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Does Shared Treatment Decision-Making Improve Asthma Adherence and Outcomes? Supported by grants from the National Heart, Lung and Blood Institute 1R01.

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Presentation on theme: "Does Shared Treatment Decision-Making Improve Asthma Adherence and Outcomes? Supported by grants from the National Heart, Lung and Blood Institute 1R01."— Presentation transcript:

1 Does Shared Treatment Decision-Making Improve Asthma Adherence and Outcomes? Supported by grants from the National Heart, Lung and Blood Institute 1R01 HL69358 (PI: SWilson) and 1R18 HL67092 (PI: ASBuist)

2 Only ~50% of patients take asthma medications at effective doses Documented problems: Under-use of controller medications Over-use of relievers & OTC medications Poor inhaled medication technique Failure to fill/refill prescriptions Failure to keep medications available when and where they are needed

3 Known contributors to non-adherence Patient Younger age Low socioeconomic status Lack of education Memory problems Lack of understanding of the disease Regimen Longer duration of treatment Higher cost Complexity, more frequent dosing Properties (bad taste, more side effects, etc.) Physician-patient relationship Inadequate monitoring Failure to explain side effects Failure to analyze patients medication-taking behaviors Failure to address the patients individual situation and preferences

4 Models of Clinician-Patient Interaction Traditional model: Interaction is directive; Clinician makes the treatment decision Evidence-based management usually follows a traditional model Informed decision-making model: Clinician provides information to the patient Patient makes the decision Pt MD Pt MD

5 Shared decision-making model: Mutual exchange of information and treatment preferences between clinician & patient Both participate in treatment decisions Each brings unique knowledge to the interaction Hypothesis: Involving patients in treatment decisions should result in: Better adherence to treatment Better asthma control Greater patient satisfaction Pt MD

6 Design of the BOAT trial Three-arm, randomized controlled trial SDM = shared decision making care management MBG = guidelines-based traditional care management UC = usual medical care Data collection Baseline and 12-mos. post-randomization Questionnaire PFT 12-mos. pre and 24 mos. post-randomization (36 mo.) Asthma medications dispensed All health care utilization

7 BOAT study hypotheses regarding adherence and disease outcomes SDM > MBG SDM > UC

8 Study Outcomes Primary Adherence to asthma medications Asthma-related quality of life Asthma-related health care utilization Secondary Asthma control Use of reliever medications Symptom-free days; Lung function Satisfaction with asthma care Preferences, values, & attitudes towards adherence Total asthma health care utilization Asthma-related health care costs

9 Both the SDM & MBG Interventions: Target patients with poorly controlled, moderate-severe asthma Involve 2 in-person sessions, approximately 1 mo. apart, plus 3 follow-up calls at 3 mo. intervals Conducted by asthma care managers: Clinical pharmacists Nurse practitioners and registered nurses Physician assistants Respiratory therapists Parallel written protocols (scripts) guide both SDM and MBG clinician-patient interactions Structured to enable tailoring to the individual patient Instructional aides and worksheets are included in the interventionist manual

10 SDM and MBG Interventions* Provide information Assess understanding of asthma Review asthma and how it is treated Confirm comprehension Negotiate (SDM)/Prescribe (MBG) Summarize patient goals and priorities Review PFTs with patient Assess symptom control using objective criteria Determine asthma severity per GINA guidelines Define medication preferences Discuss +/- of each treatment option per patient goals and preferences Negotiate a treatment decision Wrap Up Write Rx Give Asthma Action & Management Plan Teach proper inhaler use Give asthma diary Schedule follow-up appointment Set the Stage Establish rapport Describe session schedule Describe shared decision making approach Gather patient information Asthma symptoms Perceptions of control Medication use Use of alternative therapies Environmental triggers Patient goals & preferences * White = MBG and SDM Gold = SDM only



13 Inclusion Criteria Recent ED/hospital visit for asthma and/or evidence of over-use of rescue medication 18-70 years of age KFHP member 1 year Self-reported, doctor-diagnosed asthma Currently R x ed asthma medications Meets obstruction reversibility criterion One or more asthma control problems (ATAQ score 1)

14 Exclusion Criteria Mild intermittent/seasonal asthma Regular use of oral corticosteroids Currently receiving asthma care-management Not able to speak, read, and understand English Planning to move out of area within two years

15 Eligible Patients (N=613) SDM (N= 204) MBG (N= 205) UC (N= 204) Randomization* * Adaptive randomization algorithm (Pocock, 1983) - ensures better than chance balance and increases likelihood of better than chance balance on correlated characteristics.

16 Demographic characteristics * N=613 % Age18-34 yrs.20 35-50 yrs.42 51-70 yrs.38 GenderMale44 Female56 EthnicityHispanic 4 Asian10 Native Hawaiian/Pacific Islander 8 Black/African American 16 White/Caucasian62 % Level of education < High School Diploma 2 HS Diploma/GED16 Technical/Some College43 4-Year Degree/BA/BS22 Graduate Degree17 Annual family income $20,000 8 $20,001 - $40,00021 $40,001 - $60,00025 $60,001 - $80,00018 $80,001 24 DK/Refused To Answer 4 * No significant group differences. 38% 80%

17 Baseline asthma status* * No significant group differences in symptom frequency, nocturnal symptoms, or FEV 1 % predicted at baseline. Symptom FrequencyNocturnal Symptoms FEV1 % predicted

18 De facto medication regimen and asthma control* * No significant group differences at baseline. Medication regimen Asthma Control

19 Did the SDM patients medication choices differ from the MBG care managers guidelines-based R x ? Medication SDM N=191 MBG N=186 p- value 1 Beclomethasone 8090 (50%)108 (61%) Fluticasone 22078 (43%)53 (30%)0.03 Other ICS 2 13 (7%)17 (10%) Any ICS181 (95%)178 (96%)0.67 Leukotriene modifier14 ( 7%)14 (8%)0.94 Theophylline4 ( 2%)1 (1%)0.37 Any Controller 3 186 (97%)181 (97%)1.00 1. Chi-square or Fishers exact test. 2. Includes Beclomethasone and Fluticasone at lower strengths, and Budesonide. 3. Includes ICSs, leukotriene modifiers, and theophylline; excludes LABAs and oral prednisone.

20 Adherence measure = Continuous Measure of Medication Acquisition (CMA) CMA = Number of days supply of a medication dispensed/365 days Proportion of days on which medication was available for use on R x ed regimen A commonly used indicator of adherence to the intended daily regimen Data from the HMOs pharmacy database ~95% of patients obtain all their medications from the HMO pharmacy

21 Cumulative medication acquisition (CMA) values pre and post randomization, by experimental group UCMBGSDMNp-value Baseline Yr. N=203 N=204N=610 Any ICS0.32 (0.32) 0.32 (0.31) 0.33 (0.34) 0.8986 Any Controller N=204 0.41 (0.47) N=205 0.38 (0.37) N=204 0.40 (0.43) N=613 0.9490 Follow-up Yr. N=203N=202N=204N=609 Any ICS0.39 (0.37) 0.54 (0.36) 0.62 (0.38) SDM vs MBG p=0.0162 SDM vs UC p<0.0001 MBG vs UC p<0.0001 Any Controller N=204 0.49 (0.52) N=205 0.59 (0.45) N=204 0.69 (0.45) N=613 SDM vs MBG p=0.0095 SDM vs UC p<0.0001 MBG vs UC p=0.0014 CMA index – Mean (SD)

22 Conclusions: For non-adherent patients with poorly controlled asthma -- Involving patients in a meaningful way in treatment decisions does not result treatment regimens that conflict with standard guidelines, assuming patients have a basic understanding of: asthma their current level of disease control the medical rationale for asthma treatment.

23 Conclusions: For non-adherent patients with poorly controlled asthma, care management that utilizes a shared clinician-patient approach to selection of the treatment regimen significantly improves adherence to asthma controllers over a one year period when compared with both: usual medical care, and traditional, prescriptive care management Intervention effects did not differ as a function of ethnic group (Caucasian, Asian and African American)

24 Conclusions - continued Clinical approaches of asthma care managers can be shaped such that treatment decision making is shared with the patient in a meaningful way. This required use of a detailed intervention protocol, training, and ongoing feedback. Patients evaluate their own vs. the clinicians influence on treatment decisions differently when they experience a shared decision making approach than when they experience prescriptive care management

25 Does shared decision-making lead to: better asthma control? better asthma-related quality of life? reduced asthma health care utilization? increased patient satisfaction? Are adherence outcomes mediated by patient perceptions of their influence on treatment decisions? Are disease outcomes mediated by medication adherence? Questions being investigated by analyses in process

26 Decision Roles - Treatment decisions were made by: 1 = Care manager alone 2 = Care manager mostly 3 = Patient and care manager equally 4 = Patient mostly 5 = Patient alone Protocol Adherence - 1 = Relevant elements not covered 3 = All elements covered, but some briefly, incompletely, or inadequately 5 = All topics covered completely, thoroughly, and accurately Process outcomes Rating scales: How closely did interventionists follow the protocol Who made the treatment decisions?

27 Investigators Sandra Wilson, PhD, PI (PAMFRI, SUSM) Sonia Buist, MD, PI (OHSU, CHR) William Vollmer, PhD (CHR) Tom Vogt, MD (CHR) Nancy L. Brown, PhD (PAMFRI, SU) Philip Lavori, PhD (SUSM) Margaret Strub, MD (TPMG) Stephen VanDenEeden, PhD (KRFI/DOR) Consultants Amiram Gafni, PhD Elizabeth Juniper, PhD Cynthia Rand, PhD Sean Sullivan, PhD Kevin Weiss, MD Clinical Site Co-investigators Faith Bocobo, MD (TPMG) Christine Fukui, MD (TPMG) Donald German, MD (TPMG) John Hoehne, MD (TPMG) Matthew Lau, MD (TPMG) Myngoc Nguyen, MD (TPMG)



30 (SDM only)

31 Post-randomization CMA indices for inhaled corticosteroids, by group 1 1.N=504. Excludes 4 patients with mild persistent asthma for whom no ICS was prescribed. 2.Overall test of group differences, Wilcoxon/Kruskal Wallis test. 3.Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p<0.0001. Mn = 0.54 N = 202 Mn = 0.62 N = 204 Mn = 0.39 N = 203 Overall p<0.0001 2,3

32 Post-randomization CMA indices for all asthma controllers combined, by group 1 1.N = 504. Excludes 4 patients with mild persistent asthma, for whom no controller was prescribed. 2.Overall test of group differences, Wilcoxon/Kruskal Wallis test. 3.Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p=0.0023. Overall p<0.0001 2,3 Mn = 0.59 N = 205 Mn = 0.69 N = 204 Mn = 0.49 N = 204

33 Pre-randomization CMA for all controllers, by ethnicity, within relevant sites Northern CA & Portland Northern CA & Hawaii Mn = 0.40 N = 94 Mn = 0.41 N = 344 Mn = 0.47 N = 205 Mn = 0.36 N = 59

34 Post-randomization CMA for all controllers, by group, separately for Whites and Asians. WhiteAsian Regression model Group comparison: p-value <=0.0001. Group x Ethnicity interaction: p-value = 0.4478 Mn=0.66 N = 68 Mn=0.74 N = 68 Mn=0.52 N = 69 Mn=0.78 N = 18 Mn=0.87 N = 19 Mn=0.52 N = 22

35 Post-randomization CMA for all controllers, by group, separately for Whites and African Americans Regression model Group comparison: p-value <=0.0001; Group X Ethnicity interaction: p-value = 0.6993. WhiteAfrican American Mn = 0.63 N = 113 Mn = 0.74 N = 115 Mn = 0.53 N = 116 Mn = 0.55 N = 33 Mn = 0.51 N = 32 Mn = 0.34 N = 29

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