The Community Participation Transition after Stroke (COMPASS) randomized controlled trial: Impact 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 pre-stroke (n=183). INTERVENTION: Participants randomized to intervention (n=85) received home modifications and self-management training from an occupational therapist over 4 visits in the home. Participants randomized to control (n=98) received the same number of visits consisting of stroke education. MAIN OUTCOME MEASURES: Death, skilled nursing facility (SNF) admission, 30-day rehospitalization, 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% to 96.6%%) and death (cumulative survival 100%) compared to the control group (SNF cumulative survival 78.9%, 95% CI 70.4% to 87.4%; P=0.039; death cumulative survival 87.3%, 95% CI 79.9% to 94.7%, P=0.001). Thirty-day rehospitalization also appeared lower among intervention participants (cumulative survival 95.1%, 95% CI 90.5% to 99.8%) compared to control participants (cumulative survival 86.3%, 95% CI 79.4% to 93.2%, P=0.050) 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 to 6.5) and the control group (7.2 falls per 1000 participant-days, 95% CI 6.2 to 8.3; incidence rate ratio [IRR] 0.78, 95% CI 0.46 to 1.33, P=0.361). CONCLUSIONS: A home-based OT-led intervention that helps stroke survivors transition 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.

Economic Evaluation of Ultrasound-guided Central Venous Catheter Confirmation vs Chest Radiography in Critically Ill Patients: A Labor Cost Model.

INTRODUCTION: Despite evidence suggesting that point-of-care ultrasound (POCUS) is faster and non-inferior for confirming position and excluding pneumothorax after central venous catheter (CVC) placement compared to traditional radiography, millions of chest radiographs (CXR) are performed annually for this purpose. Whether the use of POCUS results in cost savings compared to CXR is less clear but could represent a relative advantage in implementation efforts. Our objective in this study was to evaluate the labor cost difference for POCUS-guided vs CXR-guided CVC position confirmation practices. METHODS: We developed a model to evaluate the per patient difference in labor cost between POCUS-guided vs CXR-guided CVC confirmation at our local urban, tertiary academic institution. We used internal cost data from our institution to populate the variables in our model. RESULTS: The estimated labor cost per patient was $18.48 using CXR compared to $14.66 for POCUS, resulting in a net direct cost savings of $3.82 (21%) per patient using POCUS for CVC confirmation. CONCLUSION: In this study comparing the labor costs of two approaches for CVC confirmation, the more efficient alternative (POCUS-guided) is not more expensive than traditional CXR. Performing an economic analysis framed in terms of labor costs and work efficiency may influence stakeholders and facilitate earlier adoption of POCUS for CVC confirmation.

The Impact of Sugar-Sweetened Beverage Taxes by Household Income: A Multi-City Comparison of Nielsen Purchasing Data.

Due to the role that sugar-sweetened beverages (SSBs) play in the obesity epidemic, SSB taxes have been enacted in the United States in the California cities of Albany, Berkeley, Oakland, and San Francisco, as well as in Boulder, Philadelphia, and Seattle. We pooled five years of Nielsen Consumer Panel and Retail Scanner Data (2014-18) to examine purchasing behaviors in and around these cities that have instituted SSB taxes. We included households that were either subject to the tax during the study period or were in surrounding areas within the same state. The goal was to test for the differential impact of SSB taxes by income level and type of tax. Multivariate analyses of beverage purchases found that (1) there is a dose-response relationship with the size of the SSB tax; (2) the Philadelphia tax, which is the only one that includes low-calorie beverages, is associated with greater reductions in SSB purchases and an increase in bottled water purchase; and (3) approximately 72% of the tax is passed through to consumers, but this does not vary by income level of the household. Few income-related effects were detected. Overall, our findings suggest that the Philadelphia model may be the most effective at encouraging healthy habits in beverage choice.

Understanding contributors to racial and ethnic inequities in COVID-19 incidence and mortality rates.

BACKGROUND: Racial inequities in Coronavirus 2019 (COVID-19) have been reported over the course of the pandemic, with Black, Hispanic/Latinx, and Native American individuals suffering higher case rates and more fatalities than their White counterparts. METHODS: We used a unique statewide dataset of confirmed COVID-19 cases across Missouri, linked with historical statewide hospital data. We examined differences by race and ethnicity in raw population-based case and mortality rates. We used patient-level regression analyses to calculate the odds of mortality based on race and ethnicity, controlling for comorbidities and other risk factors. RESULTS: As of September 10, 2020 there were 73,635 confirmed COVID-19 cases in the State of Missouri. Among the 64,526 case records (87.7% of all cases) that merged with prior demographic and health care utilization data, 12,946 (20.1%) were Non-Hispanic (NH) Black, 44,550 (69.0%) were NH White, 3,822 (5.9%) were NH Other/Unknown race, and 3,208 (5.0%) were Hispanic. Raw cumulative case rates for NH Black individuals were 1,713 per 100,000 population, compared with 2,095 for NH Other/Unknown, 903 for NH White, and 1,218 for Hispanic. Cumulative COVID-19-related death rates for NH Black individuals were 58.3 per 100,000 population, compared with 38.9 for NH Other/Unknown, 19.4 for NH White, and 14.8 for Hispanic. In a model that included insurance source, history of a social determinant billing code in the patient's claims, census block travel change, population density, Area Deprivation Index, and clinical comorbidities, NH Black race (OR 1.75, 1.51-2.04, p<0.001) and NH Other/Unknown race (OR 1.83, 1.36-2.46, p<0.001) remained strongly associated with mortality. CONCLUSIONS: In Missouri, COVID-19 case rates and mortality rates were markedly higher among NH Black and NH Other/Unknown race than among NH White residents, even after accounting for social and clinical risk, population density, and travel patterns during COVID-19.

Predicting Future Utilization Using Self-Reported Health and Health Conditions in a Longitudinal Cohort Study: Implications for Health Insurance Decision Support.

Decision support techniques and online algorithms aim to help individuals predict costs and facilitate their choice of health insurance coverage. Self-reported health status (SHS), whereby patients rate their own health, could improve cost-prediction estimates without requiring individuals to share personal health information or know about undiagnosed conditions. We compared the predictive accuracy of several models: (1) SHS only, (2) a "basic" model adding health-related variables, and (3) a "full" model adding measures of healthcare access. The Medical Expenditure Panel Survey was used to predict 2015 health expenditures from 2014 data. Relative performance was assessed by comparing adjusted-R2 values and by reporting the predictive accuracy of the models for a new cohort (2015-2016 data). In the SHS-only model, those with better SHS were less likely to incur expenditures. However, after accounting for health variables, those with better SHS were more likely to incur expenses. In the full model, SHS was no longer predictive of incurring expenses. Variables indicating better access to care were associated with higher likelihood of spending and higher spending. The full model (R2 = 0.290) performed slightly better than the basic model (R2 = 0.240), but neither performed well at the upper tail of the cost distribution. While our SHS-based models perform well in the aggregate, predicting population-level risk well, they are not sufficiently accurate to guide individuals' insurance shopping decisions in all cases. Policies that rely heavily on health insurance consumers making individually optimal choices cannot assume that decision tools can accurately anticipate high costs.

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.

Unmet Need for Total Joint Arthroplasty in Medicaid Beneficiaries After Affordable Care Act Expansion.

BACKGROUND: The utilization of total hip arthroplasty (THA) and total knee arthroplasty (TKA) increased after Medicaid expansion under the U.S. Affordable Care Act (ACA), suggesting a potential unmet need for THA and TKA. We examined the timing of THA and TKA in patients after obtaining Medicaid expansion insurance coverage. We hypothesized that patients with Medicaid expansion insurance would undergo a surgical procedure sooner than patients in traditional Medicaid populations. METHODS: We used administrative data from a Medicaid managed care company to determine the timing of primary THA and TKA in patients who were 18 to 64 years of age in 4 states with Medicaid expansion (Illinois, Ohio, Oregon, and Washington) and 4 states without Medicaid expansion (Louisiana, Mississippi, Texas, and Wisconsin) from 2008 to 2015. The insurance types were Medicaid expansion, Medicaid plans for Supplemental Security Income (SSI), or Temporary Assistance for Needy Families (TANF). Roughly, these 3 groups correspond to relatively healthy childless adults, relatively unhealthy disabled adults, and parents of children with Medicaid insurance. The main outcome measure was time from enrollment to the surgical procedure. The primary exposure of interest was insurance type. We used a generalized linear regression model to adjust for patient age, sex, social deprivation, surgeon supply and reimbursement, and state-level Medicaid enrollment. RESULTS: In the unadjusted analysis of 4,117 patients, there was a significantly shorter time from enrollment to THA and TKA for the expansion group (median, 7.5 months) relative to the SSI group (median, 16.1 months; p < 0.0001) and the TANF group (median, 12.2 months; p < 0.0001). In the adjusted analysis, the time from enrollment to THA and TKA was significantly shorter in the expansion group (ß, -1.21 [95% confidence interval (CI), -1.35 to -1.07]; p < 0.001) compared with the TANF group (ß, -0.27 [95% CI, -0.38 to -0.17]; p < 0.001) and the SSI group (reference). Compared with the SSI group, these coefficients are equivalent to a 70% shorter time to the surgical procedure in the expansion group and a 24% shorter time to the surgical procedure in the TANF group. CONCLUSIONS: Our findings suggest an unmet need for THA and TKA among newly enrolled Medicaid expansion beneficiaries. This need should be considered by surgeons, hospitals, and policymakers in ensuring access to care. Furthermore, consideration should be given to existing insurance-based disparities in access to orthopaedic care, as these may be exacerbated by an increased demand for THA and TKA from Medicaid expansion beneficiaries.

The cumulative impact of health insurance on health status.

OBJECTIVE: To add to the evidence base on causal linkages between health insurance coverage and health status, controlling for sociodemographic factors, by analyzing longitudinal data. DATA SOURCE: Secondary data from the Panel Study of Income Dynamics (PSID), 2009-17, which is a longitudinal, multigenerational study covering a wide array of socioeconomic topics that began in 1968 but has only recently begun collecting useful information on individual health insurance. STUDY DESIGN: 2017 data on self-reported health status, work limitations, and death were analyzed as outcomes based upon the degree of exposure to health insurance in 2011-17. All variables were collected biannually for four years beginning in 2011. Having health insurance at each point in time was, in turn, modeled as a function of several sociodemographic factors. DATA EXTRACTION METHODS: Data were downloaded using the crosswalk tool available at the PSID website. Because individual health insurance questions were only asked of heads and spouses in households beginning in 2011, we analyzed only these records. PRINCIPAL FINDINGS: Among respondents who were not in fair or poor health in 2009, each additional 2 years of subsequent reported insurance coverage reduced the chance of reporting fair or poor health in 2017 by 10 percent; however, this effect was not present for black respondents. CONCLUSIONS: Our results suggest that the effect of health insurance on health status may compound over time, although unevenly by race. Since people who report fair or poor health status represent the bulk of utilization and spending, our findings provide evidence in support of viewing coverage expansions as investments that will pay dividends in the form of lower utilization over time. More work is needed to produce detailed estimates of cost savings, which may in turn influence policy, as well as to understand and address the source of racial disparity.

Association of Medicaid Expansion With Insurance Coverage Among Children With Cancer.

Importance: Despite evidence of improved insurance coverage under the Affordable Care Act and Medicaid expansion among adults with cancer, little is known regarding the association of these policies with coverage among children with cancer. Objective: To assess the association of early Medicaid expansion with rates of Medicaid coverage, private coverage, and no uninsurance among children with cancer. Design, Setting, and Participants: This cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2007, to December 31, 2015, to identify children diagnosed with cancer at ages 0 to 14 years in the United States. Data were analyzed from July 27, 2017, to October 7, 2019. Exposures: Changes in insurance status at diagnosis after early Medicaid expansion in California, Connecticut, Washington, and New Jersey (EXP states) were compared with changes in nonexpansion (NEXP) states (Arkansas, Georgia, Hawaii, Iowa, Kentucky, Louisiana, Michigan, New Mexico, and Utah). Main Outcomes and Measures: Difference-in-differences (DID) analyses were used to compare absolute changes in insurance status (uninsured, Medicaid, private/other) at diagnosis before (2007 to 2009) and after (2011 to 2015) expansion in EXP relative to NEXP states. Results: A total of 21 069 children (11 265 [53.5%] male; mean [SD] age, 6.18 [4.57] years) were included. A 5.25% increase (95% CI, 2.61%-7.89%; P < .001) in Medicaid coverage in children with cancer was observed in EXP vs NEXP states, with larger increases among children of counties with middle to high (adjusted DID estimates, 10.18%; 95% CI, 4.22%-16.14%; P = .005) and high (adjusted DID estimates, 6.13%; 95% CI, 1.10%-11.15%; P = .05) poverty levels (P = .04 for interaction). Expansion-associated reductions of children reported as uninsured (-0.73%; 95% CI, -1.49% to 0.03%; P = .06) and with private or other insurance (-4.52%; 95% CI, -7.16% to -1.88%; P < .001) were observed. For the latter, the decrease was greater for children from counties with middle to high poverty (-9.00%; 95% CI, -14.98% to -3.02%) and high poverty (-6.38%; 95% CI, -11.36% to -1.40%) (P = .04 for interaction). Conclusions and Relevance: In this study, state Medicaid expansions were associated with increased Medicaid coverage in children with cancer overall and in some subgroups primarily owing to switching from private coverage, particularly in counties with higher levels of poverty but also through reductions in the uninsured.

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.

Effect Of Population Size On Rural Health Insurance Premiums In The Federal Employees Health Benefits Program.

In the study of health insurance access and affordability in rural areas, a recurring issue is to understand the challenges that programs based upon the competitive market model, such as the Affordable Care Act's Marketplaces, may experience in less populated areas. This article analyzes data for 2013-16 from the Federal Employees Health Benefits Program, focusing on premium and enrollment data for "state-specific" plans-which offer insurance policies and set premiums at the regional level. In nonmetropolitan counties, each additional plan enrollee was associated with a $0.10 lower per capita biweekly premium, whereas this effect was trivial in metropolitan counties. Low health care provider counts were not associated with higher premiums in nonmetropolitan areas, nor was the degree of insurer competition an important predictor of premiums. However, there was substantial correlation over time, which suggests that some variables may be viewed less as sources of premium variation and more as influencing long-term premium levels. These findings suggest that small risk pools may contribute to the challenges faced by private plans in rural areas, in which case risk reinsurance is a potential policy solution.

A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers' Health Insurance Plan Choice.

Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.

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.

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.