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Comparative Study
. 2018 Jan 8;11(1):21.
doi: 10.1186/s13071-017-2580-z.

Understanding the relationship between egg- and antigen-based diagnostics of Schistosoma mansoni infection pre- and post-treatment in Uganda

Affiliations
Comparative Study

Understanding the relationship between egg- and antigen-based diagnostics of Schistosoma mansoni infection pre- and post-treatment in Uganda

Joaquín M Prada et al. Parasit Vectors. .

Abstract

Background: Schistosomiasis is a major socio-economic and public health problem in many sub-Saharan African countries. After large mass drug administration (MDA) campaigns, prevalence of infection rapidly returns to pre-treatment levels. The traditional egg-based diagnostic for schistosome infections, Kato-Katz, is being substituted in many settings by circulating antigen recognition-based diagnostics, usually the point-of-care circulating cathodic antigen test (CCA). The relationship between these diagnostics is poorly understood, particularly after treatment in both drug-efficacy studies and routine monitoring.

Results: We created a model of schistosome infections to better understand and quantify the relationship between these two egg- and adult worm antigen-based diagnostics. We focused particularly on the interpretation of "trace" results after CCA testing. Our analyses suggest that CCA is generally a better predictor of prevalence, particularly after treatment, and that trace CCA results are typically associated with truly infected individuals.

Conclusions: Even though prevalence rises to pre-treatment levels only six months after MDAs, our model suggests that the average intensity of infection is much lower, and is probably in part due to a small burden of surviving juveniles from when the treatment occurred. This work helps to better understand CCA diagnostics and the interpretation of post-treatment prevalence estimations.

Keywords: CCA; Diagnostics; Kato-Katz; Mathematical models; Schistosomes; Trace readings.

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Conflict of interest statement

Ethics approval and consent to participate

This manuscript uses anonymized human data previously published (see Lamberton et al. 2014 [5]) originally collected with the approval of the Uganda National Council of Science and Technology (Memorandum of Understanding: sections 1.4, 1.5, 1.6) and the Imperial College Research Ethics Committee (EC NO: 03.36. R&D No:03/SB/033E).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Prevalence comparison between raw data and model prediction. Point estimates are the predicted values based on the raw data, black circles represent estimated prevalences using Kato-Katz, while the green triangles and blue squares are the estimates using a circulating cathodic antigen diagnostic, assuming trace results as positive (CCA+) and negative (CCA-), respectively. Red point with credible interval is the prevalence estimated by the model. Individual diagnostics prevalence estimates are generally lower than model predicted values combining both diagnostics
Fig. 2
Fig. 2
Posterior distribution of average intensity of infection vs true prevalence. Each colour represent a different time point. At baseline (green) both prevalence and intensity of infection are high; one month post-treatment (black) both prevalence and infection are low; six months post-treatment (blue) the prevalence is similar to baseline, but the average population intensity of infection is still relatively low. This suggests that prevalence recovers faster in the population than intensity of infection, which builds up more slowly. The intensity of infection is shown in eggs per gram (epg) for clarity, however, the raw data are used in the model
Fig. 3
Fig. 3
Comparison between diagnostic estimates and true prevalence for simulated data. Colours represent the different diagnostics. Double KK estimates are coloured black (circles), CCA with trace results as positives (CCA+) are represented by green triangles, while CCA with trace as negative (CCA-) are blue squares. Symbols coloured red are the real data from the diagnostics for the average estimated true prevalence (Baseline and one and six months post-treatment). a Comparison of the different diagnostics and true prevalence illustrating that KK and CCA- always underestimate true prevalence, while CCA+ overestimates prevalence at low/medium prevalences. b Comparison of CCA diagnostics (CCA+ and CCA-) and KK diagnostics (two samples) showing a non-linear relationship as prevalence changes
Fig. 4
Fig. 4
Error in prevalence estimation based on simulated data. Mean and standard deviation across all simulated points. Error decreases as the number of repeated Kato-Katz measures increases but CCA assuming trace as positive (CCA+) has an overall smaller error in prevalence estimation. This suggests that multiple KK will improve precision, but not enough compared to CCA+, particularly at low levels of prevalence

References

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