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We appreciate the comments of Macleod and Davey-Smith on our recent
article reporting an association between systemic inflammation markers and
socio-economic status.[1,2] In their letter, Macleod and Davey-Smith
state that our findings, particularly the association of fibrinogen with
socio-economic status, and its interpretation is not correct, and runs
contrary to the principle of "Mendelian randomisat...
We appreciate the comments of Macleod and Davey-Smith on our recent
article reporting an association between systemic inflammation markers and
socio-economic status.[1,2] In their letter, Macleod and Davey-Smith
state that our findings, particularly the association of fibrinogen with
socio-economic status, and its interpretation is not correct, and runs
contrary to the principle of "Mendelian randomisation". As the evidence,
they refer to the finding that plasma fibrinogen concentrations are
related to a polymorphism in the b-fibrinogen gene, with presence of the
"T" allele being associated with higher levels. According to the authors,
this finding is in keeping with the evidence from controlled trials which
suggests that drugs lowering fibrinogen do not decrease the risk of
coronary heart disease (CHD) and therefore, the association between plasma
fibrinogen and CHD risk is most probably not causal.
We believe, however, that the authors have misinterpreted our
findings and conclusions to some extent. First, we did not study the
relationship of fibrinogen to the risk of cardiovascular disease, but our
aim was merely to study the association of systemic inflammation markers
and socio-economic status in a cross-sectional design. The relationship of
plasma fibrinogen level and CHD risk has been found in a number of
prospective observational studies. Data on clinical trials are scarce, and
do not in our understanding justify any conclusions about the causality on
the observed association at the moment.[2]
Furthermore, we did not state that the fibrinogen-social position
link is not a reflection of the social patterning of prevalent disease, or
other health related behavioural or biological factors (smoking, obesity
etc.). In our article, we said that systemic inflammation is a biologically
plausible mediator between socio-economic status and the risk of
cardiovascular disease but our intention was not to state that socio-
economic position as such causes chronic systemic inflammation. Therefore,
we concluded also that other factors, which were not included in the
analyses, such as prevalent or sub-clinical diseases, and behavioural and
environmental factors, such as diet, exercise and exposure to toxic
substances at work or elsewhere, and low birth weight may be involved.
We suspect also that the concept of "Mendelian Randomisation", if
used the way the authors are using it, is not going to be very helpful for
untangling the causal roles of factors that lead to the disease outcomes.
They take one single nucleotide polymorphism (SNP) of a single gene, in
this case the fibrinogen beta gene, and draw inferences from that to the
plasma fibrinogen concentration and to the causal effects of fibrinogen on
the coronary heart disease risk. This is a simplistic view, which does not
take properly into account the complex genetic background of a
multifactorial disease. Usually, the repeatability of these single gene -
single SNP studies has been poor. As to fibrinogen, there are three genes
encoding the fibrinogen molecule, fibrinogen alpha, fibrinogen beta, and
fibrinogen gamma. At least 157 SNPs are known in these three genes.[4,5]
Furthermore, other genes, such as the IL-6 gene, are likely to have an
effect on the fibrinogen concentration. There is enormous potential for
interactions between these different genetic variants as well as between
the genetic variants and 'environmental' factors. In addition,
pleiotropism and epistasis are common. Therefore, we think that the
concept of "Mendelian randomisation" is, in most cases, a cross
oversimplification of the underlying biology of a complex, multifactorial
disease. We suspect that its applicability is likely to be rare and
limited to few special occasions.
References
1. Macleod J, Davey Smith G. Fibrinogen, social position, and risk of
heart disease. J Epidemiol Community Health 2004;58:157.
2. Jousilahti P, Salomaa V, Rasi V, Vahtera E, Palosuo T. Association of
markers of systemic inflammation, C reactive protein, serum amyloid A, and
fibrinogen with socio-economic status. J Epidemiol Community Health
2003;57:730-733.
3. Danesh J, Collins R, Appleby P, Peto R. Associaitons of fibrinogen, c-
reactive protein, albumin or white cell count: meta-analyses of
prospective studies of coronary heart disease. JAMA 1998;279:1477-1482.
4. Sing CF, Stengard JH, Kardia LR. Genes, environment, and cardiovascular
disease. Arterioscler Thromb Vasc Biol 2003;23:1190-6.
My understanding, confirmed by brief review of data on the ONS
website, is that the South Asian population in the UK has a younger age
distribution than the white population. In this case, would an indicator
based on those aged over 35 need further adjustment for the age
distribution beyond 35 in order to examine prescribing? Is it possible
that we are seeing an age effect in the negative correlation...
My understanding, confirmed by brief review of data on the ONS
website, is that the South Asian population in the UK has a younger age
distribution than the white population. In this case, would an indicator
based on those aged over 35 need further adjustment for the age
distribution beyond 35 in order to examine prescribing? Is it possible
that we are seeing an age effect in the negative correlation of
prescribing rates and ethnic minority population proportions?
Conflict of Interest
YHEC Ltd is a contract research company and
carries out projects for both the NHS/DH and for the pharmaceutical
industry. We are not conducting any current research on prescribing rates
for coronary heart disease nor do we see the paper or our comment as
impinging on any of our current work.
We read with great interest the recent paper by Martins et al.[1]
concerning the influence of socioeconomic conditions on air pollution
adverse health effects in elderly people in Sao Paulo, Brazil. These
results are very interesting and may promote understandings of which
social category of persons are most sensitive to air pollution. The
authors suggest that socioeconomic deprivation represents a...
We read with great interest the recent paper by Martins et al.[1]
concerning the influence of socioeconomic conditions on air pollution
adverse health effects in elderly people in Sao Paulo, Brazil. These
results are very interesting and may promote understandings of which
social category of persons are most sensitive to air pollution. The
authors suggest that socioeconomic deprivation represents an effect
modifier of the association between air pollution and respiratory deaths
in elderly people for an increase of 10 µg/m3. They conclude that poverty
represents an important risk factor that should be taken into account when
determining the health consequences of environmental contamination. We
agree with these conclusions nevertheless, the question is to know if poor
people died because they are more illness or inaccessibility (geographic
and economic considerations) to health care system or because they were
more exposed to air pollution ?
We know that people with lower socioeconomic status are more sensitive to
a large number of risk factors according to different life habits, or to
addictive conducts as smoking habits [2].
When air pollution is considered, socioeconomic characteristics as an
effect modifier can take two aspects. First, people with low socioeconomic
status may be more sensitive in term of health effect because they have
associated pathologies and individuals with certain diseases had a greater
risk of dying during an episode of increased of air pollution than did
members of the general population [3]. Furthermore, people living in
underprivileged sectors would have both more limited access to healthcare
[4] and greater exposure to air pollution. Exposure conditions is the second
aspect of the interpretation of the effect modifier. Jerrett et al.[5]
argue, low socioeconomic conditions may be associated with manufacturing
and so with a higher workplace exposures, but also with a lower mobility.
In addition, persons with lower socioeconomic characteristics may be
exposed to a complex mix of pollution from indoor sources, as well as
outdoor pollution due to traffic, industry, and waste burning in
developing countries. It seems necessary to explore the link between
individual exposure and socioeconomic characteristics because these two
factors are strongly correlated.
More studies are need to investigate this effect modifier and particularly
the signification of this effect. To understand this effect we will need
individual data on risk factor but also data on individual exposure to
have a good interpretation of the results and to have policy implications.
References
(1) Martins MC, Fatigati FL, Vespoli TC et al. Influence of socioeconomic
conditions on air pollution adverse health effects in elderly people: an
analysis of six regions in Sao Paulo, Brazil. J Epidemiol Community Health
2004;58:41-6.
(2) Prescott E, Godtfredsen N, Vestbo J, Osler M. Social position and
mortality from respiratory diseases in males and females. Eur Resp J
2003;21:821-6.
(3) Goldberg MS, Burnett RT, Bailar JC et al. Identification of persons
with cardiorespiratory conditions who are at risks of dying from the acute
effects of ambient air particles. Environ Health Perspect 2001;109:487-94.
(4) Chen Y, Dales R, Krewski D. Asthma and the risk of hospitalization in
Canada : the role of socioeconomic and demographic factors. Chest
2001;119:708-13.
(5) Jerrett M, Burnett RT, Brook J et al. Do socioeconomic characteristics
modify the short term association between air pollution and mortality ?
Evidence from a zonal time series in Hamilton , Canada. J Epidemiol
Community Health 2004;58:31-40.
It is important to highlight shortfalls in service delivery to looked after children. However, the provision of information as in the quoted study is seldom enough to change practice.
In Northern Ireland we have had integrated health and social services
since the early 1970s. As part of Children's Services Planning we tagged
the records of Looked After Children and compared the immunisation stat...
It is important to highlight shortfalls in service delivery to looked after children. However, the provision of information as in the quoted study is seldom enough to change practice.
In Northern Ireland we have had integrated health and social services
since the early 1970s. As part of Children's Services Planning we tagged
the records of Looked After Children and compared the immunisation status
of Looked After Children (LAC) and children who were not Looked after.
The results are shown here in Table 1.
Table 1 Child health system records of looked after and other children in
Craigavon Banbridge Health and Social Services Trust, October 2001.
Number (%) of children
born 1 Jan 1987 to 31 Dec 1999
Looked after
children (n=75)
Other children
(n=23 936)
Completed Primary
immunisations
67 (89.3)
22979 (96)
Meningococcal C
68 (90.6)
22260 (93)
Measles, mumps, and rubella
69 (92)
22979 (96)
These figures have been published.[1] Integrated health and social
services can be an effective means of protecting the health of vulnerable
children in Society.
Reference
1. Farrell B., Findings for looked after children are not
generalisable. BMJ 2003; 326:1088 (letter)(17 May)
The paper by Syed et al,[1] provides observations on the use of face
masks by members of the public for protection against the severe acute
respiratory syndrome (SARS) coronavirus (CoV).
The authors’ raise an
important question as to whether masks are effective in preventing
disease. The type of masks used can generally be categorized as either
surgical or paper and are suggested to off...
The paper by Syed et al,[1] provides observations on the use of face
masks by members of the public for protection against the severe acute
respiratory syndrome (SARS) coronavirus (CoV).
The authors’ raise an
important question as to whether masks are effective in preventing
disease. The type of masks used can generally be categorized as either
surgical or paper and are suggested to offer similar protection. For
health care workers (HCW), it has been shown [2] that masks do not provide
adequate protection against SARS CoV. However, protection for HCW is
somewhat different than that for those of the general public, especially
those not directly exposed to droplet transmission on a “continous” basis
from an infected individual. The finding of a possible dose-response [3] for
exposure and infection to SARS CoV lessens the chance of infection through
droplet transmission by the general public, especially when some personal
protection is afforded. When masks are used along with other hygiene
practices, risk of infection, excluding close contact with an infected
person, like a family member, can be minimized.
Masks have been shown to provide an increased protection rate of 2.[4]
for Mycobacterium tuberculosis in comparison to no mask4. Since SARS CoV
has been suggested to be spread by aerosol droplet and not to any
significant degree by airborne transmission,[5] masks will probably provide
some increased protection to the general public. However, as noted by
Syed, it is necessary that they be properly used and changed frequently.
Since this virus can survive for 72 hours or more on surfaces, its
transmitted through fomite contact and infection can occur by mucus
membranes (e.g. conjunctiva);[5] thus, other personal hygiene practices
(e.g. hand washing) are of equal or greater importance4.
For public health protection, use of masks can have some impact on
preventing the spread of SARS CoV. However, this should be only one
health practice that is encouraged by the public since others (e.g. hand
washing) are also of great importance.
References
1. Syed Q, Sopwith W, M Regan M, Bellis MA. Behind the mask. Journey
through an epidemic: some observations of contrasting public health
responses to SARS. J Epidemiol Community Health, 2003; 57: 855 - 856.
2. Lange JH. SARS and respiratory protection. Hong Kong Medical
Journal 2004; 10: 392-3.
3. Scales DC, Green K, Chan AK, Poutanen SM, Foster D, Nowak K,
Raboud JM, Saskin R, Lapinsky SE, Stewart TE. Illness in intensive-care
staff after brief exposure to severe acute respiratory syndrome. Emerg
Infect Dis [9: 1205-10] 2003 Oct [accessed December 19, 2003]. Available
from: http://www.cdc.gov/ncidod/EID/vol9no10/03-03-0525.htm
4. Barnhart S, Sheppard L, Beaudet N, Stover B, Balmes J.
Tuberculosis in health care settings and the estimated benefits of
engineering controls and respiratory protection. J Occup Environ Med 1997;
39: 849-853.
5. Centers for Disease Control and Prevention. Public health guidance
for community-level preparedness and response to Severe Acute respiratory
Syndrome (SARS). Draft, October, 2003;
http://www.cdc.gov/ncidod/sars/sarsprepplan.htm
I heard this study reported on the Today programme on Radio 4. What effective dissemination! I applaud the inclusion of SES as a covariate in this study, and I agree that the correlational effects reported are of interest. Nevertheless, I believe that one important aspect of the interpretation of the study is omitted from the discussion. This is the small effect sizes.
I heard this study reported on the Today programme on Radio 4. What effective dissemination! I applaud the inclusion of SES as a covariate in this study, and I agree that the correlational effects reported are of interest. Nevertheless, I believe that one important aspect of the interpretation of the study is omitted from the discussion. This is the small effect sizes.
In Table 2, standardized regression coefficients relating to 140 different analyes are presented. Ignoring the sign, these range from values of 0.00 to 0.16. Squaring a standardized regression coefficient gives a value of R2, which is an estimate of the amount of variance explained by the regression model. Therefore the largest amount of variance explained by any of these models is the square of 0.16, which equals 0.026, or 2.6%. Cohen (1988) has defined small, medium and large effect sizes for values of R2. He defined small effect size as R2 = 0.02, medium effect size as R2 = 0.13, and large effect size as R2 = 0.26. Therefore, all of these are small effects.
The reported p values add credibility to these data, however, one must bear in mind the large sample size employed (n = 5352). As sample size increases, so study power increases, leading to increased sensitivity to detect, and label ‘significant’ even the smallest of effects. Therefore, this ‘superpowered’ study has, not surprisingly, detected many small, and smaller, effects. This is OK in itself, however, in my opinion, some comment as to their ‘real world’ significance should have made. Additionally, the lack of consideration of likely Type I error inflation due to multiple hypothesis testing is a conspicuous omission.
Dr Nishiura [1] accentuated that caution must be exercised in using mathematical models to ascertain the recent SARS epidemics.
The key issue, as we believe, is to understand the model and its results for what they are and, more importantly, for what they are not. It is especially true with the basic reproductive number R0, or its variant the effective reproductive number at time t Rt, which has bee...
Dr Nishiura [1] accentuated that caution must be exercised in using mathematical models to ascertain the recent SARS epidemics.
The key issue, as we believe, is to understand the model and its results for what they are and, more importantly, for what they are not. It is especially true with the basic reproductive number R0, or its variant the effective reproductive number at time t Rt, which has been estimated for the recent SARS outbreaks in Beijing, Hong Kong,, Toronto, Taiwan, and Singapore in several recent articles (e.g. [2-6]). R0, the average number of secondary infections caused by an infective person upon entering a totally susceptible population, is a useful tool to gauge the initial trend of an epidemic. It is also often misunderstood and misused. Indeed, a recent news feature in Nature [7] described the basic reproductive number R0 as "A measure of a disease's infectiousness" corresponds to how many people, on average, are infected by each patient in the absence of any control measures, which erroneously left out the important requirement that the patient must be an index case in that population, i.e. all possible contacts of that person are susceptible to infection.
The effective reproductive number at time t Rt =R0 x(t), where x is the susceptible proportion of population at time t, measures number of infections caused by a new case at time t.[3] It is more important as a mean to understand the progression of the epidemic, taking into consideration the control measures, behavior changes, and climate as they have all been proven to be important in the case of SARS. Moreover, one can approximate the average growth rate of an epidemic over a given time interval while the epidemic is underway from the cumulative case data. From which one could then estimate the "mean effective reproductive number of the observed time period" R*, i.e. the average number of secondary infections caused by one infective person during the observed time interval. The precise definition gives the public officials a clear chronology of progression (or cessation) of the epidemic, albeit retrospectively.
For illustration, we used the cumulative number of probable SARS cases in Taiwan by onset date from March 12 to June 15,[8] exponential curve fitting with first-order autocorrelation in the error structure,[9] and the period of SARS infectivity of 29.03 days (i.e. time from onset to death or discharge) estimated from [10] to obtain the mean effective reproductive numbers for the five distinct periods during March 12 - June 15 (Table 1). A chronology of relevant events of importance is given as a footnote of Table 1. Figure 1 paints a clear picture of slowly growing epidemic in the beginning, to the outbreak kindled by the admission of first SARS patient to Ho Ping Hospital, the site of first hospital cluster infections, on April 9. The peak period of infections (4/11-4/26) ended with the shutdown of Ho Ping Hospital on April 24. The series of hospital clusters in Taipei and subsequently in the southern port city of Kaohsiung finally subsided with the May 11 shutdown of Chang Gung Hospital in Kaohsiung, due to successful intervention efforts to stop nosocomial infections, the last of which occurred shortly before June 9 the onset date of the last hospital infection in Taiwan. The result clearly points to the important lesson from the outbreak in Taiwan shutdown of hospitals where cluster infections have occurred had been a crucial step in breaking the local chains of transmissions. The effect of quarantine measures, however, is less clear and requires further study, perhaps with mathematical modeling. Clearly, retrospective mathematical modeling is an important reference for public health policy makers intending to contain possible future outbreaks with the most effective intervention measures as long as we understand them for what they are and what they are not.
3/18 –
Implementation of Level A quarantine.
4/09 – Admission of first SARS patient to Ho Ping Hospital.
4/24 – Shutdown of Ho Ping Hospital.
4/28 – Implementation of
Level B quarantine.
5/11 – Shutdown of Chang Gung Hospital.
6/15 – Onset date
of the last hospital infection.
References
(1) Nishiura H. Mathematical modeling of SARS: cautious in all our movements. J Epidem Com Health 2003; In Press.
(2) Riley S, Fraser C, Donnelly C, Ghani AC, Abu-Raddad LJ, Hedley AJ, et al. Transmission dynamics of the etiological agents of SARS in Hong Kong: Impact of public health interventions. Science 2003; 300: 961-66 (20 June 2003) Published online 23 May 2003 (10.1126/science.1086478)
(3) Lipsitch, M, Cohen T, Cooper B, Robins JM, Ma S, James L, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science 2003; 300: 1966-70 (20 June 2003) Published online 23 May 2003; 10.1126/science.1086616
(4) Zhou G, & Yan G. Severe Acute Respiratory Syndrome epidemics in Asia. Emerg Infect Dis 2003; 9(12), In Press.
(5) Hsieh YH, Chen CWS, & Hsu SB. The Severe Acute Respiratory Syndrome outbreak in Taiwan: Lessons to be learned. Emerg Infect Dis 2003; To Appear.
(6) Chowell G, Fenimore PW, Castillo-Garsow MA, & Castillo-Chavez C. SARS outbreaks in Ontario, Hong Kong, and Singapore: the role of diagnosis and isolation as a control mechanism. J Theoret Biol 2003; 224: 1-8.
(7) Pearson H, Clarke T, Abbott A, Knight J, & Cyranoski D. SARS: what have we learned? Nature 2003; 424(6945):121-6. Nature 424, 121: 126(2003) (10 July 2003).
(9) Hsieh YH,. & Chen CWS. Severe Acute Respiratory Syndrome: Numbers don¡¦t tell the whole story. British Medical J 2003; 326: 1395-1396.
(10) Donnelly C, Ghani AC, Leung GM, Hedley AJ, Fraser c, Riley S, et al. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet 2003; 361(9371): 1761-66. (May 24 2003) Available at http://image.thelancet.com/extras/-3art4453web.pdf.
The focus group research by Thomson and colleagues is, as they
themselves suggest, not strictly Health Impact Assessment (HIA). Whilst it
gives valuable insights into the perceived health impacts on a local
community of closing an existing leisure facility, this was not a joint
exercise with service providers with a view to lessening any negative
impacts.
The focus group research by Thomson and colleagues is, as they
themselves suggest, not strictly Health Impact Assessment (HIA). Whilst it
gives valuable insights into the perceived health impacts on a local
community of closing an existing leisure facility, this was not a joint
exercise with service providers with a view to lessening any negative
impacts.
To achieve optimum benefits for local residents, HIA should be
prospective and collaborative. Otherwise it runs the risk of becoming a
post hoc evaluation with little benefit for the participants. In this case
a collaborative prospective assessment could have predicted some of the
less obvious negative impacts of closure, enabling residents and planners
to take avoiding action.
We have carried out two collaborative HIAs, one on a proposed new
road and one on land remediation options and both of these, together with
our 'toolkit' appear on the Health Development Agency's website
(www.hiagateway.org.uk). Local people contributed to both assessments,
together with planners and public health specialists, before action was
initiated. If HIA is to be an effective instrument for improving health
and reducing health inequalities, it needs to be both prospective and
collaborative.
Dr Bernard CK Choi and Dr Anita WP Pak recently developed a
simple approximate mathematical model to predict the cumulative incidence
and death.[1] Although it’s certainly easy to understand and to use as
they stated, every users must be cautious about misunderstanding the real
applications and evaluations for SARS epidemics. This problem originates
in their too rough assumptions.
Dr Bernard CK Choi and Dr Anita WP Pak recently developed a
simple approximate mathematical model to predict the cumulative incidence
and death.[1] Although it’s certainly easy to understand and to use as
they stated, every users must be cautious about misunderstanding the real
applications and evaluations for SARS epidemics. This problem originates
in their too rough assumptions.
Firstly, since the model is based on simplification of an epidemic
model, so called Kermack & McKendrick model,[2] the fate of an
epidemic depends on threshold theorem: that is, R0 > 1 or not. They
used a single value of the basic reproductive number, R0, throughout the
epidemic. This, however, is not an accurate description of actual
transmission dynamics because R0 is likely to decrease after the onset of
an epidemic is detected and announced. Considering threshold host
densities in case of simple circumstances,[3] R0 would be written as R0 =
N/NT. Here N is the population size, and 1/NT is the proportionality
constant whose value would be determined by all manner of biological,
social, and environmental aspects of transmission. In the real SARS
epidemic, the latter value might be largely affected by social reactions
as well as environmental factors (including weather conditions as the
authors mentioned). It is well known that the potentials for panic and
social stigma are often much greater than the risk for the disease such as
SARS. It is also notable that great variability in R0 should be a key to
consider SARS epidemic. To say that a crude picture of SARS can be
estimated with the model is quite different from saying that the model can
be used to evaluate the success of interventions.
Secondly, as authors stated ‘may not’, homogenous mixing would not be
a correct depiction of actual population interactions of SARS
transmission. Although we are still in the face of so many unknowns
including super-spreading events (SSEs), the small number of transmissions
in most of the countries that experienced SARS occurrences suggests that
the close contact required for transmission did not occur.[4] For
instance, Japan has so far not experienced domestic transmission even
though it had experienced the entrance of an SARS-CoV infected person,
whereas one index case, not SSEs, caused an epidemic originated from
Hospital in Toronto.[5] When we note that 76% of the infections in
Singapore were acquired in a health-care facility,[6] it is evident SARS
can easily be spread by close personal contact. Those facts were
relatively known at that time when authors developed their model.
Therefore, it is too optimistic to apply their assumption to the real data
that every infected person will pass the disease to exactly R0 susceptible
individuals.
I agree that user-friendly mathematical models must be developed and
the mathematical models should be kept as simple as possible so that
public health officers as well as students could predict and learn more
effectively. Such models and recommendations for general usage, however,
may send the wrong message to the local health officers and/or public. The
models for the public must be based on clear knowledge, especially of
epidemiological determinants and ecology of pathogens, when it comes to
general usage. In order to avoid misleading, the mathematical
epidemiologists cannot be too careful to describe their limitations of own
models.
References
(1) Choi BCK, & Pak AWP. A simple approximate mathematical model to
predict the number of SARS cases and deaths. J Epidem Com Health. 2003; In
Press.
(2) Kermack WO, & McKendrick AG. Contributions to the mathematical
theory of epidemics – 1927. R Stat Soc J 1927;115:700-721. (Reprinted
in Bull Math Biol 1991;53:33-55).
(3) Anderson RM, & May RM. Infectious Diseases of Humans: Dynamics
and Control. Oxford: Oxford University Press, 1992.
(4) Arita I, Kojima K, & Nakane M. Transmission of Severe Acute
Respiratory Syndrome. Emerg Infect Dis 2003; 9: 1183-4.
(5) Dwosh HA, Hong HHL, Austgarden D, Herman S, & Schabas R.
Identification and containment of an outbreak of SARS in a community
hospital. CMAJ 2003; 168: 1415-20.
(6) Center for Disease Control and Prevention. Severe Acute Respiratory
Syndrome-Singapore, 2003. MMWR 2003; 52: 405-11.
NOTE:Conflict of Interests including financial interests: Nil
The report by Jousilahti and colleagues in the 2003 September's issue of JECH adds to growing evidence of a consistent association between serum inflammatory
markers – particularly fibrinogen – and social position. [1-3]
These
authors interpret their data as suggesting that the fibrinogen-social
position link is not merely a reflection of the social patterning of
prevalent disease, smoking and obesi...
The report by Jousilahti and colleagues in the 2003 September's issue of JECH adds to growing evidence of a consistent association between serum inflammatory
markers – particularly fibrinogen – and social position. [1-3]
These
authors interpret their data as suggesting that the fibrinogen-social
position link is not merely a reflection of the social patterning of
prevalent disease, smoking and obesity (all of which are positively
associated with increased serum fibrinogen and lower social position)
since a strong trend of increasing fibrinogen with decreasing social
status survived statistical adjustment for these covariates. Fibrinogen,
they conclude, is therefore a promising candidate for the “missing link”
between social position and cardiovascular health.
The authors’ reasoning implicitly accepts that fibrinogen is a cause
of coronary heart disease (CHD). However this runs contrary to recently
published evidence utilising the principle of “Mendelian randomisation”
(the situation where a particular genetic polymorphism strongly influences
exposure level of a putative disease risk factor, and should in turn be
related to increased risk of disease if the risk factor is indeed a
cause).[4,5] In fact we discussed this evidence in our commentary on
psychosocial explanations of health inequalities in last month’s edition
of the JECH (fibrinogen has been shown to be “independently” associated
with some psychosocial exposures leading to claims that it may mediate
relations between these exposures and health).[6] Plasma fibrinogen
levels are related to a polymorphism in the beta-fibrinogen gene, with
presence of the “T” allele being associated with higher levels. Amongst
controls of a recent large case-control study, fibrinogen increased by
0.12g/l per T allele present. Comparing cases with controls, a 0.12g/l
rise in fibrinogen was associated with a relative risk of CHD of 1.20 (95%
CI 1.13-1.26). If increased fibrinogen actually caused heart disease then
a similar per allele relative risk of CHD should be seen. In fact the per-
allele relative risk of CHD was 1.03 (0.96-1.10). Individuals whose
genotype would have subjected them to chronically elevated plasma
fibrinogen experienced no increased risk of heart disease, suggesting that
observed associations between fibrinogen and CHD risk are not causal. This
finding is in keeping with evidence from RCTs that suggests that drugs
lowering fibrinogen do not decrease the risk of CHD.[7]
Fibrinogen probably predicts cardiovascular events because of reverse
causation (atherosclerosis is an inflammatory condition and raises
circulating fibrinogen levels) and because of confounding – smoking,
abstaining from alcohol, not exercising and being poor are all associated
with elevated fibrinogen and themselves increase the risk - or are markers
for factors that increase the risk - of cardiovascular disease
The data presented by Jousilahti and colleagues illustrate how easily
one can misattribute causality to associations in social epidemiology.
Many factors are socially patterned and thus appear as possible candidates
for a causal role in the processes that generate any disease outcome that
is also socially patterned.[6,8] Demonstrating apparent statistical
independence of associations between such exposures and outcomes does
little to infer their causal basis as it is often likely to reflect issues
of residual confounding and measurement imprecision of correlated
covariates.[9] Strategies such as Mendelian randomisation can help
untangle these issues. Where feasible, they should be more widely adopted
in epidemiology.
References
(1) Jousilahti, P Salomaa, V Rasi, V Vahtera, E, T Palosuo T.
Association of markers of systemic inflammation, C reactive protein, serum
amyloid A, and fibrinogen, with socioeconomic status J Epidemiol Community
Health 2003;57:730-733.
(2) Wilson TW, Kaplan GA, Kauhanen J, et al. Association between
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Dear Editor
We appreciate the comments of Macleod and Davey-Smith on our recent article reporting an association between systemic inflammation markers and socio-economic status.[1,2] In their letter, Macleod and Davey-Smith state that our findings, particularly the association of fibrinogen with socio-economic status, and its interpretation is not correct, and runs contrary to the principle of "Mendelian randomisat...
Dear Editor
My understanding, confirmed by brief review of data on the ONS website, is that the South Asian population in the UK has a younger age distribution than the white population. In this case, would an indicator based on those aged over 35 need further adjustment for the age distribution beyond 35 in order to examine prescribing? Is it possible that we are seeing an age effect in the negative correlation...
Dear Editor,
We read with great interest the recent paper by Martins et al.[1] concerning the influence of socioeconomic conditions on air pollution adverse health effects in elderly people in Sao Paulo, Brazil. These results are very interesting and may promote understandings of which social category of persons are most sensitive to air pollution. The authors suggest that socioeconomic deprivation represents a...
Dear Editor
It is important to highlight shortfalls in service delivery to looked after children. However, the provision of information as in the quoted study is seldom enough to change practice.
In Northern Ireland we have had integrated health and social services since the early 1970s. As part of Children's Services Planning we tagged the records of Looked After Children and compared the immunisation stat...
Dear Editor
The paper by Syed et al,[1] provides observations on the use of face masks by members of the public for protection against the severe acute respiratory syndrome (SARS) coronavirus (CoV).
The authors’ raise an important question as to whether masks are effective in preventing disease. The type of masks used can generally be categorized as either surgical or paper and are suggested to off...
Dear Editor
I heard this study reported on the Today programme on Radio 4. What effective dissemination! I applaud the inclusion of SES as a covariate in this study, and I agree that the correlational effects reported are of interest. Nevertheless, I believe that one important aspect of the interpretation of the study is omitted from the discussion. This is the small effect sizes.
In...
Dear Editor
Dr Nishiura [1] accentuated that caution must be exercised in using mathematical models to ascertain the recent SARS epidemics.
The key issue, as we believe, is to understand the model and its results for what they are and, more importantly, for what they are not. It is especially true with the basic reproductive number R0, or its variant the effective reproductive number at time t Rt, which has bee...
Dear Editor
The focus group research by Thomson and colleagues is, as they themselves suggest, not strictly Health Impact Assessment (HIA). Whilst it gives valuable insights into the perceived health impacts on a local community of closing an existing leisure facility, this was not a joint exercise with service providers with a view to lessening any negative impacts.
To achieve optimum benefits for local re...
Dear Editor
Dr Bernard CK Choi and Dr Anita WP Pak recently developed a simple approximate mathematical model to predict the cumulative incidence and death.[1] Although it’s certainly easy to understand and to use as they stated, every users must be cautious about misunderstanding the real applications and evaluations for SARS epidemics. This problem originates in their too rough assumptions.
Firstly,...
Dear Editor
The report by Jousilahti and colleagues in the 2003 September's issue of JECH adds to growing evidence of a consistent association between serum inflammatory markers – particularly fibrinogen – and social position. [1-3]
These authors interpret their data as suggesting that the fibrinogen-social position link is not merely a reflection of the social patterning of prevalent disease, smoking and obesi...
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