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 ID: 932422
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Slide1
Student PresentationsSSRCA - 2014Summer Student Research and Clinical Assistantship Program
University of Wisconsin
School of Medicine and Public Health
Department of Family Medicine
Slide2Slide3Life 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 HanrahanDepartment of Family Medicine, University of Wisconsin School of Medicine and Public HealthSummer Student Research and Clinical Assistantship (SSRCA) ProgramSummer 2014
University of Wisconsin Department of Family Medicine
Slide4BackgroundUniversity of Wisconsin Department of Family Medicine
Rasmussen &
Hancox
(2014)
Slide5BackgroundUniversity of Wisconsin Department of Family Medicine
Asthma
(Control)
BMI
Saint-Pierre, et. Al (2006)
Slide6BackgroundUniversity 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)
Slide7PurposeUniversity of Wisconsin Department of Family Medicine
Asthma
(Control)
BMI
Sex
Pediatric males
Adult females
Does this relationship hold in a large clinical population?
Slide8MethodsUniversity of Wisconsin Electronic Health Record Public Health Information Exchange (
UW
eHealth
-PHINEX)University of Wisconsin Department of Family MedicineClinical DataUW Departments
o
f Family Medicine, Internal Medicine, Pediatrics
2007-2012
Community Level Data
US Census Bureau
Esri
Business Analyst
Guilbert
, et. Al (2012),
Tomasello
, et. Al (2014)
Slide9MethodsUniversity of Wisconsin Department of Family Medicine
≥2 encounters ≥2 years apart
ICD-9 493.xx
≥2 adverse events ≥90 days apart
Slide10MethodsUniversity of Wisconsin Department of Family Medicine
Age Group
BMI Category
Sex
0-4, 5-11, 12-17, 18-40, 41-59, 60+
Normal
Obese
* According to CDC age-appropriate guidelines
Male
Female
Stratified by:
Slide11ResultsUniversity of Wisconsin Department of Family Medicine
298,847
40,011
(13.4%)6,554(16.4% of patients with asthma)
Slide12University of Wisconsin Department of Family Medicine
Slide13University of Wisconsin Department of Family Medicine
Slide14University of Wisconsin Department of Family Medicine
Slide15University of Wisconsin Department of Family Medicine
Slide16SummaryAsthma prevalence
H
igher
in obese pediatric males and obese adult femalesOR of association between obesity and asthmaSimilar in pediatric males/femalesSignificantly greater in adult femalesSimilar but non-significant patterns observed for uncontrolled asthmaUniversity of Wisconsin Department of Family Medicine
Slide17ImplicationsAlignment of a large, clinical population with smaller epidemiological studies
Epidemiological predictive value
Future targeted diagnosis and treatment methods
Biology of associationFemale sex hormone interaction vs. underlying inflammation linked to both asthma and obesityUniversity of Wisconsin Department of Family Medicine
Slide18ReferencesBeckett 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
Slide19Slide20Dropout Characteristics of Opioid Dependent Offenders in Community-Based Treatment
Shawn Wayne, M2
Randy Brown, MD, PhD
Slide21Opioid Epidemic
Slide22Role Drug Treatment Court (DTC)
An
individual with untreated addiction to illicit substances commits an average of 63 crimes per
year.2Intervention!Reduces recidivism and illicit drug use, through obligatory,CounselingMedical treatmentJudicial supervisionSocial services In exchange for dismissal/ reduction of charges
Slide23Medical Treatment
Medications:
Methadone [Federally accredited facilities]
Suboxone (Buprenorphine/ Naloxone) Pilot Study:Suboxone Tx in physicians office (PO) effective, however did not reduce HIV risk behaviorsSpecialist Stabilization Period: Optimizing treatment, given limited resources Compare Suboxone Tx PO to Suboxone
Tx
Specialty Care followed by
Tx
PO.
Slide24Study 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
Tx
MHS-3 Months
PO-7 Months
Suboxone
Tx
PO-10 Months
Slide25Data Collection Instruments:
Surveys:
TLFB (Timeline Feedback)
Measures drug-use for the previous 14-daysASI (Addiction Severity Index)Accesses drug use, SES, legal circumstancesCMR (Circumstances, Motivation, and Readiness)RAB (Risk Assessment Battery)HIV/AIDS risk assessmentCourt Reports
Slide26Unforeseen Difficulties
Recruitment
18 unique subjects enrolled since November 2013
Reassessed inclusion criteria Restructuring of Dane County DC in December 2013Potential impediment to recruitment processDropout50% dropout (DO) prior TransportationRecidivismInability to fill prescriptionExpected DO (20-40%) with Suboxone Tx
Slide27Dropout Comparisons:
No difference in rate of DO between
Tx
Demographics:No difference in age or gender between DO status groupsBaseline 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 statusOther: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)
Slide28Significance 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
Slide29Discussion:
Findings:
No difference in DO status between
Tx armsHeroin use and age not be prognostic of DOMotivation 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 MHSConclusions:Preliminary support of predictive baseline figures between Tx armsPersonalized DC treatmentLimitations:Sample sizeBaseline ComparisonExtraneous Circumstances (Transportation, Legal, Family etc.)
Slide30Future Investigation: Criminality
D
rug use and criminality
Income generating crimes, disorderly conduct, possession, etc. Predictive value of CriminalityDO, recidivism, positive UAsIs Criminality a prognostic marker for Tx arms?Increased judicial supervision reduced positive UAs and sanctions amongst other “high risk” DO participantsDoes daily dispensing at MHS may have similar effect?
Slide31Future 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 revisionCCAP and Court ReportsCriminality metric (adapted Gordon et al. 2013) FrequencyVarietySeverityTo be continued... Questions?
Slide32Literature 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.
Slide33Slide34How doctors birthHow our experiences shape our practice
Carly Kruse, MSc,
Ildi
Martonffy, MD
Slide35Background and ObjectivesHistory of birthing stories as a space for women to share experiencesKen 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:Examine birthing preferences and birthing realitiesExplore maternal care approaches before and after motherhoodInvestigate breastfeeding expectationsAnalyze changes in breastfeeding counseling due to personal experiences
Slide36Methods29 question survey distributed to members of UW Family Medicine Department, UW Obstetrics and Gynecology Department, and the Academy of Breastfeeding Medicine45 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 questions20 participants interested 7 completed
Slide37Participants
*Percentages are calculated using n=43 for all questions whether or not all participants responded to that question
Slide38Prenatal MethodsNational average of doula utilization = 6%
2
“Met with a doula to talk about letting go and not always being in control” –
Interviewee 006
Slide39Delivery Methods*n=32 with 63 responses
Slide40BreastfeedingAll participants breastfed for at least 1 of their births and 90.7% breastfed all babies76.7% 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)
Slide41Impact on Care PracticesPrenatal CounselingMore breastfeeding education
Fewer birth plans:
“Goal of labor that everyone end up healthy, but how we get there is unimportant” – Interviewee 007
Labor SupportWoman-centered approaches“take more cues from the laboring woman” -005 Normalization of deliveries and expectationsBreastfeeding CounselingRemove social pressures: “Stop shoulding yourself” – Interviewee 004Become more informedPediatric Care“I considered my most important job as being a mother. My profession was being a doctor. These were mutually reinforcing roles” -015
Slide42DiscussionOverall 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
Slide43LimitationsSmall sample size GeneralizabilitySelf-selection bias
Family Medicine participants = 65.1%
OB/GYNE Participants = 18.6%
Retrospective self-reporting
Slide44ConclusionDo 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
Slide45ReferencesMurray, 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/>. 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>. 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>.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>.
Slide46Slide47Mental health predicts common cold occurrenceBy Lizzie Maxwell
Slide48BackgroundThe 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 incidence2Sheldon Cohen et al.Increased stress increased ARI incidence3Rakel D, Mundt M, Ewers T, et al. Value associated with mindfulness meditation and moderate exercise intervention in acute respiratory infection: the MEPARI study. 2013
Slide49MethodsMEPARI and MEPARI-2Spearman rank-order correlation
Psychosocial Measures (baseline
)
SF12ARI Measures (throughout study)IncidenceDuration and Severity
Slide50SF-12 Health Survey12 QuestionsGenericHealth-Related Quality of Life
2 Summary Scores:
Physical
Mental
Slide51During 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
Slide52ARI OutcomesIncidenceDo you think you have/are coming down with a cold1 of 4 common cold symptoms
Score
>
2 on Jackson ScaleDurationSeverityWURSS-24
Slide53Demographics
n
353% Female77.6%Mean Age54.2 (10.4)% Education < Bachelors degree71.4%% Income < $50,00058.1%
Slide54# ARI
SF-12 Mental
Results
Graph courtesy of Joseph Chase
Slide55ExplanationsStress as a common risk factor4Mental illness’ effect on immunity
5
Unfounded symptoms
6Healthy vs unhealthy behaviors4
Slide56LimitationsAnalysis has thus far included intervention groupsPotential effects of interventions?
Next steps…
Slide57Results
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
Slide58References1. 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): 390-72. 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 Press5. 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.
Slide59Slide60Community 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
Slide61Today’s Talk
Introduction
Who are Hmong?
What is HHCWhat is SHOWMiniSHOW of Wausau Hmong communityPreliminary Surveys
Slide62Who are Hmong?
Hmong History
U.S. Hmong cultural ancestry as ethnic minority in China
Resettled in mountains of Laos, Thailand, North VietnamAfter Vietnam War and Secret War, fled and relocated to Thailand refugee campsCame to U.S. as political refugees after 1975Hmong are largest ethnic Asian population in Wisconsin
Slide63What is HHC
Hmong Health Council
Central Wisconsin
South Central WisconsinHmong 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
Slide64What is SHOW?
Survey of the Health of Wisconsin
Gathers data across Wisconsin
Annual surveys up to 1000 people/year age 21-74Measures:health behaviors, mental health, access to health care, beliefs in health care, environmentPartner with HHC
Slide65Target Population:
Central Wisconsin Hmong
Midwest has largest Hmong population in the nation
Wausau is 2nd largest Hmong community in WisconsinHmong Health issuesPre migration/refugee campsPost migrationIncreased risk for obesity, hypertension, hyperlipidemia, cardiovascular disease, diabetes
Slide66Mini Health Assessment
Pilot project in Wausau Hmong community
General health assessment of Hmong Wisconsin Community using SHOW methods
10-30 householdsWill we be able to reproduce similar study to SHOW’s pilot neighborhood study?
Slide67Preliminary 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
Slide68What we learned so far
Survey takes 2 hours in Hmong, 1hour in
Hmonglish
Some things cannot be directly translatedSome concepts are difficult to explain or understand: Scales (rate from 0-10)Genes/DNA
Slide69Questions?
References
http://hmonghealthcouncil.wordpress.com/about
/http://www.med.wisc.edu/show/about-survey-of-the-health-of-wisconsin/36193http://www.hndinc.org/cmsAdmin/uploads/dlc/HND-Census-Report-2013.pdfHer C, Mundt M. Risk prevalence for type 2 diabetes mellitus in adult Hmong in Wisconsin: a pilot study. WMJ 2005;104(5):70-7.
Slide70Slide71Disease-Management & Financial Implications of the Addition of a Health Coach/Nutritionist in Two Family Medicine Clinics
Kristin Magliocco
Dennis Baumgardner MD, Tiffany Mullen DO, Kristen Reynolds MD
Slide72Presentation OutlineBackgroundExpected Outcomes
Methods
Data Collection
Preliminary DataLimitations of the StudyConcluding RemarksLiterature Cited
Slide73Background: Chronic Disease80% of healthcare spending1
Leads to preventable deaths
2
Lifestyle changesPatients’ disease maintenance goals are not met1Diabetes: 43%Hypertension: 50%Hyperlipidemia: 80%
Slide74Background: 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 disease
4
Slide75Background: Health CoachingEmpowerment1
Motivational interviewing
2
Active role for patient3Goal setting for what is feasible in daily life5Follow-up7,8
Slide76Expanding the Healthcare TeamTime is limiting factor for clinicians6
Non-clinician personnel
5,6,7
Medical Assistants1Dietitians9Medical/Nursing Students10Successful Peers11,12Dual-trained Nutritionist/Health Coach
Slide77Expected OutcomesPrimary outcome: Improved clinical outcomes
Secondary outcome: Financial benefits for patients
Slide78MethodsRetrospective chart review
Each patient is own control
2 integrative Family Medicine clinics
Referrals to Nutritionist/Health Coach by PCPInclusion Criteria by DiagnosisDiabetesHypertensionHyperlipidemia, HypercholesterolemiaMetabolic SyndromeObesity (BMI > 30)
Slide79Data Collection
Slide80Preliminary Data
6.2 ± 0.316
Slide81Preliminary Data
129.38 ± 8.876
82.40 ± 3.978
Slide82Preliminary Data
211.71 ± 30.587
129.86 ± 39.599
53.86 ± 13.459
140.86 ± 71.913
Slide83Preliminary Data
36.62 ± 7.915
Slide84Limitations of the StudyLow patient enrollment so farCash payments for appointments
Creates biases
Variation in follow-up
Follow-up shown to be essential14
Slide85Concluding RemarksStudy is ongoing
Potential future impact for chronic disease
Slide86Literature Cited
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.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.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.Norris SL, Engelgau MM, Narayn KMV. Effectiveness of self-management training in type 2 diabetes. Diabetes Care 2001, 24(3): 561-587.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.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.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.
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.
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.
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.
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.
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.
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.
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.
Slide87Questions?
Slide88Slide89Negative pap smear, positive hpv: what does it mean?
Lindsey Anderson
Faculty Mentor:
Sarina Schrager, M.D., M.S.
Slide90Cervical cancer 2010 Incidence: 12,200 cervical cancer diagnoses2010 Mortality: 4,200 deathsEasily treated if caught early
Human papilloma virus (HPV) infection prerequisite
Cervical intraepithelial
neoplasia I, II, III (CIN)HPV 16, 18, 31, 33, 45
Slide91Cervical cancer screeningPap smear cytologyNegativeAtypical squamous cells of undetermined significance (ASCUS)
Low grade squamous intraepithelial lesion (LSIL)
High grade squamous intraepithelial lesion (HSIL)
HPV DNA tests16, 18 DNA genotypingFollow-upConization (Cone biopsy)Loop Electrosurgical Excision Procedure (LEEP)Hysterectomy
Slide92Screening guidelinesNew guidelines in place 2012Co-testing for women ages 30-65Hope to decrease number of colposcopies
Both negative = co-test again in 5 years
Slide93Chart reviewCase finding with data lists from all UW clinicsDFM patients with colposcopies doneDFM patients with pap smears done
November 2012-April 2014
785 charts
66 negative pap/positive HPV56 had a colposcopy6 abnormal colposcopies2 referred to further procedures
Slide94Negative pap smear, positive HPV56 women59 procedures total29 biopsies
23 normal (79.3%)
30
endocervical curettage 28 normal (93.3%)18 women had both biopsy and ECC 12 all normal (66.7%)
Slide95Abnormal colposcopyThree Cervical Intraepithelial Neoplasia I (CIN I)
60% resolve to normal in one year
One CIN I/normal
One CIN II-IIIReferred for LEEPOne CIN III/carcinoma-in-situReferred for possible hysterectomy
Slide96Abnormal Colposcopy
Smoking Status
50% current/former smokers
HPV Prevalence83.3% were HPV 16+16.7% were HPV 18+Previous Abnormal Pap Smear50% had a previous abnormal pap smear***47% more likely to have had a previous abnormal pap smear***Smoking Status56% current/former smokersHPV Prevalence80% were HPV 16+18% were HPV 18+Previous Abnormal Pap Smear34% had a previous abnormal pap smear
Normal
Colposcopy
Slide97referencesSaslow 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.
Slide98Slide99Improving 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 7/18/14Faculty Mentor: Paul Hunter, M.D.Department of Family MedicineUniversity of Wisconsin School of Medicine and Public HealthSarah Bleything, PASixteenth Street Community Health Center, Milwaukee, WI
Slide100Introduction: 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 patientsTreatment 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)
Slide101Introduction
Major limitation of LTBI treatment is adherence.
Individual clinics report between 5% and 60% completion for 6 months INH of those
initiating treatmentINH monotherapy and INH/RPT have similar efficacy (Sterling, NEJM)
Slide102Study 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.
Slide103MethodsStudy 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 EMRIRB approval at SSCHCStudy Participants:All patients accepting treatment for LTBI in 2012 and 2013INH monotherapy and INH/RPT combined therapy (DOT)
Slide104MethodsData Collection:
Retrospective review of LTBI patient log and Electronic Medical Records
Clinical 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 normalRelationship with the clinicResident distance from clinic (calculated by GoogleMaps)No. visits in year preceding treatment acceptanceNo. years a patient at the clinic
Slide105Results: participant eligibility
n=139; INH/RPT – 45, INH only - 94
Slide106ResultsBaseline characteristics of study and control groups
Slide107Results: 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
Slide108Results: 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)
Slide109Discussion
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 therapyReduced hepatotoxicityMore research is needed to better predict who is most likely to complete treatment
Slide110AcknowledgmentsI would like to thank the following for their participation in this project:
Paul Hunter, M.D. - UW Department of Family Medicine
Sarah
Bleything, PA, - SSCHCSixteenth Street Community Health Center Milwaukee Health Department
Slide111Slide112Meditation for chronic low back pain in patients prescribed opioids: A cost analysis
Aleksandra
Zgierska
, MD, PhDJames Ircink, BS
Slide113Background/SignificanceUS healthcare system most expensive in worldYet lags in quality/efficiency
Chronic low back pain affects 80% of US adults
Significant economic burden
Long-term opioids is common txCurrent opioid abuse epidemic
Slide114Background/SignificanceAlternative treatments warrantedImproved quality of life, reduced cost
Meditation has promise to improve health
Limited evidence in CLBP
Low cost, sustained resultsCosts yet to be estimated in this population
Slide115Methods35 adults with CLBP treated with daily opioids
Randomized to
(
i) meditation + standard of care(ii) standard of care onlyPatient-reported data via surveys at baseline, 8 weeks, and 26 weeks:Cost: Meds, health care utilization, productivity, MVA’sQuality of life: QALY’s, ODI, Health Score
Slide116MethodsCategorical costs estimatedGroup Comparison
Statistical methods
Means, SD’s, CI’s
Small, pilot trial effect sizes
Slide117Results: Baseline
Mean
(n=35)
DemographicsAge52Years of Back Pain
14.2
Years of Opioids
7.9
Individual Gross Income
$18,291
Household Gross Income
$36,089
Health Measures
ODI Score
67
Health Score
53
QALY Score
0.581
Slide118Results: Baseline (past 6 mo.)
Health Care Utilization
Mean $ (n=35)
Office Visit Costs1138Urgent Care Visit Costs59Individual Mental Health Visit
Costs
391
Group Mental Health Visit
Cost
29
Inpatient Day
Cost
2075
Emergency Room Visit
Costs
459
Total Health Care Utilization
Cost (SD)
$4151 (6463)
Productivity
Cost Due to Missed Work
Days
1976
Cost Due to Missed Leisure
Days
2868
Total Productivity
Cost (SD)
$4844 (7243)
Motor Vehicle
Accidents
.
06
Costs Due to Motor Vehicle
Accidents
$509
Slide119Results Pending…Medication dataMeditation-efficacy analyses
Slide120Preliminary ConclusionsThe opioid-treated CLBP population is costly
Slide121Questions?
Slide1222014 SSRCAThanks