Dataset Information

Primary medication non-adherence: analysis of 195,930 electronic prescriptions.

ABSTRACT:

Background

Non-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started ("primary non-adherence") or predictors of failure to fill.

Objective

Evaluate primary non-adherence in community-based practices and identify predictors of non-adherence.

Participants

75,589 patients treated by 1,217 prescribers in the first year of a community-based e-prescribing initiative.

Design

We compiled all e-prescriptions written over a 12-month period and used filled claims to identify filled prescriptions. We calculated primary adherence and non-adherence rates for all e-prescriptions and for new medication starts and compared the rates across patient and medication characteristics. Using multivariable regressions analyses, we examined which characteristics were associated with non-adherence.

Main measures

Primary medication non-adherence.

Key results

Of 195,930 e-prescriptions, 151,837 (78%) were filled. Of 82,245 e-prescriptions for new medications, 58,984 (72%) were filled. Primary adherence rates were higher for prescriptions written by primary care specialists, especially pediatricians (84%). Patients aged 18 and younger filled prescriptions at the highest rate (87%). In multivariate analyses, medication class was the strongest predictor of adherence, and non-adherence was common for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%).

Conclusions

Many e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.

SUBMITTER: Fischer MA

PROVIDER: S-EPMC2842539 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Primary medication non-adherence: analysis of 195,930 electronic prescriptions.

Fischer Michael A MA Stedman Margaret R MR Lii Joyce J Vogeli Christine C Shrank William H WH Brookhart M Alan MA Weissman Joel S JS

Journal of general internal medicine 20100204 4

<h4>Background</h4>Non-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started ("primary non-adherence") or predictors of failure to fill.<h4>Objective</h4>Evaluate primary non-adherence in community-based practices and identify predictors of non-adherence.<h4>Participants</h4>75,589 patients treated by 1,217 prescribers in the first year of a community-ba ...[more]

PMID: 20131023

Similar Datasets

Project description:ObjectivesTo measure primary medication non-adherence (failure to fill prescription medicines) in patients discharged from the emergency department (ED), and to determine whether sociodemographic factors, smoking status and access to a general practitioner affect prescription filling. Little is known about primary medication non-adherence in EDs, and less so in New Zealand (NZ). Identifying reasons for non-adherence will enable development of strategies to improve adherence and reduce morbimortality.Design and settingAn observational study based on patient data from the ED of a large public hospital in South Auckland, NZ.ParticipantsData were collected from 1600 patients discharged between 28 April-6 May and 28 July-9 August 2014. Data were included if patients were residents within the Auckland Regional Public Health Service boundaries, admitted to ED and discharged with a prescription. Data were excluded if patients were admitted to another ward, transferred to another hospital or left the ED without seeing a doctor.Results992 patients were included in the study, the majority were under 10 years (32.6%), of Pacific Island descent (42.8%), NZ-born (67.7%) and living in the most socioeconomically deprived areas (78.1%). Almost 50% of patients failed to fill all prescription medications. Simple linear regression analysis indicated that non-adherence was significant for those 10-24 years (n=236; adherence=47.2%; p<0.05), of NZ Māori ethnicity (n=175; 51.3%; p=0.01), unemployed (n=77; 46.8%; p<0.01), homemakers (n=66; 45.7%; p<0.01), students (n=228; 55.6%; p<0.05) and cigarette smokers (n=139; 50.3%; p<0.01). Following multivariable analysis, the strongest predictors for non-adherence were those aged between 10 and 17 years (n=116; p<0.01), the unemployed (n=77; p=0.01) and homemakers (n=66; p=0.01).ConclusionsAge and occupation were the greater predictors of non-adherence; however, no other significant differences were found. Since this study, changes to prescription co-payments have been made. Further research is warranted to assess whether this change has more recently affected the rates of non-adherence.

Project description:BackgroundInappropriate medication prescription is a common cause of preventable adverse drug events among elderly persons in the primary care setting.ObjectiveThe aim of this systematic review is to quantify the extent of inappropriate prescription to elderly persons in the primary care setting.MethodsWe systematically searched Ovid-Medline and Ovid-EMBASE from 1950 and 1980 respectively to March 2012. Two independent reviewers screened and selected primary studies published in English that measured (in)appropriate medication prescription among elderly persons (>65 years) in the primary care setting. We extracted data sources, instruments for assessing medication prescription appropriateness, and the rate of inappropriate medication prescriptions. We grouped the reported individual medications according to the Anatomical Therapeutic and Chemical (ATC) classification and compared the median rate of inappropriate medication prescription and its range within each therapeutic class.ResultsWe included 19 studies, 14 of which used the Beers criteria as the instrument for assessing appropriateness of prescriptions. The median rate of inappropriate medication prescriptions (IMP) was 20.5% [IQR 18.1 to 25.6%.]. Medications with largest median rate of inappropriate medication prescriptions were propoxyphene 4.52 (0.10-23.30)%, doxazosin 3.96 (0.32 15.70)%, diphenhydramine 3.30 (0.02-4.40)% and amitriptiline 3.20 (0.05-20.5)% in a decreasing order of IMP rate. Available studies described unequal sets of medications and different measurement tools to estimate the overall prevalence of inappropriate prescription.ConclusionsApproximately one in five prescriptions to elderly persons in primary care is inappropropriate despite the attention that has been directed to quality of prescription. Diphenhydramine and amitriptiline are the most common inappropriately prescribed medications with high risk adverse events while propoxyphene and doxazoxin are the most commonly prescribed medications with low risk adverse events. These medications are good candidates for being targeted for improvement e.g. by computerized clinical decision support.

Project description:Medication adherence is an expensive and damaging problem for patients and health care providers. Patients adhere to only 50% of drugs prescribed for chronic diseases in developed nations. Digital health has paved the way for innovative smartphone solutions to tackle this challenge. However, despite numerous apps available claiming to improve adherence, a thorough review of adherence apps has not been carried out to date.The aims of this study were to (1) review medication adherence apps available in app repositories in terms of their evidence base, medical professional involvement in development, and strategies used to facilitate behavior change and improve adherence and (2) provide a system of classification for these apps.In April 2015, relevant medication adherence apps were identified by searching the Apple App Store and the Google Play Store using a combination of relevant search terms. Data extracted included app store source, app price, documentation of health care professional (HCP) involvement during app development, and evidence base for each respective app. Free apps were downloaded to explore the strategies used to promote medication adherence. Testing involved a standardized medication regimen of three reminders over a 4-hour period. Nonadherence features designed to enhance user experience were also documented.The app repository search identified a total of 5881 apps. Of these, 805 fulfilled the inclusion criteria initially and were tested. Furthermore, 681 apps were further analyzed for data extraction. Of these, 420 apps were free for testing, 58 were inaccessible and 203 required payment. Of the 420 free apps, 57 apps were developed with HCP involvement and an evidence base was identified in only 4 apps. Of the paid apps, 9 apps had HCP involvement, 1 app had a documented evidence base, and 1 app had both. In addition, 18 inaccessible apps were produced with HCP involvement, whereas 2 apps had a documented evidence base. The 420 free apps were further analyzed to identify strategies used to improve medication adherence. This identified three broad categories of adherence strategies, reminder, behavioral, and educational. A total of 250 apps utilized a single method, 149 apps used two methods, and only 22 apps utilized all three methods.To our knowledge, this is the first study to systematically review all available medication adherence apps on the two largest app repositories. The results demonstrate a concerning lack of HCP involvement in app development and evidence base of effectiveness. More collaboration is required between relevant stakeholders to ensure development of high quality and relevant adherence apps with well-powered and robust clinical trials investigating the effectiveness of these interventions. A sound evidence base will encourage the adoption of effective adherence apps, and thus improve patient welfare in the process.

Project description:PurposeThe majority of patients with glaucoma do not take their medications as prescribed. Estimates of the cost-utility value of adherence to prescribed glaucoma medication are vital to implement potentially effective interventions.DesignCost-utility analysis using Monte Carlo microsimulations incorporating a series of Markov cycles (10 000 iterations per strategy).ParticipantsPatients with glaucoma aged ≥40 years with a full lifetime horizon (up to 60 years).MethodsThe analysis estimated glaucomatous progression on the basis of data from the United Kingdom Glaucoma Treatment Study. Participants with glaucoma entered the model at age 40 years with a mean deviation in the better-seeing eye of -1.4±-1.9 decibels (dB) and -4.3±-3.4 dB in the worse-seeing eye. Participants whose glaucoma worsened each year accumulate -0.8 dB loss compared with -0.1 dB loss for those who remained stable. Data from the Glaucoma Laser Trial and the Tube versus Trabeculectomy Studies were used to assign probabilities of worsening disease among treated patients. Claims data estimating rates of glaucoma medication adherence over 4 years were used to assign probability of adherence. Those with poor adherence were modeled as having outcomes similar to the placebo arm of the clinical trials. As patients' mean deviation deteriorated, they transitioned between health states from mild (≥-6 dB), to moderate (<-6 to ≥-12 dB), to severe glaucoma (<-12 to ≥23 dB), to unilateral (<-20 dB) and bilateral blindness. At each health state, patients incurred the costs of treatment and established health utilities; ultimately, societal costs of low vision and blindness were included.Main outcome measuresCost and quality-adjusted life year (QALY) of glaucoma medication adherence.ResultsBeginning at an initial glaucoma diagnosis at age 40 years, patients proceeded to single-eye blindness as early as 19 years among those who were nonadherent and 23 years for those remaining adherent. Total healthcare costs for adherent patients averaged $62 782 (standard deviation [SD], 34 107), and those for nonadherent patients averaged $52 722 (SD, 38 868). Nonadherent patients had a mean loss of 0.34 QALYs, resulting in a cost-effectiveness ratio of $29 600 per QALY gained.ConclusionAt a conservative willingness to pay of $50 000/QALY, there is room to expand services to improve patient adherence.

Project description:BackgroundAccurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units.ObjectivesWe aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model.DesignThis was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLP-derived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model.ParticipantsPatients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study.Main measuresOdds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model.Key resultsThe non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p < 0.001).ConclusionsA six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.

Project description:Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.

Dataset Information

Primary medication non-adherence: analysis of 195,930 electronic prescriptions.

Background

Objective

Participants

Design

Main measures

Key results

Conclusions

Publications

Primary medication non-adherence: analysis of 195,930 electronic prescriptions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets