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Diabetes prevalence and diagnosis in US states: analysis of health surveys

Goodarz Danaei*, Ari B Friedman, Shefali Oza, Christopher JL Murray and Majid Ezzati

Population Health Metrics 2009, 7:16  doi:10.1186/1478-7954-7-16

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Authors' response to reader comment

Jolayne Houtz   (2009-10-30 00:12)  Population Health Metrics

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

Competing interests

No competing interests.

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Comments on the missing values of smoking and insurance status

Yiling Cheng   (2009-10-29 15:59)  Centers for Disease Control and Prevention email

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 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.

Competing interests

None declared

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