Disparities in Mammography Screening: Analyzing Barriers to Access Using Individual Patient Perspectives and the Health Belief Model.

OBJECTIVE: The objective of our study is to explore Nepali women's beliefs about access to mammography screening, and motivations to get screened or not. This work was intended to be hypothesis generating for subsequent quantitative analysis and to inform policy and decision-making to improve access. METHODS: We conducted structured qualitative interviews among nine Nepali women in the Northeast of the United States receiving care at a local community health center and among nine white women receiving mammography care at a large academic medical center in the Northeast. We analyzed the transcripts using a mixed deductive (content analysis) and inductive (grounded theory) approach. Deductive codes were generated from the Health Belief Model which states that a person's belief in the real threat of a disease with their belief in the effectiveness of the recommended health service or behavior or action will predict the likelihood the person will adopt the behavior. We compared and contrasted qualitative results from both groups. RESULTS: We found that eligible Nepali women who had not received mammography screening had no knowledge of its availability and its importance. Primary care physicians emerged as a critical link in addressing this disparity: trust was found to be high among Nepali women with their established primary care provider. CONCLUSION: The findings of this study suggest that the role of primary care practitioners in conversations around the importance and eligibility for mammography screening is of critical importance, especially for underserved groups with limited health knowledge of screening opportunities and potential health benefits. Follow-up research should focus on primary care practices.

In this study, we interviewed Nepali women in a small, rural state in in the Northeast of the United States who are eligible for breast cancer screening yet do not seek it to better understand their motivations f. We also interviewed women who did get mammography screening to understand their motivations. We found that eligible Nepali women who had not received mammography screening had no knowledge of its availability and its importance. Primary care physicians emerged as a critical link in addressing this disparity: trust was found to be high among Nepali women with their established primary care provider. The findings of this study suggest that the role of primary care practitioners in conversations around the importance and eligibility for mammography screening is of critical importance.

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.

Community Health Teams: a qualitative study about the factors influencing the decision-making process.

BACKGROUND: The purpose of this study was to explore the factors influencing how individual Community Health Teams (CHTs) make decisions about what services to offer and how to allocate their resources. METHODS: We conducted thirteen semi-structured interviews with all 13 CHTs program managers between January and March, 2021. We analyzed interviewees descriptions of their service offerings, resources allocation, and decision-making process to identify themes. RESULTS: Four major themes emerged from the interview data as factors influencing community health team program managers' decision-making process: commitment to offering high-quality care coordination, Blueprint's stable and flexible structure, use of data in priority setting, and leveraging community partnerships and local resources. CONCLUSIONS: Community-based CHTs with flexible funding allowed programs to tailor service offerings in response to community needs. It is important for teams to have access to community-level data. Teams are cultivating and leveraging community partners to increase their care coordination capacity, which is focus of their work. CHTs are a model for leveraging community partnerships to increase service capacity and pubic engagement in health services for other states to replicate.

Changes in healthcare spending attributable to obesity and overweight: payer- and service-specific estimates.

BACKGROUND: National efforts to control US healthcare spending are potentially undermined by changes in patient characteristics, and in particular increases in rates of obesity and overweight. The objective of this study was to provide current estimates of the effect of obesity and overweight on healthcare spending overall, by service line and by payer using the National Institutes of Health classifications for BMI. METHODS: We used a quasi-experimental design and analyzed the data using generalized linear models and two-part models to estimate obesity- and overweight-attributable spending. Data was drawn from the 2006 and 2016 Medical Expenditures Panel Survey. We identified individuals in the different BMI classes based on self-reported height and weight. RESULTS: Total medical costs attributable to obesity rose to $126 billion per year by 2016, although the marginal cost of obesity declined for all obesity classes. The overall spending increase was due to an increase in obesity prevalence and a population shift to higher obesity classes. Obesity related spending between 2006 and 2016 was relatively constant due to decreases in inpatient spending, which were only partially offset by increases in outpatient spending. CONCLUSIONS: While total obesity related spending between 2006 and 2016 was relatively constant, by examining the effect of different obesity classes and overweight, it provides insight into spend for each level of obesity and overweight across service line and payer mix. Obesity class 2 and 3 were the main factors driving spending increases, suggesting that persons over BMI of 35 should be the focus for policies focused on controlling spending, such as prevention.

Understanding the factors that affect the appropriateness of rheumatology referrals.

BACKGROUND: Reducing inappropriate referrals to specialists is a challenge for the healthcare system as it seeks to transition from volume to value-based healthcare. Given the projection of a severe shortage of rheumatologists in the near future, innovative strategies to decrease demand for rheumatology services may prove more fruitful than increasing the supply of rheumatologists. Efforts to increase appropriate utilization through reductions in capacity may have the unintended consequence of reducing appropriate care as well. This highlights the challenges in increasing the appropriate use of high cost services as the health system transitions to value based care. The objective of this study was to analyze factors affecting appropriateness of rheumatology services. METHODS: This was a cross-sectional study of patients receiving Rheumatology services between November 2013 and October 2019. We used a proxy for "appropriateness": whether or not there was any follow-up care after the first appointment. Results from regression analysis and physicians' chart reviews were compared using an inter-rater reliability measure (kappa). Data was drawn from the EHR 2013-2019. RESULTS: We found that inappropriate referrals increased 14.3% when a new rheumatologist was hired, which increased to 14.8% after wash-out period of 6 months; 15.7% after 12 months; 15.5% after 18 months and 16.7% after 18 months. Other factors influencing appropriateness of referrals included severity of disease, gender and insurance type, but not specialty of referring provider. CONCLUSIONS: Given the projection of a severe shortage of rheumatologists in the near future, innovative strategies to decrease demand for rheumatology services may prove more fruitful than increasing the supply of rheumatologists. Innovative strategies to decrease demand for rheumatology services may prove more fruitful than increasing the supply of rheumatologists. These findings may apply to other specialties as well. This study is relevant for health care systems that are implementing value-based payment models aimed at reducing inappropriate care.

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations.

BACKGROUND: High quality serious illness communication requires good understanding of patients' values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing value- and belief-related features of conversations in the natural clinical setting. We use a validated NLP corpus and a series of statistical analyses to capture and explain conversation features that characterize the complex domain of moral values and beliefs. The objective of this study was to examine the frequency, distribution and clustering of morality lexicon expressed by patients during palliative care consultation using the Moral Foundations NLP Dictionary. METHODS: We used text data from 231 audio-recorded and transcribed inpatient PC consultations and data from baseline and follow-up patient questionnaires at two large academic medical centers in the United States. With these data, we identified different moral expressions in patients using text mining techniques. We used latent class analysis to explore if there were qualitatively different underlying patterns in the PC patient population. We used Poisson regressions to analyze if individual patient characteristics, EOL preferences, religion and spiritual beliefs were associated with use of moral terminology. RESULTS: We found two latent classes: a class in which patients did not use many expressions of morality in their PC consultations and one in which patients did. Age, race (white), education, spiritual needs, and whether a patient was affiliated with Christianity or another religion were all associated with membership of the first class. Gender, financial security and preference for longevity-focused over comfort focused treatment near EOL did not affect class membership. CONCLUSIONS: This study is among the first to use text data from a real-world situation to extract information regarding individual foundations of morality. It is the first to test empirically if individual moral expressions are associated with individual characteristics, attitudes and emotions.

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.

Tradeoffs in Triage of Motor Vehicle Trauma by Rural 911 Emergency Medical Services Practitioners.

PURPOSE: Identification and triage of severely injured patients to trauma centers is paramount to survival. Many patients are undertriaged in rural areas and do not receive proper care. The decision-making processes involved in triage are not well understood and should be assessed to improve the triage process and outcomes. METHODS: Triage decision-making processes were explored through emergency medical services (EMS) practitioner focus groups and a discrete choice experiment (DCE). Attributes of trauma determined from focus groups and the literature included patient demography, injury mechanism, and trauma center distance. DCE data were analyzed using mixed logit models. RESULTS: High-risk mechanism, decreased age, multiple comorbidities, and pregnancy were found to increase the preference for triage. Greater trauma center distance was found to decrease preference for triage, but practitioners were willing to trade off up to 2 h of travel time to transport a third-trimester pregnancy and 48 min of travel time to transport a 25-y-old than they would a 50-y-old with the same comorbidities, injuries, and stability. CONCLUSIONS: Our findings suggest that current forms of EMS protocols may not be appropriately tailored to support the mechanisms underlying practitioner decision making. Public health professionals and researchers should consider using DCEs to better understand EMS practitioner decision making and identify structures and incentives that may improve patient outcomes and optimally guide appropriate triage decisions. HIGHLIGHTS: Discrete choice experiments are an effective method to elicit prehospital practitioners' preferences around transport of the traumatized patient.Practitioner biases observed in EMS transport data are recovered in stated preference models incorporating individual preference heterogeneity.There is a discrepancy between the triage priorities recommended by protocol and those measured from prehospital practitioners' decisions-this may have implications in over- and undertriage rates and prehospital protocol design.

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.

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.

Lives saved and lost in the first six month of the US COVID-19 pandemic: A retrospective cost-benefit analysis.

In the beginning of the COVID-19 US epidemic in March 2020, sweeping lockdowns and other aggressive measures were put in place and retained in many states until end of August of 2020; the ensuing economic downturn has led many to question the wisdom of the early COVID-19 policy measures in the US. This study's objective was to evaluate the cost and benefit of the US COVID-19-mitigating policy intervention during the first six month of the pandemic in terms of COVID-19 mortality potentially averted, versus mortality potentially attributable to the economic downturn. We conducted a synthesis-based retrospective cost-benefit analysis of the full complex of US federal, state, and local COVID-19-mitigating measures, including lockdowns and all other COVID-19-mitigating measures, against the counterfactual scenario involving no public health intervention. We derived parameter estimates from a rapid review and synthesis of recent epidemiologic studies and economic literature on regulation-attributable mortality. According to our estimates, the policy intervention saved 866,350-1,711,150 lives (4,886,214-9,650,886 quality-adjusted life-years), while mortality attributable to the economic downturn was 57,922-245,055 lives (2,093,811-8,858,444 life-years). We conclude that the number of lives saved by the spring-summer lockdowns and other COVID-19-mitigation was greater than the number of lives potentially lost due to the economic downturn. However, the net impact on quality-adjusted life expectancy is ambiguous.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

Adherence to COVID-19 policy measures: Behavioral insights from The Netherlands and Belgium.

BACKGROUND: Since the start of the global COVID-19 pandemic, countries have been mirroring each other's policies to mitigate the spread of the virus. Whether current measures alone will lead to behavioral change such as social distancing, washing hands, and wearing a facemask is not well understood. The objective of this study is to better understand individual variation in behavioral responses to COVID-19 by exploring the influence of beliefs, motivations and policy measures on public health behaviors. We do so by comparing The Netherlands and Flanders, the Dutch speaking part of Belgium. METHODS AND FINDINGS: Our final sample included 2,637 respondents from The Netherlands and 1,678 from Flanders. The data was nationally representative along three dimensions: age, gender, and household income in both countries. Our key outcome variables of interest were beliefs about policy effectiveness; stated reasons for complying with public rules; and changes in behavior. For control variables, we included a number of measures of how severe the respondent believed Covid-19 to be and a number of negative side effects that the person may have experienced: loneliness, boredom, anxiety, and conflicts with friends and neighbors. Finally, we controlled for socio-demographic factors: age, gender, income (categorical), education (categorical) and the presence of Covid-19 risk factors (diabetes, high blood pressure, heart disease, asthma, allergies). The dependent variable for each of the estimation models is dichotomous, so we used Probit models to predict the probability of engaging in a given behavior. We found that motivations, beliefs about the effectiveness of measures, and pre-pandemic behavior play an important role. The Dutch were more likely to wash their hands than the Flemish (15.4%, p<0.01), visit family (15.5%, p < .01), run errands (12.0%, p<0.05) or go to large closed spaces such as a shopping mall (21.2%, p<0.01). The Dutch were significantly less likely to wear a mask (87.6%, p<0.01). We also found that beliefs about the virus, psychological effects of the virus, as well as pre-pandemic behavior play a role in adherence to recommendations. CONCLUSIONS: Our results suggest that policymakers should consider behavioral motivations specific to their country in their COVID-19 strategies. In addition, the belief that a policy is effective significantly increased the probability of the behavior, so policy measures should be accompanied by public health campaigns to increase adherence.

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.

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic.

Given the role of human behavior in the spread of disease, it is vital to understand what drives people to engage in or refrain from health-related behaviors during a pandemic. This paper examines factors associated with the adoption of self-protective health behaviors, such as social distancing and mask wearing, at the start of the Covid-19 pandemic in the USA. These behaviors not only reduce an individual's own risk of infection but also limit the spread of disease to others. Despite these dual benefits, universal adoption of these behaviors is not assured. We focus on the role of socioeconomic differences in explaining behavior, relying on data collected in April 2020 during the early stages of the Covid-19 pandemic. The data include information on income, gender and race along with unique variables relevant to the current pandemic, such as work arrangements and housing quality. We find that higher income is associated with larger changes in self-protective behaviors. These gradients are partially explained by the fact that people with less income are more likely to report circumstances that make adopting self-protective behaviors more difficult, such as an inability to tele-work. Both in the USA and elsewhere, policies that assume universal compliance with self-protective measures-or that otherwise do not account for socioeconomic differences in the costs of doing so-are unlikely to be effective or sustainable.

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.

Using discrete choice experiments to measure preferences for hard to observe choice attributes to inform health policy decisions.

BACKGROUND: Models of preferences in health services research (HSR) and Health Economics are often defined by readily available information, such as that captured in claims data and electronic health records. Yet many important questions about patient choices cannot be easily studied because of a lack of critical data elements. The objective of this review is to outline the advantages of using stated preferences (SP) data in health services research, and to outline how these methods can be used to evaluate choices that have not yet been offered or studied. MAIN BODY: This article focuses on the application of DCE's to relevant policy and health system delivery questions currently relevant, particularly in the United States. DCE's may be helpful to collect data from patient or consumer data that we currently do not have. The article provides examples of research questions that have been answered using SP data collected with a DCE. It outlines how to construct a DCE and how to analyze the data. It also discusses the methodological challenges and emphasizes important considerations regarding the design and estimation methods. SP data can be adopted in situations where we would like to have consumer choice data, but we currently do not. These are often hypothetical situations to analyze the decision-making process of individuals. With SP data it is possible to analyze trade-offs patients make when choosing between treatment options where these hard to measure attributes are important. CONCLUSION: This paper emphasizes that a carefully designed DCE and appropriate estimation methods can open up a new world of data regarding trade-offs patients and providers in healthcare are willing to make. It updates previous "how to" guide for DCE's for health services researchers and health economists who are not familiar with these methods or have been unwilling to use them and updates previous description of these methods with timely examples.

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.

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.

Switching Costs in Medicare Advantage.

This paper estimates the magnitude of switching costs in the Medicare Advantage program. Consumers are generally assumed to pick plans that provide the combination of benefits and premiums that maximize their individual utility. However, the plan choice literature has generally omitted prior choices from choice models. The analysis is based on five years of the Medicare Current Beneficiary Survey, a nationally representative longitudinal dataset. The MCBS data were combined with data on Medicare Advantage Part C plan benefits and premiums. Individual choices are modeled as a function of individual characteristics, plan characteristics and prior year plan choices using a mixed logit model. We found relatively high rates of switching between plans within insurer (20%), although less switching between insurers. Prior year plan choices were highly significant at both the contract and plan level. Premium was negative and significant. Loyalty (contract and plan), premium and plan structure were found to be heterogeneous in preferences. We found a statistically significant willingness to pay for a lower prescription drug deductible and lower copays. Switching costs were higher for sicker individuals. Switching costs between plans offered by the same insurer are far lower than switching costs between insurers; beneficiaries will switch plans if an alternative is perceived as $233 a month better than the current choice and switch insurers if the alternative is perceived as $944 better than the current plan/contract, on average. Premium elasticities would be 34% greater in magnitude if prior choices were irrelevant. We provide evidence that the state dependence is structural rather than spurious.