# Misclassification errors in prevalence estimation: Bayesian handling with care

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This Hints and Kinks paper starts from the simple but well-known premise that “what gets measured, gets done”, which we would like to extend into “what gets measured well, gets done well”, and finally to “what does not get measured well could still get done well, if appropriate analytical methods are used”.

Imagine assessing the prevalence of an infectious disease in a population, where the presence of disease is determined by a diagnostic test. For each tested individual, the diagnostic test result gives a “signal” that does not necessarily match its true infection status. It is well known that false positive and false negative results can arise when using diagnostic tests, for example producing a positive result in a non-case owing to a factor unrelated to the infection. On a population level, the prevalence as determined by the diagnostic test will thus only be an “apparent” prevalence, which will, to some extent, differ from the “true” prevalence. This problem of diagnostic test...

## Keywords

Infection Status Malaria Prevalence Test Characteristic External Information Positive Test Result## References

- Berkvens D, Speybroeck N, Praet N, Adel A, Lesaffre E (2006) Estimating disease prevalence in a Bayesian framework using probabilistic constraints. Epidemiology 17:145–153PubMedCrossRefGoogle Scholar
- Bouwknegt M, Engel B, Herremans MMPT, Widdowson MA, Worm HC, Koopmans MPG, Frankena K, De Roda Husman AM, De Jong MCM, Van Der Poel WHM (2008) Bayesian estimation of hepatitis E virus seroprevalence for populations with different exposure levels to swine in The Netherlands. Epidemiol Infect 136:567–576PubMedCrossRefGoogle Scholar
- Branscum AJ, Gardner IA, Johnson WO (2005) Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling. Prev Vet Med 68:145–163PubMedCrossRefGoogle Scholar
- Engel B, Swildens B, Stegeman A, Buist W, De Jong M (2006) Estimation of sensitivity and specificity of three conditionally dependent diagnostic tests in the absence of a gold standard. J Agric Biol Environ Stat 11:360–380CrossRefGoogle Scholar
- Joseph L, Gyorkos TW, Coupal L (1995) Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am J Epidemiol 141(3):263–272PubMedGoogle Scholar
- Liu P, Xiao S, Shi ZX, Bi XX, Yang HT, Jin H (2011) Bayesian evaluation of the human immunodeficiency virus antibody screening strategy of duplicate enzyme-linked immunosorbent assay in Xuzhou blood center, China. Transfusion 51:793–798PubMedCrossRefGoogle Scholar
- Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS—A Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:325–337CrossRefGoogle Scholar
- Lunn D, Spiegelhalter D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions (with discussion). Stat Med 28:3049–3082PubMedCrossRefGoogle Scholar
- Rogan WJ, Gladen B (1978) Estimating prevalence from the results of a screening test. Am J Epidemiol 107:71–76PubMedGoogle Scholar
- Speybroeck N, Praet N, Claes F, Van Hong N, Torres K, Mao S, Van den Eede P, Thi Thinh T, Gamboa D, Sochantha T, Thang ND, Coosemans M, Büscher P, D’Alessandro U, Berkvens D, Erhart A (2011) True versus apparent malaria infection prevalence: the contribution of a Bayesian approach. PLoS ONE 6(2):e16705PubMedCrossRefGoogle Scholar
- Speybroeck N, Williams CJ, Lafia KB, Devleesschauwer B, Berkvens D (2012) Estimating the prevalence of infections in vector populations using pools of samples. Med Vet Entomol 26:361–371Google Scholar