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PM 515 Behavioral Epidemiology Introduction

PM 515 Behavioral Epidemiology Introduction. Jennifer Unger, Ph.D. Ping Sun, Ph.D. What is behavioral epidemiology?. The epidemiology of behaviors Prevalence of health risk and protective behaviors Doing the science of epidemiology with a behavioral focus

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PM 515 Behavioral Epidemiology Introduction

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  1. PM 515Behavioral EpidemiologyIntroduction Jennifer Unger, Ph.D. Ping Sun, Ph.D.

  2. What is behavioral epidemiology? • The epidemiology of behaviors • Prevalence of health risk and protective behaviors • Doing the science of epidemiology with a behavioral focus • Identifying behavioral (rather than biological) risk and protective factors for disease

  3. What this class will cover • Forming research questions and hypotheses in B.E. • Analyzing B.E. data • Communicating B.E. results • New methods in B.E.

  4. Research Questions • What are the risk and protective factors for specific health behaviors and/or disease outcomes?

  5. Where do we look for these risk and protective factors?

  6. Within the individual • Genetics • Demographic characteristics • Knowledge, attitudes, beliefs

  7. In the social network • Peer influences • Family influences • Perceived social norms

  8. In the larger environment • Social / cultural environment • Physical environment • Includes built environment

  9. In this class, we will • Generate research questions and hypotheses about risk and protective factors for health behaviors or health outcomes • Design data analysis strategy to test the hypotheses appropriately • Interpret the results • Write up the results for publication

  10. Testing the hypotheses • Need to choose the appropriate data analysis strategy • Need to know how to interpret the results • (Maybe) need to know what happened inside the computer

  11. Some analytic techniques we will use in this class • Bivariate analyses • Correlations, chi-squares, t-tests (just a review) • Multivariate analyses • Linear and logistic regression • Complicated stuff • Multilevel modeling • Structural equation modeling • Power calculation • Meta-analysis • Cost-benefit analysis • Attrition and missing data

  12. Communicating the results • What to put in the tables and text • How to interpret it • How to explain it

  13. Assignment • Use a dataset to investigate a hypothesis. • Example (don’t use this one): • Does depression increase the risk of smoking? • If so, does this occur in both genders? In all ethnic groups? • Is it confounded by peer influences?

  14. Datasets • China Seven-Cities Study (tons of data!) • Project RED (3 waves of data) • Cultural factors and substance use among Hispanic adolescents in Southern California • Other datasets from your projects

  15. Class project Final paper • Write up the results of your class project in the style of a journal article. • Approximately 20-25 pages, double-spaced, with Introduction, Methods, Results, Discussion, References, and Tables. • References should be in APA or Index Medicus style. They may be done with Endnote, but make sure they print out correctly in the final draft. • In the Acknowledgments section, state who did what. • Due on the last day of class.

  16. Class project Presentation – • Present the results of your class project. • The presentation should be in the form of an oral presentation at a conference. • Include Background, Hypotheses, Methods, Data Analysis, Results, Conclusions, Future Directions • 20 minutes with visual aids

  17. Introduction to Website and Archives of Datasets and Documents • URL: http://www-rcf.usc.edu/~sping/PM-515/ • Will include • Lecture notes • Readings • Datasets • Codebooks

  18. The Toward-No-Drug-Use StudyThe Motivation-Skills-Decision Making Model (MSD) of Problem Behavior--tailored to prevent drug abuse among older teens Motivation and correction of cognitive misperceptions Social and Change in Drug Use Behavior Self-control Skills Decision Making More information here

  19. The Toward-No-Drug-Use StudyStudy Design and Implementation • Group Randomized trial • School as the unit of randomization • Study Flow: • Recruit schools • Collect school level information • Create a score for each school. This score will be a measure of average risk for drug use for the students attending this school. • Match the schools by the score into groups of three schools. For each matched schools, randomly assign an intervention program (Enhanced TND, regular TND, and Control) to each school • Collect the student-level baseline data • Administer the 12-session TND curricula • Collect the postest data immediately after the treatment. • Collect the 1-year, 2-year, or longer duration follow-up data.

  20. TND Longitudinal Data Collection Control Treatment Pretest Survey Posttest Survey 1 year Follow-up Survey Treatment

  21. Variables in the TND Dataset • The SAS file to create the dataset: getdata.sas • Study Variables: • Outcomes: • drug uses (cigarette smoking, alcohol use, marijuana, hard drugs) at baseline, and follow-ups, assessed as times of uses during the last 30 days, or the status (Yes/No) during the last 30 days. • Other measures: • Age, gender, ethnicity, SES (parents’ education, and rooms per person), acculturation, intention of drug uses, depression, social self control, health as a value, etc. PM511A Session 1

  22. Project RED Dataset

  23. Project RED • Longitudinal study of acculturation patterns and substance use among Hispanic adolescents in Southern California • Respondents • 9th grade students in 7 Los Angeles high schools • Schools contained at least 70% Hispanic students • Median annual household incomes in their ZIP codes ranged from $29,000 to $73,000. • Students completed surveys in 9th, 10th, and 11th grade (2005, 2006, 2007).

  24. Student Recruitment • 3218 students invited to participate • 2420 (75%) provided parental consent and student assent. • 2222 (92%) completed the survey in 9th grade. • 1773 (80%) also completed surveys in 10th and 11th grade • Of the 1773 students with complete data for all 3 waves, 1575 (89%) self-identified as Hispanic or Latino or reported a Latin American country of origin.

  25. Survey procedure • In-school survey administered by data collectors • Data collectors also read the survey aloud • Surveys available in English and Spanish, but only 17 students chose the Spanish version • Telephone surveys of students who had consent but were not in their classrooms on the day of the survey

  26. Change in Substance Use from 9th to 11th grade among Hispanics

  27. Acculturation – Two dimensions • U.S. Orientation • Hispanic Orientation • Measure: • Short form of Cuellar ARSMA-II scale

  28. Other measures • Family cohesion and conflict • FACES-II scale (Olson) • Ethnic identity • MEIM (Phinney) • Perceived discrimination (Guyll) • Acculturative stress (Vega) • Pro-immigrant attitudes • Attitudes Toward Immigrants Scale (Hovey)

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