Aches and pains, as well as mental health conditions, are one of the biggest causes of work absence. Most people return-to-work reasonably quickly after an episode of healthcare, but around 10 in 100 go on to have a longer-term absence of more than 12 months.
If a sickness absence lasts more than 7 days and sick pay is required, a General Practitioner (GP) can issue a fit note, that contains their recommendations about a potential return to work. Fit note information is recorded in primary care electronic health records. Having access to CPRD allows us to access fit note data, and use it as a measure of work absence, to investigate work absence patterns over time. This is important, because during a consultation with a GP it is often difficult to tell who is at risk of longer-term absence. So we plan to use fit note data in patients with pain or a mental health condition to see if we can find common patterns of work absence such as having a long absence or returning to work quickly.
We also want to see if the health and sociodemographic characteristics of a person affect their chances of following a particular work absence pattern. For example, are people living in more disadvantaged neighbourhoods more likely to have a longer-term work absence?
Ultimately, the goal is to allow GPs to give more specific support to their patients at first consultation, to aid the return-to-work process.
BACKGROUND
Ability to work is one of the biggest drivers of social inequalities, leading to adverse health and social outcomes. Absence from work due to musculoskeletal and/or mental health conditions accounts for the majority of healthcare costs and productivity losses. Most people return-to-work relatively quickly following an episode of healthcare, but approximately 10% go on to have a longer-term work absence of > 12 months.
Fit notes are statements issued by GPs that record their medical recommendations regarding a potential return-to-work for patients absent for more than 7 days. They are recorded in primary care electronic health records; access to such data potentially allows uncovering of patterns of work absence over time (trajectories). Knowledge of these trajectories and associated characteristics could help GPs better distinguish patients at higher risk of sustained long-term work absence during initial consultation, and thus potentially offer earlier and more targeted intervention to such patients.
AIMS AND OBJECTIVES
1) To derive, and compare using different statistical methods, common longitudinal trajectories of work absence as measured by receipt of fit notes, for a population consulting their GP with a musculoskeletal and/or mental health condition
2) To derive health and sociodemographic characteristics associated with these trajectories
METHODS
For a population absent from work due to musculoskeletal and/or mental health conditions:
Study 1: Derivation of rates and duration of work absence (2010-2021), with differences examined by: sociodemographic characteristics (age, sex, and geographic region).
Study 2: Derivation of trajectories of work absence (2016-2018); contrasted using simple methods of modelling trajectories, against more complex approaches (such as different types of latent class analysis).
Study 3: Multivariable multinomial regression analyses to test association of each derived trajectory with the sociodemographic characteristics specified in study 1, as well as deprivation status, health characteristics, comorbidity, and treatment received.
STUDY 1
Rates and duration of work absence
STUDY 2
Trajectories of work absence (derivation)
STUDY 3
Trajectories of work absence (association of characteristics)
Gwenllian Wynne-Jones - Chief Investigator - Keele University
Amardeep Legha - Corresponding Applicant - Keele University
Clare Holdsworth - Collaborator - Keele University
James Bailey - Collaborator - Keele University
Kelvin Jordan - Collaborator - Keele University
Victoria Welsh - Collaborator - Keele University
Patient Level Index of Multiple Deprivation