Pathogen-specific burdens of community diarrhoea in developing countries: A multisite birth cohort study (MAL-ED)

Background: Most studies of the causes of diarrhoea in low-income and middle-income countries have looked at severe disease in people presenting for care, and there are few estimates of pathogen-specific diarrhoea burdens in the community.Methods: We undertook a birth cohort study with not only intensive community surveillance for diarrhoea but also routine collection of non-diarrhoeal stools from eight sites in South America, Africa, and Asia. We enrolled children within 17 days of birth, and diarrhoeal episodes (defined as maternal report of three or more loose stools in 24 h, or one loose stool with visible blood) were identified through twice-weekly home visits by fieldworkers over a follow-up period of 24 months. Non-diarrhoeal stool specimens were also collected for surveillance for months 1-12, 15, 18, 21, and 24. Stools were analysed for a broad range of enteropathogens using culture, enzyme immunoassay, and PCR. We used the adjusted attributable fraction (AF) to estimate pathogen-specific burdens of diarrhoea.|Findings: Between November 26, 2009, and February 25, 2014, we tested 7318 diarrhoeal and 24 310 non-diarrhoeal stools collected from 2145 children aged 0-24 months. Pathogen detection was common in non-diarrhoeal stools but was higher with diarrhoea. Norovirus GII (AF 5·2%, 95% CI 3·0-7·1), rotavirus (4·8%, 4·5-5·0), Campylobacter spp (3·5%, 0·4-6·3), astrovirus (2·7%, 2·2-3·1), and Cryptosporidium spp (2·0%, 1·3-2·6) exhibited the highest attributable burdens of diarrhoea in the first year of life. The major pathogens associated with diarrhoea in the second year of life were Campylobacter spp (7·9%, 3·1-12·1), norovirus GII (5·4%, 2·1-7·8), rotavirus (4·9%, 4·4-5·2), astrovirus (4·2%, 3·5-4·7), and Shigella spp (4·0%, 3·6-4·3). Rotavirus had the highest AF for sites without rotavirus vaccination and the fifth highest AF for sites with the vaccination. There was substantial variation in pathogens according to geography, diarrhoea severity, and season. Bloody diarrhoea was primarily associated with Campylobacter spp and Shigella spp, fever and vomiting with rotavirus, and vomiting with norovirus GII.Interpretation: There was substantial heterogeneity in pathogen-specific burdens of diarrhoea, with important determinants including age, geography, season, rotavirus vaccine usage, and symptoms. These findings suggest that although single-pathogen strategies have an important role in the reduction of the burden of severe diarrhoeal disease, the effect of such interventions on total diarrhoeal incidence at the community level might be limited

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.Peer reviewe

Wastewater-based epidemiological surveillance to monitor the prevalence of SARS-CoV-2 in developing countries with onsite sanitation facilities

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.This research was supported by Water Aid Bangladesh, North South University, Dhaka, COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh, the International Training Network of Bangladesh University of Engineering and Technology (ITN-BUET) - Centre for Water Supply and Waste Management, and KTH Royal Institute of Technology, Sweden. We acknowledge the sincere help and support of the staff and volunteers of NSTU-COVID-19 Diagnostic Lab, Noakhali Science and Technology University, Bangladesh during the different phases of the study. PB and MTI acknowledge the Life Science Technology Platform, Science for Life Laboratory for the seed funding to initiate the wastewater-based epidemiological studies for SARS-CoV-2 in Bangladesh. We would also like to acknowledge the two anonymous reviewers for their critical comments as well as their thoughtful insights, which has significantly improved the manuscript.Peer reviewe

Early Life Child Micronutrient Status, Maternal Reasoning, and a Nurturing Household Environment have Persistent Influences on Child Cognitive Development at Age 5 years : Results from MAL-ED

Funding Information: The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED) is carried out as a collaborative project supported by the Bill & Melinda Gates Foundation, the Foundation for the NIH, and the National Institutes of Health/Fogarty International Center. This work was also supported by the Fogarty International Center, National Institutes of Health (D43-TW009359 to ETR). Author disclosures: BJJM, SAR, LEC, LLP, JCS, BK, RR, RS, ES, LB, ZR, AM, RS, BN, SH, MR, RO, ETR, and LEM-K, no conflicts of interest. Supplemental Tables 1–5 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Address correspondence to LEM-K (e-mail: [email protected]). Abbreviations used: HOME, Home Observation for Measurement of the Environment inventory; MAL-ED, The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project; TfR, transferrin receptor; WPPSI, Wechsler Preschool Primary Scales of Intelligence.Peer reviewe

Early life child micronutrient status, maternal reasoning, and a nurturing household environment have persistent influences on child cognitive development at age 5 years: Results from MAL-ED

Background: Child cognitive development is influenced by early-life insults and protective factors. To what extent these factors have a long-term legacy on child development and hence fulfillment of cognitive potential is unknown. Objective: The aim of this study was to examine the relation between early-life factors (birth to 2 y) and cognitive development at 5 y. Methods: Observational follow-up visits were made of children at 5 y, previously enrolled in the community-based MAL-ED longitudinal cohort. The burden of enteropathogens, prevalence of illness, complementary diet intake, micronutrient status, and household and maternal factors from birth to 2 y were extensively measured and their relation with the Wechsler Preschool Primary Scales of Intelligence at 5 y was examined through use of linear regression. Results: Cognitive T-scores from 813 of 1198 (68%) children were examined and 5 variables had significant associations in multivariable models: mean child plasma transferrin receptor concentration (β: −1.81, 95% CI: −2.75, −0.86), number of years of maternal education (β: 0.27, 95% CI: 0.08, 0.45), maternal cognitive reasoning score (β: 0.09, 95% CI: 0.03, 0.15), household assets score (β: 0.64, 95% CI: 0.24, 1.04), and HOME child cleanliness factor (β: 0.60, 95% CI: 0.05, 1.15). In multivariable models, the mean rate of enteropathogen detections, burden of illness, and complementary food intakes between birth and 2 y were not significantly related to 5-y cognition. Conclusions: A nurturing home context in terms of a healthy/clean environment and household wealth, provision of adequate micronutrients, maternal education, and cognitive reasoning have a strong and persistent influence on child cognitive development. Efforts addressing aspects of poverty around micronutrient status, nurturing caregiving, and enabling home environments are likely to have lasting positive impacts on child cognitive development.publishedVersio

Data analytics for decision support in software release management

The dynamism and uncertainty in iterative software development demand for frequent releases with shorter duration, reduced scope, and strong stakeholder involvement. The lack of custom-built integrated support for release management in iterative releases mainly motivated this research. Key contribution of this thesis is the proposed Plan-Monitor-Improve (PMI) Framework. PMI integrates i) strategic planning of a release, ii) monitoring release readiness and iii) improving release readiness. Methodologically, this thesis is positioned at the intersection of three key concepts: i) release management ii) data analytics techniques, and iii) decision support systems. The PMI-Plan method introduces a multi-objective particle swarm optimization-based planning method to support the multi-dimensional nature of iterative releases. Maximum stakeholder incorporation is achieved in a three-stage interactive modeling. The research is triggered by industry collaborators in an NSERC CRD project and studies with a prototype tool in real world planning problems. Inspired by statistical process control approaches, the PMI-Monitor method continuously monitors release readiness. PMI-Monitor formulates release readiness prediction as a binary classification problem and solves using machine learning classifiers. The PMI-Improve method addresses release readiness by identifying key attributes to be used for decision-making. PMI-Improve also applies analogy-based reasoning for proactively identifying bottlenecks and their characteristics. The release readiness improvement factor identification is formulated as a search problem and solved using a genetic algorithm. The PMI is an integrated decision centric framework, custom built to support iterative release management. PMI integrates a portfolio of data analytics techniques (e.g. analogical reasoning, multi-objective optimization, classification, process control, interactive search) to gain better insight from the frequent release iterations and strong stakeholder involvements. PMI integrates the characteristics of iterative development and applies it towards an operational platform to make better data driven decisions. Initial empirical evaluation of PMI further supports applicability and benefits of the framework in real world iterative software development