Are birthweight and postnatal weight gain in childhood associated with blood pressure in early adolescence? Results from a Ugandan birth cohort

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Study Justification:
The study aimed to investigate the relationship between birthweight, postnatal weight gain, and blood pressure in early adolescence among Ugandan adolescents. This is important because in Africa, where low birthweight, malnutrition, and high blood pressure are prevalent, the understanding of these relationships is still uncertain. By examining the effects of early life growth on blood pressure, the study aimed to contribute to the knowledge on developmental programming of blood pressure and provide insights for efforts to control blood pressure.
Highlights:
– The study included 1119 adolescents from a large birth cohort in Uganda.
– Mean systolic blood pressure was 105.9 mmHg and mean diastolic blood pressure was 65.2 mmHg.
– There was little evidence of an association between birthweight and blood pressure.
– Accelerated weight gain between birth and 5 years was associated with increased blood pressure.
– The effects of accelerated weight gain on blood pressure were strongest among low birthweight children.
– The findings suggest that postnatal weight gain, rather than birthweight, is important in the developmental programming of blood pressure.
Recommendations:
– Efforts to control blood pressure should adopt a life course approach, considering the impact of postnatal weight gain.
– Interventions targeting fast-growing low birthweight children may be particularly effective in reducing the risk of high blood pressure.
Key Role Players:
– Researchers and scientists specializing in pediatric health, cardiovascular health, and public health.
– Health policymakers and government officials responsible for implementing interventions related to child health and blood pressure control.
– Healthcare providers, including doctors, nurses, and community health workers, involved in the care of children and adolescents.
Cost Items for Planning Recommendations:
– Research funding for further studies and interventions targeting postnatal weight gain and blood pressure control.
– Costs associated with implementing interventions, such as nutritional programs, growth monitoring, and health education.
– Training and capacity building for healthcare providers to effectively address the developmental programming of blood pressure.
– Monitoring and evaluation costs to assess the impact of interventions and track progress in blood pressure control.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study collected data from a large birth cohort in Uganda and used linear regression to analyze the relationship between birthweight, weight gain, and blood pressure in adolescents. The study found little evidence of association between birthweight and blood pressure, but accelerated weight gain between birth and 5 years was associated with increased blood pressure. The study also found that the effects of accelerated weight gain on blood pressure were strongest among low birthweight children. To improve the strength of the evidence, future studies could consider including a control group, conducting a randomized controlled trial, and collecting data on potential confounders such as diet and physical activity.

Background In Africa, where low birthweight (LBW), malnutrition and high blood pressure (BP) are prevalent, the relationships between birthweight (BW), weight gain and BP later in life remain uncertain. We examined the effects of early life growth on BP among Ugandan adolescents. Methods Data were collected prenatally from women and their offspring were followed from birth, with BP measured following standard protocols in early adolescence. Weight-for-age Z-scores (WAZ) were computed using World Health Organization references. Linear regression was used to relate BW, and changes in WAZ between birth and 5 years, to adolescents’ BP, adjusting for confounders. Results Among 2345 live offspring, BP was measured in 1119 (47.7%) adolescents, with mean systolic BP 105.9 mmHg and mean diastolic BP 65.2 mmHg. There was little evidence of association between BW and systolic [regression coefficient β = 0.14, 95% confidence interval (CI) (-1.00, 1.27)] or diastolic [β = 0.43, 95% CI (-0.57, 1.43)] BP. Accelerated weight gain between birth and 5 years was associated with increased BP: systolic β = 1.17, 95% CI (0.69, 1.66) and diastolic β = 1.03, 95% CI (0.59, 1.47). Between birth and 6 months of age, effects of accelerated weight gain on adolescent BP were strongest among the LBW (both premature and small-for-gestational-age) children [BW < 2.5 kg: β = 2.64, 95% CI (0.91, 4.37), BW≥2.5 kg: β = 0.58, 95% CI (0.01, 1.14), interaction P-value =0.024]. Conclusions Findings from this large tropical birth cohort in Uganda suggest that postnatal weight gain rather than BW is important in the developmental programming of BP, with fast-growing LBW children at particular risk. Efforts to control BP should adopt a life course approach.

Adolescents from the EMaBS, a randomized, double-blind, placebo-controlled trial designed to investigate the effects of worms and their treatment in pregnancy and childhood on vaccine responses and infections in the children,18 were enrolled into the BP study. As previously described,18 from 2003 to 2005, pregnant women attending antenatal care at Entebbe Hospital and residing in the study area were enrolled and randomized to receive single-dose praziquantel or matching placebo and single-dose albendazole or matching placebo in a 2 x 2 factorial design. Those with evidence of helminth-induced pathology or history of adverse reaction to anthelminthics or abnormal pregnancy, or who had enrolled for an earlier pregnancy, were excluded.19 At 15 months, the resulting offspring were randomized to receive quarterly albendazole or matching placebo up to age 5 years.18 Demographic, socioeconomic and health information was collected prenatally (from pregnant women) and from birth onwards from the live-born offspring. Birthweight was measured immediately after birth using scales (Fazzini SRL, Vimodrone, Italy) for those delivered in Entebbe hospital. For offspring delivered elsewhere, BW was recorded as written on the child health card. Weight was measured at 6 weeks and 6 months of age, using CMS weighing equipment (model MP25: Chasmors Ltd, London, UK) and then annually (close to the child’s birthday) using weighing scales (Seca, Hamburg, Germany). Height was measured as recumbent length at age 6 weeks using an adjustable child-length measuring board (Seca, Hamburg, Germany), then annually (from age 1 year) using stadiometers (Seca 213, Hamburg, Germany). BMI was weight in kilograms (kg) divided by height in metres (m) squared. Children continued under follow-up after the trial intervention ended in 2011. Between 2 May 2014 and 1 June 2016, those attending their visit at ages 10 or 11 years and not presenting with an illness were enrolled in the BP study; 11-year-olds were excluded if they were previously enrolled as 10-year-olds. Trained nurses measured BP thrice 5 minutes apart, using an appropriate-sized cuff20 on the right arm supported at the heart level, with the participant seated upright all the way to the back of the chair, legs uncrossed and feet flat on the floor. Automated Omron (M6, HEM-700) machines, validated every 6 months by the Uganda National Bureau of Standards, were used. Means of the three systolic and diastolic BP were calculated. Blood pressure percentiles were obtained using Center for Disease Control height percentile charts and National Health and Nutrition Examination Survey Working Group on Children and Adolescents BP tables.20,21 Adolescents with mean BP (systolic and/or diastolic) measurements ≥95th percentile for gender, age and height on day 1 had BP measured for up to two extra days. Those sustaining a high BP on day 3 were referred for specialist attention. Non-pharmacological management was recommended to adolescents with BP (systolic and/or diastolic) ≥90th percentile for age, gender and height. The study was approved by ethics committees of the Uganda Virus Research Institute, the London School of Hygiene and Tropical Medicine and the Uganda National Council for Science and Technology. Written informed consent and assent were obtained. Data were double-entered in Microsoft Access and analysed using Stata 14 (College Station, TX, USA). Characteristics of cohort members enrolled and not enrolled in the BP study were compared using chi-square tests. The study outcomes were systolic and diastolic BP. The mean of the second and third day-1 BP measurements was used for analysis, as these were on average different from and lower than the first day-1 measurements (Supplementary Figure 1, available as Supplementary data at IJE online). Key exposures were BW and postnatal weight gain. Postnatal weight gain was change in weight-for-age Z-score (WAZ) between birth and age 5 years, with shorter growth periods (birth to 6 months, 6 to 12 months, 12 to 24 months and 24 to 60 months) also examined. The 2006 World Health Organization standard references22,23 were used to calculate WAZ, weight-for-height Z-score (WHZ) and BMI-for-age Z-score (BMIZ). Potential confounders were maternal characteristics including sociodemographic (age, education, area of residence, socioeconomic status), BMI, pregnancy anthelminthic trial interventions, illness and infections (hypertension, HIV, malaria, worms), and child’s characteristics including sex, feeding status, BMI, age, childhood anthelminthic trial intervention, illness and infections (malaria, worms). Pearson correlation coefficients between BMI at age 10–11 years and anthropometric variables (WAZ, WHZ and BMIZ) at birth, 6 weeks, 6 months and annually from 1 to 5 years were calculated. Linear regression (fitted separately for systolic and diastolic BP) was used to assess the association between each key exposure and adolescent BP. Adolescents’ age and sex were included a priori in all models. Regression models were adjusted for each potential confounder in turn, with those causing an important change in the effect of the exposure of interest on BP retained in the final model. Final models with and without current weight were fitted. For BW, we assessed whether a linear or non-linear (categorical or quadratic) relationship provided a better fit to the data. Likelihood ratio test (LRT) was used to examine for effect modification by gender, original trial interventions and birth season. Since the timing of wet seasons in this setting is subject to variability, birth months were categorized as either dry or wet depending on whether the total monthly rainfall was below or above the median rainfall for all birth months. For weight gain, as well as for those confounders identified in the BW exposure analysis, the effect of each growth period was adjusted for earlier postnatal weight gain period(s) and feeding status at 6 weeks. Effect modification by gender or BW (<2.5 kg versus ≥2.5 kg) was assessed using LRT. Sensitivity analysis assessing the impact of missing values for the main exposures was conducted: missing BW values were replaced with the minimum (1.26 kg) and the maximum (5.50 kg) non-missing value of BW recorded, and final models re-run for both scenarios. Similarly, missing values for WAZ change were replaced with the smallest and largest change in WAZ for the given growth period.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health, including prenatal care, nutrition, and postnatal care. These apps could also include features such as appointment reminders and tracking tools for monitoring weight gain and blood pressure.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This would enable them to receive medical advice, prenatal check-ups, and guidance on managing their health during pregnancy.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can conduct regular check-ups, monitor blood pressure and weight gain, and refer women to healthcare facilities when necessary.

4. Maternal Health Clinics: Establish dedicated maternal health clinics that provide comprehensive care for pregnant women, including prenatal check-ups, nutritional counseling, and education on pregnancy and childbirth. These clinics could also offer postnatal care and support for new mothers.

5. Health Education Programs: Develop and implement health education programs that target pregnant women and their families, focusing on topics such as nutrition, hygiene, and the importance of regular prenatal care. These programs can be delivered through community workshops, radio broadcasts, or mobile messaging platforms.

6. Transportation Support: Address transportation barriers by providing transportation services or subsidies for pregnant women to access healthcare facilities for prenatal check-ups, delivery, and postnatal care. This could involve partnering with local transportation providers or implementing community-based transportation initiatives.

7. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to cover the costs of prenatal care, delivery, and postnatal care. These vouchers can be distributed through healthcare facilities or community organizations to ensure that women have access to essential maternal health services.

8. Maternal Health Hotlines: Establish dedicated hotlines staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women. These hotlines can be accessed via phone or text message and can offer assistance with questions related to prenatal care, nutrition, and common pregnancy concerns.

These innovations aim to improve access to maternal health services, enhance education and support for pregnant women, and address barriers such as transportation and limited healthcare resources in underserved areas.
AI Innovations Description
The study mentioned in the description explores the relationship between birthweight, postnatal weight gain, and blood pressure in early adolescence among Ugandan adolescents. The findings suggest that postnatal weight gain, rather than birthweight, is important in the developmental programming of blood pressure. Specifically, accelerated weight gain between birth and 5 years was associated with increased blood pressure. This association was strongest among low birthweight (LBW) children. The study recommends adopting a life course approach to control blood pressure and highlights the importance of monitoring postnatal weight gain, particularly in LBW children.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase access to prenatal care: Implement strategies to ensure that pregnant women have access to regular prenatal check-ups, including screenings for potential complications and interventions to address any identified issues.

2. Improve transportation infrastructure: Enhance transportation networks and infrastructure in rural areas to facilitate easier access to healthcare facilities for pregnant women, particularly those living in remote or underserved regions.

3. Strengthen community health worker programs: Expand and strengthen community health worker programs to provide education, support, and basic healthcare services to pregnant women in their own communities, reducing the need for long-distance travel.

4. Enhance telemedicine capabilities: Utilize telemedicine technologies to provide remote consultations, monitoring, and support for pregnant women, especially in areas with limited access to healthcare facilities or specialized services.

5. Increase availability of skilled birth attendants: Train and deploy more skilled birth attendants, such as midwives and nurses, to ensure that women have access to skilled care during childbirth, reducing the risk of complications and improving maternal and neonatal outcomes.

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 group or geographic area that will be the focus of the simulation, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather relevant data on the current state of maternal health access in the target population, including indicators such as the number of prenatal care visits, distance to healthcare facilities, availability of skilled birth attendants, and maternal and neonatal health outcomes.

3. Define simulation parameters: Determine the specific variables and parameters that will be used to model the impact of the recommendations, such as the increase in prenatal care visits, the reduction in travel distance to healthcare facilities, or the number of skilled birth attendants to be deployed.

4. Develop a simulation model: Use statistical or mathematical modeling techniques to create a simulation model that incorporates the baseline data and the defined parameters. This model should simulate the potential changes in access to maternal health services based on the recommended interventions.

5. Run the simulation: Apply the simulation model to the baseline data to generate simulated outcomes, such as the projected increase in prenatal care visits, the reduction in travel distance, or the improvement in maternal and neonatal health outcomes.

6. Analyze the results: Evaluate the simulated outcomes to assess the potential impact of the recommendations on improving access to maternal health. This analysis may involve comparing the simulated outcomes to the baseline data and identifying any significant improvements or changes.

7. Refine and iterate: Based on the analysis of the simulation results, refine the simulation model and parameters as needed to better reflect the potential impact of the recommendations. Repeat the simulation process to generate updated and more accurate projections.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions about which interventions to prioritize and implement.

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