Evidence map›Paper›PMID 35647260›Full record

ArticleSSM - population health2022

Estimating uncertainty in a socioeconomic index derived from the American community survey.

Francis P Boscoe, Bian Liu, Jordana Lafantasie, Li Niu, Furrina F Lee

Abstract read
In one paragraph

Article in SSM - population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Francis P BoscoePumphandle, LLC, Camden, ME, USA.
Bian LiuDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jordana LafantasieSchool of Public Health, University at Albany, Rensselaer, NY, USA.
Li NiuDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Furrina F LeeNew York State Department of Health, Albany, NY, USA.

Funding

Exploration of dynamic spatiotemporal exposure profiles via patient residential and healthcare utilization historyR21CA235153 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LIU, BIAN · 2019 to 2020
$377k
NCI NIH HHS R21 CA235153
6 · The paper itself

Abstract

Socioeconomic indexes are widely used in public health to facilitate neighborhood-scale analyses. Although they are calculated with high levels of precision, they are rarely reported with accompanying measures of uncertainty (e.g., 90% confidence intervals). Here we use the variance replicate tables that accompany the United States Census Bureau's American Community Survey to report confidence intervals around the Yost Index, a socioeconomic index comprising seven variables that is frequently used in cancer surveillance. The Yost Index is reported as a percentile score from 1 (most affluent) to 100 (most deprived). We find that the average uncertainty for a census tract in the United States is plus or minus 8 percentiles, with the uncertainty a function of the value of the index itself. Scores at the extremes of the distribution are more precise and scores near the center are less precise. Less-affluent tracts have greater uncertainty than corresponding more-affluent tracts. Fewer than 50 census tracts of 72,793 nationally have unusual distributions of socioeconomic conditions that render the index uninformative. We demonstrate that the uncertainty in a census-based socioeconomic index is calculable and can be incorporated into any analysis using such an index.

Identifiers

PMID35647260
PMCPMC9130578

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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.