Analyzing Alcohol Behavior of San Luis Obispo County

Ariana (Audi) MontesStatistics Major

California Polytechnic University, San Luis Obispo

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Abstract

The Behavioral Risk Factor Surveillance System (BRFSS) conducted by the Centers for Disease Control and Prevention (CDC) is the world’s largest, on-going telephone health survey system. Data is collected routinely throughout the nation to obtain information regarding behavioral health risk factors of individuals. The BRFSS data is a rich data source at the state and county level that is freely available through the CDC website. This presentation will review survey based analyses conducted with SAS® utilizing the BRFSS data in order to assess alcohol behavior among San Luis Obispo County residents.

Objectives

Successfully download and use SAS® University Edition on my mac.

Explore demographic characteristics in comparison to alcohol use and binge drinking in San Luis Obispo County.

Examine the relationship, if any, between college-aged people and binge drinking.

• proc surveyfreq data=sasdata.drinkers;• strata _ststr;• cluster _psu;• weight _finalwt;• by BingeDrinker;• tables AgeGroup sex EmploymentStatus

• EducationLevel Income MaritalStatus

• Race / row nofreq nototal nostd;

• run;

PROC SURVEYFREQ

• proc surveylogistic data=sasdata.drinkers;• strata _ststr;• cluster _psu;• weight _finalwt;• class AgeGroup EducationLevel (ref='High School Graduate or Below') Race/param=ref;

• model BingeDrinker (ref='No') = AgeGroup EducationLevel Race;

• run;

PROC SURVEYLOGISTIC

Methods

I used SAS® (v9.3) to read and combine the datasets from each year and I used SAS® University Edition to run my analyses.

I collected data from the years 2005-2012. For these seven years, I narrowed the dataset to California first, then San Luis Obispo County. The data collected from these years in the county totaled 1148 observations. With these observations, I decided to narrow down to variables focusing on demographics and alcohol behavior.

The variables I created to assess alcohol behavior were Drinker and Binge Drinker. The Binge Drinker variable is characterized as follows: men having more than 4 drinks in one occasion and women having more than 5 drinks in one occasion. This variable does not include non-drinkers.

To build my models, I initially ran a univariate logistic regression for each of the variables of interest. With these, I made a list of which variables were borderline significant at the alpha .20 level. I then ran a model with these variables and took out the least significant variables one by one until I came upon a model with all variables whose p-values were less than an alpha .05 level. With these models, I tested all of the interactions and the significant ones are shown on slide 5 in the complete model.

I used PROC SURVEYFREQ and PROC SURVEYLOGISTIC as seen to the left, with corresponding STRATA, CLUSTER, and WEIGHT statements to analyze the data.

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Demographics

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Note: The p-values presented are from the type III chi-square test outputted from the univariate logistic regressions with Drinker/Binge Drinker referenced as “No”

Note: The p-values in blue are significant at the alpha .05 level and the p-values in green are borderline significant at the .20 level and were also included in the model

Drinker Binge Drinker  Yes No

P-valueYes No

P-valueWeighted%

Weighted%

Weighted%

Weighted%

Frequency Frequency Frequency FrequencySex            

Male 528,348 50.69 272,073 39.02 0.0094 319,617 48.95 479,353 44.22 0.4180Female 514,027 49.31 425,160 60.98   333,298 51.04 604,666 55.78

           Age Group            

18-24 102,617 9.84 112,427 16.12 0.1413 42,732 6.54 172,312 15.90 0.071925+ 939,758 90.16 584,805 83.88   610,184 93.46 911,707 84.10

           Race            

White 805,011 77.23 438,273 62.86 0.0111 551,813 84.52 688,798 63.54 0.0013Hispanic 180,077 17.28 180,235 25.85   66,590 10.20 293,721 27.10Other 29,032 2.79 43,280 6.21   21,800 3.34 50,513 4.66Asian 13,697 1.31 25,707 3.69   6,129 0.94 33,275 3.07Black 14,558 1.40 9,737 1.40   6,584 1.01 17,712 1.63

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Marital Status            Married 672,216 64.49 354,116 50.79 0.0003 435,666 66.73 589,445 54.38 <.0001Never Married 210,078 20.15 180,916 25.95   113,675 17.41 277,319 25.58Divorced 101,959 9.78 82,263 11.80   63,534 9.73 120,688 11.13Widowed 56,427 5.41 78,855 11.31   38,346 5.87 95,485 8.81

           Education Level            

High School Graduate or Below 265,094 25.43 290,793 41.71 0.0002 116,633 17.86 439,254 40.52 0.0002College Graduate 777,281 74.57 404,768 58.05   536,282 82.14 643,094 59.32

           Employment Status            

Employed 621,644 59.64 322,551 46.26 0.0003 392,160 60.06 550,814 50.81 0.0734Retired 240,478 23.07 146,767 21.05   165,982 25.42 219,813 20.28Not Working 83,243 7.99 107,681 15.44   47,774 7.32 143,151 13.21Homemaker 58,492 5.61 93,803 13.45   22,318 3.42 129,976 11.99Student 36,526 3.50 26,431 3.79   22,691 3.48 40,264 3.71

           Income Level            

Less than $35,000 272,253 26.12 292,209 41.91 0.0001 124,261 19.03 440,201 40.61 0.0036$35,000 to $49,999 137,621 13.20 102,078 14.64   87,131 13.35 152,568 14.07$50,000 to $74,999 192,498 18.47 81,133 11.64   119,239 18.26 154,392 14.24$75,000 or more 381,692 36.62 153,224 21.98   281,820 43.16 253,096 23.35

Drinker Binge Drinker  Yes No

P-valueYes No

P-valueWeighted%

Weighted%

Weighted%

Weighted%

Frequency Frequency Frequency Frequency

Demographics (continued)

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Drinkers Binge Drinkers

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Variable Effect P-valueMarital Status Widowed vs. Married <.0001Employment Status Homemaker vs. Employed <.0001Employment Status Not Working vs. Employed 0.009Education Level College Graduate vs. High School or Below 0.0128Race Asian vs. White 0.0392Race Other vs. White 0.0185

Variable Effect P-value

Education Level College Graduate vs. High School or Below 0.0086

Race Hispanic vs. White 0.0057

The significant (alpha<.05) effects from the model displayed above in the forest plot are outlined below:

The significant (alpha<.05) effects from the model displayed above in the forest plot are outlined below:

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Next Steps

I plan to continue working with this rich dataset and diving further into analyses as I continue my senior project. I plan to explore interactions that seem to be potentially significant and possibly meaningful. It would also be interesting to add other characteristics to my model (e.g. mental status, physical status, women of child-bearing age, etc…) and assess how alcohol relates to them.

References

http://www.cdc.gov/brfss/annual_data/annual_data.htm

http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_introsamp_sect007.htm

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ®

Acknowledgements

I would like to thank my senior project advisors, Rebecca Ottesen and Heather Smith, for their assistance throughout this project.

I would also like to thank SAS® Technical Support for assisting me in the process of successfully downloading SAS® University Edition.

Finally, a huge thanks to WUSS 2014 for accepting my abstract and for the opportunity to share my findings with other conference attendees.

Conclusion

I did not find much evidence of an association between college-aged people and binge drinking. I did, however, find other interesting relationships and was able to compare drinkers to binge drinkers and study the demographic changes among the two groups. For example, college graduates appear to be more likely to drink and binge drink than people who graduated high school or below. Another interesting association for drinkers is that homemakers and people who do not work are less likely to drink than people who are employed. These relationships, as well as the other significant effects on slide 5, give insight to what behaviors are encapsulated in San Luis Obispo County.

Contact Information:

Ariana (Audi) MontesStatistics MajorCalifornia Polytechnic University, San Luis Obispoamonte01@calpoly.edu831.240.8547

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