baseline uninsured rates relative to non-expansion states (-0.038 95% p = 0.027). Conclusions: Early changes in payer mix identified in the first two quarters of 2014 continued through the Medicaid expansion’s first year and are distributed across all condition types studied. We found no change in total discharges between Medicaid expansion and non-expansion states, however residents of states that should have been most affected by the Medicaid expansion were less likely to be hospitalized for diabetes. [ABSTRACT FROM AUTHOR]

The Model for End-Stage Liver Disease (MELD) score has been successfully used to prioritize patients on the United States liver transplant waiting list since its adoption in 2002. The United Network for Organ Sharing (UNOS)/Organ Procurement Transplantation Network (OPTN) allocation policy has evolved over the years, and notable recent changes include Share 35, inclusion of serum sodium in the MELD score, and a ‘delay and cap’ policy for hepatocellular carcinoma (HCC) patients. We explored the potential of a registrant’s change in 30-day MELD scores (ΔMELD30) to improve allocation both before and after these policy changes. Current MELD and ΔMELD30 were evaluated using cause-specific hazards models for waitlist dropout based on US liver transplant registrants added to the waitlist between 06/30/2003 and 6/30/2013. Two composite scores were constructed and then evaluated on UNOS data spanning the current policy era (01/02/2016 to 09/07/2018). Predictive accuracy was evaluated using the C-index for model discrimination and by comparing observed and predicted waitlist dropout probabilities for model calibration. After the change to MELD-Na, increased dropout associated with ΔMELD30 jumps is no longer evident at MELD scores below 30. However, the adoption of Share 35 has potentially resulted in discrepancies in waitlist dropout for patients with sharp MELD increases at higher MELD scores. Use of the ΔMELD30 to add additional points or serve as a potential tiebreaker for patients with rapid deterioration may extend the benefit of Share 35 to better include those in most critical need. [ABSTRACT FROM AUTHOR]

Background: Five guideline-recommended medication categories are available to treat patients who have heart failure (HF) with reduced ejection fraction. However, adherence to these medications is often suboptimal, which places patients at increased risk for poor health outcomes, including hospitalization. We aimed to examine the association between adherence to these medications and potentially preventable HF hospitalizations among younger insured adults with newly diagnosed HF. Methods and results: Using the 2008–2012 IBM MarketScan Commercial database, we followed 26,439 individuals aged 18–64 years with newly diagnosed HF and calculated their adherence (using the proportion of days covered (PDC) algorithm) to the five guideline-recommended medication categories: angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers; beta blockers; aldosterone receptor antagonists; hydralazine; and isosorbide dinitrate. We determined the association between PDC and long-term preventable HF hospitalizations (observation years 3–5) as defined by the United States (U.S.) Agency for Healthcare Research and Quality. Overall, 49.0% of enrollees had good adherence (PDC≥80%), which was more common among enrollees who were older, male, residing in higher income counties, initially diagnosed with HF in an outpatient setting, and who filled prescriptions for fewer medication categories assessed. Adherence differed by medication category and was lowest for isosorbide dinitrate (PDC = 60.7%). In total, 7.6% of enrollees had preventable HF hospitalizations. Good adherers, compared to poor adherers (PDC<40%), were 15% less likely to have a preventable hospitalization (HR 0.85, 95% confidence interval, 0.75–0.96). Conclusion: We found that approximately half of insured U.S. adults aged 18–64 years with newly diagnosed HF had good adherence to their HF medications. Patients with good adherence, compared to those with poor adherence, were less likely to have a potentially preventable HF hospitalization 3–5 years after their initial diagnosis. Because HF is a chronic condition that requires long-term management, future studies may want to assess the effectiveness of interventions in sustaining adherence. [ABSTRACT FROM AUTHOR]

E-cigarettes are promoted as healthier alternatives to conventional cigarettes. Many cigarette smokers use both products. It is unknown whether the additional use of e-cigarettes among cigarette smokers (dual users) is associated with reduced exposure to tobacco-related health risks. Cross-sectional analysis was performed using baseline data from the Health eHeart Study, among English-speaking adults, mostly from the United States. Cigarette use (# cigarettes/day) and/or e-cigarette use (# days, # cartridges, and # puffs) were compared between cigarette only users vs. dual users. Additionally, we examined cardiopulmonary symptoms/ conditions across product use: no product (neither), e-cigarettes only, cigarettes only, and dual use. Among 39,747 participants, 573 (1.4%) reported e-cigarette only use, 1,693 (4.3%) reported cigarette only use, and 514 (1.3%) dual use. Dual users, compared to cigarette only users, reported a greater median (IQR) number of cigarettes per day, 10.0 (4.0–20.0) vs. 9.0 (3.0–15.0) (p < .0001), a lower (worse) median (IQR) SF-12 general health score, 3.3 (2.8–3.8) vs. 3.5 (2.8–3.9) (p = .0014), and a higher (worse) median (IQR) breathing difficulty score in the past month, 2.0 (1.0–2.0) vs. 1.0 (1.0–2.0) (p = .001). Of the 19 cardiopulmonary symptoms/ conditions, having a history of arrhythmia was significantly different between cigarette only users (14.2%) and dual users (17.8%) (p = .02). In this sample, dual use was not associated with reduced exposure to either (i) cigarettes, compared to cigarette only users or (ii) e-cigarettes, compared to e-cigarette only users. E-cigarette only use, compared to no product use, was associated with lower general health scores, higher breathing difficulty scores (typically and past month), and greater proportions of those who responded ‘yes’ to having chest pain, palpitations, coronary heart disease, arrhythmia, COPD, and asthma. These data suggest the added use of e-cigarettes alone may have contributed to cardiopulmonary health risks particularly respiratory health risks. [ABSTRACT FROM AUTHOR]

Introduction: As atrial fibrillation (AF) is often asymptomatic, it may remain undiagnosed until or even after development of complications, such as stroke. Consequently the observed prevalence of AF may underestimate total disease burden. Methods: To estimate the prevalence of undiagnosed AF in the United States, we performed a retrospective cohort modeling study in working age (18–64) and elderly (≥65) people using commercial and Medicare administrative claims databases. We identified patients in years 2004–2010 with incident AF following an ischemic stroke. Using a back-calculation methodology, we estimated the prevalence of undiagnosed AF as the ratio of the number of post-stroke AF patients and the CHADS2-specific stroke probability for each patient, adjusting for age and gender composition based on United States census data. Results: The estimated prevalence of AF (diagnosed and undiagnosed) was 3,873,900 (95%CI: 3,675,200–4,702,600) elderly and 1,457,100 (95%CI: 1,218,500–1,695,800) working age adults, representing 10.0% and 0.92% of the respective populations. Of these, 698,900 were undiagnosed: 535,400 (95%CI: 331,900–804,400) elderly and 163,500 (95%CI: 17,700–400,000) working age adults, representing 1.3% and 0.09% of the respective populations. Among all undiagnosed cases, 77% had a CHADS2 score ≥1, and 56% had CHADS2 score ≥2. Conclusions: Using a back-calculation approach, we estimate that the total AF prevalence in 2009 was 5.3 million of which 0.7 million (13.1% of AF cases) were undiagnosed. Over half of the modeled population with undiagnosed AF was at moderate to high risk of stroke. [ABSTRACT FROM AUTHOR]

Background: Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015. Methods and findings: We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice. Conclusions: We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching. [ABSTRACT FROM AUTHOR]

Objective: To examine whether investments made in public health research align with the health burdens experienced by white and black Americans. Methods: In this cross-sectional study of all deaths in the United States in 2015, we compared the distribution of potential years of life lost (PYLL) across 39 causes of death by race and identified key differences. We examined the relationship between cause-of-death-specific PYLL and key indicators of public health investment (federal funding and number of publications) by race using linear spline models. We also compared the number of courses available at the top schools of public health relevant to the top causes of death contributor to PYLL for black and white Americans. Results: Homicide was the number one contributor to PYLL among black Americans, while ischemic heart disease was the number one contributor to PYLL among white Americans. Firearm-related violence accounted for 88% of black PYLL attributed to homicide and 71% of white PYLL attributed to homicide. Despite the high burden of PYLL, homicide research was the focus of few federal grants or publications. In comparison, ischemic heart disease garnered 341 grants and 594 publications. The number of public health courses available relevant to homicide (n = 9) was similar to those relevant to ischemic heart disease (n = 10). Conclusions: Black Americans are disproportionately affected by homicide, compared to white Americans. For both black and white Americans, the majority of PYLL due to homicide are firearm-related. Yet, homicide research is dramatically underrepresented in public health research investments in terms of grant funding and publications, despite available public health training opportunities. If left unchecked, the observed disproportionate distribution of investments in public health resources threatens to perpetuate a system that disadvantages black Americans. [ABSTRACT FROM AUTHOR]

Few studies have examined rates and causes of short-term readmissions among adults across age and insurance types. We compared rates, characteristics, and costs of 30-day readmission after all-cause hospitalizations across insurance types in the US. We retrospectively evaluated alive patients ≥18 years old, discharged for any cause, 1/1/13-11/31/13, 2006 non-federal hospitals in 21 states in the Nationwide Readmissions Database. The primary stratification variable of interest was primary insurance. Comorbid conditions were assessed based on Elixhauser comorbidities, as defined by administrative billing codes. Additional measures included diagnoses for index hospitalizations leading to rehospitalization. Hierarchical multivariable logistic regression models, with hospital site as a random effect, were used to calculate the adjusted odds of 30-day readmissions by age group and insurance categories. Cost and discharge estimates were weighted per NRD procedures to reflect a nationally representative sample. Diagnoses for index hospitalizations leading to rehospitalization were determined. Among 12,533,551 discharges, 1,818,093 (14.5%) resulted in readmission within 30 days. Medicaid insurance was associated with the highest adjusted odds ratio (AOR) for readmission both in those ≥65 years old (AOR 1.12, 95%CI 1.10–1.14; p <0.001), and 45–64 (AOR 1.67, 95% CI 1.66–1.69; p < 0.001), and Medicare in the 18–44 group (Medicare vs. private insurance: AOR 1.99, 95% CI 1.96–2.01; p <0.001). Discharges for psychiatric or substance abuse disorders, septicemia, and heart failure accounted for the largest numbers of readmissions, with readmission rates of 24.0%, 17.9%, 22.9% respectively. Total costs for readmissions were 50.7 billion USD, highest for Medicare (29.6 billion USD), with non-Medicare costs exceeding 21 billion USD. While Medicare readmissions account for more than half of the total burden of readmissions, costs of non-Medicare readmissions are nonetheless substantial. Medicaid patients have the highest odds of readmission in individuals older than age 44, commonly due to hospitalizations for psychiatric illness and substance abuse disorders. Medicaid patients represent a population at uniquely high risk for readmission. [ABSTRACT FROM AUTHOR]

Background: Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment. Methods and Findings: We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R2 between 18–64% of variability in mortality outcomes, 3–39% of variability in patient safety outcomes, and 22–70% of variability in prevention outcomes. Conclusion: The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system. [ABSTRACT FROM AUTHOR]

A retrospective cohort analysis of the US MarketScan claims databases was performed to compare persistence and discontinuation rates between the vitamin K antagonist warfarin and the non-vitamin K antagonist oral anticoagulants rivaroxaban and dabigatran in patients with non-valvular atrial fibrillation. The analysis included adult patients with non-valvular atrial fibrillation newly initiated on rivaroxaban, dabigatran, or warfarin between November 1, 2011 and December 31, 2013, with a baseline CHA2DS2-VASc score ≥2, two or more non-valvular atrial fibrillation diagnosis codes (427.31), and ≥6 months’ continuous medical and pharmacy benefit enrollment before oral anticoagulant initiation. Propensity score matching was performed to match patients receiving rivaroxaban with those receiving dabigatran (1:1) and warfarin (1:1). Patients were followed until the first event of inpatient death, end of continuous enrollment, or end of study period. Medication persistence was defined as absence of a refill gap of >60 days. Discontinuation was defined as no additional refill for >90 days and through to end of follow-up. Hazard ratios (HRs) of oral anticoagulant persistence and discontinuation were estimated using Cox proportional hazard models. In total, 3,2634 patients were included (n = 10878/oral anticoagulant group). Rivaroxaban was associated with better persistence than both dabigatran (HR 0.64, 95% confidence interval [CI] 0.62–0.67) and warfarin (HR 0.62, 95% CI 0.59–0.64) and lower discontinuation than dabigatran (HR 0.61, 95% CI 0.58–0.64) and warfarin (HR 0.65, 95% CI 0.62–0.68). Real-world analysis of oral anticoagulant use may reveal whether the relatively high persistence/low discontinuation demonstrated for rivaroxaban translates into lower rates of stroke. [ABSTRACT FROM AUTHOR]

Background: Respiratory syncytial virus (RSV) causes significant pediatric morbidity and is the most common cause of bronchiolitis. Bronchiolitis hospitalizations declined among US infants from 2000‒2009; however, rates in infants at high risk for RSV have not been described. This study examined RSV and unspecified bronchiolitis (UB) hospitalization rates from 1997‒2012 among US high-risk infants. Methods: The Kids’ Inpatient Database (KID) infant annual RSV (ICD-9 079.6, 466.11, 480.1) and UB (ICD-9 466.19, 466.1) hospitalization rates were estimated using weighted counts. Denominators were based on birth hospitalizations with conditions associated with high-risk for RSV: chronic perinatal respiratory disease (chronic lung disease [CLD]); congenital airway anomalies (CAA); congenital heart disease (CHD); Down syndrome (DS); and other genetic, metabolic, musculoskeletal, and immunodeficiency conditions. Preterm infants could not be identified. Hospitalizations were characterized by mechanical ventilation, inpatient mortality, length of stay, and total cost (2015$). Poisson and linear regression were used to test statistical significance of trends. Results: RSV and UB hospitalization rates were substantially elevated for infants with higher-risk CHD, CLD, CAA and DS without CHD compared with all infants. RSV rates declined by 47.0% in CLD and 49.7% in higher-risk CHD infants; no other declines in high-risk groups were observed. UB rates increased in all high-risk groups except for a 22.5% decrease among higher-risk CHD. Among high-risk infants, mechanical ventilation increased through 2012 to 20.4% and 13.5% of RSV and UB hospitalizations; geometric mean cost increased to $31,742 and $25,962, respectively, and RSV mortality declined to 0.9%. Conclusions: Among high-risk infants between 1997 and 2012, RSV hospitalization rates declined among CLD and higher-risk CHD infants, coincident with widespread RSV immunoprophylaxis use in these populations. UB hospitalization rates increased in all high-risk groups except higher-risk CHD, suggesting improvement in the health status of higher-risk CHD infants, potentially due to enhanced surgical interventions. Mechanical ventilation use and RSV and UB hospitalization costs increased while RSV mortality declined. [ABSTRACT FROM AUTHOR]

Background: Several studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis. Methods: We construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system. Findings: For the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs. Conclusions: Classifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients. [ABSTRACT FROM AUTHOR]

Objectives: To determine the association between myocardial infarction (AMI) and clinical outcome in patients with primary admissions diagnosis of acute cerebral ischemia (ACI) in the US. Methods: Data from Nationwide Inpatient Sample (NIS) was queried from 2002–2011 for inpatient admissions of patients with a primary diagnosis of ACI with and without AMI using International Classification of Diseases, Ninth Revision, Clinical Modification coding (ICD-9). A multivariate stepwise regression analysis was performed to assess the correlation between identifiable risk factors and clinical outcomes. Results: During 10 years the NIS recorded 886,094 ACI admissions with 17,526 diagnoses of AMI (1.98%). The overall cumulative mortality of cohort was 5.65%. In-hospital mortality was associated with AMI (aOR 3.68; 95% CI 3.49–3.88, p≤0.0001), rTPA administration (aOR 2.39 CI, 2.11–2.71, p<0.0001), older age (aOR 1.03, 95% CI, 1.03–1.03, P<0.0001) and women (aOR 1.06, 95% CI 1.03–1.08, P<0.0001). Overall, mortality risk declined over the course of study; from 20.46% in 2002 to 11.8% in 2011 (OR 0.96, 95% CI 0.95–0.96, P<0.0001). Survival analysis demonstrated divergence between the AMI and non-AMI sub-groups over the course of study (log-rank p<0.0001). Conclusion: Our study demonstrates that although the prevalence of AMI in patients hospitalized with primary diagnosis of ACI is low, it negatively impacts survival. Considering the high clinical burden of AMI on mortality of ACI patients, a high quality monitoring in the event of cardiac events should be maintained in this patient cohort. Whether prompt diagnosis and treatment of associated cardiovascular diseases may improve outcome, deserves further study. [ABSTRACT FROM AUTHOR]

Objective: We investigated the role of state-level differences in modifiable cardiovascular (CV) risk factors in contributing to state disparities in cardiovascular mortality rates in the US. Methods: Adults aged 45–74 in 2010 were examined. We constructed a CV risk index summarizing state-level exposure to current smoking, obesity, physical inactivity, alcohol abstinence, hypertension, elevated cholesterol, and diabetes using the Behavioral Risk Factor Surveillance System. Outcomes were cardiovascular, coronary heart disease, and stroke mortality. Linear regression was used to estimate associations between the CV risk index and mortality outcomes. Models accounted for state-level socioeconomic characteristics and other potential confounders. Results: Risk factors were highly correlated at the state-level (Cronbach's alpha 0.85 (men) and 0.92 (women). Each +1SD difference in the cardiovascular risk index was associated with higher adjusted cardiovascular mortality rates by 41.0 (95%CI = 26.3, 55.7) and 33.3 (95%CI = 24.4, 42.2) deaths per 100,000 for men and women, respectively. The index accounted for 8% (men) and 11% (women) of the variation in state-level cardiovascular mortality. Comparable associations were also observed for coronary heart disease and stroke mortality. Conclusions: CV risk factors were highly correlated at the state-level and were independently associated with state CV mortality, suggesting the utility of generalized CV risk reduction. [ABSTRACT FROM AUTHOR]

Objective: Previous studies suggest that unemployment predicts increased cardiovascular disease (CVD) risk, but whether unemployment insurance programs mitigate this risk has not been assessed. Exploiting US state variations in unemployment insurance benefit programs, we tested the hypothesis that more generous benefits reduce CVD risk. Methods: Cohort data came from 16,108 participants in the Health and Retirement Study (HRS) aged 50–65 at baseline interviewed from 1992 to 2010. Data on first and recurrent CVD diagnosis assessed through biennial interviews were linked to the generosity of unemployment benefit programmes in each state and year. Using state fixed-effect models, we assessed whether state changes in the generosity of unemployment benefits predicted CVD risk. Results: States with higher unemployment benefits had lower incidence of CVD, so that a 1% increase in benefits was associated with 18% lower odds of CVD (OR:0.82, 95%-CI:0.71–0.94). This association remained after introducing US census regional division fixed effects, but disappeared after introducing state fixed effects (OR:1.02, 95%-CI:0.79–1.31).This was consistent with the fact that unemployment was not associated with CVD risk in state-fixed effect models. Conclusion: Although states with more generous unemployment benefits had lower CVD incidence, this appeared to be due to confounding by state-level characteristics. Possible explanations are the lack of short-term effects of unemployment on CVD risk. Future studies should assess whether benefits at earlier stages of the life-course influence long-term risk of CVD. [ABSTRACT FROM AUTHOR]

Background: Coronary heart disease (CHD) incidence has declined significantly in the US, as have levels of major coronary risk factors, including LDL-cholesterol, hypertension and smoking, but whether trends in subclinical atherosclerosis mirror these trends is not known. Methods and Findings: To describe recent secular trends in subclinical atherosclerosis as measured by serial evaluations of coronary artery calcification (CAC) prevalence in a population over 10 years, we measured CAC using computed tomography (CT) and CHD risk factors in five serial cross-sectional samples of men and women from four race/ethnic groups, aged 55–84 and without clinical cardiovascular disease, who were members of Multi-Ethnic Study of Atherosclerosis (MESA) cohort from 2000 to 2012. Sample sizes ranged from 1062 to 4837. After adjusting for age, gender, and CT scanner, the prevalence of CAC increased across exams among African Americans, whose prevalence of CAC was 52.4% in 2000–02, 50.4% in 2003–04, 60.0% is 2005–06, 57.4% in 2007–08, and 61.3% in 2010–12 (p for trend <0.001). The trend was strongest among African Americans aged 55–64 [prevalence ratio for 2010–12 vs. 2000–02, 1.59 (95% confidence interval 1.06, 2.39); p = 0.005 for trend across exams]. There were no consistent trends in any other ethnic group. Risk factors generally improved in the cohort, and adjustment for risk factors did not change trends in CAC prevalence. Conclusions: There was a significant secular trend towards increased prevalence of CAC over 10 years among African Americans and no change in three other ethnic groups. Trends did not reflect concurrent general improvement in risk factors. The trend towards a higher prevalence of CAC in African Americans suggests that CHD risk in this population is not improving relative to other groups. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the main source of morbidity and mortality in the United States with costs of more than $170 billion. Repetitive respiratory disorders during sleep are assumed to be a major cause of these diseases. Therefore, the understanding of the cardio-respiratory regulation during these events is of high public interest. One of the governing mechanisms is the mutual influence of the cardiac and respiratory oscillations on their respective onsets, the cardio-respiratory coordination (CRC). We analyze this mechanism based on nocturnal measurements of 27 males suffering from obstructive sleep apnea syndrome. Here we find, by using an advanced analysis technique, the coordigram, not only that the occurrence of CRC is significantly more frequent during respiratory sleep disturbances than in normal respiration (p-value<10−51) but also more frequent after these events (p-value<10−15). Especially, the latter finding contradicts the common assumption that spontaneous CRC can only be observed in epochs of relaxed conditions, while our newly discovered epochs of CRC after disturbances are characterized by high autonomic stress. Our findings on the connection between CRC and the appearance of sleep-disordered events require a substantial extension of the current understanding of obstructive sleep apneas and hypopneas. [ABSTRACT FROM AUTHOR]