A Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM).

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.

[1]  S. Kenny,et al.  A Follow-Up Study of Diabetic Oklahoma Indians: Mortality and causes of death , 1993, Diabetes Care.

[2]  K. Hsiao,et al.  A Population Survey on the Prevalence of Diabetes in Kin-Hu, Kinmen , 1994, Diabetes Care.

[3]  J D Habbema,et al.  Modelling issues in cancer screening , 1995, Statistical methods in medical research.

[4]  R. Klein,et al.  Onset of NIDDM occurs at Least 4–7 yr Before Clinical Diagnosis , 1992, Diabetes Care.

[5]  A. Morrison,et al.  Basic issues in population screening for cancer. , 1980, Journal of the National Cancer Institute.

[6]  J. Habbema,et al.  A model‐based analysis of the hip project for breast cancer screening , 1990, International journal of cancer.

[7]  K. Hsiao,et al.  Community-Based Epidemiological Study on Diabetes in Pu-Li, Taiwan , 1992, Diabetes Care.

[8]  L. Tabár,et al.  Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase. , 1995, Statistics in medicine.

[9]  J. C. Tressler,et al.  Fourth Edition , 2006 .

[10]  R. Jarrett Duration of Non‐insulin‐dependent Diabetes and Development of Retinopathy: Analysis of Possible Risk Factors , 1986, Diabetic medicine : a journal of the British Diabetic Association.

[11]  E. P. Kao,et al.  An Introduction to Stochastic Processes , 1996 .

[12]  R H Jones,et al.  Multi-state models and diabetic retinopathy. , 1995, Statistics in medicine.

[13]  Mitchell H. Gail,et al.  AIDS Epidemiology: A Quantitative Approach , 1994 .

[14]  N. Day,et al.  Simplified models of screening for chronic disease: estimation procedures from mass screening programmes. , 1984, Biometrics.

[15]  P. Prorok,et al.  Design and analysis of cancer screening trials , 1995, Statistical methods in medical research.

[16]  D. Parkin A computer simulation model for the practical planning of cervical cancer screening programmes. , 1985, British Journal of Cancer.

[17]  Erhan Çinlar,et al.  Introduction to stochastic processes , 1974 .

[18]  L. Stitt,et al.  Evaluating the survival of cancer cases detected by screening. , 1987, Statistics in medicine.