Title |
Pooled PCR testing strategy and prevalence estimation of submicroscopic infections using Bayesian latent class models in pregnant women receiving intermittent preventive treatment at Machinga District Hospital, Malawi, 2010
|
---|---|
Published in |
Malaria Journal, December 2014
|
DOI | 10.1186/1475-2875-13-509 |
Pubmed ID | |
Authors |
Zhiyong Zhou, Rebecca Mans Mitchell, Julie Gutman, Ryan E Wiegand, Dyson A Mwandama, Don P Mathanga, Jacek Skarbinski, Ya Ping Shi |
Abstract |
Low malaria parasite densities in pregnancy are a diagnostic challenge. PCR provides high sensitivity and specificity in detecting low density of parasites, but cost and technical requirements limit its application in resources-limited settings. Pooling samples for PCR detection was explored to estimate prevalence of submicroscopic malaria infection in pregnant women at delivery. Previous work uses gold-standard based methods to calculate sensitivity and specificity of tests, creating a challenge when newer methodologies are substantially more sensitive than the gold standard. Thus prevalence was estimated using Bayesian latent class models (LCMs) in this study. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Turkey | 1 | 33% |
United States | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Burkina Faso | 1 | 1% |
Unknown | 74 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 14 | 19% |
Researcher | 14 | 19% |
Student > Bachelor | 7 | 9% |
Student > Postgraduate | 5 | 7% |
Student > Doctoral Student | 5 | 7% |
Other | 16 | 21% |
Unknown | 14 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 20 | 27% |
Agricultural and Biological Sciences | 11 | 15% |
Nursing and Health Professions | 5 | 7% |
Design | 4 | 5% |
Veterinary Science and Veterinary Medicine | 3 | 4% |
Other | 17 | 23% |
Unknown | 15 | 20% |