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Surveillance of medication use: early identification of poor adherence.

ABSTRACT: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence.Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence.All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point.Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted.

SUBMITTER: Jonikas MA

PROVIDER: S-EPMC3384104 | biostudies-literature | 2012 Jul-Aug

REPOSITORIES: biostudies-literature

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Surveillance of medication use: early identification of poor adherence.

Jonikas Magdalena A MA Mandl Kenneth D KD

Journal of the American Medical Informatics Association : JAMIA 20111119 4

<h4>Background</h4>We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence.<h4>Methods</h4>Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication poss ...[more]

PMID: 22101969

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Project description:BackgroundCannabis use following the onset of first-episode psychosis has been linked to both increased risk of relapse and non-adherence with antipsychotic medication. Whether poor outcome associated with cannabis use is mediated through an adverse effect of cannabis on medication adherence is unclear.MethodsIn a prospective analysis of data acquired from four different adult inpatient and outpatient units of the South London and Maudsley Mental Health National Health Service Foundation Trust in London, UK, 245 patients were followed up for 2 years from the onset of first-episode psychosis. Cannabis use after onset of psychosis was assessed by self-reports in face-to-face follow-up interviews. Relapse data were collected from clinical notes using the WHO Life Chart Schedule. This measure was also used to assess medication adherence on the basis of both face-to-face interviews and clinical notes. Patients were included if they had a diagnosis of first-episode non-organic or affective psychosis according to ICD-10 criteria, and were aged between 18 and 65 years when referred to local psychiatric services. We used structural equation modelling analysis to estimate whether medication adherence partly mediated the effects of continued cannabis use on risk of relapse. The primary outcome variable was relapse, defined as admission to a psychiatric inpatient unit after exacerbation of symptoms within 2 years of first presentation to psychiatric services. Information on cannabis use over the first 2 years after onset of psychosis was investigated as a predictor variable for relapse. Medication adherence was assessed as a mediator variable on the basis of clinical records and self-report data. Study researchers (TS, NP, EK, and EF) rated the adherence.Findings397 patients who presented with their first episode of psychosis between April 12, 2002, and July 26, 2013 had a follow-up assessment until September, 2015. Of the 397 patients approached for followed up, 133 refused to take part in this study and 19 could not be included because of missing data. 91 (37%) of 245 patients with first-episode psychosis had a relapse over the 2 years of follow-up. Continued cannabis use predicted poor outcome, including risk of relapse, number of relapses, length of relapse, and care intensity at follow-up. In controlled structural equation modelling analyses, medication adherence partly mediated the effect of continued cannabis use on outcome, including risk of relapse (proportion mediated=26%, βindirect effects=0·08, 95% CI 0·004 to 0·16), number of relapses (36%, βindirect effects=0·07, 0·003 to 0·14), time until relapse (28%, βindirect effects=-0·26, -0·53 to 0·001) and care intensity (20%, βindirect effects=0·06, 0·004 to 0·11) but not length of relapse (6%, βindirect effects=0·03, -0·03 to 0·09). The adjusted models explained moderate amounts of variance for outcomes defined as risk of relapse (R2=0·25), number of relapses (R2=0·21), length of relapse (R2=0·07), time until relapse (R2=0·08), and care intensity index (R2=0·15).InterpretationBetween 20% and 36% of the adverse effects of continued cannabis use on outcome in psychosis might be mediated through the effects of cannabis use on medication adherence. Interventions directed at medication adherence could partly help mitigate the harm from cannabis use in psychosis.FundingThis study is funded by the National Institute of Health Research (NIHR) Clinician Scientist award.

Project description:IntroductionAchieving optimal adherence to antiretroviral therapy (ART) among adolescents living with HIV (ALWHIV) is challenging, especially in low-resource settings. To help accurately determine who is at risk of poor adherence, we developed and internally validated models comprising multi-level factors that can help to predict the individualized risk of poor adherence among ALWHIV in a resource-limited setting such as Uganda.MethodsWe used data from a sample of 637 ALWHIV in Uganda who participated in a longitudinal study, "Suubi+Adherence" (2012 to 2018). The model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression to select the best subset of multi-level predictors (individual, household, community or economic-related factors) of poor adherence in one year's time using 10-fold cross-validation. Seventeen potential predictors included in the model were assessed at 36 months of follow-up, whereas adherence was assessed at 48 months of follow-up. Model performance was evaluated using discrimination and calibration measures.ResultsFor the model predicting poor adherence, five of the 17 predictors (adherence history, adherence self-efficacy, family cohesion, child poverty and group assignment) were retained. Its ability to discriminate between individuals with and without poor adherence was acceptable; area under the curve (AUC) = 69.9; 95% CI: 62.7, 72.8. There was no evidence of possible areas of miscalibration (test statistic = 1.20; p = 0.273). The overall performance of the model was good.ConclusionsOur findings support prediction modelling as a useful tool that can be leveraged to improve outcomes across the HIV care continuum. Utilizing information from multiple sources, the risk prediction score tool applied here can be refined further with the ultimate goal of being used in a screening tool by practitioners working with ALWHIV. Specifically, the tool could help identify and provide early interventions to adolescents at the highest risk of poor adherence and/or viral non-suppression. However, further fine-tuning and external validation may be required before wide-scale implementation.

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Surveillance of medication use: early identification of poor adherence.

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Surveillance of medication use: early identification of poor adherence.

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