Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data.
Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Efficient identification of communities with high infection burden could help target more intensive control efforts. We hypothesized that IgG seroprevalence in c...
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ftdoajarticles:oai:doaj.org/article:157f2843a5524a1b80ff995075f99447 2023-05-15T15:12:58+02:00 Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. Christine Tedijanto Solomon Aragie Zerihun Tadesse Mahteme Haile Taye Zeru Scott D Nash Dionna M Wittberg Sarah Gwyn Diana L Martin Hugh J W Sturrock Thomas M Lietman Jeremy D Keenan Benjamin F Arnold 2022-03-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0010273 https://doaj.org/article/157f2843a5524a1b80ff995075f99447 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0010273 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010273 https://doaj.org/article/157f2843a5524a1b80ff995075f99447 PLoS Neglected Tropical Diseases, Vol 16, Iss 3, p e0010273 (2022) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1371/journal.pntd.0010273 2022-12-30T21:15:28Z Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Efficient identification of communities with high infection burden could help target more intensive control efforts. We hypothesized that IgG seroprevalence in combination with geospatial layers, machine learning, and model-based geostatistics would be able to accurately predict future community-level ocular Ct infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia to assess this hypothesis. Median Ct infection prevalence among children 0-5 years old increased from 6% at enrollment, in the context of recent mass drug administration (MDA), to 29% by month 36, following three years without MDA. At baseline, correlation between seroprevalence and Ct infection was stronger among children 0-5 years old (ρ = 0.77) than children 6-9 years old (ρ = 0.48), and stronger than the correlation between active trachoma and Ct infection (0-5y ρ = 0.56; 6-9y ρ = 0.40). Seroprevalence was the strongest concurrent predictor of infection prevalence at month 36 among children 0-5 years old (cross-validated R2 = 0.75, 95% CI: 0.58-0.85), though predictive performance declined substantially with increasing temporal lag between predictor and outcome measurements. Geospatial variables, a spatial Gaussian process, and stacked ensemble machine learning did not meaningfully improve predictions. Serological markers among children 0-5 years old may be an objective tool for identifying communities with high levels of ocular Ct infections, but accurate, future prediction in the context of changing transmission remains an open challenge. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 16 3 e0010273 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Christine Tedijanto Solomon Aragie Zerihun Tadesse Mahteme Haile Taye Zeru Scott D Nash Dionna M Wittberg Sarah Gwyn Diana L Martin Hugh J W Sturrock Thomas M Lietman Jeremy D Keenan Benjamin F Arnold Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Efficient identification of communities with high infection burden could help target more intensive control efforts. We hypothesized that IgG seroprevalence in combination with geospatial layers, machine learning, and model-based geostatistics would be able to accurately predict future community-level ocular Ct infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia to assess this hypothesis. Median Ct infection prevalence among children 0-5 years old increased from 6% at enrollment, in the context of recent mass drug administration (MDA), to 29% by month 36, following three years without MDA. At baseline, correlation between seroprevalence and Ct infection was stronger among children 0-5 years old (ρ = 0.77) than children 6-9 years old (ρ = 0.48), and stronger than the correlation between active trachoma and Ct infection (0-5y ρ = 0.56; 6-9y ρ = 0.40). Seroprevalence was the strongest concurrent predictor of infection prevalence at month 36 among children 0-5 years old (cross-validated R2 = 0.75, 95% CI: 0.58-0.85), though predictive performance declined substantially with increasing temporal lag between predictor and outcome measurements. Geospatial variables, a spatial Gaussian process, and stacked ensemble machine learning did not meaningfully improve predictions. Serological markers among children 0-5 years old may be an objective tool for identifying communities with high levels of ocular Ct infections, but accurate, future prediction in the context of changing transmission remains an open challenge. |
format |
Article in Journal/Newspaper |
author |
Christine Tedijanto Solomon Aragie Zerihun Tadesse Mahteme Haile Taye Zeru Scott D Nash Dionna M Wittberg Sarah Gwyn Diana L Martin Hugh J W Sturrock Thomas M Lietman Jeremy D Keenan Benjamin F Arnold |
author_facet |
Christine Tedijanto Solomon Aragie Zerihun Tadesse Mahteme Haile Taye Zeru Scott D Nash Dionna M Wittberg Sarah Gwyn Diana L Martin Hugh J W Sturrock Thomas M Lietman Jeremy D Keenan Benjamin F Arnold |
author_sort |
Christine Tedijanto |
title |
Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
title_short |
Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
title_full |
Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
title_fullStr |
Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
title_full_unstemmed |
Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
title_sort |
predicting future community-level ocular chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pntd.0010273 https://doaj.org/article/157f2843a5524a1b80ff995075f99447 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 16, Iss 3, p e0010273 (2022) |
op_relation |
https://doi.org/10.1371/journal.pntd.0010273 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010273 https://doaj.org/article/157f2843a5524a1b80ff995075f99447 |
op_doi |
https://doi.org/10.1371/journal.pntd.0010273 |
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PLOS Neglected Tropical Diseases |
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16 |
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3 |
container_start_page |
e0010273 |
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