Estimating true prevalence of Schistosoma mansoni from population summary measures based on the Kato-Katz diagnostic technique

Background: The prevalence of Schistosoma mansoni infection is usually assessed by the Kato-Katz diagnostic technique. However, Kato-Katz thick smears have low sensitivity, especially for light infections. Egg count models fitted on individual level data can adjust for the infection intensity-dependent sensitivity and estimate the ‘true’ prevalence in a population. However, application of these models is complex and there is need for adjustments that can be done without modelling expertise. This study provides estimates of the ‘true’ S. mansoni prevalence from population summary measures of observed prevalence and infection intensity using extensive simulations parametrized with data from different settings in sub-Saharan Africa. Methodology: An individual-level egg count model was applied to Kato-Katz data to determine the S. mansoni infection intensity-dependent sensitivity for various sampling schemes. Observations in populations with varying forces of transmission were simulated, using standard assumptions about the distribution of worms and their mating behavior. Summary measures such as the geometric mean infection, arithmetic mean infection, and the observed prevalence of the simulations were calculated, and parametric statistical models fitted to the summary measures for each sampling scheme. For validation, the simulation-based estimates are compared with an observational dataset not used to inform the simulation. Principal findings: Overall, the sensitivity of Kato-Katz in a population varies according to the mean infection intensity. Using a parametric model, which takes into account different sampling schemes varying from single Kato-Katy to triplicate slides over three days, both geometric and arithmetic mean infection intensities improve estimation of sensitivity. The relation between observed and ‘true’ prevalence is remarkably linear and triplicate slides per day on three consecutive days ensure close to perfect sensitivity. Conclusions/Significance: Estimation of ‘true’ S. mansoni prevalence is improved when taking into account geometric or arithmetic mean infection intensity in a population. We supply parametric functions and corresponding estimates of their parameters to calculate the ‘true’ prevalence for sampling schemes up to 3 days with triplicate Kato-Katz thick smears per day that allow estimation of the ‘true’ prevalence. Author summary: The World Health Organization (WHO) recommends the Kato-Katz diagnostic method, i.e., counting eggs in a thick-smear of stool using light microscopy, for estimation of Schistosoma mansoni infection prevalence. While the diagnostic specificity of Kato-Katz very high, the sensitivity varies strongly with infection intensity and the number of samples collected and thick smears per sample tested. Therefore, the performance of Kato-Katz in a population depends on the distribution of infections in the population and individual-level data is needed to determine the ‘true’ prevalence of infection. However, modelling capacity to determine ‘true’ prevalence from individual-level data is often not available to programme managers. In this study, we therefore provide simple equations to estimate the ‘true’ prevalence and associated uncertainty from observed prevalence and arithmetic or geometric mean infection intensity for a variety of common sampling schemes. We find that by including information about the mean infection intensity in a population the estimation of ‘true’ prevalence can be improved compared to assuming a constant value for the diagnostic sensitivity and supply parameters and functions to calculate the ‘true’ prevalence of infection.