Newborn weight change and predictors of underweight in the neonatal period in Guinea-Bissau, Nepal, Pakistan and Uganda

Maternal and Child Nutrition, Volume 18, No. 4, Year 2022

Ukuhunyushwa

In low- and middle-income countries (LMIC), growth impairment is common; however, the trajectory of growth over the course of the first month has not been well characterised. To describe newborn growth trajectory and predictors of growth impairment, we assessed growth frequently over the first 30 days among infants born ≥2000 g in Guinea-Bissau, Nepal, Pakistan and Uganda. In this cohort of 741 infants, the mean birth weight was 3036 ± 424 g. For 721 (98%) infants, weight loss occurred for a median of 2 days (interquartile range, 1–4) following birth until weight nadir was reached 5.9 ± 4.3% below birth weight. At 30 days of age, the mean weight was 3934 ± 592 g. The prevalence of being underweight at 30 days ranged from 5% in Uganda to 31% in Pakistan. Of those underweight at 30 days of age, 56 (59%) had not been low birth weight (LBW), and 48 (50%) had reached weight nadir subsequent to 4 days of age. Male sex (relative risk [RR] 2.73 [1.58, 3.57]), LBW (RR 6.41 [4.67, 8.81]), maternal primiparity (1.74 [1.20, 2.51]) and reaching weight nadir subsequent to 4 days of age (RR 5.03 [3.46, 7.31]) were highly predictive of being underweight at 30 days of age. In this LMIC cohort, country of birth, male sex, LBW and maternal primiparity increased the risk of impaired growth, as did the modifiable factor of delayed initiation of growth. Interventions tailored to infants with modifiable risk factors could reduce the burden of growth impairment in LMIC. At health facilities in Guinea‐Bissau, Nepal, Pakistan and Uganda between April 2019 and March 2020, we enroled 741 singleton infants who weighed ≥2000 g, a weight which was eligible for routine clinical care at all enroling sites. Additionally, infants were eligible if their mothers were ≥18 years old and intended to breastfeed for at least 6 months. The health facilities included Simoa Mendes Hospital in Bissau, Guinea‐Bissau, Bissora Hospital in Bissora, Guinea‐Bissau and village facilities and home births in Guinea‐Bissau; Dhulikhel Hospital in Dhulikhel, Nepal; Aga Khan University, Karimabad Hospital and Koohi Goth Health Center in Karachi, Pakistan; and Mukono Health Center in Mukono, Uganda and Kitebi Health Center and Kawala Health Center in Kampala, Uganda. We excluded infants with major congenital anomalies, danger signs, respiratory distress or maternal or infant contraindications to breastfeeding, but did not exclude infants with economic or environmental constraints on growth or specify gestational age parameters for study participation. We used a convenience sampling strategy for the selection of enrolment sites and infants. Sites in Guinea‐Bissau, Pakistan and Uganda had completed study activities before the onset of the COVID‐19 pandemic; the Nepal site had just completed enrolment at the time of the first COVID‐19 shutdown and was able to complete study activities. Trained study staff recruited, screened and enroled mothers and infants and informed consent was obtained from the mother for herself and her infant. This study was approved by the UCSF Institutional Review Board, the Guinea‐Bissau National Committee on Ethics in Health (Comite Nacional de Etica na Saude), the Nepal Health Research Council, the Institutional Review Committee of Kathmandu University Teaching Hospital, the Ethical Review Committee at the Aga Khan University in Pakistan, the Higher Degrees, Research and Ethics Committee of Makerere University and the Uganda National Council of Science and Technology. Using a standardised protocol, trained study staff obtained duplicate weights and lengths for naked infants at study visits, which were within 6 h of birth and at 1, 2, 3, 4, 5, 12 and 30 days of age, with a Seca 334 scale (Seca Inc.) accurate to ±5 g and stadiometer (Seca Inc and Pelstar, LLC); two additional measurements were taken if the initial two measurements varied by 15 g and 0.5 cm, respectively, for weight and length. We excluded weights obtained on Day 1 from analysis if they varied by 15% or more from birth weight and weights obtained on Days 2, 3 and 4 if they varied by 10% or more from the prior day’s weight. Weights were excluded in this manner from seven infants on Day 1, three infants on Day 2, five infants on Day 3, five infants on Day 4 and four infants on Day 5. We did not exclude any weights obtained on Days 12 or 30 due to a lack of certainty regarding plausible weight change in those time intervals. Infant dietary intake including breastfeeding and any supplementary feeding was assessed at these study visits using an instrument previously validated for breastfeeding infants in the first week of life in LMIC (Tylleskär et al., 2011). All enroled mothers were also surveyed regarding covariates related to enroled infant growth, including maternal age, educational attainment, marital status, parity, location of delivery, water source and type of toilet facility. All study visits occurred at the enrolment health facilities or during home visits as preferred by the participants. If necessary, participants were traced and located using provided contact information and maps. The study was strictly observational: the study team did not have access to data on changes in weight, did not provide health care to enroled infants and encouraged all mothers to access their usual sources of care after study enrolment. Referrals to medical care were made by the study team as needed. No direct care was provided by the study team, and ill infants were referred. Travel reimbursement was provided; no other incentives were provided. Birth weight was defined as weight measured by trained study staff at <6 h of age. Low birth weight (LBW) was determined using the WHO definition of birth weight less than 2500 g. Underweight, stunting, and wasting were defined as weight‐for‐age z‐score (WAZ) <−2, length‐for‐age z‐score (LAZ) <−2 and weight‐for‐length z‐score (WLZ) <−2, respectively, and calculated using the WHO Anthro Survey Analyzer (World Health Organization, 2010, 2020) which was selected because reliable data on gestational age was not available for most of the cohort. Of note, this approach was unable to generate WLZ for lengths <45 cm, so WLZ was not used as a prespecified outcome. Our prespecified primary outcome was WAZ at 30 days of age because WAZ was expected to change more substantially over the first 30 days than LAZ. Quantile regression methods appropriate for data with repeated measures were used to estimate 10th, 25th, 50th (median), 75th and 90th percentiles of weight (in g) as a function of time after birth separately for each country to depict weight changes during this period (Koenker, 2004). A restricted cubic spline with four degrees of freedom was used to generate nonlinear quantile curves, and the tuning parameter (λ) was set to 10 (Koenker et al., 1994). To test associations with dichotomous outcomes, χ 2 and Student's t‐test were used for the bivariate analysis of dichotomous and normally distributed continuous variables, respectively; Wilcoxon rank sum test was used for bivariate analysis of continuous variables that were not normally distributed. We used Wald‐based confidence intervals to report the relative risk (RR) of dichotomous outcomes. Modified Poisson regression with robust standard errors was used to determine the relationship between baseline characteristics present at birth and dichotomous outcomes at 30 days of age while adjusting for potential confounders. All analyses were conducted in Stata/IC 16.0 (Stata Corp).

I-AI Digest

Study Justification:

– The study aimed to characterize the growth trajectory of newborns in low- and middle-income countries (LMIC) during the first month of life.
– Growth impairment is common in LMIC, but the specific patterns of growth in the neonatal period have not been well understood.
– By studying the predictors of growth impairment, the researchers aimed to identify modifiable risk factors that could be targeted with interventions to reduce the burden of growth impairment in LMIC.

Study Highlights:

– The study enrolled 741 singleton infants weighing ≥2000 g from health facilities in Guinea-Bissau, Nepal, Pakistan, and Uganda.
– Weight loss occurred in 98% of infants for a median of 2 days following birth until reaching a nadir of 5.9% below birth weight.
– At 30 days of age, the mean weight was 3934 g.
– The prevalence of being underweight at 30 days ranged from 5% in Uganda to 31% in Pakistan.
– Male sex, low birth weight, maternal primiparity, and reaching weight nadir after 4 days of age were highly predictive of being underweight at 30 days.

Recommendations:

– Interventions tailored to infants with modifiable risk factors, such as delayed initiation of growth, could help reduce the burden of growth impairment in LMIC.
– Policy makers should prioritize strategies to address the identified risk factors, including improving access to healthcare, promoting early initiation of growth, and addressing socio-economic factors that contribute to growth impairment.

Key Role Players:

– Health facility staff in Guinea-Bissau, Nepal, Pakistan, and Uganda
– Trained study staff who recruited, screened, and enrolled mothers and infants
– Researchers and data analysts
– Policy makers and government officials responsible for healthcare and nutrition programs

Cost Items for Planning Recommendations:

– Healthcare infrastructure and resources for providing access to healthcare services
– Training and capacity building for health facility staff and study staff
– Data collection and analysis tools
– Travel reimbursement for study participants
– Communication and outreach materials for promoting early initiation of growth and addressing socio-economic factors
Please note that the cost items provided are general categories and may vary depending on the specific context and implementation strategy.

Amandla Obufakazi

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study had a large sample size (741 infants) and collected data from multiple countries, which increases the generalizability of the findings. The study also used a standardized protocol and trained staff to collect data, which enhances the reliability of the measurements. However, there are a few actionable steps to improve the evidence. First, the study could have included a control group to compare the growth trajectory and predictors of growth impairment. This would provide a stronger basis for causal inference. Second, the study could have included more detailed information about the methodology, such as the specific criteria used for participant selection and the process of obtaining informed consent. This would increase transparency and allow for better assessment of potential biases. Finally, the study could have conducted a sensitivity analysis to assess the robustness of the findings to different assumptions or methods. Overall, the evidence in the abstract is solid, but these improvements would further strengthen it.

Abstract

At health facilities in Guinea‐Bissau, Nepal, Pakistan and Uganda between April 2019 and March 2020, we enroled 741 singleton infants who weighed ≥2000 g, a weight which was eligible for routine clinical care at all enroling sites. Additionally, infants were eligible if their mothers were ≥18 years old and intended to breastfeed for at least 6 months. The health facilities included Simoa Mendes Hospital in Bissau, Guinea‐Bissau, Bissora Hospital in Bissora, Guinea‐Bissau and village facilities and home births in Guinea‐Bissau; Dhulikhel Hospital in Dhulikhel, Nepal; Aga Khan University, Karimabad Hospital and Koohi Goth Health Center in Karachi, Pakistan; and Mukono Health Center in Mukono, Uganda and Kitebi Health Center and Kawala Health Center in Kampala, Uganda. We excluded infants with major congenital anomalies, danger signs, respiratory distress or maternal or infant contraindications to breastfeeding, but did not exclude infants with economic or environmental constraints on growth or specify gestational age parameters for study participation. We used a convenience sampling strategy for the selection of enrolment sites and infants. Sites in Guinea‐Bissau, Pakistan and Uganda had completed study activities before the onset of the COVID‐19 pandemic; the Nepal site had just completed enrolment at the time of the first COVID‐19 shutdown and was able to complete study activities. Trained study staff recruited, screened and enroled mothers and infants and informed consent was obtained from the mother for herself and her infant. This study was approved by the UCSF Institutional Review Board, the Guinea‐Bissau National Committee on Ethics in Health (Comite Nacional de Etica na Saude), the Nepal Health Research Council, the Institutional Review Committee of Kathmandu University Teaching Hospital, the Ethical Review Committee at the Aga Khan University in Pakistan, the Higher Degrees, Research and Ethics Committee of Makerere University and the Uganda National Council of Science and Technology. Using a standardised protocol, trained study staff obtained duplicate weights and lengths for naked infants at study visits, which were within 6 h of birth and at 1, 2, 3, 4, 5, 12 and 30 days of age, with a Seca 334 scale (Seca Inc.) accurate to ±5 g and stadiometer (Seca Inc and Pelstar, LLC); two additional measurements were taken if the initial two measurements varied by 15 g and 0.5 cm, respectively, for weight and length. We excluded weights obtained on Day 1 from analysis if they varied by 15% or more from birth weight and weights obtained on Days 2, 3 and 4 if they varied by 10% or more from the prior day’s weight. Weights were excluded in this manner from seven infants on Day 1, three infants on Day 2, five infants on Day 3, five infants on Day 4 and four infants on Day 5. We did not exclude any weights obtained on Days 12 or 30 due to a lack of certainty regarding plausible weight change in those time intervals. Infant dietary intake including breastfeeding and any supplementary feeding was assessed at these study visits using an instrument previously validated for breastfeeding infants in the first week of life in LMIC (Tylleskär et al., 2011). All enroled mothers were also surveyed regarding covariates related to enroled infant growth, including maternal age, educational attainment, marital status, parity, location of delivery, water source and type of toilet facility. All study visits occurred at the enrolment health facilities or during home visits as preferred by the participants. If necessary, participants were traced and located using provided contact information and maps. The study was strictly observational: the study team did not have access to data on changes in weight, did not provide health care to enroled infants and encouraged all mothers to access their usual sources of care after study enrolment. Referrals to medical care were made by the study team as needed. No direct care was provided by the study team, and ill infants were referred. Travel reimbursement was provided; no other incentives were provided. Birth weight was defined as weight measured by trained study staff at <6 h of age. Low birth weight (LBW) was determined using the WHO definition of birth weight less than 2500 g. Underweight, stunting, and wasting were defined as weight‐for‐age z‐score (WAZ) <−2, length‐for‐age z‐score (LAZ) <−2 and weight‐for‐length z‐score (WLZ) <−2, respectively, and calculated using the WHO Anthro Survey Analyzer (World Health Organization, 2010, 2020) which was selected because reliable data on gestational age was not available for most of the cohort. Of note, this approach was unable to generate WLZ for lengths <45 cm, so WLZ was not used as a prespecified outcome. Our prespecified primary outcome was WAZ at 30 days of age because WAZ was expected to change more substantially over the first 30 days than LAZ. Quantile regression methods appropriate for data with repeated measures were used to estimate 10th, 25th, 50th (median), 75th and 90th percentiles of weight (in g) as a function of time after birth separately for each country to depict weight changes during this period (Koenker, 2004). A restricted cubic spline with four degrees of freedom was used to generate nonlinear quantile curves, and the tuning parameter (λ) was set to 10 (Koenker et al., 1994). To test associations with dichotomous outcomes, χ 2 and Student's t‐test were used for the bivariate analysis of dichotomous and normally distributed continuous variables, respectively; Wilcoxon rank sum test was used for bivariate analysis of continuous variables that were not normally distributed. We used Wald‐based confidence intervals to report the relative risk (RR) of dichotomous outcomes. Modified Poisson regression with robust standard errors was used to determine the relationship between baseline characteristics present at birth and dichotomous outcomes at 30 days of age while adjusting for potential confounders. All analyses were conducted in Stata/IC 16.0 (Stata Corp).

Izinto zokwakha

N/A

Emisha

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with information and resources related to maternal health, including growth monitoring, breastfeeding support, and nutrition guidance.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women and new mothers in remote or underserved areas to consult with healthcare professionals, receive prenatal and postnatal care, and access medical advice without the need for physical travel.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women and new mothers in their local communities. These workers can help monitor newborn growth, provide breastfeeding assistance, and identify and refer high-risk cases to healthcare facilities.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access essential maternal health services, including antenatal care, skilled birth attendance, and postnatal care. This can help reduce financial barriers and increase utilization of these services.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers and facilities to expand service coverage, improve infrastructure, and enhance the quality of care.

6. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health, including the benefits of early and regular prenatal care, exclusive breastfeeding, and proper nutrition. These campaigns can be conducted through various channels, such as mass media, community outreach programs, and social media platforms.

7. Maternal Health Monitoring Systems: Develop and implement digital health systems that enable real-time monitoring of maternal health indicators, such as weight gain during pregnancy, newborn weight changes, and postnatal care utilization. These systems can help identify high-risk cases and facilitate timely interventions.

8. Maternal Health Financing Models: Explore innovative financing models, such as microinsurance or community-based health financing schemes, to ensure financial protection for pregnant women and new mothers. These models can help reduce out-of-pocket expenses and improve access to quality maternal health services.

It’s important to note that the specific context and needs of each country or region should be considered when implementing these innovations.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health is to develop interventions tailored to infants with modifiable risk factors. This can help reduce the burden of growth impairment in low- and middle-income countries (LMIC).

The study found that factors such as delayed initiation of growth, low birth weight, maternal primiparity, and reaching weight nadir subsequent to 4 days of age were highly predictive of being underweight at 30 days of age. By targeting these modifiable risk factors, interventions can be designed to address them and improve infant growth.

These interventions could include promoting early initiation of growth through breastfeeding support and education, providing nutritional supplementation for infants at risk of growth impairment, and implementing programs to improve maternal nutrition and healthcare during pregnancy.

It is important to tailor these interventions to the specific context of each LMIC, taking into account cultural, social, and economic factors. Additionally, collaboration between healthcare providers, policymakers, and communities is crucial for the successful implementation of these interventions.

By implementing targeted interventions that address modifiable risk factors, access to maternal health can be improved, leading to better outcomes for both mothers and infants in LMICs.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in low- and middle-income countries (LMIC) can improve access to maternal health services. This includes establishing well-equipped maternity wards, labor rooms, and neonatal care units.

2. Increasing skilled birth attendance: Encouraging pregnant women to seek skilled birth attendance by trained healthcare professionals, such as midwives or doctors, can improve maternal and neonatal outcomes. This can be achieved through community awareness campaigns and providing incentives for women to deliver in healthcare facilities.

3. Enhancing antenatal care services: Expanding and improving antenatal care services can help identify and manage potential complications during pregnancy. This includes regular check-ups, screening for high-risk pregnancies, and providing necessary interventions and counseling.

4. Promoting breastfeeding and nutrition: Educating mothers about the importance of breastfeeding and proper nutrition during pregnancy and postpartum can improve maternal and infant health outcomes. This can be done through community-based programs, support groups, and counseling services.

5. Implementing telemedicine and mobile health solutions: Utilizing technology, such as telemedicine and mobile health applications, can help overcome geographical barriers and improve access to maternal health services in remote areas. This includes providing remote consultations, health education, and monitoring of maternal and neonatal health.

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 or region where the recommendations will be implemented. This could be a specific LMIC or a particular community within a country.

2. Collect baseline data: Gather relevant data on the current state of maternal health in the target population. This includes information on maternal mortality rates, access to healthcare facilities, utilization of antenatal care services, and breastfeeding rates.

3. Develop a simulation model: Create a simulation model that incorporates the recommended interventions and their potential impact on maternal health outcomes. This model should consider factors such as population size, healthcare infrastructure, and socio-economic conditions.

4. Input data and parameters: Input the collected baseline data into the simulation model, along with parameters related to the recommended interventions. This includes data on the number of healthcare facilities, trained healthcare professionals, and expected changes in behavior and utilization of services.

5. Run simulations: Run multiple simulations using the model to simulate different scenarios and assess the potential impact of the recommendations on improving access to maternal health. This can include estimating changes in maternal mortality rates, increase in skilled birth attendance, improvement in breastfeeding rates, and reduction in complications during pregnancy.

6. Analyze results: Analyze the simulation results to evaluate the effectiveness of the recommendations in improving access to maternal health. This includes comparing the outcomes of different scenarios and identifying the most impactful interventions.

7. Refine and iterate: Based on the simulation results, refine the recommendations and the simulation model if necessary. Iterate the process to further optimize the interventions and improve access to maternal health.

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

Umbhali

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1111/mcn.13396

ISSN: 17408695

Research Areas:

Community Interventions, COVID, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Social Determinants, Technology and Innovations

Study Countries:

Guinea, Guinea-Bissau, Multi-Countries, Uganda

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male

Yabelana ngalokhu: