The study showed a great way to measure socio-economic position based
on housing condition and income, and also showed a strong association
between socio-economic status and cardiovascular disease mortality among
middle age people.[1]
However, if we remove the effect of smoking, diet pattern, and
physical activity level, this association would likely to become weaker,
or if the mortality o...
The study showed a great way to measure socio-economic position based
on housing condition and income, and also showed a strong association
between socio-economic status and cardiovascular disease mortality among
middle age people.[1]
However, if we remove the effect of smoking, diet pattern, and
physical activity level, this association would likely to become weaker,
or if the mortality of interest is among old age people, the association
may be diluted by other risk factors, which are not related to socio-economic status.
In terms of impact on health, “Socio-economic status” may influence a
cluster of risk factors, especially, behavioural and psychological
factors, that affect people’s health and are hard to measure. Moreover
socio-economic status itself may be considered as a cluster of variables,
and some of these are highly correlated with others. A measurement of
income may reflect other social variables such as housing condition,
access to health care…However the relationship among these “social
variables” or the strength of their relationships may vary greatly in
different societies and over different periods. Therefore it may be
necessary to identify and measure the key “social variables” that strongly
associate with risk factors in the community instead of measuring “the
real socio-economic status”.
Reference:
1. Claussen, B., G. Davey Smith, and D. Thelle, Impact of childhood
and adulthood socioeconomic position on cause specific mortality: the Oslo
Mortality Study. J Epidemiol Community Health, 2003. 57(1): p.40-5.
In their recent article, Jousilahti et al.[1] compare mortality rates
among participants and non-participants of population surveys on health in
Finland, and report higher mortality rates among male and female non-participants. Since information regarding non-response is usually limited
to a few background characteristics available from population
registers,[2] the study by Jousilahti et al. is a valuab...
In their recent article, Jousilahti et al.[1] compare mortality rates
among participants and non-participants of population surveys on health in
Finland, and report higher mortality rates among male and female non-participants. Since information regarding non-response is usually limited
to a few background characteristics available from population
registers,[2] the study by Jousilahti et al. is a valuable contribution to
our knowledge on possible bias in population health studies due to non-
participation. However relevant and interesting their effort, we object to
some of the conclusions reached by the authors.
In particular, we take issue with the authors’ fourth key point on
page 313: “Differences in mortality show that health behaviour and health
status substantially differ between people who participate and who do not
participate in health surveys.” This point is drawn from page 314, second
paragraph: “The observed difference in mortality … is caused by
differences … in health status at baseline”, and a bit further “A
difference in health status and existing disease at baseline also seems to
be evident”. We think that this conclusion could not be substantiated by
the results from their study simply because they do not have (collected)
any information on health status at baseline of non-participants.
‘Evidence’ on health behaviour is also indirect, assessed by data on
‘mortality’ attributed by the authors to smoking and alcohol consumption.
What is presented as evident facts and key outcomes of the study is a
speculation on part of the authors, inferred as a possible explanation for
the observed differences in mortality between participants and non-
participants. That this inference may be flawed is illustrated in our
Dutch prospective cohort study.[3] We found the paradoxical results that
although mortality rates at follow up were lower among participants, as in
the Finish study, coronary heart disease was found to be more prevalent at
baseline for participants compared with non-participants.
Another, yet minor point is their contradictory statements regarding
the potential impact of participation rate, in the last two paragraphs of
the article. They concluded that “… low participation rate may
considerably bias the results of population based health surveys”. We
agree that one way to reduce bias is to increase the response rate. But
this is, obviously, not a general rule. Stang and Jockel[4] for example,
showed that under certain conditions studies with low response rates may
be less biased than studies with high response rates. Austin et al.[5]
showed that when subjects’ participation was related only to either the
exposure or to the outcome of the study the odds ratio was not biased. So,
besides trying to increase the response rates it remains necessary to
compare respondents with non-respondents on both exposure and outcome[6]
to determine whether non-response is random and, hence, ignorable.
MY Veenstra,
Addiction Research Institute, Erasmus University Rotterdam, Rotterdam, the
Netherlands
PHHM Lemmens
Department of Health Care Studies, Division Medical Sociology, Care and
Public Health research Institute, University of Maastricht, Maastricht,
the Netherlands
Correspondence to:
Marja Y. Veenstra
Department of Health Care Studies
Division Medical Sociology
P.O. box 616
6200 MD Maastricht
Netherlands
Telephone: +31-(0)43-388 22 23
Fax: +31-(0)43-388 41 69
email: m.veenstra@zw.unimaas.nl
References
[1] Jousilahti P, Salomaa V, Kuulasmaa K, Niemela M, Vartiainen E. Total
and cause specific mortality among participants and non-participants of
population based health surveys: a comprehensive follow up of 54 372
Finnish men and women. J Epidemiol Community Health 2005;59:310-5.
[2] Stang A. Nonresponse research--an underdeveloped field in
epidemiology. Eur J Epidemiol 2003;18:929-31.
[3] Veenstra MY, Friesema IHM, Zwietering PJ, Garretsen HFL, Knottnerus
JA, Lemmens PHHM. Lower registered prevalence of heart disease among
survey nonrespondents, but higher mortality risk during follow-up for the
middle aged general population. Accepted for publication in J Clin
Epidemiol.
[4] Stang A, Jockel KH. Studies with low response proportions may be less
biased than studies with high response proportions. Am J Epidemiol
2004;159:204-10.
[5] Austin MA, Criqui MH, Barrett Connor E, Holdbrook MJ. The effect of
response bias on the odds ratio. Am J Epidemiol 1981;114:137-43.
Stanistreet, Bambra, and Scott-Samuel’s article[1] on the connection
between patriarchy and higher male mortality rates addresses issues which
we are currently investigating, although with a very different theoretical
framework. The universality of sex differences in mortality rates begs for
a valid explanation that can account for both similarities and differences
across diverse human cultures. We belie...
Stanistreet, Bambra, and Scott-Samuel’s article[1] on the connection
between patriarchy and higher male mortality rates addresses issues which
we are currently investigating, although with a very different theoretical
framework. The universality of sex differences in mortality rates begs for
a valid explanation that can account for both similarities and differences
across diverse human cultures. We believe that human mortality patterns
result from the interaction of features shaped by our evolutionary history
of sexual selection and our developmental environment.[2] Briefly, because
females (and especially human women) make a greater parental investment in
offspring, they are more selective in choosing mating partners.[3] Males
generally compete with each other for reproductive access to females3 and
in humans this includes competing for social status and material
resources, features that are much more important for males than females in
attracting mates for across cultures.[4] Because humans are a mildly
polygynous species (as evidenced by the degree of our physical sexual
dimorphism5), throughout our evolutionary history some men have been able
to obtain multiple partners at the expense of others. This skew in
reproductive success creates an incentive for behavioural and physiological
strategies that carry higher risks of early mortality in men than in
women.
We believe that the Male to Female Mortality Ratio (M:F MR, the ratio
of male to female mortality rates) may be a useful indicator of systematic
characteristics of cultures, such as the severity of male-male
competition, levels of political instability, and/or inequality in social
status and control of resources.[6] In previous work, we found that the
overall M:F MR across nations was inversely correlated with women's
empowerment, as measured by the UN Gender Empowerment Measure, r(44) = -.654, p<.001. A stepwise linear regression including the GDP
indicator from Stanistreet et al. found that gender empowerment accounted
for 51% of the variance in M:F MR levels across nations, but did not find
a unique contribution of GDP, F(1,39) = 41.16, p<.001. The UN Gender
Empowerment Measure is a composite of the percentage of; members of
parliament, legislators, senior officials and managers, professional and
technical workers who are female; and the ratio of estimated female to
male earned income. Although Stanistreet et al., note concerns with the
reliability of some of these measures, error would most likely decrease
the strength of the relationship found between gender empowerment and the
M:F MR. We also find the inference of a causal relationship between social
attributes and mortality solely from consistencies in mortality patterns
to be somewhat tautological.
Our theoretical framework does not exclude a feminist perspective on
or provide a moral justification for inequality, but it does provide an
ultimate theoretical explanation for the widespread existence of both
patriarchy and sex differences in mortality. Societies differ in the
degree to which resources and power are held by elite men (which is
probably related to various ecological and historical factors), and the
degree to which the majority of women and men are dominated by elite men
affects the intensity of male-male competition for this power. We have
demonstrated elsewhere that the degree of socio-economic inequality both
within and across nations is related to the M:F MR6,7. Our approach
highlights interactions between adaptations developed during human
evolutionary history and current environmental factors, so we can
supercede the unproductive debates over the respective roles of biology
and culture. Within and cross national differences in death rates
demonstrate human flexibility and the potential for change as well as the
importance of illuminating the causal framework for mortality patterns.
References
1 Stanistreet D, Bambra C, Scott-Samuel A. Is patriarchy the source
of men’s higher mortality? J. Epidemiol. Community Health 2005;59:873-876.
2 Kruger D, Nesse R. Sexual selection and the Male:Female Mortality
Ratio. Evolutionary Psychology 2004;2:66-77.
3 Trivers R. Parental investment and sexual selection. In: B.
Campbell B ed. Sexual selection and the descent of man: 1871-1971 Chicago:
Aldine, 1972:136-179.
4 Buss D. Sex difference in human mate preferences: Evolutionary
hypotheses tested in 37 cultures. Beh Brain Sci 1989; 12:1-49.
5 Leutenegger W, Kelley J. Relationship of sexual dimorphism in
canine size and body size to social, behavioral, and ecological correlates
in anthropoid primates. Primates 1977;18:117-136.
6 Kruger D, Nesse R. Economic transition, male competition, and sex
differences in mortality rates. Manuscript submitted for publication.
7 Kruger D, Nesse R. Inequalities in money, mate value, and
mortality. Manuscript in preparation.
As the percentages in table 1 are referred to PREVALENCE,[1] the
ratios of these percentages are PREVALENCE RATIO, and are unlikely to
equal to RELATIVE RISK: INCIDENCE RATIO. In the method part of the
articles the percentages are explained as the proportion of “patient
years”,[1] but not prevalence.
As the percentages in table 1 are referred to PREVALENCE,[1] the
ratios of these percentages are PREVALENCE RATIO, and are unlikely to
equal to RELATIVE RISK: INCIDENCE RATIO. In the method part of the
articles the percentages are explained as the proportion of “patient
years”,[1] but not prevalence.
The data used in the study[1] are based on patient records. Cases
counted in the studies which would not include patients who do not refer
to a GP for the two diseases of interest. Therefore the subjects included
in the study were from a “special” subset of the patient population, and
caution is needed to generalise the result.
The diagnosis of the two diseases may be associated: GPs may be more
likely to diagnosis a case as psychiatric given the substance abuse
history. GPs may be also more likely to discover that the patient has
substance abuse behaviour, because they may pay more attention to that
particular problem.
Reference
1. Frisher, M., et al., Substance misuse and psychiatric illness:
prospective observational study using the general practice research
database. J Epidemiol Community Health, 2005. 59(10): p. 847-50.
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.
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.
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.
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.
Richard Morris and Peter Whincup compare their results based on the
British Regional Heart Study (BRHS) to our results based on the Health
Surveys for England and Scotland. The degree of similarity is encouraging
(we particularly agree with them about the role of genetics, a suggestion
we included only for the sake of completeness). We can think of two
possible reasons for the different conclusions with...
Richard Morris and Peter Whincup compare their results based on the
British Regional Heart Study (BRHS) to our results based on the Health
Surveys for England and Scotland. The degree of similarity is encouraging
(we particularly agree with them about the role of genetics, a suggestion
we included only for the sake of completeness). We can think of two
possible reasons for the different conclusions with respect to the role of
risk factors. It is possible that the situation has changed between BRHS
baseline in 1978-80 and the Health Surveys’ data from 1998. More likely
however is the difference in the nature of the samples. The BRHS sample
was designed to represent the full range of water hardness within Britain,
while the Health Surveys are representative samples of the populations of
their respective countries. Further, BRHS respondents were drawn from
medium-sized towns, thereby excluding all residents of rural areas, large
cities and metropolitan conurbations. The whole of Scotland, for example,
was represented by persons drawn from the patient registers of the one
collaborating general practice in each of three Scottish towns: Falkirk,
Ayr and Dunfermline. The BRHS Scottish sample, in consequence, does not
include respondents from Glasgow, Edinburgh, Dundee, Aberdeen, nor from
the Southern Uplands, nor the Highlands and Islands. Such differences in
the nature of the study samples means that any comparison of results
needs to be made with great caution.
Shaw et al. find that during a period of substantial decline in child
mortality, relative differences among socioeconomic classes increased,
while absolute differences remained stable (actually declining very
slightly). Such findings are entirely to be expected.
Socioeconomic mortality differences, measured in relative terms, tend
almost invariably to increase during times of declining mortality...
Shaw et al. find that during a period of substantial decline in child
mortality, relative differences among socioeconomic classes increased,
while absolute differences remained stable (actually declining very
slightly). Such findings are entirely to be expected.
Socioeconomic mortality differences, measured in relative terms, tend
almost invariably to increase during times of declining mortality. The
tendency is a consequence of the fact that disadvantaged groups comprise
larger proportions of the part of the population that is very susceptible
to some adverse outcome than they comprise of the part of the population
that is only somewhat susceptible to the outcome. Thus, as effective
measures to reduce mortality increasingly limit avoidable mortality to
only the most susceptible segments of the population, disadvantaged groups
comprise a larger proportion of those continuing to experience avoidable
mortality. Correspondingly, the ratio of the mortality rate of a
disadvantaged group to that of an advantaged group will increase.
This pattern can be observed in almost every data set that allows one
to examine the demographic makeup of the population falling below various
points in a continuum. Published U.S. income data provide a ready
illustration. In 2004, the black poverty rate (24.7 percent) was 2.3
times the white poverty rate (10.8 percent); but the black rate of falling
below 50 percent of the poverty line (11.7 percent) was 2.7 times the
white rate (4.4 percent).[1] Suppose, then, that a program to reduce
poverty enabled everyone with incomes between the poverty line and 50
percent of the poverty line to escape poverty. The ratio of the black
poverty rate to the white poverty rate would increase from 2.3 to 2.7.
Anyone inclined to regard the increase in the black-white poverty
ratio as reflecting a true worsening of the status of U.S. blacks compared
with whites should examine the opposite outcome -- the avoidance of
poverty. For that same across-the-board decline in poverty would reduce
the difference between black and whites rates of avoiding poverty --
changing the black-white ratio of avoiding poverty from .84 (75.3/89.2)
to .92 (88.3/95.6). But the pattern is not unique to income
distributions. Rather, with virtually every set of dichotomous outcomes
disparately affecting two groups, the decline in the prevalence of one
outcome tends both to increase the relative difference in experiencing
the outcome and to reduce the relative difference in avoiding the outcome.
And, if we accept changes in relative differences as reflecting meaningful
changes in quality, whether we regard inequality to be increasing or
decreasing will turn on whether we examine the unfavourable or the
favourable outcome.[2,3]
On the other hand, absolute differences are the same regardless of
whether one examines the favourable or the unfavourable outcome.
Nevertheless, absolute differences fail to provide a satisfactory means of
evaluating changes in the inequality of mortality rates over time because,
like relative differences, absolute differences also change when there
occurs an across-the-board change in the prevalence of an outcome. When
the rates at which two groups experience the unfavourable outcome are close
to 100 percent, the absolute difference will be close to zero, as it also
will be when the rates of experiencing the favourable outcome are close to
100 percent. But as the prevalence of an outcome declines from 100
percent, the absolute difference will rise for a time and then decrease on
its way back towards zero. At the points on the distribution where we
usually examine mortality, the absolute difference will tend to decline as
the overall prevalence of the unfavourable outcome declines. This is what
we observe in the case of the hypothetical across-the-board reduction in
poverty described above, where the absolute difference between black and
white poverty rates would fall from 15.5 to 3.5.
Thus both the increasing mortality disparity measured in relative
terms and declining disparity measured in absolute terms are to be
expected in times of declining mortality. The patterns may not occur in
every case, particularly when rates approach an irreducible minimum.[3]
But the tendency is a strong one, and without recognizing it, one cannot
draw meaningful conclusions about whether inequalities are increasing or
decreasing over time.
References
[1] U.S. Census Bureau, Current Population Survey, 2004 Annual Social
and Economic Supplement, Table POV01: "Age and Sex of All People, Family
Members and Unrelated Individuals Iterated by Income-to-Poverty Ratio
Below 100% of Poverty -- White Alone or in Combination (A.O.I.C.)," May
19, 2005. http://pubdb3.census.gov/macro/032005/pov/new01_000.htm
Dear Editor,
The study showed a great way to measure socio-economic position based on housing condition and income, and also showed a strong association between socio-economic status and cardiovascular disease mortality among middle age people.[1]
However, if we remove the effect of smoking, diet pattern, and physical activity level, this association would likely to become weaker, or if the mortality o...
Dear Editor,
In their recent article, Jousilahti et al.[1] compare mortality rates among participants and non-participants of population surveys on health in Finland, and report higher mortality rates among male and female non-participants. Since information regarding non-response is usually limited to a few background characteristics available from population registers,[2] the study by Jousilahti et al. is a valuab...
Dear Editor,
Stanistreet, Bambra, and Scott-Samuel’s article[1] on the connection between patriarchy and higher male mortality rates addresses issues which we are currently investigating, although with a very different theoretical framework. The universality of sex differences in mortality rates begs for a valid explanation that can account for both similarities and differences across diverse human cultures. We belie...
Dear Editor,
As the percentages in table 1 are referred to PREVALENCE,[1] the ratios of these percentages are PREVALENCE RATIO, and are unlikely to equal to RELATIVE RISK: INCIDENCE RATIO. In the method part of the articles the percentages are explained as the proportion of “patient years”,[1] but not prevalence.
The dat...
Dear Editor,
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...
Dear Editor,
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...
Dear Editor,
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...
Dear Editor,
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...
Dear Editor
Richard Morris and Peter Whincup compare their results based on the British Regional Heart Study (BRHS) to our results based on the Health Surveys for England and Scotland. The degree of similarity is encouraging (we particularly agree with them about the role of genetics, a suggestion we included only for the sake of completeness). We can think of two possible reasons for the different conclusions with...
Dear Editor,
Shaw et al. find that during a period of substantial decline in child mortality, relative differences among socioeconomic classes increased, while absolute differences remained stable (actually declining very slightly). Such findings are entirely to be expected.
Socioeconomic mortality differences, measured in relative terms, tend almost invariably to increase during times of declining mortality...
Pages