1. Epidemiology and Global Health
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Enteropathogen antibody dynamics and force of infection among children in low-resource settings

  1. Benjamin F Arnold  Is a corresponding author
  2. Diana L Martin
  3. Jane Juma
  4. Harran Mkocha
  5. John B Ochieng
  6. Gretchen M Cooley
  7. Richard Omore
  8. E Brook Goodhew
  9. Jamae F Morris
  10. Veronica Costantini
  11. Jan Vinjé
  12. Patrick J Lammie
  13. Jeffrey W Priest
  1. University of California, Berkeley, United States
  2. United States Centers for Disease Control and Prevention, United States
  3. Kenya Medical Research Institute, Kenya
  4. Kongwa Trachoma Project, United Republic of Tanzania
  5. Georgia State University, United States
  6. Task Force for Global Health, United States
Research Article
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Cite this article as: eLife 2019;8:e45594 doi: 10.7554/eLife.45594

Abstract

Little is known about enteropathogen seroepidemiology among children in low-resource settings. We measured serological IgG responses to eight enteropathogens (Giardia intestinalis, Cryptosporidium parvum, Entamoeba histolytica, Salmonella enterica, enterotoxigenic Escherichia coli, Vibrio cholerae, Campylobacter jejuni, norovirus) in cohorts from Haiti, Kenya, and Tanzania. We studied antibody dynamics and force of infection across pathogens and cohorts. Enteropathogens shared common seroepidemiologic features that enabled between-pathogen comparisons of transmission. Overall, exposure was intense: for most pathogens the window of primary infection was <3 years old; for highest transmission pathogens primary infection occurred within the first year. Longitudinal profiles demonstrated significant IgG boosting and waning above seropositivity cutoffs, underscoring the value of longitudinal designs to estimate force of infection. Seroprevalence and force of infection were rank-preserving across pathogens, illustrating the measures provide similar information about transmission heterogeneity. Our findings suggest antibody response can be used to measure population-level transmission of diverse enteropathogens in serologic surveillance.

Data availability

Analyses were conducted in R version 3.5.3. Data and computational notebooks used to complete the analyses are available through GitHub and the Open Science Framework (osf.io/r4av7).

The following data sets were generated

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Author details

  1. Benjamin F Arnold

    Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States
    For correspondence
    benarnold@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6105-7295

  2. Diana L Martin

    Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Jane Juma

    Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
    Competing interests
    The authors declare that no competing interests exist.

  4. Harran Mkocha

    Kongwa Trachoma Project, Kongwa, United Republic of Tanzania
    Competing interests
    The authors declare that no competing interests exist.

  5. John B Ochieng

    Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
    Competing interests
    The authors declare that no competing interests exist.

  6. Gretchen M Cooley

    Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Richard Omore

    Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
    Competing interests
    The authors declare that no competing interests exist.

  8. E Brook Goodhew

    Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Jamae F Morris

    Department of African-American Studies, Georgia State University, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Veronica Costantini

    Division of Viral Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  11. Jan Vinjé

    Division of Viral Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  12. Patrick J Lammie

    Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, United States
    Competing interests
    The authors declare that no competing interests exist.

  13. Jeffrey W Priest

    Division of Foodborne, Waterborne, and Environmental Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (K01-AI119180)

  • Benjamin F Arnold

Bill and Melinda Gates Foundation (OPP1022543)

  • Patrick J Lammie

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: In Haiti, the human subjects protocol was reviewed and approved by the Ethical Committee of St. Croix Hospital (Leogane, Haiti) and the institutional review board at the US Centers for Disease Control and Prevention (CDC). After listening to an overview of the study, individuals were asked for verbal consent to participate. Verbal consent was deemed appropriate by both review boards because of low literacy rates in the study population. With each longitudinal visit, the study team re-consented participants before specimen collection. Mothers provided consent for children under 7, and children 7 years and older provided additional verbal assent. In Kenya, the human subjects protocol was reviewed and approved by institutional review boards at the Kenya Medical Research Institute (KEMRI) and at the US CDC. Primary caretakers provided written informed consent for their infant child's participation in the trial and blood specimen collection and testing. The original trial was registered at clinicaltrials.org (NCT01695304). In Tanzania, the human subjects protocol was reviewed and approved by the Institute for Medical Research Ethical Review Committee in Dar es Salaam, Tanzania and the institutional review board at the US CDC. Parents of enrolled children provided consent, and children 7 years and older also provided verbal assent before specimen collection.

Reviewing Editor

  1. Mark Jit, London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom

Publication history

  1. Received: January 29, 2019
  2. Accepted: August 15, 2019
  3. Accepted Manuscript published: August 19, 2019 (version 1)
  4. Version of Record published: September 16, 2019 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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    1. Epidemiology and Global Health

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    Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape).

    Methods:

    The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability.

    Results:

    Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively).

    Conclusions:

    The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/).

    Funding:

    DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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    Methods:

    We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

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    Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors.

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