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Understanding Temporal Patternsin Hypertensive Drug Therapy
1Margret Bjarnadottir, 2Sana Malik, 2Catherine Plaisant, 3Eberechukwu Onukwugha
1Smith School of Business, University of Maryland, College Park 2Department of Computer Science, University of Maryland, College Park 3School of Pharmacy, University of Maryland, Baltimore
May 28, 2015 — HCIL 32nd Annual Symposium
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2
๏ Worsened conditions
๏ Adverse outcomes
๏ Increased risk of death
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Hospitalization costs due to medication non-adherence are estimated as high as
$13 billion annually.
3
Sullivan et al., “Noncompliance with medication regimens and subsequent hospitalizations: a literature analysis and cost of hospitalization estimate,” Journal of Research in Pharmaceutical Economics, Vol. 2, No. 2. (1990), pp. 19-33.
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Medication Possession Ratio (MPR)
4
period nobservatio of lengthsupplied days∑
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Medication Possession Ratio (MPR)
4
period nobservatio of lengthsupplied days∑
time
Study start Study end
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Medication Possession Ratio (MPR)
4
period nobservatio of lengthsupplied days∑
time
Study start Study end
= 100%
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Medication Possession Ratio (MPR)
4
period nobservatio of lengthsupplied days∑
time
Study start Study end
= 83%
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Medication Possession Ratio (MPR)
5
period nobservatio of lengthsupplied days∑ = 75%
time
Study start Study end
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Hypertensive Drug Therapy
Angiotension-Converting Enzyme-Inhibitors
Angiotension II Receptor Blockers
Calcium Channel Blockers
Beta Blockers
Diuretics
6
ACE
ARB
CCB
Beta
Diur
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time
Hypertensive Drug Therapy
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Hypertensive Drug Therapy
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Can we use visualization tools to more accurately understand adherence
patterns in hypertensive drug therapy?
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๏ Can we identify good vs. bad patterns?
๏ Can we understand patient behavior?
Research Questions
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Data๏ Commercial prescription claims
๏ 900,000 individuals (16 million claims)
๏ 5 Drug Classes
12
Angiotension-Converting Enzyme-Inhibitors
Angiotension II Receptor Blockers
Calcium Channel Blockers
Beta Blockers
Diuretics
ACE
ARB
CCB
Beta
Diur
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
13
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging Dataset
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
— Single
} 2 Drugs
} 3 Drugs
} 4 Drugs
— All 5
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
timeStart of record
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
time2 yearsStart of record
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
time
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Data Simplification
๏ Partitioning
๏ Temporal Windowing
๏ Interval Merging
time
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How do allowable gap and overlap assumptions affect adherence
analysis?
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Sensitivity Analysis: Allowable Gap
18
0 days 7 days
15 days 30 days
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Sensitivity Analysis: Allowable Gap
18
0 days 7 days
15 days 30 days
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Sensitivity Analysis: Allowable Gap
18
0 days 7 days
15 days 30 days
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Sensitivity Analysis: Allowable Gap
18
0 days 7 days
15 days 30 days
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0 days 7 days
15 days 30 days
Sensitivity Analysis: Allowable Overlap
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19
0 days 7 days
15 days 30 days
Sensitivity Analysis: Allowable Overlap
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0 days 7 days
15 days 30 days
Sensitivity Analysis: Allowable Overlap
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19
0 days 7 days
15 days 30 days
Sensitivity Analysis: Allowable Overlap
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Single DrugsACE
ARB
CCB
Beta
Diur
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Single DrugsACE
ARB
CCB
Beta
Diur
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2 DrugsACE
Beta
Both
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2 DrugsACE
Beta
Both
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2 Drugs
false overlap
ACE
Beta
Both
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2 DrugsACE
Beta
Both
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2 DrugsACE
Beta
Both
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All 5 Drugs
25
2,200 patients
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2,200 patientsAll 5 Drugs
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2,200 patientsAll 5 Drugs
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2,200 patientsAll 5 Drugs
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2,200 patientsAll 5 Drugs
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All 5 Drugs
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2,200 patients
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All 5 Drugs
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2,200 patients
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Starting on Diuretic
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ACE
ARB
CCB
Beta
Diur150,000 patients
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Starting on Diuretic
28
ACE
ARB
CCB
Beta
Diur150,000 patients
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Starting on Diuretic
28
ACE
ARB
CCB
Beta
Diur150,000 patients
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Summary
๏ Visualization facilitated rapid understanding of complex patient behavior
๏ Interactions allow systematic analysis of episode modeling
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Understanding Temporal Patternsin Hypertensive Drug Therapy
Sana [email protected]/hcil/eventflow
Thanks to Sophia Wu. Support from the University of Maryland Center for Health-related Informatics and Bioimaging (CHIB).