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 sui...
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.
Recent 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.Partia...
Recent 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.
In 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,...
In 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
Erlangsen 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 tre...
Erlangsen 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.
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.
I 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.
I 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.
One 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.
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...
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.
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...
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.
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...
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.
Assumptions 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 sui...
Recent 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.Partia...
In 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,...
Erlangsen 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 tre...
Dear 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.
I 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...
One 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.
Dear 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...
Dear 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...
Dear 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...
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