http://www.pophealthmetrics.com/content/7/1/16/comments The latest comments on the article 'Diabetes prevalence and diagnosis in US states: analysis of health surveys' 2009-10-30T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ http://www.pophealthmetrics.com/content/7/1/16/comments#375658 <p>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. <br/>Goodarz Danaei and Majid Ezzati, on behalf of the authors</p> Jolayne Houtz 2009-10-30T00:00:00Z http://www.pophealthmetrics.com/content/7/1/16/comments#374660 <p>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 &#8220;&#8230;50.2% of observations in NHANES were missing either smoking or insurance status&#8230;&#8221; 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 &#8220;Smoked at least 100 cigarettes in life&#8221; (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 &#8220;Covered by health insurance&#8221;(http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/hiq_c.pdf). The authors might ignore the skip pattern of these two variables. Incorrectly handling these variables may make incorrect predictions and incorrect conclusions. I am wondering whether the authors can check the document and dataset again and rerun the analyses.</p> Yiling Cheng 2009-10-29T00:00:00Z