In the past years, several factors such as evidence-based healthcare culture, quality-linked incentives, and patient-centered actions, associated with an important increase of financial constraints and pressures on healthcare budgets, resulted in a growing interest by policy-makers in enlarging pharmacists’ roles in care. Numerous studies have demonstrated positive therapeutic outcomes associated with pharmaceutical services in a wide array of diseases. Yet, the evidence of the economic impact of the pharmacist in decreasing total health expenditures, unnecessary care, and societal costs relies on well-performed, reliable, and transparent economic evaluations, which are scarce. Pharmacoeconomics is a branch of health economics that usually focuses on balancing the costs and benefits of an intervention towards the use of limited resources, aiming at maximizing value to patients, healthcare payers and society through data driven decision making. These decisions can be guide by a health technology assessment (HTA) process that inform governmental players about medical, social, and economic implications of development, diffusion, and use of health technologies – including clinical pharmacy interventions. This paper aims to provide an overview of the important concepts in costing in healthcare, including studies classification according to the type of analysis method (e.g. budget-impact analysis, cost-minimization analysis, cost-effectiveness analysis, cost-utility analysis), types of costs (e.g. direct, indirect and intangible costs) and outcomes (e.g. events prevented, quality adjusted life year - QALY, disability adjusted life year - DALY). Other key components of an economic evaluation such as the models’ perspective, time horizon, modelling approaches (e.g. decision trees or simulation models as the Markov model) and sensitivity analysis are also briefly covered. Finally, we discuss the methodological issues for the identification, measurement and valuation of costs and benefits of pharmacy services, and suggest some recommendations for future studies, including the use of Value of Assessment Frameworks.

Purpose: The main purpose of this paper is to develop a brand love measurement scale, adopting an etic approach and testing for differences on how consumers experience brand love across different cultures. Design/methodology/approach: An integrative model of brand love was first developed and then data was collected through an online survey (N = 322) in two countries (the USA and Portugal) with different characteristics (individualistic vs collectivistic). A structural equation model method was followed, including an exploratory factor analysis, confirmatory factor analysis and multi-group analysis to test the set of hypotheses. Findings: As proposed in the initial model, results indicate that brand love is the result of a dynamic interaction between five integrated emotional dimensions: passionate driven behavior, commitment, affection and connection, consumer-brand identification and brand relationship. The multi-group analysis across countries suggests that the socio-cultural context, namely, the collectivistic vs individualistic nature of the sample, significantly influences the brand love experience. Originality/value: This is the first brand love measurement scale developed from an etic perspective, encompassing complex and dynamic emotional dimensions that in combination, form the brand love experience. Results indicate that the brand love experience may significantly differ from culture to culture, pointing out the most relevant dimensions in each of the socio-cultural contexts that better predict brand love and its consequences. These findings are particularly relevant for practitioners working on global brand management.

Medication adherence is a crucial factor for managing chronic conditions, especially in aging adults. Previous studies have identified predictors of medication adherence. However, current methods fail to capture the time-varying nature of how risk factors can influence adherence behavior. This objective of this study was to implement multitrajectory group-based models to compare a time-varying to a time-fixed approach to identifying non-adherence risk factors. The study population comprised 11,068 Medicare beneficiaries aged 65 and older taking select medications for hypertension, high blood cholesterol, and oral diabetes medications, between 2008 and 2016. Time-fixed predictors (e.g., sex, education) were examined using generalized multinomial logistic regression, while time-varying predictors were explored through multitrajectory group-based modeling. Several predisposing, enabling, and need characteristics were identified as risk factors for following at least one non-adherence trajectory. Time-varying predictors displayed an alternative representation of those risk factors, especially depression symptoms. This study highlights the dynamic nature of medication adherence predictors and the utility of multitrajectory modeling. Findings suggest that targeted interventions can be developed by addressing the key time-varying factors affecting adherence.