Neither care delivery nor public health systems have grappled with widening disparities as life expectancy gaps increase in the US. Reimagining health care and public health requires aligned incentives including attention to vulnerable populations, financial incentives to improve total population health, effective deployment of community assets, and adoption of a continuous learning system. We argue that Big Hairy Audacious Goals-targets for a Health GDP (similar to the economy's gross domestic product [GDP]), Life Expectancy, Safe and Sound Children, One Earth Policy, Social Spending, and Political Healing-can focus our attention and propel needed action.
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http://dx.doi.org/10.37765/ajmc.2024.89649 | DOI Listing |
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFCirc Res
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada (C.P., S.A., J.W.A., R.L., F.N., J.S., I.C.).
Background: Iron is an essential micronutrient for cell survival and growth; however, excess of this metal drives ferroptosis. Although maternal iron imbalance and placental hypoxia are independent contributors to the pathogenesis of preeclampsia, a hypertensive disorder of pregnancy, the mechanisms by which their interaction impinge on maternal and placental health remain elusive.
Methods: We used placentae from normotensive and preeclampsia pregnancy cohorts, human H9 embryonic stem cells differentiated into cytotrophoblast-like cells, and placenta-specific preeclamptic mice.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
View Article and Find Full Text PDFCirc Genom Precis Med
January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
Afr J Prim Health Care Fam Med
December 2024
Division of Rural Health (Ukwanda), Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; and, Department of Health Professions Education, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town.
Background: Interprofessional education (IPE) during undergraduate training (UGT) is considered important for new graduates to collaborate inter-professionally. There are, however, well-documented workplace challenges that hinder their involvement in interprofessional collaborative practice (IPCP) such as professional hierarchy, poor role clarification and communication challenges.
Aim: This article explores graduates' perceptions of the value rural undergraduate IPE had on their IPCP during their first year of work.
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