Soil-transmitted helminth infection and nutritional status among urban slum children in Kenya

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Study Justification:
The study aimed to evaluate the nutritional impact of soil-transmitted helminth (STH) infection among urban slum children in Kenya. This is important because STH infections are common in low-resource settings and can lead to malnutrition, anemia, and other health issues. Understanding the relationship between STH infection and nutritional status can help inform interventions and strategies to improve the health of these vulnerable populations.
Highlights:
– The study found that approximately 40% of pre-school children (PSC) and school-aged children (SAC) in the Kibera slum had STH infection, primarily Ascaris and Trichuris.
– Malnutrition prevalence among PSC and SAC included anemia, iron deficiency, vitamin A deficiency, and stunting.
– In multivariate analysis, STH infection in PSC was associated with vitamin A deficiency and iron deficiency.
– Integrated deworming and micronutrient supplementation strategies should be evaluated in this population.
Recommendations:
Based on the study findings, the following recommendations are suggested:
1. Implement integrated deworming programs targeting STH infections among urban slum children.
2. Evaluate the effectiveness of micronutrient supplementation strategies, particularly for vitamin A and iron, in improving the nutritional status of children in urban slums.
3. Strengthen healthcare services in urban slums, including access to free care and clinics staffed by trained personnel.
4. Conduct further research to understand the long-term effects of STH infection on nutritional status and overall health outcomes in urban slum children.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Government health departments and ministries responsible for implementing public health programs.
2. Non-governmental organizations (NGOs) working in the field of child health and nutrition.
3. Community health workers and volunteers who can assist in the implementation of deworming and supplementation programs.
4. Healthcare providers and clinics that can provide free care and support for children in urban slums.
Cost Items:
While the actual cost of implementing the recommendations will vary depending on the specific context, some key cost items to consider in planning the recommendations include:
1. Procurement and distribution of deworming medications.
2. Development and production of educational materials and resources for awareness campaigns.
3. Training and capacity building for healthcare providers and community health workers.
4. Monitoring and evaluation activities to assess the effectiveness of interventions.
5. Infrastructure and equipment for healthcare facilities and clinics in urban slums.
6. Research funding for further studies on the long-term effects of STH infection and interventions.
Please note that the provided cost items are general and may not reflect the actual cost in the specific context. A detailed budget analysis would be required for accurate cost estimation.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is cross-sectional, which limits the ability to establish causality. Additionally, the sample size is relatively small, which may affect the generalizability of the findings. To improve the evidence, future studies could consider using a longitudinal design to establish causality and increase the sample size to improve generalizability.

To evaluate the nutritional impact of soil-transmitted helminth (STH) infection, we conducted a cross-sectional survey of 205 pre-school (PSC) and 487 school-aged children (SAC) randomly selected from the surveillance registry of the Centers for Disease Control and Prevention of the Kibera slum in Kenya. Hemoglobin, iron deficiency (ID), vitamin A deficiency (VAD), inflammation, malaria, anthropometry, and STH ova were measured. Poisson regression models evaluated associations between STH and malnutrition outcomes and controlled for confounders. Approximately 40% of PSC and SAC had STH infection, primarily Ascaris and Trichuris; 2.9% of PSC and 1.1% of SAC had high-intensity infection. Malnutrition prevalence among PSC and SAC was anemia (38.3% and 14.0%, respectively), ID (23.0% and 5.0%, respectively), VAD (16.9% and 4.5%, respectively), and stunting (29.7% and 16.9%, respectively). In multivariate analysis, STH in PSC was associated with VAD (prevalence ratio [PR] = 2.2, 95% confidence interval = 1.1-4.6) and ID (PR = 3.3, 95% confidence interval = 1.6-6.6) but not anemia or stunting. No associations were significant in SAC. Integrated deworming and micronutrient supplementation strategies should be evaluated in this population. Copyright © 2014 by The American Society of Tropical Medicine and Hygiene.

The study was conducted in Kibera in southern Nairobi, Kenya, which is one of the largest contiguous urban slums in Africa.10 The Centers for Disease Control and Prevention’s International Emerging Infections Program (IEIP), in collaboration with the Kenya Medical Research Institute (KEMRI), has conducted community-based morbidity surveillance in Kibera since 2005.11 IEIP performs active household surveillance for major infectious disease syndromes for approximately 28,000 participants living in 2 of Kibera’s 13 villages. Field workers make home visits every 2 weeks to ask about recent illnesses; they also maintain an up-to-date registry by enrolling new arrivals and updating records to reflect outmigration.10,11 Kibera is characterized by high population density, semipermanent housing, and lack of official city water or sewage services. Participants can access free healthcare at a centrally located clinic staffed by study-supported trained personnel and other clinics run by non-governmental organizations and government institutions bordering the slum. Among households in the IEIP participant registry, approximately 25% of households were designated as potential sources for enrollment of pre-school children (PSC; ages 6–59 months), and the other 75% of households were designated as potential sources for enrollment of school-aged children (SAC; ages 5–14 years); this designation ensured that PSC and SAC were not from the same household. Households were then selected with probability proportional to size from each target group, and one child was randomly chosen from each selected household. Sampling weights were calculated to account for non-response and post-stratified to the IEIP cluster size populations. Sample size was capped by the laboratory capacity for daily processing of fresh stools for STH. At 80% power and accounting for 20% non-response, the achievable target sample size of 293 PSC and 899 SAC was powered to detect an odds ratio of 2.0 for STH infection associated with having an infected sibling. Written informed consent was obtained from all participating households. Subjects with anemia, positive malaria test, wasting, or STH infection were referred for free care at the nearby IEIP-run clinic. Institutional review boards of KEMRI and the Centers for Disease Control and Prevention (CDC) approved this study. Fieldwork took place from April to June of 2012. Trained fieldworkers administered a mobile device-based questionnaire to obtain demographic and socioeconomic data and child breastfeeding, deworming, and nutrition supplementation history. Anthropometric measurements of height and length were taken using a wooden measuring board accurate to 0.1 cm (Irwin Shorr Productions, Olney, MD). Weight was measured to the nearest 0.1 kg using a digital scale (Seca Corp, Hanover, MD). Trained fieldworkers completed the measurements using standard techniques. Capillary blood samples were collected for hemoglobin (Hb) measurements, malaria testing, and analysis of micronutrient and inflammatory status. A goal of three stool samples was collected from selected children. Stools were collected over 3 consecutive days when possible, accepted only if produced after midnight the previous night, and maintained in cool boxes before delivery to the laboratory for analysis before 14:00 hours each day. Stool samples were processed at KEMRI using the Kato–Katz technique. Two slides were prepared per sample; approximately 7% of all slides were reread for quality control. Details of the nutrition laboratory analysis are described elsewhere.12 In brief, Hb was determined using a HemoCue Hb301 machine (Ängelholm, Sweden). Anemia was defined by age and corrected for altitude according to WHO guidelines13: Hb < 11.0 g/dL in children 6–59 months of age, Hb < 11.5 g/dL in children 5–11 years of age, and Hb < 12 g/dL in children 12–14 years of age; 0.6 g/dL was added to cutoffs to account for an altitude of 1,800 m. A rapid diagnostic test kit (SD BIOLINE Malaria Ag P.f/Pan, Hagal-dong, Korea) was used to test for malaria. Blood samples were centrifuged, and frozen plasma samples were transported to VitA-Iron Tech (Willstaett, Germany), where levels of ferritin, retinol binding protein (RBP), C-reactive protein (CRP), and α-1-acid glycoprotein (AGP) were measured by sandwich enzyme-linked immunosorbent assay (ELISA).14 The following thresholds were used to define abnormal values for these biochemical indicators: ferritin < 12 μg/L in PSC and ferritin < 15 μg/L in SAC15; RBP 5 mg/L; AGP > 1 g/L.16 Because biomarkers of nutrition are influenced by inflammation, iron deficiency, vitamin A deficiency, and anemia were defined using a correction factor approach to adjust ferritin, RBP, and Hb values for the presence of inflammation, which was described previously.16,17 Because the prevalence of inflammation varied by age, separate correction factors were calculated for PSC and SAC. Among PSC, correction factors for ferritin, RBP, and Hb were as follows: 0.87, 1.0, and 0.91 for early inflammation (elevated CRP and normal AGP); 0.36, 1.51, and 1.08 for early convalescent inflammation (elevated CRP and AGP); 0.78, 1.1, and 1.0 for late convalescent inflammation (elevated AGP and normal CRP). Among SAC, correction factors for ferritin, RBP, and Hb were as follows: 0.70, 1.36, and 1.01 for early inflammation; 0.66, 1.36, and 1.09 for early convalescent inflammation; 0.99, 1.21, and 1.08 for late convalescent inflammation. We used the WHO Child Growth Standards (WHO Anthro, Geneva, Switzerland) to calculate z-scores and categorized underweight as a weight-for-age z-score of < −2, stunting as a height/length-for-age z-score of < −2, and wasting as a body mass index (BMI)-for-age z-score of < −2 to allow for comparisons between PSC and SAC. We used the weekly expenditure on food by the household to classify respondents by socioeconomic status. Children were classified as having received or not received vitamin A supplementation in the past 1 year based on written documentation when available or otherwise, respondent recollection. Statistical analyses were performed using R version 2.15.2 (R Core Team, 2012). The survey package was used to incorporate survey weights. Comparisons between PSC and SAC were performed with χ2 test. Means and standard deviations (SDs) were computed for continuous variables, and t test was used to compare means. Multivariable analyses used Poisson regression to provide prevalence ratios (PR).18 All analyses were run separately for PSC and SAC. Five nutritional outcomes were investigated: anemia, vitamin A deficiency (VAD), iron deficiency (ID), iron deficiency anemia (IDA), and stunting. Inflammation was also investigated as an outcome (e.g., elevated CRP and/or elevated AGP). These outcomes were dichotomized according to previously described cutoffs. Several alternative measures of the primary exposure (STH infection) were used: presence/absence of any STH infection; presence/absence of species-specific infection with Ascaris or Trichuris; presence/absence of coinfection with both Ascaris and Trichuris; and intensity of infection (none, light, medium/moderate, or high according to standard egg per 1 gram-based WHO cutoffs19 using the highest-intensity species found in the case of coinfection, with eggs per 1 gram determined as an average among all the subjects' stools). Demographic and clinical confounders were determined independently for each outcome a priori based on knowledge from published literature that they were potential predictors of the various nutritional outcomes. Demographic factors considered were age, sex, maternal education, ethnicity, socioeconomic status, and breastfeeding. Clinical factors included were malaria, stunting, wasting, ID, and VAD. To test the robustness of the model, we also performed a sensitivity analysis for the VAD outcome, which included history of recent vitamin A supplementation. Unadjusted prevalence ratios and 95% confidence intervals were computed for association between a specific outcome and primary exposure. Age, sex, and socioeconomic status were forced in the models as potential confounders. When the primary exposure was Ascaris infection, presence of Trichuris infection was also forced in the models. The decision to include the other demographic variables and clinical variables in the models was based on at least a 10% change in the prevalence ratio of the primary exposure. Variables that were originally continuous were treated as both continuous and dichotomized variables in separate models. The results of these models were compared to assess residual confounding and whether associations were linear. The best models selected were those models with confidence intervals that were more precise.

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

1. Integrated deworming and micronutrient supplementation strategies: The study suggests that deworming and providing micronutrient supplementation could be effective in improving the nutritional status of children in urban slums. Implementing integrated programs that combine deworming and micronutrient supplementation could help address both soil-transmitted helminth infections and malnutrition.

2. Mobile device-based questionnaire: The study mentions the use of a mobile device-based questionnaire to collect demographic and socioeconomic data. This innovation could be further developed and utilized to collect data on maternal health, allowing for more efficient and accurate data collection in resource-limited settings.

3. Community-based morbidity surveillance: The Centers for Disease Control and Prevention’s International Emerging Infections Program (IEIP) has been conducting community-based morbidity surveillance in the Kibera slum since 2005. This approach could be expanded to include monitoring and surveillance of maternal health indicators, allowing for early detection and intervention in cases of maternal health complications.

4. Free healthcare access: The study mentions that participants in the surveillance registry can access free healthcare at a centrally located clinic. Expanding access to free healthcare services for maternal health, including antenatal care, skilled birth attendance, and postnatal care, could help improve access to essential maternal health services in urban slum areas.

5. Collaboration between non-governmental organizations and government institutions: The study mentions that there are clinics run by non-governmental organizations and government institutions bordering the slum. Strengthening collaboration and coordination between these organizations and institutions could help improve the availability and quality of maternal health services in urban slum areas.

It is important to note that these recommendations are based on the information provided and may need to be further evaluated and adapted to the specific context and needs of the population.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health would be to implement integrated deworming and micronutrient supplementation strategies in urban slum areas, specifically targeting pregnant women and mothers of young children. This recommendation is based on the findings of the study, which showed a significant association between soil-transmitted helminth (STH) infection and nutritional deficiencies, such as vitamin A deficiency (VAD) and iron deficiency (ID), among pre-school children (PSC) in the Kibera slum in Kenya.

By integrating deworming and micronutrient supplementation programs, pregnant women and mothers of young children can receive both interventions simultaneously, leading to improved maternal and child health outcomes. Deworming can help reduce the burden of STH infection, which has been shown to contribute to malnutrition and anemia. Micronutrient supplementation, specifically targeting deficiencies in vitamin A and iron, can help address the nutritional deficiencies identified in the study.

To implement this recommendation, it would be important to collaborate with local healthcare providers, non-governmental organizations, and government institutions to ensure the availability and accessibility of deworming medications and micronutrient supplements in urban slum areas. Additionally, community education and awareness campaigns can be conducted to promote the importance of deworming and micronutrient supplementation during pregnancy and early childhood.

Regular monitoring and evaluation of the program’s impact on maternal and child health outcomes, as well as the prevalence of STH infection and nutritional deficiencies, should be conducted to assess the effectiveness of the integrated approach and make any necessary adjustments to the program.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Integrated deworming and nutrition supplementation programs: Based on the study’s findings that soil-transmitted helminth (STH) infection is associated with nutritional deficiencies in children, implementing integrated deworming and nutrition supplementation programs can help improve maternal health. These programs can include regular deworming treatments for pregnant women and providing them with essential nutrients such as iron and vitamin A.

2. Community-based health education: Conducting community-based health education programs can help raise awareness about the importance of maternal health and the risks associated with STH infection. These programs can provide information on preventive measures, such as proper hygiene practices and the importance of regular antenatal care visits.

3. Strengthening healthcare infrastructure: Improving access to maternal health requires a well-functioning healthcare infrastructure. This can involve increasing the number of healthcare facilities in slum areas, ensuring availability of essential medical supplies and equipment, and training healthcare providers to deliver quality maternal healthcare services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Baseline data collection: Collect data on the current status of maternal health in the target population, including indicators such as maternal mortality rates, antenatal care coverage, and access to essential maternal healthcare services.

2. Define simulation parameters: Determine the specific parameters to be simulated, such as the coverage and effectiveness of integrated deworming and nutrition supplementation programs, the reach and impact of community-based health education programs, and the improvements in healthcare infrastructure.

3. Model development: Develop a simulation model that incorporates the baseline data and the defined parameters. This model should simulate the potential impact of the recommendations on maternal health indicators over a specified time period.

4. Data analysis: Run the simulation model using different scenarios and analyze the results to assess the potential impact of the recommendations on improving access to maternal health. This can include evaluating changes in maternal mortality rates, antenatal care coverage, and other relevant indicators.

5. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation results by varying the input parameters and evaluating their impact on the outcomes. This helps identify the key factors that influence the effectiveness of the recommendations.

6. Interpretation and recommendations: Interpret the simulation results and provide recommendations based on the findings. This can include identifying the most effective interventions, determining the resources required for implementation, and suggesting strategies for monitoring and evaluation.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

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