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Keywords:

  • vocational rehabilitation;
  • disability pension;
  • occupation;
  • neoplasms;
  • mental health;
  • psychiatric comorbidity

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

BACKGROUND

The authors investigated whether cancer patients who have comorbid mental health disorders (MD) are at greater risk of early retirement compared with those who do not have MD.

METHODS

Individuals ages 18 to 55 years from a consecutive sample of patients who were admitted for inpatient oncologic treatment were interviewed using structured clinical interviews to ascertain MD. The patients were followed for 15 months, and the date of early retirement was documented. Rates of early retirement per 100 person-years (py) in patients with and without MD were compared using multivariate Poisson regression models.

RESULTS

At baseline, 491 patients were interviewed, and 150 of those patients (30.6%) were diagnosed with MD. Forty-one patients began full early retirement during follow-up. In patients with MD, the incidence of early retirement was 9.3 per 100 py compared with 6.1 per 100 py in mentally healthy patients. The crude rate ratio (RR) was 1.5 (95% confidence interval [CI], 0.8-2.8). The effect of MD on early retirement was modified in part by income: in patients with low income, the adjusted RR was 11.7, whereas no effect was observed in higher income groups. Patients with depression were at greater risk of retirement when they had higher income (RR, 3.4; P = .05). The effects of anxiety (RR, 2.4; P = .05), adjustment disorders (RR, 1.7; P = .21), and alcohol dependence (RR, 1.8; P = .40) on early retirement were equal across income groups.

CONCLUSIONS

Mental health conditions are risk factors for early retirement in cancer patients, although this effect differs according to the type of disorder and the patient's income level. Cancer 2014;120:2199–2206. © 2014 American Cancer Society.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Early retirement is an important health policy issue, because its incidence is increasing[1] and the resources of most societies to pay pensions are decreasing at the same time. Vocational rehabilitation is also an important topic for many patients with malignant diseases because 1) survival rates are on the increase and patients are able to work,[2-4] and 2) many survivors wish to return to work.[5]

Early retirement depends not only on the individual's health status[6-11] but also on nonmedical factors, such as working conditions,[12-14] socioeconomic status,[9, 12, 15-17] and sex.[18] At the macro level, the society's social security system[18-21] as well as unemployment rates[22] influence the likelihood of returning to work.

There is good evidence that mental health is associated with early retirement in the general population.[23-25] This also appears to be true in clinical samples: a Danish cohort study indicated that, based on health care utilization data, cancer survivors with depression had a 2-fold increased risk of early retirement compared with nondepressed cancer survivors.[12] Therefore, the association between mental health and early retirement in cancer patients needs to be investigated in more detail. For example, because retirement rates depend heavily on the country-specific health care and social security systems, it should be investigated whether this association is present in countries other than Denmark. In addition, information on mental health in the Danish study was obtained from the Danish Psychiatric Central Register, which contains data on admissions to psychiatric hospitals and psychiatric wards in general hospitals. Consequently, if depression in a cancer survivor was treated solely on an outpatient basis or if it was not treated at all, then the patient would have been classified as nondepressed. Other studies used questionnaires only to measure depression,[23, 24] bearing a potentially higher risk of misclassification. Therefore, studies ascertaining the state of mental health that use clinical interviews independent from hospital admission would add valuable information to the existing evidence. Third, mental disorders other than depression also could play a role in vocational rehabilitation and should be investigated.

The objective of the current study was to answer the following question: Do patients who have comorbid mental health conditions, ascertained with structured clinical interviews rather than with questionnaires or health care utilization data, have higher rates of early retirement compared with those who do not have comorbid mental health conditions?

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Data Collection

Data were collected prospectively at 4 different hospitals in Leipzig, Germany. Patients who attended the clinics for oncologic treatment were approached consecutively and asked to participate in the study. Inclusion criteria were to be between ages 18 and 55 years and to have sufficient command of German. Exclusion criteria were full early retirement at baseline and the presence of metastatic disease disease.

Consenting patients were interviewed at the beginning of their stay in the clinic and were followed until 15 months after baseline. In Germany, patients can receive a salary substitute for a maximum of 78 weeks after diagnosis. After that time, they either have to decide together with their physician whether they are able to work again, even on a part-time basis, or they have to request a disability pension. This was the reason for using 15 months of follow-up. It was decided to enroll patients over the entire course of the study duration, even if it was clear upfront that the follow-up time points could not be entirely completed in all patients. Sample size considerations were based on the following assumptions: the number of events needed to establish statistical significance for a hazard ratio of 3.0 with a P value of .05 and power of 0.80 was 26 events per group.

According to previous research, approximately 25% to 30% of cancer patients present with a comorbid mental health condition,[26-28] and early retirement occurs in approximately 50% of patients with such conditions,[24] whereas only approximately 10% of those without a comorbid mental condition retire early.[11] Therefore, we determined that 260 patients without and 52 patients with a mental health condition would be needed for the current study. Ethical approval for this study was obtained from Leipzig University's Institutional Review Board.

Hypothesized Causal Model

The exposure variable in the hypothesized causal model was mental health at baseline, whereas early retirement within the subsequent 15 months was the outcome. Potential confounders based on the literature were sex, age, living alone or with others, education, employment at baseline, income, and comorbid chronic somatic diseases. Intention to return to work was considered to be a factor on the causal pathway between mental health and return to work.

Measurement

Mental health was ascertained at baseline using the Structured Clinical Interview (SCID) from the Diagnostic and Statistical Manual of Mental Disorders.[29] To constitute a diagnosis of a mental disorder (MD), certain criteria have to be met, including severity and duration of symptoms and their impact on everyday activities. Reliability and validity of the SCID have been reported as good,[30, 31] although specific training of interviewers and supervision are necessary to achieve reliable results.[32] In this study, all interviewers were carefully trained and supervised. All patients with potential MD were discussed in team conferences. In cases of doubt, no diagnosis was given, ensuring a conservative approach.

Retirement status was self-reported by the patients at each time point (time zero [t0], admission to hospital; t1, 3 months after baseline; t2, 9 months after baseline; and t3, 15 months after baseline). Patients were considered “early retired” if they received a full health-related early retirement pension according to the German Statutory Pension Insurance Scheme.

Education was assessed by asking for the highest level of formal academic education attained. Subsequently, this variable was categorized into groups with <10 years, 10 years, and >10 years of education.

Employment

Patients were asked whether they were employed (excluding mini-jobs; ie, minor employment according to the German social security system with earnings of <400 Euros per month or short-term employment with a duration of <3 months within 1 year) or self-employed during the 3 months preceding the baseline interview.

Income was measured using the Organization for Economic Cooperation and Development-modified equivalence scale.[33] With the help of equivalence scales, each household type is assigned a value in proportion to its needs. In addition, income was dichotomized into below and above the poverty threshold, which is classified as an equivalence net income ≤60% of the German median. This threshold defines an income that puts an individual at risk of poverty according to the Organization for Economic Cooperation and Development. We used the German median income for the year 2009 as a threshold, because that year represented the middle of the study period. In 2009, the threshold was an equivalence net income of 940 Euros per month.[34]

Comorbid somatic diseases were self-reported by the patients. Intention to retire was assessed at baseline with the following question: 1) “Are you considering applying for a health-related retirement pension (early retirement)?” Response categories were no, yes, and already applied.

Statistical Analysis

Possible differences in baseline characteristics between the 2 groups (with or without MD) were evaluated using chi-square tests. The incidence rate of early retirement was computed by dividing the number of patients who were receiving a full retirement pension by the sum of person-years (py). This was done first for the entire sample and then separately for patients with MD and without MD.

Next, the crude rate ratio (RRcrude) was computed by comparing incidence rates between patients with and without an MD. In a third step, potential confounding factors were investigated using the Mantel-Haenszel method. Finally, a multivariate Poisson regression model was used to estimate the RR with 95% confidence interval (CI) for taking an early retirement pension, and the model was adjusted for sex, attained age, living alone or with others, education, employment at baseline, equivalence income, and somatic comorbidity.

Study entry was defined as 60 days before start of treatment for the current disease. Follow-up was split into 5-year age bands to account for the age attained during follow-up.

To assess whether the association between mental health and early retirement was modified by other factors, tests of interaction were performed using likelihood ratio tests if the Mantel-Haenszel procedure indicated an effect modification for that variable. These models were repeated in subgroups of MDs, namely, any depression, any anxiety, any adjustment disorder, and alcohol dependence. Because of smaller sample sizes, income was modeled for these subgroups using 2 categories instead of 4 categories; and we used a poverty threshold of 940 Euros. Intention to return to work was assessed descriptively but was not included in the regression model, because it was considered to be a factor on the causal pathway. Statistical analyses were performed using the STATA statistical software package (version 10; StataCorp, College Station, Tex).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Sample Characteristics at Baseline

During the study period, 601 eligible patients were contacted, and 505 agreed to participate in this study. In addition, 502 patients also agreed to participate in an SCID interview. Of those, 11 already were fully retired at baseline and consequently were excluded. The remaining 491 patients constitute the basis for the following analyses. Nearly half of these patients (47%) were women, and the average age was 46 years (range, 19-55 years). Tumor sites were as follows: breast (79 patients; 16%), cervix (78 patients; 16%), other gynecologic (23 patients; 5%), prostate (86 patients; 18%), testicle (29 patients; 6%), other urogenital (36 patients; 7%), gastrointestinal (43 patients; 9%), head and neck (68 patients; 14%), lungs (20 patients; 4%), and other (29 patients; 6%). Further demographic and medical characteristics are presented in Table 1.

Table 1. Baseline Characteristics According to Mental Health Status
  No. of Patients (%) 
CharacteristicTotal No. of PatientsWithout MDWith MDP
  1. Abbreviations: MD, mental disorder (diagnosed using Structured Clinical Interviews from the Diagnostic and Statistical Manual of Mental Disorders).

Sex491341 (69.5)150 (30.5) 
Women231150 (64.9)81 (35.1).04
Men260191 (73.5)69 (26.5) 
Age at baseline, y    
<2044 (100)0 (0).16
20-292821 (75)7 (25) 
30-397546 (61.3)29 (38.7) 
40-49218147 (67.4)71 (32.6) 
≥50166123 (74.1)43 (25.9) 
Living situation    
With others383270 (70.5)113 (29.5).23
Alone10771 (66.4)36 (33.6) 
Unknown10 (0)1 (100) 
Education, y    
<105038 (76)12 (24).35
10309206 (66.7)103 (33.3) 
>1012693 (73.8)33 (26.2) 
Unknown64 (66.7)2 (33.3) 
Employment at baseline    
Unemployed or on training11164 (57.7)47 (42.3).01
(Self)-employed380277 (72.9)103 (27.1) 
Income, €    
<5003320 (60.6)13 (39.4).18
500 to 99912880 (62.5)48 (37.5) 
1000 to 149912993 (72.1)36 (27.9) 
>1500184135 (73.4)49 (26.6) 
Unknown1713 (76.5)4 (23.5) 
Comorbid somatic disease    
No other somatic disease350246 (70.3)104 (29.7).28
Comorbid somatic disease14095 (67.9)45 (32.1) 
Unknown10 (0)1 (100) 

Mental Health

At baseline, 150 patients (30%) were diagnosed with an MD according to the SCID. Thirty-eight (8%) had any type of depressive disorder, 42 (9%) had any type of anxiety disorder, 83 (17%) had any type of adjustment disorder (including acute stress disorder and post-traumatic stress disorder), and 17 (3%) had alcohol dependence. In total, 115 patients (23%) were diagnosed with 1 mental health condition, and 35 (7%) had more than 1 diagnosis.

Retirement

During the 15 months of follow-up, 41 patients retired. One hundred eighty patients did not retire before that time point. In total, 270 patients were censored: 59 because they dropped out during the follow-up before the end of the study (19 of them died, and 40 either declined participation at follow-up points or moved to an unknown place) and the remainder because the study ended (n = 211). In total, 572 py at risk were under study. The incidence rate of early retirement was 7.2 per 100 py (95% CI, 5.3-9.7 per 100 py) for the entire sample. All health-related early retirement pensions were granted because of malignant disease, and no pension was granted because of mental health.

Effect of Mental Health on Retirement

Of the participants who had an MD, 18 (12%) retired during the follow-up period compared with 23 (7%) who did not have an MD (P = .05). In patients with MD, the incidence of early retirement was 9.3 per 100 py (95% CI, 5.7-14.8 per 100 py), whereas it was 6.1 per 100 py in mentally healthy patients (95% CI, 4.0-9.1 per 100 py). The RRcrude was 1.5 (95% CI, 0.8-2.8).

After adjusting for attained age, the pooled RR was 1.6 (95% CI, 0.8-3.0). There was no effect modification by attained age. However, there was evidence that the effect of mental health on early retirement was modified by income group (P = .05). In the lowest income group (<500 Euros per month), the RRcrude was 7.8 (95% CI, 1.0-65.0). In patients who had an income from 500 to 999 Euros per month, the RRcrude was 0.5 (95% CI, 0.2-1.4); in patients who had an income from 1000 to 1499 Euros per month, the RRcrude was 1.9 (95% CI, 0.5-7.1); and, in patients who had an income >1500 Euros per month, the RRcrude was 2.9 (95% CI, 0.7-12.9). No other effect modification was observed.

Because of this effect modification by income, no pooled estimate was calculated, but stratum-specific RRs for income groups were calculated in the Poisson regression model. After adjusting for potential confounders, there was evidence that patients with low income (<500 Euros per month equivalent net income) who had an MD had an 11.7 increased risk of early retirement compared with those who did not have an MD in the same income group. In the other income groups, the risk of early retirement was not increased in patients with a mental health condition (Table 2).

Table 2. Income-Specific Rates of Early Retirement in Patients With and Without Mental Disorders
Income Equivalent, € per moMental HealthNo. of EventsRate per 100 Person-YearsUnadjusted HR (95% CI)PAdjusted HR (95% CI)aP
  1. Abbreviations: CI, confidence interval; HR, hazard ratio; MD, mental disorder.

  2. a

    The analysis was adjusted for attained age, sex, living alone versus with others, education, baseline employment, and somatic comorbidity.

<500No MD15.51.00 1.00 
 MD642.77.8 (0.9-65.0).0611.7 (1.3-105.6).03
500 to 999No MD1415.01.00 1.00 
 MD47.00.5 (0.2-1.4).180.5 (0.2-1.7).27
1000 to 1499No MD54.91.00 1.00 
 MD49.41.9 (0.5-7.1).341.4 (0.4-5.4).64
>1500No MD31.91.00 1.00 
 MD45.63.8 (0.7-14.0).162.9 (0.4-9.2).15

In the subsequent analyses, we investigated the effect of specific mental health problems (depression, anxiety, adjustment disorders, and alcohol dependence) on early retirement (Table 3). Because of smaller sample sizes, we grouped income into 2 categories: below and above the poverty threshold. Patients with anxiety disorders had a 2-fold increased risk of early retirement compared with those without any MD (RRcrude, 2.3; P = .04), which did not change considerably after adjusting for confounders (adjusted rate ratio [RRadj], 2.4; P = .05). There was no evidence for an effect of adjustment disorders on early retirement (RRcrude, 1.5; P = .26; RRadj, 1.7; P = .21). Patients with alcohol dependence had a 3-fold increased risk of early retirement (RRcrude, 3.1; P = .05), but this effect decreased after adjusting for confounders (RRadj, 1.8; P = .40). The effect of depression on early retirement was modified by income. In patients below the poverty threshold, depression had no effect (RRcrude, 0.8; P = .79; RRadj, 1.0; P = .95), whereas patients above the poverty threshold were 3 times more likely to retire early when they were depressed (RRcrude, 3.6; P = .03; RRadj, 3.4; P = .05).

Table 3. Early Retirement in Patients With Depression, Anxiety, or Alcohol Dependence Versus Patients Without Mental Disorder
Poverty Level/Mental HealthNo. of EventsRate per 100 Person-YearsUnadjusted HR (95% CI)PAdjusted HR (95% CI)aP
  1. Abbreviations: CI, confidence interval; HR, hazard ratio; MD, mental disorder.

  2. a

    The analysis was adjusted for attained age, sex, living situation, education, baseline employment, and somatic comorbidity. Because there was an effect modification of depression and retirement by poverty, income-specific rates and HRs are presented. In the other disease groups, no effect modification by income was observed.

Above poverty threshold      
No MD103.71.00 1.00 
Depression413.63.6 (1.1-11.6).033.4 (1.0-11.4).05
Below poverty threshold      
No MD1313.11.00 1.00 
Depression210.70.8 (0.2-3.6).791.0 (0.2-4.8).95
No MD236.11.00 1.00 
Anxiety813.92.3 (1.0-5.1).042.4 (1.0-5.6).05
No MD236.11.00 1.00 
Adjustment disorder109.31.5 (0.7-3.2).261.7 (0.8-3.5).21
No MD236.11.00 1.00 
Alcohol dependence319.03.1 (0.9-10.5).051.8 (0.5-6.9).40

Intention to Retire

At baseline, 389 participants (79%) said they did not intend to apply for early retirement, 81 (16%) had thought about it, 18 (4%) had already applied for it, and 3 (1%) did not answer this question. Of those who did not intend to apply for early retirement, 4% eventually retired; of those who did intend to apply, 20% retired; and, of those who already had applied, 50% retired (P < .001). The respective rates were 3.4 per 100 py, 18.4 per 100 py, and 57.6 per 100 py, respectively. The age-adjusted RR of retiring for those who intended to retire versus those who did not intend to retire was 5.4 (95% CI, 2.7-10.8), and the rate for those who had already applied for early retirement versus those who did not intend to retire was 15.6 (95% CI, 6.7-36.3).

Intention to retire was associated with mental health at baseline. Twenty-six percent of patients with an MD had considered applying for early retirement compared with 12% of those without an MD, and 7% had already applied for early retirement among those with an MD compared with 2% of those without an MD (P < .001).

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

The objective of this study was to investigate the effect of psychiatric comorbidity on early retirement in patients with malignant diseases. For this purpose, we conducted structured clinical interviews for the diagnosis of mental health disorders with patients aged ≤55 years who were admitted to the hospital for oncologic treatment. Subsequently, these patient were followed for up to 15 months, and their dates of early retirement were documented.

Our results indicate that mental health is a predictor of early retirement. However, we also observed that this effect is potentially modified by income: patients with low income are at greater risk of early retirement when they suffer from poor mental health in general, whereas this effect does not hold in patients with higher income. Patients with depression are at greater risk of early retirement only if they have an income above the poverty threshold. Patients with anxiety disorders, in contrast, retire more frequently than patients with good mental health, regardless of their income level. Adjustment disorders, the most frequent diagnoses in cancer patients,[27, 28] appear to be unrelated to early retirement. Finally, alcohol dependence is associated with a 3 times increased risk of early retirement. However, this effect can be largely explained by confounding factors, in particular employment status before the cancer diagnosis, living alone or with others, and the patient's sex. It also should kept in mind that this effect modification by income resulted in subgroups and, thus, reduced the power of our statistical tests. In particular, our analyses of separate MD categories (depression, anxiety, etc) should be interpreted with caution.

We had proposed a priori that the intention to retire was related not only to subsequent retirement status but also to mental health. Our data confirm this assumption. We further assumed that these factors were related in a causal way, ie, poor mental health leads to the intention to retire and this, in turn, leads to early retirement.

Our study had several limitations that must be mentioned. First, mental health was assessed only at the time the patients were admitted to the hospital. To date, little is known about the course of psychiatric comorbid disorders in cancer patients. There are indications that mental health conditions tend to become chronic[35] if not treated appropriately, and we also know that they often go unnoticed by health care professionals,[36, 37] leading to under-treatment in many patients. Therefore, although we can assume that the prevalence of mental health conditions in our sample did not change considerably over the course of the study, we have to take into account that such a change may have been possible. This could have influenced our results. For example, if a patient with an anxiety disorder recovers from his anxiety, then, because anxiety is related to early retirement, this patient would be counted as “anxious,” although he continues working because he has recovered from his anxiety. In this case, we would have underestimated the effect of anxiety on retirement. Similarly, and even more likely, a patient with an adjustment disorder may have recovered and was able to fully return to work during the study. This may have been the reason why we did not observe an association between adjustment disorders and early retirement, in contrast to the other MDs. If new MDs had emerged during the course of the study, then we also would have underestimated their effects.

Another problem with our study is that many patients were censored before completion of the 15-month follow-up because the study ended. Because we used incidence rates instead of percentages of early retirement, we could account for this problem. However, it did decrease the power of our statistical analyses. Therefore, it is possible that we overlooked some relevant effects of mental health on early retirement, especially because we had to take effect modification by income into account, and that also decreased statistical power.

In addition, only about 81% of the initially contacted patients could be included in the study. It recently was demonstrated[38] that individuals who were older, had less education, and were unemployed tended to decline study participation more frequently. Mental health does not appear to be related to participation.[38] Therefore, it is possible that, in our sample, unemployed and/or poorly educated patients were under-represented. Because unemployment is related to income and we observed a correlation between income, mental health, and early retirement, this potential selection could have introduced an underestimation of the true relation between mental health and early retirement. It also should be kept in mind that the type of MD is related to the patient's tumor entity; eg, alcohol dependence occurs more frequently in patients with head and neck cancer than in other patient groups.

Finally, when analyzing predictors of early retirement, we should consider factors beyond the individual. For example, during times of high unemployment rates in the society, patients probably would chose early retirement over the risk of unemployment, whereas they would try to enter the workforce again in times with low unemployment rates.[22] Unfortunately, in the current study, we were not able to consider such factors in a systematic way. At the time of data collection, unemployment rates in the study region ranged from 11.8 to 14.7 (available at: www.destatis.de; Accessed April 4, 2014). This change was not large, but it is possible that it influenced patients' decisions regarding vocational rehabilitation.

A strength of our study was the use of clinical interviews to ascertain MD in contrast to questionnaires[23, 24] or health care utilization data.[12] This increased the validity of psychiatric diagnoses. Health care utilization data may underestimate the prevalence of MD, because not every patient with an MD seeks support from mental health care providers. Moreover, health care use depends on many factors that can be related to mental health on the one hand and early retirement on the other hand,[39, 40] making it prone to confounding.

However, even if various measures of MD assessment were used in the different studies, the findings of others[12, 23, 24] and our current results are consistent in demonstrating that mental health is an important predictor of early retirement not only in the general population[23-25] but in cancer patients as well. Early retirement because of mental health problems is increasingly common in Germany. The age of early retirement because of MD was 48 years in 2011 (available at: http://www.gbe-bund.de; Accessed April 4, 2014), whereas the regular age of retirement was 65 years. It is noteworthy that none of the patients in our study retired early because of mental health problems, but all of the early retirements were because of cancer. However, early retirement was applied for more frequently for and received by patients with poor mental health.

In conclusion, mental health problems in cancer patients not only cause emotional suffering for the patients but also have an impact on their working abilities, causing significant health-related expenditures for society. This effect may have been unnoticed by health insurance companies to date, because the official reason why these patients retire is their malignant disease and not any mental health problem. However, our study demonstrated that poor mental health increases the risk of early retirement because of the cancer itself. Because it has been established that psychotherapy and other psychosocial support systems efficiently improve the mental health of cancer patients,[41, 42] we emphasize the need to detect and appropriately treat mental health conditions in cancer patients.

FUNDING SUPPORT

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

This work was supported by German Pension Insurance (8011-106-31/3181).

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES
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