Eating and feeding behaviours in children in low-income areas in Nairobi, Kenya

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Study Justification:
– Child eating and caregiver feeding behaviors are important factors in determining food intake in undernourished children.
– However, these behaviors are poorly understood in undernourished children, particularly in low-income areas.
– This study aimed to describe the differences in appetite, food refusal, and force-feeding between undernourished and healthy children in Nairobi, Kenya.
– The study also aimed to identify potential variables that could be used to develop a child eating behavior scale for international use.
Study Highlights:
– The study was conducted in seven clinics in low-income areas of Nairobi.
– A total of 407 child-caregiver pairs were recruited, with 55% of the children being undernourished.
– Undernourished children were found to be less likely to “love food” and more likely to have high food refusal compared to healthy children.
– Caregivers of undernourished children were more likely to use high force-feeding techniques.
– The study suggests that undernourished children in low-income areas are more difficult to feed, and force-feeding is commonly used.
– The study identified a range of variables that could be used to measure child eating behavior and assess the impact of interventions.
Recommendations for Lay Reader and Policy Maker:
– The study highlights the importance of understanding child eating and caregiver feeding behaviors in undernourished children.
– Policy makers should consider the findings of this study when designing interventions to improve nutrition in low-income areas.
– Recommendations may include providing education and support to caregivers on appropriate feeding practices and promoting positive eating behaviors in children.
– Further research is needed to develop and validate a child eating behavior scale for international use.
Key Role Players:
– Researchers and scientists specializing in child nutrition and behavior.
– Health professionals and caregivers working with undernourished children.
– Policy makers and government officials responsible for nutrition programs and interventions.
– Non-governmental organizations (NGOs) and community-based organizations involved in child nutrition and development.
Cost Items for Planning Recommendations:
– Research funding for further studies and development of a child eating behavior scale.
– Training and capacity building for health professionals and caregivers on appropriate feeding practices.
– Development and dissemination of educational materials and resources.
– Monitoring and evaluation of interventions to assess their impact on child eating behaviors and nutritional outcomes.
– Collaboration and coordination between different stakeholders involved in child nutrition programs.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional study conducted in low-income areas of Nairobi, Kenya. The study recruited both undernourished and healthy children aged 6-24 months. The study used a structured interview schedule to assess child appetite, food refusal, and caregiver feeding behaviors. The findings suggest that undernourished children were less likely to ‘love food’ and more likely to have high food refusal, while their caregivers were more likely to use high force-feeding. The study provides valuable insights into eating and feeding behaviors in undernourished children in low-income areas. However, the evidence is limited to a specific population and may not be generalizable to other settings. To improve the evidence, future studies could consider a larger sample size and include a more diverse population to enhance the external validity of the findings.

Child eating and caregiver feeding behaviours are critical determinants of food intake, but they are poorly characterized in undernourished children. We aimed to describe how appetite, food refusal and force-feeding vary between undernourished and healthy children aged 6–24 months in Nairobi and identify potential variables for use in a child eating behaviour scale for international use. This cross-sectional study was conducted in seven clinics in low-income areas of Nairobi. Healthy and undernourished children were quota sampled to recruit equal numbers of undernourished children (weight for age [WAZ] or weight for length [WLZ] Z scores ≤2SD) and healthy children (WAZ > 2SD). Using a structured interview schedule, questions reflecting child appetite, food refusal and caregiver feeding behaviours were rated using a 5-point scale. Food refusal and force-feeding variables were then combined to form scores and categorized into low, medium and high. In total, 407 child–caregiver pairs, aged median [interquartile range] 9.98 months [8.7 to 14.1], were recruited of whom 55% were undernourished. Undernourished children were less likely to ‘love food’ (undernourished 78%; healthy 90% p = < 0.001) and more likely to have high food refusal (18% vs. 3.3% p = <0.001), while their caregivers were more likely to use high force-feeding (28% vs. 16% p = 0.03). Undernourished children in low-income areas in Nairobi are harder to feed than healthy children, and force-feeding is used widely. A range of discriminating variables could be used to measure child eating behaviour and assess the impact of interventions.

This cross‐sectional study was conducted in seven of the 80 health facilities, which offer child welfare services and outpatient treatment for undernutrition in Nairobi. All were located in or on the periphery of major slums, where undernutrition remains a major public health problem, (Abuya, Ciera, & Kimani‐Murage, 2012; Kimani‐Murage et al., 2015). Five facilities, Mbagathi District Hospital, Kayole II Sub County Hospital, Makadara, Embakasi and Mukuru kwa Njenga Health Centre, were government run and two, Ruben Medical Clinic, Soweto PhC clinic, were faith‐based. Children aged 6–24 months were quota sampled based on the severity of their nutritional status and whether they had started treatment with ready to use therapeutic foods. Undernourished children were eligible if they had weight for age (WAZ) or weight for length (WLZ) Z scores ≤ − 2SD. Any child with WAZ and or WLZ < −3SD was defined as SAM, with the remainder defined as MAM. Severely stunted children (<−3SD LAZ) and wasted (<−3SD WLZ) and moderately stunted (LAZ between −2SD and −3SD) children were classified as undernourished, as captured with a low WAZ. However, children who had low height ( −2SD) were classified as healthy. Children were excluded if they either had medical complications such as edema (n = 2), other medical conditions such as congenital heart disease (n = 1) or cleft lip and palate (n = 1). Undernourished children were recruited between February and July 2015, with an aim to include 150 children each with moderate versus severe undernutrition and 150 on treatment and 150 not on treatment. All eligible children identified in each heath facility were included until the quota for their subgroup was fulfilled. The healthy children were recruited in a second round of data collection between July and August 2016 and were eligible if they had WAZ > −2 SD, using gender specific WHO growth charts. Healthy children were to be excluded if they had medical conditions, which required specialized care, but this did not arise in practice. Questions used to assess eating and feeding behaviours were developed, drawing on questions used in the Gateshead Millennium Study (GMS), a UK cohort study (Wright et al., 2006) supplemented by relevant questions from the Child Eating Behaviour Questionnaire (Wardle, Guthrie, Sanderson, & Rapoport, 2001) as well as behaviours observed during preliminary meal observations in low‐income areas in Nairobi (Mutoro, Garcia, & Wright, 2019). Descriptions of all the items tested are shown in Tables S1–S4. Eating behaviour was assessed using nine variables, three hypothesized to relate to appetite and avidity and six to food refusal. The variables easy to feed, loves food and easily satisfied were used to assess appetite because we hypothesized that a child who is easy to feed and loves food is likely to have good appetite. The food refusal variables, spits out food, turns head away and holds food in mouth were selected because they have previously been shown to be associated with failure to thrive (Wilensky et al., 1996) and with slow weight gain in the GMS (Wright et al., 2006). Other refusal variables were included based on meal observations carried out by our group in similar settings in Nairobi (Mutoro et al., 2019). Caregiver behaviour during meals was assessed using eight behaviours, of which four represented coercion or force‐feeding. The force‐feeding behaviours were selected based on meal observations in Kenya. Laissez faire feeding is relatively common LMICs (Dettwyler, 1989; Moore et al., 2006); to assess this, caregivers were asked how often they left their child alone when they refused to eat. There were also two questions about stress and anxiety related to feeding taken from the GMS (Wright et al., 2006) and two about whether children fed themselves during meals and snacks. Self‐feeding was assessed because studies show that self‐feeding is associated with increased food acceptance (Moore et al., 2006). These were used to construct a structured interview schedule to be administered in Swahili, after forward and back translation to ensure accurate translation. All responses were coded using a 5‐point Likert scale, which ranged from 1 (all the time) to 5 (not at all). Data were collected by the researcher and five trained research assistants. Interviews lasted between 20 and 30 min and where possible were carried out in secluded areas of the health centres. The research team aimed to take all anthropometric measurements themselves using standardized equipment, but because of lack of space in the health facilities, in most cases, the anthropometric equipment available at the health facilities were used, but the research team assisted in taking measurements to standardize the techniques used (Lohman, Roache, & Martorell, 1992; WHO, 2008). Weight was measured using a digital weighing scale (SECA 385 digital weighing Scale III) to the nearest 0.1 kg where possible. Supine length was measured to the nearest 0.1 cm using a portable Rollameter (Raven Equipment Ltd., Dunmow, UK) or a UNICEF length board. Mid‐upper arm circumference (MUAC) was measured using MUAC tapes (S0145620 MUAC, Child 11.5 Red/PAC‐50) placed on the left arm at the midpoint between the elbow and shoulder recorded to nearest 0.1 cm. We planned to examine a wider range of novel behavioural and dietary variables between three subgroups and findings from preliminary meal observations suggested large differences in interest in food between healthy and undernourished children and in the proportion becoming upset during meals (Mutoro et al., 2019). We therefore aimed for a sample size sufficient (80% power, alpha 0.05) to detect a prevalence of 15% for any behaviour in one group compared with 30% in another (relative risk = 2). This required 150 subjects in each of the three subgroups. Analyses were conducted using Statistical Package for the Social Science (SPSS) IBM Corp. Released 2010 Version 19.0. Armonk, NY: IBM Corp and Epiinfo 7.1.5.2 Statcalc. Weight and length measurements were converted into Z scores using the WHO Anthro software version 3.2.2. Children were further classified as wasted, stunted, wasted and stunted if they had WLZ or LAZ ≤ −2SD or WLZ and LAZ ≤ −2SD, respectively. Spearman’s (nonparametric) correlations were used to assess the strength and direction of interrelationships between individual child and caregiver variables and with WAZ scores, as a composite summary of the degree of stunting and/or wasting. Cronbach’s alpha was used to assess internal consistency of variables. Variables, which showed reasonable consistency were then combined to create scores, a method used in previous studies (Bentley, Stallings, Fukumoto, & Elder, 1991, Gittelsohn et al., 1998, Wright et al., 2006). Where individual variables were used, the 5‐point Likert scale was recoded into three categories: (a) all or most of the time (1 & 2); (b) sometimes (3); and, (c) rarely or never (4 & 5). Food refusal and force‐feeding scores were created by first subtracting each variable in the score with six to get an inverted value where by high scores reflected high frequency of occurrence. The mean of food refusal and force‐feeding variables was then calculated. Indices were used to assess the degree of interest in food, food refusal, force‐feeding and maternal anxiety. This was based on the assumption that children and caregivers were likely to experience these behaviours at one point during meals, but only the frequency of occurrence and the number of behaviours during meals are a likely indicator of extreme behaviour (Dettwyler, 1989). The mean of behaviours was then used to create categories reflecting high, moderate and low occurrence. Logistic regression was used to test the association between eating and feeding behaviour indices and nutritional status (healthy vs. undernourished). All children were included in descriptive analysis, but when creating scores, children with missing data were excluded. Ethics approval for the study was obtained from the University of Glasgow Ethics review committee (200140057), University of Nairobi and Kenyatta National Hospital Ethics Review committee (P651/11/2014) and the National Council for Science, Technology and Innovation (NACOSTI/P/15/9164/5185). Access to health facilities was granted by the Nairobi county and subcounty health offices.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources on maternal health, including nutrition, breastfeeding, and child feeding behaviors. These apps can be easily accessible to caregivers in low-income areas, providing them with guidance and support.

2. Community Health Workers: Train and deploy community health workers to provide education and support to caregivers in low-income areas. These workers can visit households, conduct workshops, and provide personalized guidance on child eating and caregiver feeding behaviors.

3. Behavior Change Communication: Implement behavior change communication campaigns to raise awareness and promote positive child eating and caregiver feeding behaviors. These campaigns can use various channels such as radio, television, and community events to reach a wide audience.

4. Peer Support Groups: Establish peer support groups for caregivers in low-income areas, where they can share experiences, learn from each other, and receive support from trained facilitators. These groups can provide a supportive environment for caregivers to discuss and address challenges related to child eating and caregiver feeding behaviors.

5. Integration of Services: Integrate maternal health services with existing healthcare facilities and programs, such as immunization clinics or antenatal care services. This can ensure that caregivers have access to comprehensive support and resources for child feeding and caregiver behaviors.

6. Training and Capacity Building: Provide training and capacity building programs for healthcare providers and community health workers on child eating and caregiver feeding behaviors. This can enhance their knowledge and skills in supporting caregivers and addressing challenges related to child nutrition.

7. Policy and Advocacy: Advocate for policies and programs that prioritize maternal health and child nutrition, and ensure their implementation and enforcement. This can help create an enabling environment for caregivers to access the necessary resources and support for optimal child feeding practices.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the target population.
AI Innovations Description
Based on the description provided, the recommendation to improve access to maternal health would be to develop an intervention program that focuses on improving child eating and caregiver feeding behaviors in low-income areas in Nairobi, Kenya. This program should aim to address the following key areas:

1. Increase awareness and education: Implement educational programs to raise awareness among caregivers about the importance of healthy eating habits for children and the impact of caregiver feeding behaviors on their nutritional status. This can be done through community workshops, health clinics, and outreach programs.

2. Provide nutrition counseling: Offer individualized nutrition counseling sessions for caregivers to provide guidance on appropriate feeding practices, including responsive feeding, introducing diverse and nutritious foods, and promoting positive mealtime interactions.

3. Supportive feeding environment: Create a supportive feeding environment by providing resources such as affordable and accessible nutritious foods, cooking demonstrations, and recipes that cater to the local context and cultural preferences.

4. Training for healthcare providers: Provide training for healthcare providers on child eating and caregiver feeding behaviors, so they can effectively assess and address these issues during routine maternal health visits. This can include incorporating screening tools and assessment protocols into existing healthcare systems.

5. Community engagement and empowerment: Engage the community through community health workers, local leaders, and support groups to promote positive feeding practices and provide ongoing support to caregivers. Empower caregivers to make informed decisions about their child’s nutrition and health.

6. Monitoring and evaluation: Establish a system for monitoring and evaluating the effectiveness of the intervention program. This can include tracking changes in child eating behaviors, caregiver feeding practices, and child nutritional status over time.

By implementing these recommendations, it is expected that access to maternal health will be improved by addressing the underlying factors that contribute to undernourishment in children. This intervention program has the potential to positively impact child health outcomes and contribute to the overall well-being of families in low-income areas in Nairobi, Kenya.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Mobile Clinics: Implementing mobile clinics that can reach remote and underserved areas, providing maternal health services such as prenatal care, vaccinations, and health education.

2. Telemedicine: Utilizing telemedicine technologies to connect pregnant women in remote areas with healthcare professionals, allowing them to receive virtual consultations and guidance.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, conduct health education sessions, and facilitate referrals to healthcare facilities.

4. Maternal Health Vouchers: Introducing voucher programs that provide financial assistance to pregnant women, enabling them to access essential maternal health services at healthcare facilities.

5. Transportation Support: Establishing transportation support systems to ensure that pregnant women have access to reliable and affordable transportation to reach healthcare facilities for prenatal care, delivery, and postnatal care.

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

1. Define the indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women accessing prenatal care, the number of deliveries attended by skilled birth attendants, or the reduction in maternal mortality rates.

2. Data collection: Gather data on the current status of maternal health access in the target area, including the number of healthcare facilities, the availability of services, and the utilization rates.

3. Baseline assessment: Establish a baseline by measuring the current access to maternal health services and the associated indicators.

4. Intervention implementation: Implement the recommended interventions, such as mobile clinics or telemedicine, and track the implementation process.

5. Data analysis: Analyze the data collected after the intervention implementation to assess the impact on the identified indicators. Compare the post-intervention data with the baseline data to measure the improvements.

6. Evaluation: Evaluate the effectiveness of the interventions by comparing the post-intervention data with the expected outcomes. Assess the strengths and weaknesses of each recommendation and make adjustments if necessary.

7. Reporting and dissemination: Prepare a comprehensive report summarizing the methodology, findings, and recommendations. Share the results with relevant stakeholders, policymakers, and the community to promote awareness and support for improving access to maternal health.

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