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Recent eLetters

Displaying 1-10 letters out of 251 published

  1. 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:

    None declared

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  2. 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:

    None declared

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  3. 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:

    None declared

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  4. 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:

    None declared

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  5. Abnormal liver enzymes and diabetes risk: potential contribution of chronic viral hepatitis was underplayed

    Sir,

    Xu et al report an association between deranged liver enzymes e.g. alanine transaminase (ALT) in Chinese people residing in Southern China and the incidence of diabetes [1].

    The authors examined multiple potential confounding factors which could influence liver enzymes e.g. alcohol consumption & adiposity. However, although the authors briefly mentioned chronic viral hepatitis infections i.e. chronic hepatitis B virus (HBV) & hepatitis C virus (HCV), I think they did not give this confounder sufficient weight and did not report HBV/HCV prevalence data in the cohort studied. This is a significant shortcoming of this study.

    As the authors acknowledged, chronic HBV & HCV are relatively common in China, with the prevalence of chronic HBV of 11.3% for males, 8.2% for females & for chronic HCV 3.1% for males, 3.3% for females [2].

    Therefore, as these are common infections in China, the underlying population prevalence of chronic HBV & HCV infection in the study cohort could account for at least some of the deranged liver enzymes in the study population. Unfortunately, the lack of data on these infections prevents quantitative analysis of the effect these hepatitides may have on the liver enzyme profiles in this cohort.

    The authors acknowledged a possible association between HCV and diabetes, but there is also emerging evidence that chronic HBV infection is associated with insulin resistance [3]. Furthermore, chronic HBV complicated by cirrhosis may be associated with diabetes mellitus [4]. The apparent association between deranged liver enzymes and the development of diabetes may not be due to hepatitis/transaminitis per se, but may reflect underlying specific pathologies such as chronic HBV and HCV.

    The high prevalence of chronic HBV and HCV in the Chinese population and the associations of these infections with insulin resistance & diabetes mellitus means that the authors' conclusions underplay the possible contribution of chronic viral hepatitis to the development of diabetes. Thus, the conclusions should perhaps have included recommendations that patients with deranged liver enzymes should be screened for these viral hepatitis infections, so that they can be considered for antiviral treatment which would be beneficial not just in terms of future diabetes risk, but also the well-known risks of cirrhosis & hepatocellular carcinoma in these patients.


    References:
    1. Xu L, Jian CQ, Schooling CM et al. Liver enzymes and incident diabetes in China: a prospective analysis of 10764 participants in the Guangzhou Biobank Cohort Study. J Epidemiol Community Health 2015;69:1040-1044
    2. Huang H, Hu XF, Zhao FH et al. Estimation of cancer burden attributable to infection in Asia. J Epidemiol 2015 doi:10.2188/jea.JE20140215
    3. Lee JG, Lee S, Kim YJ et al. Association of chronic viral hepatitis B with insulin resistance. World J Gastro 2012;18(42):6120-6126
    4. Zhang J, Shen Y, Cai H et al. Hepatitis B virus infection status and risk of type 2 diabetes mellitus: A meta-analysis. Hepatol Res 2015 doi:10.1111.hepr.12481

    Conflict of Interest:

    None declared

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  6. 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:

    None declared

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    Submit response
  7. Evidence of a lack of beneficial effect of outdoor physical exercise

    Dear sir

    The author commented on the paucity of research on the point where physical exercise in polluted air becomes more harmful than beneficial. I would like to share our research findings conducted more than 10 years ago in Hong Kong (Yu et al, 2004). We compared the physical fitness of school children who regularly performed physical exercise with those who did not. In a less polluted district (annual mean PM10=44.9 ug/m3), children who regularly did physical exercise had significantly better cardiopulmonary fitness (with a higher predicted maximum oxygen update of 1.8 mL/Kg/min among children who did regular exercise). By contrast, among children in a 'high pollution district' (PM10=57.6 ug/m3), there was no significant difference in their cardiopulmonary fitness whether they exercised regularly or not (the difference in VO2 max between children with regular physical exercise and those without was insignificant, at 0.6 mL/Kg/min). The concentration of PM10 in mainland Chinese cities are much higher than our 'high pollution district', and outdoor physical exercise may be more harmful than beneficial.

    Tze Wai Wong

    Reference: Yu ITS, Wong TW, Liu HJ. Impact of air pollution on cardiopulmonary fitness of schoolchildren. Journal of Occupational and Environmental Medicine 2004; 46:946-954.

    Conflict of Interest:

    None declared

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  8. Self-reported passive smoking and salivary cotinine concentration

    Batty et al. conducted a follow-up study to know the effect of passive smoking on subsequent mortality [1]. In men in their study, self- reported passive smoking, not salivary cotinine, could predict mortality. From their Tables 1 and 2, salivary cotinine level was categorized into three groups, and self-reported passive smoking was categorized binary. I have a query on the association between self-reported passive smoking and salivary cotinine.

    Martinez-Sanchez et al. reported that geometric mean (95% confidence interval (CI)) of salivary cotinine in general population was 1.62 (1.41- 1.87) in 210 men and 1.34 (1.19-1.51) in 299 women, whose age ranged from 16 to 64 years. They also reported that the number of second-hand smoke (SHS) at home was 133 among 509 non-smokers, and that at work was 153 among 378 non-smoking workers. The geometric mean (95% CI) of salivary cotinine in subjects with SHS exposure at home was 1.57 (1.32-1.86) and that without SHS exposure was 1.42 (1.27-1.58). In contrast, the geometric mean (95% CI) of salivary cotinine in subjects with SHS exposure at work was 1.44 (1.23-1.69) and that without SHS exposure was 1.42 (1.24-1.63). From this study, information on self-reported passive smoking does not reflect the level of salivary cotinine concentration.

    Taking together, I recommend Batty et al. presenting data on the association between self-reported passive smoking and salivary cotinine concentration by presenting 2*3 cross tables, stratified by sex. By this information, superiority of self-reported passive smoking on the predictive ability against salivary cotinine concentration for mortality would be partly clarified.

    References

    1 Batty GD, Gale CR, Jefferis B, et al. Passive smoking assessed by salivary cotinine and self-report in relation to cause-specific mortality: 17-year follow-up of study participants in the UK Health and Lifestyle Survey. J Epidemiol Community Health 2014;68:1200-3.

    2 Martinez-Sanchez JM, Fu M, Perez-Rios M, et al. Comparing salivary cotinine concentration in non-smokers from the general population and hospitality workers in Spain. Eur J Public Health. 2009;19:662-4.

    Conflict of Interest:

    None declared

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  9. Re:Extreme temperatures and paediatric emergency

    Dear Sir, Thank you very much for your interest in our research. In our JECH paper, we analyzed data on a broad range of pediatric diseases and found, generally, children aged 10-14 years are more vulnerable to both hot and cold effects, compared with children of other age groups. In the OEM paper, we analyzed pediatric asthma data and found, specifically, children aged 10-14 years are more sensitive to the adverse impact of cold on asthma. We used consistent statistical approach in the two papers and have controlled for relative humidity and air pollutants. Due to limited space, we were not be able to present the difference between different age groups in terms of vulnerability to temperature effects on every disease. Best, Zhiwei

    Conflict of Interest:

    None declared

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  10. Trajectories for each cohort need to span the same/ similar age range

    The three cohorts in this study were as follows:

    Cohort 1: born 1970s and followed between ~15-35 years of age

    Cohort 2: born 1950s and followed between ~35-55 years of age

    Cohort 3: born 1930s and followed between ~55-75 years of age

    This study design has serious limitations for the investigation of cohort differences in BMI trajectories (and therefore also for the investigation of cohort differences in the associations of socio-economic position with BMI trajectory). Looking at Figure 1 - how, for example, can you calculate cohort differences in BMI at age 45 years when only cohort 2 had data at that age? How, in fact, can you make any cohort comparison of trajectories at ages when only one cohort had data? The mixed effects model used in the present paper will provide estimates, but these are based on trajectories fitted beyond the age range of the data (for at least one cohort). The only ages were cohort differences could have reliably been estimated are those were there is overlap (e.g., difference in BMI at age 35 years between cohort 1 and cohort 2), and this could have been done with cross-sectional analyses.

    Failure to consider the limitations of the data has led to potentially misleading interpretation of the results, such as "adiposity increased most quickly with age in the youngest cohort". This is expected given that cohort 1 was of an age where BMI is known to increase more rapidly than later in life. The design of the study makes it impossible to disentangle age and cohort effects (except at overlapping ages). For example, is BMI at baseline greater in cohort 3 than in cohort 1 because they are different cohorts (exposed to different environments) or because they are different ages? The difference will, of course, be due to a combination of age and cohort effects, plus any possible period effect.

    Cross-cohort comparisons of trajectories are a powerful strategy, but in nearly all instances they require the trajectories for each cohort to span the same or similar age range.

    Conflict of Interest:

    None declared

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