Factors affecting medication adherence in elderly people Hyekyung Jin,1 Yeonhee Kim,2 Sandy Jeong Rhie1,3 1College of Pharmacy, 2Center for Excellence in Teaching & Learning, 3Division of Life and Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea Background: Little is known about the functional health literacy (FHL) associated with medication adherence in elderly patients. The aim of this study was to examine the FHL among older adults and identify influencing factors that can predict medication adherence. Methods: This was a cross-sectional survey. Participants (n=160) aged 65 years and older were selected from outpatient clinics of 3 tertiary care hospitals, 6 community pharmacies, and 2 senior centers between November 1 and 30, 2014. The participants’ FHL was measured using the Korean Functional Health Literacy Test, which consists of 15 items including 8 numeracy and 7 reading comprehension items. Medication adherence was measured by the Adherence to Refills and Medication Scale. Descriptive statistics, chi-square or Fisher’s exact test, and multiple regression analyses were used to analyze the data. Results: The mean score of the total FHL was 7.72±3.51 (range 0–15). The percentage of the total number of correct answers for the reading comprehension subtest and numeracy subtest were 48.1% and 54.4%, respectively. Among 160 participants, 52.5% showed low adherence to medication. The factors affecting medication adherence included the patient’s degree of satisfaction with the service (β=-0.215, P=0.022), sufficient explanation of medication counseling (β=-0.335, P=0.000), education level (β=-0.153, P=0.045), health-related problems (β=-0.239, P=0.004), and dosing frequency (β=0.189, P=0.018). Conclusion: In this study, we found medication adherence of elderly patients was associated with education level, health-related problems, dosing frequency, satisfaction with patient counseling, and explanation of medication, but no association was found with FHL. Pharmacists should consider elderly patients’ individual characteristics such as educational background and specific patient-related health problems, provide sufficient information and explanation of medication, and ensure patient satisfaction with the counseling. Keywords: ARMS, elderly, health literacy, medication adherence

The curative effects of nanoparticle albumin-bound (nab)-paclitaxel in the first-line treatment of metastatic breast cancer (MBC) are still controversial, with even more after the removal of marketing approval of indication of bevacizumab. Five electronic databases and the related resources were searched for eligible randomized clinical trials (RCTs) without year and language restrictions to perform a meta-analysis. The studies were comparing the efficacy and safety between nab-paclitaxel chemotherapy versus solvent-based (sb)-taxanes chemotherapy such as sb-paclitaxel and docetaxel. The primary end points were overall response rate (ORR) and disease control rate (DCR). Secondary end points were progression-free survival (PFS), overall survival (OS), adverse events (AEs), and dose discontinuation rate (DDR). Five RCTs (1,554 patients) were finally identified from 1,902 studies. When compared to sb-paclitaxel, nab-paclitaxel showed significant beneficial effects in terms of ORR (OR 2.39, 95% CI 1.69–3.37, p < 0.001), DCR (OR 1.89, 95% CI 1.07–3.35, p = 0.03), and PFS (HR 0.75, 95% CI 0.62–0.90, p = 0.002). Nab-paclitaxel also showed significantly longer OS (HR 0.73, 95% CI 0.54–0.99, p = 0.04) than docetaxel. AEs and DDR were comparable between the two arms. Using nab-paclitaxel could significantly improve efficacy with comparable toxicities in the treatment of MBC.

Traditionally, dosage for pediatric patients has been optimized using simple weight-scaled methods, but these methods do not always meet the requirements of children. To overcome this discrepancy, population pharmacokinetic (PK) modeling of size and maturation functions has been proposed. The main objective of the present study was to evaluate a new modeling method for pediatric patients using clinical data from three different clinical studies. To develop the PK models, a nonlinear mixed effect modeling method was employed, and to explore PK differences in pediatric patients, size with allometric and maturation with Michaelis–Menten type functions were evaluated. Goodness of fit plots, visual predictive check and bootstrap were used for model evaluation. Single application of size scaling to PK parameters was statistically significant for the over one year old group. On the other hand, simultaneous use of size and maturation functions was statistically significant for infants younger than one year old. In conclusion, population PK modeling for pediatric patients was successfully performed using clinical data. Size and maturation functions were applied according to established criteria, and single use of size function was applicable for over one year ages, while size and maturation functions were more effective for PK analysis of neonates and infants.