Trajectories of work absence in England due to a musculoskeletal or mental health condition: an electronic health record cohort study

Trajectories of work absence in England due to a musculoskeletal or mental health condition: an electronic health record cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trajectories of work absence in England due to a musculoskeletal or mental health condition: an electronic health record cohort study Amardeep Legha, James Bailey, Victoria K Welsh, Kelvin P Jordan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907087/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Journal of Occupational Rehabilitation → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose To derive common patterns (trajectories) of work absence over time due to a musculoskeletal (MSK) or mental health (MH) condition in an English population and determine associations of these absence trajectories with health and sociodemographic characteristics. Methods This retrospective cohort study used primary care data for 43,130 and 62,355 economically active individuals with an incident work absence (as measured by receipt of fit notes) due to a MSK or MH condition, respectively, between 2016–2018. Latent class growth analysis was used to define trajectories (through issuance of fit notes), and trajectory-covariate association analysis performed through multivariable multinomial logistic regression. Results Five common trajectories of work absence associated with MSK and MH conditions were determined over a one-year follow-up. The two most common trajectories consisted of low absence (a ‘Single’ fit note and ‘Short Term’ absence), whilst the two least common trajectories were characterised by longer-term absence of six months or more (‘Chronic Sustained’ and ‘Chronic Fast Decreasing’), and the fifth by intermittent absence. Individuals associated with the two longer-term absence trajectories were: older, living in the North or Midlands or most deprived areas of England, prescribed opioids, and current smokers. Conclusions This study has highlighted different patterns of sickness absence due to a MSK or MH condition and profiles of individuals associated with longer-term absence. Earlier and more targeted health and work intervention towards these high-risk subgroups, alongside policy interventions to reduce health inequalities, could help alleviate the rising rate of long-term sickness absence and economic inactivity. Sickness Absence Fit Notes Musculoskeletal Conditions Mental Health Conditions Trajectories Latent Class Analysis Figures Figure 1 Introduction Long-term sickness absence has been rising in the UK. Between spring 2019 and summer 2022, the number of working-age adults that were economically inactive (not working nor seeking employment) due to long-term sickness absence increased by half a million [ 1 ]. Being in work is generally beneficial for physical and mental health, whilst being off work is associated with poorer health and well-being [ 2 ]. Whilst the majority of people with a sickness absence tend to return-to-work (RTW) quickly, approximately 10% go on to have longer-term absences of > 12 months [ 3 ]. Once an individual is on a long-term absence, it becomes progressively harder to RTW, and more adverse health and social problems are experienced [ 2 ]. In the UK, if an individual is off work for more than seven consecutive days due to a medical problem, a ‘fit note’ is issued from a healthcare professional that details the medical advice the individual has received regarding their fitness to work and can be used to claim statutory sick pay through the state [ 4 ],[ 5 ]. Fit notes are recorded in primary care as electronic health records. The most common reason for fit note issuance in England is due to a mental health (MH) condition, followed by a musculoskeletal (MSK) condition (with 37.6% and 17.5% of all fit notes issued between April 2021 and March 2024 due to MH and MSK conditions, respectively) [ 6 ]. Many existing studies of individuals with a sickness absence involve use of a dichotomous absence measure (absent / not absent) and analysis through either a cross-sectional approach based on a single time point (using methods such as logistic regression), or a time-to-event approach (such as Cox regression) [ 7 ],[ 8 ]. However, RTW is a complex and dynamic process that changes with time, and both the cross-sectional and time-to-event approaches may be sub-optimal in capturing this as they treat RTW as a fixed status. In contrast, trajectory analysis is an approach that allows for repeated measures of absence data to be used to assign individuals into common subgroups (trajectories) based on duration and number of RTW spells. During an initial consultation with a healthcare professional for sickness absence, it is challenging to determine which patients are at the highest risk of a sustained long-term work absence; determining common trajectories of work absence and the patient characteristics associated with them may assist with this. Aims This study has two main aims: 1) To derive common longitudinal trajectories of work absence as measured by receipt of fit notes, for a population consulting their healthcare professional with a MSK or MH condition 2) To identify health and sociodemographic characteristics associated with longer-term absence trajectories Methods Defining the Study Population The study used data from a large UK primary care database: the Clinical Practice Research Datalink (CPRD) Aurum [9], and the study was approved by the CPRD Research Governance (protocol reference number 21_000665 made available to reviewers). The February 2022 release of CPRD Aurum (https://doi.org/10.48329/gcgx-f815) [10] was used, which contained over 13.4 million individuals that were currently registered in UK practices (this corresponded to 19.9% of the UK population). CPRD Aurum has been shown to be representative of the general English population in terms of age and gender, as well as deprivation and geographical spread (comparing a mid-2017 snapshot of CPRD Aurum data to mid-2017 data on the broader English population from the Office for National Statistics) [9]. The study population was based in English practices, aged 16-66 at time of a first ever recorded fit note (defined through searching a person’s lifetime history of fit notes recorded in the CPRD Aurum database) due to either a MSK or MH condition between 2016-2018, and had at least two years prior registration at their practice. As reason for fit note data is not available in CPRD, an assumption was made that the fit note could be attributed to a MSK or MH condition if there was a recorded MSK or MH consultation, on the day of, or in the two weeks prior to first ever fit note issuance. MSK and MH conditions were defined using Read/SNOMED codes developed in a previous CPRD study [11]. MSK conditions were osteoarthritis, inflammatory MSK, and the most common regional pain (back pain, knee pain, hip pain, hand/wrist pain). MH conditions were depression, anxiety, and stress. These code lists are publicly available on the Keele Research Repository (https://doi.org/10.21252/878s-x990). Included patients were censored at earliest of three years of follow-up after index fit note date, age ≥67 years (defined using year of birth and subtracting from year of follow-up), death, de-registration, and last collection date. Defining Trajectory Follow-Up and Interval Lengths As fit note duration data was largely not available in the CPRD Aurum database, the repeated fit note measure used to assess patterns of absence was a binary yes/no for a recorded fit note issuance (coded as 1/0) in each given time interval during follow-up. The initial MSK or MH condition incident fit note was excluded from the trajectory definition, as all individuals received this. Five different approaches to follow-up and interval length were used in this study to identify the optimal method for analysis (Table 1). A minimum of two-monthly time interval lengths were chosen to avoid underestimating recurrent fit note issuance (around 50% of all fit notes issued in England due to a MSK or MH condition in England have a duration of more than one month [12], therefore, choosing monthly intervals would be too short). Table 1 . The Five Interval Approaches Used for Trajectory Derivation in this Study Approach Follow-up Length Year One Time Interval Length Years Two-Three Time Interval Length 1 (Short-Term) One Year Three-Monthly N/A 2 (Short-Term) One Year Two-Monthly N/A 3 (Long-Term) Three Years Six-Monthly Six-Monthly 4 (Long-Term) Three Years Three-Monthly Six-Monthly 5 (Long-Term) Three Years Two-Monthly Six-Monthly Abbreviations: N/A = Not Applicable Model Building Strategy The trajectory models considered in this study were based on Latent Class Growth Analysis (LCGA). LCGA allows two or more trajectories ‘classes’ to be fitted to a study population. The probability of belonging to a trajectory class (or cluster), known as posterior probability, is estimated for each individual using the observed data, and the individual is assigned to the class that they have the greatest probability of belonging to. LCGA assumes that individuals follow the trajectory class that they are assigned to. The model building strategy began with interval approach 1 (Table 1) for the index MSK condition cohort (follow up of 1 year broken down into 4 three-monthly intervals), and initially with a two-class LCGA model. The number of classes in succeeding LCGA models were then progressively increased by one. The following criteria were used to indicate better performing models: Akaike Information Criterion (AIC) [13] and Bayesian Information Criterion (BIC) [14] (lower values indicating better model fit); Statistically significant likelihood ratio tests (both the Lo-Mendell-Rubin [15] and the bootstrapped [16] likelihood ratio tests were used) – this indicates if a ( k class) LCGA model fits the data better than a corresponding LCGA model with one fewer class (i.e., k -1 classes). Entropy and average posterior probabilities ≥0.7 (indicating better trajectory class separability) Minimum trajectory class prevalence ≥1% (to reduce the possibility of uncovering spurious trajectory classes) Graphical assessment of observed compared to predicted probabilities of fit note issuance within trajectories was also performed to assess model fit, and trajectories were evaluated for interpretability and clinical meaningfulness. Finally, when comparing two competing models with k and k +1 classes, if all of the criteria mentioned thus far were similar, the more parsimonious model (i.e., with fewer trajectory classes) was selected. All LCGAs were fitted with a linear functional form. Next, this strategy was repeated for individuals with an index MSK fit note for the four remaining approaches (Table 1). This process was repeated for individuals with a first ever fit note due to a MH condition. For the two selected optimal LCGA trajectory models, a summary of the observed patterns of fit note issuance for individuals within a trajectory class were derived, to assess individual variability of the final trajectory classes. The reporting in this study was carried out in line with the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist [17]. Stata MP version 17.0 (StataCorp, College Station, TX, USA) and Mplus Version 8.9 (Muthén & Muthén, Los Angeles, CA, USA) were used for the analyses in this study. In an effort to limit convergence issues, all trajectory analyses involved use of 500 random sets of starting values with 100 iterations [18]. Any absence measurement data that was missing due to censoring before end of follow-up was assumed to be Missing At Random, and Full Information Maximum Likelihood estimation was used to incorporate the non-missing repeated absence measurement data of such individuals (i.e., individuals were retained in the analysis as long as they had some non-missing data). A complete case sensitivity analysis to retain only individuals with complete follow-up data was also performed and did not change findings (results omitted for brevity). Characteristics Tested for Association with Trajectories In total 14 characteristics (see Table 2 for definitions and summaries) were included in the association analysis with the derived optimal absence trajectories: Sociodemographic Characteristics (4 variables) Health Characteristics (4 variables) Types of Treatment Received (4 variables) Comorbidity (2 variables) Table 2 . Summary of the n=14 Characteristics Explored in this Study Type of Characteristic Characteristic Category Definitions MSK Index Fit Note MH Index Fit Note Sociodemographic Sex Female, Male ✔ ✔ Age 16-25, 26-35, 36-45, 46-55, 56-65 (years) ✔ ✔ Region a North of England, Middle of England, South of England ✔ ✔ IMD b 1-5, Missing ✔ ✔ Health Number of MSK Consultations - Prior 2 Years c 0, 1, 2, ≥3 ✔ ✗ Number of MH Consultations - Prior 2 Years d 0, 1, 2, ≥3 ✗ ✔ Baseline MSK Condition Back pain, Knee pain, Hand/Wrist pain, Hip pain, Inflammatory MSK, Osteoarthritis ✔ ✗ Baseline MH Condition Stress, Anxiety, Depression, Anxiety and Depression ✗ ✔ Smoking Status e Never, Current, Ex Smoker, Not Recorded ✔ ✔ BMI f Underweight/Normal, Overweight, Obese, Not Recorded ✔ ✔ Types of Treatment Received g Opioids Yes, No ✔ ✔ NSAIDs Yes, No ✔ ✔ Gabapentinoids Yes, No ✔ ✔ Antidepressants Yes, No ✔ ✔ Comorbidity Polypharmacy h 0, 1-4, 5-9, ≥10 ✔ ✔ CCI Score g 0, 1, ≥2 ✗ ✔ Modified CCI Score i 0, 1, ≥2 ✔ ✗ Abbreviations: IMD = Index of Multiple Deprivation; MSK = Musculoskeletal; MH = Mental Health; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index a This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: North East, North West, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: South East, South West, London b Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas c Defined as the number of MSK consultations in the two years prior to index fit note date (excluding the index MSK consultation) d Defined as the number of MH consultations in the two years prior to index fit note date (excluding the index MH consultation) e In the five years prior to index fit note date and latest data retained f Underweight/Normal: 10<=BMI<25; Overweight: 25<=BMI<30; Obese: 30<=BMI<80; Not Recorded: no BMI data available or BMI=80. BMI data were searched for in the five years prior to index fit note date and latest data retained g In the two years prior to index fit note date h Defined as a drug count (excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants) in the two years prior to index fit note date i In the two years prior to index fit note date, excluding Rheumatic Disease All of these characteristics were accessed directly from the CPRD Aurum database, except Index of Multiple Deprivation (IMD) [19] which was accessed through a data-linkage. The latest version of patient level linked IMD data was used (the 2019 version) and in quintile format; this is a relative measure, rather than absolute, that compares levels of deprivation of English neighbourhoods at a lower-layer super output area level. Trajectory-Covariate Association Analyses S eparately, for the index MSK and MH condition fit note cohorts, multivariable multinomial logistic regression was used to test for association between the sociodemographic and health characteristics with the optimal trajectories identified. The least severe absence trajectory class was used as the reference. To preserve the original optimal trajectory classes a three-step approach was used for this analysis, as developed by Vermunt (2010) [20]. This was achieved by first estimating the original optimal derived trajectory classes (step 1, as performed earlier in Methods Section), then adding relevant covariate data (step 2), and finally performing the multinomial logistic regression analysis by accounting for the uncertainty in class membership through specifying uncertainty rates associated with the derived classes (step 3). Adjusted odds ratios (ORs) of association with 95% confidence intervals (CIs) are presented. Patient and Public Involvement A patient and public involvement and engagement meeting was held to disseminate the findings of this study to members of the public with lived experience of work absence, and to seek their feedback on the interpretation of our study results. Results There were 43,130 people included in this study with an initial MSK condition fit note (45.5% female), and 62,355 people with an initial MH condition fit note (59.2% female). Descriptive statistics are presented in Supplementary Materials S1. Optimal Trajectories of Work Absence The optimal trajectory models chosen in this study were the same for both the index MSK and MH condition fit note cohorts: a five-class LCGA, under interval approach 2 (based on two-monthly recurring intervals of a one-year follow-up post index fit note date). The process to choose the optimal models is explained in Supplementary Materials S2. The optimal LCGA models are displayed graphically in Figure 1, and corresponding model fit and class meaningfulness statistics presented in Supplementary Materials S3. In particular, these five-class models converged without issues, had a likelihood ratio test p value <0.0001 (based on both the Lo-Mendell-Rubin and the bootstrapped approaches; suggesting the five-class model fitted the data better than a four-class model), and had entropy and average posterior probabilities above our study guideline threshold of 0.7 for all trajectory classes, indicating good class separability. These optimal models also contained a variety of different and plausible trajectory class shapes that made sense in a work absence context (Figure 1). The trajectory shapes of these five class models were similar across the two cohorts, and consisted first of two classes characterised by low sickness absence throughout follow-up: ‘Single’, whereby the probability of fit note issuance remained close to 0 for all two-monthly intervals in the first year of follow-up (MSK cohort 45.5% of the population, MH cohort 32.1%) ‘Short Term’, whereby there was an initial high probability of fit note issuance in the first two months post index fit note (of around 0.85-0.90), which then decreased sharply to under 0.25 between months three and four of follow-up, and from months five to twelve were close to 0 (MSK 27.7%, MH 36.5%). Then there were two classes characterised by a high probability of absence: ‘Chronic Sustained’, whereby the probability of fit note issuance remained largely high (between 0.68 to 0.94) throughout all two-monthly intervals in the first year of follow-up (MSK 3.7%, MH 5.6%). ‘Chronic Fast Decreasing’, this trajectory started off similar to the ‘Chronic Sustained’ class, with a high and sustained probability (of close to 1) of fit note issuance in the first six months of follow-up. However, from months seven to eight there was a rapid decrease to around 0.3 probability of fit note issuance, and then from months nine to twelve this probability dropped close to 0 (MSK 2.6%, MH 5.5%). The final derived trajectory was: ‘Intermittent Low’, here the probability of fit note issuance was low, between 0.2 and 0.45 throughout all two-monthly intervals in the first year of follow-up. Generally, individuals in this trajectory had either one or two recorded fit notes during the one-year follow up period (MSK 20.6%, MH 20.4%). The variability in patterns of fit note issuance over the 12 months follow-up within each of the derived classes is shown in Supplementary Material S4. Apart from the ‘Intermittent Low’ trajectory class, the other four trajectory classes exhibited low variability in patterns of fit note. Characteristics Associated with MSK Condition Absence Trajectories For the MSK cohort, statistically significant adjusted associations common to both of the two most severe absence trajectories (‘Chronic Sustained’ and ‘Chronic Fast Decreasing’) were (Table 3): Older age Living in the North of England or the Midlands Living in the most deprived areas of England Having more (≥2) prior MSK consultations Having baseline knee pain, osteoarthritis, or hip pain, compared to back pain Being prescribed opioids, gabapentinoids, or antidepressants in the two years prior to index fit note Being current smokers Table 3 . Characteristics Associated with Optimal Trajectories of Work Absence Due to a MSK Condition Using the ‘Single’ Trajectory Class as the Reference (Adjusted Model) Trajectory Class Chronic Sustained a Chronic Fast Decreasing a Intermittent Low a Short Term a n=1,261 n=1,333 n=7,272 n=11,154 Sex Male 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Female 0.93 (0.81, 1.07) 1.17 (1.01, 1.37) 1.15 (1.06, 1.24) 1.10 (1.03, 1.17) Age 16-25 years 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 26-35 years 1.38 (1.02, 1.87) 1.04 (0.79, 1.37) 1.00 (0.88, 1.14) 1.24 (1.13, 1.36) 36-45 years 1.67 (1.25, 2.23) 1.15 (0.88, 1.50) 0.84 (0.74, 0.95) 1.15 (1.05, 1.26) 46-55 years 2.10 (1.59, 2.77) 1.42 (1.10, 1.85) 0.83 (0.73, 0.93) 1.37 (1.24, 1.50) 56-66 years 3.26 (2.47, 4.30) 2.38 (1.83, 3.10) 1.04 (0.90, 1.20) 1.54 (1.38, 1.71) Region b South of England 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) North of England 1.36 (1.16, 1.60) 1.70 (1.45, 2.00) 1.02 (0.92, 1.13) 1.36 (1.27, 1.46) Middle of England 1.45 (1.24, 1.70) 1.24 (1.02, 1.51) 1.13 (1.03, 1.25) 1.20 (1.11, 1.29) IMD c 1 (least deprived) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 2 1.02 (0.78, 1.34) 1.17 (0.90, 1.54) 1.15 (0.99, 1.34) 1.02 (0.92, 1.12) 3 1.22 (0.94, 1.60) 1.19 (0.91, 1.55) 1.32 (1.15, 1.52) 1.01 (0.92, 1.12) 4 1.68 (1.30, 2.16) 1.27 (0.97, 1.66) 1.61 (1.41, 1.84) 0.99 (0.90, 1.09) 5 (most deprived) 2.50 (1.97, 3.17) 1.64 (1.28, 2.09) 1.78 (1.55, 2.04) 0.95 (0.87, 1.05) Missing 1.22 (0.79, 1.90) 0.38 (0.15, 0.97) 0.75 (0.53, 1.07) 1.30 (1.12, 1.52) MSK Consultations - Prior 2 Years d 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1 1.27 (1.07, 1.51) 1.06 (0.87, 1.29) 1.06 (0.96, 1.17) 1.09 (1.01, 1.17) 2 1.63 (1.30, 2.03) 1.70 (1.35, 2.14) 1.29 (1.13, 1.47) 1.21 (1.09, 1.35) ≥3 1.81 (1.44, 2.27) 1.48 (1.16, 1.90) 1.47 (1.29, 1.67) 1.3 (1.17, 1.44) Baseline MSK Condition Back pain 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Knee pain 1.34 (1.11, 1.62) 1.47 (1.21, 1.79) 1.34 (1.21, 1.49) 0.95 (0.87, 1.03) Hand/wrist pain 1.01 (0.72, 1.41) 1.21 (0.87, 1.69) 1.02 (0.86, 1.22) 0.81 (0.71, 0.93) Inflammatory MSK 1.09 (0.80, 1.49) 0.95 (0.66, 1.38) 1.06 (0.86, 1.30) 0.78 (0.66, 0.92) Osteoarthritis 2.60 (2.00, 3.39) 1.84 (1.33, 2.53) 2.10 (1.70, 2.59) 0.87 (0.72, 1.05) Hip pain 2.62 (2.00, 3.43) 1.68 (1.18, 2.39) 1.43 (1.15, 1.78) 1.13 (0.95, 1.34) Opioids No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.40 (1.20, 1.65) 1.58 (1.32, 1.90) 1.18 (1.08, 1.30) 1.14 (1.06, 1.23) NSAIDs No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.16 (1.00, 1.35) 1.08 (0.91, 1.28) 1.15 (1.05, 1.26) 1.06 (0.99, 1.14) Gabapentinoids No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.60 (1.22, 2.11) 2.08 (1.58, 2.75) 1.51 (1.24, 1.83) 1.25 (1.05, 1.49) Antidepressants No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.79 (1.52, 2.10) 1.48 (1.23, 1.77) 1.50 (1.35, 1.67) 1.07 (0.98, 1.17) Polypharmacy e 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1-4 0.97 (0.80, 1.17) 0.82 (0.67, 1.01) 1.07 (0.96, 1.20) 0.99 (0.92, 1.07) 5-9 1.26 (1.00, 1.59) 1.14 (0.89, 1.45) 1.36 (1.19, 1.55) 1.01 (0.91, 1.12) ≥10 1.48 (1.13, 1.95) 1.37 (1.00, 1.88) 1.84 (1.54, 2.20) 1.00 (0.86, 1.15) Smoking Status Never 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Current 1.80 (1.53, 2.10) 1.41 (1.19, 1.69) 1.36 (1.25, 1.49) 1.07 (1.00, 1.15) Ex Smoker 1.24 (1.01, 1.52) 1.17 (0.94, 1.46) 0.99 (0.87, 1.11) 1.08 (0.99, 1.17) Not Recorded 1.22 (0.95, 1.57) 1.03 (0.79, 1.34) 0.95 (0.83, 1.08) 1.00 (0.91, 1.10) BMI f Underweight/Normal 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Overweight 0.95 (0.78, 1.17) 0.94 (0.76, 1.18) 1.16 (1.04, 1.30) 1.12 (1.02, 1.22) Obese 0.99 (0.82, 1.21) 0.97 (0.78, 1.20) 1.19 (1.07, 1.33) 1.05 (0.96, 1.14) Not Recorded 1.20 (0.98, 1.48) 1.11 (0.90, 1.37) 1.07 (0.96, 1.20) 1.06 (0.98, 1.15) Modified CCI Score g 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1 1.34 (1.12, 1.61) 0.90 (0.72, 1.13) 1.01 (0.90, 1.14) 0.96 (0.88, 1.05) ≥2 1.28 (0.95, 1.72) 0.89 (0.62, 1.29) 1.10 (0.91, 1.34) 0.95 (0.80, 1.13) Abbreviations: IMD = Index of Multiple Deprivation; MSK = Musculoskeletal; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index Notes: Values are presented as adjusted odds ratios with 95% confidence intervals (adjustments were made for all the other variables shown in the Table) Statistically significant estimates (where 95% CI doesn't include the value 1) are shown in bold a All odds ratios are calculated with respect to the reference trajectory: Single. b This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: Northeast, Northwest, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: Southeast, Southwest, London c Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas d Excluding the index MSK consultation e Excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants f Underweight/Normal: 10<=BMI<25; Overweight: 25<=BMI<30; Obese: 30<=BMI<80; Not Recorded: no BMI data available or BMI=80 g Excluding Rheumatic Disease Characteristics Associated with MH Condition Absence Trajectories In the MH cohort, individuals in the two most absence severe trajectories (‘Chronic Sustained’ and ‘Chronic Fast Decreasing’) were more likely to (Table 4): Be male Be older Live in the North of England or the Midlands Live in any of the three most deprived areas of England (IMD from 3 to 5) Have baseline anxiety, depression, or anxiety and depression combined, compared to stress Be prescribed an opioid in the two years prior to index fit note issue Have excessive polypharmacy Be current smokers Be obese or with a ‘not recorded’ BMI Table 4 . Characteristics Associated with Optimal Trajectories of Work Absence Due to a MH Condition Using the ‘Single’ Trajectory Class as the Reference Trajectory Class Chronic Sustained a Chronic Fast Decreasing a Intermittent Low a Short Term a n=2,881 n=3,848 n=10,835 n=21,534 Sex Male 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Female 0.79 (0.71, 0.87) 0.88 (0.80, 0.96) 1.00 (0.93, 1.07) 0.98 (0.93, 1.03) Age 16-25 years 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 26-35 years 0.83 (0.73, 0.95) 1.05 (0.92, 1.19) 0.85 (0.78, 0.93) 1.38 (1.30, 1.47) 36-45 years 1.18 (1.04, 1.35) 1.52 (1.33, 1.74) 0.79 (0.72, 0.88) 1.63 (1.52, 1.74) 46-55 years 1.66 (1.43, 1.92) 2.17 (1.90, 2.49) 0.91 (0.82, 1.01) 1.80 (1.66, 1.94) 56-66 years 2.30 (1.92, 2.77) 2.72 (2.29, 3.23) 1.08 (0.94, 1.24) 1.87 (1.69, 2.08) Region b South of England 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) North of England 1.31 (1.15, 1.49) 1.65 (1.48, 1.83) 0.99 (0.91, 1.08) 1.34 (1.26, 1.42) Middle of England 1.51 (1.33, 1.72) 1.34 (1.20, 1.50) 0.95 (0.88, 1.03) 1.20 (1.13, 1.27) IMD c 1 (least deprived) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 2 1.21 (1.00, 1.47) 1.19 (1.01, 1.41) 1.12 (1.00, 1.26) 0.96 (0.89, 1.04) 3 1.49 (1.24, 1.79) 1.36 (1.16, 1.60) 1.28 (1.14, 1.43) 0.95 (0.88, 1.02) 4 1.86 (1.55, 2.23) 1.49 (1.27, 1.76) 1.52 (1.36, 1.71) 0.95 (0.88, 1.03) 5 (most deprived) 2.67 (2.24, 3.18) 2.00 (1.71, 2.33) 1.83 (1.64, 2.04) 0.93 (0.86, 1.01) Missing 0.68 (0.48, 0.95) 0.93 (0.72, 1.19) 0.54 (0.41, 0.71) 1.11 (0.99, 1.25) MH Consultations - Prior 2 Years d 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1 1.03 (0.90, 1.17) 1.07 (0.94, 1.22) 1.08 (0.98, 1.18) 0.97 (0.91, 1.04) 2 1.17 (0.98, 1.41) 1.14 (0.95, 1.36) 1.12 (0.99, 1.26) 0.99 (0.90, 1.09) ≥3 1.20 (1.02, 1.42) 1.06 (0.91, 1.25) 1.28 (1.15, 1.43) 0.94 (0.86, 1.03) Baseline MH Condition Stress 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Anxiety and Depression 3.72 (3.17, 4.36) 3.31 (2.89, 3.80) 1.69 (1.53, 1.86) 1.63 (1.52, 1.75) Depression 3.32 (2.85, 3.87) 2.77 (2.42, 3.18) 1.55 (1.40, 1.70) 1.50 (1.40, 1.60) Anxiety 2.04 (1.74, 2.38) 1.56 (1.35, 1.80) 1.19 (1.09, 1.31) 1.13 (1.06, 1.21) Opioids No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.37 (1.20, 1.56) 1.16 (1.01, 1.33) 1.33 (1.21, 1.46) 0.97 (0.89, 1.04) NSAIDs No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.06 (0.92, 1.22) 0.97 (0.85, 1.11) 1.04 (0.95, 1.14) 1.02 (0.95, 1.10) Gabapentinoids No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 0.98 (0.73, 1.32) 0.87 (0.62, 1.22) 1.14 (0.91, 1.43) 0.82 (0.67, 1.01) Antidepressants No 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Yes 1.09 (0.97, 1.23) 0.95 (0.85, 1.07) 1.25 (1.14, 1.36) 0.85 (0.80, 0.90) Polypharmacy e 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1-4 0.93 (0.82, 1.06) 0.93 (0.83, 1.04) 1.05 (0.95, 1.15) 1.00 (0.94, 1.07) 5-9 1.03 (0.88, 1.21) 1.01 (0.87, 1.17) 1.22 (1.09, 1.36) 0.99 (0.92, 1.07) ≥10 1.27 (1.02, 1.59) 1.29 (1.05, 1.59) 1.59 (1.36, 1.86) 0.96 (0.85, 1.09) Smoking Status Never 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Current 1.75 (1.56, 1.95) 1.53 (1.38, 1.70) 1.44 (1.33, 1.55) 1.05 (0.99, 1.11) Ex Smoker 0.99 (0.84, 1.17) 0.99 (0.85, 1.14) 1.06 (0.96, 1.18) 1.04 (0.96, 1.11) Not Recorded 1.17 (1.00, 1.38) 1.11 (0.96, 1.28) 1.2 (1.08, 1.33) 1.09 (1.01, 1.17) BMI f Underweight/Normal 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Overweight 1.00 (0.87, 1.16) 0.97 (0.85, 1.11) 0.97 (0.88, 1.07) 1.04 (0.97, 1.11) Obese 1.28 (1.11, 1.48) 1.21 (1.05, 1.39) 1.19 (1.08, 1.31) 1.12 (1.04, 1.21) Not Recorded 1.22 (1.07, 1.39) 1.18 (1.04, 1.33) 1.06 (0.97, 1.15) 1 (0.94, 1.06) CCI Score 0 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) 1 1.14 (0.99, 1.31) 0.96 (0.83, 1.10) 1.21 (1.09, 1.33) 0.97 (0.90, 1.05) ≥2 1.10 (0.84, 1.43) 0.99 (0.76, 1.29) 1.34 (1.10, 1.63) 0.98 (0.84, 1.15) Abbreviations: IMD = Index of Multiple Deprivation; MH = Mental Health; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index Notes: Values are presented as adjusted odds ratios with 95% confidence intervals (adjustments were made for all the other variables in the Table). Statistically significant estimates (where 95% CI doesn't include the value 1) are shown in bold a All odds ratios are calculated with respect to the reference trajectory: Single. b This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: Northeast, Northwest, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: Southeast, Southwest, London c Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas d Excluding the index MH consultation e Excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants f Underweight/Normal: 10<=BMI<25; Overweight: 25<=BMI<30; Obese: 30<=BMI<80; Not Recorded: no BMI data available or BMI=80 Discussion Summary of Main Findings and Comparison with Other Research In this large study of trajectories of work absence in England, five common trajectories associated with MSK and MH conditions were determined over a one-year follow-up. The two most prevalent trajectories consisted of low absence (issuance of either a ‘Single’ index fit note or a ‘Short Term’ absence lasting two to four months); these two trajectories comprised 73.2% and 68.6% of economically active individuals in England who experienced a first absence due to a MSK or MH condition, respectively. In contrast, our two least prevalent trajectories were characterised by longer-term absence lasting six months or more (‘Chronic Sustained’ or ‘Chronic Fast Decreasing’ absence); these two patterns of sickness absence were experienced by 6.3% and 11.1% of our study MSK and MH cohort populations, respectively. This is concerning, as long-term sickness absence has risen to record numbers and is the main reason for economic inactivity in the UK (currently accounting for 30.2% of the economically inactive population) [1],[21]. This puts the UK economy at a disadvantage compared to those of other Western countries whose economies have since shown recovery towards pre-pandemic levels [22]. The fifth trajectory subgroup, ‘Intermittent Low’ (prevalence 20% in both MSK and MH cohorts), had a less clearly identifiable pattern other than being episodic fit notes, suggesting a subgroup of individuals who were in-and-out of absence during the one-year follow-up. The findings that trajectories of longer-absence occur with lower prevalence, and trajectories of less severe absence occur with higher prevalence, have also been demonstrated in other studies [7], [8], [23], [24], [25]. Furthermore, similar shapes of the five trajectories we identified have also been found in other studies. For example, similar trajectories involving a sustained high level of work absence throughout follow-up, analogous to our most severe ‘Chronic Sustained’ class (3.7% and 5.6% prevalence, for the MSK and MH condition fit note cohorts, respectively), have been identified by: Farrants et al (2019) [7] in a Swedish population (a ‘Late Decrease’ class, 8.0%), Rysstad et al (2023) [8] in a Norwegian population (a ‘Persistent High’ class, 18.2% prevalence), and McLeod et al (2018) [23] in a Canadian population (a ‘Constant Sickness Absence’ class, 3.0% prevalence). The international differences in prevalence of slow RTW trajectories may be due to varying absence management systems and primary care across countries. Finally, we found that a set of common characteristics were associated with both of our two longer-term absence trajectories (‘Chronic Sustained’ and ‘Chronic Fast Decreasing’); individuals who were: older, living in the North or Midlands or more deprived areas of England, prescribed opioids in the two years preceding their index fit note, and current smokers. Older individuals have also been shown to be associated with longer-term absence trajectories in other studies by: Farrants et al (2019) [7], Rysstad et al (2023) [8], Farrants et al (2018) [24], and Spronken et al (2020) [25]. Our trajectory-covariate association analysis also highlighted presence of health inequalities. For example, those from the most deprived neighbourhoods were more likely to follow one of the two longer-term absence trajectories. Health inequality by deprivation status has been also demonstrated Marmot et al (2010) [26] who showed in their Strategic Review of Health Inequalities in England post-2010 report that those in the most deprived neighbourhoods of England, compared to the least deprived, lived shorter lives and with more disability. More recently, Parker et al (2020) [27] showed that these people from worse off areas of England also had a lower healthy working life expectancy than those from better off areas. The key findings from this study, identifying profiles of individuals most at risk of longer-term sickness absence due to a MSK or MH condition, provide useful evidence to target upcoming Government initiatives to those who need support the most (such as those from more economically deprived areas of England). These Government incentives include WorkWell [22], a low-intensity, holistic early intervention of work and health support designed to help people RTW, funded in 15 different areas across England. Additionally, the fit note reform [28], is a Government initiative to evaluate the state of the current fit note process, with the aim to improve access to timely health and work support. Alongside Government initiatives, policy to incentivise employers for engaging in training concerning RTW management of their employees may also be beneficial, to help employers become more accommodating and supportive of their employees’ needs, as well as broader policies to reduce national health inequalities [26]. Study Strengths and Limitations One of the key strengths of this study was use of a large, nationally representative primary care database [9]. A further key strength was that high quality trajectory reporting was conducted using the GRoLTS checklist [17], and a variety of statistical measures were used to guide the choice of the optimal models, to ensure robustness of the final selection. Face validity of the findings from this study were affirmed through discussions with a wide range of stakeholders including General Practitioners, our patient and public involvement and engagement group, and based on feedback from meetings which included members of the Office for Health Improvement and Disparities, the Department for Work and Pensions, and Versus Arthritis. However, a limitation was that it was not possible to perform a trajectory derivation analysis based on duration of fit note as this was largely missing in the formatted primary care data available for analysis. Nonetheless, a continuous fit note definition may have led to models with an increased complexity which may have more convergence issues and be more difficult to interpret, and therefore such models may be less practical. We could have considered other trajectory derivation methods also, such as growth mixture modelling, which assumes there are variations between people within trajectories but is more complex. Analyses not presented here using growth mixture modelling had convergence issues but derived similar trajectories (Figure S.1 in Supplementary Materials S5). Our models were based on issued fit notes and not workplace data. A limitation of using primary care electronic health records is that RTW data is not available. Finally, as reason for fit note was not available in the formatted primary care electronic health records used in this study, we assumed that the index fit note was due to a MSK or MH condition if there was a MSK or MH consultation, respectively, up to two weeks prior to or on the first ever fit note date. Nonetheless, it is not expected that the true number of MSK and MH condition fit notes was substantially underestimated, as, of the index fit notes analysed in this study, over 85% had MSK and MH consultations that occurred on the day of the index fit note. Recommendations for Future Research Further external validation of these trajectories would be valuable, including internationally. Qualitative work may also help to better understand what kind of support might benefit those in or at risk of the more severe trajectories of long-term sickness absence. Such a study could aim to understand what barriers these people face to RTW, and what support could help them reach a sustained RTW more quickly. Conclusion Using a representative sample of economically active English workers experiencing a first ever sickness absence due to a MSK or MH condition, long term sickness absence patterns lasting six months or more occurred in 6.3% and 11.1% of the MSK and MH cohorts, respectively. Health inequalities were demonstrated; individuals associated with these longer-term absence patterns were: older, living in the North or Midlands or most deprived areas of England, prescribed opioids, and current smokers. These findings have implications for prevention and management strategies for individuals experiencing an incident work absence due to either a MSK or MH condition. Declarations Competing interests: None to declare Funding: This study was funded by the Economic and Social Research Council and Northwest Social Science Doctoral Training Partnership, and carried out at the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC). KPJ and VKW are partly funded by the NIHR Applied Research Collaboration West Midlands. The views expressed are those of the authors alone. Acknowledgements: The study was approved by the CPRD Research Data Governance (study reference number 21_000665). This study is based in part on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. Data Sharing Statement: Data may be obtained from a third party and are not publicly available. The data were obtained from the Clinical Practice Research Datalink. Clinical Practice Research Datalink data governance does not allow us to distribute patient data to other parties. Researchers may apply for data access at http://www.CPRD.com/. Ethics Approval: Ethics approval was not required as this study used data from the Clinical Practice Research Datalink (CPRD), which was granted ethics approval on 10 th January 2022 from the Health Research Authority (through the East Midlands - Derby Research Ethics Committee, with reference 21/EM/0265). Contributors: AL, GWJ, KJ and CH designed the study. AL acquired and analysed the data; JB coordinated the data management and JB and KJ supported AL with statistical analyses. VKW provided clinical interpretation to the findings. All authors contributed to the revision of the manuscript and approved the final version. 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Documentation and Data Dictionary (set 22/January 2022). 2022. Vermunt JK. Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Polit Anal. 2010;18:450–69. Office for National Statistics (ONS). INAC01 SA: Economic inactivity by reason (seasonally adjusted). 2024. DWP, DHSC. Guidance. WorkWell prospectus: guidance for Local System Partnerships [Internet]. 2024. Available from: https://www.gov.uk/government/publications/workwell/workwell-prospectus-guidance-for-local-system-partnerships McLeod CB, Reiff E, Maas E, Bultmann U. Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders. Scand J Work Environ Health. 2018;44:147–55. Farrants K, Friberg E, Sjölund S, Alexanderson K. Work disability trajectories among individuals with a sick-leave spell due to depressive episode ≥ 21 days: A prospective cohort study with 13-month follow up. J Occup Rehabil. 2018;28:678–90. Spronken M, Brouwers EPM, Vermunt JK, Arends I, Oerlemans WGM, van der Klink JJL, et al. Identifying return to work trajectories among employees on sick leave due to mental health problems using latent class transition analysis. BMJ Open. 2020;10:e032016. Marmot M, Allen J, Goldblatt P, Boyce T, McNeish D, Grady M, et al. Fair Society, Healthy Lives - The Marmot Review: Strategic Review of Health Inequalities in England post-2010. 2010. Parker M, Bucknall M, Jagger C, Wilkie R. Population-based estimates of healthy working life expectancy in England at age 50 years: analysis of data from the English Longitudinal Study of Ageing. Lancet Public Heal. 2020;5:e395–403. Department of Health and Social Care, Department for Work and Pensions. Employment support launched for over a million people [Internet]. 2023 [cited 2024 Feb 20]. Available from: https://www.gov.uk/government/news/employment-support-launched-for-over-a-million-people Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6907087","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475616347,"identity":"384c0f2b-03c1-4768-93ff-6b346b06aa22","order_by":0,"name":"Amardeep Legha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFAC5gYGxgYGOQMJmAAPQS2MYC3GpGtJ3EC0Fv4GxsaHX3fYpW+Xbn66gaHGjsHgzAH8WiQOMDYby55Jzt0555jZDYZjyQwGZxsIWHOAsU1aso05d8ONBKAWtgMMBucJ6JA/wNj+W7KtPt3gRvq3Gwz/iNBiALSF8WPb4QSDGzlmNxjbDhB2mOFhxmZpxrbjhjtn5JTdSOxL5pEk5H25480HP/5sq5Y3l0jfduPDNzs5vjMJBFzGDETwmEggJiJBgPEHUcpGwSgYBaNgxAIAJUNH4UZtve0AAAAASUVORK5CYII=","orcid":"","institution":"Keele University","correspondingAuthor":true,"prefix":"","firstName":"Amardeep","middleName":"","lastName":"Legha","suffix":""},{"id":475616348,"identity":"44e7d045-a05e-4630-a0ca-a5ee54abb534","order_by":1,"name":"James Bailey","email":"","orcid":"","institution":"Keele University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Bailey","suffix":""},{"id":475616349,"identity":"b051dc57-f18f-4221-b288-0c7b11972a5a","order_by":2,"name":"Victoria K Welsh","email":"","orcid":"","institution":"Keele University","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"K","lastName":"Welsh","suffix":""},{"id":475616350,"identity":"3d1afe2f-75d7-4f35-97f5-c2bc04cb9974","order_by":3,"name":"Kelvin P Jordan","email":"","orcid":"","institution":"Keele University","correspondingAuthor":false,"prefix":"","firstName":"Kelvin","middleName":"P","lastName":"Jordan","suffix":""},{"id":475616351,"identity":"6e4421f1-ce9c-404b-89f8-d7e2e0b8aa54","order_by":4,"name":"Clare Holdsworth","email":"","orcid":"","institution":"Keele University","correspondingAuthor":false,"prefix":"","firstName":"Clare","middleName":"","lastName":"Holdsworth","suffix":""},{"id":475616352,"identity":"9a5c5cc0-476b-4504-ab3d-6515a5e4db26","order_by":5,"name":"Gwenllian Wynne-Jones","email":"","orcid":"","institution":"Keele University","correspondingAuthor":false,"prefix":"","firstName":"Gwenllian","middleName":"","lastName":"Wynne-Jones","suffix":""}],"badges":[],"createdAt":"2025-06-16 15:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6907087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6907087/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10926-025-10342-y","type":"published","date":"2025-11-28T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85429888,"identity":"410f5517-e953-44ff-9696-d738d4a83d42","added_by":"auto","created_at":"2025-06-25 18:03:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112505,"visible":true,"origin":"","legend":"\u003cp\u003eFive-Class LCGA Models, Based on Interval Approach 2 (Two Monthly Intervals, Year One Data Only), for the Cohort with Index Fit Note Due to a MSK Condition (Left) and MH Condition (Right)\u003c/p\u003e\n\u003cp\u003eAbbreviations: LCGA = Latent Class Growth Analysis; MSK = Musculoskeletal; MH = Mental Health. \u003cbr\u003e\nFor each trajectory, the solid lines represent the probability of fit note issuance in the given time interval (model estimated data), whilst the dotted lines represent the proportion of individuals issued a fit note (observed data). Trajectory class prevalence is shown, derived from a count based on posterior probability.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6907087/v1/4238b5e1b961f407f1697bc3.png"},{"id":97178242,"identity":"38e68d4d-1909-4aed-bd76-9fdb618e0c3d","added_by":"auto","created_at":"2025-12-01 16:05:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2292864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907087/v1/7f85f6d0-8d6e-4a11-9382-8d7556dd2144.pdf"},{"id":85429893,"identity":"89c9265e-8e73-44d3-a59e-ccdbf417284f","added_by":"auto","created_at":"2025-06-25 18:03:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":264381,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialTrajectoriesofAbsenceFormattedforJORFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6907087/v1/fe5100e8652ffcd4d27183a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trajectories of work absence in England due to a musculoskeletal or mental health condition: an electronic health record cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLong-term sickness absence has been rising in the UK. Between spring 2019 and summer 2022, the number of working-age adults that were economically inactive (not working nor seeking employment) due to long-term sickness absence increased by half a million [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Being in work is generally beneficial for physical and mental health, whilst being off work is associated with poorer health and well-being [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Whilst the majority of people with a sickness absence tend to return-to-work (RTW) quickly, approximately 10% go on to have longer-term absences of \u0026gt;\u0026thinsp;12 months [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Once an individual is on a long-term absence, it becomes progressively harder to RTW, and more adverse health and social problems are experienced [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the UK, if an individual is off work for more than seven consecutive days due to a medical problem, a \u0026lsquo;fit note\u0026rsquo; is issued from a healthcare professional that details the medical advice the individual has received regarding their fitness to work and can be used to claim statutory sick pay through the state [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e],[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Fit notes are recorded in primary care as electronic health records. The most common reason for fit note issuance in England is due to a mental health (MH) condition, followed by a musculoskeletal (MSK) condition (with 37.6% and 17.5% of all fit notes issued between April 2021 and March 2024 due to MH and MSK conditions, respectively) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany existing studies of individuals with a sickness absence involve use of a dichotomous absence measure (absent / not absent) and analysis through either a cross-sectional approach based on a single time point (using methods such as logistic regression), or a time-to-event approach (such as Cox regression) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, RTW is a complex and dynamic process that changes with time, and both the cross-sectional and time-to-event approaches may be sub-optimal in capturing this as they treat RTW as a fixed status. In contrast, trajectory analysis is an approach that allows for repeated measures of absence data to be used to assign individuals into common subgroups (trajectories) based on duration and number of RTW spells. During an initial consultation with a healthcare professional for sickness absence, it is challenging to determine which patients are at the highest risk of a sustained long-term work absence; determining common trajectories of work absence and the patient characteristics associated with them may assist with this.\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e\n\u003cp\u003eThis study has two main aims:\u003c/p\u003e\n\u003cp\u003e1) To derive common longitudinal trajectories of work absence as measured by receipt of fit notes, for a population consulting their healthcare professional with a MSK or MH condition\u003c/p\u003e\n\u003cp\u003e2) To identify health and sociodemographic characteristics associated with longer-term absence trajectories\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eDefining the Study Population\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study used data from a large UK primary care database: the Clinical Practice Research Datalink (CPRD) Aurum [9], and the study was approved by the CPRD Research Governance (protocol reference number 21_000665 made available to reviewers). The February 2022 release of CPRD Aurum (https://doi.org/10.48329/gcgx-f815) [10] was used, which contained over 13.4 million individuals that were currently registered in UK practices (this corresponded to 19.9% of the UK population). CPRD Aurum has been shown to be representative of the general English population in terms of age and gender, as well as deprivation and geographical spread (comparing a mid-2017 snapshot of CPRD Aurum data to mid-2017 data on the broader English population from the Office for National Statistics) [9].\u003c/p\u003e\n\u003cp\u003eThe study population was based in English practices, aged 16-66 at time of a first ever recorded fit note (defined through searching a person\u0026rsquo;s lifetime history of fit notes recorded in the CPRD Aurum database) due to either a MSK or MH condition between 2016-2018, and had at least two years prior registration at their practice. As reason for fit note data is not available in CPRD, an assumption was made that the fit note could be attributed to a MSK or MH condition if there was a recorded MSK or MH consultation, on the day of, or in the two weeks prior to first ever fit note issuance.\u003c/p\u003e\n\u003cp\u003eMSK and MH conditions were defined using Read/SNOMED codes developed in a previous CPRD study [11]. MSK conditions were osteoarthritis, inflammatory MSK, and the most common regional pain (back pain, knee pain, hip pain, hand/wrist pain). MH conditions were depression, anxiety, and stress. These code lists are publicly available on the Keele Research Repository (https://doi.org/10.21252/878s-x990).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncluded patients were censored at earliest of three years of follow-up after index fit note date, age \u0026ge;67 years (defined using year of birth and subtracting from year of follow-up), death, de-registration, and last collection date. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDefining Trajectory Follow-Up and Interval Lengths\u003c/h2\u003e\n\u003cp\u003eAs fit note duration data was largely not available in the CPRD Aurum database, the repeated fit note measure used to assess patterns of absence was a binary yes/no for a recorded fit note issuance (coded as 1/0) in each given time interval during follow-up. The initial MSK or MH condition incident fit note was excluded from the trajectory definition, as all individuals received this.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFive different approaches to follow-up and interval length were used in this study to identify the optimal method for analysis (Table 1). A minimum of two-monthly time interval lengths were chosen to avoid underestimating recurrent fit note issuance (around 50% of all fit notes issued in England due to a MSK or MH condition in England have a duration of more than one month [12], therefore, choosing monthly intervals would be too short). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe Five Interval Approaches Used for Trajectory Derivation in this Study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear One\u0026nbsp;\u003cbr\u003e\u0026nbsp;Time Interval Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears Two-Three Time Interval Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1 (Short-Term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOne Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eThree-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2 (Short-Term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOne Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTwo-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e3 (Long-Term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eThree Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSix-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eSix-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e4 (Long-Term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eThree Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eThree-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eSix-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e5 (Long-Term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eThree Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTwo-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eSix-Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: N/A = Not Applicable\u003c/p\u003e\n\u003ch2 id=\"_Toc160711343\"\u003eModel Building Strategy\u003c/h2\u003e\n\u003cp\u003eThe trajectory models considered in this study were based on Latent Class Growth Analysis (LCGA). LCGA allows two or more trajectories \u0026lsquo;classes\u0026rsquo; to be fitted to a study population. The probability of belonging to a trajectory class (or cluster), known as posterior probability, is estimated for each individual using the observed data, and the individual is assigned to the class that they have the greatest probability of belonging to. LCGA assumes that individuals follow the trajectory class that they are assigned to.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe model building strategy began with interval approach 1 (Table 1) for the index MSK condition cohort (follow up of 1 year broken down into 4 three-monthly intervals), and initially with a two-class LCGA model. The number of classes in succeeding LCGA models were then progressively increased by one.\u003c/p\u003e\n\u003cp\u003eThe following criteria were used to indicate better performing models:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eAkaike Information Criterion (AIC) [13] and Bayesian Information Criterion (BIC) [14] (lower values indicating better model fit);\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStatistically significant likelihood ratio tests (both the Lo-Mendell-Rubin [15] and the bootstrapped [16]\u0026nbsp;likelihood ratio tests were used) \u0026ndash; this indicates if a (\u003cem\u003ek\u003c/em\u003e class) LCGA model fits the data better than a corresponding LCGA model with one fewer class (i.e., \u003cem\u003ek\u003c/em\u003e-1 classes).\u003c/li\u003e\n \u003cli\u003eEntropy and average posterior probabilities \u0026ge;0.7 (indicating better trajectory class separability)\u003c/li\u003e\n \u003cli\u003eMinimum trajectory class prevalence \u0026ge;1% (to reduce the possibility of uncovering spurious trajectory classes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGraphical assessment of observed compared to predicted probabilities of fit note issuance within trajectories was also performed to assess model fit, and trajectories were evaluated for interpretability and clinical meaningfulness. Finally, when comparing two competing models with \u003cem\u003ek\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e+1 classes, if all of the criteria mentioned thus far were similar, the more parsimonious model (i.e., with fewer trajectory classes) was selected.\u003c/p\u003e\n\u003cp\u003eAll LCGAs were fitted with a linear functional form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, this strategy was repeated for individuals with an index MSK fit note for the four remaining approaches (Table 1). This process was repeated for individuals with a first ever fit note due to a MH condition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the two selected optimal LCGA trajectory models, a summary of the observed patterns of fit note issuance for individuals within a trajectory class were derived, to assess individual variability of the final trajectory classes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reporting in this study was carried out in line with the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist [17]. Stata MP version 17.0 (StataCorp, College Station, TX, USA) and Mplus Version 8.9 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, Los Angeles, CA, USA) were used for the analyses in this study. In an effort to limit convergence issues, all trajectory analyses involved use of 500 random sets of starting values with 100 iterations [18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAny absence measurement data that was missing due to censoring before end of follow-up was assumed to be Missing At Random, and Full Information Maximum Likelihood estimation was used to incorporate the non-missing repeated absence measurement data of such individuals (i.e., individuals were retained in the analysis as long as they had some non-missing data). A complete case sensitivity analysis to retain only individuals with complete follow-up data was also performed and did not change findings (results omitted for brevity).\u003c/p\u003e\n\u003ch2\u003eCharacteristics Tested for Association with Trajectories\u003c/h2\u003e\n\u003cp\u003eIn total 14 characteristics (see Table 2 for definitions and summaries) were included in the association analysis with the derived optimal absence trajectories:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSociodemographic Characteristics (4 variables)\u003c/li\u003e\n \u003cli\u003eHealth Characteristics (4 variables)\u003c/li\u003e\n \u003cli\u003eTypes of Treatment Received (4 variables)\u003c/li\u003e\n \u003cli\u003eComorbidity (2 variables)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSummary of the n=14 Characteristics Explored in this Study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory Definitions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSK Index Fit Note\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH Index Fit Note\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale, Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e16-25, 26-35, 36-45,\u0026nbsp;\u003cbr\u003e\u0026nbsp;46-55, 56-65 (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eRegion\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNorth of England, Middle of England, South of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eIMD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1-5, Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNumber of MSK Consultations\u0026nbsp;\u003cbr\u003e- Prior 2 Years\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0, 1, 2, \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNumber of MH Consultations\u0026nbsp;\u003cbr\u003e- Prior 2 Years\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0, 1, 2, \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBaseline MSK Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eBack pain,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Knee pain,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Hand/Wrist pain,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Hip pain,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Inflammatory\u0026nbsp;\u003cbr\u003e\u0026nbsp;MSK, Osteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBaseline MH Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eStress,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Anxiety,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Depression,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Anxiety and Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSmoking Status\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNever,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Current,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Ex Smoker,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Not Recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eUnderweight/Normal, Overweight,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Obese,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Not Recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of Treatment Received\u003c/strong\u003e\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eOpioids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNSAIDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eGabapentinoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAntidepressants\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePolypharmacy\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0, 1-4, 5-9, \u0026ge;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCCI Score\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0, 1, \u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eModified CCI Score\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0, 1, \u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5864%;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5865%;\"\u003e\n \u003cp\u003e\u003cspan style='text-align: start;color: rgb(0, 29, 53);background-color: rgb(255, 255, 255);font-size: 18px;font-family: \";'\u003e✗\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IMD = Index of Multiple Deprivation; MSK = Musculoskeletal; MH = Mental Health; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: North East, North West, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: South East, South West, London\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eDefined as the number of MSK consultations in the two years prior to index fit note date (excluding the index MSK consultation)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003eDefined as the number of MH consultations in the two years prior to index fit note date (excluding the index MH consultation)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u0026nbsp;\u003c/sup\u003eIn the five years prior to index fit note date and latest data retained\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u0026nbsp;\u003c/sup\u003eUnderweight/Normal: 10\u0026lt;=BMI\u0026lt;25; Overweight: 25\u0026lt;=BMI\u0026lt;30; Obese: 30\u0026lt;=BMI\u0026lt;80; Not Recorded: no BMI data available or BMI\u0026lt;10 or BMI\u0026gt;=80. BMI data were searched for in the five years prior to index fit note date and latest data retained\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eg\u0026nbsp;\u003c/sup\u003eIn the two years prior to index fit note date\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eh\u0026nbsp;\u003c/sup\u003eDefined as a drug count (excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants) in the two years prior to index fit note date\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ei\u0026nbsp;\u003c/sup\u003eIn the two years prior to index fit note date, excluding Rheumatic Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;All of these characteristics were accessed directly from the CPRD Aurum database, except Index of Multiple Deprivation (IMD) [19] which was accessed through a data-linkage. The latest version of patient level linked IMD data was used (the 2019 version) and in quintile format; this is a relative measure, rather than absolute, that compares levels of deprivation of English neighbourhoods at a lower-layer super output area level.\u003c/p\u003e\n\u003ch2\u003eTrajectory-Covariate Association Analyses\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003eeparately, for the index MSK and MH condition fit note cohorts, multivariable multinomial logistic regression was used to test for association between the sociodemographic and health characteristics with the optimal trajectories identified. The least severe absence trajectory class was used as the reference.\u003c/p\u003e\n\u003cp\u003eTo preserve the original optimal trajectory classes a three-step approach was used for this analysis, as developed by Vermunt (2010) [20]. This was achieved by first estimating the original optimal derived trajectory classes (step 1, as performed earlier in Methods Section), then adding relevant covariate data (step 2), and finally performing the multinomial logistic regression analysis by accounting for the uncertainty in class membership through specifying uncertainty rates associated with the derived classes (step 3). Adjusted odds ratios (ORs) of association with 95% confidence intervals (CIs) are presented.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePatient and Public Involvement\u003c/h2\u003e\n\u003cp\u003eA patient and public involvement and engagement meeting was held to disseminate the findings of this study to members of the public with lived experience of work absence, and to seek their feedback on the interpretation of our study results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 43,130 people included in this study with an initial MSK condition fit note (45.5% female), and 62,355 people with an initial MH condition fit note (59.2% female). Descriptive statistics are presented in Supplementary Materials S1.\u003c/p\u003e\n\u003ch2 id=\"_Toc160711347\"\u003eOptimal Trajectories of Work Absence\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe optimal trajectory models chosen in this study were the same for both the index MSK and MH condition fit note cohorts: a five-class LCGA, under interval approach 2 (based on two-monthly recurring intervals of a one-year follow-up post index fit note date). The process to choose the optimal models is explained in Supplementary Materials S2.\u003c/p\u003e\n\u003cp\u003eThe optimal LCGA models are displayed graphically in Figure 1, and corresponding model fit and class meaningfulness statistics presented in Supplementary Materials S3. In particular, these five-class models converged without issues, had a likelihood ratio test \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.0001 (based on both the Lo-Mendell-Rubin and the bootstrapped approaches; suggesting the five-class model fitted the data better than a four-class model), and had entropy and average posterior probabilities above our study guideline threshold of 0.7 for all trajectory classes, indicating good class separability.\u003c/p\u003e\n\u003cp\u003eThese optimal models also contained a variety of different and plausible trajectory class shapes that made sense in a work absence context (Figure 1). The trajectory shapes of these five class models were similar across the two cohorts, and consisted first of two classes characterised by low sickness absence throughout follow-up:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003e\u0026lsquo;Single\u0026rsquo;, whereby the probability of fit note issuance remained close to 0 for all two-monthly intervals in the first year of follow-up (MSK cohort 45.5% of the population, MH cohort 32.1%)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026lsquo;Short Term\u0026rsquo;, whereby there was an initial high probability of fit note issuance in the first two months post index fit note (of around 0.85-0.90), which then decreased sharply to under 0.25 between months three and four of follow-up, and from months five to twelve were close to 0 (MSK 27.7%, MH 36.5%).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen there were two classes characterised by a high probability of absence:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u0026lsquo;Chronic Sustained\u0026rsquo;, whereby the probability of fit note issuance remained largely high (between 0.68 to 0.94) throughout all two-monthly intervals in the first year of follow-up (MSK 3.7%, MH 5.6%).\u003c/li\u003e\n \u003cli\u003e\u0026lsquo;Chronic Fast Decreasing\u0026rsquo;, this trajectory started off similar to the \u0026lsquo;Chronic Sustained\u0026rsquo; class, with a high and sustained probability (of close to 1) of fit note issuance in the first six months of follow-up. However, from months seven to eight there was a rapid decrease to around 0.3 probability of fit note issuance, and then from months nine to twelve this probability dropped close to 0 (MSK 2.6%, MH 5.5%).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe final derived trajectory was:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u0026lsquo;Intermittent Low\u0026rsquo;, here the probability of fit note issuance was low, between 0.2 and 0.45 throughout all two-monthly intervals in the first year of follow-up. Generally, individuals in this trajectory had either one or two recorded fit notes during the one-year follow up period (MSK 20.6%, MH 20.4%).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe variability in patterns of fit note issuance over the 12 months follow-up within each of the derived classes is shown in Supplementary Material S4. Apart from the \u0026lsquo;Intermittent Low\u0026rsquo; trajectory class, the other four trajectory classes exhibited low variability in patterns of fit note.\u003c/p\u003e\n\u003ch2\u003eCharacteristics Associated with MSK Condition Absence Trajectories\u003c/h2\u003e\n\u003cp\u003eFor the MSK cohort, statistically significant adjusted associations common to both of the two most severe absence trajectories (\u0026lsquo;Chronic Sustained\u0026rsquo; and \u0026lsquo;Chronic Fast Decreasing\u0026rsquo;) were (Table 3):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOlder age\u003c/li\u003e\n \u003cli\u003eLiving in the North of England or the Midlands\u003c/li\u003e\n \u003cli\u003eLiving in the most deprived areas of England\u003c/li\u003e\n \u003cli\u003eHaving more (\u0026ge;2) prior MSK consultations\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHaving baseline knee pain, osteoarthritis, or hip pain, compared to back pain\u003c/li\u003e\n \u003cli\u003eBeing prescribed opioids, gabapentinoids, or antidepressants in the two years prior to index fit note\u003c/li\u003e\n \u003cli\u003eBeing current smokers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Characteristics Associated with Optimal Trajectories of Work Absence Due to a MSK Condition Using the \u0026lsquo;Single\u0026rsquo; Trajectory Class as the Reference (Adjusted Model)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"692\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 551px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrajectory Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic\u0026nbsp;\u003cbr\u003e Sustained\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic\u0026nbsp;\u003cbr\u003e Fast Decreasing\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntermittent\u0026nbsp;\u003cbr\u003e Low\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eShort Term\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=1,261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=1,333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=7,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=11,154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.81, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.17 (1.01, 1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.06, 1.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.10 (1.03, 1.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e16-25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e26-35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.38 (1.02, 1.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.79, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.88, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (1.13, 1.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e36-45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.67 (1.25, 2.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15 (0.88, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.84 (0.74, 0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.05, 1.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e46-55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.10 (1.59, 2.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.42 (1.10, 1.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.73, 0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.37 (1.24, 1.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e56-66 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.26 (2.47, 4.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.38 (1.83, 3.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.90, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.54 (1.38, 1.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.16, 1.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.70 (1.45, 2.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.92, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.27, 1.46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.45 (1.24, 1.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (1.02, 1.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.03, 1.25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.11, 1.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIMD\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (least deprived)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.78, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (0.90, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15 (0.99, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.92, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.22 (0.94, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.19 (0.91, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.32 (1.15, 1.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.92, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.68 (1.30, 2.16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.27 (0.97, 1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.61 (1.41, 1.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.90, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (most deprived)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.50 (1.97, 3.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.64 (1.28, 2.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.78 (1.55, 2.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.87, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.22 (0.79, 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.38 (0.15, 0.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75 (0.53, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (1.12, 1.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMSK Consultations - Prior 2 Years\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.07, 1.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.87, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.96, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.01, 1.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.63 (1.30, 2.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.70 (1.35, 2.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.13, 1.47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.09, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.81 (1.44, 2.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.16, 1.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.47 (1.29, 1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.3 (1.17, 1.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline MSK Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBack pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKnee pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.11, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.47 (1.21, 1.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.21, 1.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.87, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHand/wrist pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.72, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21 (0.87, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.86, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.81 (0.71, 0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInflammatory MSK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (0.80, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.66, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.86, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.78 (0.66, 0.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.60 (2.00, 3.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.84 (1.33, 2.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.10 (1.70, 2.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87 (0.72, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHip pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.62 (2.00, 3.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.68 (1.18, 2.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.15, 1.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.13 (0.95, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOpioids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.20, 1.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.58 (1.32, 1.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (1.08, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.14 (1.06, 1.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNSAIDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.16 (1.00, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.91, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.05, 1.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.99, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGabapentinoids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.60 (1.22, 2.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.08 (1.58, 2.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.51 (1.24, 1.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.25 (1.05, 1.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAntidepressants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.79 (1.52, 2.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.23, 1.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.50 (1.35, 1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.98, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePolypharmacy\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.80, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82 (0.67, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.96, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.92, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26 (1.00, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.89, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.19, 1.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.91, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.13, 1.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37 (1.00, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.84 (1.54, 2.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.86, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.80 (1.53, 2.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.41 (1.19, 1.69)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.25, 1.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (1.00, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEx Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (1.01, 1.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (0.94, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.87, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.99, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot Recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.22 (0.95, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.79, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.83, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.91, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnderweight/Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.78, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94 (0.76, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.16 (1.04, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (1.02, 1.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.82, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.78, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.07, 1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (0.96, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot Recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20 (0.98, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.90, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.96, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.98, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModified CCI Score\u003csup\u003eg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.12, 1.61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90 (0.72, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.90, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96 (0.88, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28 (0.95, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.62, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (0.91, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.80, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IMD = Index of Multiple Deprivation; MSK = Musculoskeletal; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eNotes: Values are presented as adjusted odds ratios with 95% confidence intervals (adjustments were made for all the other variables shown in the Table)\u003c/p\u003e\n\u003cp\u003eStatistically significant estimates (where 95% CI doesn\u0026apos;t include the value 1) are shown in bold\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e All odds ratios are calculated with respect to the reference trajectory: Single.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: Northeast, Northwest, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: Southeast, Southwest, London\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Excluding the index MSK consultation\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003e Underweight/Normal: 10\u0026lt;=BMI\u0026lt;25; Overweight: 25\u0026lt;=BMI\u0026lt;30; Obese: 30\u0026lt;=BMI\u0026lt;80; Not Recorded: no BMI data available or BMI\u0026lt;10 or BMI\u0026gt;=80\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eg\u003c/sup\u003e Excluding Rheumatic Disease\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCharacteristics Associated with MH Condition Absence Trajectories\u003c/h2\u003e\n\u003cp\u003eIn the MH cohort, individuals in the two most absence severe trajectories (\u0026lsquo;Chronic Sustained\u0026rsquo; and \u0026lsquo;Chronic Fast Decreasing\u0026rsquo;) were more likely to (Table 4):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBe male\u003c/li\u003e\n \u003cli\u003eBe older\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLive in the North of England or the Midlands\u003c/li\u003e\n \u003cli\u003eLive in any of the three most deprived areas of England (IMD from 3 to 5)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHave baseline anxiety, depression, or anxiety and depression combined, compared to stress\u003c/li\u003e\n \u003cli\u003eBe prescribed an opioid in the two years prior to index fit note issue\u003c/li\u003e\n \u003cli\u003eHave excessive polypharmacy\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBe current smokers\u003c/li\u003e\n \u003cli\u003eBe obese or with a \u0026lsquo;not recorded\u0026rsquo; BMI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Characteristics Associated with Optimal Trajectories of Work Absence Due to a MH Condition Using the \u0026lsquo;Single\u0026rsquo; Trajectory Class as the Reference\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 532px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrajectory Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Sustained\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic\u0026nbsp;\u003cbr\u003e Fast Decreasing\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntermittent\u0026nbsp;\u003cbr\u003e Low\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eShort Term\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=2,881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=3,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=10,835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=21,534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.79 (0.71, 0.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.88 (0.80, 0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.93, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.93, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e16-25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e26-35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.73, 0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (0.92, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.85 (0.78, 0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.38 (1.30, 1.47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e36-45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (1.04, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.33, 1.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.79 (0.72, 0.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.63 (1.52, 1.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e46-55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.66 (1.43, 1.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.17 (1.90, 2.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91 (0.82, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.80 (1.66, 1.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e56-66 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.30 (1.92, 2.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.72 (2.29, 3.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.94, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.87 (1.69, 2.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.31 (1.15, 1.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.65 (1.48, 1.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.91, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.26, 1.42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.51 (1.33, 1.72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.20, 1.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.88, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.13, 1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIMD\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (least deprived)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21 (1.00, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.01, 1.41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12 (1.00, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96 (0.89, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.49 (1.24, 1.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.16, 1.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (1.14, 1.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.88, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.86 (1.55, 2.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.49 (1.27, 1.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.36, 1.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.88, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (most deprived)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.67 (2.24, 3.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.00 (1.71, 2.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.83 (1.64, 2.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.86, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.68 (0.48, 0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.72, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.54 (0.41, 0.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.99, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMH Consultations - Prior 2 Years\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.90, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.94, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.98, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.91, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (0.98, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.95, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12 (0.99, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.90, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.02, 1.42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.91, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (1.15, 1.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94 (0.86, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline MH Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnxiety and Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.72 (3.17, 4.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.31 (2.89, 3.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.69 (1.53, 1.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.63 (1.52, 1.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.32 (2.85, 3.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.77 (2.42, 3.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.55 (1.40, 1.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.50 (1.40, 1.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.04 (1.74, 2.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.56 (1.35, 1.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.09, 1.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.06, 1.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOpioids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.37 (1.20, 1.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.16 (1.01, 1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 (1.21, 1.46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.89, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNSAIDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.92, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.85, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.95, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.95, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGabapentinoids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.73, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87 (0.62, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.91, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82 (0.67, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAntidepressants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (0.97, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.85, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.25 (1.14, 1.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.85 (0.80, 0.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePolypharmacy\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.82, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.83, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (0.95, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.94, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.88, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.87, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.09, 1.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.92, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.02, 1.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.05, 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(1.05, 1.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.08, 1.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (1.04, 1.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot Recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.07, 1.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (1.04, 1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.97, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.94, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCCI Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.99, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96 (0.83, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.09, 1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.90, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (0.84, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.76, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.10, 1.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.84, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IMD = Index of Multiple Deprivation; MH = Mental Health; NSAIDs = Non-Steroidal Anti-Inflammatory Drugs; BMI = Body Mass Index; CCI = Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eNotes: Values are presented as adjusted odds ratios with 95% confidence intervals (adjustments were made for all the other variables in the Table).\u003c/p\u003e\n\u003cp\u003eStatistically significant estimates (where 95% CI doesn\u0026apos;t include the value 1) are shown in bold\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e All odds ratios are calculated with respect to the reference trajectory: Single.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e This is defined as the region of the General Practitioner Practice (primary care clinic) of the patient. North of England is defined as: Northeast, Northwest, Yorkshire and the Humber; Middle of England: East Midlands, West Midlands, East of England; and South of England: Southeast, Southwest, London\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Quintiles are used for IMD (1-5), where a higher score represents more deprived areas. An IMD of 5 represents the most deprived areas of England, and an IMD of 1 the least deprived areas\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Excluding the index MH consultation\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Excluding Opioids, NSAIDs, Gabapentinoids, Antidepressants\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003e Underweight/Normal: 10\u0026lt;=BMI\u0026lt;25; Overweight: 25\u0026lt;=BMI\u0026lt;30; Obese: 30\u0026lt;=BMI\u0026lt;80; Not Recorded: no BMI data available or BMI\u0026lt;10 or BMI\u0026gt;=80 \u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003eSummary of Main Findings and Comparison with Other Research\u003c/h2\u003e\n\u003cp\u003eIn this large study of trajectories of work absence in England, five common trajectories associated with MSK and MH conditions were determined over a one-year follow-up. The two most prevalent trajectories consisted of low absence (issuance of either a \u0026lsquo;Single\u0026rsquo; index fit note or a \u0026lsquo;Short Term\u0026rsquo; absence lasting two to four months); these two trajectories comprised 73.2% and 68.6% of economically active individuals in England who experienced a first absence due to a MSK or MH condition, respectively.\u003c/p\u003e\n\u003cp\u003eIn contrast, our two least prevalent trajectories were characterised by longer-term absence lasting six months or more (\u0026lsquo;Chronic Sustained\u0026rsquo; or \u0026lsquo;Chronic Fast Decreasing\u0026rsquo; absence); these two patterns of sickness absence were experienced by 6.3% and 11.1% of our study MSK and MH cohort populations, respectively.\u0026nbsp;This is concerning, as long-term sickness absence has risen to record numbers and is the main reason for economic inactivity in the UK (currently accounting for 30.2% of the economically inactive population)\u0026nbsp;[1],[21].\u0026nbsp;This puts the UK economy at a disadvantage compared to those of other Western countries whose economies have since shown recovery towards pre-pandemic levels\u0026nbsp;[22].\u003c/p\u003e\n\u003cp\u003eThe fifth trajectory subgroup, \u0026lsquo;Intermittent Low\u0026rsquo; (prevalence 20% in both MSK and MH cohorts), had a less clearly identifiable pattern other than being episodic fit notes, suggesting a subgroup of individuals who were in-and-out of absence during the one-year follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings that trajectories of longer-absence occur with lower prevalence, and trajectories of less severe absence occur with higher prevalence, have also been demonstrated in other studies [7], [8], [23],\u0026nbsp;[24],\u0026nbsp;[25]. Furthermore, similar shapes of the five trajectories we identified have also been found in other studies. For example, similar trajectories involving a sustained high level of work absence throughout follow-up, analogous to our most severe \u0026lsquo;Chronic Sustained\u0026rsquo; class (3.7% and 5.6% prevalence, for the MSK and MH condition fit note cohorts, respectively), have been identified by: Farrants et al (2019)\u0026nbsp;[7]\u0026nbsp;in a Swedish population (a \u0026lsquo;Late Decrease\u0026rsquo; class, 8.0%), Rysstad et al (2023)\u0026nbsp;[8]\u0026nbsp;in a Norwegian population (a \u0026lsquo;Persistent High\u0026rsquo; class, 18.2% prevalence), and McLeod et al (2018)\u0026nbsp;[23]\u0026nbsp;in a Canadian population (a \u0026lsquo;Constant Sickness Absence\u0026rsquo; class, 3.0% prevalence). The international differences in prevalence of slow RTW trajectories may be due to varying absence management systems and primary care across countries.\u003c/p\u003e\n\u003cp\u003eFinally, we found that a set of common characteristics were associated with both of our two longer-term absence trajectories (\u0026lsquo;Chronic Sustained\u0026rsquo; and \u0026lsquo;Chronic Fast Decreasing\u0026rsquo;); individuals who were: older, living in the North or Midlands or more deprived areas of England, prescribed opioids in the two years preceding their index fit note, and current smokers. Older individuals have also been shown to be associated with longer-term absence trajectories in other studies by: Farrants et al (2019) [7], Rysstad et al (2023) [8],\u0026nbsp;Farrants et al (2018)\u0026nbsp;[24], and\u0026nbsp;Spronken et al (2020)\u0026nbsp;[25].\u003c/p\u003e\n\u003cp\u003eOur trajectory-covariate association analysis also highlighted presence of health inequalities. For example, those from the most deprived neighbourhoods were more likely to follow one of the two longer-term absence trajectories. Health inequality by deprivation status has been also demonstrated\u0026nbsp;Marmot et al (2010) [26] who showed in their Strategic Review of Health Inequalities in England post-2010 report that those in the most deprived neighbourhoods of England, compared to the least deprived, lived shorter lives and with more disability. More recently,\u0026nbsp;Parker et al (2020)\u0026nbsp;[27]\u0026nbsp;showed that these people from worse off areas of England also had a lower healthy working life expectancy than those from better off areas.\u003c/p\u003e\n\u003cp\u003eThe key findings from this study, identifying profiles of individuals most at risk of longer-term sickness absence due to\u0026nbsp;a MSK or MH condition, provide\u0026nbsp;useful evidence to target upcoming Government initiatives to those who need support the most (such as those from more economically deprived areas of England). These Government incentives include WorkWell [22], a low-intensity, holistic early intervention of work and health support designed to help people RTW, funded in 15 different areas across England. Additionally, the fit note reform [28], is a Government initiative to evaluate the state of the current fit note process, with the aim to improve access to timely health and work support.\u003c/p\u003e\n\u003cp\u003eAlongside Government initiatives, policy to incentivise employers for engaging in training concerning RTW management of their employees may also be beneficial, to help employers become more accommodating and supportive of their employees\u0026rsquo; needs, as well as broader policies to reduce national health inequalities [26].\u003c/p\u003e\n\u003ch2 id=\"_Toc160711366\"\u003eStudy Strengths and Limitations\u003c/h2\u003e\n\u003cp\u003eOne of the key strengths of this study was use of a large, nationally representative \u0026nbsp;primary care database [9]. A further key strength was that high quality trajectory reporting was conducted using the GRoLTS checklist [17], and a variety of statistical measures were used to guide the choice of the optimal models, to ensure robustness of the final selection. Face validity of the findings from this study were affirmed through discussions with a wide range of stakeholders including General Practitioners, our patient and public involvement and engagement group, and based on feedback from meetings which included members of the Office for Health Improvement and Disparities, the Department for Work and Pensions, and Versus Arthritis.\u003c/p\u003e\n\u003cp\u003eHowever, a limitation was that it was not possible to perform a trajectory derivation analysis based on duration of fit note as this was largely missing in the formatted primary care data available for analysis. Nonetheless, a continuous fit note definition may have led to models with an increased complexity which may have more convergence issues and be more difficult to interpret, and therefore such models may be less practical.\u0026nbsp;We could have considered other trajectory derivation methods also, such as growth mixture modelling, which assumes there are variations between people within trajectories but is more complex. Analyses not presented here using growth mixture modelling had convergence issues but derived similar trajectories (Figure S.1 in Supplementary Materials S5).\u003c/p\u003e\n\u003cp\u003eOur models were based on issued fit notes and not workplace data. A limitation of using primary care electronic health records is that RTW data is not available. Finally, as reason for fit note was not available in the formatted primary care electronic health records used in this study, we assumed that the index fit note was due to a MSK or MH condition if there was a MSK or MH consultation, respectively, up to two weeks prior to or on the first ever fit note date. Nonetheless, it is not expected that the true number of MSK and MH condition fit notes was substantially underestimated, as, of the index fit notes analysed in this study, over 85% had MSK and MH consultations that occurred on the day of the index fit note.\u003c/p\u003e\n\u003ch2\u003eRecommendations for Future Research\u003c/h2\u003e\n\u003cp\u003eFurther external validation of these trajectories would be valuable, including internationally. Qualitative work may also help to better understand what kind of support might benefit those in or at risk of the more severe trajectories of long-term sickness absence. Such a study could aim to understand what barriers these people face to RTW, and what support could help them reach a sustained RTW more quickly.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing a representative sample of economically active English workers experiencing a first ever sickness absence due to a MSK or MH condition, long term sickness absence patterns lasting six months or more occurred in 6.3% and 11.1% of the MSK and MH cohorts, respectively. Health inequalities were demonstrated; individuals associated with these longer-term absence patterns were: older, living in the North or Midlands or most deprived areas of England, prescribed opioids, and current smokers. These findings have implications for prevention and management strategies for individuals experiencing an incident work absence due to either a MSK or MH condition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e None to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was funded by the Economic and Social Research Council and Northwest Social Science Doctoral Training Partnership, and carried out at the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC). KPJ and VKW are partly funded by the NIHR Applied Research Collaboration West Midlands. The views expressed are those of the authors alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The study was approved by the CPRD Research Data Governance (study reference number 21_000665).\u003c/p\u003e\n\u003cp\u003eThis study is based in part on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement:\u0026nbsp;\u003c/strong\u003eData may be obtained from a third party and are not publicly available. The data were obtained from the Clinical Practice Research Datalink. Clinical Practice Research Datalink data governance does not allow us to distribute patient data to other parties. Researchers may apply for data access at http://www.CPRD.com/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eEthics approval was not required as this study used data from the Clinical Practice Research Datalink (CPRD), which was granted ethics approval on 10\u003csup\u003eth\u003c/sup\u003e January 2022 from the Health Research Authority (through the East Midlands - Derby Research Ethics Committee, with reference 21/EM/0265).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors:\u0026nbsp;\u003c/strong\u003eAL, GWJ, KJ and CH designed the study. AL acquired and analysed the data; JB coordinated the data management and JB and KJ supported AL with statistical analyses. VKW provided clinical interpretation to the findings. All authors contributed to the revision of the manuscript and approved the final version.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOffice for National Statistics (ONS). Half a million more people are out of the labour force because of long-term sickness. 2022. \u003c/li\u003e\n\u003cli\u003eWaddell G, Burton KA. Is work good for your health and well-being? 2006. \u003c/li\u003e\n\u003cli\u003eWynne-Jones G, Cowen J, Jordan JL, Uthman O, Main CJ, Glozier N. Absence from work and return to work in people with back pain: A systematic review and meta-analysis. Occup Environ Med. 2014;71:448\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eDepartment for Work and Pensions. Guidance for GPs, employers, hospital doctors and occupational health practitioners on using fit notes (statements of fitness for work) [Internet]. 2018 [cited 2021 Aug 5]. Available from: https://www.gov.uk/government/collections/fit-note\u003c/li\u003e\n\u003cli\u003eUK Government. Statutory Sick Pay (SSP) [Internet]. [cited 2023 Oct 20]. Available from: https://www.gov.uk/statutory-sick-pay\u003c/li\u003e\n\u003cli\u003eNHS Digital. Fit Notes Issued by GP Practices, England, March 2024. 2024. \u003c/li\u003e\n\u003cli\u003eFarrants K, Friberg E, Sjolund S, Alexanderson K. Trajectories of future sickness absence and disability pension days among individuals with a new sickness absence spell due to osteoarthritis diagnosis \u0026gt;=21 days: a prospective cohort study with 13-month follow-up. BMJ Open. 2019;9:e030054. \u003c/li\u003e\n\u003cli\u003eRysstad T, Grotle M, Aasdahl L, Dunn KM, Tveter AT. Identification and Characterisation of Trajectories of Sickness Absence Due to Musculoskeletal Pain: A 1-Year Population-based Study. J Occup Rehabil. 2023;33:277\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eWolf A, Dedman D, Campbell J, Booth H, Lunn D, Chapman J, et al. Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum. Int J Epidemiol. 2019;48:1740-1740g. \u003c/li\u003e\n\u003cli\u003eCPRD. CPRD Aurum February 2022 (Version 2022.02.001) [Data set]. Clinical Practice Research Datalink. 2022. \u003c/li\u003e\n\u003cli\u003eMason KJ, Jordan KP, Heron N, Edwards JJ, Bailey J, Achana FA, et al. Musculoskeletal pain and its impact on prognosis following acute coronary syndrome or stroke: A linked electronic health record cohort study. Musculoskeletal Care. 2023;21:749\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eNHS Digital. Fit Notes Issued by GP Practices, England, September 2023. 2024. \u003c/li\u003e\n\u003cli\u003eAkaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19:716\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eSchwarz G. Estimating the Dimension of a Model. Ann Stat. 1978;6:461\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eLo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88:767\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eMcLachlan G, Peel D. Finite Mixture Models. New York: Wiley; 2004. \u003c/li\u003e\n\u003cli\u003evan de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: Guidelines for reporting on latent trajectory studies. Struct. Equ. Model. van de Schoot, Rens: Department of Methods and Statistics, Utrecht University, P.O. Box 80.140, Utrecht, Netherlands, TC 3508, [email protected]: Taylor \u0026amp; Francis; 2017. p. 451\u0026ndash;67. \u003c/li\u003e\n\u003cli\u003eJung T, Wickrama KAS. An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Soc Personal Psychol Compass. 2008;2:302\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eCPRD. Small area level data based on patient postcode. Documentation and Data Dictionary (set 22/January 2022). 2022. \u003c/li\u003e\n\u003cli\u003eVermunt JK. Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Polit Anal. 2010;18:450\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eOffice for National Statistics (ONS). INAC01 SA: Economic inactivity by reason (seasonally adjusted). 2024. \u003c/li\u003e\n\u003cli\u003eDWP, DHSC. Guidance. WorkWell prospectus: guidance for Local System Partnerships [Internet]. 2024. Available from: https://www.gov.uk/government/publications/workwell/workwell-prospectus-guidance-for-local-system-partnerships\u003c/li\u003e\n\u003cli\u003eMcLeod CB, Reiff E, Maas E, Bultmann U. Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders. Scand J Work Environ Health. 2018;44:147\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eFarrants K, Friberg E, Sj\u0026ouml;lund S, Alexanderson K. Work disability trajectories among individuals with a sick-leave spell due to depressive episode \u0026ge; 21 days: A prospective cohort study with 13-month follow up. J Occup Rehabil. 2018;28:678\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eSpronken M, Brouwers EPM, Vermunt JK, Arends I, Oerlemans WGM, van der Klink JJL, et al. Identifying return to work trajectories among employees on sick leave due to mental health problems using latent class transition analysis. BMJ Open. 2020;10:e032016. \u003c/li\u003e\n\u003cli\u003eMarmot M, Allen J, Goldblatt P, Boyce T, McNeish D, Grady M, et al. Fair Society, Healthy Lives - The Marmot Review: Strategic Review of Health Inequalities in England post-2010. 2010. \u003c/li\u003e\n\u003cli\u003eParker M, Bucknall M, Jagger C, Wilkie R. Population-based estimates of healthy working life expectancy in England at age 50 years: analysis of data from the English Longitudinal Study of Ageing. Lancet Public Heal. 2020;5:e395\u0026ndash;403. \u003c/li\u003e\n\u003cli\u003eDepartment of Health and Social Care, Department for Work and Pensions. Employment support launched for over a million people [Internet]. 2023 [cited 2024 Feb 20]. Available from: https://www.gov.uk/government/news/employment-support-launched-for-over-a-million-people\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joor","sideBox":"Learn more about [Journal of Occupational Rehabilitation](https://www.springer.com/journal/10926)","snPcode":"10926","submissionUrl":"https://submission.nature.com/new-submission/10926/3","title":"Journal of Occupational Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sickness Absence, Fit Notes, Musculoskeletal Conditions, Mental Health Conditions, Trajectories, Latent Class Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6907087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo derive common patterns (trajectories) of work absence over time due to a musculoskeletal (MSK) or mental health (MH) condition in an English population and determine associations of these absence trajectories with health and sociodemographic characteristics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective cohort study used primary care data for 43,130 and 62,355 economically active individuals with an incident work absence (as measured by receipt of fit notes) due to a MSK or MH condition, respectively, between 2016\u0026ndash;2018. Latent class growth analysis was used to define trajectories (through issuance of fit notes), and trajectory-covariate association analysis performed through multivariable multinomial logistic regression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFive common trajectories of work absence associated with MSK and MH conditions were determined over a one-year follow-up. The two most common trajectories consisted of low absence (a \u0026lsquo;Single\u0026rsquo; fit note and \u0026lsquo;Short Term\u0026rsquo; absence), whilst the two least common trajectories were characterised by longer-term absence of six months or more (\u0026lsquo;Chronic Sustained\u0026rsquo; and \u0026lsquo;Chronic Fast Decreasing\u0026rsquo;), and the fifth by intermittent absence. Individuals associated with the two longer-term absence trajectories were: older, living in the North or Midlands or most deprived areas of England, prescribed opioids, and current smokers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study has highlighted different patterns of sickness absence due to a MSK or MH condition and profiles of individuals associated with longer-term absence. Earlier and more targeted health and work intervention towards these high-risk subgroups, alongside policy interventions to reduce health inequalities, could help alleviate the rising rate of long-term sickness absence and economic inactivity.\u003c/p\u003e","manuscriptTitle":"Trajectories of work absence in England due to a musculoskeletal or mental health condition: an electronic health record cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 18:02:58","doi":"10.21203/rs.3.rs-6907087/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-24T04:24:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-17T04:11:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-17T04:11:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Occupational Rehabilitation","date":"2025-06-16T15:29:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joor","sideBox":"Learn more about [Journal of Occupational Rehabilitation](https://www.springer.com/journal/10926)","snPcode":"10926","submissionUrl":"https://submission.nature.com/new-submission/10926/3","title":"Journal of Occupational Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c9c156d-d647-423a-8787-a995d1cc0426","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T15:59:40+00:00","versionOfRecord":{"articleIdentity":"rs-6907087","link":"https://doi.org/10.1007/s10926-025-10342-y","journal":{"identity":"journal-of-occupational-rehabilitation","isVorOnly":false,"title":"Journal of Occupational Rehabilitation"},"publishedOn":"2025-11-28 15:57:03","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-06-25 18:02:58","video":"","vorDoi":"10.1007/s10926-025-10342-y","vorDoiUrl":"https://doi.org/10.1007/s10926-025-10342-y","workflowStages":[]},"version":"v1","identity":"rs-6907087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907087","identity":"rs-6907087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}