Blood lead level in infants and subsequent risk of malaria: A prospective cohort study in Benin, Sub-Saharan Africa

PLoS ONE, Volume 14, No. 7, Year 2019

Interpretation

Lead and malaria both present significant health risks to children in Sub-Saharan Africa. Previous studies have shown that high blood lead levels in children act as a protective factor against subsequent malaria incidence. The main objective of this study was to investigate associations between blood lead level and malaria outcomes prospectively in Beninese children from 12 to 24 months of age. Two-hundred and four children were assessed for lead at 12 months and closely followed until 24 months for malaria; when symptoms and parasite density were also recorded. Univariate and multivariate negative binomial and linear regression models tested associations between blood lead level quartile and total episodes of malaria (total symptomatic and asymptomatic episodes) and parasite density, respectively. Median blood lead level among children measured at 12 months was 56.50 (4.81–578) μg/ L. During the 12-month follow-up, 172 (84.31%) children had at least one malaria episode. Univariate and multivariate negative binomial and linear regressions did not reveal significant associations between blood lead level quartile and malaria outcomes. Iron deficiency was not found to be an effect modifier. Results from this prospective child-cohort study investigating associations between blood lead level and malaria did not confirm results from previous cross-sectional studies. Further research is needed to further explore this relationship and other co-morbidities due to malaria and lead. This was an observational, prospective cohort study following children born to women enrolled in a randomized clinical trial (Malaria in Pregnancy Preventive Alternative Drugs, MiPPAD, {“type”:”clinical-trial”,”attrs”:{“text”:”NCT00811421″,”term_id”:”NCT00811421″}}NCT00811421) investigating two intervention therapies for malaria during pregnancy in the semi-rural area of Allada, 40 kilometers north of Cotonou, in Benin, Sub-Saharan Africa. Study protocol and inclusion criteria of participants for the clinical trial is explained elsewhere [15]. All singleton children born to mothers from the MiPPAD trial were invited to participate in a subsequent cross-sectional study (TOVI) at 12 months of age [16]. A sub-cohort of these children was then followed closely to measure malaria incidence and parasite density between 12 to 24 months of age within the TOLIMMUNPAL study. Participating children in our analyses included those who met the following inclusion criteria: had mothers who met the original inclusion criteria for the MiPPAD clinical trial, were assessed for blood lead level within the TOVI study between 10 to 13 months of age, and were followed for malaria incidence within the TOLIMMUNPAL study from 12 to 24 months of age. Blood lead level, the primary exposure of interest in this study, was measured once in children ages 10–13 months within the TOVI study. Eight mL of venous blood was collected from each child and 4mL put into a tube with dipotassium EDTA. An aliquot of EDTA blood was diluted 20-fold in ammonia 0.5% v/v and 0.1% v/v surfactant Triton-X and analyzed by inductively coupled plasma mass spectrometry (ICP-MS; Perkin Elmer Sciex Elan DRC II ICP-MS instrument) at the Centre de Toxicologie, Institut National de Santé Publique du Québec (Québec, Canada). The limit of detection for blood lead analysis was 0.2 μg/L [9]. Blood lead level was analyzed in quartiles due to the non-linear relationship between lead and malaria and in order to better interpret results. There were two primary outcomes of interest within this study, malaria and parasite density in children. Malaria was diagnosed in children from 12 to 24 months of age through a positive thick blood smear test and/or rapid diagnostic test (RDT) within the TOLIMMUNPAL study. A positive thick blood smear test, the gold standard of malaria diagnosis, involves the examination of blood samples using microscopy in order to diagnosis the presence of parasites [17,18]. Pan/Pf RDT (Parascreen), able to give results within 15 minutes of administration, identifies the presence of Plasmodium-specific histidine-rich protein-2 produced in the infected human blood [19]. Each month during the 12-month follow-up, participating children were visited by a nurse, who performed a thick blood smear test to diagnose malaria, regardless of symptoms, and an RDT if they presented with fever (temperature equal to or greater than 37.5°C). A nurse also visited children at home every 15 days, during which time a thick blood smear test and an RDT were administered if children had fever or history of fever in the previous 24 hours. Additionally, children and their families had access to free emergency care and were able to attend the clinic if symptoms occurred. During these visits, a thick blood smear test and an RDT were administered. Symptomatic malaria was defined as a positive thick blood smear and/or RDT and the presence of fever within three days of diagnosis. Parasite density (in parasites/μL) was measured in children positively diagnosed with malaria by a thick blood smear test, through the use of a multiplication factor applied to the average parasitemia/field [14]. Malaria was analyzed as the total number of malaria episodes, total number of symptomatic episodes, and total number of asymptomatic episodes during the 12-month follow-up. Parasite density was log-transformed and analyzed as mean logarithm parasite density per child. Potential confounders included in adjusted models were socioeconomic status (SES), maternal education, iron deficiency, mosquito-net use, malaria status before 12 months, maternity ward location, and environmental risk of infection. Information regarding maternal education, mosquito-net use, and maternity ward were collected via questionnaire given to mothers during follow-up. Serum ferritin concentrations were measured in children using an AxSym Immuno-Assay Analyzer (Abbott Laboratories, Abbott Park, IL) with a sample of 500 mL of serum. Iron deficiency was defined as a serum ferritin concentration less than 12 μg/L or as serum ferritin concentration of 12 to 70 μg/L in the presence of inflammation (CRP concentration > 5mg/L) [20]. In addition to being considered as a potential confounding factor, iron deficiency was also tested as an effect modifier. Malaria status before 12 months was defined as having at least one malaria episode before the lead assessment at 12 months of age. TOLIMMUNPAL researchers collected information on mosquito density, as well as environmental (rainfall, soil type, nearby water sources, vegetation index) and biological data (number of inhabitants per room in household, use of bed nets and/or insecticides) in order to calculate a time- and space-dependent environmental risk of infection quantifying each child’s exposure to malaria vectors using a predictive model [21]. Univariate analyses of associations between the primary exposure of interest, blood lead level, the primary outcomes of interest, total malaria episodes and mean logarithm parasite density, and potential confounders were performed. Confounders found to be associated to the primary exposure and/or outcomes (p<0.20) were kept in final, adjusted models. Multivariate negative binomial regression models were run to examine associations between blood lead level and number of malaria episodes, including number of symptomatic and asymptomatic malaria episodes separately. Linear regression models tested associations between blood lead level and mean logarithm parasite density measured in children. Additionally, characteristics of children lost to follow-up (i.e. not present at 24 months) were compared to those present for the entire follow-up using t-tests, Wilcoxon-rank sum tests, and Fisher exact tests when appropriate. Two sensitivity analyses were conducted: (1) regression analyses after removal of children potentially exposed to lead through contaminated paint chips, and (2) regression analyses including only malaria episodes diagnosed within first 6 months after lead assessment, due to the 30-day half life of lead. Statistical analyses were completed using STATA 14.2 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP) for Windows. All studies from which data were used for the purpose of this study were approved by the following bodies: the Hospital Clinic of Barcelona (Spain), the Comité Consultatif de Déontologie et d’Éthique of the Institut de Recherche pour le Développement (France) [15], the University of Abomey-Calavi in Benin and New York University in the United States (IRB#09–1253) [9], and the Beninese Ethical Committee of the Faculté des Sciences de la Santé (FSS).

AI Digest

Study Justification:

– Lead and malaria are significant health risks to children in Sub-Saharan Africa.
– Previous studies have shown a potential protective effect of high blood lead levels against subsequent malaria incidence.
– This study aimed to investigate the associations between blood lead levels and malaria outcomes in Beninese children aged 12 to 24 months.

Study Highlights:

– The study followed 204 children from 12 to 24 months of age.
– Blood lead levels were measured at 12 months, and malaria episodes and parasite density were recorded during the follow-up period.
– Median blood lead level at 12 months was 56.50 μg/L.
– 84.31% of children had at least one malaria episode during the 12-month follow-up.
– Statistical analyses did not reveal significant associations between blood lead levels and malaria outcomes.
– Iron deficiency was not found to modify the relationship between lead levels and malaria.

Recommendations for Lay Reader:

– The study did not find a significant association between blood lead levels and malaria outcomes in children.
– Further research is needed to explore this relationship and other co-morbidities related to malaria and lead.

Recommendations for Policy Maker:

– The findings of this study suggest that blood lead levels may not have a significant impact on malaria outcomes in children.
– Policymakers should consider the need for additional research to better understand the relationship between lead and malaria, as well as other factors that may contribute to malaria incidence in Sub-Saharan Africa.

Key Role Players:

– Researchers and scientists involved in conducting further research on the relationship between lead and malaria.
– Public health officials and policymakers responsible for implementing interventions to reduce lead exposure and prevent malaria in children.
– Healthcare providers and clinicians who can educate parents and caregivers about the risks of lead exposure and malaria prevention strategies.

Cost Items for Planning Recommendations:

– Funding for further research studies to investigate the relationship between lead and malaria.
– Resources for public health interventions to reduce lead exposure, such as lead abatement programs and education campaigns.
– Budget allocation for malaria prevention and control measures, including mosquito control programs, distribution of insecticide-treated bed nets, and access to malaria diagnosis and treatment services.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is a prospective cohort study, which is generally considered to provide stronger evidence compared to cross-sectional studies. The study also includes a relatively large sample size of 204 children. However, the main findings of the study did not reveal significant associations between blood lead level and malaria outcomes, which suggests that the evidence may not be very strong. To improve the strength of the evidence, the study could consider the following actionable steps: 1) Increase the sample size to improve statistical power and detect smaller associations, 2) Conduct a randomized controlled trial to establish causality, 3) Consider adjusting for additional potential confounders that may influence the relationship between blood lead level and malaria outcomes, and 4) Replicate the study in different populations to assess generalizability.

Abstract

This was an observational, prospective cohort study following children born to women enrolled in a randomized clinical trial (Malaria in Pregnancy Preventive Alternative Drugs, MiPPAD, {“type”:”clinical-trial”,”attrs”:{“text”:”NCT00811421″,”term_id”:”NCT00811421″}}NCT00811421) investigating two intervention therapies for malaria during pregnancy in the semi-rural area of Allada, 40 kilometers north of Cotonou, in Benin, Sub-Saharan Africa. Study protocol and inclusion criteria of participants for the clinical trial is explained elsewhere [15]. All singleton children born to mothers from the MiPPAD trial were invited to participate in a subsequent cross-sectional study (TOVI) at 12 months of age [16]. A sub-cohort of these children was then followed closely to measure malaria incidence and parasite density between 12 to 24 months of age within the TOLIMMUNPAL study. Participating children in our analyses included those who met the following inclusion criteria: had mothers who met the original inclusion criteria for the MiPPAD clinical trial, were assessed for blood lead level within the TOVI study between 10 to 13 months of age, and were followed for malaria incidence within the TOLIMMUNPAL study from 12 to 24 months of age. Blood lead level, the primary exposure of interest in this study, was measured once in children ages 10–13 months within the TOVI study. Eight mL of venous blood was collected from each child and 4mL put into a tube with dipotassium EDTA. An aliquot of EDTA blood was diluted 20-fold in ammonia 0.5% v/v and 0.1% v/v surfactant Triton-X and analyzed by inductively coupled plasma mass spectrometry (ICP-MS; Perkin Elmer Sciex Elan DRC II ICP-MS instrument) at the Centre de Toxicologie, Institut National de Santé Publique du Québec (Québec, Canada). The limit of detection for blood lead analysis was 0.2 μg/L [9]. Blood lead level was analyzed in quartiles due to the non-linear relationship between lead and malaria and in order to better interpret results. There were two primary outcomes of interest within this study, malaria and parasite density in children. Malaria was diagnosed in children from 12 to 24 months of age through a positive thick blood smear test and/or rapid diagnostic test (RDT) within the TOLIMMUNPAL study. A positive thick blood smear test, the gold standard of malaria diagnosis, involves the examination of blood samples using microscopy in order to diagnosis the presence of parasites [17,18]. Pan/Pf RDT (Parascreen), able to give results within 15 minutes of administration, identifies the presence of Plasmodium-specific histidine-rich protein-2 produced in the infected human blood [19]. Each month during the 12-month follow-up, participating children were visited by a nurse, who performed a thick blood smear test to diagnose malaria, regardless of symptoms, and an RDT if they presented with fever (temperature equal to or greater than 37.5°C). A nurse also visited children at home every 15 days, during which time a thick blood smear test and an RDT were administered if children had fever or history of fever in the previous 24 hours. Additionally, children and their families had access to free emergency care and were able to attend the clinic if symptoms occurred. During these visits, a thick blood smear test and an RDT were administered. Symptomatic malaria was defined as a positive thick blood smear and/or RDT and the presence of fever within three days of diagnosis. Parasite density (in parasites/μL) was measured in children positively diagnosed with malaria by a thick blood smear test, through the use of a multiplication factor applied to the average parasitemia/field [14]. Malaria was analyzed as the total number of malaria episodes, total number of symptomatic episodes, and total number of asymptomatic episodes during the 12-month follow-up. Parasite density was log-transformed and analyzed as mean logarithm parasite density per child. Potential confounders included in adjusted models were socioeconomic status (SES), maternal education, iron deficiency, mosquito-net use, malaria status before 12 months, maternity ward location, and environmental risk of infection. Information regarding maternal education, mosquito-net use, and maternity ward were collected via questionnaire given to mothers during follow-up. Serum ferritin concentrations were measured in children using an AxSym Immuno-Assay Analyzer (Abbott Laboratories, Abbott Park, IL) with a sample of 500 mL of serum. Iron deficiency was defined as a serum ferritin concentration less than 12 μg/L or as serum ferritin concentration of 12 to 70 μg/L in the presence of inflammation (CRP concentration > 5mg/L) [20]. In addition to being considered as a potential confounding factor, iron deficiency was also tested as an effect modifier. Malaria status before 12 months was defined as having at least one malaria episode before the lead assessment at 12 months of age. TOLIMMUNPAL researchers collected information on mosquito density, as well as environmental (rainfall, soil type, nearby water sources, vegetation index) and biological data (number of inhabitants per room in household, use of bed nets and/or insecticides) in order to calculate a time- and space-dependent environmental risk of infection quantifying each child’s exposure to malaria vectors using a predictive model [21]. Univariate analyses of associations between the primary exposure of interest, blood lead level, the primary outcomes of interest, total malaria episodes and mean logarithm parasite density, and potential confounders were performed. Confounders found to be associated to the primary exposure and/or outcomes (p<0.20) were kept in final, adjusted models. Multivariate negative binomial regression models were run to examine associations between blood lead level and number of malaria episodes, including number of symptomatic and asymptomatic malaria episodes separately. Linear regression models tested associations between blood lead level and mean logarithm parasite density measured in children. Additionally, characteristics of children lost to follow-up (i.e. not present at 24 months) were compared to those present for the entire follow-up using t-tests, Wilcoxon-rank sum tests, and Fisher exact tests when appropriate. Two sensitivity analyses were conducted: (1) regression analyses after removal of children potentially exposed to lead through contaminated paint chips, and (2) regression analyses including only malaria episodes diagnosed within first 6 months after lead assessment, due to the 30-day half life of lead. Statistical analyses were completed using STATA 14.2 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP) for Windows. All studies from which data were used for the purpose of this study were approved by the following bodies: the Hospital Clinic of Barcelona (Spain), the Comité Consultatif de Déontologie et d’Éthique of the Institut de Recherche pour le Développement (France) [15], the University of Abomey-Calavi in Benin and New York University in the United States (IRB#09–1253) [9], and the Beninese Ethical Committee of the Faculté des Sciences de la Santé (FSS).

Materials

Innovations

The study mentioned in the description does not provide specific innovations or recommendations to improve access to maternal health. However, based on the information provided, here are some potential innovations that could be considered to improve access to maternal health:

1. Mobile health (mHealth) interventions: Utilizing mobile technology to provide maternal health information, reminders, and access to healthcare services. This could include text message reminders for prenatal appointments, educational videos or apps on maternal health topics, and telemedicine consultations.

2. Community health workers: Training and deploying community health workers to provide maternal health education, support, and basic healthcare services in remote or underserved areas. These workers can help bridge the gap between communities and formal healthcare systems.

3. Telemedicine: Using telecommunication technology to provide remote consultations and medical advice to pregnant women in areas with limited access to healthcare facilities. This can help overcome geographical barriers and provide timely care to pregnant women.

4. Maternal health clinics: Establishing dedicated maternal health clinics in areas with high maternal mortality rates. These clinics can provide comprehensive prenatal care, skilled birth attendance, and postnatal care services to ensure the well-being of both mothers and infants.

5. Maternal health financing schemes: Implementing innovative financing mechanisms, such as community-based health insurance or conditional cash transfer programs, to improve financial access to maternal health services. This can help reduce out-of-pocket expenses and increase utilization of essential maternal health services.

6. Health information systems: Strengthening health information systems to collect, analyze, and utilize data on maternal health indicators. This can help identify gaps in service delivery, monitor progress, and inform evidence-based decision-making for improving maternal health outcomes.

7. Public-private partnerships: Collaborating with private sector organizations, such as pharmaceutical companies or technology companies, to leverage their resources, expertise, and networks to improve access to maternal health services. This can include initiatives like subsidized or donated medications, medical equipment, or technology solutions.

It is important to note that the specific context and needs of each community should be considered when implementing these innovations.

AI Innovations Description

The study mentioned in the description investigates the association between blood lead levels in infants and subsequent risk of malaria in Benin, Sub-Saharan Africa. The study found that there were no significant associations between blood lead levels and malaria outcomes in children aged 12 to 24 months. However, further research is needed to explore this relationship and other co-morbidities due to malaria and lead.

Based on this study, a recommendation to improve access to maternal health and potentially reduce the risk of malaria in infants could be to implement interventions that focus on reducing lead exposure in pregnant women and young children. This could include:

1. Public awareness campaigns: Educate pregnant women and their families about the sources and health risks of lead exposure. Provide information on how to minimize exposure, such as avoiding lead-based paint, using clean water sources, and practicing good hygiene.

2. Lead testing and monitoring: Implement routine lead testing for pregnant women and young children to identify individuals at risk of high lead levels. This can help healthcare providers intervene early and provide appropriate support and guidance.

3. Environmental interventions: Improve access to clean water sources and promote safe housing practices to reduce lead exposure. This could involve providing water filters or improving water infrastructure in communities with high lead levels. Additionally, implementing regulations and policies to prevent lead-based paint use and promote safe housing conditions can also be effective.

4. Nutritional interventions: Promote a healthy diet rich in nutrients that can help reduce lead absorption and mitigate its effects. This could include providing nutritional supplements, such as iron and calcium, which have been shown to reduce lead absorption in the body.

5. Collaboration and coordination: Foster collaboration between healthcare providers, public health agencies, and community organizations to ensure a comprehensive approach to reducing lead exposure. This can involve sharing resources, expertise, and best practices to maximize the impact of interventions.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the risk of lead exposure, which may indirectly contribute to reducing the risk of malaria in infants. However, it is important to note that further research is needed to fully understand the relationship between lead exposure and malaria outcomes.

AI Innovations Methodology

Based on the provided information, it seems that the study you mentioned focuses on investigating the association between blood lead levels in infants and the subsequent risk of malaria in Benin, Sub-Saharan Africa. The study followed a cohort of children from 12 to 24 months of age and assessed their blood lead levels at 12 months. Malaria outcomes, including total episodes of malaria and parasite density, were recorded during the follow-up period.

To improve access to maternal health, it is important to consider innovations that address the specific challenges faced in this context. Here are a few potential recommendations:

1. Mobile health (mHealth) solutions: Develop mobile applications or SMS-based systems to provide pregnant women and new mothers with information on prenatal care, nutrition, and postnatal care. These platforms can also be used to schedule appointments, send reminders, and provide access to teleconsultations with healthcare providers.

2. Community health workers: Train and deploy community health workers to provide maternal health education, antenatal care, and postnatal care services in remote or underserved areas. These workers can also facilitate referrals to higher-level healthcare facilities when necessary.

3. Telemedicine: Establish telemedicine networks to connect healthcare providers in urban areas with pregnant women and new mothers in rural or remote areas. This can enable remote consultations, monitoring of high-risk pregnancies, and timely interventions.

4. Maternal health clinics: Set up dedicated maternal health clinics in areas with limited access to healthcare facilities. These clinics can provide comprehensive prenatal care, delivery services, and postnatal care, ensuring that pregnant women receive the necessary care in a timely manner.

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

1. Define the target population: Identify the specific population that would benefit from the innovations, such as pregnant women in rural areas or low-income communities.

2. Collect baseline data: Gather information on the current state of maternal health access, including factors such as healthcare facility availability, distance to healthcare facilities, utilization rates, and health outcomes.

3. Model the impact: Use mathematical modeling techniques to simulate the potential impact of the recommended innovations on improving access to maternal health. This could involve estimating the number of additional women who would receive prenatal care, the reduction in travel time to healthcare facilities, or the increase in the number of teleconsultations.

4. Consider contextual factors: Take into account contextual factors that may influence the effectiveness of the innovations, such as cultural beliefs, infrastructure limitations, and healthcare workforce capacity.

5. Sensitivity analysis: Conduct sensitivity analyses to assess the robustness of the results and explore different scenarios or assumptions.

6. Evaluate cost-effectiveness: Assess the cost-effectiveness of implementing the recommended innovations by comparing the costs of implementation with the expected health outcomes and benefits.

7. Policy recommendations: Based on the simulation results, provide policy recommendations on the implementation and scale-up of the innovations to improve access to maternal health.

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

Author

Dima Health

Statistics:

Citations: 3

Identifiers:

DOI: 10.1371/journal.pone.0220023

Research Areas:

Community Interventions, Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants

Study Countries:

Benin

Study Design:

Cohort Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female