Hintsa et al. examined the effect of effort, reward and job control
on the exit from the labour market by a 6-year follow-up study in workers
at the age of 61 years or younger [1]. The author adopted binary logistic
regression analysis by adjusting several variables, and concluded that
effort-reward imbalance (ERI), effort and job control were significant
predictors for exit from the labour market. In contrast, reward wa...
Hintsa et al. examined the effect of effort, reward and job control
on the exit from the labour market by a 6-year follow-up study in workers
at the age of 61 years or younger [1]. The author adopted binary logistic
regression analysis by adjusting several variables, and concluded that
effort-reward imbalance (ERI), effort and job control were significant
predictors for exit from the labour market. In contrast, reward was not
selected as a significant predictor. I have some concerns on their study.
First, the authors did not use effort, reward and job control
simultaneously as independent variables for predicting exit from the
labour market. Schmidt et al. recently reported that the sum score of
effort significantly increased and the sum score of reward significantly
decreased as ERI increased in 4141 samples in Germany [2]. If
multicollinearity among independent variables cannot be solved, the
simultaneous use of ERI, effort and reward should be handled with caution,
because ERI was simply calculated as the logarithmic value of [(sum score
of effort)*7]/[(sum score of reward)*3]. But inset of effort and reward
simultaneously as independent variables into logistic model seems
appropriate, because the authors measured stressful psychological work
environment by ERI model, and each factor has a different dimension for
psychometry. In addition, simultaneous use of job control from another
theoretical stress model should also be considered in combination with ERI
model after evaluating multicollinearity.
Second, statistical results differed by different combination of
adjusting variables in their study. The adjusting variables were selected
to know the net association between exit from the labour market and
effort, reward or job control, and I recommend selecting full-adjudging
model in their study. If discrepancies of statistical results by selecting
different combination of adjusting variable exist, stability of
significance cannot be guaranteed in the statistical model.
Finally, there are other reasons of exit from the labour market than
the causes of working environment. On this point, explanation rate of exit
from the labour market by factors form ERI model and job control should be
presented. The authors selected workers at the age of 61 years or younger,
and cause of exit by family support is suspected especially in women. This
would partly be related to the sex differences in the average age of
withdrawal from the labour market. Anyway, further study is commended to
confirm the causal association.
References
1 Hintsa T, Kouvonen A, McCann M, et al. Higher effort-reward
imbalance and lower job control predict exit from the labour market at the
age of 61 years or younger: evidence from the English Longitudinal Study
of Ageing. J Epidemiol Community Health 2015;69:543-9.
2 Schmidt B, Bosch JA, Jarczok MN, et al. Effort-reward imbalance is
associated with the metabolic syndrome - findings from the Mannheim
Industrial Cohort Study (MICS). Int J Cardiol 2015;178:24-8.
Dear editor,
We have read with great interest the meta-analysis submitted by Li and colleagues1, which investigated the association between fish consumption and depression risk. We warmly and greatly congratulate and applaud for their important work. However, an issue existed in this study should be noted.
These authors stated that observational study including cross-sectional, case-control, and cohort study was eligible for their...
Dear editor,
We have read with great interest the meta-analysis submitted by Li and colleagues1, which investigated the association between fish consumption and depression risk. We warmly and greatly congratulate and applaud for their important work. However, an issue existed in this study should be noted.
These authors stated that observational study including cross-sectional, case-control, and cohort study was eligible for their inclusion criteria in inclusion criteria subsection. However, the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA)2 was used to guide reporting their meta-analysis. Important is that the PRISMA is designed to use mainly in systematic review and meta-analysis with randomized controlled trials (RCTs) rather than meta-analysis with observational studies in epidemiology. Controversially, the Meta-analysis of Observational Studies in Epidemiology (MOOSE)3 is developed for reporting this given meta-analysis. And thus, the authors should adopted the MOOSE to guide reporting their meta-analysis on this given topic preferably we suggested in order to further improve reporting quality.
1. Li F, Liu X, Zhang D. Fish consumption and risk of depression: a meta-analysis. J Epidemiol Community Health 2015; doi: 10.1136/jech-2015-206278.
2. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meya-analyses: the PRISMA statement. J Clin Epidemiol 2009; 62:1006-1012.
3. Stroup DF, Berlin JA, Moton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000; 283:2008-2012.
Lauderdale et al. examined the association between perceived
fair/poor health and sleep duration by several methods [1]. The authors
concluded that U-shaped relationship between sleep duration and prevalence
of fair/poor health was observed only with measuring sleep with survey
sleep hours and survey calculated sleep time. In contrast, there was no
association between long sleep duration and increased prevalence of
fair/...
Lauderdale et al. examined the association between perceived
fair/poor health and sleep duration by several methods [1]. The authors
concluded that U-shaped relationship between sleep duration and prevalence
of fair/poor health was observed only with measuring sleep with survey
sleep hours and survey calculated sleep time. In contrast, there was no
association between long sleep duration and increased prevalence of
fair/poor health by actigraphy. However, I am not fully convinced by their
arguments.
First, the authors well understand the limitation of actigraphy,
named Actiwatch Spectrum, for sleep evaluation and the need of validation
study of actigraphy against sleep polysomnography. There is a difference
between brain activity and physical movement during sleep, and the
discrepancy of sleep parameters between polysomnography and actigraphy is
obvious for insomniacs [2]. When calculating total sleep time by
actigraphy, the authors selected default sleep/awake sensitivity setting
(40 counts per minutes), and also carried out a sensitivity analysis with
a lower threshold of 20. Although the correlation coefficient between
sleep duration with different threshold setting was greater than 0.99,
actual sleep duration differs 18 minute in an average. Kushida et al.
reported the best sleep/awake threshold of Actiwatch for detecting
wakefulness as "high-sensitivity" setting (20 counts per minutes) [3].
Peterson et al. adopted default sleep/awake sensitivity setting of
Actiwatch, and described the overestimation of total sleep time and
underestimation of wake-after sleep onset [4]. These reports present that
level of sleep/awake threshold is important for estimating sleep duration
by actigraphy.
Second, the numbers of subjects in each category of sleep duration
seems useful information in their study. The authors described the mean
value of sleep duration by survey sleep hours and by actigraph total sleep
time were 7.5 hours and 7.2 hours, but the prevalence of fair/poor health
in subjects with sleep duration >9 hours by survey sleep hours was two-
fold higher than that by actigraph total sleep time. The correlation
coefficient between sleep duration by survey sleep hours and by actigraph
total sleep time was 0.29, and I suspect that some subjects with long
sleep duration by actigraphy do not actually keep enough sleep duration.
Anyway, sleep polysomnography study is required to confirm the lack
of U-shaped association between sleep duration and prevalence of fair/poor
health.
References
1 Lauderdale DS, Chen JH, Kurina LM, et al. Sleep duration and health
among older adults: associations vary by how sleep is measured. J
Epidemiol Community Health 2015 Nov 3. doi: 10.1136/jech-2015-206109
2 Natale V, Leger D, Martoni M, et al. The role of actigraphy in the
assessment of primary insomnia: a retrospective study. Sleep Med
2014;15:111-5.
3 Kushida CA, Chang A, Gadkary C, et al. Comparison of actigraphic,
polysomnographic, and subjective assessment of sleep parameters in sleep-
disordered patients. Sleep Med 2001;2:389-96.
4 Peterson BT, Chiao P, Pickering E, et al. Comparison of actigraphy
and polysomnography to assess effects of zolpidem in a clinical research
unit. Sleep Med 2012;13:419-24.
Hintsa et al. examined the effect of effort, reward and job control on the exit from the labour market by a 6-year follow-up study in workers at the age of 61 years or younger [1]. The author adopted binary logistic regression analysis by adjusting several variables, and concluded that effort-reward imbalance (ERI), effort and job control were significant predictors for exit from the labour market. In contrast, reward wa...
Lauderdale et al. examined the association between perceived fair/poor health and sleep duration by several methods [1]. The authors concluded that U-shaped relationship between sleep duration and prevalence of fair/poor health was observed only with measuring sleep with survey sleep hours and survey calculated sleep time. In contrast, there was no association between long sleep duration and increased prevalence of fair/...
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