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Sturmberg JP, Marcum JA. From cause and effect to causes and effects. J Eval Clin Pract 2024; 30:296-308. [PMID: 36779244 DOI: 10.1111/jep.13814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/14/2023]
Abstract
It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.
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Affiliation(s)
- Joachim P Sturmberg
- School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Holgate, New South Wales, Australia
- Foundation President, International Society for Systems and Complexity Sciences for Health, Waitsfield, Vermont, USA
| | - James A Marcum
- Department of Philosophy, Baylor University, Waco, Texas, USA
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2
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Chen H, Wang B, Lv R, Zhou T, Shen J, Song H, Xu X, Ma Y, Yuan C. Progression and trajectory network of age-related functional impairments and their combined associations with mortality. iScience 2023; 26:108368. [PMID: 38058300 PMCID: PMC10696261 DOI: 10.1016/j.isci.2023.108368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/06/2023] [Accepted: 10/26/2023] [Indexed: 12/08/2023] Open
Abstract
Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.
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Affiliation(s)
- Hui Chen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Binghan Wang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rongxia Lv
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tianjing Zhou
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Shen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huan Song
- West China Biomedical Big Data Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Yuan Ma
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Changzheng Yuan
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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3
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Jung YH, Yun IL, Park EC, Jang SI. New-onset dyslipidemia in adult cancer survivors from medically underserved areas: a 10-year retrospective cohort study. BMC Cancer 2023; 23:904. [PMID: 37752422 PMCID: PMC10521396 DOI: 10.1186/s12885-023-11384-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 09/07/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Cancer survival rates are increasing; however, studies on dyslipidemia as a comorbidity of cancer are limited. For efficient management of the disease burden, this study aimed to understand new-onset dyslipidemia in medically underserved areas (MUA) among cancer survivors > 19 years. METHODS This study used 11-year (2009-2019) data from the Korean National Health Insurance Service sample cohort. Cancer survivors for five years or more (diagnosed with ICD-10 codes 'C00-C97') > 19 years were matched for sex, age, cancer type, and survival years using a 1:1 ratio with propensity scores. New-onset dyslipidemia outpatients based on MUA were analyzed using the Cox proportional hazards model. RESULTS Of the 5,736 cancer survivors included in the study, the number of new-onset dyslipidemia patients was 855 in MUA and 781 in non-MUA. Cancer survivors for five years or more from MUA had a 1.22-fold higher risk of onset of dyslipidemia (95% CI = 1.10-1.34) than patients from non-MUA. The prominent factors for the risk of dyslipidemia in MUA include women, age ≥ 80 years, high income, disability, complications, and fifth-year cancer survivors. CONCLUSIONS Cancer survivors for five years or more from MUA had a higher risk of new-onset dyslipidemia than those from non-MUA. Thus, cancer survivors for five years or more living in MUA require healthcare to prevent and alleviate dyslipidemia.
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Affiliation(s)
- Yun Hwa Jung
- Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea
| | - I L Yun
- Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea
| | - Eun-Cheol Park
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea
- Department of Preventive Medicine &, Institute of Health Services Research, Yonsei University College of Medicine, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea
| | - Sung-In Jang
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea.
- Department of Preventive Medicine &, Institute of Health Services Research, Yonsei University College of Medicine, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.
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Kwon MJ, Kim JK, Kim JH, Kim JH, Kim MJ, Kim NY, Choi HG, Kim ES. Exploring the Link between Chronic Kidney Disease and Parkinson's Disease: Insights from a Longitudinal Study Using a National Health Screening Cohort. Nutrients 2023; 15:3205. [PMID: 37513623 PMCID: PMC10385674 DOI: 10.3390/nu15143205] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Chronic kidney disease (CKD) and Parkinson's disease (PD) are common illnesses found in the geriatric population. A potential link between CKD and PD emergence has been hypothesized; however, existing conclusions are disputed. In this longitudinal research, we analyzed data acquired from the Korean National Health Insurance Service-Health Screening Cohort. The dataset comprised the health information of 16,559 individuals clinically diagnosed with CKD and 66,236 control subjects of comparable ages, all aged ≥40 years. These subjects participated in health examinations from 2002 to 2019. To assess the correlation between CKD and PD, we employed overlap-weighted Cox proportional hazard regression models. The unadjusted, crude hazard ratio for PD was greater in the CKD group than in the control group (crude hazard ration (HR) 1.20; 95% confidence interval (CI) = 1.04-1.39; p = 0.011). However, the Cox proportional hazard regression analysis, incorporating propensity score overlap weighting, revealed no significant discrepancy after considering confounding variables such as demographic factors, socio-economic status, lifestyle, and concurrent health conditions (adjusted HR (aHR), 1.09; 95% CI = 0.97-1.22; p = 0.147). Subgroup analyses showed a higher probability of PD development among certain CKD individuals, including those who resided in rural areas (aHR, 1.19; 95% CI = 1.03-1.37; p = 0.022), maintained a normal weight (aHR, 1.29; 95% CI = 1.08-1.56; p = 0.006), or had fasting blood glucose levels ≥100 mg/dL (aHR, 1.18; 95% CI = 1.00-1.39; p = 0.046). Therefore, these clinical or environmental factors may influence the incidence of PD in CKD patients. In conclusion, our results suggest that the general CKD population may not exhibit a greater propensity for PD than their non-CKD counterparts. However, this might be contingent upon specific lifestyle and comorbid conditions. Thus, certain lifestyle alterations could be crucial in mitigating the potential manifestation of PD in patients diagnosed with CKD.
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Affiliation(s)
- Mi Jung Kwon
- Department of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Jwa-Kyung Kim
- Division of Nephrology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Ji Hee Kim
- Department of Neurosurgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Joo-Hee Kim
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Min-Jeong Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Nan Young Kim
- Hallym Institute of Translational Genomics and Bioinformatics, Hallym University Medical Center, Anyang 14068, Republic of Korea
| | - Hyo Geun Choi
- Suseo Seoul E.N.T. Clinic and MD Analytics, 10, Bamgogae-ro 1-gil, Gangnam-gu, Seoul 06349, Republic of Korea
| | - Eun Soo Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
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Lee HI, Yoon S, Kim JH, Ahn W, Lee S. Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships. Arch Osteoporos 2023; 18:79. [PMID: 37272994 DOI: 10.1007/s11657-023-01290-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 05/26/2023] [Indexed: 06/06/2023]
Abstract
We performed comorbidity-network analysis to obtain global view of comorbidity related with osteoporosis. We selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. We found 45-significant disease-clusters. Of these, 14-disease-clusters were related to fra, while 10 were related to musculoskeletal diseases. Our findings will serve as basic data for further studies. PURPOSE Osteoporosis causes devastating fractures; however, its exact etiology remains unknown. Elucidating associated comorbidities and their temporal relationships could provide better insights into its pathogenesis. Comorbidity-network analysis was performed to obtain global view of these associations. METHODS We randomly selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. These patients were identified using ICD-10 codes M81-M82, which represent osteoporosis without pathological fractures. Control group was created through propensity score matching. The comorbidities in each group were grouped into similar classifications to form "disease cluster"; 126 such clusters were identified. To create a comorbidity network, we selected disease clusters with high associations (i.e., odds ratios and relative risks ranked in the upper 50th percentile). To identify the temporal relationships between these clusters and osteoporosis, trajectories of directions were identified. RESULTS Finally, we found 45 significant disease clusters. Of these, 14 disease clusters were related to fractures or injuries, while 10 were related to musculoskeletal diseases. Temporal analysis revealed that 15 disease clusters preceded osteoporosis; these included the following three with the strongest associations: "other fracture", "disorders of bone density and structure (M83-M85)", and "sequelae of injuries of neck and trunk (T91)". Thirty disease clusters followed osteoporosis; these included the following three with the strongest associations: "spine fracture," "spondylopathies (M45-M49)", and "pelvic region and thigh fracture,". CONCLUSION We obtained a global view of the osteoporosis comorbidity network, which is otherwise difficult to achieve through study of individual diseases. Our findings will serve as the basic data for further studies.
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Affiliation(s)
- Hyun Il Lee
- Department of Orthopaedic Surgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang-si, Gyeonggi-do, 10380, Republic of Korea
| | - Siyeong Yoon
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea
| | - Jin Hwan Kim
- Department of Orthopaedic Surgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang-si, Gyeonggi-do, 10380, Republic of Korea
| | - Wooyeol Ahn
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea
| | - Soonchul Lee
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
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Yang X, Xu W, Leng D, Wen Y, Wu L, Li R, Huang J, Bo X, He S. Exploring novel disease-disease associations based on multi-view fusion network. Comput Struct Biotechnol J 2023; 21:1807-1819. [PMID: 36923471 PMCID: PMC10009443 DOI: 10.1016/j.csbj.2023.02.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.
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Affiliation(s)
- Xiaoxi Yang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.,Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Wenjian Xu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.,Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.,MOE Key Laboratory of Major Diseases in Children, Beijing 100045, China.,Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing 100045, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Huang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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Monchka BA, Leung CK, Nickel NC, Lix LM. The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study. BMC Med Res Methodol 2022; 22:165. [PMID: 35676621 PMCID: PMC9175465 DOI: 10.1186/s12874-022-01607-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/15/2022] [Indexed: 11/29/2022] Open
Abstract
Background Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures. Methods A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges. Results Relative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes. Conclusions Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01607-8.
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Affiliation(s)
- Barret A Monchka
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada. .,George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, 3rd Floor, 753 McDermot Ave, Winnipeg, Manitoba, R3E 0T6, Canada.
| | - Carson K Leung
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Nathan C Nickel
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lisa M Lix
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, 3rd Floor, 753 McDermot Ave, Winnipeg, Manitoba, R3E 0T6, Canada
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Kim HJ, Shin SY, Jeong SH. Nature and Extent of Physical Comorbidities Among Korean Patients With Mental Illnesses: Pairwise and Network Analysis Based on Health Insurance Claims Data. Psychiatry Investig 2022; 19:488-499. [PMID: 35753688 PMCID: PMC9233950 DOI: 10.30773/pi.2022.0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/29/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis. METHODS A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity. RESULTS In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks. CONCLUSION Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.
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Affiliation(s)
- Ho Joon Kim
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - Sam Yi Shin
- Department of Psychiatry, The Healer's Hospital, Busan, Republic of Korea
| | - Seong Hoon Jeong
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
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Akmatov MK, Ermakova T, Bätzing J. Psychiatric and Nonpsychiatric Comorbidities Among Children With ADHD: An Exploratory Analysis of Nationwide Claims Data in Germany. J Atten Disord 2021; 25:874-884. [PMID: 31364481 DOI: 10.1177/1087054719865779] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Objective: This study examined the full spectrum of comorbid disorders in all statutory-health-insured children aged 5 to 14 years with ADHD in 2017 by using nationwide claims data in Germany. Method: Children with ADHD (n = 258,662) were compared for the presence of 864 comorbid diseases with a control group matched by gender, age, and region of residence (n = 2,327,958). Results: Among others, metabolic disorders (odds ratio [OR] = 9.18; 95% confidence interval [CI] = [8.43, 9.99]), viral pneumonia (OR = 4.95; 95% CI = [2.37, 10.33]), disorders of white blood cells (OR = 4.55; 95% CI = [3.83, 5.40]), kidney failure (OR = 3.33; 95% CI = [2.65, 4.18]), hypertension (OR = 3.26; 95% CI = [3.00, 3.55]), obesity (OR = 2.85; 95% CI = [2.80, 2.91]), type 2 diabetes (OR = 2.61; 95% CI = [2.11, 3.23]), migraine (OR = 2.49; 95% CI = [2.37, 2.61]), asthma (OR = 2.19; 95% CI = [2.16, 2.22]), atopic dermatitis (OR = 2.10; 95% CI = [2.16, 2.23]), juvenile arthritis (OR = 1.56; 95% CI = [1.39, 1.76]), glaucoma (OR = 1.51; 95% CI = [1.30, 1.75]), and type 1 diabetes (OR = 1.30; 95% CI = [1.20, 1.40]) were more likely to be diagnosed in ADHD children. Conclusion: Along with psychiatric diseases, various somatic diseases were more common in ADHD children. The results have direct implications for patient care, including fine-grained diagnostics and personalized therapy.
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Affiliation(s)
- Manas K Akmatov
- Central Research Institute of Ambulatory Health Care in Germany, Berlin, Germany
| | - Tatiana Ermakova
- Central Research Institute of Ambulatory Health Care in Germany, Berlin, Germany
| | - Jörg Bätzing
- Central Research Institute of Ambulatory Health Care in Germany, Berlin, Germany
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Hwang S, Lee T, Yoon Y. Exploring disease comorbidity in a module-module interaction network. J Bioinform Comput Biol 2020; 18:2050010. [PMID: 32404015 DOI: 10.1142/s0219720020500109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module-module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules - sets of genes that have a higher probability of taking part in specific functions - and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein-protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases.
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Affiliation(s)
- Soyoun Hwang
- Department of IT Convergence Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Taekeon Lee
- Department of Computer Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Youngmi Yoon
- Department of Computer Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea
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Brunson JC, Agresta TP, Laubenbacher RC. Sensitivity of comorbidity network analysis. JAMIA Open 2020; 3:94-103. [PMID: 32607491 PMCID: PMC7309234 DOI: 10.1093/jamiaopen/ooz067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/12/2019] [Accepted: 12/10/2019] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVES Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques. MATERIALS AND METHODS We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters. RESULTS Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative. DISCUSSION Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach. CONCLUSION We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.
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Affiliation(s)
- Jason Cory Brunson
- Center for Quantitative Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA
| | - Thomas P Agresta
- Center for Quantitative Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA
- Department of Family Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA
| | - Reinhard C Laubenbacher
- Center for Quantitative Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr, Farmington, CT 06032, USA
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Kim MH, Banerjee S, Zhao Y, Wang F, Zhang Y, Zhu Y, DeFerio J, Evans L, Park SM, Pathak J. Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls. J Biomed Inform 2018; 87:88-95. [PMID: 30300713 PMCID: PMC6262847 DOI: 10.1016/j.jbi.2018.09.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 09/25/2018] [Accepted: 09/28/2018] [Indexed: 01/09/2023]
Abstract
OBJECTIVE We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. MATERIALS AND METHODS Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling. RESULTS By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms. CONCLUSION Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.
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Affiliation(s)
- Min-Hyung Kim
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Samprit Banerjee
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yize Zhao
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Fei Wang
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yiye Zhang
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Yongjun Zhu
- Department of Library and Information Science, Sungkyungkwan University, Seoul, Republic of Korea
| | - Joseph DeFerio
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Lauren Evans
- Division of Biostatistics and Epidemiology, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA
| | - Sang Min Park
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Jyotishman Pathak
- Division of Health Informatics, Department of Health Policy and Research, Weill Cornell Medical College of Cornell University, NY, USA.
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Brunson JC, Laubenbacher RC. Applications of network analysis to routinely collected health care data: a systematic review. J Am Med Inform Assoc 2018; 25:210-221. [PMID: 29025116 PMCID: PMC6664849 DOI: 10.1093/jamia/ocx052] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 04/18/2017] [Accepted: 04/23/2017] [Indexed: 01/21/2023] Open
Abstract
Objective To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research. Materials and Methods A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis. Results The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities. Discussion We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.
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