Depression during pregnancy and the postpartum among HIV-infected women on antiretroviral therapy in Uganda

Journal of Acquired Immune Deficiency Syndromes, Volume 67, Year 2014

Interpretation

Background: Among HIV-infected women, perinatal depression compromises clinical, maternal, and child health outcomes. Antiretroviral therapy (ART) is associated with lower depression symptom severity but the uniformity of effect through pregnancy and postpartum periods is unknown. Uganda is an important setting in which to investigate depression over perinatal periods given a total fertility rate of 6.2 births per woman41 and HIV prevalence of 7.2%,20,42 higher among women (8.2%) than among men (6.1%). In addition, 70% of treatment-eligible Ugandans access ART.20,28 ART coverage among women with pregnancy is expected to increase with Uganda’s adoption of “Option B+” to prevent perinatal HIV transmission.43,44 Mbarara District is a primarily rural setting located approximately 265 km southwest of Kampala. Adult HIV prevalence in the district is estimated at 10%45 and is higher among women compared with men. The Mbarara University HIV clinic is located within the Mbarara Regional Referral Hospital and offers comprehensive HIV care services, including ART, at no cost to patients.46 Study participants were enrolled in the Uganda AIDS Rural Treatment Outcomes (UARTO) cohort study, initiated in July 2005 with the primary objective of determining predictors of virologic failure and antiretroviral resistance. Participants were recruited from HIV-infected, treatment-naive adults initiating ART at the Mbarara University HIV clinic. Patients who were at least 18 years old and living within 60 km of the clinic were eligible to enroll. At the time of this analysis, 447 women were enrolled in UARTO. Loss-to-follow-up (participants for whom we were unable to confirm vital status after ≥180 days without cohort follow-up) was 2% at 1 year and 5% at 2 years after enrollment. Participants were seen at baseline (ie, at ART initiation) and quarterly for bloodwork and to complete standardized interviewer-administered questionnaires detailing sociodemographics, mental and physical health, sexual risk behaviour, and partner dynamics. Incident pregnancies were assessed by self-report. Interviews were administered in the dominant local language (Runyankole). This analysis includes data from female participants (18–49 years) enrolled from June 2005 followed through December 2012. Depression symptom severity was measured using a modified version of the Hopkins Symptom Checklist (HSCL)-15 for depression.47 Based on previous studies using HSCL in Uganda, we included a 16th item, “Feeling like I don’t care about my health.”48 Each symptom was scored on a 4-item Likert scale ranging from 1 (not at all) to 4 (extremely) and the total depression severity score was calculated as the mean of the 16 items, with higher scores indicating greater depression symptom severity. We also assessed a dichotomous measure of “probable depression” defined as an HSCL score >1.75, a commonly used threshold for a positive screen of depression.47–50 The depression subscale of HSCL has been used to assess depression in general population samples and among people living with HIV in sub-Saharan African countries.47–49,51–54 The HSCL has been further shown to have good reliability55 and construct validity56 among people living with HIV in Uganda specifically. Although the HSCL was not designed to specifically screen for perinatal depression, it and other generic depression scales are commonly used to measure depression among antenatal women.54,57–59 The most reliable, valid, and frequently used measure of perinatal depression in sub-Saharan Africa is the Edinburgh Postnatal Depression Scale54; however, the reliability and validity of the Edinburgh Postnatal Depression Scale outside of perinatal periods is unknown. The primary predictor variable is a 3-level variable indicating pregnancy status, which we classified as being pregnant, up to 1 year postpartum or neither pregnant nor post-partum (“non–pregnancy-related”). Women who reported sterilization (tubal ligation or hysterectomy) at baseline were excluded from the analysis. If the procedure was reported during follow-up, women were censored upon reported date of the procedure. Periods of pregnancy were defined based on self-report at baseline and over the follow-up period and included both first and subsequent pregnancies. Pregnancy start was defined as the visit date when pregnancy was first reported and pregnancy end was the subsequent date at which women reported no longer being pregnant. For the few women (n = 8) who reported a live birth outcome and had a computed period of pregnancy of <7 or >11 months, live birth date was used to back-calculate a start date to account for an estimated 9-month gestation. The postpartum period was defined as the period from the end of pregnancy until 12 months after any pregnancy outcome.60,61 All other follow-up times were assessed as non–pregnancy-related. At study enrollment, women reported whether they were or had been pregnant in the previous 12 months but did not report dates of pre-enrollment pregnancy or postpartum status. Thus, no women were classified as postpartum at study entry. We examined the association between depression and pregnancy status, adjusting for baseline and time-updated covariates considered potential confounders. Baseline variables included sociodemographic characteristics (including age, education, employment, household income, and marital status), reproductive history (including parity), and clinical history (including time since HIV diagnosis, AIDS defining illnesses, CD4 cell count at enrollment, and body mass index). Time-updated variables were measured quarterly and included age, time on HIV treatment, CD4 cell count, HIV viral load <400 copies per milliliter (ie, viral suppression), the Medical Outcomes Study HIV Health Survey Physical Health Summary score (scored on a 0–100 scale, where a higher score indicates better health),62,63 and sexual activity in the previous 3 months. Descriptive statistics were used to characterize baseline distributions of study variables. Baseline differences between women with and without incident pregnancy were compared using Wilcoxon rank sum test for continuous variables and Pearson χ2 test for categorical variables. Although our regression models are based on time-varying pregnancy-related status, comparison of fixed categories of pregnancy are provided to inform understanding of differences between women who exclusively contributed to non-pregnant periods and those who contributed to all 3 periods of follow-up. We calculated the mean (SD) HSCL score at baseline and then compared mean HSCL scores across pregnant, postpartum, and non–pregnancy-related periods using analysis of variance. We plotted mean HSCL scores (with residual standard error bars) across the 3 periods by time on ART. We fit a multivariable linear regression model to the data, using generalized estimating equations to estimate model parameters. We used an autoregressive working correlation structure. Pregnancy and the postpartum period were investigated as independent predictors of depression symptom severity. After testing normality assumptions and collinearity, variables with a significant association with depression in bivariate analyses (at P < 0.20) were considered for the full model to obtain the relative contribution of each covariate. Model selection was achieved by minimizing the Akaike Information Criterion while maintaining P-values for covariates below 0.20.64 All statistical tests were 2-sided and were considered statistically significant at α = 0.05. Using a binary variable for “probable depression,” we also fit a multivariable GEE logistic regression model. The same model selection procedures and covariates were used. Data were analyzed with SAS version 9.3 (SAS Institute Inc., Cary, NC).65 All participants provided voluntary, written informed consent at study enrollment. Ethical approval for all study procedures was obtained from the Institutional Review Committee, Mbarara University of Science and Technology; the Partners Human Research Committee, Massachusetts General Hospital; the Committee on Human Research, University of California at San Francisco; and the Research Ethics Board of Simon Fraser University. Consistent with national guidelines, we received clearance for the study from the Uganda National Council for Science and Technology and from the Research Secretariat in the Office of the President.

AI Digest

Study Justification:

This study aims to investigate the impact of depression during pregnancy and the postpartum period among HIV-infected women on antiretroviral therapy (ART) in Uganda. Perinatal depression can have negative effects on clinical, maternal, and child health outcomes. Understanding the relationship between depression and pregnancy/postpartum periods in this population is important for improving healthcare interventions and support.

Study Highlights:

– The study was conducted in Uganda, which has a high total fertility rate and HIV prevalence among women.
– The study included HIV-infected women on ART from the Mbarara University HIV clinic.
– Depression symptom severity was measured using a modified version of the Hopkins Symptom Checklist (HSCL)-15.
– The study examined the association between depression and pregnancy status, adjusting for various covariates.
– The findings provide insights into the prevalence and impact of depression during pregnancy and the postpartum period among HIV-infected women on ART in Uganda.

Study Recommendations:

Based on the study findings, the following recommendations can be made:

1. Healthcare providers should screen HIV-infected women on ART for depression during pregnancy and the postpartum period.
2. Interventions should be developed to address and manage depression in this population, including counseling, support groups, and access to mental health services.
3. Further research is needed to explore the specific factors contributing to depression in this population and to evaluate the effectiveness of different interventions.

Key Role Players:

1. Healthcare providers: Responsible for screening and providing support for HIV-infected women on ART.
2. Mental health professionals: Involved in the assessment and treatment of depression in this population.
3. Policy makers: Responsible for implementing policies and guidelines to address depression during pregnancy and the postpartum period among HIV-infected women on ART.
4. Community organizations: Can provide additional support and resources for women experiencing depression.

Cost Items for Planning Recommendations:

1. Training and capacity building for healthcare providers and mental health professionals.
2. Development and implementation of screening tools and protocols.
3. Provision of counseling and mental health services.
4. Awareness campaigns and educational materials.
5. Collaboration with community organizations and support groups.
6. Monitoring and evaluation of interventions.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is fairly strong, but there are some areas for improvement. The study provides detailed information about the study population, methods, and statistical analysis. However, the abstract does not mention the sample size or provide any specific results or conclusions. To improve the evidence, the abstract could include a summary of the main findings and their implications for clinical practice or future research.

Abstract

Uganda is an important setting in which to investigate depression over perinatal periods given a total fertility rate of 6.2 births per woman41 and HIV prevalence of 7.2%,20,42 higher among women (8.2%) than among men (6.1%). In addition, 70% of treatment-eligible Ugandans access ART.20,28 ART coverage among women with pregnancy is expected to increase with Uganda’s adoption of “Option B+” to prevent perinatal HIV transmission.43,44 Mbarara District is a primarily rural setting located approximately 265 km southwest of Kampala. Adult HIV prevalence in the district is estimated at 10%45 and is higher among women compared with men. The Mbarara University HIV clinic is located within the Mbarara Regional Referral Hospital and offers comprehensive HIV care services, including ART, at no cost to patients.46 Study participants were enrolled in the Uganda AIDS Rural Treatment Outcomes (UARTO) cohort study, initiated in July 2005 with the primary objective of determining predictors of virologic failure and antiretroviral resistance. Participants were recruited from HIV-infected, treatment-naive adults initiating ART at the Mbarara University HIV clinic. Patients who were at least 18 years old and living within 60 km of the clinic were eligible to enroll. At the time of this analysis, 447 women were enrolled in UARTO. Loss-to-follow-up (participants for whom we were unable to confirm vital status after ≥180 days without cohort follow-up) was 2% at 1 year and 5% at 2 years after enrollment. Participants were seen at baseline (ie, at ART initiation) and quarterly for bloodwork and to complete standardized interviewer-administered questionnaires detailing sociodemographics, mental and physical health, sexual risk behaviour, and partner dynamics. Incident pregnancies were assessed by self-report. Interviews were administered in the dominant local language (Runyankole). This analysis includes data from female participants (18–49 years) enrolled from June 2005 followed through December 2012. Depression symptom severity was measured using a modified version of the Hopkins Symptom Checklist (HSCL)-15 for depression.47 Based on previous studies using HSCL in Uganda, we included a 16th item, “Feeling like I don’t care about my health.”48 Each symptom was scored on a 4-item Likert scale ranging from 1 (not at all) to 4 (extremely) and the total depression severity score was calculated as the mean of the 16 items, with higher scores indicating greater depression symptom severity. We also assessed a dichotomous measure of “probable depression” defined as an HSCL score >1.75, a commonly used threshold for a positive screen of depression.47–50 The depression subscale of HSCL has been used to assess depression in general population samples and among people living with HIV in sub-Saharan African countries.47–49,51–54 The HSCL has been further shown to have good reliability55 and construct validity56 among people living with HIV in Uganda specifically. Although the HSCL was not designed to specifically screen for perinatal depression, it and other generic depression scales are commonly used to measure depression among antenatal women.54,57–59 The most reliable, valid, and frequently used measure of perinatal depression in sub-Saharan Africa is the Edinburgh Postnatal Depression Scale54; however, the reliability and validity of the Edinburgh Postnatal Depression Scale outside of perinatal periods is unknown. The primary predictor variable is a 3-level variable indicating pregnancy status, which we classified as being pregnant, up to 1 year postpartum or neither pregnant nor post-partum (“non–pregnancy-related”). Women who reported sterilization (tubal ligation or hysterectomy) at baseline were excluded from the analysis. If the procedure was reported during follow-up, women were censored upon reported date of the procedure. Periods of pregnancy were defined based on self-report at baseline and over the follow-up period and included both first and subsequent pregnancies. Pregnancy start was defined as the visit date when pregnancy was first reported and pregnancy end was the subsequent date at which women reported no longer being pregnant. For the few women (n = 8) who reported a live birth outcome and had a computed period of pregnancy of 11 months, live birth date was used to back-calculate a start date to account for an estimated 9-month gestation. The postpartum period was defined as the period from the end of pregnancy until 12 months after any pregnancy outcome.60,61 All other follow-up times were assessed as non–pregnancy-related. At study enrollment, women reported whether they were or had been pregnant in the previous 12 months but did not report dates of pre-enrollment pregnancy or postpartum status. Thus, no women were classified as postpartum at study entry. We examined the association between depression and pregnancy status, adjusting for baseline and time-updated covariates considered potential confounders. Baseline variables included sociodemographic characteristics (including age, education, employment, household income, and marital status), reproductive history (including parity), and clinical history (including time since HIV diagnosis, AIDS defining illnesses, CD4 cell count at enrollment, and body mass index). Time-updated variables were measured quarterly and included age, time on HIV treatment, CD4 cell count, HIV viral load <400 copies per milliliter (ie, viral suppression), the Medical Outcomes Study HIV Health Survey Physical Health Summary score (scored on a 0–100 scale, where a higher score indicates better health),62,63 and sexual activity in the previous 3 months. Descriptive statistics were used to characterize baseline distributions of study variables. Baseline differences between women with and without incident pregnancy were compared using Wilcoxon rank sum test for continuous variables and Pearson χ2 test for categorical variables. Although our regression models are based on time-varying pregnancy-related status, comparison of fixed categories of pregnancy are provided to inform understanding of differences between women who exclusively contributed to non-pregnant periods and those who contributed to all 3 periods of follow-up. We calculated the mean (SD) HSCL score at baseline and then compared mean HSCL scores across pregnant, postpartum, and non–pregnancy-related periods using analysis of variance. We plotted mean HSCL scores (with residual standard error bars) across the 3 periods by time on ART. We fit a multivariable linear regression model to the data, using generalized estimating equations to estimate model parameters. We used an autoregressive working correlation structure. Pregnancy and the postpartum period were investigated as independent predictors of depression symptom severity. After testing normality assumptions and collinearity, variables with a significant association with depression in bivariate analyses (at P < 0.20) were considered for the full model to obtain the relative contribution of each covariate. Model selection was achieved by minimizing the Akaike Information Criterion while maintaining P-values for covariates below 0.20.64 All statistical tests were 2-sided and were considered statistically significant at α = 0.05. Using a binary variable for “probable depression,” we also fit a multivariable GEE logistic regression model. The same model selection procedures and covariates were used. Data were analyzed with SAS version 9.3 (SAS Institute Inc., Cary, NC).65 All participants provided voluntary, written informed consent at study enrollment. Ethical approval for all study procedures was obtained from the Institutional Review Committee, Mbarara University of Science and Technology; the Partners Human Research Committee, Massachusetts General Hospital; the Committee on Human Research, University of California at San Francisco; and the Research Ethics Board of Simon Fraser University. Consistent with national guidelines, we received clearance for the study from the Uganda National Council for Science and Technology and from the Research Secretariat in the Office of the President.

Materials

https://efashare.b-cdn.net/materials/413920b7838ecd9ba9e5d9e9e852194f3538f9d21479d9a49bc94b3c6f0a1a52/qai-67-s179-s001.pdf

Innovations

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

1. Telemedicine: Implementing telemedicine services can provide remote access to mental health support for pregnant women and new mothers, including counseling and therapy for perinatal depression. This can be particularly beneficial for women in rural areas who may have limited access to mental health professionals.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information, resources, and support for pregnant women and new mothers can help improve access to maternal health services. These apps can include features such as symptom tracking, appointment reminders, educational materials, and access to virtual support groups.

3. Community health workers: Training and deploying community health workers who can provide support and education to pregnant women and new mothers in their local communities can help improve access to maternal health services. These workers can offer guidance on mental health, provide referrals to healthcare facilities, and assist with navigating the healthcare system.

4. Integrated care models: Implementing integrated care models that combine maternal health services with HIV care can help address the specific needs of HIV-infected women. This can include providing comprehensive mental health screenings and interventions as part of routine antenatal and postpartum care.

5. Task-shifting: Training and empowering non-specialist healthcare providers, such as nurses and midwives, to deliver mental health services can help increase access to care. These providers can be trained to identify and manage perinatal depression, offer counseling, and provide referrals to specialized mental health services when needed.

6. Peer support programs: Establishing peer support programs where women who have experienced perinatal depression can provide guidance, empathy, and encouragement to pregnant women and new mothers can help reduce stigma and improve access to support services. These programs can be facilitated through in-person meetings or online platforms.

7. Health education campaigns: Conducting targeted health education campaigns to raise awareness about perinatal depression, its symptoms, and available resources can help reduce stigma and encourage women to seek help. These campaigns can be conducted through various channels, including social media, community events, and healthcare facilities.

It’s important to note that the specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health would be to implement routine screening and treatment for perinatal depression among HIV-infected women on antiretroviral therapy in Uganda. This recommendation is based on the findings that perinatal depression can compromise clinical, maternal, and child health outcomes among HIV-infected women. By identifying and addressing depression symptoms during pregnancy and the postpartum period, healthcare providers can improve the overall well-being of these women and potentially enhance their adherence to antiretroviral therapy. This can be achieved by training healthcare providers to administer validated depression screening tools, such as the Edinburgh Postnatal Depression Scale, and providing appropriate mental health support and interventions for those identified with depression symptoms. Additionally, integrating mental health services into existing maternal health programs and ensuring access to affordable and quality mental health care can further enhance the effectiveness of this recommendation.

AI Innovations Methodology

To improve access to maternal health in Uganda, here are some potential recommendations:

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, particularly in rural areas, can help increase access to maternal health services. This includes ensuring the availability of skilled healthcare providers, essential medical equipment, and necessary medications.

2. Community-based interventions: Implementing community-based interventions can help reach pregnant women in remote areas who may have limited access to healthcare facilities. This can involve training and empowering community health workers to provide basic prenatal care, education on maternal health, and referrals to healthcare facilities when necessary.

3. Mobile health (mHealth) solutions: Utilizing mobile technology can help overcome geographical barriers and improve access to maternal health services. This can include sending reminders for prenatal appointments, providing educational resources via mobile apps, and enabling telemedicine consultations for pregnant women in remote areas.

4. Financial incentives: Implementing financial incentives, such as conditional cash transfers or subsidies, can help reduce financial barriers to accessing maternal health services. This can encourage pregnant women to seek antenatal care, deliver in healthcare facilities, and receive postpartum care.

To simulate the impact of these recommendations on improving access to maternal health, a possible methodology could include the following steps:

1. Define the target population: Identify the specific population group that will be the focus of the simulation, such as pregnant women in rural areas of Uganda.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population. This can include information on healthcare infrastructure, availability of healthcare providers, utilization rates of maternal health services, and any existing barriers to access.

3. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations on improving access to maternal health. This can include indicators such as the number of pregnant women receiving antenatal care, the percentage of deliveries in healthcare facilities, and the rate of postpartum care utilization.

4. Develop a simulation model: Create a simulation model that incorporates the potential recommendations and their expected impact on the defined indicators. This can involve using mathematical equations, statistical models, or computer simulations to estimate the changes in access to maternal health services based on the implemented interventions.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations on improving access to maternal health. This can involve varying parameters, such as the coverage of interventions or the timeframe for implementation, to assess different scenarios.

6. Analyze results: Analyze the simulation results to determine the projected changes in access to maternal health services. This can include comparing the indicators before and after implementing the recommendations, as well as assessing the potential cost-effectiveness of the interventions.

7. Validate and refine the model: Validate the simulation model by comparing the projected results with real-world data, if available. Refine the model based on feedback and further data analysis to improve its accuracy and reliability.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health in Uganda. This can inform decision-making and resource allocation to prioritize interventions that are likely to have the greatest positive impact.

Author

Dima Health

Statistics:

Citations: 46

Identifiers:

DOI: 10.1097/QAI.0000000000000370

ISSN: 15254135

Research Areas:

Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Uganda

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female