Diarrhea, which is caused by bacteria such as Salmonella or by viruses like norovirus, is the fourth leading cause of death among children worldwide, with children in low-resource settings being at highest risk. The pathogens that cause diarrhea spread when stool from infected people comes into contact with new hosts, for example, through inadequate sanitation or by drinking contaminated water. Currently, the best way to track these infections is to collect stool samples from people and test them for the presence of the pathogens. Unfortunately, this is costly and difficult to do on a large scale outside of clinical settings, making it hard to track the spread of diarrhea-causing pathogens.

The body produces antibodies – small proteins that can detect specific pathogens – in response to an infection. These antibodies help ward off future infections by the same pathogen, so if they are present in the blood, this indicates a current or previous infection. Scientists already collect blood samples to track malaria, HIV and vaccine-preventable diseases in low-resource settings. These samples could be tested more broadly to measure the levels of antibodies against diarrhea-causing pathogens.

Now, Arnold et al. have used blood samples collected from children in Haiti, Kenya, and Tanzania to measure antibody responses to 8 diarrhea-causing pathogens. The results showed that many children in these settings had been infected with all 8 pathogens before age three, and that all of the pathogens shared similar age-dependent patterns of antibody response. This finding enabled Arnold et al. to combine antibody measurements with statistical models to estimate each pathogen’s force of infection, that is, the rate at which susceptible individuals in the population become infected. This is a key step for epidemiologists to understand which pathogens cause the most infections in a population.

The experiments show that testing blood samples for antibodies could provide scientists with a new tool to track the transmission of diarrhea-causing pathogens in low-resource settings. This information could help public health officials design and test efforts to prevent diarrhea, for example, by improving water treatment or developing vaccines.

A broad set of viral, bacterial, and parasitic enteropathogens are leading causes of the global infectious disease burden, with the highest burden among young children living in lower income countries (GBD 2016 DALYs and HALE Collaborators, 2016). Infections that result in acute diarrhea and related child deaths drive disease burden estimates attributed to enteropathogens, but asymptomatic infections are extremely common and the full scope of sequelae is only partially understood (Liu et al., 2016; Platts-Mills et al., 2018). Much of what we know about enteropathogen transmission is based on passive clinical surveillance, which reflects a small fraction of all infections. For example, antibody-based incidence of infection to Salmonella enterica and Campylobacter jejuni were 2–6 orders of magnitude higher than case-based surveillance in European populations (Simonsen et al., 2008; Falkenhorst et al., 2012; Teunis et al., 2012; Teunis et al., 2013), and a study of Salmonella enterica serotype Typhi in Fiji found similarly high discordance between antibody-based incidence and case-based surveillance (Watson et al., 2017). A more complete picture of enteropathogen infection in populations would help understand drivers of transmission, disease burden, naturally acquired immunoprotection, as well as to design public health prevention measures, and measure intervention effects.

Stool-based, high-throughput PCR assays have helped solve the logistical difficulties of single-pathogen testing for enterics and have provided new insights into pathogen-specific infections and disease burden (Liu et al., 2016; Platts-Mills et al., 2018). Yet, stool is not routinely collected in population-based surveys, and infection with many globally important enteric pathogens can be sufficiently rare and relatively short-lived to require designs with almost continuous surveillance (Platts-Mills et al., 2018; Lin et al., 2018). At the same time, large-scale serological surveillance platforms create new opportunities for expanded enteropathogen surveillance alongside other infectious diseases (Metcalf et al., 2016; Arnold et al., 2018). These challenges and opportunities have generated interest in antibody-based measurement as a complement to PCR for population-based enteropathogen surveillance (Griffin et al., 2011; Exum et al., 2016; Moss et al., 2014; Arnold et al., 2017), and for endpoints in observational and randomized studies (Crump et al., 2007; Zambrano et al., 2017; Chard et al., 2018; Vargas et al., 2017; Mosites et al., 2018; Wade et al., 2018; Egorov et al., 2018).

After infection, many enteropathogens elicit a transiently elevated antibody response that wanes over time. In lower transmission settings where antibody responses could be monitored longitudinally after distinct infections, Salmonella enterica, Campylobacter jejuni, Cryptosporidium parvum, and Giardia intestinalis (syn. Giardia lamblia, Giardia duodenalis) immunoglobulin G (IgG) levels in blood have been shown to wane over a period of months since infection; IgM and IgA levels decline even more quickly (Teunis et al., 2012; Strid et al., 2001; Priest et al., 2001; Priest et al., 2010; Falkenhorst et al., 2013; Hjøllo et al., 2018). Compared with permanently immunizing infections such as measles, transient immunity adds a layer of complexity to seroepidemiologic inference and methods. To our knowledge, there has been no detailed study of enteropathogen seroepidemiology among children in low-resource settings where transmission is intense beginning early in life (Platts-Mills et al., 2018). Such studies are needed to determine if serology is a viable approach to measure enteropathogen transmission in low-resource settings.

We conducted a series of analyses in cohorts from Haiti, Tanzania, and Kenya that measured serological antibody responses to eight enteropathogens using multiplex bead assays. Our objectives were to identify common patterns in antibody dynamics shared across enteropathogens and populations, and to evaluate serological methods to compare between-pathogen heterogeneity in infection, including estimates of force of infection. Our results provide new insights into the seroepidemiology of enteropathogens among children living in low-resource settings, and contribute advances to inform the design and analysis of surveillance efforts whose goal is to quantify heterogeneity in enteropathogen transmission through antibody response.

In cohorts from Haiti, Kenya, and Tanzania we identified consistent patterns in IgG responses that provide new insights into enteropathogen seroepidemiology among children in low-resource settings. Most population-level heterogeneity in IgG levels and seroconversion was between birth and 3 years, reflecting high transmission and early life primary infection. For particularly high transmission pathogens (e.g., ETEC, Campylobacter), most variation in IgG levels was observed among children < 1 year old. In these study populations, endemic force of infection for enteropathogens was as high, and in many cases several fold higher, than force of infection estimated during epidemics of new dengue serotype introductions in Nicaragua and Peru (approximately 0.4 to 0.6 seroconversions per year) (Katzelnick et al., 2018; Reiner et al., 2014). Significant boosting and waning of antibody levels above seropositivity cutoffs identified through longitudinal profiles in Haiti and Kenya reinforce the value of longitudinal designs to derive antibody distributions among unexposed and to estimate force of infection.

The shift of IgG distributions from bimodal to unimodal for many pathogens (Giardia, Cryptosporidium, E. histolytica, and Campylobacter), resulting from a combination of antibody boosting, waning and acquired immunity, complicates the interpretation of seroprevalence at older ages: among older children a seronegative response could either mean the children were never exposed or they were previously exposed but antibody levels waned below seropositivity cutoffs. The age-dependent shift contrasts with more stable differentiation of seronegative and seropositive groups observed for some other antibody responses (e.g., C. trachomatis pgp3 in Figure 1—figure supplement 2) and likely results from less robust and sustained IgG response following infection. Estimates of IgG half-life in the Haiti cohort were on the order of 10 weeks for most pathogens (Supplementary file 8), implying that time to seroreversion would be approximately 1 year without additional exposure (assuming exponential decay λ = 0.01 corresponding to a 10 week half-life, a starting level of 10,000 MFI, and seropositivity cutoff of 300 MFI; $t=-\log (N_t/N_0)/\lambda$= 350 days). Seroreversion is therefore possible at young ages, but population-level mean IgG levels and seroprevalence declined for only three pathogens, Giardia, ETEC, and Campylobacter, and only above age 3 years (Figure 3, Figure 3—figure supplement 2). Increases in mean IgG levels and seroprevalence with age imply IgG boosting from new infections or repeated infections outpaced IgG decay for all enteropathogens studied until at least age 3 years, and for many pathogens through age 10 years; seroprevalence thus reflects a conservative lower bound of a population’s cumulative exposure over this age range.

The age range over which seroprevalence provided useful epidemiologic information varied by pathogen and cohort. In Haiti, 100% of children were seropositive to ETEC LT B toxin before age 12 months, though seroprevalence did not exceed 90% for most other pathogens until age 5 years in Haiti (Figure 3). In Kenya, the age range of 4–18 months captured wide variation in seroprevalence for most pathogens. The Tanzania study enrolled children ages one and older due to a primary focus on trachoma monitoring, but missed the key window of variation in antibody response for all enteropathogens except E. histolytica and Salmonella (Figure 3—figure supplements 1 and 2). Studies that extend beyond 10 years into adolescence and adulthood would help determine whether enteropathogen seroprevalence remains sufficiently high that it no longer provides useful epidemiologic information. The shift in IgG distributions for some enteropathogens raises the question of whether population mean IgG levels stabilize at a new ‘set point’ with repeated infections as has been observed for dengue serotypes (Salje et al., 2018); if so, then the use of fold-changes in IgG would be preferred to seropositivity cutoffs to identify incident infections among older ages.

In low-resource settings, measuring a sufficient number of young children before primary infection, preferably with longitudinal measurements, will help ensure that within-sample seropositivity cutoff estimation is possible. Two-component mixture models fit the data and provided reasonable cutoff estimates only when restricted to an age range that included clearly delineated subpopulations of seronegative and seropositive responses. For most pathogens studied, this required measurements among children < 1 year old, an age range during which IgG responses still followed a bimodal distribution. The only reliable approach to estimate seropositivity cutoffs for the highest transmission pathogens like ETEC and Campylobacter was to estimate a distribution among presumed unexposed by identifying measurements among children who subsequently experienced a large increase in IgG, a strategy only possible in a longitudinal design. Although not attempted here, studies that wish to compare antibody response and seroprevalence between different sites should use a common assay platform and materials (e.g., shared bead coupling) with jointly estimated seropositivity cutoffs to help ensure comparability across sites, since there are currently no global reference standards to translate arbitrary units into antibody titers for enteropathogens.

Seroepidemiologic measures that can be estimated from cross-sectional surveys are of particular interest for infectious diseases because most large-scale, population-based serosurveillance platforms use cross-sectional designs (Arnold et al., 2018). Our results show that seroprevalence and force of infection estimated from seroprevalence models adequately summarize between-pathogen heterogeneity in transmission when compared with longitudinal estimates of force of infection. Pathogens with fastest rising mean IgG and seroprevalence with age (e.g., ETEC, Campylobacter, norovirus GII.4; Figure 3, Figure 3—figure supplement 1) had highest force of infection measured prospectively over the study period, and seroprevalence was rank-preserving with prospective force of infection in both Haiti and Kenya (Figure 5). Seroprevalence alone thus appears to be sufficient to assess relative pathogen transmission if measured in an age range that captures ample heterogeneity in response (in these cohorts < 3 years old). Our findings align with modeling studies of other infectious diseases such as malaria (Corran et al., 2007), trachoma (Pinsent et al., 2018), and dengue (Katzelnick et al., 2018), and suggest that enteropathogens share similar seroepidemiologic features conducive to population-based surveillance in cross-sectional surveys despite different underlying immunology. Two caveats are that cross-sectional methods considered in this study will generally underestimate force of infection because they fail to rigorously account for seroreversion and incident boosting above seropositivity cutoffs (Figure 6), and seroprevalence is not a clear marker of cumulative exposure as children age beyond the window of primary infection because of waning IgG responses.

The relative intensity of pathogen transmission based on serological measures in this analysis aligns with relative differences in infection from large-scale molecular testing in stool. For example, rapid rises in IgG levels, seroprevalence, and force of infection for ETEC and Campylobacter beginning very early in life is consistent with their prominence in molecular testing of stool specimens in the MAL-ED studies (Platts-Mills et al., 2018; Platts-Mills et al., 2015). Higher force of infection for norovirus GII.4 compared with norovirus GI.4 in Haiti is consistent with the relative prominence of circulating genogroups (Bányai et al., 2018; Rouhani et al., 2016). The results lend additional support to the idea that broader serological testing in existing serosurveillance platforms represents a useful opportunity to measure enteropathogen transmission in populations without routine stool-based testing (Metcalf et al., 2016; Arnold et al., 2018).

These analyses had limitations. High correlation between Salmonella LPS Groups B and D, between ETEC LT B subunit and cholera toxin B subunit, and between norovirus GI and norovirus GII at the individual level (Figure 2) shows that for these pathogens seroepidemiologic analyses will be less specific than analyses based on molecular detection. Salmonella LPS Groups B and D have antigenic overlap in their lipopolysaccharides (Grimont and Weill F-X, 2007), and ETEC LT B subunit and cholera toxin B subunit are known to be immunologically cross-reactive (Smith and Sack, 1973). There was no known cholera transmission in the study populations, so we assumed the elevated responses to the cholera toxin B subunit reflected exposure to LT-producing ETEC; this assumption could only be confirmed with specific measures of cholera infection. Norovirus GI and GII virus-like particles are antigenically different, but cross-reactivity between norovirus genogroups is possible (Tamminen et al., 2012). In addition, a recent study from Uganda found high seroprevalence for both GI and GII norovirus suggesting repeated infections by viruses of the same or different genogroup could potentially boost cross-reactive antibody production (Thorne et al., 2018). Despite lower specificity between genogroups or serogroups in these cases, the consistency of antibody relationships with predictions based on antigenic overlap, and absence of correlation between unrelated antibodies (Supplementary file 3), lends support to the internal validity of the assays.

Our reliance on IgG as a sole measure of immune response did not enable finer distinction between pathogens in age-dependent acquired immunity that might be possible with additional measures of immune function. For example, ETEC LT and Campylobacter IgG levels and seroprevalence consistently rose quickly by age 1 year across all of the cohorts (Figure 3 and its supplements). We interpret the steep rise in IgG early in life to reflect higher transmission and exposure to ETEC and Campylobacter compared with other pathogens studied, but the steeper rise in IgG levels could also reflect a difference in immune mechanism. For example, it could reflect a more robust response to T-cell dependent antigens compared with T-cell independent antigens (e.g., Salmonella LPS), which might require a more mature immune system to mount a robust B-cell response (Siegrist and Aspinall, 2009). Yet, the steep rise in Salmonella LPS IgG among Haitian children (Figure 3), and the faster age-dependent rise in IgG response to Salmonella LPS compared with ETEC LT B subunit among children in the United States (Arnold et al., 2017) suggest that differences in IgG acquisition by age likely reflect differences in early life pathogen exposure.

Children were measured relatively infrequently in the Kenya and Haiti cohorts, which could have led to missed seroconversions if IgG levels waned below the seropositivity cutoff before a child’s next measurement. Our simulations that studied the effect of more frequent sampling showed that monthly measurement could lead to higher force of infection estimates in settings with frequent exposure, and that more frequent measurements would be most valuable for highest transmission pathogens, such as ETEC and Campylobacter in this study (Figure 7). Higher resolution longitudinal measurements would also enable study of seasonal differences in enteropathogen transmission. Measurement timing in analysis populations precluded extensive evaluation of seasonal differences in IgG response, but age-adjusted seroprevalence was 1–5% higher across pathogens during the wet season in the Haiti cohort (Supplementary file 1). This exploratory result suggests a potential avenue of future research that could join high resolution temperature, precipitation, and antibody measurements to study seasonal drivers of enteropathogen transmission dynamics.

Another limitation is that without measures of patent infection in stool we were unable to compare multiple independent measures of transmission. Paired stool and blood specimens could help resolve some of the questions raised above related to serological assay specificity and differential immune development across pathogens. The Haiti and Tanzania cohorts were not originally designed to assess enteropathogens, and antibody measurements were not paired with measures of clinical symptoms of diarrhea or with measures of patent infection in stool. In Haiti, Moss et al. (2014) previously reported limited microscopy testing for protozoan cysts alongside IgG markers, but infrequent stool measurements prevented extensive comparisons. In Kenya, diarrheal stool testing during weekly follow-up visits for Cryptosporidium and Giardia showed that serology identified nearly all infections detected in stool, plus a substantial number of presumed infections not detected through diarrheal surveillance (Supplementary file 4); however, for other pathogens studied we did not have stool-based measures of infection. All included antigens have been characterized extensively with respect to patent infections among adults and children in other settings (details in Materials and methods). Furthermore, we observed high levels of consistency across cohorts in seroepidemiologic patterns, and consistency with general age-dependent patterns documented across diverse pathogens for which transmission begins early in life (Arnold et al., 2017; Hens et al., 2012; Corran et al., 2007; Katzelnick et al., 2018). Together, these observations suggest that the antibody dynamics and force of infection estimates from IgG responses reflect actual pathogen transmission in these cohorts, but paired stool and blood testing would provide a more definitive test.

High-resolution, longitudinal assessment of paired enteropathogen infection and antibody measurements among children could provide valuable, additional insights into the pathogen-specific antibody dynamics following primary- and secondary infections. Analyses of Plasmodium falciparum (White et al., 2014; Helb et al., 2015; Rodriguez-Barraquer et al., 2018) and dengue virus (Salje et al., 2018) illustrate how paired, longitudinal measurements of patent infection and antibody response enable richer characterizations of antibody dynamics and pathogen transmission. Paired longitudinal stool and antibody testing among children and adults would enable studies of age-dependent enteropathogen antibody kinetics, and if present, disentangle them from age-varying incidence rates when using serology to estimate force of infection. Studies that pair stool-based molecular testing with antibody response could also assess whether approaches to estimate enteropathogen incidence from cross-sectional samples that rely on estimates of antibody decay with time since infection (Teunis et al., 2012; Simonsen et al., 2009) could be used among children in low-resource settings. A similar approach was recently described to estimate cholera incidence among all ages (Azman et al., 2019), but broader development across enteropathogens and among young children remains an open area of research.

Analyses were conducted in R version 3.5.3. Data and computational notebooks used to complete the analyses are available through GitHub (https://github.com/ben-arnold/enterics-seroepi; copy archived at https://github.com/elifesciences-publications/enterics-seroepi) and the Open Science Framework (osf.io/r4av7).

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Author details

1. Benjamin F Arnold

   1. Division of Epidemiology and Biostatistics, University of California, Berkeley, Berkeley, United States
   2. Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, United States
   3. Department of Ophthalmology, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   ben.arnold@ucsf.edu

   Competing interests

   No competing interests declared

   "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

   Contribution

   Resources, Data curation, Supervision, Validation, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Jane Juma

   Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya

   Contribution

   Resources, Validation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Harran Mkocha

   Kongwa Trachoma Project, Kongwa, United Republic of Tanzania

   Contribution

   Resources, Validation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. John B Ochieng

   Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya

   Contribution

   Supervision, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

6. Gretchen M Cooley

   Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States

   Contribution

   Resources, Supervision, Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Richard Omore

   Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya

   Contribution

   Resources, Validation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

8. E Brook Goodhew

   Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States

   Contribution

   Resources, Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

9. Jamae F Morris

   Department of African-American Studies, Georgia State University, Atlanta, United States

   Contribution

   Resources, Data curation, Supervision, Investigation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

10. Veronica Costantini

    Division of Viral Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States

    Contribution

    Resources, Validation, Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

11. Jan Vinjé

    Division of Viral Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States

    Contribution

    Resources, Validation, Writing—review and editing

    Competing interests

    No competing interests declared

12. Patrick J Lammie

    1. Division of Parasitic Diseases and Malaria, United States Centers for Disease Control and Prevention, Atlanta, United States
    2. Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

13. Jeffrey W Priest

    Division of Foodborne, Waterborne, and Environmental Diseases, United States Centers for Disease Control and Prevention, Atlanta, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing

    Competing interests

    No competing interests declared

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