The association of pain with labor force participation, absenteeism, and presenteeism in Spain

Abstract

Objectives:

The aims of this paper are to generate estimates of the association between the severity and frequency of pain in Spain and (i) labor force participation and workforce status and (ii) patterns of absenteeism and presenteeism for the employed workforce.

Methods:

Data are from the internet-based 2010 National Health and Wellness Survey (NHWS). This survey covers both those who report experiencing pain in the last month as well as the no-pain population. An estimated 17.25% of adults in Spain report experiencing pain in the past month. A series of regression models are developed with the no-pain group as the reference category. The impact of pain, categorized by severity and frequency, is assessed within a labor supply framework for (i) labor force participation and (ii) absenteeism and presenteeism. Both binomial and multinomial logistic models are estimated.

Results:

The results demonstrate that severe and moderate pain has a significant, substantive, and negative association with labor force participation and, together with the experience of mild pain, a substantive impact on absenteeism and presenteeism within the employed workforce. Compared to no-pain controls, the strongest association is seen in the case of severe pain, notably severe daily pain and labor force participation (odds ratio 0.363; 95% CI: 0.206–0.637). The association of severe pain with labor force participation is also significant (odds ratio 0.356; 95% CI: 0.217–0.585). There is a clear gradient in the association of pain severity and frequency with labor force participation. The impact of pain is far greater than the potential impact of other health status measures (e.g., chronic comorbidities and BMI). Labor force participation is also adversely associated with pain experience. Persons reporting severe daily pain are far more likely not to be in the labor force (relative probabilities 0.339 vs 0.611). The experience of pain, notably severe and frequent pain, also outstrips the impact of other health status factors in absenteeism and presenteeism. In the former case, the odds ratio associated with severe daily pain is 16.216 (95% CI: 5.127–51.283), which contrasts to the odds ratio for the Charlson comorbidity index of 1.460 (95%CI: 1.279–1.666). Similar results hold for presenteeism. The contribution of moderate and mild pain to absenteeism and presenteeism is more marked than for labor force participation.

Conclusions:

The experience of pain, in particular severe daily pain, has a substantial negative impact both on labor force participation in Spain as well as reported absenteeism and presenteeism. As a measure of health status, it clearly has an impact that outstrips other health status measures. Whether or not pain is considered as a disease in its own right, the experience of chronic pain, as defined here, presents policy-makers with a major challenge. Programs to relieve the burden of pain in the community clearly have the potential for substantial benefits from societal, individual, and employer perspectives.

Key words::

• Pain
• Severity
• Labor force
• Absenteeism
• Presenteeism

Introduction

Health status is seen as a major determinant of labor supply. In the human capital framework of labor supply there are theoretical reasons for predicting that deteriorating health and the presence of chronic disease and associated comorbidities, to include severe and frequent pain will be associated with reduced labor supply1. This would be expected to be seen both in the decision to participate in the labor force as well as in a higher incidence of absenteeism and presenteeism among those who continue to be employed.

In countries such as the US, the UK, Australia, and Sweden, the association between chronic health conditions, associated disabilities, and the presence of pain-specific conditions on labor force status, absenteeism, and presenteeism are well established. While the majority of these have looked at claims for general health status and labor market outcomes, a particular focus has been on health risk factors such as obesity, smoking, and alcohol use, although specific chronic disease states such as diabetes have been evaluated together with disease states or conditions associated with chronic pain such as depression2–4. Chronic low back pain has been examined extensively in terms of both its correlates with employment activity as well as attempts to establish predictors of absenteeism and reduced labor force participation5,6. In the US workforce a range of common pain conditions have been shown to impact productive time7. Pain has been shown in small scale employer studies to impact physical and mental health, to limit work performance, and increase absenteeism and presenteeism8. In respondents with chronic pain, psychosocial problems such as abuse, mood disorders, employment handicaps, and poor coping skills are common9. Regional studies have also evaluated the association of widespread pain with work status, showing, in a recent Swedish study, pain to be a significant negative factor10. Most recently, Langley et al.11, in a multivariate analysis, have shown a strong negative association between the severity and frequency of pain and labor force participation, absenteeism, and presenteeism in five countries—the UK, France, Spain, Germany, and Italy. The experience of the individual countries was not assessed.

Evidence for the association between the experience of pain, in terms of both its severity and frequency, and labor force participation, absenteeism, and presenteeism, in Spain is limited. As far as can be ascertained, there are no studies which have attempted to evaluate the quantitative relationship between the experience of pain and employment status, absenteeism, and presenteeism at the national level for Spain. There have, however, been a number of studies that have considered the association between the experience of specific disease states and labor force activity. Particularly noteworthy are studies that have looked at the impact of migraine12, HIV/AIDS13, and disability14. To these should be added the earlier studies by Bassols et al.15 describing the impact of pain in the Catalonian population. More recently, Fernández-de-las-Peñas et al.16 report on estimates from the 2006 Spanish National Health Survey to assess the prevalence of neck and low back pain with occupation and income as part of a profile of socio-economic status. Two recent companion Spanish studies have reported on the overall prevalence and correlates of pain17 and the impact of pain on health-related quality-of-life and healthcare resource utilization18.

Importantly, from a labor market perspective, chronic and recurrent pain is now considered by many to be a disease in its own right and not merely a symptom. Pain is no longer seen as related to an evolving injury but as reflecting pathophysiological changes within the nociceptive system with psychosocial responses that perpetuate the problem19. It is important, therefore, to evaluate the potential impact of this often debilitating condition as the basis for assessments of the benefits and costs of pain management programs20.

The purpose of this paper is to attempt to fill these gaps by providing an evaluation of (i) estimates of the prevalence of pain within the Spanish workforce; (ii) the contribution of pain severity and frequency to reduced labor force participation in Spain; and (iii) the contribution of pain severity and frequency to increased rates of absenteeism and presenteeism for those in employment. In all cases the impact of pain will be assessed in terms of a control ‘no pain’ reference group, those who did not experience pain in the adult Spanish population in the past 30 days.

Methods

For the purpose of this analysis pain is considered to be exogenous. It is recognized that there may be an argument for endogeneity where the experience of pain may be an outcome of prior workforce decisions (e.g., occupational choice), the presence of chronic disease (and their associated risk factors), or unobserved individual or household characteristics. Even so, evidence for the impact of endogeneity is mixed2,21,22. In the present case experience of chronic disease and its contribution to the experience of pain is only considered through the presence of comorbidities as a potential confounding characteristic, recognizing that there are pain conditions such as fibromyalgia which appear unrelated to any underlying disease etiology.

National health and wellness survey

The NHWS is a syndicated, annual and biannual, internet-based, cross-sectional study of the healthcare attitudes, behaviors, and characteristics of the adult population. It is designed to be representative of the population 18 years of age and over in each country and has been undertaken in the US, UK, France, Spain, Germany Italy, urban China, and Japan. Since its initiation in 1998, over 600,000 survey responses across ∼140 conditions have been collected. In addition, several supplementary studies have been conducted in which NHWS respondents were re-contacted and asked further questions. The present analysis is based on the results of the 2010 NHWS for Spain. The NHWS has also supported three recent pan-European studies of the prevalence, correlates, and impact of pain11,23,24.

Prevalence, severity, and frequency of pain

Respondents to the 2010 NHWS who indicated that they had had pain or fibromyalgia in the last 12 months were asked if they had experienced pain in the last month and the condition(s) that had caused pain. If respondents indicated that they had only experienced menstrual pain, migraine, dental pain, or headache in the last month, they were excluded from the pain category. The 2010 NHWS survey of Spain yielded a total of 5039 respondents 18 years of age and over. A total of 868 (17.22%) indicated that they had experienced pain in the last month and met the inclusion criteria. In weighted terms, with weights applied by age and gender, these responses yield an estimated population of 35.39 million, with 6.10 million reporting pain (17.25%).

Pain severity and pain frequency reported are detailed in Table 1. Overall, of the 6.10 million estimated to have experienced pain in the last month, 11.69% reported severe pain, 64.17% moderate pain, and 24.14% mild pain. Severe daily pain was experienced by 9.45%. The prevalence of pain between those in the labor force and those not in the labor force is similar (17.36% vs 17.10%). Where the difference lies is in the prevalence by pain severity. The prevalence of severe pain in the labor force is less than one half of that for those not in the labor force. Moderate pain prevalence is somewhat lower, but the prevalence of mild pain is higher.

Table 1.  Pain severity and frequency of pain, 2010 NHWS, weighted estimates, Spain.

Pain dimension	Distribution (%)
Pain level
Mild	24.17
Moderate	64.17
Severe	11.69
Total	100.00
Pain frequency
Daily	40.29
4–6 times a week	12.75
2–3 times a week	17.74
Weekly or less	29.22
Total	100.00
Pain level and frequency
Severe daily pain	9.45
Moderate daily pain	28.41
Pain and labor force status
Prevalence: labor force	17.36
Prevalence: not in the labor force	17.10
Prevalence: Pain severity and labor force status
Labor force: severe pain	1.43
Labor force: moderate pain	10.98
Labor force: mild pain	4.94
Not in the labor force: severe pain	2.93
Not in the labor force: moderate pain	11.20
Not in the labor force: mild pain	2.98

Source: NHWS, 2010.

Dependent variables

The NHWS asks respondents to report on their current workforce status in terms of (i) employment status—full-time, part-time, self-employed; (ii) unemployment status—actively looking for work; and (iii) not in the labor force. The weighted distribution of respondents by workforce status is shown in Table 2. Among those not reporting pain, 37.65% were employed full time, with a further 6.30% employed part time. Among those reporting pain only 34.58% were employed full time, with a further 7.95% employed part time. There is a marked decline in labor force participation with increasing severity of pain. Among those with moderate pain labor force participation is 60.27%; falling to 43.00% for those with severe pain. In the case of persons employed full time, those with mild pain report 44.94% in full time employment, falling to 20.16% for those with severe pain.

Table 2.  Distribution by labor force status of persons reporting pain, 2010 NHWS, weighted estimates, Spain.

Labor force status	Persons reporting no pain (%)	Persons reporting pain (%)	Persons reporting mild pain (%)	Persons reporting moderate pain (%)	Persons reporting severe pain (%)
Employed full-time	37.65	34.57	44.94	33.31	20.16
Employed part-time	6.30	7.94	9.99	7.53	6.02
Self-employed	6.12	7.02	5.72	8.30	2.67
Unemployed	10.58	11.51	11.33	11.13	14.16
Not in labor force	39.34	38.92	28.01	39.73	57.00
Total	100.00	100.00	100.00	100.00	100.00

Source: NHWS, 2010.

All respondents to the 2010 NHWS were also asked to report their labor force status and to complete the Workplace Productivity and Activity Impairment Scale (WPAI) to measure the impact of health status on employment related activities. The WPAI questionnaire measures work time missed and work and activity impairment because of a specified health problem during the past 7 days25. The validity and accuracy of the instrument has been established in a number of disease states (e.g., irritable bowel syndrome, asthma, dermatitis, Crohn’s disease)26,27. The instrument has been validated in Spanish28.

The WPAI absenteeism and workplace questions are only relevant to those in employment—where the NHWS identifies persons who are currently employed full-time, employed part-time, or self-employed. Respondents are asked to indicate:

• During the past 7 days, how many hours did you miss from work because of your health problems? (Range 0–112 h);

• During the past 7 days, how many hours did you miss from work because of any other reason such as vacation, holidays, time off to participate in this study? (Range 0–112 h);

• During the past 7 days, how many hours did you actually work? (Range 0–112 h); and

• During the past 7 days how much did your health problems affect your productivity while you were working? (Response on a 0–10 scale from ‘health problems had no effect on work’ to ‘health problems completely prevented me from working).

Two measures of employment impact are generated from these responses. These are:

1. Absenteeism: percentage of work time missed in the past 7 days; and

2. Presenteeism: extent to which productivity at work was impaired.

For the purpose of the present analysis, the absenteeism responses are put on the same basis as the presenteeism responses. That is, they are translated into the percentage of total non-work time and presented as class intervals from 0–9% of time lost to 100% of time lost.

The distribution of days lost due to absenteeism as a result of ill-health is presented in Table 3. Overall, among those experiencing pain, 20.80% reported some degree of absenteeism. The corresponding estimate for those not experiencing pain is 7.35%.

Table 3.  Workplace productivity and activity impairment scale: Impact of health problems on absenteeism in the past 7 days, 2010 NHWS, weighted estimates, Spain.

Absenteeism: Percentage of worktime lost	All persons (%)	Persons reporting ‘no pain’ (%)	Persons reporting ‘pain’ (%)
0–9 (Health problems had no effect on my work)	90.36	92.65	79.20
10–19	2.56	2.02	5.19
20–29	1.69	1.32	3.45
30–49	1.45	1.33	2.01
50 and over	3.96	2.68	10.14
Total	100.00	100.00	100.00

Source: NHWS, 2010.

The distribution of presenteeism responses is detailed in Table 4. Among those reporting ‘no pain’ 55.09% indicated that health problems had no effect on their work, while the corresponding figure for those experiencing pain was 34.54%.

Table 4.  Workplace productivity and activity impairment scale: Impact of health problems on presenteeism in the past 7 days, 2010 NHWS, weighted estimates, Spain.

Presenteeism responses	All persons (%)	Persons reporting ‘no pain’ (%)	Persons reporting ‘pain’ (%)
0 (Health problems had no effect on my work)	51.79	55.09	34.54
1	12.36	12.31	12.67
2	9.82	9.07	13.77
3	5.49	5.22	6.87
4	2.66	2.07	5.78
5	5.99	5.91	6.44
6	5.66	5.09	8.66
7	3.22	2.64	6.28
8	1.86	1.53	3.56
9 and over	1.14	1.08	1.44
Total	100.00	100.00	100.00

Source: NHWS, 2010.

Independent variables

The choice of independent variables reflects their anticipated impact on labor supply decisions—as these impact both labor force status as well as absenteeism and presenteeism. The variables (Table 5) are considered under the following heads:

• Socio-demographic variables;

• Health risk behaviors; and

• Comorbidity status.

Table 5.  Distribution of independent variables, 2010 NHWS, weighted estimates, Spain.

Independent variables	Weighted estimates
	No pain (%)	Pain (%)
Socio-demographic variables
Age: 18–39 years	32.49	24.49
Age: 40–59 years	34.59	47.17
Age: 60 years and older	32.92	28.34
Gender: female	49.25	60.33
Gender: male	50.75	39.67
Education: University or higher	25.17	23.19
Education: High school completed	55.05	53.68
Education: Other	19.77	23.13
Income: Under €20,000	29.02	28.75
Income: €20,000–€39,999	44.45	47.02
Income: €40,000 and above	10.89	11.20
Income reporting declined	15.64	13.03
Health risk behaviors
BMI: Underweight	2.01	2.05
BMI: Normal weight	38.47	32.18
BMI: Overweight	42.13	41.90
BMI: Obese or morbidly obese	17.39	23.87
Current smoker	21.97	23.81
Alcohol user (>2 days month)	10.38	9.00
Morbidity/Comorbidity Status
Charlson Comorbidity Index (SD)	0.328 (0.820)	0.575 (1.060)

SD, standard deviation.

Source: NHWS, 2010.

Within a life cycle model of labor supply, the interaction of age, gender, and education on labor force participation is well established. Labor force participation is expected to increase with education, but decline with age, reflecting both the impact of poorer health on workforce decisions, the impact of wealth accumulation on household decisions, as well as institutional factors determining retirement status. Absenteeism and presenteeism would also be expected to increase with age—again reflecting the impact of poorer health. To the extent that pain is associated with characteristics such as age, gender, and education, these would be expected to have a negative impact.

In the present context persons reporting pain are focused on the 40–59 year age group (47.17% vs 34.59%). In the pain group, the percentage of females reporting pain (60.13%) is substantially greater than for males (39.67%) compared to the gender distribution in the no-pain group. This higher reported prevalence of chronic pain among females is well documented, with females at a higher risk of developing several chronic pain disorders29,30. The association of pain and educational level is also well documented. Blyth et al.31 report on pain being significantly associated with lower levels of completed education in Australia, while Callahan and Pincus32 find that poorer clinical status in rheumatoid arthritis is associated with lower levels of educational attainment. This is not seen in the case of Spain, with similar distributions between the pain and control populations. Similar distributions also hold in respect of household income.

Three health risk behaviors are identified: body mass index (BMI), current smoking, and current alcohol consumption. The NHWS does not allow a more detailed assessment of actual alcohol consumption or number of cigarettes per day and duration of smoking behavior. The relationship between BMI and labor force participation, absenteeism, and presenteeism is less clear cut, although there is evidence to show a negative relationship between BMI and wages33. To the extent that BMI is taken as a marker for health status persons reporting a BMI in the range of obesity and morbid obesity would be expected to have a lower participation rate and a higher probability of absenteeism and presenteeism.

As far as the other health risk behaviors of smoking and alcohol are concerned, their relationship to labor force participation, absenteeism, and presenteeism is most appropriately seen through their potential role as health status markers. The expected impact of these health risk factors is unclear.

The presence of morbid/comorbid conditions is captured by the Charlson Comorbidity Index (CCI)34. The CCI was originally designed as a measure of the risk of 1-year mortality attributable to comorbidity. The overall comorbidity score reflects the cumulative increased likelihood of 1-year mortality; the higher the score, the more severe the burden of comorbidity. To the extent that the CCI, together with the impact of age, reflects ill health, it is expected to impact adversely work activity decisions in a number of ways. First, the presence of chronic comorbidities would be expected to increase the disutility of work and workplace productivity. This would be reflected in a reduced willingness to seek work or to remain in the workforce. Second, for those in the workforce the presence of chronic comorbidities would be expected to increase both absenteeism and presenteeism. Third, the possibility of receiving ill-health-related disability benefits would further reduce work incentives. Finally, to the extent that poor health indicated reduced life expectancy, this would increase the present value of wealth and encourage early retirement.

In the present analysis, persons reporting pain have a higher CCI (0.575) compared to those without pain (0.328).

Estimation

The impact of pain severity and frequency on labor force participation is estimated (i) through a binary logit model where the dependent variable is whether or not the respondent is in the labor force, and (ii) through a multinomial logit model where the unordered dependent variable captures the five labor force and not in the labor force categories. Not being in the labor force is the reference category. The impact of pain on absenteeism and presenteeism is assessed, for those respondents in employment, through the specification of ordered logit models. The dependent variables are (i) the estimated percentage of worktime lost in the last 7 days due to ill health and (ii) the estimated percentage impact on work productivity of ill-health experienced over the last 7 days. All models are estimated: (i) for persons reporting severity of pain (Model I); and (ii) for persons reporting severity and frequency of pain as dependent variables (Model II). In both cases the no-pain respondents are the reference category.

Because of the small number of observations in certain independent variable categories the number of categories has been reduced for the regression analysis. The changes are: (i) the severity and frequency of pain experience is summarized in daily vs other for severe and moderate pain and mild pain is one category; and (ii) persons not assessed for BMI are dropped from the model.

Results

Results for the binary logistic labor force participation model are presented in Table 6 and the multinomial logistic model in Table 7. The same set of independent variables is utilized in each model. In the latter case the results are presented only for the predicted labor force category outcomes for those reporting severe and moderate pain and those reporting severe daily and moderate daily pain. (The complete set of multinomial logit results are available on request.)

Table 6.  Labor force participation regressions, 2010 NHWS, Spain.

Regression model	Logit Model I			Logit Model II
independent variables	Odds ratio	p > |z|	95% CI	Odds ratio	p > |z|	95% CI
Pain level reported in last month*
Mild	1.555	0.035	1.031–2.344
Moderate	0.852	0.198	0.667–1.087
Severe	0.356	0.000	0.217–0.585
Pain level and frequency*
Severe daily pain				0.363	0.000	0.206–0.637
Severe pain less than daily				0.322	0.017	0.127–0.814
Moderate daily pain				0.618	0.005	0.441–0.867
Moderate pain less then daily				1.093	0.599*	0.784–1.524
Mild pain				1.554	0.035	1.031–2.343
Socio-demographic variables
Age: 40–59 years^a	1.588	0.000	1.338–1.884	1.599	0.000	1.347–1.899
Age: 60 years and older^a	0.063	0.000	0.051–0.077	0.064	0.000	0.052–0.078
Gender: male^b	1.173	0.053*	0.998–1.379	1.168	0.059*	0.994–1.373
Education: high school completed^c	1.818	0.000	1.486–2.224	1.822	0.000	1.489–2.230
Education: University or higher^c	3.293	0.000	2.610–4.155	3.289	0.000	2.607–4.150
Health risk behaviors
BMI: Underweight^d	1.158	0.534*	0.729–1.839	1.168	0.512*	0.734–1.859
BMI: Overweight^d	1.165	0.096*	0.973–1.394	1.173	0.082*	0.980–1.404
BMI: Obese or morbidly obese^d	0.998	0.989*	0.801–1.245	1.009	0.939*	0.809–1.258
Current smoker^e	1.526	0.000	1.262–1.846	1.527	0.000	1.263–1.846
Alcohol user^f	0.968	0.816*	0.739–1.269	0.971	0.834*	0.741–1.274
Morbidity/comorbidity status
Charlson Comorbidity Index	0.857	0.000	0.788–0.931	0.857	0.000	0.789–0.932
n	4992			4992
Log pseudo likelihood	−3587.48			−3582.40
Pseudo R^2	0.329			0.330

All variables significant at 5% level except (*).

Reference categories: * no pain reported in last month; ^aaged 18–39 years; ^bfemales; ^cnot completed high school; ^dBMI normal weight; ^enon-smoker; ^fnon-drinker.

Logistic regression results

In Model I, the experience of severe pain has the greatest negative impact (odds ratio <1) on labor force participation with an odds ratio of 0.356 (95% CI: 0.217–0.585) (Table 6). The impact of moderate and mild pain is not statistically significant.

Once the frequency of pain is considered, Model II, the impact of pain experience on labor force participation is more pronounced. The experience of severe daily pain is similar to the results for severe pain (0.363; 95% CI: 0.206–0.637). The association of severe pain expressed less than daily is also significant (0.322; 95% CI: 0.127–0.814). Moderate daily pain enters with a significant odds ratio (0.618; 95% CI: 0.441–0.867), although the association of moderate pain experienced less than daily is not significant.

The impact of the socio-demographic variables is mixed. Compared to the reference group, persons in the age group 40–59 years report higher labor force participation (in Model I odds ratio 1.588, 95% CI: 1.338–1.884). Males have a higher labor force participation than females. Labor force participation also increases with educational attainment. In both models, the impact of health risk factors is mixed. BMI is not significant, although current smoking is significant in both models (odds ratio >1). Alcohol use has no impact. The CCI is negative (odds ratio <1) and significant in its impact in both models.

Multinomial logit regression results

Rather than attempt to present the full results for the multinomial model, summary results are presented for labor force status probabilities (Table 7). For persons reporting severe pain the predicted probability of being employed full time is 0.192 against 0.408 in the control no-pain group. Once the frequency of pain is factored in the impact is more marked. For persons reporting severe daily pain the predicted probability of being in full-time employment is 0.209 compared to an estimated 0.407 for those not experiencing pain. Conversely, the predicted probability of not being in the labor force is 0.615 for those experiencing severe pain compared to 0.338 for those without pain.

Table 7.  Labor force participation multinomial regression: Predicted relative probabilities for severe and moderate daily pain, 2010 NHWS, Spain.

Labor force status	Mild pain	Moderate pain	Severe pain	Moderate daily pain	Severe daily pain
Employed full-time
Pain	0.461	0.334	0.192	0.282	0.209
No pain	0.401	0.413	0.408	0.410	0.407
Employed part-time
Pain	0.108	0.078	0.058	0.058	0.061
No pain	0.069	0.069	0.070	0.071	0.070
Self-employed
Pain	0.065	0.098	0.033	0.103	0.027
No pain	0.073	0.070	0.074	0.071	0.074
Unemployed
Pain	0.114	0.104	0.104	0.093	0.093
No pain	0.110	0.111	0.110	0.111	0.110
Not in the labor force
Pain	0.253	0.387	0.615	0.465	0.611
No pain	0.347	0.337	0.338	0.337	0.339

Ordered logit regression results

Results for absenteeism and presenteeism are presented in Tables 8 and 9, respectively. In Model I severe pain and in Model II the combination of severe and moderate pain combined with pain frequency dominate the ordered logit results in their positive impact (odds ratio >1) on absenteeism (absenteeism increases with pain severity and frequency). In the case of absenteeism, severe pain in Model I enters with an odds ratio of 13.766 (95% CI: 5.251–36.085), while severe daily pain in Model II enters with an odds ratio of 16.216 (95% CI: 5.127–51.283). Moderate and mild pain experience has a substantially lower impact, but both are statistically significant. These results hold for pain severity and frequency with a clear gradient of absenteeism response.

Table 8.  Absenteeism ordered regression results, 2010 NHWS, Spain.

Regression model	Logit Model I			Logit Model II
independent variables	Odds ratio	p > |z|	95% CI	Odds ratio	p > |z|	95% CI
Pain level reported in last month*
Mild	2.146	0.005	1.255–3.671
Moderate	3.192	0.000	2.230–4.567
Severe	13.766	0.000	5.251–36.085
Pain level and frequency*
Severe daily pain				16.216	0.000	5.127–51.283
Severe pain and less than daily				7.086	0.007	1.716–29.256
Moderate daily pain				4.559	0.000	2.640–7.873
Moderate pain and less than daily				2.582	0.000	1.679–3.972
Mild pain				2.146	0.005	1.254–3.672
Socio-demographic variables
Age: 40–59 years^a	0.659	0.008	0.484–0.898	0.659	0.008	0.484–0.898
Age: 60 years and older^a	0.884	0.675*	0.496–1.576	0.875	0.656*	0.486–1.576
Gender: male^b	0.811	0.186*	0.594–1.106	0.815	0.201*	0.596–1.115
Education: high school completed^c	0.847	0.496*	0.525–1.366	0.858	0.531*	0.530–1.387
Education: University or higher^c	0.681	0.154*	0.401–1.155	0.695	0.181*	0.409–1.184
Income: €20,000–€39,999^d	0.828	0.325*	0.568–1.206	0.816	0.293*	0.558–1.193
Income: €40,000 and above^d	0.986	0.952*	0.618–1.573	0.981	0.936*	0.612–1.572
Income: Reporting declined^d	1.029	0.911*	0.629–1.681	1.028	0.911*	0.629–1.680
Health risk behaviors
BMI: Underweight^e	2.639	0.007*	1.297–5.368	2.612	0.007	1.293–5.278
BMI: Overweight^e	1.151	0.424	0.816–1.623	1.153	0.418*	0.817–1.628
BMI: Obese or morbidly obese^e	1.560	0.035	1.031–2.359	1.554	0.038	1.024–2.360
Current smoker^f	0.890	0.487*	0.640–1.236	0.894	0.506*	0.643–1.243
Alcohol user^g	1.180	0.490*	0.737–1.888	1.201	0.444*	0.751–1.921
Morbidity/comorbidity status
Charlson Comorbidity Index	1.458	0.000	1.276–1.666	1.460	0.000	1.279–1.666
n	2615			2615
Log pseudo likelihood	−1821.81			−1818.73
Pseudo R^2	0.0557			0.0573

All variables significant at the 5% level except (*).

Reference categories: * no pain reported in last month; ^aaged 18–39 years; ^bfemales; ^cnot completed high school; ^dincome under €20,000; ^eBMI normal weight; ^fnon-smoker; ^gnon-drinker.

Table 9.  Presenteeism ordered regression results, 2010 NHWS, Spain.

Regression model	Logit Model I			Logit Model II
independent variables	Odds ratio	p > |z|	95% CI	Odds ratio	p > |z|	95% CI
Pain level reported in last month*
Mild	1.684	0.001	1.228–2.308
Moderate	2.275	0.000	1.809–2.862
Severe	6.294	0.000	2.302–17.206
Pain level and frequency*
Severe daily pain				6.797	0.000	2.743–16.842
Severe pain and less than daily				3.757	0.532*	0.059–237.906
Moderate daily pain				2.271	0.000	1.480–3.483
Moderate pain and less than daily				2.278	0.000	1.763–2.943
Mild pain				1.684	0.001	1.228–2.308
Socio-demographic variables
Age: 40–59 years^a	0.663	0.000	0.563–0.780	0.663	0.000	0.563–0.780
Age: 60 years and older^a	0.543	0.000	0.396–0.745	0.542	0.000	0.395–0.744
Gender: male^b	0.873	0.101	0.742–1.027	0.872	0.100	0.742–1.026
Education: high school completed^c	1.001	0.992*	0.766–1.310	1.002	0.989*	0.766–1.310
Education: University or higher^c	0.956	0.764*	0.715–1.280	0.957	0.767*	0.715–1.281
Income: €20,000–€39,999^d	0.954	0.652*	0.776–1.172	0.955	0.662*	0.777–1.174
Income: €40,000 and above^d	0.945	0.691*	0.715–1.249	0.947	0.700*	0.716–1.251
Income: Reporting declined^d	0.925	0.583*	0.699–1.222	0.926	0.592*	0.701–1.225
Health risk behaviors
BMI: Underweight^e	0.820	0.510*	0.455–1.478	0.820	0.510*	0.455–1.478
BMI: Overweight^e	0.988	0.894*	0.830–1.176	0.989	0.898*	0.831–1.177
BMI: Obese or morbidly obese^e	1.033	0.784*	0.819–1.302	1.034	0.776*	0.820–1.304
Current smoker^f	0.782	0.007	0.654–0.933	0.782	0.007	0.655–0.934
Alcohol user^g	1.001	0.992*	0.749–1.339	1.003	0.986*	0.750–1.340
Morbidity/comorbidity status
Charlson Comorbidity Index	1.345	0.000	1.229–1.471	1.344	0.000	1.229–1.470
n	2565			2565
Log pseudo likelihood	−6235.89			−6235.72
Pseudo R^2	0.0184			0.0184

All variables significant at 5% level except (*).

Reference categories: * no pain reported in last month; ^aaged 18–39 years; ^bfemales; ^cnot completed high school; ^dincome under €20,000; ^eBMI normal weight; ^fnon-smoker; ^gnon-drinker.

The majority of other independent variables are not statistically significant. Exceptions are age (40–59 years) being obese or morbidly obese and the CCI.

The results for presenteeism (Table 9) are similar to those for absenteeism in both Model I and Model II. There is a clear gradient by pain severity, although the experience of severe pain on a less than daily basis is not significant. The impact of the other independent variables is mixed. Age is significant, together with the negative impact of the CCI.

Discussion

The observation that the experience of pain, notably chronic severe pain, reduces labor force participation and increases absenteeism and presenteeism, is a recurring one in the pain literature. Breivik et al.35, in their survey of chronic pain in Europe, reported that one in four said their pain had impacted their employment status, 19% had lost their job because of pain, 16% had changed job responsibilities because of chronic pain, and 13% had changed jobs entirely. At the same time, in the 6 months prior to the survey those in employment reported a mean time lost from work of 7.8 days due to pain. In terms of absence and reduced performance due to common pain conditions—lost productive time—the American Productivity Audit concluded that pain was a common and disabling condition in the US workforce4. Although not as comprehensive, the Bassols et al.15 study of Catalonia points to the negative impact of pain on both labor force participation and absenteeism.

The results presented here at the national level for Spain would echo these findings for the US. In the 2010 NHWS the estimated impact of pain in Spain on labor force participation is substantial (Table 2). Among those persons reporting pain in the last month, 34.35% report working full-time compared to 37.48% of those not experiencing pain. In terms of experiencing severe pain the estimate is 19.98%. The multinomial logit model confirms the magnitude of this difference (Table 7) in terms of the independent effect of pain. The estimated relative probability of being employed full-time if severe pain is experienced is 0.192 compared to an estimated 0.408 among the no-pain population. The impact on persons reporting severe daily pain is similar (0.209). What is important to note is the impact of severe and moderate pain vs the daily experience of severe or moderate pain. Because the majority of those experiencing severe pain do so on a daily basis, it is the impact of severe daily pain that is the key attribute.

At the same time, the analysis presented here supports other, disease-specific, Spanish studies of the impact of chronic disease on employment status, absenteeism, and presenteeism. Oliva13 found that the employment status of HIV-positive individuals is directly related to their health status—after controlling for age, gender, and education level.

The importance of these results is in the magnitude of the pain effect—notably the impact of severe and moderate daily pain—allied with the overall prevalence of pain experienced. These provide an entirely new perspective on the association of pain with labor force participation and workplace experience in Spain. Previous assessments of the impact of pain in Spain have not considered these issues at the national level. The pre-eminent contribution of pain as a determinant of labor supply decisions is brought out in the results of the logistic model (Table 6). The experience of severe pain and the combination of pain experience with pain frequency are the key dimensions of pain experience. Mild pain has no impact. The pain variables (most notably in Model II) dominate the regression results. If pain is considered a disease in its own right then measures such as the CCI which attempt to capture the impact of comorbidities are failing to capture a key determinant of health status and workforce participation. Although CCI is significant in both Model I and Model II, the impact on participation is relatively small.

Taken at face value, these estimates of the impact of pain on labor force participation represent a potentially significant output loss as well as a potential and possibly avoidable claim on healthcare and social security budgets. Reducing the prevalence of pain and returning people to full-time or part-time employment offers substantial output gains.

The experience of pain is not only associated with a lower probability of labor force participation, it also has a substantial association with absenteeism. This result confirms previous studies in Spain and elsewhere which have assessed the impact of chronic disease and specific pain conditions on both absenteeism and presenteeism. Once again, however, pain has been subsumed as an aspect of the chronic disease state considered. It has not been considered as an attribute or disease in its own right. Of particular interest here is not the fact that pain is associated with increased rates of absenteeism, but the magnitude of the effect. There are a large number of studies that have assessed the impact of both acute and chronic disease on absenteeism—to include the experience of pain. None, however, have attempted to compare the severity and frequency of the experience of pain at a national level against the absenteeism experience of a no-pain reference group. Given this perspective, it is noteworthy that the presence of comorbidities, health risk factors, and even socio-demographic characteristics are eclipsed by the presence not only of severe frequent pain, but also, to a lesser extent, by moderate and mild pain experience. Indeed the association of mild pain with absenteeism is more marked than for labor force participation.

The association of pain with presenteeism mirrors that for absenteeism. The experience of pain, irrespective of severity or frequency, has a significant negative impact on presenteeism. There is a clear gradient relating the severity and frequency of pain to the incidence of presenteeism. Once again, the experience of pain overshadows the impact of other health status characteristics.

Seen against the background of an aging Spanish population and projections of a more adverse ratio of active workforce members to dependents, pain presents not only as a major health problem but also as a legitimate target of microeconomic policy. This conclusion holds irrespective of whether or not the experience of long-term or chronic pain is seen as a condition that transcends specific disease conditions that may have been seen as the initial cause or more prosaically as a symptom that has failed to subside. If the results presented here are considered a guide, the experience of both severe and moderate pain is quantitatively more important that the more traditional health risk factors of BMI, alcohol use, and smoking in its impact on workforce activities. These have been long-standing targets of health policy. It is perhaps worth noting at this point that these results mirror those reported using a similar data set for the five major EU countries together11.

Limitations of the analysis

There are a number of limitations to the present study that should be noted. First, as an internet-based observational study, there is the possibility of bias in the responses as only persons with internet-access will be asked to participate. To this should be added the potential impact of recall bias. While the extent of such biases is unknown, it is worth noting that internet penetration in Spain is in excess of 50% of individuals and households. In the case of the sampling for Spain, the internet interviews were supplemented by telephone interviews in the older population. Second, respondents are asked to report on their experience of pain. Apart from the potential impact of recall bias, there is no clinical confirmation of, for example, reported pain severity or frequency. Third, respondents are also asked to report their employment status and recall their workplace experience; there is no independent check on the accuracy of these responses. Finally, this study has focused on the experience of pain. Apart from excluding more obvious acute pain types there is no attempt to try and impose an arbitrary distinction between acute and chronic pain or between, for example, primarily neuropathic and primarily nociceptive pain. Nor is it possible to assess pain chronicity.

Conclusions

The experience of pain, in particular severe daily pain, has a substantial negative impact both on labor force participation in Spain as well as on reported absenteeism and presenteeism. As a measure of health status, the experience of pain clearly has an impact that outstrips other health status measures—notably the presence of comorbidities and BMI. Whether or not pain is considered as a disease in its own right, the experience of chronic pain, as defined here, presents policy-makers with a major challenge. The experience of pain not only represents a major health problem but also a challenge in microeconomic policy. Programs to relieve the burden of pain in the community clearly have the potential for substantial benefits both from an individual and employer perspective, of returning to work as well as reducing losses from absenteeism and presenteeism. This would be seen in terms of both increasing labor force participation—with the focus on returning people to full-time employment—but also in reducing absenteeism and presenteeism for those in employment.

Transparency

Declaration of interest

This study was supported by the Grünenthal Foundation, Spain.

Declaration of financial/other interests

PCL has disclosed he is a consultant for Kantar Health, a company that undertook the analysis for the Grünenthal Foundation, Spain. JTM, CMF, CPH, ATV, and MAR all disclose they are consultants for the Grünenthal Foundation, Spain.

Acknowledgements

No assistance in the preparation of this article is to be declared.