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Abstract
Objectives We describe the development and initial validation of a new scale for measuring non-illness factors that are important in predicting occupational outcomes, called the NIPSA (non-illness predictors of sickness absence) scale.
Methods Forty-two questions were developed which covered a broad range of potential non-illness-related risk factors for sickness absence. 682 participants in the South East London Community Health study answered these questions and a range of questions regarding both short-term and long-term sickness absence. Factor analysis was conducted prior to examining the links between each identified factor and sickness absence outcomes.
Results Exploratory factor analysis using the oblique rotation method suggested the questionnaire should contain 26 questions and extracted four factors with eigenvalues greater than 1: perception of psychosocial work environment (factor 1), perceived vulnerability (factor 2), rest-focused attitude towards recovery (factor 3) and attitudes towards work (factor 4). Three of these factors (factors 1, 2 and 3) showed significant associations with long-term sickness absence measures (p<0.05), meaning a final questionnaire that included 20 questions with three subscales.
Conclusions The NIPSA is a new tool that will hopefully allow clinicians to quickly assess for the presence of non-illness factors that may be important in predicting occupational outcomes and tailor treatments and interventions to address the barriers identified. To the best of our knowledge, this is the first time that a scale focused on transdiagnostic, non-illness-related predictors of sickness absence has been developed.
- Sickness absence
- return to work
- psychosocial work environment
- vulnerability
- rest
- recovery
- work
- occupational outcomes
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- Sickness absence
- return to work
- psychosocial work environment
- vulnerability
- rest
- recovery
- work
- occupational outcomes
What this paper adds
Regardless of the underlying medical diagnosis, symptom severity alone is not a strong predictor of occupational outcomes, such as sickness absence.
There are a range of other factors relating to individual perceptions, response to symptoms and the workplace that can influence an individual’s sickness absence behaviour.
We have described a new measure, called the NIPSA (non-illness predictors of sickness absence) scale, which consists of 20 questions that can provide reliable measures on three subscales: perception of psychosocial work environment (factor 1), perceived vulnerability (factor 2) and rest-focused attitude towards recovery (factor 3).
We have also demonstrated how an individual’s scores on each of these factors are associated with measures of long-term sickness absence behaviour.
Once this new scale has been further validated, it is hoped that clinicians will be able to use NIPSA to identify non-illness factors that are risk factors for short-term episodes of sickness absence progressing to long-term sickness absence or recurrent episodes of sickness absence.
Introduction
Sickness absence is a major public health and economic problem across the developed world.1 Over recent decades there has been a gradual change in the ascribed medical causes of sickness absence, with mental disorders now being the leading diagnosis in cases of long-term sickness absence (LTSA) and disability benefits in most developed countries.2–4 Regardless of whether an episode of sickness absence is due to mental or physical illness, there is increasing evidence that objective measures of symptom severity are not a reliable predictor of the duration of any period of sickness absence.5–7 Indeed, at a population level over the last century, rates of incapacity benefits and LTSA have tended to move in the opposite direction to most overall measures of population health.8 These observations indicate that sickness absence is a complicated behavioural response that can be influenced by many factors other than the medical diagnosis and simple symptom levels.4
Researchers studying individual disorders have begun to identify a number of non-illness-related predictors of sickness absence relevant for each disorder. For example, among those with back pain, fear-avoidance beliefs and behaviours have been found to consistently predict sickness absence, even after controlling for pain intensity and physical disability.9 Avoidant behaviour in response to symptoms and rest-focused attitudes to recovery have also been found to predict LTSA among those with chronic fatigue.5 Life course studies, which have followed individuals from childhood through to later life, have identified a number of other individual factors, such as temperament, perceived health vulnerability and general intelligence, that are predictors of occupational incapacity in later life, independent of the level of physical or mental health symptoms.10–13
Not surprisingly, a range of workplace factors have also been identified that appear to influence rates of sickness absence. In the occupational health literature, there are three main models that are typically used to examine the impact of the psychosocial work environment on workers’ health: the job demand-control-support model14; the effort–reward imbalance model15; and the organisational justice model.16 The psychosocial work environment, as measured by these models, is known to be associated with a range of health outcomes, such as depression, cardiovascular disease and overall mortality.17–20 Given this, it is not surprising that these same measures of the workplace environment are often found to be robust predictors of sickness absence.21–23 However, there is now emerging evidence that these workplace factors may predict sickness absence independently of their impact on health. A recent study of more than 7000 Norwegian workers followed for 12 months replicated the association between job strain (the combination of high job demand and low decision latitude) and sickness absence, but then went on to show that this effect could not be explained by extensive measures of physical and mental health.24
The importance of recognising non-illness-related predictors of sickness absence is highlighted by the observation that symptom-based treatments alone are often not enough to return an individual to work after a period of sick leave.25 In order to maximise occupational recovery, the individual and workplace factors that are contributing to sickness absence behaviours need to be identified and addressed in addition to symptom-focused treatments. However, to date there has not been a simple way to measure the different factors that may contribute to sickness absence in individual cases. In this paper we describe the development and initial validation of a new scale for measuring non-illness factors that may be important in predicting occupational outcomes, called the NIPSA (non-illness predictors of sickness absence) scale.
Methods
Questionnaire development
The NIPSA questionnaire was designed to measure a broad range of individual and work-related factors that have been suggested to predict occupational health outcomes. A total of 42 questions were developed, which covered the following topics: perceived vulnerability, attitude towards employment, the psychosocial work environment, relationship with employer, coping style at work and response to symptoms. The choice of which psychosocial work risk factors to include was informed by a systematic review on this topic, which our research team was conducting at the same time.26 The selections of the other domains covered were based on a series of conversations with colleagues familiar with each topic area. Each question was posed as a statement (eg, ‘I am better than most people at handling stressful situations’), with participants asked to rate how much they agree or disagree with each statement. A Likert-type scale with five choices (strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree, strongly agree) each assigned with a numerical value between 1 and 5 was used. Participants were instructed to think about their current or last job when answering the work-related questions. Half of the items were reverse-scored in order to reduce response biases such as acquiescence or agreement response tendency.27 28
Study population
The South East London Community Health (SELCoH) study is a psychiatric and physical morbidity survey carried out on communities in the south London boroughs of Southwark and Lambeth.29 Households (defined as one person or a group of people who share accommodation as their only or main residence) were identified using the Small User Postcode Address File. Trained interviewers visited each selected household at least four times at different times of the day. Interviews were conducted on as many adults aged 16 years and older who lived at the given address and were available during any one of the visits. Interviews were conducted using a computer-assisted interview schedule. As part of the interview, participants completed the 42-item NIPSA questionnaire.
Of the 1439 residents who were contacted between 2011 and 2013, interviews were conducted with 1052 participants (73% response rate) using a computer-assisted interview schedule. A previous study using SELCoH data has shown that this sample had similar demographic and socioeconomic indicators to UK Census Information for the catchment area.30 In order to avoid the potential issues that may arise as a result of clustering by household, only one individual from each household was randomly selected and kept in the analysis, while the remaining members of the same household were excluded. In addition, those who were not of working age (aged 16–65 years for men, aged 16–59 years for women) were excluded (156 individuals), leaving a final sample of 682 individuals used for the development and evaluation of the scale.
Factor analysis and selection of questions
Suitability for factor analysis was assessed using a series of tests: Bartlett’s test of sphericity, Kaiser-Meyer-Olkin measure of sampling adequacy test31 and a sample size to variable ratio of more than 5:1.32 Once these requirements were adequately satisfied, an exploratory factor analysis (EFA) of principal factors was performed to investigate the latent structure of the questionnaire. The number of unrotated factors to be extracted was determined using Kaiser’s criteria of eigenvalues greater than 133 and the scree plot.34 The point at which the drop of the curve ceases and levels on the scree plot indicates the maximum number of factors to be extracted. As it was established a priori that the proposed factors would likely be correlated, we used the oblique rotation (ie, promax approach method) to extract the factors. Individual questions that did not load adequately (factor loadings <0.30) onto any of the extracted factors or those that loaded strongly (factor loadings ≥0.3) onto more than one factor were removed. This process was repeated, with reducing number of questions, until an interpretable factor structure was obtained. Once a factor structure was established, the appropriateness of each question to the overall factor label was assessed by the authors.
The internal consistencies of the extracted factors were calculated using Cronbach’s alpha coefficient.35 An alpha coefficient >0.70 denotes satisfactory reliability for exploratory research. Item–total correlation, which is the correlation between an item in the factor and the aggregate of the other items of the same factor, was also inspected; a minimum correlation of 0.20 is the typical threshold used for retaining an item in the factor.36
Factor scores were created by summing the raw scores of the items, with additional Bartlett factor scores created using the score prediction command (predict) in Stata V.12.0. This refined method for computing factor scores maximises validity and produces unbiased estimates of the factor scores that are highly correlated with a given factor. Reverse scoring was required for the negatively phrased items. Factor score distribution was described using the mean and SD where data were normally distributed. All statistical analyses were performed on Stata V.12.0 for Windows.37
Validation against measures of occupational outcomes
Among those who were in employment at the time of the interview, recent sickness absence was enquired about in two ways. First, participants were asked about LTSA, which for this study was defined as an episode of sick leave lasting more than 2 weeks had occurred over the past 2 years. Participants were also required to complete questions from the WHO Health and Work Performance Questionnaire concerning absenteeism. They were asked about the number of hours they are expected to work every week, as well as the number of full or partial days of work that they missed in the preceding 4 weeks due to their own physical or mental ill health. Relative absenteeism was calculated as the percentage of work hours missed during the 4 weeks prior to the interview, where a value of 0 indicates no sickness absence and 100% equates to total sickness absence. Previous studies have shown the validity of the questions and the scoring method and have demonstrated that the estimates correlate closely with employer records of absenteeism. Any participant who reported receiving incapacity benefits was classified as permanently sick/disabled or unemployed respectively.
Spearman’s correlation analysis was performed to examine associations between each of the factors and relative absenteeism. Student’s t-tests were conducted to determine the relationship between the factors and permanent sickness/disability or LTSA (at least one episode of sick leave of 2 weeks or more in the past 2 years).
Results
Demographic information
The baseline characteristics of the study participants are presented in table 1. The mean age of the population was 39.2 years, the percentage of female participants was 59% (n=400), and a slight majority had an education level higher than a General Certificate of Secondary Education or equivalent qualifications (n=467).
Demographic information and baseline characteristics of study participants (n=682)
Construct validity
EFA using the oblique rotation method extracted four factors with eigenvalues greater than 1. Factor 1 had an eigenvalue of 3.89 and explained 45% of the variance; factor 2 had an eigenvalue of 1.89 and accounted for 21% of the variance; factors 3 and 4 had eigenvalues of 1.40 (explained variance of 16%) and 1.32 (explained variance of 14%), respectively. The four-factor solution, demonstrated in table 2, explained 96% of the total variance. The scree plot also suggested a four-factor structure for the questionnaire. Factor rotation using the oblique method was administered to extract the four factors, which were labelled as follows: perception of psychosocial work environment (factor 1), perceived vulnerability (factor 2), rest-focused attitude towards recovery (factor 3) and attitudes towards work (factor 4). Of the original 42 proposed questions, 12 were removed by the stepped EFA. The items ‘At work I feel part of a team that works well’, ‘I easily feel criticized by my co-workers’, ‘I am easily embarrassed by health problems’, and ‘I tend to be a bit of an all or nothing person’ were removed because they were deemed by the authors as poor matches for their respective factors. After the removal of these items, factor analysis was repeated on the remaining 26 items and 10 items loaded onto factor 1, with loadings ranging from 0.34 to 0.78. Six items loaded onto factor 2 with factor loadings between 0.32 and 0.60. Factor 3 was loaded with four items (factor loadings ranging from 0.63 to 0.67), while factor 4 had six items with factor loadings between 0.30 and 0.60. Generally, items from each factor had loadings of less than 0.1 on the other factors.
Principal factor analysis of the NIPSA (factor loadings and internal consistency)
Internal consistency
Cronbach’s alpha coefficient was calculated for each of the factors to determine the internal consistency of the derived subscales. Factors 1 and 3 showed acceptable internal consistency (α=0.79 and 0.70, respectively); however, the alpha coefficients for factor 2 (α=0.61) and factor 4 (α=0.62) indicated suboptimal internal consistency. Chronological removal of items from the subscales resulted in no additional improvements to the internal consistencies. None of the items had item–total correlations lower than 0.2, and therefore were all retained in the questionnaire.
Correlations with occupational outcomes
Factor scores were computed for the four extracted factors using both the non-refined (summed raw scores) and refined (Bartlett scores) approaches. The distributions of the summed raw scores are summarised in table 3. Bartlett factor scores are standardised, and therefore for all factors the mean and SD were approximately 0 and 1, respectively. A higher score for psychosocial work environment represents more positive perceptions of the workplace and less work-based risk factors; a higher score on perceived vulnerability indicates stronger feelings or perceptions of vulnerability at work; a higher rest-focused attitude towards recovery score suggests greater beliefs that rest from work is important for recovery from illness; and a higher score for the factor attitudes towards work represents stronger views on the importance of work. Relative absenteeism was non-normally distributed, and therefore the Spearman’s rank correlation coefficient was calculated to investigate its association with each of the factors (table 4). There were no significant correlations between any of the factors and relative absenteeism (all p>0.05), regardless of whether the score was calculated using the refined (Bartlett scores) or non-refined (summed scores) method.
Factor score distribution (summed raw scores)
Correlation between factor scores (summed and Bartlett scores) and relative absenteeism (population in full-time or part-time employment only)
Table 5 displays the comparison of the summed factor scores among the working population with or without an LTSA episode in the past 2 years. The differences between the summed factor scores for those who were in employment and those who were permanently sick or disabled are also presented in the same table. LTSA was associated with significantly lower scores for psychosocial work environment (p=0.04) and higher perceived vulnerability scores (p=0.02). Similarly, compared with those in employment, the sick or disabled participants had significantly lower scores for psychosocial work environment (p<0.01), significantly higher scores for perceived vulnerability (p<0.01) and marginally higher scores for the rest-focused attitude towards recovery factor (p=0.05). Among the whole sample, the scores for attitudes towards work were not associated with either LTSA or permanent sickness/disability (p>0.05 for both). However, among women, sick or disabled participants had significantly higher scores for attitudes towards work (21.6 vs 19.9, p=0.01). The same comparisons (group with LTSA compared with group without LTSA; employed compared with permanently sick or disabled) were performed using the factor scores generated using Bartlett’s approach. Similar results were achieved. Stratified analyses were also carried out in order to look at the association between factor scores and each occupational measure among those less than or more than 40 years of age. Similar associations were observed in both age categories.
Raw sum factor scores comparison among those with or without long-term sickness absence (full-time/part-time employment population only) and permanent sickness/disability
Discussion
This paper describes a new questionnaire, the Non-illness Predictors of Sickness Absence, or NIPSA, scale. This 20-item scale provides scores for three factors: psychosocial work environment, perceived vulnerability and rest-focused attitude toward recovery, each of which we have shown to be associated with long-term or permanent sickness absence. While a fourth factor, attitudes to work, was identified, it was only associated with sickness absence among women and thus is not included in this final proposed questionnaire. The three-factor scale proposed has an additional degree of face validity, with a factor for each of work, self and health. To the best of our knowledge, this is the first time that a scale focused on transdiagnostic, non-illness-related predictors of sickness absence has been developed.
Given the enormous public health, social and economic impacts of LTSA,1 8 the development of this type of scale is overdue. Importantly, we feel this simple, relatively short scale will have practical uses in clinical settings, particularly when health professionals are being asked to advise workers or employers about the likelihood of return to work and the barriers that need to be overcome to achieve this. The NIPSA scale should provide occupational health clinicians with a simple and quick method to evaluate what, if any, non-illness factors they need to be considering in addition to symptomatic management in order to maximise the possibility of good functional and occupational recovery. Each of the three subscales described can be linked to a specific suite of additional interventions. Individuals scoring highly on the psychosocial work environment subscale may benefit from work-based interventions (focused either on their own perceptions or the workplace itself), while those scoring highly on the perceived vulnerability or rest-focused attitude toward recovery subscales may need additional cognitive or behaviour interventions, respectively. Our findings also highlight the importance of perceptions of self, work and health in predicting functional outcomes from ill health, each of which are open to influence in a number of ways. From an academic point of view, it is hoped that NIPSA will also be able to be used to explore the impact of various interventions on these three factors.
There are some limitations related to the development and initial validation of this scale that require some consideration. The initial 42 questions used in the development were developed by the authors and based on the available literature and theoretical models. Attempts were made to cover all potentially modifiable non-illness-related predictors of sickness absence, but it is likely that not all relevant factors were covered in the initial group of questions. There are a number of workplace factors that have been shown to be associated with sickness absence that are not directly addressed in the included questions, such as bullying or workplace conflicts.38–40 It is also possible that some physical attributes of the workplace may be important barriers for return to work, especially in the case of physical ill health. The inclusion of additional questions in order to cover such topics may potentially improve the ability of NIPSA to predict sickness absence behaviour, but at the expense of making the questionnaire longer and therefore less likely to be used. Our hope is that NIPSA becomes a tool that evolves as information on new risk factors becomes available. However, we would argue that new questions should only be added if they describe a new factor beyond the three already described or if they substantially improve the ability of the questionnaire to measure individual’s alignment within the already defined categories. One of the subscales identified, perceived vulnerability, had an alpha value that suggests suboptimal internal consistency, although this does not mean the items contained in this subscale are not important, rather that they are not conforming to a single dimension as well as some of the other factors. The importance of the fourth factor, attitudes to work, remains unclear. Our analyses suggest it may be important in subsections of the population, particularly women. Further analyses on new samples will be required to determine if the additional six questions that measure this factor should be included in the NIPSA questionnaire.
In terms of the initial validation, the main limitation is the use of cross-sectional data that enquire about past sickness absence, which makes it impossible to know the temporal sequence, meaning reverse causality may explain some of the associations observed. This is likely to be particularly problematic, along with recall bias, among those who are not currently at work and are recalling their prior work environment. This is an important limitation and means that NIPSA requires further validation with prospective data and that tools such as the NIPSA should not yet be used for screening employees prior to employment or at the commencement of a period of sickness absence. Cultural influences can be important in predicting sickness absence behaviour. The population used for this study, based in South East London, is very culturally diverse, which is a strength, but it is also more deprived and mentally unwell compared with the rest of the UK,30 which may limit the generalisability of our results. As such, it will also be important for the NIPSA questionnaire to be translated into other languages and tested in different cultural and economic settings.
In summary, the 20-item NIPSA questionnaire is a new tool that in the future will hopefully allow clinicians to quickly assess for the presence of non-illness-related factors that may be important in predicting occupational outcomes. While this tool needs to be further validated with prospective data and evidence-based cut-off scores developed for each subscale, we hope that in time it will provide clinicians with an easy way to tailor treatment and interventions that can reduce the occupational impact of ill health.
References
Footnotes
Contributors The idea for a new scale measuring non-illness predictors of sickness absence was developed by SBH in consultation with MHe and MHo. M-JW undertook the analysis. SLH, IM, MHe, SD, MHo and SBH assisted M-JW with the interpretation of the results. All authors contributed to the manuscript preparation.
Funding This paper represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The research was further supported by the Biomedical Research Nucleus joint infrastructure grant from Guy’s and St Thomas’ Charity and the Maudsley Charity, NSW Health Australia and by the Economic and Social Research Council (grant number RES-177-25-0015). SBH and M-JW were funded by NSW Health. SD was funded by the Donald Dean Research Fellowship in Work and Mental Health, awarded by the Royal College of Psychiatrists.
Disclaimer The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. None of the funders had any role in the study design, collection, analysis, interpretation of data, writing of the manuscript or the decision to submit this manuscript for publication.
Competing interests None declared.
Ethics approval King’s College London research ethics committee for non-clinical research populations: reference CREC/07/08-152.
Provenance and peer review Not commissioned; externally peer reviewed.