Use of quantitative molecular diagnostic methods to investigate the effect of enteropathogen infections on linear growth in children in low-resource settings: longitudinal analysis of results from the MAL-ED cohort study

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Study Justification:
The study aimed to investigate the effect of enteropathogen infections on linear growth in children in low-resource settings. This is important because enteropathogen infections not only cause diarrhea but also contribute to poor growth in children. By using quantitative molecular diagnostic methods, the study aimed to identify specific enteropathogens associated with decreased linear growth in order to inform interventions and reduce global stunting.
Study Highlights:
– The study used molecular diagnostics to detect 29 enteropathogens in stool samples collected from children in low-resource settings.
– Diarrheal episodes attributed to bacteria and parasites, but not viruses, were associated with small decreases in length after 3 months and at age 2 years.
– Subclinical, non-diarrheal infections with certain enteropathogens, including Shigella, enteroaggregative Escherichia coli, Campylobacter, and Giardia, were associated with substantial decrements in length at 2 years.
– Successfully reducing exposure to these pathogens could increase the mean length of children by 0.12-0.37 length-for-age Z score (LAZ), equivalent to 0.4-1.2 cm, at the study sites.
Study Recommendations:
Based on the findings, the study recommends interventions that can decrease exposure to Shigella, enteroaggregative E. coli, Campylobacter, and Giardia. These interventions have the potential to increase the linear growth of children and reduce global stunting.
Key Role Players:
To address the study recommendations, the following key role players are needed:
1. Researchers and scientists: To further investigate the associations between enteropathogen infections and linear growth, and to develop and evaluate interventions.
2. Healthcare providers: To implement interventions and provide appropriate treatment for enteropathogen infections.
3. Public health officials: To develop policies and guidelines for preventing and managing enteropathogen infections in low-resource settings.
4. Community leaders and organizations: To raise awareness about the importance of hygiene and sanitation practices to prevent enteropathogen infections.
Cost Items for Planning Recommendations:
While the actual cost of implementing the recommendations will vary depending on the specific interventions and context, some potential cost items to consider in planning include:
1. Research and data collection: Funding for further research, data collection, and analysis to better understand the associations between enteropathogen infections and linear growth.
2. Intervention development and implementation: Resources for developing and implementing interventions to reduce exposure to Shigella, enteroaggregative E. coli, Campylobacter, and Giardia, such as hygiene promotion programs, improved sanitation facilities, and access to clean water.
3. Healthcare services: Funding for healthcare services, including diagnosis, treatment, and follow-up care for enteropathogen infections.
4. Training and capacity building: Investment in training healthcare providers, community health workers, and public health officials to effectively implement interventions and promote behavior change.
5. Monitoring and evaluation: Resources for monitoring and evaluating the impact of interventions on linear growth and enteropathogen infections, and making necessary adjustments to improve effectiveness.
Please note that the above cost items are general considerations and may not cover all potential costs associated with implementing the recommendations.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a longitudinal analysis of results from the MAL-ED cohort study. The study used quantitative PCR to detect 29 enteropathogens in stool samples collected from children in low-resource settings. The associations between enteropathogen infections and linear growth were estimated at different time intervals and adjusted for confounding factors. The findings suggest that certain enteropathogens, such as Shigella, enteroaggregative E coli, Campylobacter, and Giardia, have a substantial negative association with linear growth. The study provides actionable steps to improve child health by reducing exposure to these pathogens, which could potentially increase the mean length of children. However, to further strengthen the evidence, it would be beneficial to include more details on the study population, sampling methods, and statistical analyses in the abstract.

Background: Enteropathogen infections in early childhood not only cause diarrhoea but contribute to poor growth. We used molecular diagnostics to assess whether particular enteropathogens were associated with linear growth across seven low-resource settings. Methods: We used quantitative PCR to detect 29 enteropathogens in diarrhoeal and non-diarrhoeal stools collected from children in the first 2 years of life obtained during the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) multisite cohort study. Length was measured monthly. We estimated associations between aetiology-specific diarrhoea and subclinical enteropathogen infection and quantity and attained length in 3 month intervals, at age 2 and 5 years, and used a longitudinal model to account for temporality and time-dependent confounding. Findings: Among 1469 children who completed 2 year follow-up, 35 622 stool samples were tested and yielded valid results. Diarrhoeal episodes attributed to bacteria and parasites, but not viruses, were associated with small decreases in length after 3 months and at age 2 years. Substantial decrements in length at 2 years were associated with subclinical, non-diarrhoeal, infection with Shigella (length-for-age Z score [LAZ] reduction −0·14, 95% CI −0·27 to −0·01), enteroaggregative Escherichia coli (−0·21, −0·37 to −0·05), Campylobacter (−0·17, −0·32 to −0·01), and Giardia (−0·17, −0·30 to −0·05). Norovirus, Cryptosporidium, typical enteropathogenic E coli, and Enterocytozoon bieneusi were also associated with small decrements in LAZ. Shigella and E bieneusi were associated with the largest decreases in LAZ per log increase in quantity per g of stool (−0·13 LAZ, 95% CI −0·22 to −0·03 for Shigella; −0·14, −0·26 to −0·02 for E bieneusi). Based on these models, interventions that successfully decrease exposure to Shigella, enteroaggregative E coli, Campylobacter, and Giardia could increase mean length of children by 0·12–0·37 LAZ (0·4–1·2 cm) at the MAL-ED sites. Interpretation: Subclinical infection and quantity of pathogens, particularly Shigella, enteroaggregative E coli, Campylobacter, and Giardia, had a substantial negative association with linear growth, which was sustained during the first 2 years of life, and in some cases, to 5 years. Successfully reducing exposure to certain pathogens might reduce global stunting. Funding: Bill & Melinda Gates Foundation.

The MAL-ED study design has been described previously.10 Children were enrolled within 17 days of birth at eight locations between November, 2009, and February, 2012. Linear anthropometric measurements were available from seven locations: Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Fortaleza, Brazil; Loreto, Peru; Venda, South Africa; and Haydom, Tanzania. 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, they had no other siblings enrolled in the study, and had a birthweight or enrolment weight of more than 1500 g. Children diagnosed with congenital disease or severe neonatal disease were excluded. All sites received ethical 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 until age 2 years for active surveillance of child illnesses, antibiotic use, breastfeeding, and food intake. Sociodemographic information was collected every 6 months. Linear anthropometric measurements were obtained by fieldworkers monthly to age 2 years (length) and once at age 5 years (±6 months; height).11 Diarrhoeal stools were defined by maternal report of three or more loose stools in 24 h or one stool with visible blood. Non-diarrhoeal stool samples were collected monthly (at least 3 days before or after a diarrhoea episode) from birth to age 2 years. We tested all stool specimens using custom-designed TaqMan Array Cards (ThermoFisher, Carlsbad, CA, USA) that compartmentalised probe-based quantitative PCR assays for 29 enteropathogens (appendix). Assay validation, nucleic acid extraction, quantitative PCR conditions, and quality control have been previously described.13, 14 Both Shigella and enteroinvasive E coli are detected using the ipaH target; however, on the basis of previous findings that Shigella flexneri and Shigella sonnei account for the majority of ipaH detections,13 and Shigella positive stool cultures are metagenomically similar to ipaH positive stools,15 for simplicity the presence of ipaH was considered diagnostic of Shigella. Pathogen-specific aetiology of diarrhoea was determined using attributable fractions (AFe) to adjust for subclinical pathogen infections, as previously described.13, 16, 17 We defined pathogen-attributable episodes when the pathogen quantity-derived AFe was 0·5 or higher (ie, majority attribution). Episodes with a sum of all pathogen-specific AFes of less than 0·5 (ie, the majority of the episode was not attributed to pathogens) were considered non-attributable. We assessed the associations between diarrhoeal aetiologies and growth for diarrhoea episodes attributable to any infection, and to viral, parasitic, and bacterial pathogen groups (appendix). We also assessed individual pathogens, specifically the ten enteropathogens with the highest attributable diarrhoeal incidence in the MAL-ED study (identified in the companion Article16): Shigella, typical enteropathogenic E coli, Campylobacter jejuni or Campylobacter coli, enterotoxigenic E coli, Cryptosporidium, astrovirus, sapovirus, norovirus, rotavirus, and adenovirus 40/41. For the subclinical infection and growth analysis, we assessed all 29 pathogens (appendix) and included the 13 most prevalent pathogens as covariates in the models (including all pathogens with significant associations with growth in the height attainment model): enteroaggregative E coli, enterotoxigenic E coli, Giardia, Campylobacter, atypical enteropathogenic E coli, adenovirus 40/41, sapovirus, typical enteropathogenic E coli, norovirus, Shigella, astrovirus, Enterocytozoon bieneusi and Cryptosporidium. Length measurements were converted into length-for-age Z scores (LAZ) using 2006 WHO child growth standards.18 Socioeconomic status was summarised using a construct of water, assets, maternal education, and income11, 15 and was averaged over four biannual surveys. Exclusive breastfeeding was defined as the proportion of days in a specified time period in which children were breastfed and received no liquids or solids. Potential confounders were included on the basis of previous associations with enteropathogen exposure19, 20, 21 and linear growth.11 To estimate the associations between aetiology-specific diarrhoea and linear growth after 3 months, we used repeated measures linear regression with general estimating equations to account for correlation between children’s outcomes over time. Models were adjusted for age, site, sex, socioeconomic status, maternal height, LAZ at the beginning of the interval, exclusive breastfeeding, and number of non-attributable diarrhoea episodes in the same period. We also estimated the associations of diarrhoea with fever, dehydration, vomiting, blood, prolonged duration (diarrhoea for 7 days or longer), and high severity (modified Vesikari score >6)22 with LAZ after 3 months. We used linear regression to estimate associations between aetiology-specific diarrhoea episodes and LAZ measured at 2 years (acceptable window for measurement was age 731 days ±15 days) in a height attainment model. Models were adjusted for enrolment LAZ, sex, socioeconomic status, exclusive breastfeeding in the first 6 months, maternal height, number of non-attributable diarrhoea episodes, and number of episodes treated with any antibiotics. Effects were estimated for the difference in LAZ at 2 years and scaled to compare a high burden of attributable diarrhoea episodes with a low burden (ie, the difference between the 90th and 10th percentile). We used linear regression to estimate associations between subclinical enteropathogen infections and LAZ measured at 2 years in a height attainment model, adjusting for site, enrolment LAZ, sex, socioeconomic status, exclusive breastfeeding in the first 6 months, and maternal height. Exposure to each enteropathogen was summarised as the proportion of non-diarrhoeal stools obtained between age 1 and 24 months that were positive for that enteropathogen. The summative effect of pathogen groups was assessed by calculating the mean number of detections between age 1 and 24 months. The difference in LAZ at 2 years associated with each pathogen was scaled to compare the 90th percentile with the 10th percentile for stool positivity (appendix). In a second analysis, we specified pathogen quantity in non-diarrhoeal stools as the exposure, defined by mean log-copy number per g of stool (appendix) and scaled effects per one log increase in pathogen quantity. In a sensitivity analysis, we specified enteropathogen exposure as the proportion of all positive stools (non-diarrhoeal and diarrhoeal) obtained between age 1 and 24 months. We also estimated the associations of enteropathogen exposures with weight-for-age and weight-for-length Z scores in models with the same structure, additionally adjusting for enrolment weight-for-age Z scores. We estimated the associations with height-for-age Z score at 5 years of age (±6 months) in a model with the same structure. To ensure temporality and include potential lag periods between exposures and growth, we investigated enteropathogens in longitudinal models. We defined exposures in 6 month intervals from birth to 2 years and used the parametric g-formula23 to model interim effects on LAZ at the end of the intervals and overall effect on LAZ at 2 years. The parametric g-formula was fitted for each pathogen individually, specified first as the proportion of positive non-diarrhoeal stools and second as the mean quantity in non-diarrhoeal stools during the 6 month interval. Pathogen exposures were assessed with a flexible lag structure including the exposure in the current and previous intervals (appendix). All models were adjusted for the five pathogens with the strongest associations with LAZ at 2 years. We first used the observed data to estimate β-coefficients in longitudinal repeated measures models for each time-dependent covariate in the 6 month intervals (appendix). We used Monte Carlo simulations with the estimated coefficients to predict the time-dependent covariates, pathogen exposures, and LAZ outcomes for each interval to age 2 years in a random sample of 50 000 replicates from the study population at baseline. Simulations were run for high (90th percentile in each interval) and low (10th percentile in each interval) pathogen exposure conditions and the difference due to a one log increase in pathogen quantity. We estimated the cumulative effect of pathogens on LAZ by calculating the difference of the mean predicted outcomes at 2 years between the high and low exposure conditions (population-standardised LAZ difference). 95% CIs were constructed by bootstrapping at the individual level to account for correlation between observations over time with 1000 replicates. 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 data in the study and had final responsibility for the decision to submit for publication.

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Development of point-of-care molecular diagnostic tests: Creating portable and affordable diagnostic tests that can accurately detect enteropathogens in low-resource settings would enable healthcare providers to quickly identify infections and provide appropriate treatment.

2. Mobile health (mHealth) applications: Developing mobile applications that can provide real-time information and guidance to pregnant women and new mothers about enteropathogen infections, prevention strategies, and available healthcare services could improve access to maternal health information and support.

3. Community-based interventions: Implementing community-based interventions that focus on improving hygiene practices, sanitation, and access to clean water can help reduce the transmission of enteropathogens and improve maternal and child health outcomes.

4. Targeted interventions for high-risk populations: Identifying high-risk populations, such as those living in areas with high prevalence of enteropathogen infections, and implementing targeted interventions, such as vaccination campaigns or distribution of clean water filters, can help reduce the burden of these infections and improve maternal health.

5. Integration of maternal health services: Integrating maternal health services with existing healthcare systems, such as primary care clinics or community health centers, can improve access to prenatal care, screening for enteropathogen infections, and appropriate treatment for pregnant women.

6. Capacity building and training: Providing training and capacity building programs for healthcare providers in low-resource settings on the detection, management, and prevention of enteropathogen infections can improve the quality of maternal health services and outcomes.

7. Policy and advocacy: Advocating for policies that prioritize maternal health and allocate resources for the prevention and treatment of enteropathogen infections can help improve access to maternal health services and reduce the burden of these infections on pregnant women and their children.
AI Innovations Description
The recommendation to improve access to maternal health based on the findings of the MAL-ED cohort study is to implement interventions that successfully decrease exposure to specific enteropathogens, such as Shigella, enteroaggregative E. coli, Campylobacter, and Giardia. These pathogens have been found to have a substantial negative association with linear growth in children during the first 2 years of life, and in some cases, up to 5 years. By reducing exposure to these pathogens, it is possible to increase the mean length of children by 0.12-0.37 length-for-age Z scores (LAZ), which corresponds to a 0.4-1.2 cm increase in length at the MAL-ED study sites. This intervention has the potential to reduce global stunting in children.
AI Innovations Methodology
The study described in the provided text focuses on the use of quantitative molecular diagnostic methods to investigate the impact of enteropathogen infections on linear growth in children in low-resource settings. The goal is to understand the association between specific enteropathogens and poor growth in order to identify potential interventions to improve child health.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Based on the findings of the study, specific recommendations can be formulated to improve access to maternal health. These recommendations could include interventions to decrease exposure to enteropathogens such as Shigella, enteroaggregative E coli, Campylobacter, and Giardia.

2. Define the simulation parameters: Determine the variables and parameters that will be used to simulate the impact of the recommendations. This could include factors such as the prevalence of enteropathogen infections, the effectiveness of interventions, and the baseline levels of maternal health access.

3. Develop a simulation model: Create a mathematical or computational model that represents the relationship between the recommendations and access to maternal health. This model should incorporate the relevant variables and parameters identified in the previous step.

4. Input data: Gather data on the current state of access to maternal health, including factors such as healthcare infrastructure, availability of healthcare providers, and utilization rates. This data will be used as input for the simulation model.

5. Run simulations: Use the simulation model to run multiple iterations, varying the parameters to simulate different scenarios. This could involve comparing the impact of different levels of intervention effectiveness or different levels of enteropathogen prevalence.

6. Analyze results: Analyze the results of the simulations to determine the impact of the recommendations on access to maternal health. This could include measuring changes in key indicators such as the number of women receiving prenatal care, the number of skilled birth attendants, or the reduction in maternal mortality rates.

7. Validate the model: Validate the simulation model by comparing the results of the simulations with real-world data. This will help ensure that the model accurately represents the impact of the recommendations on access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of specific recommendations on improving access to maternal health. This information can then be used to inform decision-making and prioritize interventions that have the greatest potential for positive impact.

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