Examining alignment of community health teams' preferences for health, equity, and spending with state all-payer waiver priorities: A discrete choice experiment.

OBJECTIVE: The state of Vermont has a statewide waiver from the centers for medicare and medicaid services to allow all-payer Accountable Care Organizations (ACOs). The Vermont all-payer model (VAPM) waiver is layered upon previous reforms establishing regional community health teams (CHTs) and medical homes. The waiver is intended to incentivize healthcare value and quality and create alignment between health system payers, providers, and CHTs. The objective of this study was to examine CHT's trade-offs and preferences for health, equity, and spending and the alignment with VAPM priorities. DATA SOURCES/STUDY SETTING: Data were gathered from a survey and discrete choice experiment among CHT leadership and CHT team members of the 13 CHTs in Vermont. STUDY DESIGN: We used conditional logit models to model the choice as a function of its characteristics (attributes) and mixed logit models to analyze whether preferences for programs varied by persons and roles within CHTs. DATA COLLECTION/EXTRACTION METHODS: There were 60 respondents who completed the survey online with 14 choice tasks, with three program options in each task, for a total sample size of 2520. PRINCIPAL FINDINGS: We found that CHTs prioritized programs in the community health plan and those with quantitative evidence of effectiveness. They were less likely to choose either programs targeting racial and ethnic minorities or programs having a small effect on a large population. Preferences did not vary across individual or community attributes. Program priorities of the VAPM, especially healthcare spending, were not prioritized. CONCLUSIONS: The results suggest that the new VAPM does not automatically create system alignment: CHTs tended to prioritize local needs and voices. The statewide priorities are less important to CHTs, which have excellent internal alignment. This creates potential disconnection between state and community health goals. However, CHTs and the VAPM prioritize similar populations, indicating an opportunity to increase alignment by allowing flexible programs tailored to local needs. CHTs also prioritized programs with a strong evidence base, suggesting another potential avenue to create system alignment.

A Quantitative Framework for Medication Non-Adherence: Integrating Patient Treatment Expectations and Preferences.

Introduction: Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios. Methods: We present an empirical framework that includes a health production function, specifically applied to antihypertensive medications nonadherence. Data collection involved a pilot study that utilized a double-bound contingent-belief (DBCB) questionnaire. Through this questionnaire, participants could express how efficacy and side effects were affected by controlled levels of non-adherence, allowing for the estimation of sensitivity in health outcomes and costs. Results: Parameters derived from the DBCB questionnaire revealed that on average, patients with hypertension anticipated that treatment efficacy was less sensitive to non-adherence than side effects. Our derived health production function suggests that patients may strategically manage adherence to minimize side effects, without compromising efficacy. Patients' inclination to manage medication intake is closely linked to the relative importance they assign to treatment efficacy and side effects. Model outcomes indicate that patients opt for full adherence when efficacy outweighs side effects. Our findings also indicated an association between income and patient expectations regarding the health of antihypertensive medications. Conclusion: Our framework represents a pioneering effort to quantitatively link non-adherence to patient preferences. Preliminary results from our pilot study of patients with hypertension suggest that the framework offers a viable alternative for evaluating the potential impact of interventions on treatment adherence.

Understanding factors associated with increases in Medicare Advantage enrollment, 2007-2018.

OBJECTIVES: Private managed care plans in the Medicare Advantage (MA) program have been gaining market share relative to traditional fee-for-service Medicare (TM), yet there are no obvious structural changes to Medicare that would explain this growth. Our goal is to explain the growth in MA market share during a period when it increased dramatically. STUDY DESIGN: Data are drawn from a representative sample of the Medicare population from 2007 to 2018. METHODS: We decomposed MA growth into changes in the values of explanatory variables that influence MA enrollment (eg, income and payment rate) and changes in preferences for MA vs TM (estimated coefficients) using a nonlinear version of the Blinder-Oaxaca decomposition to distinguish the sources of MA growth. We find that the relatively smooth growth in MA market share masks 2 distinct growth periods. RESULTS: From 2007 to 2012, 73% of the increase was due to changes in the values of the explanatory variables, and only 27% was due to changes in coefficients. In contrast, from 2012 to 2018, changes in explanatory variables, particularly MA payment levels, would have led to a decline in MA market share if that effect had not been offset by changes in the coefficients. CONCLUSIONS: Overall, we find that MA is becoming more appealing to more educated and nonminority beneficiaries than in the past, although minority and lower-income beneficiaries are still more likely to pick the program. Over time, if preferences continue to shift, the nature of the MA program will change as it moves more toward the middle of the Medicare distribution.

The effect of medical innovation on the cost-effectiveness of Covid 19-related policies in the United States using a SIR model.

BACKGROUND: During 2020-21, the United States used a multifaceted approach to control SARS-CoV-2 (Covid-19) and reduce mortality and morbidity. This included non-medical interventions (NMIs), aggressive vaccine development and deployment, and research into more effective approaches to medically treat Covid-19. Each approach had both costs and benefits. The objective of this study was to calculate the Incremental Cost Effectiveness Ratio (ICER) for three major Covid-19 policies: NMIs, vaccine development and deployment (Vaccines), and therapeutics and care improvements within the hospital setting (HTCI). METHODS: To simulate the number of QALYs lost per scenario, we developed a multi-risk Susceptible-Infected-Recovered (SIR) model where infection and fatality rates vary between regions. We use a two equation SIR model. The first equation represents changes in the number of infections and is a function of the susceptible population, the infection rate and the recovery rate. The second equation shows the changes in the susceptible population as people recover. Key costs included loss of economic productivity, reduced future earnings due to educational closures, inpatient spending and the cost of vaccine development. Benefits included reductions in Covid-19 related deaths, which were offset in some models by additional cancer deaths due to care delays. RESULTS: The largest cost is the reduction in economic output associated with NMI ($1.7 trillion); the second most significant cost is the educational shutdowns, with estimated reduced lifetime earnings of $523B. The total estimated cost of vaccine development is $55B. HTCI had the lowest cost per QALY gained vs "do nothing" with a cost of $2,089 per QALY gained. Vaccines cost $34,777 per QALY gained in isolation, while NMIs alone were dominated by other options. HTCI alone dominated most alternatives, except the combination of HTCI and Vaccines ($58,528 per QALY gained) and HTCI, Vaccines and NMIs ($3.4 m per QALY gained). CONCLUSIONS: HTCI was the most cost effective and was well justified under any standard cost effectiveness threshold. The cost per QALY gained for vaccine development, either alone or in concert with other approaches, is well within the standard for cost effectiveness. NMIs reduced deaths and saved QALYs, but the cost per QALY gained is well outside the usual accepted limits.

The effect of unobserved preferences and race on vaccination hesitancy for COVID-19 vaccines: implications for health disparities.

BACKGROUND: Reducing the extra burden COVID-19 has on people already facing disparities is among the main national priorities for the COVID-19 vaccine rollout. Early reports from states releasing vaccination data by race show that White residents are being vaccinated at significantly higher rates than Black residents. Public health efforts are being targeted to address vaccine hesitancy among Black and other minority populations. However, health care interventions intended to reduce health disparities that do not reflect the underlying values of individuals in underrepresented populations are unlikely to be successful. OBJECTIVE: To identify key factors underlying the disparities in COVID-19 vaccination. METHODS: Primary data were collected from an online survey of a representative sample of the populations of the 4 largest US states (New York, California, Texas, and Florida) between August 10 and September 3, 2020. Using latent class analysis, we built a model identifying key factors underlying the disparities in COVID-19 vaccination. RESULTS: We found that individuals who identify as Black had lower rates of vaccine hesitancy than those who identify as White. This was true overall, by latent class and within latent class. This suggests that, contrary to what is currently being reported, Black individuals are not universally more vaccine hesitant. Combining the respondents who would not consider a vaccine (17%) with those who would consider one but ultimately choose not to vaccinate (11%), our findings indicate that more than 1 in 4 (28%) persons will not be willing to vaccinate. The no-vaccine rate is highest in White individuals and lowest in Black individuals. CONCLUSIONS: Results suggest that other factors, potentially institutional, are driving the vaccination rates for these groups. Our model results help point the way to more effective differentiated policies. DISCLOSURES: No funding was received for this study. The authors have nothing to disclose.

Jobs, Housing, and Mask Wearing: Cross-Sectional Study of Risk Factors for COVID-19.

BACKGROUND: Many studies have focused on the characteristics of symptomatic patients with COVID-19 and clinical risk factors. This study reports the prevalence of COVID-19 in an asymptomatic population of a hospital service area (HSA) and identifies factors that affect exposure to the virus. OBJECTIVE: The aim of this study is to measure the prevalence of COVID-19 in an HSA, identify factors that may increase or decrease the risk of infection, and analyze factors that increase the number of daily contacts. METHODS: This study surveyed 1694 patients between April 30 and May 13, 2020, about their work and living situations, income, behavior, sociodemographic characteristics, and prepandemic health characteristics. This data was linked to testing data for 454 of these patients, including polymerase chain reaction test results and two different serologic assays. Positivity rate was used to calculate approximate prevalence, hospitalization rate, and infection fatality rate (IFR). Survey data was used to analyze risk factors, including the number of contacts reported by study participants. The data was also used to identify factors increasing the number of daily contacts, such as mask wearing and living environment. RESULTS: We found a positivity rate of 2.2%, a hospitalization rate of 1.2%, and an adjusted IFR of 0.55%. A higher number of daily contacts with adults and older adults increases the probability of becoming infected. Occupation, living in an apartment versus a house, and wearing a face mask outside work increased the number of daily contacts. CONCLUSIONS: Studying prevalence in an asymptomatic population revealed estimates of unreported COVID-19 cases. Occupational, living situation, and behavioral data about COVID-19-protective behaviors such as wearing a mask may aid in the identification of nonclinical factors affecting the number of daily contacts, which may increase SARS-CoV-2 exposure.

Patient preferences for provider choice: a discrete choice experiment.

OBJECTIVES: There is an ongoing policy discussion regarding an adequate breadth of provider networks. Health plans with "restricted networks" of providers have proved surprisingly popular on the Affordable Care Act health insurance exchanges because of a substantial gap in premiums between plans with open networks and closed networks. The objective of this paper is to assess which other attributes of the provider network matter to patients when choosing health insurance. STUDY DESIGN: We used a discrete choice experiment to analyze the effect of previously unobserved characteristics regarding provider networks on plan choice, including wait time, breadth, travel time, whether the plan covers care for their personal doctor, and monthly premium. Hypothetical plan options were offered to respondents of an online survey using Qualtrics software. METHODS: We used mixed multinomial logit models to estimate preference-based utilities for attributes of primary care provider networks and willingness to pay. RESULTS: Coverage of a personal doctor was the most important attribute, followed by premium, wait time to see a primary care provider, the breadth of the network, and travel time to the closest doctor covered by the plan. Respondents were willing to pay $95 per month to have a plan that covers care for their personal doctor, and they were willing to wait 6 days for an appointment to have a plan covering care for their personal doctor. CONCLUSIONS: The results of this study provide new insights to federal and state legislators developing new models or standards on network adequacy and patient decision support tools.

The relation between selective contracting and healthcare expenditures in private health insurance plans in the United States.

Many healthcare systems, including The Netherlands, Germany and Switzerland, have incorporated elements of managed competition, whereby insurers compete for enrollees in a marketplace organized or facilitated by a government or governing entity. In these countries, managed competition was introduced with the idea that the system would contain cost growth while maximizing value for consumers and employers. An important mechanism to control costs is selective contracting: the process of contracting providers into a network and offer insurance packages with varying levels of provider coverage. In these systems, enrollees are expected to choose lower cost plans which offer access to only contracted providers in the network. The questions is, however, if restricting provider choice leads to reduced healthcare expenditures. In the United States, enrollees often have a choice between plans with restricted networks of providers and plans that offer more provider choice, where care outside the contracted network of providers is (partly) covered. The purpose of this study is to understand whether insurance plans with restrictions on provider access in the United States have reduced healthcare expenditures and to identify the mechanism by which that reduction occurred. We used data from the Medical Expenditure Panel Survey (MEPS), a nationally representative sample of families and individuals. We estimated expenditures for enrollees in restricted network plans using two-part models and generalized linear models. We found that restricted network plans, on average, save $761 per enrollee. Our results suggest that cost savings due to restricted network plans are largely a result of price reductions rather than utilization reductions, although both play a role in cost savings. When introducing reforms shifting from a supply-oriented to a demand-oriented health care system, these findings might be worth considering by other countries.

The Effect of an Inpatient Smoking Cessation Treatment Program on Hospital Readmissions and Length of Stay.

OBJECTIVE: The objective of this study is to estimate the impact of an inpatient tobacco cessation treatment program on 30-day readmission rates and length of stay (LOS). METHODS: Participants were 28,994 patients admitted to the hospital between July 2012 and July 2014. Smokers were identified through the electronic medical records system and were offered cessation treatment. Program effects were estimated by using a difference-in-differences approach, comparing all smokers to all nonsmokers before versus after introduction of the program. Readmission rates were modeled by using probit regression; LOS was modeled by using truncated negative binomial regression. Models controlled for age, sex, race, payer, hospital department, severity of illness, and intensive care unit days. RESULTS: The hospital-initiated smoking cessation intervention had no significant effect on 30-day readmission rates or LOS. Other control variables had the expected signs and were statistically significant. CONCLUSIONS: The evaluation of an inpatient tobacco dependence treatment did not find significant short-term changes in healthcare utilization in the first 30 days after initial hospitalization.