Epidemiology and Impact of Campylobacter Infection in Children in 8 Low-Resource Settings: Results from the MAL-ED Study

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Study Justification:
The study aimed to investigate the epidemiology and impact of Campylobacter infection in children in low-resource settings. This is important because enteropathogen infections have been linked to enteric dysfunction and impaired growth in these settings. Understanding the prevalence and factors associated with Campylobacter infection can help inform interventions to reduce the burden of infection and improve child growth.
Study Highlights:
– The study included 1892 children from 8 low-resource settings.
– Most children (84.9%) had a Campylobacter-positive stool sample by 1 year of age.
– Factors associated with a reduced risk of Campylobacter detection included exclusive breastfeeding, treatment of drinking water, access to an improved latrine, and recent macrolide antibiotic use.
– A high Campylobacter burden was associated with lower length-for-age Z scores at 24 months compared to a low burden.
– Campylobacter infection was also associated with increased intestinal permeability and intestinal and systemic inflammation.
Study Recommendations:
Based on the findings, the following recommendations can be made:
1. Promote exclusive breastfeeding to reduce the risk of Campylobacter infection.
2. Implement measures to treat drinking water and improve latrine facilities to reduce the burden of Campylobacter infection.
3. Targeted antibiotic treatment may be beneficial in reducing Campylobacter infection.
4. Interventions to improve child growth should consider the impact of Campylobacter infection on intestinal permeability and inflammation.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Public health officials and policymakers to develop and implement interventions.
2. Healthcare providers to educate caregivers about the importance of exclusive breastfeeding and proper water treatment.
3. Engineers and sanitation experts to improve latrine facilities.
4. Researchers to further investigate the effectiveness of targeted antibiotic treatment.
Cost Items for Planning Recommendations:
While the actual cost will vary depending on the specific context, the following cost items should be considered in planning the recommendations:
1. Costs associated with promoting exclusive breastfeeding, such as education materials and support programs.
2. Costs of implementing water treatment measures, including equipment and training.
3. Costs of improving latrine facilities, including construction and maintenance.
4. Costs of targeted antibiotic treatment, including medication and monitoring.
5. Costs of research and evaluation to assess the effectiveness of interventions and inform future strategies.
Please note that the provided cost items are general categories and the actual cost will depend on the local context and specific interventions implemented.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is robust, with a large sample size and data collected from multiple sites. The statistical analysis is thorough, using appropriate methods to adjust for confounders. The results show a high prevalence of Campylobacter infection in children in low-resource settings and its association with growth shortfalls. The conclusions are supported by the data. However, to improve the evidence, it would be helpful to provide more details on the specific methods used for data collection and analysis, as well as the limitations of the study. Additionally, including information on the funding source and potential conflicts of interest would enhance transparency.

Background. Enteropathogen infections have been associated with enteric dysfunction and impaired growth in children in low-resource settings. In a multisite birth cohort study (MAL-ED), we describe the epidemiology and impact of Campylobacter infection in the first 2 years of life. Methods. Children were actively followed up until 24 months of age. Diarrheal and nondiarrheal stool samples were collected and tested by enzyme immunoassay for Campylobacter. Stool and blood samples were assayed for markers of intestinal permeability and inflammation. Results. A total of 1892 children had 7601 diarrheal and 26 267 nondiarrheal stool samples tested for Campylobacter. We describe a high prevalence of infection, with most children (n = 1606; 84.9%) having a Campylobacter-positive stool sample by 1 year of age. Factors associated with a reduced risk of Campylobacter detection included exclusive breastfeeding (risk ratio, 0.57; 95% confidence interval,. 47-.67), treatment of drinking water (0.76; 0.70-0.83), access to an improved latrine (0.89; 0.82-0.97), and recent macrolide antibiotic use (0.68; 0.63-0.74). A high Campylobacter burden was associated with a lower length-for-age Z score at 24 months (-1.82; 95% confidence interval, -1.94 to -1.70) compared with a low burden (-1.49; -1.60 to -1.38). This association was robust to confounders and consistent across sites. Campylobacter infection was also associated with increased intestinal permeability and intestinal and systemic inflammation. Conclusions. Campylobacter was prevalent across diverse settings and associated with growth shortfalls. Promotion of exclusive breastfeeding, drinking water treatment, improved latrines, and targeted antibiotic treatment may reduce the burden of Campylobacter infection and improve growth in children in these settings.

The MAL-ED study design and methodology have been described elsewhere [18]. The study was conducted at 8 sites: Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru. Children were enrolled from November, 2009 to February, 2012 and followed up through 24 months of age. Monthly anthropometry was performed [21]. Stool samples were collected in the absence of diarrhea at 1–12, 15, 18, 21, and 24 months of age as well as from each diarrhea episode, defined as maternal report of ≥3 loose stools in 24 hours or visible blood in stool and identified through twice-weekly home visits. Caregivers were surveyed biannually from 6 months of age, including questions about maternal income and education, the home environment, drinking water source and treatment, and the presence of animals. Crowding was defined as >2 persons per room living in the home. An improved latrine and water source were defined following World Health Organization guidelines [22]. Treatment of drinking water was defined as boiling, filtering, or adding bleach. Poor access to water was defined as having a primary drinking water source more than a 10-minute walk from the home. Twice-weekly home surveillance assessed breastfeeding in the prior day as exclusive (no consumption of other food or liquid), partial, or none, identified the introduction of specific foods, and recorded antibiotic use. All sites received ethical approval from their respective governmental, local institutional, and collaborating institutional review boards. Written informed consent was obtained from the parent or guardian of each child. The laboratory methods used in the MAL-ED study have been described elsewhere [10, 23]. Pertinently, EIA was performed for Campylobacter (ProSpecT) as well as Giardia and Cryptosporidium (TechLab). Monthly surveillance stool samples were also tested for myeloperoxidase (MPO; measured in nanograms per milliliter), a marker of neutrophil activity in the intestinal mucosa (Alpco); neopterin (NEO; measured in nanomoles per liter), a marker of T-helper cell 1 activity (GenWay Biotech); and α-1-antitrypsin (AAT; measured in milligrams per gram), a marker of intestinal permeability (Biovendor). Blood samples collected at 7, 15 and 24 months were tested for α-1-acid glycoprotein (AGP; measured in milligrams per deciliter), a marker of systemic inflammation. To identify factors associated with Campylobacter detection in surveillance stool samples, we used generalized estimating equations to fit a generalized linear model with a first-order autoregressive working correlation matrix and robust variance to account for nonindependence of stool testing within each child. To estimate risk ratios for Campylobacter detection, we used Poisson regression as an approximation of log-binomial regression since the log-binomial models did not converge [24]. First, we estimated the association for each factor of interest with Campylobacter detection, adjusting for age (using a natural spline with knots at 6, 12, and 18 months), sex, site, and season via the terms sin(2mπ/12) + cos(2mπ/12), where m is the month of the year as well as—given possible variation in seasonality between sites—an interaction between these terms and site [25]. Then, based on statistical significance, model fit based on the quasi-likelihood information criterion and an assessment of covariance between individual factors, we fit a multivariable model. We also fit site-specific models, excluding those variables from the multivariable model that did not vary within specific sites. Finally, to further describe any association with recent antibiotic use, we fit 2 multivariable models, adjusted as described above, which included (1) class-specific antibiotic use in the prior month and (2) class-specific use in 15-day windows over the prior 60 days. For the analysis of linear growth, included individuals were required to have a length-for-age Z (LAZ) score at 24 months of age. We excluded children from the Pakistan site, owing to bias noted in a subset of length measurements at this site. To estimate the association between the burden of detection of an individual pathogen and 24-month LAZ score, we calculated the enteropathogen burden for each subject using the surveillance stool samples (namely, samples positive/samples tested). Similar burden indices were calculated for the 0–6, 7–12, and 13–24 month intervals. Persistent infection was defined as detection of Campylobacter from all surveillance stool samples tested during a 3-month period. We then fit a multiple linear regression, including enrollment LAZ score, sex, site, and Campylobacter burden and further adjusted for possible confounders, including factors associated with Campylobacter infection in the multivariable model as well as highly-correlated pathogens. To calculate model-predicted 24-month LAZ scores, we calculated predicted population marginal effects using least-squares means [26]. Overall and site-specific high and low burdens of Campylobacter were defined as the 90th and 10th percentile of Campylobacter burden, respectively. To estimate the association between Campylobacter detection and fecal markers of intestinal permeability and inflammation collected in surveillance stool samples (MPO, NEO, and AAT), we used generalized estimating equations to fit a generalized linear model, as described previously but using a gaussian distribution. These models adjusted for age, sex and site, as previously described, and we also fit site-specific models. Finally, to describe the association between Campylobacter burden and systemic inflammation, we fit multiple linear regression models and calculated predicted population marginal effects using least-squares means, both for the entire cohort and for each site, as described for the analysis of linear growth, but with the mean AGP value for each individual as the response variable instead of 24-month LAZ score. All statistical analysis was performed using R software, version 3.2.2 (Foundation for Statistical Computing).

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health. These apps could include features such as prenatal care reminders, nutrition guidance, and access to telemedicine consultations.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in low-resource settings to consult with healthcare professionals remotely. This could help overcome barriers such as distance and lack of transportation to healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal healthcare services. These vouchers could cover costs such as prenatal care, delivery, and postnatal care.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in low-resource settings to provide comprehensive care for pregnant women. These clinics could offer prenatal check-ups, delivery services, postnatal care, and family planning services.

6. Health Education Programs: Develop and implement health education programs that focus on maternal health. These programs could provide information on topics such as nutrition during pregnancy, breastfeeding, and the importance of prenatal care.

7. Improved Infrastructure: Invest in improving healthcare infrastructure in low-resource settings, including the construction and renovation of healthcare facilities. This would ensure that pregnant women have access to clean and safe environments for childbirth.

8. Maternal Health Monitoring Systems: Implement systems for monitoring and tracking maternal health indicators in real-time. This could help identify areas with high maternal mortality rates and enable targeted interventions to improve access to care.

9. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This could involve leveraging private sector resources and expertise to enhance healthcare delivery in low-resource settings.

10. Maternal Health Financing: Develop innovative financing mechanisms to ensure sustainable funding for maternal health programs. This could include exploring options such as social impact bonds, health insurance schemes, and public-private partnerships for funding maternal health initiatives.
AI Innovations Description
Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Promote exclusive breastfeeding: Encourage and support mothers to exclusively breastfeed their infants for the first six months of life. Breast milk provides essential nutrients and antibodies that can help protect infants from infections, including Campylobacter.

2. Improve access to clean drinking water: Implement strategies to ensure that communities have access to safe and clean drinking water. This can include treating drinking water through boiling, filtering, or adding bleach to reduce the risk of Campylobacter contamination.

3. Enhance sanitation facilities: Increase access to improved latrines and promote proper hygiene practices to reduce the transmission of Campylobacter. This can include educating communities on the importance of using latrines and practicing good hand hygiene.

4. Targeted antibiotic treatment: Develop guidelines and protocols for the appropriate use of antibiotics to treat Campylobacter infections in children. This can help reduce the burden of infection and minimize the impact on growth and development.

By implementing these recommendations, it is possible to reduce the prevalence of Campylobacter infection and improve maternal and child health outcomes in low-resource settings.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Promote exclusive breastfeeding: Encourage and support mothers to exclusively breastfeed their infants for the first six months of life. This can be done through education programs, community support groups, and healthcare provider guidance.

2. Improve access to clean drinking water: Implement measures to ensure that drinking water is safe and free from contamination. This can include water treatment methods such as boiling, filtering, or adding bleach, as well as improving infrastructure for water supply and storage.

3. Enhance sanitation facilities: Increase access to improved latrines and sanitation facilities to reduce the risk of fecal contamination and the spread of infections. This can involve building and maintaining proper sanitation infrastructure, promoting hygiene practices, and raising awareness about the importance of sanitation.

4. Targeted antibiotic treatment: Develop strategies to ensure appropriate and targeted use of antibiotics for the treatment of infections, including Campylobacter infection. This can involve training healthcare providers on proper antibiotic prescribing practices, implementing guidelines for antibiotic use, and promoting antibiotic stewardship.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as maternal mortality rates, antenatal care coverage, skilled birth attendance, and postnatal care utilization.

2. Collect baseline data: Gather data on the current status of these indicators in the target population or setting. This can involve conducting surveys, reviewing existing data sources, and engaging with local stakeholders.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, demographic characteristics, healthcare infrastructure, and resource availability.

4. Input data and parameters: Input the collected baseline data into the simulation model, along with relevant parameters such as the coverage and effectiveness of the recommendations. This may require estimating or obtaining data on factors such as breastfeeding rates, access to clean water, sanitation coverage, and antibiotic use patterns.

5. Run simulations: Run the simulation model using different scenarios that reflect the implementation of the recommendations. This can involve varying the coverage and effectiveness of each recommendation to assess their individual and combined impacts.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on the selected indicators of access to maternal health. This can include comparing the outcomes of different scenarios, identifying trends or patterns, and assessing the magnitude of the expected improvements.

7. Validate and refine the model: Validate the simulation model by comparing its results with real-world data or expert opinions. Refine the model based on feedback and further insights gained from the validation process.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community members. Use the results to advocate for the implementation of the recommended interventions and inform decision-making processes.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data. Additionally, collaboration with local experts and stakeholders is crucial to ensure the relevance and applicability of the simulation study.

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