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also their degree of depression was assessed. The authors applied a mixed model analysis of covariance for the initial analyses and more compound models including interaction effects for the final analyses. During the analyses the patients were divided into three groups (based on the interviews): persistent depression, improved and remission. Regarding the employment status the results showed that the probability of paid employment was positively related to the clinical outcome. In other words those patients with the greatest improvement also had a higher probability of paid employment. Compared to earlier research this study observes the patients over a longer time period. Contrary to earlier studies there is found no time delay between the improvement in clinical outcome and an improved economic outcome. But this could be due to the fact that this study includes only patients with mild and moderate depression, and it is therefore not possible to generalize to patients with severe depression. Finally, the authors mention that although they control for a lot of variables, they cannot reject the possibility that there is an unobservable effect that causes the relation between the clinical and the economic outcome. Schoenbaum et al. (2002) study the effects of depression treatment on clinical outcome and on employment outcomes. They use a panel with 938 adults suffering from a depressive disorder, observed over six months. The observational study was carried out in the primary care environment, which means that the patients were treated by their usual health care providers and with the regular payment mechanisms. The participating clinics were randomized to either conventional treatment or one of two quality improvement programs with the aim of improving care, either through focus on medication or through extended therapy. The improvement programs also differed from the usual treatment since there was a higher degree of follow-up by the clinicians. The data were collected through a survey assessing the dosage of antidepressants taken and the patient’s degree of depression on a mental health scale. Furthermore the data included a large number of covariates among which age, sex, marital status, education and employment status. Based on the follow-up survey, it was evaluated whether the patient received appropriate care or not. The results showed that patients in the quality improvement programs were more likely to receive appropriate care. Thereafter the authors used multivariate regression models to analyse the effects of the treatment. They used instrumental variables methods to take account of the self-selection into treatment, since their initial analyses showed a positive relationship between the degree of illness and the appropriateness of the care. As an instrument they used the randomized intervention status that is the likelihood of receiving one of the quality improving programs compared to the conventional treatment. The analyses showed positive treatment effects on employment, since a larger percentage 25
of patients receiving appropriate care were employed, compared to those patients receiving inappropriate care. Apart from these results proper treatment of depression also resulted in better health outcomes. A recent overview of the research in antidepressant treatment and employment is provided by Greener and Guest (2005). According to the authors this is an area that has been understudied and to a certain extent this can be explained by the fact that most studies looking at the effect of antidepressants only have a horizon of a few weeks, which is not always enough to evaluate the effects on work productivity and employment. The authors conduct a systematic literature review by searching healthcare databases such as Medline and Cochrane. In general there is evidence in the literature that antidepressants can have a positive effect on occupational outcomes. One of the articles reviewed found that the patients with a reduction in the symptoms of depression were 16 % more likely to stay employed or find a job, if they did not have a job to begin with, compared to the patients with continuing symptoms. Also the patients with reduced symptoms had less missed days of work on a yearly basis. The authors conclude that there are still parts in this literature that remain unexplored, but that the complex nature of depression does not make this straightforward. 3.3 The effect of methotrexate (MTX) on employment This section gives an introduction to the scarce literature on the effect of the drug MTX on employment. The remaining analysis builds on the hypothesis that MTX has a positive effect on employment and therefore evidence from the literature is presented, which documents this claim. Smolen et al. (2006) estimated the effect of infliximab 37 treatment on the employability of patients with rheumatoid arthritis (RA). They observed a group of 856 patients with early RA, who were treated with MTX plus infliximab or MTX plus placebo infusions. During a 54 week period the patients were evaluated several times, where their disease activity was measured and through interviews their self-report employability was assessed. The two groups of patients are compared in order to analyse whether infliximab has an effect on the patient’s employability. The authors used a logistic regression to estimate the employability rates. Comparing the employment status after 54 weeks of treatment (with MTX plus infliximab or MTX plus placebo) with the baseline employment status, there is a net increase in employability of respectively 8 % and 2 %. The authors also segregated the results according to the patients’ improvement in RA. When doing this, it became apparent that the patients with the best clinical outcome also were the ones with the best 37 Infliximab is a drug that is used for treating among others rheumatoid arthritis. 26
Page 1 and 2: Estimating the Impact of An
Page 3 and 4: Abstract This thesis aims at analys
Page 5 and 6: 1 Introduction According to the Wor
Page 7 and 8: consisting of lack of data on the d
Page 9 and 10: experience symptoms of depression o
Page 11 and 12: (SSRI’s), also called the new ant
Page 13 and 14: heumatologists. But since descripti
Page 15 and 16: H > 0 or a corner solution, H = 0 .
Page 17 and 18: employers more reluctant to hire ne
Page 19 and 20: One of the most cited models when a
Page 21 and 22: One of the factors greatly influenc
Page 23 and 24: Galarraga et al. (2006) have chosen
Page 25 and 26: channel runs through enhanced compl
Page 27: 3.2 The effect of antidepressants o
Page 31 and 32: Inspired by Galarraga et al. (2006)
Page 33 and 34: are used by other patients groups,
Page 35 and 36: Consequently I am dealing with a se
Page 37 and 38: constructed, and will be explained
Page 39 and 40: As mentioned in chapter 1, one of t
Page 41 and 42: Number of observations 2364 252 211
Page 43 and 44: is perhaps not surprising, since th
Page 45 and 46: Figure 7 The distribution of the us
Page 47 and 48: Figure 9 depicts the defined daily
Page 49 and 50: where φ ( z) is the standard norma
Page 51 and 52: 5.3 The binary response model and p
Page 53 and 54: The log of equation [5.7] gives the
Page 55 and 56: antidepressant use are displayed in
Page 57 and 58: As established in chapter 4, the pa
Page 59 and 60: probit estimation, respectively. Ha
Page 61 and 62: estimation. And ag
Page 63 and 64: As can be seen from table 7 the ext
Page 65 and 66: mtx 0.0002 0.0003 0.0001 0.0002 wag
Page 67 and 68: probit estimation produces more sig
Page 69 and 70: health. As a consequence, further a
Page 71 and 72: 8 Conclusion The aim of this thesis
Page 73 and 74: References Agerbo, E., T. Eriksson,
Page 75 and 76: Zimmerman, F. and W. Katon. 2005. S
Page 77 and 78: wage2 Dummy=1 if wage income up to
Page 79 and 80:
data medold; set b.lmdb_pct10_2003;
Page 81 and 82:
if Last.aar then output; /*output o
Page 83 and 84:
KEEP AAR ALDER ANC02 ANC36 ANC79 AN
Page 85 and 86:
set b.register; yr=0+aar; DROP aar;
Page 87 and 88:
HIGHER='Middle-range and higher edu
Page 89 and 90:
dayemp4='Between 8 months and 364 d
Page 91 and 92:
data collect; merge test1 (in=a) te
Page 93 and 94:
data test1; set b.finaldata1; proc
Page 95 and 96:
antidep_lastyr=lag(AD_dummy); if (u
Page 97 and 98:
Estimation results Probit yr 2003 -
Page 99 and 100:
Probit estimates Number of obs = 58
Page 101 and 102:
single | -.3439072 .0893125 -3.85 0
Page 103 and 104:
(Assumption: . nested in A) Prob >
Page 105 and 106:
. /*with proxy, generalised residua
Page 107 and 108:
. . /*Panel probit same as Galarrag
Page 109 and 110:
Iteration 7: log pseudolikelihood =
Page 111 and 112:
-----------------------------------
Page 113 and 114:
. . probit emp antidep mtx wageinc
Page 115 and 116:
ho = 0.2 log likelihood = -681.4270
Page 117 and 118:
ab79 | -.2830833 .3452789 -0.82 0.4
Page 119 and 120:
-----------------------------------
Page 121 and 122:
. predict ohat, resid . . probit em
Page 123 and 124:
y99 | -.1676129 .4540083 -0.37 0.71
Page 125 and 126:
ab02*| -.0132912 .01201 -1.11 0.269
Page 127 and 128:
. gen numerator=normdenxb*[ad_dummy
Page 129 and 130:
Iteration 2: log likelihood = -306.
Page 131 and 132:
. . probit emp antidep_last5yrs mtx
Page 133 and 134:
note: 0 failures and 14 successes c
Page 135 and 136:
y = Pr(emp) (predict) = .64904538 -
Page 137 and 138:
Probit yr 2003 - Table 9: . use /ak
Page 139 and 140:
higher | .0160771 .2154333 0.07 0.9
Page 141 and 142:
ieland1*| .2476048 .11071 2.24 0.02
Page 143 and 144:
Random-effects tobit regression Num
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