Prevalence of Chronic Medical Conditions Among Medicare Advantage and Traditional Medicare Beneficiaries

Background: The federal government spends billions of dollars per year on payments to Medicare Advantage (MA) plans based, in part, on beneficiaries’ risk scores. Despite this, little is known about the true burden of chronic medical conditions among MA beneficiaries compared with those in fee-for-service (FFS) Medicare. Objective: To determine whether the prevalence of chronic medical conditions is higher among MA compared with FFS beneficiaries. Design: Cross-sectional. Setting: Population based. Participants: Adults aged 65 years or older enrolled in MA or FFS Medicare. Measurements: Using direct physical examination and laboratory data from the National Health and Nutrition Examination Survey (2015 to 2018), we compared the age- and sex-standardized prevalence of obesity, hypertension, hyperlipidemia, diabetes, and chronic kidney disease between MA and FFS beneficiaries. Results: The unweighted study population included 2446 respondents corresponding to a weighted total of 45 426 711 adults (34.4% MA, 65.6% FFS Medicare). The prevalence of obesity (41.1% vs. 40.6%; standardized difference [SDiff], 0.48 percentage points [pp] [95% CI, −5.2 to 6.2 pp]), hypertension (70.9% vs. 71.0%; SDiff, −0.05 pp [CI, −5.8 to 5.7 pp]), hyperlipidemia (79.4% vs. 82.3%; SDiff, −2.86 pp [CI, −7.0 to 1.3 pp]), and chronic kidney disease (19.2% vs. 22.8%; SDiff, −3.48 pp [CI, −9.2 to 2.3 pp]) was not higher among MA beneficiaries compared with FFS beneficiaries. However, the prevalence of diabetes was higher in MA (33.3% vs. 26.3%; SDiff, 7.00 pp [CI, 3.3 to 10.7 pp]). Limitation: Differences in the severity of specific medical conditions between groups could not be assessed. Conclusion: In this nationally representative study from 2015 to 2018, the prevalence of obesity, hypertension, hyperlipidemia, and chronic kidney disease was not higher among MA compared with FFS beneficiaries; however, the prevalence of diabetes was higher among MA beneficiaries. Primary Funding Source: National Heart, Lung, and Blood Institute (NHLBI) and American Heart Association (AHA).

Clinical Characteristics and Current Management of U.S. Adults at Elevated Risk for Heart Failure Using the PREVENT Equations: A Cross-Sectional Analysis

Strategies to Address Racial and Ethnic Disparities in Health and Health Care for Chronic Conditions: An Evidence Map of Research From 2017 to 2024: Annals of Internal Medicine: Vol 178, No 1

Background: Racial and ethnic disparities in health and health care persist in the United States, adversely affecting outcomes in prevention and treatment of chronic conditions among adults. Purpose: To map interventions aimed at reducing racial and ethnic disparities and improving health outcomes in the prevention and treatment of chronic conditions in adults. Data Sources: Searches of MEDLINE, CINAHL, and Scopus from January 2017 to April 2024, supplemented with gray literature. Study Selection: U.S.-based studies of interventions targeting racial and ethnic disparities in adults with chronic conditions. Data Extraction: Information on intervention types, targets, outcomes, study designs, study settings, chronic conditions, and delivery personnel was extracted and categorized. Data Synthesis: Among 174 unique studies, 12 intervention types were identified, with self-management support and patient navigation the most common. Most interventions targeted patient behaviors; few studies addressed disparities directly or focused on underrepresented racial and ethnic marginalized groups. Limitations: The lack of standardized terminology and the underrepresentation of certain racial and ethnic groups limit the evidence base. Although the literature search accurately reflects the current state of the literature, it also limits the body of evidence by excluding health disparities research conducted before January 2017, so significant findings from earlier studies may have been overlooked. Conclusion: The literature highlights diverse interventions targeting health disparities, but few studies evaluated their effectiveness in reducing the health disparities gaps. There is an urgent need for research focused on underrepresented racial and ethnic groups, particularly in promising areas such as patient navigation for cancer and diabetes self-management. Future research should prioritize robust study designs to assess the long-term effect and broader applicability of interventions, thus helping organizations and stakeholders to tailor strategies to community-specific needs. Primary Funding Source: Agency for Healthcare Research and Quality.

Effectiveness and Cost-Effectiveness of Expanded Targeted Testing and Treatment of Latent Tuberculosis Infection Among the Medicare Population in 2022

Background: In the United States, older adults have elevated prevalence of latent tuberculosis infection (LTBI) and incidence of tuberculosis (TB). Objective: To estimate the health benefits and cost-effectiveness of LTBI testing and treatment among the Medicare-eligible population. Design: Model-based cost-effectiveness analysis. Data Sources: Nationally representative surveys and published evidence. Target Population: Medicare-eligible persons aged 65 years or older with at least 1 of 15 factors associated with elevated TB risk, as identified by guidelines from the U.S. Preventive Services Task Force (USPSTF) and other organizations. Time Horizon: Lifetime. Perspective: Societal. Intervention: One-time offer of LTBI testing and treatment versus no intervention. Outcome Measures: Lifetime TB cases and deaths averted, quality-adjusted life-years (QALYs) gained, costs, and incremental cost-effectiveness ratios (ICERs). Results of Base-Case Analysis: In 2022, there were an estimated 29.9 million Medicare-eligible persons (95% uncertainty interval [UI], 28.4 to 31.6 million persons) aged 65 years or older with elevated TB risks, including 14.7 million (95% UI, 13.4 to 16.0 million) with USPSTF-recommended factors. In the target population, 4.9 million persons (95% UI, 4.0 to 5.8 million persons) (16.4% [95% UI, 13.9% to 19.1%]) were estimated to have LTBI. Testing and treatment of LTBI was estimated to prevent 10 946 TB cases (95% UI, 4684 to 20 579 cases) and 2579 TB deaths (95% UI, 1106 to 4882 deaths), with 13 234 lifetime QALYs (95% UI, 5343 to 25 519 lifetime QALYs) gained. For the overall target population and for persons with USPSTF-recommended factors, ICERs were $192 000 (95% UI, $92 000 to $503 000) and $155 000 (95% UI, $77 000 to $393 000) per QALY gained, respectively. Results of Sensitivity Analysis: The ICER was $109 000 (95% UI, $49 000 to $285 000) per QALY gained for 65-year-olds newly eligible for Medicare. Limitation: Health benefits from averted post-TB sequelae were not estimated. Conclusion: Medicare-eligible persons represent approximately one third of all U.S. persons with LTBI. Testing and treatment of LTBI in this population could lead to substantial reductions in TB and TB-related mortality, particularly among 65-year-olds newly eligible for Medicare. Primary Funding Source: Centers for Disease Control and Prevention.

The Risks of Risk Assessment: Causal Blind Spots When Using Prediction Models for Treatment Decisions

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions. “Causal blind spots” were identified in 3 common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to persons taking a certain treatment, and ignoring treatment. Through several real examples, this article illustrates how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision making. The discussion covers issues attributable to confounding, selection, mediation, and changes in treatment protocols over time. An extension of guidelines for the development, reporting, and evaluation of prediction models is advocated to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as “prediction under interventions.” Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.

Prevention and Initial Management of HIV Infection

Since July 2017, when In the Clinic last addressed management of HIV infection, there have been meaningful improvements in our ability to prevent HIV and to manage patients living with HIV. New approaches to preexposure prophylaxis and more effective treatments have made the elimination of HIV infection a feasible goal. The federal “Ending the HIV Epidemic” initiative aims at a 90% reduction in new HIV diagnoses by 2030. This article provides updated information on how clinicians should use these improvements to manage their patients who are at risk for HIV infection or are newly diagnosed with HIV.

Review: Revascularization and medical treatment may be similar in atherosclerotic renal artery stenosis

Source Citation Raman G, Adam GP, Halladay CW, et al. Comparative effectiveness of management strategies for renal artery stenosis: an updated systematic review. Ann Intern Med. 2016;165:635-49. 27536808

In black Africans with hypertension, amlodipine-based therapy vs perindopril–hydrochlorothiazide improved BP control

Source Citation Ojji DB, Mayosi B, Francis V, et al; CREOLE Study Investigators. Comparison of dual therapies for lowering blood pressure in black Africans. N Engl J Med. 2019. [Epub ahead of print]. 30883050

Cardiovascular Disease Mortality Among Native Hawaiian and Pacific Islander Adults Aged 35 Years or Older, 2018 to 2022

Background: Native Hawaiian and Pacific Islander (NHPI) adults have historically been grouped with Asian adults in U.S. mortality surveillance. Starting in 2018, the 1997 race and ethnicity standards from the U.S. Office of Management and Budget were adopted by all states on death certificates, enabling national-level estimates of cardiovascular disease (CVD) mortality for NHPI adults independent of Asian adults. Objective: To describe CVD mortality among NHPI adults. Design: Race-stratified age-standardized mortality rates (ASMRs) and rate ratios were calculated using final mortality data from the National Vital Statistics System for 2018 to 2022. Setting: Fifty states and the District of Columbia. Participants: Adults aged 35 years or older at the time of death. Measurements: CVD deaths were identified from International Classification of Diseases, 10th Revision codes indicating CVD (I00 to I99) as the underlying cause of death. Results: From 2018 to 2022, 10 870 CVD deaths (72.6% from heart disease; 19.0% from cerebrovascular disease) occurred among NHPI adults. The CVD ASMR for NHPI adults (369.6 deaths per 100 000 persons [95% CI, 362.4 to 376.7]) was 1.5 times higher than for Asian adults (243.9 deaths per 100 000 persons [CI, 242.6 to 245.2]). The CVD ASMR for NHPI adults was the third highest in the country, after Black adults (558.8 deaths per 100 000 persons [CI, 557.4 to 560.3]) and White adults (423.6 deaths per 100 000 persons [CI, 423.2 to 424.1]). Limitation: Potential misclassification of underlying cause of death or race group. Conclusion: NHPI adults have a high rate of CVD mortality, which was previously masked by aggregation of the NHPI population with the Asian population. The results of this study support the need for continued disaggregation of the NHPI population in public health research and surveillance to identify opportunities for intervention. Primary Funding Source: National Institute of General Medical Sciences, National Institutes of Health.

Long COVID Definitions and Models of Care: A Scoping Review: Annals of Internal Medicine: Vol 177, No 7

Background: Definitions of long COVID are evolving, and optimal models of care are uncertain. Purpose: To perform a scoping review on definitions of long COVID and provide an overview of care models, including a proposed framework to describe and distinguish models. Data Sources: English-language articles from Ovid MEDLINE, PsycINFO, the Cochrane Library, SocINDEX, Scopus, Embase, and CINAHL published between January 2021 and November 2023; gray literature; and discussions with 18 key informants. Study Selection: Publications describing long COVID definitions or models of care, supplemented by models described by key informants. Data Extraction: Data were extracted by one reviewer and verified for accuracy by another reviewer. Data Synthesis: Of 1960 screened citations, 38 were included. Five clinical definitions of long COVID varied with regard to timing since symptom onset and the minimum duration required for diagnosis; 1 additional definition was symptom score–based. Forty-nine long COVID care models were informed by 5 key principles: a core “lead” team, multidisciplinary expertise, comprehensive access to diagnostic and therapeutic services, a patient-centered approach, and providing capacity to meet demand. Seven characteristics provided a framework for distinguishing models: home department or clinical setting, clinical lead, collocation of other specialties, primary care role, population managed, use of teleservices, and whether the model was practice- or systems-based. Using this framework, 10 representative practice-based and 3 systems-based models of care were identified. Limitations: Published literature often lacked key model details, data were insufficient to assess model outcomes, and there was overlap between and variability within models. Conclusion: Definitions of long COVID and care models are evolving. Research is needed to optimize models and evaluate outcomes of different models. Primary Funding Source: Agency for Healthcare Research and Quality. (Protocol posted at https://effectivehealthcare.ahrq.gov/products/long-covid-models-care/protocol.)