Pharmacoepidemiology resources in Ireland-an introduction to pharmacy claims data.

INTRODUCTION: Administrative health data, such as pharmacy claims data, present a valuable resource for conducting pharmacoepidemiological and health services research. Often, data are available for whole populations allowing population level analyses. Moreover, their routine collection ensures that the data reflect health care utilisation in the real-world setting compared to data collected in clinical trials. SETTING AND METHODS: The Irish Health Service Executive-Primary Care Reimbursement Service (HSE-PCRS) community pharmacy claims database is described. The availability of demographic variables and drug-related information is discussed. The strengths and limitations associated using this database for conducting research are presented, in particular, internal and external validity. Examples of recently conducted research using the HSE-PCRS pharmacy claims database are used to illustrate the breadth of its use. RESULTS AND CONCLUSIONS: The HSE-PCRS national pharmacy claims database is a large, high-quality, valid and accurate data source for measuring drug exposure in specific populations in Ireland. The main limitation is the lack of generalisability for those aged <70 years and the lack of information on indication or outcome

Prescriber variation in potentially inappropriate prescribing in older populations in Ireland.

BACKGROUND: Health care policy-makers look for prescribing indicators at the population level to evaluate the performance of prescribers, improve quality and control drug costs. The aim of this research was to; (i) estimate the level of variation in potentially inappropriate prescribing (PIP) across prescribers in the national Irish older population using the STOPP criteria; (ii) estimate how reliably the criteria could distinguish between prescribers in terms of their proportion of PIP and; (iii) examine how PIP varies between prescribers and by patient and prescriber characteristics in a multilevel regression model. METHODS: 1,938 general practitioners (GPs) with 338,375 registered patients\u27 ≥70 years were extracted from the Health Service Executive Primary Care Reimbursement Service (HSE-PCRS) pharmacy claims database. HSE-PCRS prescriptions are WHO ATC coded. Demographic data for claimants\u27 and prescribers\u27 are available. Thirty STOPP indicators were applied to prescription claims in 2007. Multilevel logistic regression examined how PIP varied between prescribers and by individual patient and prescriber level variables. RESULTS: The unadjusted variation in PIP between prescribers was considerable (median 35%, IQR 30-40%). The STOPP criteria were reliable measures of PIP (average \u3e0.8 reliability). The multilevel regression models found that only the patient level variable, number of different repeat drug classes was strongly associated with PIP (\u3e2 drugs v none; adjusted OR, 4.0; 95% CI 3.7, 4.3). After adjustment for patient level variables the proportion of PIP varied fourfold (0.5 to 2 times the expected proportion) between prescribers but the majority of this variation was not significant. CONCLUSION: PIP is of concern for all prescribers. Interventions aimed at enhancing appropriateness of prescribing should target patients taking multiple medications

Proton pump inhibitors: potential cost reductions by applying prescribing guidelines.

BACKGROUND: There are concerns that proton pump inhibitors (PPI) are being over prescribed in both primary and secondary care. This study aims to establish potential cost savings in a community drug scheme for a one year period according to published clinical and cost-effective guidelines for PPI prescribing. METHODS: Retrospective population-based cohort study in the Republic of Ireland using the Health Services Executive (HSE) Primary Care Reimbursement Services (PCRS) pharmacy claims database. The HSE-PCRS scheme is means tested and provides free health care including medications to approximately 30% of the Irish population. Prescription items are WHO ATC coded and details of every drug dispensed and claimants\u27 demographic data are available. Potential cost savings (net ingredient cost) were estimated according to UK NICE clinical guidelines for all HSE-PCRS claimants on PPI therapy for ≥3 consecutive months starting in 2007 with a one year follow up (n=167,747). Five scenarios were evaluated; (i) change to PPI initiation (cheapest brand); and after 3 months (ii) therapeutic switching (cheaper brand/generic equivalent); (iii) dose reduction (maintenance therapy); (iv) therapeutic switching and dose reduction and (v) therapeutic substitution (H2 antagonist). RESULTS: Total net ingredient cost was €88,153,174 for claimants on PPI therapy during 2007. The estimated costing savings for each of the five scenarios in a one year period were: (i) €36,943,348 (42% reduction); (ii) €29,568,475 (34%); (iii) €21,289,322 (24%); (iv) €40,505,013 (46%); (v) €34,991,569 (40%). CONCLUSION: There are opportunities for substantial cost savings in relation to PPI prescribing if implementation of clinical guidelines in terms of generic substitution and step-down therapy is implemented on a national basis

Identifying the determinants of adjuvant hormonal therapy medication taking behaviour in women with stages I-III breast cancer: A systematic review and meta-analysis

Objective: This systematic review and meta-analysis aimed to identify the modifiable determinants of adjuvant hormonal therapy medication taking behaviour (MTB) in women with stage I-III breast cancer in clinical practice settings. Methods: We searched PubMed EMBASE, PsycINFO and CINAHL for articles investigating determinants of adjuvant hormonal therapy. Potentially modifiable determinants were identified and mapped to the 14 domains of the Theoretical Domains Framework (TDF), an integrative framework of theories of behavioural change. Meta-analysis was used to calculate pooled odds ratios for selected determinants. Results: Potentially modifiable determinants were identified in 42 studies and mapped to 9 TDF domains. In meta-analysis treatment side-effects (Domain: Beliefs about Capabilities) and follow-up care with a general practitioner (vs. oncologist) (Social Influences) were significantly negatively associated with persistence (p&lt;0.001) and number of medications (Behaviour Regulation) was significantly positively associated with persistence (p&lt;0.003). Studies did not examine several domains (including Beliefs about Consequences, Intentions, Goals, Social Identity, Emotion and Knowledge) which have been reported to influence MTB in other disease groups. Conclusions: There is some evidence that the domains Beliefs about Capabilities, Behaviour Regulation and Social Influences influence hormonal therapy MTB. Practice implications: Further research is needed to develop effective interventions to improve hormonal therapy MTB

The need for patient adherence standard measures for Big Data

Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of 21st century. Ageing leads to multimorbidity and complex therapeutic regimens that create fertile ground for non-adherence. As this scenario is a global problem, it needs a worldwide answer. Might this answer be provided, given the new opportunities created by the digitization of healthcare? Day by day health-related information is collected in electronic health records, pharmacy dispensing databases, health insurance systems and national health system records. These Big Data repositories offer a unique chance to study adherence both retrospectively and prospectively, at population level, as well as its related factors. In order to make the full use of this opportunity, there is a need to develop standardised measures of adherence, which can be applied globally to Big Data and will inform scientific research, clinical practice and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for producing a consensus on global standards for measuring adherence with Big Data. More specifically, sound standards of formatting, and analysing Big Data are needed in order to assess, uniformly present and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence, and make healthcare systems more effective and sustainable

Identifying determinants of adherence to adjuvant endocrine therapy following breast cancer : a systematic review of reviews

Background: In oestrogen- receptor positive breast cancer, daily oral adjuvant endocrine therapy (ET) for at least 5 years significantly reduces risks of recurrence and breast cancer- specific mortality. However, many women are poorly adherent to ET. Development of effective adherence support requires comprehensive understanding of influences on adherence. We undertook an umbrella review to identify determinants of ET adherence.Methods: We searched PubMed, Embase, CINAHL, PsycINFO, Cochrane and PROSPERO (inception to 08/2022) to identify systematic reviews on factors influencing ET adherence. Abstracted determinants were mapped to the World Health Organization's dimensions of adherence. Reviews were quality appraised and overlap assessed.Results: Of 5732 citations screened, 17 reviews were eligible (9 quantitative primary studies; 4 qualitative primary studies; 4 qualitative or quantitative studies) including 215 primary papers. All five WHO dimensions influenced ET non- adherence: The most consistently identified non- adherence determinants were patient- related factors (e.g. lower perceived ET necessity, more treatment concerns, perceptions of ET ‘cons’ vs. ‘pros’). Healthcare system/healthcare professional- related factors (e.g. perceived lower quality health professional interaction/relationship) were also important and, to a somewhat lesser extent, socio- economic factors (e.g. lower levels of social/economic/material support). Evidence was more mixed for medication- related and condition- related factors, but several may be relevant (e.g. experiencing side- effects, cost). Potentially modifiable factors are more influential than non- modifiable/fixed factors (e.g. patient characteristics)