Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years. Several ML and statistical models predicting CVD time-to-event outcomes have been developed. However, it is not known as to which of the two model types—ML and statistical models—have higher discrimination and calibration in this regard. Hence, this planned work aims to systematically review studies that compare ML with statistical methods in terms of their predictive abilities in the case of time-to-event data with censoring.

Original research articles published as prognostic prediction studies, which involved the development and/or validation of a prognostic model, within a peer-reviewed journal, using cohort or experimental design with at least a 12-month follow-up period will be systematically reviewed. The review process will adhere to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist.

Ethical approval is not required for this review, as it will exclusively use data from published studies. The findings of this study will be published in an open-access journal and disseminated at scientific conferences.

CRD42023484178.

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.