Cancer diagnosis and treatment in working-age adults: Implications for employment, health insurance coverage, and financial hardship in the United States.

The rising costs of cancer care and subsequent medical financial hardship for cancer survivors and families are well documented in the United States. Less attention has been paid to employment disruptions and loss of household income after a cancer diagnosis and during treatment, potentially resulting in lasting financial hardship, particularly for working-age adults not yet age-eligible for Medicare coverage and their families. In this article, the authors use a composite patient case to illustrate the adverse consequences of cancer diagnosis and treatment for employment, health insurance coverage, household income, and other aspects of financial hardship. They summarize existing research and provide nationally representative estimates of multiple aspects of financial hardship and health insurance coverage, benefit design, and employee benefits, such as paid sick leave, among working-age adults with a history of cancer and compare them with estimates among working-age adults without a history of cancer from the most recently available years of the National Health Interview Survey (2019-2021). Then, the authors identify opportunities for addressing employment and health insurance coverage challenges at multiple levels, including federal, state, and local policies; employers; cancer care delivery organizations; and nonprofit organizations. These efforts, when informed by research to identify best practices, can potentially help mitigate the financial hardship associated with cancer.

Health insurance status and cancer stage at diagnosis and survival in the United States.

Previous studies using data from the early 2000s demonstrated that patients who were uninsured were more likely to present with late-stage disease and had worse short-term survival after cancer diagnosis in the United States. In this report, the authors provide comprehensive data on the associations of health insurance coverage type with stage at diagnosis and long-term survival in individuals aged 18-64 years who were diagnosed between 2010 and 2013 with 19 common cancers from the National Cancer Database, with survival follow-up through December 31, 2019. Compared with privately insured patients, Medicaid-insured and uninsured patients were significantly more likely to be diagnosed with late-stage (III/IV) cancer for all stageable cancers combined and separately. For all stageable cancers combined and for six cancer sites-prostate, colorectal, non-Hodgkin lymphoma, oral cavity, liver, and esophagus-uninsured patients with Stage I disease had worse survival than privately insured patients with Stage II disease. Patients without private insurance coverage had worse short-term and long-term survival at each stage for all cancers combined; patients who were uninsured had worse stage-specific survival for 12 of 17 stageable cancers and had worse survival for leukemia and brain tumors. Expanding access to comprehensive health insurance coverage is crucial for improving access to cancer care and outcomes, including stage at diagnosis and survival.

Hospital Prices for Physician-Administered Drugs for Patients with Private Insurance.

BACKGROUND: Hospitals can leverage their position between the ultimate buyers and sellers of drugs to retain a substantial share of insurer pharmaceutical expenditures. METHODS: In this study, we used 2020-2021 national Blue Cross Blue Shield claims data regarding patients in the United States who had drug-infusion visits for oncologic conditions, inflammatory conditions, or blood-cell deficiency disorders. Markups of the reimbursement prices were measured in terms of amounts paid by Blue Cross Blue Shield plans to hospitals and physician practices relative to the amounts paid by these providers to drug manufacturers. Acquisition-price reductions in hospital payments to drug manufacturers were measured in terms of discounts under the federal 340B Drug Pricing Program. We estimated the percentage of Blue Cross Blue Shield drug spending that was received by drug manufacturers and the percentage retained by provider organizations. RESULTS: The study included 404,443 patients in the United States who had 4,727,189 drug-infusion visits. The median price markup (defined as the ratio of the reimbursement price to the acquisition price) for hospitals eligible for 340B discounts was 3.08 (interquartile range, 1.87 to 6.38). After adjustment for drug, patient, and geographic factors, price markups at hospitals eligible for 340B discounts were 6.59 times (95% confidence interval [CI], 6.02 to 7.16) as high as those in independent physician practices, and price markups at noneligible hospitals were 4.34 times (95% CI, 3.77 to 4.90) as high as those in physician practices. Hospitals eligible for 340B discounts retained 64.3% of insurer drug expenditures, whereas hospitals not eligible for 340B discounts retained 44.8% and independent physician practices retained 19.1%. CONCLUSIONS: This study showed that hospitals imposed large price markups and retained a substantial share of total insurer spending on physician-administered drugs for patients with private insurance. The effects were especially large for hospitals eligible for discounts under the federal 340B Drug Pricing Program on acquisition costs paid to manufacturers. (Funded by Arnold Ventures and the National Institute for Health Care Management.).

The Affordable Care Act and access to care across the cancer control continuum: A review at 10 years.

Lack of health insurance coverage is strongly associated with poor cancer outcomes in the United States. The uninsured are less likely to have access to timely and effective cancer prevention, screening, diagnosis, treatment, survivorship, and end-of-life care than their counterparts with health insurance coverage. On March 23, 2010, the Patient Protection and Affordable Care Act (ACA) was signed into law, representing the largest change to health care delivery in the United States since the introduction of the Medicare and Medicaid programs in 1965. The primary goals of the ACA are to improve health insurance coverage, the quality of care, and patient outcomes, and to maintain or lower costs by catalyzing changes in the health care delivery system. In this review, we describe the main components of the ACA, including health insurance expansions, coverage reforms, and delivery system reforms, provisions within these components, and their relevance to cancer screening and early detection, care, and outcomes. We then highlight selected, well-designed studies examining the effects of the ACA provisions on coverage, access to cancer care, and disparities throughout the cancer control continuum. Finally, we identify research gaps to inform evaluation of current and emerging health policies related to cancer outcomes.

Mortality and morbidity ramifications of proposed retractions in healthcare coverage for the United States.

In the absence of universal healthcare in the United States, federal programs of Medicaid and Medicare are vital to providing healthcare coverage for low-income households and elderly individuals, respectively. However, both programs are under threat, with either enacted or proposed retractions. Specifically, raising Medicare age eligibility and the addition of work requirements for Medicaid qualification have been proposed, while termination of continuous enrollment for Medicaid was recently effectuated. Here, we assess the potential impact on mortality and morbidity resulting from these policy changes. Our findings indicate that the policy change to Medicare would lead to over 17,000 additional deaths among individuals aged 65 to 67 and those to Medicaid would lead to more than 8,000 deaths among those under the age of 65. To illustrate the implications for morbidity, we further consider a case study among those people with diabetes who would be likely to lose their health insurance under the policy changes. We project that these insurance retractions would lead to the loss of coverage for over 700,000 individuals with diabetes, including more than 200,000 who rely on insulin.

Minimizing the burden of cancer in the United States: Goals for a high-performing health care system.

Between 1991 and 2015, the cancer mortality rate declined dramatically in the United States, reflecting improvements in cancer prevention, screening, treatment, and survivorship care. However, cancer outcomes in the United States vary substantially between populations defined by race/ethnicity, socioeconomic status, health insurance coverage, and geographic area of residence. Many potentially preventable cancer deaths occur in individuals who did not receive effective cancer prevention, screening, treatment, or survivorship care. At the same time, cancer care spending is large and growing, straining national, state, health insurance plans, and family budgets. Indeed, one of the most pressing issues in American medicine is how to ensure that all populations, in every community, derive the benefit from scientific research that has already been completed. Addressing these questions from the perspective of health care delivery is necessary to accelerate the decline in cancer mortality that began in the early 1990s. This article, part of the Cancer Control Blueprint series, describes challenges with the provision of care across the cancer control continuum in the United States. It also identifies goals for a high-performing health system that could reduce disparities and the burden of cancer by promoting the adoption of healthy lifestyles; access to a regular source of primary care; timely access to evidence-based care; patient-centeredness, including effective patient-provider communication; enhanced coordination and communication between providers, including primary care and specialty care providers; and affordability for patients, payers, and society.

The risk of losing health insurance in the United States is large, and remained so after the Affordable Care Act.

Health insurance coverage in the United States is highly uncertain. In the post-Affordable Care Act (ACA), pre-COVID United States, we estimate that while 12.5% of individuals under 65 are uninsured at a point in time, twice as many-one in four-are uninsured at some point over a 2-y period. Moreover, the risk of losing insurance remained virtually unchanged with the introduction of the landmark ACA. Risk of insurance loss is particularly high for those with health insurance through Medicaid or private exchanges; they have a 20% chance of losing coverage at some point over a 2-y period, compared to 8.5% for those with employer-provided coverage. Those who lose insurance can experience prolonged periods without coverage; about half are still uninsured 6 mo later, and almost one-quarter are uninsured for the subsequent 2 y. These facts suggest that research and policy attention should focus not only on the "headline number" of the share of the population uninsured at a point in time, but also on the stability and certainty (or lack thereof) of being insured.

A Blueprint for U.S. Health Insurance Reform.

The current U.S. health insurance "system" was not deliberately planned and constructed but has emerged piecemeal over the past half-century through a series of incremental and haphazard reforms. That policy history also reveals a clear but unfulfilled societal commitment to providing access to essential health care regardless of resources. To fulfill this obligation, the solution proposed in this article has 2 key elements: 1) universal coverage that is automatic, free, and basic, and 2) the option to buy supplemental coverage in a well-designed market. Such a system could, if desired, be created without raising taxes and without disrupting or changing the delivery of medical care.

Incidence trend of type 2 diabetes from 2012 to 2021 in Germany: an analysis of health claims data of 11 million statutorily insured people.

AIMS/HYPOTHESIS: The aim of the study is to describe the time trend of type 2 diabetes incidence in the largest state of Germany, Bavaria, from 2012 to 2021, and to compare the incidence rates during the pandemic period (2020-2021) to the pre-pandemic period (2012-2019). METHODS: This secondary data analysis uses health claims data provided by the Bavarian Association of Statutory Health Insurance Physicians (KVB), covering approximately 11 million insurees, accounting for 85% of the total population of Bavaria, Germany. Newly diagnosed type 2 diabetes cases in adults (≥20 years) coded as E11 (Diabetes mellitus, Type 2) or E14 (Unspecified diabetes mellitus) under ICD-10, German modification (ICD-10-GM) for the study period 2012 to 2021 were included. Annual and quarterly age-standardised incidence rates (ASIR) stratified by sex, age and region were calculated using the European standard population. Sex-specific crude incidence rates (CIR) were calculated using 10-year age groups. Regression analyses adjusted for time trends, seasonal effects, and pandemic effects were used to analyse the incidence trend and to assess the effect of the pandemic. RESULTS: Overall, 745,861 new cases of type 2 diabetes were diagnosed between 2012 and 2021: 50.4% (376,193 cases) in women. The male/female ratio remained stable over the observation period, while the median age at diagnosis decreased from 61 to 58 years in men and from 66 years to 61 years in women. ASIR were consistently higher for men compared with women, with the yearly difference remaining stable over time (2012: 18%; 2021: 20%). An overall decreasing trend in ASIR was observed during the study period, with a strong decrease from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021 for both sexes. For men, ASIR decreased from 1514 per 100,000 person-years in 2012 to 995 per 100,000 person-years in 2021 (4.6% average annual reduction), and for women from 1238 per 100,000 person-years in 2012 to 796 per 100,000 person-years in 2021 (4.8% average annual reduction). This downward trend was also observed for age groups above 50 years. Regression analyses showed no significant change in incidence rates during the pandemic period (2020 and 2021) compared with the pre-pandemic period. CONCLUSIONS/INTERPRETATION: For the first time, a 10-year incidence trend of type 2 diabetes is reported for Germany, showing a strong decline from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021. The incidence trend of type 2 diabetes appears not to have been affected by the first 2 years of the COVID-19 pandemic. Despite an overall increasing prevalence, the incidence is decreasing, potentially resulting from robust screening by family physicians, reducing the median age at diagnosis by 3 to 5 years. However, further investigation is needed to fully identify the reasons for the declining incidence trend. Continued incidence monitoring is necessary to identify the long-term trend and the potential effect of the pandemic on diagnoses of type 2 diabetes.

Avoidance of care: how health-care affordability influenced COVID-19 disease severity and outcomes.

In this study we examined the association between payor type, a proxy for health-care affordability, and presenting COVID-19 disease severity among 2108 polymerase chain reaction-positive nonelderly patients admitted to an acute-care hospital between March 1 and June 30, 2020. The adjacent-category logit model was used to fit pairwise odds of individuals' having (1) an asymptomatic-to-mild modified sequential organ failure assessment (mSOFA) score (0-3) versus a moderate-to-severe mSOFA score (4-7) and (2) a moderate-to-severe mSOFA score (4-7) versus a critical mSOFA score (>7). Despite representing the smallest population, Medicare recipients experienced the highest in-hospital death rate (19%), a rate twice that of the privately insured. The uninsured had the highest rate of critical mSOFA score on admission and had twice the odds of presenting with a critical illness when compared with the privately insured (odds ratio = 2.08, P =.03). Because payor type was statistically related to the most severe presentations of COVID-19, we question whether policy changes affecting health-care affordability might have prevented deaths and rationing of scarce resources, such as intensive care unit beds and ventilators.

Association of state Medicaid expansion policies with pediatric liver transplant outcomes.

Children from minoritized/socioeconomically deprived backgrounds suffer disproportionately high rates of uninsurance and graft failure/death after liver transplant. Medicaid expansion was developed to expand access to public insurance. Our objective was to characterize the impact of Medicaid expansion policies on long-term graft/patient survival after pediatric liver transplantation. All pediatric patients (<19 years) who received a liver transplant between January 1, 2005, and December 31, 2020 in the US were identified in the Scientific Registry of Transplant Recipients (N = 8489). Medicaid expansion was modeled as a time-varying exposure based on transplant and expansion dates. We used Cox proportional hazards models to evaluate the impact of Medicaid expansion on a composite outcome of graft failure/death over 10 years. As a sensitivity analysis, we conducted an intention-to-treat analysis from time of waitlisting to death (N = 1 1901). In multivariable analysis, Medicaid expansion was associated with a 30% decreased hazard of graft failure/death (hazard ratio, 0.70; 95% confidence interval, 0.62, 0.79; P < .001) after adjusting for Black race, public insurance, neighborhood deprivation, and living in a primary care shortage area. In intention-to-treat analyses, Medicaid expansion was associated with a 72% decreased hazard of patient death (hazard ratio, 0.28; 95% confidence interval, 0.23-0.35; P < .001). Policies that enable broader health insurance access may help improve outcomes and reduce disparities for children undergoing liver transplantation.

Commercial Versus Medicaid Insurance and Use of High-Priced Anticancer Treatments.

BACKGROUND: Because the markups on cancer drugs vary by payor, providers' financial incentive to use high-price drugs is differential according to each patient's insurance type. We evaluated the association between patient insurer (commercial vs Medicaid) and the use of high-priced cancer treatments. MATERIALS AND METHODS: We linked cancer registry, administrative claims, and demographic data for individuals diagnosed with cancer in North Carolina from 2004 to 2011, with either commercial or Medicaid insurance. We selected cancers with multiple FDA-approved, guideline-recommended chemotherapy options and large price differences between treatment options: advanced colorectal, lung, and head and neck cancer. The outcome was a receipt of a higher-priced option, and the exposure was insurer: commercial versus Medicaid. We estimated risk ratios (RRs) for the association between insurer and higher-priced treatment using log-binomial models with inverse probability of exposure weights. RESULTS: Of 812 patients, 209 (26%) had Medicaid. The unadjusted risk of receiving higher-priced treatment was 36% (215/603) for commercially insured and 27% (57/209) for Medicaid insured (RR: 1.31, 95% CI: 1.02-1.67). After adjustment for confounders the association was attenuated (RR: 1.15, 95% CI: 0.81-1.65). Exploratory subgroup analysis suggested that commercial insurance was associated with increased receipt of higher-priced treatment among patients treated by non-NCI-designated providers (RR: 1.53, 95% CI: 1.14-2.04). CONCLUSIONS: Individuals with Medicaid and commercial insurance received high-priced treatments in similar proportion, after accounting for differences in case mix. However, modification by provider characteristics suggests that insurance type may influence treatment selection for some patient groups. Further work is needed to determine the relationship between insurance status and newer, high-price drugs such as immune-oncology agents.

Opioid and Non-Opioid Pharmacotherapy Use for Pain Management Among Privately Insured Pediatric Patients With Cancer in the United States.

BACKGROUND: This study examined the trends and patterns of opioid and non-opioid pharmacotherapy use among a large national sample of privately insured pediatric patients with cancer in the United States. MATERIALS AND METHODS: We identified pediatric (aged < 21) patients diagnosed with central nervous system (CNS), lymphoma, gonadal, leukemia, or bone cancer from MarketScan data 2005-2019. We examined the proportion of patients who filled a prescription for the following 5 types of pharmacotherapy: opioid, anticonvulsant, non-steroidal anti-inflammatory drug (NSAID), antidepressant, and muscle relaxant during active cancer treatment. We assessed the trends and patterns in pharmacotherapy using multivariable logistic regressions. RESULTS: Among 4174 patients included, 2979 (71%) had an opioid prescription; 746 (18%), 384 (9%), 202 (5%), and 169 (4%) had anticonvulsant, NSAID, antidepressant and muscle relaxant prescriptions, respectively. Multivariable logistic regression showed a nonlinear trend in the use of opioids among pediatric patients with cancer over time such that use slightly increased until 2012 (OR of 1.40 [95% CI, 1.12-1.73] for 2012 vs. 2006) but then decreased thereafter (OR of 0.51 [0.37-0.68] for 2018 vs. 2012). The use of anticonvulsants, NSAIDs, and muscle relaxants increased significantly linearly over time (all P < .005). CONCLUSION: There has been a downward trend in the use of opioids in recent years among pediatric patients with cancer and an upward trend in the use of non-opioid pharmacotherapy for pain management potentially as an alternative to opioids.

Survival among lung cancer patients: comparison of the U.S. military health system and the surveillance, epidemiology, and end results (SEER) program by health insurance status.

PURPOSE: The U.S. military health system (MHS) provides beneficiaries with universal health care while health care access varies in the U.S. general population by insurance status/type. We divided the patients from the U.S. general population by insurance status/type and compared them to the MHS patients in survival. METHODS: The MHS patients were identified from the Department of Defense's Automated Central Tumor Registry (ACTUR). Patients from the U.S. general population were identified from the Surveillance, Epidemiology, and End Results (SEER) program. Multivariable Cox regression analysis was conducted to compare different insurance status/type in SEER to ACTUR in overall survival. RESULTS: Compared to ACTUR patients with non-small cell lung cancer (NSCLC), SEER patients showed significant worse survival. The adjusted hazard ratios (HRs) were 1.08 [95% Confidence Interval (CI) = 1.03-1.13], 1.22 (95% CI = 1.16-1.28), 1.40 (95% CI = 1.33-1.47), 1.50 (95% CI = 1.41-1.59), for insured, insured/no specifics, Medicaid, and uninsured patients, respectively. The pattern was consistently observed in subgroup analysis by race, gender, age, or tumor stage. Results were similar for small cell lung cancer (SCLC), although they were only borderline significant in some subgroups. CONCLUSION: The survival advantage of patients receiving care from a universal health care system over the patients from the general population was not restricted to uninsured or Medicaid as expected, but was present cross all insurance types, including patients with private insurance. Our findings highlight the survival benefits of universal health care system to lung cancer patients.

Financial burden among metastatic breast cancer patients: a qualitative inquiry of costs, financial assistance, health insurance, and financial coping behaviors.

PURPOSE: Metastatic breast cancer (MBC) patients often face substantial financial burden due to prolonged and expensive therapy. However, in-depth experiences of financial burden among MBC patients are not well understood. METHODS: Qualitative interviews were conducted to describe the experiences of financial burden for MBC patients, focusing on the drivers of financial burden, their experience using their health insurance, accessing financial assistance, and any resulting cost-coping behaviors. Interviews were transcribed and qualitatively analyzed using a descriptive phenomenological approach to thematic analysis. RESULTS: A total of n = 11 MBC patients or caregiver representatives participated in the study. MBC patients were on average 50.2 years of age (range: 28-65) and 72.7% non-Hispanic White. MBC patients were diagnosed as metastatic an average of 3.1 years (range: 1-9) before participating in the study. Qualitative analysis resulted in four themes including (1) causes of financial burden, (2) financial assistance mechanisms, (3) health insurance and financial burden, and (4) cost-coping behaviors. Both medical and non-medical costs drove financial burden among participants. All participants reported challenges navigating their health insurance and applying for financial assistance. Regardless of gaining access to assistance, financial burden persisted for nearly all patients and resulted in cost-coping behaviors. CONCLUSION: Our findings suggest that current systems for health insurance and financial assistance are complex and difficult to meet patient needs. Even when MBC patients accessed assistance, excess financial burden persisted necessitating use of financial coping-behaviors such as altering medication use, maintaining employment, and taking on debt.

Hospital Encounters Within 1 Year Postpartum Across Insurance Types, Oregon 2012-2017.

BACKGROUND: Little is known about the timing and frequency of postpartum hospital encounters and postpartum visit attendance and how they may be associated with insurance types. Research on health insurance and its association with postpartum care utilization is often limited to the first 6 weeks. OBJECTIVE: To assess whether postpartum utilization (hospital encounters within 1 year postpartum and postpartum visit attendance within 12 weeks) differs by insurance type at birth (Medicaid, high deductible health plans, and other commercial plans) and whether rates of hospital encounters differ by postpartum visit attendance and insurance status. METHODS: Time-to-event analysis of Oregon hospital births from 2012 to 2017 using All Payer All Claims data. We conducted weighted Cox Proportional Hazard regressions and accounted for differences in insurance type at birth using multinomial propensity scores. RESULTS: Among 202,167 hospital births, 24.9% of births had at least 1 hospital encounter within 1 year postpartum. Births funded by Medicaid had a higher risk of a postpartum emergency department (ED) visit (hazard ratio: 2.05, 95% CI: 1.99, 2.12) and lower postpartum visit attendance (hazard ratio: 0.71, 95% CI: 0.70, 0.72) compared with commercial plans. Among Medicaid beneficiaries, missing the postpartum visit in the first 6 weeks was associated with a lower risk of subsequent readmissions (adjusted hazard ratio 0.77, 95% CI: 0.68, 0.87) and ED visits (adjusted hazard ratio: 0.87 (0.85, 0.88). CONCLUSIONS: Medicaid beneficiaries received more care in the ED within 1 year postpartum compared with those enrolled in other commercial plans. This highlights potential issues in postpartum care access.

Average daily glucocorticoid dose, number of prescription days, and cumulative dose in the initial 90 days of glucocorticoid therapy are associated with subsequent hip and clinical vertebral fracture risk: a retrospective cohort study using a nationwide health insurance claims database in Japan.

PURPOSE: Fracture risk assessment is recommended at three months after glucocorticoid (GC) therapy initiation. This study aimed to assess whether GC exposure in the initial 90 days of GC therapy is associated with subsequent hip and clinical vertebral fracture risk using the nationwide health insurance claims database of Japan (NDBJ). METHODS: Patients aged ≥ 50 years who were prescribed GC (≥ 70 mg prednisolone or equivalent; PSL) in the initial 90 days of GC therapy and were followed for hip and clinical vertebral fracture incidences for the subsequent 1080 days were selected from NDBJ. Associations of GC exposure with hip or clinical vertebral fracture risk were evaluated by Cox regression analysis adjusted for potential confounders. RESULTS: We selected 316,396 women and 299,871 men for the GC-exposed group and 43,164 women and 33,702 men for the reference group. Higher GC doses and longer prescription days in the initial 90 days of GC therapy were significantly and dose-dependently associated with increased fracture risk relative to the reference group. Patients receiving GC ≥ 5 mg PSL/day had a significantly increased fracture risk in the stratum of 30-59 days of GC prescription. In addition, female patients who received GC (≥ 1 and < 2.5 mg PSL/day) for 90 days in the initial 90 days of GC therapy had a significantly increased fracture risk. CONCLUSIONS: GC exposure in the initial 90 days of GC therapy was dose-dependently associated with hip and clinical vertebral fracture risk. GC may increase fracture risk with lower doses for shorter durations than previously reported. Fracture risk assessment three months after glucocorticoid (GC) therapy initiation is recommended. We found that GC exposure in the initial 90 days of GC therapy at lower daily doses for shorter durations than previously reported were significantly and dose-dependently associated with fracture risk using a nationwide health insurance claims database.

Machine learning in health financing: benefits, risks and regulatory needs.

There is increasing use of machine learning for the health financing functions (revenue raising, pooling and purchasing), yet evidence lacks for its effects on the universal health coverage (UHC) objectives. This paper provides a synopsis of the use cases of machine learning and their potential benefits and risks. The assessment reveals that the various use cases of machine learning for health financing have the potential to affect all the UHC intermediate objectives - the equitable distribution of resources (both positively and negatively); efficiency (primarily positively); and transparency (both positively and negatively). There are also both positive and negative effects on all three UHC final goals, that is, utilization of health services in line with need, financial protection and quality care. When the use of machine learning facilitates or simplifies health financing tasks that are counterproductive to UHC objectives, there are various risks - for instance risk selection, cost reductions at the expense of quality care, reduced financial protection or over-surveillance. Whether the effects of using machine learning are positive or negative depends on how and for which purpose the technology is applied. Therefore, specific health financing guidance and regulations, particularly for (voluntary) health insurance, are needed. To inform the development of specific health financing guidance and regulation, we propose several key policy and research questions. To gain a better understanding of how machine learning affects health financing for UHC objectives, more systematic and rigorous research should accompany the application of machine learning.

Alors que l'apprentissage machine connaît un usage croissant pour les fonctions de financement de la santé (collecte de revenus, mise en commun et achat), les preuves manquent quant à ses effets sur les objectifs de la couverture sanitaire universelle (CSU). Ce document présente une synthèse des cas d'utilisation de l'apprentissage machine et de leurs avantages et risques potentiels. L'évaluation révèle que les différents cas d'utilisation de l'apprentissage machine pour le financement de la santé sont susceptibles d'affecter tous les objectifs intermédiaires de la CSU: la distribution équitable des ressources (à la fois positivement et négativement), l'efficacité (principalement positivement) et la transparence (à la fois positivement et négativement). Il existe également des effets positifs et négatifs sur les trois objectifs finaux de la CSU, à savoir l'utilisation des services de santé en fonction des besoins, la protection financière et la qualité des soins. Lorsque l'utilisation de l'apprentissage machine facilite ou simplifie des tâches de financement de la santé qui vont à l'encontre des objectifs de la CSU, différents risques se font jour, comme la sélection des risques, la réduction des coûts au détriment de la qualité des soins, la réduction de la protection financière ou la surveillance excessive. Les effets positifs ou négatifs de l'utilisation de l'apprentissage machine dépendent de la manière dont la technologie est appliquée et de l'objectif poursuivi. C'est pourquoi s'imposent des orientations et des réglementations spécifiques en matière de financement de la santé, en particulier pour l'assurance maladie (volontaire). Afin d'éclairer l'élaboration de telles orientations et réglementations, nous proposons plusieurs questions clés en matière de politique et de recherche. Pour mieux comprendre la façon dont l'apprentissage machine affecte le financement de la santé dans le cadre des objectifs de la CSU, une recherche plus systématique et plus rigoureuse devrait accompagner la mise en œuvre de l'apprentissage machine.

Aunque el uso del aprendizaje automático para las funciones de financiación sanitaria (recaudación de ingresos, mancomunación y compra) es cada vez mayor, no hay evidencias de sus efectos sobre los objetivos de la cobertura sanitaria universal (CSU). Este documento ofrece una sinopsis de los casos de uso del aprendizaje automático y sus posibles beneficios y riesgos. La evaluación revela que los diversos casos de uso del aprendizaje automático para la financiación sanitaria tienen el potencial de afectar a todos los objetivos intermedios de la CSU: la distribución equitativa de los recursos (tanto positiva como negativamente), la eficiencia (principalmente positiva) y la transparencia (tanto positiva como negativamente). También hay efectos positivos y negativos en los tres objetivos finales de la CSU, es decir, la utilización de los servicios sanitarios en función de las necesidades, la protección financiera y la atención de calidad. El uso del aprendizaje automático para facilitar o simplificar tareas de financiación sanitaria contraproducentes para los objetivos de la CSU plantea diversos riesgos, como la selección de riesgos, la reducción de costes a expensas de la calidad de la atención, la disminución de la protección financiera o el exceso de vigilancia. El carácter positivo o negativo de los efectos del aprendizaje automático depende de cómo y con qué fin se aplique la tecnología. Por lo tanto, se necesitan directrices y reglamentos específicos para la financiación sanitaria, en particular para los seguros de salud (voluntarios). Proponemos varias preguntas clave en materia de política e investigación para contribuir a la elaboración de directrices y reglamentos específicos sobre financiación sanitaria. A fin de comprender mejor cómo afecta el aprendizaje automático al logro de los objetivos de la CSU en el ámbito de la financiación sanitaria, la aplicación del aprendizaje automático debería ir acompañada de una investigación más sistemática y rigurosa.

Insurance-based Disparities in Outcomes and Extracorporeal Membrane Oxygenation Utilization for Hospitalized COVID-19 Patients.

BACKGROUND: The objective of this study was to examine insurance-based disparities in mortality, nonhome discharges, and extracorporeal membrane oxygenation utilization in patients hospitalized with COVID-19. METHODS: Using a national database of U.S. academic medical centers and their affiliated hospitals, the risk-adjusted association between mortality, nonhome discharge, and extracorporeal membrane oxygenation utilization and (1) the type of insurance coverage (private insurance, Medicare, dual enrollment in Medicare and Medicaid, and no insurance) and (2) the weekly hospital COVID-19 burden (0 to 5.0%; 5.1 to 10%, 10.1 to 20%, 20.1 to 30%, and 30.1% and greater) was evaluated. Modeling was expanded to include an interaction between payer status and the weekly hospital COVID-19 burden to examine whether the lack of private insurance was associated with increases in disparities as the COVID-19 burden increased. RESULTS: Among 760,846 patients hospitalized with COVID-19, 214,992 had private insurance, 318,624 had Medicare, 96,192 were dually enrolled in Medicare and Medicaid, 107,548 had Medicaid, and 23,560 had no insurance. Overall, 76,250 died, 211,702 had nonhome discharges, 75,703 were mechanically ventilated, and 2,642 underwent extracorporeal membrane oxygenation. The adjusted odds of death were higher in patients with Medicare (adjusted odds ratio, 1.28 [95% CI, 1.21 to 1.35]; P < 0.0005), dually enrolled (adjusted odds ratio, 1.39 [95% CI, 1.30 to 1.50]; P < 0.0005), Medicaid (adjusted odds ratio, 1.28 [95% CI, 1.20 to 1.36]; P < 0.0005), and no insurance (adjusted odds ratio, 1.43 [95% CI, 1.26 to 1.62]; P < 0.0005) compared to patients with private insurance. Patients with Medicare (adjusted odds ratio, 0.47; [95% CI, 0.39 to 0.58]; P < 0.0005), dually enrolled (adjusted odds ratio, 0.32 [95% CI, 0.24 to 0.43]; P < 0.0005), Medicaid (adjusted odds ratio, 0.70 [95% CI, 0.62 to 0.79]; P < 0.0005), and no insurance (adjusted odds ratio, 0.40 [95% CI, 0.29 to 0.56]; P < 0.001) were less likely to be placed on extracorporeal membrane oxygenation than patients with private insurance. Mortality, nonhome discharges, and extracorporeal membrane oxygenation utilization did not change significantly more in patients with private insurance compared to patients without private insurance as the COVID-19 burden increased. CONCLUSIONS: Among patients with COVID-19, insurance-based disparities in mortality, nonhome discharges, and extracorporeal membrane oxygenation utilization were substantial, but these disparities did not increase as the hospital COVID-19 burden increased.