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Archived Comments for: How common is chronic fatigue syndrome; how long is a piece of string?

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  1. Caution required when extrapolating prevalence rates to the full population

    Tom Kindlon, Irish ME/CFS Support Group

    11 June 2007

    This editorial [1] says, with regard to the CDC study[2]: "The CDC has now repeated and extended the Wichita study in Georgia, and found a prevalence of between six and ten times greater, with 2.5% of the population suffering from CFS. If this prevalence was both accurate and representative of the USA as a whole, this would suggest that some 7.5 million Americans were sufferers, compared to the previous estimates of 0.7 to 1.2 million."

    Before the 7.5 million figure is quoted, it might be useful to point out that the figure makes a number of assumptions, including that the prevalence rate for those under 18 and over 60 would be similar. However previous studies have suggested this is unlikely to be the case, with prevalence rates for young children in particular being much lower.

    The round figure of 7.5 million would be equivalent to a population of 295,275,591.

    Using this data the population estimate for 2005 was 296,410,404 (i.e. a similar figure).

    Using the same data: The population under 18 years was 73,469,580, the population over 60 was 49,791,976 and

    population aged 18-60 was 173,148,444.

    For a population of those aged over 18 and under 60 of this size, a back of the envelope calculation for CFS prevalence using the prevalence rate of 2.64%[2] would give: (173,148,444*0.0264)= 4,397,971.

    Tom Kindlon

    [1] How common is chronic fatigue syndrome; how long is a piece of string? Peter D White

    Population Health Metrics 2007, 5:6 doi:10.1186/1478-7954-5-6

    [2] Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia

    William C Reeves , James F Jones , Elizabeth Maloney , Christine Heim , David C Hoaglin , Roumiana S Boneva , Marjorie Morrissey and Rebecca Devlin

    Population Health Metrics 2007, 5:5 doi:10.1186/1478-7954-5-5

    Competing interests

    No competing interests

  2. Obesity: A Secondary Factor in CFS

    cort johnson, Phoenix-cfs.org

    11 June 2007

    Dr White suggests that obesity could be responsible for the some of the symptoms present in a subset of CFS patients described in the CDC Georgia studies. This is undoubtedly correct. Approximately 12% of the CFS patients in the CDC Wichita studies were classified as being ‘morbidly obese’, an exclusionary factor in most CFS studies but one that was controlled for (Vernon and Reeve 2006). The results from Dr. Whites Wichita studies suggest, however, that obesity plays at most a secondary factor in producing the symptoms found in CFS and probably little in producing the central symptoms of the disease.

    The first two classes of patients Dr. White was able to segregate out in his Wichita studies were equally obese (Conna et. al. 2006, Aslakson et. al. 2006). One called ‘obese-hypnoea’ had approximately 80% CFS and idiopathic fatigue patients; the second the ‘well’ group was dominated by the healthy controls. The BMI’s of both groups were similar (BMI 32-30) with the fatigue group having somewhat higher inflammatory markers (CRP: 5.3-4.2; IL-6: 68-56). These markers were similar, however, to those found in other classes dominated by non-obese but still fatigued patients; inflammatory markers in CFS patients are not necessarily a function of obesity.

    Where the obese but well and obese but ill groups did differ dramatically was in their symptoms; the obese fatigued group suffered from much higher levels of post-exertional fatigue (78-0), sleep problems (100-29), unrefreshing sleep (98-13), muscle pain (93-47), etc. than did the obese but well group; obesity in itself did not appear to contribute to many of the symptoms associated with CFS in either CFS patients or healthy controls.

    Interestingly, the two groups in which CFS patients were most prevalent (classes 5, 6) and had and displayed the severest symptoms had amongst the lowest BMI ratings (26/27) found. Over 80% of both these groups suffered from sleep problems, post-exertional fatigue and joint pains.

    Obesity estimates also vary. Dr White notes the very high rate (approx. 40%) of obesity in CFS patients.The NHANES Survey estimates, however, that 32% of Americans are obese. Other groups posit lower figures. This figure rises as the population ages; the highest incidence of obesity occurs from ages 45-64 – the same age frame that most of the CFS patients in the Wichita study fit into. This will shrink the difference between the obesity rates in CFS patients and the population at large.

    Some CFS patients are obese and obesity will add to their health issues but a closer look indicates that obesity is not associated with the symptoms most characteristic of CFS or considered most disabling by CFS patients (post-exertional fatigue, concentration problems, sleep issues); obesity is at most a secondary factor in CFS.

    Aslakson, E., Wollmer-Connar, U. and P. White. 2006. The validity of heterogeneity in chronic unexplained fatigue. Pharmacogenomics 7, 365-373.

    Conna, U., Aslakson, E. and P. White. 2006. An empirical delineation of the heterogeneity of chronic unexplained fatigue in women. Pharmacogenomics 7, 355-364.

    http://win.niddk.nih.gov/publications/PDFs/stat904z.pdf

    Vernon, S. and Reeve, W. 2006. The challenge of integrating disparate high-content data: epidemiological, clinical and laboratory data collected during an in-hospital study of chronic fatigue syndrome. Pharmacogenomics 7

    Competing interests

    None

  3. Reference to obesity a red herring?

    Tom Kindlon, Irish ME/CFS Support Group

    12 June 2007

    In his editorial, Prof White says:

    "Georgia may not be representative of the USA as a whole. For instance, we do not know the body mass index (BMI) of the Georgian sample. The Wichita sample of CFS cases contained 43% of subjects with a BMI of 30 or over, representing significant obesity [9]. This compares with 20% in the USA as a whole [13]. Since obesity is associated with fatigue [14], a similar proportion in Georgia might inflate the prevalence of CFS."

    Firstly, just because obesity can cause fatigue is quite a different from obesity causing the syndrome CFS. Using this logic, perhaps we should be saying that prevalence studies on any condition which can involve disabling fatigue (for example multiple sclerosis) may be questionable if there is a higher rate of obesity within the sample population. It is important to consider cause and effect i.e. just because people with a condition may be more obese when they are sampled years after having the illness is not the same as saying they were more obese before getting the illness and this caused them to develop the condition.

    Also the Wichita study[1], to which Prof. White refers, found a relatively low prevalence rate, of 0.235%, for CFS compared to other random-number studies including the one under review. So it seems curious to refer to this study to try to justify a hypothesis that the obesity rate in the Georgia study artificially increased the prevalence rate.

    Tom Kindlon

    [1] Prevalence and Incidence of Chronic Fatigue Syndrome in Wichita, Kansas

    Michele Reyes, PhD; Rosane Nisenbaum, PhD; David C. Hoaglin, PhD; Elizabeth R. Unger, PhD, MD; Carol Emmons, PhD; Bonnie Randall, MCP; John A. Stewart, MD; Susan Abbey, MD; James F. Jones, MD; Nelson Gantz, MD; Sarah Minden, MD; William C. Reeves, MD, MSPH

    Arch Intern Med. 2003;163:1530-1536.

    Competing interests

    No competing interest

  4. A correction - and obesity

    Peter White, Barts and the London, Queen Mary School of Medicine and Dentistry

    13 June 2007

    I am grateful to Tom Kindlon for pointing out the error I made when estimating the prevalence of CFS in the whole of the USA from the prevalence rate found by Reeves and colleagues in their study of the state of Georgia [1, 2]. As Kindlon rightly points out, the estimated 7.5 million sufferers did not allow for the lower prevalence found in the young and old. Previous research suggests that these two groups probably do have a lower incidence of the diagnosis of CFS [3]. Therefore a better estimate of the US prevalence of CFS, based on the findings of Reeves and colleagues [1], would be considerably less than 7.5 million, and closer to the 4 million that Kindlon quotes.

    It is timely to remember that my commentary at the same time suggested that we should be cautious before accepting the validity of a prevalence of 2.54% and generalising from this figure.

    As to the role of obesity in CFS, Cort Johnson rightly points to work suggesting that obesity does contribute to, but does not totally explain, the heterogeneity that underlies CFS [4]. But my larger point was that this was merely one example of how we should be cautious before generalising from one American state survey to different populations. Kindlon's point about the Wichita study population being relatively obese yet having a lower prevalence of CFS than the Georgian sample is moot, since the two surveys used different methods of ascertainment [2].

    1. WC Reeves, JF Jones, E Maloney, C Heim, DC Hoaglin, RS Boneva, M Morrissey, R Devlin. Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia. Population Health Metrics 2007, 5:5 doi:10.1186/1478-7954-5-5

    2 PD White. How common is chronic fatigue syndrome; how long is a piece of string? Population Health Metrics 2007, 5:6 doi:10.1186/1478-7954-5-6

    3 Gallagher AM, Thomas JM, Hamilton WT, White PD. Incidence of fatigue symptoms and diagnoses presenting in UK primary care from 1990 to 2001. Journal of the Royal Society of Medicine 2004;97:571-5.

    4 Vollmer-Conna U, Aslakson E, White PD. An empirical delineation of the heterogeneity of chronic unexplained fatigue. Pharmacogenomics 2006;7(3):355-364.

    Competing interests

    I have collaborated with Dr Reeves and colleagues in research into CFS.

  5. Obesity and arbitrary criteria

    Tom Kindlon, Irish ME/CFS Support Group

    13 June 2007

    Firstly, I thought I would clarify that I did not make my point about obesity rates based simply on the one study, the Wichita study[1]: the Chicago Study[2] found a prevalence of 0.422% using the same (or very similar) methodology and method of operationalizing the criteria as the Wichita study, producing a much higher score than the 0.235% score found in the Wichita study.

    However this correspondence has caused to me to reflect on the issue: I still remain to be convinced that because the residents of Wichita were more obese than the general population, the prevalence figure for CFS (as defined then) of 0.235% was artifically increased; however perhaps if the new broadened criteria lack sensitivity and specificity, the figures in the latest studies could be artificially inflated because of a higher background obesity rate?

    I think there is an important issue of a lack of sensitivity and specificity with the new method of operationalizing the criteria. As Peter White says, the current criteria are "abitrary". Whether they are being used by a "jobbing

    physician", an epidemiologist or a researcher, one of the aims of criteria should be that they have good sensitivity and specificity rates. Perhaps a direction for discourse and research in the future should be trying to arrive at CFS

    criteria that reach that aim? If necessary, having different criteria for different circumstances: for example, have one set of criteria when looking for expensive biological work but perhaps less strigent criteria for use in some clinical settings?

    Tom Kindlon

    [1] Prevalence and Incidence of Chronic Fatigue Syndrome in Wichita, Kansas Michele Reyes, PhD; Rosane Nisenbaum, PhD; David C. Hoaglin, PhD; Elizabeth R. Unger, PhD, MD; Carol Emmons, PhD; Bonnie Randall, MCP; John A. Stewart, MD; Susan Abbey, MD; James F. Jones, MD; Nelson Gantz, MD; Sarah Minden, MD; William C. Reeves, MD, MSPH Arch Intern Med. 2003;163:1530-1536.

    [2]. Jason LA, Richman JA, Rademaker AW, Jordan KM, Plioplys AV, Taylor RR, McCready W, Huang CF, Plioplys S: A community-based study of chronic fatigue syndrome. Arch Int Med 1999, 159:2129-2137.

    Competing interests

    No competing interest

  6. Recent developments

    doug fraser, ME-letterforce National e-group UK

    18 June 2007

    Professor Peter White states that: "One of the most difficult tasks in medicine is to accurately measure how common illnesses are."

    However, assuming that sufficient financial resources and the relevant expertise are available, it is quite difficult to understand why accurately measuring the frequency of illnesses should have become one of the most difficult tasks in medicine.

    "Why do we do it? Justifications include being able to plan health care and public health priorities, as well as highlighting specific diseases for extra funding for both health care and research.

    Yet the jobbing physician at the sharp edge of clinical practice cares little about the exact prevalence of a disease or illness, since this is all too obvious from the frequency of the problems presented by patients who come through the door."

    It is also unclear precisely how the 'exact prevalence' of a 'disease or illness' can be obviously ascertained from the frequency of the 'problems' presented by patients who come through the door of 'the jobbing physician'.

    Nor is it clear whether or not, or why if it is indeed the case, the jobbing physician should care 'little' about the exact prevalence of a disease or illness.

    Perhaps the caring physician may be concerned to know about the exact prevalence of a disease or illness because of an unusual relative increase in frequency in the local area, which may require the alerting of relevant authorities. Or perhaps the physician may be concerned that the patient know more about the exact frequency in order to avoid feeling 'alone', or to better understand the situation, or to prepare the patient for misunderstandings from others, who may be unaware of the rarity or otherwise involved, and who may be inclined to make unwarranted assumptions about the disease and the patient in question. Or the physician may have a family member affected by a particular disease, who might also wonder about the figures involved. The jobbing physician may have an interest in the accuracy of any implications flowing from the frequencies of various diseases.

    The caring physician may also feel entitled to the most accurate figures available so that he can use those figures to lobby authorities more effectively on behalf of his patients if need be, for better research and appropriate funding, in areas most relevant to his patients, that only he will be uniquely familiar with. There are no doubt many reasons for the physician to care about the exact prevalence of a given disease or illness.

    "How do you measure a syndrome?"

    Perhaps one would need to clarify the precise meaning of 'syndrome' in the first instance.

    According to the Mayo Clinic definition, which is about on average, it is 'a collection of symptoms that characterize a specific disease or condition', and according to the Oxford Concise Medical Dictionary, a syndrome is 'a combination of signs and/or symptoms that forms a distinct clinical picture indicative of a particular disorder.'

    Some examples of syndromes might include: Reye syndrome, Post splenectomy syndrome, Waterhouse-Friderichsen syndrome, Aortic arch syndrome, Klippel-Trenaunay syndrome, Horner syndrome, Dubin-Johnson syndrome, Chylomicronemia syndrome, Gray syndrome, HELLP syndrome, Sheehan syndrome, Alström syndrome, Caplan’s syndrome, Blind loop syndrome, Bartter syndrome, Sjogren syndrome, Parinaud syndrome, Potter syndrome, Beckwith-Wiedemann syndrome, Hurler syndrome, Aase syndrome, Short bowel syndrome, and Asherman's syndrome.

    How one might precisely 'measure' a syndrome is unclear. That many syndromes are well described and amenable to accurate diagnosis, seems rather certain however.

    "If the disease in question has no biological marker and is difficult to define clinically, the problem of working out the accurate prevalence becomes esoteric."

    Presently, Kerr JR and the Collaborative Clinical Study Group based in England are aware of '15 highly significant protein bio-markers' which could be human or viral but which are 'statistically significantly associated with CFS'.

    (http://www.abc.net.au/rn/allinthemind/stories/2007/1945419.htm)

    In addition, in 2003, an 'expert panel of 11 physicians—who have diagnosed and/or treated more than 20,000 ME/CFS patients between them—has developed a clinical case definition that provides a flexible conceptual framework based on the characteristic patterns of symptom clusters, which reflect specific areas of pathogenesis. The expert subcommittee of Health Canada selected the expert consensus panel.' http://www.haworthpress.com/store/product.asp?sku=4958 (Health Canada is the Federal department responsible for helping Canadians maintain and improve their health, while respecting individual choices and circumstances).

    Hopefully these recent developments may allow the uninitiated to assist in helping overcome the problem of working out the accurate prevalence of Chronic Fatigue Syndrome.

    Douglas T Fraser.

    Competing interests

    None declared

  7. Caution required when making numerical comparisons between Wessely (1997) and the current study

    Tom Kindlon, Irish ME/CFS Support Group

    3 October 2007

    In his editorial[1], Prof. White says:

    "Comorbid psychiatric conditions may have inflated the prevalence. A previous study found an equally high point prevalence of CFS (2.6%), by surveying United Kingdom primary care patients [10]. However, when those patients who also had a comorbid psychiatric disorder were excluded, the prevalence fell to 0.5%."

    Reference to this paper[2] is also made in the editorial's concluding paragraph and in the accompanying Reeves paper[3].

    A close inspection of table 2 of the referenced paper[2] reveals some strange figures (with regard to the estimates for the CDC '94 criteria mentioned above):

    (i) The Oxford criteria for CFS were found to have a lower prevalence, of 2.2%. Given that the CDC 94 criteria would be seen as more restrictive than the Oxford criteria (e.g. requiring symptoms as well as fatigue lasting six months), this suggests an error with one or both of the figures?

    (ii) the mean and 95% confidence intervals given for the prevalence rates without co-morbid psychological disorders for CFS (CDC 94) are given as 0.5 (0.1, 0.3) which makes no sense (the confidence intervals should be above and below the mean).

    So these two observations mean that I'm not sure how much faith should be placed with some of the figures given in that study.

    The methodology of the Wessely study was also different, using attendance at primary care physicians to screen for patients, which could lead to skewed data. The random number methodology in the Reeves study seems stronger.

    It should also be remembered that the authors of the Reeves study[3] did exclude many patients with psychological disorders before giving the diagnosis of CFS. So even if one accepts the curious data presented in Table 2 in Wessely et al[2], it seems unlikely we can extrapolate from the drop in the figures found the Wessely study to produce a similar drop in figures found in the current study[3].

    Tom Kindlon

    [1] How common is chronic fatigue syndrome; how long is a piece of string? Peter D White Population Health Metrics 2007, 5:6 doi:10.1186/1478-7954-5-6

    [2] Wessely S, Chalder T, Hirsch S, Wallace P, Wright D. The prevalence and morbidity of chronic fatigue and chronic fatigue syndrome: a prospective primary care study. Am J Pub Health 1997, 87:1449-1455.

    Available online at:

    http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1380968

    [3] Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia. William C Reeves, James F Jones, Elizabeth Maloney, Christine Heim, David C Hoaglin, Roumiana S Boneva , Marjorie Morrissey and Rebecca Devlin. Population Health Metrics 2007, 5:5 doi:10.1186/1478-7954-5-5

    Competing interests

    No competing interests

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