DOI: 10.7910/dvn/qnjv0a
title: Life + Health Study: Advancing novel methods to measure and analyze multiple types of discrimination for population health research (R01 MD012793, 2019–2026)

description: The Life + Health Study ("Advancing novel methods to measure and analyze multiple types of discrimination for population health research"; &lt;a href="https://reporter.nih.gov/search/CdmqEcCLK0-rRMfzaUZXaQ/project-details/9733462" target="_blank"&gt;NIH R01MD012793&lt;/a&gt;) is a cross‑sectional, population‑based study designed to improve how exposure to discrimination is measured and analyzed in relation to health. The study compares novel implicit measures with conventional explicit (self‑report) measures of discrimination, evaluates approaches to modeling exposure to multiple types of (discrimination, and tests hypothesized links between discrimination and health.

&lt;br/&gt;&lt;br/&gt;The study was conducted among U.S.‑born adults ages 25–64 recruited between 2020 and 2022 from three community health centers in Boston, Massachusetts (Fenway Health, Mattapan Community Health Center, and Harvard Street Neighborhood Health Center). The target sample size under the R01 was 1,092 participants; the analytic dataset documented here comprises approximately 699 participants who completed both the implicit and explicit components and met inclusion criteria for the core analyses. Participants may have experienced discrimination across multiple social dimensions (e.g., race/ethnicity, gender identity, age, sexual orientation, weight). Data collection included: (1) a Brief Implicit Association Test (B‑IAT) battery for discrimination, using a format refined for time‑efficient assessment of lifetime exposure to multiple types of discrimination; and (2) a validated explicit self‑report survey measuring experiences of discrimination, alongside health and sociodemographic information. Primary health outcomes include psychological distress and sleep‑related disorders.

&lt;br/&gt;&lt;br/&gt;&lt;strong&gt;Public Materials and Documentation:&lt;/strong&gt; This dataset record provides formal citation information and publicly accessible materials including documentation, study instruments, codebooks, and analysis code. All public materials are also available on &lt;strong&gt;&lt;a href="https://github.com/LifeAndHealthStudy" target="_blank"&gt;GitHub&lt;/a&gt;&lt;/strong&gt;.

&lt;br/&gt;&lt;br/&gt;&lt;strong&gt;Data Storage:&lt;/strong&gt; The de-identified analytic dataset is stored on secure Harvard Chan School servers managed by Harvard Chan School Information Technology.

&lt;br/&gt;&lt;br/&gt;&lt;strong&gt;Data Access:&lt;/strong&gt; Because the data contain sensitive health and social information, the underlying analytic data files are not openly available for download from this repository. Researchers interested in accessing the data may be able to do so via a governed access process and Data Use Agreement (DUA). &lt;strong&gt;To apply for data access, please complete the &lt;a href="https://harvard.az1.qualtrics.com/jfe/form/SV_0dhd6tjArmB9fKK"&gt;application form&lt;/a&gt;. &lt;/strong&gt; For additional information on eligibility criteria, application procedures, and data use conditions, please see the PDF "L+H_ANALYTIC_DATASET_ACCESS+TERMS_OF_USE" located in the "Create-Analytic-Dataset" folder included in this record.
description: R
description: Qualtrics
description: Inquisit
description: Core authors/contributors to the study are as follows: Nancy Krieger (PI), Jarvis T. Chen, Nykesha Johnson, Meg Matthews, Sari Reisner, Merrily LeBlanc, Maddalena Marini, Ken Mayer, Donna Bright, Apriani Oendari, Guale Valdez, and Tanveer Khan, and also (not active at time of end of study) Pamela D. Waterman and Christian Testa; see co-author lists and acknowledgments in our published papers for additional contributors to the work.