ArticleHealth services research2022
Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities).
Article in Health services research, 2022. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The Impact of Social Drivers of Health on Patient Portal Utilization for Total Joint Arthroplasty Patients: A Qualitative Study.Arthroplasty today · 2026Article
- Probabilistic bias analysis for exposure misclassification of household income by neighbourhood in a cohort of individuals with colorectal cancer.International journal of epidemiology · 2024Article
- Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities).Health services research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo assess a new approach (weighting by "income probabilities [IP]") that uses US Census data from the patients' communities to approximate individual-level income, an important but often missing variable in health services research. DATA SOURCES: Community (census tract level) income data came from the 2017 5-year American Community Survey (ACS). The patient data included those diagnosed with cancer in 2017 in Ohio (n = 65,759). The reference population was the 2017 5-year ACS Public Use Microdata Sample (n = 564,357 generalizing to 11,288,350 Ohioans). STUDY DESIGN/
methodsWe applied the traditional approach of income approximation using median census tract income along with two IP based approaches to estimate the proportions in the patient data with incomes of 0%-149%, 150%-299%, 300%-499%, and 500%+ of the federal poverty level (FPL) ("class-relevant income grouping") or 0%-138%, 139%-249%, 250%-399%, and 400%+ FPL ("policy-relevant income grouping"). These estimated income distributions were then compared with the known income distributions of the reference population. DATA COLLECTION/EXTRACTION
methodsThe patient data came from Ohio's cancer registry. The other data were publicly available. PRINCIPAL
findingsBoth IP based approaches consistently outperformed the traditional approach overall and in subgroup analyses, as measured by the weighted average absolute percentage point differences between the proportions of each of the income categories of the reference population and the estimated proportions generated by the income approximation approaches ("average percent difference," or APD). The smallest APD for an IP based method, 0.5%, was seen in non-Hispanic White females in the class-relevant income grouping (compared with 16.5% for the conventional method), while the largest APD, 7.1%, was seen in non-Hispanic Black females in the policy-relevant income grouping (compared with 18.0% for the conventional method).
conclusionsWeighting by IP substantially outperformed the conventional approach of estimating the distribution of incomes in patient data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.