We appreciate the comments from Cope et al on our paper reporting the
association between smoking cessation and smoking reduction and subsequent
risk of myocardial infarction (1). Specifically, Cope et al propose that
the lack of a beneficial effect of reduced smoking - in contrast to
smoking cessation - could be due to inaccuracy (underreporting) of the
self-reported tobacco consumption. In addition, Cope et al raise the...
We appreciate the comments from Cope et al on our paper reporting the
association between smoking cessation and smoking reduction and subsequent
risk of myocardial infarction (1). Specifically, Cope et al propose that
the lack of a beneficial effect of reduced smoking - in contrast to
smoking cessation - could be due to inaccuracy (underreporting) of the
self-reported tobacco consumption. In addition, Cope et al raise the
important question of which measurement method most accurately reflects
tobacco exposure in the individual smoker.
We agree that nowadays almost every study of smoking habits apply one
or more measurement of biochemical marker of smoking in addition to self-
report. It is also correct that in our paper the study participants are
divided into the different smoking categories on basis of self-reported
smoking and changes in smoking. However, as mentioned in the discussion,
measurements of expired carbon monoxide (CO) and serum cotinine were
undertaken in one of the follow-up examinations. We found increasing
levels of CO (Table 2) and cotinine (not shown) with increasing self-
reported tobacco consumption, indicating that underreporting of smoking
alone cannot explain our results, but clearly misclassification cannot be
ruled out in this observational study. Furthermore, a previous review and
meta-analysis (2) concluded that self-reported smoking is an accurate
measure of tobacco exposure in population based studies, whereas this is
not the case in intervention and clinical studies. Our data were based on
a sample of the general population; participants with known coronary heart
disease prior to study entrance were excluded. In addition, information on
smoking habits and changes in smoking were part of a large questionnaire
initiated in the late 1970's and the 1980's, thus minimizing the risk of
"social desirability bias" in this study.
Cope et al describe a pilot study using a urine cotinine test for
measuring nicotine intake. There are various methods of validating tobacco
intake including biochemical markers, and cotinine is one of the better
due to its relatively long half-life and the possible linear relationship
with number of cigarettes smoked. However, cotinine is not very useful in
smoking reduction studies since most of the participants in these trials
are supplied with nicotine replacement medications. Interestingly, the
intervention studies of smoking reduction all report that despite nicotine
replacement the percentage decline in amount of tobacco is followed by a
smaller decline in biochemical markers of smoking exposure.
Evidence of the effects of reduced smoking on risk of coronary heart
disease is limited. The few ongoing smoking reduction trials report
favorable changes in blood analyses of parameters of arteriosclerosis up
to two years after smoking reduction. Unfortunately, these studies have a
very high "drop-out" percentage, but it will be interesting to see the
clinical results of a long-term follow-up in this type of "risk reduction"
study.
In summary, we believe that the self-reported smoking habits in our
study are fairly precise. However, biochemical verification of smoking is
necessary in intervention and clinical studies although there are no ideal
markers of tobacco exposure specifically with respect to assessment of
smoking reduction.
(1) Godtfredsen NS et al. Smoking reduction, smoking cessation, and
incidence of fatal and non-fatal myocardial infarction in Denmark 1976-
1998: a pooled cohort study. J Epidemiol Community Health 2003;57:412-416.
(2)Patrick DL et al. The validity of self-reported smoking: a review
and meta-analysis. Am J Public Health 1994;84:1086-1093.
I have read with high interest the comments made by Petronis and
Anthony on my editorial.[1] I have also read their forthcoming article,[2] and
I believe they apply an analytical approach that seems to be, in my
opinion, a step in the right direction for research on contextual
influences and health that focus on investigation of clustering. I will be
very pleased of writing a larger comment and send i...
I have read with high interest the comments made by Petronis and
Anthony on my editorial.[1] I have also read their forthcoming article,[2] and
I believe they apply an analytical approach that seems to be, in my
opinion, a step in the right direction for research on contextual
influences and health that focus on investigation of clustering. I will be
very pleased of writing a larger comment and send it for consideration and
possible publication in the Journal.
Measuring clustering with the aim of obtaining substantive scientific
information is so far an uncommon approach in social epidemiology and most
multilevel analyses have in fact been plain “contextual analysis”[3] focused
on measures of association. This seems be true not only for studies using
GGE techniques but also for studies using multilevel hierarchical
regression. However, the original standpoint of multilevel hierarchical
regression analyses is the investigation of complex patterns of variation [4]
rather than dealing with residual correlation.
Regarding the use of “GEE2” and measurement of clustering, the
comment of Petronis and Anthony is certainly right.
From an epidemiological point of view the most interesting question
is the conceptual rather than the mathematical approach used. I agree with
Petronis and Anthony in their conceptual approach and I believe that they
put context back in epidemiology using “GEE2” techniques.[5] The Parwise
Odds Ratio and other techniques for measuring neighbourhood heterogeneity
and clustering like the Median Odds Ratio and the Interval Odds Ratio [6,7]
deserve more development and spreading.
References
(1) Merlo J. Multilevel analytical approaches in social epidemiology:
measures of health variation compared with traditional measures of
association. J Epidemiol Community Health 2003;57:550-2.
(2) Petronis KR,.Anthony JC. A different kind of contextual effect:
geographic clustering of cocaine incidence in the US. J Epidemiol
Community Health 2003;in press.
(3) Diez Roux AV. A glossary for multilevel analysis. J Epidemiol
Community Health 2002;56:588-94.
(4) Goldstein H. Multilevel Statistical Models. London: Hodder Arnold, 2003.
(5) Carey V, Zeger SL, Diggle P. Modelling multivariate binary data
with alternating logistic regressions. Biometrika 1993;80:517-26.
(6) Larsen K, Petersen JH, Budtz-Jorgensen E, Endahl L. Interpreting
parameters in the logistic regression model with random effects.
Biometrics 2000;56:909-14.
(7)Larsen K,.Merlo J. Appropriate assessment of neighborhood effects
on individual health -integrating random and fixed effects in multilevel
logistic regression. Working paper 2003.
Daniel D Reidpath is slightly misguided to observe that social
inequalities in
health have "not yet been elevated to the status of having its own
National
Library of Medicine MeSH (medical subject) heading", and his remark hence
calls for some additional precision.
As a MeSH search for inequalit* will indicate, the National Library
of Medicine
lists "Inequality" and "Inequalities" as entry terms for its
"Soc...
Daniel D Reidpath is slightly misguided to observe that social
inequalities in
health have "not yet been elevated to the status of having its own
National
Library of Medicine MeSH (medical subject) heading", and his remark hence
calls for some additional precision.
As a MeSH search for inequalit* will indicate, the National Library
of Medicine
lists "Inequality" and "Inequalities" as entry terms for its
"Socioeconomic
Factors" heading, which was introduced in 1968. The other MeSH entry for
social factors, namely "Social Environment", does not mention inequalities
in
that respect. Admittedly, these headings do not allow for a clear mapping
of
the health inequalities literature, which is better accessed through user-submitted search strings like "health inequalit*[tw]."
Nevertheless, much more recently, and as a MeSH search for disparit*
indicates, the National Library of Medicine has introduced "Health Status
Disparities" in its topical indexing. The heading coverage is described as
"variation in rates of disease occurrence and disabilities between
socioeconomic and/or geographically defined population groups." A PubMed
search on the heading already returns over 500 references, consistent with
Daniel D Reidpath's view that health inequalities have become a central
element of the research agenda for social epidemiologists, sociologists of
health and illness, and virtually any scholar interested in health
economics
and welfare.
Another new heading introduced in 2008 is "Healthcare Disparities,"
which
covers "Differences in access to or availability of facilities and
services" -- a
partial definition of social inequalities in health, as it is much more
restrictive
in scope and focuses on the single but crucial dimension of access to
health
services. The provision of a specific heading for inequalities in access
to
healthcare, however, comes with several cross-national and cross-sectoral
advantages, and constitutes a valuable bibliographic tool for researchers
in
that respect.
What Daniel D Reidpath's comment underlines is the variety of terms
associated with "social inequalities in health" in international research.
In the
American context, research on the differential distribution of mortality
and
morbidity by socio-economic status (and other social factors) is
susceptible
to be flagged as research on health "disparities" instead of
"inequalities" or
"inequities." Acknowledging another geographical bias, Paul Braveman has
observed that "‘health equity’ is a term rarely encountered in the United
States but more familiar to public health professionals elsewhere." [1].
In
Britain, the political history of health inequalities shows that the more
neutral
terms "health variations" were instrumental in maintaining health
inequalities
on the political agenda, following governmental reluctancy towards the
conclusions of the Black report [2,3].
An explanation for variations in research terminology might relate to
national
contexts of health politics. In the case of the United States, it would be
seemingly unsurprising to observe that a nation that tolerates high levels
of
inequality within its population would prefer the term "disparity" to
address a
phenomenon otherwise addressed as "inequality", a term which would appear
to be politically connoted in the American polity. Existing data on
inequality
and public opinion tend to reveal, however, a more complex picture, in
which
attitudes towards inequality exhibit less cross-national variation than
policy
preferences [4,5].
More generally, research on health inequalities will inevitably
intersect with
other forms of social research, such as racial and ethnic inequality [6].
In fact,
health inequalities are shaped by a large array of factors, not all of
which
should not be expected to be linguistic or rhetorical like the ones
mentioned
here; in France, for instance, institutional factors are constraining the
development of both research and policy regarding health inequalities
[7,8].
Current scholarship, however, has given considerably less attention to the
relationship between health inequalities and institutional variables
derived
from the political context.
This brief response aims not only at rectifying the slight inadequacy
in Daniel
D Reidpath's review, but also at showing how social inequalities in health
call
for interdisciplinary research perspectives in health and society, as
actively
promoted by the JECH. One might finally want to stress the dearth of
political
analysis in this field of research [9].
References
[1] Braveman, P. Health disparities and health equity: concepts and
measurement. Annu Rev Public Health 2006;27:167–94.
[2] Berridge V. Blume S, eds. Poor health. Social inequality before
and after
the Black Report. London: Frank Cass, 2003.
[3] Macintyre S. Before and after the Black report: four fallacies.
In: Berridge V.
Blume S, eds. Poor health. Social inequality before and after the Black
Report.
London: Frank Cass, 2003:198–219.
[4] Pontusson J. Inequality and prosperity in contemporary
capitalism. Ithaca,
NY: Cornell University Press, 2005.
[5] McCall L, Kenworthy L. Americans' social policy preferences in
the era of
rising inequality. Unpublished manuscript, 2008.
[6] Gamble VN, Stone D. US policy on health inequities: the interplay
of
politics and research. J Health Polit Policy Law 2006;31:93–126.
[7] Berthod-Wurmser M. Programmes de recherche et débat public sur
les
inégalités de santé: la France est-elle en retrait ? In: Leclerc A, Fassin
D,
Grandjean H, et al. Les inégalités sociales de santé. Paris: La
Découverte,
2000: 69–80.
[8] Briatte F. Lutter contre les inégalités de santé en France et en
Grande-
Bretagne. [Master's thesis]. Grenoble: Institute of Political Studies,
University
of Grenoble, 2006.
[9] Judge, K. Politics and health: policy design and implementation
are even
more neglected than political values? Eur J Pub Health 2008;18:355–356.
Mortality data are obtained from table 1a and 1b in the
article[1], and SPSS version 11 is used for analysis.
There are total 56 “subjects”: 4 (different centralize birth cohort)*7
(different cou...
Mortality data are obtained from table 1a and 1b in the
article[1], and SPSS version 11 is used for analysis.
There are total 56 “subjects”: 4 (different centralize birth cohort)*7
(different countries)*2(sex type). Each “subject” with five variables, 3 categorical variables: year
according to which they were centralized, country, and sex.; 2 continuous
variables: mortality rate(*10000) at age 55-69, and mortality rate(*10000)
at age 80-89.
Findings:
1) The assumptions of the regression model or the analysis of
correlation between mortalities in late middle age and mortalities in old
age for all sub-cohorts (N=56) do not hold. Thus the validity of the
regression model needs to be evaluated, and the correlation coefficients
may be meaningless.
2) The associations between mortality in two different age groups for
all male sub-cohorts (N=28) seems to be great different between sub-
cohorts with early mortality rate (*10000) <240 and sub-cohorts with
early mortality rate (*10000) >240.
3) The strong association between mortalities in two different age
for both sex of all sub-cohorts (N=56) or sub-cohorts from different
countries are likely to be confounded by sex.
Explanation:
1) The assumptions of the regression model—Normal distribution of
dependent variable, Normal distribution of residuals, equal variance of
residuals, and independence of residuals[2] are tested. Normality test:
Shapiro-Wilk are conducted for both dependent and independent variables:
mortality rate(*10000) in 55-69 age, and mortality rate(*10000) in 80-89
age, results for both of them are significant at 0.01 level, therefore it
would be more than 95% confident to say the two variables are not Normal
distributed.
Then two regressions analysis are preformed a) Mortality in
80-89 age set as independent variable while Mortality in 55-69 age set as
dependent variable, b) Mortality in 80-89 age set as dependent variable
while Mortality in 55-56 age set as independent variable. Standardized
residuals are saved for analysis. Normality test :Shapiro-Wilk again are
conducted for the two residuals showing that residuals of the first
model is significant at 0.01:assumption of Normality does not hold. Though
the test for the residuals of the second model is not statistical
significant. In the third step, two scatterplots are constructed by
plotting residuals against dependent variable for the above two models,
and both of the scatterplots shows patterns indicating that residuals are
likely lack of randomness. Based on these, it may not be suitable that
using regression models or correlation to describe the relationship
between mortalities in two different age for all sub-cohorts (N=56)
directly.
2) Two scatterplots are constructed by plotting mortality in 55-69
age groups against mortality in 80-89 age groups for men (N=28) and for
Women (N=28). Among men sub-cohorts the patterns of plots are great
different between sub-cohorts with early mortality rate(*10000) lower than
240 and sub-cohorts with early mortality rate(*10000)higher than 240. Two
regression models are conducted by choosing men cohorts with mortality in
55-69 age higher than 240 (*10000) or lower than that. Only among men
cohorts with middle age mortality higher than 240, the regression model is
significant with R=0.716. While assumptions for the model are found to be
able to assumed.
3) Two-block regression model(old age mortality as dependent
variable, block one including sex as independent variable, block two
includes both sex and middle age mortality) is constructed to determine
the impact of sex on the “relation” between middle age mortality and old
age mortality among all sub-cohorts N=56, cohorts from different countries
N=8, (7 groups). The results show that among all countries, R increase
less than 12% when middle age mortality is added into the model given that
sex is already in the model. Though the assumption of the models are not
hold, it is still likely that sex is a great confounder.
References
1. Janssen, F., Peeters, A, Mackenbach P J, Kunst A E for NEDCOM,
Relation between trends in late middle age mortality and trends in old age
mortality—is there evidence for mortality selection? J Epidemiol Community
Health, 2005. 59.
Lovasi et al. [1]) document the relationship between the density of
street trees and the prevalence of childhood asthma in New York City.
Their findings suggest street trees are associated with a lower
prevalence, although no causality was inferred. I would like to point out
a number of methodological issues which should benefit future studies on
this subject.
Prevalence of asthma was determined for 4-year-old a...
Lovasi et al. [1]) document the relationship between the density of
street trees and the prevalence of childhood asthma in New York City.
Their findings suggest street trees are associated with a lower
prevalence, although no causality was inferred. I would like to point out
a number of methodological issues which should benefit future studies on
this subject.
Prevalence of asthma was determined for 4-year-old and 5-year-old
children using data from school screenings in 1999. Street tree density
was derived from the 1995 street tree census completed by the Parks and
Recreation Department of the City of New York and expressed as the total
number of trees on streets segments divided by the land area. Data was
aggregated at the level of United Hospital Fund (UHF) areas. Additional
variables used in the analysis were population density, racial/ethnic
composition and a measure of proximity to pollution sources. The initial
correlation analysis suggested a negative association between street tree
density and prevalence of asthma. However, one of the strongest positive
associations was between street tree density and population density. This
initially appears somewhat counterintuitive, until it is recognized
exactly which types of trees are included in the analysis. The street tree
census conducted by the Parks and Recreation Department of the City of New
York only considered trees along city streets, and trees in parks and open
space are not included. As a result, the street tree density derived by
Lovasi et al. [1] is a substantial underestimate of the actual number
of trees within a UHF area, in particular in areas with large areas of
parks and open space. In fact, Nowak et al. [2] estimate that New York
City has about 5.2 million trees, while the latest street tree census in
2005-2006 counted 592,130 trees [3]. Street trees,
therefore, account only for around 11% of trees within the study area. The
strong correlation between street tree density and population density is
strongly driven by the fact that the total length of streets segments per
unit area increases with population density. Logic suggests that overall
tree density and population density are likely negatively associated since
many parks and open space occur in areas with lower (average) population
density. The pattern in street tree density by UHF areas is therefore a
poor representation of the overall tree density.
Figure 1 below provides an example of a park in Brooklyn adjacent to
a residential area. While numerous trees are visible on the residential
streets, the number of trees in the park far exceeds the number of street
trees. While Figure 1 is not representative for the entire study area, it
illustrates how not including trees in parks and open space presents a
misleading picture of the potential effects of trees on local air quality
in urban areas
Figure 1 Digital orthophoto of a portion of Prospect Park in
Booklyn, New York City adjacent to a residential area.
Source: United States Geological Survey 2006.
The argument could be made that street trees are more relevant than
those in parks and open space since street trees are much closer to the
residential homes. However, Lovasi et al [1] do not make this argument
and instead aggregate all variables at the level of UHFs. This aggregation
does not allow for a determination of street tree density in close
proximity to the residential addresses of asthma cases. If street trees in
close proximity are deemed of greater relevance than trees in parks and
open spaces at greater distances, an individual or street segment level
analysis is required.
A second methodological issue relates to the determination of the
measure of proximity to pollution sources. Relying on the methodology
presented by Maantay [4], Lovasi et al. [1] create uniform distance
buffers around toxic release inventory sites, stationary point sources and
major truck routes and then determine the percentage of each UHF falling
within one or more of these buffers. While the specific distances and
types of sources were derived from the Maantay [4] study, the authors
fail to highlight the many limitations of this approach as detailed at
length in the original study, including the use of single buffer
distances, treating all pollution sources as being similar, and ignoring
cumulative effects from multiple sources. Perhaps more importantly,
Maantay [4] used the individual geocoded residential locations of
asthma hospitalization cases and determined if they fell within a
particular buffer or not. Lovasi et al. [1] instead determine the
prevalence of asthma as a rate based on the number of children within each
UHF and compared this to the percentage of the area of the UHF falling
within one of more of the buffers around the pollution sources, without
considering the proximity of individual cases to pollution sources. The
data aggregation to the level of UHFs represents a very substantial loss
of information. No evidence is presented that aggregation at the level of
UHF areas is justified given the nature of the research question since it
remains unclear at what (spatial) scale the potential effects of street
trees on air quality are expected to occur.
Future research efforts in this area should consider the following
three refinements:
1) Developing a more robust measure of tree density
which includes trees in parks and open spaces. This could be addressed by
using land use or land cover maps supplemented with field sampling as
employed by Nowak et al. [2].
2) Using individual level analysis
instead of aggregation to coarse units. This would involve geocoding
individual address locations of asthma cases and creating individual-level
measures of tree density and proximity to pollution sources, as well as
creating a meaningful sample of non-asthma cases for comparison.
3)
Employing more robust measures of proximity to pollution sources. One
approach to accomplishing this is to use cumulative distribution functions
as employed by Waller et al. [5] and Zandbergen and Chakraborty [6].
While each of these three elements requires considerable effort, they
should contribute to a much improved understanding of the complex
relationships between tree density and asthma prevalence.
Paul Zandbergen
University of New Mexico
Department of Geography
References
1. Lovasi GS, Quin JW, Neckerman KM, Perzanowksi MS Rundle A. 2008.
Children living in areas with more street trees have lower prevalence of
asthma. J Epidemiol Community Health 2008;62:647-649.
2. Nowak DJ, Hoehn III RE, Crane D E, Stevens JC, Walton JT. Assessing
urban forest effects and values, New York City's urban forest. Resource
Bulletin NRS-9. United States Department of Agriculture, Forest Service,
Northern Research Station, 2007.
3. Peper PJ, Mcpherson EG, Simpson JR, Gardner SL, Vargas KE, Xiao Q.
New York City, New York municipal forest resource analysis. Center for
Urban Forest Research, United States Department of Agriculture, Forest
Service, Pacific Southwest Research Station, 2007.
4. Maantay J. Asthma and air pollution in the Bronx: methodological and
data considerations in using GIS for environmental justice and health
research. Health Place 2007;13:32-56
5. Waller LA, Louis TA, Carlin BP. 2007. Environmental justice and
statistical summaries of differences in exposure distributions. J Exposure
Analysis and Env Epi 1999;9:56–65.
6. Zandbergen PA, Chakraborty J. Improving environmental exposure
analysis using cumulative distribution functions and individual geocoding.
Int J Health Geographics 2006;5:23.
For several decades, sociologists have debated how best to measure
socio-economic status, noting that popular measures may not be equally
appropriate for use with women and men. Occupation - the measure used for
the Registrar General's classifcation - is particularly problematic where
gender comparisons are involved. (See [1] for several examples). Gender discrepancies in the meanings of
the SES measu...
For several decades, sociologists have debated how best to measure
socio-economic status, noting that popular measures may not be equally
appropriate for use with women and men. Occupation - the measure used for
the Registrar General's classifcation - is particularly problematic where
gender comparisons are involved. (See [1] for several examples). Gender discrepancies in the meanings of
the SES measure may have contributed to the findings of Tiffin and
colleagues (2005).
References
1. Annandale E, Hunt, K. (eds). 2000. Gender Inequalities in Health. Open University Press,
Buckingham.
The mortalities of the 56 sub-cohorts in the study are not randomly
chosen from a defined population.[1] However the validity of many
statistical analysis methods including regression analysis are based on
the condition that samples are random drawn.[2] Therefore the regression
model fitted this set of data, in my point of view would be better taken
as a description of this set of data rather than pr...
The mortalities of the 56 sub-cohorts in the study are not randomly
chosen from a defined population.[1] However the validity of many
statistical analysis methods including regression analysis are based on
the condition that samples are random drawn.[2] Therefore the regression
model fitted this set of data, in my point of view would be better taken
as a description of this set of data rather than predicting the
relationship between middle age mortality and old age mortality of other
populations chosen from other countries or chosen from the future.
The changes of mortality are greatly affected by the development of
technology, improvement of medical services, changes in people’s life
style, and many other unknown determinants, which may rise in the future,
while it’s impossible to use statistical methods to control such factors,
which are unknown, moreover it would not be possible to draw samples from
the future.
Obviously it is necessary to have a well classification and clear
understanding of the determinants on
an observed relationship from this kind of vital statistics data, before
any prediction can be made.
Reference
1. Janssen, F., Peeters, A, Mackenbach P J, Kunst A E for NEDCOM,
Relation between trends in late middle age mortality and trends in old age
mortality—is there evidence for mortality selection? J Epidemiol Community
Health, 2005. 59.
Whilst not disputing the originality of the review of community-based participatory research by Cook, and published in the August 2008 edition of JECH, it does raise questions about what the agreed minimum methodological requirements are for JECH to describe a review as “systematic”.[1]
Specifically, the Cook review meets none of the criteria which are widely considered to differentiate systemati...
Whilst not disputing the originality of the review of community-based participatory research by Cook, and published in the August 2008 edition of JECH, it does raise questions about what the agreed minimum methodological requirements are for JECH to describe a review as “systematic”.[1]
Specifically, the Cook review meets none of the criteria which are widely considered to differentiate systematic reviews (SRs) from traditional literature reviews (Table 1).[2] Most notably, the Cook study employed a very minimalist search strategy not least as in terms of the electronic searches, only MEDLINE was searched. This is widely considered as inadequate for SRs, particularly public health ones, as many relevant studies are missed.[3, 4] Further, citation follow-ups, hand searching and author contact, all considered important aspects of a sound SR search strategy, are absent.[2, 5] Clear descriptions of study inclusion, data extraction and quality appraisal were similarly lacking. Indeed, quality appraisal seems limited to a description of the various study designs employed by the included studies. No quality appraisal criteria are presented. As table 1 demonstrates, transparency is lacking in many other aspects of the review methodology and it is unclear why studies are included (or indeed excluded). In addition, there is only one author of the review (Cook). This is very rare for a SR, as having a second reviewer to independently check judgements is an important component of the rigour of SRs – minimising bias and enhancing reproducibility. [2, 5] Indeed all other SRs published in JECH since 2000 have had more than two reviewers.
It is therefore highly misleading to describe the Cook literature review as a “systematic” review.[1] Doing so within the pages of JECH undermines the extensive efforts (time, cost, management) put in by actual SR teams, ignores the evidence of the benefits of taking a systematic approach to evidence synthesis,[2-5] and brushes aside the extensive guidelines developed about the good conduct of SRs.[5] The essential methodological elements of SRs (transparency, rigour, reproducibility, quality appraisal), not only differentiate them from traditional reviews, but they are also what gives SRs their high status within the hierarchy of evidence and makes them such a valuable source of research evidence. The Cook review lacks the methodological rigour which characterises a SR and cannot be fairly described as such: it is a traditional literature review and JECH readers should re-consider its findings in this context.
Clare Bambra
References
1. Cook, WK. Integrating research and action: a systematic review of community-based participatory research to address health disparities in environmental and occupational health in the USA. J Epidemiol Comm Heal. 2008, 62: 668-676.
2. Petticrew, M. Systematic reviews from astronomy to zoology: myths and misconceptions. Brit Med J. 2001, 322;98-101.
3. Suarez-Almazor, ME., Belseck, E., Homik, J., et al. Identifying clinical trials in the medical literature with electronic databases: MEDLINE alone is not enough. Control Clin Trials 2000, 21: 476.87.
4. Ogilivie, D., Hamilton, V., Egan, M., et al Systematic reviews of health effects of social interventions: How far should you go? J Epidemiol Comm Heal. 2005, 59: 804-808.
5. Higgins, JPT., Green, S. (eds). Cochrane Handbook for Systematic Reviews of Intervention Studies (v5.0). 2008. Available at: http://www.cochrane-handbook.org/ [accessed 20/08/08]
Personally, I believe that downward socioeconomic trajectory may lead
to poor mental health, which is suggested in the paper.[1]
However, poor mental health at earlier age may positive associate with
mental health problems in later life, and may also have negative effect on
people’s employments. Therefore, poor mental health at earlier age may act
as a confounder, which should be assessed and con...
Personally, I believe that downward socioeconomic trajectory may lead
to poor mental health, which is suggested in the paper.[1]
However, poor mental health at earlier age may positive associate with
mental health problems in later life, and may also have negative effect on
people’s employments. Therefore, poor mental health at earlier age may act
as a confounder, which should be assessed and controlled in the study.
Reference
1. Tiffin P., Pearce M., Parker L. 2005, 'Social mobility over the
lifecourse and self reported mental health at age 50: prospective cohort
study', J Epidemiol Community Health, vol. 59:870-872.
Proposed obesity body mass index correction for self-reported data
may not be appropriate
David Faeh and Matthias Bopp
Institute of Social and Preventive Medicine (ISPM), University of
Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
The study published by Dauphinot et al. in the February issue of the
Journal of Epidemiology and Community Health compared self-reported and
measured BMI in a...
Proposed obesity body mass index correction for self-reported data
may not be appropriate
David Faeh and Matthias Bopp
Institute of Social and Preventive Medicine (ISPM), University of
Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
The study published by Dauphinot et al. in the February issue of the
Journal of Epidemiology and Community Health compared self-reported and
measured BMI in a sample of the general population of Geneva (Switzerland)
and a smaller French sample [1]. In order to account for underestimation
of BMI arising from self-reports, the authors proposed an universal cut-
off of 29.2 kg/m2 instead of 30 kg/m2 for the definition of obesity. In a
previous study, we compared the obesity prevalence based on measured and
self-reported BMI in ten population-based studies carried out in
Switzerland between 1977 and 2003 (N=54,315) [2]. As shown in the figure,
we found considerably higher (1.6 times) prevalence of obesity in the five
studies using measured BMI (black solid line and squares) compared to the
five surveys with self-reports (grey solid line triangles, for details see
[2]). The use of the Dauphinot cut-off in our data suggests that this
could indeed improve estimates of obesity. However, the proposed cut-off
of 29.2 kg/m2 (dotted line, diamonds) may not sufficiently account for
self-report bias and for sex-differences. The latter is supported by
figure 1 in the Dauphinot publication and by the majority of population
studies included in the review of Connor Gorber et. al [3]. According to
our data, a cut-off of 28.6 kg/m2 in men and 28.2 kg/m2 in women (dashed
line, crosses) could be more appropriate. Probably, persons participating
in the Geneva study have a lower body weight than inhabitants of other
Swiss areas. For example, in the CoLaus population based study carried out
in Lausanne, the obesity prevalence was 15.6% (vs 12.6% in the Geneva
population).[1, 4] Furthermore the validation with the French study may be
problematic, since the BMI underestimation mainly arose from an
overestimation of height in that population, while it predominantly
resulted from an underestimation of weight in the sample from Geneva.[1]
Finally, it is questionable whether an ubiquitary valid BMI threshold can
be derived. This is also suggested by the rather large differences between
studies (see [3]) and by the fact that rounding of height strongly depends
on cultural affiliation.[5] Nevertheless, all lines derived from linear
regression are quite parallel (particularly in men) suggesting that a
single threshold for self-reported BMI works independently of time.
Figure. Mean (%) prevalence of obesity with trend line (obteined with
linear regression) by study.
References
1. Dauphinot V, Wolff H, Naudin F, et al. New obesity body mass index
threshold for self-reported data. J Epidemiol Community Health
2009;63(2):128-32.
2. Faeh D, Marques-Vidal P, Chiolero A, et al. Obesity in Switzerland: do
estimates depend on how body mass index has been assessed? Swiss Med Wkly
2008;138(13-14):204-10.
3. Connor Gorber S, Tremblay M, Moher D, et al. A comparison of direct vs.
self-report measures for assessing height, weight and body mass index: a
systematic review. Obes Rev 2007;8(4):307-26.
4. Firmann M, Mayor V, Vidal PM, et al. The CoLaus study: a population-
based study to investigate the epidemiology and genetic determinants of
cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord
2008;8:6.
5. Bopp M, Faeh D. Who gives me five? Rounding preference for self
reported height depends on language. BMJ 2008;337(a2950):1463.
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Proposed obesity body mass index correction for self-reported data may not be appropriate
David Faeh and Matthias Bopp
Institute of Social and Preventive Medicine (ISPM), University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
The study published by Dauphinot et al. in the February issue of the Journal of Epidemiology and Community Health compared self-reported and measured BMI in a...
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