Burden, risk factors, and comorbidities of behavioural and emotional problems in Kenyan children: a population-based study

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Study Justification:
– Three-quarters of the burden of mental health problems occur in low-and-middle-income countries, but there is a lack of epidemiological studies on these problems in preschool children from sub-Saharan Africa.
– Behavioural and emotional problems often start in early childhood, which is particularly important in Africa where there are high incidences of perinatal and early risk factors.
– This study aims to estimate the prevalence and risk factors of behavioural and emotional problems in young children in a rural area on the Kenyan coast.
Study Highlights:
– The study was conducted in the Kilifi Health and Demographic Surveillance System (KHDSS) in Kilifi County, Kenya.
– The prevalence of total behavioural and emotional problems in preschool children was 13%, with 10% for externalising problems and 22% for internalising problems.
– The most common CBCL syndrome was somatic problems (21%), and the most common DSM-IV-oriented scale was anxiety problems (13%).
– Risk factors associated with behavioural and emotional problems included consumption of cassava, perinatal complications, seizure disorders, and house status.
– Seizure disorders, burn marks, and respiratory problems were important comorbidities of behavioural and emotional problems.
Recommendations for Lay Reader and Policy Maker:
– Behavioural and emotional problems are common in preschool children in rural areas of Kenya and are associated with preventable risk factors.
– It is important to identify and address behavioural and emotional problems and their comorbidities in young children.
– Interventions should focus on reducing risk factors such as cassava consumption, perinatal complications, and improving housing conditions.
– Policy makers should prioritize mental health services and support for young children in rural areas.
Key Role Players:
– Researchers and epidemiologists
– Health professionals (doctors, nurses, psychologists)
– Community health workers
– Educators and school administrators
– Government officials and policymakers
– Non-governmental organizations (NGOs) working in mental health
Cost Items for Planning Recommendations:
– Training and capacity building for health professionals and community health workers
– Development and implementation of mental health screening and intervention programs
– Provision of therapeutic interventions and counseling services
– Awareness campaigns and community outreach programs
– Infrastructure improvements for healthcare facilities
– Research and data collection activities
– Monitoring and evaluation of interventions
– Collaboration and coordination between different stakeholders and organizations

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 was population-based and used a large sample size, which increases the generalizability of the findings. The study also used a validated tool, the Child Behaviour Checklist (CBCL), to assess behavioural and emotional problems. The prevalence rates of behavioural and emotional problems were reported with confidence intervals, which adds to the reliability of the findings. The study also identified several risk factors and comorbidities associated with these problems. However, the abstract could be improved by providing more details on the methodology, such as the sampling strategy and data analysis techniques. Additionally, it would be helpful to include information on the limitations of the study and any potential biases. Overall, the evidence in the abstract is strong, but providing more methodological details and addressing potential limitations would further enhance its strength.

Background Three-quarters of the burden of mental health problems occurs in low-and-middle-income countries, but few epidemiological studies of these problems in preschool children from sub-Saharan Africa have been published. Behavioural and emotional problems often start in early childhood, and this might be particularly important in Africa, where the incidence of perinatal and early risk factors is high. We therefore aimed to estimate the prevalence and risk factors of behavioural and emotional problems in young children in a rural area on the Kenyan coast. Methods We did a population-based epidemiological study to assess the burden of behavioural and emotional problems in preschool children and comorbidities in the Kilifi Health and Demographic Surveillance System (KHDSS, a database formed of the population under routine surveillance linked to admissions to Kilifi County Hospital). We used the Child Behaviour Checklist (CBCL) to assess behavioural and emotional problems. We then determined risk factors and medical comorbidities associated with behavioural and emotional problems. The strength of associations between the risk factors and the behavioural and emotional problems was estimated using generalised linear models, with appropriate distribution and link functions. Findings 3539 families were randomly selected from the KHDSS. Of these, 3273 children were assessed with CBCL. The prevalence of total behavioural and emotional problems was 13% (95% CI 12–14), for externalising problems was 10% (9–11), and for internalising problems was 22% (21–24). The most common CBCL syndrome was somatic problems (21%, 20–23), whereas the most common DSM-IV-oriented scale was anxiety problems (13%, 12–14). Factors associated with total problems included consumption of cassava (risk ratio 5·68, 95% CI 3·22–10·03), perinatal complications (4·34, 3·21–5·81), seizure disorders (2·90, 2·24–3·77), and house status (0·11, 0·08–0·14). Seizure disorders, burn marks, and respiratory problems were important comorbidities of behavioural and emotional problems. Interpretation Behavioural and emotional problems are common in preschool children in this Kenyan rural area and are associated with preventable risk factors. Behavioural and emotional problems and associated comorbidities should be identified and addressed in young children. Funding Wellcome Trust.

This study was done within the Kilifi Health and Demographic Surveillance System (KHDSS), which is located in Kilifi County, about 60 km north of Mombasa city.12 The KHDSS is both an area (divided into enumeration zones under regular surveillance) and a database (formed of the population under routine surveillance linked to admissions to Kilifi County Hospital). The KHDSS has a population of about 280 000 residents who are predominantly of the Mijikenda tribe and has an estimated 50 000 children aged 1–6 years. The KHDSS has a northern and southern region covering an area of 891 km2. Epilepsy and neurodevelopmental clinics at Kilifi County Hospital provide therapeutic interventions and counselling services. Screening in stage 1 was done by trained fieldworkers who read out the content of the questionnaires to the parents owing to low literacy levels in this area, taking short breaks every 10 min. The three questionnaires (for risk factors, behavioural and emotional problems, and seizures) in stage 1 were administered in a random order. A random sample of those with and without behavioural and emotional problems predetermined through a sample calculation was invited in stage 2 for a clinical evaluation study to determine medical comorbidities. This study was approved by the Scientific and Ethics Review Unit of the Kenya Medical Research Institute and written informed consent was obtained from parents or carers of children participating in the study. Behavioural and emotional problems were assessed in stage 1 with the Child Behaviour Checklist (CBCL), which was adapted and piloted in the local population and languages.13 The CBCL is used in children aged 1·5–5·5 years,2 and has been applied on 1–6-year-old children; it identifies seven syndromes and five DSM-IV-oriented scales.14 The CBCL has acceptable psychometric properties on a sample of Kenyan preschool children in this area.13 The CBCL items had an internal consistency Cronbach’s α of 0·95, and inter-informant agreement (Pearson’s correlation coefficient, r>0·80), test–retest reliability (r=0·76), and the fit index of the seven-CBCL syndromes (eg, root mean square error of approximation <0·05) were within acceptable ranges.13 Because of the literacy challenges in this area, CBCL questions were read out to the respondents (parents or guardians) by trained neuropsychological assessors fluent in the local languages. The language of administration was primarily Kiswahili (lingua franca), but Giriama was also used for a few respondents who could not comprehend Kiswahili. We used a systematic approach of translation and adaptation of the tools. The initial translation was done by two independent translators fluent in the original language (English) and the target language (Kiswahili and Giriama). These translations were then back translated into English by two independent translators and inconsistencies were resolved. The scoring system used and the grouping of the CBCL items into syndromes and externalising and internalising subscales followed recommendations by the Achenbach System of Empirically Based Assessments.2 The total score for the CBCL was formed by summing ratings from all of the 99 items. Items that formed the seven syndromes of the CBCL, externalising and internalising scores, and the DSM-oriented scales are outlined in the appendix. The 90th percentile of the CBCL scores was used as the cutoff according to recommendations from the developers of the tool, and produced cutoff scores similar to those applied in children in the USA.15 These cutoffs were piloted and found to discriminate between children with and without adverse perinatal events.13 Parents and carers of children assessed with the CBCL were interviewed using a parental risk factor questionnaire. The risk factor questionnaire consisted of the following items: maternal sociodemographic information such as employment, schooling, religion, and ethnicity; pregnancy and perinatal histories; socioeconomic status indicators such as housing status; medical histories such as seizure disorders or other chronic illnesses; and child health and nutrition habits, such as food types consumed, eating soil, and snoring at night. A thorough literature search informed the choice of risk factors included in the questionnaire. About 20% of the children were invited to take part in stage 2 of the study. One clinician saw 243 children with CBCL scores of more than 60 and 377 children with CBCL scores of less than 60. The children were randomly selected from those screened in stage 1 using the RAND command of MySQL (Oracle Corp, USA). The clinician who was blinded to the screening status in stage 1 asked questions about the history of seizures to determine whether the seizures were acute seizures or epilepsy. The clinician was blinded to the status of the children screening positive for seizures in stage 1 in the community. The clinician did a clinical examination to assess for gross and fine motor deficits, sensory function, abnormal limb activity, cognitive or mental status, cranial nerve function, sensory ability, and skin integrity. Anthropometric measures of the child were also taken and included head circumference, mid-upper arm circumference, height, and weight. Abnormal pregnancy was defined as premature or prolonged labour, post-dated pregnancy, pre-eclampsia, eclampsia, or any other health problems during pregnancy.16 Adverse perinatal events were defined as delays in crying, breathing, and breastfeeding after birth. Seizure disorders included both epilepsy and febrile acute seizures, with febrile seizures defined as seizures associated with a febrile illness or fever in those younger than 6 years, and epilepsy as a history of two unprovoked seizures.17 Intellectual disability was assessed by a clinician observing young children who had problems performing the standardised test of a locally adapted developmental inventory.18 Malnutrition was defined as a weight-for-age z score value of −2 or lower or a mid-upper arm circumference less than 11·5 cm. Sensory function was considered impaired if a child could not localise touch from cotton wool or a painful stimulus. Motor impairments were defined as an inability to hold toys and walk or sit upright if of an appropriate age. We estimated that screening approximately 3500 children aged between 1 and 6 years, randomly selected from a surveillance database of 50 000 children using RAND command would be sufficient to identify psychopathology with a precision of 1%. We assessed whether the CBCL scores had a normal distribution by plotting quintile, Kernel density (of predicted regression residuals), and histogram plots (appendix) on raw and (natural) log-transformed scores. Prevalence of behavioural problems was computed first as a probability (where those with CBCL problems are treated as positive and those without CBCL problems as negative), applying the inverse logit function to the intercept coefficient of a logistic regression model to provide outcome probabilities on the logit (log odds) scale. The probability was then multiplied by 100 to obtain the prevalence. Prevalence estimates stratified by age group and sex were computed, fitting fractional polynomial equations to smooth the prevalence by age. Risk factors associated with behavioural and emotional problems were determined using log binomial regression models implemented in a generalised linear model, with robust variance-covariance matrix of the estimators. β coefficients for each risk factor above were then computed with log-transformed CBCL scores as the dependent variable using a generalised linear model that assumes a normal distribution and has an identity link function, and a robust variance–covariance matrix. We built penultimate models that accounted for child factors (age, sex, schooling, and region of residence) and final models that accounted for both child factors and maternal factors (age, marital status, economic or employment status, education level, ethnicity, and religion). A test for linear trend was performed for risk factors categorised into three or more levels using likelihood ratio tests. Age for the child and mother were entered into the risk factor models as first degree fractional polynomials. We measured associations between discrete variables using Pearson's χ2 tests, or Fisher's exact tests, where the number of observations in a cell was less than five. Student's t test or the Mann-Whitney U test were used to compare the distribution of behavioural and emotional scores. Internal consistency was computed using the cialpha command in Stata. A p value of 0·05 or less was considered significant for exploratory comparisons, whereas associations were significant if the lower CIs did not include 1. All analyses were done with Stata, version 13. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Based on the provided information, it seems that the study focuses on assessing the burden, risk factors, and comorbidities of behavioral and emotional problems in preschool children in a rural area on the Kenyan coast. The study utilizes the Child Behavior Checklist (CBCL) to assess these problems and identifies several risk factors associated with them. However, the study does not directly address innovations to improve access to maternal health. Therefore, based on the information provided, it is not possible to recommend specific innovations for improving access to maternal health.
AI Innovations Description
The study titled “Burden, risk factors, and comorbidities of behavioural and emotional problems in Kenyan children: a population-based study” aimed to estimate the prevalence and risk factors of behavioural and emotional problems in young children in a rural area on the Kenyan coast. The study used the Child Behaviour Checklist (CBCL) to assess behavioural and emotional problems in preschool children. The findings of the study showed that behavioural and emotional problems are common in preschool children in this Kenyan rural area and are associated with preventable risk factors. The study recommended that these problems and associated comorbidities should be identified and addressed in young children.

To develop this recommendation into an innovation to improve access to maternal health, the following steps can be taken:

1. Raise awareness: Develop campaigns and educational programs to raise awareness about the importance of maternal mental health and the impact it has on the overall well-being of both the mother and child. This can be done through community outreach programs, workshops, and partnerships with local healthcare providers.

2. Screening and assessment: Implement routine screening and assessment tools, such as the CBCL, to identify behavioural and emotional problems in pregnant women and new mothers. This can be done during prenatal visits, postpartum check-ups, and home visits by healthcare professionals.

3. Integrated care: Establish integrated care models that bring together maternal health services and mental health services. This can involve training healthcare providers to identify and address behavioural and emotional problems in pregnant women and new mothers, as well as providing access to mental health professionals for further assessment and treatment.

4. Support and intervention: Develop support programs and interventions tailored to the specific needs of pregnant women and new mothers experiencing behavioural and emotional problems. This can include individual counseling, group therapy, parenting classes, and peer support groups.

5. Collaboration and partnerships: Foster collaboration and partnerships between healthcare providers, community organizations, and government agencies to ensure a comprehensive approach to improving access to maternal health. This can involve sharing resources, coordinating services, and advocating for policy changes that prioritize maternal mental health.

By implementing these recommendations, access to maternal health can be improved by addressing behavioural and emotional problems in pregnant women and new mothers, ultimately leading to better outcomes for both the mother and child.
AI Innovations Methodology
The study you provided focuses on the burden, risk factors, and comorbidities of behavioral and emotional problems in preschool children in a rural area on the Kenyan coast. While the study does not directly address access to maternal health, I can provide some general recommendations for innovations that can improve access to maternal health and a methodology to simulate the impact of these recommendations.

Innovations to improve access to maternal health:
1. Telemedicine: Implementing telemedicine programs can provide remote access to healthcare professionals for prenatal and postnatal care, allowing pregnant women to receive medical advice and support without the need for physical travel.
2. Mobile health (mHealth) applications: Developing mobile applications that provide information on maternal health, track pregnancy progress, and send reminders for appointments and medication can help improve access to essential maternal healthcare services.
3. Community health workers: Training and deploying community health workers who can provide basic maternal healthcare services, education, and support in remote or underserved areas can help bridge the gap in access to maternal health services.
4. Transport and referral systems: Establishing efficient transport and referral systems that connect pregnant women in remote areas to healthcare facilities equipped to handle maternal health complications can help ensure timely access to emergency obstetric care.
5. Maternal health clinics: Setting up dedicated maternal health clinics in underserved areas can provide comprehensive care, including prenatal check-ups, antenatal classes, and postnatal care, in a convenient and accessible manner.

Methodology to simulate the impact of recommendations on improving access to maternal health:
1. Define the target population: Identify the specific population or geographic area for which access to maternal health needs to be improved.
2. Collect baseline data: Gather data on the current status of maternal health access in the target population, including factors such as distance to healthcare facilities, availability of healthcare providers, and utilization rates of maternal health services.
3. Define indicators: Determine key indicators that reflect access to maternal health, such as the percentage of pregnant women receiving prenatal care, the distance to the nearest healthcare facility, or the time taken to reach a healthcare facility.
4. Simulate interventions: Use modeling techniques to simulate the impact of different interventions, such as the innovations mentioned above, on the defined indicators. This can involve creating scenarios where the interventions are implemented and estimating the resulting changes in access to maternal health.
5. Analyze and interpret results: Analyze the simulated results to assess the potential impact of the interventions on improving access to maternal health. Consider factors such as cost-effectiveness, scalability, and feasibility of implementation.
6. Refine and prioritize interventions: Based on the simulation results, refine and prioritize the interventions that show the most promising impact on improving access to maternal health.
7. Implement and evaluate: Implement the selected interventions and continuously monitor and evaluate their effectiveness in improving access to maternal health. Adjustments and improvements can be made based on ongoing evaluation and feedback.

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

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