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Background: Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study. Methods: We re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0–2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics. Findings: We analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6–71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8–38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6–39·5) was more common than bacterial (25·0%, 23·4–28·4) and parasitic diarrhoea (3·5%, 3·0–5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8–29·9), sapovirus (22·8, 18·9–27·5), rotavirus (20·7, 18·8–23·0), adenovirus 40/41 (19·0, 16·8–23·0), enterotoxigenic Escherichia coli (18·8, 16·5–23·8), norovirus (15·4, 13·5–20·1), astrovirus (15·0, 12·0–19·5), Campylobacter jejuni or C coli (12·1, 8·5–17·2), Cryptosporidium (5·8, 4·3–8·3), and typical enteropathogenic E coli (5·4, 2·8–9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7–54·1], specificity 84·0% [83·0–84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1–17·3], specificity 96·5% [96·0–97·0]). Interpretation: Quantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings. Funding: Bill & Melinda Gates Foundation.
The MAL-ED study design has been previously described.13 Between November, 2009, and February 2012, children were enrolled from the community within 17 days of birth at eight locations: Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru. Children were included if their mother was aged 16 years or older, their family intended to remain in the study area for at least 6 months from enrolment, they were from a singleton pregnancy, and they had no other siblings enrolled in the study. Children with a birthweight or enrolment weight of less than 1500 g and children diagnosed with congenital disease or severe neonatal disease were excluded. All sites received ethics approval from their respective governmental, local institutional, and collaborating institutional ethics review boards. Written informed consent was obtained from the parent or guardian of every child. Fieldworkers visited children twice weekly for surveillance of child illnesses, antibiotic use, and vaccine administration. Diarrhoea was defined as maternal report of three or more loose stools in 24 h, or one stool with visible blood. Episodes separated by 48 h without study-defined diarrhoea were considered distinct episodes. The duration, number of loose stools in 24 h, presence of dehydration, fever, and vomiting were also recorded for each episode of diarrhoea. Dehydration was defined as irritability that was difficult to console, increased thirst, loss of skin turgor, sunken eyes, or lethargy.7 Diarrhoea for 7 days or more was considered a prolonged episode, and a high frequency was defined as more than 6 stools in any 24 h period during an episode. A diarrhoea severity score was calculated for every episode as previously described.14 Diarrhoeal and monthly non-diarrhoeal surveillance stool specimens were collected. Diarrhoea was considered to be treated by antibiotics if antibiotic use was reported on any day of the diarrhoea episode, and inappropriate antibiotic use was defined post hoc on the basis of a microbiological gold standard as either unnecessary treatment (ie, for non-Shigella diarrhoea) or inappropriate antibiotic selection (ie, use of an antibiotic class other than fluoroquinolones or macrolides for Shigella diarrhoea).15, 16, 17, 18 All diarrhoeal stools and non-diarrhoeal stools collected for surveillance for months 1–12, 15, 18, 21, and 24 were analysed according to a standardised protocol, as previously described.14, 19 We used all available diarrhoeal and monthly non-diarrhoeal stool specimens from children who had complete follow-up to age 24 months. We used custom-designed TaqMan Array Cards (Thermo Fisher, Carlsbad, CA, USA) that compartmentalised probe-based quantitative PCR assays for 29 enteropathogens. Assays for Plesiomonas shigelloides were included on a subset of cards. All procedures, including assay validation, nucleic acid extraction, quantitative PCR setup, and quality control have been described previously (appendix).20, 21 Raw stool aliquots were stored at −80°C before extraction. Bacteriophage MS2 was used as an external control to monitor efficiency of nucleic acid extraction and amplification. We included one extraction blank per batch and one no-template amplification control per ten cards to exclude laboratory contamination. The detection of rotavirus was considered false positive if obtained within 28 days of rotavirus vaccine administration. Both Shigella and enteroinvasive E coli can be detected using the ipaH target; however, on the basis of previous findings6, 22 and for simplicity, we considered the detection of ipaH to be consistent with Shigella infection. For all analyses, we used the quantification cycle value as an inverse measure of pathogen quantity, whereby one quantification cycle unit corresponds to a doubling in quantity. A quantification cycle of 35 was considered the analytical limit of detection. We estimated pathogen-specific burdens of diarrhoea by calculating attributable fractions, which incorporate both the frequency of pathogen detection in diarrhoea and the association between pathogen quantity and diarrhoea.23 This allowed for differential attribution of aetiology based on the amount of pathogen nucleic acid present. To estimate this association, a generalised linear mixed-effects model (GLMM) was fit for each pathogen, whereby the outcome was diarrhoea, and predictors were the quantity of the modelled pathogen, the quantity of each other pathogen, child sex, test batch, child age in 3 month intervals, an interaction between pathogen quantity and child age, a random slope for each site, and a random intercept for each individual. A quadratic term for the quantity of the modelled pathogen was considered if the prevalence in diarrhoea at any quantity was at least 5% and included if it improved model fit on the basis of the Akaike information criterion. Non-diarrhoeal stools were required to be collected at least 7 days before and after any diarrhoea episode. All pathogens with at least one detection at any quantity in diarrhoeal stools and any association with diarrhoea in single-pathogen analysis (ie, the same model but without adjustment for other pathogens) were included in the final analysis. Attributable fractions were calculated by summing the pathogen attributable fraction for each episode (AFe) across each of j episodes with the following equation: where and ORe is the pathogen-specific and quantity-specific odds ratio from the GLMM for each episode. Attributable incidence rates were calculated as the product of the number of episodes identified by surveillance and the attributable fractions divided by the number of child years at risk and expressed as rates per 100 child-years. 95% CIs were estimated by bootstrapping with 1000 iterations. To estimate the association between pathogen-attributable diarrhoea and clinical characteristics, a single GLMM was fit for each characteristic and included the AFe for each pathogen, child age, a quadratic term for age, and nested random effects for site and individual. Coefficients were scaled to the AFe range for each pathogen. An AFe for non-infectious diarrhoea was defined as one minus the sum of all pathogen-specific AFes with a lower bound of zero. To assess model-based prediction of pathogen-attributable episodes, we first identified aetiologic detections for each episode, using a stringent quantitative cutoff (AFe ≥0·5; appendix). If more than one aetiology was identified, the pathogen with the highest AFe was considered the primary aetiology, and all others were considered secondary aetiologies. We then derived a prediction score from a GLMM, with an outcome of an aetiologic episode, and predictors of blood in stool, fever, prolonged duration, dehydration, vomiting, high stool frequency, and child age in 3 month intervals and nested random effects for site and individual. The fixed effects coefficients were scaled, rounded, and summed. We fit a receiver operating characteristic curve, and the lowest score that achieved at least 80% specificity was selected as the cutoff. For each prediction score, a diarrhoea episode was considered positive if it had a score greater than or equal to the cutoff. The Youden Index24 was calculated as sensitivity + specificity – 1, and 95% CIs were calculated using the binomial distribution. All analyses were done in R version 3.4.3. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
