An Example of SAS Application in Public Health Research --- Predicting Smoking Behavior in Changqiao District, Shanghai, China

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1 An Example of SAS Application in Public Health Research --- Predicting Smoking Behavior in Changqiao District, Shanghai, China Ding Ding, San Diego State University, San Diego, CA ABSTRACT Finding predictors of health-related behaviors is an important step in public health research. According to the Behavioral Ecological Model (BEM), people behave in certain ways because physical and social contingencies have reinforced this behavior (Hovell, Wahlgren, Gehrman, 2002). So far the BEM has still not been applied to study the smoking epidemic in China. To fill the gap of previous research, a multiple logistic model, which contained a hierarchy of variables on different levels of matecontingencies, was constructed with the help of SAS out of a sample from Changqiao District, Shanghai, China. INTRODUCTION As the world s most populous developing country, with a population of 1.3 billion, and more than 350 million smokers, China is the world s largest producer and consumer of tobacco (Yong et al., 1999). From the 2002 National Smoking Survey, smoking prevalence among Chinese males was 66.0%, (Yang, Ma, Li, & Zhou, 2005). Smoking is already responsible for more than 12% of Chinese male adult deaths (Liu, Peto, Chen, Boreham, Wu, & Li, 1998), and the percentage is still growing. Minimal research has been conducted in China on smoking behavior, and includes only univariate and bivariate analyses, which couldn t include potential predictors on different levels of environment that reinforce smoking behavior. In this study, in order to test potential predictors simultaneously, examine interactions between variables, and adjust for confounders, multiple logistic regression analysis was performed to construct a final model, which predicted current smoking behavior in the study population. All statistical analysis was conducted using SAS DATA COLLECTION/ DATA ENTRY Data were collected by the Fudan University, School of Public Health in Shanghai, China. Potential participants were selected by random number dialing through a resident information network at the Changqiao Residents Committee. Questionnaire based interviews were done by trained interviewers and data were collected during the interviews. Data were entered in to an SPSS file (Version 14.0). Ten percent of the data were double entered for quality control. DATA INPUT In order to input data from SPSS file into an SAS file, the original dataset was saved as a portable file in SPSS and then inputted into SAS using the following statement:

2 libname final spss 'C:\Shanghai Project\Data\final.por'; data chq ; set final. chqdat; run; In this way, a data set named CHQ was created in SAS. In order to examine whether the data set was inputted right, the PROC CONTENTS statement was used and the output showed that all the variables were inputted correctly. DATA CLEANING The layout of the data file CHQ is presented in Table 1. Table 1 Description of the data file CHQ Variable Names Description Variable Type Valid Value ID ID Number for participants Numeric 1-3 digits AGE Age Numeric 18-80* CHILDREN Having children living at home Numeric 1 or 2 EDU Education level Numeric 1-8 EMPLOY Employment status Numeric 1-9 FDSMK Friend smoke Numeric 1-3 FDTOL Friend tolerate others smoking Numeric 1-3 HEALTH Health status Numeric 1-5 INCOME Family monthly income Numeric 1-7 KNOW Knowledge about adverse health Numeric 3-15 effects of smoking MAR Marital status Numeric 1-4 SEX Gender Numeric 1 or 2 SMK Smoking status** Numeric 1 or 2 SPT_M Social acceptability for male smoking Numeric 9-27 * The valid range of age is determined by the recruiting criteria. ** Smokers have been defined as smoking more than 100 cigarettes lifetime First, FROC FREQ was used for every variable of interest to check if there were any data out of the range of valid values. The next step was to determine where the invalid data occurred in the raw data set. The following program was used for age, the rest of the variables were similar. if age < 18 or age > 80 then put age= ID=; run; After the locations of invalid data were found, the raw data was examined in order to attain the true value of the variable.

3 DATA MANIPULATION Once the data were checked for accuracy, PROC FREQ was used to obtain the distribution of variables. The outcome showed that variables EDU, HEALTH, INCOME, KNOW followed highly skewed distributions. Variables EMPLOY SPT_M had too many categories and not enough scores in certain categories. Age was a continuous variable. In order to facilitate further analysis, variables EDU, HEALTH, INCOME, KNOW, EMPLOY, SPT_M were recoded into new variables with less categories; AGE was collapsed into a categorical variable. IF-THEN statements were used for recoding. Cut-off points were determined by the data distribution and the theoretical meaning of each category. Table 2 shows the variables after recoding. Table 2 Original and recoded variables in data file CHQ Original variable Recoded Variable Categories AGE AGECAT 1=19-42; 2=42-48; 3=48-60; 4=60-79 CHILDREN AT HOME 1= Yes; 0=No EDU EDUCAT 1=0-9 year education; 2=10-12 years; 3=13+ years EMPLOY EMCAT 1=retired; 2=current working; 3=students; 4=laid offs FDSMK 1=most friends; 2=some friends; 3= No friends FDTOL 1=most friends; 2=some friends; 3= No friends HEALTH HLTHCAT 1=good; 0= average INCOME ICCAT 1= RMB; 2= RMB; 3=3000+RMB KNOW KWCAT 1=high; 0=low MAR MARCAT 1=not married (including devoiced, widowed); 2= married SEX 1=male; 0=female SMK 1=yes; 0=no SPT_M SPTCAT 1=low; 2= average; 3=high DATA ANALYSIS 1. Descriptive Analysis Table 3 shows the frequencies of all of the independent variables. Table 3 Participant Characteristics (N=243) Categorical Variable Number % Age Gender Male Female

4 Education 0-9 years years years Marital Status Married Single/divorced/widowed Having children living in same household Yes No Current Employment Status Retired Working Full time student Laid off Family monthly income (after tax, Unit: RMB) Health Status Good Average Friends smoking Most friends Some friends Almost no friends Friends tolerate others smoking Most friends Some friends Almost no friends Social acceptability of smoking Low Average High Knowledge about negative health effects of smoking High Low Smoking status Yes No Bivariate Analysis Previous studies and statistics have stated a remarkable discrepancy between the smoking rate among Chinese males and females. To justify further analysis and the conceptual model, the relationship between gender and current smoking must be looked into first. Using the following program, a contingency table was created and a Chi- Square statistic was calculated. proc freq data=chq; table sex*smk/chisq; title 'smoking by gender'; run;

5 SAS output showed that no female participants were smokers and 75 out of 123 male participants were smokers. In this way, further analysis would only be limited to the subgroup of male participants. The WHERE statement was used to create this subgroup. In the male subgroup, bivariate analysis was preformed individually between each independent variable and the dependent variable (smoking status) to identify the association between potential predictors and smoking status. The results are shown in Table 4. Table 4 Bivariate Analysis of independent variables (N=123) Categorical Variable smokers ( n=75) nonsmokers (n=48) p-value Age P< (14.67%) 15 (31.25%) (40.00%) 5 (10.42%) (30.67%) 7 (14.58%) (14.67%) 21 (43.75%) Education P= years 23 (30.67%) 12 (25.00%) years 41 (54.67%) 20 (41.67%) 13+ years 11 (14.67%) 16 (33.33%) Marital Status P= Married 57 (76.00%) 34 (70.83%) Single/divorced/widowed 18 (24.00%) 14 (29.17%) Having children living in at home P= Yes 28 (37.33%) 12 (25.00%) No 47 (62.67%) 36 (75.00%) Current Employment Status P= Retired 19 (25.68%) 24 (51.06%) Working 36 (48.00%) 19 (39.58%) Full time student and housewives 1 (1.35%) 3 (6.38%) Laid off 19 (25.68%) 2 (4.26%) Family monthly income (RMB) P = (36.00%) 9 (18.75%) (41.33%) 25 (52.08%) (22.67%) 14 (29.17%) Health Status p= Good 36 (48.00%) 26 (54.17%) Not so good 39 (52.00%) 22 (45.83%) Friends smoking P< Most friends 44 (58.67%) 7 (14.58%) Some friends 25 (33.33%) 31(64.58%) Almost no friends 5 (6.67%) 10 (20.83%) Friends tolerate others smoking P= Most friends 37(49.33%) 13 (27.08%) Some friends 26(34.67%) 22 (45.83%) Almost no friends 11 (14.67%) 13 (27.08%) Social acceptability of smoking P= Low 6 (8.00%) 10 (20.83%) Average 59 (78.67%) 29 (60.42%) High 10 (13.33%) 9 (18.75%) Knowledge about negative health effects of smoking P= High 56 (74.67%) 40 (83.33%) Low 47 (62.67%) 8 (16.67%)

6 3. Multiple Logistic Regression Analysis Subsequent to bivariate analysis, variables with a significance level less than 0.05 were included in the logistic regression model. Even though some variables had a significance level higher than 0.05, they might still confound the association between the independent variables and the dependent variable. Considering this, variables with a significance level were also included into the logistic regression model. However, the decision of whether or not to keep these variables needed to be tested. A PROC LOGISTIC statement was used to test the model. proc logistic data=chq; class agecat(ref='1') educat(ref='1') emcat (ref='2')iccat(ref='1') children (ref='0') fdsmk (ref='3')fdtol(ref='3') sptcat (ref='1')/ param=reference; model smk(ref='0')= fdsmk agecat emcat educat fdtol sptcat iccat children / rl lackfit aggregate scale=none rsquare; run; The first model sequentially included friends smoke, age, employment status, educational level, friends tolerance of others smoking, social support for smoking, family income, children living at home. This model explained 35.46% of the variance of smoking status (F=2.94, P<0.0001). The next step was to test whether the exclusion of variables CHILDREN, ICCAT, SPTCAT, FDTOL from the first model would change the association between the independent variables and dependent variable by a significantly large amount. PROC LOGISTIC was used, removing one of the four potential confounders each time from the original model. By removing variables CHILDREN and INCOME from the model, none of the odds ratios of other variables had changed by more than 10%, thus these two variables were left out of the model. By excluding FDTOL and SPTCAT from the model, with a percent change more than 10%, FDTOL and SPTCAT were left inside the model as confounders because they distorted the relationship between variable FDSMK and SMK. The final model included friends smoke, age, employment status, educational level, friends tolerance of others smoking, perceived social acceptability of smoking. This model explained 35.16% (F=3.50, P<0.0001) of the variance in smoking status outcome. With a P-value =0.60, this logistic regression model showed a good fit to the sample data. Odds Ratios, confidence levels and p-values from the Logistic Regression model are summarized in Table 5.

7 Table 5 Multiple analysis of factors associated with smoking status using logistic regression (N=123) Variable OR 95% CI p-value Age * Education 0-9 years years years Current Employment Status Currently Working 1 Retired Full time student Laid off * Friends smoking Almost no friends 1 Some friends Most friends * Friends tolerate others smoking Almost no friends 1 Some friends Most friends Perceived social acceptability of smoking Low 1 Average * High * P-value <0.05 According to the final model, adjusting for educational level and friends tolerance of others smoking, males aged were 6.39 times more likely to smoke than males aged Males who were laid-off were times as likely to smoke as males who were currently smoking. Males whose friends were mostly smokers were times more likely to smoke than males with almost no friends smoking. Males who felt somewhat accepted by society to smoke had a 6.98 times higher risk to smoke compared to males who felt unaccepted by the society to smoke. CONCLUSION SAS has been widely used in qualitative research in the field of Public Health. This study is an example of the application of SAS in an epidemiological study on smoking behavior. The findings of this study have verified the Behavioral Ecological Model statistically regarding the smoking behavior in the Chinese male. This will help understand smoking behavior in the social environment and tailor intervention for future health promotion in China.

8 CONTACT INFORMATION Ding Ding Graduate School of Public Health, San Diego State University Center for Behavioral Epidemiology and Community Health, 9245 Sky Park Court, Suite 230 San Diego, CA,