Comparison of Different Strategies to Measure Medication Adherence via Claims Data in Patients With Chronic Heart Failure

Ihle, Peter; Krueger, Katrin; Schubert, Ingrid; Griese‐Mammen, Nina; Parrau, Natalie; Laufs, Ulrich; Schulz, Martin

doi:10.1002/cpt.1378

Cited by 15 publications

(16 citation statements)

References 46 publications

(89 reference statements)

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“…When interpreting the results of this study, the following limitations need to be considered. The first limitation relates to the data source and calculation of adherence: limitations of data generated for billing purposes are well known 53 and concern, for example, the correctness of the codes. This has been addressed as far as possible by using established and validated code sets to select the population and to define outcomes and covariates (internal validity) and by aligning the study design with previous work (external reference as a surrogate for external validity).…”

Section: Discussionmentioning

confidence: 99%

Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants – A Retrospective Cohort Study in Health Insurance Claims Data

Wirbka¹,

Haefeli²,

Meid³

2021

PPA

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Background An essential contribution regarding the prevention of thromboembolic events in patients with (non-valvular) atrial fibrillation (AF) is good adherence to direct oral anticoagulants (DOACs). However, it is an open question what “good” adherence means for DOACs or below which threshold non-adherence is clinically relevant for AF patients. Ultimately, such a classification could prevent strokes and associated costs through adjusted treatment regimens or supportive measures. Methods We selected 10,092 AF patients from health insurance claims data between 2014 and 2018 who were issued a majority (at least half of the number) of maximum approved strength prescriptions of one of the following DOACs, namely rivaroxaban, apixaban, or dabigatran. Due to the limited sample size, the prescriptions of dabigatran had to be finally excluded for the cut-off analysis. DOAC adherence was calculated as the proportion of days covered (PDC) by dividing the days of theoretical use (days covered) of the drug by the duration in days of the observation interval. PDC cut-off values were derived from stroke risk as a function of continuous PDC values in time-to-event analyses and corresponding dose-response models. The influence of adherence-promoting interventions (targeted and untargeted) on the occurrence of strokes and related costs was then projected, considering intervention costs per patient. Results The population had a mean age of 74.5 years and 50% were female. The median PDC was 0.79 ± 0.28 with a median follow-up time of 1218 days, in which 2% of all DOAC patients had a stroke. The adherence cut-offs for good adherence were identified at 0.78 for rivaroxaban and 0.8 for apixaban. Targeted interventions appeared to be far more cost-effective than untargeted interventions. Conclusion Clear adherence cut-offs enable healthcare professionals to identify patients with clinically relevant non-adherence. Interventions based on these cut-offs appear to be a promising means to optimize DOAC treatment.

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Section: Discussionmentioning

confidence: 99%

Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants – A Retrospective Cohort Study in Health Insurance Claims Data

Wirbka¹,

Haefeli²,

Meid³

2021

PPA

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“… 16 Moreover, a previous study showed that different methods of assuming antihypertensive medications’ prescribed daily dose had significant differences on calculating PDC in patients with chronic heart failure using claims data. 35 Further research is warranted to validate these factors. Third, each antihypertensive drug class has a specific indication and regimen.…”

Section: Discussionmentioning

confidence: 99%

Comparing the Sensitivities of Measures of Adherence to Antihypertensive Drugs Using Korean National Health Insurance Claims Data

Choo

Jung

Shin

et al. 2021

PPA

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Purpose Numerous studies have utilized various forms of adherence measures. However, methods for measuring adherence are inconsistent. Moreover, few studies are available that have compared sensitivities of the effects of several criteria on medication adherence. This study aims to compare measures of adherence using varied analytical decisions. Materials and Methods We included three measures for adherence: proportion of days covered with one or more medications (PDC with≥1 ), duration weighted mean PDC (PDC wm ), and daily polypharmacy possession ratio (DPPR). We compared the sensitivities of the measures by changing parameters in the Korean nationwide claims database. First, we used PDC with≥1 as our base model. Then, we divided an adherence measure algorithm into three categories: (1) definition of data cleaning, (2) inclusion criteria and observation period, and (3) calculation methods of medication adherence. The categories included eight decision nodes that incorporated 25 alternative options. Finally, we assessed the medication adherence for the base-case with commonly used values and then varied to measure with each alternative option. Results The base-case included 14,288 beneficiaries with antihypertensives. Among eight decisions, both handling an end-date-of-study and overlaps had the strongest impacts on measuring PDC with≥1 , PDC wm , and DPPR, with small differences in sample size. Instead of the estimates of adherence from PDC wm , those of PDC with≥1 and DPPR were similar. Furthermore, a tendency toward a higher medication adherence was observed with a smaller study population. Conclusion The decisions regarding identifying an end-date-of-study and overlaps showed meaningful impacts of all three measures including PDC with≥1 , PDCwm, and DPPR on measuring medication adherence.

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“…Indirect measures based on pharmacy records of prescription filling are commonly used (e.g. Hoogendoorn et al, 2019 ; Ihle et al, 2019 ; Simon et al, 2018 ), providing inexpensive but valid and objective adherence information ( Nieuwlaat et al, 2014 ) with good predictive validity (e.g. Choo et al, 1999 ; Steiner & Prochazka, 1997 ) and close correlation with measures based on electric monitoring and pill count ( Choo et al, 1999 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying temporal patterns of adherence to antidepressants, bisphosphonates and statins, and associated patient factors

Park

Tickle

Cutler

2022

SSM - Population Health

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Comparison of Different Strategies to Measure Medication Adherence via Claims Data in Patients With Chronic Heart Failure

Cited by 15 publications

References 46 publications

Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants – A Retrospective Cohort Study in Health Insurance Claims Data

Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants – A Retrospective Cohort Study in Health Insurance Claims Data

Comparing the Sensitivities of Measures of Adherence to Antihypertensive Drugs Using Korean National Health Insurance Claims Data

Identifying temporal patterns of adherence to antidepressants, bisphosphonates and statins, and associated patient factors

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