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Estimates of cutoffs with specificities and sensitivities for urine cotinine and hydroxycotinine for US adults aged ≥ 20 years to classify smokers and nonsmokers.

Ram Baboo Jain
Published in: Environmental science and pollution research international (2020)
Data for urine cotinine and hydroxycotinine became available for the first time in the 2013-2014 cycle of the National Health and Nutrition Examination Survey administered by the US Centers for Disease Control and Prevention. Cutoffs to classify smokers and nonsmokers for both cotinine and hydroxycotinine for US adults aged ≥ 20 years were developed by using receiver operating characteristic curve methodology. The optimality criterion used to determine cutoffs simultaneously maximized both specificity and sensitivity. Cutoffs were determined for the total population, males, females, non-Hispanic whites, non-Hispanic blacks, Hispanics, and non-Hispanic Asians. Cutoffs for both cotinine and hydroxycotinine were determined with a minimum sensitivity of 95.5% and with a minimum specificity of 95.4%. For the total population, cutoff for urine cotinine was 91.7 ng/mL estimated with a specificity as well as a sensitivity of 97.1%. The cutoff for the total population for urine hydroxycotinine was 128.0 ng/mL estimated with a specificity as well as a sensitivity of 96.5%. The order in which cutoffs were observed for cotinine was non-Hispanic blacks (283.0 ng/mL) > non-Hispanic whites (111.0 ng/mL) > males (109.0 ng/mL) > females (91.7 ng/mL) > total population (91.7 ng/mL) > Hispanics (20.8 ng/mL) > non-Hispanic Asians (7.39 ng/mL). The order in which cutoffs were observed for hydroxycotinine was non-Hispanic blacks (530.0 ng/mL) > non-Hispanic whites (180.0 ng/mL) > females (97.0 ng/mL) > total population (96.5 ng/mL) > males (95.9 ng/mL) > Hispanics (20.6 ng/mL) > non-Hispanic Asians (13.8 ng/mL). Thus, the largest cutoffs were observed for non-Hispanic blacks and the lowest cutoffs were observed for non-Hispanic Asians.
Keyphrases
  • african american
  • smoking cessation
  • electronic health record
  • data analysis