Improving the validity of determining medication adherence from electronic health record medications orders

Discontinuation of Renin-Angiotensin System Inhibitors During the Early Stage of the COVID-19 Pandemic

BACKGROUND

In March and April 2020, medical societies published statements recommending continued use of renin-angiotensin system (RAS) inhibitors despite theoretical concerns that these medications could increase COVID-19 severity. Determining if patients discontinued RAS inhibitors during the COVID-19 pandemic could inform responses to future public health emergencies.

METHODS

We analyzed claims data from US adults with health insurance in the Marketscan database. We identified patients who filled a RAS inhibitor and were persistent, defined by not having a ≥30-day gap without medication available, and high adherence, defined by having medication available on ≥80% of days, from March 2019 to February 2020. Among these patients, we estimated the proportion who discontinued their RAS inhibitor (i.e., had ≥30 consecutive days without a RAS inhibitor available to take) between March and August 2020. For comparison, we estimated the proportion of patients that discontinued a RAS inhibitor between March and August 2019 after being persistent with high adherence from March 2018 to February 2019.

RESULTS

Among 816,380 adults who were persistent and adherent to a RAS inhibitor from March 2019 to February 2020, 10.8% discontinued this medication between March and August 2020. Among 822,873 adults who were persistent and adherent to a RAS inhibitor from March 2018 to February 2019, 11.7% discontinued this medication between March and August 2019. The multivariable-adjusted relative risk for RAS inhibitor discontinuation in 2020 vs. 2019 was 0.94 (95% CI 0.93-0.95).

CONCLUSIONS

There was no evidence of an increase in RAS inhibitor discontinuation during the early stage of the COVID-19 pandemic.

Adherence to self-administered biologic disease-modifying antirheumatic drugs across health-system specialty pharmacies

Disclaimer

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Purpose

Adherence to self-administered biologic disease-modifying antirheumatic drugs (bDMARDs) is necessary for therapeutic benefit. Health-system specialty pharmacies (HSSPs) have reported high adherence rates across several disease states; however, adherence outcomes in rheumatoid arthritis (RA) populations have not yet been established.

Methods

We performed a multisite retrospective cohort study including patients with RA and 3 or more documented dispenses of bDMARDs from January through December 2018. Pharmacy claims were used to calculate proportion of days covered (PDC). Electronic health records of patients with a PDC of <0.8 were reviewed to identify reasons for gaps in pharmacy claims (true nonadherence or appropriate treatment holds). Outcomes included median PDC across sites, reasons for treatment gaps in patients with a PDC of <0.8, and the impact of adjusting PDC when accounting for appropriate therapy gaps.

Results

There were 29,994 prescriptions for 3,530 patients across 20 sites. The patient cohort was mostly female (75%), with a median age of 55 years (interquartile range [IQR], 42-63 years). The original(ie, prereview) median PDC was 0.94 (IQR, 0.83-0.99). Upon review, 327 patients had no appropriate treatment gaps identified, 6 patients were excluded due to multiple unquantifiable appropriate gaps, and 420 patients had an adjustment in the PDC denominator due to appropriate treatment gaps (43 instances of days’ supply adjusted based on discordant days’ supply information between prescriptions and physician administration instructions, 11 instances of missing fills added, and 421 instances of clinically appropriate treatment gaps). The final median PDC after accounting for appropriate gaps in therapy was 0.95 (IQR, 0.87-0.99).

Conclusion

This large, multisite retrospective cohort study was the first to demonstrate adherence rates across several HSSPs and provided novel insights into rates and reasons for appropriate gaps in therapy.

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance

BACKGROUND

Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models.

OBJECTIVE

We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization.

METHODS

We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders.

RESULTS

The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates.

CONCLUSIONS

Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

Why do we observe a limited impact of primary care access measures on clinical quality indicators?

The study assessed the effects of enhanced primary care access and continuity on clinical quality in a large, multipayer, multispecialty ambulatory care organization with fee-for-service provider incentives. The difference-in-differences estimates indicate that access to own primary care physician is a statistically significant predictor of improved clinical quality, although the effect size is small such that clinical significance may be negligible. Reduced time for own primary care physician appointment and increased enrollment in electronic personal health record are positive predictors of chronic disease management processes and preventive screening but are inconsistently associated with clinical outcomes. Challenges in identifying relationships between access and quality outcomes in a real-world setting are also discussed.

Standardizing terminology and definitions of medication adherence and persistence in research employing electronic databases

OBJECTIVE

To propose a unifying set of definitions for prescription adherence research utilizing electronic health record prescribing databases, prescription dispensing databases, and pharmacy claims databases and to provide a conceptual framework to operationalize these definitions consistently across studies.

METHODS

We reviewed recent literature to identify definitions in electronic database studies of prescription-filling patterns for chronic oral medications. We then develop a conceptual model and propose standardized terminology and definitions to describe prescription-filling behavior from electronic databases.

RESULTS

The conceptual model we propose defines 2 separate constructs: medication adherence and persistence. We define primary and secondary adherence as distinct subtypes of adherence. Metrics for estimating secondary adherence are discussed and critiqued, including a newer metric (New Prescription Medication Gap measure) that enables estimation of both primary and secondary adherence.

DISCUSSION

Terminology currently used in prescription adherence research employing electronic databases lacks consistency. We propose a clear, consistent, broadly applicable conceptual model and terminology for such studies. The model and definitions facilitate research utilizing electronic medication prescribing, dispensing, and/or claims databases and encompasses the entire continuum of prescription-filling behavior.

CONCLUSION

Employing conceptually clear and consistent terminology to define medication adherence and persistence will facilitate future comparative effectiveness research and meta-analytic studies that utilize electronic prescription and dispensing records.

Determinants of patient adherence: a review of systematic reviews

PURPOSE

A number of potential determinants of medication non-adherence have been described so far. However, the heterogenic quality of existing publications poses the need for the use of a rigorous methodology in building a list of such determinants. The purpose of this study was a systematic review of current research on determinants of patient adherence on the basis of a recently agreed European consensus taxonomy and terminology.

METHODS

MEDLINE, EMBASE, CINAHL, Cochrane Library, IPA, and PsycINFO were systematically searched for systematic reviews published between 2000/01/01 and 2009/12/31 that provided determinants on non-adherence to medication. The searches were limited to reviews having adherence to medication prescribed by health professionals for outpatient as a major topic.

RESULTS

Fifty-one reviews were included in this review, covering 19 different disease categories. In these reviews, exclusively assessing non-adherence to chronic therapies, 771 individual factor items were identified, of which most were determinants of implementation, and only 47-determinants of persistence with medication. Factors with an unambiguous effect on adherence were further grouped into 8 clusters of socio-economic-related factors, 6 of healthcare team- and system-related factors, 6 of condition-related factors, 6 of therapy-related factors, and 14 of patient-related factors. The lack of standardized definitions and use of poor measurement methods resulted in many inconsistencies.

CONCLUSIONS

This study provides clear evidence that medication non-adherence is affected by multiple determinants. Therefore, the prediction of non-adherence of individual patients is difficult, and suitable measurement and multifaceted interventions may be the most effective answer toward unsatisfactory adherence. The limited number of publications assessing determinants of persistence with medication, and lack of those providing determinants of adherence to short-term treatment identify areas for future research.

Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data

BACKGROUND

Data from the memberships of large, integrated health care systems can be valuable for clinical, epidemiologic, and health services research, but a potential selection bias may threaten the inference to the population of interest.

METHODS

We reviewed administrative records of members of Kaiser Permanente Southern California (KPSC) in 2000 and 2010, and we compared their sociodemographic characteristics with those of the underlying population in the coverage area on the basis of US Census Bureau data.

RESULTS

We identified 3,328,579 KPSC members in 2000 and 3,357,959 KPSC members in 2010, representing approximately 16% of the population in the coverage area. The distribution of sex and age of KPSC members appeared to be similar to the census reference population in 2000 and 2010 except with a slightly higher proportion of 40 to 64 year olds. The proportion of Hispanics/Latinos was comparable between KPSC and the census reference population (37.5% vs 38.2%, respectively, in 2000 and 45.2% vs 43.3% in 2010). However, KPSC members included more blacks (14.9% vs 7.0% in 2000 and 10.8% vs 6.5% in 2010). Neighborhood educational levels and neighborhood household incomes were generally similar between KPSC members and the census reference population, but with a marginal underrepresentation of individuals with extremely low income and high education.

CONCLUSIONS

The membership of KPSC reflects the socioeconomic diversity of the Southern California census population, suggesting that findings from this setting may provide valid inference for clinical, epidemiologic, and health services research.

Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders

BACKGROUND

Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients.

OBJECTIVE

This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia.

DESIGN

This is a retrospective observational cohort study. PARTICIPANTS (OR PATIENTS OR SUBJECTS): Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period.

MAIN MEASURES

Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings.

KEY RESULTS

The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics = 0.61-0.63).

CONCLUSIONS

Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.