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To carry out an economic evaluation of a task-shifting intervention for the treatment of depressive and anxiety disorders in primary-care settings in Goa, India. Cost-utility and cost-effectiveness analyses based on generalized linear models were performed within a trial set in 24 public and private primary-care facilities. Subjects were randomly assigned to an intervention or a control arm. Eligible subjects in the intervention arm were given psycho-education, case management, interpersonal psychotherapy and/or antidepressants by lay health workers. Subjects in the control arm were treated by physicians. The use of health-care resources, the disability of each subject and degree of psychiatric morbidity, as measured by the Revised Clinical Interview Schedule, were determined at 2, 6 and 12 months. Complete data, from all three follow-ups, were collected from 1243 (75.4%) and 938 (81.7%) of the subjects enrolled in the study facilities from the public and private sectors, respectively. Within the public facilities, subjects in the intervention arm showed greater improvement in all the health outcomes investigated than those in the control arm. Time costs were also significantly lower in the intervention arm than in the control arm, whereas health system costs in the two arms were similar. Within the private facilities, however, the effectiveness and costs recorded in the two arms were similar. Within public primary-care facilities in Goa, the use of lay health workers in the care of subjects with common mental disorders was not only cost-effective but also cost-saving.

Studies show increasing use of urgent care centers (UCCs) and there is interest in evaluating their potential for cost savings. Previous research provides limited information on generalizable methods of identifying urgent care centers and does not validate these methods. The objective of this study is to describe and validate two claims-based UCC definitions. We used FAIR Health insurance claims from 444,263 organization National Provider Identifiers (NPIs) with at least 10 claims, January 2016–March 2019 and merged this data with National Plan and Provider Enumeration System data. The first definition required (1) a UCC place of service code (POS), (2) ≥ 10% Current Procedure Terminology (CPT) codes specific to UCCs, or (3) a UCC taxonomy code in the primary field. The second definition relaxed these criteria. A random sample of 5% of NPIs identified as UCCs were validated through internet searches. Prevalence and positive predictive value (PPV) were calculated for both definitions. The first definition identified 6669 (1.5%) of NPIs as UCCs resulting in a PPV of 92%. The second definition identified 8261 (1.9%) of NPIs as UCCs and had a PPV of 87%. Out of NPIs identified under the first definition, 96% were identified using POS codes, 50% were identified using taxonomy codes, and 46% using CPT codes, with 62% of NPIs meeting multiple criteria. Findings suggest that these methods may be used by researchers to identify UCCs in studies of cost or utilization in different healthcare settings.

EXECUTIVE SUMMARYThe article examines whether subjective performance assessments from health system executives match objective performance assessments and qualitatively explores ways to achieve high performance. We interviewed 138 C-suite executives of 24 health systems in California, Minnesota, Washington, and Wisconsin between 2017 and 2019. We used maximum variation sampling to select health systems to achieve diversity in performance on objective measures of clinical performance. Our interviews focused on executives’ perceptions of their own health system’s performance and factors they thought generally contributed to high performance. In our analysis, we grouped health systems based on objective performance levels (high, medium, and low) used in sampling, compared objective performance ratings with executives’ subjective performance assessments, and used thematic analysis to identify reasons for subjective assessment of health system performance and levers of high performance in general. There was poor agreement between objective and subjective performance assessments (kappa = 0.082). Subjective assessments were higher than objective assessments and captured more factors than are typically considered in performance accountability and value-based payment initiatives. Executives whose views were inconsistent with objective performance assessments did not cite clinical care quality per se as the basis for their assessment, focusing instead on market competition, financial performance, and high customer satisfaction and loyalty. Executives who cited clinical quality metrics as the basis of their assessment offered subjective ratings consistent with objective ratings. Executives identified organizational culture, organizational governance, and staff engagement as levers for achieving high performance. Future research should explore the benefits and drawbacks of considering subjective performance assessments in value-based payment initiatives.

A demonstration project designed to improve primary care at federally qualified health centers improved patients’ access to primary care but did not achieve the desired reductions in Medicare expenditures or improvements on most measures of quality and patients’ experience.

Objectives. We compared the strength of association between average 5-year county-level mortality rates and area-level measures, including air quality, sociodemographic characteristics, violence, and economic distress. Methods. We obtained mortality data from the National Vital Statistics System and linked it to socioeconomic and demographic data from the Census Bureau, air quality data, violent crime statistics, and loan delinquency data. We modeled 5-year average mortality rates (1998–2002) for all-cause, cancer, heart disease, stroke, and respiratory diseases as a function of county-level characteristics using ordinary least squares regression models. We limited analyses to counties with population of 100 000 or greater (n = 458). Results. Demographic and socioeconomic characteristics, particularly the percentage older than 65 years and near poor, were top predictors of all-cause and condition-specific mortality, as were a high concentration of construction and service workers. We found weaker associations for air quality, mortgage delinquencies, and violent crimes. Protective characteristics included the percentage of Hispanics, Asians, and married residents. Conclusions. Multiple factors influence county-level mortality. Although county demographic and socioeconomic characteristics are important, there are independent, although weaker, associations of other environmental characteristics. Future studies should investigate these factors to better understand community mortality risk.

AimsWe examine whether primary care continuity of care (PCCOC) improved for Medicare beneficiaries under a 3-year demonstration to help federally qualified health centers (FQHCs) become patient-centered medical homes (PCMH).Subjects and methodsWe used a difference-in-differences analysis to compare changes over time in PCCOC for beneficiaries in 503 demonstration sites to those in 827 comparison sites. We measured PCCOC using the claims-based usual provider of care (UPC) index (range 0–1) indicating the proportion of visits to the most commonly seen provider or practice over a 1-year period.ResultsAverage baseline UPC index values were 0.77 at the provider level and 0.88 at the practice level, with similar values for demonstration and comparison sites. UPC decreased more over time in demonstration clinics than comparison clinics, but the magnitude of these changes were small.ConclusionsFQHCs already have high levels of PCCOC. These levels did not increase in association with the 3-year PCMH demonstration. Continuity for practices is higher than for providers, suggesting that Medicare beneficiaries may see multiple providers within one FQHC.

Study Design: We projected US spending on biologics over a 5-year period under 3 scenarios: (1) a baseline scenario holding quarter 4 (Q4) of 2020 market conditions constant; (2) under main assumptions allowing for biosimilar market growth and entry; and (3) an upper-bound scenario assuming greater biosimilar uptake, more robust price competition, and quicker biosimilar entry. Whereas conventional drugs are synthesized chemically and are typically easier to describe scientifically, biologics are manufactured in living systems and are more complex and harder to characterize.1 Average prices for biologics are much higher than those for small molecule drugs for several reasons, including differences in therapeutic classes and clinical use, payment policies, and limited competition.2 In 2017, biologics accounted for 2% of US prescriptions by volume but 37% of net spending on prescription drugs.3 Due in part to favorable returns on investments, many recent and upcoming drugs are biologics, including those to treat cancer and other life-threatening diseases.4 Biosimilars, which are highly similar versions of brand-name reference biologics, are a key component of the national strategy to tackle high and growing spending on prescription drugs.5 Compared with other countries, the United States was relatively late in establishing a pathway for biosimilar market entry. In practice, the magnitude of these reductions varies depending on the competitive environment for each biologic; the extent to which insurers and pharmacy benefit managers use formularies, utilization management, and other tools to encourage the use of lower-cost alternatives; and patient and prescriber perceptions of biosimilars.7,8 Managed care organizations, payers, and policy makers have an interest in better understanding the potential for existing and future biosimilars to help control price and spending growth for biologics. [...]ASP data were available for only some biologics.

Background: The number of people living with multiple chronic conditions is increasing, but we know little about the impact of multimorbidity on life expectancy. Objective: We analyze life expectancy in Medicare beneficiaries by number of chronic conditions. Research Design: A retrospective cohort study using single-decrement period life tables. Subjects: Medicare fee-for-service beneficiaries (N = 1,372,272) aged 67 and older as of January 1, 2008. Measures: Our primary outcome measure is life expectancy. We categorize study subjects by sex, race, selected chronic conditions (heart disease, cancer, chronic obstructive pulmonary disease, stroke, and Alzheimer disease), and number of comorbid conditions. Comorbidity was measured as a count of conditions collected by Chronic Conditions Warehouse and the Charlson Comorbidity Index. Results: Life expectancy decreases with each additional chronic condition. A 67-year-old individual with no chronic conditions will live on average 22.6 additional years. A 67-year-old individual with 5 chronic conditions and ≥ 10 chronic conditions will live 7.7 fewer years and 17.6 fewer years, respectively. The average marginal decline in life expectancy is 1.8 years with each additional chronic condition—ranging from 0.4 fewer years with the first condition to 2.6 fewer years with the sixth condition. These results are consistent by sex and race. We observe differences in life expectancy by selected conditions at 67, but these differences diminish with age and increasing numbers of comorbid conditions. Conclusions: Social Security and Medicare actuaries should account for the growing number of beneficiaries with multiple chronic conditions when determining population projections and trust fund solvency.

Background Cannabis may be a substitute for opioids but previous studies have found conflicting results when using data from more recent years. Most studies have examined the relationship using state-level data, missing important sub-state variation in cannabis access. Objective To examine cannabis legalization on opioid use at the county level, using Colorado as a case study. Colorado allowed recreational cannabis stores in January 2014. Local communities could decide whether to allow dispensaries, creating variation in the level of exposure to cannabis outlets. Design Observational, quasi-experimental design exploiting county-level variation in allowance of recreational dispensaries. Subjects Colorado residents Measures We use licensing information from the Colorado Department of Revenue to measure county-level exposure to cannabis outlets. We use the state’s Prescription Drug Monitoring Program (2013–2018) to construct opioid-prescribing measures of number of 30-day fills and total morphine equivalents, both per county resident per quarter. We construct outcomes of opioid-related inpatient visits (2011–2018) and emergency department visits (2013–2018) with Colorado Hospital Association data. We use linear models in a differences-in-differences framework that accounts for the varying exposure to medical and recreational cannabis over time. There are 2048 county-quarter observations used in the analysis. Results We find mixed evidence of cannabis exposure on opioid-related outcomes at the county level. We find increasing exposure to recreational cannabis is associated with a statistically significant decrease in number of 30-day fills (coefficient: −117.6, p -value<0.01) and inpatient visits (coefficient: −0.8, p -value: 0.03), but not total MME nor ED visits. Counties with no medical exposure prior to recreational legalization experience greater reductions in the number of 30-day fills and MME than counties with prior medical exposure ( p =0.02 for both). Conclusions Our mixed findings suggest that further increases in cannabis beyond medical access may not always reduce opioid prescribing or opioid-related hospital visits at a population level.

The use of generics in Medicare Part D generates cost savings for plan sponsors, beneficiaries, and the federal government. However, there is considerable variation in generic use across plans, even within a therapeutic class. Our objective is to understand the extent of variation in generic use in Part D and to understand factors associated with generic use. We used an observational study design using Medicare Part D claims from 2006 to 2016. We used descriptive statistics and regression analysis to examine the variation in generic and brand use across plans and the extent to which patient, plan, and area characteristics are associated with the choice of medication within a therapeutic class. Although generic use has increased markedly over time in Part D, substantial variation across plans persists in a number of common therapeutic classes. Beneficiary characteristics such as gender and health status are associated with higher/lower generic use, as are plan characteristics such as plan type (stand-alone prescription drug plan or Medicare Advantage), premium, and parent company. Because we cannot study the impact of brand-name drug rebates on generic use, we can study the variation in generic use across Part D plans as an indirect way to assess pharmacy benefit manager and plan incentives. We find circumstantial evidence that, in certain classes, rebates may play a role in influencing brand over generic use, although the exact relationship is unknowable given the proprietary nature of rebates.