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Recent eLetters

Displaying 1-10 letters out of 257 published

  1. Early life social conditions and adult cancers: a fundamental research question

    We commend the authors for taking the hypothesis that cancer may have its roots in early life social conditions seriously [1]. Social inequalities exist for many cancer types and are usually attributed to differences in lifestyles and behaviours. Thus, attempts at primary prevention are often confined to relatively proximal disease risk factors at the individual level.

    Cancer development has mainly been considered as a consequence of DNA mutations and consequently tends to be viewed through the lens of molecular biology. However, cancer may be a disease model particularly relevant to investigate how the social becomes biological, via a lifecourse approach. Many socially stratified biological mechanisms have been identified as being part of the causal chain of risk for cancers: material exposures (including inert ones such as asbestos, or living ones such as the Helicobacter pylori virus) and behavioural mechanisms (including tobacco consumption and fatty food intake). Furthermore, the lifecourse approach is of particular interest when studying cancer because of the long latent time period during which exposures occur, before the onset of disease. There is increasing evidence for the role of chronic stress from early life in cancer development and progression.

    Upstream, cancer aetiologies differ, some being exogenous in origin (viruses, smoking) and others endogenous (hormonal cancers). However all cancers are at some point rooted in the immune and inflammatory system, and thereby their initiation is susceptible to factors affecting biological mechanisms at some point along this aetiological pathway. If the immune system is impaired from killing-off damaged cells, the risk of developing tumour cells is heightened [2]. A damaged immune system is also an important accelerator of cancer progression [3]. This root in the immune system is currently the source of the most promising immune-therapy treatment for many cancers [4]. In fact, there is increasing evidence that chronic stress may influence immune and inflammatory systems, implicating socially driven epigenetic mechanisms, which may represent an overall set of early life social determinants of cancer development over the lifecourse [5]

    Evidence on the link between early life stress and cancer in epidemiology remains sparse and inconclusive, and presents difficult methodological issues. Measuring early life physiological stress remains a challenge, but one worth investing in since acute versus chronic stress, and the timing of stress in early life may be important for subsequent stress responses. Stressful events are likely to be experienced differently depending on an individual's hierarchical position on the social gradient. Individuals lower on the social gradient may be more vulnerable to the physiological or behavioural effects of stressful environmental exposures with fewer resources and buffering strategies at their disposal compared to individuals with a higher social position [6]. Social gradients may also confer stress to individuals via status anxiety, which has also been shown in non-human primates [7]. Intra-familial conditions occurring from conception into adolescence may program physiological responses during sensitive periods of development, altering an individual's biology, rendering them susceptible when faced with exposures later in life [8]. We hypothesize that taking into account socially driven early life exposures to chronic stress is important to understanding the aetiology of cancers, which have a common root in the immune and inflammatory systems. These systems form part of the overall physiological stress response.

    We have inadequately attempted to examine this hypothesis, showing that early life adversity and social position is associated with adult cancers before the age of 50 [9]. However, due to a gaping hole in the available lifecourse data combined with good quality cancer incidence data in adulthood, the hypothesis remains to be fully investigated. As well as having mortality data, cancer incidence data provided by registries would allow for a better understanding of cancer aetiological pathways from early life social conditions onwards, and potentially highlight new areas for the primary prevention of cancers. We agree with the authors' point about investing resources into data, and we would emphasise the need for quality data on cancer incidence specifically. To truly get to grips with the aetiology of cancers, concerted investment in the birth cohort studies, and their linkage with registries is paramount.

    1 Vohra J, Marmot MG, Bauld L, et al. Socioeconomic position in childhood and cancer in adulthood: a rapid-review. J Epidemiol Community Health 2016;70:629-34. 2 Stewart TJ, Abrams SI. How tumours escape mass destruction. Oncogene 2008;27:5894-903. 3 Kim R, Emi M, Tanabe K. Cancer immunoediting from immune surveillance to immune escape. Immunology 2007;121:1-14. 4 Dustin ML. Cancer immunotherapy: Killers on sterols. Nature 2016;531:583 -4. 5 Kelly-Irving M, Mabile L, Grosclaude P, et al. The embodiment of adverse childhood experiences and cancer development: potential biological mechanisms and pathways across the life course. International Journal of Public Health 2013;58:3-11. 6 Baum A, Garofalo JP, Yali AM. Socioeconomic Status and Chronic Stress: Does Stress Account for SES Effects on Health? Ann N Y Acad Sci 1999;896:131-44. 7 Sapolsky RM. The Influence of Social Hierarchy on Primate Health. Science 2005;308:648-52. 8 Bailey DBJ, Bruer JT, Symons FJ, et al., eds. Critical thinling about critical periods. Baltimore: Paul H. Brookes publishing co. 2001. 9 Kelly-Irving M, Lepage B, Dedieu D, et al. Childhood adversity as a risk for cancer: findings from the 1958 British birth cohort study. BMC Public Health 2013;13:767.

    Conflict of Interest:

    None declared

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  2. Measuring the impact of green space interventions in deprived neighbourhoods on physical activity and health

    Droomers et al. examined the association between green space interventions in Dutch deprived neighborhoods and short-term impacts on physical activity (PA) and perceived general health (PGH) among adults. The authors reported an absence of short-term positive effects on PA and health from improvements in green space in deprived neighbourhoods.[1]

    The authors made significant efforts to control for the clustering of individuals within neighbourhoods and to adjust analyses at both individual and neighbourhood levels.[1] They acknowledged several shortcomings in the research design including the wide range of socioeconomic interventions undertaken by the Dutch District Approach, in addition to the varied mix of locally tailored green space interventions.[1, 2] However, effect modifiers and information bias were possibly introduced in the study design leading to inaccurate conclusions.

    Social neighbourhood environments are considered to be effect modifiers.[3, 4] However, the overall intensity of the Dutch District Approach method used to adjust for the impact of interventions not related to green space was based on the number of interventions;[1] not on the type of interventions. Therefore, a fully justified sample size allowing stratified analysis was needed.

    Furthermore, I argue that exposure and outcome measures possibly introduced information bias limiting opportunities to capture positive impacts of green space interventions on PA and health. Information about respondents' use of green space is vital to examine their impacts on PA and health.[2, 4] However, green space exposure was not measured in terms of use, access or proximity to green space interventions, but in terms of their implementation. This information bias might have pulled the results toward the null hypothesis, as the assumption was that all respondents were "exposed" to green space interventions, but this was not necessarily true. Differential misclassification of outcomes was also potentially introduced. Given the variety of green space interventions (e.g. community gardens), PA domains specifically related to these green space interventions might have shed light on the impact of the neighbourhood green space attributes.[4] In addition to PGH, the study would have been more robust if physical, mental, and social health constructs had been operationalized.[5]

    Doomers et al. reported that green space interventions do not impact PA and health. Given the wide variety of interventions: 1) a fully justified sample size allowing stratified analysis and 2) measures, which accurately classify exposure to green space and capture PA and health domains specifically related to green space interventions are needed to limit potential type II error.

    References

    1 Droomers M, Jongeneel-Grimen B, Kramer D, et al. The impact of intervening in green space in Dutch deprived neighbourhoods on physical activity and general health: results from the quasi-experimental URBAN40 study. J Epidemiol Community Health 2016;70:147-154. doi:10.1136/jech-2014 -205210

    2 Droomer M, Harting J, Jongeneel-Grimen B, et al. Area-based interventions to ameliorate deprived Dutch neighborhoods in practice: Does the Dutch District Approach address the social determinants of health to such an extent that future health impacts may be expected? Prev Med 2014;61:122-127. http://dx.doi.org.qe2a- proxy.mun.ca/10.1016/j.ypmed.2014.01.009

    3 Diez Roux AV, Mair C. Neighborhoods and health. Ann NY Acad Sci 2010;1186:125-145. doi: 10.1111/j.1749-6632.2009.05333.x

    4 Hunter RF, Christian H, Veitch J, et al. The impact of interventions to promote physical activity in urban green space: A systematic review and recommendations for future research. Soc Sci & Med 2015;124:246-256. http://dx.doi.org.qe2a- proxy.mun.ca/10.1016/j.soscimed.2014.11.051

    5 van den Berg AE, Maas J, Verheij RA, et al. Green space as a buffer between stressful life events and health. Soc Sci & Med 2010;70:1203- 1210. doi:10.1016/j.socscimed.2010.01.002

    Conflict of Interest:

    None declared

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  3. Response to: Ribeiro, et al. "Where do people live longer and shorter lives? An ecological study of old-age survival across 4404 small areas from 18 European countries"

    Ribeiro and colleagues' identify high mortality rates at older ages in the post-industrial UK areas of Merseyside and West Central Scotland (WCS). They suggest that poverty and a lack of social cohesion may be part of the explanation for this finding (1). Merseyside and WCS are characterised by wide intra-regional variation in mortality rates compared to other deindustrialised areas across Europe, possibly reflecting greater inequalities (2).

    Although these areas are relatively deprived in a UK context, they are not so in European context and the substantially higher mortality in WCS compared to Merseyside is not explained by poverty. Glasgow's mortality remains higher than expected, when compared to Liverpool, even taking into account its relative age, sex and deprivation profile - a phenomenon known as Glasgow's 'excess mortality'(3). Thus, there is more to the explanation of health differences between these post-industrial areas than poverty alone. Levels of social cohesion have been suggested as a potential contributory cause for this 'excess mortality' (3). A survey of Glasgow, Liverpool and Manchester, found that the population of Liverpool had substantially higher social cohesion (e.g. trust and reciprocity) than Glasgow, despite almost identical poverty and deprivation levels (4). Research is underway to understand why the social fabric of Liverpool differs from Glasgow, and in particular the role of historical regional and urban policy in fostering (e.g. through popular political activity) or damaging (e.g. through the diversion of investment away from the city) this.

    The findings of Ribeiro and colleagues point to an important mortality phenomenon, but caution should be exercised before we explain these findings without due attention to historical and political context.

    1. Ribeiro AI, Krainski ET, Carvalho MS, de F?tima de Pina M. Where do people live longer and shorter lives? An ecological study of old-age survival across 4404 small areas from 18 European countries. Journal of Epidemiology and Community Health. 2016. 2. Taulbut M., Walsh D., McCartney G., Parcell S., Hartmann A., Poirier G., et al. Spatial inequalities in life expectancy within post-industrial regions of Europe. BMJ. 2014;4(6). 3. McCartney G, Collins. C, Walsh D, Batty D. Accounting for Scotland's Excess Mortality: Towards a Synthesis Glasgow Centre for Population Health, 2011. 4. Walsh D, Gerry McCartney, Sarah McCullough, Marjon van der Pol, Duncan Buchanan, Jones R. Exploring potential reasons for Glasgow's 'excess' mortality: Results of a three-city survey of Glasgow, Liverpool and Manchester. Glasgow Centre for Population Health, NHS Health Scotland and the University of Aberdeen, 2013.

    Conflict of Interest:

    None declared

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  4. How stakeholder participation could increase inequities. The inverse equity hypothesis lens.

    Recent review from Harris et al [1] sets the alternative hypothesis that greater degree of stakeholders participation can produce a contextually valid synthesis. Aware of the importance of stakeholders in healthcare, a null hypothesis may come to mind: Stakeholders increase inequity by biasing synthesis. Let us give you some theoretical framework. Research on public health interventions should include academic researchers and local stakeholders to ensure that results are relevant to, and useful for, decision-makers. The idea is that stakeholders involvement also increases transparency and truthfulness of research process [2]. Thus, the partnership "researcher-stakeholder" may be successful in improving health outcomes. Given its goal, the scope of stakeholders involvement is wide, including patients, caregivers, clinicians, researchers, advocacy groups, professional societies, businesses, policymakers, or others. These people that emerge from general population are also vulnerable to health inequalities. Then, what if there is a gap, within stakeholder, between the most and the least disadvantaged? This scenario can lead to biased results because of two reasons: 1) Marginalization of vulnerable populations that are valuable for the researcher-stakeholder partnership, and 2) Unfairness selection of stakeholders that reproduces inequality in a particular way by allowing some people to mobilize capital for their own benefit, blending self-interest and public interest which is a difficult enterprise [3]. Allowing some individuals to have greater probability of become stakeholder could, as I already mention, produce biased results. Biased results leads to wrong decision-making and to interventions that initially benefit only those with higher socio-economic status and therefore inadvertently increase inequities, a situation called "the inverse equity hypothesis" [4] Thus, one can easily conclude one big issue in the role of stakeholders in research: we must assure significant inclusion of the most disadvantaged. How? Using proper procedures for obtaining persons from the target population to build the stakeholder team. Finally, based on this short dissertation a question emerges to pose to Dr. Harris: Did you use special procedures to assure a significant inclusion of individuals to stakeholder team? In other words: do you think is important to consider selection bias when setting up the stakeholder team? Acknowledgments: Victor C. Kok MD. PhD. Asia University Conflict of interest: No one to declare. References:

    1 Harris J, Croot L, Thompson J, et al. How stakeholder participation can contribute to systematic reviews of complex interventions: Figure 1. J Epidemiol Community Health 2015;70:jech - 2015- 205701. doi:10.1136/jech-2015-205701 2 AHRQ. Stakeholder Guide 2014. Stakehold Guid 2014 2014;:15- 6.http://www.ahrq.gov/research/findings/evidence-based- reports/stakeholderguide/stakeholdr.pdf 3 Klenk NL, Meehan K, Pinel SL, et al. GLOBAL CHANGE SCIENCE. Stakeholders in climate science: Beyond lip service? Science 2015;350:743- 4. doi:10.1126/science.aab1495 4 Victora CG, Vaughan JP, Barros FC, et al. Explaining trends in inequities: evidence from Brazilian child health studies. Lancet (London, England) 2000;356:1093-8. doi:10.1016/S0140-6736(00)02741-0

    Conflict of Interest:

    None declared

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  5. Options in Breech Delivery: the woman's choice?

    Hehir (2015) gives an interesting account on the current trends in breech delivery and gives discusses several large studies whose findings suggest that differences in mortality between vaginal breech delivery and elective cesarean section are minimal. Considering the down sides of cesarean delivery, such as increased risks during future pregnancies and births, what is the best option for women? And who should make that decision?

    Vaginal breech delivery rates are decreasing, with more obstetricians opting for cesarean delivery and some even using a 'no- option' approach when it comes to vaginal breech delivery (Hehir, 2015). This 'no-option' approach means that women are not given the chance to make informed decisions about their own healthcare- a principle which is fundamental to current medical practice (Dyer, 2015). Of course there are challenges, and the fast pace and anxiety provoking environment of an emergency breech delivery is not the ideal situation to be making these decisions. It is therefore important that women are given information about their options early on in their pregnancy so that a plan can be agreed between the patient and the rest of the medical team.

    References Dyer. C. 2015. Doctors should not cherry pick what information to give to patients, court rules. British Medical Journal. 350.

    Hehir. M. 2015. Trends in Vaginal Breech Delivery. Journal of epidemiology and Community Health. 69:12.

    Conflict of Interest:

    None declared

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  6. a cup of tea

    It appears somewhat bizarre that authorities have ignored a widely consumed source of fluoride from tea although insisting on community water fluoridation (CWF) to reduce dental decay. Notably, black tea in commercial teabags contains significant levels of fluoride. This is especially so when sourced from Kenya with volcanic soils compounded by fluoride from superphosphate fertilisers. Mechanical harvesting then includes older leaves with higher fluoride content than the young hand- picked tips. Habitual tea drinkers with a daily consumption of 3 cups of tea could already be obtaining more than adequate or safe fluoride intake based on the WHO (2002) upper limit recommendations of 2mg/day for children and 4mg for adults. Peckham's multicentre GP study revealing 30% higher hypothyroidism in areas where the water supply exceeded 0.3ppm of fluoride must raise serious questions regarding HFSA toxicity as nationwide, the British are traditionally tea drinkers. Thus all areas would already be getting fluoride from tea yet only those with CWF had increased hypothyroidism rates.

    Conflict of Interest:

    None

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  7. Potential Challenges to Using Paternal Education as a Proxy for SES

    Oksuzyan et al. report an association between race/ethnicity and two subtypes of childhood leukemia: acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML).1 Accordingly, the researchers suggest that there are genetic, cultural, and environmental factors involved in the etiology of childhood leukaemia [1].

    Importantly, Oksuzyan et al. made a significant effort to examine and control for the potential role of socioeconomic status (SES) on this association. In particular, the authors used paternal education levels used as a proxy for SES given that a significant percentage of information on maternal education was missing from the birth registry used [1]. This strategy faces several challenges given the complex association of race, education, and SES.

    The use of paternal education might not appropriately account for the adverse health effects which might result (at least in part) from residing in single-parent homes. It has been widely documented that children frequently exhibit poor health outcomes in single-parent homes due to causal chain of effects related to parent's education, SES, and income [2]. Moreover, the information regarding paternal education as a proxy for SES was obtained from the California Birth Registry [1]. It is possible that this data does not adequately represent the SES of the cases used in the study given that the father identified might not have provided financial support for the child.

    In addition, the use of parental education as a proxy for SES may not be an appropriate method as average income frequently varies in jobs requiring similar education levels [3]. It is possible that these variations occur more frequently among different ethnic/racial groups; potentially as a result of systemic prejudice and/or unequal employment opportunities [3].

    Oksuzyan et al. report an association between race/ethnicity and childhood leukaemia [1]. Due to potential challenges in using paternal education as a proxy for SES, the inclusion of maternal education and a discussion regarding SES in single-parent homes would have been valuable.

    References

    1 Oksuzyan S, Crespi CM, Cockburn M, Mezei G, Vergara X, Kheifets L. Race/ethnicity and the risk of childhood leukaemia: A case-control study in California. J Epidemiol Community Health Published Online First: 19 March 2015. doi:10.1136/jech-2014-204975

    2 Gucciardi E, Celasun N, Stewart DE. Single-mother families in Canada. Can J Public Health 2004;95(1):70-73.

    3 Williams DR. Race, socioeconomic status, and health. The added effects of racism and discrimination. Ann N Y Acad Sci 1999;896:173-188.

    Conflict of Interest:

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  8. Sleep duration by actigraphy in relation to perceived health among older adults

    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.

    Conflict of Interest:

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  9. It is important that selecting appropriate reporting guidance

    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.

    Conflict of Interest:

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  10. Stressful psychosocial work and exit from the labour market

    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.

    Conflict of Interest:

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