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Protocol for the analysis of a natural experiment on the impact of the Affordable Care Act on diabetes care in community health centers

Overview of attention for article published in Implementation Science, February 2017
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Title
Protocol for the analysis of a natural experiment on the impact of the Affordable Care Act on diabetes care in community health centers
Published in
Implementation Science, February 2017
DOI 10.1186/s13012-017-0543-6
Pubmed ID
Authors

Nathalie Huguet, Heather Angier, Miguel Marino, K. John McConnell, Megan J. Hoopes, Jean P. O’Malley, Lewis A. Raynor, Sonja Likumahuwa-Ackman, Heather Holderness, Jennifer E. DeVoe

Abstract

It is hypothesized that Affordable Care Act (ACA) Medicaid expansions could substantially improve access to health insurance and healthcare services for patients at risk for diabetes mellitus (DM), with pre-DM, or already diagnosed with DM. The ACA called for every state to expand Medicaid coverage by 2014. In a 2012 legal challenge, the US Supreme Court ruled that states were not required to implement Medicaid expansions. This 'natural experiment' presents a unique opportunity to learn whether and to what extent Medicaid expansion can affect healthcare access and services for patients with DM risk, pre-DM, or DM. Data from electronic health records (EHRs) from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network, which has data from >700 community health centers (CHCs), was included in the study. EHR data will be linked to Oregon Medicaid claims data. Data collection will include information on changes in health insurance, service receipt, and health outcomes, spanning 9 years (pre- and post-expansion), comparing states that expanded Medicaid, and those that did not. Patients included in this study will be diagnosed with DM, be at risk for DM, or have pre-DM, between the ages of 19 and 64, with ≥1 ambulatory visit. Sample size is estimated to be roughly 275,000 patients. Biostatistical analyses will include the difference-in-differences (DID) methodology and a generalized linear mixed model. Econometric analyses will include a DID two-part method to calculate the difference in Medicaid expenditures in Oregon among newly insured CHC patients. Findings will have national relevance on DM health services and outcomes and will be shared through national conferences and publications. The findings will provide information needed to impact the policy as it is related to access to health insurance and receipt of healthcare among a vulnerable population. This project is registered with ClinicalTrials.gov ( NCT02685384 ). Registered 18 May 2016.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Master 7 12%
Student > Bachelor 7 12%
Student > Ph. D. Student 6 10%
Student > Doctoral Student 3 5%
Other 7 12%
Unknown 16 28%
Readers by discipline Count As %
Nursing and Health Professions 10 17%
Social Sciences 9 16%
Medicine and Dentistry 3 5%
Psychology 3 5%
Economics, Econometrics and Finance 2 3%
Other 6 10%
Unknown 25 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 July 2023.
All research outputs
#7,337,492
of 24,027,644 outputs
Outputs from Implementation Science
#1,178
of 1,749 outputs
Outputs of similar age
#135,159
of 428,981 outputs
Outputs of similar age from Implementation Science
#32
of 46 outputs
Altmetric has tracked 24,027,644 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,749 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 428,981 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.