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Cited by 10Statistical Methods in EpidemiologySurvey Sampling and Estimation TechniquesHealth disparities and outcomes

Extension of the Peters–Belson method to estimate health disparities among multiple groups using logistic regression with survey data

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Li, Y., Graubard, B. I., Huang, P., Gastwirth, J. L.·2015·Statistics in Medicine
DOI 10.1002/sim.6357PMC PMC4630005
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Abstract

Determining the extent of a disparity, if any, between groups of people, for example, race or gender, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters–Belson (PB) method fits a regression model with covariates to the AG to predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and the mean observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on binary/multinomial/proportional odds logistic regression models using data collected from complex surveys with more than one DG. Estimators of the unexplained disparity, an analytic variance–covariance estimator that is based on the Taylor linearization variance–covariance estimation method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between two or more minority groups and a majority group, are provided. Simulation studies with data selected from simple random sampling and cluster sampling, as well as the analyses of disparity in body mass index in the National Health and Nutrition Examination Survey 1999–2004, are conducted. Empirical results indicate that the Taylor linearization variance–covariance estimation is accurate and that the proposed Wald test maintains the nominal level. Copyright © 2014 John Wiley & Sons, Ltd.

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  • The resulting odds ratios are interpreted as descriptive measures of differential association rather than causal estimates.

    “The resulting odds ratios are interpreted as descriptive measures of differential association rather than causal estimates (41).”

    From Health misinformation, digital health literacy, and attitudes toward science among university students: an exploratory cross-sectional survey (2026) · cites this paper as [41]

    The Methods (section 2.1; page unavailable) define observed and unexplained disparities as differences in outcome probabilities. A search for odds ratios and causal interpretation found no passage saying that resulting odds ratios should be read as descriptive associations rather than causal estimates. (LLM, conf=0.74)

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Research works printed but not linked: 3

  1. [1]Altonji JG Blank RM Ashenfelter O Card D Race and Gender in the Labor Market. Handbook of Labor Economics 1999 3 3143 3259 Elsevier Science B. V. New York
  2. [6]Blinder AA Wage discrimination: reduced form structural estimates. Journal of Human Resources 1973 8 433 455
  3. [14]Fortin N Lemieux T Firpo S Decomposition methods in economics, Technical Report Working paper 16045 National Bureau of Economic Research Cambridge, MA. (Available from: http://www.nber.org/papers/w16045), 2010. 11/03/2014

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Authors
Li, Y., Graubard, B. I., Huang, P., & Gastwirth, J. L.
Affiliations
  • University of Maryland, College Park (US)
  • National Cancer Institute (US)
  • George Washington University (US)
Corresponding author
Li, Y.
ORCID
Venue
Statistics in Medicine
Publisher
Wiley
Published
2014-11-10
Volume
34
Issue
4
Pages
595-612
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.6357
PMC
PMC4630005
OpenAlex
W2000541238
Cited by
10
References
27
Citations by year
2026: 2 · 2025: 1 · 2024: 2 · 2022: 1 · 2020: 1 · 2019: 1 · 2018: 1 · 2015: 1
FWCI
0.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Statistics in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (Z99 CA999999)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
  • Social Sciences › Health › Health disparities and outcomes
Keywords
health disparities, logistic regression, multinomial logistic regression, proportional odds model, complex survey data, Wald test, body mass index, NHANES
MeSH terms
Analysis of Variance, Data Collection, Ethnicity, Female, Humans, Nutrition Surveys, Obesity, Linear Models, Logistic Models, Healthcare Disparities, Biostatistics

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