Cargando…

Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation

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 inte...

Descripción completa

Detalles Bibliográficos
Autores principales: Smith, Jennifer L., Sturrock, Hugh J. W., Olives, Casey, Solomon, Anthony W., Brooker, Simon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3749968/
https://www.ncbi.nlm.nih.gov/pubmed/23991238
http://dx.doi.org/10.1371/journal.pntd.0002389
_version_ 1782477052680601600
author Smith, Jennifer L.
Sturrock, Hugh J. W.
Olives, Casey
Solomon, Anthony W.
Brooker, Simon J.
author_facet Smith, Jennifer L.
Sturrock, Hugh J. W.
Olives, Casey
Solomon, Anthony W.
Brooker, Simon J.
author_sort Smith, Jennifer L.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-3749968
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-37499682013-08-29 Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation Smith, Jennifer L. Sturrock, Hugh J. W. Olives, Casey Solomon, Anthony W. Brooker, Simon J. PLoS Negl Trop Dis Research Article 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. Public Library of Science 2013-08-22 /pmc/articles/PMC3749968/ /pubmed/23991238 http://dx.doi.org/10.1371/journal.pntd.0002389 Text en © 2013 Smith et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Smith, Jennifer L.
Sturrock, Hugh J. W.
Olives, Casey
Solomon, Anthony W.
Brooker, Simon J.
Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title_full Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title_fullStr Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title_full_unstemmed Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title_short Comparing the Performance of Cluster Random Sampling and Integrated Threshold Mapping for Targeting Trachoma Control, Using Computer Simulation
title_sort comparing the performance of cluster random sampling and integrated threshold mapping for targeting trachoma control, using computer simulation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3749968/
https://www.ncbi.nlm.nih.gov/pubmed/23991238
http://dx.doi.org/10.1371/journal.pntd.0002389
work_keys_str_mv AT smithjenniferl comparingtheperformanceofclusterrandomsamplingandintegratedthresholdmappingfortargetingtrachomacontrolusingcomputersimulation
AT sturrockhughjw comparingtheperformanceofclusterrandomsamplingandintegratedthresholdmappingfortargetingtrachomacontrolusingcomputersimulation
AT olivescasey comparingtheperformanceofclusterrandomsamplingandintegratedthresholdmappingfortargetingtrachomacontrolusingcomputersimulation
AT solomonanthonyw comparingtheperformanceofclusterrandomsamplingandintegratedthresholdmappingfortargetingtrachomacontrolusingcomputersimulation
AT brookersimonj comparingtheperformanceofclusterrandomsamplingandintegratedthresholdmappingfortargetingtrachomacontrolusingcomputersimulation