1. Division of Population Health Sciences Royal College of Surgeons in Ireland Coláiste Ríoga na Máinleá in Éirinn Enhancing rational and safe prescribing in primary care Tom Fahey Professor of General Practice, RCSI Medical School & Principal Investigator, HRB Centre for Primary Care Research

2. Division of Population Health Sciences Overview Background –Potentially inappropriate prescribing (PIP) OPTI-SCRIPT –Development of intervention –Results Summary

3. Division of Population Health Sciences Overview Background –Potentially inappropriate prescribing (PIP) OPTI-SCRIPT –Development of intervention –Results Summary

4. Division of Population Health Sciences Background Prescribing is a challenging and complex process Appropriate prescribing Potentially inappropriate prescribing (PIP) Overprescribing, underprescribing and misprescribing Factors that contribute to PIP

5. Division of Population Health Sciences An overview of prescribing indicators

6. Division of Population Health Sciences Overview cont’d Following a ‘systematic literature search’, identified 46 different tools –English and German publications only 36 named older people as target patients –10 did not specify target age group –Various settings Consensus methods used in development of 19 tools Over-, under- and mis-prescribing

7. Division of Population Health Sciences No perfect set of indicators The ideal set of indicators- –Cover all aspects of appropriateness –Be developed using evidence-based methods –Show significant correlation between degree of appropriateness and clinical outcomes –Be applicable not only in research but in daily health care practice Kaufmann et al, 2013

8. Division of Population Health Sciences What contributes to PIP? Multimorbidity “Presence of two or more long-term conditions” 64.9% of people aged 65-84years [1] 30.4% of people aged 45-64 years [1] Polypharmacy “the ingestion of four or more medications”

9. Division of Population Health Sciences Prevalence of PIP PIP is prevalent in the older population (> 70 years) Republic of Ireland 36% Northern Ireland 34% United Kingdom 29%

10. Division of Population Health Sciences The prevalence of the most common STOPP/START PIP indicators across three regions

11. Division of Population Health Sciences Overview Background –Potentially inappropriate prescribing (PIP) OPTI-SCRIPT –Development of intervention –Results Summary

12. Division of Population Health Sciences OPTI-SCRIPT study development

13. Division of Population Health Sciences Study design & methodology – cluster RCT GPs inclusion criteria: Based in greater Dublin area 80+ patients aged over 70 Patients inclusion criteria: Aged 70+ Had PIP as per study list Recruited and baseline data collection prior to minimisation

14. Division of Population Health Sciences Study overview Minimisation Intervention - Academic detailing with a pharmacist - Medicines review with web-based treatment algorithms - Patient information leaflets Control - Letter with recruited patients and identified PIP - Continue to provide usual care PCRS – National Contemporaneous Control - Observational comparison to national prescribing data (376,858 patients, 2,000+ practices)

15. Division of Population Health Sciences OPTI-SCRIPT website

16. Division of Population Health Sciences

17. OPTI-SCRIPT RCT results Participants 21 GP practices (32% cluster response rate) 196 patients (37% response rate) Minimisation InterventionControl 11 practices 99 patients 10 practices 97 patients

18. Division of Population Health Sciences Study design & methodology – cluster RCT Primary outcome measure: Proportion of patients with no PIP Mean PIP per group Data collection baseline & immediate post intervention Between group differences: Random effects logistic regression Cluster mean Random effects poisson regression Process evaluation

19. Division of Population Health Sciences Outcome – Proportion with no PIP GroupNNumber of patients with no PIP % of patients with no PIP Intervention994747.5 Control972222.7 Adjusted odds ratio = 3.06 (95% CI 1.4,6.5; P=0.004)* *adjusted for gender, age, baseline PIP, number repeat medications, GP practice size

20. Division of Population Health Sciences National contemporaneous control – PCRS Intervention period, Sep 2012 – August 2013 prevalence of 38% Odds of having no PIP in OPTI-SCRIPT intervention compared to odds of having no PIP in the national PCRS cohort Odds Ratio95% CI 2.491.68, 3.69

21. Division of Population Health Sciences Process evaluation – main findings Participants positive about study –Barriers identified: GP time, communication, reimbursement Revealed intervention not delivered as expected: –Patient information leaflets not used at all –1 intervention practice did not complete reviews –2 Intervention practices conducted reviews without patients –2 control practices did alter patient medication

22. Division of Population Health Sciences Future work National trial of OPTI-SCRIPT Lessons learned? –Computerise PIP identification –Focus on top 10 PIP –Embedded in practice software –Practice incentives – reimbursement –Economic evaluation

23. Division of Population Health Sciences Overview Background –Potentially inappropriate prescribing (PIP) OPTI-SCRIPT –Development of intervention –Results Summary

24. Division of Population Health Sciences Summary Prevalence of PIP high in Ireland & UK Developed web-based intervention to target PIP in primary care Process evaluation gave insight into intervention delivery and barriers Further implementation of decision support to improve quality & safety are planned

25. Division of Population Health Sciences Acknowledgements This research is funded by the HRB Centre for Primary Care Research and the HRB PhD Scholars programme in Health Service Research Barbara Clyne, Susan Smith, Marie Bradley, Carmel Hughes, Janine Cooper, Fiona Boland, Ronan McDonnell, David Williams, Nicola Motterlini, Marie-Claire Kennedy, Daniel Clear, Frank Moriarty, Caitriona Cahir

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28. Baseline characteristics CharacteristicInterventionControl N%N% Male5555.65051.5 Mean age77.176.4 Marital status Married Widowed 56 26 56.6 26.3 51 32 53.1 33.3 GMS card8888.99597.9 Mean number of repeat medications 10.29.5

29. Division of Population Health Sciences PIP at baseline

30. Division of Population Health Sciences PPI 60% of participants had a PPI 53% of intervention, 65% of control at baseline At follow-up the odds of not having a PPI at maximum therapeutic dose were 3 times higher in intervention than control (OR = 3.41, P = 0.006, 95% CI 1.43, 8.14)