Population Enumeration and Household Utilization Survey Methods in the Enterics for Global Health (EFGH): Shigella Surveillance Study

Background

Accurate estimation of diarrhea incidence from facility-based surveillance requires estimating the population at risk and accounting for case patients who do not seek care. The Enterics for Global Health (EFGH) Shigella surveillance study will characterize population denominators and healthcare-seeking behavior proportions to calculate incidence rates of Shigella diarrhea in children aged 6-35 months across 7 sites in Africa, Asia, and Latin America.

Methods

The Enterics for Global Health (EFGH) Shigella surveillance study will use a hybrid surveillance design, supplementing facility-based surveillance with population-based surveys to estimate population size and the proportion of children with diarrhea brought for care at EFGH health facilities. Continuous data collection over a 24 month period captures seasonality and ensures representative sampling of the population at risk during the period of facility-based enrollments. Study catchment areas are broken into randomized clusters, each sized to be feasibly enumerated by individual field teams.

Conclusions

The methods presented herein aim to minimize the challenges associated with hybrid surveillance, such as poor parity between survey area coverage and facility coverage, population fluctuations, seasonal variability, and adjustments to care-seeking behavior.

The Enterics for Global Health (EFGH) Shigella Surveillance Study in Peru

Background

The Enterics for Global Health (EFGH) Peru site will enroll subjects in a periurban area of the low Amazon rainforest. The political department of Loreto lags behind most of Peru in access to improved sources of water and sanitation, per capita income, children born <2.5 kg, and infant and child mortality. Chronic undernutrition as manifested by linear growth shortfalls is common, but wasting and acute malnutrition are not.

Methods

The recruitment of children seeking care for acute diarrheal disease takes place at a geographic cluster of government-based primary care centers in an area where most residents are beneficiaries of free primary healthcare.

Results

Rates of diarrheal disease, dysentery, and Shigella are known to be high in the region, with some of the highest rates of disease documented in the literature and little evidence in improvement over the last 2 decades. This study will update estimates of shigellosis by measuring the prevalence of Shigella by polymerase chain reaction and culture in children seeking care and deriving population-based estimates by measuring healthcare seeking at the community level.

Conclusions

Immunization has been offered universally against rotavirus in the region since 2009, and in a context where adequate water and sanitation are unlikely to obtain high standards in the near future, control of principal enteropathogens through immunization may be the most feasible way to decrease the high burden of disease in the area in the near future.

The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

BACKGROUND

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

METHODS

The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

DISCUSSION

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

STUDY REGISTRATION

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.

Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea

Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Etiology of acute febrile illness in the peruvian amazon as determined by modular formatted quantitative PCR: a protocol for RIVERA, a health facility-based case-control study

BACKGROUND

The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI.

METHODS

A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21-28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI.

DISCUSSION

The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 h and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific prevalent pathogens as a cause of acute illness.

STUDY REGISTRATION

Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

Etiology of Acute Febrile Illness in the Peruvian Amazon as determined by modular formatted quantitative PCR: A Protocol for RIVERA, a Health Facility-Based Case-Control Study

Background

The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI.

Methods

A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21-28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI.

Discussion

The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 hours and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific, prevalent pathogens as a cause of acute illness.

Study Registration

Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility

Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (R _t ) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire 15-month period and several shorter, 3-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and R _t . We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on R _t varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis

Background

The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.

Methods

Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted.

Findings

Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance.

Interpretation

Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.

Funding

NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

Spatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant data

BACKGROUND

Diarrhoeal disease is a leading cause of childhood illness and death globally, and Shigella is a major aetiological contributor for which a vaccine might soon be available. The primary objective of this study was to model the spatiotemporal variation in paediatric Shigella infection and map its predicted prevalence across low-income and middle-income countries (LMICs).

METHODS

Individual participant data for Shigella positivity in stool samples were sourced from multiple LMIC-based studies of children aged 59 months or younger. Covariates included household-level and participant-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted and prevalence predictions obtained by syndrome and age stratum.

FINDINGS

20 studies from 23 countries (including locations in Central America and South America, sub-Saharan Africa, and south and southeast Asia) contributed 66 563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Probability of Shigella infection exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhoea cases at 33°C temperatures, above which it decreased. Compared with unimproved sanitation, improved sanitation decreased the odds of Shigella infection by 19% (odds ratio [OR]=0·81 [95% CI 0·76-0·86]) and open defecation decreased them by 18% (OR=0·82 [0·76-0·88]).

INTERPRETATION

The distribution of Shigella is more sensitive to climatological factors, such as temperature, than previously recognised. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, although hotspots also occur in South America and Central America, the Ganges-Brahmaputra Delta, and the island of New Guinea. These findings can inform prioritisation of populations for future vaccine trials and campaigns.

FUNDING

NASA, National Institutes of Health-The National Institute of Allergy and Infectious Diseases, and Bill & Melinda Gates Foundation.

Incorporating COVID-19 into Acute Febrile Illness Surveillance Systems, Belize, Kenya, Ethiopia, Peru, and Liberia, 2020-2021

Existing acute febrile illness (AFI) surveillance systems can be leveraged to identify and characterize emerging pathogens, such as SARS-CoV-2, which causes COVID-19. The US Centers for Disease Control and Prevention collaborated with ministries of health and implementing partners in Belize, Ethiopia, Kenya, Liberia, and Peru to adapt AFI surveillance systems to generate COVID-19 response information. Staff at sentinel sites collected epidemiologic data from persons meeting AFI criteria and specimens for SARS-CoV-2 testing. A total of 5,501 patients with AFI were enrolled during March 2020-October 2021; >69% underwent SARS-CoV-2 testing. Percentage positivity for SARS-CoV-2 ranged from 4% (87/2,151, Kenya) to 19% (22/115, Ethiopia). We show SARS-CoV-2 testing was successfully integrated into AFI surveillance in 5 low- to middle-income countries to detect COVID-19 within AFI care-seeking populations. AFI surveillance systems can be used to build capacity to detect and respond to both emerging and endemic infectious disease threats.

Enteropathy Markers in Early Life Were Associated with Adipokine, Apolipoprotein, and Cytokine Profiles Consistent with an Adverse Cardiometabolic Disease Risk Profile Later in Childhood in a Peruvian Birth Cohort

Metabolic syndrome is a cluster of risk factors for cardiovascular disease afflicting more than 1 billion people worldwide and is increasingly being identified in younger age groups and in socioeconomically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may contribute to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood. A total of 217 subjects previously enrolled in a birth cohort in Amazonian Peru were monitored annually from ages 2 to 5 years. A total of 197 blood samples collected in later childhood were analyzed for 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, which were matched to extant early-life markers of enteropathy ascertained between birth and 2 years. Multivariate and multivariable regression models were fitted to test for associations, adjusting for confounders. Fecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose, and mannitol) measured in infancy were associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also associated with later levels of omentin-1. Enteric protozoa exposure showed stronger associations with later cardiometabolic markers than viruses, bacteria, and overall diarrheal episodes. Early-life enteropathy markers were associated with altered adipokine, apolipoprotein, and cytokine profiles later in childhood consistent with an adverse cardiometabolic disease risk profile in this cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy may predict metabolic syndrome in adulthood.

Antibiotic Use and Stewardship Practices in a Pediatric Community-based Cohort Study in Peru: Shorter Would be Sweeter

BACKGROUND

There is a need to evaluate antibiotic use, duration of therapy, and stewardship in low- and middle-income countries to guide the development of appropriate stewardship programs that are global in scope and effectively decrease unnecessary antibiotic use.

METHODS

We prospectively collected information on illness occurrence and antibiotic use from a cohort of 303 children. We evaluated the incidence, duration of therapy, and appropriateness of antibiotic prescriptions by 5 main antibiotic prescribers (physicians and nurses, pharmacists, nursing assistants, self-prescriptions, and neighbors or family members).

RESULTS

Ninety percent of children received an antibiotic during follow-up, and on average, by the end of follow-up a child had spent 4.3% of their first 5 years of life on antibiotics. The most frequent prescribers were physicians/nurses (79.4%), followed by pharmacists (8.1%), self-prescriptions (6.8%), nursing assistants (3.7%), and family or neighbors (1.9%). Of the 3702 courses of antibiotics prescribed, 30.9% were done so for the occurrence of fever, 25.3% for diarrhea, 2.8% for acute lower respiratory disease, 2.7% for dysentery, and 38.2% for an undetermined illness. Courses exceeding the recommended duration were common for the principal diseases for which treatment was initiated, with 27.3% of courses exceeding the recommended length duration, representing a potential reduction in 13.2% of days on which this cohort spent on antibiotics.

CONCLUSIONS

Stewardship programs should target medical personnel for a primary care stewardship program even in a context in which antibiotics are available to the public with little or no restrictions and appropriate duration should be emphasized in this training.

Associations Between Eight Earth Observation-Derived Climate Variables and Enteropathogen Infection: An Independent Participant Data Meta-Analysis of Surveillance Studies With Broad Spectrum Nucleic Acid Diagnostics

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28°C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes.

Intestinal Colonization With Bifidobacterium longum Subspecies Is Associated With Length at Birth, Exclusive Breastfeeding, and Decreased Risk of Enteric Virus Infections, but Not With Histo-Blood Group Antigens, Oral Vaccine Response or Later Growth in Three Birth Cohorts

Bifidobacterium longum subspecies detected in infant stool have been associated with numerous subsequent health outcomes and are potential early markers of deviation from healthy developmental trajectories. This analysis derived indicators of carriage and early colonization with B. infantis and B. longum and quantified their associations with a panel of early-life exposures and outcomes. In a sub-study nested within a multi-site birth cohort, extant stool samples from infants in Bangladesh, Pakistan and Tanzania were tested for presence and quantity of two Bifidobacterium longum subspecies. The results were matched to indicators of nutritional status, enteropathogen infection, histo-blood group antigens, vaccine response and feeding status and regression models were fitted to test for associations while adjusting for covariates. B. infantis was associated with lower quantity of and decreased odds of colonization with B. longum, and vice versa. Length at birth was associated with a 0.36 increase in log₁₀ B. infantis and a 0.28 decrease in B. longum quantity at 1 month of age. B. infantis colonization was associated with fewer viral infections and small reductions in the risk of rotavirus and sapovirus infections, but not reduced overall diarrheal disease risk. No associations with vaccine responses, HBGAs or later nutritional status were identified. Suboptimal intrauterine growth and a shorter duration of exclusive breastfeeding may predispose infants to early intestinal colonization with the B. longum subspecies at the expense of B. infantis, thus denying them potential benefits of reduced enteric virus episodes.

Associations among Household Animal Ownership, Infrastructure, and Hygiene Characteristics with Source Attribution of Household Fecal Contamination in Peri-Urban Communities of Iquitos, Peru

Using previously validated microbial source tracking markers, we detected and quantified fecal contamination from avian species and avian exposure, dogs, and humans on household cooking tables and floors. The association among contamination, infrastructure, and socioeconomic covariates was assessed using simple and multiple ordinal logistic regressions. The presence of Campylobacter spp. in surface samples was linked to avian markers. Using molecular methods, animal feces were detected in 75.0% and human feces in 20.2% of 104 households. Floors were more contaminated than tables as detected by the avian marker Av4143, dog marker Bactcan, and human marker Bachum. Wood tables were consistently more contaminated than non-wood surfaces, specifically with the mitochondrial avian markers ND5 and CytB, fecal marker Av4143, and canine marker Bactcan. Final multivariable models with socioeconomic and infrastructure characteristics included as covariates indicate that detection of avian feces and avian exposure was associated with the presence of chickens, maternal age, and length of tenancy, whereas detection of human markers was associated with unimproved water source. Detection of Campylobacter in surface samples was associated with the avian fecal marker Av4143. We highlight the critical need to detect and measure the burden of animal fecal waste when evaluating household water, hygiene, and sanitation interventions, and the possibility of decreasing risk of exposure through the modification of surfaces to permit more effective household disinfection practices. Animals may be a more important source of household fecal contamination than humans in many low-resource settings, although interventions have historically focused almost exclusively on managing human waste.

Validation of microbial source tracking markers for the attribution of fecal contamination in indoor-household environments of the Peruvian Amazon

The performance of eight microbial source tracking (MST) markers was evaluated in a low-resource, tropical community located in Iquitos, Peru. Fecal samples from humans, dogs, cats, rats, goats, buffalos, guinea-pigs, chickens, ducks, pigeons, and parrots were collected (n = 117). All samples were tested with human (BacHum, HF183-Taqman), dog (BactCan), pig (Pig-2-Bac), and avian (LA35, Av4143, ND5, cytB) markers using quantitative PCR (qPCR). Internal validity metrics were calculated using all animal fecal samples, as well as animal fecal samples contextually relevant for the Peruvian Amazon. Overall, Pig-2-Bac performed best, with 100% sensitivity and 88.5% specificity to detect the correct fecal source. Human-associated markers showed a sensitivity of 80.0% and 76.7%, and specificity of 66.2% and 67.6%. When limiting the analysis to contextually relevant animal fecal samples for the Peruvian Amazon, Av143 surpassed cytB with 95.7% sensitivity and 81.8% specificity. BactCan demonstrated 100% sensitivity and 47.4% specificity. The gene copy number detected by BacHum and HF183-Taqman were positively correlated (Pearson's correlation coefficient: 0.785), as well as avian markers cytB with Av4143 (Pearson's correlation coefficient: 0.508) and nd5 (Pearson's correlation coefficient: 0.949). These findings suggest that markers such as Av4143, Pig2Bac, cytb and BacHum have acceptable performance to be impactful in source attribution studies for zoonotic enteric disease transmission in this and similar low-resource communities.

Associations between Household-Level Exposures and All-Cause Diarrhea and Pathogen-Specific Enteric Infections in Children Enrolled in Five Sentinel Surveillance Studies

Diarrheal disease remains a major cause of childhood mortality and morbidity causing poor health and economic outcomes. In low-resource settings, young children are exposed to numerous risk factors for enteric pathogen transmission within their dwellings, though the relative importance of different transmission pathways varies by pathogen species. The objective of this analysis was to model associations between five household-level risk factors-water, sanitation, flooring, caregiver education, and crowding-and infection status for endemic enteric pathogens in children in five surveillance studies. Data were combined from 22 sites in which a total of 58,000 stool samples were tested for 16 specific enteropathogens using qPCR. Risk ratios for pathogen- and taxon-specific infection status were modeled using generalized linear models along with hazard ratios for all-cause diarrhea in proportional hazard models, with the five household-level variables as primary exposures adjusting for covariates. Improved drinking water sources conferred a 17% reduction in diarrhea risk; however, the direction of its association with particular pathogens was inconsistent. Improved sanitation was associated with a 9% reduction in diarrhea risk with protective effects across pathogen species and taxa of around 10-20% risk reduction. A 9% reduction in diarrhea risk was observed in subjects with covered floors, which were also associated with decreases in risk for zoonotic enteropathogens. Caregiver education and household crowding showed more modest, inconclusive results. Combining data from diverse sites, this analysis quantified associations between five household-level exposures on risk of specific enteric infections, effects which differed by pathogen species but were broadly consistent with hypothesized transmission mechanisms. Such estimates may be used within expanded water, sanitation, and hygiene (WASH) programs to target interventions to the particular pathogen profiles of individual communities and prioritize resources.

Effects of Child and Maternal Histo-Blood Group Antigen Status on Symptomatic and Asymptomatic Enteric Infections in Early Childhood

Background

Histo-blood group antigens (HBGAs) such as fucosyltransferase (FUT)2 and 3 may act as innate host factors that differentially influence susceptibility of individuals and their offspring to pediatric enteric infections.

Methods

In 3 community-based birth cohorts, FUT2 and FUT3 statuses were ascertained for mother-child dyads. Quantitative polymerase chain reaction panels tested 3663 diarrheal and 18 148 asymptomatic stool samples for 29 enteropathogens. Cumulative diarrhea and infection incidence were compared by child (n = 520) and mothers’ (n = 519) HBGA status and hazard ratios (HRs) derived for all-cause diarrhea and specific enteropathogens.

Results

Children of secretor (FUT2 positive) mothers had a 38% increased adjusted risk of all-cause diarrhea (HR = 1.38; 95% confidence interval (CI), 1.15–1.66) and significantly reduced time to first diarrheal episode. Child FUT2 and FUT3 positivity reduced the risk for all-cause diarrhea by 29% (HR = 0.81; 95% CI, 0.71–0.93) and 27% (HR = 0.83; 95% CI, 0.74–0.92), respectively. Strong associations between HBGAs and pathogen-specific infection and diarrhea were observed, particularly for noroviruses, rotaviruses, enterotoxigenic Escherichia coli, and Campylobacter jejuni/coli.

Conclusions

Histo-blood group antigens affect incidence of all-cause diarrhea and enteric infections at magnitudes comparable to many common disease control interventions. Studies measuring impacts of interventions on childhood enteric disease should account for both child and mothers’ HBGA status.

Penalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort

Environmental enteric dysfunction (EED) is associated with chronic undernutrition. Efforts to identify minimally invasive biomarkers of EED reveal an expanding number of candidate analytes. An analytic strategy is reported to select among candidate biomarkers and systematically express the strength of each marker’s association with linear growth in infancy and early childhood. 180 analytes were quantified in fecal, urine and plasma samples taken at 7, 15 and 24 months of age from 258 subjects in a birth cohort in Peru. Treating the subjects’ length-for-age Z-score (LAZ-score) over a 2-month lag as the outcome, penalized linear regression models with different shrinkage methods were fitted to determine the best-fitting subset. These were then included with covariates in linear regression models to obtain estimates of each biomarker’s adjusted effect on growth. Transferrin had the largest and most statistically significant adjusted effect on short-term linear growth as measured by LAZ-score–a coefficient value of 0.50 (0.24, 0.75) for each log₂ increase in plasma transferrin concentration. Other biomarkers with large effect size estimates included adiponectin, arginine, growth hormone, proline and serum amyloid P-component. The selected subset explained up to 23.0% of the variability in LAZ-score. Penalized regression modeling approaches can be used to select subsets from large panels of candidate biomarkers of EED. There is a need to systematically express the strength of association of biomarkers with linear growth or other outcomes to compare results across studies.

Childhood undernutrition is widespread throughout the world and has severe, long-lasting health impacts. Substances measured in blood, urine and stool could be used as biomarkers to identify children undergoing growth failure before these impacts occur. However, it is not yet known which of the many markers that can be identified are accurate and clinically useful predictors of poor growth in infants and children. This study used a large number of candidate biomarkers of immune activation, metabolism and hormones and applied statistical methods to narrow them down from 110 different substances, to the 36 best predictors of growth in 258 Peruvian infants. It also estimated how large the effect of each of these markers was on height two months later. The biomarker with the largest effect was transferrin, a glycoprotein that can be measured in blood samples. 15-month old children with elevated transferrin were around two thirds of a centimeter taller on average at 17 months than those with low levels. Transferrin and other proteins, glycoproteins, hormones and antibodies that this study identified, can be measured easily and affordably in standard laboratories making them feasible to be used broadly as prognostic markers as part of child health and nutrition programs in under-resourced settings.

Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study

BACKGROUND

Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance.

METHODS

For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination.

FINDINGS

All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites.

INTERPRETATION

These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting.

FUNDING

Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.

Antibiotic Resistance of Campylobacter Species in a Pediatric Cohort Study

The objective of this study was to determine the phenotypic patterns of antibiotic resistance and the epidemiology of drug-resistant Campylobacter spp. from a low-resource setting. A birth cohort of 303 patients was followed until 5 years of age. Stool samples from asymptomatic children (n = 10,008) and those with diarrhea (n = 3,175) were cultured for Campylobacter Disk diffusion for ciprofloxacin (CIP), nalidixic acid (NAL), erythromycin (ERY), azithromycin (AZM), tetracycline (TE), gentamicin (GM), ampicillin (AMP), amoxicillin and clavulanic acid (AMC), ceftriaxone (CRO), chloramphenicol (C), and trimethoprim-sulfamethoxazole (TMS) was determined. Antibiotic resistances in Campylobacter jejuni and non-C. jejuni isolates from surveillance and diarrhea samples were compared, and the association between personal macrolide exposure and subsequent occurrence of a macrolide-resistant Campylobacter spp. was assessed. Of 917 Campylobacter isolates, 77.4% of C. jejuni isolates and 79.8% of non-C. jejuni isolates were resistant to ciprofloxacin, while 4.9% of C. jejuni isolates and 24.8% of non-C. jejuni isolates were not susceptible to azithromycin. Of the 303 children, 33.1% had been diagnosed with a Campylobacter strain nonsusceptible to both azithromycin and ciprofloxacin. Personal macrolide exposure did not affect the risk of macrolide-resistant Campylobacter Amoxicillin and clavulanic acid (94.0%) was one of the antibiotics with the highest rates of susceptibility. There is a high incidence of quinolone- and macrolide-resistant Campylobacter infections in infants under 24 months of age. Given the lack of association between personal exposure to macrolides and a subsequent Campylobacter infection resistant to macrolides, there is a need to evaluate the source of multidrug-resistant (MDR) Campylobacter This study provides compelling evidence to propose amoxicillin/clavulanic acid as a treatment for campylobacteriosis.

Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study

BACKGROUND

Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest.

METHODS

Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates.

RESULTS

The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true.

CONCLUSION

Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates.

A Longitudinal Study of Household Water, Sanitation, and Hygiene Characteristics and Environmental Enteropathy Markers in Children Less than 24 Months in Iquitos, Peru

Poor child gut health, resulting from a lack of access to an improved toilet or clean water, has been proposed as a biological mechanism underlying child stunting and oral vaccine failure. Characteristics related to household sanitation, water use, and hygiene were measured among a birth cohort of 270 children from peri-urban Iquitos Peru. These children had monthly stool samples and urine samples at four time points and serum samples at (2–4) time points analyzed for biomarkers related to intestinal inflammation and permeability. We found that less storage of fecal matter near the household along with a reliable water connection were associated with reduced inflammation, most prominently the fecal biomarker myeloperoxidase (MPO) (no sanitation facility compared with those with an onsite toilet had −0.43 log MPO, 95% confidence interval [CI]: −0.74, −0.13; and households with an intermittent connection versus those with a continuous supply had +0.36 log MPO, 95% CI: 0.08, 0.63). These results provide preliminary evidence for the hypothesis that children less than 24 months of age living in unsanitary conditions will have elevated gut inflammation.

A methodologic framework for modeling and assessing biomarkers of environmental enteropathy as predictors of growth in infants: an example from a Peruvian birth cohort

Background: Environmental enteropathy (EE) impairs the gut's absorptive capacity and immune function and causes decelerations in statural growth that manifest gradually over time.Objective: To illustrate an approach for assessing emerging biomarkers of EE, we separately assessed the associations between 3 such markers and subsequent nutritional status.Design: Stool samples were routinely collected between January 2010 and November 2014 from a cohort of 303 Peruvian infants and analyzed for concentrations of the biomarkers α-1-antitrypsin (AAT), myeloperoxidase, and neopterin. For each marker, a mixed-effects linear regression model was fitted for length-for-age z scores (LAZs) obtained from anthropometric assessments that incorporated covariate predictors, polynomial terms for age, and product interaction terms to test associations over varying lag lengths. The biomarkers' contribution to the models was assessed with the use of the likelihood ratio test and partial R² statistics.Results: Test statistics for the combined inclusion of the 4-model terms that involved the biomarker were highly statistically significant for AAT (28.71; P < 0.0001) and myeloperoxidase (62.79; P < 0.0001) over a 3-mo lag and moderately so for neopterin (13.97; P = 0.0074). AAT and myeloperoxidase seemed to interact strongly with age, with the magnitude and direction of the effect varying considerably over the first 3 y of life. The largest proportion of the variance explained by any biomarker (2.8%) and the largest difference in LAZ predicted between the 5th and 95th percentile (0.25) was by myeloperoxidase over a 2-mo lag.Conclusions: Of the 3 fecal biomarkers studied, 2 that related to intestinal function-AAT and myeloperoxidase-were associated with small but highly statistically significant differences in future statural growth trajectories in infants in this cohort, lending further evidence to the EE hypothesis that increased gut permeability and inflammation adversely affects subsequent nutritional status. This association exhibited a complex interaction with age. This trial was registered at clinicaltrials.gov as NCT02441426.