Tuberculosis (TB) remains a public health concern in Kenya despite the massive global efforts towards ending TB. The impediments to TB prevention and care efforts include poor health systems, resource limitations and other sociopolitical contexts that inform policy and implementation. Notably, TB cases are much higher in men than women. Therefore, the political economy analysis (PEA) study provides in-depth contexts and understanding of the gender gaps to access and successful treatment for TB infection.

PEA adopts a qualitative, in-depth approach through key informant interviews (KII) and documentary analysis.

The KIIs were distributed among government entities, academia, non-state actors and community TB groups from Kenya.

The themes identified were mapped onto the applied PEA analysis framework domains. The contextual and institutional issues included gender concerns related to the disconnect between TB policies and gender inclusion aspects, such as low prioritisation for TB programmes, limited use of evidence to inform decisions and poor health system structures. The broad barriers influencing the social contexts for TB programmes were social stigma and cultural norms such as traditional interventions that negatively impact health-seeking behaviours. The themes around the economic situation were poverty and unemployment, food insecurity and malnutrition. The political context centred around the systemic and governance gaps in the health system from the national and devolved health functions.

Broad contextual factors identified from the PEA widen the disparity in targeted gender efforts toward men. Following the development of effective TB policies and strategies, it is essential to have well-planned gendered responsive interventions with a clear implementation plan and monitoring system to enhance access to TB prevention and care.

The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland.

We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021.

Our validation dataset comprised 465 058 individuals, aged 19–100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell’s C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death.

We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.