Approaching Health Disparities From a Population Perspective: The National Institutes of Health Centers for Population Health and Health Disparities

Warnecke, Richard B.; Oh, April; Breen, Nancy; Gehlert, Sarah; Paskett, Electra D.; Tucker, Katherine L.; Lurie, Nicole; Rebbeck, Timothy R.; Goodwin, James S.; Flack, John M.; Srinivasan, Shobha; Kerner, Jon; Heurtin-Roberts, Suzanne; Abeles, Ronald P.; Tyson, Frederick L.; Patmios, Georgeanne; Hiatt, Robert A.

doi:10.2105/ajph.2006.102525

Cited by 460 publications

(417 citation statements)

References 38 publications

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“…Thus, housing reflects physical context of daily living which can potentially contribute to disparate health outcomes in the model for disparate health outcome suggested by Warnecke. 17 Yet, no systematic study of the use of housing data to construct a measure of individual SES has been reported. To address these concerns, we developed a housing-asset-based measure of SES (hereafter, HOUSES) and compare it to existing SES measures and health outcomes known to be associated with SES.…”

Section: Introductionmentioning

confidence: 99%

Development and Initial Testing of a New Socioeconomic Status Measure Based on Housing Data

et al. 2011

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Socioeconomic status (SES) has been associated with many health outcomes. Commonly used datasets such as medical records often lack data on SES but do include address information. The authors sought to determine whether an SES measure derived from housing characteristics is associated with other SES measures and outcomes known to be associated with SES. The data come from a telephone survey of parents/ guardians of children aged 1-17 years who resided in Olmsted County, Minnesota, and Jackson County, Missouri. Seven variables related to housing and six neighborhood characteristics obtained from local government assessor's offices in Olmsted County, Minnesota, were appended to survey responses. An SES index derived from housing characteristics (hereafter, HOUSES) was constructed using principal components factor analysis. For criterion validity, we assessed Pearson's correlation coefficients between HOUSES and other SES measures, including self-reported parents' educational levels, income, Hollingshead Index, and Nakao-Treas Index. For construct validity, we determined the association between HOUSES and outcomes, risks of low birth weight, overweight, and smoking exposure at home. We applied HOUSES to subjects in another community by formulating HOUSES from housing data of subjects in Jackson County, Missouri, using the same statistical algorithm as HOUSES for subjects in Olmsted County, Minnesota. We found that HOUSES had modest to good correlation with other SES measures. Overall, as hypothesized, HOUSES was inversely associated with outcome measures assessed among subjects from both counties. HOUSES may be a useful surrogate measure of individual SES in epidemiologic research, especially when SES measures for individuals are not available.

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Section: Introductionmentioning

confidence: 99%

Development and Initial Testing of a New Socioeconomic Status Measure Based on Housing Data

et al. 2011

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“…56 To fully address racial disparities, additional potential barriers at the health care system, community, and policy level also merit consideration. 57,58 Several limitations of our study should be noted. First, our method of ascertaining adherence excludes patients who obtained less than two dispensings of a medication; thus we are unable to identify non-adherent patients in cases where medications were prescribed but never dispensed or where a new medication was only dispensed a single time.…”

Section: Discussionmentioning

confidence: 89%

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

et al. 2015

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BACKGROUND: Among patients with diabetes, racial differences in cardiometabolic risk factor control are common. The extent to which differences in medication adherence contribute to such disparities is not known. We examined whether medication adherence, controlling for treatment intensification, could explain differences in risk factor control between black and white patients with diabetes. METHODS: We identified three cohorts of black and white patients treated with oral medications and who had poor risk factor control at baseline (2009): those with glycated hemoglobin (HbA1c) >8 % (n=37,873), low-density lipoprotein cholesterol (LDL-C) >100 mg/dl (n=27,954), and systolic blood pressure (SBP) >130 mm Hg (n=63,641). Subjects included insured adults with diabetes who were receiving care in one of nine U.S. integrated health systems comprising the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium. Baseline and follow-up risk factor control, sociodemographic, and clinical characteristics were obtained from electronic health records. Pharmacydispensing data were used to estimate medication adherence (i.e., medication refill adherence [MRA]) and treatment intensification (i.e., dose increase or addition of new medication class) between baseline and follow-up. County-level income and educational attainment were estimated via geocoding. Logistic regression models were used to test the association between race and follow-up risk factor control. Models were specified with and without medication adherence to evaluate its role as a mediator. RESULTS: We observed poorer medication adherence among black patients than white patients (p < 0.01): 50.6 % of blacks versus 39.7 % of whites were not highly adherent (i.e., MRA <80 %) to HbA1c oral medication(s); 58.4 % of blacks and 46.7 % of whites were not highly adherent to lipid medication(s); and 33.4 % of blacks and 23.7 % of whites were not highly adherent to BP medication(s). Across all cardiometabolic risk factors, blacks were significantly less likely to achieve control (p<0.01): 41.5 % of blacks and 45.8 % of whites achieved HbA1c <8 %; 52.6 % of blacks and 60.8 % of whites achieved LDL-C <100; and 45.7 % of blacks and 53.6 % of whites achieved SBP <130. Adjusting for medication adherence/treatment intensification did not alter these patterns or model fit statistics. CONCLUSIONS: Medication adherence failed to explain observed racial differences in the achievement of HbA1c, LDL-C, and SBP control among insured patients with diabetes.

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“…At the same time, the Coordination Center fostered and facilitated the sharing of resources and expertise across sites. Secondly, because TREC was able to promote and support work across disciplines and site, in part through the Coordination Center, TREC did not experience the two to three-year lag in publications to the same degree as TTURC and CPHHD [2][3][4].…”

Section: Discussionmentioning

confidence: 99%

“…The Centers for Population Health and Health Disparities (CPHHD) [2] TTURC was the first to undergo evaluation of its impact on research output, as measured by the timing of publications. A retrospectively-conducted bibliographic evaluation of TTURC in comparison with R01s found that after an initial two to three-year lag period, TTURC had higher overall publication rates over a 10-year comparison period [3].…”

Section: Nih Transdisciplinary Initiatives and Their Evaluationmentioning

confidence: 99%

The Structure of Distributed Scientific Research Teams Affects Collaboration and Research Output

Gehlert

Lee

Gill

et al. 2017

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T o understand how the nature of scientific collaboration between individuals and sites in team-based research initiatives affect collaboration and research output, we examined four waves of prospective survey data to measure collaboration across investigators, disciplines, and sites to measure structural determinants of research success. 116 investigators in the five sites of the NIH-funded U54 Transdisciplinary Research on Energetics and Cancer (TREC) initiative were surveyed about their research ties with a 2011 baseline measure and followed by three additional iterations and augmented by bibliometric data.Social network analysis describes the changing structure of contact and cooperation. We found that the network structure of a team science project affects the nature and rate of publications, implying that funded projects vary in research output based on how investigators interact with each other and that the design of scientific research projects affects research output by determining levels of contact between actual and potential collaborators.

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Approaching Health Disparities From a Population Perspective: The National Institutes of Health Centers for Population Health and Health Disparities

Cited by 460 publications

References 38 publications

Development and Initial Testing of a New Socioeconomic Status Measure Based on Housing Data

Development and Initial Testing of a New Socioeconomic Status Measure Based on Housing Data

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

The Structure of Distributed Scientific Research Teams Affects Collaboration and Research Output

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