Mild autonomous cortisol secretion predominantly affects women, associated with risk for hypertension, diabetes

1. Mild autonomous cortisol secretion predominantly affects women, associated with risk for hypertension, diabetes

Abstract: https://www.acpjournals.org/doi/10.7326/M21-1737

Editorial: https://www.acpjournals.org/doi/10.7326/M21-4526

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A cross-sectional multi-center study of patients with adrenal tumors found that mild autonomous cortisol secretion (MACS) predominantly affects women and is associated with increased frequency and severity of hypertension and type 2 diabetes compared to having nonfunctioning adrenal tumors (NFATs). The study was conducted using the largest ever prospectively recruited group of people with benign adrenal tumors. The findings are published in Annals of Internal Medicine.

Adrenal masses, including NFATs and steroid-overproducing masses, are found during approximately 5 percent of cross-sectional imaging studies and MACS, previously called subclinical Cushing Syndrome, is the most common hormonal abnormality in benign adrenal tumors. MACS has been reported to be associated with type 2 diabetes and hypertension, but little is known about the precise extent of the impact of MACS on cardiometabolic disease risk.

Researchers from the University of Birmingham, Birmingham, United Kingdom, studied data from the European Network for the Study of Adrenal Tumours (ENSAT) to determine cardiometabolic disease burden and steroid excretion in 1,305 persons with benign adrenal tumors with and without MACS. The data showed that many more women than men had MACS and the prevalence of hypertension and diabetes were higher in patients with MACS. Diabetes in these patients more often required insulin therapy to achieve adequate glycemic control. Persons with MACS carried an increased cardiometabolic burden similar to that seen in Cushing Syndrome, even if they did not display typical features of clinically overt cortisol excess. Based on these findings, patients diagnosed with adrenal tumors should have a cardiovascular risk assessment at the time of diagnosis, with particular attention to blood pressure and glucose metabolism.

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with corresponding author, Wiebke Arlt, MD, DSc, please contact Emma McKinney at E.J.McKinney@bham.ac.uk.

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2. Risk adjusted performance measures may not be an accurate measure of health plan performance

Abstract: https://www.acpjournals.org/doi/10.7326/M21-0881

Editorial: https://www.acpjournals.org/doi/10.7326/M21-4665

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There may be substantial residual confounding in risk-adjustment models used to evaluate health plan performance due to differences in patient characteristics between plans. This means that they may not be able to accurately or fairly identify differences between plans and should caution policymakers against assuming that risk adjustment is sufficient to isolate real differences in plan performance. These findings are published in Annals of Internal Medicine.

Nearly 70% of the Medicaid-eligible population is enrolled in a Medicaid managed care plan. Managed care plans are private healthcare plans that receive prospective per-enrollee per-month capitation payments from states and are then responsible for managing and paying for enrollees’ health care. Capitation payments to plans are “risk-adjusted”, meaning that they differ to reflect differences in health care needs across patient populations. However, our results suggest that inadequate adjustment for patient risk penalizes plans (and providers) with unobservably higher-risk patients, incentivizes plans and providers to engage in risk-selection strategies that are wasteful and can undermine quality of care, and leads public-reporting initiatives to potentially misinform patients.

Researchers from Yale School of Public Health analyzed Louisiana Medicaid data to assess the degree to which risk-adjusted measures of health plan performance reflect differences in performance across plans versus differences in patient characteristics (residual confounding). The authors examined data from 2013 and 2014, the period in which Louisiana Medicaid transitioned to Medicaid managed care. The analyses focused on 137,933 eligible residents in the first region to transition to Medicaid managed care. Of those, 94,972 did not select a plan and were randomly assigned to one of 5 plans, creating a natural experiment. The remaining 42,961 chose among the same 5 plans. The authors compared each of the 5 plans’ risk-adjusted performance between the patients who selected a plan and “gold standard” estimates of plan performance based on patients who were randomly assigned. The authors found that risk-adjusted measures of plan performance based on enrollees that chose plans differed substantially from estimates based on randomly assigned enrollees, with residual confounding only modestly reduced by risk adjustment . The authors suggest that the results should serve as a warning to policymakers who assume current risk adjustment is sufficient to measure the performance of plans (or providers) and the study discusses several implications of the findings for how payers and providers assess performance and deploy risk-adjustment in public insurance programs.

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with corresponding author, Jacob Wallace, PhD, please contact Michael Greenwood at michael.greenwood@yale.edu.

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3. NIH Workshop participants find that health and prevention research on Asian American, Native Hawaiian and Pacific Islander populations is urgently needed to eliminate disparities and promote health equity

Abstract: https://www.acpjournals.org/doi/10.7326/M21-3729

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Researchers who were convened by the National Heart, Lung, and Blood Institute (NHLBI) and 8 other National Institutes of Health (NIH) institutes say that Asian Americans (AsA), Native Hawaiian and Pacific Islander (NHPI) populations must be included in research studies to eliminate health disparities, achieve health equity, and identify untapped scientific potentials. A summary of findings from their multidisciplinary workshop, “Identifying Research Opportunities for Asian American, Native Hawaiian, and Pacific Islander Health,” is published in Annals of Internal Medicine.

In 2020, AsA and NHPI comprised 7.7% of the total U.S. population yet accounted for only 2% of the nearly 245,000 participants in the 14 largest NHLBI-supported cohorts. A review of clinical research funded by the NIH found that studies focusing on AsA and NHPI comprised only 0.17% of the total NIH budget between 1992 and 2018.

To address these discrepancies, the NHLBI/NIH workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. The researchers found very limited data available for the target populations on the epidemiology, risk factors, and outcomes for most conditions, and most existing data are not disaggregated by subgroup. The experts say these findings are particularly important given the tremendous heterogeneity among the 40 AsA and NHPI ethnic subgroups with respect to indigeneity, nativity or ancestry, culture, immigration patterns, acculturation, educational attainment, income, language, and English proficiency, all of which influence health, health care access, and outcomes. To minimize disparity and improve equity, more funding is needed to improve research support and infrastructure for health research in AsA and NHPI populations. And to improve inclusion and inform prevention and intervention efforts, researchers must seek collaborations with community partners, invest in infrastructure support for cohort studies, enhance existing data sources to enable data disaggregation, and incorporate novel technology for objective measurement.

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with corresponding author, Ann W. Hsing, PhD, MPH, please contact Julie Greicius at jgreicius@stanford.edu.

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