Oliver Bärenbold1,2, Amadou Garba3, Daniel G Colley4,5, Fiona M Fleming6, Rufin K Assaré1,2,7,8, Edridah M Tukahebwa9, Biruck Kebede10, Jean T Coulibaly1,2,7,8, Eliézer K N'Goran7,8, Louis-Albert Tchuem Tchuenté11,12, Pauline Mwinzi13, Jürg Utzinger1,2, Penelope Vounatsou1,2. 1. Swiss Tropical and Public Health Institute, Basel, Switzerland. 2. University of Basel, Basel, Switzerland. 3. Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland. 4. Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, United States of America. 5. Department of Microbiology, University of Georgia, Athens, Georgia, United States of America. 6. Schistosomiasis Control Initiative, Imperial College, London, United Kingdom. 7. Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Côte d'Ivoire. 8. Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire. 9. Vector Control Division, Ministry of Health, Kampala, Uganda. 10. Ministry of Health, Addis Ababa, Ethiopia. 11. Laboratory of Parasitology and Ecology, University of Yaoundé I, Yaoundé, Cameroon. 12. Centre for Schistosomiasis and Parasitology, Yaoundé, Cameroon. 13. Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya.
Abstract
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 a need for adjustments that can be done without modeling 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-Katz 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.
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 a need for adjustments that can be done without modeling 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-Katz 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.
Authors: Ian Kim B Tabios; Marcello Otake Sato; Ourlad Alzeus G Tantengco; Raffy Jay C Fornillos; Masashi Kirinoki; Megumi Sato; Raniv D Rojo; Ian Kendrich C Fontanilla; Yuichi Chigusa; Paul Mark B Medina; Mihoko Kikuchi; Lydia R Leonardo Journal: Front Immunol Date: 2022-06-14 Impact factor: 8.786