Fig 1.
Overview of the different operational steps for the different FEC methods.
The distinctive steps to perform a Kato-Katz (KK), Mini-FLOTAC or FECPAKG2 on a single stool sample are provided in chronological order per method. The procedures are grouped per main subject (blue: entry of demographic data; green: preparation of the sample; yellow: reading of the slide/device or the image to count STH eggs; red: entry of fecal egg count data). Waiting steps included in the procedure are indicated in grey and represent a fixed amount of time. The small clock symbol indicates what steps have been timed as part of this experiment. Clock clip art from https://openclipart.org/detail/125725/time-temps.
Table 1.
Parameterization of the simulation framework for variability in fecal egg counts before and after treatment.
Fig 2.
Time required to quantify soil-transmitted helminth infections in stool by four fecal egg count methods.
The height of the bars represents the mean time (in sec) needed to enter demographic data (blue), to perform the preparation phase (green), to count eggs (yellow) and to enter egg count data (red) for a single (1xKK) and duplicate Kao-Katz (2xKK), Mini-FLOTAC (MF) and FECPAKG2 (FP). The relative proportion (in %) of total time required to perform the preparation phase and to count is reported inside the bars.
Fig 3.
The reading time as a function of the number of STH eggs counted in a sample.
This figure represents the reading time as a function of the number of STH eggs counted in a sample for single Kato-Katz (KK), Mini-FLOTAC and FECPAKG2 separately. All egg counts represent raw egg counts (not in eggs per gram of stool). The red line represents the linear regression line. The function of the regression line is also provided.
Table 2.
Overview of parameters that determine the time required to process a single stool sample.
Table 3.
Overview of cost per unit of consumables, salary and travel.
Fig 4.
The failure rate, the probability of correctly concluding reduced drug efficacy and the total survey cost across six survey designs.
This figure shows the impact of the survey design and sample size on the failure rate (Panel A), probability of correctly detecting truly reduced efficacy (probreduced; Panel B) and the mean total survey cost (costtotal; Panel C). To gain more insights into the most cost-efficient survey design, the probability of correctly detecting reduced drug efficacy probreduced was plotted as a function of the mean costtotal (Panel D). For each of the four panels, we only consider the use of Kato-Katz in areas with low levels of hookworm infection (mean FEC = 3.7 EPG). NS = no selection; SS = screen and select; SSR = screen, select, and retest. Note, for panel A, all survey designs other than SS1x2/1x2 are identical to SSR1x1/1x2.
Fig 5.
The probability of correctly detecting presence of reduced drug efficacy and the total survey cost for three FEC methods across six survey designs.
This figure plots the probability of correctly identifying reduced therapeutic efficacy (probreduced) as a function of the mean total survey costs (costtotal) for the three different FEC methods (Kato-Katz thick smear (KK), Mini-FLOTAC and FECPAKG2; colored lines) and six survey designs (different panels). For each panel, we only consider areas that are low endemic for hookworm (mean FEC = 3.7 EPG). NS = no selection; SS = screen and select; SSR = screen, select, and retest.
Fig 6.
The probability of correctly detecting presence of reduced drug efficacy and the total survey cost for six survey designs across four levels of endemicity when deploying Kato-Katz.
This figure plots the probability of correctly identifying reduced therapeutic efficacy (probreduced) as a function of the mean total survey costs (costtotal) across six survey designs for the three soil-transmitted helminth species and four levels of endemicity (see Table 1).
Table 4.
The most cost-efficient survey design and FEC method to monitor the therapeutic drug efficacy against STHs.