It’s Complicated: Methods to assess medication nonadherence and regimen complexity John Billimek, PhD Department of Medicine Grand Rounds | August 12, 2014 Division of General Internal Medicine | Health Policy Research Institute | UC Irvine School of Medicine
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It’s Complicated: Methods to assess medication nonadherence and regimen complexity
It’s Complicated: Methods to assess medication nonadherence and regimen complexity. John Billimek, PhD Department of Medicine Grand Rounds | August 12, 2014 Division of General Internal Medicine | Health Policy Research Institute | UC Irvine School of Medicine. Two patients. - PowerPoint PPT Presentation
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It’s Complicated: Methods to assess medication
nonadherence and regimen complexity
John Billimek, PhDDepartment of Medicine Grand Rounds | August 12, 2014
Division of General Internal Medicine | Health Policy Research Institute | UC Irvine School of Medicine
Two patients 58 year-old man Type 2 diabetes Middle class,
educated Good overall health
Prescribed
4 medications
58 year-old man Type 2 diabetes Middle class,
educated Good overall health
Prescribed
7 medications
Patient Complexity in Chronic Disease Management
Multiple Chronic Conditions Nationwide (CDC) Among all adults in the US
50% have at least one chronic condition 25% have two or more
Adults over age 65 86% have at least one chronic condition 61% have two or more
Two-thirds of health care spending
Ward 2014 Prev Chronic Dis 2014;11:130389Anderson 2010. Chronic Care: Making the Case for Ongoing Care, RWJ
Complex Patients, Complex Regimens
More Chronic Conditions
More medications
indicated
Over- and under-
prescribing
Worse adherence
More adverse events
Higher costs
Increased hospitalizati
on
Increased readmission
s
Increased mortality
Mansur et al 2012. Am J Geriatr Pharmacother 10;223-229Wilson et al 2014. Ann Pharmacother 48(1);26-32
Medication Nonadherence Over 50% of patients
either Never fill Rx Delay refills Discontinue, and/or Skip doses
Contributes to up to 69% of hospital admissions And $100 billion
Osterweil 2005. NEJM
How much nonadherence is too much?
Varies by condition, treatment and situation
In VA patients with diabetes “Skipping” 20% of doses
+81% mortality risk +58% all-cause admission rate
“Skipping” 50% of doses 12-fold mortality risk
Ho. et al. 2006. Arch Intern Med 166:1836-41 Egede et al. 2011. The Annals of Pharmacotherapy 45: 169 –78
R2D2C2 Study
NIDDK, RWJ, Novo Nordisk funded RCT Disparities in diabetes management Poor, ethnically diverse sample (N=1484) Data collection
MRCI = Total A + Total B + Total Cfor all current prescription medications
Dosage Form Dosing FrequencySpecial Instructions+ +
Medication Regimen Complexity Index (MRCI)A weighted count of currently prescribed medications
A B C
A B C
All polypharmacy is not created equal
Putting it together: Population management of medication issues
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
Stage 1 R2D2C2 Dataset: Preliminary Findings
Hospital admission
ER Visit
LDL > 100 mg/dl
A1c > 8%
Medication nonadherence
0.0 0.5 1.0 1.5 2.0 2.5
1.8
1.9
1.1
1.4
1.5
Stage 1 R2D2C2 Dataset: Linking MRCI to outcomes
Higher rates with high MRCI
Odds ratios comparing MRCI above vs. below 17Adult UCI patients with type 2 diabetes (N=998)adjusted for: Age, Sex, Race/ethnicity, Education, Insurance type, Nativity, duration of diabetes and comorbidity (TIBI)*
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
Acknowledgments
Funders DOM Chair’s Award ICTS Pilot Awards program NIDDK
Collaborators Sheldon Greenfield Sherrie Kaplan Dara Sorkin Quyen Ngo-Metzger Shaista Malik Dana Mukamel Lisa Dahm Andrea Hwang UC Irvine Health Informatics &
Research Computing
Patient Advisory Group (La Voz de la Esperanza) Marco Angulo Anabel Arroyo
MRCI/MPR Development team Travis Nesbit Daniel Orlovich
Audiocoding Team Herlinda Guzman Linh Vu Katherine Vu Sophia Nguyen Kimberly Gardner Taylor Gardner Mylon Remley Mei Chang Sana Moosaji Stephanie Torrez Maria Paula Gonzalez Alejandro Avina Jessica Colin Escobar Linda Nguyen
Summary Nonadherence and Complex regimens are
common Problems with regimens are rarely discussed
Regimen complexity Outcomes Independent of comorbid disease burden
EMR-based approaches can identify patients struggling with medication regimen Help direct interventions and resources