title: [Multi-state Markov model in expressing the outcome of mild cognitive impairment among community-based elderly residents].
abstract: OBJECTIVE
  To introduce the Multi-state Markov model in studying the outcome prediction of mild cognitive impairment (MCI).
  
  
  METHODS
  Based on the intelligence quotient (IQ) changes that reflecting the trends in cognitive function in the patients under follow-up program, we constructed a four states model and used Multi-state Markov model to analyze the patients.
  
  
  RESULTS
  Among 600 MCI patients, there were 174 (29.0%) males and 426 (71.0%) females, with age range of 65-90 years-old (average 69.7 ± 6.6). For univariate analysis, gender, age, education level, marital status, smoking, household income, cerebral hemorrhage, hypertension, high cholesterol, diabetes, LDL-C, SBP and DBP were found to be associated with cognitive function. For multivariate analysis, female, older age, cerebral hemorrhage and higher SBP were shown to be the risk factors for transition from the state of cognitive stability to the state of severe deterioration, and their coefficients were 0.762, 0.366, 0.885, and 0.069, respectively. Age (0.038) could influence the transition from the state of cognitive stability to slight deterioration. Higher education level was shown to be the protective factor for these transitions (-0.219 and -0.297). Transition intensity from the state of cognitive stability to the state of slight and severe deterioration was 1.2 times that of transition to the state of improving. Transition intensity from state of slight deterioration to cognitive stability was 11.4 times that of transition to severe deterioration.
  
  
  CONCLUSIONS
  Multi-state Markov model was an effective tool in dealing with longitudinal data.
authors.affiliation: Community Health Service Centers of Xuanwu Community, Beijing 100055, China.
authors.forename: Shan-shan
authors.initials: SS
authors.lastname: Yang
authors.forename: Li-ye
authors.initials: LY
authors.lastname: Zhou
authors.forename: Rui-feng
authors.initials: RF
authors.lastname: Liang
authors.forename: Tian-e
authors.initials: TE
authors.lastname: Luo
authors.forename: Cheng-yi
authors.initials: CY
authors.lastname: Qu
authors.forename: Hong-mei
authors.initials: HM
authors.lastname: Yu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D003072:Cognition Disorders / Q000523:psychology; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000523:psychology*; D005260:Female; D006801:Humans; D007361:Intelligence Tests; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D011379:Prognosis; D012307:Risk Factors