Child growth in urban deprived settings: Does household poverty status matter? At which stage of child development?

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Study Justification:
– The study aims to examine patterns of child growth in urban deprived settings in Nairobi, Kenya.
– It investigates the impact of different dimensions of poverty (expenditures poverty, assets poverty, food poverty, and subjective poverty) on child growth.
– The study provides insights into the vulnerability of urban poor infants and children and the influences of various aspects of poverty measures.
Highlights:
– The prevalence of overall stunting among children in the age group 15-17 months is nearly 60% and remains constant thereafter.
– Food poverty is strongly associated with stunting among children aged 6-11 months, while assets poverty and subjective poverty have stronger relationships with undernutrition at older ages.
– Expenditures poverty does not have a statistically significant effect on child growth in any age group.
Recommendations:
– Address food poverty among children aged 6-11 months to reduce stunting.
– Address assets poverty and subjective poverty among older children to improve nutritional status.
– Further research is needed to understand the specific factors contributing to stunting and undernutrition in urban deprived settings.
Key Role Players:
– African Population and Health Research Centre (APHRC)
– Nairobi Urban Health and Demographic Surveillance System (NUHDSS)
– Researchers and data collectors
– Local community organizations and leaders
– Government agencies and policymakers
Cost Items for Planning Recommendations:
– Research and data collection expenses
– Staff salaries and training
– Community engagement and outreach activities
– Data analysis and interpretation
– Publication and dissemination of findings
– Monitoring and evaluation of interventions

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on longitudinal data from two informal settlements in Nairobi, Kenya. The study examines the relationship between different dimensions of poverty and child growth. The descriptive results show a high prevalence of stunting among children in the study population. The study also identifies specific dimensions of poverty that have stronger associations with undernutrition at different ages. To improve the evidence, the abstract could provide more details on the methodology used, such as the sampling strategy and statistical analysis techniques.

This paper uses longitudinal data from two informal settlements of Nairobi, Kenya to examine patterns of child growth and how these are affected by four different dimensions of poverty at the household level namely, expenditures poverty, assets poverty, food poverty, and subjective poverty. The descriptive results show a grim picture, with the prevalence of overall stunting reaching nearly 60% in the age group 15-17 months and remaining almost constant thereafter. There is a strong association between food poverty and stunting among children aged 6-11 months (p<0.01), while assets poverty and subjective poverty have stronger relationships (p<0.01) with undernutrition at older age (24 months or older for assets poverty, and 12 months or older for subjective poverty). The effect of expenditures poverty does not reach statistical significant in any age group. These findings shed light on the degree of vulnerability of urban poor infants and children and on the influences of various aspects of poverty measures. © 2011 Elsevier Ltd.

The study settings are two informal settlements of Nairobi, Kenya, namely, Viwandani and Korogocho where the African Population and Health Research Centre (APHRC) runs a longitudinal demographic surveillance system referred to as the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The NUHDSS has been in operation since 2002 and has about 60,000 registered inhabitants in nearly 20,000 households. These two densely populated communities have higher unemployment, poverty, crime, poor sanitation and generally poorer health indicators than Nairobi as a whole (APHRC, 2002). The two communities, however, exhibit structural differences: Viwandani is bordered by an industrial area and attracts relatively younger, more educated, and shorter term migrants, while the population in Korogocho is more stable and has higher levels of co-residence of spouses (Emina et al., 2011). This study uses data from the Maternal and Child Health (MCH) component of a broader project entitled “Urbanization, Poverty and Health Dynamics” being implemented in the NUHDSS. All NUHDSS female members who gave birth since September 2006 and their children were enrolled in the project, and anthropometric measurements taken. Updates were done during follow-up visits every four months, and also when new children were recruited into the study for the first time to form new cohorts. Some children could not be immediately traced until after several visits due to the high population mobility in urban poor settings. For the purpose of this study, we use data on 3693 children from six cohorts as described in Table 1. These children contribute data at different time-points (surveys) totaling 14,410 observations. The first baseline observations (Cohort 1 and survey 1) took place between February and April 2007 with follow-up visits and new recruitments made routinely thereafter. The first wave of cohort 3 was done during a prolonged period (between October 2007 and May 2008) as a result of the political and social instability that followed Kenya's 2007 elections. Sample size. Note: The total number of children enrolled across all six cohorts is 3693. The data in Table 1 show a relatively high level of attrition across successive waves. For instance, of the 568 children enrolled in the first cohort, 474 were successfully re-contacted in the first follow-up, and only 178 were reached during the eighth visit, for an average annual attrition rate of about 24%. The average annual attrition rate for the other cohorts ranged from 21 to 28%. Once a year the NUHDSS collects data on various aspects of well-being at the household level. The questionnaires cover monthly expenditures (on rent, food, energy, water, transport, electricity, health care, and school fees), assets (or possessions), dwelling characteristics (floor, wall, roof, drinking water, toilets, and garbage collection), subjective poverty on a scale from 1 (poorest) to 10 (richest), and access to food (e.g. number and quantity of meals, failure to eat, going to bed hungry). These data for 2007, 2008 and 2009 are also used in the analysis. The dependent variable is based on height-for-age Z-scores (HAZ), computed using the 2000 CDC growth reference standards using zanthro command in STATA. While child weight-for-age fluctuates with environmental influences such as acute infections and poor nutritional intake, the height-for-age indicator represents a long term measure of health or chronic undernourishment (FAO, 1997). As recommended by the World Health Organization, overall stunting is defined as HAZ below −2 standard deviations (SD) from the median of the WHO/NCHS reference, while severe stunting is defined as HAZ below −3SD from the median of the WHO/NCHS reference (WHO, 1995, 2010). HAZ score below −2SD for children in the age group below 2–3 years represents stunted growth which reflects a continuing process of ‘failing to grow’ or chronic malnutrition. In a healthy, well-nourished population of children, it is expected that approximately 2.3% of children will fall below two standard deviations of the reference population and will be classified as stunted, wasted or underweight (WHO, 1995). The World Health Organization considers the severity of malnutrition to be ‘high’ when the prevalence of stunting exceeds 30% and wasting reaches 10%. High levels of stunted growth are often associated with poor socio-economic conditions, frequent illness and inappropriate healthcare practices (WHO, 1995). In this and other similar studies, infants were measured in the recumbent position and ‘length’ was used rather than ‘height’. We operationalize alternative measures of poverty which capture not only the money-metric dimension, but also the broader aspects of human deprivation. First, we constructed a money-metric indicator of poverty using information on monthly household consumption. This indicator allows us to assess the relationship between access to cash income and child growth. Second, we derived an assets index using information on household ownership of durable assets. As indicated earlier, the assets index is considered a good measure of long-term wealth, and is expected to have an impact on stunting which represents a long term nutritional deficiency. Third, we derived a food poverty index using information of household's access to food. This index allows us to assess the effect of household food insecurity on child growth. Lastly, we included a measure of subjective poverty, derived from households' perceptions of their relative wealth status in the community, on a scale from 1 (poorest) to 10 (richest). Table 2 describes the five alternative measures of household welfare. Alternative measures of household welfare used in the study. Apart from the subjective poverty variable which was recoded in three categories using the cut-off points of three and six, the three other welfare indicators were recoded as tertiles. The categories were labeled “poorest”, “middle” and “least poor”. All four measures of poverty are time varying: the 2007 poverty indices were linked to the 2007 anthropometric data, the same for the 2008 and 2009 data. All poverty variables were measured at the NUHDSS level and tertiles derived after merging with the MCH data. There was about eight percent of missing values due to the fact that not all households had poverty information for the three time points. These missing values were imputed using the STATA add-on for imputation by chained equations (ICE) procedures (Royston, 2005). The following variables were used in the imputation equations: village where the household is located; mother's marital status, age, education and parity at the time of the first interview; household size; slum of residence; as well as the values of poverty measures for the preceding and/or the following time point. There were 100 observations with missing welfare data that were excluded from the analyses, hence a final sample of 14,310 observations from 3692 children. In the models we control for a set of characteristics and the child, mother, household and community levels which previous studies have hypothesized to have an impact on child growth. These include the sex and age of the child, and child's mother's education, length of stay in the study area, marital status, and parity. Besides mother's parity, we also control for household size since children may not necessary live with their biological parents. Using PCA, we also construct a household environment index from information on the type of dwelling's floor, wall and roof; toilet facilities, the type of drinking water source and garbage collection – factors expected to have a direct effect on risk of infections. Finally, we control for the slum of residence (Korogocho or Viwandani). The analysis is conducted in three steps: First, univariate and bivariate analyses are used to describe the patterns of stunting as the children age, and to depict the differences across the five poverty measures. Second, four multivariate models are used to test robustness of each poverty measure as a predictor of child growth and development and the statistical significance of the differences observed in the descriptive phase. Third, we stratify the analysis by age to examine how the overall effect of poverty on child nutritional status may vary by age. Given that the data are made up of repeated longitudinal observations, we use the random intercept multilevel models to control for clustering of observations at child level. The model is specified as follows: where i and j refer to the observation and child, respectively; πij is the probability that the child referenced (i, j) is stunted; xij(k) is the kth covariate; β0j represents the intercept modelled to randomly vary between children; βk is the regression coefficients of the kth explanatory variables; and u0j is the random coefficient distributed as N(0,σu2) (Rasbash et al., 2002). The equations used to fit the interaction models are derived from eq. (1). Models are fitted using the STATA “xtlogit” command. The third category (least poor) is used as the reference group for all five measures of poverty. The presentation of results will focus primarily on the coefficient of the first category (poorest).

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to informal settlements like Viwandani and Korogocho in Nairobi, Kenya. These clinics can provide essential maternal health services, including prenatal care, postnatal care, and family planning, directly to the community.

2. Telemedicine: Introducing telemedicine services that allow pregnant women in informal settlements to consult with healthcare professionals remotely. This can help overcome barriers to accessing healthcare facilities and provide timely advice and support to pregnant women.

3. Community health workers: Training and deploying community health workers in informal settlements to provide education, support, and basic healthcare services to pregnant women. These community health workers can act as a bridge between the community and formal healthcare systems, ensuring that pregnant women receive the necessary care and support.

4. Maternal health vouchers: Introducing a voucher system that provides pregnant women in informal settlements with access to essential maternal health services. These vouchers can cover the cost of prenatal care, delivery, and postnatal care, making healthcare more affordable and accessible for pregnant women.

5. Public-private partnerships: Establishing partnerships between public healthcare facilities and private healthcare providers to expand access to maternal health services in informal settlements. This collaboration can help leverage the resources and expertise of both sectors to improve the quality and availability of maternal healthcare.

6. Health education programs: Implementing comprehensive health education programs in informal settlements to raise awareness about maternal health, nutrition, and hygiene practices. These programs can empower pregnant women with knowledge and skills to take care of their own health and the health of their babies.

7. Improved infrastructure: Investing in the development of healthcare infrastructure in informal settlements, including the construction of well-equipped maternity clinics and hospitals. This can ensure that pregnant women have access to safe and quality healthcare facilities for prenatal care, delivery, and postnatal care.

It is important to note that these recommendations are based on the information provided and may need to be further tailored and evaluated to suit the specific context and needs of Viwandani and Korogocho in Nairobi, Kenya.
AI Innovations Description
Based on the provided description, it seems that the study aims to examine the relationship between different dimensions of poverty and child growth in urban deprived settings, specifically in two informal settlements in Nairobi, Kenya. The study uses longitudinal data from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) and focuses on four poverty measures: expenditures poverty, assets poverty, food poverty, and subjective poverty.

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

1. Strengthening social support systems: Implement programs that provide financial assistance and resources to households living in urban deprived settings. This can help alleviate the burden of poverty and improve access to essential maternal health services, such as prenatal care, nutrition support, and postnatal care.

2. Enhancing community-based healthcare services: Establish community health centers or mobile clinics in informal settlements to provide accessible and affordable maternal health services. These centers can offer prenatal check-ups, vaccinations, family planning services, and health education programs tailored to the specific needs of the community.

3. Promoting maternal health awareness: Conduct targeted awareness campaigns to educate women and families in urban deprived settings about the importance of maternal health. These campaigns can focus on topics such as proper nutrition during pregnancy, the benefits of prenatal care, and the importance of early childhood development.

4. Collaborating with local organizations and stakeholders: Engage with local NGOs, community leaders, and healthcare providers to develop collaborative initiatives that address the unique challenges faced by women in urban deprived settings. By working together, it is possible to create sustainable solutions that improve access to maternal health services and support systems.

5. Empowering women through education and skill-building: Implement programs that provide vocational training and educational opportunities for women in urban deprived settings. By empowering women with knowledge and skills, they can have better economic opportunities, which can positively impact their access to maternal health services.

It is important to note that these recommendations are based on the provided description and may need to be further tailored and adapted to the specific context and needs of the target population.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations for improving access to maternal health in the informal settlements of Nairobi, Kenya:

1. Strengthening healthcare infrastructure: Invest in improving the healthcare facilities and services available in the informal settlements. This could include building more clinics or health centers, ensuring they have adequate medical supplies and equipment, and training healthcare professionals to provide quality maternal health services.

2. Mobile health clinics: Implement mobile health clinics that can reach remote areas within the informal settlements. These clinics can provide essential maternal health services, such as prenatal care, vaccinations, and health education, directly to the communities.

3. Community health workers: Train and deploy community health workers who can provide basic maternal health services, educate women about pregnancy and childbirth, and refer them to healthcare facilities when necessary. These workers can also play a crucial role in raising awareness about the importance of maternal health and encouraging women to seek care.

4. Financial support: Establish programs that provide financial support to pregnant women and new mothers in the informal settlements. This could include cash transfers or vouchers that can be used to access maternal health services, transportation subsidies, or incentives for attending prenatal and postnatal care visits.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of women receiving prenatal care, the percentage of births attended by skilled health personnel, or the reduction in maternal mortality rates.

2. Baseline data collection: Gather baseline data on the selected indicators before implementing the recommendations. This could involve surveys, interviews, or data collection from healthcare facilities and community health workers.

3. Implement recommendations: Roll out the recommended interventions, such as strengthening healthcare infrastructure, deploying mobile health clinics, training community health workers, and providing financial support.

4. Monitoring and evaluation: Continuously monitor and evaluate the impact of the interventions on the selected indicators. This could involve collecting data on the number of women accessing maternal health services, tracking changes in maternal mortality rates, or conducting surveys to assess women’s satisfaction with the implemented interventions.

5. Data analysis: Analyze the collected data to assess the effectiveness of the recommendations in improving access to maternal health. This could involve comparing the baseline data with the post-intervention data to identify any significant changes or trends.

6. Adjustments and improvements: Based on the analysis, make any necessary adjustments or improvements to the interventions to further enhance their impact on improving access to maternal health.

7. Repeat evaluation: Periodically repeat the evaluation process to assess the long-term impact of the recommendations and identify areas for further improvement.

By following this methodology, policymakers and stakeholders can gain insights into the effectiveness of the recommended interventions and make informed decisions to improve access to maternal health in the informal settlements of Nairobi, Kenya.

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