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...

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Published inPLoS neglected tropical diseases Vol. 16; no. 3; p. e0010273
Main Authors Tedijanto, Christine, Aragie, Solomon, Tadesse, Zerihun, Haile, Mahteme, Zeru, Taye, Nash, Scott D., Wittberg, Dionna M., Gwyn, Sarah, Martin, Diana L., Sturrock, Hugh J. W., Lietman, Thomas M., Keenan, Jeremy D., Arnold, Benjamin F.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.03.2022
Public Library of Science (PLoS)
Subjects
Age groups
Analysis
Anti-Bacterial Agents - therapeutic use
Antibiotics
Antigens
Azithromycin
Biology and Life Sciences
Blindness
Care and treatment
Child
Child, Preschool
Children
Chlamydia
Chlamydia trachomatis
Complications and side effects
Computer and Information Sciences
Context
Correlation
Development and progression
Disease transmission
Ethiopia - epidemiology
Gaussian process
Geospatial data
Geostatistics
Humans
Immunoglobulin G
Infant
Infant, Newborn
Infections
Infectious diseases
Inflammation
Learning algorithms
Machine learning
Medicine and Health Sciences
Methods
Nucleotide sequence
PCR
People and Places
Performance prediction
Physical Sciences
Prevalence
Prevention
Public health
Research and Analysis Methods
Risk factors
Sanitation
Seroepidemiologic Studies
Serology
Sexually transmitted diseases
Spatial data
STD
Trachoma
Trachoma - prevention & control
Transmission
Tropical diseases
Ethiopia
Africa
Online AccessGet full text
ISSN1935-2735
1935-2727
1935-2735
DOI10.1371/journal.pntd.0010273

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Abstract 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 R 2 = 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.
AbstractList 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.
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 ([rho] = 0.77) than children 6-9 years old ([rho] = 0.48), and stronger than the correlation between active trachoma and Ct infection (0-5y [rho] = 0.56; 6-9y [rho] = 0.40). Seroprevalence was the strongest concurrent predictor of infection prevalence at month 36 among children 0-5 years old (cross-validated R.sup.2 = 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.
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 R 2 = 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.
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.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.
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. Trachoma, one of the leading infectious causes of blindness globally, is targeted for elimination as a public health problem by 2030. District-level estimates of active trachoma among children 1–9 years old are currently used to guide control programs and assess elimination. However, active trachoma, based on diagnosis of clinical signs, is a subjective indicator. Serological markers present an objective, scalable alternative that could be measured in integrated platforms. In a hyperendemic region, community-level seroprevalence aligned more closely with concurrent infection prevalence than active trachoma. The correlation between seroprevalence and infection prevalence was stronger among 0–5-year-olds compared to 6–9-year-olds and was consistent over a three-year period of increasing transmission. Serosurveillance among children 0–5 years old may be a promising monitoring strategy to identify communities with the highest burdens of ocular chlamydial infection.
Audience Academic
Author Wittberg, Dionna M.
Gwyn, Sarah
Tedijanto, Christine
Sturrock, Hugh J. W.
Zeru, Taye
Nash, Scott D.
Martin, Diana L.
Lietman, Thomas M.
Arnold, Benjamin F.
Tadesse, Zerihun
Haile, Mahteme
Keenan, Jeremy D.
Aragie, Solomon
AuthorAffiliation King Saud University College of Medicine, SAUDI ARABIA
1 Francis I. Proctor Foundation, University of California, San Francisco, California, United States of America
3 Amhara Public Health Institute, Bahir Dar, Ethiopia
4 The Carter Center, Atlanta, Georgia, United States of America
5 Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
7 Department of Ophthalmology, University of California, San Francisco, California, United States of America
6 Locational, Poole, United Kingdom
9 Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
2 The Carter Center Ethiopia, Addis Ababa, Ethiopia
8 Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/35275911$$D View this record in MEDLINE/PubMed
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crossref_primary_10_1371_journal_pone_0287464
crossref_primary_10_1093_infdis_jiad602
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DocumentTitleAlternate Prediction of ocular Chlamydia trachomatis infection prevalence
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Snippet Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis ( Ct ) that may ultimately lead to blindness. Efficient...
Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Efficient...
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SubjectTerms Age groups
Analysis
Anti-Bacterial Agents - therapeutic use
Antibiotics
Antigens
Azithromycin
Biology and Life Sciences
Blindness
Care and treatment
Child
Child, Preschool
Children
Chlamydia
Chlamydia trachomatis
Complications and side effects
Computer and Information Sciences
Context
Correlation
Development and progression
Disease transmission
Ethiopia - epidemiology
Gaussian process
Geospatial data
Geostatistics
Humans
Immunoglobulin G
Infant
Infant, Newborn
Infections
Infectious diseases
Inflammation
Learning algorithms
Machine learning
Medicine and Health Sciences
Methods
Nucleotide sequence
PCR
People and Places
Performance prediction
Physical Sciences
Prevalence
Prevention
Public health
Research and Analysis Methods
Risk factors
Sanitation
Seroepidemiologic Studies
Serology
Sexually transmitted diseases
Spatial data
STD
Trachoma
Trachoma - prevention & control
Transmission
Tropical diseases
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Title Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data
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