Background & Significance

1. Assessing Diagnosis Risk by Smoking ClassificationAcross the Lifespan: Where do Former Smokers Belong? S.Nichol Munk1, Michelle Cardi1,Faika Zanjani, PhD1, K.Warner Schaie, PhD2, & Sherry L.Willis, PhD2 1Graduate Center for Gerontology, University of Kentucky, Lexington, KY; 2Human Development and Family Studies, Pennsylvania State University, University Park, PA Please direct inquiries to Niki Munk at niki.munk@uky.edu Abstract Methods Results Introduction:The purpose of this study was to (1) examine the relationship between smoking behavior and selected disease diagnoses, based on how former smokers are classified and (2) determine how age contributed to the relationship between smoking and disease diagnosis outcomes. We hypothesized that (1) risk of diagnosis will vary according to how smoking is coded and healthy smoking status is defined. Further, (2) age will have a significant effect on disease diagnosis risk outcomes along with be identified as having an interaction effect with smoking behavior. Methods:Participants (N = 997) from the 1993 wave of the Seattle Longitudinal Study were used to analyze the relationship between smoking, disease diagnosis outcomes (obtained from 1992-1998 medical records), and age group: young = 25-44, middle-aged = 45-64, young-old = 65-74, old-old = 75-91. Three smoking classifications were used: (1) current, former, and lifetime non-smokers, (2) current smokers vs. former and lifetime non-smokers, and (3) current and former smokers vs. lifetime non-smokers. Results:Chi-squares results indicated that cataracts (p=0.03), circulatory disease (p=0.05), diabetes (p=0.03), and hypertension (p=0.007) were predicted by three tired smoking status. Diabetes (p=0.01), hypertension (p=0.004), and ischemia/heart disease (p=0.05) diagnosis risks were predicted by smoking status when former smokers were classified as risky along with current smokers. Current smokers vs. former and lifetime non-smokers classification failed to predict any disease outcomes. Overall, former smokers were at the greatest risk for selected disease diagnosis. In addition, higher age was associated with greater risk of disease diagnosis and logistical regressions indicated that smoking continued to predict diabetes and hypertension, after including the age effect. Conclusions:Former smokers have the greatest risk for specific disease diagnoses; when dichotomous coding is necessary for smoking status former smokers should be categorized as “risky” with current smokers. Table 3. Prevalence (%) of Diagnoses among Individuals per Smoking Classification Model; n = 997 Study Sample (Table 1) Participants (n=997) from the 1993 Wave of the Seattle Longitudinal Study (SLS) with data from the Health Behaviour Questionnaire and HMO data from disease diagnosis outcomes, 1992-1998. Table 1. Participant Characteristics; n = 997 Note. Bolder values are considered significant; DCI, risky = current smokers only& healthy = former and lifetime non-smokers; DCII, risky = current and former smokers & healthy = lifetime non-smokers only; Age and Smoking Risk Analysis for Disease Diagnoses • In logistic regressions controlling for age, smoking behavior still significantly predicted (p ≤ 0.05) diagnosis outcomes for diabetes and hypertensive disease. (Figures 3 and 4) * Indicates smoking classification still predicts outcome for disease diagnosis when controlling for age; p ≤ 0.05 Smoking Classification Predicting Disease Diagnosis (Table 3) • Former smokers were the most at risk of diagnosis for cataracts (p=.03), circulatory disease (p=.05), diabetes (p=.03), and hypertension than current or lifetime non-smokers. • Of the two dichotomous smoking classifications, analysis using DCII significantly predicted diabetes (p=.01), hypertension (p=.004), and ischemia/heart disease (p=.05). Figure 3. Diabetes Diagnosis Percentage per Smoking Classification Age Analysis (Figure 1) • When smoking status was evaluated over age groups: (p < 0.001) • When diagnosis risk was evaluated over age groups: (p < 0.001) (Figure 2) Background & Significance Figure 1. Age Percentage per Smoking Categorization • In studying health behaviors, it is important to clearly delineate risky and healthy behaviors for each variable. However, clear dichotomous delineations for some health behaviors (e.g. smoking) is difficult due to the complexities associated with the variable. The effect of smoking behavior on health outcomes is often considered across three smoking delineations; current, former, and lifetime non-smoker. In order to dichotomously assign smoking behavior, it must be determined where it is most appropriate to categorize former smokers. Considering the connection between disease risk level and the passage of time for former smokers (American Heart Association, 2007), it is reasonable to expect that while some former smokers are at the same relative health risk as lifetime non-smokers (e.g. those who have not smoked for a substantial number of years), former smokers who have not had a substantial time pass since smoking cessation may be at relatively higher risks of developing disease than a majority of those in the “healthy” category for smoking. This issue contributes to potential confounds in study results when dichotomous coding of smoking behavior is used to investigate health outcomes. American Heart Association. (2007). Learn and Live. Retrieved 5-10-2007 from: http://www.americanheart.org/presenter.jhtml?identifier=4735 Figure 4. Hypertension Diagnosis Percentage per Smoking Classification Note.†Indicates significant age differences; p ≤ 0.001 Assessments • Smoking • Disease Diagnoses • Participants coded 1 if receiving physician diagnoses of selected diseases (see Table 1) as categorized by ICD-9 codes any year from 1992 through 1998. Table 2. Smoking Classifications and Descriptions Reference: Figure 2. Age Group Diagnosis Percentages across Diagnoses Conclusions Purpose • Hypothesis 1 – Risk of diagnosis will vary according to how smoking is coded and healthy smoking status is defined: Supported DCI smoking classification (former smokers as healthy) did not predict any disease diagnosis outcomes. However, significant effects of disease risk are identified when former smokers are dichotomously coded as healthy with lifetime non-smokers for select diagnoses. Due to having the highest diagnosis risk for selected diseases, former smokers should be categorized as risky when dichotomous coding is necessary for health behavior study. Replication studies are needed to see if these results are supported when focus is only on former and lifetime non-smokers or only former and current smokers. • Hypothesis 2 – Age will have a significant effect on disease diagnosis risk outcomes along with be identified as having an interaction effect with behavior: Partially Supported Although age and smoking classification were each significant predictors of selected disease diagnosis, interactions between these two predictors were not found. Replication studies are needed with greater representation of current smokers to better determine these findings. For some disease outcomes (e.g. diabetes and hypertension), advanced age coupled with current or former smoking status places individuals for greater risk of diagnosis. • Examine the relationship between smoking behavior and selected disease diagnoses, based on how former smokers are classified. • Determine how age contributes to the relationship between smoking and disease diagnosis outcomes. Analysis Windows 9.0 SAS was used for statistical analyses for this study. • Descriptive statistics • Chi squares • Examine relationship between diagnosis and three smoking behavior coding models • Examine relationship between diagnosis and age groups • Logistical regressions • determine whether age relationships existed within the relationship of disease diagnosis and the three smoking behavior coding models. *Note. X-axis Diagnoses: 1 = Arthropathies 2 = Cataract 3 = Cerebrovascular Disease 4 = Circulatory Disease 5 = Cancer of the Skin 6 = Diabetes 7 = Diseases of Other Endocrine Glands 8 = Glaucoma 9 = Heart/cerebrovascular Disease 10 = Hypertensive Disease 11 = Ischemia/heart Disease 12 = Malignant Neoplasm 13 = Other Heart Disease Hypotheses • Risk of diagnosis will vary according to how smoking status is coded and healthy smoking status is defined. • Age will have a significant effect on disease diagnosis risk outcomes along with be identified as having an interaction effect with smoking behavior. Acknowledgements: This research was supported by grant from the NIA (R37 AG08055) to K. Warner Schaie, NIA SLS (AG024102) to Sherry Willis, and the Research Trust Challenge Grant awarded to the Graduate Center for Gerontology at the University of Kentucky.