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Additional file 1: Supplementary file 1. Description of how frailty, vulnerability and robustness defined. Supplementary Table 1. Frailty Phenotype. Supplementary Table 2. Frailty Index. Supplementary Table 3. Edmonton Frailty Scale. Supplementary Table 4a. Multiple linear regression of FP by BATT. Model 3. Supplementary Table 4b. Multiple linear regression of Frailty Phenotype by Low Muscle Mass. Model 3. Supplementary Table 4c. Multiple linear regression of Frailty Phenotype by Sarcopenia. Model 3. Supplementary Table 5a. Multiple linear regression of Frailty Index by BATT. Model 3. Supplementary Table 5b. Multiple linear regression of Frailty Index by Low Muscle Mass. Model 3. Supplementary Table 5c. Multiple linear regression of Frailty Index by Sarcopenia. Model 3. Supplementary Table 6a. Multiple linear regression of Edmonton Frailty Scale by BATT. Model 3. Supplementary Table 6b. Multiple linear regression of Edmonton Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 6c. Multiple linear regression of Edmonton Frailty Scale by Sarcopenia. Model 3. Supplementary Table 7a. Multiple linear regression of Clinical Frailty Scale by BATT. Model 3. Supplementary Table 7b. Multiple linear regression of Clinical Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 7c. Multiple linear regression of Clinical Frailty Scale by Sarcopenia. Model 3. Supplementary Table 8a. Multivariable logistic regression of Frailty Phenotype by BATT. Model 3. Supplementary Table 8b. Multivariable logistic regression of Frailty Phenotype by Low Muscle Mass. Model 3. Supplementary Table 8c. Multivariable logistic regression of Frailty Phenotype by Sarcopenia. Model 3. Supplementary Table 9a. Multivariable logistic regression of Frailty Index by BATT. Model 3. Supplementary Table 9b. Multivariable logistic regression of Frailty Index by Low Muscle Mass. Model 3. Supplementary Table 9c. Multivariable logistic regression of Frailty Index by Sarcopenia. Model 3. Supplementary Table 10a. Multivariable logistic regression of Edmonton Frailty Scale by BATT. Model 3. Supplementary Table 10b. Multivariable logistic regression of Edmonton Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 10c. Multivariable logistic regression of Edmonton Frailty Scale by Sarcopenia. Model 3. Supplementary Table 11a. Multivariable logistic regression of Clinical Frailty Scale by BATT. Model 3. Supplementary Table 11b. Multivariable logistic regression of Clinical Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 11c. Multivariable logistic regression of Clinical Frailty Scale by Sarcopenia. Model 3. Supplementary Table 12. Sensitivity Analyses of Frailty Score by multiple linear regression models in reverse order. Supplementary Table 13. Sensitivity analyses of multivariable logistic regression models of frailty. Multivariable models reversed.

Additional file 1.

Solving complex challenges requires data collected for different purposes and from different sources and geographical locations. However, combining data is challenging. There are public concerns about data security/access. Data governance (the legal and ethical framework for data access) is critical. There are technical challenges when combining data collected in different data languages and building secure computer networks which enable collaborative work but protect privacy. FED-NET builds on the operational Trusted Research Environments (TREs) offered by PIONEER, the acute care data hub. A TRE is a secure database, with complete control over what data is placed in or removed from the system and how it is analysed. FED-NET tested whether federated analytics could offer a scalable solution to the technical and governance challenges of analysing datasets separated by geography and data language. Federated analytics is where different datasets are stored within separate TREs, and instead of the data moving to one location for analysis, the analytical code is sent to each TRE and the results pooled. This work was funded by UK Research & Innovation [Grant Number: MC_PC_21028] as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK)programme, which is delivered in partnership with Health Data Research UK (HDR UK) and ADR UK (Administrative Data Research UK).

Objective: Financial incentives are often applied to motivate desirable performance across organisations in healthcare systems. In the 2016/17 financial year, the National Health Service (NHS) in England set a national performance-based incentive to increase uptake of the influenza vaccination amongst front-line staff. Since then, the threshold levels needed for hospital trusts to achieve the incentive (i.e., the targets) have ranged from 70% to 80%. The present study examines the impact of this financial incentive across eight vaccination seasons.\ud \ud Design: A retrospective observational study examining routinely recorded rates of influenza vaccination amongst staff in all acute NHS hospital trusts across eight vaccination seasons (2012/13-2019/20). The number of trusts included varied per year, from 127 to 137, due to organisational changes. McCrary’s density test is conducted to determine if the number of hospital trusts narrowly achieving the target by the end of each season is higher than would be expected in the absence of any responsiveness to the target. We refer to this bunching above the target threshold as a “threshold effect”.\ud \ud Results: In the years before a national incentive was set, 9%-31% of NHS Trusts reported achieving the target, compared with 43%-74% in the four years after. Threshold effects did not emerge before the national incentive for payment was set; however, since then, threshold effects have appeared every year. Some trusts report narrowly achieving the target each year, both as the target rises and falls. Threshold effects were not apparent at targets for partial payments.\ud \ud Conclusions: We provide compelling evidence that performance-based financial incentives produced threshold effects. Policymakers who set such incentives are encouraged to track threshold effects since they contain information on how organisations are responding to an incentive, what enquiries they may wish to make, how the incentive may be improved and what unintended effects it may be having.

Additional file 1. STROBE Statement—Checklist of items that should be included in reports of cohort studies.

Additional file 2: Supplementary Table 1. Complete linear regression analysis of Waterlow grading and length of stay (in days) after kidney transplantation. Supplementary Table 2. Complete logistic regression analysis of Waterlow grading and emergency 90-day readmission after kidney transplantation. Supplementary Table 3. Complete Cox proportional hazard regression analysis of Waterlow grading and mortality after kidney transplantation.

Additional file 1. Comorbidity codes associated with an altered risk of mortality, where prevalence of the comorbidity code changedin those with a primary diagnosis of septicaemia.