Recent eLetters
Displaying 1-10 letters out of 208 published
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Re:Conceptual fallacy
Submit responseAssumptions beyond the current level of evidence
We hereby acknowledge the e-letter by Prof. Goran Isacsson (dated 27th October, 2009) to our study on antidepressants and the change of the suicide rate in older adults [1,2]. We indeed appreciate the comment, even as our opinions differ.
Prof. Isacsson postulates that antidepressants (primarily SSRIs) are accountable - not only for the reduction in suicides occurring among those currently treated with the drug - but also for the rate reduction in the population not receiving treatment. This implies assuming that doctors have improved their rate of identifying and treating depression to such an extent that proportionally more suicidal persons are commencing a treatment (and/or fewer are dropping out of treatment).
It is true that the number of persons beginning a treatment with SSRIs has increased over time. However, we know that SSRIs are prescribed for a wider range of symptoms apart from mood disorders, such as: pain syndromes, anxiety, irritable bowel syndrome, and sexual dysfunction [3- 6]. Over the studied period proportionally more SSRI were prescribed [2]. This could indicate that the specificity with which antidepressants were prescribed for depressive symptoms might have decreased over time.
Evidence-based studies show that suicide, also among older adults, is an outcome of a many factors, of which many are likely to vary over time [7,8]. Opposed to the current years of economic recession[9], the financial prosperity of the examined period might be accountable for some of the decline in the suicide rate. The decline in the suicide rate both among those who are in treatment with antidepressants as well as those who are not in treatment is, thus, likely to be influenced by many other factors.
Opposed to ecological studies, it should be mentioned that our study used individual-level data where it is possible to establish a direct link between antidepressant users and persons dying by suicide.
Based on the current state of evidence, we feel that it is untenable to conclude that SSRI is the only contributing factor to the decline in the overall suicide rate.
References
1 Isacsson G. Conceptual fallacy [electronic letter]. http://jech.bmj.com/content/62/5/448/reply#jech_el_2395
2 Erlangsen A, Canudas-Romo V, Conwell Y. Increased use of antidepressants and decreasing suicide rates: A population-based study using Danish register data. J Epidemiol Community Health 2008;62:448-54.
3 Sindrup SH, Bjerre U, Dejgaard A. The selective serotonin reuptake inhibitor citalopram relieves the symptoms of diabetic neuropathy. Clin Pharmacol Ther 1992;52:547-52.
4 Creed F. How do SSRIs help patients with irritable bowel syndrome? Gut 2006;55:1065-7.
5 Giuliano F. 5-Hydroxytryptamine in premature ejaculation: opportunities for therapeutic intervention. Gut 2007;30:79-84.
6 Rahme E, Dasgupta K, Turecki G, et al. Risks of Suicide and Poisoning Among Elderly Patients Prescribed Selective Serotonin Reuptake Inhibitors: A Retrospective Cohort Study. J Clin Psychiatry 2008;69:349- 57.
7 Hawton K, van HK. Suicide. Lancet 2009;373:1372-81.
8 Conwell Y, Thompson C. Suicidal behavior in elders. Psychiatr Clin North Am 2008;31:333-56.
9 Gunnell D, Platt S, Hawton K. The economic crisis and suicide. BMJ 2009;338:b1891.
Conflict of Interest:
None declared
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Ethnic differences and HPV Vaccination - an Australian comparison
Submit responseRecent findings from a small qualitative study with Australian parents of Aboriginal,Anglo and Chinese backgrounds offered similar findings with key differences and indicate that generalisations cannot be made about ethnic groups globally.The soon to be published data indicates:
1.Vaccine acceptability - all parents supported HPV vaccination as a cervical cancer preventative but not as a STI preventative.Partial cervical cancer protection was a factor where access to cervical screening can be difficult.
2.Age of vaccination - varied between age 9 and 18 due to key factors associated with sexual norms and school attendance.
3.Acculturation - Chinese parents residing <5 years and who had strong Christian principles held strong normative values negating adolescent sexual behaviours and need for vaccination.Those resident >5 years and Christian were pragmatic that Western paradigms of adolescent sexuality influenced their children and necessitated the need for early vaccination.
4.Barriers to consent- long term side effects;'fatalism';and 'selective pressure' were key factors.Promiscuity was not a major concern but marginalisation of the vaccinated cohort was feared.
5.Education - all had low or no awareness of HPV.Diverse culturally specific education needs emerged.School based HPV education was not supported by all parents due to child maturation factors.
6.HPV information - transubstantive error was evident in media developed for Aboriginal and Chinese populations .Concerns with 'female centric'information .Support for males to be educated, as males are single parents and consentors.There was anger that HPV information had not been given at Pap test visits.
Conclusions:
(i)HPV education strategies need to incorporate cultural and gender distinctions. (ii)HPV needs to be communicated as a shared public health issue and emphasis on the immune benefit of HPV vaccination at age 12. (iii) HPV information is needed at Pap testing to facilitate regular screening; and psychosocial counselling with abnormal Pap results to destigmatise HPV infection.
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Effects of standard adjustment approaches on relative and absolute inequalities
Submit responseIn a 2006 comment [1] on the article by Lynch et al.,[2] I pointed out that the authors’ findings of different contributions of risk factors to relative and absolute inequalities in CHD rates were functions of the fact that the authors studied the effects of the elimination of risk factors rather the effects of adjusting for the implications of differing risk profiles in different education groups. In making this point, I noted that “while various approaches to such an adjustment yield somewhat different results,” information available in the Lynch article showed that an adjustment that attributed the risk profile of the highest education group to the lowest education group yielded exactly the same 19% percent reduction of relative and absolute inequalities in CHD. The quoted language was meant merely to suggest that the percentage reductions in the two inequalities effected by one adjustment method might differ from those effected by another method. The intended implication, however, was that under any standard approach to adjustment for risk factors, the percentage reduction of the relative difference between rates would be the same as the percentage reduction of the absolute difference between rates.
In a 2008 comment in a different forum on another article that had made an argument similar to that in Lynch et al.,[3] I pointed out that adjusting for differing risk profiles by attributing the advantaged group’s risk profile to the disadvantaged group and by attributing the disadvantaged group’s risk profile to the advantaged group, while yielding somewhat different results, yield exactly the same percentage reductions in the relative difference between rates that they yield for the absolute difference between rates. There, too, the point was the all standard adjustment techniques yield the same percentage reductions in relative differences between rates that they yield for absolute differences between rates. But the point is not correct.
In a 2008 article in Epidemiology, Singh-Manoux et al.[4] addressed the article by Lynch et al., making points similar to those in my 2006 comment. Singh-Manoux et al. also explained that adjusting for risk factors by attributing the risk profile of the advantaged group to the disadvantaged group yields exactly the same percentage reductions of relative and absolute risk differences. But the authors’ main adjustment approach (which they applied to data from the Whitehall II study) involved attributing the risk profile of the entire population both to the advantaged group and to the disadvantaged group. Such approach yielded similar, but not identical, percentage reductions in relative and absolute differences.
The authors’ point was that standard approaches to adjustment yielded similar percentage reductions in relative and absolute inequalities and that the findings by Lynch et al. of substantially different percentage reductions in relative and absolute inequalities were the consequence of addressing a different question from that addressed by standard approaches to adjustment for risk factors. But the fact is that Singh-Manoux et al. did not find identical reductions in relative and absolute differences.
Thus, whereas adjusting for risk factors either by attributing the advantaged group’s risk profile to the disadvantaged group or by attributing the disadvantaged group’s risk profile to the advantaged group yields exactly the same percentage reduction in a relative between rates as in an absolute differences between rates, adjusting for risk factors by attributing the entire population’s risk profile to both groups apparently does not. Further, while Singh-Manoux et al. seem to read their results as indicating that adjusting for risk factors in such a manner typically will yield similar reductions in relative and absolute differences, that will not necessarily be the case. Singh-Manoux et al. examined a setting where 29,121 person years were analysed for the advantaged group but only 3,387 person years were analyzed for the disadvantaged group. Hence, the risk profile of the entire population that underlay the authors’ adjustment approach was based on a population that was almost entirely (94.7%) comprised of the advantaged group. In such circumstances, adjusting according to the risk profile of the entire population will tend to yield results that are little different from adjusting according to the advantaged group’s profile – both with respect to the size of the reduction generally and with respect to the extent to which the percentage reductions in the relative and absolute differences are similar. Neither of these patterns will necessarily hold when the disadvantaged group comprises a much larger proportion of the entire population than was the case in the Singh-Manoux study.
The fact that adjustment according the risk profile of the entire population can yield different relative and absolute risk reductions would seem to militate against use of that adjustment approach – even though, as I have discussed in reference 5 and many other places, both relative and absolute differences between rates are problematic measures of the size of inequalities because each is affected by the overall prevalence of an outcome. The fact that, whatever the measure of inequality, the relative size of the groups being compared may generally affect the size of an adjustment for differing risk profiles would seem even more strongly to militate against adjusting according to the risk profile of the entire population. Further, an important reason society is interested in learning the contribution of differing risk profiles to health inequalities is to assess the impact of efforts to bring the disadvantaged group’s risk profile into line with the advantaged group’s risk profile. There is no similarly practical purpose in learning the implications of causing the advantaged group’s risk profile to worsen at the same time that the disadvantaged group’s risk profile improves, since no society would undertake to do so.
At any rate, the suggestion in my earlier comments that any standard approach to adjustment for risk factors will yield the same percentage change in the relative difference between rates that it yields for the absolute difference between rates is not correct.
References:
1. Scanlan JP. Understanding social gradients in adverse health outcomes within high and low risk populations. J Epidemiol Community Health May 18, 2006.
2. Lynch J, Davey Smith G, Harper S, Bainbridge K. Explaining the social gradient in coronary heart disease: comparing relative and absolute risk approaches. J Epidemiol Community Health 2006:60:436-441.
3. Scanlan JP. Study shows different adjustment approaches rather than different relative and absolute perspectives. Journal Review May 1, 2008 (responding to Khang YH, Lynch JW, Jung-Choi K, Cho HJ. Explaining age-specific inequalities in mortality from all causes, cardiovascular disease and ischaemic heart disease among South Korean public servants: relative and absolute perspectives. Heart 2008;94:75- 82):http://journalreview.org/v2/articles/view/17591645.html
4. Singh-Manoux A, Nabi H, Shipley M, et al. The role of conventional risk factors in explaining social inequalities in coronary heart disease – the relative and absolute approaches. Epidemiology 2008;19:599-605
5. Scanlan JP. Can we actually measure health disparities? Chance 2006:19(2):47-51: http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf
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Conceptual fallacy
Submit responseErlangsen et al. linked data from an individual-based prescription database in Denmark with data from the cause-of-death register for the two years 1996 and 2000[1]. They found 88 fewer suicides in 2000 than in 1996, 7 fewer among the population that was treated with antidepressants and 81 fewer among the untreated population. Based on “decomposition” analysis of these data, the authors concluded, “Individuals in active treatment with antidepressants seem to account for 10 % of the decrease in the suicide rate” and “the major share (…) seems to be explained through other components.” This conclusion would contradict the large number of ecological studies and some individual-based studies that suggest the opposite, that antidepressant usage was the main cause of the decrease in suicide[2-4]. I believe there is a major conceptual fallacy in the Erlangsen et al. basic assumption, however. The authors obviously assumed that they should expect the main decrease in suicide in the population treated with antidepressants if antidepressant medication was the main cause of the decrease in suicide. When their “decomposition” instead indicated that 90 % of the decrease had occurred in the population not treated with antidepressants, they concluded that antidepressant medication was not the main cause. I believe the correct conclusion, however, is the opposite, since the basic assumption was wrong. The increased treatment in the general population (+27,835) will increase the probability of suicides as well as non-suicides to be “treated”. Thus, the actual decrease of 81 “untreated suicides” may be a consequence of just the increased treatment; they were no more untreated. If these individuals still commit suicide when treated, the number of “treated suicides” will increase as much as the number of “untreated suicides” has decreased (i.e. +81 cases). If antidepressants prevent suicide, however, the number of “treated suicides” will INCREASE LESS (e.g. only 40 cases more if one assumes 50 % effectiveness). That is the correct basic assumption that Erlangsen et al. should have made. Thus, the observed result of 81 less “untreated suicides” and 7 less “treated suicides” suggests that antidepressants indeed have been the main cause of the decrease in suicide. The fact that “treated suicides” did not increase by about 40 cases as might be expected may be interpreted as fewer therapeutic failures resulting from improved quality of treatment with antidepressants.
1 Erlangsen A, Canudas-Romo V, Conwell Y. Increased use of antidepressants and decreasing suicide rates: a population-based study using Danish register data. J Epidemiol Community Health 2008;62:448-54. 2 Isacsson G, Rich CL. Antidepressant drug use and suicide prevention. Int Rev Psychiatry 2005;17:153-62. 3 Isacsson G, Holmgren A, Ösby U, et al. Decrease in suicide among the individuals treated with antidepressants. A controlled study of antidepressants in suicide, Sweden 1995-2005. Acta Psychiatr Scand 2009;120:37-44. 4 Ludwig J, Marcotte DE, Norberg K. Anti-depressants and suicide. J Health Econ 2009;28:659-76.
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Querying the mathematics of a 74% increase in incidence
Submit responseDear Editor,
The authors state in their conclusions the incidence of diabetes in the UK between 1997 and 2003 increased from 2.84 to 4.66 per 1000 person- years. I believe these figures represent a 64% increase and not the 74% reported in the article.
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Cross sectional studies- Odds ratios or prevalence ratios
Submit responseI read with interest the article by Joshy et al on prevalence of diabetes. The authors aimed at assessing the influence of deprivation on the prevalence of diabetes and have used cross-sectional study design. The authors estimated odds ratios using logistic regression. There are two fundamental interpretative issues in using odds ratio as a measure of association in cross sectional studies.
The first issue is that the odds ratios tend to overestimate the risk ratio when the outcome events are common (more than 10%). For instance, if 80 out of 100 exposed persons have a particular disease and 40 out of 100 non -exposed persons have the disease, the OR is 6, but the exposed persons are only 2 times more likely to have the disease as the non-exposed. The second issue relates to the common tendency to interpret it as relative risk, which is misleading both in theoretical and practical terms. In the previous example, it is misleading if the exposure is considered to be related to six-fold increase in the chances of getting disease. So an appropriate measure of association for cross-sectional study designs is the prevalence ratios.
I suppose the common use of ORs as effect measures in cross-sectional studies was due to greater availability of computer programs which produce OR as standard output of the fitting of logistic regression models. Though the use of OR is not intrinsically wrong, with recent advancements in computing methods, alternative statistical models like log-binomial regression can be used to directly estimate the prevalence ratios.
References
1.Barros AJ, Hirakata VN. Alternatives for logistic regression in cross- sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology. 2003;3:21.
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One important confounder missing?
Submit responseOne reason for a higher mortality in abstainers from alcohol could be that they are former heavy drinkers. Streppel et al do not seem to have adjusted for this factor.
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EFFECTS OF MENTAL DISORDERS ON THE UNEMPLOYMENT-SUICIDE ASSOCIATION
Submit responseDear Editor
It is a pleasure to read the stringent and thorough study by Andreas Lundin and colleagues on whether the effect of unemployment on mortality is causal or due to health-selection.[1]
Among other important findings, the authors conclude that “Our study showed that the unemployment–suicide association to a large extent was explained by risk factors measured before exposure to unemployment.” and the authors further state that “No other study known to us has shown the effect of mental disorder on the unemployment – suicide association.”
Other studies have reported the “effect of mental disorders” on the association between unemployment and suicide, including studies published in the Journal of Epidemiology and Community Health. [2][3][4][5] One important conclusion from these studies is that psychiatric patients who previously were employed or otherwise privileged are at higher risk of suicide. This counteracts a health selection effect – especially because this opposite effect is particularly pronounced in recently admitted psychiatric patients. These studies further suggest that transitions into unemployment and labour market marginalisation increase the suicide risk among psychiatric patients.
In conclusion, Andreas Lundin and colleagues’ study suggest that health selection explains the unemployment-suicide association.[1] However, it may also simply reflect that it is not possible with the data at hand to fully investigate this thorny problem.
Competing interests: none.
References
1. Lundin A, Lundberg I, Hallsten L et al. Unemployment and mortality - a longitudinal prospective study on selection and causation in 49 321 Swedish middle aged men. J Epidemiol Community Health 2009;In Press.
2. Agerbo E. Effect of psychiatric illness and labour market status on suicide: a healthy worker effect? J Epidemiol.Community Health 2005;59:598 -602.
3. Agerbo E, Qin P, Mortensen PB. Psychiatric illness, socioeconomic status, and marital status in people committing suicide: a matched case- sibling-control study. J Epidemiol Community Health 2006;60:776-81.
4. Agerbo E. High income, employment, postgraduate education, and marriage: a suicidal cocktail among psychiatric patients. Arch Gen Psychiatry 2007;64:1377-84.
5. Mortensen PB, Agerbo E, Erikson T et al. Psychiatric illness and risk factors for suicide in Denmark. Lancet 2000;355:9-12.
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Half a glass of wine/day : 5 years longer life?
Submit responseDear Editor,
The authors state that "Light wine consumption was associated with five years longer life expectancy". Light is defined as drinking 1-20 grams of wine alcohol/day "(on average 6g/day)".
Alcohol content of wine is measured by volume, not by weight: Since ethyl alcohol specific gravity is .785 g/mL, and the average alcohol content of wine is 11%, 1-20 grams is the equivalent of 14mL - 280mL of wine/day. The difference between 14 mL (less than a tablespoon) and 280 (more than one third of a 750 mL bottle) is so gigantic, that using the average of 6 g/day is very misleading...
What results would have been obtained if the "light" group had been divided into quartiles: 1-5, 6-10, 11-15 and 16-20 Gm/day? Would there have been a dose effect? Did the people who drank 16-20 g/day have greater life expectancy than the average? Did those who drank 1-5 g/day also benefit? One would expect that 14 mL of wine/day would have no measurable effect, the dose being homeopathic...
Another problem is that most of the wine drinkers also drank beer and/or spirits. We are not told whether those who drank little wine drank more or less alcohol from other sources than those who drank 280 mL/day. This also makes harder to interpret the conclusions for the "light" wine drinking subjects.
Cordially yours, Carlos A Camargo MD
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Glaucoma from PC usage
Submit responseDear Editor
As a PC user this article interests me greatly, as I have noticed my eyesight deteriorating since working 11 hour a day shifts in front of 4 monitors and sometimes also using the computer again after work at home.
From a lay man's perspective I would like to know more about how different types of screen affect the eyes, and indeed whether the problem may stem simply from levels of artificial light (which are greater when staring at a monitor). Often the combination of artificial light levels from room/office lights as well as the computer screens themselves contribute to a worsening state of vision in my own eyes. This could be tiredness of the eyes in addition to the above, however in natural daylight the same cannot be said, except perhaps if using a computer screen out of doors under sunlight (this I have not tried).
Anyway, any further study or pointers to further study would be much appreciated as I worry that the correlation here combined with my own experience may mean that I should change my lifestyle in order to avoid glaucoma or blindness in later life. I am currently already nearsighted +1.25.
Thanks Michael
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