Building a Bayesian Bridge From Evidence to Guidelines: Comment on "Bayesian Classification of Clinical Practice Guidelines", Aug 10/24, 2009, Goodman 169 (15): 1436

Building a Bayesian Bridge From Evidence to Guidelines: Comment on
"Bayesian Classification of Clinical Practice Guidelines"

http://archinte.ama-assn.org/cgi/content/full/169/15/1436?etoc

If interested Bayesian, this is a nice article with some good
references.

ACSM addresses myths about weight loss, exercise

Dear ACSM members and certified professionals, Last Friday, an article appeared in Time magazine making statements that we believe run counter to fact and the public interest. The article claimed that exercise, contrary to the research with which we’re all familiar, is not an effective health tool, particularly as it pertains to weight loss. While an ACSM member and expert was consulted for the story, he agrees that his research and opinions were selectively reported. Among its numerous claims, the story would have us believe that: Losing weight matters more than being aerobically fit in preventing heart disease One can’t lose weight from exercise because exercise makes you hungrier – and willpower can’t conquer the hunger enough to make good food choices Exercising 60 to 90 minutes most days of the week in order to lose weight (a recommendation from an ACSM Position Stand) is unrealistic Leisure-time physical activity – just moving around more during the day – is more effective for weight loss than dedicated exercise Vigorous exercise depletes energy resources so much that it leads to overeating – i.e., weight gain Your assistance is needed in getting the right health message out to the public. These suggested talking points will help you dispel myths and confirm the value of exercise to your patients, clients and colleagues.   Also, we encourage you to adapt this letter to the editor and submit it to your local news outlets, helping readers and viewers get the best evidence-based facts and information.   Thank you for your involvement as we continue to increase awareness of the true benefits and advantages of a regular physical activity program.   Sincerely, The American College of Sports Medicine

401 W. Michigan St., Indianapolis, IN  46202  |  Privacy Policy To unsubscribe from future emails, click here. © 2009 American College of Sports Medicine

 

FW: Three decade change in the prevalence of hearing impairment and its association with diabetes in the United States

Three decade change in the prevalence of hearing impairment and its
association with diabetes in the United States
Yiling J. Cheng, Edward W. Gregg, Jinan B. Saaddine, Giuseppina
Imperatore, Xinzhi Zhang and Ann L. AlbrightAbstract
Objective: to examine the secular change of the prevalence of hearing impairment over three decades in U.S. adults with and without diabetes.Methods: the cross-sectional National Health and Nutrition Examination Surveys (NHANES, the 1971-1973 [NHANES I] and the 1999-2004 [NHANES 1999-2004]) were used. Average pure-tone audiometry thresholds in decibels (dB) at 1, 2, 3, 4 kHz frequencies of the worse ear were used to represent the participants' hearing status. Any hearing impairment was defined as average pure-tone audiometry threshold of the worse ear >25 dB.Results: From 1971 to 2004, among adults without diabetes aged 25 to 69 years, the unadjusted prevalence of hearing impairment decreased from 27.9% to 19.1% (P <0.001), but among adults with diabetes there was no significant change (46.4% to 48.5%). After adjustment for age, sex, race, and education, the prevalence of hearing impairment in the NHANES I and NHANES 1999-2004, respectively, was 24.4% (95% confidence interval
[CI], 22.3-26.6%) and 22.3% (95% CI, 20.4-24.2) for adults without diabetes and 28.5% (95% CI, 20.4-36.6%) and 34.4 (95% CI, 29.1-39.7%) for adults with diabetes. The adjusted prevalence ratios of hearing impairment for persons with diabetes vs. those without diabetes was 1.17 (95% CI, 0.87-1.57) for the NHANES I and 1.53 (95% CI, 1.28-1.83) for NHANES 1999-2004.Conclusions: Persons with diabetes have a higher prevalence of hearing impairment, and they have not achieved the same reductions in hearing impairment over time as have persons without diabetes.

DOMAIN statement of PROC SURVEYLOGISTIC

DOMAIN statement of PROC SURVEYLOGISTIC

In SAS 9.2 there is a DOMAIN statement. Prior to SAS 9.2, there is a
trick to getting the a close approximate analysis might be helpful.

What you can do is to assign a near zero weight to observations that
don't belong to your current domain. The reason that you can not simply
make the weight zero is that the procedure will exclude the observation
with zero weight. For example, if you have a domain gender=male or
female, and if you do

if gender=male then newweight=weight; else newweight=1e-6;

Then you perform the logistic regression using the newweight:

weight newweight;

Do Contaminants Play a Role in Diabetes?

Do Contaminants Play a Role in Diabetes?
A study linking a pesticide in fish to diabetes adds to the growing chorus of studies suggesting that environmental contaminants may play a role in the widespread disease.

By Andrew McGlashen and Environmental Health News, http://www.scientificamerican.com/article.cfm?id=pesticide-and-diabetes&print=true

Eat right and exercise, conventional wisdom has it, if you want to avoid joining the diabetes epidemic.
But a new study adds some muscle to a growing body of research suggesting those steps, though beneficial, might not be enough for people exposed to chemicals in the environment.

The scientists linked diabetes and people’s body burdens of DDE, a chemical produced as the body breaks down the pesticide DDT, banned in the United States more than 35 years ago.

“Even though we haven’t used DDT in decades, its metabolites are still detected in almost everyone in the country,” said lead researcher Mary Turyk, an epidemiologist at the University of Illinois-Chicago’s School of Public Health.

Since the early 1990s, researchers have monitored a group of Great Lakes charter boat captains, recreational fishermen and others to learn about the health effects of eating fish tainted with persistent organic pollutants – chemicals that remain in the environment for decades and grow more concentrated as they move up food chains.

For the new study, blood samples from the Great Lakes group showed “consistent, dose-related associations of DDE” with diabetes, the researchers wrote in the July issue of Environmental Health Perspectives.

Among 471 adults, including 36 with diabetes, there was no link to the disease based on the amount of fish consumed or exposure to other pollutants. But the higher the concentration of DDE in the blood, the more likely they were to develop diabetes.

The study is among the strongest voices in a chorus of research supporting the link between environmental chemicals and diabetes, according to David O. Carpenter, director of the Institute for Health and the Environment at the University of Albany in New York. He was not involved in the study.

“Most people have not thought of diabetes as a disease related to environmental exposure,” he said, “and these studies show that it is. The science has been growing very, very rapidly, and to my mind, it’s one of the most exciting developments in the study of diabetes.”

Diabetics cannot produce or use enough insulin, a hormone that lets glucose – the body’s fuel – enter cells. More than 23 million Americans, or eight percent of the population, are diabetic, and that group swelled by 13.5 percent from 2005 to 2007, according to the American Diabetes Assn.

For the most common type of diabetes, Type 2, obesity and lack of exercise play a key role. The bulk of studies searching for a cause have focused on lifestyle factors, while research on environmental influences hasn’t been prominent in journals devoted to the disease, said Henry Kahn, an epidemiologist with the Centers for Disease Control and Prevention’s Division of Diabetes Translation.

“But maybe it should be. It would be foolish to overlook pollution as a factor,” he said, adding that he and colleagues have lately taken a greater interest in the role of pollutants. “We recognize it’s possibly a very important thing,” he said. “We agree it’s on the list of things worth studying.”

Oliver Jones, a biochemist at the University of Cambridge, wrote in the journal Lancet last year that “if there is indeed a link” between contaminants and diabetes, “the health implications could be tremendous. There has been almost no consideration for the possible influence of environmental factors such as pollution."

Among the reasons to believe that the environment might be involved in diabetes, according to Carpenter, is that its prevalence varies across geographic areas, and people who move to places where it’s more common become more likely to get sick. Kahn, however, said that effect could be due to people migrating to more developed areas, where a richer diet and more sedentary lifestyle are the norm.

Further evidence came from a sweeping study of more than 2,000 adults, conducted by the U.S. Centers for Disease Control and Prevention, that found people with the highest levels of six pollutants were 38 times more likely to have diabetes than those with the lowest exposure. The chemicals, including PCBs, dioxins and DDE, were chosen because they were present in at least 80 percent of participants.

“That’s just mind-boggling,” Carpenter said.
Also, Vietnam veterans exposed to the dioxin-laced defoliant Agent Orange were significantly more likely than average to become diabetic, prompting the government to offer compensation to diabetic veterans.

The way the new Great Lakes study was conducted makes its findings especially convincing, according to the authors and other experts.

Other research has found similar links between diabetes and pollutants, but they were cross-sectional studies, which means “you measure the level of a chemical and ask people if they have diabetes,” Turyk said.

Those studies could easily be skewed, Turyk said, because they don’t indicate whether diabetes developed before a person was exposed to pollutants. But in the new paper, she and colleagues measured participants’ exposure to DDE from blood samples taken in the mid-1990s, then followed up with them for nearly a decade to see who among them became diabetic, thereby ensuring that diabetics were exposed before they were diagnosed.

The paper further bulwarked the claim by discrediting the hypothesis that the link between the two is a statistical fluke. Critics have suggested that pollutants like DDE only appear to be potential causes of the disease because diabetics more slowly break down the chemicals, and therefore carry more of them.

But Turyk, sharing principal research duties with Henry Anderson of the Wisconsin Division of Public Health and Victoria Persky at the University of Illinois-Chicago, quashed that theory by showing no difference in DDE metabolism rates between diabetics and non-diabetics.

“This paper clearly shows that’s not the case,” said Carpenter. “It’s a very important contribution because of that fact.”

The researchers controlled for obesity, age and other risk factors, and still found a link to DDE exposure. The study didn’t distinguish between Type 2 and Type 1, or early-onset diabetes, but most diabetics in the study suffered from Type 2, which is more common in adults.

The authors said the relatively small number of participants and short duration limit the reliability of the findings. In addition, the link to DDE was relatively weak compared with past research.

Like nearly all human health research, it doesn’t directly show that chemicals in the environment cause diabetes.
“With epidemiology, you gather a body of evidence against something,” she said. “You can never really prove something causes something else.”

Scientists still don’t understand the mechanism by which DDE and other chemicals might contribute to diabetes, according to Carpenter, though he said pollution seems to disrupt the way genes produce proteins and “basically change the biochemistry of the cell.”

“It may be that they’re toxic to the pancreas,” which produces insulin Kahn added. “We don’t know.”
For now, common-sense precautions are everyone's best bet, Carpenter said.
“Obese people are usually obese because they eat too many animal fats, and animal fats are where these contaminants are commonly found,” he said.

Turyk added that “people should definitely follow sport fish advisories,” which warn about contaminants in waterways.
This article originally ran at Environmental Health News, a news source published by Environmental Health Sciences, a nonprofit media company.

HOMA2 calculator

HOMA2 calculator

New insights on the simultaneous assessment of insulin sensitivity and beta-cell function with the HOMA2 method

Use and Abuse of HOMA Modeling
Homeostatic model assessment (HOMA) is a method for assessing β-cell function and insulin resistance (IR) from basal (fasting) glucose and insulin or C-peptide concentrations. It has been reported in >500 publications, 20 times more frequently for the estimation of IR than β-cell function.
This article summarizes the physiological basis of HOMA, a structural model of steady-state insulin and glucose domains, constructed from physiological dose responses of glucose uptake and insulin production. Hepatic and peripheral glucose efflux and uptake were modeled to be dependent on plasma glucose and insulin concentrations. Decreases in β-cell function were modeled by changing the β-cell response to plasma glucose concentrations. The original HOMA model was described in 1985 with a formula for approximate estimation. The computer model is available but has not been as widely used as the approximation formulae. HOMA has been validated against a variety of physiological methods.
We review the use and reporting of HOMA in the literature and give guidance on its appropriate use (e.g., cohort and epidemiological studies) and inappropriate use (e.g., measuring β-cell function in isolation). The HOMA model compares favorably with other models and has the advantage of requiring only a single plasma sample assayed for insulin and glucose.
In conclusion, the HOMA model has become a widely used clinical and epidemiological tool and, when used appropriately, it can yield valuable data. However, as with all models, the primary input data need to be robust, and the data need to be interpreted carefully.

Trends in nutrient intake among adults with diabetes in the United States: 1988-2004

REENA OZA-FRANK, MS, MPH, RD; YILING J. CHENG, PhD; K. M. VENKAT NARAYAN, MD; EDWARD W. GREGG, PhD
BACKGROUND: Weight loss through dietary modification is key to type 2 diabetes self-management, yet few nationally representative data exist on dietary trends among people with diabetes. OBJECTIVE: To examine dietary changes, via nutrient intakes, among US adults with diabetes between 1988 and 2004. DESIGN: Nutrition data from the cross-sectional National Health and Nutrition Examination Surveys (Phase I: 1988-1990 and Phase II: 1991-1994) and 1999-2004 of adults with self-reported diabetes were examined. Twenty-four-hour dietary recall data were used to assess changes in energy; carbohydrate; protein; total, saturated,
polyunsaturated, and monounsaturated fat; cholesterol; fiber; sodium; and alcohol intake. STATISTICAL ANALYSES: Consumption of total energy and specific nutrients per day were estimated by survey, controlled for age and sex, using multiple linear regression and adjusted means (with standard errors). RESULTS: Between 1988 and 2004 there was no significant change in self-reported total energy consumption among adults with self-reported diabetes (1,941 kcal/day in 1988-1990 to 2,109 kcal/day in 2003-2004, P for trend=0.22). However, there was a significant increase in the consumption of carbohydrate (209 g/day in 1988-1990 to 241 g/day in 2003-2004; P for trend=0.02). In analyses stratified by age group, changes in dietary consumption were noted among persons aged 45 to 64 years; specifically, increases in total energy (1,770 to 2,100 kcal/day, P for trend =0.01) and carbohydrate consumption (195 to 234 g/day, P for trend=0.02). CONCLUSIONS: Despite recommendations to lose weight, daily energy consumption by individuals with diabetes showed no significant change, except in individuals aged 45 to 64 years, where an increase was observed. Overall, there was an increase in carbohydrate consumption. Emphasizing the equal importance of energy reduction and changes in dietary composition for people with diabetes is important for optimal self-management.

Exercise Training for Type 2 Diabetes Mellitus: Impact on
Cardiovascular Risk: A Scientific Statement From the American Heart
Association -- Marwick et al. 119 (25): 3244 -- Circulation

http://circ.ahajournals.org/cgi/content/full/119/25/3244

Abdominal obesity slide show that is downloadable

Abdominal obesity slide show

The Controversies in Obesity, Diabetes and Hypertension (CODHy) Meeting

http://care.diabetesjournals.org/content/vol31/Supplement_2/

My First Stata Program

My first Stata Program

capture program drop tabmm program tabmm version 12 syntax varlist [if][in][,col] local varnum : word count `varlist' local varnumminus1 = `varnum' -1 forvalues i=1/`varnumminus1' { local x : word `i' of `varlist' local j=`i'+1 forvalues k=`j'/`varnum' { local y: word `k' of `varlist' svy: tabulate `x' `y' } } end capture program drop tabm program tabm version 12 syntax varlist[,cell count column row se ci cv percent proportion] local varnum : word count `varlist' local x : word 1 of `varlist' forvalues i=2/`varnum' { local y: word `i' of `varlist' svy:tabulate `x' `y',`col' `cell' `se' `percent' format(%5.1f) } end tabm sex race5grp diabetes,cell se percent

The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors

http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.
1000058

Background
Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and
comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight-obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.

Methods and Findings
We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000-500,000) and 395,000 (372,000-414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight-obesity (216,000; 188,000-237,000) and physical inactivity (191,000; 164,000-222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000-107,000), low dietary omega-3 fatty acids (84,000; 72,000-96,000), and high dietary trans fatty acids (82,000; 63,000-97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000-40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000-94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis,
alcohol use disorders, road traffic and other injuries, and violence.

Conclusions
Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.

Brown Fat Identified as Heat-Yielding Cells in Humans - NYTimes.com

Brown Fat Identified as Heat-Yielding Cells in Humans
http://www.nytimes.com/2009/04/09/health/research/09fat.html?_r=2&partne
r=rss&emc=rss

Not about visceral fat or subcutaneous fat, but more information about
brown fat.

For more than 30 years, scientists have been intrigued by brown fat, a
cell that acts like a furnace, consuming calories and generating heat.
Rodents, unable to shiver effectively to keep warm, use brown fat
instead. So do human infants, who do not shiver very well. But it was
generally believed that humans lose brown fat after infancy, no longer
needing it once the shivering response kicks in.

That belief, three groups of researchers report, is wrong.

Medical Calculator/Unit Converter

Medical Calculator/Unit Converter
Clinical Analyte Unit Conversions
(http://dwjay.tripod.com/conversion.html).
Medical Algorithmas (http://www.medal.org/visitor/login.aspx): More than
12,500 Scales, Tools, Assessments, Scoring Systems, and other Algorithms
intended for Medical Education and for Biomedical Research.
Conversion & Calculation Center
(http://www.convertit.com/Go/ConvertIt/).
Converber (http://www.xyntec.com/): Converber is a unit converter. It is
a powerful software utility that will help make easy conversions between
1241 various units of measure in 33 categories. Converber converts
everything from length and force to flow and temperature. See some of
the features listed below.

The Diabetes Prevention Program: How the Participants Did It

The Diabetes Prevention Program: How the Participants Did It
http://www.medscape.com/viewarticle/587049?src=top10

Blood glucose self-monitoring in type 2 diabetes: a randomised controlled trial

Blood glucose self-monitoring in type 2 diabetes: a randomised
controlled trial

This link gets you the EXEC SUMMARY -- better than reading all 72 pages!

http://www.hta.ac.uk/execsumm/summ1315.htm

Hedgehog reappears, loses to fox

Hedgehog reappears, loses to fox
International Journal of Epidemiology 2007; 36:3-10
========================================

In a famous essay, Isaiah Berlin used a fragment from an ancient Greek poem to characterize '[O]ne of the deepest differences which divide writers and thinkers, and, it may be, human beings in general.' That fragment is: 'The fox knows many things, but the hedgehog knows one big thing.' He continued, [T]here exists a great chasm between those, on one side, who relate everything to a single central vision, one system less or more coherent or articulate, in terms of which they understand, think and feel ... and, on the other side, those who pursue many ends, often unrelated and even contradictory, connected, if at all, only in some de facto way ... The first kind of intellectual and artistic personality belongs to the hedgehogs, the second to the foxes.40 Hedgehogs are likely to think of prediction as a deductive exercise, whether based upon functionalism, free market economics or Marxism, whereas foxes are likely to make predictions based upon careful observations of particular cases. And studies of political forecasting indicate that foxes are better forecasters than hedgehogs, precisely because foxes are not committed to an overarching theory but are able to learn from their mistakes and remain open to new information. In a study of the forecasting accuracy of political experts, Philip Tetlock41 found that those who were least accurate looked very much like hedgehogs: '[T]hinkers who "know one big thing", aggressively extend the explanatory reach of that one big thing into new domains, display bristly impatience with those who "do not get it", and express considerable confidence that they are already pretty proficient forecasters, at least in the long term.'42 They are people who are likely to 'trivialize evidence that undercuts their preconceptions and to embrace evidence that reinforces their preconceptions.'43 Those who were more accurate 'look like foxes': [T]hinkers who know many small things (tricks of their trade), are skeptical of grand schemes, see explanation and prediction not as deductive exercises but rather as exercises in flexible 'ad hocery' that require stitching together diverse sources of information, and are rather diffident about their own forecasting  prowess, and ... rather dubious that the cloudlike subject of politics can by the object of a clocklike science.44 Foxes have a 'more balanced style of thinking about the world-a style of thought that elevates no thought above criticism.'45 Social epidemiology is more nearly akin to political forecasting than to physics. When considering the ssociations between sex, race and social roles on the one hand and health and disease on the other, accurate prediction is unlikely to rest upon deductive science and more likely to result from stitching together all that one can know about the context-institutional, cultural, political, epidemiological-in which particular populations live and work. Thus, social epidemiology is scientific as it reconstructs the past and explains the present, but it is not likely to be powerfully predictive. When it is successfully predictive, it is not likely to be because it is based upon deductions from scientifically valid generalizations that are true across time and place, but because analysts understand more or less intimately the people and places with which they are concerned, and because they can extrapolate sensibly from relevant experiences and groups elsewhere. 

QOL and exercise

The effects of exercise interventions on quality of life in clinical and healthy populations; a meta-analysis

Fiona Bridget Gillison, Suzanne M. Skevington, Ayana Sato, Martyn Standage and Stella Evangelidou
^aUniversity of Bath, Claverton Down, Bath BA2 7AY, United Kingdom

Available online 18 March 2009.
Abstract
The aim of the study was to provide an overview of the effect of exercise interventions on subjective quality of life (QoL) across adult clinical populations and well people, and to systematically investigate the impact of the exercise setting, intensity and type on these outcomes. From a systematic search of six electronic databases, 56 original studies were extracted, reporting on 7937 sick and well people. A meta-analysis was conducted on change in QoL from pre- to post-intervention compared with outcomes from a no-exercise control group, using weighted (by the study's sample size) pooled mean effect sizes and a fixed-effects model. Significant differences in outcome were found when treatment purpose was compared; prevention/promotion (well populations), rehabilitation, or disease management. Three to 6 months post-baseline, a moderate positive effect of exercise interventions was found for overall QoL in rehabilitation patients, but no significant effect for well or disease management groups. However, physical and psychological QoL domains improved significantly relative to controls in well participants. Psychological QoL was significantly poorer relative to controls in the disease management group. This pattern of results persisted over 1 year. With some exceptions, better overall QoL was reported for light intensity exercise undertaken in group settings, with greater improvement in physical QoL following moderate intensity exercise. The implications for future health care practice and research are discussed.

Linkage of HDR capabilities software

Linkage of HDR capabilities software

http://www.hdrlabs.com/tools/links.html

Everything you need to know about HDRI
By Christian Bloch
http://www.hdrlabs.com/news/index.php
High Dynamic Range Imaging is a method to digitally capture and edit all light in a scene. It represents a quantum leap in imaging technology, as revolutionary as the leap from Black & White to Color imaging. If you are serious about photography, you will find that HDRI is the final step that places digital ahead of analog. The old problem of over- and underexposure in analog photography, which was never fully solved, is elegantly bypassed here. A huge variety of subjects can now be photographed for the first time ever.

HDRI emerged from the movie industry, and was once Hollywood's best kept secret. It is now a mature technology available to everyone. The only problem was that it was poorly documented until now. The HDRI Handbook is the manual that was missing.

Many questions remain open even for the hip CG artists that have been using HDRI for years. This is where The HDRI Handbook comes in. Included here is everything you need to build a comprehensive knowledge base that will enable you to become really creative with HDRI. This book is packed with practical hints and tips, software evaluations, workshops, and hands-on tutorials. Whether you are a photographer, CG artist, compositor, or cinematographer, this book is sure to enlighten you.