Identifying obesity‐related multimorbidity combinations in the United States

Autor: Rebecca Reynolds, Charisse R. Madlock-Brown
Rok vydání: 2019
Předmět:
Zdroj: Clinical Obesity. 9
ISSN: 1758-8111
1758-8103
DOI: 10.1111/cob.12336
Popis: 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.
Databáze: OpenAIRE
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