Group Project

Psychological Well-Being During the Great Recession: Changes in Mental Health Care Utilization in an Occupational Cohort Sepideh Modrek, PhD, Rita Hamad, MD, and Mark R. Cullen, MD

A vast literature documents a robust relation- ship between unemployment and a variety of mental health disorders.1,2 Although studies show that mental health deteriorates because of job loss,3,4 which increases during recessions, less evidence exists on the impacts of recession on the mental health of remaining workers. However, the impacts of recessions on mental health may extend beyond the direct effect of unemployment.5,6 Increased job insecurity,6---9

feelings of powerlessness,10 increased workload, and changes in job scope—as well as anger or sympathy for laid-off coworkers—may affect mental health.

It is difficult to study the mental health of workers during recessions because measuring changes in mental health requires sensitive measures and panel data. We previously ex- amined changes in new diagnoses of depres- sion for an employed population during the recent recession and found no significant as- sociation, but this may be attributable to the high specificity of our measure, which might not have been sensitive enough to capture subtle changes in how workers feel.11 Although self-reported symptoms or depression scales are thought to be sensitive to small changes in mental health, surveys with these measures tend to be cross-sectional and therefore cannot account for workers’ previous mental health.1

If remaining workers are selected on mental health, then cross-sectional estimates may un- derestimate the true effect of recessions on the mental health of remaining workers, similar to the well-known healthy worker bias.12 To estimate changes in mental health, panel data are necessary to take into account workers’ previous mental health.

As an alternative to self-reported depression scales, we exploited detailed claims data for a panel of continuously employed, continu- ously insured workers during 2007 to 2010 to

examine changes in use of mental health services and medications during the recent recession. We explored the yearly number of mental health inpatient and outpatient visits before and during the recession, as well as the yearly supply of prescriptions filled for opiates, antidepressants, sleep aids, and anxiolytics. We examined these 4 drug classes because pre- vious research suggests that job stress is related to the onset of generalized anxiety disorder and depression13 and that sleep disorders and drug abuse are often precursors to depression.14 In addition, we examined opiates because stress is known to relate to a wide variety of psycho- somatic illnesses, including chronic pain.15,16

We focused on discontinuous changes in the trend for these 4 medication classes because evidence shows an increasing trend in their use across developed countries.17 We found de- scriptive evidence of selection into our panel on both physical and mental health, making the remaining continuously employed and insured cohort healthier. We used piecewise regres- sion, a method used to compare trends in an outcome variable before and after a defined discontinuity, to examine changes in trends

after 2009. We accounted for individual-level unobservable time-invariant characteristics (e.g., gender, race, underlying health) with fixed-effects regressions. We also accounted for area-level changes in the unemployment rate. We explored the extent to which heightened job insecurity, caused by local plant layoffs, may have led to an increase in mental health services and treatments. We also examined the medium-term patterns in the mental health outcomes for the subset of workers who remained employed and insured through 2012, to investigate whether any of the ob- served changes in mental health---related utili- zation were long lasting or dissipated with the improvement in the economy in 2011 to 2012.

METHODS

We selected workers for our sample from linked administrative personnel and claims data sets from a multisite US manufacturing firm. These data are described elsewhere.8,11,18

We focused on 2 cohorts: a panel of 11625 employees who were continuously employed at the 25 largest plants, in 15 states, from

Objectives. We examined the mental health effects of the Great Recession of

2008 to 2009 on workers who remained continuously employed and insured.

Methods. We examined utilization trends for mental health services and medica-

tions during 2007 to 2012 among a panel of workers in the 25 largest plants, located in

15 states, of a US manufacturing firm. We used piecewise regression to compare

trends from 2007 to 2010 in service and medication use before and after 2009, the

year of mass layoffs at the firm and the peak of the recession. Our models

accounted for changes in county-level unemployment rates and individual-

level fixed effects.

Results. Mental health inpatient and outpatient visits and the yearly supply of

mental health–related medications increased among all workers after 2009. The

magnitude of the increase in medication usage was higher for workers at plants

with more layoffs.

Conclusions. The negative effects of the recession on mental health extend to

employed individuals, a group considered at lower risk of psychological distress.

(Am J Public Health. 2015;105:304–310. doi:10.2105/AJPH.2014.302219)

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January 2007 to December 2010, in which we examined short-term changes in outcomes, and a panel of 10 242 employees who were contin- uously employed at the same 25 plants from January 2007 to December 2012, in which we examined medium-term changes. All employees in the sample had insurance coverage through- out the study period, ensuring access to mental health services. The firm provided health in- surance benefits with identical provider net- works to employees and families through a local preferred provider organization, subject to choice in family coverage and deductible rates. Limited changes in preferred provider coverage, copays, and deductibles occurred during the study period.

We used claims data to quantify utilization of mental health services and medications. These data provided detailed claims records for each inpatient and outpatient medical en- counter and for each prescription filled and included International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9- CM)19 codes and Current Procedural Terminol- ogy (CPT) codes.20 We used ICD-9-CM codes to identify relevant diagnoses and CPT codes to categorize face-to-face physician visits.

Measures

We used the following ICD-9-CM codes to identify encounters with any mental health component: 296 (bipolar disorder), 300 (anxiety disorder), 304 (opiate dependence), 305 (alcohol abuse), 309 (mood disorder), and 311 (major depression). For each worker, we totaled the number of encounters an individual had in each year in 2 domains: outpatient physician visits (including emergency room visits) and inpatient hospitalizations. For outpatient visits, we only considered encounters that had a face-to-face visit with a doctor, as indicated by the CPT code.

We used the pharmacy claims data to calculate use of mental health---related medi- cations, according to the number of days’ supply that an individual filled in a year for 4 categories of medications: opiates, antidepres- sants, sleep aids, and anxiolytics (Appendix A, Table A, available as a supplement to the online version of this article at http://www. ajph.org, lists the drugs in each class). We included opiates because they may be used for self-medication, especially among individuals with a history of substance abuse.21 We also

included sleep aids because insomnia often precedes the onset of depression, and they are often prescribed for insomnia related to de- pression.22 We assumed that an increase in supply was directly related to an increase in the use of these medications, so we refer generally to medication use here. Although many of these medications are prescribed for multiple diagnoses, our analysis examined deviations from the mean use for each person across the years. Therefore, if individuals consistently used these drugs for other purposes, that would not affect our results. The analysis only cap- tured marked changes in use of these drugs.

The period of our study was marked by a rapid change in the national unemployment rate. We accounted for changes in regional labor market conditions by including the yearly county-level unemployment rate from the Bu- reau of Labor Statistics23 as a control variable in all of our models. We used unemployment rates for the county in which each plant was located.

We used the personnel data set to identify plants experiencing high layoff rates. If a plant had a mass termination event in which 40 or more employees were laid off on a single day, we considered it a high-layoff plant. We as- sumed that surviving employees at these 7 plants experienced greater job insecurity than did those at the 18 other plants. We used mass termination events to categorize job instability in previous studies.8,11

Data Analysis

We described selection on health and mental health in particular to establish that continuously employed workers were healthier than their counterparts who dropped out of the sample. We categorized workers’ baseline health status in 2007 with a health risk score, computed with a third-party algorithm (DxCG software, Verisk Health Inc, Waltham, MA), which uses an in- dividual’s historical CPT and ICD-9-CM codes and utilization of health care services to de- termine a risk stratification score. This score has been used to adjust for underlying health, particularly in health services research.24---26

A score of1indicates that the individual’s health expenditures are likely to fall at the mean in the following year. Each unit increase predicts a 100% increase in expenditures above the mean. We also compared mental health---related

inpatient and outpatient visits and use of the 4 classes of mental health---related medications for workers who remained employed and those who left in 2008 to 2010.

Our primary analysis involved the cohort of continuously employed and insured workers. We used piecewise regression, which parti- tioned the data into 2 intervals, allowing us to estimate a separate trend line for each interval. The boundaries between the segments are known as breakpoints. We modeled the breakpoint in 2009, the peak of major layoffs at the company and the nadir of the gross national product nationally, comparing trends in 2007 to 2008 with those in 2009 to 2010. We used this breakpoint in previous studies of the cohort,8,11 and it coincided with a company press release in January 2009 announcing plans for a reduction in the workforce of 13%, citing “extraordinary times requiring extraor- dinary actions.”27 Although layoffs at the company ended in March 2010, hiring and pay freezes continued at least through 2013. We therefore investigated the medium-term impacts by extending the postrecession seg- ment from 2009 to 2012. We also included county-level unemployment rates to account for changes in the regional labor market.

Because our data included multiple obser- vations for each individual—that is, 1 observa- tion per person per year—we controlled for time-invariant characteristics by including a fixed-effects parameter at the individual level. The inclusion of individual-level fixed effects in the model allowed us to use only within-individual variation to identify discontinuous changes in utilization of mental health services and med- ications after 2009. The inclusion of the fixed effect precluded controlling for age directly, but all specifications accounted for aging by in- cluding a second- and third-degree polynomial for age. Robust standard errors clustered at the individual level. We assessed heterogeneity in employees’ responses to the recession through the use of an interaction term between the time trend variable and whether an individual’s plant experienced a mass termination event.

RESULTS

Table 1 presents the sample characteristics for 3 groups: (1) 11 625 workers who were active on January 1, 2007, and continuously

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employed and insured from 2007 to 2010; (2) 10 242 workers who were active on January 1, 2007, and continuously employed and insured from 2007 to 2012; and (3) 2946 workers who were active on January 1, 2007, and whose contract of employment was termi- nated between 2008 and 2010. The second group was a subset of the first group. The workers in groups 1 and 2 were predomi- nantly White men, aged 45 years on average; 56% worked at high-layoff plants. These workers’ average risk score was 1, suggesting that their overall health care utilization in 2007 fell at the mean of a working aged population. By contrast, the workers who left in 2008 to 2010 (group 3) were on average 3 years older, were more likely to be female, and had higher risk scores in 2007 (all, P < .001).

Table A2 (available as an online supplement) shows baseline differences in mental health utili- zation between those who left the firm and those who remained, confirming that the workers who left had higher mental health inpatient and outpatient utilization in 2007. They also used more antidepressants, anxiolytics, and opiates. Results presented here focus only on the contin- uously employed and insured workforce.

Changes in Utilization of Mental Health

Services After 2009

Table 2 presents results on inpatient visits with a mental health---related diagnosis for the 2007 to 2010 and 2007 to 2012 cohorts.

We found a marginally significant increase in inpatient utilization in 2009 to 2010 from 2007 to 2008 (b = 0.00189; P = .078). The magnitude of the postrecession trend line was about 4 times as great as that of the prere- cession trend line in the short term. In the medium term, the prerecession and postre- cession trends were almost identical, suggest- ing that the increase in inpatient visits was abrupt and short lived.

Table 2 also shows models that included an interaction term for working at a high-layoff plant. We found no difference in the trend among those with higher job insecurity. We documented that changes in county-level un- employment rate were related to decreases in mental health inpatient visits.

Table 3 presents results for the number of outpatient visits with a mental health---related diagnosis. The results suggested a statistically significant increase in the trends in outpatient utilization in 2009 to 2010 over 2007 to 2008. The increase in the trend remained elevated and significant, but the magnitude was substantially reduced in a comparison of the yearly increases of 2009 to 2012 and 2007 to 2008. These results were driven by a decreasing prerecession trend and a discon- tinuous and increasing postrecession trend. We observed no difference between em- ployees at high-layoff plants and those at other plants. We also documented that changes in county-level unemployment rates were

related to increases in mental health outpa- tient visits. When we allowed for differences by plant-level layoffs, the magnitude between increases in unemployment rates and in- creases in mental health outpatient visits was similar across both the 2007 to 2010 and the 2007 to 2012 periods.

Changes in Utilization of Mental Health

Medications After 2009

Table 4 presents changes in the supply of opiates, antidepressants, sleep aids, and anxio- lytics. We examined the number of days’ supply of each drug category separately, and we exam- ined differential patterns among workers who worked in high-layoff plants. Workers used more opiates, antidepressants, sleep aids, and anxio- lytics in 2009 to 2010 than in 2007 to 2008. The increases in use of opiates and anxiolytics were only marginally significant. Opiate use increased 8.8% a year (a figure derived from the mean initial use of 8.8 pills), or almost 5 times the prerecession trend. We found a 13% increase in antidepressant use (derived from the mean initial use of 27.1 pills). The increasing trend in the use of antidepressants in the post- recession period was particularly notable because of the decreasing trend in antidepressant use before the recession.

Workers increased their use of sleep aids by 23% per year (derived from the mean initial use of 4.4 pills). The difference in the trend for sleep aids was driven by a small increasing trend in the use of sleep aids in the post- recession period and a large decreasing trend before the recession. The 11% increase in use of anxiolytics (derived from the mean initial use of 7.2 pills) was almost 5 times the prerecession trend. Use of antidepressants was also statistically significantly higher in high- layoff plants than in other plants. We found no difference in trends for opiates, sleep aids, or anxiolytics at high-layoff plants. Results also suggested that changes in county-level unemployment rates were robustly related to increased use of opiates, and sleep aids.

In the medium term, our results suggested that the increase in the trend for antidepres- sants and sleep aids remained significant among the 2007 to 2012 cohort, but the magnitude of the increase was smaller. The increase in use of antidepressants in high-layoff plants remained significant, though the

TABLE 1—Characteristics of Workers in Continuously Employed Cohorts and Those

Employed in 2007 but Terminated Before 2010: 2007–2012

Characteristic

2007–2010 Cohort,a

No. or % or Mean 6SD

2007–2012 Cohort,a

No. or % or Mean 6SD

Workers Laid Off in 2008–2010,

No. or % or Mean 6SD

Sample size 11 625 10 242 2946

Female 20.1 19.3 25

Age in 2007, y 45.6 69.0 45.2 68.7 48.7 612.2

Race

White 82 82 80.9

Black 8.7 8.6 9.4

Hispanic 6.3 6.3 6.5

Other 3 3.1 3.2

2007 Risk score 1.0 61.0 0.98 60.96 1.42 61.95

Employed at high-layoff plant b

55.9 56.4 52.6

aInclusion criteria were continuous employment and continuous insurance coverage during study period. bPlant that laid off ‡ 40 employees in 1 day.

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magnitude was diminished. We found no difference in trend for sleep aids, anxiolytics, or opiates at high-layoff plants in the longer period. However, in the models that accounted for differences by plant-level job insecurity, changes in county-level unemployment rates were robustly related to increased use of opiates and sleep aids.

DISCUSSION

We examined changes in utilization of mental health services and medications in a cohort of continuously employed and insured workers employed at a firm that experienced significant downsizing events during the 2008 to 2009 Great Recession. We documented that

remaining workers had better overall health and significantly less utilization of mental health services and medications in the years before the recession.

Despite their lower use of mental health services and medications at baseline, workers who remained employed increased their utili- zation of inpatient and outpatient mental health

TABLE 2—Mental Health Inpatient Utilization Among Continuously Employed and Insured Workers: 2007–2012

Yearly Mental Health Inpatient Visits

Variable 2007–2010 Cohort, b (95% CI)

2007–2012 Cohort,

b (95% CI)

2007–2010 Cohort, With

Interactions at High-Layoff

Plants, a b (95% CI)

2007–2012 Cohort, With

Interactions at

High-Layoff Plants, a b (95% CI)

Time trend before 2009 0.00056 (–0.00034, 0.00146) 0.00035 (–0.00047, 0.00117) 0.00087 (–0.00034, 0.00208) 0.00051 (–0.00065, 0.00167)

Time trend after 2009 0.00245* (0.00058, 0.00433) 0.00036 (–0.00010, 0.00083) 0.00126 (–0.00100, 0.00351) 0.00004 (–0.00061, 0.00069)

Interaction terms: job security

Before 2009 x high layoff –0.00046 (–0.00170, 0.00078) –0.00037 (–0.00153, 0.00079)

After 2009 x high layoff 0.00215 (–0.00144, 0.00575) 0.00058 (–0.00026, 0.00143)

High layoff 0.00027 (–0.00157, 0.00210) –0.00032 (–0.00193, 0.00129)

Unemployment rate –0.00029* (–0.00054, –0.00005) –0.00011 (–0.00031, 0.00010) –0.00031* (–0.00057, –0.00006) –0.00009 (–0.00028, 0.00011)

Constant 0.00423 (–0.00190, 0.01037) 0.00082 (–0.00249, 0.00413) 0.00447 (–0.00181, 0.01074) 0.00087 (–0.00267, 0.00442)

Model comparisons

Difference in trend, before vs after recession 0.00189 0.00028

Note. CI = confidence interval. Analyses were conducted with piecewise regression with a discontinuity in 2009, with fixed effects at the individual level. Models controlled for age squared and age cubed. The 2007–2010 cohort had 46 500 observations for 11 625 individuals; the 2007–2012 cohort had 61 452 observations for 10 242 individuals. aA high-layoff plant laid off ‡ 40 employees in 1 day (n = 7). Reference group = all other plants (n = 18). *P < .05.

TABLE 3—Mental Health Outpatient Utilization Among Continuously Employed and Insured Workers: 2007–2012

Yearly Mental Health Outpatient Visits

Variable

2007–2010 Cohort,

b (95% CI)

2007–2012 Cohort,

b (95% CI)

2007–2010 Cohort, With Interactions

at High-Layoff Plants, a b (95% CI)

2007–2012 Cohort, With

Interactions at High-Layoff

Plants, a b (95% CI)

Time trend before 2009 –0.0107*** (–0.0171, –0.0043) –0.0051 (–0.0112, 0.0010) –0.0102** (–0.0172, –0.0032) –0.0093* (–0.0168, –0.0017)

Time trend after 2009 0.0085* (0.0006, 0.0163) 0.0060*** (0.0029, 0.0092) 0.0066 (–0.00353, 0.0166) 0.0091 (–0.0017, 0.0199)

Interaction terms: job security

Before 2009 x high layoff 0.0006 (–0.0067, 0.0079) 0.0010 (–0.0067, 0.0087)

After 2009 x high layoff 0.0037 (–0.0120, 0.0193) –0.0000 (–0.0167, 0.0167)

High layoff 0.0173** (0.0065, 0.0281) 0.0169** (0.0056, 0.0283)

Unemployment rate 0.0050*** (0.0025, 0.0076) 0.0029* (0.0005, 0.0053) 0.0046*** (0.0021, 0.0072) 0.0042** (0.0015, 0.0069)

Constant 0.0331 (–0.0059, 0.0722) 0.0631** (0.0222, 0.1041) 0.0298 (–0.0089, 0.0685) 0.0395 (–0.0029, 0.0819)

Model comparisons

Difference in trend, before vs after recession 0.0192*** 0.0112**

Note. CI = confidence interval. Analyses were conducted with piecewise regression with a discontinuity in 2009, with fixed effects at the individual level. Models controlled for age squared and age cubed. The 2007–2010 cohort had 46 500 observations for 11 625 individuals; the 2007–2012 cohort had 61 452 observations for 10 242 individuals. aA high-layoff plant laid off ‡ 40 employees in 1 day (n = 7). Reference group = all other plants (n = 18). *P < .05; **P < .01; ***P < .001.

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TA B LE

4 — M e n ta l H e a lt h M e d ic a ti o n S u p p ly fo r W o rk e rs

in 2 0 0 7 – 2 0 1 0 a n d 2 0 0 7 – 2 0 1 2 C o n ti n u o u sl y E m p lo ye d C o h o rt s

Ye ar ly M ed ic at io n Co un t

Ye ar ly M ed ic at io n Co un t, H ig h- La yo ff Pl an ts a

Va ria bl e

O pi at es , b (9 5%

CI )

An tid ep re ss an ts , b (9 5%

CI )

Sl ee p Ai ds , b (9 5%

CI )

An xi ol yt ic s, b (9 5%

CI )

O pi at es , b (9 5%

CI )

An tid ep re ss an ts , b (9 5%

CI )

Sl ee p Ai ds , b (9 5%

CI )

An xi ol yt ic s, B (9 5%

CI )

20 07 –2 01 0 C oh or t

Ti m e tr en d be fo re 20 09

0. 20 8 (– 0. 43 5, 0. 85 2)

–0 .2 89

(– 1. 54 5, 0. 96 6)

–0 .7 49 **

(– 1. 25 2, –0 .2 46 )

0. 19 3 (– 0. 37 1, 0. 75 6)

0. 12 0 (– 0. 59 6, 0. 83 7)

–0 .0 86 8 (– 1. 42 6, 1. 25 2)

–0 .6 56 * (– 1. 18 0, –0 .1 32 )

0. 17 8 (– 0. 39 7, 0. 75 2)

Ti m e tr en d af te r 20 09

0. 98 5* **

(0 .4 39 , 1. 53 1)

3. 25 0* **

(2 .2 54 , 4. 24 6)

0. 24 8 (– 0. 14 8, 0. 64 4)

0. 96 3* **

(0 .4 40 , 1. 48 6)

0. 65 4 (– 0. 08 3, 1. 39 0)

1. 41 8*

(0 .0 60 , 2. 77 7)

–0 .1 42

(– 0. 69 2, 0. 40 9)

0. 72 3 (– 0. 03 9, 1. 48 5)

In te ra ct io n te rm s: jo b se cu rit y

Be fo re 20 09

x hi gh

la yo ff

0. 24 1 (– 0. 40 7, 0. 88 9)

–0 .3 35

(– 1. 55 3, 0. 88 4)

–0 .1 36

(– 0. 66 9, 0. 39 7)

0. 04 05

(– 0. 51 3, 0. 59 3)

Af te r 20 09

x hi gh

la yo ff

0. 60 6 (– 0. 42 6, 1. 63 8)

3. 28 3* **

(1 .3 04 , 5. 26 1)

0. 70 2 (– 0. 06 8, 1. 47 1)

0. 43 1 (– 0. 60 4, 1. 46 6)

H ig h la yo ff

1. 07 8 (– 0. 82 2, 2. 97 8)

1. 97 6 (– 1. 37 2, 5. 32 3)

1. 10 0 (– 0. 02 3, 2. 22 4)

0. 06 5 (– 1. 58 9, 1. 71 9)

U ne m pl oy m en t ra te

0. 69 6* **

(0 .4 10 , 0. 98 3)

0. 47 4 (– 0. 02 5, 0. 97 3)

0. 29 2* * (0 .1 04 , 0. 48 1)

0. 12 9 (– 0. 10 5, 0. 36 2)

0. 67 5* **

(0 .3 91 , 0. 96 0)

0. 46 8 (– 0. 03 6, 0. 97 3)

0. 28 5* * (0 .0 94 , 0. 47 6)

0. 12 5 (– 0. 11 6, 0. 36 6)

Co ns ta nt

–5 .0 60 * (– 9. 62 8, –0 .4 91 )

–5 .7 79

(– 14 .7 29 , 3. 17 0)

–5 .1 65 **

(– 8. 48 0, –1 .8 50 )

–4 .5 28 * (– 8. 53 6, –0 .5 19 )

–5 .3 65 * (– 9. 94 7, –0 .7 83 )

–6 .4 86

(– 15 .5 54 , 2. 58 1)

–5 .5 24 ** * (– 8. 88 1, –2 .1 68 )

–4 .5 27 * (– 8. 48 5, –0 .5 68 )

M od el co m pa ri so ns

D iff er en ce

in tr en d, be fo re

vs af te r re ce ss io n

0. 77 7b

3. 54 0* **

0. 99 7* *

0. 77 0c

20 07 –2 01 2 C oh or t

Ti m e tr en d be fo re 20 09

0. 92 7* * (0 .3 53 , 1. 50 1)

0. 23 9 (- 0. 98 7, 1. 46 6)

–0 .4 12

(- 0. 88 1, 0. 05 6)

0. 35 7 (- 0. 17 7, 0. 89 2)

0. 55 4 (- 0. 09 2, 1. 20 1)

–0 .2 40

(- 1. 59 4, 1. 11 3)

–0 .3 74

(- 0. 87 8, 0. 13 0)

0. 15 4 (- 0. 43 1, 0. 73 8)

Ti m e tr en d af te r 20 09

0. 99 9* **

(0 .6 97 , 1. 30 2)

2. 58 3* **

(1 .9 92 , 3. 17 5)

0. 27 3* * (0 .0 69 , 0. 47 6)

0. 49 5* **

(0 .2 08 , 0. 78 2)

0. 78 4* **

(0 .3 89 , 1. 17 9)

1. 52 8* **

(0 .7 37 , 2. 31 9)

0. 11 4 (- 0. 13 5, 0. 36 4)

0. 40 4*

(0 .0 15 , 0. 79 2)

In te ra ct io n te rm s: jo b se cu rit y

Be fo re 20 09

x hi gh

la yo ff

0. 47 6 (- 0. 18 4, 1. 13 5)

–0 .0 76

(- 1. 40 0, 1. 24 8)

–0 .1 85

(- 0. 74 1, 0. 37 0)

0. 28 4 (- 0. 30 9, 0. 87 6)

Af te r 20 09

x hi gh

la yo ff

0. 42 1 (- 0. 11 2, 0. 95 3)

2. 05 2* **

(0 .9 93 , 3. 11 0)

0. 30 5 (- 0. 05 9, 0. 67 0)

0. 17 9 (- 0. 33 9, 0. 69 6)

H ig h la yo ff

1. 26 8 (- 0. 53 6, 3. 07 3)

2. 33 0 (- 1. 16 5, 5. 82 5)

1. 07 2 (- 0. 04 8, 2. 19 3)

0. 79 2 (- 0. 92 3, 2. 50 6)

U ne m pl oy m en t ra te

0. 29 1*

(0 .0 62 , 0. 51 9)

0. 21 4 (- 0. 24 5, 0. 67 4)

0. 15 5 (- 0. 00 6, 0. 31 5)

0. 00 3 (- 0. 19 8, 0. 20 3)

0. 33 6* * (0 .1 07 , 0. 56 4)

0. 43 7 (- 0. 02 8, 0. 90 2)

0. 18 3*

(0 .0 21 , 0. 34 6)

0. 02 2 (- 0. 18 7, 0. 23 0)

Co ns ta nt

–1 .9 68

(- 6. 62 6, 2. 68 9)

–5 .5 4 (- 15 .0 28 , 3. 94 9)

–3 .9 76 * (- 7. 51 5, –0 .4 36 )

–4 .0 02 * (- 7. 99 3, –0 .0 11 )

–3 .0 40

(- 7. 85 0, 1. 77 0)

–8 .6 77

(- 18 .3 53 , 0. 99 9)

–4 .7 06 * (- 8. 37 5, –1 .0 38 )

–4 .5 86 * (- 8. 60 4, –0 .5 67 )

M od el co m pa ri so ns

D iff er en ce

in tr en d, be fo re

vs af te r re ce ss io n

0. 07 2

2. 34 4* *

0. 68 5*

0. 13 8

N ot e. CI = co nfi de nc e in te rv al .A na ly se s w er e co nd uc te d w ith

pi ec ew is e re gr es si on

w ith

a di sc on tin ui ty in 20 09 ,w ith

fix ed

ef fe ct s at th e in di vi du al le ve l. M od el s co nt ro lle d fo r ag e sq ua re d an d ag e cu be d. Th e 20 07 –2 01 0 co ho rt ha d 46

50 0 ob se rv at io ns fo r 11

62 5 in di vi du al s; th e 20 07 –2 01 2 co ho rt ha d

61 45 2 ob se rv at io ns

fo r 10

24 2 in di vi du al s.

a A hi gh -la yo ff pl an t la id of f ‡ 40

em pl oy ee s in 1 da y (n = 7) . Re fe re nc e gr ou p = al l ot he r pl an ts (n = 18 ).

b Ap pr oa ch in g si gn ifi ca nc e at P = .0 57 .

c A pp ro ac hi ng

si gn ifi ca nc e at P = .0 52 .

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308 | Research and Practice | Peer Reviewed | Modrek et al. American Journal of Public Health | February 2015, Vol 105, No. 2

services immediately after the downturn. Con- sistent with this evidence, we also found an increase in the use of mental health---related medications, specifically opiates, antidepres- sants, sleep aids, and anxiolytics, for all workers. When we looked at a longer period, including data for those who remained working in 2011 to 2012, we observed no difference in the rate of utilization of mental health---related inpatient visits. Use of antidepressants and sleep aids was still higher among all workers, and workers at high-layoff plants still used more antidepressants, though the magnitudes were diminished. We confirmed that beyond the local plant environment, county-level un- employment rates were robustly associated with increases in mental health outpatient visits and medication use.

Our analysis helps reconcile divergent re- sults from 2 previous studies we conducted among the same population of workers. One study found an insignificant increase in new depression diagnoses at high-layoff plants,11

and another examined self-reported work stress and found it elevated at high-layoff plants.8 In the analysis we report here, we found an increase in the rate of mental health services in the short run and a prolonged increase in antidepressant use in high-layoff plants. This suggests that new diagnoses for depression may be too stringent a measure to capture subtler changes in psychological dis- tress, but that some mental health deterioration occurred in our cohort. This is particularly important because surviving workers should have been the least affected by the economic downturn, yet our study showed that this healthy population also experienced adverse mental health outcomes.

Few studies have examined mental health changes in working populations in the context of the Great Recession. Consistent with our findings, the American Panel Survey found that in 2008 to 2009 one third of Americans reported having problems with sleeping; in late 2010, this figure was one fourth.28 Our finding about changes in unemployment rate and use of mental health medication was both novel and consistent with previous literature. A re- cent study found increases in opiate consump- tion in states with higher unemployment rates between 2007 and 2010 in the United States, but no increase in antidepressants as a class.29

Our study substantiated results from other studies regarding opiates, but we used more stringent methods—more localized change in unemployment, a sample comprising only con- tinually employed populations, and analysis of within-person variation only—and found an in- crease in the use of sleep aids.

Our study had several advantages over pre- vious studies. First, few studies have the de- tailed data necessary to examine small changes in utilization of mental health services and medications over time. Our data contained multiple observations per worker, allowing for the inclusion of individual-level fixed effects to estimate changes in utilization of mental health services and medications after 2009 through analysis of within-person variation only. This model was conservative because it con- trolled for both area-level unemployment rates and local plant-level employment conditions.

Another advantage was our large sample size, combined with continuous measures of the outcomes. Although some detailed panel studies with self-reported depression scales have been conducted, such as the Michigan Recession and Recovery study30 and the American Life Panel study,31 they had rela- tively small sample sizes and did not continu- ously measure outcomes over longer periods. In light of the moderate and dissipating effect sizes of mental health changes in working populations, only a large data set with contin- uous measures would allow differentiation of short- and medium-term effects.

Limitations

Our sample comprised only workers in the heavy and light manufacturing industries, which were heavily affected by the recent recession. It could be that employees in other sectors of the economy had different experi- ences. Also, although we examined yearly medication supply, we were unable to deter- mine whether employees actually took the pre- scriptions that were filled. We were not able to determine whether increases in medication use were driven by workers with preexisting mental health problems, because our sample included very few such people. The fixed-effect model captured only increases in use among new users and increases or decreases in existing users.

Our results may have underestimated the true effect. We selected workers who

maintained employment throughout the reces- sion and therefore had access to insurance, had better overall health than their laid-off col- leagues, and used mental health services less than their laid-off colleagues in the prereces- sion period. Other studies have shown that remaining workers are healthier,11 and population-level surveys show that employed populations have generally better mental health.28 This selection could limit generaliza- tion to the less stably employed general US working population, which may have experi- enced greater effects from the recession.

Conclusions

Our study provides evidence that even those who remained employed during the Great Recession experienced worsening mental health. The results suggested a modest increase in use of medications and an increase in mental health---related inpatient and outpatient visits in the short term. Our measure of medication supply was likely sensitive to small changes in mental health and was consistent with the idea that medication use is a coping mechanism in times of uncertainty. Our results suggested that the mental health consequences of the recent recession on the US workforce were wide- spread.

Although these mental health issues may be resolving as the economy emerges from the recession, other downturns will undoubtedly occur. Employers should be aware of the negative consequences of layoffs on their remaining workforce and their work culture. In such times, employers can mitigate the negative consequences of layoffs by promoting and providing increased support services, such as employee assistance programs and targeted wellness programs. In addition, they should communicate upcoming layoffs transparently. Disclosing the scope and duration of the layoffs may help limit uncertainty and mental health distress that remaining workers face. During recessions, physicians should also be more vigilant in assessing patients’ work environ- ment, including its stability, when diagnosing and treating mental health concerns. j

About the Authors The authors are with the Department of General Medical Disciplines, Stanford University School of Medicine, Palo Alto, CA.

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Correspondence should be sent to Sepideh Modrek, PhD, 1070 Arastradero Rd, Palo Alto, CA 94304 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted July 19, 2014.

Contributors S. Modrek originated the study, searched the literature, analyzed and interpreted the data, and wrote the article. R. Hamad and M. R. Cullen helped interpret data and write the article.

Acknowledgments This research was funded by from the National In- stitute on Aging (grants, 1R01 AG026291-06 and 1R01AG032449-01) and Alcoa Inc.

Human Participant Protection The Stanford University institutional review board pro- vided ethical approval for this study. Individual consent was waived because of an epidemiological exemption.

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