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by Megan Wiggins, Marie Varughese, Ellen Rafferty, Sasha van Katwyk, Christopher McCabe, Jeff Round, Erin Kirwin
BackgroundDuring public health crises such as the COVID-19 pandemic, decision-makers relied on infectious disease models to evaluate policy options. Often, there is a high degree of uncertainty in the evidence base underpinning these models. When there is increased uncertainty, the risk of selecting a policy option that does not align with the intended policy objective also increases; we term this decision risk. Even when models adequately capture uncertainty, the tools used to communicate their outcomes, underlying uncertainty, and associated decision risk have often been insufficient. Our aim is to support infectious disease modellers and decision-makers in interpreting and communicating decision risk when evaluating multiple policy options.
MethodsWe developed the Decision Uncertainty Toolkit by adapting methods from health economics and infectious disease modelling to improve the interpretation and communication of uncertainty. Specifically, we developed a quantitative measure of decision risk as well as a suite of risk visualizations. We refined the toolkit contents based on feedback from early dissemination through conferences and workshops.
ResultsThe Decision Uncertainty Toolkit: (i) adapts and extends existing health economics methods for characterization, estimation, and communication of uncertainty to infectious disease modelling, (ii) introduces a novel risk measure that quantitatively captures the downside risk of policy alternatives, (iii) provides visual outputs for dissemination and communication of uncertainty and decision risk, and (iv) includes instructions on how to use the toolkit, standard text descriptions and examples for each component. The use of the toolkit is demonstrated through a hypothetical example.
ConclusionThe Decision Uncertainty Toolkit improves existing methods for communicating infectious disease model results by providing additional information regarding uncertainty and decision risk associated with policy alternatives. This empowers decision-makers to consider and evaluate decision risk more effectively when making policy decisions. Improved understanding of decision risk can improve outcomes in future public health crises.
This study aims to determine key workforce variables (demographic, health and occupational) that predicted National Health Service (NHS) staff’s absence due to illness and expressed intention to leave their current profession.
Staff from 18 NHS Trusts were surveyed between April 2020 and January 2021, and again approximately 12 months later.
Logistic and linear regression were used to explore relationships between baseline exposures and four 12-month outcomes: absence due to COVID-19, absence due to non-COVID-19 illness, actively seeking employment outside current profession and regularly thinking about leaving current profession.
22 555 participants (out of a possible 152 286 employees; 15%) completed the baseline questionnaire. 10 831 participants completed the short follow-up questionnaire at 12 months and 5868 also completed the long questionnaire; these participants were included in the analyses of sickness absence and intention to leave, respectively. 20% of participants took 5+ days of work absence for non-COVID-19 sickness in the 12 months between baseline and 12-month questionnaire; 14% took 5+ days of COVID-19-related sickness absence. At 12 months, 20% agreed or strongly agreed they were actively seeking employment outside their current profession; 24% thought about leaving their profession at least several times per week. Sickness absence (COVID-19 and non-COVID-19 related) and intention to leave the profession (actively seeking another role and thinking about leaving) were all more common among NHS staff who were younger, in a COVID-19 risk group, had a probable mental health disorder, and who did not feel supported by colleagues and managers.
Several factors affected both workforce retention and sickness absence. Of particular interest are the impact of colleague and manager support because they are modifiable. The NHS workforce is likely to benefit from training managers to speak with and support staff, especially those experiencing mental health difficulties. Further, staff should be given sufficient opportunities to form and foster social connections. Selection bias may have affected the presented results.
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