Association of State Medicaid Income Eligibility Limits and Long-Term Survival After Cancer Diagnosis in the United States.

PURPOSE: To examine the association between historic state Medicaid income eligibility limits and long-term survival among patients with cancer. METHODS: 1,449,144 adults age 18-64 years newly diagnosed with 19 common cancers between 2010 and 2013 were identified from the National Cancer Database. States' Medicaid income eligibility limits were categorized as ≤ 50%, 51%-137%, and ≥ 138% of federal poverty level (FPL). Survival time was measured from diagnosis date through December 31, 2017, for up to an 8-year follow-up. Multivariable Cox proportional hazards models with age as time scale were used to assess associations of eligibility limits and stage-specific survival, adjusting for the effects of sex, metropolitan statistical area, comorbidities, year of diagnosis, facility type and volume, and state. RESULTS: Among patients with newly diagnosed cancer age 18-64 years, patients living in states with lower Medicaid income eligibility limits had worse survival for most cancers in both early and late stages, compared with those living in states with Medicaid income eligibility limits ≥ 138% FPL. A dose-response relationship was observed for most cancers with lower income limits associated with worse survival (13 of 17 cancers evaluated for early-stage cancers, and 11 of 17 cancers evaluated for late-stage cancers, and leukemia and brain tumors with P-trend < .05). CONCLUSION: Lower Medicaid income eligibility limits were associated with worse long-term survival within stage; increasing Medicaid income eligibility may improve survival after cancer diagnosis.

Analysis of Clinician and Patient Factors and Completion of Telemedicine Appointments Using Video.

Importance: Telemedicine provides patients access to episodic and longitudinal care. Policy discussions surrounding future support for telemedicine require an understanding of factors associated with successful video visits. Objective: To assess patient and clinician factors associated with successful and with failed video visits. Design, Setting, and Participants: This was a quality improvement study of 137 846 scheduled video visits at a single academic health system in southeastern Wisconsin between March 1 and December 31, 2020, supplemented with patient experience survey data. Patient information was gathered using demographic information abstracted from the electronic health record and linked with block-level socioeconomic data from the US Census Bureau. Data on perceived clinician experience with technology was obtained using the survey. Main Outcomes and Measures: The primary outcome of interest was the successful completion of a scheduled video visit or the conversion of the video visit to a telephone-based service. Visit types and administrative data were used to categorize visits. Mixed-effects modeling with pseudo R2 values was performed to compare the relative associations of patient and clinician factors with video visit failures. Results: In total, 75 947 patients and 1155 clinicians participated in 137 846 scheduled video encounters, 17 190 patients (23%) were 65 years or older, and 61 223 (81%) patients were of White race and ethnicity. Of the scheduled video encounters, 123 473 (90%) were successful, and 14 373 (10%) were converted to telephone services. A total of 16 776 patients (22%) completed a patient experience survey. Lower clinician comfort with technology (odds ratio [OR], 0.15; 95% CI, 0.08-0.28), advanced patient age (66-80 years: OR, 0.28; 95% CI, 0.26-0.30), lower patient socioeconomic status (including low high-speed internet availability) (OR, 0.85; 95% CI, 0.77-0.92), and patient racial and ethnic minority group status (Black or African American: OR, 0.75; 95% CI, 0.69-0.81) were associated with conversion to telephone visits. Patient characteristics accounted for systematic components for success; marginal pseudo R2 values decreased from 23% (95% CI, 21.1%-26.1%) to 7.8% (95% CI, 6.3%-9.4%) with exclusion of patient factors. Conclusions and Relevance: As policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should guide decisions. In particular, this quality improvement study suggests that underserved patients may become disproportionately vulnerable by cuts in coverage for telephone-based services.

Community burden and prognostic impact of reduced kidney function among patients hospitalized with acute decompensated heart failure: The Atherosclerosis Risk in Communities (ARIC) Study Community Surveillance.

BACKGROUND: Kidney dysfunction is prevalent and impacts prognosis in patients with acute decompensated heart failure (ADHF). However, most previous reports were from a single hospital, limiting their generalizability. Also, contemporary data using new equation for estimated glomerular filtration rate (eGFR) are needed. METHODS AND RESULTS: We analyzed data from the ARIC Community Surveillance for ADHF conducted for residents aged ≥55 years in four US communities between 2005-2011. All ADHF cases (n = 5, 391) were adjudicated and weighted to represent those communities (24,932 weighted cases). The association of kidney function (creatinine-based eGFR by the CKD-EPI equation and blood urea nitrogen [BUN]) during hospitalization with 1-year mortality was assessed using logistic regression. Based on worst and last serum creatinine, there were 82.5% and 70.6% with reduced eGFR (<60 ml/min/1.73m2) and 37.4% and 26.6% with severely reduced eGFR (<30 ml/min/1.73m2), respectively. Lower eGFR (regardless of last or worst eGFR), particularly eGFR <30 ml/min/1.73m2, was significantly associated with higher 1-year mortality independently of potential confounders (odds ratio 1.60 [95% CI 1.26-2.04] for last eGFR 15-29 ml/min/1.73m2 and 2.30 [1.76-3.00] for <15 compared to eGFR ≥60). The association was largely consistent across demographic subgroups. Of interest, when both eGFR and BUN were modeled together, only BUN remained significant. CONCLUSIONS: Severely reduced eGFR (<30 ml/min/1.73m2) was observed in ~30% of ADHF cases and was an independent predictor of 1-year mortality in community. For prediction, BUN appeared to be superior to eGFR. These findings suggest the need of close attention to kidney dysfunction among ADHF patients.

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Assessment of clinical risk factors to validate the probability of osteoporosis and subsequent fractures in Korean women.

This cross-sectional, observational study was designed to identify clinical risk factors of osteoporosis and fractures in Korean women to validate the probability of osteoporosis and subsequent fractures. A total of 1541 Korean women were recruited nationally. Fracture history of any site, risk factors of osteoporosis, and fall-related risk factors were surveyed and physical performance tests were conducted. Peripheral dual-energy X-ray absorptiometry was used to measure calcaneus bone mineral density (BMD). The number of positive responses on the modified 1-min osteoporosis risk test was related to the risk of osteoporosis. The frequency of osteoporosis was higher in those with a height reduction of >4 cm and a reduced body mass index (BMI). Multivariate analysis showed that older age and lower BMI were related to higher relative risk of osteoporosis. Time required to stand up from a chair and questions related to fall injury were significantly related to clinical fracture history of any site. Multivariate analysis showed that the relative risk of fractures at any site was higher in older subjects with a lower T-score and parental hip fracture history. This study shows that age and BMI are the most significant clinical risk factors for osteoporosis and that age, BMD, and parental history of hip fracture are highly applicable risk factors for validating the probability of osteoporotic fractures in Korean women.