Student Presentations SSRCA - 2014 Summer Student Research and Clinical Assistantship Program...

Student PresentationsSSRCA - 2014

Summer Student Research and Clinical Assistantship Program

University of Wisconsin School of Medicine and Public Health

Department of Family Medicine

Life course predictors of asthma risks in a large clinical population: age, sex, and BMI

Saamia Masoom, Aman Tandias, Jarjieh Fang, Dr. David Hahn, Dr. Theresa Guilbert, Dr. Yingqi Zhao &

Dr. Larry Hanrahan

Department of Family Medicine, University of Wisconsin School of Medicine and Public Health

Summer Student Research and Clinical Assistantship (SSRCA) ProgramSummer 2014

University of Wisconsin Department of Family Medicine

Background

University of Wisconsin Department of Family Medicine

AsthmaBMI

Family history

of asthma

History of

allergies Exposure to

allergens

Others

Rasmussen & Hancox (2014)

Background

University of Wisconsin Department of Family Medicine

Asthma(Control

)BMI

Saint-Pierre, et. Al (2006)

Background

University of Wisconsin Department of Family Medicine

Asthma(Control

)BMI

Sex • Pediatric males• Adult females

Sex hormone interactions? Linked underlying inflammation?Egan, et. Al (2013), Chen, et. Al (2013), Beckett, et. Al (2001), Zierau, et. Al (2012)

Purpose

University of Wisconsin Department of Family Medicine

Asthma(Control

)BMI

Sex • Pediatric males• Adult females

Does this relationship hold in a large clinical population?

MethodsUniversity of Wisconsin Electronic Health Record

Public Health Information Exchange (UW eHealth-PHINEX)

University of Wisconsin Department of Family Medicine

Clinical Data• UW Departments of Family Medicine,

Internal Medicine, Pediatrics• 2007-2012

Community Level Data• US Census Bureau• Esri Business Analyst

Guilbert, et. Al (2012), Tomasello, et. Al (2014)

Methods

University of Wisconsin Department of Family Medicine

PHINEX

Asthma

Controlled Uncontrolled

No Asthma

• ≥2 encounters ≥2 years apart

• ICD-9 493.xx

• ≥2 adverse events ≥90 days apart

Methods

University of Wisconsin Department of Family Medicine

Age Group

BMI Categor

y

Sex

• 0-4, 5-11, 12-17, 18-40, 41-59, 60+

• Normal• Obese* According to CDC age-appropriate guidelines

• Male• Female

Stratified by:

Results

University of Wisconsin Department of Family Medicine

PHINEX

Asthma

Controlled Uncontrolled

No Asthma

298,847

40,011(13.4%)

6,554(16.4% of patients

with asthma)

University of Wisconsin Department of Family Medicine

05 to 11 12 to 17 18 to 40 41 to 59 60+0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Asthma Prevalence vs. Age (BMI x Sex)

Normal BMI FemaleObese FemaleNormal BMI MaleObese Male

Age as of last BMI measurement

Asth

ma

Prev

alen

ce

University of Wisconsin Department of Family Medicine

05 to 11 12 to 17 18 to 40 41 to 59 60+0

0.5

1

1.5

2

2.5

Odds ratio of asthma prevalence in obese patients compared to normal BMI vs. age, stratified by sex

MaleFemale

Age as of last BMI measurement

Odd

s Ra

tio

University of Wisconsin Department of Family Medicine

05 to 11 12 to 17 18 to 40 41 to 59 60+0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Uncontrolled Asthma Prevalence vs. Age (BMI x Sex)

Normal BMI FemaleObese FemaleNormal BMI MaleObese Male

Age as of last BMI measurement

Unc

ontr

olle

d As

thm

a Pr

eval

ence

University of Wisconsin Department of Family Medicine

05 to 11 12 to 17 18 to 40 41 to 59 60+0

0.5

1

1.5

2

2.5

Odds ratio of uncontrolled asthma prevalence in obese patients compared to normal BMI vs. age, stratified by

sex

MaleFemale

Age as of last BMI measurement

Odd

s ra

tio

Summary• Asthma prevalence

• Higher in obese pediatric males and obese adult females

• OR of association between obesity and asthma• Similar in pediatric males/females• Significantly greater in adult females

• Similar but non-significant patterns observed for uncontrolled asthma

University of Wisconsin Department of Family Medicine

Implications• Alignment of a large, clinical population with

smaller epidemiological studies• Epidemiological predictive value

• Future targeted diagnosis and treatment methods

• Biology of association• Female sex hormone interaction vs.

underlying inflammation linked to both asthma and obesity

University of Wisconsin Department of Family Medicine

ReferencesBeckett WS, Jacobs DR, Yu X, Iribarren C, Williams OD (2001) Asthma Is Associated with Weight Gain in Females but Not Males, Independent of Physical Activity. Am J Respir Crit Care Med 164: 2045–2050. doi:10.1164/ajrccm.164.11.2004235.

Chen YC, Dong GH, Lin KC, Lee YL (2013) Gender difference of childhood overweight and obesity in predicting the risk of incident asthma: a systematic review and meta-analysis. Obes Rev 14: 222–231. doi:10.1111/j.1467-789X.2012.01055.x.

Egan KB, Ettinger AS, Bracken MB (2013) Childhood body mass index and subsequent physician-diagnosed asthma: a systematic review and meta-analysis of prospective cohort studies. BMC Pediatr 13: 121. doi:10.1186/1471-2431-13-121.

Guilbert TW, Arndt B, Temte J, Adams A, Buckingham W, et al. (2012) The theory and application of UW ehealth-PHINEX, a clinical electronic health record-public health information exchange. WMJ Off Publ State Med Soc Wis 111: 124–133.

Rasmussen F, Hancox RJ (2014) Mechanisms of obesity in asthma. Curr Opin Allergy Clin Immunol 14: 35–43. doi:10.1097/ACI.0000000000000024.

Saint-Pierre P, Bourdin A, Chanez P, Daures J-P, Godard P (2006) Are overweight asthmatics more difficult to control? Allergy 61: 79–84. doi:10.1111/j.1398-9995.2005.00953.x.

Tomasallo CD, Hanrahan LP, Tandias A, Chang TS, Cowan KJ, et al. (2014) Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data. Am J Public Health 104: e65–e73. doi:10.2105/AJPH.2013.301396.

Zierau O, Zenclussen AC, Jensen F (2012) Role of female sex hormones, estradiol and progesterone, in mast cell behavior. Mol Innate Immun 3: 169. doi:10.3389/fimmu.2012.00169.

University of Wisconsin Department of Family Medicine

Dropout Characteristics of Opioid Dependent Offenders in Community-Based Treatment

Shawn Wayne, M2Randy Brown, MD, PhD

Opioid Epidemic

Role Drug Treatment Court (DTC)

• An individual with untreated addiction to illicit substances commits an average of 63 crimes per year.2

– Intervention!

• Reduces recidivism and illicit drug use, through obligatory,– Counseling– Medical treatment– Judicial supervision– Social services

• In exchange for dismissal/ reduction of charges

Medical Treatment

Medications:• Methadone [Federally accredited facilities]• Suboxone (Buprenorphine/ Naloxone)

Pilot Study:• Suboxone Tx in physicians office (PO) effective, however did not

reduce HIV risk behaviors

Specialist Stabilization Period: • Optimizing treatment, given limited resources • Compare Suboxone Tx PO to Suboxone Tx Specialty Care followed

by Tx PO.

Study Structure

Treatment (Tx):• Suboxone (Buprenorphine/ Naloxone)

Study Arms:• Physician Office (PO) for 10 months• Specialty Care at Madison Health Services (MHS) followed by 7 months of PO care.

Data Collection• Baseline• Monthly

Opioid Offenders Dane County Drug Treatment Court

Subjects Consented and Randomized

Suboxone TxMHS-3 MonthsPO-7 Months

Suboxone TxPO-10 Months

Data Collection Instruments:

Surveys:• TLFB (Timeline Feedback)

– Measures drug-use for the previous 14-days• ASI (Addiction Severity Index)

– Accesses drug use, SES, legal circumstances• CMR (Circumstances, Motivation, and Readiness)• RAB (Risk Assessment Battery)

– HIV/AIDS risk assessment

Court Reports

Unforeseen Difficulties

• Recruitment– 18 unique subjects enrolled since November 2013– Reassessed inclusion criteria – Restructuring of Dane County DC in December 2013

• Potential impediment to recruitment process

• Dropout– 50% dropout (DO) prior

– Transportation– Recidivism– Inability to fill prescription

– Expected DO (20-40%) with Suboxone Tx

Dropout Comparisons:

No difference in rate of DO between Tx

Demographics:• No difference in age or gender between DO status groups

Baseline Drug Use:• No difference in drug use 14-days prior to intake between DO (p=0.36)• Heroin use was not statistically different between Tx or DO status

Other:• Individuals with self-reported drug participated depression, anxiety, and confusion,

may be less likely to drop out • Motivation difference observed between DO statuses, on the importance of

stopping use over everything else (p=0.049)

Significance of Motivation:

• MHS• A lack of self-reported motivation associated with DO status

amongst participants assigned to MHS. (N=8)

– Importance of treatment (p=0.013)– Serious legal problems (p=0.035)– Importance of legal counsel (p=0.031)– Outside interference (p=0.057)

• No significant difference between DO status across Tx

Discussion:Findings:• No difference in DO status between Tx arms• Heroin use and age not be prognostic of DO• Motivation significant in DO outcome for MHS Tx

Rationale:• MHS requires daily dosing, a more intensive treatment model than weekly PO • Motivation, thus may be important for predicting success at MHS

Conclusions:• Preliminary support of predictive baseline figures between Tx arms• Personalized DC treatment

Limitations:• Sample size• Baseline Comparison• Extraneous Circumstances (Transportation, Legal, Family etc.)

Future Investigation: Criminality

• Drug use and criminality– Income generating crimes, disorderly conduct, possession, etc.

• Predictive value of Criminality– DO, recidivism, positive UAs

Is Criminality a prognostic marker for Tx arms?

• Increased judicial supervision reduced positive UAs and sanctions amongst other “high risk” DO participants

Does daily dispensing at MHS may have similar effect?

Future Investigation: Criminality

Hypothesis: MHS Tx improves DC outcomes; graduation rate, recidivism, and drug use, amongst DC participants with a more extensive criminal history than PO Tx.

• IRB revision– CCAP and Court Reports

• Criminality metric (adapted Gordon et al. 2013) – Frequency– Variety– Severity

To be continued... Questions?

Literature Cited

• Brown, Randall. Community‐Based Treatment for Opioid Dependent Offenders: A Pilot Study. The American Journal on Addictions, 22: 500–502, 2013.

• SAMHSA. Results from the 2008 National Survey on Drug Use and Health: National Findings. Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies;2009.

• Nurco DN. A long-term program of research on drug use and crime. Subst. Use Misuse. Jul 1998;33(9):1817- 1837.

• Stein MD, Cioe P, Friedmann PD. Buprenorphine retention in primary care. J Gen Internal Med. 2005;20:1038–1041.

• Sinha R, Easton C. Substance abuse and criminality. Journal of the American Academy of Psychiatry & the Law. 1999;27(4):513-526.

• Brecht ML, Anglin MD, Wang JC. Treatment effectiveness for legally coerced versus voluntary methadone maintenance clients. The American Journal of Drug and Alcohol Abuse. 1993;19(1):89-106.

• “Early-Phase Outcomes from a Randomized Trial of Intensive Judicial Supervision in an Australian Drug Court,” Jones C.G.A. (2013) Criminal Justice and Behavior, 40 (4), pp. 453-468.

• Gordon, Michael. “The Severity, Frequency, and Variety of Crime in Heroin-Dependent Prisoners Enrolled in a Buprenorphine Clinical Trial” 2012. The Prison Journal December 2013 vol. 93 no. 4 390-410.

HOW DOCTORS BIRTHHow our experiences shape our practice

Carly Kruse, MSc, Ildi Martonffy, MD

Background and Objectives• History of birthing stories as a space for women to share

experiences• Ken Murray’s “How Doctor’s Die: It’s not like the rest of

us, but should be”1

• Descriptive study utilizing both qualitative and quantitative tools to explore birthing experience of female physicians

• Objectives:1. Examine birthing preferences and birthing realities2. Explore maternal care approaches before and after motherhood3. Investigate breastfeeding expectations4. Analyze changes in breastfeeding counseling due to personal

experiences

Methods• 29 question survey distributed to members of UW Family

Medicine Department, UW Obstetrics and Gynecology Department, and the Academy of Breastfeeding Medicine• 45 physicians and 1 Nurse practitioner responded• 43 eligible participants based on medical specialty, gender, and

experience of at least one live birth delivery• 30 minute in-person follow-up interviews

• General interview guide approach with standardized open-ended questions

• 20 participants interested • 7 completed

Participants

*Percentages are calculated using n=43 for all questions whether or not all participants responded to that question

Prenatal Methods

National average of doula utilization = 6%2

“Met with a doula to talk about letting go and not always being in control” – Interviewee 006

Birthing Class Birth Plan Doula0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

71.4%

35.7%25.0%

50.0%

14.3%0.0%

Percentage of respondents utilizing prenatal support methods during one or more pregnancy

Fam Med OBY/GYNE

Delivery Methods

*n=32 with 63 responses

Pitocin

Cervican Ripening

Intrauterine Pressure Catheter

Artificial Rupture of the membrane

Forceps

Vacuum Extraction

Episiotomy

0 10 20 30 40 50 60 70 80 90 100

65.6

28.1

18.8

53.13

12.5

12.5

21.88

Interventional Method used during one or more deliveries

% of Respondents reporting use of intervention

0.010.020.030.040.050.060.070.080.090.0

100.0

58.1

4.7 4.711.6

18.6

Type of Delivery for First BirthPe

rcen

tage

of s

urve

y pa

rtici

pant

s (n

=43)

Breastfeeding• All participants breastfed for at least 1 of their births and

90.7% breastfed all babies• 76.7% breastfed for more than 6 months on average

“There was no question whether or not I would breastfeed.” – Interviewee 001

• Publically shamed for breastfeeding in public while simultaneously feeling social pressure to breastfeed exclusively (Interviewees 001, 002, 004)

• Undertrained and Unknowledgeable“she [my daughter] was teaching me about breastfeeding” –

Interviewee 003 • Expected “to be successful” (009) and breastfeed

“exclusively” (008)

Impact on Care Practices• Prenatal Counseling

• More breastfeeding education• Fewer birth plans:

“Goal of labor that everyone end up healthy, but how we get there is unimportant” – Interviewee 007

• Labor Support• Woman-centered approaches

“take more cues from the laboring woman” -005 • Normalization of deliveries and expectations

• Breastfeeding Counseling• Remove social pressures: “Stop shoulding yourself” – Interviewee 004• Become more informed

• Pediatric Care“I considered my most important job as being a mother. My profession was

being a doctor. These were mutually reinforcing roles” -015

Discussion• Overall approach to care today shaped by experience of

entire course of pregnancy from prenatal to postnatal to motherhood

• Three common themes• Increased Empathy“I can help frame their expectation for their own experience better than I could

before my own pregnancies and births” -006 • Increased awareness of social pressure put on women to parent

or birth in a particular way“Mostly that I try to reassure women that the societal pressures about what

pregnancy, labor, birth and new motherhood look like are kind of BS ways to make women feel bad about themselves” -001

• Increased advocacy for empowermentBecause I was able to achieve my birth and breastfeeding goals, I believe

other women have the power to do it too, when they have the right support” -002

Limitations• Small sample size • Generalizability• Self-selection bias

• Family Medicine participants = 65.1%• OB/GYNE Participants = 18.6%

• Retrospective self-reporting

Conclusion• Do doctors birth differently than other women?• Evidence that personal experiences construct the way physicians

approach and counsel patients• Future research:

• How successful are physicians with leveraging empathy to address empowerment?

• How do we teach non-parents in medical training all that doctors have garnered from personal experiences?

“It’s hard for physicians to have a clue if they haven’t breastfed before—a nuanced skill that is learned and passed on through

generations.”- Interviewee 001

References1. Murray, K. How Doctors Die: It’s Not Like the Rest of Us, But It Should Be.

Zocalo Public Square. Nov. 2011 retrieved from <http://www.zocalopublicsquare.org/2011/11/30/how-doctors-die/ideas/nexus/>.

2. Declercq, E., et al. Listening to Mothers III Pregnancy and Birth: Report of the Third National U.S. Survey of Women’s Childbearing Experiences. May 2013 retrieved from <http://transform.childbirthconnection.org/wp-content/uploads/2013/06/LTM-III_Pregnancy-and-Birth.pdf>.

3. Osterman, M., et al. Primary Cesarean Delivery Rates, by State: Results from the Revised Birth Certificate, 2006-2012. National Vital Statistics Reports. 63(1) Jan. 2014 retrieved from <http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_01.pdf>.

4. Division of Nutrition, Physical Activity, and Obesity. Breastfeeding Report Card: United States 2013. National Center for Chronic Disease Prevention and Health Promotion. 2013 retrieved from <http://www.cdc.gov/breastfeeding/pdf/2013breastfeedingreportcard.pdf>.

MENTAL HEALTH PREDICTS COMMON COLD OCCURRENCEBy Lizzie Maxwell

Background The cost of ARI in US

$40 billion non-influenza ARI1

Poor mental health as risk factor for ARI Yuki Adam et al.

DSM-IV mental disorders increased ARI incidence2

Sheldon Cohen et al. Increased stress increased ARI incidence3

Rakel D, Mundt M, Ewers T, et al. Value associated with mindfulness meditation and moderate exercise intervention in acute respiratory infection: the MEPARI study. 2013

Methods MEPARI and MEPARI-2

Spearman rank-order correlationPsychosocial Measures (baseline)

SF12ARI Measures (throughout study)

IncidenceDuration and Severity

SF-12 Health Survey12 QuestionsGenericHealth-Related Quality of Life2 Summary Scores:

Physical Mental

During the last 4 weeks did you… Accomplish less than you would like? Do work/activities less carefully than usual?

How many times over the last 4 weeks have you… Felt calm and peaceful? Had a lot of energy? Felt downhearted and blue?

SF-12 Mental

ARI Outcomes Incidence

Do you think you have/are coming down with a cold

1 of 4 common cold symptoms Score > 2 on Jackson Scale

Duration Severity

WURSS-24

Demographicsn 353% Female 77.6%Mean Age 54.2 (10.4)% Education < Bachelors degree

71.4%

% Income < $50,000 58.1%

# ARI

SF-1

2 M

enta

lResults

Graph courtesy of Joseph Chase

Explanations Stress as a common risk factor4

Mental illness’ effect on immunity5

Unfounded symptoms6

Healthy vs unhealthy behaviors4

Limitations Analysis has thus far included

intervention groups Potential effects of interventions?

Next steps…

Results

Instrument Incidence rho (p-value)

Duration rho (p-value)

Severity rho (p-value)

SF-12 Mental

-0.11 (0.045)

-0.09 (0.080)

-0.09 (0.078)

PANAS - 0.09 (0.086) 0.08 (0.137) 0.11 (0.040)

PHQ 9 -0.06 (0.230)

-0.02 (0.663)

0.01 (0.913)

MAAS -0.11 (0.045)

-0.1 (0.065) -0.09 (0.095)

Table courtesy of Joseph Chase

References 1. Rakel D, Mundt M, Ewers T, et al. Value associated with mindfulness

meditation and moderate exercise intervention in acute respiratory infection: the MEPARI study. Family Practice. 2013; 30(4): 390-7

2. Adam Y, Meinlschmidt G, Lieb R. Associations between mental disorders and the common cold in adults: A population-based cross-sectional study. Journal of Psychosomatic Research. 2012; 74(2013): 69-73.

3. Cohen S, Tyrrell DA, Smith AP. Psychological stress and susceptibility to the common cold. New England Journal of Medicine. 1991; 325(9): 606-612.

4. Cohen S, Miller G. (2001). Stress, immunity, and susceptibility to upper respiratory infection. In Psychoneuroimmunology (3rd Ed., Vol. 2, pp: 499-509). Academic Press

5. Copeland W, Shanahan L, Costello EJ. Cumulative depression episodes predicts later c-reactive protein levels: a prospective analysis. Biology Psychiatry. 2012; 71(1):15-21.

6. Cohen S, Doyle W, Turner R, et al. Emotional style and susceptibility to the common cold. Psychosomatic Medicine.2003; 65(4):652-657.

Community Health Assessment in the Wausau Hmong Population: Preliminary Survey of Wausau Hmong Community

Leaders

Pajin Vang MPH, MD candidate Dr. Kevin Thao MD, MPH

SSRCA Department of Family Medicine

Today’s Talk

• Introduction– Who are Hmong?– What is HHC– What is SHOW

• MiniSHOW of Wausau Hmong community• Preliminary Surveys

Who are Hmong?

• Hmong History– U.S. Hmong cultural ancestry as ethnic minority in China– Resettled in mountains of Laos, Thailand, North Vietnam– After Vietnam War and Secret War, fled and relocated to

Thailand refugee camps– Came to U.S. as political refugees after 1975

– Hmong are largest ethnic Asian population in Wisconsin

What is HHC

Hmong Health Council• Central Wisconsin• South Central Wisconsin

• Hmong Health Council( HHC) is an independent coalition of Hmong healthcare providers, community leaders, members and partners working together to improve the health of Hmong Americans

What is SHOW?

• Survey of the Health of Wisconsin• Gathers data across Wisconsin• Annual surveys

– up to 1000 people/year age 21-74• Measures:

– health behaviors, – mental health,– access to health care,– beliefs in health care,– environment

• Partner with HHC

Target Population:Central Wisconsin Hmong

• Midwest has largest Hmong population in the nation– Wausau is 2nd largest Hmong community in Wisconsin

• Hmong Health issues– Pre migration/refugee camps– Post migration

• Increased risk for obesity, hypertension, hyperlipidemia, cardiovascular disease, diabetes

• Mini Health Assessment– Pilot project in Wausau Hmong community– General health assessment of Hmong Wisconsin

Community using SHOW methods– 10-30 households– Will we be able to reproduce similar study to

SHOW’s pilot neighborhood study?

• Preliminary Surveys– Introduce the project to the community leaders

• 10 community leaders to be surveyed– Will they want to participate?– Will they answer all the questions?– Survey translated to Hmong

What we learned so far

• Survey takes 2 hours in Hmong, 1hour in Hmonglish

• Some things cannot be directly translated• Some concepts are difficult to explain or

understand: – Scales (rate from 0-10)– Genes/DNA

Questions?

• References– http://hmonghealthcouncil.wordpress.com/about/– http://www.med.wisc.edu/show/about-survey-of-t

he-health-of-wisconsin/36193

– http://www.hndinc.org/cmsAdmin/uploads/dlc/HND-Census-Report-2013.pdf

– Her C, Mundt M. Risk prevalence for type 2 diabetes mellitus in adult Hmong in Wisconsin: a pilot study. WMJ 2005;104(5):70-7.

Disease-Management & Financial Implications of the Addition of a Health Coach/Nutritionist in Two Family Medicine Clinics

Kristin MaglioccoDennis Baumgardner MD, Tiffany Mullen DO, Kristen Reynolds MD

Presentation Outline•Background•Expected Outcomes•Methods•Data Collection•Preliminary Data•Limitations of the Study•Concluding Remarks•Literature Cited

Background: Chronic Disease

•80% of healthcare spending1

•Leads to preventable deaths2

•Lifestyle changes•Patients’ disease maintenance goals are

not met1

▫Diabetes: 43%▫Hypertension: 50%▫Hyperlipidemia: 80%

Background: Self-Management Support•“Systematic provision of education and

supportive interventions to increase patients’ skills and confidence in managing their health conditions”

-Institute of Medicine

•Improves clinical outcomes for chronic disease4

Background: Health Coaching

•Empowerment1

•Motivational interviewing2

•Active role for patient3

•Goal setting for what is feasible in daily life5

•Follow-up7,8

Expanding the Healthcare Team•Time is limiting factor for clinicians6

•Non-clinician personnel5,6,7

▫Medical Assistants1

▫Dietitians9

▫Medical/Nursing Students10

▫Successful Peers11,12

•Dual-trained Nutritionist/Health Coach

Expected Outcomes

•Primary outcome: Improved clinical outcomes

•Secondary outcome: Financial benefits for patients

Methods•Retrospective chart review•Each patient is own control•2 integrative Family Medicine clinics•Referrals to Nutritionist/Health Coach by

PCP▫Inclusion Criteria by Diagnosis

Diabetes Hypertension Hyperlipidemia, Hypercholesterolemia Metabolic Syndrome Obesity (BMI > 30)

Data Collection

Preliminary Data

6.2 ± 0.316

Preliminary Data

129.38 ± 8.876 82.40 ± 3.978

Preliminary Data

211.71 ± 30.587

129.86 ± 39.599

53.86 ± 13.459

140.86 ± 71.913

Preliminary Data

36.62 ± 7.915

Limitations of the Study•Low patient enrollment so far•Cash payments for appointments

▫Creates biases•Variation in follow-up

▫Follow-up shown to be essential14

Concluding Remarks

•Study is ongoing

•Potential future impact for chronic disease

Literature Cited1. Willard-Grace R, DeVore D, Chen EH, Danielle H, Bodenheimer T, and Thom DH.The effectiveness of medical

assistant health coaching for low-income patients with uncontrolled diabetes, hypertension, and hyperlipidemia: protocol for a randomized controlled trial and baseline characteristics of the study population. BMC Family Practice 2013, (14):27.

2. Bennett H, Laird K, Margolius D, Ngo V, Thom DH, and Bodenheimer T. The effectiveness of health coaching, home blood pressure monitoring, and home-titration in controlling hypertension among low-income patients: protocol for a randomized controlled trial. BMC Public Health 2009, (9): 456.

3. Howard LM and Hagen BF. Experiences of person with type 2 diabetes receiving health coaching: an exploratory qualitative study. Education for Health 2012, 25(1): 66-69.

4. Norris SL, Engelgau MM, Narayn KMV. Effectiveness of self-management training in type 2 diabetes. Diabetes Care 2001, 24(3): 561-587.

5. Chen EH, Thom DH, Hessler DM, Phengrasamy L, Hammer H, Saba G, and Bodenheimer, T. Using the teamlet model to improve chronic care in an academic primary care practice. Journal of General Internal Medicine 2010, 25 Suppl 4:S610-614.

6. Yarnall KSH, Ostbye T, Krause KM, Pollak KI, Gradison M, Michener JL. Family physicians as team leaders: “time” to share the care. Prev Chronic Dis. 2009, 6(2): A59.

7. Margolius D, Wong J, Goldman ML, Rouse-Iniguez J, and Bodenheimer T. Delegating responsibility from clinicians to nonprofessional personnel: the example of hypertension control. Journal of the American Board of Family Medicine 2012, 5(2): 209-215.

8. Margolius D, Bodenheimer T, Bennett H, Wong J, Ngo V, Padilla G, and Thom DH. Health coaching to improve hypertension treatment in a low-income, minority population. Annals of Family Medicine 2012, 10(3): 199-205.

9. Battista MC, Labonte M, Menard J, Jean-Denis F, Houde G, Ardilouze JL, and Perron P. Dietitian-coached management in combination with annual endocrinologist follow up improves global metabolic and cardiovascular health in diabetic participants in 24 months. Applied Physiology, Nutrition, and Metabolism 2012, 37(4): 610-620.

10. Leung LB, Busch AM, Nottage SL, Arellano N, Glieberman E, Busch NJ, and Smith SR. Approach to antihypertensive adherence: a feasibility study on the use of student health coaches for uninsured hypertensive adults. Behavioral Medicine 2012, 38(1): 19-27.

11. Leahey TM and Wing RR. A randomized controlled pilot study testing three types of health coaches for obesity treatment: professional, peer, and mentor. Obesity 2013, 21(5): 928-934.

12. Ghorob A, Vivas MM, De Vore D, Ngo V, Bodenheimer T, Chen E, and Thom DH. The effectiveness of peer health coaching in improving glycemic control among low-income patients with diabetes: protocol for a randomized controlled trial. BMC Public Health 2011, (11):208.

13. Evans JG, Sutton DR, Dajani LH, Magee JS, Silva RA, Roura MF, Wadud K, Pucell JA, Travaglini S, Segel SA, Sultan S, Roffman MS, Ayad SS, Boria-Hart NL, and Smith SM. A novel endocrinology-based wellness program to reduce medication expenditures and improve glycemic outcomes. Diabetes & Metabolic Syndrome: Clinical Research & Review 2013, (7): 87-90.

14. Siminerio L, Ruppert KM, and Gabbay RA. Who can provide diabetes self-management support in primary care? Findings from a randomized controlled trial. The Diabetes Educator 2013, 39(5): 705-713.

Questions?

NEGATIVE PAP SMEAR, POSITIVE HPV: WHAT DOES IT MEAN?Lindsey AndersonFaculty Mentor: Sarina Schrager, M.D., M.S.

CERVICAL CANCER 2010 Incidence: 12,200 cervical cancer diagnoses 2010 Mortality: 4,200 deaths Easily treated if caught early Human papilloma virus (HPV) infection prerequisite Cervical intraepithelial neoplasia I, II, III (CIN) HPV 16, 18, 31, 33, 45

CERVICAL CANCER SCREENING Pap smear cytology

Negative Atypical squamous cells of undetermined significance (ASCUS) Low grade squamous intraepithelial lesion (LSIL) High grade squamous intraepithelial lesion (HSIL)

HPV DNA tests 16, 18 DNA genotyping

Follow-up Conization (Cone biopsy) Loop Electrosurgical Excision Procedure (LEEP) Hysterectomy

SCREENING GUIDELINES New guidelines in place 2012

Co-testing for women ages 30-65 Hope to decrease number of colposcopies Both negative = co-test again in 5 years

CHART REVIEW Case finding with data lists from all UW clinics

DFM patients with colposcopies done DFM patients with pap smears done November 2012-April 2014

785 charts

66 negative pap/positive HPV

56 had a colposcopy

6 abnormal colposcopies

2 referred to further procedures

NEGATIVE PAP SMEAR, POSITIVE HPV 56 women 59 procedures total

29 biopsies 23 normal (79.3%)

30 endocervical curettage 28 normal (93.3%)

18 women had both biopsy and ECC 12 all normal (66.7%)

ABNORMAL COLPOSCOPY Three Cervical Intraepithelial Neoplasia I (CIN I)

60% resolve to normal in one year One CIN I/normal One CIN II-III

Referred for LEEP One CIN III/carcinoma-in-situ

Referred for possible hysterectomy

ABNORMAL COLPOSCOPY Smoking Status

50% current/former smokers HPV Prevalence

83.3% were HPV 16+ 16.7% were HPV 18+

Previous Abnormal Pap Smear 50% had a previous abnormal

pap smear ***47% more likely to have

had a previous abnormal pap smear***

Smoking Status 56% current/former smokers

HPV Prevalence 80% were HPV 16+ 18% were HPV 18+

Previous Abnormal Pap Smear

34% had a previous abnormal pap smear

NORMAL COLPOSCOPY

REFERENCES Saslow et al. 2012. “American Cancer Society, American Society

for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology Screening Guidelines for the Prevention and Early Detection of Cervical Cancer” Journal of Lower Genital Tract Disease 16(3):0.

Discacciati MG et al. 2014. “Prognostic value of DNA and mRNA e6/e7 of human papillomavirus in the evolution of cervical intraepithelial neoplasia grade 2”. Biomark Insights 13(9):15-22.

American Cancer Society. Cancer Facts & Figures 2010. Atlanta: American Cancer Society; 2010.

Improving treatment completion rates for latent tuberculosis infection: a

review of two treatment regimens at a community health center

Gregory Lines, MPH MD candidate 2017

University of Wisconsin School of Medicine and Public Health 7/18/14

Faculty Mentor: Paul Hunter, M.D.Department of Family Medicine

University of Wisconsin School of Medicine and Public Health

Sarah Bleything, PASixteenth Street Community Health Center, Milwaukee, WI

Introduction: Latent tuberculosis infection (LTBI)

• Estimated that 11 million people in the U.S. are infected with M. tuberculosis.

• 10% lifetime risk of conversion to active TB among healthy patients

• Treatment of LTBI is necessary for controlling and eliminating active TB in the United States.

• 9 months daily isoniazid (INH)• 12 weekly doses of isoniazid (INH) and rifapentine

(RPT) directly observed (CDC recommendation 2011)• 4 months daily rifampin (RIF)

Introduction

• Major limitation of LTBI treatment is adherence. • Individual clinics report between 5% and 60%

completion for 6 months INH of those initiating treatment

• INH monotherapy and INH/RPT have similar efficacy (Sterling, NEJM)

Study objective:

To compare treatment completion rates among patients accepting LTBI treatment with 12 weekly doses of isoniazid (INH) and rifapentine (RPT) directly observed to those accepting 9 months of daily isoniazid (INH) monotherapy.

MethodsStudy setting: Sixteenth Street Community Centers Parkway Health Center, Milwaukee, WI

• Federally Qualified Health Center• Patient population is low-income, predominantly Hispanic

Study Design and Ethics: • Retrospective cohort study, review of EMR• IRB approval at SSCHC

Study Participants:• All patients accepting treatment for LTBI in 2012 and 2013• INH monotherapy and INH/RPT combined therapy (DOT)

MethodsData Collection:

• Retrospective review of LTBI patient log and Electronic Medical RecordsClinical Outcome:

• Treatment completionPredictor of Interest:

• Treatment group (INH/RPT vs. INH only)Variables :

• Demographic information (age, sex, race, ethnicity)• Comorbidities (Smoking status, Diabetes mellitus, history of Injection drug use,

chronic kidney disease, HIV status)• Elevated liver function tests (ALT, AST) , above normal and 3x normal• Relationship with the clinic

• Resident distance from clinic (calculated by GoogleMaps)• No. visits in year preceding treatment acceptance• No. years a patient at the clinic

Results: participant eligibilityn=139; INH/RPT – 45, INH only - 94

ResultsBaseline characteristics of study and control groups

Results: overall completion rates

INH only INH/RPT (DOT) Total 52.1 % (49/94) 77.8% (35/45) 60.4 (84/139)

Patients agreeing to LTBI treatment, n = 139:

INH only INH/RPT (DOT) Total

73.1 % (49/67) 100% (35/35) 82.4% (84/102)

Patients initiating LTBI treatment, n=102

Results: Logistic regression analysis, n = 139

Univariate logistic regression for DOT group compared to INH only:(OR 3.21; 95% CI, 1.43 – 7.23; P=0.005)

Discussion• 12 week DOT regimen with INH/RPT combined therapy

can achieve higher completion rates than self-administered INH monotherapy in a community health center serving predominantly low-income Hispanics

• Greater success may be attributed to:• shorter treatment regimen • directly observed therapy• Reduced hepatotoxicity

• More research is needed to better predict who is most likely to complete treatment

Acknowledgments

I would like to thank the following for their participation in this project:

Paul Hunter, M.D. - UW Department of Family Medicine

Sarah Bleything, PA, - SSCHC

Sixteenth Street Community Health Center

Milwaukee Health Department

Meditation for chronic low back pain in patients prescribed opioids:

A cost analysis

Aleksandra Zgierska, MD, PhDJames Ircink, BS

Background/Significance

• US healthcare system most expensive in world– Yet lags in quality/efficiency

• Chronic low back pain affects 80% of US adults– Significant economic burden

• Long-term opioids is common tx– Current opioid abuse epidemic

Background/Significance

• Alternative treatments warranted– Improved quality of life, reduced cost

• Meditation has promise to improve health– Limited evidence in CLBP– Low cost, sustained results

• Costs yet to be estimated in this population

Methods

• 35 adults with CLBP treated with daily opioids

• Randomized to (i) meditation + standard of care(ii) standard of care only

• Patient-reported data via surveys at baseline, 8 weeks, and 26 weeks:– Cost: Meds, health care utilization, productivity, MVA’s– Quality of life: QALY’s, ODI, Health Score

Methods

• Categorical costs estimated

• Group Comparison

• Statistical methods – Means, SD’s, CI’s– Small, pilot trial effect sizes

Results: BaselineMean (n=35)

DemographicsAge 52Years of Back Pain 14.2Years of Opioids 7.9Individual Gross Income $18,291Household Gross Income $36,089

Health MeasuresODI Score 67Health Score 53QALY Score 0.581

Results: Baseline (past 6 mo.)Health Care Utilization Mean $ (n=35)

Office Visit Costs 1138Urgent Care Visit Costs 59Individual Mental Health Visit Costs 391Group Mental Health Visit Cost 29Inpatient Day Cost 2075Emergency Room Visit Costs 459

Total Health Care Utilization Cost (SD) $4151 (6463)Productivity

Cost Due to Missed Work Days 1976Cost Due to Missed Leisure Days 2868

Total Productivity Cost (SD) $4844 (7243)

Motor Vehicle Accidents .06Costs Due to Motor Vehicle Accidents $509

Results Pending…

• Medication data• Meditation-efficacy analyses

Preliminary Conclusions

• The opioid-treated CLBP population is costly

Questions?

2014 SSRCA

Thanks