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how much variance explainable by IQ? (M Stewart, 30 August 2011)

Your longevity map looks like an IQ map, and Gottfredson has clearly demonstrated that IQ causes SES, not the reverse. How much of the variance in longevity or "falling behind" is explainable through county average IQ differences? Or was this not explored? read full comment

Comment on: Kulkarni et al. Population Health Metrics, 9:16

Social autopsy (Neil Pakenham-Walsh, 30 August 2011)

AbouZahr describes ascertainment by interview of (1) cause of death and (2) social, care-seeking and other factors leading to death. These are two quite different challenges with different underlying purposes. The first may be described as verbal autopsy, but the second is more usually described a social autopsy. Social autopsy is rarely conducted but could, as AbouZahr relates, provide very important information about how to break the chain of the 'road to death', whereby a chain of events, decisions, and circumstances lead to most, if not all, avoidable deaths. That chain could be broken by empowering parents and healthcare providers - including empowerment with basic clinical healthcare knowledge such as recognition of danger signs. read full comment

Comment on: AbouZahr Population Health Metrics, 9:19

Exploring the details of these findings (Peter Byass, 30 August 2011)

I would love to be able to comment in detail on these findings, which as you point out in the paper are surprising given the subsrtantial number of published studies in which the InterVA suite of interpretative models has performed well. However, I would need to see the datasets you used in order to do this. read full comment

Comment on: Lozano et al. Population Health Metrics, 9:50

Huffington Post blog about this article (Peter Byass, 30 August 2011)

There's a blog on Huffington Post relating to this article at http://www.huffingtonpost.co.uk/peter-byass/counting-hivaids-deaths_b_935993.html read full comment

Comment on: Byass et al. Population Health Metrics, 9:46

Correction of Table 2 (Henry Kalter, 30 August 2011)

In addition to the factors listed in Table 2, the updated Pathway Analysis questionnaire includes the following social, behavioral, and preventive factors that should have been included in the table:
• Care of pre-pregnancy health conditions, number of antenatal care visits, and timing of the last visit
• Decision maker for delivery place, and factors constraining institutional delivery
• Decision maker for maternal complications and newborn and child illnesses
• Newborn care (cord care, bathing, warmth, breastfeeding, post-partum counseling, well-baby checks)
• Child care (breastfeeding and nutrition, vaccinations, vitamin A, smoke exposure, insecticide-treated bed net use, care of pre-fatal illness conditions) read full comment

Comment on: Kalter et al. Population Health Metrics, 9:45

Author response to reader comment (Jolayne Houtz, 28 March 2011)

In re-examining the results, the author discovered a typographical error. The HR with 95% CI for the South West region in Model 4 of the analysis is:
1.30 (1.26 - 2.20).
read full comment

Comment on: Antai Population Health Metrics, 9:6

HR or CI incorrect (Matthew Stevens, 28 March 2011)

"The risks of under-5 deaths were significantly higher for children of mothers residing in the South South (Niger Delta) region (HR: 1.30; 95% CI: 1.76-2.20) and..."

The CI given or the HR are incorrect in the abstract. read full comment

Comment on: Antai Population Health Metrics, 9:6

Why are we only given information on 3 out of the 8 SF-36 subscales? (Tom Kindlon, 05 November 2009)

Reading this paper, one could be forgiven for thinking that the SF-36 questionnaire only has 3 subscales: Physical Functioning, Mental Health and Social Functioning as that is all we are given information on. In fact, of course, the SF-36 questionnaire has 8 subscales: Physical function, Role physical, Bodily pain, General health, Vitality, Social function, Role emotional and Mental health[1].

The authors use the empiric definition for CFS[2] which requires at a minimum that the "role physical" and "role emotional" subscales also be measured.

We also know that all 8 subscales were measured in this cohort[3]. So why was the information not given? If one was not giving the authors the benefit of the doubt, one could speculate that it was because Table 4 would not... read full comment

Comment on: Aslakson et al. Population Health Metrics, 7:17

There has been criticism of how CFS is defined in this study (Tom Kindlon, 05 November 2009)

I thought it would be useful to point out that there is controversy [1,2] with regard to the criteria [3] used in this study to define Chronic Fatigue Syndrome (CFS).

For example, the criteria for CFS used in this study do not even require a patient to have fatigue. The authors say: “We used the Multidimensional Fatigue Inventory (MFI-20) [4] to measure characteristics of fatigue” but they do not give the thresholds. Given the MFI-20 has five subscales: (General fatigue, Physical fatigue, Mental fatigue, Activity reduction and Motivation reduction), one would probably suspect that a patient would have to score poorly on one of the headings which have fatigue in their title. But the actual criteria are: a patient needs to score >=13 on MFI general fatigue or... read full comment

Comment on: Aslakson et al. Population Health Metrics, 7:17

A more detailed comparison would need to be made before one could say this replicates the previous study (Tom Kindlon, 05 November 2009)

Part of the aim of this study [1] appears to be to compare the classes that were drawn up with a previous cohort[2-4]. However it does not, to my mind, deal with this in a particularly rigorous fashion.


The main quantitative comparisons are the percentages that fall in each class (Tables 6 and 7). However, the percentages will be influence by the quantity and type of non-CFS controls used which are not the same in each cohort [to explain why this is important using an extreme example: if there were 1000 non-CFS cases for everyone one CFS case in one cohort, the percentages in each class would be different than if there was a 1:1 ratio of CFS to non-CFS cases in the other cohort].


The first study involved the following[5]:... read full comment

Comment on: Aslakson et al. Population Health Metrics, 7:17

Authors' response to reader comment (Jolayne Houtz, 30 October 2009)

We appreciate the attention to this detail by Dr Cheng. The point raised is correct and was indeed due to a skip pattern in the NHANES questionnaire. We repeated the analysis to evaluate the influence on the coefficients of regression within NHANES and predicted diabetes prevalence. Three coefficients (smoking, age 60-69, and age 70+) changed by less than 10%, and the rest remained unchanged. Predicted diabetes prevalence for different state-sex-age-race-insurance categories changed on average by 1.3% and at the most by 3.5% of the values reported in the manuscript, and hence were not sensitive to this error.
Goodarz Danaei and Majid Ezzati, on behalf of the authors read full comment

Comment on: Danaei et al. Population Health Metrics, 7:16

Comments on the missing values of smoking and insurance status (Yiling Cheng, 29 October 2009)

This article demonstrated a simple and innovative approach to answer an important question that is what the total diabetes prevalences by US states are. I read it with great interesting and noticed the authors mentioned that there were “…50.2% of observations in NHANES were missing either smoking or insurance status…” According to the documentations, this is extremely too high. For example, in NHANES 2003-2004, persons aged 20 years or older had one missing value on question “Smoked at least 100 cigarettes in life” (http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/smq_c.pdf) and persons aged 0 years or older had only 133 missing values on question “Covered by health insurance”(http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/hiq_c.pdf). The... read full comment

Comment on: Danaei et al. Population Health Metrics, 7:16

Caution required when making numerical comparisons between Wessely (1997) and the current study (Tom Kindlon, 03 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... read full comment

Comment on: White Population Health Metrics, 5:6

Many possibilities to consider for metropolitan, urban, and rural differences in sex ratio. (Claire C., 15 August 2007)

One of the most interesting and potentially informative findings from this study is the finding that the gender ratio of CFS was strikingly different among metropolitan, urban, and rural populations.The only suggestion that the authors make regarding this finding is that "The striking differences between female and male rates in the 3 strata may indicate risk effects of gender (a social construct) in distinction to sex (a biologic attribute)."This is a very interesting suggestion, in light of all the research demonstrating that CFS is a physical disorder with biological markers. We should not fall under the line of thinking that medical observations whose causes are not yet explained are psychological or social constructs, or psychosomatic. This suggestion also assumes that gender... read full comment

Comment on: Reeves et al. Population Health Metrics, 5:5

Observations on apparent changes in methods of assessing symptoms (Tom Kindlon, 06 July 2007)

I notice that the "Symptom Inventory collects information about the presence, frequency, and intensity of .. symptoms during the month preceding the interview".However the Fukuda et al '94 definition [1] is supposed to look for "the concurrent occurrence of four or more of the following symptoms, all of which must have persisted or recurred during 6 or more consecutive months of illness and must not have predated the fatigue".Was there a particular reason why a time frame of one month was chosen? This would suggest that relatively short-lived symptoms would be counted. If the reasoning was that asking people detailed questions about symptom severity and frequency over a longer period would might not be as accurate, perhaps a two-stage question could be asked: firstly... read full comment

Comment on: Wagner et al. Population Health Metrics, 3:8

More symptoms could be added to a CFS Symptom Inventory (Tom Kindlon, 21 June 2007)

Many would feel that the 8 symptoms used in the CDC '94 definition [1] were chosen in a somewhat arbitrary fashion; so it is to be welcomed that the CDC itself has started to look beyond these symptoms with the CDC CFS Symptom Inventory. The idea of a Short Form of the CDC Symptom Inventory is also interesting.However, it is not clear to me where the extra symptoms that are on the CDC CFS Symptom Inventory came from. For example, I didn't see some of the symptoms listed in Reeves et al [2].In 2001, De Becker et al [3] published data on the symptoms found in over 2500 patients. They tried to improve on the 1988 [4] and 1994 CDC criteria. They suggested a list of symptoms that could be used to strengthen the ability to select ME/CFS patients. Many of the symptoms they mentioned are not... read full comment

Comment on: Wagner et al. Population Health Metrics, 3:8

Whither Post-exertional Fatigue? (cort johnson, 21 June 2007)

The Empirical Definition has many positive aspects; better characterization of CFS patients, a way to track treatment efficacy and perhaps identify symptom based subsets and it does appear to identify a very ill population. But does it single out the peculiar condition called CFS. Some aspects of it suggest to me that it does not. Some researchers have proposed that post-exertional fatigue is a hallmark symptom in CFS. The Canadian Consensus and 1990 Australian definition require post-exertional fatigue to be present for a CFS diagnosis. The Fukuda definition does not; although it is one of eight major symptoms it is not required for a CFS diagnosis. The empirical definition appears to dilute the importance of this symptom further. Some evidence produced by CDC studies and others, however... read full comment

Comment on: Reeves et al. Population Health Metrics, 5:5

Questioning the use of the Role Emotional (RE) subscale of the SF-36 questionnaire in the diagnosis of CFS (Tom Kindlon, 19 June 2007)

As background to the previous two comments, I thought I'd point out that if people would like to see what makes up the Role Emotional (RE) subscale of the SF-36, a copy of a sample SF-36 questionnaire can be seen at: <http://www.nhlbi.nih.gov/resources/deca/wave/w11.pdf> .It is question 6 i.e. 3 questions with only yes or no as possible answers.The cut off point used in the current study is less than or equal to a score of 66 [1], so two "yes" answers (out of the three questions) is the cut off point for functional impairment.Tom Kindlon[1] Chronic Fatigue Syndrome – A clinically empirical approach to its definition and study. William C Reeves, Dieter Wagner, Rosane Nisenbaum, James F Jones, Brian Gurbaxani, Laura Solomon, Dimitris A Papanicolaou, Elizabeth R Unger, Suzanne D... read full comment

Comment on: Reeves et al. Population Health Metrics, 5:5

Does the use of the 'role emotional' subscale of the SF-36 help with sensitivty and specificity rates? Can we find out the prevalence rate if this subscale had not been used? (Sarah LaBelle, 18 June 2007)

This paper presents results long awaited, prevalence of CFS beyond metropolitan areas. The huge difference in the metropolitan area rate of the Georgia study as compared to prior rates based on studies in other metropolitan areas is not well explained. The pre-publication discussion includes comment by the authors that this difference is not important, rather it is important that CFS is not diagnosed by simple physical test measurements. The prevalence rate of this study is its single most important result. Huge variation needs more exploration of reasons why they occurred, and whether the result is reliable. Inclusion of the 'role emotional' subscale constitutes a substantial change in diagnostic method from prior work by the same lead author. The authors state they are using... read full comment

Comment on: Reeves et al. Population Health Metrics, 5:5

Recent developments (doug fraser, 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... read full comment

Comment on: White Population Health Metrics, 5:6

Does the use of the "Role emotional" subscale of the SF-36 help with sensitivity and specificity rates? Can we find out the prevalence rate if this subscale hadn't been used? (Tom Kindlon, 15 June 2007)

It is to be welcomed that attempts are being made to operationalize the CDC (94) CFS criteria [1], enabling easier comparisons between studies and making it easier for researchers to try to replicate findings.So for example, having some sort of numerical value on a symptom so that one can say whether a symptom is present or not in a patient seems to be a good idea.However if one is aiming to do this, it would seem preferable to choose methods that have good sensitivity and specificity rates for the condition in question. And it's questionnable whether the methods used in this study have good sensitivity and specificity.The authors claim that they "used stringent (i.e., <= 25th percentile population norms on any of the 4 SF-36 scales) to define severe functional impairment".... read full comment

Comment on: Reeves et al. Population Health Metrics, 5:5

Obesity and arbitrary criteria (Tom Kindlon, 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... read full comment

Comment on: White Population Health Metrics, 5:6

A correction - and obesity (Peter White, 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... read full comment

Comment on: White Population Health Metrics, 5:6

Reference to obesity a red herring? (Tom Kindlon, 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... read full comment

Comment on: White Population Health Metrics, 5:6

Obesity: A Secondary Factor in CFS (cort johnson, 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... read full comment

Comment on: White Population Health Metrics, 5:6