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Ryu E, Jenkins GD, Wang Y, Olfson M, Talati A, Lepow L, Coombes BJ, Charney AW, Glicksberg BS, Mann JJ, Weissman MM, Wickramaratne P, Pathak J, Biernacka JM. The importance of social activity to risk of major depression in older adults. Psychol Med 2023; 53:2634-2642. [PMID: 34763736 PMCID: PMC9095757 DOI: 10.1017/s0033291721004566] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/04/2021] [Accepted: 10/20/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.
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Affiliation(s)
- Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Gregory D. Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Yanshan Wang
- Department of AI and Informatics, Mayo Clinic, Rochester, USA
| | - Mark Olfson
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Ardesheer Talati
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Lauren Lepow
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Brandon J. Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Alexander W. Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Benjamin S. Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
| | - J. John Mann
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Myrna M. Weissman
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Priya Wickramaratne
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | | | - Joanna M. Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, USA
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Employment Characteristics and Risk of Hospitalization Among Older Adults Participating in the Mayo Clinic Biobank. Mayo Clin Proc Innov Qual Outcomes 2022; 6:552-563. [PMID: 36299252 PMCID: PMC9588999 DOI: 10.1016/j.mayocpiqo.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective To determine the relationship between characteristics of employment and future hospitalization in older adults. Patients and Methods We conducted a survey of adults aged 65 years or older participating in the Mayo Clinic Biobank. Using a frequency-matched, case-control design, we compared patients who were hospitalized within 5 years of biobank enrollment (cases) with those who were not hospitalized (controls). We assessed the duration of work, age at first job, number of jobs, disability, retirement, and reasons for leaving work. We performed logistic regression analysis to assess the association of these factors with hospitalization, accounting for age, sex, comorbid conditions, and education level. Results Among 3536 participants (1600 cases and 1936 controls; median age, 68.5 years; interquartile range, 63.4-73.9 years), cases were older, more likely to be male, and had lower education levels. Comorbid illnesses had the largest association with hospitalization (odds ratio [OR], 4.09; 95% CI, 3.37-4.97 [highest vs lowest quartile]). On adjusted analyses, odds of hospitalization increased with the presence of disability (OR, 1.31; 95% CI, 1.01-1.69) and decreased with having 1 or 2 lifetime jobs vs no employment (OR, 0.77; 95% CI, 0.60-1.00). The length of work, furlough, age of retirement, childcare issues, and reasons for leaving a job were not associated with hospitalization. Conclusion This study reports an association between disability during work and hospitalization. On the basis of our findings, it may be important to obtain a more detailed work history from patients because it may provide further insight into their future health.
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Girwar SM, Jabroer R, Fiocco M, Sutch SP, Numans ME, Bruijnzeels MA. A systematic review of risk stratification tools internationally used in primary care settings. Health Sci Rep 2021; 4:e329. [PMID: 34322601 PMCID: PMC8299990 DOI: 10.1002/hsr2.329] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 06/19/2021] [Accepted: 06/27/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND AND AIMS In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance. METHODS We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes. RESULTS The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7). CONCLUSION Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.
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Affiliation(s)
- Shelley‐Ann M. Girwar
- Department of Public Health and Primary Care, LUMC Campus the HagueLeiden University Medical CentreThe HagueThe Netherlands
- Jan van Es InstituutEdeThe Netherlands
| | - Robert Jabroer
- Department of Public Health and Primary Care, LUMC Campus the HagueLeiden University Medical CentreThe HagueThe Netherlands
| | - Marta Fiocco
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
- Medical Statistics Department of Biomedical Data ScienceLeiden University Medical CenterLeidenThe Netherlands
- Princess Maxima Center for Pediatric OncologyUtrechtThe Netherlands
| | - Stephen P. Sutch
- Department of Public Health and Primary Care, LUMC Campus the HagueLeiden University Medical CentreThe HagueThe Netherlands
- Department of Health Policy and ManagementBloomberg School of Public Health Johns Hopkins UniversityBaltimoreMarylandUSA
| | - Mattijs E. Numans
- Department of Public Health and Primary Care, LUMC Campus the HagueLeiden University Medical CentreThe HagueThe Netherlands
| | - Marc A. Bruijnzeels
- Department of Public Health and Primary Care, LUMC Campus the HagueLeiden University Medical CentreThe HagueThe Netherlands
- Jan van Es InstituutEdeThe Netherlands
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Takahashi PY, Ryu E, Bielinski SJ, Hathcock M, Jenkins GD, Cerhan JR, Olson JE. No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:229-237. [PMID: 33603442 PMCID: PMC7886254 DOI: 10.2147/pgpm.s281645] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/29/2020] [Indexed: 02/06/2023]
Abstract
Background The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits). Methods This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes. Results During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period. Conclusion We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.
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Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Gregory D Jenkins
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Olson JE, Ryu E, Hathcock MA, Gupta R, Bublitz JT, Takahashi PY, Bielinski SJ, St Sauver JL, Meagher K, Sharp RR, Thibodeau SN, Cicek M, Cerhan JR. Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile. BMJ Open 2019; 9:e032707. [PMID: 31699749 PMCID: PMC6858142 DOI: 10.1136/bmjopen-2019-032707] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/14/2019] [Accepted: 10/15/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case-control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies. PARTICIPANTS A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents. FINDINGS TO DATE Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case-control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution. FUTURE PLANS The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case-control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.
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Affiliation(s)
- Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew A Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Ruchi Gupta
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Joshua T Bublitz
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Karen Meagher
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard R Sharp
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mine Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
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Ryu E, Olson JE, Juhn YJ, Hathcock MA, Wi CI, Cerhan JR, Yost KJ, Takahashi PY. Association between an individual housing-based socioeconomic index and inconsistent self-reporting of health conditions: a prospective cohort study in the Mayo Clinic Biobank. BMJ Open 2018; 8:e020054. [PMID: 29764878 PMCID: PMC5961601 DOI: 10.1136/bmjopen-2017-020054] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Using surveys to collect self-reported information on health and disease is commonly used in clinical practice and epidemiological research. However, the inconsistency of self-reported information collected longitudinally in repeated surveys is not well investigated. We aimed to investigate whether a socioeconomic status based on current housing characteristics, HOUsing-based SocioEconomic Status (HOUSES) index linking current address information to real estate property data, is associated with inconsistent self-reporting. STUDY SETTING AND PARTICIPANTS We performed a prospective cohort study using the Mayo Clinic Biobank (MCB) participants who resided in Olmsted County, Minnesota, USA, at the time of enrolment between 2009 and 2013, and were invited for a 4-year follow-up survey (n=11 717). PRIMARY AND SECONDARY OUTCOME MEASURES Using repeated survey data collected at the baseline and 4 years later, the primary outcome was the inconsistency in survey results when reporting prevalent diseases, defined by reporting to have 'ever' been diagnosed with a given disease in the baseline survey but reported 'never' in the follow-up survey. Secondary outcome was the response rate for the 4-year follow-up survey. RESULTS Among the MCB participants invited for the 4-year follow-up survey, 8508/11 717 (73%) responded to the survey. Forty-three per cent had at least one inconsistent self-reported disease. Lower HOUSES was associated with higher inconsistency rates, and the association remained significant after pertinent characteristics such as age and perceived general health (OR=1.46; 95% CI 1.17 to 1.84 for the lowest compared with the highest HOUSES decile). HOUSES was also associated with lower response rate for the follow-up survey (56% vs 77% for the lowest vs the highest HOUSES decile). CONCLUSION This study demonstrates the importance of using the HOUSES index that reflects current SES when using self-reporting through repeated surveys, as the HOUSES index at baseline survey was inversely associated with inconsistent self-report and the response rate for the follow-up survey.
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Affiliation(s)
- Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Young J Juhn
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew A Hathcock
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Chung-Il Wi
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - James R Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Kathleen J Yost
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Ryu E, Juhn YJ, Wheeler PH, Hathcock MA, Wi CI, Olson JE, Cerhan JR, Takahashi PY. Individual housing-based socioeconomic status predicts risk of accidental falls among adults. Ann Epidemiol 2017. [PMID: 28648550 DOI: 10.1016/j.annepidem.2017.05.019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PURPOSE Accidental falls are a major public health concern among people of all ages. Little is known about whether an individual-level housing-based socioeconomic status measure is associated with the risk of accidental falls. METHODS Among 12,286 Mayo Clinic Biobank participants residing in Olmsted County, Minnesota, subjects who experienced accidental falls between the biobank enrollment and September 2014 were identified using ICD-9 codes evaluated at emergency departments. HOUSES (HOUsing-based Index of SocioEconomic Status), a socioeconomic status measure based on individual housing features, was also calculated. Cox regression models were utilized to assess the association of the HOUSES (in quartiles) with accidental fall risk. RESULTS Seven hundred eleven (5.8%) participants had at least one emergency room visit due to an accidental fall during the study period. Subjects with higher HOUSES were less likely to experience falls in a dose-response manner (hazard ratio: 0.58; 95% confidence interval: 0.44-0.76 for comparing the highest to the lowest quartile). In addition, the HOUSES was positively associated with better health behaviors, social support, and functional status. CONCLUSIONS The HOUSES is inversely associated with accidental fall risk requiring emergency care in a dose-response manner. The HOUSES may capture falls-related risk factors through housing features and socioeconomic status-related psychosocial factors.
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Affiliation(s)
- Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Young J Juhn
- Asthma Epidemiology Research Unit and Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | - Philip H Wheeler
- Asthma Epidemiology Research Unit and Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | | | - Chung-Il Wi
- Asthma Epidemiology Research Unit and Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - James R Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN.
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Takahashi PY, Ryu E, Hathcock MA, Olson JE, Bielinski SJ, Cerhan JR, Rand-Weaver J, Juhn YJ. A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population. J Epidemiol Community Health 2015; 70:286-91. [PMID: 26458399 DOI: 10.1136/jech-2015-205925] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 09/27/2015] [Indexed: 11/04/2022]
Abstract
BACKGROUND Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population. METHODS Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 2010, with follow-up until 31 December 2011. Primary outcome was all-cause hospitalisation over 1 calendar-year. Secondary outcome was MCC determined through a Minnesota Medical Tiering score. A logistic regression model was used to assess the association of HOUSES with the Minnesota tiering score. With adjustment for age, sex and MCC, the association of HOUSES with hospitalisation risk was tested using the Cox proportional hazards model. RESULTS Eligible patients totalled 6402 persons (median age, 57 years; 25th-75th quartiles, 45-68 years). The lowest quartile of HOUSES was associated with a higher Minnesota tiering score after adjustment for age and sex (OR (95% CI) 2.4 (2.0 to 3.1)) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalisation (age, sex, MCC-adjusted HR (95% CI) 1.53 (1.18 to 1.98)) compared with those in the highest quartile. CONCLUSIONS Low SES, as assessed by HOUSES, was associated with increased risk of hospitalisation and greater MCC health burden. HOUSES may be a clinically useful surrogate for SES to assess risk stratification for patient care and clinical research.
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Affiliation(s)
- Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew A Hathcock
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - James R Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Young J Juhn
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Takahashi PY, Ryu E, Olson JE, Winkler EM, Hathcock MA, Gupta R, Sloan JA, Pathak J, Bielinski SJ, Cerhan JR. Health behaviors and quality of life predictors for risk of hospitalization in an electronic health record-linked biobank. Int J Gen Med 2015; 8:247-54. [PMID: 26316799 PMCID: PMC4540136 DOI: 10.2147/ijgm.s85473] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Hospital risk stratification models using electronic health records (EHRs) often use age and comorbid health burden. Our primary aim was to determine if quality of life or health behaviors captured in an EHR-linked biobank can predict future risk of hospitalization. Methods Participants in the Mayo Clinic Biobank completed self-administered questionnaires at enrollment that included quality of life and health behaviors. Participants enrolled as of December 31, 2010 were followed for one year to ascertain hospitalization. Data on comorbidities and hospitalization were derived from the Mayo Clinic EHR. Hazard ratios (HR) and 95% confidence interval (CI) were used, adjusted for age and sex. We used gradient boosting machines models to integrate multiple factors. Different models were compared using C-statistic. Results Of the 8,927 eligible Mayo Clinic Biobank participants, 834 (9.3%) were hospitalized. Self-perceived health status and alcohol use had the strongest associations with risk of hospitalization. Compared to participants with excellent self-perceived health, those reporting poor/fair health had higher risk of hospitalization (HR =3.66, 95% CI 2.74–4.88). Alcohol use was inversely associated with hospitalization (HR =0.57 95% CI 0.45–0.72). The gradient boosting machines model estimated self-perceived health as the most influential factor (relative influence =16%). The predictive ability of the model based on comorbidities was slightly higher than the one based on the self-perceived health (C-statistic =0.67 vs 0.65). Conclusion This study demonstrates that self-perceived health may be an important piece of information to add to the EHR. It may be another method to determine hospitalization risk.
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Affiliation(s)
- Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA ; Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Erin M Winkler
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Ruchi Gupta
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jeff A Sloan
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - James R Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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10
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Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank. Health Qual Life Outcomes 2015; 13:95. [PMID: 26138599 PMCID: PMC4490595 DOI: 10.1186/s12955-015-0285-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Accepted: 06/11/2015] [Indexed: 12/21/2022] Open
Abstract
Background Deficits in health-related quality of life (HRQOL) may be associated with worse patient experiences, outcomes and even survival. While there exists evidence to identify risk factors associated with deficits in HRQOL among patients with individual medical conditions such as cancer, it is less well established in more general populations without attention to specific illnesses. This study used patients with a wide range of medical conditions to identify contributors with the greatest influence on HRQOL deficits. Methods Self-perceived general health and depressive symptoms were assessed using data from 21,736 Mayo Clinic Biobank (MCB) participants. Each domain was dichotomized into categories related to poor health: deficit (poor/fair for general health and ≥3 for PHQ-2 depressive symptoms) or non-deficit. Logistic regression models were used to test the association of commonly collected demographic characteristics and disease burden with each HRQOL domain, adjusting for age and gender. Gradient boosting machine (GBM) models were applied to quantify the relative influence of contributors on each HRQOL domain. Results The prevalence of participants with a deficit was 9.5 % for perception of general health and 4.6 % for depressive symptoms. For both groups, disease burden had the strongest influence for deficit in HRQOL (63 % for general health and 42 % for depressive symptoms). For depressive symptoms, age was equally influential. The prevalence of a deficit in general health increased slightly with age for males, but remained stable across age for females. Deficit in depressive symptoms was inversely associated with age. For both HRQOL domains, risk of a deficit was associated with higher disease burden, lower levels of education, no alcohol consumption, smoking, and obesity. Subjects with deficits were less likely to report that they were currently working for pay than those without a deficit; this association was stronger among males than females. Conclusions Comorbid health burden has the strongest influence on deficits in self-perceived general health, while demographic factors show relatively minimal impact. For depressive symptoms, both age and comorbid health burden were equally important, with decreasing deficits in depressive symptoms with increasing age. For interpreting patient-reported metrics and comparison, one must account for comorbid health burden.
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