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1.  The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010 
BMC Medicine  2015;13:74.
The escalating use of prescribed drugs has increasingly raised concerns about polypharmacy. This study aims to examine changes in rates of polypharmacy and potentially serious drug-drug interactions in a stable geographical population between 1995 and 2010.
This is a repeated cross-sectional analysis of community-dispensed prescribing data for all 310,000 adults resident in the Tayside region of Scotland in 1995 and 2010. The number of drug classes dispensed and the number of potentially serious drug-drug interactions (DDIs) in the previous 84 days were calculated, and age-sex standardised rates in 1995 and 2010 compared. Patient characteristics associated with receipt of ≥10 drugs and with the presence of one or more DDIs were examined using multilevel logistic regression to account for clustering of patients within primary care practices.
Between 1995 and 2010, the proportion of adults dispensed ≥5 drugs doubled to 20.8%, and the proportion dispensed ≥10 tripled to 5.8%. Receipt of ≥10 drugs was strongly associated with increasing age (20–29 years, 0.3%; ≥80 years, 24.0%; adjusted OR, 118.3; 95% CI, 99.5–140.7) but was also independently more common in people living in more deprived areas (adjusted OR most vs. least deprived quintile, 2.36; 95% CI, 2.22–2.51), and in people resident in a care home (adjusted OR, 2.88; 95% CI, 2.65–3.13). The proportion with potentially serious drug-drug interactions more than doubled to 13% of adults in 2010, and the number of drugs dispensed was the characteristic most strongly associated with this (10.9% if dispensed 2–4 drugs vs. 80.8% if dispensed ≥15 drugs; adjusted OR, 26.8; 95% CI 24.5–29.3).
Drug regimens are increasingly complex and potentially harmful, and people with polypharmacy need regular review and prescribing optimisation. Research is needed to better understand the impact of multiple interacting drugs as used in real-world practice and to evaluate the effect of medicine optimisation interventions on quality of life and mortality.
Electronic supplementary material
The online version of this article (doi:10.1186/s12916-015-0322-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4417329  PMID: 25889849
Drug interactions; Family practice; Physician; Polypharmacy; Prescribing patterns; Primary care
2.  Developing a complex intervention to improve prescribing safety in primary care: mixed methods feasibility and optimisation pilot study 
BMJ Open  2014;4(1):e004153.
(A) To measure the extent to which different candidate outcome measures identified high-risk prescribing that is potentially changeable by the data-driven quality improvement in primary care (DQIP) intervention.(B) To explore the value of reviewing identified high-risk prescribing to clinicians.(C) To optimise the components of the DQIP intervention.
Mixed method study.
General practices in two Scottish Health boards.
4 purposively sampled general practices of varying size and socioeconomic deprivation.
Outcome measures
Prescribing measures targeting (1) high-risk use of the non-steroidal anti-inflammatory drugs (NSAIDs) and antiplatelets; (2) ‘Asthma control’ and (3) ‘Antithrombotics in atrial fibrillation (AF)’.
The prescribing measures were used to identify patients for review by general practices. The ability of the measures to identify potentially changeable high-risk prescribing was measured as the proportion of patients reviewed where practices identified a need for action. Field notes were recorded from meetings between researchers and staff and key staff participated in semistructured interviews exploring their experience of the piloted intervention processes.
Practices identified a need for action in 68%, 25% and 18% of patients reviewed for prescribing measures (1), (2) and (3), respectively. General practitioners valued being prompted to review patients, and perceived that (1) ‘NSAID and antiplatelet’ and (2) ‘antithrombotics in AF’ were the most important to act on. Barriers to initial and ongoing engagement and to sustaining improvements in prescribing were identified.
‘NSAIDs and antiplatelets’ measures were selected as the most suitable outcome measures for the DQIP trial, based on evidence of this prescribing being more easily changeable. In response to the barriers identified, the intervention was designed to include a financial incentive, additional ongoing feedback on progress and reprompting review of patients, whose high-risk prescribing was restarted after a decision to stop.
Trial registration number NCT01425502.
PMCID: PMC3902335  PMID: 24448848
Clinical Pharmacology
5.  Process evaluations for cluster-randomised trials of complex interventions: a proposed framework for design and reporting 
Trials  2013;14:15.
Process evaluations are recommended to open the ‘black box’ of complex interventions evaluated in trials, but there is limited guidance to help researchers design process evaluations. Much current literature on process evaluations of complex interventions focuses on qualitative methods, with less attention paid to quantitative methods. This discrepancy led us to develop our own framework for designing process evaluations of cluster-randomised controlled trials.
We reviewed recent theoretical and methodological literature and selected published process evaluations; these publications identified a need for structure to help design process evaluations. We drew upon this literature to develop a framework through iterative exchanges, and tested this against published evaluations.
The developed framework presents a range of candidate approaches to understanding trial delivery, intervention implementation and the responses of targeted participants. We believe this framework will be useful to others designing process evaluations of complex intervention trials. We also propose key information that process evaluations could report to facilitate their identification and enhance their usefulness.
There is no single best way to design and carry out a process evaluation. Researchers will be faced with choices about what questions to focus on and which methods to use. The most appropriate design depends on the purpose of the process evaluation; the framework aims to help researchers make explicit their choices of research questions and methods.
Trial registration NCT01425502
PMCID: PMC3600672  PMID: 23311722
Process evaluation; Complex intervention; Cluster-randomised controlled trial; Qualitative; Quantitative; Reporting
6.  Study protocol of a mixed-methods evaluation of a cluster randomized trial to improve the safety of NSAID and antiplatelet prescribing: data-driven quality improvement in primary care 
Trials  2012;13:154.
Trials of complex interventions are criticized for being ‘black box’, so the UK Medical Research Council recommends carrying out a process evaluation to explain the trial findings. We believe it is good practice to pre-specify and publish process evaluation protocols to set standards and minimize bias. Unlike protocols for trials, little guidance or standards exist for the reporting of process evaluations. This paper presents the mixed-method process evaluation protocol of a cluster randomized trial, drawing on a framework designed by the authors.
This mixed-method evaluation is based on four research questions and maps data collection to a logic model of how the data-driven quality improvement in primary care (DQIP) intervention is expected to work. Data collection will be predominately by qualitative case studies in eight to ten of the trial practices, focus groups with patients affected by the intervention and quantitative analysis of routine practice data, trial outcome and questionnaire data and data from the DQIP intervention.
We believe that pre-specifying the intentions of a process evaluation can help to minimize bias arising from potentially misleading post-hoc analysis. We recognize it is also important to retain flexibility to examine the unexpected and the unintended. From that perspective, a mixed-methods evaluation allows the combination of exploratory and flexible qualitative work, and more pre-specified quantitative analysis, with each method contributing to the design, implementation and interpretation of the other.
As well as strengthening the study the authors hope to stimulate discussion among their academic colleagues about publishing protocols for evaluations of randomized trials of complex interventions.
Data-driven quality improvement in primary care trial registration NCT01425502
PMCID: PMC3502604  PMID: 22929598
Complex intervention; Process evaluation; Protocol; Mixed methods; Randomized controlled trial
7.  High-risk prescribing and monitoring in primary care: how common is it, and how can it be improved? 
The safety of medication use in primary care is an area of increasing concern for health systems internationally. Systematic reviews estimate that 3–4% of all unplanned hospital admissions are due to preventable drug-related morbidity, the majority of which have been attributed to shortcomings in the prescribing and monitoring stages of the medication use process. We define high-risk prescribing as medication prescription by professionals, for which there is evidence of significant risk of harm to patients, and which should therefore either be avoided or (if avoidance is not possible) closely monitored and regularly reviewed for continued appropriateness. Although prevalence estimates vary depending on the instrument used, cross-sectional studies conducted in primary care equivocally show that it is common and there is evidence that it can be reduced. Quality improvement strategies, such as clinical decision support, performance feedback and pharmacist-led interventions have been shown to be effective in reducing prescribing outcomes but evidence of improved patient outcomes remains limited. The increasing implementation of electronic medical records in primary care offer new opportunities to combine different strategies to improve medication safety in primary care and to integrate services provided by different stakeholders. In this review article, we describe the spectrum of high-risk medication use in primary care, review approaches to its measurement and summarize research into its prevalence. Based on previously developed interventions to change professional practice, we propose a systematic approach to improve the safety of medication use in primary care and highlight areas for future research.
PMCID: PMC4110851  PMID: 25083235
adverse drug event; clinical decision support system; medication error; medication safety; performance feedback; primary healthcare
8.  A cluster randomised stepped wedge trial to evaluate the effectiveness of a multifaceted information technology-based intervention in reducing high-risk prescribing of non-steroidal anti-inflammatory drugs and antiplatelets in primary medical care: The DQIP study protocol 
High-risk prescribing of non-steroidal anti-inflammatory drugs (NSAIDs) and antiplatelet agents accounts for a significant proportion of hospital admissions due to preventable adverse drug events. The recently completed PINCER trial has demonstrated that a one-off pharmacist-led information technology (IT)-based intervention can significantly reduce high-risk prescribing in primary care, but there is evidence that effects decrease over time and employing additional pharmacists to facilitate change may not be sustainable.
We will conduct a cluster randomised controlled with a stepped wedge design in 40 volunteer general practices in two Scottish health boards. Eligible practices are those that are using the INPS Vision clinical IT system, and have agreed to have relevant medication-related data to be automatically extracted from their electronic medical records. All practices (clusters) that agree to take part will receive the data-driven quality improvement in primary care (DQIP) intervention, but will be randomised to one of 10 start dates. The DQIP intervention has three components: a web-based informatics tool that provides weekly updated feedback of targeted prescribing at practice level, prompts the review of individual patients affected, and summarises each patient's relevant risk factors and prescribing; an outreach visit providing education on targeted prescribing and training in the use of the informatics tool; and a fixed payment of 350 GBP (560 USD; 403 EUR) up front and a small payment of 15 GBP (24 USD; 17 EUR) for each patient reviewed in the 12 months of the intervention. We hypothesise that the DQIP intervention will reduce a composite of nine previously validated measures of high-risk prescribing. Due to the nature of the intervention, it is not possible to blind practices, the core research team, or the data analyst. However, outcome assessment is entirely objective and automated. There will additionally be a process and economic evaluation alongside the main trial.
The DQIP intervention is an example of a potentially sustainable safety improvement intervention that builds on the existing National Health Service IT-infrastructure to facilitate systematic management of high-risk prescribing by existing practice staff. Although the focus in this trial is on Non-steroidal anti-inflammatory drugs and antiplatelets, we anticipate that the tested intervention would be generalisable to other types of prescribing if shown to be effective.
Trial registration, dossier number: NCT01425502
PMCID: PMC3353207  PMID: 22444945
Adverse drug event; Non-steroidal anti-inflammatory drug; Antiplatelet; Medication error; Medication review; Decision support systems; Clinical; Stepped wedge; Randomised controlled trial; Primary healthcare
9.  Quality and safety of medication use in primary care: consensus validation of a new set of explicit medication assessment criteria and prioritisation of topics for improvement 
Addressing the problem of preventable drug related morbidity (PDRM) in primary care is a challenge for health care systems internationally. The increasing implementation of clinical information systems in the UK and internationally provide new opportunities to systematically identify patients at risk of PDRM for targeted medication review. The objectives of this study were (1) to develop a set of explicit medication assessment criteria to identify patients with sub-optimally effective or high-risk medication use from electronic medical records and (2) to identify medication use topics that are perceived by UK primary care clinicians to be priorities for quality and safety improvement initiatives.
For objective (1), a 2-round consensus process based on the RAND/UCLA Appropriateness Method (RAM) was conducted, in which candidate criteria were identified from the literature and scored by a panel of 10 experts for 'appropriateness' and 'necessity'. A set of final criteria was generated from candidates accepted at each level. For objective (2), thematically related final criteria were clustered into 'topics', from which a panel of 26 UK primary care clinicians identified priorities for quality improvement in a 2-round Delphi exercise.
(1) The RAM process yielded a final set of 176 medication assessment criteria organised under the domains 'quality' and 'safety', each classified as targeting 'appropriate/necessary to do' (quality) or 'inappropriate/necessary to avoid' (safety) medication use. Fifty-two final 'quality' assessment criteria target patients with unmet indications, sub-optimal selection or intensity of beneficial drug treatments. A total of 124 'safety' assessment criteria target patients with unmet needs for risk-mitigating agents, high-risk drug selection, excessive dose or duration, inconsistent monitoring or dosing instructions. (2) The UK Delphi panel identified 11 (23%) of 47 scored topics as 'high priority' for quality improvement initiatives in primary care.
The developed criteria set complements existing medication assessment instruments in that it is not limited to the elderly, can be implemented in electronic data sets and focuses on drug groups and conditions implicated in common and/or severe PDRM in primary care. Identified priorities for quality and safety improvement can guide the selection of targets for initiatives to address the PDRM problem in primary care.
PMCID: PMC3296596  PMID: 22316181
Medication error; quality indicator; primary health care; adverse drug events; preventable drug related morbidity

Results 1-9 (9)