ArticleAmerican journal of epidemiology2018
Statistical Inference on Health Disparity Indices for Complex Surveys.
Article in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Socioeconomic health equity with malaria burden: quantifying slope (Relative) index of inequity in malaria infection outcomes in the Southeastern-coastal Tanzania.BMC public health · 2026Article
- Social determinants of health and life expectancy in US adults.Journal of translational medicine · 2025Article
- Race/ethnicity and gender modify the association between diet and cognition in U.S. older adults: National Health and Nutrition Examination Survey 2011-2014.Alzheimer's & dementia (New York, N. Y.) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.
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Registered trials
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