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A data-driven typology of asthma medication adherence using cluster analysis.

ABSTRACT: Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).

SUBMITTER: Tibble H

PROVIDER: S-EPMC7490405 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A data-driven typology of asthma medication adherence using cluster analysis.

Tibble Holly H Chan Amy A Mitchell Edwin A EA Horne Elsie E Doudesis Dimitrios D Horne Rob R Mizani Mehrdad A MA Sheikh Aziz A Tsanas Athanasios A

Scientific reports 20200914 1

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extrac ...[more]

PMID: 32929109

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Project description:Background: Pharmacy administrative claims data remain an accessible and efficient source to measure medication adherence for frequently hospitalized patient populations that are systematically excluded from the landmark drug trials. Published pharmacotherapy studies use medication possession ratio (MPR) and proportion of days covered (PDC) to calculate medication adherence and usually fail to incorporate hospitalization and prescription overlap/gap from claims data. To make the cacophony of adherence measures clearer, this study created a refined hospital-adjusted algorithm to capture pharmacotherapy adherence among patients with end-stage renal disease (ESRD). Methods: The United States Renal Data System (USRDS) registry of ESRD was used to determine prescription-filling patterns of those receiving new prescriptions for oral P2Y12 inhibitors (P2Y12-I) between 2011 and 2015. P2Y12-I-naïve patients were followed until death, kidney transplantation, discontinuing medications, or loss to follow-up. After flagging/censoring key variables, the algorithm adjusted for hospital length of stay (LOS) and medication overlap. Hospital-adjusted medication adherence (HA-PDC) was calculated and compared with traditional MPR and PDC methods. Analyses were performed with SAS software. Results: Hospitalization occurred for 78% of the cohort (N = 46 514). The median LOS was 12 (interquartile range [IQR] = 2-34) days. MPR and PDC were 61% (IQR = 29%-94%) and 59% (IQR = 31%-93%), respectively. After applying adjustments for overlapping coverage days and hospital stays independently, HA-PDC adherence values changed in 41% and 52.7% of the cohort, respectively. When adjustments for overlap and hospital stay were made concurrently, HA-PDC adherence values changed in 68% of the cohort by 5.8% (HA-PDC median = 0.68, IQR = 0.31-0.93). HA-PDC declined over time (3M-6M-9M-12M). Nearly 48% of the cohort had a ≥30 days refill gap in the first 3 months, and this increased over time (P < .0001). Conclusions: Refill gaps should be investigated carefully to capture accurate pharmacotherapy adherence. HA-PDC measures increased adherence substantially when adjustments for hospital stay and medication refill overlaps are made. Furthermore, if hospitalizations were ignored for medications that are included in Medicare quality measures, such as Medicare STAR program, the apparent reduction in adherence might be associated with lower quality and health plan reimbursement.

Project description:BackgroundEmpirical research and health policies on asthma have focused on children and young adults, even though asthma morbidity and mortality are higher among older asthmatics.ObjectiveTo explore the relationship of asthma-related beliefs and self-reported controller medication adherence in older asthmatics.DesignAn observational study of asthma beliefs and self-management among older adults.ParticipantsAsthmatics ages ? 60 years (N?=?324, mean age 67.4 ± 6.8, 28 % white, 32 % black, 30 % Hispanic) were recruited from primary care practices in New York City and Chicago.Main measuresSelf-reported controller medication adherence was assessed using the Medication Adherence Report Scale. Based on the Common Sense Model of Self-Regulation, patients were asked if they believe they only have asthma with symptoms, their physician can cure their asthma, and if their asthma will persist. Beliefs on the benefit, necessity and concerns of treatment use were also assessed. Multivariate logistic regression was used to examine the association of beliefs with self-reported medication adherence.Key resultsThe majority (57.0 %) of patients reported poor adherence. Poor self-reported adherence was more common among those with erroneous beliefs about asthma illness and treatments, including the "no symptoms, no asthma" belief (58.7 % vs. 31.7 %, respectively, p?<?0.001), "will not always have asthma" belief (34.8 % vs. 12.5 %, p?<?0.001), and the "MD can cure asthma" belief (21.7 % vs. 9.6 %, p?=?0.01). Adjusting for illness beliefs, treatment beliefs and demographics, patients with a "no symptoms, no asthma" belief had lower odds of having good self-reported adherence (odds ratio [OR] 0.45, 95 % confidence interval [CI] 0.23-0.86), as did those with negative beliefs about the benefits (OR 0.73, 95 % CI 0.57-0.94) and necessity (OR 0.89, 95 % CI 0.83-0.96) of treatment.ConclusionsIllness and treatment beliefs have a strong influence on self-reported medication adherence in older asthmatics. Interventions to improve medication adherence in older asthmatics by modifying illness and treatment beliefs warrant study.

Project description:BackgroundVarious studies have found that medication adherence is generally low among patients with asthma, and that the complexity of the regimen may be a potential factor. However, there is no information on the complexity of the regimen and its relationship to adherence and asthma outcomes in Ethiopian asthma patients. Therefore, this study assessed how complex medication regimens affected medication adherence and asthma control in patients with asthma.MethodFrom February 1 to May 30, 2022, a multicenter cross-sectional study was conducted in three public referral hospitals in northwestern Ethiopia. The Medication Complexity Index (MRCI), a 65-item validated instrument, was used to represent the complexity of medication regimens The Medication Adherence Rating Scale for Asthma (MARS-A) was used to assess medication adherence, and the ACT was used to measure the level of asthma control. The association between predictor and outcome variables was determined using multivariable logistic regression analysis. P-values of < 0.05 were declared as a significant association.ResultPatients with asthma (n = 396) who met the inclusion criteria were included in the final analysis. About 21.2% and 24.5% of the participants had high asthma-specific MRCI and patient-level MRCI, respectively. The majority (84.4%) of the participants did not adhere to their medication, and 71% of the participants were classified as having uncontrolled asthma. According to the result of the multivariable analysis, moving from a high asthma-specific MRCI to a moderate asthma MRCI enhances the likelihood of medication adherence by 2.51 times (AOR = 2.51, 95%CI: (1.27, 7.71). Likewise, patients who have low asthma MRCI were four times more likely to adhere to the medication compared with high asthma MRCI (AOR = 3.80, 95%CI: (2.0, 11.1). Similarly, patients having low patient-level MRCI were eight times more likely their asthma level had been controlled (AOR = 7.84, 95%CI: 1.46 to 21.3) and patients who had moderate patient-level MRCI were three times (AOR = 2.83, 95%CI: 1.05 to 8.25) more controlled asthma compared with patients who had high patient level MRCI.ConclusionThe majority of asthma patients had low and moderate complexity of MRCI. Patients with low and moderate regimen complexity demonstrated high adherence and had well-controlled asthma. Therefore, future researchers should consider MRCI as one factor for adherence and asthma control levels.

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A data-driven typology of asthma medication adherence using cluster analysis.

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A data-driven typology of asthma medication adherence using cluster analysis.

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