To construct a data-driven composite from (a subset of) currently used quality indicators for oesophagogastric cancer surgery and to evaluate whether this approach enhances the reliability of between-hospital comparisons on outcome relative to the expert-driven composite indicator ‘textbook outcome (TO)’.

In this retrospective cohort study, we applied Item Response Theory (IRT) to construct a data-driven continuous composite indicator reflecting a single latent variable—the quality of surgical care—and estimated latent variable scores for all individual patients. Reliability was compared between the expert-driven (TO) and data-driven (IRT) composite indicators.

All Dutch hospitals providing oesophagogastric cancer surgery.

All patients who underwent oesophagectomy (n=3588) or gastrectomy (n=1782) between 2018 and 2022 as registered in the Dutch Upper GI Cancer Audit (DUCA).

We evaluated the reliability of between-hospital comparisons using ‘rankability’, which quantifies the proportion of observed variation in indicator scores between hospitals not attributable to chance.

Seven out of 15 quality indicators were included in the IRT composite indicator. Most of the patients were assigned the artificial maximum of the continuous quality score (ie, ceiling effect), resulting in similar average hospital scores. Relative to TO, rankability increased when using the IRT composite for oesophagectomy (57% vs 41%) but declined for gastrectomy (38% vs 47%).

The selected seven quality indicators for oesophageal and gastric cancer surgery represent a single latent variable but are not yet optimal for differentiating surgical care quality due to ceiling effects. Despite using fewer indicators, the continuous IRT score showed a promising increase in rankability for oesophagectomy, suggesting that data-driven composite indicators may enhance hospital benchmarking reliability.

Breast cancer is the most common cancer among women globally. While the impact of lifestyle factors like smoking and obesity on breast cancer risk and survival is well documented, the effect of working conditions is not fully understood. Moreover, breast cancer can reduce employability, making it crucial to identify factors that facilitate return to work and improve life satisfaction. Since breast cancer is affected by sleep and lifestyle, which are related to working conditions, understanding how they affect breast cancer outcomes is key. This study aims to explore the relationship between working conditions and breast cancer outcomes, including incidence, mortality and survival within a causal framework. Our specific aims are to understand the relationship between (1) working conditions and occupational groups and breast cancer outcomes, including the extent to which sleep, lifestyle and breast cancer screening uptake explain these relationships and (2) prediagnosis working conditions, sleep and lifestyle and their effect on return to work and life satisfaction among breast cancer survivors.

We will use data from the UK Biobank, a large-scale cohort study with data on 273 825 women between 40 and 69 years old at baseline, followed from 2006 to 2022. The data has been linked with death and cancer registries and includes 8309 incident breast cancer cases. To quantify the effect of working conditions on breast cancer outcomes (aim 1) and their effect on return to work and life satisfaction (aim 2), we will implement g-methods to estimate the average causal effect and employ counterfactual-based mediation analysis to quantify how much mediating factors, such as sleep and lifestyle, explain this effect.

UK Biobank received ethical approval from the North West Multi-Centre Research Ethics Committee. No further ethical approval was required for the proposed research project. In line with the two aims, four original research manuscripts will be published in open-access peer-reviewed journals to disseminate the findings. In addition, findings will be disseminated at international conferences and scientific meetings.