Association of Mild Grade Anemia with Cognitive, Functional, Mood, and Quality of Life
Outcomes in the Elderly: the “Health and Anemia” Study
Reply [1st] to comments from Prof/Dr U. Thieme (Reviewer # 1)
Preliminary considerations
Thanks for the large harvest of considerations and suggestions.
Together with the present manuscript, we submitted a second one to PLoSONE on
the ”association of mild grade anemia with morbidity, hospitalization, and mortality”
investigated in the same population of elderly persons. Incorrectly assuming that they
would be reviewed together, we tried to avoid boring repetitions. As a result we
probably failed to report some informantion and considerations which could possibly
have addressed some of the issues raised.
In the submitted manuscript we used the word “participants” to designate both the
717 individuals who participated in the present study and the 4,501 individuals with
blood tests available (the participants of the prevalence study). In the revised manuscript,
we have now (1) left the word “participant” solely to designate the 717 participants in
the present study, and (2) substituted the word “participant” referring to the 4,501
individuals with: “Among the 4,068 eligible individuals ...” (first line of the abstract
Methodology and Findings), “All elderly individuals with blood test results ...” (the
fourth line from the bottom, on page 4 of the original manuscript), and “Among
individuals with blood tests available ... “ (first line of the Results section).
As highlighted in the cover letter to the Editor as well as in the Introduction, aim of
the study was to investigate the association of mild grade anemia with cognitive
performance, functional status, mood and quality of life (QoL). Mild grade anemia is a
common laboratory finding in the elderly population usually overlooked by the physician
as not having any clinical consequences and hence not worth further clinical
investigation and treatment. Furthermore, most mild anemic elderly individuals are
unaware of their condition. Thus, the signal we expected to detect, if present, would
likely have been of a weaker magnitude than the background noise due to those more
serious medical conditions already known to be associated with the outcomes
investigated.
In general, we are not convinced that the mere comparison of a variable between
two groups, save for dramatic but rather unlikely results, would be sufficient to establish
the presence of a selection bias. In fact, a bias is present if the association between a
factor (mild anemia, in this case) and the outcome (for example cognitive performance)
differs between “the study sample and the target population”. A difference on a variable,
unless systematic, is not sufficient to determine the presence of a selection bias,
especially in studies where sample sizes are large enough to show even meaningless
differences as statistically significant. The fact that a variable is unbalanced between the
study sample and the target population does not necessarily indicate a bias in the
association between the factor and the outcome. Moreover, even if modest differences
between groups were present, one should be able to figure out a plausible way in which
these modest differences could have affected the results. We do not see any plausible
explanation for how a difference (for example in the proportion of women) could affect
the association between being mild anemic and the outcomes studied. Trying to put all
the elements together, a good, plausible hypothesis should be able to explain why mild
anemic women (mostly considering themselves non anemic!) scoring better (if they had
been tested) than mild anemic participants on an outcome measure AND, at the same
time, elderly women free of anemia scoring worse (if they had been tested) than non
anemic participants on the same outcome would be more likely to decline to participate,
not be traced, or already be deceased at the time of the contact than participants.
We agree that the quality and quantity of the data provided must be complete
enough to make the study results sound, repeatable, convincing (see replies below).
However, we think that the answer to the core comparison requested by the Reviewer is
already present in the sentence on page 9 (second paragraph) of the results section (see
below).
“General Considerations:
The authors provide data from a large cross-sectional, presumably population-based
study of elderly people aged 65 to 84 years residing in an Italian municipality. The aim
of the study is to evaluate the relationship between mild anemia and a variety of health
outcomes including quality of life, activities of daily living, cognitive function and
mood.
It is well known that the cross-sectional design restricts conclusions to statistical
associations rather than causal inference. However, the study is of relevance for three
reasons: the sample - elderly, independently living people - is an important patient group
in primary care, the variables of interest (clinical endpoints) are clinically relevant, and
the population-base may reflect the distribution of risk factors assessed in the
community.
[1] Sample Selection:
The data provided on the recruitment process of participants are incomplete, and
data on people not entering the study for a variety of reasons are lacking completely.
This fact and the possibility of selection bias is the major drawback of the manuscript. It
offsets the potential strength of the study design, which is the population-base.
Of course, selection bias is a threat to virtually all studies that include humans as
participants. If so, it is essential to present a comparison of the selected (and analysed)
group of participants with the non-selected / excluded sample in terms of known
variables, i.e. sociodemografics, current diseases, cause of consultation of the physician
etc. This comparison is the only tool that allows a judgement on the differences between
both groups, participants and non-participants. Without such a comparison, a decision
about the representativeness of the study sample and hence the generalizability of the
study findings is impossible.
The minimum that should be provided is a comparison by age, sex and some
measure of health status or disease severity with a description of obvious differences
between the study sample and the target population. Of course, a discussion of the
impact of these differences on the interpretation of the overall results should follow.”
Having made use, in this part of the Health and Anemia Study, of exclusion criteria
(see pages 4-5 of the Methods), of course we carried out a selection of the elderly
population. In the preliminary considerations we have already anticipated the main
reasons for this choice (this point will be addressed in further detail later). Thus,
speaking of representativeness, the present study results should be generalizable “only”
to the elderly population not bearing those selection criteria (i.e. the target population),
that is to the considerable 90.4% of the general population of elderly. And what we
wanted was to investigate whether mild grade anemia was associated with the clinical
outcomes chosen precisely in this elderly population.
Before responding to the specific issues raised, it is important to keep in mind that,
as reported in the manuscript,
1. we introduced exclusion criteria;
2. mild grade anemia was significantly associated with several pathological conditions.
3. we included all eligible consenting individuals with a past or present diagnosis of
cancer;
4. we included all eligible consenting individuals with mild anemia and only a random
sample of the eligible consenting non anemic controls;
The “true excluded” (i.e. anemic and non anemic individuals meeting exclusion
criteria) of course cannot be part of the “target population” by definition (but apart from
the fact that it would be mistaken, what would be the purpose or the sense of comparing
a selected group of individuals with the remaining group of individuals from which the
selected were excluded?). To avoid any misunderstanding we have now re-arranged
Figure 1 to also show in the flow chart the group of the “eligible” to the study which in
fact represents the “target population” (n = 4068). Thus, following the Reviewer’s
suggestion, we compared the 717 elderly individuals included in the study to the 3351
eligible individuals not included for the following variables: age, sex, education,
myocardial infarction, angina, hypertension, heart failure, diabetes, respiratory failure,
neurologic disorders, and comorbid disease severity. Obviously the two groups could not
be compared for renal insufficiency (because individuals with renal insufficiency were
excluded) and for history of past or present diagnosis of cancer (because, as already
mentioned above, individuals with a history of cancer where deliberately oversampled).
For this same reason, the presence of a history of cancer was not taken into consideration
in calculating the comorbid disease severity score.
The table below shows the results of the analysis. Modest but significant
differences between groups were found for sex, education and history of myocardial
infarction. Individuals not included comprised a higher proportion of women (+ 8.7%)
and a lower proportion of individuals with a history of myocardial infarction (- 1.8%)
and were on average less educated (- 0.5 years) than the individuals included. These
variables are associated, since women are less affected than men by ischemic heart
diseases and, for the generation under investigation, less educated than men (in the
present population, on the average, men: 8.9 years and women: 7.0 years).
As expected, these results were already inferable from those reported in the
submitted manuscript, where, on page 9, individuals who refused the interview were
compared to those who accepted:
“Individuals who refused the interview (n = 964) comprised a higher proportion of
women (+13.8%) and of persons who lived alone (+11.8%), and were on the average
older (+2.4 years) and less educated (-1.9 years) than those who accepted (n = 3,050).
All the above features are of course highly associated with each other. “
This similarity of results was rather predictable since only the individuals who
refused this part of the study could have introduced a deviation from the results of the
eligible. In fact all the anemic elderly individuals were eligible while among the non
anemic it is very unlikely that the process of randomization could have introduced a
deviation (see next point). Thus the results reported below were already anticipated by
those even more marked reported in the original manuscript.
Variable
Mean age (SD), years
Included
Not included
p values
73.3 (5.3)
73.3 (5.1)
53.6
62.3
8.2 (3.9)
7.7 (3.8)
.0025
Myocardial infarction, %
6.3
4.4
.0402
Angina, %
6.4
5.0
.1194
Hypertension, %
54.5
52.5
.3288
Heart failure, %
4.8
5.4
.4904
Diabetes, %
9.2
9.0
.8905
Respiratory failure, %
6.4
5.3
.2353
Neurologic diseases, %
2.7
2.4
.7195
1.21 (0.16)
1.20 (0.16)
.0966
Women, %
Mean education (SD), years
Mean comorbid disease severity (SD) *
* Not taking into consideration oncological status.
.9364
< .0001
In the text we have now substituted the above reported sentence with a new one
reporting the results of the comparison suggested by the Reviewer:
“To control for possible differences on demographic and clinical characteristics,
elderly individuals included (n = 717) were compared to those not included (n = 3351)
for age, sex, education, myocardial infarction, angina, hypertension, heart failure,
diabetes respiratory failure, neurologic disorders, and comorbid disease severity.
Individuals not included comprised a higher proportion of women (+ 8.7%) and a lower
proportion of individuals with a history of myocardial infarction (- 1.8%) and were on
average less educated (- 0.5 years) than the individuals included. As women are less
affected by ischemic heart diseases and, for the generation under investigation, less
educated than men, these variables are associated.”
In almost any epidemiological study the group of individuals who refuses to
participate shows some modest but significant differences with the group of the
included. In studies in the elderly population, those who refuse are usually older, in
greater proportion women, and less educated. If one is eager to hypothesize the source of
these small differences, what most likely explains those found in the present study is that
the elderly persons living alone (5% more frequent among the not included), who of
course in this age class are mostly women, more frequently refused the home interview
(as often happens in door-to-door studies), were they mild anemic or non anemic (!). We
do not see how these (small) differences could have introduced a selection bias, that is a
bias capable of selecting a sample in which the associations between mild anemia status
and the various different outcomes investigated would be different from those in the
target population. Even more so considering that in the multivariable analyses we had
already adjusted all the results for these as well as many other a priori chosen potential
confounders.
In any case, considering the importance of the issue raised by the Reviewer, we have
added this sentence to the “limitations” part of the Discussion:
“Although the possible influence of a non-response bias on the associations studied
cannot be excluded, this seems rather improbable considering the condition examined
(an anemia of mild grade of which most of the elderly persons were unaware) together
with the nature of the dependent variables studied.”
[2] “Although of minor importance, I would also appreciate a comparison of all
eligible non-anemic patients with the random sample of non-anemics that was actually
chosen as a comparison group. It should be tested and than stated in the results section,
that the randomization was successful and the random sample reasonably represents all
non-anemics that were eligible. I would also suggest a sentence on the process of
randomization, as it is standard in the reporting of randomized controlled trials (for
example: The random sample was drawn during the recruitment phase by using central
computer randomization / by using block randomization with blocks of variable length
etc.).”
Representing 76.3% (547/717) and the 97.2% (3256/3351) of respectively the
previous “study sample” and “target population”, comparing the two groups suggested
by the Reviewer (all eligible non anemic individuals versus the randomized non anemic
sample) generates very similar results: -8.5 % of women and an average + 0.7% years of
education (the difference for myocardial infarction is no longer significant).
If the Reviewer was instead curious to know whether “the randomization was
successful”, then the groups to compare are the “randomized” versus the “non
randomized”, excluding the non anemic individuals with a past or present history of
cancer who cannot be included in the analyses because they did not pass through the
randomization process. In this case, all the variables investigated did not significantly
differ between groups (see Table below).
Under “Statistical analysis” we have added the sentence:
“Simple randomization was used to select those among the non anemic elderly
individuals to include in the study sample.”
Variable
Mean age (SD), years
Randomized
Not
randomized
p values
72.4 (5.2)
72.5 (4.8)
.7120
53.9
58.7
.1199
8.5 (4.0)
8.3 (3.8)
.4069
Myocardial infarction, %
6.1
4.5
.2329
Angina, %
6.8
4.5
.0973
Women, %
Mean education (SD), years
Hypertension, %
49.8
51.5
.6020
Heart failure, %
4.4
4.7
.8687
Diabetes, %
7.5
7.7
.8999
Respiratory failure, %
5.1
4.3
.5264
Neurologic diseases, %
1.0
2.3
.1252
1.20 (0.16)
1.19 (0.15)
.3172
Mean comorbid disease severity (SD) *
* Not taking into consideration oncological status.
[3] “Several clues from the data suggest that selection bias may be present. I will
provide three examples:
1. The flow chart of the study shows that approx. 23% of anemic persons (79 / 344)
were excluded and approx. 4% not found, resulting in non-participation for both reasons
in one of four persons. In non-anemic people only approx. 9% (354 / 4,157) and approx.
1% were excluded or not found (39 / 4,157), totaling in less than one in ten.
2. According to the flow chart, overall approx. 70% (2,858 / 4,157) of non-anemic
participants were eligible for randomisation to the control sample in contrast to only
50% (170 / 344) in the group of anemic participants.
3. Table 1 shows that in the group of anemic participants the prevalence of cancer is
much lower than in the non-anemic group (13,5% vs. 46,4%). This is striking, as an
association between anemia and cancer is established, and a higher incidence of cancer
in patients with baseline anemia has been shown.”
On the contrary, the “examples” provided, if anything, suggest that a selection bias
may not be present.
Example 1. Individuals who met exclusion criteria, being “not eligible” (not: “nonparticipant”) are consequently not part of the “target population”. Thus the difference
reported by the Reviewer cannot be used as an index for the potential presence of a
selection bias, but instead simply reflects the expected results of the differential selection
consequent to the association between mild grade anemia and several pathological
conditions (a result fully reported in the other paper submitted to PLoSONE but still
well reflected in the sample described in Table 1, as reported on page 9 of the
manuscript: “Characteristics of mild anemic and non anemic individuals included in the
impact study (Table 1) reflect those of the larger groups from which participants were
drawn, except for the higher prevalence of cancer in the non anemic group and, in both
groups, the absence of renal failure or the drop in prevalence of those disorders like
neurologic diseases because of the inclusion/exclusion criteria adopted.”).
The very few, and here not decisive, individuals “not found” (i.e. persons who
accepted the interview but could not be traced at their residence) cannot be summed with
the excluded, but rather, in a conservative calculation of the respose rate (75%), with the
“refused”.
Example 2. This is practically the other side of the coin of example 1. In fact, being
that the percentages of the “refused and not found” are almost the same in the two
groups (21.2% among the anemic and 22.7% among the non anemic elderly individuals),
evidently the difference reported by the Reviewer depends almost entirely on the
difference between the individuals excluded (i.e. “not eligible”) in the two groups and,
consequently, is once again the same difference reported in example 1. In other words,
here again the difference pointed out is the same as in example 1 and depends on the
individuals excluded. Thus, as in example 1, example 2 as well does not only not
represent a suggestion for the presence of a selection bias, but more properly cannot be
even considered as a possible index of selection bias.
A useful comparison actually offering a suggestion on the possible presence of a
selection bias was already concisely and cogently reported on page 9 of the manuscript:
“A similar percentage of anemic (21.9%) and non anemic (23.8%) individuals refused
the interview, while 3,104 elderly persons accepted to participate (response rate =
76.3%).” (page 9)
Example 3. Here as in another remark in Part B of the Review (see below), there
must have been some misreading. In fact, in the manuscript it was clearly written:
on page 5: “Since secondary aim of the study was to examine the association
between anemia and cancer, all eligible non anemic individuals with a past or present
diagnosis of cancer were also included in this part of the study.”;
on page 8: “Due to the inclusion criteria, all analyses had the diagnosis of cancer as
a covariate.”;
on page 9: “Characteristics of mild anemic and non anemic individuals included in
the impact study (Table 1) reflect those of the larger groups from which participants
were drawn, EXCEPT FOR the higher prevalence of cancer in the non anemic group and
…”;
in the footnote of Table 1: [Differences or odds ratios (95%) CI] Adjusted for
oncological status.
Thus: (a) individuals with a past or present diagnosis of cancer did not pass through
the randomization process; (b) the high percentage of past or present diagnosis of cancer
among non anemic individuals was the pursued result of the inclusion criteria and was
consequently always taken into consideration in all the statistical analyses performed
(see also below) (c) evidently the percentages reported in Table 1 cannot represent the
prevalences of cancer (as already explained on page 9: “Characteristics of mild anemic
and non anemic individuals included in the impact study (Table 1) reflect those of the
larger groups from which participants were drawn, except for the higher prevalence of
cancer in the non anemic group”), but rather the number of individuals (by necessity
higher among the non anemic) with a past or present diagnosis of cancer that consented
to participate; (d) the true prevalence of past or present diagnosis of cancer in the
population studied (n = 4501) (reported in the other submitted paper to PLoSONE
regarding precisely the association of mild anemia with morbidity) is 11.2% among non
anemic and 12.5% among the mild anemic.
Thus, also example 3 has nothing to do with “selection bias”.
[4a] “One possible reason for the above mentioned observations is the way people
were included into the study.”
As shown in the response to the previous issue, “the above mentioned
observations”, being ill-based, cannot “suggest that a selection bias may be present”.
Summarizing the previous answer: (1) the excluded individuals are not part of the
“target population” and thus cannot determine a selection bias; (2) taking into account
the inclusion process (individuals with a past or present diagnosis of cancer did not pass
through the randomization process) the percentages of anemic and non anemic persons
with a past or present diagnosis of cancer are those expected in this type of population.
Even though not pertinent to the discussion on the possible presence of a selection bias,
also the different percentages of “excluded” among the anemic and non anemic
individuals are those expected based on the associations between mild anemia and
higher morbidity.
[4b] “The authors state (page 4, methods section, line 4) that "all participants with
blood test results who gave their consent were considered" eligible. It is not clear which
patient actually got blood tests, and why, and for what reason blood test were omitted in
other patients. This should be clearly described and commented, if necessary.”
“all participants with blood test results who gave their consent were considered for
INCLUSION” (not: “ELIGIBLE”).
We definitely agree with the Reviewer. The first part of the prevalence study is
reported only in the other manuscript submitted to PLoSONE. Thus we have added the
same clause under “Study population” before the above mentioned sentence:
“Of the 10,110 residents (6,146 women and 3,964 men) of 65-84 years old on the
prevalence day, 1,131 could not be traced by phone, 80 died before being contacted,
4,398 refused to or could not donate a blood sample, and 4,501 agreed to take part.
Individuals with blood tests available (mean age 73.6 years, standard deviation [SD] =
5.2) were on average approximately one year younger than individuals without blood
tests (mean age 74.8 years, SD = 5.5) and the proportion of women was similar (60.1%
and 61.2% respectively).”
[5] “Furthermore, the exclusion criteria need further clarification. For the feasibility
of a home visit and an interview, eligible participants should be able to cooperate.
Therefore, it is straightforward to exclude people with terminal illness, illiteracy, severe
psychiatric diseases, advanced dementia, severely disabling stroke and the like.
However, it is not necessary to exclude all patients with neurological impairment, as it is
stated in the methods section, nor is it essential to exclude patients with heart, lung or
liver insufficiency, or hospitalized people.”
The purpose of the exclusion criteria was only secondarily to improve “the
feasibility of home visit and interview”, it served mainly, as already explained, to
exclude all those medical conditions already known to be associated with the outcomes
investigated.
Moreover:
1. We did not “exclude all patients with neurological impairment”, but individuals
with CNS disorders associated with the outcomes investigated (the list is in the
manuscript), as is stated in the Methods section and as can be ascertained in Table 1
under “Neurological diseases”.
2. Neither did we exclude “patients with heart, lung or liver insufficiency”, but only
those individuals with SEVERE organ insufficiency (the definition is in the manuscript)
as is stated in the Methods section and as can be ascertained always in Table 1 under
“Heart failure” and “Respiratory failure” (we did not find any individual with liver
insufficiency).
Thus, for the most part, there is a good agreement between the Reviewer’s views
and ours with regard to the exclusion criteria to adopt. Where our views remain at
variance is about “hospitalization”: supposing that it would be formally possible to
administer tests and scales in the hospital, would the Reviewer put and analyze together
the neuropsychological, functional, mood, and QoL data taken from individuals in
critical condition (and often fulfilling other exclusion criteria as well) and in a
completely different setting with those taken from the other individuals? Disagreement
remains also about limiting the exclusion only to individuals with “advanced dementia”
and “severely disabling stroke”. As we have already explained, we did not expect to be
able to detect the signal of a decreased cognitive performance, functional ability, mood,
or QoL associated with mild anemia against the background of a mild to moderate
dementia or stroke. And we doubt that mild anemia could be considered a major clinical
or public health concern in the elderly persons already affected by dementia or stroke.
After the description of the exclusion criteria in the Methods section we have now
made explicit that:
“Purpose of these criteria was to exclude those major medical conditions already
known to be associated with decreased cognitive performance, functional ability, mood,
or QoL, and those individuals not reliably testable.”
In our view, if one is interested in investigating the burden of (mild) anemia in
patients already affected by other serious medical conditions, the population-based study
would not have enough power to detect an association in such small subsamples of
patients identified in large scale studies like ours. Large, multicenter, clinic-based
studies would be a much more powerful setting in which to investigate these
associations, providing the opportunity to compare large cohorts of (mild) anemic versus
non anemic patients affected by a certain pathology, as has already been done in patients
affected by chronic heart failure (though for “easier” to study and more on the side of
public health outcomes such as mortality) (Sharma et al. [2004] Eur Heart J 25: 10211028; Kosiborod et al. [2005] Arch Intern Med 165: 2237-2244).
[6] “Of importance is the exclusion due to renal failure, as the criterion is not clearly
stated. Overt renal failure, for example symptomatic uremia or dialysis, may be clinically
identified. It can be justified to exclude such patients, as end stage renal disease may
lead to severe anemia that needs medical treatment. However, subclinical renal
insufficiency may be present. It is highly prevalent in the elderly and is often detectable
by means of the estimated Glomerular Filtration Rate (eGFR) only. The authors should
state which definition of renal insufficiency is used. If subclinical renal insufficiency is
not an exclusion criterion, as it seems, the authors should consider it as an additional
confounding variable and should include it in the multivariate analysis.”
The Reviewer raises an interesting issue which has often been discussed by our
research group because of the particular relationship between anemia and renal
insufficiency. Once again, for the same reasons reported in the previous responses, our
original idea tended, in agreement with the Reviewer, towards excluding only the
individuals with severe renal failure. Thus, also individuals with a mild grade renal
insufficiency were considered eligible.
As reported in the Methods (page 5) and discussed on page 12, the information on
comorbid diseases mainly relied on the individual self-report which was highly
consistent (“Cohen’s  between 0.84 and 0.932”) and reliable (“trustworthiness of the
interviews was rated as “good” or “very good” in 88% of the cases”).
When before analyses we discovered that only 19 individuals (11 mild anemic and 8
non anemic) affected by a non-severe form of renal insufficiency were included, we
decided that the easiest and most radical way to control for this variable was to exclude
these few individuals from the analyes. What would happen if instead, as suggested by
the Reviewer, we had included these 19 individuals into analyses as well (of course
controlling also for this additional confounding variable)?
In Model 3 (when adjusted for comorbid diseases), beyond those dependent
variables already reported in the manuscript (Visual Search of Matrices of Digits,
FACT-An Anemia and Fatigue subscales), also Stroop IEE (p = .0490) would reach
statistical significance, while MMSE (p = .0668), Word List Recognition (p = .0876),
Stroop IET (worst quartile) (p = .0627), IADL (p = .0668), and SF 12 Physical (score <
40) (p = .0752) would approach statistical signifincance.
In Model 3 (when adjusted for comorbid disease severity), beyond those dependent
variables already reported in the manuscript (Visual Search of Matrices of Digits, SF 12
Physical (score < 40), FACT-An Anemia and Fatigue subscales), also Stroop IET (worst
quartile) (p = .0336) and IADL (p = .0199) would reach statistical significance, while
MMSE (p = .0737), Stroop IEE (p = .0822), Stroop IET (p = .0855), and SF 12 Physical
(p = .0759) would approach statistical signifincance.
Having chosen before performing the analyses to exclude all individuals with renal
insufficiency, we considered the above reported as post-hoc results and thus decided to
report the most conservative results in the submitted manuscript. Should we have
reported the results of this post-hoc analysis too? We thought it would be rather heavy
for a reader to plod through all these findings. Following the Reviewer’s advice we have
now tried to include these results. The Reviewer can judge whether to keep them or
leave the manuscript as it was originally.
Under “Study population”:
“Individuals with a mild grade renal insufficiency were considered eligible for the
interview but were not included in the primary analyses.”
Under “Results”:
“If the 19 individuals with a mild grade renal insufficiency (11 mild anemic and 8
non anemic) were included into the analyses, in Model 3 adjusted for comorbid diseases
also Stroop IEE (p = .0490) and in Model 3 adjusted for comorbid disease severity also
Stroop IET (worst quartile) (p = .0336) and IADL (p = .0199) would reach statistical
significance, while several other variables would approach statistical significance.”
[7] “In addition to the above mentioned exclusion criteria I would suggest adding a
hemoglobin level beyond 10 g / dl, as it is stated in the methods section under
"Definition of anemia and mild grade anemia".”
“beyond 10 g / dl”: below?
Moderate to severe anemia was not an exclusion criteria. The low prevalence of this
anemia grade in the population resulted in a small number of individuals with this
condition (n = 31) which, after the subtraction of those excluded and of those who
refused or were not found, amounted to just 16 individuals, too small a group to be
analyzed separately (as secondary aim) as we had initially hoped.
[8a] “The extensive use of a variety of exclusion criteria is contradictory to the
basic idea of a population-based study, namely the evaluation of diseases, health
conditions and their risk factors in the distribution characteristic for the target
population. I would prefer emphasizing the presumed population-base as the strength
and innovation of this study rather than the independence of the relationship between
anemia and several health outcomes, however important it may seem.”
We agree with the Reviewer that, generally speaking, population-based studies
should be more “pragmatic” than “fastidious”. But the methodological approach cannot
be independent of the factors and outcomes investigated. And, as we have already stated
both in the “Preliminary considerations” and in the replies to previous remarks, we have
excluded those medical conditions known to be associated with the outcomes
investigated. The signal due to mild anemia expected to be detected, if present, would be
too weak against the background of the very serious medical conditions excluded.
With regard to the “extensive use”, we point out once again that less than 10% of
the elderly general population was excluded because of the “variety of criteria” adopted.
[8b] “A reason for the variety of exclusion criteria might be the wish to create a
sample of community-dwelling elderly, as it is often used in studies on mobility
impairment or falls, for example. The expression of an "organ insufficiency [...] severe
enough to limit the patient´s autonomy" may be a hint for this (methods section, page 4 /
5). If this was intended, however, the exclusion criteria should be applied less rigorous,
the argument of feasibility should be underlined, and of course only those patients that
proofed unable to comply with the home visit protocol should than be excluded from the
analysis. I would appreciate an clarification by the authors.”
“To create a sample of community-dwelling elderly” it would be enough to exclude
institutionalized and hospitalized individuals. As stated above, the main goal of our
choice was to exclude those medical conditions already known to be associated with the
outcomes investigated and, only secondarily, to improve the feasibility of the home
interview. Thus we did not need to create a sample of community-dwelling elderly but
rather wanted to exclude the elderly persons living in the institutions because for the
most part they are affected by some form of dementia (in traditional nursing homes in
Lombardy, Italy, we found a prevalence of 86.4%) while those left are often in critical
medical condition or with severe functional disability.
Or we did not want to exclude the individuals affected by an “organ insufficiency ...
severe enough to limit the patient’s AUTONOMY” because they would have “proofed
unable to comply with the home visit” (most elderly persons with severe organ
insufficiency already self-exclude themselves from any epidemiological survey), but
mainly because we did not expect to be able to detect a decrease in the outcome
investigated in persons already having (very) poor cognitive performance, functional
ability, mood, or QoL.
“If ... [then] the exclusion criteria should be applied less rigorous ...”. If the
definition of “less rigorous” is that outlined in the previous remark [5], then, as shown in
the relative reply, our exclusion criteria were already “less rigorous” than those reported
in the remark and, in any case, they resulted in the exclusion of only a small percentage
of the elderly persons investigated.
“Part B.
Further remarks, which I consider less important in view of the criticism of the
sampling process, follow in order of their appearance in the manuscript:
[1] Abstract:
Part Methodology and Findings, line 6 and further:
I would prefere including the abbreviations of the tests applied, i.e. MMSE, selected
items of the CERAD, basic ADL, IADL and so on. This allows to quickly capture the
information of selected standard assement tests applied in this study.”
We wanted to draw the reader’s attention to the specific cognitive domains
investigated, not to the particular tests used.
[2] “Introduction:
Shorter sentences would be easier to follow.
As an example may serve:
„Fatigue and weakness are common consequences of anemia. Several crosssectional studies in the elderly have reported the association of anemia with functional
disability and poorer physical performance [2], decreased muscular strength [3], fall
injury events at home [4], and increased frailty risk [5]. Two longitudinal studies
suggested that elderly persons with anemia are at increased risk of physical decline and
recurrent falls [6,7]."
Done.
[3] “Hospitalization and mortality have also been clinical outcomes associated with
anemia and should be cited by the authors (references: 1. Penninx, B. W., M. Pahor, et
al. (2006). Anemia in old age is associated with increased mortality and hospitalization.
J Gerontol A Biol Sci Med Sci 61(5): 474-9, 2. Semba, R. D., Ricks M. O., et al. (2007).
Types of anemia and mortality among older disabled women living in the community:
the Women's Health and Aging Study I. Aging Clin Exp Res 19(4): 259-64).”
The issues of hospitalization and mortality associated with anemia have been fully
addressed in the other submitted manuscript to PLoS ONE specifically devoted to this
different subject. Besides, the literature to cite would have been much broader.
Considering retrospective and prospective studies in the community-dwelling or in
selected populations of elderly individuals, we reported in the above-mentioned
manuscript 4 studies for the hospitalization outcome and 10 for the mortality.
Furthermore, the paper by Semba et al. (2007), reporting the results of a secondary
analysis in a selected population of moderately to severely disabled older WOMEN, is
not that relevant to the literature on anemia and mortality and the primary analysis on
mortality in the same population of disabled women had already been published: Chaves
et al. (2004) J Am Geriatr Soc 52: 1811-1816.
[4] “The authors missed references for "the relationship of anemia with cognitive
performance and mood".
We did that intentionally because we thought it was better to review the literature on
cognition, functional ability, mood, and QoL in detail in the Discussion rather than to
pack it into the Introduction.
[5] “Analysis:
I do not agree with the authors that the inclusion of cancer patients in the study
forces to adjust all analyses for cancer status. It is well established that cancer status is
an important covariate in the context of anemia. But, as already stated before, a
population-based study sample has the strength to provide data that are typical for the
target population. I would accept the concurrent reporting of crude and cancer adjusted
odds ratios in table 1, if the author wish to emphasize the importance of cancer status as
a covariate.”
We confess our surprise upon reading this remark. After the expressed concern
about the possible presence of a selection bias, in front of a deliberate oversampling of
elderly individuals with a past or present diagnosis of cancer (which is a departure from
the original frequency in the general elderly population) the Reviewer does not agree
with the inevitably required adjustment of all the analysis by oncological status. We did
not “wish to emphasize the importance of cancer status as a covariate”, but merely to
adjust for the departure from the original prevalence intentionally introduced by the
oversampling (see the reply to the remark [3], example 3).
[6] “The reporting of a secondary analysis in the discussion (see Statistical analysis
section, page 8, line 11, and Discussion section, page 11) is strongly discouraged. All
results should to be reported in the results section.”
We do not think that a Discussion should be made only of words but also of figures.
Secondary analyses are often reported in the Results section to overshadow the results of
the primary analysis. When reported in the Discussion, post-hoc analysis can instead be
useful to discuss doubtful issues or suggest future research options.
In any case, following the Reviewer’s suggestion, we have moved the finding on
IADL when the score is “set up at less or equal to 10% disability” in the Results section
and added in the Discussion:
“Post-hoc analysis seems to suggest that higher level of IADL disability could be
associated with mild anemia.”
Reply [2nd] to remarks from Reviewer # 1 (Prof/Dr U. Thieme)
“I am sorry that some disturbances occurred as a result of my comments. My intention
is to criticize in a constructive way to help clarify things and improve the understanding
of the issue under discussion.
I am grateful that the authors corrected two mistakes of mine in their answer. I
erroneously suggested an Hb level “beyond” 10 g / dl, actually meaning a level “below”
that threshold. The authors correctly interpreted my argument. I apologize for this.
Another misunderstanding occurred with regard to the prevalence of cancer in the
control group. The authors deliberately included all non anemic individuals with a past
or present history of cancer into the control group. Under this circumstances, I
understand that the authors perform all analysis with cancer status as a covariate.
A further source of disturbance has probably been the fact that a second manuscript has
been submitted to PloS Medicine. I was unaware of this. I do not know its content yet,
and therefore can not judge whether the information contained in that manuscript would
have prevented my critique.”
To PLoS ONE not to PLoS Medicine.
“I appreciate the efforts of the authors to improve the manuscript along with some of
my
recommendations. I mention as examples: renal insufficiency as an exclusion criterion,
the additional sentence on randomization, some simplification in wording.
I still disagree with the authors regarding the selection bias issue. Less than half of
people from the target population (n=10.110) accepted a blood test and health
questionnaire (n=4.501). The only available information of those without blood test is
obviously age and sex. The authors report a difference in age of on average one year.
However, it is highly improbable that this is the only difference to those individuals
with blood tests. We just do not know more.”
“I still disagree with the authors regarding the selection bias issue.” Actually, as in the
first review of the manuscript (where the different “selection bias” pointed out was
simply the result of the inclusion/exclusion criteria adopted), here too the issue at stake
is not the selection bias, but rather the response rate of the study (see below the answer
to the next remark). Though related, these are not at all synonymous concepts (that is: a
study with a high reponse rate does not exclude the presence of a selection bias, as a
study with a low response rate does not necessarily imply the presence of a selection
bias). In fact the bias, when present, is due to the presence of a SYSTEMATIC
difference between characteristics of participants and non participants. And even in
presence of a systematic difference, this could lead to a bias with respect to a certain
ASSOCIATION but not necessarily to another and thus the difference should always be
put in relation to the dependent variable examined (a consideration neither present in
the first nor in the second review).
Since “ALL 65-84 year old individuals (N = 10,110) residing in the municipality of
Biella” (page 4 of the original manuscript) were eligible for the study, the only possible
source of a selection bias was self-selection. And a distinctive feature of the present
study was that the elderly person’s decision could not be determined by the trait under
study (as it happens, when persons are invited to participate in a study on the effect of
smoking for example), simply because they did not know if they were mild anemic or
not. Moreover, mild anemia, as explained in the manuscript’s introduction, tends to be
considered by both the patient and the doctor as a condition without clinical
consequences. A selection bias would be introduced only if the ASSOCIATION
between mild anemia and the various, different outcomes examined would differ
between those who accepted the blood test and those who refused, and we do not see
any plausible basis for this (see what has already been explained in the Preliminary
considerations of the reply to the first review).
As in most door-to-door studies, the fact that only more than a half (50.6%) “accepted a
blood test” (since deceased and not found elderly could not accept or refuse) is basically
due to our particular initial request to take a blood sample.
“It is highly improbable that this is the only difference ...”: even though other
(correlated) differences between groups were also present, this would not be enough to
demonstrate the presence of a selection bias. We do not know of any large
epidemiologic survey where there is no significant difference between participants and
non participants. Moreover, in epidemiological surveys in the elderly, non participants
are usually older, are in worse health conditions, and, when the aim is not to investigate
dementia, have a higher prevalence of dementia (not considering the ethical problems
involved, demented persons and their relatives are not so fond of participating in
epidemiologic surveys in mild anemia or the like). Thus, a self-selection is almost
always present in any population-based study and to understand if this selection could
represent a bias (with respect to the association investigated) one has to consider the
dependent variable examined. And in the present study serious diseases and dementia
were precisely the confounding conditions we would have excluded from the study
sample.
“From those eligible (n=4.068) only 3.023 were actually eligible as a study sample
according to the flow chart (170 out of 265 anemic and 2.853 out of 3.803 non anemic
individuals). As a result, the study sample actually reflects only 29,9% of the target
population. On this basis, it is critical to state any further population base of the study
sample.”
Though formally possible, adding non participants to the first part of the study (aims: to
estimate the prevalence of mild anemia and its long-term association with mortality and
hospitalization) to non participants in the second part (the present study) does not help to
clarify the “selection bias issue”. In fact, in the first part of the study, elderly individuals,
if traced and alive, accepted or refused to donate a blood sample, while in the second
part they accepted or refused the cognitive, functional, mood, and QoL evaluation. It is
clear that the reasons to refuse to donate a blood sample (mainly because ill, they already
had it, or of the discomfort or fear of the test) have little to do with the reasons to refuse
the neuro/psychological assessment of the second part of the study. In other words, the
reasons for the self-selection in the first part are mostly different from those in the
second and it would seem unrealistic to speculate that the association between mild
anemia and the various, different outcomes investigated would go in opposite directions
among donors and non donors. And the response rate in the second, specific part, that is
the present study, was high …
It is quite difficult to follow the calculation of the response rate made by the Reviewer:
1st. The Reviewer fails to take into account the excluded among the elderly without
blood tests (a percentage surely much higher, for the considerations reported above,
than that of the excluded among the elderly with blood tests).
2nd. In the first lines of the sentence the Reviewer reports the denominator of those with
blood tests (4,068), where he correctly did not take into account the excluded. Then, “as
a result” (?), the Reviewer re-puts into the denominator also the 1,045 individuals
excluded (4,068-3,023=1,045 following the Reviewer’s calculation). In fact a response
rate of 29.9% can only be the result of 3,023:10,110x100.
As already stated in our first reply, the excluded individuals, not being eligible, are by
definition not part of the target population!
3rd. Even if it refers to a very small number of individuals which would not noticeably
change the calculation of the response rate, the 11 deceased and hospitalized after
acceptance and the 16 moderate to severe anemic elderly (but also the 19 individuals
with mild grade renal insufficiency) should be counted with the responders and not with
the non responders because all of them accepted to participate.
When taking into consideration also the elderly who did not or could not accept to
donate a blood sample, the response rate, in our estimates, is about 40%. And it was
exactly because of this moderate response rate that we had already added to the revised
manuscript, in the “limitations” part of the Discussion, the sentence :
“Although the possible influence of a non-response bias on the associations studied
cannot be excluded, this seems rather improbable considering the condition examined
(an anemia of mild grade of which most of the elderly persons were unaware) together
with the nature of the dependent variables studied.”
Thus, we have already taken into consideration the issue of a possible non-response bias
and cautioned the reader about it.
Incidentally, considering the meager literature on the subject cited in the Discussion
section of the manuscript (pages 11-12), the response rate of the available communitybased studies was either not reported or moderate.
We do not understand what the Reviewer means by “On this basis, it is critical to state
any further population base of the study sample.” The reply has been put together with
that of the last point.
“Furthermore, as I understand it now, the control group does not represent a unique
group of non anemic participants. It is actually the sum of a random sample of non
anemic eligible individuals plus all non anemic individuals with a history of past or
present cancer, as confirmed by the authors in their answer. In this respect, the flow
chart is misleading, as it does not indicate the special way non anemic participants with
a cancer history are dealt with.”
In our reply to the first review we did not add any new information on the oncological
component of the control group that was not already present in the original manuscript.
The fact that all non anemic elderly with a past or present diagnosis of cancer were
included in the control group because of the secondary aim of the study (page 5 of the
original manuscript) far from pointing to any possible validity issue (see Reviewer’s
last remark), represents instead a sound method to analyze all the available data which
does not change the representativeness of the control group.
1st. Instead of losing potentially important information we took into consideration, as is
usually done, the interviews of those additional “oncological” elderly studied by
including, as already stated, the oncological status as an independent factor in all the
statistical analyses performed.
2nd. In fact, if instead of all, only the expected 11.2% of the elderly with a past or
present diagnosis of cancer were randomly included in the control group (that is
recreating a simple random sample from the non anemic population), the results would
not change: in spite of the reduced sample size, in the fully adjusted model mild anemia
would remain significantly associated with the same cognitive and QoL variables
reported in the manuscript and also with Stroop IEE measure (p = .0429). The results of
this further analysis has been now added to the manuscript.
3rd. In the multivariable regressions of the above reduced control group, diagnosis of
cancer is significantly associated with no variable (p between .1126 and .9969, mean p
of .5313).
Though in the text the composition of the control group is repeatedly explained, the
Reviewer is right with regard to the flow-chart and we have changed the word
“randomized” with “included” in the last box of Figure 1.
“Taken together, it is doubtful that the comparison made between anemic participants
and the control group is valid. It is a mere assumption to state that the sample is
population based.”
“Taken together…”: of the two members of this pair the one referring to the so called
“selection bias” is actually an already reported issue of response rate, while the other,
the composition of the control group, demostrates instead how sound and robust our
findings are. Thus, if our study surely needs to be replicated, as already acknowledged
both in the Discussion and in the reply to Reviewer # 2, it is also one of the very few
available, and in the case of the QoL variable the only existing (and it is also the only
one where a wide range of cognitive domains were investigated).
“It is a mere assumption to state that the sample is population based”: no, it is not at all
an assumption because a sample is population-based or whatever because of the study
design and not because of the selection bias or the response rate, etc. In other words, a
study does not change the nature of its design (population-based, clinic-based,
institution-based, case-control, and so on) because of the response rate (unless, of
course, the number of the included were ridiculously low) or even in the presence of a
DEMONSTRATED selection bias. In these cases in fact, one would just have a
population-based study with a low response rate or a population-based study with a
selection bias.
In the light of the statement “It is a mere assumption to state that the sample is
population based”, we interpreted the sentence “On this basis, it is critical to state any
further population base of the study sample” as if it means “the authors should stop
stating that the present is a population-based study”. We have just explained why this
request sounds strange, but, even if we cannot agree with the Reviewer, we do not mind
removing the word “population-based” (from the Title and from the first lines of the
Study population Methods and of the Discussion) or replacing it with the term
“community-dwelling elderly persons” when it is necessary to describe the population
of elderly investigated (in the first sentence of the Abstract and, twice, in the last two
paragraphs of the Introduction).