Mixture analysis of tuberculin survey data from northern Malawi and critique of the method

G. R. Davies*, P. E. Fine, Emilia Vynnycky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


SETTING: Various methods have been used to estimate the prevalence of Mycobacterium tuberculosis infection from tuberculin survey data. All are complicated by prior sensitisation to environmental mycobacteria and bacille Calmette-Guérin (BCG) vaccination. Mixture analysis has recently been proposed as a means of overcoming misclassification and improving infection prevalence estimates. OBJECTIVE: To compare conventional and mixture model estimates of M. tuberculosis infection prevalence. DESIGN: Mixture models with two or three univariate normal components were fitted to the results of 53 909 tuberculin tests conducted in northern Malawi during 1980-1984. Data were stratified by BCG status, sex and age and corrected for digit preference. Prevalence estimates derived from mixture models were compared with those of conventional methods. RESULTS: The optimal model was age-dependent, with three- and one-component solutions preferred in younger and older age groups, respectively. In contrast with findings from elsewhere, a component corresponding to BCG vaccination was indistinguishable from that attributable to environmental mycobacterial exposure, and infection prevalence estimates in younger individuals with a BCG scar were inflated, irrespective of the method used. CONCLUSION: The validity of infection prevalence and incidence estimates based on mixture modelling is probably locale-dependent, and the assumptions underlying mixture models may not realistically reflect underlying immunological processes.

Original languageEnglish
Pages (from-to)1023-1029
Number of pages7
JournalInternational Journal of Tuberculosis and Lung Disease
Issue number9
Publication statusPublished - Sept 2006


  • BCG vaccine
  • Epidemiology
  • Mathematical model
  • Tuberculin tests
  • Tuberculosis


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