Understanding the impact of COVID-19 on women's access to and experiences of contraceptive services in England: a qualitative study.

BACKGROUND: The COVID-19 pandemic response prompted rapid changes to how contraceptive services were delivered in England. Our aim was to examine women's experiences of accessing contraceptive services since March 2020 and to understand any inequalities of access. METHODS: We conducted telephone interviews with 31 women aged 17-54 years who had accessed contraceptive services in England since March 2020. The sample was skewed to include participants with lower educational attainment and higher deprivation. Interview transcripts were thematically analysed using inductive and deductive approaches. RESULTS: Few differences were found regarding educational attainment. Participants using contraceptive injections (all living in areas in the most deprived quintile) reported the greatest access challenges. Some switched method or stopped using contraception as a result. More general barriers reported by participants included service closures, unclear booking processes, and lack of appointment availability. Many participants welcomed the flexibility and convenience of remote contraceptive services. However, telephone appointments posed challenges for those at school or living with parents, and some described them as rushed and inconducive to asking questions or raising concerns. Those accessing contraception for the first time or nearing menopause felt they were unable to access sufficient support and guidance during the pandemic. Some participants voiced concerns around the lasting effects of COVID-19 on appointment availability and inadequate service delivery. CONCLUSIONS: Women's experiences of accessing contraceptive services in England since March 2020 are diverse. While remote services were suitable for some, COVID-19 restrictions unequally impacted women depending on their method of contraception and life stage.

Domain-informed variational neural networks and support vector machines based leakage detection framework to augment self-healing in water distribution networks.

The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards 'self-healing' water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test dataset.

Impact of HIV and antiretroviral drug exposure on lung growth and function over 2 years in an African Birth Cohort.

OBJECTIVE: To assess the impact of HIV and antiretroviral exposure without infection on lung growth and function over the first 2 years of life. DESIGN: Prospective observational study of an African birth cohort, Drakenstein Child Health Study. METHOD: Infants enrolled antenatally had lung function measured at 6 weeks, 1 and 2 years. HIV-infected women received antiretroviral therapy (ART) as per local guidelines. The association between HIV and antiretroviral exposure with lung function was assessed using mixed effects modelling. RESULTS: Of 1143 infants born, two HIV-infected infants were excluded from analysis; 909 (80%) infants had lung function collected at 6 weeks [190 (21%) were HIV-exposed uninfected (HEU)]; 782 (69%) at 1 year and 741 (65%) at 2 years. At 6 weeks HEU infants had larger tidal volume compared with HIV-unexposed infants (1.13 ml, confidence interval: 0.02-2.23, P = 0.045). High maternal viral load was associated with a 17% lower expiratory flow over 2 years (0.17, confidence interval 0.00-0.34, P = 0.046). First-line ART initiated during pregnancy was associated with lower infant tidal volume at 6 weeks compared with those who initiated ART before pregnancy (-2.7 ml, -5.31 to -0.10, P = 0.042), and low maternal CD4 cell counts associated with lower infant tidal over 2 years (-11.1 ml, -18.58-3.58, P = 0.004). CONCLUSION: HIV exposure is associated with altered lung function in early life, with a vulnerable HEU subgroup based on maternal disease severity, immunological compromise and ART exposure. These data highlight the importance of ongoing surveillance of respiratory health in HEU children.

Intra-breath measures of respiratory mechanics in healthy African infants detect risk of respiratory illness in early life.

Lower respiratory tract illness (LRTI) is a leading cause of mortality and morbidity in children. Sensitive and noninvasive infant lung function techniques are needed to measure risk for and impact of LRTI on lung health. The objective of this study was to investigate whether lung function derived from the intra-breath forced oscillation technique (FOT) was able to identify healthy infants at risk of LRTI in the first year of life.Lung function was measured with the novel intra-breath FOT, in 6-week-old infants in a South African birth cohort (Drakenstein Child Health Study). LRTI during the first year was confirmed by study staff. The association between baseline lung function and LRTI was assessed with logistic regression and odds ratios determined using optimal cut-off values.Of the 627 healthy infants with successful lung function testing, 161 (24%) had 238 LRTI episodes subsequently during the first year. Volume dependence of respiratory resistance (ΔR) and reactance (ΔX) was associated with LRTI. The predictive value was stronger if LRTI was recurrent (n=50 (31%): OR 2.5, ΔX), required hospitalisation (n=38 (16%): OR 5.4, ΔR) or was associated with wheeze (n=87 (37%): OR 3.9, ΔX).Intra-breath FOT can identify healthy infants at risk of developing LRTI, wheezing or severe illness in the first year of life.