Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model: A Web-based program designed to evaluate the cost-effectiveness of disease management programs in heart failure.

BACKGROUND: Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. METHODS: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics; use of evidence-based medications; and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model. Projections of resource use and quality of life are modeled using relationships with time-varying Seattle Heart Failure Model scores. The model can be used to evaluate parallel-group and single-cohort study designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. RESULTS: The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. CONCLUSION: The Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure.

Prevalence and patterns of cooking dinner at home in the USA: National Health and Nutrition Examination Survey (NHANES) 2007-2008.

OBJECTIVE: To measure the prevalence of cooking dinner at home in the USA and test whether home dinner preparation habits are associated with socio-economic status, race/ethnicity, country of birth and family structure. DESIGN: Cross-sectional analysis. The primary outcome, self-reported frequency of cooking dinner at home, was divided into three categories: 0-1 dinners cooked per week ('never'), 2-5 ('sometimes') and 6-7 ('always'). We used bivariable and multivariable regression analyses to test for associations between frequency of cooking dinner at home and factors of interest. SETTING: The 2007-2008 National Health and Nutrition Examination Survey (NHANES). SUBJECTS: The sample consisted of 10 149 participants. RESULTS: Americans reported cooking an average of five dinners per week; 8 % never, 43 % sometimes and 49 % always cooked dinner at home. Lower household wealth and educational attainment were associated with a higher likelihood of either always or never cooking dinner at home, whereas wealthier, more educated households were more likely to sometimes cook dinner at home (P < 0·05). Black households cooked the fewest dinners at home (mean = 4·4, 95 % CI 4·2, 4·6). Households with foreign-born reference persons cooked more dinners at home (mean = 5·8, 95 % CI 5·7, 6·0) than households with US-born reference persons (mean = 4·9, 95 % CI 4·7, 5·1). Households with dependants cooked more dinners at home (mean = 5·2, 95 % CI 5·1, 5·4) than households without dependants (mean = 4·6, 95 % CI 4·3, 5·0). CONCLUSIONS: Home dinner preparation habits varied substantially with socio-economic status and race/ethnicity, associations that likely will have implications for designing and appropriately tailoring interventions to improve home food preparation practices and promote healthy eating.

Improving Identification of Patients at Low Risk for Major Cardiac Events After Noncardiac Surgery Using Intraoperative Data.

BACKGROUND/OBJECTIVE: Risk-stratification tools for cardiac complications after noncardiac surgery based on preoperative risk factors are used to inform postoperative management. However, there is limited evidence on whether risk stratification can be improved by incorporating data collected intraoperatively, particularly for low-risk patients. METHODS: We conducted a retrospective cohort study of adults who underwent noncardiac surgery between 2014 and 2018 at four hospitals in the United States. Logistic regression with elastic net selection was used to classify in-hospital major adverse cardiovascular events (MACE) using preoperative and intraoperative data ("perioperative model"). We compared model performance to standard risk stratification tools and professional society guidelines that do not use intraoperative data. RESULTS: Of 72,909 patients, 558 (0.77%) experienced MACE. Those with MACE were older and less likely to be female. The perioperative model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.85-0.92). This was higher than the Lee Revised Cardiac Risk Index (RCRI) AUC of 0.79 (95% CI, 0.74-0.84; P < .001 for AUC comparison). There were more MACE complications in the top decile (n = 1,465) of the perioperative model's predicted risk compared with that of the RCRI model (n = 58 vs 43). Additionally, the perioperative model identified 2,341 of 7,597 (31%) patients as low risk who did not experience MACE but were recommended to receive postoperative biomarker testing by a risk factor-based guideline algorithm. CONCLUSIONS: Addition of intraoperative data to preoperative data improved prediction of cardiovascular complication outcomes after noncardiac surgery and could potentially help reduce unnecessary postoperative testing.

Primary Care Physician Networks In Medicare Advantage.

Medicare Advantage (MA) plans often establish restrictive networks of covered providers. Some policy makers have raised concerns that networks may have become excessively restrictive over time, potentially interfering with patients' access to providers. Because of data limitations, little is known about the breadth of MA networks. Taking a novel approach, we used Medicare Part D claims data for 2011-15 to examine how primary care physician networks have changed over time and what demographic and plan characteristics are associated with varying levels of network breadth. Our findings indicate that the share of MA plans with broad networks increased from 80.1 percent in 2011 to 82.5 percent in 2015. Enrollment in broad-network plans grew from 54.1 percent to 64.9 percent over the same period. In an adjusted analysis, we detected no significant time trend. In addition, narrow networks were associated with urbanicity, higher income, higher physician density, and more competition among plans. Health maintenance organizations had narrower networks than did point-of-service plans, whose networks were narrower than those of preferred provider organizations.

Changes in primary care access at community health centers between 2012/2013 and 2016.

OBJECTIVE: To compare access at community health centers (CHCs) vs private offices (non-CHCs) under the Affordable Care Act. DATA SOURCE: Ten state primary care audit conducted in 2012/2013 and 2016. STUDY DESIGN: CHCs and non-CHCs were called. We calculated difference in differences comparing CHCs vs non-CHCs by caller insurance type. PRINCIPAL FINDINGS: In both rounds, Medicaid and uninsured callers had higher appointment rates at CHC than non-CHCs. CHC appointment rates significantly increased between 2012/2013 and 2016 for both employer-sponsored and Medicaid callers, with no significant wait time changes. Appointment rates increased (13.5% points, P < 0.001) and wait times decreased (-5.7 days, P = 0.017) at CHCs relative to non-CHCs for employer-sponsored insurance. CONCLUSION: Appointment availability at CHCs improved after ACA implementation, without increased wait times.

National Representativeness Of Hospitals And Markets In Medicare's Mandatory Bundled Payment Program.

In 2016 Medicare implemented its first mandatory alternative payment model, the Comprehensive Care for Joint Replacement (CJR) program, in which the agency pays clinicians and hospitals a fixed amount for services provided in hip and knee replacement surgery episodes. Medicare made CJR mandatory, rather than voluntary, to produce generalizable evidence on what results Medicare might expect if it scaled bundled payment up nationally. However, it is unknown how markets and hospitals in CJR compare to others nationwide, particularly with respect to baseline quality and spending performance and the structural hospital characteristics associated with early savings in CJR. Using data from Medicare, the American Hospital Association, and the Health Resources and Services Administration, we found differences in structural market and hospital characteristics but largely similar baseline hospital episode quality and spending. Our findings suggest that despite heterogeneity in hospital characteristics associated with early savings in CJR, Medicare might nonetheless reasonably expect similar results by scaling CJR up to additional urban markets and increasing total program coverage to areas in which 71 percent of its beneficiaries reside. In contrast, different policy designs may be needed to extend market-level programs to other regions or enable different hospital types to achieve savings from bundled payment reimbursement.

Model Performance Metrics in Assessing the Value of Adding Intraoperative Data for Death Prediction: Applications to Noncardiac Surgery.

We tested the value of adding data from the operating room to models predicting in-hospital death. We assessed model performance using two metrics, the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), to illustrate the differences in information they convey in the setting of class imbalance. Data was collected on 74,147 patients who underwent major noncardiac surgery and 112 unique features were extracted from electronic health records. Sets of features were incrementally added to models using logistic regression, naïve Bayes, random forest, and gradient boosted machine methods. AUROC increased as more features were added, but changes were small for some modeling approaches. In contrast, AUPRC, which reflects positive predicted value, exhibited improvements across all models. Using AUPRC highlighted the added value of intraoperative data, not seen consistently with AUROC, and that with class imbalance AUPRC may serve as the more clinically relevant criterion.

Risk Stratification for Postoperative Acute Kidney Injury in Major Noncardiac Surgery Using Preoperative and Intraoperative Data.

Importance: Acute kidney injury (AKI) is one of the most common complications after noncardiac surgery. Yet current postoperative AKI risk stratification models have substantial limitations, such as limited use of perioperative data. Objective: To examine whether adding preoperative and intraoperative data is associated with improved prediction of noncardiac postoperative AKI. Design, Setting, and Participants: A prognostic study using logistic regression with elastic net selection, gradient boosting machine (GBM), and random forest approaches was conducted at 4 tertiary academic hospitals in the United States. A total of 42 615 hospitalized adults with serum creatinine measurements who underwent major noncardiac surgery between January 1, 2014, and April 30, 2018, were included in the study. Serum creatinine measurements from 365 days before and 7 days after surgery were used in this study. Main Outcomes and Measures: Postoperative AKI (defined by the Kidney Disease Improving Global Outcomes within 7 days after surgery) was the primary outcome. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination. Results: Among 42 615 patients who underwent noncardiac surgery, the mean (SD) age was 57.9 (15.7) years, 23 943 (56.2%) were women, 27 857 (65.4%) were white, and the most frequent surgery types were orthopedic (15 718 [36.9%]), general (8808 [20.7%]), and neurologic (6564 [15.4%]). The rate of postoperative AKI was 10.1% (n = 4318). The progressive addition of clinical data improved model performance across all modeling approaches, with GBM providing the highest discrimination by AUC. In GBM models, the AUC increased from 0.712 (95% CI, 0.694-0.731) using prehospitalization variables to 0.804 (95% CI, 0.788-0.819) using preoperative variables (inclusive of prehospitalization variables) (P < .001 for AUC comparison). The AUC further increased to 0.817 (95% CI, 0.802-0.832) when adding intraoperative variables (P < .001 for comparison vs model using preoperative variables). However, the statistically significant improvements in discrimination did not appear to be clinically significant. In particular, the AKI rate among patients classified as high risk improved from 29.1% to 30.0%, a net of 15 patients were appropriately reclassified as high risk, and an additional 15 patients were appropriately reclassified as low risk. Conclusions and Relevance: The findings of the study suggest that electronic health record data may be used to accurately stratify patients at risk of perioperative AKI, but the modest improvements from adding intraoperative data should be weighed against challenges in using intraoperative data.

Closing the gaps in opioid use disorder research, policy and practice: conference proceedings.

Drug overdose deaths involving opioids have surged in recent years and the economic cost of the opioid epidemic is estimated to be over $500 billion annually. In the midst of calls for declaring a national emergency, health policy decision makers are considering the best ways to allocate resources to curb the epidemic. On June 9, 2017, 116 invited health researchers, clinicians, policymakers, health system leaders, and other stakeholders met at the University of Pennsylvania to discuss approaches to address the gaps in evidence-based substance use disorder policy and practice, with an emphasis on the opioid epidemic. The conference was sponsored by the Center for Health Economics of Treatment Interventions for Substance Use Disorder, HCV, and HIV (CHERISH), a NIDA-funded National Center of Excellence, and hosted by the Leonard Davis Institute of Health Economics of the University of Pennsylvania. The conference aims were to: (1) foster new relationships between researchers and policymakers through a collaborative work process and (2) generate evidence-based policy recommendations to address the opioid epidemic. The conference concluded with an interactive work session during which attendees self-identified as researchers or policymakers and were divided equally among 13 tables. These groups met to develop and present policy recommendations based on an opioid use disorder case study. Thirteen policy recommendations emerged across four themes: (1) quality of treatment, (2) continuity of care, (3) opioid prescribing and pain management, and (4) consumer engagement. This conference serves as a proposed model to develop equitable, working relationships among researchers, clinicians, and policymakers.

The Role of Community Health Centers in Reducing Racial Disparities in Spatial Access to Primary Care.

INTRODUCTION: Racial minorities are more likely to live in primary care shortage areas. We sought to understand community health centers' (CHCs) role in reducing disparities. METHODS: We surveyed all primary care practices in an urban area, identified low access areas, and examined how CHCs influence spatial accessibility. RESULTS: Census tracts with higher rates of public insurance (≥40% vs <10%, odds ratio [OR] = 31.06, P < .001; 30-39% vs 10%, OR = 7.84, P = 0.001) were more likely to be near a CHC and those with moderate rates of uninsurance (10%-19% vs <10%, OR = 0.42, P = .045) were less likely. Racial composition was not associated with proximity. Tracts close to a CHC were less likely (OR = 0.11, P < .0001) to be in a low access area. This association did not differ based on racial composition. DISCUSSION: Although CHCs were more likely to be in areas with a greater fraction of racial minorities, location was more strongly influenced by public insurance rates. CHCs reduced the likelihood of being in low access areas but the effect did not vary by tract racial composition.

For Third Enrollment Period, Marketplaces Expand Decision Support Tools To Assist Consumers.

The design of the Affordable Care Act's online health insurance Marketplaces can improve how consumers make complex health plan choices. We examined the choice environment on the state-based Marketplaces and HealthCare.gov in the third open enrollment period. Compared to previous enrollment periods, we found greater adoption of some decision support tools, such as total cost estimators and integrated provider lookups. Total cost estimators differed in how they generated estimates: In some Marketplaces, consumers categorized their own utilization, while in others, consumers answered detailed questions and were assigned a utilization profile. The tools available before creating an account (in the window-shopping period) and afterward (in the real-shopping period) differed in several Marketplaces. For example, five Marketplaces provided total cost estimators to window shoppers, but only two provided them to real shoppers. Further research is needed on the impact of different choice environments and on which tools are most effective in helping consumers pick optimal plans.

Comparative effectiveness and cost-effectiveness analyses frequently agree on value.

The Patient-Centered Outcomes Research Institute, known as PCORI, was established by Congress as part of the Affordable Care Act (ACA) to promote evidence-based treatment. Provisions of the ACA prohibit the use of a cost-effectiveness analysis threshold and quality-adjusted life-years (QALYs) in PCORI comparative effectiveness studies, which has been understood as a prohibition on support for PCORI's conducting conventional cost-effectiveness analyses. This constraint complicates evidence-based choices where incremental improvements in outcomes are achieved at increased costs of care. How frequently this limitation inhibits efficient cost containment, also a goal of the ACA, depends on how often more effective treatment is not cost-effective relative to less effective treatment. We examined the largest database of studies of comparisons of effectiveness and cost-effectiveness to see how often there is disagreement between the more effective treatment and the cost-effective treatment, for various thresholds that may define good value. We found that under the benchmark assumption, disagreement between the two types of analyses occurs in 19 percent of cases. Disagreement is more likely to occur if a treatment intervention is musculoskeletal and less likely to occur if it is surgical or involves secondary prevention, or if the study was funded by a pharmaceutical company.

Psychotropic medication use among Medicaid-enrolled children with autism spectrum disorders.

OBJECTIVE: The objective of this study was to provide national estimates of psychotropic medication use among Medicaid-enrolled children with autism spectrum disorders and to examine child and health system characteristics associated with psychotropic medication use. METHODS: This cross-sectional study used Medicaid claims for calendar year 2001 from all 50 states and Washington, DC, to examine 60,641 children with an autism spectrum disorder diagnosis. Logistic regression with random effects was used to examine the child, county, and state factors associated with psychotropic medication use. RESULTS: Of the sample, 56% used at least 1 psychotropic medication, 20% of whom were prescribed > or = 3 medications concurrently. Use was common even in children aged 0 to 2 years (18%) and 3 to 5 years (32%). Neuroleptic drugs were the most common psychotropic class (31%), followed by antidepressants (25%) and stimulants (22%). In adjusted analyses, male, older, and white children; those who were in foster care or in the Medicaid disability category; those who received additional psychiatric diagnoses; and those who used more autism spectrum disorder services were more likely to have used psychotropic drugs. Children who had a diagnosis of autistic disorder or who lived in counties with a lower percentage of white residents or greater urban density were less likely to use such medications. CONCLUSIONS: Psychotropic medication use is common among even very young children with autism spectrum disorders. Factors unrelated to clinical presentation seem highly associated with prescribing practices. Given the limited evidence base, there is an urgent need to assess the risks, benefits, and costs of medication use and understand the local and national policies that affect medication use.