PURPOSE: Trachoma is the leading cause of blindness from infection worldwide. Treatment programs require accurate Chlamydia trachomatis infection prevalence rates to guide decision making. The use of clinical examination is by far the most common way to monitor activity, but may yield overestimates of infection prevalence. Laboratory testing on individual specimens such as polymerase chain reaction (PCR) is highly sensitive and specific, but prohibitively expensive. Here we demonstrate simulations of pooled PCR results may estimate infection prevalence of an entire community yielding substantial cost savings if pool size is chosen correctly. METHODS: Community infection prevalence was estimated using maximum likelihood estimation with data collected from a previously described study. Simulations for communities were performed to determine the accuracy of prevalence estimation using pooled results. The root mean squared error was then used to determine an acceptable inaccuracy in estimates allowing for a pooling strategy to be formed. RESULTS: Results from simulations and empirical data suggest optimum pooling strategies to estimate community infection prevalence while keeping the root mean squared error of the estimate below 2%. Reduction of PCR testing which permits cost savings is shown to be between 5 and 80% given a community infection prevalence below 60%. CONCLUSIONS: Pooling specimens for PCR testing often provides enough data to accurately estimate infection prevalence at the community level.
PURPOSE:Trachoma is the leading cause of blindness from infection worldwide. Treatment programs require accurate Chlamydia trachomatis infection prevalence rates to guide decision making. The use of clinical examination is by far the most common way to monitor activity, but may yield overestimates of infection prevalence. Laboratory testing on individual specimens such as polymerase chain reaction (PCR) is highly sensitive and specific, but prohibitively expensive. Here we demonstrate simulations of pooled PCR results may estimate infection prevalence of an entire community yielding substantial cost savings if pool size is chosen correctly. METHODS: Community infection prevalence was estimated using maximum likelihood estimation with data collected from a previously described study. Simulations for communities were performed to determine the accuracy of prevalence estimation using pooled results. The root mean squared error was then used to determine an acceptable inaccuracy in estimates allowing for a pooling strategy to be formed. RESULTS: Results from simulations and empirical data suggest optimum pooling strategies to estimate community infection prevalence while keeping the root mean squared error of the estimate below 2%. Reduction of PCR testing which permits cost savings is shown to be between 5 and 80% given a community infection prevalence below 60%. CONCLUSIONS: Pooling specimens for PCR testing often provides enough data to accurately estimate infection prevalence at the community level.
Authors: Catherine E Oldenburg; Abdou Amza; Boubacar Kadri; Beido Nassirou; Sun Y Cotter; Nicole E Stoller; Sheila K West; Robin L Bailey; Travis C Porco; Bruce D Gaynor; Jeremy D Keenan; Thomas M Lietman Journal: Am J Trop Med Hyg Date: 2017-12-14 Impact factor: 2.345
Authors: Stanislaw P Stawicki; Rebecca Jeanmonod; Andrew C Miller; Lorenzo Paladino; David F Gaieski; Anna Q Yaffee; Annelies De Wulf; Joydeep Grover; Thomas J Papadimos; Christina Bloem; Sagar C Galwankar; Vivek Chauhan; Michael S Firstenberg; Salvatore Di Somma; Donald Jeanmonod; Sona M Garg; Veronica Tucci; Harry L Anderson; Lateef Fatimah; Tamara J Worlton; Siddharth P Dubhashi; Krystal S Glaze; Sagar Sinha; Ijeoma Nnodim Opara; Vikas Yellapu; Dhanashree Kelkar; Ayman El-Menyar; Vimal Krishnan; S Venkataramanaiah; Yan Leyfman; Hassan Ali Saoud Al Thani; Prabath Wb Nanayakkara; Sudip Nanda; Eric Cioè-Peña; Indrani Sardesai; Shruti Chandra; Aruna Munasinghe; Vibha Dutta; Silvana Teixeira Dal Ponte; Ricardo Izurieta; Juan A Asensio; Manish Garg Journal: J Glob Infect Dis Date: 2020-05-22
Authors: Abdou Amza; Boubacar Kadri; Beido Nassirou; Sun Y Cotter; Nicole E Stoller; Sheila K West; Robin L Bailey; Travis C Porco; Bruce D Gaynor; Jeremy D Keenan; Thomas M Lietman; Catherine E Oldenburg Journal: Br J Ophthalmol Date: 2017-09-11 Impact factor: 4.638
Authors: Jennifer R Evans; Anthony W Solomon; Rahul Kumar; Ángela Perez; Balendra P Singh; Rajat Mohan Srivastava; Emma Harding-Esch Journal: Cochrane Database Syst Rev Date: 2019-09-26
Authors: Bidya Prasad Pant; Ramesh C Bhatta; J S P Chaudhary; Suresh Awasthi; Sailesh Mishra; Shekhar Sharma; Puja A Cuddapah; Sarah E Gwyn; Nicole E Stoller; Diana L Martin; Jeremy D Keenan; Thomas M Lietman; Bruce D Gaynor Journal: PLoS Negl Trop Dis Date: 2016-02-12
Authors: Scott D Nash; Aisha E P Stewart; Tigist Astale; Eshetu Sata; Mulat Zerihun; Demelash Gessese; Berhanu Melak; Gedefaw Ayenew; Zebene Ayele; Belay Bayissasse; Melsew Chanyalew; Zerihun Tadesse; E Kelly Callahan Journal: Trans R Soc Trop Med Hyg Date: 2018-12-01 Impact factor: 2.184
Authors: Laura G Senyonjo; Oscar Debrah; Diana L Martin; Adwoa Asante-Poku; Stephanie J Migchelsen; Sarah Gwyn; Dzeidzom K deSouza; Anthony W Solomon; David Agyemang; Nana Biritwum-Kwadwo; Benjamin Marfo; Didier Bakajika; Ernest O Mensah; Agatha Aboe; Joseph Koroma; James Addy; Robin Bailey Journal: PLoS Negl Trop Dis Date: 2018-12-14
Authors: Scott D Nash; Aisha E P Stewart; Mulat Zerihun; Eshetu Sata; Demelash Gessese; Berhanu Melak; Tekola Endeshaw; Melsew Chanyalew; Ambahun Chernet; Belay Bayissasse; Jeanne Moncada; Thomas M Lietman; Paul M Emerson; Jonathan D King; Zerihun Tadesse; E Kelly Callahan Journal: Clin Infect Dis Date: 2018-11-28 Impact factor: 9.079
Authors: Jessica S Kim; Catherine E Oldenburg; Gretchen Cooley; Abdou Amza; Boubacar Kadri; Baido Nassirou; Sun Yu Cotter; Nicole E Stoller; Sheila K West; Robin L Bailey; Jeremy D Keenan; Bruce D Gaynor; Travis C Porco; Thomas M Lietman; Diana L Martin Journal: PLoS Negl Trop Dis Date: 2019-01-28