Community Participation Transition After Stroke (COMPASS) Randomized Controlled Trial: Effect on Adverse Health Events.

OBJECTIVE: To compare adverse health events in intervention versus control group participants in the Community Participation Transition After Stroke trial to reduce barriers to independent living for community-dwelling stroke survivors. DESIGN: Randomized controlled trial. SETTING: Inpatient rehabilitation (IR) to home and community transition. PARTICIPANTS: Stroke survivors aged ≥50 years being discharged from IR who had been independent in activities of daily living prestroke (N=183). INTERVENTIONS: Participants randomized to intervention group (n=85) received home modifications and self-management training from an occupational therapist over 4 visits in the home. Participants randomized to control group (n=98) received the same number of visits consisting of stroke education. MAIN OUTCOME MEASURES: Death, skilled nursing facility (SNF) admission, 30-day rehospitalization, and fall rates after discharge from IR. RESULTS: Time-to-event analysis revealed that the intervention reduced SNF admission (cumulative survival, 87.8%; 95% confidence interval [CI], 78.6%-96.6%) and death (cumulative survival, 100%) compared with the control group (SNF cumulative survival, 78.9%; 95% CI, 70.4%-87.4%; P=.039; death cumulative survival, 87.3%; 95% CI, 79.9%-94.7%; P=.001). Thirty-day rehospitalization also appeared to be lower among intervention participants (cumulative survival, 95.1%; 95% CI, 90.5%-99.8%) than among control participants (cumulative survival, 86.3%; 95% CI, 79.4%-93.2%; P=.050) but was not statistically significant. Fall rates did not significantly differ between the intervention group (5.6 falls per 1000 participant-days; 95% CI, 4.7-6.5) and the control group (7.2 falls per 1000 participant-days; 95% CI, 6.2-8.3; incidence rate ratio, 0.78; 95% CI, 0.46-1.33; P=.361). CONCLUSIONS: A home-based occupational therapist-led intervention that helps stroke survivors transition to home by reducing barriers in the home and improving self-management could decrease the risk of mortality and SNF admission after discharge from rehabilitation.

Policy implications of fixed-to-total-cost ratio variation across rural and urban hospitals.

PURPOSE: Hospitals with lower fixed-to-total-cost ratios may be better positioned to remain financially viable when reducing service volumes required by many value-based payment systems. We assessed whether hospitals in rural areas have higher fixed-to-total-cost ratios, which would tend to create a systematic disadvantage in such an environment. METHODS: Our observational study used a mixed-effects, repeated-measures model to analyze Medicare Hospital Cost Report Information System data for 2011-2020. We included all 4,953 nonfederal, short-term acute hospitals in the United States that are present in these years. After estimating the relationship between volume (measured in adjusted patient days) and patient-care costs in a model that controlled for a small number of hospital characteristics, we calculated fixed-to-total-cost ratios based on our model's estimates. FINDINGS: We found that nonmetropolitan hospitals tend to have higher average fixed-to-total-cost ratios (0.85-0.95) than metropolitan hospitals (0.73-0.78). Moreover, the degree of rurality matters; hospitals in micropolitan counties have lower ratios (0.85-0.87) than hospitals in noncore counties (0.91-0.95). While the Critical Access Hospital (CAH) designation is associated with higher average fixed-to-total-cost ratios, high fixed-to-total-cost ratios are not exclusive to CAHs. CONCLUSIONS: Overall, these results suggest that hospital payment policy and payment model development should consider hospital fixed-to-total-cost ratios particularly in settings where economies of scale are unattainable, and where the hospital provides a sense of security to the community it serves.

Deaths of Despair and Population Aging in Missouri.

Recent declines in life expectancy in the US, especially for middle-aged White persons, have called attention to mortality from deaths of despair - deaths due to alcohol, drugs, and suicide. Using data from the Centers for Disease Control and the U.S. Census Bureau, this paper examined deaths of despair by race/ethnicity, age, cause of death, birth cohort, and sex in Missouri. We focused on Area Agencies on Aging as geographic units of interest to increase usefulness of our findings to public administrators. Deaths of despair began trending up for all age groups beginning in 2007-2009, with the sharpest increases occurring for Black or African American non-Hispanics beginning in 2013-2015. The most dramatic increases occurred for the population age 50-59 in St. Louis City and Area Agency on Aging regions in southern Missouri. For older adults, considerable variation in rates, trends, and cause of deaths of despair is evident across the state.

Trends in Hospital Mortality for Uninsured Rural and Urban Populations, 2012-2016.

PURPOSE: Rural-urban health disparities have received increasing scrutiny as rural individuals continue to have worse health outcomes. However, little is known about how insurance status contributes to urban-rural disparities. This study characterizes how rural uninsured patients compare to the urban uninsured, determines whether rurality among the uninsured is associated with worse clinical outcomes, and examines how clinical outcomes based on rurality have changed over time. METHODS: We conducted a retrospective cohort study of the 2012-2016 National Inpatient Sample hospital discharge data including 1,478,613 uninsured patients, of which 233,816 were rural. Admissions were broken into 6 rurality categories. Logistic regression models were used to determine the independent association between rurality and hospital mortality. FINDINGS: Demographic and clinical characteristics differed significantly between rural and urban uninsured patients: rural patients were more often white, lived in places with lower median household income, and were more often admitted electively and transferred. Rurality was associated with significantly higher in-hospital mortality rates (1.44% vs 1.89%, OR 1.32, P < .001). This association strengthened after adjusting for medical comorbidities and hospital characteristics. Further, disparities between urban and rural mortality were found to be growing, with the gap almost doubling between 2012 and 2016. CONCLUSIONS: Rural and urban uninsured patients differed significantly, specifically in terms of race and median income. Among the uninsured, rurality was associated with higher in-hospital mortality, and the gap between urban and rural in-hospital mortality was widening. Our findings suggest the rural uninsured are a vulnerable population in need of informed, tailored policies to reduce these disparities.

Impact of Sugar-Sweetened Beverage Warning Labels on Consumer Behaviors: A Systematic Review and Meta-Analysis.

CONTEXT: As a primary source of added sugars, sugar-sweetened beverage consumption contributes to obesity. This study systematically synthesizes the scientific evidence regarding the impact of sugar-sweetened beverage warning labels on consumer behaviors and intentions. EVIDENCE ACQUISITION: A keyword/reference search was performed in 2019 in Cochrane Library, PubMed, Web of Science, CINAHL, Scopus, and Google Scholar. Meta-analysis was conducted in 2020 to estimate the effect of sugar-sweetened beverage warning labels on consumers' purchase decisions. EVIDENCE SYNTHESIS: A total of 23 studies (13 RCTs, 9 nonrandomized experiments, and 1 computer simulation study) met the eligibility criteria and were included. Labels were classified into 6 categories: (1) symbol with nutrient profile, (2) symbol with health effect, (3) text of nutrient profile, (4) text of health effect, (5) graphic with health effect, and (6) graphic with nutrient profile. Compared with the no-label control group, sugar-sweetened beverage warning label use was associated with reduced odds of choosing sugar-sweetened beverages (OR=0.49, 95% CI=0.41, 0.56) and a reduced sugar-sweetened beverage purchase intention (Cohen's d= -0.18, 95% CI= -0.31, -0.06). Across alternative label categories, the graphic with health effect (OR=0.34, 95% CI=0.08, 0.61), text of health effect (OR=0.47, 95% CI=0.39, 0.55), graphic with nutrient profile (OR=0.58, 95% CI=0.36, 0.81), and symbol with health effect (OR=0.67, 95% CI=0.39, 0.95) were associated with reduced odds of choosing sugar-sweetened beverages. CONCLUSIONS: Sugar-sweetened beverage warning labels were effective in dissuading consumers from choosing them. Graphic with health effect labels showed the largest impact. Future studies should delineate the psychosocial pathways linking sugar-sweetened beverage warning labels to purchase decisions, recruit socioeconomically diverse participants, and design experiments in naturalistic settings.

Improving Cancer Patients' Insurance Choices (I Can PIC): A Randomized Trial of a Personalized Health Insurance Decision Aid.

BACKGROUND: Many cancer survivors struggle to choose a health insurance plan that meets their needs because of high costs, limited health insurance literacy, and lack of decision support. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial. MATERIALS AND METHODS: Eligible individuals (18-64 years, diagnosed with cancer for ≤5 years, English-speaking, not Medicaid or Medicare eligible) were randomized to I Can PIC or an attention control health insurance worksheet. Primary outcomes included health insurance knowledge, decisional conflict, and decision self-efficacy after completing I Can PIC or the control. Secondary outcomes included knowledge, decisional conflict, decision self-efficacy, health insurance literacy, financial toxicity, and delayed care at a 3-6-month follow-up. RESULTS: A total of 263 of 335 eligible participants (79%) consented and were randomized; 206 (73%) completed the initial survey (106 in I Can PIC; 100 in the control), and 180 (87%) completed a 3-6 month follow-up. After viewing I Can PIC or the control, health insurance knowledge and a health insurance literacy item assessing confidence understanding health insurance were higher in the I Can PIC group. At follow-up, the I Can PIC group retained higher knowledge than the control; confidence understanding health insurance was not reassessed. There were no significant differences between groups in other outcomes. Results did not change when controlling for health literacy and employment. Both groups reported having limited health insurance options. CONCLUSION: I Can PIC can improve cancer survivors' health insurance knowledge and confidence using health insurance. System-level interventions are needed to lower financial toxicity and help patients manage care costs. IMPLICATIONS FOR PRACTICE: Inadequate health insurance compromises cancer treatment and impacts overall and cancer-specific mortality. Uninsured or underinsured survivors report fewer recommended cancer screenings and may delay or avoid needed follow-up cancer care because of costs. Even those with adequate insurance report difficulty managing care costs. Health insurance decision support and resources to help manage care costs are thus paramount to cancer survivors' health and care management. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial. I Can PIC provides health insurance information, supports patients through managing care costs, offers a list of financial and emotional support resources, and provides a personalized cost estimate of annual health care expenses across plan types.

Health Insurance Marketplaces: Issuer Participation and Premium Trends in Rural Places, 2018.

Purpose: Since 2014, when the Health Insurance Marketplaces (HIMs) authorized by the Patient Protection and Affordable Care Act (PPACA) were implemented, considerable premium changes have been observed in the marketplaces across the 50 states and the District of Columbia. This policy brief assesses the changes in average HIM plan premiums from 2014 to 2018, before accounting for subsidies, with an emphasis on the widening variation across rural and urban places, providing information during Congressional debates on the future of the program. Key Findings: (1) Insurance issuers reduced HIM participation across both rural and urban places (with 1.7 and 2.2 issuers, respectively), both in states that expanded Medicaid under the PPACA and in non-expansion states. (2) The average adjusted premium (before premium subsidy) continues to rise across all of the above categories, and the gap has widened between the 32 Medicaid expansion and 19 non-expansion states. Average premiums in rural counties are higher than average premiums in urban counties in both expansion and non-expansion states (by $43 per month and $27 per month, respectively). (3) Prior trends of lower premium changes at greater population densities are no longer observed in the 2018 data. (4) In 2018, 1,581 counties (52 perent) have one participating insurance issuer. Nationwide, 42 percent of all urban counties and 55 percent of all rural counties only have one issuer.

Medicaid savings from the Contraceptive CHOICE Project: a cost-savings analysis.

BACKGROUND: Forty-five percent of births in the United States are unintended, and the costs of unintended pregnancy and birth are substantial. Clinical and policy interventions that increase access to the most effective reversible contraceptive methods (intrauterine devices and contraceptive implants) have potential to generate significant cost savings. Evidence of cost savings for these interventions is needed. OBJECTIVE: The purpose of this study was to conduct a cost-savings analysis of the Contraceptive CHOICE Project, which provided counseling and no-cost contraception, to demonstrate the value of investment in enhanced contraceptive care to the Missouri Medicaid program. STUDY DESIGN: The Contraceptive CHOICE Project was a prospective cohort study of 9256 reproductive-age women who were enrolled between 2007 and 2011. Study follow-up was completed October 2013. This analysis includes 5061 Contraceptive CHOICE Project participants who were current Missouri Medicaid beneficiaries or were uninsured and reported household incomes <201% of the federal poverty line. We created a simulated comparison group of women who were receiving care through the Missouri Title X program and modeled the contraception and pregnancy outcomes that would have occurred in the absence of the Contraceptive CHOICE Project. Data about contraceptive use for the comparison group (N=5061) were obtained from the Missouri Title X program and adjusted based on age, race, ethnicity, and income. To make an accurate comparison that would account for the difference in the 2 populations, we used our simulation model to estimate total Contraceptive CHOICE Project costs and total comparison group costs. We reported all costs in 2013 dollars to account for inflation. RESULTS: Among the Contraceptive CHOICE Project participants who were included, the uptake of intrauterine devices and implants was 76.1% compared with 4.8% among the comparison group. The estimated contraceptive cost for the simulated Contraceptive CHOICE Project group was $4.0 million vs $2.3 million for the comparison group. The estimated numbers of unintended pregnancies and births averted among the simulated Contraceptive CHOICE Project group compared with the comparison group were 927 and 483, respectively, which represented a savings in pregnancy and maternity care of $6.7 million. We estimated that the total cost savings for the state of Missouri attributable to the Contraceptive CHOICE Project was $5.0 million (40.7%) over the project duration. CONCLUSION: A program providing counseling and no-cost contraception yields substantial cost savings because of the increased uptake of highly effective contraception and consequent averted unintended pregnancy and birth.

Health Insurance and Poverty in Trajectories of Out-of-Pocket Expenditure among Low-Income Middle-Aged Adults.

OBJECTIVE: To assess the effects of longitudinal patterns of health insurance and poverty on out-of-pocket expenditures among low-income late middle-aged adults. DATA SOURCES/STUDY SETTING: Six waves (2002-2012) of the Health and Retirement Study, in combination with RAND Center for the Study of Aging data, were used. STUDY DESIGN: A random coefficient regression analysis was conducted in a multilevel growth curve framework to estimate the impact of health insurance and poverty on out-of-pocket expenditures. PRINCIPAL FINDINGS: At baseline, individuals with private insurance or unstable coverage were more likely to have out-of-pocket expenditures and financial burdens than public insurance holders. Over time, the poor who had no insurance, unstable coverage, or insurance type change had higher out-of-pocket expenditures; private coverage holders had higher odds of financial burden. CONCLUSIONS: Unstable insurance coverage had a discernible effect on the long-term, out-of-pocket expenditures among low-income adults. Findings have an important policy implication to protect poor late middle-aged population; as this population enters old age, the high financial burden it faces may exacerbate persistent socioeconomic health disparity among older people with unstable insurance coverage.

2016 Rural Enrollment in Health Insurance Marketplaces, by State.

Purpose. In this brief, cumulative county-level enrollment in Health Insurance Marketplaces (HIMs) through March 2016 is presented for state HIMs operated as Federally Facilitated Marketplaces (FFMs) and for those operated as Federally Supported State-Based Marketplaces (FS-SBMs). Enrollment rates in metropolitan and non-metropolitan areas of each state, defined as the percentage of "potential market" participants selecting plans, are presented. Monitoring annual enrollment rates provides a gauge of how well state outreach and enrollment efforts are proceeding and helps identify states with strong non-metropolitan enrollment as models for other states to emulate. Key Findings. (1) Cumulative enrollment in the HIMs in non-metropolitan counties has grown to about 1.4 million in 2016, representing 40 percent of the potential market in non-metropolitan counties. (2) Estimated enrollment rates varied considerably across the United States. In particular, estimated enrollment rates in non-metropolitan areas were substantially higher than in metropolitan areas in Hawaii, Illinois, Michigan, Montana, Maine, Nebraska, Wisconsin, and Wyoming. (3) The states that achieved the highest absolute non-metropolitan enrollment totals were Michigan, Georgia, Missouri, North Carolina, Texas, and Wisconsin. Of these, Michigan, North Carolina, and Wisconsin also had non-metropolitan enrollment rates above 50 percent. (4) About half of all states, evenly distributed by Medicaid expansion status but mostly concentrated in the Midwestern census region, had higher enrollment growth in non-metropolitan areas from 2015 to 2016, and in fact aggregated non-metropolitan growth was greater than metropolitan growth in both expansion categories.

Changing Rural and Urban Enrollment in State Medicaid Programs.

Purpose. From October 2013­before implementation of the Affordable Care Act (ACA)­to November 2016, Medicaid enrollment grew by 27 percent. However, very little attention has been paid to date to how changes in Medicaid enrollment vary within states across the rural-urban continuum. This brief reports and analyzes changes in enrollment in metropolitan, micropolitan, and rural (noncore) areas in both expansion states (those that used ACA funding to expand Medicaid coverage) and nonexpansion states (those that did not use ACA funding to expand Medicaid coverage). The findings suggest that growth has been uneven across rural-urban geography, and that Medicaid enrollment growth is lower in rural counties, particularly in nonexpansion states. Key Findings. (1) Medicaid growth rates in metropolitan counties in nonexpansion states from 2012 to 2015 were twice as large as in rural counties (14 percent compared to 7 percent). (2) In contrast, the differential in growth rates between metropolitan, micropolitan, and rural counties was much less dramatic in expansion states (growth rates of 43 percent, 38 percent, and 38 percent, respectively). (3) Analysis at the state level shows much variability across the states, even when controlling for expansion status. For example, some states with an above-average rural population, such as Tennessee and Idaho, had higher-than-average enrollment increases, with strong rural increases, while other states with similar proportions of rural residents, such as Nebraska, Oklahoma, Maine, and Wyoming, experienced enrollment decreases in micropolitan and/or rural counties. (4) States' pre-ACA Medicaid eligibility levels for parents and children affected the potential for growth. For example, some states that had higher eligibility levels (e.g., Maryland and Illinois) experienced lower Medicaid growth rates from 2012 to 2015, in part because their baseline enrollment was higher. (5) In the expansion states of Colorado and Nevada, which both have State-Based Marketplaces (SBMs), enrollment increases were over four times the overall average.

Rural Medicare Advantage Market Dynamics and Quality: Historical Context and Current Implications.

Purpose. In this policy brief, we assess variation in Medicare's star quality ratings of Medicare Advantage (MA) plans that are available to rural beneficiaries. Evidence from the recent Centers for Medicare & Medicaid Services (CMS) quality demonstration suggests that market dynamics, i.e., firms entering and exiting the MA marketplace, play a role in quality improvement. Therefore, we also discuss how market dynamics may impact the smaller and less wealthy populations that are characteristic of rural places. Key Data Findings. (1) Highly rated MA plans serving rural Medicare beneficiaries are more likely to be health maintenance organizations (HMOs) and local preferred provider organizations (PPOs), as opposed to regional PPOs. HMOs and local PPOs may be better able to improve their quality scores strategically in response to the bonus payment incentive due to existing internal monitoring mechanisms. (2) On average, the rural enrollment rate is lower in plans with higher quality scores (59 percent) than the corresponding urban rate (71 percent). This differential is likely due, in part, to lack of availability of highly rated plans in rural areas: 17.8 percent of rural counties lacked access to a plan with four or more (out of five) stars, while just 3.7 percent of urban counties lacked such access. (3) MA plans with high quality scores have been operating longer, on average, and have a lower percentage of rural counties within their contract service areas than plans with lower quality scores.

Health Insurance Marketplaces: Premium Trends in Rural Areas.

Since 2014, when the Health Insurance Marketplaces (HIMs) authorized by the Patient Protection and Affordable Care Act (ACA) were implemented, considerable premium changes have been observed in the marketplaces across the 50 states and the District of Columbia. This policy brief assesses the changes in average HIM plan premiums from 2014 to 2016, before accounting for subsidies, with an emphasis on the widening variation across rural and urban places. Since this brief focuses on premiums without accounting for subsidies, this is not intended to be an analysis of the "affordability" of ACA premiums, as that would require assessment of premiums, cost-sharing adjustments, and other factors.

Impact of the Affordable Care Act on access to care for US adults with diabetes, 2011-2012.

INTRODUCTION: Lack of health insurance is a barrier to medical care, which may increase the risk of diabetes complications and costs. The objective of this study was to assess the potential of the Affordable Care Act (ACA) of 2010 to improve diabetes care through increased health care access by comparing health care and health outcomes of insured and uninsured people with diabetes. METHODS: We examined demographics, access to care, health care use, and health care expenditures of adults aged 19 to 64 years with diabetes by using the 2011 and 2012 Medical Expenditure Panel Survey. Bivariate descriptive statistics comparing insured and uninsured persons were evaluated separately by income above and below 138% of the federal poverty level (FPL), (a threshold for expanded Medicaid eligibility in select states under the ACA) using the t test and proportion and median tests. RESULTS: Uninsured adults reported poorer access to care than insured adults, such as having a usual source of health care (69.0% vs 89.5% [≤138% FPL], 77.1% vs 94.6% [>138% FPL], both P < .001) and having lower rates of 6 key diabetes preventive care services (P ≤ .05). Insured adults with diabetes had significantly higher health care expenditures than uninsured adults ($13,706 vs $4,367, $10,838 vs $4,419, respectively, both P < .001). CONCLUSION: Uninsured adults with diabetes had less access to health care and lower levels of preventive care, health care use, and expenditures than insured adults. To the extent that the ACA increases access and coverage, uninsured people with diabetes are likely to significantly increase their health care use, which may lead to reduced incidence of diabetes complications and improved health.

Methodological innovations in public health education: transdisciplinary problem solving.

In 2008, the faculty of the Brown School at Washington University in St. Louis designed a Master of Public Health program centered on transdisciplinary problem solving in public health. We have described the rationale for our approach, guiding principles and pedagogy for the program, and specific transdisciplinary competencies students acquire. We have explained how transdisciplinary content has been organized and delivered, how the program is being evaluated, and how we have demonstrated the feasibility of this approach for a Master of Public Health degree.

Behavioral economics: "nudging" underserved populations to be screened for cancer.

Persistent disparities in cancer screening by race/ethnicity and socioeconomic status require innovative prevention tools and techniques. Behavioral economics provides tools to potentially reduce disparities by informing strategies and systems to increase prevention of breast, cervical, and colorectal cancers. With an emphasis on the predictable, but sometimes flawed, mental shortcuts (heuristics) people use to make decisions, behavioral economics offers insights that practitioners can use to enhance evidence-based cancer screening interventions that rely on judgments about the probability of developing and detecting cancer, decisions about competing screening options, and the optimal presentation of complex choices (choice architecture). In the area of judgment, we describe ways practitioners can use the availability and representativeness of heuristics and the tendency toward unrealistic optimism to increase perceptions of risk and highlight benefits of screening. We describe how several behavioral economic principles involved in decision-making can influence screening attitudes, including how framing and context effects can be manipulated to highlight personally salient features of cancer screening tests. Finally, we offer suggestions about ways practitioners can apply principles related to choice architecture to health care systems in which cancer screening takes place. These recommendations include the use of incentives to increase screening, introduction of default options, appropriate feedback throughout the decision-making and behavior completion process, and clear presentation of complex choices, particularly in the context of colorectal cancer screening. We conclude by noting gaps in knowledge and propose future research questions to guide this promising area of research and practice.

Health Insurance Marketplaces: Early Findings on Changes in Plan Availability and Premiums in Rural Places, 2014-2015.

The Patient Protection and Affordable Care Act established Health Insurance Marketplaces (HIMs) in all 50 states and the District of Columbia. This policy brief assesses the changes in HIMs from 2014 to 2015 in terms of choices offered and premiums charged, with emphasis on how these measures vary across rural and urban places. Key Findings. (1) In 74 percent of HIM rating areas, the number of firms operating increased by at least one, while the number of firms decreased in only about 6 percent of rating areas. Further, 64 percent of rating areas with fewer than 50 persons per square mile gained at least one firm. (2) There was no consistent pattern of premium increases with respect to rating area population density (used as a proxy here for the degree of "ruralness" of the rating areas). Nationally, rural areas are not experiencing higher premium increases than their urban counterparts. In fact, the lowest increases in second-lowest cost silver plan premiums occurred in the medium-density population rating areas of 51 to 300 persons per square mile. (3) Average adjusted premiums increased from 2014 to 2015 by 6.7 percent in Federally-Facilitated Marketplaces (FFMs) compared to just 1.4 percent in State-Based Marketplaces (SBMs). Regardless of SBM or FFM status, premium increases across the United States were negatively correlated with the number of firms entering the market. (4) Analysis of the most rural states, in terms of percentage of the population classified as nonmetropolitan, shows that, in general, premiums fell significantly in rural places where they had been rather high, and they increased in rural places where they had been rather low. The five rural states with the lowest premium increases had an average of 0.17 firms entering the market, while the five with the highest premium increases had an average of 0.50 firms exiting the market.

Rural Enrollment in Health Insurance Marketplaces.

Our previous analysis of 2015 Health Insurance Marketplace (HIM) data on plan availability and premiums in comparison to 2014 showed only modest premium increases in many rural areas and increased firm participation in most areas. To determine whether HIM enrollment also shows a positive trend, we analyzed county-level HIM enrollment data for 2015 by geographic categories, population density, premium, and firm participation, comparing enrollment outcomes in rural places to those in urban places. Key Findings. (1) In the Northeast, Midwest, and West census regions, estimated enrollment rates in rural (micropolitan and noncore) counties were similar to estimated rates in urban counties, while in the South, rural rates lagged behind urban rates. (2) Estimated enrollment rates at the rating area level increased as the population density of the rating area increased. (3) Various measures of rurality and geography indicate that HIMs performed well in many rural areas; however, this analysis suggests that in some rural areas, enrollment outcomes may have been weak due to factors such as the geographic scope of the rating areas, plan availability in these rating areas, or potentially fewer resources devoted to outreach and enrollment efforts. (4) In general, county-level, enrollment-weighted average premiums differed more by census region than by metropolitan, micropolitan, and noncore status. (5) Low enrollment rates at the rating area level were associated with a lower numbers of firms participating in HIMs. When three or more firms participated, enrollment rates were close to or above average.

Rural Enrollment in Health Insurance Marketplaces, by State.

Since passage of the Patient Protection and Affordable Care Act (ACA), much attention has been focused on the functioning of Health Insurance Marketplaces (HIMs). In this brief, cumulative county-level enrollment in HIMs through March 2015 is presented for state HIMs operated as Federally Facilitated Marketplaces (FFMs) and Federally Supported State-Based Marketplaces (FS-SBMs). We provide comparisons between enrollment in urban and rural areas of each state and corresponding percentages of "potential market" participants enrolled. Given differences in populations eligible for HIM enrollment, we analyzed Medicaid expansion states separately. This analysis provides a gauge of how well outreach and enrollment efforts are proceeding in the states. Key Findings. (1) Overall, people living in metropolitan areas were more likely to enroll in HIMs than were people in non-metropolitan areas, as 38.9 percent of potentially eligible metropolitan residents in Medicaid expansion states and 47.5 percent in non-expansion states were enrolled in HIMs, compared to 33.9 percent and 37.3 percent in nonmetropolitan areas, respectively. (2) Estimated enrollment rates varied considerably across the United States. In particular, estimated enrollment rates in non-metropolitan areas are higher than in metropolitan areas in Illinois, Maine, Michigan, Montana, Nebraska, Nevada, New Hampshire, North Dakota, Wisconsin, and Wyoming. (3) The states with the highest rural enrollment percentages were Maine, Michigan, Montana, North Carolina, New Hampshire, South Carolina, and Wisconsin. States with high absolute rural enrollment were about as likely to be Medicaid expansion states to be as non-expansion states, and they were slightly less likely to belong to the South census region.