Food security and nutritional outcomes among urban poor orphans in Nairobi, Kenya

Journal of Urban Health, Volume 88, No. SUPPL. 2, Year 2011

Interpretation

The study examines the relationship between orphanhood status and nutritional status and food security among children living in the rapidly growing and uniquely vulnerable slum settlements in Nairobi, Kenya. The study was conducted between January and June 2007 among children aged 6-14 years, living in informal settlements of Nairobi, Kenya. Anthropometric measurements were taken using standard procedures and z scores generated using the NCHS/WHO reference. Data on food security were collected through separate interviews with children and their caregivers, and used to generate a composite food security score. Multiple regression analysis was done to determine factors related to vulnerability with regards to food security and nutritional outcomes. The results show that orphans were more vulnerable to food insecurity than non-orphans and that paternal orphans were the most vulnerable orphan group. However, these effects were not significant for nutritional status, which measures long-term food deficiencies. The results also show that the most vulnerable children are boys, those living in households with lowest socioeconomic status, with many dependants, and female-headed and headed by adults with low human capital (low education). This study provides useful insights to inform policies and practice to identify target groups and intervention programs to improve the welfare of orphans and vulnerable children living in urban poor communities. © 2011 The New York Academy of Medicine. This study uses data from a World Bank-funded study of the welfare of orphans and vulnerable children (OVCs) of primary school-going age (6–14 years) in urban poor areas29. The OVC study was carried out in two informal settlements in Nairobi, Kenya, where the African Population and Health Research Centre (APHRC) has run a health and demographic surveillance system, the Nairobi urban health and demographic surveillance system (NUHDSS), since 2001. The NUHDSS, in which the study was nested, involves a systematic quarterly recording of vital demographic events including births, deaths and migrations occurring among household residents. The NUHDSS also regularly collects data on other health and socioeconomic issues such as household assets and amenities, morbidity, and cause of death, using verbal autopsies and education. The two slum areas that comprise the study site (Korogocho and Viwandani) are densely populated (63,318 and 52,583 inhabitants per square kilometer, respectively) and are also characterized by poor housing, high unemployment rates, lack of water supply and sanitation services, high levels of violence and general insecurity and poor health indicators.8,30 Viwandani, which is located near the industrial area, has relatively higher levels of education, employment and population mobility, while the population in Korogocho is more stable and with higher levels of co-residence of spouses. The OVC study, which was carried out between January and June 2007, investigated various domains of child welfare. This paper uses data on nutritional status and food security among orphans and non-orphans. In common with other studies, the term orphan in this study refers to children who have lost either one (paternal/maternal) or both parents (double). The target minimum sample size calculated for the study was 2,122. We then sought to include all orphans in the NUHDSS database (n = 1,202), with an equal number of non-orphans, randomly selected from the NUHDSS database; matched upon age, gender and location of residence at the population level. Hence, the target sample was 2,404 children. Anthropometric measurements (height and weight) were taken from the child; and interviews regarding food security were done with both the child and his/her caregiver. Ethical approval for the study was obtained from the Kenya Medical Research Institute’s National Ethical Review Committee. Written informed consent was obtained from the child’s caregiver both for interviewing the caregiver and the child. In addition, verbal assent was obtained from the child. Dependent Variables Child nutritional status was derived from anthropometric measurements taken from all the children. All measurements were carried out using standard procedures.31 Height was measured using an inelastic tape measure with the child standing on level ground against a flat perpendicular surface and was recorded in centimeters to one decimal point. Weight was measured using an electronic scale (Seca 881 U, obtained from United Nations Children’s Fund) and was recorded in kilograms to one decimal place. Through use of the World Health Organization (WHO) 2005 Anthro program,32 height-for-age and weight-for-age z scores were generated using the 1977 National Center for Health Statistics/World Health Organization (NCHS/WHO) reference. Nutritional outcomes included height-for-age score, weight-for-age score, stunting and underweight.Food security was measured through complementary interviews with both the caregiver and the index child separately. Questions asked sought to assess perceived hunger, regularity of meals, food access and food shortage. Answers were recoded to be unidirectional, with 0/1 being the poorest/lowest and 4/5 the best/highest; a composite measure was then derived by summing up standardized scores of all responses. Cases with missing information on any of the food security variables were not included in the generation of the composite score (a total of 63 cases). Such missing information was mainly due to questions with reference to a specific date, e.g. if the child was away from the household on the reference day (see Table Table77 for questions contributing to the food security score). Questions contributing to food security score, Nairobi informal settlements, Kenya, 2007 Independent Variables The orphan status of children—our key predictor—was defined using two specifications: (1) non-orphan vs. orphan and (2) father/paternal orphan vs. mother/maternal orphan vs. double orphan. Other explanatory variables, mainly extracted from the NUHDSS database, included location of residence (Korogocho, Viwandani); child’s age, sex, ethnicity and relationship to the household head; household head’s age, sex and highest level of education; number of children <15 years in the household; household socioeconomic status (constructed using principal component analysis of the following amenities and assets: electricity supply, bicycle, television, radio, house phone, sofa, table, flush light, kerosene lamp, kerosene stove and wall clock). Household wealth tertiles were generated from the wealth index using the Stata’s xtile command and labeled as poorest (lowest 1/3), middle and least poor (highest 1/3). Analysis was carried out to test the following hypotheses: Only children for whom information from two sources (the index child and the caregiver) was captured were considered in the analysis (n = 1,235: 467 orphans and 768 non-orphans). Five hundred nineteen caregivers were unavailable for interview because of migration (314 permanent, 10 temporary), refusals (48), deaths (17) and untraceable (130). A further 797 children were excluded due to migration (312 permanent and 193 temporary), refusals (44), death (3) and untraceable (245). Overall, there were 1,550 children with a corresponding caregiver interview: 950 non-orphans and 600 orphans. Three hundred fifteen of these children were aged 15 years or older (due to some time lapse between sampling from the NUHDSS database and the actual study time), and hence excluded from analysis. Thus, 1,235 children were included in the analysis. The analysis, done using Stata version 10.0 (StataCorp LP, USA), involved both descriptive and multivariate regression methods. Chi-square test was used to test for differences in proportions by orphan status. Initially, mean group differences with regards to nutritional outcomes and food security score were analyzed through t test for orphan status (orphan/non-orphan) and a one-way ANOVA for categories of orphanhood (father, mother and double). Subsequently, random intercepts regression models were used in the multivariate analysis using the Stata’s xtmixed command (for linear regression) and xtlogit command (for logistic regression) to allow for clustering at the household level, given the structure of the sample. The 1,235 children included in the study were nested within 1,034 households: 1 household had 4 children, 29 households had 3 children each and 140 households had two children each, while the rest of the 864 households hosted one child each. The mixed effect model was used to account for both fixed effects and random effects at the child level and at the household level, respectively.

AI Digest

Study Justification:

– The study examines the relationship between orphanhood status and nutritional status and food security among children living in slum settlements in Nairobi, Kenya.
– The study provides insights into the vulnerability of orphans to food insecurity and identifies the most vulnerable orphan group.
– The study also identifies factors related to vulnerability, such as gender, socioeconomic status, and household composition.
– The findings of the study can inform policies and interventions to improve the welfare of orphans and vulnerable children in urban poor communities.

Highlights:

– Orphans were found to be more vulnerable to food insecurity compared to non-orphans, with paternal orphans being the most vulnerable.
– However, the study did not find significant differences in nutritional status between orphans and non-orphans.
– Boys, children living in households with low socioeconomic status, and those headed by adults with low education were found to be the most vulnerable.
– The study provides valuable information for identifying target groups and designing intervention programs to improve the welfare of orphans and vulnerable children in urban poor communities.

Recommendations:

– Develop targeted interventions to address the food security needs of orphans, particularly paternal orphans.
– Implement programs to improve the socioeconomic status of households with vulnerable children, including providing education and employment opportunities.
– Focus on addressing the specific needs of boys and households with many dependents.
– Provide support and resources to female-headed households and adults with low education to improve their capacity to care for vulnerable children.

Key Role Players:

– Government agencies responsible for social welfare and child protection
– Non-governmental organizations (NGOs) working on child welfare and poverty alleviation
– Community-based organizations (CBOs) operating in slum settlements
– Health professionals and nutritionists
– Educators and school administrators

Cost Items:

– Funding for targeted interventions and programs
– Resources for education and skills training for vulnerable households
– Support for health and nutrition services
– Capacity-building initiatives for government agencies, NGOs, and CBOs
– Research and data collection on the welfare of orphans and vulnerable children

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is fairly strong, but there are some areas for improvement. The study provides detailed information on the methodology, including the sample size, data collection methods, and statistical analysis. The study also presents clear results and conclusions. However, it would be helpful to have more information on the representativeness of the sample and the generalizability of the findings. Additionally, the abstract could benefit from a clearer statement of the study’s limitations. To improve the evidence, the authors could consider providing more information on the selection process for the study participants and addressing any potential biases in the data collection. They could also discuss the implications of the findings for policy and practice in more detail.

Abstract

This study uses data from a World Bank-funded study of the welfare of orphans and vulnerable children (OVCs) of primary school-going age (6–14 years) in urban poor areas29. The OVC study was carried out in two informal settlements in Nairobi, Kenya, where the African Population and Health Research Centre (APHRC) has run a health and demographic surveillance system, the Nairobi urban health and demographic surveillance system (NUHDSS), since 2001. The NUHDSS, in which the study was nested, involves a systematic quarterly recording of vital demographic events including births, deaths and migrations occurring among household residents. The NUHDSS also regularly collects data on other health and socioeconomic issues such as household assets and amenities, morbidity, and cause of death, using verbal autopsies and education. The two slum areas that comprise the study site (Korogocho and Viwandani) are densely populated (63,318 and 52,583 inhabitants per square kilometer, respectively) and are also characterized by poor housing, high unemployment rates, lack of water supply and sanitation services, high levels of violence and general insecurity and poor health indicators.8,30 Viwandani, which is located near the industrial area, has relatively higher levels of education, employment and population mobility, while the population in Korogocho is more stable and with higher levels of co-residence of spouses. The OVC study, which was carried out between January and June 2007, investigated various domains of child welfare. This paper uses data on nutritional status and food security among orphans and non-orphans. In common with other studies, the term orphan in this study refers to children who have lost either one (paternal/maternal) or both parents (double). The target minimum sample size calculated for the study was 2,122. We then sought to include all orphans in the NUHDSS database (n = 1,202), with an equal number of non-orphans, randomly selected from the NUHDSS database; matched upon age, gender and location of residence at the population level. Hence, the target sample was 2,404 children. Anthropometric measurements (height and weight) were taken from the child; and interviews regarding food security were done with both the child and his/her caregiver. Ethical approval for the study was obtained from the Kenya Medical Research Institute’s National Ethical Review Committee. Written informed consent was obtained from the child’s caregiver both for interviewing the caregiver and the child. In addition, verbal assent was obtained from the child. Dependent Variables Child nutritional status was derived from anthropometric measurements taken from all the children. All measurements were carried out using standard procedures.31 Height was measured using an inelastic tape measure with the child standing on level ground against a flat perpendicular surface and was recorded in centimeters to one decimal point. Weight was measured using an electronic scale (Seca 881 U, obtained from United Nations Children’s Fund) and was recorded in kilograms to one decimal place. Through use of the World Health Organization (WHO) 2005 Anthro program,32 height-for-age and weight-for-age z scores were generated using the 1977 National Center for Health Statistics/World Health Organization (NCHS/WHO) reference. Nutritional outcomes included height-for-age score, weight-for-age score, stunting and underweight.Food security was measured through complementary interviews with both the caregiver and the index child separately. Questions asked sought to assess perceived hunger, regularity of meals, food access and food shortage. Answers were recoded to be unidirectional, with 0/1 being the poorest/lowest and 4/5 the best/highest; a composite measure was then derived by summing up standardized scores of all responses. Cases with missing information on any of the food security variables were not included in the generation of the composite score (a total of 63 cases). Such missing information was mainly due to questions with reference to a specific date, e.g. if the child was away from the household on the reference day (see Table Table77 for questions contributing to the food security score). Questions contributing to food security score, Nairobi informal settlements, Kenya, 2007 Independent Variables The orphan status of children—our key predictor—was defined using two specifications: (1) non-orphan vs. orphan and (2) father/paternal orphan vs. mother/maternal orphan vs. double orphan. Other explanatory variables, mainly extracted from the NUHDSS database, included location of residence (Korogocho, Viwandani); child’s age, sex, ethnicity and relationship to the household head; household head’s age, sex and highest level of education; number of children <15 years in the household; household socioeconomic status (constructed using principal component analysis of the following amenities and assets: electricity supply, bicycle, television, radio, house phone, sofa, table, flush light, kerosene lamp, kerosene stove and wall clock). Household wealth tertiles were generated from the wealth index using the Stata’s xtile command and labeled as poorest (lowest 1/3), middle and least poor (highest 1/3). Analysis was carried out to test the following hypotheses: Only children for whom information from two sources (the index child and the caregiver) was captured were considered in the analysis (n = 1,235: 467 orphans and 768 non-orphans). Five hundred nineteen caregivers were unavailable for interview because of migration (314 permanent, 10 temporary), refusals (48), deaths (17) and untraceable (130). A further 797 children were excluded due to migration (312 permanent and 193 temporary), refusals (44), death (3) and untraceable (245). Overall, there were 1,550 children with a corresponding caregiver interview: 950 non-orphans and 600 orphans. Three hundred fifteen of these children were aged 15 years or older (due to some time lapse between sampling from the NUHDSS database and the actual study time), and hence excluded from analysis. Thus, 1,235 children were included in the analysis. The analysis, done using Stata version 10.0 (StataCorp LP, USA), involved both descriptive and multivariate regression methods. Chi-square test was used to test for differences in proportions by orphan status. Initially, mean group differences with regards to nutritional outcomes and food security score were analyzed through t test for orphan status (orphan/non-orphan) and a one-way ANOVA for categories of orphanhood (father, mother and double). Subsequently, random intercepts regression models were used in the multivariate analysis using the Stata’s xtmixed command (for linear regression) and xtlogit command (for logistic regression) to allow for clustering at the household level, given the structure of the sample. The 1,235 children included in the study were nested within 1,034 households: 1 household had 4 children, 29 households had 3 children each and 140 households had two children each, while the rest of the 864 households hosted one child each. The mixed effect model was used to account for both fixed effects and random effects at the child level and at the household level, respectively.

Materials

N/A

Innovations

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

1. Mobile health clinics: Implementing mobile health clinics that can reach remote or underserved areas, providing maternal health services and education to women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women with healthcare professionals remotely, allowing them to receive prenatal care and consultations without having to travel long distances.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support to women in their own communities.

4. Maternal health vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, such as prenatal care, delivery, and postnatal care, at reduced or no cost.

5. Maternity waiting homes: Establishing maternity waiting homes near healthcare facilities, where pregnant women from remote areas can stay during the final weeks of pregnancy to ensure timely access to skilled birth attendants and emergency obstetric care.

6. Mobile applications: Developing mobile applications that provide pregnant women with information, reminders, and guidance on prenatal care, nutrition, and healthy behaviors during pregnancy.

7. Public-private partnerships: Collaborating with private healthcare providers to expand access to maternal health services, leveraging their resources and expertise to reach more women in need.

8. Maternal health education programs: Implementing comprehensive maternal health education programs in schools, community centers, and workplaces to raise awareness about the importance of prenatal care, nutrition, and safe delivery practices.

9. Transportation support: Providing transportation support, such as subsidized or free transportation vouchers, to pregnant women who face challenges in accessing healthcare facilities due to distance or lack of transportation options.

10. Maternal health insurance schemes: Establishing or expanding maternal health insurance schemes that cover the cost of prenatal care, delivery, and postnatal care, ensuring that financial barriers do not prevent women from seeking necessary healthcare services.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health based on the study findings could be to implement targeted intervention programs for orphans and vulnerable children living in urban poor communities. These programs should focus on addressing food insecurity and improving nutritional outcomes among these children.

Specifically, the recommendation could include the following strategies:

1. Enhancing food security: Implement programs that aim to improve access to nutritious food for orphans and vulnerable children. This could involve initiatives such as providing food assistance, promoting community gardens, and supporting income-generating activities to increase household food security.

2. Nutrition education: Develop and implement educational programs that focus on improving caregivers’ knowledge and practices related to nutrition. This could include training on balanced diets, meal planning, and food preparation techniques to ensure adequate nutrition for children.

3. Social support: Establish support networks and community-based programs to provide emotional and social support to orphans and vulnerable children. This could involve creating safe spaces for children to interact, providing counseling services, and facilitating access to healthcare services.

4. Strengthening household socioeconomic status: Implement interventions that aim to improve the socioeconomic status of households with orphans and vulnerable children. This could include providing vocational training, job placement assistance, and access to microfinance services to enhance income-generating opportunities for caregivers.

5. Collaboration and coordination: Foster collaboration between government agencies, non-governmental organizations, and community-based organizations to ensure a comprehensive and coordinated approach to addressing the needs of orphans and vulnerable children. This could involve sharing resources, expertise, and best practices to maximize the impact of interventions.

By implementing these recommendations, it is expected that access to maternal health will be improved for orphans and vulnerable children living in urban poor communities, leading to better overall health outcomes for this population.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, particularly in slum areas, can help increase access to maternal health services. This includes ensuring the availability of skilled healthcare professionals, essential medical equipment, and necessary medications.

2. Mobile health clinics: Implementing mobile health clinics that can reach remote or underserved areas can help provide essential maternal health services to women who may have limited access to healthcare facilities. These clinics can offer prenatal care, postnatal care, and family planning services.

3. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and communities. These workers can provide education, counseling, and basic healthcare services to pregnant women and new mothers in their own communities.

4. Telemedicine: Utilizing telemedicine technologies can enable pregnant women in remote areas to access healthcare services through virtual consultations with healthcare professionals. This can help overcome geographical barriers and improve access to timely and quality care.

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 group that will benefit from the recommendations, such as pregnant women in slum areas.

2. Collect baseline data: Gather relevant data on the current state of access to maternal health services in the target population. This can include information on healthcare facilities, healthcare providers, availability of services, and utilization rates.

3. Define indicators: Determine key indicators that will be used to measure the impact of the recommendations, such as the number of pregnant women receiving prenatal care or the reduction in maternal mortality rates.

4. Develop a simulation model: Create a simulation model that incorporates the baseline data and simulates the potential impact of the recommendations. This can be done using statistical software or simulation tools.

5. Input intervention scenarios: Define different intervention scenarios based on the recommendations, such as increasing the number of healthcare facilities or deploying mobile health clinics. Input the relevant parameters and assumptions into the simulation model.

6. Run simulations: Run the simulation model using the different intervention scenarios to estimate the potential impact on access to maternal health services. This can provide insights into the expected changes in key indicators.

7. Analyze results: Analyze the simulation results to assess the effectiveness of the recommendations in improving access to maternal health services. Compare the different intervention scenarios to identify the most impactful strategies.

8. Refine and iterate: Based on the analysis, refine the recommendations and simulation model as needed. Iterate the process to further optimize the strategies and improve access to maternal health services.

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

Author

Dima Health

Statistics:

Citations: 35

Identifiers:

DOI: 10.1007/s11524-010-9491-z

ISSN: 10993460

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Kenya

Study Design:

Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female