title: A Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM).
abstract: BACKGROUND
  The high prevalence and severe consequences of non-insulin dependent diabetes mellitus (NIDDM) in Taiwan calls for urgent measures to detect this disease in the asymptomatic phase. However, the efficacy of early detection of NIDDM is highly dependent on its natural history from the disease-free state, through the asymptomatic to the symptomatic phase and death from NIDDM or other causes.
  
  
  METHODS
  In order to project the above progression, a five-state illness-and-death Markov chain model was proposed to estimate these transition parameters using data from two rounds of a blood sugar screening programme for NIDDM in Puli, in central Taiwan.
  
  
  RESULTS
  Results showed that the annual incidence for asymptomatic NIDDM was 10.67 per 1000 (95% CI: 8.26-13.79) and the average duration between the asymptomatic and symptomatic phases (the sojourn time) was 8 years (95%CI: 5.74-11.29). The 10-year survival rate for asymptomatic NIDDM (79.35%) was better than that for symptomatic NIDDM (69.45%). Prediction of deaths from NIDDM was performed to assess how the efficacy of screening for NIDDM varied by different screening frequencies (annual, biennial, 4-yearly and the control group). Results indicated there is no substantial difference in mortality reduction from NIDDM among the annual, biennial and 4-yearly screening regimens. However, a 4-yearly screening regimen significantly reduced deaths from NIDDM by 40% (95% CI: 26-51%).
  
  
  CONCLUSIONS
  A long sojourn time and the substantial reduction in mortality suggest that a 4-yearly screening regime for NIDDM would be most effective and feasible in Taiwan. The proposed five-state Markov chain model can be applied to other similar NIDDM screening projects.
authors.affiliation: Graduate Institute of Public Health, College of Medicine, National Yang-Ming University, Taipei, Taiwan.
authors.forename: H S
authors.initials: HS
authors.lastname: Kuo
authors.forename: H J
authors.initials: HJ
authors.lastname: Chang
authors.forename: P
authors.initials: P
authors.lastname: Chou
authors.forename: L
authors.initials: L
authors.lastname: Teng
authors.forename: T H
authors.initials: TH
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D002423:Cause of Death; D016001:Confidence Intervals; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D018450:Disease Progression; D005260:Female; D006306:Health Surveys; D006801:Humans; D015994:Incidence; D008297:Male; D008390:Markov Chains; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D015996:Survival Rate; D013624:Taiwan / Q000453:epidemiology