Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis.

BACKGROUND: The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown. OBJECTIVE: To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH). DESIGN: Decision analytic Markov model. DATA SOURCES: Published literature. TARGET POPULATION: Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults. TIME HORIZON: Lifetime. PERSPECTIVE: U.S. health care payer. INTERVENTION: Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands. OUTCOME MEASURES: Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs. RESULTS OF BASE-CASE ANALYSIS: Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900). RESULTS OF SENSITIVITY ANALYSIS: Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters. LIMITATIONS: Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments. CONCLUSION: Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions. PRIMARY FUNDING SOURCE: National Human Genome Research Institute.

Cost-effectiveness of population-wide genomic screening for Lynch syndrome in the United States.

PURPOSE: Genomic screening for Lynch syndrome (LS) could prevent colorectal cancer (CRC) by identifying high-risk patients and instituting intensive CRC screening. We estimated the cost-effectiveness of a population-wide LS genomic screening vs family history-based screening alone in an unselected US population. METHODS: We developed a decision-analytic Markov model including health states for precancer, stage-specific CRC, and death and assumed an inexpensive test cost of $200. We conducted sensitivity and threshold analyses to evaluate model uncertainty. RESULTS: Screening unselected 30-year-olds for LS variants resulted in 48 (95% credible range [CR] = 35-63) fewer overall CRC cases per 100,000 screened individuals, leading to 187 quality-adjusted life-years (QALYs; 95% CR = 123-260) gained at an incremental cost of $24.6 million (95% CR = $20.3 million-$29.1 million). The incremental cost-effectiveness ratio was $132,200, with an 8% and 71% probability of being cost-effective at $100,000 and $150,000 per QALY willingness-to-pay thresholds, respectively. CONCLUSION: Population LS screening may be cost-effective in younger patient populations under a $150,000 willingness-to-pay per QALY threshold and with a relatively inexpensive test cost. Further reductions in testing costs and/or the inclusion of LS testing within a broader multiplex screening panel are needed for screening to become highly cost-effective.

Insurance Expansion and Hospital Emergency Department Access: Evidence From the Affordable Care Act.

BACKGROUND: Little is known about whether insurance expansion affects the location and type of emergency department (ED) use. Understanding these changes can inform state-level decisions about the Medicaid expansion under the Patient Protection and Affordable Care Act (ACA). OBJECTIVE: To investigate the effect of the 2014 ACA Medicaid expansion on the location, insurance status, and type of ED visits. DESIGN: Quasi-experimental observational study from 2012 to 2014. SETTING: 126 investor-owned, hospital-based EDs. PARTICIPANTS: Uninsured and Medicaid-insured adults aged 18 to 64 years. INTERVENTION: ACA expansion of Medicaid in January 2014. MEASUREMENTS: Number of ED visits overall, type of visit (for example, nondiscretionary or nonemergency), and average travel time to the ED. Interrupted time-series analyses comparing changes from the end of 2013 to end of 2014 for patients from Medicaid expansion versus nonexpansion states were done. RESULTS: There were 1.06 million ED visits among patients from 17 Medicaid expansion states, and 7.87 million ED visits among patients from 19 nonexpansion states. The EDs treating patients from Medicaid expansion states saw an overall 47.1% decrease in uninsured visits (95% CI, -65.0% to -29.3%) and a 125.7% (CI, 89.2% to 162.6%) increase in Medicaid visits after 12 months of ACA expansion. Average travel time for nondiscretionary conditions requiring immediate medical care decreased by 0.9 minutes (-6.2% [CI, -8.9% to -3.5%]) among all Medicaid patients from expansion states. We found little evidence of similar changes among patients from nonexpansion states. LIMITATION: Results reflect shifts in ED care at investor-owned facilities, which limits generalizability to other hospital types. CONCLUSION: Meaningful changes in insurance status and location and type of ED visits in the first year of ACA Medicaid expansion were found, suggesting that expansion provides patients with a greater choice of hospital facilities. PRIMARY FUNDING SOURCE: Robert Wood Johnson Foundation.

Sepsis-Associated 30-Day Risk-Standardized Readmissions: Analysis of a Nationwide Medicare Sample.

OBJECTIVES: To determine national readmission rates among sepsis survivors, variations in rates between hospitals, and determine whether measures of quality correlate with performance on sepsis readmissions. DESIGN: Cross-sectional study of sepsis readmissions between 2008 and 2011 in the Medicare fee-for-service database. SETTING: Acute care, Medicare participating hospitals from 2008 to 2011. PATIENTS: Septic patients as identified by International Classification of Diseases, Ninth Revision codes using the Angus method. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We generated hospital-level, risk-standardized, 30-day readmission rates among survivors of sepsis and compared rates across region, ownership, teaching status, sepsis volume, hospital size, and proportion of underserved patients. We examined the relationship between risk-standardized readmission rates and hospital-level composite measures of quality and mortality. From 633,407 hospitalizations among 3,315 hospitals from 2008 to 2011, median risk-standardized readmission rates was 28.7% (interquartile range, 26.1-31.9). There were differences in risk-standardized readmission rates by region (Northeast, 30.4%; South, 29.6%; Midwest, 28.8%; and West, 27.7%; p < 0.001), teaching versus nonteaching status (31.1% vs 29.0%; p < 0.001), and hospitals serving the highest proportion of underserved patients (30.6% vs 28.7%; p < 0.001). The best performing hospitals on a composite quality measure had highest risk-standardized readmission rates compared with the lowest (32.0% vs 27.5%; p < 0.001). Risk-standardized readmission rates was lower in the highest mortality hospitals compared with those in the lowest (28.7% vs 30.7%; p < 0.001). CONCLUSIONS: One third of sepsis survivors were readmitted and wide variation exists between hospitals. Several demographic and structural factors are associated with this variation. Measures of higher quality in-hospital care were correlated with higher readmission rates. Several potential explanations are possible including poor risk standardization, more research is needed.

Employment Outcomes After Critical Illness: An Analysis of the Bringing to Light the Risk Factors and Incidence of Neuropsychological Dysfunction in ICU Survivors Cohort.

OBJECTIVES: To characterize survivors' employment status after critical illness and to determine if duration of delirium during hospitalization and residual cognitive function are each independently associated with decreased employment. DESIGN: Prospective cohort investigation with baseline and in-hospital clinical data and follow-up at 3 and 12 months. SETTING: Medical and surgical ICUs at two tertiary-care hospitals. PATIENTS: Previously employed patients from the Bringing to Light the Risk Factors and Incidence of Neuropsychological Dysfunction in ICU Survivors study who survived a critical illness due to respiratory failure or shock were evaluated for global cognition and employment status at 3- and 12-month follow-up. MEASUREMENTS AND MAIN RESULTS: We used multivariable logistic regression to evaluate independent associations between employment at both 3 and 12 months and global cognitive function at the same time point, and delirium during the hospital stay. At 3-month follow-up, 113 of the total survival cohort of 448 (25%) were identified as being employed at study enrollment. Of these, 94 survived to 12-month follow-up. At 3- and 12-month follow-up, 62% and 49% had a decrease in employment, 57% and 49% of whom, respectively, were newly unemployed. After adjustment for physical health status, depressive symptoms, marital status, level of education, and severity of illness, we did not find significant predictors of employment status at 3 months, but better cognition at 12 months was marginally associated with lower odds of employment reduction at 12 months (odds ratio, 0.49; p = 0.07). CONCLUSIONS: Reduction in employment after critical illness was present in the majority of our ICU survivors, approximately half of which was new unemployment. Cognitive function at 12 months was a predictor of subsequent employment status. Further research is needed into the potential relationship between the impact of critical illness on cognitive function and employment status.

The Growing Integration of Physician Practices: With a Medicaid Side Effect.

BACKGROUND: Strategic alignment and integration is currently in vogue throughout the health care industry, but its diffusion and pace have not been documented in recent years. The full range of downstream implications from greater alignment between hospitals and physicians has also not been completely explored. OBJECTIVES: We track the organizational landscape among all office-based US physician practices from 2009 to 2015 and document the degree of vertical integration over time. Then, we examine the implications of vertical integration on practices' acceptance of publicly insured patients. RESEARCH DESIGN: We use descriptive trends and linear regression models with practice level fixed effects to capture the relationships between within-office changes in integration behavior and changes in public payer acceptance. RESULTS: Independent (nonintegrated) physician practices are still the most common organizational type, but their share is declining as the share of practices integrated with a health system increases 3-fold between 2009 and 2015. Although >80% of practices that are part of a health system accept Medicaid, <60% of independent practices will see these patients. Vertically integrating with a health system makes it more likely a practice will start seeing Medicaid patients. CONCLUSIONS: Integration-and possibly consolidation-appears to be occurring and may be increasing over time in the United States. However, it also seems to increase the number of physician practices participating in the Medicaid program. This beneficial side effect has not been previously documented and should be kept in mind as policymakers weigh the pros and cons of a more integrated health care system.

Role of Geography and Nurse Practitioner Scope-of-Practice in Efforts to Expand Primary Care System Capacity: Health Reform and the Primary Care Workforce.

BACKGROUND: Little is known about the geographic distribution of the overall primary care workforce that includes both physician and nonphysician clinicians--particularly in areas with restrictive nurse practitioner scope-of-practice laws and where there are relatively large numbers of uninsured. OBJECTIVE: We investigated whether geographic accessibility to primary care clinicians (PCCs) differed across urban and rural areas and across states with more or less restrictive scope-of-practice laws. RESEARCH DESIGN: An observational study. SUBJECTS: 2013 Area Health Resource File (AHRF) and US Census Bureau county travel data. MEASURES: The measures included percentage of the population in low-accessibility, medium-accessibility, and high-accessibility areas; number of geographically accessible primary care physicians (PCMDs), nurse practitioners (PCNPs), and physician assistants (PCPAs) per 100,000 population; and number of uninsured per PCC. RESULTS: We found divergent patterns in the geographic accessibility of PCCs. PCMDs constituted the largest share of the workforce across all settings, but were relatively more concentrated within urban areas. Accessibility to nonphysicians was highest in rural areas: there were more accessible PCNPs per 100,000 population in rural areas of restricted scope-of-practice states (21.4) than in urban areas of full practice states (13.9). Despite having more accessible nonphysician clinicians, rural areas had the largest number of uninsured per PCC in 2012. While less restrictive scope-of-practice states had up to 40% more PCNPs in some areas, we found little evidence of differences in the share of the overall population in low-accessibility areas across scope-of-practice categorizations. CONCLUSIONS: Removing restrictive scope-of-practice laws may expand the overall capacity of the primary care workforce, but only modestly in the short run. Additional efforts are needed that recognize the locational tendencies of physicians and nonphysicains.

Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health.

OBJECTIVE: To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). DATA SOURCES AND STUDY SETTING: Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. STUDY DESIGN: Cross-sectional, descriptive analysis. DATA COLLECTION/EXTRACTION METHODS: The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. PRINCIPAL FINDINGS: HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. CONCLUSIONS: The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.

Primary Care Physicians In Medicare Advantage Were Less Costly, Provided Similar Quality Versus Regional Average.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9 percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8 percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4 percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

Physician patient sharing relationships within insurance plan networks.

OBJECTIVE: To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks. DATA SOURCES: Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA. STUDY DESIGN: Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage [MA], Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth. DATA EXTRACTION: We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted MA (n = 941 networks), Medicaid managed care (n = 293), and individually-purchased (n = 332) and small-group (n = 501) plans. PRINCIPAL FINDINGS: Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value >0.05 for all comparisons). An exception was among national versus local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networksvs. 64.7% for local and regional networks for PCP-cardiology). CONCLUSIONS: Given recent trends toward narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight.

Unsolicited Patient Complaints Following the 21st Century Cures Act Information-Blocking Rule.

Importance: The 21st Century Cures Act includes an information-blocking rule (IBR) that requires health systems to provide patients with immediate access to their health information in the electronic medical record upon request. Patients accessing their health information before they receive an explanation from their health care team may experience confusion and may be more likely to share unsolicited patient complaints (UPCs) with their health care organization. Objective: To evaluate the quantity of UPCs about physicians before and after IBR implementation and to identify themes in UPCs that may identify patient confusion, fear, or anger related to the release of information. Design, Setting, and Participants: This retrospective cohort study was conducted with an interrupted time-series analysis of UPCs spanning January 1, 2020, to June 30, 2022. The data were obtained from a single academic medical center, Vanderbilt University Medical Center, at which the IBR was implemented on January 20, 2021. Data analysis was performed from January 11 to July 15, 2023. Exposure: Implementation of the IBR on January 20, 2021. Main Outcomes and Measures: The primary outcome was the monthly rate of UPCs before and after IBR implementation. A qualitative analysis was performed for UPCs received after IBR implementation. The Wilcoxon rank-sum test was used to compare monthly complaints between the pre- and post-IBR groups. The Pearson χ2 test was used to compare proportions of complaints by UPC category between time periods. Results: The medical center received 8495 UPCs during the study period: 3022 over 12 months before and 5473 over 18 months after institutional IBR implementation. There was no difference in the monthly proportions of UPCs per 1000 patient encounters before (median, 0.81 [IQR, 0.75-0.88]) and after (median, 0.83 [IQR, 0.77-0.89]) IBR implementation (difference in medians, -0.02 [95% CI, -0.12 to 0.07]; P =.86). Segmented regression analysis revealed no difference in monthly UPCs (ß [SE], 0.03 [0.09]; P =.72). Conclusions and Relevance: In this cohort study, implementation of the Cures Act IBR was not associated with an increase in monthly rates of UPCs. These findings suggest that review of UPCs identified as IBR-specific complaints may allow clinicians and organizations to prepare patients that their test and procedure results may be available before clinicians are able to review them and respond.

Access to In-Network Hospitals in Tennessee During the COVID-19 Pandemic.

This cross-sectional study assesses the daily percentage of floor and intensive care unit bed available at in-network hospitals for patients with COVID-19 in Tennessee.

Difference-in-differences for categorical outcomes.

OBJECTIVE: To discuss and develop difference-in-difference estimators for categorical outcomes and apply them to estimate the effect of the Affordable Care Act's Medicaid expansion on insurance coverage. DATA SOURCES: Secondary analysis of Survey on Income and Program Participation (SIPP) data on health insurance coverage types before (January 2013) and after (December 2015) Medicaid expansion in 39 US states (19 expansion and 20 non-expansion). STUDY DESIGN: We develop difference-in-difference methods for repeated measures (panel data) of categorical outcomes. We discuss scale-dependence of DID assumptions for marginal and transition effect estimates and specify a new target estimand: the difference between outcome category transitions under treatment versus no treatment. We establish causal assumptions about transitions that are sufficient to identify this and a marginal target estimand. We contrast the marginal estimands identified by the transition approach versus an additive assumption only about marginal evolution. We apply both the marginal and transition approaches to estimate the effects of Medicaid expansion on health insurance coverage types (employer-sponsored; other private, non-group; public; and uninsured). DATA EXTRACTION: We analyzed 16,027 individual survey responses from people aged 18-62 years in the 2014 SIPP panel. PRINCIPAL FINDINGS: We show that the two identifying assumptions are equivalent (on the scale of the marginals) if either the baseline marginal distributions are identical or the marginals are constant in both groups. Applying our transitions approach to the SIPP data, we estimate a differential increase in transitions from uninsured to public coverage and differential decreases in transitions from uninsured to private, non-group coverage and in remaining uninsured. CONCLUSIONS: By comparing the assumption that marginals are evolving in parallel to an assumption about transitions across outcome values, we illustrate the scale-dependence of difference-in-differences. Our application shows that studying transitions can illuminate nuances obscured by changes in the marginals.

How The ACA Dented The Cost Curve.

Numerous provisions of the Affordable Care Act (ACA) were designed to make health care more affordable, yet the act's cumulative effects on health care costs are still debated. A key question is whether or not the ACA reduced the annual rate at which total national health care spending increased and brought per capita spending growth rates down. We review the direct and indirect effects of the ACA on spending across segments of the health insurance market. We highlight areas where the ACA has affected spending, but we emphasize that the ACA's long-run impact on spending will depend on sustaining the adjustments made to provider payment systems and expanding the emphasis on value across payers throughout the ACA's second decade and beyond.

Medicaid Expansion Slowed Rates Of Health Decline For Low-Income Adults In Southern States.

Of the fourteen states that have not expanded eligibility for Medicaid, nine are in the southern census region, and two others border that region. Ongoing debate over the merits of Medicaid expansion in these states has focused, in part, on whether the safety net provides sufficient access for uninsured low-income Americans. We analyzed longitudinal survey and vital status data from the twelve-state Southern Community Cohort Study (SCCS) for 15,356 nonelderly adult participants with low incomes, 86 percent of whom were enrolled at community health centers. In difference-in-differences analyses, we compared changes in self-reported health between participants in four expansion and eight nonexpansion states before (2008-13) and after (2015-17) Medicaid expansion. We found that a higher proportion of SCCS participants in expansion states reported increases in Medicaid coverage (a differential change of 7.6 percentage points), a lower proportion experienced a health status decline (-1.8 percentage points), and a higher proportion maintained their baseline health status (1.4 percentage points). The magnitude of estimated reductions in health declines would meaningfully affect a nonexpansion state's health ranking in our sample if that state elected to expand Medicaid. Our results suggest that for low-income adults in the South, Medicaid expansion yielded health benefits-even for those with established access to safety-net care.