Socioeconomic factors associated with anaemia among children aged 6–59 months in Namibia

Journal of Public Health in Africa, Volume 11, No. 1, Year 2020

Interpretation

Background: Anaemia remains a public health concern, and the its prevalence varies between countries as well as between age, sex and levels of poverty. This study aims at examining the association between socio-demographic factors and anaemia among children aged 6–59 months in Namibia. Methods: Data was extracted from the 2013 Namibian Demographic Health Survey. The association between anaemia and other factors was examined with logistic regression. Results are reported in odds ratio (OR), with 95% confidence intervals (CI). Results: In total, 1,383 children aged 6–59 months had complete data and included in the analyses. Our study shows that there is a statistically significantly increased risk of anaemia among children from poorer households compared with the richest quintile. Also, there was a statistically significance supporting anaemia being more common among boys than girls. There was also a statistically significant negative effect related to age. Discussion: Our study shows that young children, boys and children in poorer households have an increased risk of anaemia. Considering the adverse impact of anaemia on child development, policies must prioritise factors exacerbating anaemia risk. The NDHS aims to gather data, amongst other things, on key health indicators such as fertility, maternal and child health, and nutritional status of mothers and children, and it has been conducted four times, in 1992, 2000, 2006-2007, and 2013.17 In our study, data from the 2013 NDHS was used. The two-stage sampling frame used in the 2013 NDHS was mainly based on the frame for the Namibia Population and Housing Census during 2011, though with partial updates. Namibia consists of 6,102 enumeration areas (EAs), 2,818 in urban areas, with an average of 86 households in them, and 3,284 in rural areas, with an average of 74 households in them. A predefined number of urban and rural EAs within each of the 13 regions in Namibia, totaling 269 urban and 285 rural clusters, were decided before the randomization took place. Within these 26 areas, in the first stage, probability proportional to size was used to select the 554 clusters. In the second stage, 20 households were chosen with equal probability systematic sampling in each of the clusters, and the total sample size was, therefore, 11,080 households. Detailed information about the sampling methods and the entire survey can be found in the 2013/14 NDHS report.17 In each household, a questionnaire was used, in which all members of the household were listed, and covering information about assets, which was used to calculate a wealth index. Additionally, all women aged 15-49 years in the households responded to a face-to-face questionnaire, which included questions about their educational level, and their children’s use of vitamin A supplements and deworming medication. Additional to these questionnaires, hemoglobin, height and weight of the children were measured. In our study children aged 6–59 months whose parent participated in the 2013 NDHS and provided information for them were included. Hemoglobin was measured in 2,303 children aged 6 – 59 months.17 After restricting to children who had anemia tested, their height and weight measured, and a face-to-face interview conducted with their mother, we got a total sample size of 1,537 children. Hemoglobin testing was performed by trained health technicians, by drawing a drop of capillary blood from a child’s fingertip or heel. The blood was drawn into a micro-cuvette and analyzed with a battery portable HemoCue photometer (HemoCue AB, Ängelholm, Sweden) that displays the hemoglobin concentration. We followed the WHO criteria and defined anemia as an Hb level of ≤ 11.0 g/dL.2 Height and weight were measured lying down for children below 24 months and for older children was measured standing. In our analyses, we used sex, place of residence, age, household wealth status, maternal education, received vitamin A supplement, received deworming medication, wasted, underweight, and stunted as exposure variables. These socioeconomic variables have previously been reported as being associated with anemia in children under 5. For the variable sex, girls were defined as the exposure group. Age was grouped as 6–11 months (reference group), 12–23 months, 24–35 months, 36–47 months, and 48–59 months. For place of residence, urban was used as reference and rural as exposure. The household wealth index was compiled in the NDHS dataset using the principle component analysis of asset variables, which are then categorized into quintiles, 17 with the highest wealth quintile as reference group. Questions about household characteristics (roofing type, flooring type, cooking fuel), possession of durable goods (bicycle, radio, television) and access to basic services (electricity, toilet, source of drinking water) were used to compile the household assets.17 This method is considered the most reliable measure of household socio-economic position.27 We used the lowest quintile as a reference group. Maternal education was divided into no education (defined as never went to school), primary education (attended school for 1–7 years), secondary education (attended school for 8–12 years) and higher education (attended university studies or similar) with secondary education used as the reference group. For received vitamin A supplement, received deworming medication was used as the exposure group. In our analyses, we excluded responses of do not know regarding vitamin A supplement and deworming medication(227 individuals and 405 individuals respectively) from analyses. Children were classified as stunted for a low height-for-age, as underweight for a low weight-for-age, and as wasted for low weight-for-height, according to the WHO child growth criteria.17 Descriptive statistics were performed with adjustments for the sampling weights of the NDHS. The weights are used to ensure a national representative survey sample for NDHS’s two-stage stratified cluster sampling methodology, which leads to a non-proportional distribution of sample divisions across regions.17 Multivariable logistic regression was applied to study the association between anemia and other factors. Estimates from this were presented with odds ratios (ORs) and 95% confidence intervals (CI). Stratified estimates were applied for sex. In these analyses, 1,383 children were included, whereas 154 participants were excluded because of missing data, either for use of Vitamin A, use of deworming medicine or no data for weight and/or height. To evaluate whether the drop-out because of lack of interview data might affect our results, a complementary analysis was performed on the 2,208 children aged 6–59 months with haemoglobin data, anthropometric data, and socioeconomic data (not including mother’s education). These analyses consequently did not include the variables mother’s education, vitamin A supplements and deworming medication. Furthermore, we conducted logistic regression without sample weights. This was done as the sample weights were calculated for the whole NDHS, while our study was based on a limited part of the sample due to our inclusion criteria, which meant that only mothers of 6–59 months old children with all required measurements was included. This limits our data to on average less than 2.5 children per EA in comparison to the 20 household per EA that was sampled, and this causes potential bias due to unstable sample weights. Statistical significance was set at 0.05. The statistical analyses were performed using STATA statistical software (Version 13; The StataCorp LP, College Station, Texas). We checked whether collinearity between stunting, underweight and wasting might affect our estimates by performing analyses with one of them at a time in separate logistic regressions (results not shown). We have not evaluated whether collinearity might affect our results in any other way. The DHS is conducted in countries with which WHO have established collaborations. Ethical clearance was obtained from WHO and the participating individual countries’ ethical committees before the surveys were conducted. Informed consent was obtained from legal guardian for participation in the study before individuals were interviewed. The DHS data that was used for the current study is available freely on a public domain (downloaded from http://www.dhsprogram.com/data/dataset_a dmin/download-datasets.cfm) after completion of a user’s agreement and the granting of access. No separate permission is required for data usage and publication.

AI Digest

Study Justification:

– Anaemia is a public health concern that affects children aged 6-59 months in Namibia.
– The prevalence of anaemia varies between countries and socio-demographic factors.
– Understanding the association between socio-demographic factors and anaemia is important for developing effective policies and interventions.

Study Highlights:

– Data was extracted from the 2013 Namibian Demographic Health Survey.
– Logistic regression was used to examine the association between anaemia and socio-demographic factors.
– The study included 1,383 children aged 6-59 months.
– Results showed a statistically significant increased risk of anaemia among children from poorer households compared to the richest quintile.
– Boys had a statistically significant higher risk of anaemia compared to girls.
– Age was also found to have a statistically significant negative effect on anaemia risk.

Study Recommendations:

– Policies should prioritize factors that exacerbate anaemia risk, such as poverty and gender disparities.
– Interventions should focus on young children, boys, and children in poorer households to reduce the risk of anaemia.
– Efforts should be made to improve access to healthcare services, including vitamin A supplements and deworming medication.

Key Role Players:

– Ministry of Health: Responsible for implementing policies and interventions to address anaemia among children.
– Healthcare Providers: Involved in providing healthcare services, including screening and treatment for anaemia.
– Non-Governmental Organizations: Can support the implementation of interventions and raise awareness about anaemia.
– Community Leaders: Play a role in promoting health education and encouraging community participation in anaemia prevention.

Cost Items for Planning Recommendations:

– Healthcare Services: Budget for screening, diagnosis, and treatment of anaemia.
– Health Education: Allocate funds for health promotion campaigns and educational materials.
– Training and Capacity Building: Provide resources for training healthcare providers on anaemia prevention and management.
– Monitoring and Evaluation: Set aside funds for monitoring the effectiveness of interventions and evaluating outcomes.
– Research and Data Collection: Allocate resources for future studies and data collection to monitor anaemia prevalence and trends.

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 uses data from the 2013 Namibian Demographic Health Survey, which provides a representative sample of the population. The association between socio-demographic factors and anaemia is examined using logistic regression, and the results are reported in odds ratios with 95% confidence intervals. The study finds a statistically significant increased risk of anaemia among children from poorer households compared to the richest quintile, as well as a statistically significant higher prevalence of anaemia among boys compared to girls. The study also identifies a statistically significant negative effect related to age. The discussion highlights the need for policies to prioritize factors exacerbating anaemia risk. To improve the evidence, the abstract could provide more details about the sample size, the specific socio-demographic factors examined, and the methods used to measure anaemia and other variables. Additionally, it would be helpful to include information about potential limitations of the study and suggestions for future research.

Abstract

The NDHS aims to gather data, amongst other things, on key health indicators such as fertility, maternal and child health, and nutritional status of mothers and children, and it has been conducted four times, in 1992, 2000, 2006-2007, and 2013.17 In our study, data from the 2013 NDHS was used. The two-stage sampling frame used in the 2013 NDHS was mainly based on the frame for the Namibia Population and Housing Census during 2011, though with partial updates. Namibia consists of 6,102 enumeration areas (EAs), 2,818 in urban areas, with an average of 86 households in them, and 3,284 in rural areas, with an average of 74 households in them. A predefined number of urban and rural EAs within each of the 13 regions in Namibia, totaling 269 urban and 285 rural clusters, were decided before the randomization took place. Within these 26 areas, in the first stage, probability proportional to size was used to select the 554 clusters. In the second stage, 20 households were chosen with equal probability systematic sampling in each of the clusters, and the total sample size was, therefore, 11,080 households. Detailed information about the sampling methods and the entire survey can be found in the 2013/14 NDHS report.17 In each household, a questionnaire was used, in which all members of the household were listed, and covering information about assets, which was used to calculate a wealth index. Additionally, all women aged 15-49 years in the households responded to a face-to-face questionnaire, which included questions about their educational level, and their children’s use of vitamin A supplements and deworming medication. Additional to these questionnaires, hemoglobin, height and weight of the children were measured. In our study children aged 6–59 months whose parent participated in the 2013 NDHS and provided information for them were included. Hemoglobin was measured in 2,303 children aged 6 – 59 months.17 After restricting to children who had anemia tested, their height and weight measured, and a face-to-face interview conducted with their mother, we got a total sample size of 1,537 children. Hemoglobin testing was performed by trained health technicians, by drawing a drop of capillary blood from a child’s fingertip or heel. The blood was drawn into a micro-cuvette and analyzed with a battery portable HemoCue photometer (HemoCue AB, Ängelholm, Sweden) that displays the hemoglobin concentration. We followed the WHO criteria and defined anemia as an Hb level of ≤ 11.0 g/dL.2 Height and weight were measured lying down for children below 24 months and for older children was measured standing. In our analyses, we used sex, place of residence, age, household wealth status, maternal education, received vitamin A supplement, received deworming medication, wasted, underweight, and stunted as exposure variables. These socioeconomic variables have previously been reported as being associated with anemia in children under 5. For the variable sex, girls were defined as the exposure group. Age was grouped as 6–11 months (reference group), 12–23 months, 24–35 months, 36–47 months, and 48–59 months. For place of residence, urban was used as reference and rural as exposure. The household wealth index was compiled in the NDHS dataset using the principle component analysis of asset variables, which are then categorized into quintiles, 17 with the highest wealth quintile as reference group. Questions about household characteristics (roofing type, flooring type, cooking fuel), possession of durable goods (bicycle, radio, television) and access to basic services (electricity, toilet, source of drinking water) were used to compile the household assets.17 This method is considered the most reliable measure of household socio-economic position.27 We used the lowest quintile as a reference group. Maternal education was divided into no education (defined as never went to school), primary education (attended school for 1–7 years), secondary education (attended school for 8–12 years) and higher education (attended university studies or similar) with secondary education used as the reference group. For received vitamin A supplement, received deworming medication was used as the exposure group. In our analyses, we excluded responses of do not know regarding vitamin A supplement and deworming medication(227 individuals and 405 individuals respectively) from analyses. Children were classified as stunted for a low height-for-age, as underweight for a low weight-for-age, and as wasted for low weight-for-height, according to the WHO child growth criteria.17 Descriptive statistics were performed with adjustments for the sampling weights of the NDHS. The weights are used to ensure a national representative survey sample for NDHS’s two-stage stratified cluster sampling methodology, which leads to a non-proportional distribution of sample divisions across regions.17 Multivariable logistic regression was applied to study the association between anemia and other factors. Estimates from this were presented with odds ratios (ORs) and 95% confidence intervals (CI). Stratified estimates were applied for sex. In these analyses, 1,383 children were included, whereas 154 participants were excluded because of missing data, either for use of Vitamin A, use of deworming medicine or no data for weight and/or height. To evaluate whether the drop-out because of lack of interview data might affect our results, a complementary analysis was performed on the 2,208 children aged 6–59 months with haemoglobin data, anthropometric data, and socioeconomic data (not including mother’s education). These analyses consequently did not include the variables mother’s education, vitamin A supplements and deworming medication. Furthermore, we conducted logistic regression without sample weights. This was done as the sample weights were calculated for the whole NDHS, while our study was based on a limited part of the sample due to our inclusion criteria, which meant that only mothers of 6–59 months old children with all required measurements was included. This limits our data to on average less than 2.5 children per EA in comparison to the 20 household per EA that was sampled, and this causes potential bias due to unstable sample weights. Statistical significance was set at 0.05. The statistical analyses were performed using STATA statistical software (Version 13; The StataCorp LP, College Station, Texas). We checked whether collinearity between stunting, underweight and wasting might affect our estimates by performing analyses with one of them at a time in separate logistic regressions (results not shown). We have not evaluated whether collinearity might affect our results in any other way. The DHS is conducted in countries with which WHO have established collaborations. Ethical clearance was obtained from WHO and the participating individual countries’ ethical committees before the surveys were conducted. Informed consent was obtained from legal guardian for participation in the study before individuals were interviewed. The DHS data that was used for the current study is available freely on a public domain (downloaded from http://www.dhsprogram.com/data/dataset_a dmin/download-datasets.cfm) after completion of a user’s agreement and the granting of access. No separate permission is required for data usage and publication.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide pregnant women and new mothers with information, reminders, and support related to maternal health, including nutrition, prenatal care, and postnatal care.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare providers through video calls or phone calls, reducing the need for travel and improving access to prenatal care.

3. Community Health Workers: Train and deploy community health workers who can provide education, counseling, and basic healthcare services to pregnant women and new mothers in their own communities. This can help bridge the gap between healthcare facilities and remote or marginalized populations.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access essential maternal health services, such as prenatal care visits, delivery in a healthcare facility, and postnatal care.

5. Transportation Support: Establish transportation support systems, such as subsidized or free transportation services, to help pregnant women reach healthcare facilities for prenatal care visits, delivery, and postnatal care, particularly in rural or remote areas.

6. Maternal Health Clinics: Set up dedicated maternal health clinics or centers that offer comprehensive services, including prenatal care, delivery, postnatal care, family planning, and counseling, to ensure that pregnant women receive all necessary care in one location.

7. Maternal Health Education Programs: Develop and implement educational programs that target women and their families, providing information on the importance of maternal health, pregnancy care, nutrition, and early childhood development.

8. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, facilities, and resources to expand coverage and reach underserved populations.

9. Maternal Health Financing: Explore innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to make maternal health services more affordable and accessible to low-income women.

10. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that maternal health services are delivered in a safe, respectful, and effective manner, thereby increasing trust and utilization of these services.

It is important to note that the suitability and effectiveness of these innovations may vary depending on the specific context and needs of the population.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in Namibia would be to prioritize interventions that address the socioeconomic factors associated with anemia among children aged 6-59 months. These interventions could include:

1. Targeted support for children from poorer households: Implement programs that provide nutritional support, such as iron supplementation and fortified foods, to children from poorer households who are at a higher risk of anemia. This could be done through community-based initiatives or through existing healthcare facilities.

2. Gender-specific interventions: Develop interventions that specifically target boys, as they were found to have a higher risk of anemia compared to girls. This could involve promoting healthy eating habits, providing access to nutritious foods, and raising awareness about the importance of iron-rich foods for boys’ health.

3. Early detection and intervention: Implement regular screening programs to identify anemia in young children, particularly those aged 6-11 months who were found to be at a higher risk. Early detection can allow for timely intervention and treatment, which can prevent the long-term consequences of anemia on child development.

4. Maternal education and awareness: Promote maternal education on the importance of proper nutrition during pregnancy and early childhood. This can include providing information on iron-rich foods, the benefits of breastfeeding, and the importance of prenatal and postnatal care. Increasing awareness among mothers can lead to better nutrition practices and improved maternal and child health outcomes.

5. Strengthening healthcare infrastructure: Improve access to healthcare facilities, particularly in rural areas, to ensure that mothers and children have access to essential services, including prenatal and postnatal care, nutritional counseling, and anemia screening and treatment. This may involve increasing the number of healthcare facilities, training healthcare providers, and improving transportation networks to facilitate access to healthcare services.

By implementing these recommendations, it is expected that access to maternal health in Namibia can be improved, leading to a reduction in the prevalence of anemia among children and better overall health outcomes for mothers and children.

AI Innovations Methodology

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

1. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, especially in rural areas, by providing necessary equipment, supplies, and trained healthcare professionals.

2. Increase awareness and education: Implement comprehensive maternal health education programs to raise awareness about the importance of prenatal care, nutrition, and early detection of complications. This can be done through community outreach programs, workshops, and media campaigns.

3. Improve transportation and accessibility: Enhance transportation systems to ensure pregnant women have easy access to healthcare facilities. This can include providing transportation vouchers or mobile clinics for remote areas.

4. Provide financial support: Establish financial assistance programs to alleviate the financial burden of maternal healthcare services. This can include subsidies for prenatal care, childbirth, and postnatal care.

5. Strengthen referral systems: Develop effective referral systems to ensure timely access to specialized care for high-risk pregnancies. This can involve establishing clear protocols for transferring patients between healthcare facilities.

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

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

2. Collect baseline data: Gather data on the current state of maternal health access, including the number of pregnant women receiving prenatal care, the availability of healthcare facilities, and the transportation infrastructure.

3. Implement interventions: Implement the recommended interventions in selected areas or communities. This could involve improving healthcare facilities, conducting education programs, providing transportation support, and implementing financial assistance programs.

4. Monitor and evaluate: Continuously monitor the implementation of interventions and collect data on the selected indicators. This can be done through surveys, interviews, and data collection from healthcare facilities.

5. Analyze the data: Analyze the collected data to assess the impact of the interventions on the selected indicators. Compare the data before and after the implementation of the interventions to determine the effectiveness of the recommendations.

6. Adjust and refine: Based on the analysis, make adjustments and refinements to the interventions as needed. This could involve scaling up successful interventions, addressing any challenges or barriers, and identifying areas for further improvement.

7. Repeat the process: Continuously repeat the monitoring, evaluation, and adjustment process to ensure ongoing improvement in access to maternal health.

By following this methodology, policymakers and healthcare providers can assess the impact of the recommendations and make informed decisions to further improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.4081/jphia.2020.1131

ISSN: 20389922

Research Areas:

Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Namibia

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female