Health Services Research最新文献

Objective: To examine the effects of offering non-emergency medical transportation (NEMT) on care utilization among low-income and disabled beneficiaries in Medicare Advantage (MA).

Study setting and design: We leveraged the 2019 expansion of "primarily health related" benefits to study the impact of offering NEMT on enrollees' utilization of care. We used an event study model to compare changes in care for beneficiaries enrolled in plans that began offering a NEMT benefit in 2019 versus those in plans that did not.

Data sources and analytic sample: We used MA plan benefit package, Medicare enrollment, and MA encounter data for years 2016-2019 to identify plans offering NEMT, low-income and disabled beneficiaries enrolled in these plans, and model covariates.

Principal findings: Offering of NEMT was associated with little change in utilization. We found a statistically insignificant 1.4% increase in the probability of receiving an annual wellness visit (Coef. 0.006; 95% CI, -0.007-0.018, p = 0.371) and a 4.0% decrease in ambulance use days (Coef. -0.012; 95% CI, -0.033-0.010, p = 0.290). We did not find evidence of statistically significant or economically meaningful changes in outpatient evaluation and management, procedure, imaging, and emergency room visits.

Conclusions: In the first year of NEMT benefit offerings by MA plans, we found no detectable evidence of associated changes in care utilization among low-income and disabled beneficiaries. Conclusions about the potential value of coverage for NEMT are limited by the short evaluation period and lack of data on NEMT benefit generosity and use.

Objective: To develop and validate a clinical risk prediction algorithm to identify breast cancer survivors at high risk for adverse outcomes.

Study setting and design: Our national retrospective analysis used cross-validated random forest machine learning models to separately predict the risk of all-cause death, cancer-specific death, claims-derived risk of recurrence, and other adverse health outcomes within 3 and 5 years following treatment completion.

Data sources and analytic sample: Our study used the Surveillance and Epidemiology End Results (SEER) registry-Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey (SEER-CAHPS) linked data for survivors diagnosed between 2003 and 2011, with follow-up claims data to 2017.

Principal findings: Within the 3-year follow-up period, 372/4516 survivors (mean age 75.1; 81.7% white) in the primary cohort (8.2%) died, 111 from cancer (2.5%), 665 (14.7%) experienced cancer recurrence, and 488 (10.8%) were hospitalized for adverse health outcomes. The algorithm's prediction resulted in 91.9% out-of-sample accuracy (the percent of observations classified correctly) and a 37.6% Cohen's Kappa (i.e., improvement over an uninformed model). Out-of-sample accuracy was 97.5% (44% improvement) for predicting cancer-specific death, 85% (26% improvement) for recurrence, and 89% (28% improvement) for other adverse health outcomes. Important predictors across outcomes included geographic region, age, frailty, comorbidity, time since diagnosis, and out-of-pocket cost responsibility.

Conclusions: Machine learning models accurately predicted relevant adverse survivorship outcomes, driven primarily by non-cancer specific factors. Breast cancer survivors at high risk for adverse outcomes may benefit from more intensive care, whereas those at low risk may be more appropriately managed by primary care.

Objective: To analyze the conditional association between provider and organizational factors and routine cancer screening for older Medicaid enrollees before and during the COVID-19 pandemic.

Study setting and design: This study analyzed pre-pandemic (2018/2019; n = 110,882) and pandemic (2020/2021; n = 107,451) cohorts of New Jersey (NJ) Medicaid enrollees aged 50-75. Using linear probability models, we evaluated how provider and organizational characteristics, including interactions with pandemic years, influenced screening for breast, cervical, colorectal, and lung cancers. Models controlled for enrollees' demographic and clinical characteristics and geographic factors.

Data sources and analytic sample: Claims data from the 2016-2021 NJ Medicaid Management Information System were linked to Medicare Provider and Specialty files. The sample included Medicaid enrollees with an assigned primary care provider and no prior cancer diagnosis.

Principal findings: Higher patient panel sizes were consistently associated with increased screening for breast (20.4%, 95% confidence interval (CI): 13.9%-26.8%), cervical (24.1%, 95% CI: 16.6%-31.5%), and lung cancer (63.1%; 95% CI: 17.4%-108.6%) during the pandemic. Obstetrician-gynecologist providers were linked to higher screening rates for breast (50.6%, 95% CI: 41.6%-59.5%) and cervical cancers (70.5%, 95% CI: 52.3%-88.9%), even during the pandemic. Female providers improved screening rates for breast (7.6%, 95% CI: 2.8%-12.3%), cervical (3.8%, 95% CI: 0.10%-7.5%), and colorectal cancer (5.8%, 95% CI: -2.7%-14.4%) among female enrollees. Provider age was unrelated to breast, cervical, or colorectal screening; however, in 2021, lung cancer screening was 23% lower for patients of clinicians aged 62 and above.

Conclusions: Large group practices effectively maintained breast and cervical cancer screening during the pandemic while exhibiting mixed results for colorectal and lung cancers. Provider characteristics such as gender and specialty also significantly impacted screening rates. Supporting large practices and addressing barriers in smaller practices are key to improving cancer prevention, especially during crises.

Objective: To measure discrepancies in risk adjustment scores using only Medicaid or Veterans Health Administration (VA) diagnoses for Veterans dually enrolled in VA and Medicaid.

Study setting and design: Veterans aged 18-64 enrolled in the VA and Medicaid for at least one full calendar year during 2017-2020. We compared the number and overlap of annual diagnoses derived from VA and Medicaid data. We also calculated Charlson, Elixhauser, and Centers for Medicare and Medicaid Hierarchical Condition Categories Version 21 (CMS-V21) risk scores using VA-only, Medicaid-only, and combined VA-Medicaid data for each person-year. We used intraclass correlations within risk measures to compare scores across risk measures.

Data sources and analytic sample: We used data from the VA's Assistant Deputy Undersecretary for Health's (ADUSH) enrollment files regarding age and VA Priority Group to select our cohort of VA enrollees. We used T-MSIS Analytic Files (TAF) and the Demographics and Enrollment (DE) file to determine Medicaid enrollment.

Principal findings: Our study cohort contained 183,018 dual-enrollees with service-connected disabilities representing 405,318 person years and 219,977 dual enrollees without service-connected disabilities (531,948 person years). On average, service-connected individuals had 9.1 fewer diagnoses from Medicaid-only data than from VA-only data (95% Confidence Interval (CI): [9.0, 9.1]) and 5.0 fewer for non-service-connected Veterans (95% CI: [4.9, 5.1]). Intraclass correlations between VA-only data and combined VA-Medicaid scores had higher correlations for Charlson (0.816 vs. 0.591 for service connected, 0.722 vs. 0.638 for non-service connected) and Elixhauser (0.818 vs. 0.609 for service-connected, 0.723 to 0.702 non-service-connected) scores, while Medicaid-only scores had higher correlations for CMS V21 (0.756 vs. 0.666 for service-connected, 0.795 to 0.542 for non service-connected).

Conclusions: Medicaid and VA data represent non-overlapping diagnoses data in three common risk scores. Researchers should consider combining records to calculate disease burden for dual-enrolled Veterans to ensure complete capture of risk.

Objective: To evaluate patient- and area-level factors in relation to telehealth visit use in cancer care.

Study setting and design: We surveyed a cohort of adults with an upcoming healthcare visit related to their cancer treatment at two academic medical centers (one in central North Carolina and one in southeast Michigan) and their community affiliates. Black adults and those with a scheduled telehealth visit were purposively oversampled during recruitment. We linked respondent residential addresses to area-level measures, including broadband access. The two patient-reported outcomes of interest were (1) whether a choice in visit type (virtual or in-person) was offered and (2) scheduled visit type.

Data sources and analytic sample: We assembled a cohort of 773 adults (response rate = 15%). After excluding nonrecall for being offered a choice, the analytic sample was 725 adults.

Principal findings: The sample was 46% aged < 65 years, 42% Black, and 67% women. Black respondents were less likely than non-Black respondents to be offered a choice, 15% versus 23%, prevalence difference (PD) and 95% CI = (-8.7%, CI: -14.4, -3.0) and if offered a choice, less likely to accept a telehealth visit (20% vs. 67%; PD = -47.0%, CI: -62.0, -32.0). Compared to men, women had a lower frequency of visit choice (16% vs. 27%; PD = -10.9%. CI: -17.4, -4.4) and accepted telehealth visits (42% vs. 63%; PD = -20.8%, CI: -36.8, -4.7). Respondents who expressed technology-related worries were less likely to accept a telehealth visit. Lower area-level technology access (e.g., broadband ownership) and higher poverty were nonsignificantly associated with less offering and less scheduling of telehealth visits.

Conclusions: Interventions to improve access to telehealth in cancer care and mitigate structural inequities (namely racism and sexism) should consider patient- and area-level barriers to being offered a choice in visit type and the ability to accept a telehealth visit.

Objective: To estimate changes in lung cancer screening at age 65, the age of nearly universal Medicare coverage.

Study setting and design: Screening reduces lung cancer mortality but is underutilized. We used a regression discontinuity design to measure the impact of nearly universal Medicare coverage at age 65 on first-time receipt of screening (primary outcome) and the proportion of screened individuals with detected lung cancer (secondary outcome).

Data sources and analytic sample: First-time screens at age 60-69 in the American College of Radiology's Lung Cancer Screening Registry data, 2015-2020.

Principal findings: Nearly-universal access to Medicare at 65 increased first-time lung cancer screening by 5450 per year (CI 4911-5990), a 41% increase compared to age 64. Eighty-nine percent of additional screens were among people who met screening eligibility criteria. Increases at age 65 were larger in rural areas than nonrural areas (52% vs. 39%) and were similar for men and women (41% and 42%). There was no statistically significant change in the proportion of screened individuals with lung cancer detected.

Conclusion: First-time receipt of lung cancer screening increases at age 65, particularly among people in rural areas. Cancer detection rates did not worsen, suggesting screening remained well targeted as it increased.

Objective: To evaluate the association between implementation of "Pathways to Success" and quality among beneficiaries cared for in Shared Savings Program accountable care organizations (ACOs).

Study setting and design: Medicare initiated "Pathways to Success" in 2019 that required upside-risk only ACOs in Shared Savings Program to transition to a two-sided risk model and prior two-sided ACOs to assume even greater financial responsibility. We examined the association between Pathways and ACO-targeted (hospitalizations for congestive heart failure [CHF] and all-cause 30-day readmissions) and nontargeted (all-cause emergency department visits without hospitalization for CHF and hospital observation stays) quality measures, using a difference-in-differences framework.

Data sources and analytic sample: Data were extracted from a 20% sample of national Medicare data from 2018 to 2020. This study included 810,070 beneficiary-quarters in 514 ACOs, and 813,855 beneficiary-quarters never attributed to an ACO (i.e., controls).

Principal findings: Implementation of Pathways was not associated with significant relative changes in the quarterly number of CHF admissions (decreasing from 97.98 to 82.04 per 1000 beneficiaries in ACOs; differential change = 3.51 quarterly CHF admissions per 1000 beneficiaries, 95% CI, -4.82 to 11.85) or the quarterly number of emergency department visits for CHF (decreasing from 110.90 to 97.50 per 1000 beneficiaries in ACOs; differential change = 6.47 quarterly CHF emergency department visits per 1000 beneficiaries, 95% CI, -3.71 to 16.64). However, quarterly rates of 30-day all-cause readmissions increased slightly by 0.61% points (95% CI, 0.23 to 0.98; unadjusted readmissions increased from 14.49% to 14.81% in ACOs) after Pathways implementation. Observation stays remained unchanged (differential change = -0.16% points, 95% CI, -0.33 to 0.02; unadjusted observation stays increased from 3.64% to 3.94% in ACOs) after the launch of Pathways.

Conclusions: Medicare's Pathways to Success, which introduced two-sided risk, was not associated with improvement in select quality measures.

Objective: To examine whether patient sociodemographic and clinical characteristics and prior interactions with the healthcare system were associated with opening patient portal messages related to cancer genetic services and beginning services.

Study setting and design: The trial was conducted in the University of Utah Health (UHealth) and NYU Langone Health (NYULH) systems. Between 2020 and 2023, 3073 eligible primary care patients aged 25-60 years meeting family history-based criteria for cancer genetic evaluation were randomized 1:1 to receive a patient portal message with a hyperlink to a pretest genetics education chatbot or information about scheduling a pretest standard of care (SOC) appointment.

Data sources and analytic sample: Primary data were collected. Eligible patients had a primary care visit in the previous 3 years, a patient portal account, no prior cancer diagnosis except nonmelanoma skin cancer, no prior cancer genetic services, and English or Spanish as their preferred language. Multivariable models identified predictors of opening patient portal messages by site and beginning pretest genetic services by site and experimental condition.

Principal findings: Number of previous patient portal logins (UHealth average marginal effect [AME]: 0.32; 95% CI: 0.27, 0.38; NYULH AME: 0.33; 95% CI: 0.27, 0.39), having a recorded primary care provider (NYULH AME: 0.15; 95% CI: 0.08, 0.22), and more primary care visits in the previous 3 years (NYULH AME: 0.09; 95% CI: 0.02, 0.16) were associated with opening patient portal messages about genetic services. Number of previous patient portal logins (UHealth AME: 0.14; 95% CI: 0.08, 0.21; NYULH AME: 0.18; 95% CI: 0.12, 0.23), having a recorded primary care provider (NYULH AME: 0.08; 95% CI: 0.01, 0.14), and more primary care visits in the previous 3 years (NYULH AME: 0.07; 95% CI: 0.01, 0.13) were associated with beginning pretest genetic services. Patient sociodemographic and clinical characteristics were not significantly associated with either outcome.

Conclusions: As system-level initiatives aim to reach patients eligible for cancer genetic services, patients already interacting with the healthcare system may be most likely to respond. Addressing barriers to accessing healthcare and technology may increase engagement with genetic services.

Objective: To evaluate whether the association between receiving care at an accredited hospital and timely treatment initiation varies by county income level.

Study setting and design: This cross-sectional study compared days from diagnosis to treatment initiation among patients receiving care at CoC-accredited hospitals with patients receiving care at non-accredited hospitals. We estimated distributional effects with a quantile regression model. We stratified patients into low (median household-income < 80k) and high-income (median household-income ≥ 80k) counties.

Data sources and analytic sample: We analyzed population-based Surveillance, Epidemiological, and End Results case data (2018-2021). We excluded cancer cases that did not receive treatment. All analyses were adjusted for tumor and patient characteristics, treatment received, and geographic factors.

Principal findings: Among 2,107,188 patients receiving cancer treatment, 73.65% received care at an accredited hospital. Median time-to-treatment was 27 days (interquartile range = 1-52). Care at an accredited hospital was associated with longer median time-to-treatment (+2.6 days) in low-income counties but not high-income counties. Similarly, care at an accredited hospital was associated with widening the time-to-treatment interquartile range (+1.8 days) in low-income but not high-income counties. The magnitude of these associations was highest in patients aged 65+, unmarried, diagnosed at an early stage, and in less common cancers. Only among patients diagnosed with distant-stage cancer was accreditation associated with reduced median time-to-treatment in both low and high-income counties.

Conclusions: Treatment at an accredited hospital appeared to increase time-to-treatment differences between high-and low-income counties and low-income counties. This heterogeneity may reflect access challenges facing low-income cancer patients. Health systems seeking to provide high quality, timely care must overcome these access challenges as they navigate patients through the cancer care continuum. While a 2.6-day delay in treatment may not impact outcomes, future research should understand why patients from lower-resource communities wait longer than patients in affluent communities.