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Little is known about the longitudinal progression of cost-related medication non-adherence (CRN) among the high-need, high-cost diabetes population. We aim to document the longitudinal aspect of CRN among Medicare diabetes patients at high risk of hospitalization and the role of Medicare-Medicaid dual eligibility in CRN. 617 Medicare diabetes patients at high risk of hospitalization were followed up at 3-month intervals for a total of 16 surveys. Patients' socio-demographic and health characteristics by dual eligibility were compared using Chi-square tests. The progression of CRN was documented using a Kaplan-Meier Survival Curve. A Cox Survival Regression analysis and a Generalized Estimating Equation (GEE) analysis were conducted to evaluate the adjusted hazard ratio (HR) and population-averaged effect of dual eligibility on CRN, controlling for socio-demographic and health characteristics. 303 patients (49.1%) reported dual eligibility, among whom 151 (49.8%) reported CRN; they were more likely to be under 65 (p < 0.01), had lower income (p < 0.01), were less likely to report cardiovascular disease (p = 0.05), and were less likely to report CRN (p < 0.01) compared to those who did not report dual eligibility. Those with dual eligibility had a lower hazard ratio (HR = 0.67, p < 0.01) and lower likelihood of reporting CRN (coefficient = -0.40, p < 0.01), and those with depression had higher hazard ratio (HR = 1.31, p = 0.03) and higher likelihood of reporting CRN (coefficient = 0.32, p < 0.01) in the Cox model and GEE, respectively. While insurance coverage enables patients to overcome their major deficiency in income, many patients fall through the cracks as their disease progresses. Depression is a major risk factor for CRN. Health policy addressing CRN needs to be implemented in tandem with clinical intervention, targeting those at the increasing risk of CRN.

Vitamin D deficiency has long been associated with reduced immune function that can lead to viral infection. Several studies have shown that Vitamin D deficiency is associated with increases the risk of infection with COVID-19. However, it is unknown if treatment with Vitamin D can reduce the associated risk of COVID-19 infection, which is the focus of this study. In the population of US veterans, we show that Vitamin D 2 and D 3 fills were associated with reductions in COVID-19 infection of 28% and 20%, respectively [(D 3 Hazard Ratio (HR) = 0.80, [95% CI 0.77, 0.83]), D 2 HR = 0.72, [95% CI 0.65, 0.79]]. Mortality within 30-days of COVID-19 infection was similarly 33% lower with Vitamin D 3 and 25% lower with D 2 (D 3 HR = 0.67, [95% CI 0.59, 0.75]; D 2 HR = 0.75, [95% CI 0.55, 1.04]). We also find that after controlling for vitamin D blood levels, veterans receiving higher dosages of Vitamin D obtained greater benefits from supplementation than veterans receiving lower dosages. Veterans with Vitamin D blood levels between 0 and 19 ng/ml exhibited the largest decrease in COVID-19 infection following supplementation. Black veterans received greater associated COVID-19 risk reductions with supplementation than White veterans. As a safe, widely available, and affordable treatment, Vitamin D may help to reduce the severity of the COVID-19 pandemic.

To study cost-related medication non-adherence (CRN) for a 30-month period before and during the COVID-19 pandemic using a sample of Medicare patients at high risk of hospitalization. A novel data set of quarterly surveys of CRN was used to evaluate CRN before and during the COVID-19 pandemic. Generalized Estimating Equation (GEE) analyses were conducted to evaluate the adjusted coefficients of change in CRN behaviors controlling for socio-demographic and health characteristics. Six hundred seventy-seven Medicare beneficiaries at high risk of hospitalization who were alive on January 1, 2020 and followed up through quarterly surveys on CRN for 30 months before and during the COVID-19 pandemic. Two metrics of prevalence and persistence of CRN and their adjusted coefficients in GEE with binomial family distribution and log link function controlling for socio-demographic and health characteristics. A total of 5,990 quarterly surveys were completed by the 677 patients during the 30-month study period. Among the 677 patients, 250 (37%) were men, 591 (87%) were African American, and 288 (42%) were Medicare-Medicaid dual eligible. The unadjusted prevalence of CRN before and during the COVID-19 pandemic was 31.1% and 25.7% respectively (p = 0.02 by Chi-squared test), and persistent CRN rates were 12.1% and 9.7% respectively (p = 0.17 by Chi-squared test). The adjusted odds ratio of CRN prevalence during the pandemic compared to the pre-pandemic level was 0.75 (p<0.01), and 0.74 (p = 0.03) for persistent CRN in GEE estimations. There are coherent evidence of a reversal of CRN rates during the COVID-19 pandemic among this high-need, high-cost resource utilization Medicare population. Patients' CRN behaviors may be responsive to exogenous impacts, and the behaviors changed in the same direction with similar magnitude in terms of prevalence (the extensive margin) and persistence (the intensive margin). More research is needed to advance the understanding of the driving forces behind patients' behavioral changes and to identify factors that may be informative for reducing CRN in the long run.

Background: In the United States, $50,000 per Quality-Adjusted Life-Year (QALY) is a decision rule that is often used to guide interpretation of cost-effectiveness analyses. However, many investigators have questioned the scientific basis of this rule, and it has not been updated. Methods: We used 2 separate approaches to investigate whether the $50,000 per QALY rule is consistent with current resource allocation decisions. To infer a lower bound for the decision rule, we estimated the incremental cost-effectiveness of recent (2003) versus pre-\"modern era\" (1950) medical care in the United States. To infer an upper bound for the decision rule, we estimated the incremental cost-effectiveness of unsubsidized health insurance versus self-pay for nonelderly adults (ages 21-64) without health insurance. We discounted both costs and benefits, following recommendations of the Panel on Cost-Effectiveness in Health and Medicine. Results: Our base case analyses suggest that plausible lower and upper bounds for a cost-effectiveness decision rule are $183,000 per life-year and $264,000 per life-year, respectively. Our sensitivity analyses widen the plausible range (between $95,000 per life-year saved and $264,000 per life-year saved when we considered only health care's impact on quantity of life, and between $109,000 per QALY saved and $297,000 per QALY saved when we considered health care's impact on quality as well as quantity of life) but it remained substantially higher than $50,000 per QALY. Conclusions: It is very unlikely that $50,000 per QALY is consistent with societal preferences in the United States.

Background Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design Prospective observational cohort study. Patients Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission.

Throughout the COVID-19 pandemic, patient portals have become more widely used tools of patient care delivery. However, not all individuals have equivalent access or ability to use patient portals. The aim of this study is to evaluate the relationships between eHealth literacy (eHL) and patient portal awareness, use, and attitudes among hospitalized patients. Inpatients completed patient portal surveys; eHL was assessed (eHealth Literacy Scale). Multivariable logistic regression analyses adjusted for age, self-reported race, gender, and educational attainment were completed with significance at P<.006 (Bonferroni correction). Among 274 participants, most identified as Black (n=166, 61%) and female (n=140, 51%), mean age was 56.5 (SD 16.7) years, and 178 (65%) reported some college or higher educational attainment. One-quarter (n=79, 28%) had low eHL (mean 27, SD 9.5), which was associated with lower odds of portal access awareness (odds ratio 0.11, 95% CI 0.05-0.23; P<.001), having ever used portals (odds ratio 0.19, 95% CI 0.10-0.36; P<.001), less perceived usefulness of portals (odds ratio 0.20, 95% CI 0.10-0.38; P=.001), and lower likelihood of planning to use portals in the coming years (odds ratio 0.12, 95% CI 0.06-0.25; P<.001). As time through the COVID-19 pandemic passed, there was a trend toward increased perceived usefulness of patient portals (53% vs 62%, P=.08), but average eHL did not increase through time (P=.81). Low eHL was associated with less awareness, use, and perceived usefulness of portals. Perceived usefulness of portals likely increased through the COVID-19 pandemic, but patients' eHL did not. Interventions tailored for patients with low eHL could ensure greater equity in health care delivery through the COVID-19 pandemic.

A fundamental activity in hospital operations is patient assignment, which we define as the process of assigning hospital patients to specific physician services and clinical units based on their diagnosis. When the preferred assignment is not possible, typically due to capacity limits, hospitals often allow for overflow, which is the assignment of patients to other services and/or units. Overflow accelerates assignment, but can also reduce care quality and increase length of stay. This paper develops a discrete-event simulation model to evaluate different assignment strategies. Using a simulation-based optimization approach, we evaluate and heuristically optimize these strategies accounting for expected hospital and physician profit, care quality and patient waiting time. We apply the model using data from the University of Chicago Medical Center. We find that the strategies that use heuristically optimized designation of overflow services and units increase expected profit relative to the capacity-based strategy in which overflow patients are assigned to a service and unit with the most available capacity. We also find further improvement in the strategy that uses heuristically optimized overflow services and units as well as a holding unit that holds patients until a bed in their primary or secondary unit becomes available. Additionally, we demonstrate the effects of these strategies on other performance measures such as patient concentration, waiting time, and outcomes.

Health expenditures are a major financial burden for many persons in low and middle-income countries, where individuals often lack health insurance. We estimate the effect of purchasing cardiovascular medicines on poverty in low and middle-income populations using rural and urban India as an example. We created step-up treatment regimens for prevention of ischemic heart disease for the most common cardiovascular medications in India based on their cost and relative risk reduction. Cost was measured by Government of India mandated ceiling prices in rupees (Rs. 1 = $0·016) for essential medicines plus taxes. We calculated step-wise projected incidence and intensity of impoverishment due to medicine purchase. To do this we measured the resources available to individuals as daily per-capita expenditures from the latest National Sample Survey, subtracted daily medication costs, and compared this to 2014 poverty thresholds recommended by an expert group. Analysis of cost-effectiveness resulted in five primary prevention drug regimens, created by progressive addition of Aspirin 75 mg, Hydrochlorothiazide 12.5mg, Losartan 25 mg, and Atorvastatin 10 mg or 40mg. Daily cost from steps 1 to 5 increased from Rs. 0·13, Rs. 1.16, Rs. 3.81, Rs. 10.07, to Rs. 28.85. At baseline, 31% of rural and 27% percent of urban Indian population are poor at the designated poverty thresholds. The Rs. 28.85 regimen would be unaffordable to 81% and 58% of rural and urban people. A secondary prevention regimen with aspirin, hydrochlorothiazide, atenolol and atorvastatin could be unaffordable to 81% and 57% rural and urban people respectively. According to our estimates, 17% of the rural 32% of the urban adult population could benefit with these medications, and their out of pocket purchase could impoverish 17 million rural and 10 million urban people in India and increase respective poverty gaps by 2.9%. Medication costs for cardiovascular disease have the potential to cause financial burden to a significant proportion of people in India. These costs increase the likelihood that patients will forego needed treatment and emphasize the need for programs to reduce the costs of medications for cardiovascular patients in India, including by expansion of prescription drug coverage.

Medicare coverage for audio-only telehealth is slated to end this year after the public health emergency concludes. When the time comes, many patients may be unable to make the transition from audio-only to video telehealth due to digital inexperience. This study explores the second digital divide within video telehealth use, which is primarily characterized by skills and capabilities rather than access, by measuring eHealth literacy (eHL) and video capabilities in hospitalized patients. The aim of this study is to evaluate video capabilities, eHealth literacy, and engagement with video telehealth among hospitalized patients. The study design is a cross-sectional observational study of adult inpatients at the University of Chicago Medical Center. We assessed self-reported rates of audio versus video telehealth usage as well as the participants' self-reported willingness to use video telehealth for future health care visits. We used a multivariable binary logistic regression to determine the odds ratio for being unwilling to use video telehealth, adjusted for age, sex, race or ethnicity, educational level, eHL literacy scale (eHEALS), health literacy (brief health literacy screen), technology access, internet access, and video capability. Of the 297 enrolled participants, median age was 58 years, most (n=185, 62%) identified as Black, half (n=149, 50%) were female, one-quarter (n=66, 22%) lacked home internet access, and one-third (n=102, 34%) had inadequate eHL. Patients with low eHL reported greater participation in audio-only telehealth over video telehealth, of which the former may lose its flexible pandemic reimbursement policy. This may widen the existing health disparities as older adults and patients with low eHL face challenges in accessing video telehealth services. Low eHL is associated with lack of web-based skills, lower rates of video telehealth usage, and lower willingness to use video technology. The study results raise the question of how to improve video capability among patients who, despite having access to smartphones and laptops, face challenges in using telehealth optimally.

Background Homelessness is associated with substantial morbidity. Data linkages between homeless and health systems are important to understand unique needs across homeless populations, identify homeless individuals not registered in homeless databases, quantify the impact of housing services on health-system use, and motivate health systems and payers to contribute to housing solutions. Methods We performed a cross-sectional survey including six health systems and two Homeless Management Information Systems (HMIS) in Cook County, Illinois. We performed privacy-preserving record linkage to identify homelessness through HMIS or ICD-10 codes captured in electronic medical records. We measured the prevalence of health conditions and health-services use across the following typologies: housing-service utilizers stratified by service provided (stable, stable plus unstable, unstable) and non-utilizers (i.e., homelessness identified through diagnosis codes—without receipt of housing services). Results Among 11,447 homeless recipients of healthcare, nearly 1 in 5 were identified by ICD10 code alone without recorded homeless services ( n  = 2177; 19%). Almost half received homeless services that did not include stable housing ( n  = 5444; 48%), followed by stable housing ( n  = 3017; 26%), then receipt of both stable and unstable services ( n  = 809; 7%). Setting stable housing recipients as the referent group, we found a stepwise increase in behavioral-health conditions from stable housing to those known as homeless solely by health systems. Compared to those in stable housing, prevalence rate ratios (PRR) for those without homeless services were as follows: depression (PRR = 2.2; 95% CI 1.9 to 2.5), anxiety (PRR = 2.5; 95% CI 2.1 to 3.0), schizophrenia (PRR = 3.3; 95% CI 2.7 to 4.0), and alcohol-use disorder (PRR = 4.4; 95% CI 3.6 to 5.3). Homeless individuals who had not received housing services relied on emergency departments for healthcare—nearly 3 of 4 visited at least one and many (24%) visited multiple. Conclusions Differences in behavioral-health conditions and health-system use across homeless typologies highlight the particularly high burden among homeless who are disconnected from homeless services. Fragmented and high use of emergency departments for care should motivate health systems and payers to promote housing solutions, especially those that incorporate substance use and mental health treatment.