Evidence map›Paper›PMID 35627907›Full record

ArticleHealthcare (Basel, Switzerland)2022

Predicting Areas with High Concentration of the Long-Term Uninsured and Their Association with Emergency Department Usage by Uninsured Patients in South Carolina.

Khoa Truong, Julie Summey Bedi, Lingling Zhang, Brooke Draghi, Lu Shi

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Khoa TruongDepartment of Public Health Sciences, Clemson University, Clemson, SC 29634, USA.ORCID 0000-0002-8996-4126
Julie Summey BediScale Strategic Solutions, Cincinnati, OH 45246, USA.
Lingling ZhangCollege of Nursing and Health Sciences, University of Massachusetts, Boston, MA 02125, USA.
Brooke DraghiDepartment of Public Health Sciences, Clemson University, Clemson, SC 29634, USA.ORCID 0000-0002-2108-7382
Lu ShiDepartment of Public Health Sciences, Clemson University, Clemson, SC 29634, USA.

Funding

Blue Cross Blue Shield of South Carolina Foundation 2013-16
6 · The paper itself

Abstract

backgroundTo predict areas with a high concentration of long-term uninsured (LTU) and Emergency Department (ED) usage by uninsured patients in South Carolina.

methodsAmerican Community Survey data was used to predict the concentration of LTU at the ZIP Code Tabulation Area (ZCTA) level. In a multivariate regression model, the LTU concentration was then modeled to predict ED visits by uninsured patients. ED data came from the restricted South Carolina Patient Encounter data with patients' billing zip codes. A simulation was conducted to predict changes in the ED visit numbers and rates by uninsured patients if the LTU concentration was reduced to a lower level.

resultsOverall, there was a positive relationship between ED visit rates by the uninsured patients and areas with higher concentrations of LTU. Our simulation model predicted that if the LTU concentration for each ZCTA was reduced to the lowest quintile, the ED visit rates by the uninsured would decrease significantly. The greatest reduction in the number of ED visits by the uninsured over a two-year period was for the following primary diagnoses: abdominal pain (15,751 visits), cellulitis and abscess (11,260 visits) and diseases for the teeth and supporting structures (10,525 visits).

conclusionsThe provision of primary healthcare services to the LTU could help cut back inappropriate uses of ED resources and healthcare costs.

Indexed as

Emergency Departmenthealth disparitieshealth policylong-term uninsuredZip Code Tabulation Area

Identifiers

PMID35627907
PMCPMC9142117

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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.