Evidence map›Paper›PMID 28176307›Full record

ArticleHealth services research2018

Do People with Health Insurance Coverage Who Live in Areas with High Uninsurance Rates Pay More for Emergency Department Visits?

James B Kirby, Joel W Cohen

Abstract read
In one paragraph

Article in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

James B KirbyAgency for Healthcare Research and Quality, Center for Financing, Access and Cost Trends, Rockville, MD.
Joel W CohenAgency for Healthcare Research and Quality, Center for Financing, Access and Cost Trends, Rockville, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the relationship between the percent uninsured in a county and expenditures associated with the typical emergency department visit. DATA SOURCES: The Medical Expenditure Panel Survey linked to county-level data from the American Community Survey, the Healthcare Cost and Utilization Project, and the Area Health Resources Files. STUDY

designWe use a nationally representative sample of emergency department visits that took place between 2009 and 2013 to estimate the association between the percent uninsured in counties and the amount paid for a typical visit. Final estimates come from a diagnosis-level fixed-effects model, with additional controls for a wide variety of visit, individual, and county characteristics. PRINCIPAL

findingsAmong those with private insurance, we find that an increase of 1 percentage point in the county uninsurance rate is associated with a $20 increase in the mean emergency department payment. No such association is observed among visits covered by other insurance types.

conclusionsResults provide tentative evidence that the costs associated with high uninsurance rates spill over to those with insurance, but future research needs to replicate these findings with longitudinal data and methods before drawing causal conclusions. Recent data on changes in area uninsurance rates following the ACA's insurance expansions and subsequent changes in emergency department expenditures afford a valuable opportunity to do this.

Indexed as

AdultAgedEmergency Service, HospitalFemaleHealth ExpendituresHumansInsurance CoverageInsurance, HealthMaleMedicaidMedically UninsuredMedicareMiddle AgedResidence CharacteristicsSocioeconomic FactorsUnited StatesHealth care costshealth care financing/insurance/premiumshealth care organizations and systems

Identifiers

PMID28176307
PMCPMC5867177

What OpenQuestion holds

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Registered trials

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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.