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Schistosomiasis is among the most common parasitic diseases in the world, with over 142 million people infected in low- and middle-income countries. Measuring population-level transmission is centrally important in guiding schistosomiasis control programs. Traditionally, human Schistosoma mansoni infections have been detected using stool microscopy, which is logistically difficult at program scale and has low sensitivity when people have low infection burdens. We compared serological measures of transmission based on antibody response to S. mansoni soluble egg antigen (SEA) with stool-based measures of infection among 3,663 preschool-age children in an area endemic for S. mansoni in western Kenya. We estimated force of infection among children using the seroconversion rate and examined how it varied geographically and by age. At the community level, serological measures of transmission aligned with stool-based measures of infection (ρ = 0.94), and serological measures provided more resolution for between-community differences at lower levels of infection. Force of infection showed a clear gradient of transmission with distance from Lake Victoria, with 94% of infections and 93% of seropositive children in communities <1.5 km from the lake. Force of infection increased through age 3 y, by which time 65% (95% CI: 53%, 75%) of children were SEA positive in high-transmission communities-2 y before they would be reached by school-based deworming programs. Our results show that serologic surveillance platforms represent an important opportunity to guide and monitor schistosomiasis control programs, and that in high-transmission settings preschool-age children represent a key population missed by school-based deworming programs.

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.

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.

Antibodies are unique among biomarkers in their ability to identify persons with protective immunity to vaccine-preventable diseases and to measure past exposure to diverse pathogens. Most infectious disease surveillance maintains a single-disease focus, but broader testing of existing serologic surveys with multiplex antibody assays would create new opportunities for integrated surveillance. In this perspective, we highlight multiple areas for potential synergy where integrated surveillance could add more value to public health efforts than the current trend of independent disease monitoring through vertical programs. We describe innovations in laboratory and data science that should accelerate integration and identify remaining challenges with respect to specimen collection, testing, and analysis. Throughout, we illustrate how information generated through integrated surveillance platforms can create new opportunities to more quickly and precisely identify global health program gaps that range from undervaccination to emerging pathogens to multilayered health disparities that span diverse communicable diseases.

Antibodies are unique among biomarkers in their ability to identify persons with protective immunity to vaccine-preventable diseases and to measure past exposure to diverse pathogens. Most infectious disease surveillance maintains a single-disease focus, but broader testing of existing serologic surveys with multiplex antibody assays would create new opportunities for integrated surveillance. In this perspective, we highlight multiple areas for potential synergy where integrated surveillance could add more value to public health efforts than the current trend of independent disease monitoring through vertical programs. We describe innovations in laboratory and data science that should accelerate integration and identify remaining challenges with respect to specimen collection, testing, and analysis. Throughout, we illustrate how information generated through integrated surveillance platforms can create new opportunities to more quickly and precisely identify global health program gaps that range from undervaccination to emerging pathogens to multilayered health disparities that span diverse communicable diseases.

Rainstorms increase levels of fecal indicator bacteria in urban coastal waters, but it is unknown whether exposure to seawater after rainstorms increases rates of acute illness. Our objective was to provide the first estimates of rates of acute illness after seawater exposure during both dry- and wet-weather periods and to determine the relationship between levels of indicator bacteria and illness among surfers, a population with a high potential for exposure after rain. We enrolled 654 surfers in San Diego, California, and followed them longitudinally during the 2013-2014 and 2014-2015 winters (33,377 days of observation, 10,081 surf sessions). We measured daily surf activities and illness symptoms (gastrointestinal illness, sinus infections, ear infections, infected wounds). Compared with no exposure, exposure to seawater during dry weather increased incidence rates of all outcomes (e.g., for earache or infection, adjusted incidence rate ratio (IRR) = 1.86, 95% confidence interval (CI): 1.27, 2.71; for infected wounds, IRR = 3.04, 95% CI: 1.54, 5.98); exposure during wet weather further increased rates (e.g., for earache or infection, IRR = 3.28, 95% CI: 1.95, 5.51; for infected wounds, IRR = 4.96, 95% CI: 2.18, 11.29). Fecal indicator bacteria measured in seawater (Enterococcus species, fecal coliforms, total coliforms) were strongly associated with incident illness only during wet weather. Urban coastal seawater exposure increases the incidence rates of many acute illnesses among surfers, with higher incidence rates after rainstorms.

Rainstorms increase levels of fecal indicator bacteria in urban coastal waters, but it is unknown whether exposure to seawater after rainstorms increases rates of acute illness. Our objective was to provide the first estimates of rates of acute illness after seawater exposure during both dry- and wet-weather periods and to determine the relationship between levels of indicator bacteria and illness among surfers, a population with a high potential for exposure after rain. We enrolled 654 surfers in San Diego, California, and followed them longitudinally during the 2013-2014 and 2014-2015 winters (33,377 days of observation, 10,081 surf sessions). We measured daily surf activities and illness symptoms (gastrointestinal illness, sinus infections, ear infections, infected wounds). Compared with no exposure, exposure to seawater during dry weather increased incidence rates of all outcomes (e.g., for earache or infection, adjusted incidence rate ratio (IRR) = 1.86, 95% confidence interval (CI): 1.27, 2.71; for infected wounds, IRR = 3.04, 95% CI: 1.54, 5.98); exposure during wet weather further increased rates (e.g., for earache or infection, IRR = 3.28, 95% CI: 1.95, 5.51; for infected wounds, IRR = 4.96, 95% CI: 2.18, 11.29). Fecal indicator bacteria measured in seawater (Enterococcus species, fecal coliforms, total coliforms) were strongly associated with incident illness only during wet weather. Urban coastal seawater exposure increases the incidence rates of many acute illnesses among surfers, with higher incidence rates after rainstorms.

Biomedical laboratory experiments routinely use negative controls to identify possible sources of bias, but epidemiologic studies have infrequently used this type of control in their design or measurement approach. Recently, epidemiologists proposed the routine use of negative controls in observational studies and defined the structure of negative controls to detect bias due to unmeasured confounding. We extend this previous study and define the structure of negative controls to detect selection bias and measurement bias in both observational studies and randomized trials. We illustrate the strengths and limitations of negative controls in this context using examples from the epidemiologic literature. Given their demonstrated utility and broad generalizability, the routine use of prespecified negative controls will strengthen the evidence from epidemiologic studies.

ObjectivesTo provide summary estimates of gastroenteritis risks and illness burden associated with recreational water exposure and determine whether children have higher risks and burden.MethodsWe combined individual participant data from 13 prospective cohorts at marine and freshwater beaches throughout the United States (n = 84 411). We measured incident outcomes within 10 days of exposure: diarrhea, gastrointestinal illness, missed daily activity (work, school, vacation), and medical visits. We estimated the relationship between outcomes and 2 exposures: body immersion swimming and Enterococcus spp. fecal indicator bacteria levels in the water. We also estimated the population-attributable risk associated with these exposures.ResultsWater exposure accounted for 21% of diarrhea episodes and 9% of missed daily activities but was unassociated with gastroenteritis leading to medical consultation. Children aged 0 to 4 and 5 to 10 years had the most water exposure, exhibited stronger associations between levels of water quality and illness, and accounted for the largest attributable illness burden.ConclusionsThe higher gastroenteritis risk and associated burden in young children presents important new information to inform future recreational water quality guidelines designed to protect public health.

Biomedical laboratory experiments routinely use negative controls to identify possible sources of bias, but epidemiologic studies have infrequently used this type of control in their design or measurement approach. Recently, epidemiologists proposed the routine use of negative controls in observational studies and defined the structure of negative controls to detect bias due to unmeasured confounding. We extend this previous study and define the structure of negative controls to detect selection bias and measurement bias in both observational studies and randomized trials. We illustrate the strengths and limitations of negative controls in this context using examples from the epidemiologic literature. Given their demonstrated utility and broad generalizability, the routine use of prespecified negative controls will strengthen the evidence from epidemiologic studies.

ObjectivesTo provide summary estimates of gastroenteritis risks and illness burden associated with recreational water exposure and determine whether children have higher risks and burden.MethodsWe combined individual participant data from 13 prospective cohorts at marine and freshwater beaches throughout the United States (n = 84 411). We measured incident outcomes within 10 days of exposure: diarrhea, gastrointestinal illness, missed daily activity (work, school, vacation), and medical visits. We estimated the relationship between outcomes and 2 exposures: body immersion swimming and Enterococcus spp. fecal indicator bacteria levels in the water. We also estimated the population-attributable risk associated with these exposures.ResultsWater exposure accounted for 21% of diarrhea episodes and 9% of missed daily activities but was unassociated with gastroenteritis leading to medical consultation. Children aged 0 to 4 and 5 to 10 years had the most water exposure, exhibited stronger associations between levels of water quality and illness, and accounted for the largest attributable illness burden.ConclusionsThe higher gastroenteritis risk and associated burden in young children presents important new information to inform future recreational water quality guidelines designed to protect public health.

Discreet collection of spot check observations to measure household hygiene conditions is a common measurement technique in epidemiologic studies of hygiene in low-income countries. The objective of this study was to determine whether the collection of spot check observations in longitudinal studies could itself induce reactivity (i.e., change participant behavior). We analyzed data from a 12-month prospective cohort study in rural Tamil Nadu, India that was conducted in the absence of any hygiene or toilet promotion activities. Our data included hygiene and toilet spot checks from 10,427 household visits. We found substantial evidence of participant reactivity to spot check observations of hygiene practices that were easy to modify on short notice. For example, soap observed at the household's primary handwashing location increased from 49% at enrollment to 81% by the fourth visit and remained at or above 77% for the remainder of the study.

Discreet collection of spot check observations to measure household hygiene conditions is a common measurement technique in epidemiologic studies of hygiene in low-income countries. The objective of this study was to determine whether the collection of spot check observations in longitudinal studies could itself induce reactivity (i.e., change participant behavior). We analyzed data from a 12-month prospective cohort study in rural Tamil Nadu, India that was conducted in the absence of any hygiene or toilet promotion activities. Our data included hygiene and toilet spot checks from 10,427 household visits. We found substantial evidence of participant reactivity to spot check observations of hygiene practices that were easy to modify on short notice. For example, soap observed at the household's primary handwashing location increased from 49% at enrollment to 81% by the fourth visit and remained at or above 77% for the remainder of the study.

BackgroundEfforts to monitor malaria transmission increasingly use cross-sectional surveys to estimate transmission intensity from seroprevalence data using malarial antibodies. To date, seroconversion rates estimated from cross-sectional surveys have not been compared to rates estimated in prospective cohorts. Our objective was to compare seroconversion rates estimated in a prospective cohort with those from a cross-sectional survey in a low-transmission population.Methods and findingsThe analysis included two studies from Haiti: a prospective cohort of 142 children ages ≤ 11 years followed for up to 9 years, and a concurrent cross-sectional survey of 383 individuals ages 0-90 years old. From all individuals, we analyzed 1,154 blood spot specimens for the malaria antibody MSP-1(19) using a multiplex bead antigen assay. We classified individuals as positive for malaria using a cutoff derived from the mean plus 3 standard deviations in antibody responses from a negative control set of unexposed individuals. We estimated prospective seroconversion rates from the longitudinal cohort based on 13 incident seroconversions among 646 person-years at risk. We also estimated seroconversion rates from the cross-sectional survey using a reversible catalytic model fit with maximum likelihood. We found the two approaches provided consistent results: the seroconversion rate for ages ≤ 11 years was 0.020 (0.010, 0.032) estimated prospectively versus 0.023 (0.001, 0.052) in the cross-sectional survey.ConclusionsThe estimation of seroconversion rates using cross-sectional data is a widespread and generalizable problem for many infectious diseases that can be measured using antibody titers. The consistency between these two estimates lends credibility to model-based estimates of malaria seroconversion rates using cross-sectional surveys. This study also demonstrates the utility of including malaria antibody measures in multiplex assays alongside targets for vaccine coverage and other

BackgroundEfforts to monitor malaria transmission increasingly use cross-sectional surveys to estimate transmission intensity from seroprevalence data using malarial antibodies. To date, seroconversion rates estimated from cross-sectional surveys have not been compared to rates estimated in prospective cohorts. Our objective was to compare seroconversion rates estimated in a prospective cohort with those from a cross-sectional survey in a low-transmission population.Methods and findingsThe analysis included two studies from Haiti: a prospective cohort of 142 children ages ≤ 11 years followed for up to 9 years, and a concurrent cross-sectional survey of 383 individuals ages 0-90 years old. From all individuals, we analyzed 1,154 blood spot specimens for the malaria antibody MSP-1(19) using a multiplex bead antigen assay. We classified individuals as positive for malaria using a cutoff derived from the mean plus 3 standard deviations in antibody responses from a negative control set of unexposed individuals. We estimated prospective seroconversion rates from the longitudinal cohort based on 13 incident seroconversions among 646 person-years at risk. We also estimated seroconversion rates from the cross-sectional survey using a reversible catalytic model fit with maximum likelihood. We found the two approaches provided consistent results: the seroconversion rate for ages ≤ 11 years was 0.020 (0.010, 0.032) estimated prospectively versus 0.023 (0.001, 0.052) in the cross-sectional survey.ConclusionsThe estimation of seroconversion rates using cross-sectional data is a widespread and generalizable problem for many infectious diseases that can be measured using antibody titers. The consistency between these two estimates lends credibility to model-based estimates of malaria seroconversion rates using cross-sectional surveys. This study also demonstrates the utility of including malaria antibody measures in multiplex assays alongside targets for vaccine coverage and other

Many community-based studies of acute child illness rely on cases reported by caregivers. In prior investigations, researchers noted a reporting bias when longer illness recall periods were used. The use of recall periods longer than 2-3 days has been discouraged to minimize this reporting bias. In the present study, we sought to determine the optimal recall period for illness measurement when accounting for both bias and variance. Using data from 12,191 children less than 24 months of age collected in 2008-2009 from Himachal Pradesh in India, Madhya Pradesh in India, Indonesia, Peru, and Senegal, we calculated bias, variance, and mean squared error for estimates of the prevalence ratio between groups defined by anemia, stunting, and underweight status to identify optimal recall periods for caregiver-reported diarrhea, cough, and fever. There was little bias in the prevalence ratio when a 7-day recall period was used (<10% in 35 of 45 scenarios), and the mean squared error was usually minimized with recall periods of 6 or more days. Shortening the recall period from 7 days to 2 days required sample-size increases of 52%-92% for diarrhea, 47%-61% for cough, and 102%-206% for fever. In contrast to the current practice of using 2-day recall periods, this work suggests that studies should measure caregiver-reported illness with a 7-day recall period.

BackgroundStudies of health risks associated with recreational water exposure require investigators to make choices about water quality indicator averaging techniques, exposure definitions, follow-up periods, and model specifications; however, investigators seldom describe the impact of these choices on reported results. Our objectives are to report illness risk from swimming at a marine beach affected by nonpoint sources of urban runoff, measure associations between fecal indicator bacteria levels and subsequent illness among swimmers, and investigate the sensitivity of results to a range of exposure and outcome definitions.MethodsIn 2009, we enrolled 5674 people in a prospective cohort at Malibu Beach, a coastal marine beach in California, and measured daily health symptoms 10-19 days later. Concurrent water quality samples were analyzed for indicator bacteria using culture and molecular methods. We compared illness risk between nonswimmers and swimmers, and among swimmers exposed to various levels of fecal indicator bacteria.ResultsDiarrhea was more common among swimmers than nonswimmers (adjusted odds ratio = 1.88 [95% confidence interval = 1.09-3.24]) within 3 days of the beach visit. Water quality was generally good (fecal indicator bacteria levels exceeded water quality guidelines for only 7% of study samples). Fecal indicator bacteria levels were not consistently associated with swimmer illness. Sensitivity analyses demonstrated that overall inference was not substantially affected by the choice of exposure and outcome definitions.ConclusionsThis study suggests that the 3 days following a beach visit may be the most relevant period for health outcome measurement in recreational water studies. Under the water quality conditions observed in this study, fecal indicator bacteria levels were not associated with swimmer illness.

Many community-based studies of acute child illness rely on cases reported by caregivers. In prior investigations, researchers noted a reporting bias when longer illness recall periods were used. The use of recall periods longer than 2-3 days has been discouraged to minimize this reporting bias. In the present study, we sought to determine the optimal recall period for illness measurement when accounting for both bias and variance. Using data from 12,191 children less than 24 months of age collected in 2008-2009 from Himachal Pradesh in India, Madhya Pradesh in India, Indonesia, Peru, and Senegal, we calculated bias, variance, and mean squared error for estimates of the prevalence ratio between groups defined by anemia, stunting, and underweight status to identify optimal recall periods for caregiver-reported diarrhea, cough, and fever. There was little bias in the prevalence ratio when a 7-day recall period was used (<10% in 35 of 45 scenarios), and the mean squared error was usually minimized with recall periods of 6 or more days. Shortening the recall period from 7 days to 2 days required sample-size increases of 52%-92% for diarrhea, 47%-61% for cough, and 102%-206% for fever. In contrast to the current practice of using 2-day recall periods, this work suggests that studies should measure caregiver-reported illness with a 7-day recall period.

BackgroundStudies of health risks associated with recreational water exposure require investigators to make choices about water quality indicator averaging techniques, exposure definitions, follow-up periods, and model specifications; however, investigators seldom describe the impact of these choices on reported results. Our objectives are to report illness risk from swimming at a marine beach affected by nonpoint sources of urban runoff, measure associations between fecal indicator bacteria levels and subsequent illness among swimmers, and investigate the sensitivity of results to a range of exposure and outcome definitions.MethodsIn 2009, we enrolled 5674 people in a prospective cohort at Malibu Beach, a coastal marine beach in California, and measured daily health symptoms 10-19 days later. Concurrent water quality samples were analyzed for indicator bacteria using culture and molecular methods. We compared illness risk between nonswimmers and swimmers, and among swimmers exposed to various levels of fecal indicator bacteria.ResultsDiarrhea was more common among swimmers than nonswimmers (adjusted odds ratio = 1.88 [95% confidence interval = 1.09-3.24]) within 3 days of the beach visit. Water quality was generally good (fecal indicator bacteria levels exceeded water quality guidelines for only 7% of study samples). Fecal indicator bacteria levels were not consistently associated with swimmer illness. Sensitivity analyses demonstrated that overall inference was not substantially affected by the choice of exposure and outcome definitions.ConclusionsThis study suggests that the 3 days following a beach visit may be the most relevant period for health outcome measurement in recreational water studies. Under the water quality conditions observed in this study, fecal indicator bacteria levels were not associated with swimmer illness.

Background Estimating the required sample size and statistical power for a study is an integral part of study design. For standard designs, power equations provide an efficient solution to the problem, but they are unavailable for many complex study designs that arise in practice. For such complex study designs, computer simulation is a useful alternative for estimating study power. Although this approach is well known among statisticians, in our experience many epidemiologists and social scientists are unfamiliar with the technique. This article aims to address this knowledge gap. Methods We review an approach to estimate study power for individual- or cluster-randomized designs using computer simulation. This flexible approach arises naturally from the model used to derive conventional power equations, but extends those methods to accommodate arbitrarily complex designs. The method is universally applicable to a broad range of designs and outcomes, and we present the material in a way that is approachable for quantitative, applied researchers. We illustrate the method using two examples (one simple, one complex) based on sanitation and nutritional interventions to improve child growth. Results We first show how simulation reproduces conventional power estimates for simple randomized designs over a broad range of sample scenarios to familiarize the reader with the approach. We then demonstrate how to extend the simulation approach to more complex designs. Finally, we discuss extensions to the examples in the article, and provide computer code to efficiently run the example simulations in both R and Stata. Conclusions Simulation methods offer a flexible option to estimate statistical power for standard and non-traditional study designs and parameters of interest. The approach we have described is universally applicable for evaluating study designs used in epidemiologic and social science research.

Background Estimating the required sample size and statistical power for a study is an integral part of study design. For standard designs, power equations provide an efficient solution to the problem, but they are unavailable for many complex study designs that arise in practice. For such complex study designs, computer simulation is a useful alternative for estimating study power. Although this approach is well known among statisticians, in our experience many epidemiologists and social scientists are unfamiliar with the technique. This article aims to address this knowledge gap. Methods We review an approach to estimate study power for individual- or cluster-randomized designs using computer simulation. This flexible approach arises naturally from the model used to derive conventional power equations, but extends those methods to accommodate arbitrarily complex designs. The method is universally applicable to a broad range of designs and outcomes, and we present the material in a way that is approachable for quantitative, applied researchers. We illustrate the method using two examples (one simple, one complex) based on sanitation and nutritional interventions to improve child growth. Results We first show how simulation reproduces conventional power estimates for simple randomized designs over a broad range of sample scenarios to familiarize the reader with the approach. We then demonstrate how to extend the simulation approach to more complex designs. Finally, we discuss extensions to the examples in the article, and provide computer code to efficiently run the example simulations in both R and Stata. Conclusions Simulation methods offer a flexible option to estimate statistical power for standard and non-traditional study designs and parameters of interest. The approach we have described is universally applicable for evaluating study designs used in epidemiologic and social science research.

Background Estimating the required sample size and statistical power for a study is an integral part of study design. For standard designs, power equations provide an efficient solution to the problem, but they are unavailable for many complex study designs that arise in practice. For such complex study designs, computer simulation is a useful alternative for estimating study power. Although this approach is well known among statisticians, in our experience many epidemiologists and social scientists are unfamiliar with the technique. This article aims to address this knowledge gap. Methods We review an approach to estimate study power for individual- or cluster-randomized designs using computer simulation. This flexible approach arises naturally from the model used to derive conventional power equations, but extends those methods to accommodate arbitrarily complex designs. The method is universally applicable to a broad range of designs and outcomes, and we present the material in a way that is approachable for quantitative, applied researchers. We illustrate the method using two examples (one simple, one complex) based on sanitation and nutritional interventions to improve child growth. Results We first show how simulation reproduces conventional power estimates for simple randomized designs over a broad range of sample scenarios to familiarize the reader with the approach. We then demonstrate how to extend the simulation approach to more complex designs. Finally, we discuss extensions to the examples in the article, and provide computer code to efficiently run the example simulations in both R and Stata. Conclusions Simulation methods offer a flexible option to estimate statistical power for standard and non-traditional study designs and parameters of interest. The approach we have described is universally applicable for evaluating study designs used in epidemiologic and social science research.