Hospitalizations and Emergency Department Use in Mayo Clinic Biobank Participants Within the Employee and Community Health Medical Home

Takahashi, Paul Y.; Ryu, Euijung; Olson, Janet E.; Anderson, Kari J.; Hathcock, Matthew; Haas, Lindsey R.; Naessens, James M.; Pathak, Jyotishman; Bielinski, Suzette J.; Cerhan, James R.

doi:10.1016/j.mayocp.2013.06.015

Cited by 11 publications

(12 citation statements)

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“…HOUSES may not completely represent SES because SES is a multifactorial classification. Our cohort was derived from the MCB and may not reflect a population-based sample, and may reflect some selection bias 22. In addition, Olmsted County tends to have a greater white population and higher education levels than counties nationwide.…”

Section: Discussionmentioning

confidence: 99%

“…This study enrolled all eligible adult Mayo Clinic primary care patients who were enrolled voluntarily in the Mayo Clinic Biobank (MCB)21 and were residents of Olmsted County, Minnesota, USA 22. We limited our study patients to MCB participants living in Olmsted County at the time of MCB enrolment because HOUSES is available to the Olmsted County population only.…”

Section: Methodsmentioning

confidence: 99%

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A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population

Takahashi

Ryu

Hathcock

et al. 2015

J Epidemiol Community Health

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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 hospitalization in a community population. Methods Participants were residents of Olmsted County, Minnesota, aged >18 years who were enrolled in Mayo Clinic Biobank on December 31, 2010, with follow-up until December 31, 2011. Primary outcome was all-cause hospitalization over 1 calendar year. Secondary outcome was MCC determined through Minnesota Medical Tiering score. Logistic regression model was used to assess association of HOUSES with Minnesota tiering score. With adjustment for age, sex, and MCC, the association of HOUSES with hospitalization risk was tested using Cox proportional hazards model. Results Eligible patients totaled 6,402 persons (median age, 57 years; 25th-75th quartiles, 45-68 years). The lowest quartile of HOUSES was associated with higher Minnesota tiering score after adjustment for age and sex (odds ratio [95% CI], 2.4 [2.0-3.1]) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalization (age, sex, MCC-adjusted hazard ratio [95% CI], 1.53 [1.18-1.98]) compared with those in the highest quartile. Conclusion Low SES, as assessed by HOUSES, was associated with increased risk of hospitalization 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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population

Takahashi

Ryu

Hathcock

et al. 2015

J Epidemiol Community Health

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“…Our findings suggest that healthcare providers may need to account for comorbid health burden and age for interpretation. HRQOL outcomes may need to be reported by different age category or by different comorbid health burden like Minnesota Medical Tier [ 25 ]. Categorizing both groups may allow accurate comparison of HRQOL which is not dependent on different comorbid health burden.…”

Section: Discussionmentioning

confidence: 99%

“…All participants from the study were from the Mayo Clinic Biobank (MCB). The MCB is an institutional resource initiated by the Mayo Clinic Center for Individualized Medicine [ 23 – 25 ]. Enrollment into the MCB began in April 2009 and is ongoing with a target goal of 50,000 participants.…”

Section: Methodsmentioning

confidence: 99%

Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank

Ryu¹,

Takahashi

Olson³

et al. 2015

Health Qual Life Outcomes

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BackgroundDeficits 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.MethodsSelf-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.ResultsThe 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.ConclusionsComorbid 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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“…Various aspects of the Mayo Clinic Biobank population have been described earlier 6–10. Takahashi et al 11 helped us understand our participants by comparing participants in the Mayo Clinic Biobank who were assigned a primary care provider in Rochester, Minnesota (39% at time of publication) to the entire Rochester primary care (Employee and Community Health, ECH) population. We found that Biobank patients were older and had more chronic conditions than the underlying ECH population overall.…”

Section: Findings To Datementioning

confidence: 99%

Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile

et al. 2019

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PurposeThe 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.ParticipantsA 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 dateOverall, 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 plansThe 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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Hospitalizations and Emergency Department Use in Mayo Clinic Biobank Participants Within the Employee and Community Health Medical Home

Cited by 11 publications

References 10 publications

A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population

A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population

Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank

Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile

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