Search

Prior research has identified frailty, comorbidity, and age as predictors of outcomes for patients with coronavirus disease 2019 (COVID-19), including mortality. However, it remains unclear how these factors play different roles in COVID-19 prognosis. This study focused on correlations between frailty, comorbidity and age, and their correlations to discharge outcome and length-of-stay in hospitalized patients with COVID-19. Clinical data was collected from 56 patients who were ≥50 years old and admitted from March 2020 to June 2020 primarily for COVID-19. Frailty Risk Score (FRS) and the Charlson Comorbidity Index (CCI) were used for assessment of frailty and comorbidity burden, respectively. Age had significant positive correlation with FRS and CCI (P

Precision medicine presents an opportunity to use novel, data‐driven strategies to improve patient care. The field of precision medicine has undergone many advancements over the past few years. It has moved beyond incorporation of individualized genetic risk into medical decision‐making to include multiple other factors such as unique social, demographic, behavioral, and clinical characteristics. Geriatric medicine stands to benefit heavily from the integration of precision medicine into its standard practices. Older adults, compared with other populations, have high clinical and biological heterogeneity that can alter the risks and benefits of different approaches to patient care. These factors have not been routinely considered previously by geriatricians. Yet, geriatricians' ability to address older adults' baseline heterogeneity is increasingly recognized as a cornerstone of delivering quality care in a geriatric medical practice. Given the shared focus of individualized decision‐making, precision medicine is a natural fit for geriatric medicine. This manuscript provides, via cases and discussion, examples that illustrate how precision medicine can improve the care of our older patients today. We will share specific and existing tools and evidence, and review the existing multilevel barriers to further incorporate and implement these tools into clinical practice. We propose methods to address these barriers and to help realize the full potential of precision medicine for the care of older adults. We conclude with a brief discussion of potential future directions of research of precision medicine in the care of older adults. [ABSTRACT FROM AUTHOR]

Author(s): Benjamin Seligman, Maddalena Ferranna, David E. Bloom In Table 2, the reported results do not perfectly align with results produced by running the posted code. Please see the correct [...]

Background: Electronic frailty indices (eFIs) can expand measurement of frailty in research and practice and have demonstrated predictive validity in associations with clinical outcomes. However, their construct validity is less well studied. We aimed to assess the construct validity of the VA‐FI, an eFI developed for use in the U.S. Veterans Affairs Healthcare System. Methods: Veterans who underwent comprehensive geriatric assessments between January 31, 2019 and June 6, 2022 at VA Boston and had sufficient data documented for a comprehensive geriatric assessment‐frailty index (CGA‐FI) were included. The VA‐FI, based on diagnostic and procedural codes, and the CGA‐FI, based on geriatrician‐measured deficits, were calculated for each patient. Geriatricians also assessed the Clinical Frailty Scale (CFS), functional status (ADLs and IADLs), and 4‐meter gait speed (4MGS). Results: A total of 132 veterans were included, with median age 81.4 years (IQR 75.8–88.7). Across increasing levels of VA‐FI (<0.2; 0.2–0.4; >0.4), mean CGA‐FI increased (0.24; 0.30; 0.40). The VA‐FI was moderately correlated with the CGA‐FI (r 0.45, p < 0.001). Every 0.1‐unit increase in the VA‐FI was associated with an increase in the CGA‐FI (linear regression beta 0.05; 95% confidence interval [CI] 0.03–0.06), higher CFS category (ordinal regression OR 1.69; 95% CI 1.24–2.30), higher odds of ADL dependency (logistic regression OR 1.59; 95% CI 1.20–2.11), IADL dependency (logistic regression OR 1.68; 95% CI 1.23–2.30), and a decrease in 4MGS (linear regression beta −0.07, 95% CI −0.12 to −0.02). All models were adjusted for age and race, and associations held after further adjustment for the Charlson Comorbidity Index. Conclusion: Our results demonstrate the construct validity of the VA‐FI through its associations with clinical measures of frailty, including summary frailty measures, functional status, and objective physical performance. Our findings complement others' in showing that eFIs can capture functional and mobility domains of frailty beyond just comorbidity and may be useful to measure frailty among populations and individuals. [ABSTRACT FROM AUTHOR]

Background The COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level. Methods and findings We estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents. Conclusions Substantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19., Author(s): Benjamin Seligman 1,2,3,*, Maddalena Ferranna 4, David E. Bloom 4 Introduction The SARS-CoV-2 pandemic is a profound challenge to healthcare systems and societies. While no segment of society is [...]

Background: Alzheimer's disease and other dementias (ADODs) severely threaten the wellbeing of older people, their families, and communities, especially with projected exponential growth. Understanding the macroeconomic implications of ADODs for policy making is essential but under-researched., Methods: We used a health-augmented macroeconomic model to calculate the macroeconomic burden of ADODs for 152 countries or territories, accounting for: the effect on labour supply of reduced working hours of informal caregivers; the effect on labour supply of ADODs-related mortality and morbidity; age-sex-specific differences in education, work experience, labour market participations, and informal caregivers; and treatment and formal care costs diverting from savings and investments., Findings: ADODs will cost the world economy 14 513 billion international dollars (INT$, measured in the base year 2020; 95% uncertainty interval [UI] 12 106-17 778) from 2020 to 2050, equivalent to 0·421% (95% UI 0·351-0·515) of annual global GDP. Japan incurs the largest annual GDP loss at 1·463% (1·225-1·790). China (INT$2961 billion [2507-3564]), the USA (INT$2331 billion [1989-2829]), and Japan (INT$1758 billion [1471-2150]) face the largest absolute economic burdens. The economic burden of informal care ranges from 60·97% in high-income countries to 85·45% in lower-middle-income countries, and treatment and formal care costs range from 10·50% in lower-middle-income countries to 30·80% in high-income countries., Interpretation: The macroeconomic burden of ADODs is substantial and unequally distributed across countries and regions. Global efforts to reduce the burden, especially with regard to informal care, are urgently needed., Funding: National Institute on Aging, National Institutes of Health; Chinese Academy of Engineering; Chinese Academy of Medical Sciences; Bill & Melinda Gates Foundation; Davos Alzheimer's Collaborative through Data for Decisions., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study., Methods: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks., Results: All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender., Discussion: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.