Operationalization and validation of a novel method to calculate adherence to polypharmacy with refill data from the australian pharmaceutical benefits scheme (Pbs) database

Isabelle Arnet, Melanie Greenland, Matthew W. Knuiman, Jamie M. Rankin, Joe Hung, Lee Nedkoff, Tom G. Briffa, Frank M. Sanfilippo

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Abstract

Background: Electronic health care data contain rich information on medicine use from which adherence can be estimated. Various measures developed with medication claims data called for transparency of the equations used, predominantly because they may overestimate adherence, and even more when used with multiple medications. We aimed to operationalize a novel calculation of adherence with polypharmacy, the daily polypharmacy possession ratio (DPPR), and validate it against the common measure of adherence, the medication possession ratio (MPR) and a modified version (MPRm). Methods: We used linked health data from the Australian Pharmaceutical Benefits Scheme and Western Australian hospital morbidity dataset and mortality register. We identified a strict study cohort from 16,185 patients aged ≥65 years hospitalized for myocardial infarction in 2003–2008 in Western Australia as an illustrative example. We applied iterative exclusion criteria to standardize the dispensing histories according to previous literature. A SAS program was developed to calculate the adherence measures accounting for various drug parameters. Results: The study cohort was 348 incident patients (mean age 74.6±6.8 years; 69% male) with an admission for myocardial infarction who had cardiovascular medications over a median of 727 days (range 74 to 3,798 days) prior to readmission. There were statins (96.8%), angiotensin converting enzyme inhibitors (88.8%), beta-blockers (85.6%), and angiotensin receptor blockers (13.2%) dispensed. As expected, observed adherence values were higher with mean MPR (median 89.2%; Q1: 73.3%; Q3: 104.6%) than mean MPRm (median 82.8%; Q1: 68.5%; Q3: 95.9%). DPPR values were the most narrow (median 83.8%; Q1: 70.9%; Q3: 96.4%). Mean MPR and DPPR yielded very close possession values for 37.9% of the patients. Values were similar in patients with longer observation windows. When the traditional threshold of 80% was applied to mean MPR and DPPR values to signify the threshold for good adherence, 11.6% of patients were classified as good adherers with the mean MPR relative to the DPPR. Conclusion: In the absence of transparent and standardized equations to calculate adherence to polypharmacy from refill databases, the novel DPPR algorithm represents a valid and robust method to estimate medication possession for multi-medication regimens.

Original languageEnglish
Pages (from-to)1181-1194
Number of pages14
JournalClinical Epidemiology
Volume10
DOIs
Publication statusPublished - 1 Jan 2018

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Polypharmacy
Databases
Pharmaceutical Preparations
Cohort Studies
Myocardial Infarction
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Western Australia
Medication Adherence
Angiotensin Receptor Antagonists
Patient Compliance
Angiotensin-Converting Enzyme Inhibitors
Observation
Medicine
Morbidity
Delivery of Health Care
Mortality
Health

Cite this

@article{ce0aaa52be7a43a881ab4e5d0fedd81e,
title = "Operationalization and validation of a novel method to calculate adherence to polypharmacy with refill data from the australian pharmaceutical benefits scheme (Pbs) database",
abstract = "Background: Electronic health care data contain rich information on medicine use from which adherence can be estimated. Various measures developed with medication claims data called for transparency of the equations used, predominantly because they may overestimate adherence, and even more when used with multiple medications. We aimed to operationalize a novel calculation of adherence with polypharmacy, the daily polypharmacy possession ratio (DPPR), and validate it against the common measure of adherence, the medication possession ratio (MPR) and a modified version (MPRm). Methods: We used linked health data from the Australian Pharmaceutical Benefits Scheme and Western Australian hospital morbidity dataset and mortality register. We identified a strict study cohort from 16,185 patients aged ≥65 years hospitalized for myocardial infarction in 2003–2008 in Western Australia as an illustrative example. We applied iterative exclusion criteria to standardize the dispensing histories according to previous literature. A SAS program was developed to calculate the adherence measures accounting for various drug parameters. Results: The study cohort was 348 incident patients (mean age 74.6±6.8 years; 69{\%} male) with an admission for myocardial infarction who had cardiovascular medications over a median of 727 days (range 74 to 3,798 days) prior to readmission. There were statins (96.8{\%}), angiotensin converting enzyme inhibitors (88.8{\%}), beta-blockers (85.6{\%}), and angiotensin receptor blockers (13.2{\%}) dispensed. As expected, observed adherence values were higher with mean MPR (median 89.2{\%}; Q1: 73.3{\%}; Q3: 104.6{\%}) than mean MPRm (median 82.8{\%}; Q1: 68.5{\%}; Q3: 95.9{\%}). DPPR values were the most narrow (median 83.8{\%}; Q1: 70.9{\%}; Q3: 96.4{\%}). Mean MPR and DPPR yielded very close possession values for 37.9{\%} of the patients. Values were similar in patients with longer observation windows. When the traditional threshold of 80{\%} was applied to mean MPR and DPPR values to signify the threshold for good adherence, 11.6{\%} of patients were classified as good adherers with the mean MPR relative to the DPPR. Conclusion: In the absence of transparent and standardized equations to calculate adherence to polypharmacy from refill databases, the novel DPPR algorithm represents a valid and robust method to estimate medication possession for multi-medication regimens.",
keywords = "Administrative data, Algorithm, Claims database, DPPR, Medication adherence, Medication possession ratio",
author = "Isabelle Arnet and Melanie Greenland and Knuiman, {Matthew W.} and Rankin, {Jamie M.} and Joe Hung and Lee Nedkoff and Briffa, {Tom G.} and Sanfilippo, {Frank M.}",
year = "2018",
month = "1",
day = "1",
doi = "10.2147/CLEP.S153496",
language = "English",
volume = "10",
pages = "1181--1194",
journal = "Clinical Epidemiology",
issn = "1179-1349",
publisher = "Dove Medical Press Ltd.",

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TY - JOUR

T1 - Operationalization and validation of a novel method to calculate adherence to polypharmacy with refill data from the australian pharmaceutical benefits scheme (Pbs) database

AU - Arnet, Isabelle

AU - Greenland, Melanie

AU - Knuiman, Matthew W.

AU - Rankin, Jamie M.

AU - Hung, Joe

AU - Nedkoff, Lee

AU - Briffa, Tom G.

AU - Sanfilippo, Frank M.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Background: Electronic health care data contain rich information on medicine use from which adherence can be estimated. Various measures developed with medication claims data called for transparency of the equations used, predominantly because they may overestimate adherence, and even more when used with multiple medications. We aimed to operationalize a novel calculation of adherence with polypharmacy, the daily polypharmacy possession ratio (DPPR), and validate it against the common measure of adherence, the medication possession ratio (MPR) and a modified version (MPRm). Methods: We used linked health data from the Australian Pharmaceutical Benefits Scheme and Western Australian hospital morbidity dataset and mortality register. We identified a strict study cohort from 16,185 patients aged ≥65 years hospitalized for myocardial infarction in 2003–2008 in Western Australia as an illustrative example. We applied iterative exclusion criteria to standardize the dispensing histories according to previous literature. A SAS program was developed to calculate the adherence measures accounting for various drug parameters. Results: The study cohort was 348 incident patients (mean age 74.6±6.8 years; 69% male) with an admission for myocardial infarction who had cardiovascular medications over a median of 727 days (range 74 to 3,798 days) prior to readmission. There were statins (96.8%), angiotensin converting enzyme inhibitors (88.8%), beta-blockers (85.6%), and angiotensin receptor blockers (13.2%) dispensed. As expected, observed adherence values were higher with mean MPR (median 89.2%; Q1: 73.3%; Q3: 104.6%) than mean MPRm (median 82.8%; Q1: 68.5%; Q3: 95.9%). DPPR values were the most narrow (median 83.8%; Q1: 70.9%; Q3: 96.4%). Mean MPR and DPPR yielded very close possession values for 37.9% of the patients. Values were similar in patients with longer observation windows. When the traditional threshold of 80% was applied to mean MPR and DPPR values to signify the threshold for good adherence, 11.6% of patients were classified as good adherers with the mean MPR relative to the DPPR. Conclusion: In the absence of transparent and standardized equations to calculate adherence to polypharmacy from refill databases, the novel DPPR algorithm represents a valid and robust method to estimate medication possession for multi-medication regimens.

AB - Background: Electronic health care data contain rich information on medicine use from which adherence can be estimated. Various measures developed with medication claims data called for transparency of the equations used, predominantly because they may overestimate adherence, and even more when used with multiple medications. We aimed to operationalize a novel calculation of adherence with polypharmacy, the daily polypharmacy possession ratio (DPPR), and validate it against the common measure of adherence, the medication possession ratio (MPR) and a modified version (MPRm). Methods: We used linked health data from the Australian Pharmaceutical Benefits Scheme and Western Australian hospital morbidity dataset and mortality register. We identified a strict study cohort from 16,185 patients aged ≥65 years hospitalized for myocardial infarction in 2003–2008 in Western Australia as an illustrative example. We applied iterative exclusion criteria to standardize the dispensing histories according to previous literature. A SAS program was developed to calculate the adherence measures accounting for various drug parameters. Results: The study cohort was 348 incident patients (mean age 74.6±6.8 years; 69% male) with an admission for myocardial infarction who had cardiovascular medications over a median of 727 days (range 74 to 3,798 days) prior to readmission. There were statins (96.8%), angiotensin converting enzyme inhibitors (88.8%), beta-blockers (85.6%), and angiotensin receptor blockers (13.2%) dispensed. As expected, observed adherence values were higher with mean MPR (median 89.2%; Q1: 73.3%; Q3: 104.6%) than mean MPRm (median 82.8%; Q1: 68.5%; Q3: 95.9%). DPPR values were the most narrow (median 83.8%; Q1: 70.9%; Q3: 96.4%). Mean MPR and DPPR yielded very close possession values for 37.9% of the patients. Values were similar in patients with longer observation windows. When the traditional threshold of 80% was applied to mean MPR and DPPR values to signify the threshold for good adherence, 11.6% of patients were classified as good adherers with the mean MPR relative to the DPPR. Conclusion: In the absence of transparent and standardized equations to calculate adherence to polypharmacy from refill databases, the novel DPPR algorithm represents a valid and robust method to estimate medication possession for multi-medication regimens.

KW - Administrative data

KW - Algorithm

KW - Claims database

KW - DPPR

KW - Medication adherence

KW - Medication possession ratio

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U2 - 10.2147/CLEP.S153496

DO - 10.2147/CLEP.S153496

M3 - Article

VL - 10

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EP - 1194

JO - Clinical Epidemiology

JF - Clinical Epidemiology

SN - 1179-1349

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