EMPLOYMENT STATUS AND HEALTH TRAJECTORIES

Leeds, 18 March 2008

EMPLOYMENT STATUS AND HEALTH TRAJECTORIES

Gopalakrishnan NetuveliImperial College London1 Jan 2007 – 31 March 2008


Employment and health

“… there is a strong theoretical case, supported by a great deal of background evidence, that work and paid employment are generally beneficial for physical and mental health and well-being.” Waddel and Burton, 2006.

Debate: selection vs. causation “…there is a strong case for all health

strategies to consider employment as an outcome, where appropriate. There is also a strong case for employment policy to evaluate the health impact of all its relevant interventions.” McLean et al. 2005


Problem with the direction of causation

Employment and health may mutually influence each other and the direction of causation might depend on context and contingency.

This makes the relationship between employment and health complex.

Data form that might capture context and contingency is longitudinal trajectories.

Study of trajectories might help to understand part of this complexity


Objective

To explore trajectories of health and employment in a sample of BHPS

Data: 2852 subjects between 16 and 50 years in 1991 who were employed and reported no health problems and self-rated health as good or better

Employment trajectory: 1 =(self) employed; 0=ElseHealth trajectory: 1 = Good or better SRH; 0=ElseW9 & 14 excluded > SRH question different


Methods

1. Summarising trajectories: are there classes of trajectories?

Latent Class Growth Analysis

W1

IS

C

W14

Age

Sex

11

0

13


Methods

2. Comparing employment and health trajectories within individuals: are trajectories of health and employment similar?


Measuring similarity: requirements

Both trajectories coded similarly Same number of states in each point of each

trajectory The states coded similarly have the same

relative position in the vector of states for each trajectory

Present study:

Trajectory 0 1Health Health problems present No health problemsEmployment Out of employment In employment

Codes


Methods contd…

Common distance measures of similarity:Euclidean, Hamming, Levenshtein

Present study: a new approach using Kolmogorov-Smirnov D – statisticD-statistics is the maximum distance between the cumulative

fractile/percentile distribution of the two trajectories. A significant test for the H0:D=0 can be done (if necessary

exact test accounting for small number of points)The individual P-values can be combined using meta-analysis,

even adjusting for co-variates using meta-regression It is also possible to identify which distribution ‘dominates’

Applications used: Mplus, STATA


Results

Distribution of the sample according to W1 age and sex

Age group Men Women All16-30 564 456 102031-40 425 320 74541-50 587 500 1087All 1567 1276 2852


Results

Distribution according to W1 social class


Employment trajectories

Latent Class Growth Analysis of employment trajectory identified 7 classes. Classification forced to stop at 7 when number of people in any class fell below 5%

Employment trajectories Freq. Percent AUC* Immediate drop 446 15.48 0.06 Early rectangle 353 12.25 0.21Early drop - slow decline 137 4.76 0.37 Middle rectangle 237 8.23 0.46 Early drop- recovery 264 9.16 0.68 Late rectangle 247 8.57 0.72

Persisting 1,197 41.55 0.91

*AUC Average proportion of person-time in employment


Employment trajectories


Age and sex distribution of employment trajectories


Social class distribution of employment trajectories by sex


Propensity to different types of employment trajectories

Narrative description of a multinomial logistic regression:

Employment trajectories Characeristics Immediate drop >30years, manual class Early rectangle not 31-40 (might be manual class)Early drop - slow decline Women <31or >40 years manual class Middle rectangle >40 years Early drop- recovery Women <31 years manual class Late rectangle <30 or >40 years

Persisting Reference category


Health trajectories

LCGA identified 6 classes. Classification stopped when there was no significant statistical difference between six and seven class solutions

*AUC Average proportion of person-time in employment

Health trajectories N % AUC* CharacteristicsImmediate drop 376 15.06 0.06 <31 years manual classEarly rectangle 247 9.89 0.24 <31 yearsEarly drop - slow decline 173 6.93 0.30 Women >40 years manual classLate rectangle 248 9.93 0.53 (<31 or >40 years manual class)Early drop- recovery 402 16.1 0.54 Women <31 or >40 years manual classPersisting 1,051 42.09 0.75 Reference


Cross-tabulation of health and employment trajectories

EmploymentImmediate drop

Early rectangle

Early drop - slow dec

Early drop- recovery

Late rectangle Persisting All

Immediate drop 307 12 4 3 8 3 337Early rectangle 50 191 9 10 4 8 272Early drop - slow dec 2 1 37 24 28 19 111Middle rectangle 6 30 23 81 12 22 174Early drop- recovery 1 1 24 12 90 121 249Late rectangle 3 3 23 70 32 73 204Persisting 7 9 53 48 228 805 1150All 376 247 173 248 402 1051 2497

Health

Chi-square= 3994; df=30 p-value: <0.0001

Pivotal cells contributing to greatest to chi-square

Correlation between trajectories: 0.8


Are the health and employment trajectories within indivuals similar?


Meta-analysis of p-values: full and subgroups

Groups D-statistics (95%CI) z-value pAll 0.28 (0.27 to 0.30) -9.040 1.000Men, non-manual, 16-30 years 0.19 (0.15 to 0.23) -7.083 1.000Men, non-manual, 31-40 years 0.26 (0.22 to 0.30) -4.307 1.000Men, non-manual, 41-50 years 0.31 (0.27 to 0.34) -0.698 0.757Men, manual, 16-30 years 0.21 (0.17 to 0.25) -7.327 1.000Men, manual, 31-40 years 0.30 (0.24 to 0.35) -0.870 0.808Men, manual, 41-50 years 0.36 (0.31 to 0.40) 1.823 0.034Women, non-manual, 16-30 years 0.26 (0.22 to 0.29) -5.364 1.000Women, non-manual, 31-40 years 0.26 (0.21 to 0.31) -4.912 1.000Women, non-manual, 41-50 years 0.33 (0.29 to 0.36) 0.358 0.360Women, manual, 16-30 years 0.31 (0.24 to 0.37) -0.403 0.657Women, manual, 31-40 years 0.31 (0.25 to 0.38) -0.669 0.748Women, manual, 41-50 years 0.33 (0.28 to 0.39) 0.215 0.415


Distribution of Employment and health trajectories in men, non-manual, 41-50 years

Early rectangle

Early drop slow decline

Late rectangle

Early drop recovery

Persisting Total

Middle rectangle 0 0 1 1 2 4Early drop- recovery 0 1 0 1 7 9Late rectangle 0 3 1 0 0 4Persisting 1 3 5 19 41 69Total 1 7 7 21 50 86

Health

Employment

Pearson chi2(12) = 32.3433 Pr = 0.001


Average D according to employment and health latent classes

EmploymentImmediate drop

Early rectangle

Early drop - slow dec

Early drop- recovery

Late rectangle Persisting All

Immediate drop 0.02 . 0.07 . 0.65 0.36 0.31Early rectangle 0.14 0.14 0.11 0.11 0.07 0.48 0.32Early drop - slow dec . . 0.11 . 0.34 0.38 0.30Middle rectangle 0.21 0.29 0.26 0.23 0.25 0.36 0.28Early drop- recovery . 0.79 0.31 0.38 0.30 0.20 0.25Late rectangle 0.64 0.64 0.39 0.26 0.32 0.18 0.26Persisting 0.64 0.63 0.56 0.47 0.39 0.22 0.29All 0.41 0.57 0.44 0.40 0.37 0.23 0.28

Health

Emboldened: significant p-value after synthesis


Conclusions

Are there classes of trajectories? YES

Are trajectories of health and employment similar? YES for the majority (80%)

Selection or causation? Weak evidence (if any) for causation


Acknowledgements

ESRC and UPTAP programme

Professor David Blane, ICLProfessor Mel Bartley, UCLProfessor Richard Wiggins, IOE

Members of Q3 seminar group, Imperial College London

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EMPLOYMENT STATUS AND HEALTH TRAJECTORIES

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