Medication Nonadherence In Patients Research Articles

Applying Machine Learning Models Derived From Administrative Claims Data to Predict Medication Nonadherence in Patients Self-Administering Biologic Medications for Inflammatory Bowel Disease.

Adherence to self-administered biologic therapies is important to induce remission and prevent adverse clinical outcomes in Inflammatory bowel disease (IBD). This study aimed to use administrative claims data and machine learning methods to predict nonadherence in an academic medical center test population. A model-training dataset of beneficiaries with IBD and the first unique dispense of a self-administered biologic between June 30, 2016 and June 30, 2019 was extracted from the Commercial Claims and Encounters and Medicare Supplemental Administrative Claims Database. Known correlates of medication nonadherence were identified in the dataset. Nonadherence to biologic therapies was defined as a proportion of days covered ratio <80% at 1 year. A similar dataset was obtained from a tertiary academic medical center's electronic medical record data for use in model testing. A total of 48 machine learning models were trained and assessed utilizing the area under the receiver operating characteristic curve as the primary measure of predictive validity. The training dataset included 6998 beneficiaries (n = 2680 nonadherent, 38.3%) while the testing dataset included 285 patients (n = 134 nonadherent, 47.0%). When applied to test data, the highest performing models had an area under the receiver operating characteristic curve of 0.55, indicating poor predictive performance. The majority of models trained had low sensitivity and high specificity. Administrative claims-trained models were unable to predict biologic medication nonadherence in patients with IBD. Future research may benefit from datasets with enriched demographic and clinical data in training predictive models.

P28 Medication Non-Adherence in Patients with Varying Levels of Depression Symptoms Measured Using the Medication Adherence Reasons Scale

P28 Medication Non-Adherence in Patients with Varying Levels of Depression Symptoms Measured Using the Medication Adherence Reasons Scale

The development of a clinical nomogram to predict medication nonadherence in patients with knee osteoarthritis.

Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.

Low rates of primary medication nonadherence in patients prescribed oral oncology agents across health system specialty pharmacies.

BACKGROUND: New oral oncology medications bring novel challenges when patients are initiating treatment. Rates of primary medication nonadherence (PMN), the rate at which a medication is prescribed but not obtained, of up to 30% have been reported for oral oncology medications. More research is needed to identify causes and develop strategies for health system specialty pharmacies (HSSPs) to improve cancer treatment initiation rates. OBJECTIVE: To evaluate the rate and reasons for PMN to specialty oral oncology medications in an HSSP setting. METHODS: We performed a multisite retrospective cohort study across 7 HSSP sites. Patients were included if they had an orally self-administered oncology medication referral generated by the health system of the affiliated specialty pharmacy between May 1, 2020, and July 31, 2020. Data collected at each site using pharmacy software and the electronic health record were deidentified and aggregated for analysis. After identifying unfilled referrals within a 60-day fill window, a retrospective chart review was performed to identify final referral outcomes and reasons for unfilled referrals. Referral outcomes were categorized as unknown fill outcomes (because of being referred to another fulfillment method or if received for benefits investigation only), filled by the HSSP, or not filled. The primary outcome was PMN for each PMN-eligible referral and secondary outcomes included reason for PMN and time to fill. The final PMN rate was calculated by dividing the number of unfilled referrals by total referrals with a known fill outcome. RESULTS: Of 3,891 referrals, 947 were PMN eligible, representing patients with a median age of 65 years (interquartile range = 55-73), near equal distribution between male and female (53% vs 47%), and most commonly with Medicare pharmacy coverage (48%). The most referred medication was capecitabine (14%), and the most common diagnosis was prostate cancer (14%). Among PMN-eligible referrals, 346 (37%) had an unknown fill outcome. Of the 601 referrals with known fill outcome, 69 referrals were true instances of PMN, yielding the final PMN rate of 11%. Most referrals were filled by the HSSP (56%). Patient decision was the most common reason for not filling (25%; 17/69 PMN cases). The median time to fill after initial referral was 5 days (interquartile range = 2-10). CONCLUSIONS: HSSPs have a high percentage of patient initiation of new oral oncology medication treatments in a timely manner. More research is needed to understand patient reasons for deciding not to start therapy and to improve patient-centered cancer treatment planning decisions. DISCLOSURES: Dr Crumb was a planning committee member with Horizon CME for the Nashville APPOS 2022 Conference. Dr Patel received funding and support for attending meetings and/or travel from the University of Illinois Chicago College of Pharmacy.

A Real-World Evaluation of Primary Medication Nonadherence in Patients with Nonvalvular Atrial Fibrillation Prescribed Oral Anticoagulants in the United States.

Nonadherence to oral anticoagulants (OACs) is a challenge to stroke risk reduction in patients with nonvalvular atrial fibrillation (NVAF). Data on primary medication nonadherence (PMN) in NVAF are lacking. Our aim was to assess the rates and predictors of PMN among NVAF patients who were newly prescribed an OAC. This was a retrospective database analysis of linked healthcare claims and electronic health record data. Adult NVAF patients with a prescription order for an OAC (apixaban, rivaroxaban, dabigatran, or warfarin) between January 2016 and June 2019 were identified (date of first prescription order = index date). Patients had a 1-year baseline and a 6-month post-index period to assess the rates of PMN, defined as having a prescription order but no paid claim for any OAC on or within 30 days after the index date. Sensitivity analyses explored 60-, 90- and 180-day PMN thresholds. Logistic regression models were used to examine the predictors of PMN. Among 20,393 patients, the overall 30-day PMN rate was 28.4%; PMN rates decreased to 17% with a 180-day threshold. PMN was numerically lowest for warfarin among OACs and numerically lowest for apixaban among direct OACs. A CHA2DS2-VASc score of ≥ 3, commercial insurance, and African American race were associated with higher odds of PMN. More than one-quarter of patients experienced PMN within 30 days of their initial prescription order. This rate decreased over a longer period, suggesting a delay in fills. Understanding the factors associated with PMN is warranted to develop effective interventions for improving OAC treatment rates in NVAF.

Prevalence and Factors Associated with Medication Nonadherence in Patients with Major Psychiatric Disorders in Southern India

Abstract Objectives: Historically, nonadherence to medication has been a major problem faced by health care workers while treating chronic illnesses. Nonadherence to psychotropic medications is known to be associated with poorer treatment outcomes, remarkable risk, and cost to the medical system in managing psychiatric illness. For care providers, partial compliance or discontinuation of medications represents the difficulty of maintaining treatment successes over time. In this study, we intended to assess medication adherence and to understand the reasons for nonadherence in patients with major psychiatric disorders in southern India. Methods: This is a cross-sectional observational study, done from January to June 2023. With the International Classification of Diseases 10th Revision diagnosis, we assessed patients with psychotic disorders, bipolar affective disorders, and major depressive disorder. The Medication Adherence Rating Scale (MARS) was used to assess medication adherence. The patients also completed copies of semi-structured questionnaire on their sociodemographic characteristics, their medications, and reasons for nonadherence. Results: A total of 147 subjects were assessed. We found that 68.0% of subjects were poorly adherent to the treatment based on MARS, and that only 32.0% of them were strictly adherent to the treatment. The reasons for poor adherences to the medication treatment were carelessness or forgetfulness (21.8%), adverse effects of the medications (16.3%), and the perception of the subject of feeling better (15.6%). Conclusion: More than two thirds of the subjects was found to be non-adherent to the medications. Assessing various reasons for medication adherence will help find strategies to improve medication adherence and thus outcome of the treatment.

Medication adherence and its impact on glycemic control in type 2 diabetes mellitus patients with comorbidity: A multicenter cross-sectional study in Northwest Ethiopia.

Medication nonadherence in patients with chronic diseases, particularly in type 2 diabetes mellitus (T2DM) with comorbidity, has continued to be the cause of treatment failure. The current study assessed medication adherence and its impact on glycemic control in T2DM patients with comorbidity. An institutional-based multicenter cross-sectional study was conducted among T2DM patients with comorbidity at the selected hospitals in Northwest Ethiopia. Medication adherence was measured using a structured questionnaire of the General Medication Adherence Scale (GMAS). A logistic regression model was used to identify predictors of the level of medication adherence and glycemic control. P < 0.05 at 95% confidence interval (CI) was statistically significant. A total of 403 samples were included in the final study. This study showed that more than three-fourths (76.9%) of the participants were under a low level of medication adherence. Source of medication cost coverage [AOR = 10.593, 95% CI (2.628-41.835; P = 0.003], monthly income (P < 0.00), self-monitoring of blood glucose (SMBG) practice [AOR = 0.266, 95% CI (0.117-0.604); P = 0.002], number of medications [AOR = 0.068, 95% CI (0.004-0.813); P = 0.014] and medical conditions [AOR = 0.307, 95% CI (0.026-0.437); P = 0.018] were found to be significant predictors of medication adherence. Significantly, majority (74.7%) of participants had poor levels of glycemic control. Patients who had a high level of medication adherence [AOR = 0.003, 95% CI (0.000-0.113); P = 0.002] were found less likely to have poor glycemic control compared with patients who were low adherent to their medications. The current study concluded that medication adherence was low and significantly associated with poor glycemic control. Number of medical conditions and medications were found to be associated with medication adherence. Management interventions of T2DM patients with comorbidity should focus on the improvement of medication adherence.

Psychological Factors Associated With Adherence to Oral Treatment in Ulcerative Colitis.

Medication nonadherence in patients with ulcerative colitis (UC) can result in frequent relapses, severe disease, and higher risk of colorectal cancer. Behavioral models relying on motivation and perceived competence, like the self-determination theory (SDT), have been implicated in nonadherence; however, the SDT has not been evaluated in the adult UC population. We sought to examine the association between adherence to oral medications in patients with UC and psychological distress, relationship with health care providers, motivation, and competence. We performed a cross-sectional study within the Inflammatory Bowel Disease (IBD) Partners online registry in which participants completed a baseline survey including demographic information, IBD history, symptoms, medication adherence, and psychosocial factors. Members of the registry with a diagnosis of UC received an online follow-up survey that included baseline questionnaires and assessment of competence, motivation, and patient-physician relationship. Logistic regression models were performed to determine the relationship between psychosocial factors, adherence modifiers, and medication adherence. Of the 410 UC patients included, 29% had low adherence to their medications, 36% had medium adherence, and 34% had high adherence. In the multivariable analysis, younger patients, those with a lower perceived competence, and those with worse relationship with their providers were more likely to have lower adherence to their medications. Poor adherence to oral medications in UC was associated with lower perceived competence and worse relationship with providers. Further interventions based on the SDT can potentially improve adherence and optimize patient care.

Behavioral Factors Associated with Medication Nonadherence in Patients with Hypertension.

Medication nonadherence is associated with an increased risk of complications in hypertensive patients. We investigated behavioral factors associated with medication nonadherence in hypertensive patients in southern Taiwan. Using questionnaires, we collected data regarding clinicodemographic characteristics and nonadherence behaviors from 238 hypertensive patients. We assessed the self-reported prevalence of specific behaviors of medication nonadherence and investigated factors associated with each behavior using multivariable logistic regression analysis. The most common behavior of medication nonadherence was forgetting to take medication (28.6%), followed by discontinuing medication (9.2%) and reducing the medication dose (8.8%). Age ≥ 65 years (adjusted odds ratio [aOR] = 0.32, 95% confidence interval [CI] = 0.15–0.69) and male sex (aOR = 2.61, CI = 1.31–5.19) were associated with forgetting to take medication. The presence of comorbidities (diabetes, kidney disease, or both) and insomnia (aOR = 3.97, 95% CI = 1.30–12.1) was associated with reducing the medication dose. The use of diet supplements was associated with discontinuing the medication (aOR = 4.82, 95% CI = 1.50–15.5). Compliance with a low oil/sugar/sodium diet was a protective factor against discontinuing medication (aOR = 0.14; 95% CI = 0.03–0.75). The most pervasive behavior associated with medication nonadherence among hypertensive patients was forgetting to take medication. Age <65 years, male sex, comorbidities, insomnia, noncompliance with diet, and the use of dietary supplements were specifically associated with medication nonadherence.

A Review of Factors Relating to Medication Non-Adherence in Patients with Schizophrenia

Schizophrenia is a severe chronic mental illness affecting twenty million people worldwide. Although the incidence of schizophrenia remains low, its prevalence may remain high due to medication non-adherence. Knowing potential factors relating to non-adherence with medication among patients with schizophrenia by investigating existing literature is needed in order to understand the phenomenology of this situation. Therefore, the study aimed to explore the most common factors that affected medication non-adherence in patients with schizophrenia. A systematic review was conducted through a literature search on Cochrane Database of Systematic Reviews, MEDLINE, PsycINFO, EBSCOhost, CINAHL Complete, ERIC, and Allied Health databases published from 1980 to 2021. Database searches were conducted with the terms &amp;ldquo;non-adherence to medication&amp;rdquo; &amp;ldquo;schizophrenia,&amp;rdquo; and &amp;ldquo;factors.&amp;rdquo; Eighty-six articles were found following the first-round search. Then seventy-eight articles were excluded due to irrelevant and duplicate tiles. Only eight articles were included for the final review. Per the findings, factors associated with medication non-adherence were categorized into four main themes: individual characteristics, cognitive appraisal, social influence, and health-care service. To improve medication adherence rates in patients with schizophrenia, psychiatric nurses must consider these specific factors while establishing nursing interventions.

Predicting medication nonadherence risk in the Chinese type 2 diabetes mellitus population - establishment of a new risk nomogram model: a retrospective study.

ObjectiveTo investigate the risk factors of medication nonadherence in patients with type 2 diabetes mellitus (T2DM) and to establish a risk nomogram model.MethodsThis retrospective study enrolled patients with T2DM, which were divided into two groups based on their scores on the Morisky Medication Adherence scale. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for medication nonadherence. A risk model was then established using a nomogram. The accuracy of the prediction model was evaluated using centrality measurement index and receiver operating characteristic curves. Internal verification was evaluated using bootstrapping validation.ResultsA total of 338 patients with T2DM who included in the analysis. Logistic regression analysis showed that the educational level, monthly per capita income, drug affordability, the number of drugs used, daily doses of drugs and the time spent taking medicine were all independent risk factors for medication nonadherence. Based on these six risk factors, a nomogram model was established to predict the risk of medication nonadherence, which was shown to be very reliable. Bootstrapping validated the nonadherence nomogram model for patients with T2DM.ConclusionsThis nomogram model could be used to evaluate the risks of drug nonadherence in patients with T2DM.

Factors Associated with Medication Non-Adherence in Patients with Dyslipidemia.

Lack of medication adherence among patients with dyslipidemia negatively affects health-related outcomes. This study aims to evaluate medication adherence; we also aim to investigate the predictors of non-adherence among patients with dyslipidemia in Jordan. Medication adherence was evaluated in a total of 228 dyslipidemia patients. The Beliefs about Medicines Questionnaire was also used to assess patients’ beliefs about medications. The majority of the current study participants (73.2%) reported non-adherence to the prescribed medications. There were significant negative associations between medication adherence and concerns of prescription drug use (B = −0.41, p-value < 0.01), duration of dyslipidemia (B = −0.22, p-value < 0.01), and the number of medications (B = −0.64, p-value < 0.01). Positive associations were found between medication adherence and the necessity of prescription drug use (B = 0.43, p-value < 0.01), taking statin and fibrate (B = 2.04, p-value < 0.01), and moderate-intensity statin (B = 2.34, p-value < 0.01). As for patients’ beliefs about medications, the item “My medicine to lower my cholesterol disrupted my life” had the highest mean (3.50 ± 0.99). This study revealed a low adherence rate to medication among patients with dyslipidemia. It also demonstrates modifiable factors such as beliefs regarding perceived risk, medication harms, treatment duration, and the number of medications associated with poor adherence in patients with dyslipidemia.

PMH49 Factors Affecting Medication Nonadherence Among Patients with Psychiatric Illness in Tertiary Care Teaching Hospital in South India

To evaluate the factors associated with medication nonadherence in patients with psychiatric illness in outpatient settings. The study conducted for six months in the outpatient psychiatric department of a tertiary care teaching hospital in south India. Patients aged 18 and over who have been diagnosed with psychiatric disease (Depression, Bipolar affective disorder, Alcohol dependence syndrome and schizophrenia) as per the International Classification of Diseases (ICD)-10 and received at least one psychotropic drug for at least one month were included in the study. Medication adherence was assessed using the medication adherence rating scale (MARS). Descriptive analysis was performed for all variables. A total of 414 patients were enrolled among them 54.8% (n=227) were females. The mean age of the study population was 38.76 years (SD±13.43). The most common diagnoses among patients was depression (54.34%) followed by, bipolar disorder (28.01%), schizophrenia (11.35%) and alcohol dependency syndrome (6.28%). Of 414 patients, 58% (n=239) of patients discontinued their prescribed medication. The factors associated with medication nonadherence were categorized as patient-related, medication-related and disease-related. Under patient-related, financial reason (16.3%), Myths & Misconception (15.8%), irregular follow up (14.6%) are most common reasons. Under medication-related, adverse drug reactions (12.5%) and non-availability of medications (10.4%) were common factors. Under disease-related, lack of insight (10.8%), forgetfulness (5.4%), no improvement in treatment (4.6%) were the most common factors for medication nonadherence. Numerous factors contributed to medication nonadherence. Strategies need to be developed and implemented to enhance medication adherence and achieve a better therapeutic outcome in patients with mental illness.

Global Burden, Regional Differences, Trends, and Health Consequences of Medication Non-Adherence in Patients with Hypertension During 2010-2020: A Systematic Review and Meta-Analysis Involving 27 Million Patients

Global Burden, Regional Differences, Trends, and Health Consequences of Medication Non-Adherence in Patients with Hypertension During 2010-2020: A Systematic Review and Meta-Analysis Involving 27 Million Patients

National Estimates of Financial Hardship From Medical Bills and Cost-related Medication Nonadherence in Patients With Inflammatory Bowel Diseases in the United States

Inflammatory bowel diseases (IBDs) are associated with substantial health care needs. We estimated the national burden and patterns of financial toxicity and its association with unplanned health care utilization in adults with IBD in the United States. Using the National Health Interview survey (2015), we identified individuals with self-reported IBD and assessed national estimates of financial toxicity across domains of financial hardship due to medical bills, cost-related medication nonadherence (CRN) and adoption of cost-reducing strategies, personal and health-related financial distress (worry about expenses), and health care affordability. We also evaluated the association of financial toxicity with emergency department (ED) utilization. Of the estimated 3.1 million adults with IBD in the United States, 23% reported financial hardships due to medical bills, 16% of patients reported CRN, and 31% reported cost-reducing behaviors. Approximately 62% of patients reported personal and/or health-related financial distress, and 10% of patients deemed health care unaffordable. Prevalence of financial toxicity was substantial even in participants with higher education, with private insurance, and belonging to middle/high-income families, highlighting underinsurance. Inflammatory bowel disease was associated with 1.6 to 2.6 times higher odds of financial toxicity across domains compared with patients without IBD. Presence of any marker of financial toxicity was associated with higher ED utilization. One in 4 adults with IBD experiences financial hardship due to medical bills, and 1 in 6 adults reports cost-related medication nonadherence. These financial determinates of health-especially underinsurance-have important implications in the context of value-based care.

Psycho-demographic and clinical predictors of medication adherence in patients with bipolar I disorder in a university hospital in Egypt.

BackgroundPoor adherence to treatment is one of the main challenges to symptom control and preventing recurrence in bipolar disorder (BD). Numerous studies have established an association between patients’ poor adherence and an increased risk of recurrence, relapse of the symptoms and admission to hospital.AimTo study the socio-demographic and clinical factors associated with medication nonadherence in patients with BD who were admitted to the hospital.SettingThe study was conducted at the Institute of Psychiatry, Ain Shams University.MethodsA 1-year longitudinal prospective study of 110 patients, aged 18–60 years, with BD-I. Young Mania Rating Scale, Clinical Global Impression, Global Assessment of Functioning, Sheehan Disability Scale and Insight and Treatment Attitude Questionnaire were applied before and 6 months after discharge. Adherence was measured using the Morisky 8-Item Medication Adherence Scale. Sociodemographic data and level of functioning were studied in relation to adherence.ResultsHigher adherence was noticed in female, married and older patients and those with a higher level of education. However, low adherence was more common in male, non-married and less educated patients. Follow-up after 6 months revealed that the high adherence group scored the lowest in terms of disability. Meanwhile, the low adherence group scored the highest scores in disability.ConclusionSeveral socio-demographic and clinical variables were found to be associated with a low adherence rate to the prescribed medication in patients with BD-I. Age and impaired insight were found to be significant predictive factors for non-adherence.

Medication Non-adherence in Patients with Osteoporosis: Implications for Clinical Pharmacists and Osteoporosis Care Providers.

Poor adherence to the prescribed therapy leads to low bone mineral density and enhance the development of osteoporosis complications and unnecessary hospitalization. To explore factors associated with medication non-adherence in patients with osteoporosis. Findings would help guide the development of future pharmaceutical care interventions aim at improving health outcomes for patients with osteoporosis. The study was conducted at an outpatient osteoporosis clinic at the Royal Medical Services Hospital. Variables including socio-demographics and medical factors were collected using medical records and custom-designed questionnaire. Medication adherence was assessed using the validated 4-item Morisky Medication Adherence Scale. Logistic regression was performed to develop a model with variables that best predicted medication non-adherence in patients with osteoporosis in Jordan. A total of 296 patients participated in the study. Most of the study participants (72.3%) were found non-adherent. Patients were found less likely to adhere to the prescribed medications with each unit increase in the number of prescribed medications (OR = 2.503, CI = 1.103-5.680) and if they did not have a trust in the efficacy of the medications (OR = 5.544, CI = 0.990-31.058). Medication adherence for patients with osteoporosis has considered scope for improvement in Jordan. Simplifying dosage regimen in addition to taking patients' preferences when selecting the medications should be taken into account in future interventions designed to improve health outcomes for patients with osteoporosis.

Measuring the multiple domains of medication nonadherence: findings from a Delphi survey of adherence experts.

Consensus on a gold-standard measure of patient medication nonadherence has been elusive, in part because medication nonadherence involves multiple, distinct behaviors across three phases (initiation, implementation, and persistence). To assess these behaviors, multiple measurement approaches may be needed. The purpose of this study was to identify expert-recommended approaches to measuring nonadherence behaviors. Thirty medication nonadherence experts were e-mailed two consecutive surveys. In both, respondents rated their agreement with definitions of nonadherence behaviors and measurement approaches. In the second survey, respondents rated the suitability of each measurement approach for assessing each behavior and identified the optimal measurement approach for each behavior. Consensus was achieved for eight patient medication nonadherence behaviors: not filling initial prescription and not taking first dose (noninitiation); refilling prescription late, missing doses, taking extra doses, taking doses at wrong time, and improperly administering medication (incorrect implementation); and discontinuing medication early (nonpersistence). Consensus was achieved for seven measurement approaches: self-report, prescription fill data, pill count, drug levels, electronic drug monitoring (EDM), smart technology, and direct observation. Self-report questionnaires were most commonly rated "at least somewhat suitable" for measuring behaviors. EDM was rated as optimal for measuring missing doses, taking extra doses, and taking doses at the wrong time. Prescription fill data were rated as optimal for not filling initial prescription, refilling late, and discontinuing. Direct observation was rated as optimal for measuring improper administration. Suitable and optimal measurement approaches varied across nonadherence behaviors. Researchers should select the measurement approach best suited to assessing the behavior(s) targeted in their research.

Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool.

To develop and validate a risk assessment tool called the Prescription Medication Non-Adherence Prediction Tool (Rx-NAPT) to predict medication nonadherence in patients with fibromyalgia. This was a retrospective cohort study using claims data from South Carolina Medicaid. Patients with fibromyalgia who were ≥18 years old and who had filled at least 1 prescription medication for pregabalin, duloxetine, or milnacipran from January 1, 2005, through June 30, 2011 were included. Medication possession ratios (MPRs) were calculated to classify patients as adherent (MPR ≥ 80%) or nonadherent (MPR < 80%). Multivariable logistic models using 100 bootstrap replications (with replacement) were used to identify factors associated with medication nonadherence, including age, gender, race, days' supply, medication type, and fibromyalgia-related comorbidity score. Weighted β coefficients of the predictors were used to create the Rx-NAPT. Youden's J statistic was used to classify nonadherent patients into different levels of risk. The study sample comprised 6,626 patients with fibromyalgia, where 4,804 (72.50%) were non-adherent and 1,822 (27.50%) were adherent to their prescribed medication(s). Logistic regression models showed that 7 predictors (gender, age, race, fibromyalgia-related comorbidity score, medication type, health maintenance organization coverage, emergency room visit) were statistically significant in ≥50% of the bootstrapped samples. The final model demonstrated reasonable discrimination (area under the curve [AUC] = 0.6224) and calibration (Hosmer-Lemeshow goodness-of-fit; P > 0.05) statistics and was validated internally (AUC = 0.6372). Poor adherence with medication remains an important barrier to providing optimal care. Our risk prediction model provides an easy tool to help clinicians better identify patients with fibromyalgia who may not take their medications as prescribed.

Effects of Medication Nonadherence and Comorbidity on Health Resource Utilization in Schizophrenia.

Medication nonadherence to antipsychotic drugs, which is commonly seen in patients with schizophrenia who have comorbidities, not only affects the quality of life of individuals suffering from the condition, but can also lead to worsening of disease condition, adverse outcomes, excessive use of health care resources, and higher medical costs. To determine the effect of nonadherence to antipsychotics and related disease comorbidities on medical care utilization with respect to inpatient hospital visits, outpatient visits, office visits, and emergency room (ER) visits. Retrospective, cross-sectional research data was obtained from the Medical Expenditure Panel Surveys (MEPS) for the years 2010-2014. The proportion of days covered (PDC) adherence measure was used to identify and classify individuals as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%). A logistic regression analysis was used to further examine the effect of key study variables and comorbidity on medication nonadherence in patients with schizophrenia. Using the Student's t-test, population characteristics were statistically compared between the adherent and nonadherent populations and between populations with comorbidities and without comorbidities with respect to inpatient, outpatient, office, and ER visits. Of 1.2 million people who reported having schizophrenia in MEPS from 2010 to 2014, as many as 71% were found to be nonadherent to antipsychotic medications (PDC < 80%). Results showed that women (OR = 3.594, 95% CI = 1.33-11.40, P = 0.030) and people with less than 15 years of education (OR = 20.85, 95% CI = 3.91-111.09, P = 0.0005) were more likely to be nonadherent to antipsychotic medications than all other demographics. Compared with the adherent schizophrenia population (n = 353,349), the nonadherent population (n = 868,737) had greater utilization of outpatient visits (0.68 vs. 1.92, P < 0.0001) and office visits (10.95 vs. 18.21, P < 0.0001) but had lower utilization of inpatient visits (0.82 vs. 0.45, P < 0.0001) and ER visits (1.03 vs. 0.79, P = 0.1036). Compared with the schizophrenia population without comorbidities, the population with comorbidities (a classification based on a Charlson Comorbidity Index score of ≥ 1) had greater utilization of inpatient (0.39 vs. 0.76, P < 0.0001); office (13.39 vs. 19.34, P < 0.0001); and ER visits (0.39 vs. 1.41, P < 0.0001) but had lower utilization of outpatient visits (1.86 vs. 1.21, P < 0.0001). Greater medical care resources are used by nonadherent populations with schizophrenia and comorbidities than those without comorbidities. Together, nonadherence and comorbidities pose significant risks to patients with schizophrenia, in clinical and financial terms, and addressing problems stemming from such risks should be an area of priority in schizophrenia management. No outside funding supported this study. The authors have no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.