Examining health disparities by gender: A multimorbidity network analysis of electronic medical record

Pankush Kalgotra, Ramesh Sharda, Julie Croff

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Problem Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors. This paper aims to address the co-occurrences of diseases using network analysis. Methods In this study, we identify these multi-morbidities from a large electronic medical record (EMR) containing diagnoses, symptoms and treatment data on more than 22.1 million patients. We create multimorbidity networks from males and females medical records and compare their structural properties. Results Our macro analysis at the organ-level indicates that females have a stronger multimorbidity network than males. For example, the female multimorbidity network includes six linkages to mental health, wherein the male multimorbidity network includes only two linkages to mental health. The strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. Conclusion Our multimorbidity network analysis by gender identifies specific differences in disease diagnosis by gender, and presents questions for biological, behavioral, clinical, and policy research.

Original languageEnglish
Pages (from-to)22-28
Number of pages7
JournalInternational Journal of Medical Informatics
Volume108
DOIs
StatePublished - 1 Dec 2017

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Electronic Health Records
Comorbidity
Health
Mental Health
Lipid Metabolism
Medical Records
Morbidity
Research

Keywords

  • Gender disparity
  • Multimorbidity
  • Network analysis

Cite this

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abstract = "Problem Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors. This paper aims to address the co-occurrences of diseases using network analysis. Methods In this study, we identify these multi-morbidities from a large electronic medical record (EMR) containing diagnoses, symptoms and treatment data on more than 22.1 million patients. We create multimorbidity networks from males and females medical records and compare their structural properties. Results Our macro analysis at the organ-level indicates that females have a stronger multimorbidity network than males. For example, the female multimorbidity network includes six linkages to mental health, wherein the male multimorbidity network includes only two linkages to mental health. The strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. Conclusion Our multimorbidity network analysis by gender identifies specific differences in disease diagnosis by gender, and presents questions for biological, behavioral, clinical, and policy research.",
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Examining health disparities by gender : A multimorbidity network analysis of electronic medical record. / Kalgotra, Pankush; Sharda, Ramesh; Croff, Julie.

In: International Journal of Medical Informatics, Vol. 108, 01.12.2017, p. 22-28.

Research output: Contribution to journalArticle

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N2 - Problem Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors. This paper aims to address the co-occurrences of diseases using network analysis. Methods In this study, we identify these multi-morbidities from a large electronic medical record (EMR) containing diagnoses, symptoms and treatment data on more than 22.1 million patients. We create multimorbidity networks from males and females medical records and compare their structural properties. Results Our macro analysis at the organ-level indicates that females have a stronger multimorbidity network than males. For example, the female multimorbidity network includes six linkages to mental health, wherein the male multimorbidity network includes only two linkages to mental health. The strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. Conclusion Our multimorbidity network analysis by gender identifies specific differences in disease diagnosis by gender, and presents questions for biological, behavioral, clinical, and policy research.

AB - Problem Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors. This paper aims to address the co-occurrences of diseases using network analysis. Methods In this study, we identify these multi-morbidities from a large electronic medical record (EMR) containing diagnoses, symptoms and treatment data on more than 22.1 million patients. We create multimorbidity networks from males and females medical records and compare their structural properties. Results Our macro analysis at the organ-level indicates that females have a stronger multimorbidity network than males. For example, the female multimorbidity network includes six linkages to mental health, wherein the male multimorbidity network includes only two linkages to mental health. The strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. Conclusion Our multimorbidity network analysis by gender identifies specific differences in disease diagnosis by gender, and presents questions for biological, behavioral, clinical, and policy research.

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