Contribution of Four Comorbid Conditions to Racial/Ethnic Disparities in Mortality Risk

Daw, Jonathan

doi:10.1016/j.amepre.2016.07.036

Cited by 56 publications

(43 citation statements)

References 21 publications

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“…Hence, Blacks have disproportionate representation in the classes that infer the greatest risk of adverse outcomes, “Diabetes with Comorbidities” and “Multimorbid”. This pattern is consistent with past studies on national samples of men and women; they found higher risks of diseases, comorbidities, and mortality for Blacks and Native Americans due to a combination of biological, socioeconomic, environmental, and psychosocial causes ( Daw, 2017 ; Mujahid et al, 2017 ; Saha, Freeman, Toure, Tippens, & Weeks, 2007 ; Schoenborn & Heyman, 2009 ). The VHA may create benefit by specifically targeting Black senior-aged women in their intervention programs that are aimed at reducing obesity and preventing diabetes.…”

Section: Discussionsupporting

confidence: 92%

A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters

Gonsoulin

Durazo‐Arvizu

Goldstein

et al. 2017

Innovation in Aging

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Background and ObjectivesThis study characterizes the multiple morbidities experienced by senior-aged women Veterans so that the Veterans Health Administration (VHA) and other health care systems may be better prepared to meet the health care needs of this growing cohort.Research Design and MethodsUsing the VHA’s Corporate Data Warehouse, we conducted a retrospective observational study of the 38,597 female veteran patients who were at least 65 years old and received care in the VHA during 2013 and 2014. We use a latent class analysis model to cluster diagnoses associated with inpatient and outpatient events over the years.ResultsThe senior-aged women Veterans are characterized by six major classes of disease clusters. We defined these classes as: Healthy (16.24% of the cohort); Ophthalmological Disorders (13.84%); Musculoskeletal Disorders (14.22%); At Risk for Cardiovascular Disease (37.53%); Diabetic with Comorbidities (9.05%); and Multimorbid (9.12%). The patterns and prevalence of these condition classes vary by race, age, and marital status.Discussion and ImplicationsEach of the six clusters can be used to develop clinical practice guidelines that are appropriate for senior-aged women Veterans. Consistent with past literature, the most common conditions in this cohort are hypertension and hyperlipidemia; together they form the most common class, “At Risk of Cardiovascular Disease (CVD)”. Results also show evidence of race-related disparities, with Blacks being more likely to be in the highest risk classes. Also, members of the cohort who are currently married having improved chances of being in the healthy class. And finally, we see a “healthy survivor” effect with the oldest women in our cohort having low overall rates of disease.

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Section: Discussionsupporting

confidence: 92%

A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters

Gonsoulin

Durazo‐Arvizu

Goldstein

et al. 2017

Innovation in Aging

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“…We believe it is crucial to study these differences. Research indicates that some ethnic groups have different rates of comorbidities and controlling for these differences significantly moderates “the mortality risk disparity between African American, Hispanic, and Asian/Pacific Islanders and whites” …”

Section: Introductionmentioning

confidence: 99%

Identifying obesity‐related multimorbidity combinations in the United States

Madlock‐Brown

Reynolds

2019

Clinical Obesity

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Summary Interest in understanding the effects of multimorbidity on outcomes has increased in recent years. This paper presents the most common obesity‐related groupings of multimorbidity in the United States. Using Cerner HealthFacts data, we applied the frequent pattern growth algorithm to identify prevalent multimorbidity groupings of 3 or more diseases (one being obesity) by race using a dataset of 574 172 patients with obesity from all over the United States. We set the minimum prevalence to 10% and identified groupings of ICD10‐CM diagnoses that occur in our dataset at or above the minimum prevalence level. We provide binomial proportion confidence interval estimates to demonstrate the validity of the proportions. We performed g‐test for independence to validate differences in prevalence by race. We found 18 multimorbidity combinations with prevalence higher than or equal to 10%. Our results indicate that there are multiple common multimorbidities groupings for patients with obesity. Each multimorbidity combination is composed of diseases from the following clinical categories: endocrine, nutritional and metabolic diseases; diseases of the circulatory system; diseases of the digestive system; diseases of the nervous system; and diseases of the musculoskeletal system and connective tissue. For each multimorbidity pattern, the prevalence was found to be significantly different by race according to the g‐test with P‐value < .001. Most frequent patterns include essential hypertension or disorder of lipid metabolism. This study identifies common groupings of multimorbidity. We believe our data can be useful for those developing integrated care plans, particularly for those serving diverse communities.

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“…1 Conceptual model. CM, cardiovascular markers; MM, metabolic markers; IM, inflammation markers (adapted from Daw (2017 )). …”

Section: Introductionmentioning

confidence: 99%

The contribution of three dimensions of allostatic load to racial/ethnic disparities in poor/fair self-rated health

Santos-Lozada

Daw

2018

SSM - Population Health

Self Cite

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ObjectiveThis study evaluates whether different dimensions of physiological dysregulation, modeled individually rather than additively mediate racial/ethnic disparities in self-reported health.MethodsUsing data from the National Health and Nutrition Examination Survey (2005–2010) and the Karlson, Hold, and Breen (KHB) mediation model, this paper explores what operationalization of biomarker data most strongly mediate racial/ethnic disparities in poor/fair self-rated health (SRH) among adults in the United States, net of demographic, socioeconomic, behavioral, and medication controls.ResultsNon-Hispanic blacks and Hispanics had significantly higher odds of reporting poor/fair self-rated health in comparison to non-Hispanic whites. Operationalizations of allostatic load that disaggregate three major dimensions of physiological dysregulation mediate racial/ethnic disparities strongly between non-Hispanic blacks and non-Hispanic whites, but not between Hispanics and non-Hispanic whites. Disaggregating these dimensions explains racial/ethnic disparities in poor/fair SRH better than the continuous score. Analyses on sex-specific disparities indicate differences in how individual dimensions of allostatic load contribute to racial/ethnic disparities in poor/fair SRH differently. All individual dimensions are strong determinants of poor/fair SRH for males. In contrast, for females, the only dimension that is significantly associated with poor/fair SRH is inflammation. For the analytic sample, additive biomarker scores fit the data as well or better than other approaches, suggesting that this approach is most appropriate for explaining individual differences. However, in sex-specific analyses, the interactive approach models fit the data best for men and women.ConclusionsFuture researchers seeking to explain racial/ethnic disparities in full or sex-stratified samples should consider disaggregating allostatic load by dimension.

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Contribution of Four Comorbid Conditions to Racial/Ethnic Disparities in Mortality Risk

Cited by 56 publications

References 21 publications

A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters

A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters

Identifying obesity‐related multimorbidity combinations in the United States

The contribution of three dimensions of allostatic load to racial/ethnic disparities in poor/fair self-rated health

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