Retention in care is a major challenge for pregnant and postpartum women living with HIV (PPHIV) in the prevention of mother-to-child HIV transmission (PMTCT) continuum. However, the factors influencing retention from the perspectives of women who have become lost to follow-up (LTFU) are not well described. We explored these factors within an enhanced sub-cohort of the East Africa International Epidemiology Databases to Evaluate AIDS Consortium. From 2018–2019, a purposeful sample of PPHIV ≥18 years of age were recruited from five maternal and child health clinics providing integrated PMTCT services in Kenya. Women retained in care were recruited at the facility; women who had become LTFU (last visit >90 days) were recruited through community tracking. Interview transcripts were analyzed thematically using a social-ecological framework. Forty-one PPHIV were interviewed. The median age was 27 years, 71% were pregnant, and 39% had become LTFU. In the individual domain, prior PMTCT experience and desires to safeguard infants’ health enhanced retention but were offset by perceived lack of value in PMTCT services following infants’ immunizations. In the peer/family domain, male-partner financial and motivational support enhanced retention. In the community/society domain, some women perceived social pressure to attend clinic while others perceived pressure to utilize traditional birth attendants. In the healthcare environment, long queues and negative provider attitudes were prominent barriers. HIV-related stigma and fear of disclosure crossed multiple domains, particularly for LTFU women, and were driven by perceptions of HIV as a fatal disease and fear of partner abandonment and abuse. Both retained and LTFU women perceived that integrated HIV services increased the risk of disclosure. Retention was influenced by multiple factors for PPHIV. Stigma and fear of disclosure were prominent barriers for LTFU women. Multicomponent interventions and refining the structure and efficiency of PMTCT services may enhance retention for PPHIV.

Medical records of pregnant and postpartum women living with HIV and their infants attending a large referral facility in Kenya from 2015 to 2019 were analyzed to identify characteristics associated with retention in care and viral suppression. Women were stratified based on the timing of HIV care enrollment: known HIV-positive (KHP; enrolled pre-pregnancy) and newly HIV-positive (NHP; enrolled during pregnancy). Associations with retention at 18 months postpartum and viral suppression (< 1000 copies/mL) were determined. Among 856 women (20% NHP), retention was 83% for KHPs and 53% for NHPs. Viral suppression was 88% for KHPs and 93% for NHPs, but 19% of women were missing viral load results. In a competing risk model, viral suppression increased by 18% for each additional year of age but was not associated with other factors. Overall, 1.9% of 698 infants with ≥ 1 HIV test result were HIV-positive. Tailored interventions are needed to promote retention and viral load testing, particularly for NHPs, in the PMTCT continuum.

Although an estimated 1.4 million women living with HIV (WHIV) are pregnant each year globally, data describing the effects of the COVID-19 pandemic on postpartum women in low- and middle-income countries (LMICs) are limited. To address this gap, we conducted phone surveys among 170 WHIV ≥18 years and 18–24 months postpartum enrolled in HIV care at the Academic Model Providing Access to Healthcare in western Kenya, and assessed the effects of the pandemic across health, social and economic domains. We found that 47% of WHIV experienced income loss and 71% experienced food insecurity during the pandemic. The majority (96%) of women reported having adequate access to antiretroviral treatment and only 3% reported difficulties refilling medications, suggesting that the program’s strategies to maintain HIV service delivery during the early phase of the pandemic were effective. However, 21% of WHIV screened positive for depression and 8% for anxiety disorder, indicating the need for interventions to address the mental health needs of this population. Given the scale and duration of the pandemic, HIV programs in LMICs should work with governments and non-governmental organizations to provide targeted support to WHIV at highest risk of food and income insecurity and their associated adverse health outcomes.

In the context of a textile industry, where inconsistent maintenance scheduling and disjointed maintenance strategies could lead to breakdowns, reduced efficiency, and safety concerns, the need for reliable maintenance schedules and coherent strategies became paramount. This study endeavored to address this challenge by harnessing the power of the Weibull distribution. Its application involved scrutinizing system data and the time intervals between maintenance operations for critical equipment, with the overarching goal of deriving maintenance schedules and parameters that amplified both reliability and performance. To realize this objective, a methodological approach rooted in the Weibull distribution was employed. The analysis encompassed not only failure data examination but also the calculation of the Mean Time Between Failures (MTBF), offering insights into the system’s reliability. The study delved into the intricate connections among Weibull distribution parameters, hazard functions, and reliability functions. To validate the derived models, an array of techniques such as data fitting, probability plots, and regression analysis were systematically undertaken. Consequently, the study unveiled a spectrum of failure patterns contingent upon the shape parameters identified. These patterns encompassed premature, random, and wear-out failure modes, each necessitating specific maintenance strategies tailored to optimize equipment performance and ensure safety. The calculated MTBF values shed light on the equipment’s reliability, while the derived probability density functions, survival functions, and hazard functions enriched the comprehensive understanding of the system’s behavior. It was established that a shape of 1.46503 implies that most of the failures are associated with early wear-out failure. By pinpointing the failure modes and aligning corresponding maintenance approaches, the study not only enhanced equipment performance but also elevated safety standards.The study also proposed avenues for improving analysis accuracy through diverse data collection, real-time monitoring, and exploring dynamic parameter adjustments to accommodate evolving operational conditions.

Validating maintenance strategies is crucial for industrial equipment reliability. Regression analysis establishes correlations between plans and Mean Time Between Failures (MTBF). This study validates maintenance schedules and parameters for critical equipment in a textile factory using regression analysis of system data and maintenance intervals. Employing the Monte Carlo Simulation technique, it analyzes relationships between input variables (maintenance activities, equipment age, operating conditions) and MTBF. An R-squared value of over 0.70 confirms the significance of the regression model. Survey design identifies critical departments, and real-time equipment failure data supports the methodology. Regression analysis yields a significant model (R-squared = 85.56%) with 18 input variables contributing to MTBF variance. Sensitivity analysis reveals their hierarchical impact. Conclusions emphasize regression analysis’s efficacy in validating maintenance strategies, showcasing the input variables’ significance. Findings underscore tailored maintenance plans and suggest predictive analytics expansion. Recommendations include adaptive strategies, predictive analytics integration, optimal maintenance intervals determination, cost-benefit analyses, and spare parts inventory optimization.