title: A Bayesian Approach to Identifying New Risk Factors for Dementia: A Nationwide Population-Based Study.
abstract: Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics.We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular diseases. Then, we used Bayesian statistics to verify 2 new potential risk factors for dementia, namely hearing loss and senile cataract, determined from the Taiwan's National Health Insurance Research Database.We included a total of 6546 (6.0%) patients diagnosed with dementia. We observed older age, female sex, and lower income as independent risk factors for dementia. Moreover, we verified the 4 recognized risk factors for dementia in the older Taiwanese population; their odds ratios (ORs) ranged from 3.469 to 1.207. Furthermore, we observed that hearing loss (OR = 1.577) and senile cataract (OR = 1.549) were associated with an increased risk of dementia.We found that the results obtained using Bayesian statistics for assessing risk factors for dementia, such as head injury, depression, DM, and vascular diseases, were consistent with those obtained using classical frequentist probability. Moreover, hearing loss and senile cataract were found to be potential risk factors for dementia in the older Taiwanese population. Bayesian statistics could help clinicians explore other potential risk factors for dementia and for developing appropriate treatment strategies for these patients.
authors.affiliation: From the School of Pharmacy, College of Pharmacy, Kaohsiung Medical University (Y-HW, S-SW); Department of Psychiatry (J-HT) and Department of Neurology (Y-PC), Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University; Department of Psychiatry, Kaohsiung Medical University Hospital, Kaohsiung Medical University (PY); and Department of Computer Science and Engineering, National Sun Yat-sen University (C-HRL, K-HT), Kaohsiung, Taiwan.
authors.forename: Yen-Hsia
authors.initials: YH
authors.lastname: Wen
authors.forename: Shihn-Sheng
authors.initials: SS
authors.lastname: Wu
authors.forename: Chun-Hung Richard
authors.initials: CR
authors.lastname: Lin
authors.forename: Jui-Hsiu
authors.initials: JH
authors.lastname: Tsai
authors.forename: Pinchen
authors.initials: P
authors.lastname: Yang
authors.forename: Yang-Pei
authors.initials: YP
authors.lastname: Chang
authors.forename: Kuan-Hua
authors.initials: KH
authors.lastname: Tseng
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002386:Cataract / Q000453:epidemiology; D006259:Craniocerebral Trauma / Q000453:epidemiology; D003704:Dementia / Q000453:epidemiology*; D003863:Depression / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D034381:Hearing Loss / Q000453:epidemiology; D006801:Humans; D008297:Male; D016017:Odds Ratio; D012111:Residence Characteristics; D012307:Risk Factors; D012737:Sex Factors; D012959:Socioeconomic Factors; D013624:Taiwan / Q000453:epidemiology; D014652:Vascular Diseases / Q000453:epidemiology