Malnutrition in infants aged under 6 months attending community health centres: A cross sectional survey

Nutrients, Volume 13, No. 8, Year 2021

Interpretation

A poor understanding of malnutrition burden is a common reason for not prioritizing the care of small and nutritionally at-risk infants aged under-six months (infants u6m). We aimed to estimate the anthropometric deficit prevalence in infants u6m attending health centres, using the Composite Index of Anthropometric Failure (CIAF), and to assess the overlap of different individual indicators. We undertook a two-week survey of all infants u6m visiting 18 health centres in two zones of the Oromia region, Ethiopia. We measured weight, length, and MUAC (mid-upper arm circumference) and calculated weight-for-length (WLZ), length-for-age (LAZ), and weight-for-age z-scores (WAZ). Overall, 21.7% (95% CI: 19.2; 24.3) of infants u6m presented CIAF, and of these, 10.7% (95% CI: 8.93; 12.7) had multiple anthropometric deficits. Low MUAC overlapped with 47.5% (95% CI: 38.0; 57.3), 43.8% (95% CI: 34.9; 53.1), and 42.6% (95% CI: 36.3; 49.2) of the stunted, wasted, and CIAF prevalence, respectively. Underweight overlapped with 63.4% (95% CI: 53.6; 72.2), 52.7% (95% CI: 43.4; 61.7), and 59.6% (95% CI: 53.1; 65.9) of the stunted, wasted, and CIAF prevalence, respectively. Anthropometric deficits, single and multiple, are prevalent in infants attending health centres. WAZ overlaps more with other forms of anthropometric deficits than MUAC. The study sites were in Deder woreda, East Hararge zone and in Jimma zone, Ethiopia. Though geographically separate, these are both located in the Oromia region. They were chosen because they are the sites of our future RCT. The study was implemented in 18 health centres, ten in Jimma zone and eight in Deder woreda. Jimma zone is one of the most populous areas of the Oromia Regional State, with a population of over 3 million people. Deder woreda has a population of some 315,000 people. Both sites have a high burden of malnutrition. Their main livelihood in the area is agriculture, petty trade of cash crops such as khat and coffee, fattening of oxen, and local casual labour. Jimma zone and Deder woreda have 124 and 8 health centres, respectively, each serving an average population of between 15,000 to 30,000. We undertook a health centre-based cross-sectional survey, surveying all infants u6m who attended the selected health centres for any reason over an average period of two weeks in each centre. Reasons for attendance included: being born at the health centre; immunization clinics; growth monitoring clinics; under-5 clinics (where children present with a variety of acute illnesses). We collected data from 1060 infants u6m between 12 October 2020 and 29 January 2021. We lacked prior information on how many infants u6m attending the health centres would have anthropometric deficits. Consequently, to estimate a sufficiently robust sample, we assumed a 50% prevalence of anthropometric deficit in infants u6m, and a 3% precision [22]. Using these assumptions, we estimated that we needed a sample of 1067 infants u6m, an average of 60 infants u6m per health centre. To plan for field logistics, we assumed that each health centre would have an average attendance of 30 infants u6m per week and set the average duration of data collection for each health centre to a two-week period. In Deder woreda, we included all eight available health centres. In Jimma zone, we selected ten out of the 124 available health centres as follows: First, we undertook a register review in all 124 health centres to collect eligibility information on ease of access and patient load. We then excluded 60 health centres from which we were unable to gather complete eligibility data. We further excluded seven health centres that were difficult to access. Lastly, we ranked the remaining 57 health centres according to patient load and randomly selected ten centres from the top 50%. We undertook training for the teams of enumerators and supervisors, one team in Jimma zone and one in Deder woreda, to ensure consistency and a high quality of data collection. Our training included learning how to obtain anthropometric measurements (e.g., weight, length, MUAC), assess infant feeding practices, obtain economic and demographic data, use digital data gathering devices (DDGs), as well as to obtain informed consent and clinical history data. Our training also included the piloting of data collection, prior to initiating the actual survey data collection, to ensure the collection of high-quality data and to identify and correct any sources of data collection errors. During the pilot, we also assessed field challenges of the survey tool for final editing. All data were collected using an electronic questionnaire designed using the REDCap (Research Electronic Data Capture) project system (https://redcap.am.lshtm.ac.uk/redcap/ accessed on 14 May 2021). At the household level, we obtained information about the sex and formal education of the household head, household size, number of dependent children aged < 18 years. From mothers or primary caregivers, we obtained information on age, formal education, and religion. From all infants u6m, we obtained sex and date of birth data. We asked mothers/caregivers to recall their infants’ age in weeks. We asked whether the infant was born singleton or was a twin or a triplet; the infant’s birth order; how many siblings aged < 18 years they have, and whether any of them had died recently. We collected data on infants’ feeding practices in the past 24 h. We asked about current and past breastfeeding and whether they received any liquids, i.e., water, milk, juice, broth, runny porridge, yoghurt, or other liquids apart from those mentioned. We asked whether they were fed using a bottle and whether they received any solid, semi-solid, or soft foods [23]. We measured weight with the infant undressed using a digital weight scale (Seca 354) to the nearest 5 g if weighing < 10 kg, or to the nearest 10 g if weighing ≥ 10 kg. We measured length using a UNICEF length/height board (infant/child/adult) to the nearest completed 0.1 cm. We measured knee–heel length, from the heel to the upper part of the distal femur (knee), using a digital Vernier calliper (Sealey AK9623EV) to the nearest completed 0.1 cm. We measured MUAC using UNICEF MUAC tapes to the nearest completed 0.1 cm. We measured head circumference using standard anthropometric tapes to the nearest completed 0.1 cm. We obtained subscapular and triceps skinfolds using a Harpenden skinfold calliper to the nearest completed 0.2 mm. We collected all anthropometric measurements in pairs, as per WHO Child Growth Standards protocols [24]. In brief, for each anthropometric measurement, two enumerators independently took one measurement. If the difference between these two measurements exceeded the pre-set maximum allowed difference (see Appendix A Table A1), each enumerator took a second measurement, and we excluded the first pair of measurements. If the difference for their second pair of measurements also exceeded the allowed difference, they each took a third measurement, and we excluded the first two pairs of measurements. If the difference in their third pair of measurements exceeded the allowed difference, no further measurements were taken, and we excluded all three pairs of measurements. We handled and analysed data using Stata (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX, USA: StataCorp LLC). We estimated infant’s age in weeks as the difference between the survey’s date and the infant’s date of birth divided by seven. For infants whose date of birth was not available, we used maternal recalled age. We grouped infants by age, in 5-week brackets. We identified infants as being exclusively breastfed if their mothers or primary caregivers reported in their 24 h recall to having breastfed the infants but answered no to all questions regarding any liquid intake, bottle feeding or feeding the infant with solid, semi-solid, or soft foods. We calculated the mean value for each of the anthropometric measurements using the pair of measurements that did not exceed the allowed difference. We used weight, length, and age data to estimate the anthropometric indices weight-for-age, length-for-age, and weight-for-length z-scores (WAZ, LAZ, and WLZ, respectively) based on the 2006 WHO Child Growth Standards [13], using the zanthro Stata command [25]. We marked anthropometric indices as outliers using the 2006 WHO recommendations [26], i.e., if they were >5 or <−6, >6 or <−6, and >5 or <−5 for WAZ, LAZ, and WLZ values, respectively. We defined underweight, stunted, and wasted as WAZ, LAZ, and WLZ < −2, respectively. We used CIAF to assess overall malnutrition prevalence in infants u6m [18]. We defined CIAF as all infants u6m that were either underweight, stunted, or wasted and we generated the following subcategories: wasted only; wasted and underweight; wasted, stunted and underweight; stunted and underweight; stunted only; and underweight only [18]. We defined the Composite Index of Severe Anthropometric Failure (CISAF) as all infants u6m that were severely underweight, stunted, or wasted, as defined by a WAZ, LAZ, or WLZ < −3, respectively [27]. To explore the overlap between MUAC and CIAF in infants, we explored the use of different thresholds to define low MUAC: MUAC < 11.5 cm; MUAC < 11.0 cm if aged < 6 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 7 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 13 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 17 weeks and <11.5 thereafter; MUAC < 11.0 cm; and MUAC < 10.5 cm. These thresholds were chosen to match those in past/present use in older children; and the age thresholds to match timings of immunization clinic visits when future programmes would use MUAC for the identification of at-risk infants u6m. For the analysis, we excluded infants u6m for which we could not estimate all anthropometric indices as they had weight or length missing, or their lengths were <45 cm, or if one or more of their anthropometric indices were marked as outliers. In addition, we excluded infants from the assessment of overlap between different anthropometric indicators if they presented with oedema. We estimated means or proportions, along with the 95% confidence intervals (95% CI), for all variables. We compared basic characteristics between Jimma and Deder using t-test and z-test with the lincom Stata command. To determine the prevalence of different anthropometric deficits in infants, we estimated summary statistics for all anthropometric variables using the svy Stata commands that account for the survey design.

AI Digest

Study Justification:

– The study aimed to address the poor understanding of malnutrition burden in infants under six months, which often leads to a lack of prioritization in their care.
– By estimating the prevalence of anthropometric deficits in these infants, the study aimed to highlight the need for targeted interventions and support for this vulnerable population.

Study Highlights:

– The study surveyed infants under six months attending 18 health centers in the Oromia region, Ethiopia.
– Anthropometric measurements, including weight, length, and mid-upper arm circumference (MUAC), were taken to calculate weight-for-length, length-for-age, and weight-for-age z-scores.
– The study found that 21.7% of infants under six months presented with anthropometric deficits, and 10.7% had multiple deficits.
– Low MUAC was found to overlap with a significant proportion of stunted, wasted, and overall anthropometric deficit prevalence.
– Underweight also overlapped with a substantial percentage of stunted, wasted, and overall anthropometric deficit prevalence.

Recommendations for Lay Reader and Policy Maker:

– The study highlights the high prevalence of anthropometric deficits in infants under six months attending health centers.
– It emphasizes the importance of addressing malnutrition in this age group and implementing targeted interventions to improve their nutritional status.
– The findings suggest the need for comprehensive approaches that address multiple anthropometric deficits, particularly focusing on low MUAC and underweight.
– Policy makers should prioritize the provision of resources and support to health centers to effectively address malnutrition in infants under six months.

Key Role Players:

– Health center staff: Including doctors, nurses, and nutritionists who can provide appropriate care and support to infants under six months with anthropometric deficits.
– Community health workers: They play a crucial role in identifying and referring infants at risk of malnutrition to health centers for further assessment and treatment.
– Government officials: Responsible for allocating resources and implementing policies to address malnutrition in infants under six months.
– Non-governmental organizations (NGOs): They can provide additional support, resources, and expertise in implementing interventions and programs to address malnutrition.

Cost Items for Planning Recommendations:

– Training: Budget for training health center staff, community health workers, and other relevant personnel on identifying and managing malnutrition in infants under six months.
– Equipment and supplies: Budget for purchasing or maintaining necessary equipment and supplies for anthropometric measurements, such as weight scales, length boards, MUAC tapes, and digital data gathering devices.
– Outreach and awareness campaigns: Budget for conducting community outreach and awareness campaigns to educate caregivers about the importance of nutrition and early identification of malnutrition in infants under six months.
– Nutritional support: Budget for providing appropriate nutritional support, including therapeutic foods and supplements, to infants under six months with anthropometric deficits.
– Monitoring and evaluation: Budget for regular monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.
Please note that the above cost items are general suggestions and may vary depending on the specific context and resources available.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional survey conducted in health centers in the Oromia region of Ethiopia. The study measured weight, length, and MUAC of infants under 6 months and calculated various anthropometric indices. The prevalence of anthropometric deficits, including underweight, stunting, and wasting, was estimated using the Composite Index of Anthropometric Failure (CIAF). The study also explored the overlap between low MUAC and CIAF. The evidence is based on a large sample size (1060 infants) and provides confidence intervals for the prevalence estimates. However, the study design is cross-sectional, which limits the ability to establish causality or determine temporal relationships. To improve the evidence, future studies could consider a longitudinal design to assess the long-term impact of malnutrition in infants and explore potential risk factors. Additionally, including a control group of infants without malnutrition could provide a better comparison for estimating the burden of malnutrition. Finally, conducting the study in multiple regions or countries could enhance the generalizability of the findings.

Abstract

The study sites were in Deder woreda, East Hararge zone and in Jimma zone, Ethiopia. Though geographically separate, these are both located in the Oromia region. They were chosen because they are the sites of our future RCT. The study was implemented in 18 health centres, ten in Jimma zone and eight in Deder woreda. Jimma zone is one of the most populous areas of the Oromia Regional State, with a population of over 3 million people. Deder woreda has a population of some 315,000 people. Both sites have a high burden of malnutrition. Their main livelihood in the area is agriculture, petty trade of cash crops such as khat and coffee, fattening of oxen, and local casual labour. Jimma zone and Deder woreda have 124 and 8 health centres, respectively, each serving an average population of between 15,000 to 30,000. We undertook a health centre-based cross-sectional survey, surveying all infants u6m who attended the selected health centres for any reason over an average period of two weeks in each centre. Reasons for attendance included: being born at the health centre; immunization clinics; growth monitoring clinics; under-5 clinics (where children present with a variety of acute illnesses). We collected data from 1060 infants u6m between 12 October 2020 and 29 January 2021. We lacked prior information on how many infants u6m attending the health centres would have anthropometric deficits. Consequently, to estimate a sufficiently robust sample, we assumed a 50% prevalence of anthropometric deficit in infants u6m, and a 3% precision [22]. Using these assumptions, we estimated that we needed a sample of 1067 infants u6m, an average of 60 infants u6m per health centre. To plan for field logistics, we assumed that each health centre would have an average attendance of 30 infants u6m per week and set the average duration of data collection for each health centre to a two-week period. In Deder woreda, we included all eight available health centres. In Jimma zone, we selected ten out of the 124 available health centres as follows: First, we undertook a register review in all 124 health centres to collect eligibility information on ease of access and patient load. We then excluded 60 health centres from which we were unable to gather complete eligibility data. We further excluded seven health centres that were difficult to access. Lastly, we ranked the remaining 57 health centres according to patient load and randomly selected ten centres from the top 50%. We undertook training for the teams of enumerators and supervisors, one team in Jimma zone and one in Deder woreda, to ensure consistency and a high quality of data collection. Our training included learning how to obtain anthropometric measurements (e.g., weight, length, MUAC), assess infant feeding practices, obtain economic and demographic data, use digital data gathering devices (DDGs), as well as to obtain informed consent and clinical history data. Our training also included the piloting of data collection, prior to initiating the actual survey data collection, to ensure the collection of high-quality data and to identify and correct any sources of data collection errors. During the pilot, we also assessed field challenges of the survey tool for final editing. All data were collected using an electronic questionnaire designed using the REDCap (Research Electronic Data Capture) project system (https://redcap.am.lshtm.ac.uk/redcap/ accessed on 14 May 2021). At the household level, we obtained information about the sex and formal education of the household head, household size, number of dependent children aged < 18 years. From mothers or primary caregivers, we obtained information on age, formal education, and religion. From all infants u6m, we obtained sex and date of birth data. We asked mothers/caregivers to recall their infants’ age in weeks. We asked whether the infant was born singleton or was a twin or a triplet; the infant’s birth order; how many siblings aged < 18 years they have, and whether any of them had died recently. We collected data on infants’ feeding practices in the past 24 h. We asked about current and past breastfeeding and whether they received any liquids, i.e., water, milk, juice, broth, runny porridge, yoghurt, or other liquids apart from those mentioned. We asked whether they were fed using a bottle and whether they received any solid, semi-solid, or soft foods [23]. We measured weight with the infant undressed using a digital weight scale (Seca 354) to the nearest 5 g if weighing 5 or 6 or 5 or <−5 for WAZ, LAZ, and WLZ values, respectively. We defined underweight, stunted, and wasted as WAZ, LAZ, and WLZ < −2, respectively. We used CIAF to assess overall malnutrition prevalence in infants u6m [18]. We defined CIAF as all infants u6m that were either underweight, stunted, or wasted and we generated the following subcategories: wasted only; wasted and underweight; wasted, stunted and underweight; stunted and underweight; stunted only; and underweight only [18]. We defined the Composite Index of Severe Anthropometric Failure (CISAF) as all infants u6m that were severely underweight, stunted, or wasted, as defined by a WAZ, LAZ, or WLZ < −3, respectively [27]. To explore the overlap between MUAC and CIAF in infants, we explored the use of different thresholds to define low MUAC: MUAC < 11.5 cm; MUAC < 11.0 cm if aged < 6 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 7 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 13 weeks and <11.5 thereafter; MUAC < 11.0 cm if aged < 17 weeks and <11.5 thereafter; MUAC < 11.0 cm; and MUAC < 10.5 cm. These thresholds were chosen to match those in past/present use in older children; and the age thresholds to match timings of immunization clinic visits when future programmes would use MUAC for the identification of at-risk infants u6m. For the analysis, we excluded infants u6m for which we could not estimate all anthropometric indices as they had weight or length missing, or their lengths were <45 cm, or if one or more of their anthropometric indices were marked as outliers. In addition, we excluded infants from the assessment of overlap between different anthropometric indicators if they presented with oedema. We estimated means or proportions, along with the 95% confidence intervals (95% CI), for all variables. We compared basic characteristics between Jimma and Deder using t-test and z-test with the lincom Stata command. To determine the prevalence of different anthropometric deficits in infants, we estimated summary statistics for all anthropometric variables using the svy Stata commands that account for the survey design.

Materials

https://efashare.b-cdn.net/materials/a267cec9f5795d6b03aaec1444338afb800cf563fed75a71d6b2664fb5b02443/nutrients-13-02489-s001.zip

Innovations

Based on the provided description, 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 infant care. These apps can be easily accessible to mothers and caregivers, providing them with guidance and support.

2. Telemedicine: Implement telemedicine services to connect healthcare providers with pregnant women and new mothers in remote areas. This allows for remote consultations, monitoring, and follow-up care, reducing the need for travel and improving access to healthcare services.

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 communities. These workers can help identify and address malnutrition and other health issues at the grassroots level.

4. Integrated Care: Establish integrated care models that bring together maternal health services with other essential services such as immunization, family planning, and nutrition programs. This approach ensures comprehensive care for mothers and infants, reducing fragmentation and improving access.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure, improve service delivery, and enhance the availability of essential supplies and medications.

6. Health Information Systems: Implement robust health information systems that capture and analyze data on maternal health indicators. This data can be used to identify gaps in access and quality of care, inform decision-making, and track progress towards improving maternal health outcomes.

7. Transportation and Logistics: Develop innovative transportation and logistics solutions to overcome geographical barriers and ensure timely access to maternal health services. This can include mobile clinics, ambulances, and delivery services for essential supplies.

8. Maternal Health Financing: Explore innovative financing mechanisms to ensure affordable and accessible maternal health services. This can involve health insurance schemes, community-based financing models, and partnerships with microfinance institutions.

9. Maternal Health Education: Strengthen maternal health education programs that target both healthcare providers and communities. This includes training healthcare professionals on evidence-based practices and empowering women and families with knowledge and skills to make informed decisions about their health.

10. Quality Improvement Initiatives: Implement quality improvement initiatives that focus on enhancing the quality of care provided to pregnant women and new mothers. This can involve training healthcare providers, improving infrastructure and equipment, and implementing standardized protocols and guidelines.

These innovations can help address the challenges related to access to maternal health services and contribute to improving maternal and infant health outcomes.

AI Innovations Description

Based on the description provided, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Implement a comprehensive maternal health program: Develop and implement a comprehensive maternal health program that focuses on addressing malnutrition in infants under 6 months attending community health centers. This program should include interventions such as nutrition education, breastfeeding support, and access to nutritious foods for both mothers and infants.

2. Strengthen health center capacity: Provide training and resources to health center staff to improve their knowledge and skills in identifying and managing malnutrition in infants. This can include training on anthropometric measurements, nutrition counseling, and the use of digital data gathering devices for accurate data collection.

3. Improve access to healthcare services: Enhance the accessibility of healthcare services by increasing the number of health centers in the targeted areas. This can be achieved by establishing new health centers or expanding existing ones to ensure that there is adequate coverage and capacity to serve the population.

4. Community engagement and awareness: Conduct community outreach programs to raise awareness about the importance of maternal health and the impact of malnutrition on infants. This can involve community meetings, workshops, and educational campaigns to empower mothers and caregivers with knowledge and skills to provide optimal nutrition and care for their infants.

5. Collaboration with stakeholders: Foster collaboration between government agencies, non-governmental organizations, and other relevant stakeholders to support the implementation of the maternal health program. This can include sharing resources, expertise, and best practices to ensure a coordinated and effective approach.

6. Monitoring and evaluation: Establish a robust monitoring and evaluation system to track the progress and impact of the maternal health program. This can involve regular data collection, analysis, and reporting to identify areas of improvement and make evidence-based decisions.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in malnutrition among infants under 6 months attending community health centers.

AI Innovations Methodology

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

1. Strengthening Health Center Infrastructure: Investing in the improvement of health center infrastructure, including facilities, equipment, and resources, can help ensure that maternal health services are readily available and accessible to women in need.

2. Enhancing Health Workforce Capacity: Increasing the number of skilled healthcare providers, such as doctors, nurses, and midwives, and providing them with adequate training and support can improve the quality and availability of maternal health services.

3. Implementing Community-Based Interventions: Engaging and empowering local communities through community-based interventions, such as health education programs, outreach services, and community health workers, can help raise awareness about maternal health and improve access to care.

4. Strengthening Referral Systems: Establishing effective referral systems between health centers and higher-level healthcare facilities can ensure that pregnant women with complications receive timely and appropriate 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 specific indicators that measure access to maternal health, such as the number of pregnant women receiving antenatal care, the percentage of births attended by skilled healthcare providers, or the distance to the nearest health facility.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population. This can be done through surveys, interviews, or existing data sources.

3. Introduce the recommendations: Simulate the implementation of the recommendations by adjusting the relevant variables in the data. For example, increase the number of healthcare providers or improve the infrastructure of health centers.

4. Analyze the impact: Compare the baseline data with the simulated data to assess the impact of the recommendations on the selected indicators. This can be done by calculating the changes in the indicators and determining if they have improved access to maternal health.

5. Validate the results: Validate the simulated impact by comparing it with real-world data or conducting further research or evaluations to ensure the accuracy and reliability of the findings.

6. Refine and iterate: Based on the results, refine the recommendations and iterate the simulation process to further optimize the impact on improving access to maternal health.

It is important to note that the methodology may vary depending on the specific context and available data sources. Additionally, involving relevant stakeholders, such as healthcare providers, policymakers, and community members, in the simulation process can help ensure the relevance and feasibility of the recommendations.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.3390/nu13082489

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative