Influence of gestational weight gain on baby’s birth weight in Addis Ababa, Central Ethiopia: a follow-up study

BMJ open, Volume 12, No. 6, Year 2022

Interpretation

BACKGROUND: Gestational weight gain (GWG) is an important indicator of fetal well-being during pregnancy. Inadequate or excessive GWG could have undesirable effects on birth weight. However, information regarding the influence of GWG on birth weight is lacking from the Ethiopian setting. OBJECTIVE: This study aimed to determine the influence of GWG and other maternal-related factors on birth weight in Addis Ababa, Ethiopia. DESIGN AND METHODS: A cohort of pregnant women who received the first antenatal care before or at 16 weeks of gestation in health centres in Addis Ababa were followed from 10 January 2019 to 25 September 2019. Data were collected using a structured questionnaire and medical record reviews. We conducted a multivariable linear regression analysis to determine the independent effect of gestational weight on birth weight. RESULTS: Of the 395 women enrolled in the study, the participants’ pregnancy outcome was available for 329 (83.3%). The mean birth weight was 3130 (SD, 509) g. The proportion of low birth weight (<2500 g) was 7.5% (95% CI 4.8% to 11.0%). Babies born to underweight women were 150.9 g (95% CI 5.8 to 308.6 g, p=0.049) lighter than babies born to normal-weight women. Similarly, babies whose mothers gained inadequate weight were 248 g (95% CI 112.8 to 383.6 g, p<0.001) lighter than those who gained adequate weight. Moreover, babies whose mothers had a previous history of abortion or miscarriages or developed gestational hypertension in the current pregnancy were 147.2 g (95% CI 3.2 to 291.3 g, p=0.045) and 310.7 g (95% CI 62.7 to 552.8 g, p=0.012) lighter, respectively, compared with those whose mothers had not. CONCLUSIONS: Prepregnancy weight, GWG, having had a previous history of abortion or miscarriages, and developing gestational hypertension during a current pregnancy were independently associated with birth weight. Pregnancy-related weight management should be actively promoted through intensive counseling during routine antenatal care contacts. This study was conducted in Addis Ababa, Ethiopia’s capital and the largest city. Participants were selected from nine health centres. The previously published paper reported details of the study setting and numbers of women recruited from each facility.31 A cohort of pregnant women were followed from before or at their 16 th week of gestation until they gave birth to assess their GWG and the baby’s birth weight from 10 January 2019 to 25 September 2019. Using the double proportion formula, we calculated the sample size using Open Epi V.2.3. The assumptions for the sample size calculation were alpha value 0.05; power 80%; exposed to non-exposed ratio 1:2 (proportion of adequate GWG=28% (exposure); and proportion of inadequate GWG=69% (non-exposure))30; proportion of LBW among women who gained adequate gestational weight=1.7%; proportion of LBW among women who gained inadequate gestational weight=17.5%,21 lost to follow-up=20%. The required sample was 189 (exposed=63 and control (non-exposed)=126). However, since this study was part of another large study, we recruited a sample size of 395. The details of the sample size calculation assumptions were described in the study published elsewhere.31 Pregnant women who came to health centres before or at 16 weeks gestation for antenatal care were invited to participate, and those who agreed were recruited. We limited eligibility to women with a singleton pregnancy and no comorbidities such as diabetes and hypertension. We used structured questionnaires with trained interviewers and face-to-face semistructured interviews during the baseline data collection. Using the questionnaires, we collected information regarding sociodemographic characteristics, previous history of abortion (termination of pregnancy before the 28th week of gestation), LBW and stillbirth, pregnancy intention (planned/unplanned), gravidity, food insecurity, dietary diversity, physical activity, intimate partner violence and depression-related symptoms. Data collectors measured baseline weight and height of the women and mid-upper arm circumference. Women’s medical records were also reviewed both during baseline data collection and after birth to collect data such as gestational age (ultrasound result), blood pressure, level of haemoglobin, random blood sugar result, weight at the 36th weeks of gestation, mode of birth, episiotomy, birth weight and sex of the baby. The primary author reviewed these data. Women were followed from before or at their 16th week of gestation until they gave birth to assess their GWG and the baby’s birth weight. Sixteen women (5.2%) gave birth in a rural location, and we could not access the birth records. The birthweight information was ascertained for these women through a phone call to the mother. The primary outcome variable in this study was birth weight. However, other pregnancy outcome variables such as the occurrence of gestational hypertension, modes of birth, episiotomy and birth outcomes (live birth, miscarriage or stillbirth) were also considered as outcome variables. We assessed the household food insecurity using the Household Food Insecurity Access Scale33 and the women’s dietary diversity using the minimum dietary diversity-women tool.34 Women’s physical activity level was measured using the International Physical Activity Questionnaire-long form.35 Perinatal depression symptoms were measured using the Edinburgh Postnatal Depression Scale36 and intimate partner violence were measured using a questionnaire used by the WHO multicountry study on women’s health and domestic violence.37 We double entered the data into Census and Survey Processing System (CSPro V.7.1). We exported data to STATA (V.14, StataCorp) for cleaning and analysis. Missing data were handled by performing pairwise deletion in the study. A particular variable was excluded when it had a missing value, but the case can still be used when analysing other variables with non-missing values. Hence, the analyses were performed on subsets of the data depending on where values are missing without completely omitting a case with missing some variables from the analyses. Descriptive statistics, including frequencies, means and SD, were computed to describe the data. We calculated GWG by subtracting women’s baseline weight from their weight at the 36th week of gestation. The adequacy of GWG (inadequate, adequate or excessive) was determined using the IOM criteria. Birth weight was analysed as a categorical and continuous variable. Birth weight was classified as <2.5 kg (LBW), 2.5 kg–3.9 kg (normal birth weight), ≥4.0 kg (macrosomia). The relationship between birth weight as a categorical variable (ie, LBW, normal birth weight or macrosomia) and other variables was reported descriptively using percentage. Since the number of LBW and macrosomic babies were small, we could not perform a regression analysis using birth weight as a categorical variable. Therefore, we assessed the influence of GWG and other variables on birth weight using a linear regression model. Variables with p<0.25 in the bivariable analysis were included in the multivariable analyses. However, some variable like food insecurity was considered important and forced into the multivariable model irrespective of the p value. The assumptions for linear regression were checked. Scatter plots showed that observations were linear. Multicollinearity was checked using the variance inflation factor (VIF). The mean VIF value was 1.44. The VIF value for each predictor variable was <3, which showed no multicollinearity among variables. We performed multivariable linear regression analysis to determine the independent effect of GWG on birth weight, adjusting for other potential factors (educational status, average household monthly income, and previous history of abortion (termination of pregnancy before the 28th week of gestation), consuming meat or chicken in the last 24 hours, food insecurity, prepregnancy weight, maternal haemoglobin level, occurrence of gestational hypertension and sex of the baby).

AI Digest

Study Justification:

– Gestational weight gain (GWG) is an important indicator of fetal well-being during pregnancy.
– Inadequate or excessive GWG could have undesirable effects on birth weight.
– Information regarding the influence of GWG on birth weight is lacking from the Ethiopian setting.

Study Highlights:

– The study aimed to determine the influence of GWG and other maternal-related factors on birth weight in Addis Ababa, Ethiopia.
– A cohort of pregnant women who received the first antenatal care before or at 16 weeks of gestation in health centers in Addis Ababa were followed from January 10, 2019, to September 25, 2019.
– Data were collected using a structured questionnaire and medical record reviews.
– The study found that babies born to underweight women and those whose mothers gained inadequate weight were lighter than babies born to normal-weight women and those whose mothers gained adequate weight, respectively.
– Babies whose mothers had a previous history of abortion or miscarriages or developed gestational hypertension in the current pregnancy were also lighter compared to those whose mothers had not.
– Prepregnancy weight, GWG, previous history of abortion or miscarriages, and developing gestational hypertension were independently associated with birth weight.
– The study highlights the importance of promoting pregnancy-related weight management through intensive counseling during routine antenatal care contacts.

Recommendations for Lay Reader and Policy Maker:

– Pregnant women should be encouraged to maintain a healthy weight before and during pregnancy.
– Intensive counseling on weight management should be provided during routine antenatal care visits.
– Women with a previous history of abortion or miscarriages and those at risk of developing gestational hypertension should receive special attention and support.
– Health policies should prioritize the prevention and management of gestational hypertension and provide adequate support for women who have experienced abortion or miscarriages.

Key Role Players:

– Obstetricians and gynecologists
– Midwives and nurses
– Nutritionists and dietitians
– Psychologists and counselors
– Health educators and community health workers
– Policy makers and government officials

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare professionals on weight management counseling
– Development and dissemination of educational materials on healthy weight management during pregnancy
– Implementation of regular antenatal care visits with a focus on weight monitoring and counseling
– Provision of support services for women with a previous history of abortion or miscarriages
– Screening and management of gestational hypertension
– Research and monitoring to evaluate the effectiveness of interventions
– Collaboration with community organizations and stakeholders for awareness campaigns and outreach programs

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 design is a cohort study, which is generally considered to provide moderate to strong evidence. The sample size calculation was performed and the study included 395 participants, which is a good sample size. The statistical analysis used multivariable linear regression to determine the independent effect of gestational weight gain on birth weight, which is appropriate. However, there are a few areas for improvement. First, the abstract does not provide information on the representativeness of the study sample, which could affect the generalizability of the findings. Second, the abstract does not mention any potential limitations of the study, such as confounding factors or biases. It would be helpful to include this information to provide a more balanced assessment of the evidence. Finally, the abstract does not mention any recommendations for future research or implications for practice. Including this information would make the abstract more actionable and useful for readers.

Abstract

This study was conducted in Addis Ababa, Ethiopia’s capital and the largest city. Participants were selected from nine health centres. The previously published paper reported details of the study setting and numbers of women recruited from each facility.31 A cohort of pregnant women were followed from before or at their 16 th week of gestation until they gave birth to assess their GWG and the baby’s birth weight from 10 January 2019 to 25 September 2019. Using the double proportion formula, we calculated the sample size using Open Epi V.2.3. The assumptions for the sample size calculation were alpha value 0.05; power 80%; exposed to non-exposed ratio 1:2 (proportion of adequate GWG=28% (exposure); and proportion of inadequate GWG=69% (non-exposure))30; proportion of LBW among women who gained adequate gestational weight=1.7%; proportion of LBW among women who gained inadequate gestational weight=17.5%,21 lost to follow-up=20%. The required sample was 189 (exposed=63 and control (non-exposed)=126). However, since this study was part of another large study, we recruited a sample size of 395. The details of the sample size calculation assumptions were described in the study published elsewhere.31 Pregnant women who came to health centres before or at 16 weeks gestation for antenatal care were invited to participate, and those who agreed were recruited. We limited eligibility to women with a singleton pregnancy and no comorbidities such as diabetes and hypertension. We used structured questionnaires with trained interviewers and face-to-face semistructured interviews during the baseline data collection. Using the questionnaires, we collected information regarding sociodemographic characteristics, previous history of abortion (termination of pregnancy before the 28th week of gestation), LBW and stillbirth, pregnancy intention (planned/unplanned), gravidity, food insecurity, dietary diversity, physical activity, intimate partner violence and depression-related symptoms. Data collectors measured baseline weight and height of the women and mid-upper arm circumference. Women’s medical records were also reviewed both during baseline data collection and after birth to collect data such as gestational age (ultrasound result), blood pressure, level of haemoglobin, random blood sugar result, weight at the 36th weeks of gestation, mode of birth, episiotomy, birth weight and sex of the baby. The primary author reviewed these data. Women were followed from before or at their 16th week of gestation until they gave birth to assess their GWG and the baby’s birth weight. Sixteen women (5.2%) gave birth in a rural location, and we could not access the birth records. The birthweight information was ascertained for these women through a phone call to the mother. The primary outcome variable in this study was birth weight. However, other pregnancy outcome variables such as the occurrence of gestational hypertension, modes of birth, episiotomy and birth outcomes (live birth, miscarriage or stillbirth) were also considered as outcome variables. We assessed the household food insecurity using the Household Food Insecurity Access Scale33 and the women’s dietary diversity using the minimum dietary diversity-women tool.34 Women’s physical activity level was measured using the International Physical Activity Questionnaire-long form.35 Perinatal depression symptoms were measured using the Edinburgh Postnatal Depression Scale36 and intimate partner violence were measured using a questionnaire used by the WHO multicountry study on women’s health and domestic violence.37 We double entered the data into Census and Survey Processing System (CSPro V.7.1). We exported data to STATA (V.14, StataCorp) for cleaning and analysis. Missing data were handled by performing pairwise deletion in the study. A particular variable was excluded when it had a missing value, but the case can still be used when analysing other variables with non-missing values. Hence, the analyses were performed on subsets of the data depending on where values are missing without completely omitting a case with missing some variables from the analyses. Descriptive statistics, including frequencies, means and SD, were computed to describe the data. We calculated GWG by subtracting women’s baseline weight from their weight at the 36th week of gestation. The adequacy of GWG (inadequate, adequate or excessive) was determined using the IOM criteria. Birth weight was analysed as a categorical and continuous variable. Birth weight was classified as <2.5 kg (LBW), 2.5 kg–3.9 kg (normal birth weight), ≥4.0 kg (macrosomia). The relationship between birth weight as a categorical variable (ie, LBW, normal birth weight or macrosomia) and other variables was reported descriptively using percentage. Since the number of LBW and macrosomic babies were small, we could not perform a regression analysis using birth weight as a categorical variable. Therefore, we assessed the influence of GWG and other variables on birth weight using a linear regression model. Variables with p<0.25 in the bivariable analysis were included in the multivariable analyses. However, some variable like food insecurity was considered important and forced into the multivariable model irrespective of the p value. The assumptions for linear regression were checked. Scatter plots showed that observations were linear. Multicollinearity was checked using the variance inflation factor (VIF). The mean VIF value was 1.44. The VIF value for each predictor variable was <3, which showed no multicollinearity among variables. We performed multivariable linear regression analysis to determine the independent effect of GWG on birth weight, adjusting for other potential factors (educational status, average household monthly income, and previous history of abortion (termination of pregnancy before the 28th week of gestation), consuming meat or chicken in the last 24 hours, food insecurity, prepregnancy weight, maternal haemoglobin level, occurrence of gestational hypertension and sex of the baby).

Materials

Innovations

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information and resources related to gestational weight gain, nutrition, and overall maternal health. These apps can also offer reminders for prenatal appointments and medication schedules.

2. Telemedicine Services: Implement telemedicine services to provide remote access to healthcare professionals for prenatal check-ups, consultations, and follow-up care. This can be especially beneficial for women in rural areas who may have limited access to healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and monitoring to pregnant women in their local communities. These workers can help ensure that women receive appropriate prenatal care and guidance on gestational weight gain.

4. Maternal Health Clinics: Establish specialized maternal health clinics that focus on providing comprehensive care for pregnant women, including monitoring gestational weight gain, nutrition counseling, and regular check-ups. These clinics can also offer additional services such as mental health support and breastfeeding assistance.

5. Public Health Campaigns: Launch public health campaigns to raise awareness about the importance of gestational weight gain and its impact on birth outcomes. These campaigns can include educational materials, workshops, and community events to promote healthy behaviors during pregnancy.

6. Collaboration with Local Organizations: Partner with local organizations, such as women’s associations and community groups, to promote maternal health and provide resources for pregnant women. This collaboration can help reach a wider audience and ensure that culturally appropriate support is available.

7. Health Insurance Coverage: Advocate for health insurance policies that cover prenatal care, including monitoring gestational weight gain. This can help reduce financial barriers and improve access to necessary healthcare services for pregnant women.

It is important to note that the implementation of these innovations should be tailored to the specific context and needs of the community in Addis Ababa, Ethiopia.

AI Innovations Description

Based on the provided information, the recommendation to improve access to maternal health is to actively promote pregnancy-related weight management through intensive counseling during routine antenatal care contacts. This recommendation is based on the findings of the study, which showed that inadequate gestational weight gain (GWG) was associated with lower birth weight. Additionally, other factors such as prepregnancy weight, previous history of abortion or miscarriages, and developing gestational hypertension during the current pregnancy were also found to be independently associated with birth weight.

By providing intensive counseling on pregnancy-related weight management, healthcare providers can educate pregnant women on the importance of maintaining a healthy weight during pregnancy. This can include guidance on appropriate weight gain, balanced nutrition, and physical activity. By addressing these factors, it is possible to improve birth outcomes and reduce the risk of low birth weight.

Implementing this recommendation would require training healthcare providers to deliver effective counseling on pregnancy-related weight management. It would also involve integrating this counseling into routine antenatal care visits, ensuring that pregnant women receive consistent and comprehensive support throughout their pregnancy.

Overall, by actively promoting pregnancy-related weight management through intensive counseling, access to maternal health can be improved, leading to better birth outcomes and healthier pregnancies.

AI Innovations Methodology

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

1. Increase awareness and education: Implement comprehensive maternal health education programs that focus on the importance of gestational weight gain, nutrition, and overall maternal well-being. This can be done through community outreach programs, antenatal care visits, and educational campaigns.

2. Strengthen antenatal care services: Improve the quality and accessibility of antenatal care services by ensuring that pregnant women have regular check-ups, receive appropriate counseling on weight management, and have access to necessary medical interventions.

3. Enhance nutrition support: Provide pregnant women with access to nutritious food and supplements to support healthy weight gain during pregnancy. This can be done through food assistance programs, nutritional counseling, and supplementation initiatives.

4. Promote mental health support: Address mental health issues such as depression and intimate partner violence, which can impact maternal health outcomes. Implement screening and referral programs to ensure that pregnant women receive the necessary support and interventions.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the percentage of pregnant women receiving adequate antenatal care, the percentage of women with appropriate gestational weight gain, and the percentage of low birth weight babies.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, medical records, and interviews with healthcare providers and pregnant women.

3. Develop a simulation model: Create a mathematical model that incorporates the identified indicators and their interdependencies. This model should consider factors such as the implementation of the recommended interventions, the population size, and the timeframe for the simulation.

4. Input intervention scenarios: Define different scenarios that represent the implementation of the recommended interventions. For each scenario, specify the expected changes in the indicators based on available evidence and expert input.

5. Run the simulation: Use the simulation model to project the potential impact of each intervention scenario on the selected indicators. This can be done by adjusting the relevant parameters in the model and running multiple iterations to account for uncertainty.

6. Analyze and interpret the results: Examine the simulation outputs to assess the potential improvements in access to maternal health under different intervention scenarios. Compare the results to the baseline data to determine the effectiveness of each recommendation.

7. Refine and validate the model: Continuously update and refine the simulation model based on new data and feedback from stakeholders. Validate the model by comparing the simulated results with real-world outcomes, if available.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions on improving access to maternal health. This information can guide decision-making and resource allocation to prioritize the most effective strategies.

Authors & Co-Authors

Statistics:

Authors: 5

Identifiers:

DOI: 10.1136/bmjopen-2021-055660

Research Areas:

Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Noncommunicable Diseases, Sexual and Reproductive Health, Social Determinants, Violence and Injury

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female