Fig 1.
Study design depicting the two-pronged approach of this analysis, containing the number of included children (n) from each mass drug administration (MDA) survey.
Inclusion criteria for each prong of this study is as follows: [A] to determine the prevalence, theselection was determined by those children who provided urine samples for parasitological quantification at either the pre- or post-MDA survey, and [B] to assess efficacy measures those included were treated children (as confirmed by the school MDA registers) who were positive for schistosome infection and were followed at both pre- and post- MDA. The number of districts included in each survey is labelled, along with the date of each MDA.
Table 2.
Results of different approaches to determine persistent hotspots of S. haematobium prevalence (PPHS).
Table 1.
Measures of praziquantel efficacy (PZQ) against Schistosoma haematobium after the treatment during a mass drug administration (MDA).
Fig 2.
Choropleth maps depicting the geographical distribution of the prevalence of S. haematobium at the pre-MDA1 and pre-MDA6 surveys.
The prevalence of each district is coloured to represent the prevalence (%) at that timepoint, with the sentinel site locations highlighted by a circular data point. These data points also represent the proportion of infection intensities at the survey, with the circular pie chart indicating what percentage of infections were light (light blue) or heavy (dark blue). The date at which the MDA was carried out is depicted on the right-hand side of the choropleth maps. Both maps were generated using QGIS, Version [3.22.2]. Zimbabwe National Statistics Agency (2022 ) Administrative boundaries shapefiles (district level), Zimbabwe National Statistics Agency (ZIMSTAT). Retrieved from https://data.humdata.org/dataset/cod-ab-zwe/resource/5ed39dd6-5124-4282-81cc-5295a3a976be (accessed 25 January 2025).
Fig 3.
Map of Zimbabwe highlighting the districts detected as persistent hotspots of S. haematobium prevalence (PPHS), persistent hotspots of decreased praziquantel efficacy (EPHS), and districts of both PPHS and EPHS.
The sentinel sites at which the surveys were conducted are labelled with a circle and the number in which the hotspot was detected. Zimbabwe National Statistics Agency (2022) Administrative boundaries shapefiles (district level), Zimbabwe National Statistics Agency (ZIMSTAT). Retrieved from https://data.humdata.org/dataset/cod-ab-zwe/resource/5ed39dd6-5124-4282-81cc-5295a3a976be (accessed 25 January 2025).
Fig 4.
Box plots showing baseline prevalence and mean egg count at pre-MDA1 in districts that were responders compared to persistent hotspots of S. haematobium prevalence (PPHS).
(A) Compares baseline prevalence, (B) Compares the baseline mean egg count.
Fig 5.
Box plots showing baseline prevalence and mean egg count at pre-MDA1 in districts that were responders compared to those that were persistent hotspots of decreasing praziquantel efficacy (EPHS).
(A) Compares baseline prevalence, (B) Compares the baseline mean egg count. The EPHS were identified using the approaches of classification described in detail in this study. The Mann–Whitney U-test P-values are indicated between the two groups.
Fig 6.
The relationship between pre-treatment intensity of Schistosoma haematobium infection and cure rate (CR) after praziquantel (PZQ) treatment during six years of annual mass drug administrations (MDAs) in Zimbabwe.
Fig 7.
Linear regression of the relationship between the egg reduction rate and distance to the nearest waterway.
This model was found to be significant (R2 = 0.106, P = 0.016). The shaded area represents the 95% confidence interval (CI).
Fig 8.
Coverage of the mass drug administration programs at the national and district level, for each detected persistent hotspots of S. haematobium prevalence (PPHS).
Table 3.
Comparison of the changes in WHO risk category compared to the baseline in the previous MDAs baseline category.