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Cited by 33Medication Adherence and CompliancePharmacy and Medical PracticesPharmaceutical Practices and Patient Outcomes

Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy

Open access
Torres-Robles, Andrea, Wiecek, Elyssa, Cutler, Rachelle, Drake, Barry, Benrimoj, Shalom I., Fernandez-Llimos, Fernando, Garcia-Cardenas, Victoria·2019·Frontiers in Pharmacology
DOI 10.3389/fphar.2019.00130PMC PMC6399119
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Abstract

Background: Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study is to analyze the changes on adherence implementation rates before and after a community pharmacy intervention integrated in usual practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n= 11,257 on rosuvastatin, n= 6,797 on irbesartan, and n= 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Results: Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenlafaxine. In terms of the proportion of adherent patients (PDC>80.0%) the trend was similar, increasing after the pharmacist intervention from overall 17.4% to 41.2% and decreasing after 1 year of analysis to 35.3%. Conclusion: Big database analysis techniques provided results on adherence implementation over two years of analysis. Although an increase on the rates was observed after the pharmacist intervention, there is still a prevalence on sub-optimal implementation over time. Enhancing the current intervention using an evidence-based approach and integrating big database analysis techniques to a real-time measurement of adherence could help community pharmacies improve medication adherence.

AI snapshot

Written from the paper’s full text.

TL;DR

This retrospective observational study used pharmacy dispensing data and big-data techniques to assess adherence implementation (PDC) before and after a community pharmacist-led educational intervention in Australia. Average PDC and the proportion of adherent patients increased shortly after the intervention but declined over 12 months.

Key findings

  • Among 20,335 patients, average PDC rose 3 months after the pharmacist intervention: rosuvastatin from 50.2% (SD 30.1) to 66.9% (SD 29.9); irbesartan from 50.8% (SD 30.3) to 68.0% (SD 29.3); desvenlafaxine from 47.3% (SD 28.4) to 66.3% (SD 27.3).
  • By 12 months after intervention, average PDC declined to 62.1% (SD 32.0) for rosuvastatin, 62.4% (SD 32.5) for irbesartan, and 58.1% (SD 31.1) for desvenlafaxine.
  • The proportion of adherent patients (PDC ≥80%) increased from overall 17.4% to 41.2% after the intervention and decreased to 35.3% after one year.
  • Sensitivity analysis among patients claiming all dispensings in affiliated pharmacies showed average PDC increasing 15.7% from 59.0% (SD 27.3) to 74.7% (SD 27.7), then declining 9.6% to 65.1% (SD 24.6) over 12 months.

Methods

  • Retrospective observational study using de-identified GuildLink Pty Ltd GuildCare dispensing and intervention data from Australian community pharmacies; included 20,335 patients (11,257 on rosuvastatin, 6,797 on irbesartan, 2,281 on desvenlafaxine) who received the MedScreen Compliance pharmacist-led educational adherence intervention and had dispensing records before and after it.
  • The intervention was triggered when calculated MPR was below 70% and included exploration of barriers, patient education, goal-setting, and recording of the interaction; data covered 1 year before and 1 year after the first intervention, excluding patients with prescribed quantity <28 or >30 doses per dispense and those with fewer than two dispensing dates before and after intervention.
  • PDC was used as the adherence implementation measure; analysis was performed by trimesters using descriptive statistics (average PDC and SD) with SQL, Python, and PyCharm, plus a sub-analysis of adherent patients (PDC ≥80%) and a sensitivity analysis on patients claiming all dispensings in affiliated pharmacies.

Limitations

  • Stated: dispensing records were only associated with the pharmacy where patients were intervened; medications claimed elsewhere were not recorded, so discontinuation could not be determined, and only implementation adherence from first to last available dispensing record was reported.
  • Stated: there was variability of the intervention between pharmacists and pharmacies, and no fidelity/process measures were available because this was retrospective real-life data; the authors note this also reflects real-world practice.
  • Evident: there was no control or comparator group and the pre-post observational design means observed changes cannot be causally attributed to the intervention; also, days’ supply was not recorded, so once-daily dosing was assumed and patients with <28 or >30 doses per dispense were excluded.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fphar.2019.00130

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 4 cited sources checked · 0 supported · 0 flagged

  • Study design and conductreasoning

    No process indicators to validate the fidelity of the intervention were available.

    “No process indicators to validate the fidelity of the intervention were available as it was a real-life intervention.”
    Quote in the paperSubstantial claim
  • Premisecitation

    PDC has been affirmed to be a more accurate and conservative representation of adherence compared to MPR.

    “PDC has been affirmed to be a more accurate and conservative representation of adherence compared to MPR”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Martin et al., 2009 source

      Not checked in the free run.

  • Interpretationcitation

    The study findings on real practice settings follow similar trends to those reported in randomized controlled trials.

    “our study findings on real practice settings follow similar trends to those reported in randomized controlled trials.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Al-Jumah and Qureshi, 2012

      Not checked in the free run.

    • cites Pousinho et al., 2016 source

      Not checked in the free run.

  • Study design and conductnone given

    The database did not record days' supply for each individual dispense.

    “The database did not record days’ supply for each individual dispense.”
    Quote in the paperSubstantial claim
  • Interpretationreasoning

    Because dispense records were only associated with the intervention pharmacy, it is not possible to know if patients actually discontinued their treatment.

    “Because of this, it is not possible to know if patients actually discontinued their treatment.”
    Quote in the paperSubstantial claim
  • May combine two claims. 10
  • Classified as probably not a substantial claim. 4

  • May combine two claims.

    Findingown data

    For desvenlafaxine, average PDC increased from 47.3% (SD 28.4) to 66.3% (SD 27.3) three months after the pharmacist intervention and declined to 58.1% (SD 31.1) over 12 months.

    “from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine”
    • Also stated in: § Abstract · plain
      “to 58.1% (SD: 31.1) for desvenlafaxine”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For irbesartan, average PDC increased from 50.8% (SD 30.3) to 68.0% (SD 29.3) three months after the pharmacist intervention and declined to 62.4% (SD 32.5) over 12 months.

    “from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan”
    • Also stated in: § Abstract · plain
      “to 62.4% (SD: 32.5) for irbesartan”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Overall, the proportion of adherent patients (PDC >= 80.0%) increased from 17.4% to 41.2% after the pharmacist intervention and decreased to 35.3% after one year of analysis.

    “increasing after the pharmacist intervention from overall 17.4 to 41.2% and decreasing after one year of analysis to 35.3%.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In sensitivity analysis on patients claiming all dispensings in affiliated pharmacies, average PDC increased by 15.7% from 59.0% (SD 27.3) to 74.7% (SD 27.7) after the pharmacist intervention and declined by 9.6% to 65.1% (SD 24.6) over the following 12 months.

    “Sensitivity analysis performed on patients claiming all dispensings in the affiliated pharmacies resulted in a similar trend with the PDC average increasing 15.7% from 59.0% (SD: 27.3) to 74.7% (SD: 27.7) after the pharmacist intervention and declining over the following 12 months on 9.6% to 65.1% (SD: 24.6).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    For rosuvastatin, average PDC increased from 50.2% (SD 30.1) to 66.9% (SD 29.9) three months after the pharmacist intervention and declined to 62.1% (SD 32.0) over 12 months.

    “Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin”
    • Also stated in: § Abstract · plain
      “These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    With the majority of patients not reaching the common threshold of a PDC of 80%, there remains opportunity for improvement.

    “With the majority of patients not reaching the common threshold of a PDC of 80%, there remains opportunity for improvement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Trends observed before and after the intervention for each drug were a gradual decrease in average PDC during the 1 year pre-intervention, an increase after the intervention, followed by a subsequent decrease over time.

    “Trends observed before and after the intervention in each of the drugs showed: (1) a gradual decrease in average PDC rates during a 1 year pre-intervention, (2) an increase after the pharmacist’s intervention was delivered, followed by (3) a subsequent decrease over time.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed increase in PDC after the intervention is attributable to the pharmacist intervention rather than other temporal factors.

  • May combine two claims.

    Study design and conductnone given

    Dispense records were only associated with the pharmacy where patients were intervened; if the patient claimed a medication in a different pharmacy, this data was not recorded in the database.

    “Dispense records were only associated to the pharmacy where patients were intervened. If the patient claimed a medication in a different pharmacy, this data was not recorded in this database.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    An increase in adherence rates was observed after the pharmacist intervention, followed by a gradual decrease over time.

    “An increase in adherence rates was observed after the pharmacist intervention, followed by a gradual decrease over time.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed increase in PDC after the intervention is attributable to the pharmacist intervention rather than other temporal factors.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    A sensitivity analysis was performed on patients who claimed the initial dispensing and their corresponding repeats.

    “A sensitivity analysis was performed on patients who claimed the initial dispensing and their corresponding repeats”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study was approved by the University of Technology Sydney Human Research Ethics Committee (approval number ETH18-2312).

    “University of Technology Sydney Human Research Ethics Committee (HREC) approved this study (approval number ETH18-2312).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    A cut-off for optimal adherence of PDC equal to or higher than 80% is reasonable for predictable hospitalizations in chronic diseases.

    “Cut-off for optimal adherence was defined as PDC equal or higher than 80% as this has been found to be reasonable for predictable hospitalizations in chronic diseases”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Karve et al., 2009 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Sub-optimal implementation adherence is a prevalent problem with the average PDC decreasing over time.

    “Sub-optimal implementation adherence is a prevalent problem with the average PDC decreasing over time.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed increase in PDC after the intervention is attributable to the pharmacist intervention rather than other temporal factors.

  • Probably not a substantial claim: may not pass S4 (based).

    Study design and conductreasoning

    There was variability of the intervention between pharmacists and pharmacies.

    “we must also crucially consider the variability of the intervention between pharmacists and pharmacies.”
    Quote in the paperNot a substantial claim
14 of 14

  • The authors state this as an assumption.

    A once daily prescribed dose can be assumed for all included patients.

    “we assumed a once daily prescribed dose”

  • The paper relies on this without support.

    The observed increase in PDC after the intervention is attributable to the pharmacist intervention rather than other temporal factors.

    “In terms of the effect of the pharmacist intervention, there was an increase in average PDC rates for all of the drugs after the intervention.”

  • The paper relies on this without support.

    Whether clinical trials of adherence interventions produce results applicable to everyday practice and whether effects are maintained in real-life settings usually remains unknown.

    “However, whether these trials produce results that are applicable to everyday practice and whether the effects are maintained in real-life settings usually remains unknown.”

  • The paper relies on this without support.

    In real-life practice, no analysis of the impact of the intervention on improving adherence long-term is usually conducted.

    “In real-life practice, patients are often exposed to community pharmacist interventions during the dispensing of medicines but no analysis of the impact of the intervention on improving adherence long-term is usually conducted.”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

  • Irbesartan PDC decline is understated

    Minor

    A decline from 68.0% to 62.4% is 5.6 percentage points, not the reported 4.8.

    Reported 4.8 · recomputed 5.6

  • Patients-per-pharmacy average does not match the reported totals

    Minor

    The reported 20,335 patients across 1,805 pharmacies give about 11.27 patients per pharmacy, not 8.59.

    Reported 8.59 · recomputed 11.265928

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Horne, 2005, Concordance, Adherence and Compliance in Medicine Taking: a Conceptual Map and Research Priorities.
  2. 2018, Adherence.

Cited by

Sorted by citations

Related

Authors
Torres-Robles, A., Wiecek, E., Cutler, R., Drake, B., Benrimoj, S. I., Fernandez-Llimos, F., & Garcia-Cardenas, V.
Affiliations
University of Technology Sydney (AU)
ORCID
Venue
Frontiers in Pharmacology
Publisher
Frontiers Media SA
Published
2019-02-26
Volume
10
Pages
130-130
ISSN
1663-9812 (ISSN-L)
DOI
10.3389/fphar.2019.00130
PMC
PMC6399119
OpenAlex
W2916722889
Semantic Scholar
048c21fd281da32a7b115310ae1872459eb32c1f
Cited by
33
References
37
Citations by year
2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 9 · 2021: 8 · 2020: 2 · 2019: 2
FWCI
5.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Pharmacology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacy and Medical Practices
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
Keywords
medication adherence, community pharmacy, pharmacist intervention, dispensing data, proportion of days covered, rosuvastatin, irbesartan, desvenlafaxine, big data analytics, adherence measurement, medication adherence monitoring, integrated care model, electronic health data

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