Research Presentations 2013 Summer Student Research and Clinical Assistantship Program

1. University of Wisconsin School of Medicine and Public Health Department of Family Medicine Research Presentations2013Summer Student Research and Clinical Assistantship Program

3. Evaluating Clinical Response to Electronic Health Record Surveillance of Childhood Obesity Brittney Golbach University of Wisconsin School of Medicine and Public Health Summer Student Research and Clinical Assistantship Program Summer 2013

4. At a Glance I. Background II. Goals III. Methods IV. Results V. Discussion VI. Future directions

5. Background: In the Literature From 2003-2007 obesity prevalence increased by 10% for all U.S. children 10-17 years old (Singh,2010) 2007-2008 NHANES found 28.8% of children 2-19 to be obese (OB) and 31.7% overweight (OV) (Ogden,2010) The probability of obesity as an adult is >50% for OV/OB children over 6 years old (Whitaker,1997)

6. Background: Work in DFM • UW E-Health Public Health Information Exchange (PHINEX); survey of 25 UW Family Medicine residency clinics (Pillai,2012) • 30% of children 2-17 OV/OB • 2% of those children coded in Epic as OV • <1% coded as OB • 2011: UW Health EHR begins auto-coding visit BMI percentiles by category

7. Background: Current Guidelines 2007 Expert Committee Recommendations for the Prevention, Assessment and Treatment of Childhood and Adolescent Overweight and Obesity (Barlow, 2007) Healthcare Effectiveness Data and Information Set (HEDIS) Measures

8. Project Goals 1. Determine frequency at which UW Family Medicine providers adhere to best practice recommendations for OV/OB prevention and assessment 2. Offer targeted feedback to family practice providers 3. Establish a baseline for measuring future progress in response to a QI intervention

9. Methods: Sample & Setting Patients: children 2-19 in well-child checks (WCC’s) Providers: family medicine resident and faculty physicians, physician assistants Settings: Northeast, Verona and Belleville UWDFM residency clinics Quality improvement (QI) study

10. Methods: Nutrition & Activity Measures • Electronic appointment evaluation form completed by researcher (me) during/following each WCC • Evaluation form criteria reflect the Expert Committee Recommendations for WCC’s for all pediatric patients • Compare to relevant Epic Checklist counseling items

11. Methods: Nutrition & Activity Measures Expert Committee Epic • I. Nutrition • Sugar-Sweetened Beverages • Fruits/Vegetables • Breakfast • Eating Out • Family Meals • Portions • Fruit Juice • Meal Frequency/Snacking • II. Physical Activity • III. Sedentary Behavior • TV in Bedroom • Screentime • I. Nutrition • Food Groups • Eating Patterns/Preferences • Family Meals • II. Physical activity • III. Sedentary Behavior • TV in Bedroom • Screentime • Sleep

12. Methods: Additional Measures • I. Prevention & Assessment • Family History • Physical Exam & Review of Systems • Labs • II. Treatment • Referral • III. Office Routines • BMI Documentation & Growth Charts • OV/OB Diagnosis • Language & Motivational Interviewing

13. Methods: Procedures 1. Observation of WCC appointments 2. Reference to EHR patient charts 3. Qualitative observations 4. Analysis of frequencies with Pivot Tables

14. Results: Demographics N = 26 cases

15. Results: Counseling Practices

16. Results: Counseling Practices

17. Results: Qualitative Observations • Appropriate language • Family Medical History for risk factors • FMH generally incomplete and/or vague • Complete absence of obesity in FMH • Problem list diagnosis vs. “Today’s visit” diagnosis

18. Results: Qualitative Observations • Review of Systems: max. 4/7 items covered • 6 patients in OV/OB categories were asked no OV/OB-related ROS questions • Recommended set of lab tests were not ordered for any OV/OB patients • Referrals: 1 referral to physical therapy in this study

19. Discussion: Limitations Sample size Relying on EHR for Epic criteria completion Hawthorne Effect

20. Discussion: Findings 1. BMI documentation is ahead of other primary care facilities (Brandt,2013) 2. A sensitive approach 3. Documentation of diagnosis & FMH (Whitaker,1997) 4. Epic vs. Expert Committee 5. Assessing vs. Counseling

21. Future Directions • 1. Best practice alert & modify auto-coding (Taveras,2013) • 2. Assess provider-perceived barriers • 3. Fit Epic to Expert Committee Rec’s(Taveras,2013) • 4. Future QI intervention (?)

22. Acknowledgements Brian Arndt, MD Larry Hanrahan, PhD,MS Jon Temte, MD, MS, PhD AmanTandias Melissa Behren Peter Capelli Tracy Flood, MD,PhD Emily Tomayko, PhD, RD Aaron Carrel, MD Alex Adams, MD, PhD Clinic Staff at Verona, Northeast and Belleville PHINEX Team

23. Final Thoughts & Questions

25. Climate Change and Health in Primary Care: an exploratory study Temte, Jonathan, MD/PhD; Holzhauer, John, BS; Kushner, Kenneth, PhD University of Wisconsin School of Medicine and Public Health Summer Research and Clinical Assistantship July 19, 2013

26. Overview Purpose Background Methods Results Discussion Acknowledgements

27. Determine recognition rate of climate change within convenient population • To assess whether adult primary care patients perceive anyeffects of climate change on their health • Evaluate any association between acknowledgement of climate change and depression and/or anxiety • Encourage discussion and future investigation into subject Purpose

28. Background • Human health effects of climate change • Direct •  temperature • Injury/death from extreme weather events •  air pollution/aeroallergens • Indirect • Changes in infectious disease • Changes in food production/delivery McMichael 2003, 2011; Patz 2000, 2005

29. Background • Mental health effects 3 likely ways • Directly • Experience of extreme weather event/novel weather patterns • Disruptions in social, economic and environmental determinates of health • Displacement • Loss of connection • Emotional distress and anxiety about the future Fritze et al. 2008

30. Methods • 23 Question survey – anonymous • Convenience samples • 4 University of Wisconsin-Madison- Department of Family Medicine affiliated clinics

31. Methods • The survey • capture data on symptoms, attitudes, and experiences associated with climate change • PHQ-2 (depression) and GAD-2 (anxiety); validated screening instruments • demographic information

32. Methods • 9 Questions regarding awareness/effect of climate change • Scored 0- “Not at all” to 5- “All the time” • Response rates were analyzed • Individually • “Composite” • 4 questions of PHQ-2/GAD-2 • Scored 0- “Not at all” to 3 “Nearly every day” • Results analyzed • Individually • combined into “Dysphoria” score

33. Results • 728 people approached • 157 refusals • 571 surveys attempted (78%) • Common reasons for refusal • Unwilling • Too sick • No glasses • No English

34. Results

35. Results

36. Results

37. Results • Is global climate change occurring? • Is global climate change due to human activities? Political leaning = only significant predictor of responses. (p < .001)

38. Results • Highest mean “Composite” questions • “Paying more attention to changes in climate” • “Are you troubled by the lack of action on climate change by leaders” • Lowest mean… • “Have you noted an health effects in you or your family members from climate change?”

39. Results Selected “Composite” Means Rarely Sometimes Frequently Most of the time

40. Results • Rank Correlation: Dysphoria, Composite • R= 0.345 • P < 0.001

41. Discussion Nationwide: • 64% Cautious, Concerned, Alarmed • 47% Anthropogenic • Majority expect direct health impacts • Less than half expect indirect

42. Discussion • Determining causality between climate change and dysphoria? • Does it matter? • Acceptance of climate change acts as force multiplier for mental health problems? • Patients are aware and topic should be discussed • % of “climate change accepters” makes it a good population to study effects of accepting climate change

43. Acknowledgements • Drs. Temte, Kushner • Staff at DFM affiliated clinics • Department of Family Medicine- University of Wisconsin School of Medicine and Public Health (Summer Research and Clinical Assistantship)

44. Questions?

46. Current Practices: Miscarriage Management in Wisconsin K. Hope Wilkinson, MS Jess Dalby, MD

47. Background • Miscarriage is not a rare event • Multiple studies have validated that there are four safe treatment options for women experiencing a miscarriage prior to 12 weeks gestational age. • These options are: expectant waiting, medical management, uterine aspiration in the office, and uterine aspiration in the operating room.

48. Methods – Chart Review 83 65

49. What options are women offered?

50. What happens in miscarriage?