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Recent eLetters

Displaying 1-10 letters out of 244 published

  1. 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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  2. 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:

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  3. 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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  4. Is 7 a day really better than 5 a day?

    These impressive data have been widely reported. Eating plenty of fruit and vegetables seems to be a good idea, but I am concerned at how the need to eat 7+ portions a day to obtain maximum benefit has been reported. As far as I can tell, the estimates for "daily" consumption were based on a single day. Few people eat exactly the same every day and regression to the mean suggests that most of those who ate 7+ portions on the day of the survey will not have sustained such high levels of consumption over the 7.7 years of follow-up. While these people probably continued to eat a lot of fruit and vegetables and gain health benefits from doing so, isn't it misleading to extrapolate the quantity of fruit and vegetable eaten in one day into a recommendation for regular daily consumption?

    Conflict of Interest:

    None declared

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  5. Re:What exactly is a vegetable?

    Dear Prof. Bland,

    Please see our supplementary material where you can see the questions that were asked during the interview. You will note that survey participants were explicitly advised not to include potatoes when considering their answers.

    http://jech.bmj.com/content/suppl/2014/03/04/jech-2013- 203500.DC1/jech-2013-203500supp1.pdf

    Conflict of Interest:

    None declared

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  6. Air pollution and respiratory symptoms in children

    Molter et al. reported the effects of long-term exposure to particulate matter with aerodynamic diameter <10 micrometer (PM10) and nitrogen dioxide (NO2) on the prevalence of asthma and wheeze within a population-based birth cohort (1). They concluded that no significant association between long-term exposure to PM10 and NO2 and the prevalence of either asthma or wheeze was found. In contrast, the same authors reported that lifetime exposure to PM10 and NO2 was associated significantly with reductions in lung volume growth by using the same cohort (2).

    On this point, Gehring et al. conducted meta-analysis with random- effect model on the effects of air pollution on lung function in children (3). They concluded that that current levels of NO2, total nitrogen oxides, and particulate matter with aerodynamic diameter <2.5 micrometer (PM2.5) were significantly associated with the change in forced expiratory volume in 1 second (FEV1), but PM10 showed no significance. As the level of statistical significance in studies by Molter et al. or Gehring et al. was not highly enough, final conclusion cannot be determined. Relating to these studies, Urman et al. reported health survey to assess the effects of near-roadway air pollution (NRAP) and regional pollution on childhood lung function (4). They concluded that the contribution of regional pollution to adverse lung function, evaluated by FEV1 and forced vital capacity (FVC), was relatively larger than that of NRAP, and NO2 contributed little to the decrease in FEV1 and FVC than other air pollution indicators such as PM2.5 and PM10.

    I have some concerns on the study outcomes. First, physiological lung functions and subjective respiratory complains for dependent variables would sometimes lead to the different study outcome, and both dependent factors should be evaluated to know the adverse effect of air pollution on respiratory organs. As one of the co-authors, I conducted questionnaire survey for 16,663 pairs of junior high school students and their mothers in Indonesian cities to measure the effect of air pollution on respiratory health (5). Nine communities were set and there were inter-class and intra -class variation of NO2. As a main result, the prevalence rates of the symptoms of cough, phlegm, persistent cough, wheezing without a cold, and asthma of the student were significantly correlated with the NO2 emitted along large roads near their residences.

    Second, the superiority of PM2.5 compared with PM10 could not be confirmed, and the best aerodynamic diameter of particulate matter as an air pollution indicator should be specified by further studies.

    Finally, I recommend for considering indoor air pollution especially by smoking (6). Anyway, cause-effect relationship would be clarified by epidemiological cohort studies.

    References

    1 Molter A, Agius R, de Vocht F, et al. Effects of long-term exposure to PM10 and NO2 on asthma and wheeze in a prospective birth cohort. J Epidemiol Community Health 2014;68:21-8.

    2 Molter A, Agius RM, de Vocht F, et al. Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort. Environ Health Perspect 2013;121:1232-8.

    3 Gehring U, Gruzieva O, Agius RM, et al. Air Pollution Exposure and Lung Function in Children: The ESCAPE Project. Environ Health Perspect 2013;121:1357-64.

    4 Urman R, McConnell R, Islam T, et al. Associations of children's lung function with ambient air pollution: joint effects of regional and near-roadway pollutants. Thorax 2013 Nov 19. doi: 10.1136/thoraxjnl-2012- 203159.

    5 Duki MI, Sudarmadi S, Suzuki S, et al. Effect of air pollution on respiratory health in Indonesia and its economic cost. Arch Environ Health 2003;58:135-43.

    6 Guerra S, Stern DA, Zhou M, et al. Combined effects of parental and active smoking on early lung function deficits: a prospective study from birth to age 26 years. Thorax 2013;68:1021-8.

    Conflict of Interest:

    None declared

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  7. What exactly is a vegetable?

    I may have missed it, but Oyebode et al. do not seem to say exactly what they mean by a vegetable or what the exact question was. This is important, because they do refer to a UK Department of Health website, which states that potatoes, yams, plantain, and casava should not be included the 5-a-day count, but sweetcorn should. I thought that sweetcorn was a grain and so would not count it as a vegetable, though I may be wrong, but if asked how many portions of vegetables I ate yesterday would definitely have included potatoes, a staple food in the UK, if I had eaten them. I think that the authors should clarify this.

    Conflict of Interest:

    None declared

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  8. Extreme temperatures and paediatric emergency

    Sir, the recent report on "extreme temperatures and paediatric emergency" is very interesting [1]. Xu et al. concluded that "children are at particular risk of a variety of diseases which might be triggered by extremely high temperatures [1]."Xu et al. also mentioned for the effect of climate change. In fact, Xu et al. reported a highly similar publication in Occup Environ Med and also noted for the effect of climate change on childhood illness with special focus on asthma [2]. The two works should share the same groups of patients but the conclusion is different. In the present report, Xu et al. make a conclusion that "children aged 10-14 years were more sensitive to both hot and cold effects [1]" whereas they proposed that "children aged 10-14 years were most vulnerable to cold effects [2]." This implies that there are many bias in both reports. Hence, it cannot conclude on any effects from hot and cold temperature on pediatric illness. In addition, not only temperature but also other climatic factors can affect the disease incidence. The good example is the effect of humidity [3], pollutants [3] and ozone levels [4].

    References

    1. Xu Z, Hu W, Su H, Turner LR, Ye X, Wang J, Tong S. Extreme temperatures and paediatric emergency department admissions. J Epidemiol Community Health. 2013 Nov 23. doi: 10.1136/jech-2013-202725. [Epub ahead of print] 2. Xu Z, Huang C, Hu W, Turner LR, Su H, Tong S. Extreme temperatures and emergency department admissions for childhood asthma in Brisbane, Australia. Occup Environ Med. 2013 Oct;70(10):730-5. 3. Vandini S, Corvaglia L, Alessandroni R, Aquilano G, Marsico C, Spinelli M, Lanari M, Faldella G. Respiratory syncytial virus infection in infants and correlation with meteorological factors and air pollutants. Ital J Pediatr. 2013 Jan 11;39(1):1. 4. Jones GN, Sletten C, Mandry C, Brantley PJ. Ozone level effect on respiratory illness: an investigation of emergency department visits. South Med J. 1995 Oct;88(10):1049- 56.

    Conflict of Interest:

    None declared

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  9. Re: Social determinants of mental health: a Finnish nationwide follow up study on mental disorders

    Dear Editor, The published paper by Paananen et al, entitled "Social determinants of mental health: a Finnish nationwide follow up study on mental disorders"1 was an interesting and rigorous study. Through a longitudinal approach, all Finnish children who were born in a certain year (1987) were followed through adolescence in order to examine the development of mental disorders and assess potential SDH-related risk factors. For this purpose, the authors created six multivariate models between different groups of variables and the outcome. In addition, a full model was developed to include the variables which showed significant associations with the outcome in at least one of these six models. In Table 2 of that paper, the determinants of mental disorders according to various models were demonstrated. Nevertheless, it was not clear for us why parent's highest educational level, parent's highest SES, parental social assistance, cohort member's education and cohort member's received social assistance which showed association with the outcome in models 2, 3 or 5, were not included in the full model. At the first look, one might speculate that these variables were included in the full model, but were not shown in Table 2; as they might not have proved significant. But, this might not be the correct justification as some non-significant variables e.g. "mother's age (<20)" and "single mothers" could be found in the full model within the same table without being significant in the full model. It would be very kind of the authors to respond to this question and explain whether it would be a concern in the main findings of the full model or not. Sincerely Yours, * Narjes Hazar, MD, Resident in Community Medicine, Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran * Mojgan Karbakhsh, MD, Associate Professor in Community Medicine, Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran Reference: 1. Paananen R, Ristikari T, Merikukka M, Gissler M. Social determinants of mental health: a Finnish nationwide follow-up study on mental disorders. J Epidemiol Community Health. 2013 Aug 1. doi:10.1136/jech-2013-202768 ________________________________________________________________________ Corresponding author: Dr Narjes Hazar, MD, Resident in Community Medicine, Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran Address: Department of Community Medicine, School of Medicine, Tehran University of Medical Sciences, PoorSina St, Qods St, Enqelab Av, Tehran, Iran Email: n-hazar@razi.tums.ac.ir Tel/Fax: +9821 88962357

    Conflict of Interest:

    None declared

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  10. Cochrane review of health & socio-economic impacts of housing improvement

    This is a great contribution to the literature on fuel poverty, cold housing, and health.

    The authors call for a review of qualitative and intervention research related to this topic. It may be useful for readers to be made aware of a recently updated version of a systematic review of the health and socio-economic impacts of housing improvement published by the Campbell and Cochrane Collaborations 1. In this review we looked at physical improvements to housing infrastructure and this included a group of 15 quantitative (including five RCTs) and seven qualitative studies of warmth & energy efficiency improvements. We did not include studies which only looked at financial help with fuel bills, for example the winter fuel allowance distributed to the elderly in the UK.

    1. Thomson H, Thomas S, Sellstrom E, Petticrew M. Housing improvements for health and associated socio-economic outcomes. Cochrane Database of Systematic Reviews 2013;2:Art. No.: CD008657 DOI: 10.1002/14651858.CD008657.pub2. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD008657.pub2/pdf/standard

    Conflict of Interest:

    Flagging up my own work.

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