| Title |
Impact of Patient-Centered Medical Home Implementation on Diabetes Control in the Veterans Health Administration
|
|---|---|
| Published in |
Journal of General Internal Medicine, April 2018
|
| DOI | 10.1007/s11606-018-4386-x |
| Pubmed ID | |
| Authors |
LeChauncy D. Woodard, Omolola E. Adepoju, Amber B. Amspoker, Salim S. Virani, David J. Ramsey, Laura A. Petersen, Lindsey A. Jones, Lea Kiefer, Praveen Mehta, Aanand D. Naik |
| Abstract |
Given its widespread dissemination across primary care, the Veterans Health Administration (VA) is an ideal setting to examine the impact of the patient-centered medical home (PCMH) on diabetes outcomes. To assess the impact of PCMH implementation on diabetes outcomes among patients receiving care in the Veterans Health Administration. Retrospective cohort analysis and multilevel logistic regression. Twenty thousand eight hundred fifty-eight patients in one Midwest VA network who had a diabetes diagnosis in both 2009 and 2012 and who received primary care between October 1, 2008 and September 30, 2009. Glycemic and lipid control using VA quality indicators [hemoglobin (Hb) A1c < 9%, low-density lipoprotein cholesterol (LDL-C) < 100 mg/dL]. Odds of glycemic control were lower in 2012 than 2009 (OR = 0.72, 95% CI = 0.67-0.77, p < 0.001), and this change in control over time varied by race (OR of the interaction between time and race = 1.18, 95% CI = 1.02-1.36, p = 0.028). While the disparity in glycemic control between white and black patients persisted post-PCMH, the magnitude of the disparity was smaller in 2012 compared to 2009 (2012: OR = 1.32, 95% CI = 1.18-1.47, p < 0.0001 and 2009: OR = 1.59, 95% CI = 1.39-1.82, p < 0.0001). Odds of lipid control did not significantly change between 2009 and 2012 and change did not vary by race and/or gender. Although there were no significant improvements in odds of lipid control, and odds of glycemic control decreased following PCMH implementation, there was evidence of reduced racial disparities in glycemic control post-PCMH implementation. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 13% |
| Librarian | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Other | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 6 | 15% |
| Unknown | 14 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 23% |
| Nursing and Health Professions | 4 | 10% |
| Social Sciences | 4 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 4 | 10% |
| Unknown | 16 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 15 May 2018.
All research outputs
#14,759,948
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#5,426
of 7,806 outputs
Outputs of similar age
#183,738
of 332,287 outputs
Outputs of similar age from Journal of General Internal Medicine
#86
of 133 outputs
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