Evidence map›Paper›PMID 24800153›Full record

ArticleMedicare & medicaid research review2012

The role of data in health care disparities in Medicaid managed care.

David Moskowitz, Bruce Guthrie, Andrew B Bindman

Abstract read
In one paragraph

Article in Medicare & medicaid research review, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

David MoskowitzAlameda County Medical Center.
Bruce GuthrieUniversity of Dundee.
Andrew B BindmanUniversity of California San Francisco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Affordable Care Act includes provisions to standardize the collection of data on health care quality that can be used to measure disparities. We conducted a qualitative study among leaders of Medicaid managed care plans, that currently have access to standardized quality data stratified by race and ethnicity, to learn how they use it to address disparities.

methodsWe conducted semi-structured interviews with 21 health plan leaders across 9 Medicaid managed care plans in California. We used purposive sampling to maximize heterogeneity in geography and plan type (e.g., non-profit, commercial). We performed a thematic analysis based on iterative coding by two investigators.

resultsWe found 4 major themes. Improving overall quality was tightly linked to a focus on standardized metrics that are integral to meeting regulatory or financial incentives. However, reducing disparities was not driven by standardized data, but by a mix of factors. Data were frequently only examined by race and ethnicity when overall performance was low. Disparities were attributed to either individual choices or cultural and linguistic factors, with plans focusing interventions on recently immigrated groups.

conclusionsWhile plans' efforts to address overall quality were often informed by standardized data, actions to reduce disparities were not, at least partly because there were few regulatory or financial incentives driving meaningful use of data on disparities. Standardized data, as envisaged by the Affordable Care Act, could become more useful for addressing disparities if they are combined with policies and regulations that promote health care equity.

Indexed as

CaliforniaEthnicityHealthcare DisparitiesHumansInterviews as TopicManaged Care ProgramsMedicaidQualitative ResearchRacial GroupsUnited StatesEthnic Differences in Health and Health CareHealth PolicyLawPoliticsRegulation, Medicaid, Qualitative Research, Racial

Identifiers

PMID24800153
PMCPMC4006475

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.