Predictors of Workplace Disability in a Premanifest Huntington’s Disease Cohort
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
Huntington’s disease (HD) is an inherited neurodegenerative disease involving motor, cognitive, and psychiatric/behavioral impairments that will eventually affect work role functioning. Few objective data exist regarding predictors of workplace disability in HD. The authors explored the predictors of work impairment and disability in a cross-sectional cohort of 656 employed, premanifest HD (preHD) individuals. In this cohort—the majority of whom were female, urban-dwelling, married/partnered, and working full-time, with minimal cognitive impairment, good function, minimal motor abnormality, and no indication of significant mental health issues—the number of participants who reported that they had missed work due to HD was low (2.4%). However, 12% of the study sample reported experiencing impairment while working due to preHD, 12.2% reported work-related activity impairment due to preHD, and 12.7% reported impairment in their overall work ability. Higher numbers of CAG repeats on the mutant allele and having more motor symptoms were associated with significantly higher odds of experiencing workplace impairment. Importantly, several modifiable factors were also found to predict workplace disability. Specifically, higher levels of anxiety symptoms were associated with significantly higher odds of experiencing workplace impairment. Good mental and physical health served as protective factors, where good physical health was associated with 6% lower odds of experiencing impairment or missing work time and good mental health was associated with of 10%−12% lower. The results provide important new knowledge for the development of future targeted intervention trials to support preHD individuals in maintaining their work roles as long as possible.
Huntington’s disease (HD) is an inherited neurodegenerative disease involving motor, cognitive, and psychiatric/behavioral symptoms that will eventually affect work role functioning. Symptoms progressively worsen until full care is needed. The onset of motor symptoms and subsequent formal clinical diagnosis typically occur during the fourth or fifth decade of life, the age range when many individuals are at the peak of their professional careers. HD has a long presymptomatic period. Although most HD gene carriers are not clinically diagnosed until they experience motor signs, changes in cognition1 and mental health2 have been detected up to 15 years prior to formal diagnosis.3,4 These cognitive and psychiatric symptoms can have a greater impact on function and quality of life than that of the motor symptoms.5 Thus, the changes that occur in premanifest HD (preHD) may be crucial to a person’s ability to maintain day to day functions, including work functions and occupational status.
Numerous empirical studies have documented the value of employment in terms of financial reward, self-esteem, social support, personal satisfaction, community connectedness, and overall physical and mental health.6 In contrast, extended work disability is associated with negative outcomes, including financial hardship, loss of self-esteem, social isolation, and increased mental health problems.7 Work disability is associated with increased costs, such as health service provision; sick leave; and indirect costs due to the loss of work productivity, wage replacement, retirement, and disability pensions. The reduction or loss of the ability to work as a result of a neurological illness, including HD, has negative impacts on mental health, the family budget, and family dynamics.8 It is therefore important that employment be maintained for as long as possible in individuals with preHD, who may eventually develop HD with progressive disablement. While studies have considered the potential for employer discrimination as a result of genetic legacy,9–11 overall there has been a paucity of research that has considered the importance of employment in terms of preHD and, indeed, other neurodegenerative diseases.
Changes in work function may be one of the most reliable initial indicators of functional changes or decline in preHD.12,13 Data from a small qualitative study of seven individuals with preHD and their family members suggest that work function may be compromised in preHD.14 Beglinger and colleagues reported that occupational decline was common in those with preHD, with 65.1% reporting some loss of ability to engage in their typical work, as measured by the Unified Huntington’s Disease Rating Scale (UHDRS) Total Functional Capacity (TFC) scale.12 However, individuals with preHD have reported that they can continue to be productive in their work environment when workplace accommodations can be made.15 Identifying factors that may facilitate or hinder engagement in work-related functions would therefore enhance the ability to develop work accommodations that would enable preHD individuals to continue to maintain employment for as long as they wish and are able.
The aim of this study was to explore the predictors of work impairment and disability in a cohort of employed, preHD individuals. Given the lack of research in this area, the findings may inform the development of strategies or interventions to help preHD individuals maintain employment.
Methods
Study Design and Sample
We used data from Enroll-HD (ClinicalTrials.gov identifier: NCT01574053). Enroll-HD is a global clinical research platform designed to facilitate clinical research in HD. There are currently 142 study sites, located in North America, Latin America, Australasia, and Europe. Individuals with preHD or manifest HD, genotype negative or unknown, family members, and community controls participate in this study. Only gene-expanded participants with less than unequivocal signs of HD were included as preHD participants in this study. We used Enroll-HD’s baseline HD categorization (preHD versus manifest HD) of the participant, where HD status is classified using two variables: investigator determined status (based on clinical signs and symptoms and genotyping performed as part of medical care), and research genotyping status. The classification in Enroll-HD can also be based on motor signs (“certainty”) not classified as 4 (the diagnostic confidence level from the UHDRS, which is associated with motor abnormalities that are considered unequivocal signs of HD, scored by the investigator).
All enrolled participants underwent an annual assessment using a core data set covering motor, cognitive, and behavioral symptoms, with optional assessments exploring work productivity measures, quality of life, and physical functioning. All sites are required to obtain and maintain local Ethics Committee approvals, and the project conforms to the provisions of the Declaration of Helsinki. Informed consent was obtained from all participants. Data are monitored for quality and accuracy using a risk-based monitoring approach and go through a deidentification process. The third Enroll-HD periodic data set was extracted from the database on October 31, 2016, and made available on December 15, 2016. This data set includes clinical data from 8,714 participants, including 3,598 from the European REGISTRY study, which preceded Enroll-HD, and has collected data since 2011.
For the purposes of this study, we analyzed the Enroll-HD baseline data set. The sample consisted of 656 participants who were assessed as preHD, were gene positive (CAG score >36), were currently engaged in paid full- or part-time employment, and provided data (at least one subscale total) about work disability outcomes as measured by the Work Productivity and Activity Impairment—Specific Health Problem (WPAI-SHP) questionnaire.
Assessments
Sociodemographic and lifestyle factors.
The following demographic information was collected at the baseline assessment: age, gender, region (Europe, Australasia, and Latin/North America), marital status, smoking and alcohol consumption status, employment status, and years of education.
Clinical and neuropsychological measures.
The following validated and reliable scales collected data. Motor symptoms were assessed using the UHDRS Total Motor Score (TMS) scale.16 Functional assessment was conducted using the UHDRS TFC scale.16 Global cognitive functioning was assessed using the Mini-Mental State Examination (MMSE).17 Anxiety and depressive symptoms were assessed using the Hospital Anxiety and Depression Scale (HADS).18 Irritability was assessed using the Snaith Irritability Scale.19,20 Overall physical and mental health were assessed using the 12-item Short-Form Health Survey (SF-12).21 CAG repeat length was determined using laboratory genotyping.
Work productivity.
The impact of HD on work productivity was assessed using the WPAI-SHP questionnaire,22 a self-report instrument used to assess work productivity loss due to general health or a specified health problem.22 The WPAI-SHP consists of four subscales, the total scores for which are calculated as impairment percentages (from 0% to 100%), with higher numbers indicating greater impairment and less productivity (i.e., worse outcomes). The first subscale, Absenteeism (work time missed due to illness/disease), includes any time taken off from work due to preHD. The second subscale, Presenteeism, is the reported percentage of impairment while at work due to illness/disease. The third subscale, Activity Impairment is a global assessment of any work-related activity impairment due to illness or disease. The fourth subscale, Work Productivity Loss, refers to the participant’s overall productivity (on average) on days he or she actually went to work. The formulas for calculating these items are available online.23 The WPAI-SHP is a robust measure of work productivity that has been validated as a measure of work-related impairment for numerous diseases, such as multiple sclerosis, psoriasis, ankylosing spondylitis, and Crohn’s disease.24–26
Statistical Analyses
Due to small cell sizes for certain categories of the predictor variables, some of the predictor variable categories were combined prior to analysis. Region was combined into three categories: Australasia, Europe, and the Americas (which included North and Latin America). Marital status was combined into three categories: single, married/in a partnership, and divorced/separated/widowed. Years of education was combined into three categories: below diploma (International Standard Classification of Education [ISCED] levels 0, 1, 2, 3), diploma/certificate (ISCED level 4), and tertiary studies (ISCED levels 5 and 6). Residence was combined into two categories (town/city or rural/village). Smoking and drinking status were categorized as dichotomous categorical variables (drinker/nondrinker, smoker/nonsmoker). Employment status was categorized as full time, part time, or self-employed. To address study aims (and because the WPAI-SHP data for our sample were zero-inflated), the WPAI-SHP variables were dichotomized into presence or absence of any impairment for analysis using binary logistic regression models, following the suggestion of the literature (see Table 1).27
| Characteristic | N (%) | Mean (SD) | Range |
|---|---|---|---|
| Sociodemographic and lifestyle factors | |||
| Age (years) | 39.6 (10.8) | 18–71 | |
| Female gender | 375 (57.2) | ||
| Residence | |||
| Town/city | 502 (76.5) | ||
| Rural/village | 154 (23.5) | ||
| Education | |||
| Below diploma | 244 (37.2) | ||
| Diploma/certificate | 137 (20.9) | ||
| Tertiary study | 275 (41.9) | ||
| Region | |||
| The Americas | 282 (43.0) | ||
| Europe | 306 (46.6) | ||
| Australasia | 68 (10.4) | ||
| Marital status | |||
| Single | 200 (30.5) | ||
| In a partnership | 456 (69.5) | ||
| Employment status | |||
| Full-time | 504 (76.8) | ||
| Part-time | 127 (19.4) | ||
| Self-employed | 25 (3.8) | ||
| Current drinker (N=655) | 371 (56.6) | ||
| Current smoker (N=655) | 145 (22.1) | ||
| Clinical and neuropsychological measures | |||
| MMSE (N=582) | 28.7 (1.7) | 16–30 | |
| UHDRS-TMS (N=653) | 2.9 (4.0) | 0–26 | |
| UHDRS-TFC (N=656) | 12.9 (0.7) | 0–13 | |
| HADS-A subscore (N=631) | 5.2 (3.8) | 0–19 | |
| HADS-D subscore (N=631) | 3.3 (3.3) | 0–16 | |
| Irritability score (N=631) | 4.8 (3.6) | 0–18 | |
| SF-12 PCS (N=648) | 55.9 (5.8) | 26.1–73.8 | |
| SF-12 MCS (N=648) | 48.9 (9.9) | 10.5–67.0 | |
| CAG repeat length | 42.5 (2.7) | 36–54 | |
| WPAI-SHP | |||
| Absenteeism (missed work time; N=640) | 16 (2.4) | ||
| Presenteeism (impairment while working; N=555) | 79 (12.0) | ||
| Activity impairment (N=552) | 80 (12.2) | ||
| Work productivity loss/overall work impairment (N=653) | 83 (12.7) |
a
The sample size for some variables is less than 656 due to missing values. Abbreviations: HADS-A=Hospital Anxiety and Depression Scale Anxiety subscale; HADS-D=Hospital Anxiety and Depression Scale Depression subscale; MMSE=Mini-Mental State Examination; SF-12=12-item Short-Form Health Survey; SF-12 MCS=SF-12 Mental Component summary; SF-12 PCS=SF-12 Physical Component summary; UHDRS-TFC=Unified Huntington’s Disease Rating Scale Total Functional Capacity; UHDRS-TMS=Unified Huntington’s Disease Rating Scale Total Motor Score; WPAI-SHP=Work Productivity and Activity Impairment-Specific Health Problem.
The proportion of missing data for the outcome and independent variables was below 20%. No values were imputed. Mean values, standard deviations, or ranges and frequencies for sociodemographic, lifestyle, clinical, and neuropsychological measures were calculated. To identify predictors for the WPAI-SHP subscales, we first conducted a series of univariate logistic regression analyses for each outcome variable. Predictor variables with p<0.25 were included in a backward multinomial logistic regression analysis,28 with each WPAI-SHP subscale as the dependent variable, and the sociodemographic, lifestyle, clinical, and neuropsychological variables as predictors. Collinearity of all predictor variables was tested using the linear regression procedure. Statistical significance was assessed using p<0.05 and 95% confidence interval in logistic regression models. Odds ratios (ORs) were used to quantify the association between independent and outcome variables. All analyses were performed in SPSS 22 (IBM Corporation).
Results
Demographic and Clinical Characteristics
Sociodemographic, lifestyle, clinical, and neuropsychological demographics and characteristics are summarized in Table 1. The majority of participants were female (57.2%), lived in a town or city (76.5%), were married or in a partnership (69.5%), and worked full time (76.8%). Participants were from Europe (46.6%), the Americas (43%), and Australasia (10.4%). The average MMSE score was 28.7 out of a possible 30 (SD=1.7). One participant scored 16 on the MMSE, and another scored 20, but including or excluding these two cases did not affect the associations between MMSE scores and the outcome variables.
Mean TFC scores were close to the maximum level of functioning of 13 (mean=12.9 [SD=0.7]). Higher scores on the TMS indicate more impaired motor function, and the maximum possible total score is 124. Patients with manifest HD often have total motor scores in excess of 50. The mean TMS score in our overall sample (mean=2.9 [SD=4]) suggests the presence of only very minimal motor symptoms.
Scores between 0 and 7 on the HADS subscales do not indicate the presence of clinical symptoms of anxiety or depression.18 Mean total score on both the HADS Anxiety and Depression subscales (HADS-A and HADS-D) indicated absence of clinically relevant symptoms of depression and anxiety in our cohort (HADS-A, mean=5.2 [SD=3.8], and HADS-D, mean=3.3 [SD=3.3]). Participants fell far below the cut-off score of ≥14 points20 for irritability (mean=4.8 [SD=3.6]) on the Snaith Irritability Scale. SF-12 Physical Composite scores (mean=55.9 [SD=5.8]) and Mental Health composite scores (mean=48.9 [SD=9.9]) indicated an “average” level of health, according to the normative mean of 50.21
As shown in Table 1, the number of participants who reported that they had missed work due to HD symptoms was low (2.4%). Twelve percent of the study sample reported experiencing impairment while working due to preHD, 12.2% reported work-related activity impairment due to preHD, and 12.7% reported impairment in their overall work ability/work productivity loss due to preHD.
Significant Factors Associated With the Work Disability-Related Outcomes
Based on the results of univariate regression analyses for each independent variable/outcome variable, using the significance level of 0.25, the potential significant factors for each outcome were as follows:
Absenteeism: Residence (p=0.06), HADS-A (p≤0.01), HADS-D (p≤0.01), irritability score (p≤0.01), MMSE (p=0.11), SF-12 Physical Component summary (PCS; p≤0.01).
Presenteeism: Region (p=0.13), MMSE (p=0.09), TMS (p≤0.01), TFC (p=0.09), HADS-A (p≤0.01), HADS-D (p≤0.01), irritability score (p≤0.01), SF-12 Mental Component summary (MCS; p≤0.01), SF-12 PCS (p=0.12), CAG repeat length (p≤0.01).
Activity impairment: Age (p=0.13), region (p=0.19), employment status (p=0.07), TMS (p≤0.01), TFC (p≤0.06), CAG repeat length (p=0.06), HADS-A (p≤0.01), HADS-D (p≤0.01), irritability score (p≤0.01), SF-12 MCS (p≤0.01), SF-12 PCS (p=0.03).
Overall work impairment: Marital status (p=0.21), MMSE (p=0.08), TMS (p≤0.01), TFC (p=0.10), HADS-A (p≤0.01), HADS-D (p≤0.01), irritability score (p≤0.01), SF-12 MCS (p≤0.01), SF-12 PCS (p=0.05), CAG repeat length (p≤0.01).
Table 2 reports the significant (p<0.05) factors associated with the four work-disability-related outcomes using multivariable binary logistic regression.
| Item | Odds Ratio | SE | p | 95% CI |
|---|---|---|---|---|
| Absenteeism | ||||
| Predictor | ||||
| SF-12 MCS | 0.84 | 0.03 | <0.001 | 0.79–0.89 |
| Constant | 19.88 | 1.01 | 0.003 | |
| Presenteeism | ||||
| Predictor | ||||
| CAG repeat length | 1.18 | 0.06 | 0.003 | 1.06–1.32 |
| SF-12 MCS | 0.90 | 0.02 | <0.001 | 0.87–0.93 |
| SF-12 PCS | 0.94 | 0.02 | 0.007 | 0.90–0.98 |
| HADS-A | 1.09 | 0.05 | 0.046 | 1.00–1.20 |
| UHDRS-TMS score | 1.07 | 0.03 | 0.039 | 1.00–1.14 |
| Constant | 0.23 | 2.73 | 0.588 | |
| Activity impairment | ||||
| Predictor | ||||
| CAG repeat length | 1.14 | 0.06 | 0.002 | 1.01–1.29 |
| SF-12 PCS | 0.94 | 0.02 | 0.002 | 0.90–0.98 |
| SF-12 MCS | 0.90 | 0.02 | <0.001 | 0.87–0.93 |
| Constant | 0.28 | 3.49 | 0.716 | |
| Work productivity loss | ||||
| Predictor | ||||
| UHDRS-TMS score | 1.07 | 0.03 | 0.038 | 1.00–1.14 |
| CAG repeat length | 1.18 | 0.06 | 0.003 | 1.06–1.32 |
| HADS-A | 1.09 | 0.05 | 0.048 | 1.00–1.20 |
| SF-12 MCS | 0.90 | 0.02 | <0.001 | 0.87–0.93 |
| SF-12 PCS | 0.94 | 0.02 | 0.007 | 0.9–0.98 |
| Constant | 0.23 | 2.73 | 0.589 |
a
Only significant factors are presented. HADS-A=Hospital Anxiety and Depression Scale Anxiety subscale; SF-12=12-item Short Form Health Survey; SF-12 MCS=SF-12 Mental Component summary; SF-12 PCS=SF-12 Physical Component summary; UHDRS-TFC=Unified Huntington’s Disease Rating Scale Total Functional Capacity; UHDRS-TMS=Unified Huntington’s Disease Rating Scale Total Motor Score (TMS) scale.
Absenteeism: missed work time.
Participants with lower SF-12 mental component scores (OR=0.84; 95% confidence interval [CI]=0.79–0.89) were more likely to have missed work time. No other factors achieved significance.
Presenteeism: impairment while working.
Participants with longer CAG repeat lengths (OR=1.18; 95% CI=1.06–1.32), lower SF-12 Mental Component scores (OR=0.90; 95% CI=0.87–0.93), lower SF-12 Physical Component scores (OR=0.94; 95% CI=0.90–0.98), higher scores on the HADS Anxiety subscale (HADS-A; OR=1.09; 95% CI=1.00–1.20), and higher Motor scores on the TMS (OR=1.07; 95% CI=1.00–1.14) were more likely to experience impairment while working.
Activity impairment.
Participants with longer CAG repeat lengths (OR=1.14; 95% CI=1.01–1.29), or who had lower scores on the SF-12 mental (OR=0.90; 95% CI=0.87–0.93), and physical components (OR=0.94; 95% CI=0.90–0.98), were more likely to experience activity impairment.
Work productivity loss/overall work impairment.
Participants with longer CAG repeat lengths (OR=1.18; 95% CI=1.06–1.32) or who had higher scores on the HADS Anxiety subscale (HADS-A; OR=1.09; 95% CI=1.00–1.20), higher scores on the UHDRS-TMS subscale (OR=1.07; 95% CI=1.00–1.14), or lower scores on the mental (OR=0.90; 95% CI=0.87–0.93) and physical (OR=0.94; 95% CI=0.9–0.98) components of the SF-12 were more likely to experience impaired overall work ability.
No collinearity within the independent variables was identified based on the Collinearity Statistics (tolerance value above 0.2). The Hosmer and Lemeshow test was used to assess the goodness of fit, with a significant p value indicating poor model fit.28 Using this criterion, all regression models were assessed as having a good model fit.
Discussion
This exploratory cross-sectional study investigated factors associated with workplace impairment and disability in a cohort of employed preHD individuals. In general, the study participants were cognitively healthy, had minimal motor symptoms, and were functioning well in terms of their mental and physical health, with no evidence of clinical depression, anxiety, or irritability. The proportion of participants reporting having missed work due to preHD was low (2.4%). However, around one in eight participants reported experiencing impairment while working, work-related activity impairment, and loss of overall work productivity due to their preHD. Several predictors of this workplace disability were identified. We found that good mental health was a consistent significant factor of all four outcomes. CAG repeat length and self-reported physical health were significantly associated with three outcomes: presenteeism, activity impairment, and work productivity loss. Increased anxiety and motor symptoms were also significantly associated with the Presenteeism and Work Productivity Loss subscales.
Based on the strength of the odds ratios and 95% confidence intervals, the results suggest that the predictors identified were not overly strong predictors of workplace disability (odds ratios are all between 0.84–1.18; 95% confidence intervals range from 0.79–1.32). However, these predictors were, importantly, statistically significant in a clinically healthy preHD sample; are clinically relevant; and have implications in the workplace. Based on the results, the strongest predictor of workplace disability outcomes was CAG repeat length, where nearly one in five individuals (14%−18%) with more CAG repeats had more odds of experiencing workplace disability in three domains of work productivity (impairment while working, activity impairment, and overall work impairment). CAG repeat length is inversely correlated with age of onset, and it may be that the preHD participants in this study reporting more workplace impairment were those nearest to clinical onset. Indeed, we found that having more motor symptoms was significantly associated with around 1 in 14 people having greater odds of impairment while working and reporting overall work impairment. This is consistent with the literature, as it is known that the motor disorder of HD is a major source of functional disability29 and that disability is correlated with motor score.30 This study group had an average of three symptoms, which suggests that even a small number of motor impairments can cause disability.
The results also suggest that work related impairment in this group can be predicted by a number of potentially modifiable factors. Self-report measures of physical health were significantly associated with three domains of work productivity, where around one in 16 people with better physical health scores had less odds of experiencing impairment while working, experiencing activity impairment, and reporting overall work impairment.
Self-report measures of mental health were significantly associated with all four domains of work productivity, with better mental health scores associated with 12% less odds to miss work time, 10% less odds to experience impairment while working, 10% less odds to experience activity impairment, and 10% less odds to report overall work impairment. One in ten people with preHD with higher levels of anxiety symptoms reported greater odds of impairment while working and overall work impairment. The domains of mental health measured included factors as vitality/energy, social engagement, accomplishments or limitations of emotional roles, and mental health, particularly depression and anxiety symptoms. Indeed, depression and anxiety are common, frequently coexist, and are leading causes of disability worldwide. They are important known causes of absence from work and permanent disability.31
Our results support previous literature in healthy populations, where depression and anxiety symptoms have been reported to increase the risk of early retirement.32 The strongest association with retirement due to ill health has been found to be self-reported health status, and significant associations were found between depression, anxiety, and early retirement.33 In terms of comparison with workplace disability in other neurodegenerative disorders, no other such disorder has the same scope of predictive genetic testing clinically available as HD, and assessing workplace disability preclinically is therefore challenging, with scarce published literature. Findings are consistent with the very limited HD literature, suggesting that work function may be compromised in the early stages of HD.12,14 Our findings also support previous studies in Parkinson’s disease (PD), and a cross-sectional study in patients found that anxiety and older age were significant contributing factors for unavailability in the workforce.34 Mental health in early PD has also been found to have a greater influence on the likelihood of individuals leaving the workforce than motor symptoms, with those with greater baseline depression, anxiety, and overall psychiatric distress more likely to stop working in a prospective study.35
Limitations and Future Research
The rates of the missing values for the four WPAI-SHP outcomes were all below 20%, which fall in the range reported for the majority of published psychological studies.36 Since different missing data handling strategies have their own advantages and disadvantages and are based on different assumptions, and the missing data rates in general are acceptable,37 we decided not to impute the missing data. Therefore, we adopted an available-case analysis approach, which we appreciate may bias the results and weaken the generalizability of the findings.36 Additionally, due to the low number of events for each work disability subscale (and unbalanced outcome variables), some analyses lacked adequate power, including insufficient power to include interaction terms in the regression model. We were therefore unable to determine whether there were interaction effects and, if so, how they would affect the estimated associations. We were also limited in our choice of variables to those collected by the Enroll-HD study and might have missed important factors like job stress, job demands, and type of occupation that are significant predictors of work disability in other populations.38
This study relies on self-report data, which may be affected by the participant’s capacity for self-insight, often limited in the latter stages of preHD.39 Future studies could collect collateral information from family members regarding functional changes in preHD, and also from employers (although this is often a sensitive area due to privacy and discrimination concerns). It would also be interesting to investigate the causal relationship between significant predictors and outcomes. The inclusion of the estimated time to onset (based on the CAG-Age Product score) would also be informative. A self-report work function measure specific for persons with preHD has been created—the HD Work Function measure40—which distinguishes differences in work function between people with preHD and comparison participants, but so far has not been used widely in the field and is not included in the Enroll-HD protocol. This could be cross validated with the WPAI-SHP in future studies.
By using a pre-HD sample, we found that increased motor and anxiety scores were significantly associated with work disability. This suggests that motor and anxiety symptoms could be targeted areas for interventions in this group of individuals. However, since this is a single-group cross-sectional study, we were unable to determine whether the two symptoms were specifically relevant to pre-HD individuals compared with non-HD individuals. Future research involving both pre-HD and non-HD groups would help to establish this evidence. Future research should also investigate factors related to workplace disability in a cohort of symptomatic HD individuals. This would increase understanding of whether similar or different targeted strategies are needed to maintain employment given that these individuals are at a different stage of HD. Despite limitations, this study is an exploratory analysis that includes a large number of international participants. Results form the basis for future studies with more adequate sample sizes and power.
Implications for Individuals, Practitioners, Employers, and Policy Makers
This study identifies a number of key predictors of work disability-related outcomes due to preHD. Several of these key predictors are modifiable and are potentially responsive to interventions. For example, there exist effective pharmacological and nonpharmacological treatments for mental health disorders, particularly for depression and anxiety. Promotion of social support and engagement is now a target for workplace health interventions. Physical functioning, accomplishments, limitations of physical roles, extent of bodily pain, and general health can be boosted via lifestyle interventions, and asymptomatic preHD participants should thus focus on keeping physically healthy, ideally collaboratively with their healthcare providers.
Supporting people’s mental health and physical health, and the effective prevention and treatment of depression and anxiety, can be strategically targeted to help maintain preHD individuals in their work roles. Raising awareness via education and developing evidence-based effective health promotion strategies for this community will be key in any approach designed to delay workplace disability. Similar health promotion approaches are already being undertaken in other populations with the intention of delaying decline in the early or prodromal stages of neurodegenerative diseases, such as Alzheimer’s disease. This study emphasizes the importance of ongoing good physical and mental health in all workplaces, including implementing regular health checks as well as “work friendly” environments, which are strategies that support health, regardless of gene status.
Although HD is not considered a “common” disorder, HD is perhaps the most amenable of the dementias to early intervention, due to its genetic predictability and known biomarkers, allowing study in a definite preclinical, predementia phase. Thus, preHD can be used as a model to inform early-intervention strategies for more prevalent neurodegenerative disorders. Study results highlight issues that are becoming increasingly more topical, given that genetic testing has been extended to include testing for multifactorial diseases. Findings can be used to inform issues in other dementias that are becoming better determined and detected preclinically (e.g., Alzheimer’s disease, frontotemporal dementia).
Conclusions
Currently, there is very limited research and knowledge regarding determinants of workplace impairment and disability in preHD, and these important study findings encourage future research into delaying workplace disability in this population. Results stem from the large, worldwide, prospective data set of Enroll-HD and may be useful in informing future policy or strategy development designed to maintain individuals with preHD and work capacity in the workplace for as long as possible. Findings improve the international understanding of preHD and factors associated with work disability and inform initiatives and interventions to address modifiable risk and protective factors that are responsive to health promotion interventions.
Acknowledgments
The authors thank the research participants and their families.
References
1.
Paulsen JS, Langbehn DR, Stout JC, et al: Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J Neurol Neurosurg Psychiatry 2008; 79:874–880
2.
Van Duijn E, Kingma EM, Timman R, et al: Cross-sectional study on prevalences of psychiatric disorders in mutation carriers of Huntington’s disease compared with mutation-negative first-degree relatives. J Clin Psychiatry 2008; 69:1804–1810
3.
Paulsen JS: Early detection of Huntington’s disease. Future Neurol 2010; 5:85–104
4.
Paulsen JS, Nance M, Kim JI, et al: A review of quality of life after predictive testing for and earlier identification of neurodegenerative diseases. Prog Neurobiol 2013; 110:2–28
5.
Marder K, Zhao H, Myers RH, et al: Rate of functional decline in Huntington’s disease. Neurology 2000; 54:452–458
6.
Warr P: Work values: some demographic and cultural correlates. J Occup Organ Psychol 2008; 81:751–775
7.
Warr P, Jackson P: Factors influencing the psychological impact of prolonged unemployment and of re-employment. Psychol Med 1985; 15:795–807
8.
McCabe MP, Roberts C, Firth L: Work and recreational changes among people with neurological illness and their caregivers. Disabil Rehabil 2008; 30:600–610
9.
Goh AM, Chiu E, Yastrubetskaya O, et al: Perception, experience, and response to genetic discrimination in Huntington’s disease: the Australian results of the International RESPOND-HD study. Genet Test Mol Biomarkers 2013; 17:115–121
10.
Otlowski MF, Taylor SD, Barlow-Stewart KK: Major study commencing into genetic discrimination in Australia. J Law Med 2002; 10:41–48
11.
Taylor S, Treloar S, Barlow-Stewart K, et al: Investigating genetic discrimination in Australia: perceptions and experiences of clinical genetics service clients regarding coercion to test, insurance, and employment. Aust J Emerg Technol Soc 2007; 5:63–83
12.
Beglinger LJ, O’Rourke JJ, Wang C, et al: Earliest functional declines in Huntington disease. Psychiatry Res 2010; 178:414–418
13.
Paulsen J, Wang C, Duff K, et al: Challenges assessing functional outcomes in early Huntington’s disease. Clin Genet 2009; 76(S1):A20
14.
Downing NR, Williams JK, Paulsen JS: Couples’ attributions for work function changes in prodromal Huntington disease. J Genet Couns 2010; 19:343–352
15.
Vaccarino AL, Sills T, Anderson KE, et al: Assessment of day-to-day functioning in prodromal and early Huntington disease. PLoS Curr 2011; 3:RRN1262
16.
Huntington Study Group: Unified Huntington’s disease rating scale: reliability and consistency. Mov Disord 1996; 11:136–142
17.
Folstein MF, Folstein SE, McHugh PR: “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189–198
18.
Zigmond AS, Snaith RP: The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 1983; 67:361–370
19.
Snaith RP, Taylor CM: Irritability: definition, assessment and associated factors. Br J Psychiatry 1985; 147:127–136
20.
Reedeker N, Bouwens JA, Giltay EJ, et al: Irritability in Huntington’s disease. Psychiatry Res 2012; 200:813–818
21.
Ware J Jr, Kosinski M, Keller SD: A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996; 34:220–233
22.
Reilly MC, Zbrozek AS, Dukes EM: The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics 1993; 4:353–365
23.
Reilly Associates: WPAI scoring. http://www.reillyassociates.net/WPAI_Scoring.html. Accessed April 7, 2017
24.
Andréasson E, Svensson K, Berggren F: The validity of the Work Productivity and Activity Impairment Questionnaire for patients with asthma (WPAI-asthma): results from a web-based study. Value Health 2003; 6:780
25.
Bolge SC, Doan JF, Kannan H, et al: Association of insomnia with quality of life, work productivity, and activity impairment. Qual Life Res 2009; 18:415–422
26.
Boonen A, Boone C, Albert A, et al: Understanding limitations in at-work productivity in patients with active ankylosing spondylitis: the role of work-related contextual factors. J Rheumatol 2015; 42:93–100
27.
Delucchi KL, Bostrom A: Methods for analysis of skewed data distributions in psychiatric clinical studies: working with many zero values. Am J Psychiatry 2004; 161:1159–1168
28.
Hosmer DW, Lemeshow S, Sturdivant RX: Applied logistic regression. Hoboken, NJ: Wiley, 2013. http://onlinelibrary.wiley.com/book/10.1002/9781118548387. Accessed April 7, 2017
29.
Ross CA, Pantelyat A, Kogan J, et al: Determinants of functional disability in Huntington’s disease: role of cognitive and motor dysfunction. Mov Disord 2014; 29:1351–1358
30.
Mahant N, McCusker EA, Byth K, et al: Huntington’s disease: clinical correlates of disability and progression. Neurology 2003; 61:1085–1092
31.
Koopmans PC, Bültmann U, Roelen CAM, et al: Recurrence of sickness absence due to common mental disorders. Int Arch Occup Environ Health 2011; 84:193–201
32.
Wedegaertner F, Arnhold-Kerri S, Sittaro NA, et al: Depression- and anxiety-related sick leave and the risk of permanent disability and mortality in the working population in Germany: a cohort study. BMC Public Health 2013; 13:145
33.
Pit SW, Shrestha R, Schofield D, et al: Health problems and retirement due to ill-health among Australian retirees aged 45–64 years. Health Policy 2010; 94:175–181
34.
Timpka J, Svensson J, Nilsson MH, et al: Workforce unavailability in Parkinson’s disease. Acta Neurol Scand 2017; 135:332–338
35.
Armstrong MJ, Gruber-Baldini AL, Reich SG, et al: Which features of Parkinson’s disease predict earlier exit from the workforce? Parkinsonism Relat Disord 2014; 20:1257–1259
36.
Dong Y, Peng C-YJ: Principled missing data methods for researchers. Springerplus 2013; 2:222
37.
Pigott TD: A review of methods for missing data. Educ Res Eval 2001; 7:353–383
38.
Hannerz H, Holbæk Pedersen B, Poulsen OM, et al: A nationwide prospective cohort study on return to gainful occupation after stroke in Denmark 1996–2006. BMJ Open 2011; 1:e000180
39.
Duff K, Paulsen JS, Beglinger LJ, et al: “Frontal” behaviors before the diagnosis of Huntington’s disease and their relationship to markers of disease progression: evidence of early lack of awareness. J Neuropsychiatry Clin Neurosci 2010; 22:196–207
40.
Brossman B, Williams JK, Downing N, et al: Development of the Huntington Disease Work Function Scale. J Occup Environ Med 2012; 54:1300–1308
Information & Authors
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Received: 30 April 2017
Revision received: 27 July 2017
Accepted: 5 August 2017
Published online: 29 November 2017
Published in print: Spring 2018
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The authors report no financial relationships with commercial interests.
Funding Information
CHDI Foundation10.13039/100005725: Enroll-HD
Enroll-HD, a longitudinal observational study for Huntington’s disease families intended to accelerate progress toward therapeutics, is sponsored by CHDI Foundation, a nonprofit biomedical research organization exclusively dedicated to developing therapeutics for Huntington’s disease.Metrics & Citations
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