03308nas a2200373 4500000000100000008004100001653001600042653001300058653002100071653001500092653001600107653000900123653001100132653001100143653001100154653002600165653002400191653003300215653002100248653001000269653001000279653001500289100001300304700001500317700001300332700001400345700001400359245014900373856007800522300001000600490000600610520230400616022001402920 2013 d10aYoung Adult10aTrachoma10aSampling Studies10aPrevalence10aMiddle Aged10aMale10aInfant10aHumans10aFemale10aEpidemiologic Methods10aComputer Simulation10aCommunicable Disease Control10aChild, Preschool10aChild10aAdult10aAdolescent1 aSmith JL1 aSturrock H1 aOlives C1 aSolomon A1 aBrooker S00aComparing the performance of cluster random sampling and integrated threshold mapping for targeting trachoma control, using computer simulation. uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3749968/pdf/pntd.0002389.pdf ae23890 v73 a
BACKGROUND: Implementation of trachoma control strategies requires reliable district-level estimates of trachomatous inflammation-follicular (TF), generally collected using the recommended gold-standard cluster randomized surveys (CRS). Integrated Threshold Mapping (ITM) has been proposed as an integrated and cost-effective means of rapidly surveying trachoma in order to classify districts according to treatment thresholds. ITM differs from CRS in a number of important ways, including the use of a school-based sampling platform for children aged 1-9 and a different age distribution of participants. This study uses computerised sampling simulations to compare the performance of these survey designs and evaluate the impact of varying key parameters.
METHODOLOGY/PRINCIPAL FINDINGS: Realistic pseudo gold standard data for 100 districts were generated that maintained the relative risk of disease between important sub-groups and incorporated empirical estimates of disease clustering at the household, village and district level. To simulate the different sampling approaches, 20 clusters were selected from each district, with individuals sampled according to the protocol for ITM and CRS. Results showed that ITM generally under-estimated the true prevalence of TF over a range of epidemiological settings and introduced more district misclassification according to treatment thresholds than did CRS. However, the extent of underestimation and resulting misclassification was found to be dependent on three main factors: (i) the district prevalence of TF; (ii) the relative risk of TF between enrolled and non-enrolled children within clusters; and (iii) the enrollment rate in schools.
CONCLUSIONS/SIGNIFICANCE: Although in some contexts the two methodologies may be equivalent, ITM can introduce a bias-dependent shift as prevalence of TF increases, resulting in a greater risk of misclassification around treatment thresholds. In addition to strengthening the evidence base around choice of trachoma survey methodologies, this study illustrates the use of a simulated approach in addressing operational research questions for trachoma but also other NTDs.
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