Determining the Optimal Outcome Measures for Studying the Social Determinants of Health.

anti-poverty policies and health outcome measures randomized-controlled trial social determinants of health social policies and health

Journal

International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455

Informations de publication

Date de publication:
27 04 2020
Historique:
received: 26 03 2020
revised: 19 04 2020
accepted: 25 04 2020
entrez: 1 5 2020
pubmed: 1 5 2020
medline: 3 10 2020
Statut: epublish

Résumé

Americans have significantly poorer health outcomes and shorter longevity than citizens of other industrialized nations. Poverty is a major driver of these poor health outcomes in the United States. Innovative anti-poverty policies may help reduce economic malaise thereby increasing the health and longevity of the most vulnerable Americans. However, there is no consensus framework for studying the health impacts of anti-poverty social policies. In this paper, we describe a case study in which leading global experts systematically: (1) developed a conceptual model that outlines the potential pathways through which a social policy influences health, (2) fits outcome measures to this conceptual model, and (3) estimates an optimal time frame for collection of the selected outcome measures. This systematic process, called the Delphi method, has the potential to produce estimates more quickly and with less bias than might be achieved through expert panel discussions alone. Our case study is a multi-component randomized-controlled trial (RCT) of a workforce policy called MyGoals for Healthy Aging.

Identifiants

pubmed: 32349268
pii: ijerph17093028
doi: 10.3390/ijerph17093028
pmc: PMC7246501
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIA NIH HHS
ID : R56 AG062485
Pays : United States

Déclaration de conflit d'intérêts

The authors declare no conflict of interest.

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Auteurs

Peter Muennig (P)

Department of Health Policy and Management, Columbia University, New York, NY 10032, USA.

Bruce McEwen (B)

Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology, The Rockefeller University, New York, NY 10065, USA.

Daniel W Belsky (DW)

Department of Epidemiology, Columbia University, New York, NY 10032, USA.

Kimberly G Noble (KG)

Neuroscience and Education Teachers College, Columbia University, New York, NY 10027, USA.

James Riccio (J)

Low-Wage Workers and Communities division of MDRC, New York, NY 10281, USA.

Jennifer Manly (J)

Physicians and Surgeons, Columbia University, New York, NY 10032, USA.

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Classifications MeSH