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Displaying 471 - 480 of 6915 in Annals of Internal Medicine
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Effect of Starting Dialysis Versus Continuing Medical Management on Survival and Home Time in Older Adults With Kidney Failure: A Target Trial Emulation Study: Annals of Internal Medicine: Vol 177, No 9
Background: For older adults with kidney failure who are not referred for transplant, medical management is an alternative to dialysis. Objective: To compare survival and home time between older adults who started dialysis at an estimated glomerular filtration rate (eGFR) less than 12 mL/min/1.73 m2 and those who continued medical management. Design: Observational cohort study using target trial emulation. Setting: U.S. Department of Veterans Affairs, 2010 to 2018. Participants: Adults aged 65 years or older with chronic kidney failure and eGFR below 12 mL/min/1.73 m2 who were not referred for transplant. Intervention: Starting dialysis within 30 days versus continuing medical management. Measurements: Mean survival and number of days at home. Results: Among 20 440 adults (mean age, 77.9 years [SD, 8.8]), the median time to dialysis start was 8.0 days in the group starting dialysis and 3.0 years in the group continuing medical management. Over a 3-year horizon, the group starting dialysis survived 770 days and the group continuing medical management survived 761 days (difference, 9.3 days [95% CI, −17.4 to 30.1 days]). Compared with the group continuing medical management, the group starting dialysis had 13.6 fewer days at home (CI, 7.7 to 20.5 fewer days at home). Compared with the group continuing medical management and forgoing dialysis completely, the group starting dialysis had longer survival by 77.6 days (CI, 62.8 to 91.1 days) and 14.7 fewer days at home (CI, 11.2 to 16.5 fewer days at home). Limitation: Potential for unmeasured confounding due to lack of symptom assessments at eligibility; limited generalizability to women and nonveterans. Conclusion: Older adults starting dialysis when their eGFR fell below 12 mL/min/1.73 m2 who were not referred for transplant had modest gains in life expectancy and less time at home. Primary Funding Source: U.S. Department of Veterans Affairs and National Institutes of Health.
Where Are All the Specialists? Current Challenges of Integrating Specialty Care Into Population-Based Total Cost of Care Payment Models
The Centers for Medicare & Medicaid Services Innovation Center (CMMI) has set the goal for 100% of traditional Medicare beneficiaries to be part of an accountable care relationship by 2030. Lack of meaningful financial incentives, intolerable or unpredictable risk, infrastructure costs, patient engagement, voluntary participation, and operational complexity have been noted by the provider and health care delivery community as barriers to participation or reasons for exiting programs. In addition, most piloted and implemented population-based total cost of care (PB-TCOC) payment models have focused on the role of the primary care physician being the accountability (that is, attributable) leader of a patient’s multifaceted care team as well as acting as the mayor of the “medical neighborhood,” leaving the role of specialty care physicians undefined. Successful provider specialist integration into PB-TCOC models includes meaningful participation of specialists in achieving whole-person, high-value care where all providers are financially motivated to participate; there is unambiguous prospective attribution and clearly defined accountability for each participating party throughout the care journey or episode; there is a known care attribution transition accountability plan; there is actionable, transparent, and timely data available with appropriate data development and basic analytic costs covered; and there is advanced payment to the accountable person or entity for management of the care episode that is part of a longitudinal care plan. Payment models should be created to address the 7 challenges raised here if specialists are to be incented to join TCOC models that achieve CMMI’s goal.
Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021: An Analysis of National Disease Registry Data: Annals of Internal Medicine: Vol 177, No 4
Background: Elevated tuberculosis (TB) incidence rates have recently been reported for racial/ethnic minority populations in the United States. Tracking such disparities is important for assessing progress toward national health equity goals and implementing change. Objective: To quantify trends in racial/ethnic disparities in TB incidence among U.S.-born persons. Design: Time-series analysis of national TB registry data for 2011 to 2021. Setting: United States. Participants: U.S.-born persons stratified by race/ethnicity. Measurements: TB incidence rates, incidence rate differences, and incidence rate ratios compared with non-Hispanic White persons; excess TB cases (calculated from incidence rate differences); and the index of disparity. Analyses were stratified by sex and by attribution of TB disease to recent transmission and were adjusted for age, year, and state of residence. Results: In analyses of TB incidence rates for each racial/ethnic population compared with non-Hispanic White persons, incidence rate ratios were as high as 14.2 (95% CI, 13.0 to 15.5) among American Indian or Alaska Native (AI/AN) females. Relative disparities were greater for females, younger persons, and TB attributed to recent transmission. Absolute disparities were greater for males. Excess TB cases in 2011 to 2021 represented 69% (CI, 66% to 71%) and 62% (CI, 60% to 64%) of total cases for females and males, respectively. No evidence was found to indicate that incidence rate ratios decreased over time, and most relative disparity measures showed small, statistically nonsignificant increases. Limitation: Analyses assumed complete TB case diagnosis and self-report of race/ethnicity and were not adjusted for medical comorbidities or social determinants of health. Conclusion: There are persistent disparities in TB incidence by race/ethnicity. Relative disparities were greater for AI/AN persons, females, and younger persons, and absolute disparities were greater for males. Eliminating these disparities could reduce overall TB incidence by more than 60% among the U.S.-born population. Primary Funding Source: Centers for Disease Control and Prevention.
The Impact of Health Care Algorithms on Racial and Ethnic Disparities: A Systematic Review: Annals of Internal Medicine: Vol 177, No 4
Background: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities. Purpose: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities. Data Sources: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023. Study Selection: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Data Extraction: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies – of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension. Data Synthesis: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non–race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques. Limitation: Results are mostly based on modeling studies and may be highly context-specific. Conclusion: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes. Primary Funding Source: Agency for Healthcare Quality and Research.
COVID-19 Vaccine Side Effects and Long-Term Neutralizing Antibody Response: A Prospective Cohort Study: Annals of Internal Medicine: Vol 177, No 7
Background: Concern about side effects is a common reason for SARS-CoV-2 vaccine hesitancy. Objective: To determine whether short-term side effects of SARS-CoV-2 messenger RNA (mRNA) vaccination are associated with subsequent neutralizing antibody (nAB) response. Design: Prospective cohort study. Setting: San Francisco Bay Area. Participants: Adults who had not been vaccinated against or exposed to SARS-CoV-2, who then received 2 doses of either BNT162b2 or mRNA-1273. Measurements: Serum nAB titer at 1 month and 6 months after the second vaccine dose. Daily symptom surveys and objective biometric measurements at each dose. Results: 363 participants were included in symptom-related analyses (65.6% female; mean age, 52.4 years [SD, 11.9]), and 147 were included in biometric-related analyses (66.0% female; mean age, 58.8 years [SD, 5.3]). Chills, tiredness, feeling unwell, and headache after the second dose were each associated with 1.4 to 1.6 fold higher nAB at 1 and 6 months after vaccination. Symptom count and vaccination-induced change in skin temperature and heart rate were all positively associated with nAB across both follow-up time points. Each 1 °C increase in skin temperature after dose 2 was associated with 1.8 fold higher nAB 1 month later and 3.1 fold higher nAB 6 months later. Limitations: The study was conducted in 2021 in people receiving the primary vaccine series, making generalizability to people with prior SARS-CoV-2 vaccination or exposure unclear. Whether the observed associations would also apply for neutralizing activity against non-ancestral SARS-CoV-2 strains is also unknown. Conclusion: Convergent self-report and objective biometric findings indicate that short-term systemic side effects of SARS-CoV-2 mRNA vaccination are associated with greater long-lasting nAB responses. This may be relevant in addressing negative attitudes toward vaccine side effects, which are a barrier to vaccine uptake. Primary Funding Source: National Institute on Aging.
School Mask Mandates and COVID-19: The Challenge of Using Difference-in-Differences Analysis of Observational Data to Estimate the Effectiveness of a Public Health Intervention
Background: There are considerable challenges when using difference-in-differences (DiD) analysis of ecological data to estimate the effectiveness of public health interventions in rapidly changing situations. Objective: To discuss the shortcomings of DiD methodology for the estimation of the effects of public health interventions using ecological data. Design: As an example, the authors consider an analysis that used DiD methodology and reported a causal reduction in COVID-19 cases due to the maintenance of school mask mandates. They did alternate analyses using various control groups to assess the robustness of the prior analysis. Setting: School districts in the greater Boston area and Massachusetts during the 2021-to-2022 academic year. Participants: Students and school staff. Measurements: Changes in COVID-19 case rates in districts that did and did not lift mask mandates. Results: Important potential confounders rendered DiD methodology inappropriate for causal inference, including prior immunity, temporal variation in rates of infection, and changes in testing practices. The racial composition and income of intervention and control groups also differed substantially. Compared with maintaining the mask requirement, dropping the requirement was associated with anywhere from an increase of 5.64 cases (95% CI, 3.00 to 8.29 cases) per 1000 persons to a decrease of 2.74 cases (CI, 0.63 to 4.85 cases) per 1000 persons, depending on choice of control group and whether students or staff were examined. Limitation: Ecological data were used; detailed data on all potential confounders were unavailable. Conclusion: Alternate analyses yielded estimates consistent with a wide range of both negative and positive associations in COVID-19 case rates after removal of mask mandates. The findings highlight the challenges of using DiD analysis of ecological data to estimate the effectiveness of interventions in divergent intervention and control groups during rapidly changing circumstances. Primary Funding Source: None.