Individual factors associated with time to non-adherence to ART pick-up within HIV care and treatment services in three health facilities of Zambézia Province, Mozambique

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Study Justification:
– Adherence to visit schedules remains a challenge in Mozambique’s HIV care and treatment services.
– Low adherence to antiretroviral therapy (ART) pick-up can impair the gains made in the HIV program.
– This study aims to investigate individual factors associated with non-adherence to ART pick-up in Mozambique.
Study Highlights:
– The study included 1,413 participants, with 77% being female.
– Only 19% of patients remained adherent to ART pick-up during the evaluation period.
– Factors associated with improved adherence included being 35 years old, receiving efavirenz, and living in an urban area.
– Non-participation in a Community ART Support Group (CASG) was associated with increased non-adherence to ART pick-up.
– Interventions should focus on the first 6 months following ART initiation, targeting younger individuals and widows.
– Joining CASGs earlier in the treatment course may improve adherence.
– Efforts should be made to scale up viral load capacity and HIV resistance monitoring.
Recommendations for Lay Reader and Policy Maker:
– Focus on improving adherence to ART pick-up in the first 6 months after initiation.
– Target younger individuals and widows to better understand facilitators and barriers to visit schedule adherence.
– Encourage participation in Community ART Support Groups (CASGs) earlier in the treatment course.
– Increase efforts to scale up viral load capacity and HIV resistance monitoring.
Key Role Players:
– Ministry of Health (MOH)
– Health facility staff (clinicians, counselors, pharmacists)
– Community ART Support Group (CASG) leaders and members
– Provincial Health Directorate of Zambézia Province (DPS-Z)
– Institutional Bioethics Committee for Health of Zambézia Province (CIBS-Z)
– Institutional Review Boards (IRBs) of Federal University of São Paulo (UNIFESP) and Vanderbilt University
Cost Items for Planning Recommendations:
– Training and capacity building for health facility staff on adherence support strategies
– Resources for implementing and sustaining Community ART Support Groups (CASGs)
– Equipment and supplies for scaling up viral load capacity and HIV resistance monitoring
– Monitoring and evaluation activities to assess the impact of interventions on adherence rates

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study provides a clear description of the methods used, including the study population, data collection, and statistical analysis. The results are presented with relevant statistics and associations between variables are discussed. However, the abstract could be improved by providing more specific details about the findings, such as effect sizes and confidence intervals. Additionally, the abstract could benefit from a clearer statement of the implications of the findings and suggestions for future research or interventions.

Introduction Mozambique has made significant gains in addressing its HIV epidemic, yet adherence to visit schedules remains a challenge. HIV programmatic gains to date could be impaired if adherence and retention to ART remains low. We investigate individual factors associated with non-adherence to ART pick-up in Mozambique. Methods This was a retrospective cohort of patients initiating ART between January 2013 and June 2014. Non-adherence to ART pick-up was defined as a delay in pick-up 15 days. Descriptive statistics were used to calculate socio-demographic and clinical characteristics. Adherence to ART pick-up was assessed using Kaplan Meier estimates. Cox proportional hazards model was used to determine factors associated with non-adherence. Results 1,413 participants were included (77% female). Median age was 30.4 years. 19% of patients remained adherent to ART pick-up during the evaluation period, while 81% of patients were non-adherent to ART pick-up. Probability of being non-adherent to ART pick-up by 166 days following initiation was 50%. In univariate analysis, being widowed was associated with higher adherence to ART pick-up than other marital status groups (p = 0.01). After adjusting, being 35 years (aHR: 0.843, 95% CI: 0.738–0.964, p = 0.012); receiving efavirenz (aHR: 0.932, 95% CI: 0.875–0.992, p = 0.026); and being urban (aHR: 0.754, 95% CI: 0.661–0.861, p<0.0001) were associated with improved adherence. Non-participation in a Community ART Support Group (CASG) was associated with a 43% increased hazard of non-adherence to ART pick-up (aHR 1.431, 1.192–1.717, p<0.0001) Conclusions Interventions should focus on the first 6 months following ARV initiation for improvements. Younger persons and widows are two target groups for better understanding facilitators and barriers to visit schedule adherence. Future strategies should explore the benefits of joining CASGs earlier in one´s treatment course. Finally, greater efforts should be made to accelerate the scale-up of viral load capacity and HIV resistance monitoring.

This retrospective cohort study was conducted in three health facilities of Zambézia Province, Mozambique, representing three different urban-rural zones: 24th of July, an urban health facility in the provincial capital city of Quelimane; the District Hospital of Maganja da Costa, a rural district capital hospital; and Nante Health Facility, an even more rural peripheral health facility in the district of Maganja da Costa (Fig 2). We identified HIV infected individuals at these three sites, aged 15 years or older who started ART between January 1, 2013 and June 30, 2014. For purposes of service delivery, the Ministry of Health (MOH) considers adults to be ≥15 years of age. Each patient was followed from the point of initiation of ART treatment, until June 30, 2016 for a minimum 24-month follow-up (Fig 3). *Charlotte Buehler Cherry, November 08, 2017, WGS_1984 Vanderbilt Institute for Global Health. *Patients LTFU, transferred, or who had died were censored at time of becoming LTFU, transfer, or death. Data were extracted from patients' paper medical records, filled out by clinicians, counselors and pharmacists, and aggregated into an open source HIV electronic patient tracking system called Open Medical Record System (OpenMRS); including demographic data, clinical information, laboratory results, and pharmacy data. Indicators evaluated included age, sex, education level, profession, marital status, whether urban or rural, health facility, CD4+ T cell count, World Health Organization (WHO) clinical staging, body mass index (BMI), ART regimen, and whether involved or not in a Community Adherence Support Group (CASG). Data collection comprised two phases. First, authorization was requested to access the database of each of the three health facilities. After obtaining approvals, data was extracted based on eligibility criteria and the variables to be analyzed were exported into Microsoft Excel. Second, data was linked with pharmacy records (called FILA, Formulário de Levantamento dos ARVs), comparing unique patient identification numbers (NID) of the patient record in OpenMRS and the NID of each FILA. Data were further linked with the NID of patient registers from the tuberculosis services, maternal child health services, and the health facilities death registry. FILAs are a paper-based record that is filled out prospectively at each pharmacy pick-up visit. From each FILA record that the study team could find and then confirm as being a study patient, data were extracted on both scheduled and actual ART pick-up dates for the defined study period. Data systems and patient NIDs are facility specific without ability to track patients across facilities. Due to limitations in space of each health facility´s pharmacy for archiving records, the health facilities routinely discard FILAs after an unspecified period of time. As a result, it was not possible to manually confirm the ART pick-up dates of all eligible patients identified from the OpenMRS system as planned at study outset. As such, analysis of patient retention for this study was limited to only those patients from which we could link data from the electronic medical record, or other registries, and the FILAs that were located. Outcomes of interest were calculated by subtracting the actual ART pick-up date from the scheduled ART pick-up date and assumed, based on Mozambican protocols, that the quantity of ART provided was always the same and sufficient for a 30-day supply, regardless if you received your ART at the facility or through a CASG. One exception to this were patients who first initiated an ART regimen during the study period that consisted of AZT+3TC+NVP. These patients only received 15 pills for their first two-week induction. Following this, they received the full 30-day supply as described. While medication stock-outs do continue in Mozambique, it is beyond the capacity and scope of this work to analyze if each patient actually received a 30-day supply of ART or not. We then categorized patients based on the number of uninterrupted days without ART. Patients were categorized as “adherent to ART pick-up” if the number of uninterrupted days was 0–14 days and “non-adherent to ART pick-up” if ≥15 days, our primary outcome of interest. For purpose of analysis, patients who were classified as LTFU, transferred or who had died, were censored at the time of becoming LTFU, transfer or death. Statistical analysis was performed using IBM SPSS Statistics, version 20 (International Business Machines Corporation Statistical—Package for the Social Sciences). Baseline socio-demographic and clinical characteristics of patients who were adherent and not-adherent to ART pick-up were compared using the chi-square test. Adherence to ART pick-up was assessed using the Kaplan Meier estimate (survival analysis), with graphical visualization of the mean time to first non-adherence to ART pick-up of ≥15 days (survival curve). Patients were followed for a minimum of 24 months but depending on their initiation date it could be longer. Data were censored after 1,278 days (3.5 years) of follow-up. The variables with a significant association with non-adherence to ART pick-up (p< 0.2) in univariate analysis were individually stratified into survival curves (Kaplan Meier) by comparing the two curves using the Breslow test. Continuous variables with significant association with non-adherence to ART pick-up were categorized after analysis using a Receiver Operating Characteristic (ROC) curve to determine the cutoff point that maximizes the division between adherence and non-adherence. Finally, variables significantly associated with non-adherence to ART pick-up in univariable analysis (p<0.2) were analyzed using Cox proportional hazards models. The co-factor Marital Status lost significance in the multivariate analysis and was therefore not included in the final model. The evolution of dropout during the study period was illustrated by Kaplan Meier's estimates in which we used scheduled dates for ART pick-up and added 14 days to those dates, rather than actual dates of ART pick-up. As described above, patients who started an ART regimen during the study period consisting of AZT+3TC+NVP, received a two-week supply of ART for their initial induction period. As such, it is possible for non-adherence to ART pick-up outcomes to occur as early as 30 days post ART initiation. This study protocol was approved by the Institutional Bioethics Committee for Health of Zambézia Province (Comité Institucional de Bioética para Saúde- Zambézia, CIBS-Z), as well as the Institutional Review Boards (IRBs) of Federal University of São Paulo (Universidade Federal de São Paulo, UNIFESP) and Vanderbilt University. Additional approval was obtained from the Provincial Health Directorate of Zambézia Province (Direção Provincial de Saúde da Zambézia, DPS-Z). All data were fully de-identified prior to accessing them and all three research ethics review committees waived the need for informed consent.

Based on the provided information, it seems that the study focuses on factors associated with non-adherence to ART pick-up in Mozambique. To improve access to maternal health, some potential innovations and recommendations could include:

1. Mobile health (mHealth) interventions: Implementing mobile phone-based reminders or text message alerts to remind pregnant women about their scheduled ART pick-up dates and appointments for maternal health services.

2. Community-based support groups: Strengthening and expanding Community Adherence Support Groups (CASGs) to provide social support, education, and counseling for pregnant women on ART, promoting adherence to medication pick-up and antenatal care visits.

3. Integrated services: Integrating maternal health services with HIV care and treatment services to ensure that pregnant women receive comprehensive care, including regular ART pick-up, antenatal care, and prevention of mother-to-child transmission (PMTCT) services.

4. Task-shifting and training: Training and empowering community health workers or lay health workers to provide basic maternal health services, including ART pick-up, counseling, and support, in order to increase accessibility and reduce the burden on formal healthcare providers.

5. Supply chain management: Strengthening the supply chain management system to ensure consistent availability of ART medications and other essential maternal health commodities at health facilities, reducing stock-outs and improving access for pregnant women.

6. Health information systems: Enhancing the use of electronic medical records (EMRs) and data management systems to track and monitor pregnant women on ART, ensuring timely follow-up and identification of those at risk of non-adherence.

7. Health education and awareness: Conducting targeted health education campaigns to raise awareness among pregnant women about the importance of ART adherence, regular pick-up, and antenatal care visits for their own health and the health of their babies.

These innovations and recommendations aim to address individual factors associated with non-adherence to ART pick-up and improve access to maternal health services, ultimately contributing to better maternal and child health outcomes.
AI Innovations Description
The study titled “Individual factors associated with time to non-adherence to ART pick-up within HIV care and treatment services in three health facilities of Zambézia Province, Mozambique” investigated the factors associated with non-adherence to antiretroviral therapy (ART) pick-up in Mozambique. The study found that 81% of patients were non-adherent to ART pick-up, indicating a challenge in adherence and retention to ART.

The study identified several factors associated with improved adherence to ART pick-up. These factors include being widowed, being 35 years old, receiving efavirenz, and living in an urban area. On the other hand, non-participation in a Community ART Support Group (CASG) was associated with increased non-adherence to ART pick-up.

Based on the findings, the study recommends interventions to improve adherence to ART pick-up, particularly in the first 6 months following ART initiation. Targeting younger individuals and widows can help understand the facilitators and barriers to visit schedule adherence. Additionally, strategies should explore the benefits of joining CASGs earlier in the treatment course. Efforts should also be made to accelerate the scale-up of viral load capacity and HIV resistance monitoring.

It is important to note that this study focused on ART adherence within HIV care and treatment services and did not specifically address maternal health. Therefore, the recommendations provided may not directly address improving access to maternal health.
AI Innovations Methodology
The study you provided focuses on individual factors associated with non-adherence to antiretroviral therapy (ART) pick-up in Mozambique. To improve access to maternal health, the following innovations and recommendations could be considered:

1. Mobile Health (mHealth) Applications: Develop and implement mobile health applications that provide pregnant women with information on prenatal care, nutrition, and appointment reminders. These apps can also facilitate communication between healthcare providers and pregnant women, allowing them to ask questions and receive guidance remotely.

2. Telemedicine Services: Establish telemedicine services that enable pregnant women in remote areas to consult with healthcare professionals through video calls. This can help address the shortage of healthcare providers in rural areas and improve access to prenatal care.

3. Community Health Workers: Train and deploy community health workers to provide maternal health education, conduct prenatal visits, and assist pregnant women in accessing healthcare services. These workers can serve as a bridge between the community and healthcare facilities, ensuring that pregnant women receive the necessary care and support.

4. Maternal Health Vouchers: Implement a voucher system that provides pregnant women with financial assistance to cover the costs of prenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase access to quality maternal healthcare services.

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 number of prenatal visits, percentage of pregnant women receiving prenatal care, and maternal mortality rate.

2. Data collection: Collect baseline data on the selected indicators before implementing the recommendations. This can be done through surveys, interviews, and analysis of existing health records.

3. Implement the recommendations: Introduce the recommended innovations and interventions in the targeted areas. Ensure proper training and support for healthcare providers and community health workers involved.

4. Monitor and evaluate: Continuously monitor the implementation of the recommendations and collect data on the selected indicators. This can be done through regular reporting, surveys, and data analysis.

5. Analyze the impact: Compare the data collected after the implementation of the recommendations with the baseline data to assess the impact. Use statistical methods to determine if there are significant improvements in access to maternal health.

6. Adjust and refine: Based on the findings, make adjustments and refinements to the recommendations as needed. Continuously monitor and evaluate the impact to ensure sustained improvements in access to maternal health.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make evidence-based decisions for further interventions.

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