Childhood eczema prevalence in New Zealand using topical corticosteroid dispensing data.

OBJECTIVES: To determine the prevalence of eczema among children in New Zealand. METHODS: Population-based retrospective observational study utilising national pharmaceutical dispensing records for topical corticosteroids and emollients for all New Zealand children aged 0-14 years from 1st January 2006 to 31st December 2019. Data are reported using descriptive statistics, with comparisons between ethnicities and socioeconomic quintiles undertaken with rate ratios. RESULTS: Based on dispensing data, the prevalence of eczema for New Zealand children aged 0-14 years in 2018 was 14.0% (95% CI 14.0%-14.1%), with prevalence decreasing in older age groups (children aged <1 year 26.0% (25.6%-26.4%); children aged 10-14 years 8.8% (8.7%-8.9%)). Prevalence was higher in Pacific children (23.6% (23.3%-24.0%)), but slightly lower in Maori children (13.2% (13.0%-13.3%)). CONCLUSION: Eczema is a common condition affecting a considerable proportion of children in New Zealand. This study provides nationwide paediatric prevalence data for New Zealand, and highlights the increased burden of eczema in Pacific children. Inequity in dispensing of topical corticosteroids is postulated to explain the reduced rates found for Maori children compared to previous studies. These results support the need for further research to determine factors contributing to differing eczema prevalence rates in New Zealand.

Using pharmacy dispensing data to predict falls in older individuals.

AIMS: Associations between individual medication use and falling in older individuals are well-documented. However, a comprehensive risk score that takes into account overall medication use and that can be used in daily pharmacy practice is lacking. We, therefore, aimed to determine whether pharmacy dispensing records can be used to predict falls. METHODS: A retrospective cohort study was conducted using pharmacy dispensing data and self-reported falls among 3454 Dutch individuals aged ≥65 years. Two different methods were used to classify medication exposure for each person: the drug burden index (DBI) for cumulative anticholinergic and sedative medication exposure as well as exposure to fall risk-increasing drugs (FRIDs). Multinomial regression analyses, adjusted for age and sex, were conducted to investigate the association between medication exposure and falling classified as nonfalling, single falling and recurrent falling. The predictive performances of the DBI and FRIDs exposure were estimated by the polytomous discrimination index (PDI). RESULTS: There were 521 single fallers (15%) and 485 recurrent fallers (14%). We found significant associations between a DBI ≥1 and single falling (adjusted odds ratio: 1.30 [95% confidence interval {CI}: 1.02-1.66]) and recurrent falling (adjusted odds ratio: 1.60 [95%CI: 1.25-2.04]). The PDI of the DBI model was 0.41 (95%CI: 0.39-0.42) and the PDI of the FRIDs model was 0.45 (95%CI: 0.43-0.47), indicating poor discrimination between fallers and nonfallers. CONCLUSION: The study shows significant associations between medication use and falling. However, the medication-based models were insufficient and other factors should be included to develop a risk score for pharmacy practice.

Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system.

Background: Scarcity of prospective medication non-adherence cost measurements for the Australian population with no directly measured estimates makes determining the burden medication non-adherence places on the Australian health care system difficult. This study aims to indirectly estimate the national cost of medication non-adherence in Australia comparing the cost prior to and following a community pharmacy-led intervention. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n=11,257 on rosuvastatin, n=6,797 on irbesartan and n=2,281 on desvenlafaxine) was analyzed and average adherence rate determined through calculation of PDC. Included patients received a pharmacist-led medication adherence intervention and had twelve months dispensing records; six months before and six months after the intervention. The national cost estimate of medication non-adherence in hypertension, dyslipidemia and depression pre- and post-intervention was determined through utilization of disease prevalence and comorbidity, non-adherence rates and per patient disease-specific adherence-related costs. Results: The total national cost of medication non-adherence across three prevalent conditions, hypertension, dyslipidemia and depression was $10.4 billion equating to $517 per adult. Following enrollment in the pharmacist-led intervention medication non-adherence costs per adult decreased $95 saving the Australian health care system and patients $1.9 billion annually. Conclusion: In the absence of a directly measured national cost of medication non-adherence, this estimate demonstrates that pharmacists are ideally placed to improve patient adherence and reduce financial burden placed on the health care system due to non-adherence. Funding of medication adherence programs should be considered by policy and decision makers to ease the current burden and improve patient health outcomes moving forward.

Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy.

Background: Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study was to analyze the changes on adherence implementation rates before and after a community pharmacist intervention integrated in usual real life practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n = 11,257 on rosuvastatin, n = 6,797 on irbesartan, and n = 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Results: Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenlafaxine. In terms of the proportion of adherent patients (PDC >= 80.0%) the trend was similar, increasing after the pharmacist intervention from overall 17.4 to 41.2% and decreasing after one year of analysis to 35.3%. Conclusion: Big database analysis techniques provided results on adherence implementation over 2 years of analysis. An increase in adherence rates was observed after the pharmacist intervention, followed by a gradual decrease over time. Enhancing the current intervention using an evidence-based approach and integrating big database analysis techniques to a real-time measurement of adherence could help community pharmacies improve and sustain medication adherence.