Weight gain during pregnancy among women initiating dolutegravir in Botswana

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Study Justification:
The study aimed to investigate the weight gain during pregnancy among women in Botswana who initiated dolutegravir-based antiretroviral therapy (ART). This was important because recent data suggested significant weight gain among non-pregnant HIV-positive adults after starting dolutegravir-based ART, but limited data was available for pregnant women. Excess or insufficient weight gain during pregnancy can have adverse effects on pregnancy outcomes, so it was crucial to understand the impact of dolutegravir on weight gain in this population.
Highlights:
The study found that women who initiated dolutegravir during pregnancy gained more weight between 18 and 36 weeks gestation compared to women who initiated efavirenz-based ART or HIV-uninfected women. However, neither group gained as much weight as HIV-uninfected women. Women initiating dolutegravir were more likely to experience excess weight gain but less likely to have insufficient weight gain or weight loss compared to women initiating efavirenz. These findings have both positive and negative consequences for pregnancy outcomes.
Recommendations for Lay Reader:
Based on the study findings, it is recommended that healthcare providers closely monitor weight gain in pregnant women who initiate dolutegravir-based ART. Excessive weight gain during pregnancy can increase the risk of complications, so healthcare providers should provide appropriate counseling and support to manage weight gain. It is also important for pregnant women to maintain a healthy lifestyle, including a balanced diet and regular physical activity, to promote optimal weight gain during pregnancy.
Recommendations for Policy Maker:
Policy makers should consider incorporating guidelines for monitoring and managing weight gain in pregnant women who initiate dolutegravir-based ART. This may include providing training and resources for healthcare providers to effectively counsel and support pregnant women in managing their weight. Additionally, policy makers should promote initiatives that encourage healthy lifestyles during pregnancy, such as nutrition education programs and access to physical activity resources.
Key Role Players:
1. Healthcare providers: Responsible for monitoring weight gain in pregnant women and providing appropriate counseling and support.
2. Policy makers: Responsible for developing guidelines and initiatives to address weight gain during pregnancy among women initiating dolutegravir-based ART.
3. Researchers: Responsible for conducting further studies to explore the long-term effects of dolutegravir on weight gain and pregnancy outcomes.
4. Community organizations: Responsible for promoting healthy lifestyles and providing resources to support pregnant women in managing their weight.
Cost Items for Planning Recommendations:
1. Training and education programs for healthcare providers: Budget for developing and implementing training programs to educate healthcare providers on monitoring and managing weight gain in pregnant women.
2. Counseling and support services: Budget for providing counseling and support services to pregnant women, including nutrition counseling and access to resources for physical activity.
3. Research funding: Budget for conducting further studies to explore the long-term effects of dolutegravir on weight gain and pregnancy outcomes.
4. Community initiatives: Budget for implementing community programs and initiatives that promote healthy lifestyles during pregnancy, such as nutrition education programs and access to physical activity resources.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides detailed information about the study design, data collection, and analysis. However, it does not mention the sample size or statistical significance of the findings. To improve the evidence, the abstract could include the sample size and p-values for the reported differences in weight gain between groups.

Background: Recent data suggests clinically significant weight gain among non-pregnant HIV-positive adults after starting dolutegravir-based ART (DTG). Excess or insufficient weight gain in pregnancy could adversely impact pregnancy outcomes, but data for pregnant women receiving DTG are limited. Methods: The Tsepamo Study captured data at delivery sites in Botswana from 2014 to 2019. HIV testing, HIV treatment information, and weight measurements during antenatal care were abstracted from the maternity obstetric record at delivery. HIV-positive women initiating DTG or efavirenz-based ART (EFV) between conception and 17 weeks gestation and HIV-uninfected women first presenting for antenatal care before 17 weeks gestation were included. We evaluated weekly weight gain, total 18-week weight gain, excess weight gain (>0.59 kg/week), insufficient weight gain (99% of all births that take place at the included sites as almost all women bring their antenatal medical records (‘maternity card’) to delivery [6,24]. In Botswana, approximately 95% of women deliver at a hospital [25]. Information collected from the maternity obstetric record includes demographics, past medical history, diagnoses, hospitalizations and complications during pregnancy, medications prescribed during pregnancy, HIV history (including timing of diagnoses, ART regimens, CD4 count and viral loads), and clinical information including lab results, blood pressure, and weight measurements. All weight measurements ascertained and recorded by nurses or midwives from the time of the first antenatal care (ANC) visit to delivery are captured in the maternity obstetric record with associated dates. Self-reported pre-pregnancy weight is recorded when available. Height is measured but rarely recorded (approximately 1%) and upper arm circumference is not measured. Gestational age is documented by midwives at the time of delivery based on the estimated date of delivery (EDD). EDD is calculated at the first ANC visit using the reported last menstrual period and confirmed by ultrasound when available. If the last menstrual period date is unknown or suspected to be incorrect, fundal height measurements are used by the midwives to estimate gestational age. Before May 2016, Botswana recommended initiation of TDF/emtricitabine(FTC)/EFV for all ART naïve adults with CD4 <350 cells/mm3 and for all pregnant women, regardless of CD4 cell count. In May 2016, TDF/FTC/DTG replaced TDF/FTC/EFV as the first-line regimen for all adults and all pregnant women and CD4 restrictions were removed. In September 2018, Botswana began to transition from TDF/FTC/DTG to TDF/lamivudine (3TC)/DTG to decrease the pill burden from 2 pills per day (TDF/FTC plus DTG) to 1 pill per day (TDF/3TC/DTG combined formulation). Women with kidney dysfunction or intolerance/resistance to TDF/FTC could access abacavir/3TC or zidovudine/3TC. TAF is not yet available in Botswana's national HIV program. HIV-positive women who initiated ART for the first time between the estimated last menstrual period and 17 weeks gestation were included in our analysis. We excluded women who initiated an ART regimen other than a DTG-based or EFV-based regimen. We also included HIV-uninfected women within one standard deviation of the mean age of the HIV-positive women who attended an antenatal clinic between the estimated last menstrual period and 17 weeks gestation. Multiple pregnancies were included. Baseline was defined as the date of ART initiation for HIV-positive women and as the date of the first ANC visit for HIV-uninfected women. Our study population was restricted to women who gave birth from August 2014 to March 2019. We evaluated two primary outcomes. First, we calculated weekly weight gain from 18±2 to 36±2 weeks gestation as the difference in weight measured at 36±2 weeks and 18±2 weeks divided by the number of weeks between the measurements. Second, we calculated total 18-week weight gain from 18±2 to 36±2 weeks gestation as the difference in weight measured at 36±2 weeks and 18±2 weeks among women who had two weight measurements recorded 18 weeks apart. We required the 18±2 measurement to occur at or after baseline. We chose 18±2 and 36±2 weeks as the timeframes for the weight measurements to capture a weight measurement soon after baseline (ART initiation if HIV-positive) and to avoid the majority of the variation in gestational duration. Secondary outcomes included weekly weight gain greater than 0.59 kg/week from 18±2 to 36±2 weeks (‘excess weight gain’), weekly weight gain less than 0.18 kg/week from 18±2 to 36±2 weeks (‘insufficient weight gain’), and any weight loss from 18±2 to 36±2 weeks. These cut-points are based on the Institute of Medicine (IOM) 2009 guidelines on gestational weight gain (converted from pounds to kilograms), which recommend gaining no more than 0.59 kg/week and no less than 0.18 kg/week in the second and third trimesters, regardless of pre-pregnancy BMI category (IOM guidelines recommend women with BMI<18.5 gain the most weight of all BMI categories, up to 0.59 kg/week, and women with BMI≥30 gain the least week of all BMI categories, at least 0.18 kg/week; therefore, these values can be used to define excess and insufficient weight gain regardless of pre-pregnancy BMI) [13]. Follow-up ended at 36±2 weeks for the purpose of all of our analyses. We examined demographic information by exposure group using sample means and proportions. For weekly weight gain and total 18-week weight gain, we fit linear regression models to estimate mean differences and 95% confidence intervals comparing women initiating DTG to women initiating EFV, and comparing HIV-uninfected women to women initiating EFV. Our models included a 3-level exposure variable (with EFV as the referent) and were adjusted for several baseline covariates: age (200 cells/μl or HIV-uninfected, ≤200 cells/μl or missing), employment (salaried, other or unknown), education (secondary education or higher, other or unknown), parity (≥1, 0 or unknown), gravidity (≥2, 1 or unknown), marital status (yes, no or unknown), site (tertiary referral hospital, other), smoking during pregnancy (yes, no or unknown), alcohol use during pregnancy (yes, no or unknown), pre-pregnancy weight (<50 kg, 50–80 kg, ≥80 kg, unknown), baseline weight in pregnancy (<50 kg, 50–80 kg, ≥80 kg, unknown), gestational age at baseline (<12 weeks, ≥12 weeks), and any medical diagnosis prior to pregnancy other than HIV (yes, no or unknown). Examples of common diagnoses prior to pregnancy include sexually transmitted infections (STI), anemia, hypertension, and asthma. For weekly weight gain greater than 0.59 kg/week, weekly weight gain less than 0.18 kg/week, and weight loss from 18±2 to 36±2 weeks, we fit log-binomial regression models [26] to estimate risk ratios (an appropriate measure of association for non-rare outcomes) and 95% confidence intervals comparing women initiating DTG to women initiating EFV, and comparing HIV-uninfected women to women initiating EFV. Our models were adjusted for the same baseline covariates listed above. We conducted subgroup analyses to evaluate effect modification by baseline weight in pregnancy (<50 kg and ≥80 kg) and by gravidity (primigravid and non-primigravid). Women who did not have a weight measurement at 18±2 weeks and/or at 36±2 weeks had missing outcome data for weekly weight gain from 18±2 to 36±2 weeks gestation and women who did not have two weight measurements recorded 18 weeks apart had missing outcome data for total 18-week weight gain. If the factors associated with having a missing weight were also related to the weekly or total weight gain, restricting our analysis to only women who had the weight measurements of interest could induce selection bias. We attempted to adjust for this potential selection bias by estimating inverse probability of censoring weights in a sensitivity analysis [27]. To do so, we fit a logistic regression model for not having missing data on weekly weight gain conditional on the exposure group, the baseline covariates listed above, the number of ANC visits (14), and any maternal diagnosis during pregnancy (yes, no or unknown). Our weights were stabilized [27] and used in the models evaluating weekly weight gain. A similar analysis was conducted for total 18-week weight gain. To further explore the sensitivity of our findings to missing outcome data, we performed additional sensitivity analyses where we restricted our analysis to individuals with baseline weight and where we evaluated weight at 36±2 weeks gestation as an outcome. To evaluate the potential for residual confounding by missing data for baseline weight in pregnancy, we also conducted a sensitivity analysis restricted to women with a known baseline weight. CD4 cell count was infrequently measured in Botswana after CD4 restrictions were removed from treatment initiation guidelines in 2016. [6,28] To account for unmeasured or residual confounding by CD4 cell count, we varied how we categorized CD4 cell count in several sensitivity analyses (e.g., including a missing indicator for CD4 cell count, dichotomizing CD4 cell count as <200 cells/μl versus ≥200 cells/μl or missing, and using cut-points of 350 cells/μl and 500 cells/μl). In a final sensitivity analysis, we restricted our analysis to singleton pregnancies. All analyses were conducted using SAS. The reporting of this study conforms to the STROBE statement. The funders had no role in study design, data collection and analysis, data interpretation, or preparation of the manuscript. The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication. Ethics approval for this study was granted by the Health Research and Development Committee in Botswana and by the Office of Human Research Administration at the Harvard T.H. Chan School of Public Health. Maternal consent was waived as data were collected anonymously and via chart abstraction.

Based on the provided information, here are some potential innovations that could 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 maternal health, including weight gain during pregnancy. These apps can offer personalized recommendations, track weight gain progress, and provide reminders for prenatal appointments.

2. Telemedicine Services: Implement telemedicine services to enable pregnant women in remote or underserved areas to access healthcare professionals for prenatal care. This would allow them to receive guidance on weight management and address any concerns or questions they may have.

3. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women in their local communities. These workers can conduct regular check-ins, offer guidance on healthy weight gain, and connect women to appropriate healthcare services.

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

5. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of healthy weight gain during pregnancy. These campaigns can be conducted through various channels, such as radio, television, social media, and community outreach programs.

6. Collaboration with HIV Programs: Collaborate with existing HIV programs to integrate maternal health services and support for women receiving dolutegravir-based ART. This can ensure that pregnant women on this medication receive appropriate monitoring and guidance regarding weight gain.

7. Data Collection and Analysis: Improve data collection and analysis systems to track weight gain patterns and outcomes among pregnant women. This data can help identify trends, inform policy decisions, and guide the development of targeted interventions.

It is important to note that these recommendations are general and may need to be tailored to the specific context and needs of Botswana’s healthcare system.
AI Innovations Description
The study titled “Weight gain during pregnancy among women initiating dolutegravir in Botswana” aimed to investigate the weight gain patterns among pregnant women receiving dolutegravir-based antiretroviral therapy (ART) in Botswana. The study analyzed data from the Tsepamo Study, which collected information from maternity obstetric records at delivery sites in Botswana from 2014 to 2019.

The study found that women initiating dolutegravir (DTG) during pregnancy gained more weight between 18 and 36 weeks gestation compared to women initiating efavirenz-based ART (EFV). However, both groups did not gain as much weight as HIV-uninfected women. Women initiating DTG were more likely to experience excess weight gain but less likely to experience insufficient weight gain or weight loss compared to women initiating EFV.

Based on these findings, the study suggests that initiating DTG during pregnancy could increase the risk of excess weight gain but decrease the risk of insufficient weight gain and weight loss. These findings have both positive and negative consequences in pregnancy. It is important to note that the study was conducted in Botswana and may not be generalizable to other settings.

To improve access to maternal health, the study’s recommendations could be developed into an innovation. For example, healthcare providers could use the findings to develop personalized weight management programs for pregnant women receiving DTG-based ART. These programs could include regular monitoring of weight gain, nutritional counseling, and physical activity recommendations to ensure healthy weight gain during pregnancy. Additionally, healthcare providers could use the findings to educate pregnant women about the potential risks and benefits of different ART regimens, helping them make informed decisions about their treatment options.

Overall, the study provides valuable insights into weight gain patterns among pregnant women receiving DTG-based ART and highlights the importance of monitoring and managing weight during pregnancy. By implementing the study’s recommendations, healthcare providers can contribute to improving access to maternal health and promoting positive pregnancy outcomes for women receiving DTG-based ART.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthen Antenatal Care (ANC) Services: Enhance the quality and availability of ANC services to ensure that pregnant women receive comprehensive care, including regular weight monitoring, nutritional counseling, and education on healthy weight gain during pregnancy.

2. Increase Access to HIV Testing and Treatment: Improve access to HIV testing and treatment services for pregnant women, ensuring early initiation of antiretroviral therapy (ART) and regular monitoring of weight gain among HIV-positive women.

3. Promote Maternal Nutrition: Implement programs that promote healthy eating habits and provide nutritional support to pregnant women, with a focus on balanced diets and adequate weight gain during pregnancy.

4. Enhance Health Information Systems: Develop and implement robust health information systems that capture accurate and timely data on maternal health, including weight measurements, HIV status, and ART regimens. This will enable better monitoring and evaluation of interventions aimed at improving access to maternal health.

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

1. Define Key Indicators: Identify key indicators that reflect access to maternal health, such as the percentage of pregnant women receiving ANC services, the proportion of HIV-positive pregnant women on ART, and the average weight gain during pregnancy.

2. Collect Baseline Data: Gather baseline data on the selected indicators from relevant sources, such as health facilities, surveys, and health information systems. This will provide a starting point for comparison.

3. Develop a Simulation Model: Build a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. The model should consider factors such as population size, resource availability, and implementation timelines.

4. Input Data and Parameters: Input the baseline data and relevant parameters into the simulation model. This may include information on the current coverage of ANC services, HIV testing and treatment rates, and average weight gain during pregnancy.

5. Simulate Scenarios: Run the simulation model to simulate different scenarios based on the recommended interventions. This could involve adjusting parameters such as the coverage of ANC services, the proportion of HIV-positive pregnant women on ART, and the average weight gain during pregnancy.

6. Analyze Results: Analyze the results of the simulation to assess the potential impact of the recommended interventions on improving access to maternal health. This may involve comparing the indicators between different scenarios and identifying the most effective interventions.

7. Refine and Iterate: Refine the simulation model based on the analysis results and iterate the simulation process to further explore different scenarios and refine the recommendations.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on implementing the most effective strategies.

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