la situation face à l'activité et à l'emploi deux ans...
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La situation face à l'activité et à l'emploi deux ans après un diagnostic de cancer
Alain Paraponaris, Luis Sagaon-Teyssier, Aurélia Tison
Contact: [email protected]
Research funded by: National Cancer Institute (INCa) and Cancer Research Association (ARC), Programme Cancer: maintien dans l’emploi et retour au travail
Fondation MGEN pour la Santé Publique Jeudi 9 octobre 2014, 14.00-15h30
Salle Plein Ciel
Introduction Cancer, a model for chronicisation of illnesses and treatments?
Cancer, a key-public health issue in France
358,000 new cases
150,000 deaths
800,000 persons in treatment
2 million cancer survivors
Major advances in cancer treatments
Survival rates and quality-of-life (QoL) improve
Half of adult cancer survivors < 65 years [de Boer et al, JAMA, 2009]
In France (2005), mean age (male/female) at diagnosis: 67/64 years [INCa, 2010]
New opportunities to participate to the labor force
Return to work/job tenure, component of cancer survivors QoL
Cancer, transitory or permanent shock on occupational status? Cancer survivors are more likely to be unemployed
Rate of job tenure after cancer diagnosis
From 24% (3 months) to 75% (5 years)
Varies with clinical (cancer site, treatment), demographic (gender, age), but also
social (occupational status, educational level, SES) characteristics
[Bradley et al, JHE, 2005; Bradley et al, Psycho-Onco, 2002; de Boer et al, JAMA, 2009]
Impact of cancer on job tenure depends on Productivity loss (limitations in functional/psychological abilities)
Adjustment of workstation and general working conditions
[van der Wouden et al, J Occup Med, 1992]
Social representations (discrimination, stigma, self-esteem)
[Rothstein et al, Oncology, 1995; Paraponaris et al, Health Policy, 2011]
Introduction
Cancer, transitory or permanent shock on occupational status?
What usually matters Possible confounding effects between SES, cancer prognosis and treatment
after-effects In France, gender inequalities in job tenure due to individual characteristics (age, marital
status) rather than clinical characteristics of cancer and its treatment
[Marino et al, JCO, 2013]
Deleterious impact of cancer and usual events in the job market to be disentangled
In what way does cancer induce job insecurity?
Most often, employment to employment transitions only considered
Non-employment transitions (unemployed, retired, other non-working) neglected
[Joutard et al, Ann. Eco. Stat., 2012]
Introduction
2012 French cancer survey (French National Cancer Institute) Sample of 4,349 French cancer survivors two years after cancer
diagnosis in 2010
Representative of the French population aged 20-85 years insured by the three main Health Insurance schemes
Salaried Farmers Self-employed
Sub-sample of 2,508 cancer survivors aged 57 years and less when diagnosed in 2010
12 cancer locations Breast, prostate, thyroid, melanoma (good prognosis) Bowel, aero-digestive tract, bladder, kidney, non-Hodgkin lymphoma, uterus and cervix
(intermediate prognosis) Lung (bad prognosis)
Cancer survivors in long-term sick leave for 2 years excluded
>90% of the French population (special schemes excluded)
Data and method
2012 French cancer survey (French National Cancer Institute) Random stratified sampling with unequal sampling probabilities
(rare cancers overrepresented)
Sample made representative of the reference population after: Weighting according to age, Health Insurance Scheme and cancer location Adjusting for non-response bias
Male (+), Age (+), SES (-), Cancer location: breast (-), lung (+), prostate (+), aerodigestive tract (+), non-Hodgkin lymphoma (-), worsening of cancer (+)
Response rate (AAPOR): 44% (4,349/9,885) Melanoma: 33% / non-Hodgkin lymphoma: 52%
Data Healthcare consumption (Health Insurance)
inpatient and outpatient care
CATI (survivors) postal questionnaire for some survivors with lung or aerodigestive tract cancer
Clinical data (medical staff) Tumor characteristics (tumor size and grade, TNM,…) Treatment (surgery, chemotherapy, radiotherapy, hormonotherapy,…) Support (psychological,…)
Data and method
Data and method Are cancer survivors comparable to the general population?
No, because of differences in the occupations distribution
2%
7%
13%
24%
29%
13%
2%
10%
1%
5%
12%
19%
24%
20% 19%
0% 0%
5%
10%
15%
20%
25%
30%
Farmers Craftsmen,shopkeepers
(self-employed)
Executives,knwoledge
workers
Foremen,supervisors
Clerks,assistants
Workmen No occupation Not defined
Cancer survey (2010)
General population (2010)
82% of cancer survivors employed in 2010 (+10% compared to general population)
Differences in employment rate (population employed/population) and in unemployment rate (population unemployed/working population) potentially due to structural differences in working populations as well as to cancer
Data matched with the help of propensity score matching (PSM) Controls for observed heterogeneity
Gives a control group with a non-experimental (ex post) method Mitigates the sampling bias in measured characteristics
[Rosenbaum et al, Biometrika, 1983; Rosenbaum et al, J of Am Stat Assoc, 1984; Heckman et al, Econometrics Journal, 2009; Becker et al, Stata J., 2002]
2010-2012 French employment survey Conducted by the National Institute of Statistics and Economic Studies 22,359 individuals aged 57 years and less in 2010
Principle Logit estimation of the probability to be in the cancer survivors sample Regressors: gender, age, educational level, occupational status in 2010,
income, diploma, work contract Cancer survivors paired with the best non-cancer individuals with the same
predicted probability to be treated (nearest neighbor method, probability difference<.0001)
87% of 2,508 cancer survivors matched a control 324 individuals with no control: men (53%), employed (66%), occupation not
documented (85%)
Data and method
Results
Occ
up
atio
nal
sta
tus
in 2
01
0 Occupational status of cancer survivors in 2012 (n=2,184)
Employed Sick leave Unemployed Retired Other Total
Employed 76,8 12,0 5,8 0,5 4,8 100
Sick leave 23,8 33,3 14,3 0,0 28,6 100
Unemployed 30,2 1,4 43,9 1,4 23,0 100
Retired 0,0 0,0 0,0 93,8 6,3 100
Other 7,9 0,0 4,7 0,8 86,6 100
Total 68,7 10,8 8,2 1,3 11,0 100
Evolution in the occupational status, matched samples (2010-2012)
¾ of cancer survivors employed in 2010 still employed 2 years after
Good prognosis melanoma: 85% thyroid: 84% breast: 75% prostate: 67%
Intermediate prognosis uterus: 81% kidney: 78% cervix: 73% non-Hodgkin lymphoma: 72% bowel: 62% aero-digestive tract: 53% bladder: 52%
Bad prognosis lung: 39%
Results
Occ
up
atio
nal
sta
tus
in 2
01
0 Occupational status of cancer survivors in 2012 (n=2,184)
Employed Sick leave Unemployed Retired Other Total
Employed 76,8 12,0 5,8 0,5 4,8 100
Sick leave 23,8 33,3 14,3 0,0 28,6 100
Unemployed 30,2 1,4 43,9 1,4 23,0 100
Retired 0,0 0,0 0,0 93,8 6,3 100
Other 7,9 0,0 4,7 0,8 86,6 100
Total 68,7 10,8 8,2 1,3 11,0 100
Occ
up
atio
nal
sta
tus
in 2
01
0
Occupational status of matched general population in 2012 (n=2,184)
Employed Unemployed Retired Other Total
Employed 93,8 2,8 0,5 2,9 100
Unemployed 42,6 38,3 1,1 18,1 100
Retired 0,0 0,0 100,0 0,0 100
Other 8,6 3,7 1,2 86,4 100
Total 84,7 5,1 1,7 8,5 100
Evolution in the occupational status, matched samples (2010-2012)
17% drop in the probability to be still employed
Compared to people with no cancer, cancer survivors employed in 2010
3% increase in the probability to become unemployed
2% increase in the probability to become inactive (and not retired: disabled)
Results
Occ
up
atio
nal
sta
tus
in 2
01
0 Occupational status of cancer survivors in 2012 (n=2,184)
Employed Sick leave Unemployed Retired Other Total
Employed 76,8 12,0 5,8 0,5 4,8 100
Sick leave 23,8 33,3 14,3 0,0 28,6 100
Unemployed 30,2 1,4 43,9 1,4 23,0 100
Retired 0,0 0,0 0,0 93,8 6,3 100
Other 7,9 0,0 4,7 0,8 86,6 100
Total 68,7 10,8 8,2 1,3 11,0 100
Occ
up
atio
nal
sta
tus
in 2
01
0
Occupational status of matched general population in 2012 (n=2,184)
Employed Unemployed Retired Other Total
Employed 93,8 2,8 0,5 2,9 100
Unemployed 42,6 38,3 1,1 18,1 100
Retired 0,0 0,0 100,0 0,0 100
Other 8,6 3,7 1,2 86,4 100
Total 84,7 5,1 1,7 8,5 100
Evolution in the occupational status, matched samples (2010-2012)
12% drop in the probability to get employed
5% increase in the probability to be still unemployed
5% increase in the probability to become inactive (and not retired: disabled)
Compared to people with no cancer, cancer survivors unemployed in 2010
Results
Probability not to be on sick leave
Probability to remain on sick leave
men
women
production staff
executive staff
men
women
production staff
executive staff
Months from cancer diagnosis to sick leave start Months from cancer diagnosis to sick leave start
Sick leave duration (months)
Pro
bab
ility
no
t t
o b
e o
n s
ick
leav
e
Pro
bab
ility
no
t t
o b
e o
n s
ick
leav
e
Sick leave duration (months)
Pro
bab
ility
to
rem
ain
on
sic
k le
ave
Pro
bab
ility
to
rem
ain
on
sic
k le
ave
Log-rank test, p<0.001 Log-rank test, p=0.031
Log-rank test, p=0.026 Log-rank test, p<0.001
Employment to employment transition probability, cancer survivors and non-cancer individuals (n=2,184)
Results
Gender
.77
.76
.94
Cancer
yes
no
female
male
.96
.93
.77
female
male
Employment to employment transition probability, cancer survivors and non-cancer individuals (n=2,184)
Results
Prognosis SES
.77
.84
.71
.48
.89
.94
Cancer
yes
no
Production
Executive
Production
Executive
.95
.94
good
bad
.28
.74 good
bad
Relative prognosis 5 years-expected survival rate based on cancer site and level and age
Good: > 80% Bad : 20%
SES (occupation) Executive and supervisory staff executives, knowledge workers,
foremen, supervisors Production staff
farmers, craftsmen, shopkeepers, clerks, assistants, workmen
Unemployment to employment transition probability, cancer survivors and non-cancer individuals (n=2,184)
Results
Gender
.30
.27
.43
Cancer
yes
no
female
male
.32
.45
.30
female
male
Unemployment to employment transition probability, cancer survivors and non-cancer individuals (n=2,184)
Results
Prognosis SES
.30
.49
.20
.50
.50
.43
Cancer
yes
no
Production
Executive
Production
Executive
.58
.30
good
bad
.00
.30 good
bad
Relative prognosis 5 years-expected survival rate based on cancer site and level and age
Good: > 80% Bad : 20%
SES (occupation) Executive and supervisory staff executives, knowledge workers,
foremen, supervisors Production staff
farmers, craftsmen, shopkeepers, clerks, assistants, workmen
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.83
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.83
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
2.78 3.60
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.83
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
2.78 3.60
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.53
3.76 2.93
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
2.78 3.60
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.53
3.76 2.93
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “Production” and “supervisory “ models rejected)
% job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Gender Male
Female (ref)
14.29 12.80
10.30 10.14
3.98 2.66
0.77 -1-
0.71 -1-
1.06 -1-
Age in 2010 Age
Age2
0.70***
1.004***
0.69**
1.005**
0.74
1.004
Marital status In couple
Single (ref)
11.60 17.53
8.82
13.93
2.78 3.60
1.45*
-1-
1.62*
-1-
1.04 -1-
Diploma <A-level (ref)
≥A-level
33.08 11.46
29.32 8.53
3.76 2.93
-1-
0.38***
-1-
0.38**
-1-
0.36**
Occupation Production
Supervisory-executive (ref)
18.07 6.87
1.56*
-1-
Work contract Temporary (ref)
Permanent
22.65 10.27
18.83 7.52
3.82 2.74
-1-
0.43***
-1-
0.40***
-1-
0.38*
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Firm size <10 (ref)
10-499 ≥500
16.29 14.25 6.62
13.44 11.20 4.02
2.85 3.04 2.60
-1-
1.24 0.65
-1-
1.66* 0.63
-1-
0.58 0.41
Business sector Primary/secondary (ref)
Tertiary
15.51 12.37
12.50 9.38
3.01 2.99
-1-
0.74
-1-
0.82
-1-
0.55
Workstation arrangt
no (ref) yes
21.00 5.21
16.84 3.39
4.15 1.82
-1-
0.16***
-1-
0.12***
-1-
0.28***
Workplace discrimtion no (ref)
yes
12.19 20.50
9.53
15.00
2.65 5.50
-1-
2.29***
-1-
2.08*
-1-
2.99**
5-year prognosis >80%
50-80% 20-50%
<20% (ref) Nd
11.32 14.73 18.11 34.21 14.12
8.89
12.79 13.39 21.05 9.60
2.43 1.94 4.72
13.16 4.52
0.33* 0.43 0.47 -1-
0.38*
0.57 0.73 0.65 -1-
0.46
0.10**
0.09 0.23 -1-
0.27
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Firm size <10 (ref)
10-499 ≥500
16.29 14.25 6.62
13.44 11.20 4.02
2.85 3.04 2.60
-1-
1.24 0.65
-1-
1.66* 0.63
-1-
0.58 0.41
Business sector Primary/secondary (ref)
Tertiary
15.51 12.37
12.50 9.38
3.01 2.99
-1-
0.74
-1-
0.82
-1-
0.55
Workstation arrangt
no (ref) yes
21.00 5.21
16.84 3.39
4.15 1.82
-1-
0.16***
-1-
0.12***
-1-
0.28***
Workplace discrimtion no (ref)
yes
12.19 20.50
9.53
15.00
2.65 5.50
-1-
2.29***
-1-
2.08*
-1-
2.99**
5-year prognosis >80%
50-80% 20-50%
<20% (ref) Nd
11.32 14.73 18.11 34.21 14.12
8.89
12.79 13.39 21.05 9.60
2.43 1.94 4.72
13.16 4.52
0.33* 0.43 0.47 -1-
0.38*
0.57 0.73 0.65 -1-
0.46
0.10**
0.09 0.23 -1-
0.27
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Firm size <10 (ref)
10-499 ≥500
16.29 14.25 6.62
13.44 11.20 4.02
2.85 3.04 2.60
-1-
1.24 0.65
-1-
1.66* 0.63
-1-
0.58 0.41
Business sector Primary/secondary (ref)
Tertiary
15.51 12.37
12.50 9.38
3.01 2.99
-1-
0.74
-1-
0.82
-1-
0.55
Workstation arrangt
no (ref) yes
21.00 5.21
16.84 3.39
4.15 1.82
-1-
0.16***
-1-
0.12***
-1-
0.28***
Workplace discrimtion no (ref)
yes
12.19 20.50
9.53
15.00
2.65 5.50
-1-
2.29***
-1-
2.08*
-1-
2.99**
5-year prognosis >80%
50-80% 20-50%
<20% (ref) Nd
11.32 14.73 18.11 34.21 14.12
8.89
12.79 13.39 21.05 9.60
2.43 1.94 4.72
13.16 4.52
0.33* 0.43 0.47 -1-
0.38*
0.57 0.73 0.65 -1-
0.46
0.10**
0.09 0.23 -1-
0.27
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Firm size <10 (ref)
10-499 ≥500
16.29 14.25 6.62
13.44 11.20 4.02
2.85 3.04 2.60
-1-
1.24 0.65
-1-
1.66* 0.63
-1-
0.58 0.41
Business sector Primary/secondary (ref)
Tertiary
15.51 12.37
12.50 9.38
3.01 2.99
-1-
0.74
-1-
0.82
-1-
0.55
Workstation arrangt
no (ref) yes
21.00 5.21
16.84 3.39
4.15 1.82
-1-
0.16***
-1-
0.12***
-1-
0.28***
Workplace discrimtion no (ref)
yes
12.19 20.50
9.53
15.00
2.65 5.50
-1-
2.29***
-1-
2.08*
-1-
2.99**
5-year prognosis >80%
50-80% 20-50%
<20% (ref) Nd
11.32 14.73 18.11 34.21 14.12
8.89
12.79 13.39 21.05 9.60
2.43 1.94 4.72
13.16 4.52
0.33* 0.43 0.47 -1-
0.38*
0.57 0.73 0.65 -1-
0.46
0.10**
0.09 0.23 -1-
0.27
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Firm size <10 (ref)
10-499 ≥500
16.29 14.25 6.62
13.44 11.20 4.02
2.85 3.04 2.60
-1-
1.24 0.65
-1-
1.66* 0.63
-1-
0.58 0.41
Business sector Primary/secondary (ref)
Tertiary
15.51 12.37
12.50 9.38
3.01 2.99
-1-
0.74
-1-
0.82
-1-
0.55
Workstation arrangt
no (ref) yes
21.00 5.21
16.84 3.39
4.15 1.82
-1-
0.16***
-1-
0.12***
-1-
0.28***
Workplace discrimtion no (ref)
yes
12.19 20.50
9.53
15.00
2.65 5.50
-1-
2.29***
-1-
2.08*
-1-
2.99**
5-year prognosis >80%
50-80% 20-50%
<20% (ref) Nd
11.32 14.73 18.11 34.21 14.12
8.89
12.79 13.39 21.05 9.60
2.43 1.94 4.72
13.16 4.52
0.33* 0.43 0.47 -1-
0.38*
0.57 0.73 0.65 -1-
0.46
0.10**
0.09 0.23 -1-
0.27
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “Production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “Production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “Production” and “supervisory “ models rejected)
Results % job loss Logistic regression (OR for job loss)
Variable All Production Supervisory- executive
All Production Supervisory- executive
Chemotherapy yes
no (ref)
13.86 12.50
10.75 9.62
3.11 2.88
1.32 -1-
1.19 -1-
1.72 -1-
After-effects no (ref)
moderate Important
8.17
12.90 20.24
6.35 9.13
16.90
1.81 3.77 3.33
-1-
2.25*** 3.18***
-1-
2.32*** 4.08***
-1-
2.19 1.91
Opiates use
no (ref) yes
8.96
15.30
6.63
11.97
2.33 3.33
-1-
1.52*
-1-
1.54
-1-
1.62
Psychotropics use no (ref)
yes
12.67 29.41
9.77
23.53
2.90 5.88
-1-
2.77**
-1-
3.61*
-1-
2.23
Anxiolytics use no (ref)
yes
10.43 15.03
7.45
12.02
2.98 3.01
-1-
1.11
-1-
1.21
-1-
0.86
Hypnotics use no (ref)
yes
10.27 19.26
7.17
16.48
3.10 2.78
-1-
1.46*
-1-
1.8**
-1-
0.72
Factors associated with cancer survivors’ job loss (employed in 2010, n=1,669) p-value (Khi-2 Wald test for OR: ***<0.001, **<0.01, *<0.05) LR test = 42.99 (H0 : equivalence of “Production” and “supervisory “ models rejected)
In the short run, cancer has a detrimental impact on careers No obvious gender inequalities
No systematic differences due to gender Potential differences linked to job characteristics
Strong discrepancies across occupations Ability to remain employed heavily depends on occupation Severity of cancer enhances differences within and between
occupations Job access or tenure always possible for executives and knowledge
workers two years after cancer diagnosis, even for bad prognosis cancers
Unemployment prevails for blue-collars whatever the severity of cancer
Size of the firm more likely to protect insiders (employed survivors) than to give a chance to outsiders (unemployed survivors)
Conclusion
Conclusion Limits/required refinements
Estimates conditional to survival People diagnosed with cancer in 2010 and who died between 2010 and 2012
are censored impact of cancer on careers deeper
What about sick leaves? 87% of people employed when cancer was diagnosed took benefit from one
sick leave at least Cancer survivors with no sick leave
Melanoma, thyroid, prostate No sequelae Female workers Living as a couple Diploma < A-level Production staff No workstation arrangement No workplace discrimination Temporary work contract
What about durations and employment loss/finding? 22% of people employed in 2010 lost their job (mean time: 4 months to be
compared to 7 months in general population) Among them, 30% (6.6% of people employed in 2010) found a new job (mean
time: 11 months to be compared to 5 months in general population)
Conclusion Limits/required refinements
What consequences for earnings? A drop in earnings for 3 cancer survivors out of 4
At least €296 for 1 household out of 4
Cancer survivors’ poverty rate: 21% (2010)24% (2012) (general population: 14% 14,3% )
Populations at higher risks of earnings loss Older people Bad prognosis cancers Diploma<A-level Chemotherapy Production staff No workstation arrangement Temporary work-contract Small to medium firms
How do socioeconomic inequalities in job retention evolve over time?
Comparison with 2004 Cancer Survey by DREES and INSERM Comparison with Employment Survey by INSEE News are bad
What about consequences for long-term cancer survivors? Forthcoming survey on 5 years cancer survivors (with longitudinal follow up)
La situation face à l'activité et à l'emploi
deux ans après un diagnostic de cancer
Alain Paraponaris, Luis Sagaon-Teyssier, Aurélia Tison
Contact: [email protected]
Research funded by: National Cancer Institute (INCa) and Cancer Research Association (ARC), Programme Cancer: maintien dans l’emploi et retour au travail
Fondation MGEN pour la Santé Publique Jeudi 9 octobre 2014, 14.00-15h30
Salle Plein Ciel