IntroductionAdministrative 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 methodsThe 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 conclusionsThe 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.

IntroductionCopayments are intended to decrease third party expenditure on pharmaceuticals, particularly those regarded as less essential. However, copayments are associated with decreased use of all medicines. Publicly insured populations encompass some vulnerable patient groups such as older individuals and low income groups, who may be especially susceptible to medication non-adherence when required to pay. Non-adherence has potential consequences of increased morbidity and costs elsewhere in the system.ObjectiveTo quantify the risk of non-adherence to prescribed medicines in publicly insured populations exposed to copayments.MethodsThe population of interest consisted of cohorts who received public health insurance. The intervention was the introduction of, or an increase, in copayment. The outcome was non-adherence to medications, evaluated using objective measures. Eight electronic databases and the grey literature were systematically searched for relevant articles, along with hand searches of references in review articles and the included studies. Studies were quality appraised using modified EPOC and EHPPH checklists. A random effects model was used to generate the meta-analysis in RevMan v5.1. Statistical heterogeneity was assessed using the I2 test; p>0.1 indicated a lack of heterogeneity.ResultsSeven out of 41 studies met the inclusion criteria. Five studies contributed more than 1 result to the meta-analysis. The meta-analysis included 199, 996 people overall; 74, 236 people in the copayment group and 125,760 people in the non-copayment group. Average age was 71.75years. In the copayment group, (verses the non-copayment group), the odds ratio for non-adherence was 1.11 (95% CI 1.09–1.14; P = <0.00001). An acceptable level of heterogeneity at I2 = 7%, (p = 0.37) was observed.ConclusionThis meta-analysis showed an 11% increased odds of non-adherence to medicines in publicly insured populations where copayments for medicines are necessary. Policy-makers should be wary of potential negative clinical outcomes resulting from non-adherence, and also possible knock-on economic repercussions.

Objective To estimate the incidence and prevalence of resistant hypertension among a UK population treated for hypertension from 1995 to 2015. Design Cohort study. Setting Electronic health records from the UK Clinical Practice Research Datalink in primary care. Participants 1 317 290 users of antihypertensive drugs with a diagnosis of hypertension. Main outcome measures Resistant hypertension was defined as concurrent use of three antihypertensive drugs inclusive of a diuretic, uncontrolled hypertension (≥140/90 mm Hg), and adherence to the prescribed drug regimen, or concurrent use of four antihypertensive drugs inclusive of a diuretic and adherence to the prescribed drug regimen. To determine incidence, the numerator was new cases of resistant hypertension and the denominator was person years of those with treated hypertension and at risk of developing resistant hypertension. To determine prevalence, the numerator was total number of cases with resistant hypertension and the denominator was those with treated hypertension. Prevalence and incidence were age standardised to the 2015 hypertensive population. Results The age standardised incidence of resistant hypertension increased from 0.93 cases per 100 person years (95% confidence interval 0.87 to 1.00) in 1996 to a peak level of 2.07 cases per 100 person years (2.03 to 2.12) in 2004. Incidence then decreased to 0.42 cases per 100 person years (0.40 to 0.44) in 2015. Age standardised prevalence increased from 1.75% (95% confidence interval 1.66% to 1.83%) in 1995 to a peak of 7.76% (7.70% to 7.83%) in 2007. Prevalence then plateaued and subsequently declined to 6.46% (6.38% to 6.54%) in 2015. Compared with patients aged 65-69 years, those aged 80 or more years were more likely to have prevalent resistant hypertension throughout the study period. Conclusions Prevalent resistant hypertension has plateaued and decreased in recent years, consistent with a decrease in incidence from 2004 onwards. Despite this, resistant hypertension is common in the UK hypertensive population. Given the importance of hypertension as a modifiable risk factor for cardiovascular disease, reducing uncontrolled hypertension should remain a population health focus.

Objective To determine the concordance between two methods to measure drug exposure duration from pharmacy claims data. Study design and setting We conducted a cohort study using 2002–2007 US Medicaid data. Initiators of eight drug groups were indentified: statins, metformin, atypical antipsychotics, warfarin, proton pump inhibitors (PPIs), angiotensin converting enzyme (ACE)-inhibitors, non-steroidal anti-inflammatory drugs (ns-NSAIDs) and coxibs. For each patient, we calculated two measures of exposure duration using 1) observed days’ supply available in US pharmacy claims and 2) the World Health Organisation Daily Defined Doses (DDD) methodology. We used Wilcoxon signed rank tests to compare medians and Spearman correlations to assess correlation between the two measures. Results Cohort sizes ranged from 143,885 warfarin users to >3,000,000 ns-NSAID users. Similar median exposure durations were observed for ACE-inhibitors (70days vs. 75days), PPIs (44days vs. 45days) and coxibs (44days vs. 45days). The DDD method overestimated exposure duration for ns-NSAIDs and underestimated for the remaining drug groups, relative to days’ supply. Spearman correlation coefficients ranged from 0.2–0.8. Conclusion Using DDDs to estimate drug exposure duration can result in misclassification. The magnitude of this misclassification might depend on doses used which can vary according to factors such as local prescribing practices, renal function and age.

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