Improving Health Outcomes for an Aging Population - Project 2

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 3P01AG005842-32S2

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Key facts

  • Disease

    COVID-19
  • Start & end year

    1997
    2023
  • Known Financial Commitments (USD)

    $161,388
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    Katherine Baicker
  • Research Location

    United States of America
  • Lead Research Institution

    National Bureau Of Economic Research
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease susceptibility

  • Special Interest Tags

    N/A

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Adults (18 and older)Older adults (65 and older)

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

OTHER PROJECT INFORMATION - Project Summary/AbstractDrug Treatments and the Impact of COVID-19 on Alzheimer's Patients and Other Vulnerable PopulationsInterventions in health policy and care management have the potential to reduce COVID-19 infections anddeaths, particularly if they can be targeted to the most vulnerable populations such as patients with Alzheimer'sDisease and Related Dementias (ADRD). In this supplement proposal, we compile information and develop toolsthat can accelerate and target such interventions. The first aim is to identify the medical and socioeconomiccharacteristics of people that make them most vulnerable to COVID-19. We create cohorts of patients with ADRDand for other vulnerable populations based on their Fall 2019 characteristics, and follow them through 2020 toidentify those at greatest risk of both "direct" COVID-19 outcomes (e.g., critical illness, mortality) and "indirect"increases in non-COVID outcomes. The second aim is an ambitious proof of concept: using natural experimentsto shed light on novel drugs to treat or prevent COVID-19 with a particular focus on drugs most heavily used byADRD patients (e.g., anticholinesterase inhibitors). We will develop and apply a machine learning approach totest the potential effect of drug classes on COVID-19, measured by diagnosis, hospitalization, ICU admission,and death. This supplement is made possible by a unique opportunity: Access to near-real-time Medicare claimsdata (one-month lag), which CMS appears willing to make available through an expedited data use agreement.The application will supplement an ongoing program project on Improving Health Outcome for an AgingPopulation, whose overarching aim is to better understand health trends and disparities, determinants of health,and approaches to improving health for an aging population in an evolving landscape.