Understanding Jason's 38% of MDD selected under Reeves

[WillowJ]

Just an FYI on the statistic from Jason's study... it is always cited backwards. the correct statistic is 38% of people with MDD qualify as CFS under Reeves' criteria. This is not the same as 38% of the CFS cohort (which is how I generally see the statistic presented). There are many more people with so-called "affective disorders" than CFS, and it is a much larger percent of CFS than 30-40%.

In fact, Jason notes that the incidence of "CFS" jumped by 10-fold with the introduction of the so-called Empirical criteria, to coincidentally approximate the incidence of the "affective disorders".
So it is highly unlikely for any more than 10% of Reeves disease to be Fukuda-CFS. It is probably much less than 10%, given the fact that CDC has taken to criticising Fukuda-diagnostic S&S like tender lymphs and Fukuda-update noted S&S like balance difficulties... as indicating the patient probably does not have CFS.

 

[In Vitro Infidelium]

Just an FYI on the statistic from Jason's study... it is always cited backwards. the correct statistic is 38% of people with MDD qualify as CFS under Reeves' criteria. This is not the same as 38% of the CFS cohort (which is how I generally see the statistic presented). There are many more people with so-called "affective disorders" than CFS, and it is a much larger percent of CFS than 30-40%.

In fact, Jason notes that the incidence of "CFS" jumped by 10-fold with the introduction of the so-called Empirical criteria, to coincidentally approximate the incidence of the "affective disorders".
So it is highly unlikely for any more than 10% of Reeves disease to be Fukuda-CFS. It is probably much less than 10%, given the fact that CDC has taken to criticising Fukuda-diagnostic S&S like tender lymphs and Fukuda-update noted S&S like balance difficulties... as indicating the patient probably does not have CFS.

The Jason et al study certainly serves as a criticism of Reeves but doesn’t really answer any questions outside of a Fukuda/Reeves comparison, and Jason’s study population was hardly representative – only 9% male and even amongst the CFS group a notably high level of ‘never married’ compared to the US average. The ‘never married’ may simply be a function of the small sample size but given the average age and gender distribution it’s up to 5 times the expected average , which suggests that Jason’s study might only be accurate within a particular population range.

There are other questions which Jason doesn’t address. For instance what is the level of preferential diagnosis of depression as opposed to CFS, in patients with CFS like symptoms where cognitive dysfunction predominated over physical dysfunction ? Jason considers cognitive dysfunction relative to CFS, but treats the MDD diagnosis as inviolable so while Jason’s study shows the inadequacy of Reeves, it doesn’t tell us anything about misdiagnosis of CFS as depression. Jason also does not address the issue of co-morbidity – why should someone not have both MDD and CFS, and what difficulties do both Fukuda and Reeves present in dealing with co-morbidity ?

IVI

 

[WillowJ]

this particular study of Jason's group was intended only as a criticism of the so-called Empirical criteria. The other points you bring up are certainly excellent points worthy of discussion, and that can certainly be done, but it needs to be noted that these are not valid criticisms of this particular Jason study because the objective of this study was to criticise the Empirical criteria as not selecting the same population of patients as Fukuda selects (CDC claims to be using operationalized Fukuda when using "Empirical"), but instead selecting evidently mainly primary depressive condition population patients.

Some of Jason et al.'s other studies actually support the points you wish to discuss.

My point in posting this was to educate people not to say "38% of people included in so-called CFS populations under the CDC's current inclusion have MDD" because that is an inaccurate and misleading statement--the percentage of people included in the co-called CFS populations in CDC's publications who really have primary depressive disorders and not also ME/CFS is much greater than 38%. If we want to educate people about CDC's errors, we need to do it properly and use the statistic the right way around.

I don't have a graphic so I will try to explain a visual with words. Say you have a 10" pie plate filled with marbles, and you take 40% of those marbles and put them in a 4" tart tin. Do those same marbles take up the same percentage of space in the tart tin, as they did in the pie plate? No, they do not. They are a different percentage of the tart tin. That's the same thing that happens when you take 38% of those with MDD qualify as having "CFS" [Reeves Disease] and change it to 38% of those with "CFS" actually have MDD instead.

 

[SOC]

I don't have a graphic so I will try to explain a visual with words. Say you have a 10" pie plate filled with marbles, and you take 40% of those marbles and put them in a 4" tart tin. Do those same marbles take up the same percentage of space in the tart tin, as they did in the pie plate? No, they do not. They are a different percentage of the tart tin. That's the same thing that happens when you take 38% of those with MDD qualify as having "CFS" [Reeves Disease] and change it to 38% of those with "CFS" actually have MDD instead.

Some very rough numbers for those who like them:
People in US with MDD = 39,000,000
(13% of the general population)
People in US with MDD that qualify as "CFS" under Reeves' defn = 15,000,000
(38% of people with MDD)

People in US with CFS = 600,000
(0.2% of the general population (per Jason))
People in US with CFS and comorbid MDD = 180,000
(13% of the CFS population)

People Reeves classifies as "CFS" who have only MDD = 14,820,000

Total number of people that Reeves would classify as "CFS" = 15,420,000

Therefore, a Reeves criteria CFS population is composed of
96% MDD patients without CFS
4% CFS patients (including those with comorbid MDD)

I did these number on the fly and we all know how well that works for us. So feel free to check my numbers, change the percentages to ones you think are more accurate... whatever floats your boat. I'm not claiming these numbers are perfect -- they're just very rough estimates to demonstrate WillowJ's point. MDD-only patients are not 38% of the Reeves' "CFS" population, they are closer to 90%.

If anyone finds flaws (and they could be many and large), please post corrections.

 

[WillowJ]

thanks, SOC. I didn't have (and didn't feel like looking up) figures for # of MDD pts so couldn't attempt those calcs

 

[heapsreal]

The bottom line is almost everyone with a chronic illness suffers from some degree of depression as its life changing for the worst. Maybe they should also compare a study of those with rhuematoid arthritis, hiv and MS, maybe they just need to exercise more.

 

[In Vitro Infidelium]

this particular study of Jason's group was intended only as a criticism of the so-called Empirical criteria. The other points you bring up are certainly excellent points worthy of discussion, and that can certainly be done, but it needs to be noted that these are not valid criticisms of this particular Jason study because the objective of this study was to criticise the Empirical criteria as not selecting the same population of patients as Fukuda selects (CDC claims to be using operationalized Fukuda when using "Empirical"), but instead selecting evidently mainly primary depressive condition population patients.

Some of Jason et al.'s other studies actually support the points you wish to discuss.

My point in posting this was to educate people not to say "38% of people included in so-called CFS populations under the CDC's current inclusion have MDD" because that is an inaccurate and misleading statement--the percentage of people included in the co-called CFS populations in CDC's publications who really have primary depressive disorders and not also ME/CFS is much greater than 38%. If we want to educate people about CDC's errors, we need to do it properly and use the statistic the right way around.

I don't have a graphic so I will try to explain a visual with words. Say you have a 10" pie plate filled with marbles, and you take 40% of those marbles and put them in a 4" tart tin. Do those same marbles take up the same percentage of space in the tart tin, as they did in the pie plate? No, they do not. They are a different percentage of the tart tin. That's the same thing that happens when you take 38% of those with MDD qualify as having "CFS" [Reeves Disease] and change it to 38% of those with "CFS" actually have MDD instead.

I wasnt intending criticism of your point about the percentages and yes we should all get our quotes right. What struck me about the study though was its limitation and how that could be argued as limiting its scope to serve as a critique of Reeves.

There are peculiarities arising from the data for instance it would seem that having children is a significant differential between CFS categorisation and both MDD and MDD/CFS categorisation. Does that mean that being a parent is a predisposing factor for CFS, or does Fukuda select for over representation of parenthood and that is something which Reeves avoids, or does Reeves select for under representation of parents ? And are these differentials a general function of the Fukada/Reeves case definitions or are they specific to a limited demographic (women with a median age of 37+) ?

It seems to me that the biggest question about Reeves is the massive increase in prevalence rate over Fukuda the burden to explain that should be on Reeves, rather than there being a need for other researchers to demonstrate where/ how the increase can be exists. Indeed it should be Reeves and those who support the Reeves case definition who should be required to demonstrate what their ten fold increase consists of - does it for instance vastly increase the number of children classified under CFS, or is the increase to be found disporportinaely amongst adults or older adults, and is the increase predominatly amongst females or does the ten fld increase affect the gender ratio ? These are pertinent questions for the CDC because they all impact on the CDC's current description of CFS, as extrapolated from Fukuda.

IVI

 

[Angela Kennedy]

Try star signs as a "predisposing factor", eh?

 

[In Vitro Infidelium]

Some very rough numbers for those who like them:
People in US with MDD = 39,000,000
(13% of the general population)
People in US with MDD that qualify as "CFS" under Reeves' defn = 15,000,000
(38% of people with MDD)

True (though see NIMH for alternate prevalence data) if the Jason results are applied to the MDD population, however there are problems with that. The MDD population is demographically distinct from any described CFS populations and is highly differentiated from the Jason and Reeves study populations, theres also a difficulty in that both Jason and Reeves accept the 6 month duration of CFS prior to diagnosis whereas MDD requires only two weeks of symptoms and is characterised as an episodic condition with relatively short acute phases (Jason = 1-month prevalence is 2.2%, and lifetime prevalence is 5.8%, NIMH = 12 month 6.7%, lifetime = 16.5%), in addition MDD is highly associated with co-morbidity. Jason addressed some of these issues in patient selection, on that basis alone the 38% figure can not really be applied to the whole US MDD population in a meaningful way. Demographic matching would be required to make any useful deductions.

People in US with CFS = 600,000
(0.2% of the general population (per Jason))
People in US with CFS and comorbid MDD = 180,000
(13% of the CFS population)

I dont understand the 180,000 figure. 13% x 600,000 = 78,000, but in any case the same demographic problems that arise when applying the 38% figure to the MDD population, also arise when applying the (reverse) 13% to the CFS population. The problems of age matching and the question of diagosis of CFS in children (Reeves ignores this ?) are especially significant with high prevalence of MDD in under 18 year olds compared to adults and a relatively higher 12month prevalence amongst 18 -29 year olds than older age groups. These prevalence rates are almost the complete reverse of those ascribed to M.E/CFS.

People Reeves classifies as "CFS" who have only MDD = 14,820,000

Total number of people that Reeves would classify as "CFS" = 15,420,000

Therefore, a Reeves criteria CFS population is composed of
96% MDD patients without CFS
4% CFS patients (including those with comorbid MDD)

I did these number on the fly and we all know how well that works for us. So feel free to check my numbers, change the percentages to ones you think are more accurate... whatever floats your boat. I'm not claiming these numbers are perfect -- they're just very rough estimates to demonstrate WillowJ's point. MDD-only patients are not 38% of the Reeves' "CFS" population, they are closer to 90%. If anyone finds flaws (and they could be many and large), please post corrections.

Because of the problems Ive already suggested are involved I dont think your figures can be correct, however I cant see any way to use simple data to offer any correction. Reeves clearly provides a case definition that produces a higher prevalence rate than does application of the Fukuda criteria how that applies to a given population is a much more complicated question. Jason certainly demonstrates that as a diagnostic tool the Reeves case definition is unhelpfully lax, however the laxity is proved only in a distinct demographic and it maybe that the problem is greater, lesser or even non existent in other demographics.

IVI

 

[SOC]

True (though see NIMH for alternate prevalence data) if the Jason results are applied to the MDD population, however there are problems with that. The MDD population is demographically distinct from any described CFS populations and is highly differentiated from the Jason and Reeves study populations, theres also a difficulty in that both Jason and Reeves accept the 6 month duration of CFS prior to diagnosis whereas MDD requires only two weeks of symptoms and is characterised as an episodic condition with relatively short acute phases (Jason = 1-month prevalence is 2.2%, and lifetime prevalence is 5.8%, NIMH = 12 month 6.7%, lifetime = 16.5%), in addition MDD is highly associated with co-morbidity. Jason addressed some of these issues in patient selection, on that basis alone the 38% figure can not really be applied to the whole US MDD population in a meaningful way. Demographic matching would be required to make any useful deductions.

Demographic matching, my @$$. Maybe a reading comprehension class would help. I will try to assist by selecting and explaining a couple of my key sentences in isolation:

Some very rough numbers for those who like them:

Notice the emphasis on "very rough". That explains that these numbers are NOT to be taken as highly accurate on the details.

I'm not claiming these numbers are perfect -- they're just very rough estimates to demonstrate WillowJ's point.

This second sentence also explains that these numbers are not intended to be accurate in every detail, and are only used to demonstrate WillowJ's point.

I dont understand the 180,000 figure. 13% x 600,000 = 78,000,

You are quite correct. Not sure where I got 180,000.... brain fade, no doubt. Thank you for that correction.

This is the kind of correction that is in scale to the very rough calculations I used.

That would change the very rough calculations of percentages to
99% of Reeves defined "CFS" patients who have MDD only
1% of Reeves defined "CFS" patients with CFS
but doesn't change the fundamental point

Because of the problems Ive already suggested are involved I dont think your figures can be correct, however I cant see any way to use simple data to offer any correction.

Correct by what standard? Very rough estimates for the purposes of demonstrating a simple mathematical point are very, very, very rarely accurate to the nth degree of detail. For example:

If you have 3 apples and I have 2 apples and we divide them evenly among 5 children, each child gets 1 apple. Correct?

But wait, let's get anal and nitpick -- Were all the apples exactly the same size? Were all the children the same size? How do we divide 5 unequally sized apples "evenly" among 5 unequally sized children? Are all the apples the same type of apple? Some apples are Granny Smiths and some are Golden Delicious and some of the children don't like Granny Smith apples. Is it "fair" that they got apples they won't eat? Have we defined what makes a "child"? Perhaps one of the children is 17yo, is that still considered a child? Maybe one of the children is an adult child of one of us, can we count that person as a child?

All interesting questions, but all totally irrelevant to the point of the demonstration which was to be an exemplification of the concept: (3+2)/5 = 1

Reeves clearly provides a case definition that produces a higher prevalence rate than does application of the Fukuda criteria how that applies to a given population is a much more complicated question. Jason certainly demonstrates that as a diagnostic tool the Reeves case definition is unhelpfully lax, however the laxity is proved only in a distinct demographic and it maybe that the problem is greater, lesser or even non existent in other demographics.

IVI

No? Really? I had no idea! Thank you so much for that in-depth analysis. I always enjoy a good lecture on obvious topics.

Since a shorter summary of my simple demonstration of a concept seems necessary:

As WillowJ said:

38% of people with MDD qualify as CFS under Reeves' criteria. This is not the same as 38% of the CFS cohort

38% of MDD patients (15,000,000 -- give or take a couple million)
is much bigger than
38% of CFS patients (228,000 -- give or take a few hundred thousand)
or even
38% of Reeves' "CFS" cohort (5,700,000-- give or take a million)

WillowJ, I now see the wisdom of not using numbers in an example when one is dealing with a certain demographic. Too bad we didn't have a handy picture -- it would have made the point clearer to a larger segment of the PR population.

And yes, I'm still too cranky and impatient of idiots to read or post on PR atm. Back to the Burnout Bench until my patience returns.

 

[WillowJ]

What struck me about the study though was its limitation and how that could be argued as limiting its scope to serve as a critique of Reeves.

so you are saying we should NOT critique Reeves? or not critique Reeves when not in an impossibly long paper that exhaustively covers all topics related to definition? as I mentioned, this paper is meant as a critique of Reeves and the Jason group has indeed published other papers that take up other topics related to definition. It is not necessary for every paper to cover every aspect, and it is in fact often expedient and more clear, for a paper to focus on a particular aspect.

It is true that CDC should justify their own switch from Fukuda to the Reeves surveys, but they so far have only insisted that the Reeves method (a) is the same as Fukuda, and (b) has excellent test-retest validity. But both claims are entirely false. They "consider" aspects of Fukuda met with items that are not the same (fatigue and debility are both considered met with depression, for instance, whether or not fatigue and debility are actually present), and patients enrolled as "CFS" patients do not necessarily continue to have "CFS" over the course of the study, destroying their control matching (they have had to mention that "matching was not maintained between cases and controls" due to this issue).

Since we cannot expect the CDC to be either true or sensible, we can instead be thankful to those such as the Jason group who have the spunk to openly contradict a Health Authority.

 

[In Vitro Infidelium]

so you are saying we should NOT critique Reeves? or not critique Reeves when not in an impossibly long paper that exhaustively covers all topics related to definition? as I mentioned, this paper is meant as a critique of Reeves and the Jason group has indeed published other papers that take up other topics related to definition. It is not necessary for every paper to cover every aspect, and it is in fact often expedient and more clear, for a paper to focus on a particular aspect.

Im not suggesting anyone should/or should not do anything. What I am saying is that very great care needs to be taken when extropolating from a) a comparison of specific statistical models, b) complex statistics.

At first reading I thought your argument as supported by SOCs numbers was compelling for the case that the Reeves case definition produces a ridiculously high prevalence number, having gone through the basic data MDD prevalence etc, I no longer see how the extrapolations are possible. To use your pie and tart tray analogy, it's as though one were dealing with differently coloured marbles (demographic differences) and needing to specify a particular mix of colours in each tray before one could actually assess the size of trays required.

SOC may see it in terms of very simple maths but the maths are not the issue its the data that matters. MDD is diagnosed at higher rates amongst the young, at a female to male ratio of 2:1, CFS is diagnosed at higher rates amongst a 30+ population at a female to male ratio of +3:1. CFS is a chronic condition lasting years MDD can be acute or chronic, meaning that someone who had a three month spell involving two brief episodes at age 18 will be included in the the life time prevalence figures (as used by SOC) but will have no further involvement in the ongoing data. The NIMH 12 month prevalence data would suggest a US MDD rate of about 20 million people affected in the last year, but many of those will have experienced a brief episode and may never be given an MDD diagnosis ever again. None of these acute MDD cases could ever be confused with CFS because they lack the 6 month qualifier that Reeves invokes.

What Jason shows is that there is a worrying level of confusion of diagnosis resulting from application of the Reeves case definition, when that is applied to a particular demographic group a group in which existing diagnosis produces relatively higher prevalence of CFS than in other groups, and in which there is a relatively lower prevalence of MDD. Whether Jasons results can be applied to a wider population is an open question, my guess is that the staistical manipulations necessary would make any extrapolation dubious.

Your point in starting this thread was that a particular statistic from Jasons results is consistently misquoted, my arguments here have only continued the theme that accuracy matters, if others do not agree with my analysis thats fine but if my friendly criticisms cause problems, what chance of the CDC being forced to take notice of any challenge ?

IVI

 

[WillowJ]

The prevalence of mood disorders used by Jason as the figure which the Reeves definition approximates the incidence of, is the month prevalance, not the lifetime prevalence.

Using these new criteria, the estimated rate of CFS has increased to 2.54% (Reeves et al., 2007), a rate that is about 10 times higher than prior CDC estimates (Reyes et al., 2003)

Jason LA, Najar N, Porter N, Reh C. "Evaluating the Centers for Disease Control's Empirical Chronic Fatigue Syndrome Case Definition". Journal of Disability Policy Studies. September 2009 vol. 20 no. 2 93-100.

(Mood disorders are the most prevalent psychiatric disorders after anxiety disorders: For major depressive episode, the one-month prevalence is 2.2% and lifetime prevalence is 5.8%; Regier et al., 1988).

Jason et al. Politics, Science, and the Emergence of a New Disease: The Case of CFS Am Psychol. 1997 Sep;52(9):973-83. PMID: 9301342 http://www.cfs-news.org/jason.htm (also a very helpful read when trying to understand this topic of differentiating MDD from ME/CFS, or not, and the psychogenic versus disease view)

Side note: I'm told that this is a better reference than the first:
Jason LA, Evans M, Brown A, Brown M, Porter N, et al. "Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition." Psych. 2010 Apr; 1(1):9-16

If about 5% of the population has 6 or more months of fatigue, and about half of this is due to clear medical or psychiatric reasons [31], then the critical question is how many of the remaining 2.5% have CFS. The empirical CFS case definition estimates that 2.54% do have this illness, so that research group would suggest that almost all of the remaining 2.5% would fall within the CFS category.

However, Jason et al. [7] believe that within this 2.54% are mood disorders, which are one of the most prevalent psychiatric disorders (one-month prevalence rate of major depressive episode is 2.2%) [33]. As an example, one mood disorder is MDD, which can be confused with CFS, as it has some overlapping symptoms with CFS. It is possible that some patients with MDD also have chronic fatigue and four CFS Fukuda et al. [1] symptoms that can occur with depression (e.g., unrefreshing sleep, joint pain, muscle pain, impairment in concentration). Fatigue and these four minor symptoms are also defining criteria for CFS, so it is possible that some patients with a primary affective disorder could be misdiagnosed as having CFS.

Yet, these are distinct illnesses, as several CFS symptoms are not commonly found in depression, including prolonged fatigue after physical exertion, night sweats, sore throat, and swollen lymph nodes. Illness onset with CFS often occurs over a few hours or days, whereas primary depression generally shows a more gradual onset. Biological findings also differentiate the two conditions [34]. Including the latter type of patients in the current CFS case definition could confound the interpretation of epidemiologic and treatment studies, and complicate efforts to identify biological markers for this illness.

It is important for screening tests to have high sensitivity and specificity, particularly for disorders with low prevalence rates such as CFS (about 4.2 in a thousand) [31].

It's important to note that the method of diagnosing in Reeves (to attain this prevalence estimate) is random-digit dialing, asking people if they are fatigued, and then administering (moderate-level) exclusionary testing and "diagnosing" "CFS" via surveys (which deem patients to meet Fukuda criteria when they, in fact, do not). Because of the random dialing, age considerations and other factors of who is normally thought to be diagnosed with CFS do not apply.

When they go to put together studies, the CDC tends to find that matching is not maintained between cases and controls. Perhaps because they ask about the eight diagnostic-contributory symptoms only in the past single month, some of the people deemed to have "CFS" at the time of case-matching, are not the same ones deemed to have it at the time the study commences.

 

[Snow Leopard]

Because of the random dialing, age considerations and other factors of who is normally thought to be diagnosed with CFS do not apply.

Instead it introduces new biases based on phone answering behaviour - young people in general will not answer phone surveys. There is probably a bias towards proportinally more women answering surveys as well.

 

[WillowJ]

true that it would introduce new biases. also some people have caller ID and won't even answer their phone to a center they don't recognize, so there could be a socioeconomic bias as well (although once they answer, people still may decline if not wishing to participate, so that may lessen the socioeconomic bias)

 

[Snow Leopard]

They claim that prewarning via letters boosts compliance (especially when it is a survey associated with a government department), but the rates are still not that impressive. They then attempt to 'correct' for various demographic (and socioeconomic) biases.

 

[oceanblue]

It's important to note that the method of diagnosing in Reeves (to attain this prevalence estimate) is random-digit dialing, asking people if they are fatigued, and then administering (moderate-level) exclusionary testing and "diagnosing" "CFS" via surveys (which deem patients to meet Fukuda criteria when they, in fact, do not). Because of the random dialing, age considerations and other factors of who is normally thought to be diagnosed with CFS do not apply.

But I thought Jason and Reeves both used telephone dialling for the intial screen. I seem to remember in both cases there was under 50% response rate leaving lots of scope for responder bias [ETA: 65% response for Jason]. Also, neither study matched their responders against the population on demographics, which you would think was important for a population survey. Jason chose to call in districts of Chicago close to the evaluation centre - which is a good pragmatic reason but not likely to lead to a representative sample. Comparing demographics of responders vs population would at least allow weighted estimates of prevalance.

 

[Dolphin]

The Jason et al study certainly serves as a criticism of Reeves but doesnt really answer any questions outside of a Fukuda/Reeves comparison, and Jasons study population was hardly representative only 9% male and even amongst the CFS group a notably high level of never married compared to the US average. The never married may simply be a function of the small sample size but given the average age and gender distribution its up to 5 times the expected average , which suggests that Jasons study might only be accurate within a particular population range.

The more interesting group in this study is the MDD group, not the CFS group. Also, we don't know what the true "never married" rate is in CFS - I doubt it's 8.2% (=41%/5) - I imagine it's higher than the general population for various reasons due to the illness. Also, the never married rate in the MDD group is 62% and there might be an advantage if these were more similar; similarly the male percentage in the MDD group is 11% so not a huge. But I can't see that the "never married" rate in the CFS group is of much importance in this study.

There are peculiarities arising from the data for instance it would seem that having children is a significant differential between CFS categorisation and both MDD and MDD/CFS categorisation. Does that mean that being a parent is a predisposing factor for CFS, or does Fukuda select for over representation of parenthood and that is something which Reeves avoids, or does Reeves select for under representation of parents ? And are these differentials a general function of the Fukada/Reeves case definitions or are they specific to a limited demographic (women with a median age of 37+) ?

If one wanted to know if being a parent is a predisposing factor for CFS, one wouldn't compare it to figures for a small percentage of the population (the MDD group) but for the rest of the population. But being ill with CFS would likely affect the likelihood that one had children so one would need pre-illness data (which I don't think is always accurate currently). There is no evidence from this that the Fukuda gives an unrepresentative figure for the rate of parenthood - again one needs to look at figures for the whole population to even start looking at that. People with MDD in this study seem less likely to most people to have children - that may be a function of the fact that 62% have never been married.

Again, I don't think the rate of having children is particularly interesting in discussions of Fukuda vs Reeves.

 

[Dolphin]

From the paper:

Participants

We recruited a total of 64 individuals, 27 with CFS and
37 with MDD. We obtained our sample of participants
with CFS from two sources: local CFS support groups in
Chicago and a previous research study conducted at
DePaul University. To be included in the study, participants
were required to have been diagnosed with CFS,
using the Fukuda et al. (1994) diagnostic criteria, by a
certified physician and were required to currently meet
CFS criteria using the Fukuda et al. criteria. We excluded
individuals who had other current psychiatric conditions
in addition to major depression or who reported having
untreated medical illnesses (e.g., diabetes, anemia).

We solicited 37 participants with a diagnosis of MDD
to participate in this study. We found participants from
three sources: local chapters of the Depression and
Bipolar Support Alliance group in Chicago; Craigslist, a
free local classified ads forum that is community moderated;
and online depression support groups. To be
included in the study, all participants were required to
have been diagnosed with MDD by a licensed psychologist
or psychiatrist. We excluded individuals who had
other current psychiatric conditions in addition to MDD
(e.g., bipolar, schizophrenia) and those who reported
having untreated medical illnesses.

Participants who met criteria completed questionnaires
that are described below. Participants reported any
previous physical and mental illnesses and the date of
diagnosis as well as current medications being taken to
ensure that no other illness could account for the fatigue.
We carefully screened participants to ensure that participants
from the MDD group did not have CFS as defined
by the Fukuda et al. (1994) criteria.

 

[Dolphin]

It seems to me that the biggest question about Reeves is the massive increase in prevalence rate over Fukuda the burden to explain that should be on Reeves, rather than there being a need for other researchers to demonstrate where/ how the increase can be exists. Indeed it should be Reeves and those who support the Reeves case definition who should be required to demonstrate what their ten fold increase consists of - does it for instance vastly increase the number of children classified under CFS, or is the increase to be found disporportinaely amongst adults or older adults, and is the increase predominatly amongst females or does the ten fld increase affect the gender ratio ? These are pertinent questions for the CDC because they all impact on the CDC's current description of CFS, as extrapolated from Fukuda.

Yes, Reeves et al. should look at this issue.

If anyone is interested, they could look:

Free full text at: http://www.pophealthmetrics.com/content/5/1/5

Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia.

Popul Health Metr. 2007 Jun 8;5:5.

Reeves WC, Jones JF, Maloney E, Heim C, Hoaglin DC, Boneva RS, Morrissey M, Devlin R.

Chronic Viral Diseases Branch, Coordinating Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA. wcr1@cdc.gov

Abstract
BACKGROUND: Chronic fatigue syndrome (CFS) is a debilitating illness with no known cause or effective therapy. Population-based epidemiologic data on CFS prevalence are critical to put CFS in a realistic context for public health officials and others responsible for allocating resources.

METHODS: Based on a random-digit dialing survey we ascertained CFS cases and controls to estimate the prevalence of CFS in metropolitan, urban, and rural populations of Georgia. This report focuses on the 5,623 of 19,381 respondents ages 18 to 59 years old. Fatigued (2,438), randomly selected unwell not fatigued (1,429) and randomly selected well (1,756) respondents completed telephone questionnaires concerning fatigue, other symptoms, and medical history. Subsets of those identified by interview as having CFS-like illness (292), chronic unwellness which was not CFS-like (268 - randomly selected), and well subjects (223, matched to those with CFS-like illness on sex, race, and age) completed a clinical evaluation.

RESULTS: We estimated that 2.54% of persons 18 to 59 years of age suffered from CFS. There were no significant differences in prevalence of CFS between metropolitan, urban or rural populations or between white and black residents of the three regions. However, there were significant differences in female-to-male ratios of prevalence across the strata (metropolitan female: male 11.2 : 1, urban 1.7 : 1, rural 0.8 : 1).

CONCLUSION: We estimated that 2.54% of the Georgia population suffers from CFS, which is 6- to 10-fold higher than previous population-based estimates in other geographic areas. These differences may reflect broader screening criteria and differences in the application of the case definition. However, we cannot exclude the possibility that CFS prevalence may be higher in Georgia than other areas where it has been measured. Although the study did not identify differences in overall prevalence between metropolitan, urban, and rural Georgia populations, it did suggest the need for additional stratified analyses by geographic strata.

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Free full text at: http://archinte.ama-assn.org/cgi/content/full/163/13/1530

Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas.

Arch Intern Med. 2003 Jul 14;163(13):1530-6.

Reyes M, Nisenbaum R, Hoaglin DC, Unger ER, Emmons C, Randall B, Stewart JA, Abbey S, Jones JF, Gantz N, Minden S, Reeves WC.

Division of Viral and Rickettsial Diseases, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Public Health Service, US Department of Health and Human Services, Atlanta, GA 30333, USA.

Abstract

BACKGROUND: Chronic fatigue syndrome (CFS) is a debilitating illness with no known cause or effective therapy. Population-based epidemiologic data on CFS prevalence and incidence are critical to put CFS in a realistic context for public health officials and others responsible for allocating resources and for practicing physicians when examining and caring for patients.

METHODS: We conducted a random digit-dialing survey and clinical examination to estimate the prevalence of CFS in the general population of Wichita, Kan, and a 1-year follow-up telephone interview and clinical examination to estimate the incidence of CFS. The survey included 33 997 households representing 90 316 residents. This report focuses on 7162 respondents aged 18 to 69 years. Fatigued (n = 3528) and randomly selected nonfatigued (n = 3634) respondents completed telephone questionnaires concerning fatigue, other symptoms, and medical history. The clinical examination included the Diagnostic Interview Schedule for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, laboratory testing, and a physical examination.

RESULTS: The overall weighted point prevalence of CFS, adjusted for nonresponse, was 235 per 100,000 persons (95% confidence interval, 142-327 per 100,000 persons). The prevalence of CFS was higher among women, 373 per 100,000 persons (95% confidence interval, 210-536 per 100,000 persons), than among men, 83 per 100,000 persons (95% confidence interval, 15-150 per 100,000 persons). Among subjects nonfatigued and fatigued for less than 6 months, the 1-year incidence of CFS was 180 per 100,000 persons (95% confidence interval, 0-466 per 100,000 persons).

CONCLUSIONS: Chronic fatigue syndrome constitutes a major public health problem. Longitudinal follow-up of this cohort will be used to further evaluate the natural history of this illness.

PMID:12860574[PubMed - indexed for MEDLINE]