Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors
IntroductionThe utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored.MethodsThis study aims to...
| প্রধান লেখক: | , , , , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
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Frontiers Media S.A.
2024-01-01
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| মালা: | Frontiers in Immunology |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1309549/full |
| _version_ | 1827387836134850560 |
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| author | Qinmengge Li Matthew T. Patrick Sutharzan Sreeskandarajan Jian Kang J. Michelle Kahlenberg J. Michelle Kahlenberg Johann E. Gudjonsson Zhi He Lam C. Tsoi Lam C. Tsoi Lam C. Tsoi |
| author_facet | Qinmengge Li Matthew T. Patrick Sutharzan Sreeskandarajan Jian Kang J. Michelle Kahlenberg J. Michelle Kahlenberg Johann E. Gudjonsson Zhi He Lam C. Tsoi Lam C. Tsoi Lam C. Tsoi |
| author_sort | Qinmengge Li |
| collection | DOAJ |
| description | IntroductionThe utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored.MethodsThis study aims to assess the capability of a comprehensive claims database in identifying comorbidities linked to 14 specific skin and skin-related conditions and examining temporal changes in their association patterns. This study employed a retrospective case-control cohort design utilizing 13 million skin/skin-related patients and 2 million randomly sampled controls from Optum’s de-identified Clinformatics® Data Mart Database spanning the period from 2001 to 2018. A broad spectrum of comorbidities encompassing cancer, diabetes, respiratory, mental, immunity, gastrointestinal, and cardiovascular conditions were examined for each of the 14 skin and skin-related disorders in the study.ResultsUsing the established type-2 diabetes (T2D) and psoriasis comorbidity as example, we demonstrated the association is significant (P-values<1x10-15) and stable across years (OR=1.15-1.31). Analysis of the 2014-2018 data reveals that celiac disease, Crohn’s disease, and ulcerative colitis exhibit the strongest associations with the 14 skin/skin-related conditions. Systemic lupus erythematosus (SLE), leprosy, and hidradenitis suppurativa show the strongest associations with 30 different comorbidities. Particularly notable associations include Crohn’s disease with leprosy (odds ratio [OR]=6.60, 95% confidence interval [CI]: 3.09-14.08), primary biliary cirrhosis with SLE (OR=6.07, 95% CI: 4.93-7.46), and celiac disease with SLE (OR=6.06, 95% CI: 5.49-6.69). In addition, changes in associations were observed over time. For instance, the association between atopic dermatitis and lung cancer demonstrates a marked decrease over the past decade, with the odds ratio decreasing from 1.75 (95% CI: 1.47-2.07) to 1.02 (95% CI: 0.97-1.07). The identification of skin-associated comorbidities contributes to individualized healthcare and improved clinical management, while also enhancing our understanding of shared pathophysiology. Moreover, tracking these associations over time aids in evaluating the progression of clinical diagnosis and treatment.DiscussionThe findings highlight the potential of utilizing comprehensive claims databases in advancing research and improving patient care in dermatology. |
| first_indexed | 2024-03-08T16:08:02Z |
| format | Article |
| id | doaj.art-0fbcd6f6234f491598f3c09a1839c789 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-08T16:08:02Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-0fbcd6f6234f491598f3c09a1839c7892024-01-08T04:28:41ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-01-011410.3389/fimmu.2023.13095491309549Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factorsQinmengge Li0Matthew T. Patrick1Sutharzan Sreeskandarajan2Jian Kang3J. Michelle Kahlenberg4J. Michelle Kahlenberg5Johann E. Gudjonsson6Zhi He7Lam C. Tsoi8Lam C. Tsoi9Lam C. Tsoi10Department of Biostatistics, University of Michigan, Ann Arbor, MI, United StatesDepartment of Dermatology, University of Michigan, Ann Arbor, MI, United StatesThe Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United StatesDepartment of Dermatology, University of Michigan, Ann Arbor, MI, United StatesRheumatology, Internal Medicine, University of Michigan, Ann Arbor, MI, United StatesDepartment of Dermatology, University of Michigan, Ann Arbor, MI, United StatesDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United StatesDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United StatesDepartment of Dermatology, University of Michigan, Ann Arbor, MI, United StatesDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United StatesIntroductionThe utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored.MethodsThis study aims to assess the capability of a comprehensive claims database in identifying comorbidities linked to 14 specific skin and skin-related conditions and examining temporal changes in their association patterns. This study employed a retrospective case-control cohort design utilizing 13 million skin/skin-related patients and 2 million randomly sampled controls from Optum’s de-identified Clinformatics® Data Mart Database spanning the period from 2001 to 2018. A broad spectrum of comorbidities encompassing cancer, diabetes, respiratory, mental, immunity, gastrointestinal, and cardiovascular conditions were examined for each of the 14 skin and skin-related disorders in the study.ResultsUsing the established type-2 diabetes (T2D) and psoriasis comorbidity as example, we demonstrated the association is significant (P-values<1x10-15) and stable across years (OR=1.15-1.31). Analysis of the 2014-2018 data reveals that celiac disease, Crohn’s disease, and ulcerative colitis exhibit the strongest associations with the 14 skin/skin-related conditions. Systemic lupus erythematosus (SLE), leprosy, and hidradenitis suppurativa show the strongest associations with 30 different comorbidities. Particularly notable associations include Crohn’s disease with leprosy (odds ratio [OR]=6.60, 95% confidence interval [CI]: 3.09-14.08), primary biliary cirrhosis with SLE (OR=6.07, 95% CI: 4.93-7.46), and celiac disease with SLE (OR=6.06, 95% CI: 5.49-6.69). In addition, changes in associations were observed over time. For instance, the association between atopic dermatitis and lung cancer demonstrates a marked decrease over the past decade, with the odds ratio decreasing from 1.75 (95% CI: 1.47-2.07) to 1.02 (95% CI: 0.97-1.07). The identification of skin-associated comorbidities contributes to individualized healthcare and improved clinical management, while also enhancing our understanding of shared pathophysiology. Moreover, tracking these associations over time aids in evaluating the progression of clinical diagnosis and treatment.DiscussionThe findings highlight the potential of utilizing comprehensive claims databases in advancing research and improving patient care in dermatology.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1309549/fullepidemiologyclaimsskin diseasecomorbidityOptum |
| spellingShingle | Qinmengge Li Matthew T. Patrick Sutharzan Sreeskandarajan Jian Kang J. Michelle Kahlenberg J. Michelle Kahlenberg Johann E. Gudjonsson Zhi He Lam C. Tsoi Lam C. Tsoi Lam C. Tsoi Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors Frontiers in Immunology epidemiology claims skin disease comorbidity Optum |
| title | Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| title_full | Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| title_fullStr | Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| title_full_unstemmed | Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| title_short | Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| title_sort | large scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors |
| topic | epidemiology claims skin disease comorbidity Optum |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1309549/full |
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