Australian Department of Health and Ageing · 2020-06-23 · Australian Department of Health and...

Australian Department ofHealth and AgeingEvaluation of the DAA/PMP Programs

June 2010

Table of contents

Australian Department of Health and AgeingPricewaterhouseCoopers

Table of contents

1 Executive summary 6

1.1 Background and context 6

1.2 Purpose of the evaluation 7

1.3 Key findings of the evaluation 8

1.4 Limitations of the evaluation 13

1.5 Further considerations for Government 14

2 Roadmap to the report 15

3 Background and summary of literature 16

3.1 The Australian context 16

3.1.1 General health of the Australian population 16

3.1.2 Hospitalisations in Australia 20

3.1.3 Admissions to residential aged care 20

3.1.4 Medication taking in Australia 20

3.2 Medication adherence 25

3.2.1 Definition of adherence 25

3.2.2 Extent of non-adherence 26

3.2.3 The burden of poor adherence 27

3.3 The role of community pharmacy in the management ofmedications 28

3.3.1 Strategies for improving medicationcompliance 29

3.4 Overview of the DAA and PMP service 31

3.4.1 Dose Administration Aids 31

3.4.2 Patient Medication Profiling 33

3.5 Benefits of DAAs & PMPs 33

3.5.1 People likely to benefit most from theDAA/PMP service 35

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4 Objectives and overview of the evaluation 36

4.1 Objectives of the evaluation 36

4.2 Evaluation approach 36

4.2.2 DAA and PMP data 37

4.2.3 Admitted patient care National Minimum DataSet 39

4.2.4 Pharmaceutical Benefits Scheme data 40

5 Dose Administration Aids evaluation results 42

5.1 Pharmacy results 43

5.1.1 What was the recruitment of pharmacies to theDAA program? 43

5.1.2 What were the rates of completion amongstpharmacies in the DAA program? 45

5.1.3 What were the characteristics of pharmacieswho participated in the DAA program? 47

5.1.4 How do the pharmacies who participatedcompare to pharmacies nationally? 49

5.1.5 What did the DAA service provided bypharmacies look like? 50

5.2 Patient results 55

5.2.1 What was the recruitment of patients to theDAA service? 56

5.2.2 What were the rates of completion amongstpatients in the DAA program? 57

5.2.3 What were the characteristics of patients whoparticipated? 59

5.2.4 How many and what types of medications werepatients taking? 62

5.2.5 What conditions did DAA patients have? 64

5.2.6 What proportion of patients were classified asbeing ‘at risk’? 64

5.2.7 What were the characteristics of the DAAservice? 65

5.2.8 What other services were patients receiving? 69

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6 Patient Medication Profiling evaluation results 71

6.1 Pharmacy results 72

6.1.1 What was recruitment of pharmacies to thePMP program? 72

6.1.2 What were the rates of completion amongstpharmacies in the PMP program? 74

6.1.3 What were the characteristics of pharmacieswho participated? 76

6.1.4 How do the pharmacies who participatedcompare to pharmacies nationally? 78

6.1.5 What did the service provided by pharmacieslook like? 79

6.2 Patient results 82

6.2.1 What was the recruitment of patients to thePMP program? 83

6.2.2 What were the rates of completion amongstpatients in the PMP program? 84

6.2.3 What were the characteristics of patients whoparticipated? 85

6.2.4 How many and what types of medications werepatients taking? 89

6.2.5 What conditions did PMP patients have? 91

6.2.6 What proportion of patients were classified asbeing ‘at risk’? 92

6.2.7 What were the characteristics of the PMPservice that patients received? 92

6.2.8 What other services were patients receiving? 96

7 Who is the population at risk? 97

7.1 Who are the Australian population taking medicationsand what are the associated costs? 98

7.1.1 What is the average age of Australians takingmedications and what will happen to them overtime? 98

7.1.2 What is the average number of medicationstaken by Australians and what will happen tothem over time? 101

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7.1.3 What is the average PBS benefit amount paidby Medicare Australia per month and what willhappen over time? 106

7.2 Medication-related incidents in Australian hospitals 109

7.2.1 What is the extent of medication-relatedincidents in the hospital setting? 110

7.2.2 What are the characteristics of patients whoare admitted due to medication-relatedincidents? 112

7.2.3 How long do patients stay in hospital formedication-related admissions? 116

7.2.4 What happens to patients who are hospitaliseddue to medication-related incidents? 117

8 Who is the population that benefits most? 121

8.1 Overview 121

8.2 Patient age 122

8.3 Number of medications used 122

8.4 Nature of medication problem 123

8.5 Assistance and support available 123

8.5.1 Summary of ‘at risk’ patients 124

9 Costs and benefits 125

9.1 Costs of delivering the DAA and PMP services 125

9.1.1 Unique DAA service delivery costs 126

9.1.2 Unique PMP service delivery costs 127

9.2 Impact of multiple patient services 128

9.3 Costs to patients for the DAA and PMP services 129

9.3.1 What did pharmacists charge their patients forthe DAA service? 129

9.3.2 What did pharmacists charge their patients forthe PMP service? 132

9.4 Potential benefits of the DAA and PMP services 135

9.4.1 Improved medication adherence as a result ofDAA and/or PMP 135

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9.4.2 Impact on health outcomes 136

10 Key findings 138

10.1 With the expansion of morbidity and an increasingnumber of medications being taken by Australians,medication non-adherence will increasingly become anissue in the health landscape of the future 138

10.2 Medication adherence strategies to support effectivemedication management are complex: the role of DAAsand PMPs as part of a suite of services 139

10.3 A broad range of pharmacies participated in the DAAand PMP programs and the reported costs associatedwith delivering the programs were highly varied 143

10.4 Appropriate reimbursement and incentivisation for theDAA and PMP services in community pharmacy is likelyto reflect a combination of factors 144

10.5 Limitations of the evaluation and areas for furtherinvestigation 145

11 Bibliography 147

Executive Summary

Australian Department of Health and Ageing6 PricewaterhouseCoopers

1 Executive summary

1.1 Background and context

The current Australian health landscape reflects an ageing population, an increasing prevalenceand burden of chronic disease, and decreasing mortality rates amongst more common diseases.The combination of these three factors have meant that there is a rapidly growing need for thelong-term management of many health conditions, which is resulting in increasing pressure andfinancial burden for the Australian health system.

Since 1971, Australia’s population over 65 years of age has increased from 1.1 million to 2.9million and Australia’s median age has increased by 5.1 years over the past two decades (1). In2007–08, an estimated 75% of Australians had at least one long-term condition (1) and theprevalence of certain conditions is rapidly increasing.

The ageing population and expansion of morbidity in Australia requires the use of effectivemedications for the better management of chronic and complex care needs and has resulted in asignificant increase in the demand for medications under the Pharmaceutical Benefits Scheme(PBS), this trend is likely to continue into the future.

With the increase in the use of effective medications comes the need for the effectivemanagement of medications. While the use of medications is on the rise, poor adherence tomedication regimens is a significant challenge which impacts health outcomes and health carecosts. Existing literature suggests that approximately 50.0% of patients do not take theirmedications as prescribed by their health care professional. Medication non-adherencecompromises the effectiveness of treatments, increases the risk of medication misadventures,results in unnecessary escalation of therapy and reduces a patients’ quality of life.

In addition to the adverse effects on patients, the economic costs of medication non-adherenceare high. This is particularly evident in terms of avoidable hospital admissions to hospitals whichare estimated to cost the Australian health system $660 million per year (2).

Non-adherence with medication regimens is a challenging and complex problem, resulting inpoorer health outcomes and increased health care costs. This problem is further exacerbated bythe fact that medication non-adherence can be a hidden problem, often undisclosed by patientsand unrecognised by prescribers. Efforts to assist patients to improve their adherence withmedication regimens’ represent an opportunity to improve population health and the efficiency ofthe health care system.

The Dose Administration Aids and Patient Medication Profile programs

Research has shown that Dose Administration Aids (DAA) and Patient Medication Profile (PMP)services can assist in reducing non-adherence and improve patient safety (3)(4)(5), therefore aspart of the Research and Development program undertaken as part of the Third CommunityPharmacy Agreement (3CPA), the Pharmacy Guild of Australia implemented the DAA and PMPprograms, representing a combined investment of $106.5 million by the Australian Government.

A DAA is an adherence device designed to assist medication management for a patient byhaving their medications divided into individual doses and arranged according to the doseschedule throughout the day. A DAA can be either a unit-dose pack (one single type ofmedication per compartment) or a multi-dose pack (different types of medication percompartment).

Executive Summary

Australian Department of Health and AgeingPricewaterhouseCoopers 7

The aim of the DAA program was to provide an opportunity for eligible patients to remain livingeffectively and confidently within their own homes, through better medication management fromaccessing a DAA service through their local community pharmacy. The DAA program aimed toreduce medication-related hospitalisation and adverse events through improving medicationmanagement and adherence for people in the community. The DAA Program was implementedin two phases and was available to all Section 90 pharmacies in Australia:

Phase 1: September 2007 to June 2009.

Phase 2: July 2009 to June 2010.

A PMP service is a comprehensive review of all regular medicines taken by a patient includingprescription, non-prescription, herbal and complementary medicines. A medication profile is aneasy-to-read summary list of the patient’s medicines. It includes the name of the medicine(including the brand name and active ingredient(s), directions for the use of each medicine andother useful information which could include who has prescribed the medicine, and the reasonsfor its use.

The aim of the PMP program was to reduce the risk of medication-related adverse events byassisting people to better understand and manage their medications, including prescription,over-the-counter and complementary medicines. The objective of the programs was to identifypatients who would most benefit from the supply of a PMP and to develop a sustainable serviceand payment model capable of meeting the program aim and trialling the broader use within thecommunity setting. The PMP Program was implemented in two phases and was available to allSection 90 pharmacies in Australia:

Phase 1: April 2008 to June 2009.

Phase 2: July 2009 to June 2010.

1.2 Purpose of the evaluation

The objective of the DAA and PMP programs were to provide benefit to community patients byimproving adherence and thereby reducing medication related misadventure and increasingquality of life and improving health status. The purpose of the evaluation was to review existingdata and evidence for the DAA and PMP programs to inform the potential patient and pharmacybenefits in providing these services to the Australian community, as well as inform any potentialvalue of future government investment. The specific objectives of the evaluation were to:

analyse the DAA and PMP data to better understand the service provided

identify how the DAA/PMP services interacts with other medication management programsunder the 4CPA

undertake a review of local and international evidence

identify the numbers of people nationally who are on multiple medications and areconsidered ‘at-risk’ of adverse medication events

identify rates of admission to hospital due to medication related incidents

identify rates of discharge from hospital to residential aged care for those hospitalised dueto a medication related incident

identify the potential offset (if any) that future investment would offer Government were anaccess scheme to DAA and/or PMP services implemented in the future.

Executive Summary


Evaluation approach

The overall approach to the evaluation was one of program effectiveness evaluation, rather thanan intervention efficacy approach. The biggest challenge for the evaluation was the fact that allof the relevant data, covering DAA/PMP usage, adverse events and medication usage trends,was not available in a single data set. Rather, all of this information was available in separatedata sets where the information on core sub-groups could be extrapolated from one data set toanother. Accordingly, the evaluation utilised three key sources of data:

DAA and PMP service data – provides data concerning trends in participation in DAA andPMP services provided via community pharmacy

Admitted patient care National Minimum Data Set (NMDS) – provides data concerningtrends in adverse medication related outcomes occasioning acute care

PBS data – provides data concerning trends in medication related risk factors

These three data sets together provided an overview of the key dimensions for the evaluationand formed the basis of the analysis for the evaluation. These data and their limitations arediscussed further in Section 1.4.

1.3 Key findings of the evaluation

With the expansion of morbidity and an increasing number of medications beingtaken by Australians, medication non-adherence will increasingly become an issuein the health landscape of the future.

The current Australian health landscape is well documented. It reflects an ageing population,increasing prevalence and burden of chronic disease, and decreasing mortality rates amongstmore common diseases. The aging population and increasing prevalence of chronic disease andchronic disease risk factors (e.g. smoking and obesity) are placing unprecedented burden on thehealth system.

The expansion of morbidity in Australia is integrally linked to the importance of medications aspart of the suite of tools for better management of chronic and complex care needs. A keyfeature of the Australian health landscape, therefore, is the significant increase in the demand formedications under the PBS. In 2006, there were approximately 179 million community PBSprescriptions, this represents a 44.0% increase in the number of community PBS prescriptionssince 1996 (1).

The trends in the use of prescription medications are set to continue to increase dramatically inthe near future. In 2009, PBS data revealed that Australians on medications were taking anaverage of 3.4 medications daily; this will potentially increase to 4.4 medications in 2013,indicating that the use of multiple medications is becoming increasingly prominent in the healthlandscape of the future. The average monthly PBS benefit paid per person by Medicare Australiahas increased from $96 in 2004, to $131 in 2009 and will potentially increase to $204 in 2013,reflecting that medications will also be an increasing cost to the Australian government.

With the increase in the contribution of medication to the management of chronic conditionscomes the need for the effective management of medications. While the use of medications is onthe rise, poor adherence to medication regimens remains a significant challenge – with farreaching impacts on health outcomes and health care costs. In addition to the adverse outcomesfor patients, the economic costs of medication non-adherence are high. This is particularly

Executive Summary


evident in terms of avoidable hospital admissions to hospitals which are estimated to cost theAustralian health system $660 million per year (2).

Overall, these findings reflect the changing health landscape in terms of the increasing burden ofchronic and complex conditions and the increasing use of medications. As such, the effectivemanagement of medications has a central role to play in both reduced health burden for theAustralian population and reduced financial burden on the Australian health system.

Medication adherence strategies to support effective medication management arecomplex: the role of DAAs and PMPs as part of a suite of services.

Existing literature supports the effectiveness of using a combination of services or strategies toimprove medication adherence, and, in particular, strategies that target the particular source ofnon-compliance. Programs such as DAAs and PMPs, which focus on medication adherence inthe community setting, are becoming increasingly important not only to reduce the incidence ofadverse effects on patients but to relieve the growing pressure placed on the Australian healthsystem. There is a significant body of evidence supporting the view that DAA’s and PMP’s aremore beneficial when provided as part of a suite of services targeted at effective medicationmanagement.

The results of the DAA and PMP program data were in line with the literature, reflecting that inthe ‘real world’ it is more common that patients receive multiple services, rather than a singleservice in isolation. This was demonstrated by the fact that nearly all of the participatingpharmacies offered multiple 4CPA funded services in their pharmacies and a large proportion ofpatients were receiving more than one 4CPA funded services. Thus the study findings reflect theevidence. The benefits of a suite of services that are targeted to the needs of the individualpatient and the various factors influencing adherence are likely to be far greater than theprovision of an individual service. Identification and targeting of those at risk of non-adherence islikely to benefit from a multifaceted approach, rather than a ‘one-size-fits-all’ approach.

Existing research indicates that there are risk factors for non-adherence and patients can betargeted for services such as DAAs and PMPs.

The use of DAAs and PMPs as effective strategies for improving medication managementamongst patients in the community is well documented. These strategies enable individuals tobetter self-manage their medications and maintain their independence. Such research suggeststhat the benefits of these strategies can be maximised by targeting those patients who areconsidered to be at-risk of medication non-adherence and related adverse events and, therefore,will benefit most from the service.

Broadly, the evidence suggests that medication complexity including the number of medications,the number of doses and the type of medication, can be reliable predictors of risk of medicationnon-adherence and its adverse outcomes. Risk factors identified in the literature include takingfive or more medications daily, being elderly, poor compliance, cognitive / physical impairmentand having complex medication regimens.

Evaluation results also indicate that there are potential benefits based on the targeting patientswith key risk factors.

The current evaluation utilised multiple sources of data (existing literature, DAA and PMP servicedata, admitted patient care data and PBS data) to identify the characteristics of those patientswho might be considered to be at-risk of medication non-adherence. Together, these data wereused to confirm the key risk factors for non-adherence in the Australian medication takingpopulation. These are outlined below.

Executive Summary


1. Individuals on five or more medications.

It is well documented that there is a strong correlation between the number of medications anindividual is taking and the risk of non-adherence due to the complexity associated with takingmultiple medications. Of the patients recruited to Phase 2 of the DAA and PMP programs,almost half of the patients were taking between five to seven medications.

The PBS data indicated that approximately 20.0% of all Australians who take medications takefive or more, with the average number of medications being taken by older Australianscontinuing to increase over time. This increase could be quite dramatic for those aged 75 yearsand older. In 2009, individuals in this age group were taking on average approximately fivemedications each; projections of the PBS data indicate that this age group may potentially betaking nearly double the number of medications in 2019.

Moreover, the proportion of Australians who are likely to be taking five or more medications isalso expanding. While the increase is not likely to be as dramatic, those individuals aged 65 to75 years are also estimated to be taking more medications over the next ten years. In 2009, theywere taking an average of four medications and, based on current trends, it is estimated thatthey will be taking, on average, six medications by 2019.

Thus, the proportion of the population taking more than five medications is expanding. Thoseaged 75 and over have long been recognised as being at risk of non-adherence due to thenumbers of medications being taken by them. Older Australians are clearly likely to remain themost in need of services such as DAAs and PMPs, as the average number of medications takenby those in these older age groups increases significantly in coming years. However, theexpansion of the ‘at-risk’ category (ie those taking five or more medications) to those aged 65 to75 is also predicted over the next ten years, suggesting that this younger group will also fallwithin the ambit of adherence targeting.

2. Individuals aged 65 years and over.

Existing literature suggests that one of the strongest associations with poor medicationmanagement is patient age, with the risk of medication non-adherence increasing with age.

The admitted patient care data for medication-related admissions revealed that in 2007/08almost half of all medication-related admissions were for patients aged 65 years and over andthat the average length of stay increased to over 10 days for this age group. The PBS dataindicates that those aged 65 years and over equate to approximately 40.0% of the Australianpopulation who take medications, with the average age of Australians taking medicationscontinuing to increase with the ageing population and as individuals live longer.

Despite there being no specified patient enrolment criteria, the majority of patients recruited toboth the DAA and PMP programs were aged 65 years and over, with the majority of thesepatients falling between the ages of 75 to 84. Again, while those aged 75 years and over weretargeted by pharmacists in the DAA and PMP programs, these data suggest that pharmacists inthe program recognised the benefits that might accrue with the provision of DAAs and PMPs tothose in the emerging ‘at risk’ group, those aged 65 years and over.

3. Individuals who do not have access to social support or live alone

A significant body of evidence supports the association between social support and adherence.Patients who receive little support or assistance have been shown to be at greater risk of non-adherence. Results of the DAA and PMP program showed that just under half of patientsreported living alone, and almost a third of all patients did not receive assistance with managingtheir medications. Thus this risk factor for non-adherence was well represented among thepatients recruited to the program.

Executive Summary


The flow-on broader benefits of medication management for those living alone and/or withoutsupport through services such as DAAs and PMPs are also clear. Research suggests thatservices such as DAAs and PMPs increase the capacity of patients to independently managetheir medications, which allows them to remain in their own home for longer and delay the needfor admission to residential aged care facilities. These data suggest that broader benefits ofservices such as DAAs and PMPs may be maximised through targeting individuals who do nothave access to support services and live on their own.

4. Nature of the condition and complex medication regimens

Disease or medication types have been shown in the literature to influence a patient’s capacityto manage their medications. Disease types include cardiovascular, musculoskeletal, diabetes,asthma, and nervous system diseases. The common denominator suggested by the literature forthe added risk of non-adherence for these diseases, is that they tend to involve greatercomplexity of the medication regimen.

Of those medications listed on the PBS, cardiovascular system medications have consistentlybeen the most common at almost 70 million services in 2009. The second and third mostcommon medications are nervous system and alimentary tract medications at approximately 40million and 30 million respectively (16). Results of the DAA and PMP data are aligned with theliterature; approximately 90.0% of patients were taking cardiovascular medications, just over60.0% were taking nervous system medications and fewer than 60.0% were taking alimentarytract drugs.

These results suggest that consideration of a patient’s condition and medication regime whentargeting patients for DAA and PMP is warranted based on the literature and possible in practicalterms. Considering the cohort of patients participating in the program, it is clear that pharmacistsrecruited to the program in line with the current evidence base.

Pharmacists recruited appropriately, with no enforced recruitment criteria

Pharmacists who participated in the DAA and PMP programs were encouraged to recruitpatients to the service who they thought were most likely to benefit. However, there were nospecified patient criteria for participation. Despite this, the vast majority of patients who weresuccessfully recruited to the services were individuals who could be considered to be at risk (iethey had multiple risk factors). Less than ten percent of patients recruited to the services wereidentified as having no risk factors.

A broad range of pharmacies participated in the DAA and PMP programs and thereported costs associated with delivering the programs were highly varied

Pharmacies recruited to the programs were broadly representative of community pharmaciesnationally

A broad range of pharmacies participated in both the DAA and PMP programs. The distributionof participating pharmacies across State, Pharmacy Access/Remoteness Index of Australia(PhARIA) and Socio-Economic Indexes for Areas (SEIFA) was representative of communitypharmacies nationally, suggesting that there may be no ‘type’ of pharmacy which is more likelyto opt-in to providing the DAA and PMP services. The broadly representative nature of theparticipating pharmacy cohort also suggests that the evaluation results are likely to begeneralisable to pharmacies nationally.

Executive Summary


The costs associated with providing the DAA and PMP services are largely driven by the level ofstaff involved in providing the service

Based on pharmacy staff time involved in delivering the DAA and PMP services, results showedthat the average weekly cost for delivering the DAA and PMP service was $565.89 and $122.10respectively. The cost per patient per week of the DAA service was $17.25 and $25.44 for thePMP service, with the difference in cost for the two services attributable to the level of staffinvolved in providing the service.

It is important to recognise the nature of the services being costed. The service delivery costs ofthe DAA and PMP services described above consider the services separately. However, resultsfrom the DAA and PMP service data suggest that, in reality, many pharmacies offer multipleprograms and many patients receive multiple services. The implication of this is that it is difficultto consider the unique costs of either service in isolation. Instead, consideration of theincremental costs and benefits, with patients receiving multiple services, requires furtherexploration.

It is noteworthy that the qualitative data collected during the program indicated that, while theservices may have had little cost benefit for many of the participating pharmacies, this wasoutweighed by the perceived benefits for their patients (eg provides a better form of care) andthe other perceived benefits for the pharmacy (eg improved patient loyalty and pharmacyreputation).

The costs associated with receiving the DAA and PMP service for patients was highly varied

Approximately 93.0% of pharmacies reported that they charge patients for the DAA servicecompared with 38.5% for PMPs. The costs to patients for the DAA and PMP services varied,with the costs of PMPs being slightly higher than DAAs. For DAA’s, the majority of pharmacies(63.0%) charged less than $5.00, while approximately 30.0% charged between $5.00 and$10.00. For PMP’s 43.0% of pharmacies charged less than $5.00 and 56.5% charged between$5.00 and $10.00.

Appropriate reimbursement and incentivisation for the DAA and PMP services incommunity pharmacy is likely to reflect a combination of factors

Thus the DAA and PMP program data indicated that costs were variable, and that while mostpharmacies charged for the services, the charges to patients were variable as well. Yet, somepharmacies reported that there was little cost benefit in providing the programs, indicating thatthe charges did not reflect particular reimbursement models alone, but perhaps a range ofinteractive factors, including non-financial benefits described above. These findings raise thequestion then, of what might be an appropriate model for reimbursement or incentive for theprovision of such services in community pharmacies.

To further consider the factors that might be part of a reimbursement model, the evaluationexamined the characteristics of the pharmacy which may influence the costs charged to patientsfor the DAA and PMP services. These characteristics included prescription volume (as anindicator of pharmacy size), number of patients who received a DAA service during a one weekperiod (as an indicator of pharmacy activity) and pharmacy location (as an indicator ofremoteness).

Pharmacy size and activity

Overall, the provision of the DAA service was significantly more common among pharmacieswith a higher prescription volume. This was not the case for PMPs, where no correlations were

Executive Summary


found between script volume and the number of patients receiving PMPs. When there was arelationship between activity level (number of services – either PMP or DAA – in a week) andpharmacy volume (number of scripts per week), the pattern of findings was reasonably similar,however;

there was no significant relationship between prescription volume and the amount chargedto patients for the DAA or PMP service

there was a significant relationship between the number of patients receiving the DAA orPMP service and the amount charged to patients for the service

together, prescription volume and number of patients receiving the DAA or PMP servicewas significantly related to the amount charged to patients for the service.

The program data indicated that the levels of DAA and PMP activity were the primary drivers ofamount charged by pharmacies, with higher activity levels being associated with lower charges.Moreover, that impact of service activity level is particularly evident as script volume alsoincreases. Taken together, these data suggest that where pharmacies were in a higher band ofservice provision and script volume they were likely to charge lower rates to their patients.

Pharmacy location

The evaluation examined the impact of PhARIA category (an indicator of remoteness) and theDAA/PMP provision price. The data suggested that there were no meaningful differences in theamount charged to patients for a DAA or PMP based on pharmacy location (remoteness).

1.4 Limitations of the evaluation

The main limitation of the evaluation emanates from the DAA and PMP program data. Some ofthe key limitations of these data include:

missing data were significant in Phase 1

there was a lack of comparable data across phases, particularly at the patient level

there were limited data on the health outcomes of patients

for some key variables it was unable to be determined which data were missing and thosethat were not applicable, undermining any aggregated analysis

the data were not available to undertake a robust cost benefit analysis.

Due to the limitations of the DAA and PMP program data, strategies for maximal use of thesedata were explored, including the use of corroborative data from existing research, the PBS andthe admitted patient care NMDS. These data sets were used in a ‘meta-analytic’ fashion, that is,the information on core sub-groups were extrapolated from one data set to another. The largecohort of patients participating in the DAA and PMP programs were referenced to the trends andoutcomes on similar sub groups in the national data sets describing acute patient care andmedications usage. Despite the limitations associated with the DAA and PMP program data, thekey findings reported here were supported by other reliable sources of data. Thus, the keyfindings and implications of the evaluation are also well supported.

A second important shortcoming of the evaluation was the limited availability of data on thetransfer to residential aged care due to medication related events. One key premise of the DAAand PMP programs, as with other medication management programs, is the support it providesto independence and self care. Premature transfer to aged care, due to medication management

Executive Summary


issues, is therefore a central indicator for such programs. On the other hand, it needs to berecognised that the identification of a causal link would have been a complex task. A number offactors are likely to contribute to transfer to residential aged care. However, effective medicationmanagement is thought to play a key role for these patients and consideration should be given tohow to best collect data to describe the extent of its contribution to premature residential agedcare admission.

1.5 Further considerations for Government

The existing literature, and this evaluation, supports the use of strategies such as DAAs andPMPs to assist patients in the management of their medications and to improve medicationmanagement and adherence. Equally importantly, the evaluation demonstrated the viability ofoffering such services in community pharmacy. Unquestionably, the program indicated that thereis an appetite in community pharmacy to provide the service, pharmacists are able to recruitpatients who fit evidence based criteria for being at risk of non adherence, and there is a highlevel of retention of patients once they are recruited. A range of data attest to the benefits ofrecruiting those at risk of medication non adherence and retaining them in appropriate regimesdesigned to assist medication management.

Perhaps the key area that warrants further investigation concerns the opportunity afforded interms of longer term impact (and return on investment) of such strategies and services, offeredas a package of care in the community pharmacy setting. This opportunity reflects a range oftrends including increasing recognition of the multiplicative impact of a suite of strategies tomanage non-adherence, the likelihood that an individual patient’s needs might change over time,and, that with appropriate fostering of pharmacist skills the community pharmacy setting maywell provide the opportunity to tailor strategies as patient needs change. A question for focus inthe future might therefore be how to better understand the demonstrable return on investment ofincentivising relatively broad and non-specific service offerings in community pharmacy (withquite specific components) in the community pharmacy setting.

This evaluation demonstrated that it was very difficult to separate out unique costs and benefitsattributable to strategies such as DAAs and PMPs. Current evidence suggests that pursuingincreasing accuracy and specificity is in fact not likely to be as helpful to the larger agenda ofmedication non adherence as careful documentation of exposure of patients to systematic,responsive and long term strategies to assist in their medication management. Accordingly, asthe mediation management agenda inevitably grows in significance and as a focus ofGovernment investment, consideration might best be given to definition of outcomes andbenefits of the investment, and better measurement frameworks to inform future investment.

Roadmap to the report


2 Roadmap to the report

This report comprises eight sections:

Section 1: is the executive summary

Section 2: reports the findings from the literature and provides reference information andcontext for the evaluation sections

Section 3: is an overview of the objectives of the evaluation and the evaluation approach.

Section 4: is an overview of the results of the evaluation of the DAA program, at both thepharmacy and the patient level

Section 5: is an overview of the results of the evaluation of the PMP program, at both thepharmacy and the patient level

Section 6: describes the population at risk

Section 7: discusses patients who may benefit most

Section 8: is an overview of the costs and benefits of the services

Section 9: provides a discussion of the key findings from the evaluation along with theimplications of these findings.

Background


3 Background and summary of literature

3.1 The Australian context

3.1.1 General health of the Australian population

The health of the Australian population reflects many of the trends that characterise other firstworld countries. The key features of the health landscape reflect an aging population, anincrease in the prevalence of chronic disease, a socioeconomic gradient evident in the incidenceand prevalence of disease, a decrease in the death rates of common diseases, and an increasein the social and economic burden of disease. Alongside these trends in health status, therehave been concomitant changes in the pressures on and costs to the health and aged caresystems, as well as the PBS.

The current and future importance of medication management, particularly through strategies toimprove medication adherence, needs to be considered in this context.

Australia’s aging population

Since 1971, Australia’s population over 65 years of age has increased from 1.1 million (8.3% ofpopulation) to 2.9 million (13.3% of population) (1). The result of this is that Australia’s medianage has increased by 5.1 years over the past two decades alone (1). The aging population andincreasing prevalence of chronic disease and chronic disease risk factors (e.g. smoking andobesity) are placing increasing stress on health systems, both in Australia and internationally (6).Together, cancer, cardiovascular disease, diabetes and renal failure account for approximately45% of the total burden of disease and injury in Australia (7). It is of critical importance forAustralia to address the challenges posed by the increasing burden of chronic disease,manifested as increasing morbidity, with commensurate pressure on the health system(including hospital admissions and transfers to residential aged care) and increasing social andeconomic costs. Effective medication and medication management are a central part of bettermanagement of the chronic and complex care needs.

Changes in self-assessed health status with age

Australia, like many other first world countries, collects population level self reported health dataon a recurring basis (every three years). Self-reported data provide a robust proxy measure ofpopulation health status trends, with strong predictive power for future morbidity and mortality(8). Research has found perceived health status to be a useful indicator as subjective indices ofhealth also widen theoretical frameworks to include perceived occupational stress, domesticissues, sexual conflicts etc. (9). In the 2007-2008 National Health Survey, 56% of respondentsaged 15 years and over assessed their health as very good or excellent(1). This was a marginalincrease from the 52% in 2001 and the 54% in 1995 (1).

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Table 1: Change in self-assessed health status, persons aged 15 years and over

Males (%) Females (%) Persons (%)

1995 2001 2007-2008 1995 2001 2007-2008 1995 2001 2007-2008

Excellent/very good 53.9 50.1 54.8 54.6 52.9 57.3 54.3 51.5 56.1

Good 28.6 31.4 29.6 28.5 29.2 28.5 28.5 30.2 29.0

Fair/poor 17.5 18.5 15.6 16.8 17.9 14.2 17.2 18.2 14.9

Source: Australian Institute of Health and Welfare (AIHW): Australia’s Health Reports (2008 and 2010)

Self-assessed health (2004-05) when analysed in terms of age, consistently showed that theproportions of respondents who reported very good and excellent health decreasedprogressively with age (6).

Figure 1: Self assessed health status by age group (2004-05) (AIHW, 2008)

Similarly, self-reported health can be seen to deteriorate with an increasing number of long-termconditions. At five or more long-term conditions (which are likely to be accompanied by anequivalent number of medications), more respondents reported fair/poor health than verygood/excellent health.

Figure 2: Self assessed health status by number of long term conditions (2004-05) (AIHW, 2008)

Long term conditions & diseases

In 2007–2008, an estimated 75.0% of Australians had at least one long-term condition (adisease/health problem lasting/expected to last six months or more) (1). The proportion of

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respondents reporting long-term conditions in the 2004–05 national health survey increased withage, from 41% of those under 15 years of age to over 95.0% of persons aged over 45 years (6).

Of the broad disease categories, cancer is the leading cause of disease burden (19.0%),followed by cardiovascular disease (16.0%) and mental disorders (13.0%) (1). With theexception of mental disorders, death rates are falling for many health concerns, including theaforementioned conditions (1). Moreover the prevalence of certain conditions is rapidlyincreasing: diabetes and end-stage kidney disease have increased substantially over the past 25years (diabetes has more than doubled; end-stage kidney disease has tripled over that period)(6).

The implication of falling mortality rates is that there is an increase in the long term managementof most health conditions. This exemplifies the need for medication compliance measures in thecommunity setting.

Consultations with health professionals

Approximately 65.0% of Australians aged 15 years and over claim to have check-ups with a GP(1). Of these people, 59.0% were 45 years and over (1). To manage patients’ health problems,GPs prescribed medications in 86.4 out of 100 encounters, supplied medications directly in 11out of 100 encounters and advised for over-the-counter purchases in 8.9 out of 100 encounters(1).

Of those patients with five or more long-term conditions, approximately 25% sought medicaladvice from a chemist, which is around double that of patients with 2-4 conditions and aroundtriple that of patients with one or no long term conditions (1). This indicates that chemists arelikely to be more influential to those patients with five or more long-term conditions (and hencean increased number of medications).

Cause of death

In Australians over the age of 45, the major causes of death include cancer, cardiovasculardisease, injury and poisoning, respiratory system disease and endocrine diseases(1). Cancer isthe most common cause of death in the 45-64 age group (43.0% males; 56.0% females), butdecreases with increasing age (1). Conversely, cardiovascular disease increases with age untilage 85, where it is the leading cause of death for both males (42.0%) and females (49.0%) (1).

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Table 2: Leading causes of death (AIHW 2010 report)

Males Females

Age group Cause of death % Cause of death %

45-64 Cancer 42.6 Cancer 55.8

Cardiovascular disease 24.5 Cardiovascular disease 13.9

Injury and poisoning 10.0 Injury and poisoning 7.1

Digestive disorders 5.4 Respiratory system diseases 5.9

65-84 Cancer 37.5 Cancer 33.3

Cardiovascular disease 31.6 Cardiovascular disease 32.4

Respiratory system diseases 9.5 Respiratory system diseases 8.8

Endocrine 4.1 Endocrine 4.8

85+ Cardiovascular disease 42.3 Cardiovascular disease 48.6

Cancer 20.2 Cancer 12.1

Respiratory system diseases 11.6 Respiratory system diseases 8.7

Socioeconomic status

Population sub-groups who are socially and economically disadvantaged have been shown tohave reduced life expectancy, early mortality, increased disease incidence and prevalence,increased biological and behavioural risk factors for ill health, and lower overall health status(10). Social gradients have been linked to many of the major chronic diseases and their riskfactors in Australia (11)(6). These include risk factors for a number health conditions such asrespiratory diseases, lung cancer and cardiovascular diseases. Among the long-term healthconditions covered in the 2004–2005 National Health Survey, those reported most often bydisadvantaged people were diabetes, diseases of the circulatory system (e.g. heart disease andstroke), arthritis, mental health problems and respiratory diseases (including asthma) (6). Thesurvey also identified that those who were socio-economically disadvantaged reported morevisits to doctors, hospital outpatient, accident and emergency services (6).

Health of Remote/Rural Australia

People living in ‘Rural’ and ‘Remote’ areas are commonly reported to have poorer health thanthe rest of the nation based on common indicators. Life expectancy has been shown to decreasewith increasing remoteness (1). Compared with ‘Major Cities’, the life expectancy in ‘Remote’areas can be up to seven years lower (6). The lower life expectancy in ‘Remote’ areas canlargely be attributed to the reduced life expectancy of Indigenous Australians, which isapproximately 17 years below that of Australians overall (12). Data from population healthsurveys and cancer registries show people in ‘Rural’ and ‘Remote’ areas are also more likely tohave certain chronic diseases than people living in ‘Major Cities’(13). In 2001–03, the incidenceof cancer was approximately 4% higher among people in regional areas than among those in‘Major Cities’, but it was about 10% lower in ‘Very Remote’ areas (14). The cancers showinghigher incidence outside ‘Major Cities’ tend to be preventable and occur primarily due tosmoking, alcohol consumption or sun exposure (1).

Overall, hospitalisation rates in 2007-08 were highest in ‘Very Remote’ areas, which correspondwith the lower availability of health professionals present (1). For potentially preventablehospitalisations (e.g. illnesses preventable via vaccination such as whooping cough),occurrences were highest in ‘Very Remote’ areas (1).

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3.1.2 Hospitalisations in Australia

In 2005–06, there were 7,311,983 separations in Australian hospitals, of which 4,466,076 werein public hospitals and 2,845,907 were in private hospitals (6). Between 1996–1997 and 2005–2006, separations from all hospitals increased by 37.3%. Separations increased by 22.8% inpublic acute hospitals and by 68.9% in private hospitals and between 1996–1997 and 2005–2006, the number of patient days in public acute hospitals increased by 7.2% and 25.8% forprivate hospitals (6).

The average length of stay in public acute hospitals was 3.2 days overall in 2007-2008 (3.6 inpublic acute; 2.5 in private) – excluding public psychiatric hospitals (1). Excluding same dayseparations, the average length of stay was 6.2 days in public acute and 5.4 in private hospitals(1).

As the number of hospitalisations increase, so do the associated costs to the government. Overthe period 2001-2002 to 2005-2006, the average cost per casemix-adjusted separation inAustralia has been approximately 6.0% annually, reaching $4,215 (1). Another impact of theincreased burden on hospitals is the impact on waiting times. The median waiting time forelective surgery has shown a steady increase from 27 days in 2001-2002 to 34 days in 2007-2008 (1).

The contribution of medication related adverse events to hospitalisations in Australia hasremained fairly constant betweeen 2001-2002 and 2005–2006. Over that period, the number ofseparations reporting ‘adverse effects of drugs, medicaments and biological substances’increased from 1.1% to 1.3% of separations(6). In a recent speech by Health Minister Hon.Nicola Roxon, the cost of medication related adverse events over the period 2009-2010 wasreported to be $660 million (2).

3.1.3 Admissions to residential aged care

According to the AIHW (2008), there were 105,030 admissions to residential aged care between1 July 2007 and 30 June 2008, of which 51.0% were for permanent care. This is a 22.0%increase since 1999, which equates to an average compounded annual growth rate ofapproximately 2.2% The remainder of people admitted for respite care in 2008 were slightlyyounger than those admitted for permanent care, with 69.0% of respite admissions aged 80+years compared with 73.0% of permanent admissions (15). The average length of stay forpermanent residents leaving residential aged care between 1 July 2007 and 30 June 2008 was147.8 weeks, which is approximately 12.6% longer than in 1998-1999 (15).

3.1.4 Medication taking in Australia

Another key feature of the changing Australian health landscape concerns the trends in use ofprescription medications. Alongside dramatic increases in service utilisation in Australia, therehave been significant increases in the demand for medications under the PBS.

Prescription volumes

There were approximately 179 million community PBS prescriptions in 2006 – 26.4 million forgeneral patients and 152.6 million for concessional patients (1). This represents a 44.0%increase in the number of community PBS prescriptions since 1996, which is considerably largerthan the growth in the Australian population (11%) and the growth in the population ofAustralians aged 65 years and over (18.0%) (6) (1). Additionally, there were 15 millionRepatriation Pharmaceutical Benefits Scheme (RPBS) prescriptions in 2006 and 0.5 million PBS

Background


doctor’s bag prescriptions (ie emergency drugs that the doctor can provide to patients free ofcharge) (6).

There has been a progressive increase in the total number of community prescriptions, from 166million in 1994 to 262 million in 2008 (6)(1). This increase represents an average annual growthof 3.2% or a total growth over the period of 46.0% (1).

Prescribing patterns

Medications for the nervous system, including analgesics (pain-killers) and antidepressants,were the most commonly prescribed medication group, accounting for 21.6% of all prescriptions(1). The next most common drugs were cardiovascular (20.1%), antibiotics (19.3%), alimentarytract and metabolism medications (9.6%) and respiratory medications (6.2%) (1).

Table 3 below shows the change in prescription volumes over the period of 1994 to 2008. Allprescription types increased over the period with the exception of doctor’s bag medicationswhich decreased from 0.8 million to 0.4 million over the period. RPBS prescriptions showed thelargest growth, almost tripling over the period from 5.4 million prescriptions to 14.1 millionprescriptions (1).

Table 3: Prescription volumes from 1994 to 2008

Type

Calendar year Change1994-2008(%)

Avg.annualchange(%)

1994 1996 1998 2000 2002 2004 2006 2008

(Million)

PBS concession 97 105.8 107.3 120.5 132.3 141.4 141.9 152.6 57.3 3.3

PBS general 17.2 18.5 18.8 21.8 25.2 29.5 25.8 26.4 53.5 3.1

RPBS 5.4 8.7 10.2 12.5 15 15.7 14.7 14.1 161.1 7.1

PBS and RPBS total 119.6 133 136.3 154.8 172.5 186.6 182.4 193.1 61.5 3.5

Private 11.9 11.7 15.1 14.3 16 18.1 19.3 18.0 51.3 3.0

Under co-payment 33.6 34.1 35.4 30.7 27.6 28.2 34.6 50.2 49.4 2.9

Doctor's bag 0.8 0.8 0.8 0.5 0.5 0.5 0.5 0.4 -50.0 -4.8

Total 165.9 179.5 187.5 200.3 216.6 233.3 236.7 261.7 57.7 3.3

Source: AIHW Australia’s Health Reports (2008 and 2010)

Figure 3 below shows that, over the last two decades, of those medications listed on the PBS,cardiovascular system drugs have consistently been the most common at almost 70 millionservices in 2009 as indicated by services processed from claims presented by approvedpharmacies to Medicare Australia (16). However, the extent to which cardiovascular systemmedications represent the leading prescribed medication type by volume has increased in a non-linear fashion over that time. In fact, these drugs are now nearly double the volume of thesecond leading medication type. Nervous system and general anti-infectives for systemic usehave been consistently the second and third most common medications at approximately 40million and 30 million services respectively (16).

Background


Figure 3: Graph of medication volume (services) for various medication types (total of PBS and RPBS)

Medication benefits paid by the Government

In terms of aggregate benefits paid by the government (Figure 4), cardiovascular system drugsaccounted for the highest overall value – reaching over $2 billion in 2009 (16). This is followedby anti-neoplastic and immunomodulating agents (~$1.3 billion), nervous system drugs (~$1.3billion) and alimentary tract and metabolism drugs (~$1 billion). Over the last two decades,strong growth is evident in expenditure in these therapeutic areas (anatomical therapeuticclassification (ATC)) – particularly in anti-neoplastic and immunomodulating agents (which hasalmost tripled since 2003) (16).

Background


Figure 4: Graph of benefits paid by the government (in aggregate) for various medications (total of PBSand RPBS)

The average benefit for drugs under each therapeutic area, expressed as the aggregate amountdivided by the volume (number of services) is also informative about trends over time. Figure 5below indicates that neoplastic and immunomodulating agents are by far the most expensive atover $600 per service paid out by the government (16). Moreover, the cost for these medicationshas increased sixfold over the last two decades. In contrast, the average benefit for drugs underall remaining therapeutic areas has consistently been, and remains less than $50 (16).

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Figure 5: Graph of average benefit paid by the government (by year) in each therapeutic area

Medication taking in rural and remote Australia

Medicines for endocrine therapy were more likely (1.1 times) to be prescribed in ‘Inner Regional’areas and significantly less likely to be prescribed in ‘Remote’ (0.8 times) and ‘Very Remote’ (0.6times) areas as compared with ‘Major Cities’ (14). This is broadly consistent with the pattern ofcancer incidence – higher in ‘All regional’ areas and lower (about 0.9 times) in ‘Very Remote’areas. Compared with ‘Major Cities’, prescription rates for drugs used in the treatment of heartdisease (lipid modifying drugs) were significantly lower in ‘Remote’ and ‘Very Remote’ areas –10.0% and 40.0% respectively (14). This is an interesting finding considering death rates due tocoronary heart disease were up to 1.3 times as high as ‘Major Cities’ in ‘All regional’ areas andup to 3.4 times as high in ‘Very Remote’ areas (14). The prescription rates of drugs for thetreatment of bone disease, such as osteoporosis and bone cancer, decreased with increasingremoteness. Compared with Major Cities, prescriptions rates in ‘Very Remote’ areas weresignificantly lower (70.0%) (14).

Evidently, from the national survey data collected and processed by the Australian Bureau ofStatistics (ABS) and AIHW, the population is aging, people are reporting better self-assessedhealth, more people are living with chronic conditions for longer periods and death rates arefalling for many health conditions. With these factors, among others, contributing to the largeincrease in prescription volumes (particularly in RPBS drugs), it has become increasinglyimportant to ensure that medicines are being used appropriately by patients within thecommunity.

Background


3.2 Medication adherence

A significant part of the modern health landscape in Australia, and more increasingly its cost,relates to medication. However, poor adherence to medication regimens is a significantchallenge which significantly impacts health outcomes and health care costs. This sectionoutlines the key considerations from the vast literature on the nature and extent of non-adherence and then considers the burden of poor adherence.

3.2.1 Definition of adherence

A review of the literature has revealed that the terms adherence, compliance and concordanceare all commonly used to describe patients’ medication taking behaviour. There is continueddebate about which term should be adopted as the best descriptor for essentially the samedefinition, but in recent years the term ‘compliance’ which has been traditionally used has beenincreasingly replaced with the term ‘adherence’ (17).

Adherence can be described in both in the medical context and in the context of drug therapy(18). Siegel, Lopez and Meier (2007) define adherence in the medical context as ‘the extent towhich a patient’s behaviour follows the recommendations of his or her health care practitioner’.However in the context of drug therapy adherence is generally defined as the extent to whichpatients take medications exactly as prescribed by their health care provider (19)(18).

Definition of non-adherence

Literature addressing problems around defining medication adherence have suggested that apatient be classified as non-adherent when they consume less than 80% of their medication(20). Others have reported in various clinical trials that above 80% adherence is acceptablewhilst others require above 95% (19). This observed difference may be due to some treatmentsrequiring a higher rate of adherence to be effective. Treatment for HIV for example, requiresadherence rates of at least 95% in order to be effective (19).

Another important factor to recognise is that non-adherence can be both patients omitting dosesor consumption of extra doses. It has been noted from the literature that the consumption ofextra doses is less of a problem than patients omitting doses (21).

Recognising these difficulties, for the purpose of this literature review, non-adherence will bedefined broadly as patients who fail to take greater than 80.0% of their medication as prescribedby their health care professional unless otherwise stated, as in the case of HIV antiviral therapy.

Primary and secondary non-adherence

It is important to recognise that there is more than one form of non-adherence. Primary non-adherence describes patients who fail to fill their prescription after being handed a script fromtheir health care provider (22). Secondary non-adherence infers those who after initially fillingtheir prescription either cease to take the medication or fail to obtain a repeat prescription (23).

Background


Intentional and unintentional non-adherence

It is important to understand that there is the phenomenon of intentional and unintentional non-adherence. Intentional non-adherence involves “a well thought out, purposeful decision to omitor cease to take a medication dose”, whereas non-intentional non-adherence involves “patientsomitting to take a medication because they are away from home and do not have access to theirmedication or simply forgetting to take a medication dose”(24).

Research has found that approximately 40.0% of non-adherent patients are non-intentionallynon-adherent (21). Figure 6 below, illustrates the process behind the decision to comply,highlighting the difference between primary and secondary non-adherence, as well as intentionaland unintentional non-adherence (24).

Figure 6: Non-adherent pathway

3.2.2 Extent of non-adherence

Poor adherence to medication regimens is a global challenge which significantly impacts healthoutcomes and health care costs (17)(19)(25)(26)(27). Existing literature reveals thatapproximately 50.0% of patients do not take their medication doses exactly as prescribed bytheir health care professional (28)(25)(26).

There have been many trials conducted in the literature on the extent of medication non-adherence in the general population. DiMatteo published a comprehensive review of 569adherence trials conducted up to March 1998 with an adherence range of 4.6%-100% with amedian of 76.0% and an overall mean of 75.2% (29). Some have suggested that clinical trials

Background


investigating various conditions yielded 70.0 to 90.0% adherence rates (30) and others between43.0 to 78.0% (19).

Despite these differences there have been estimations that in clinical practice the range could beas low as 10.0 to 40.0% (30). The reasoning between the higher rates of adherence seen inclinical trials is potentially due to the attention study patients receive and selection bias (that theyare willing to participate in the trial in the first place) (19) or possibly due to what is known as theHawthorn effect, defined as a change in response due to knowledge of being observed (31).

Other studies have found that of the 50.0% to 70.0% of United States of America (USA) patientswho do not adhere with their prescribed medication, 14.0% to 21.0% of these fail to even fill theiroriginal prescriptions (32). A more recent Australian report found consistency with the abovefindings, by which they estimate that 41.0% of Australians have stopped taking prescribedmedicine before they were meant to, on at least one occasion (33).

3.2.3 The burden of poor adherence

Poor adherence to medication regimens compromises the effectiveness of treatments, increasesthe risk of morbidity, unnecessary escalation of therapy and reduces a patients’ quality of life(30)(34). In a review of asthma literature (35), poor adherence resulted in more intense relapseof symptoms, resistance to therapies and decreased quality of life for the patient. One study byRoughead et al. (2004), found that in a group of 1000 community dwelling elderly patients(median age 75.5) 90.0% had at least one medication related problem and that 10.0% of thesewere usually due to adherence issues (36). Another study in the UK found that only 3.0% ofpatients with ulcerative colitis (UC) who were adherent to their medications developed colorectalcancer (CRC) compared to 31.0% who were non-adherent to longer term therapy (37).Furthermore, Hawthorne et al. (2008) reported that adherent patients exhibit a significantlyhigher probability of maintaining remissions when compared to non-adherent patients (89.0%adherent patients; 39.0% non-adherent patients).

Evidence advocating concentrated efforts on medication adherence continues in multipledisease conditions. Non-adherence in cardiovascular patients is of particular interest as drugsunder this anatomical therapeutic classification (ATC) are the most highly prescribed PBSmedication in Australia (16). The majority of patients using antihypertensive medications arefailing to meet target blood pressure as a result of poor adherence and perseverance to theirmedication regimens (38). Lau et al. reports that this places these patients at an increased riskof acute coronary syndrome (ACS) in addition to stroke, peripheral vascular disease and renalfailure (38).

In addition to the adverse effects on patients, the economic costs of non-adherence are high.There have been a number of studies undertaken that examine the impact of increasedcompliance on hospital admissions, admissions to residential aged care and costs to thehealthcare system.

Preventable hospital admissions attributable to non-adherence vary considerably internationally,ranging from 4.3% to 11.0%. Malhortra et al. investigated various emergency hospitaldepartments and found that 8.0% of admissions were directly attributable to non-adherence (39).Explanations from patients range from non-intentional non-adherence and forgetfulness torunning out, difficulty in reading labels, opening containers, halving tablets, and confusion aboutthe regimen itself (40). In a different study Col et al. found that 11.0% of elderly patients in anacute care hospital were admitted due to non-adherence (41). Winterstein et al. found through asystematic review that drug-related morbidity accounted for 4.3% of preventable hospitalpresentations (31) (42). Furthermore, it has been found that adherent patients who had eitherhypertension or hyperlipidemia had up to 50.0% lower hospitalisation risks (43).

Background


Others have reported that, in addition to hospital admissions, medication non-adherencecontributes to nursing home admissions as well. It has been reported that up to 23.0% of nursinghome admissions are the result of medication non-adherence in the elderly (44). This suggeststhat increasing the adherence of medication regimens in the elderly population may contribute tothe reduction of premature admissions to residential aged care.

More broadly, the economic costs attributable to medication non-adherence have beenestimated. It has been estimated, for example, that healthcare costs associated with non-adherence in the USA alone is between $77 billion and $300 billion a year (29)(45)(26) includingloss of productivity and early mortality (26). Another study by Kane and Shaya (2008) reportedthat the increased health care costs were associated across all types of health care services,with adherent patients costing less to treat by 62.0%, 45.0% and 12.0% for hospital admissions,emergency department and outpatient visits respectively (46). In Australia, it was estimated thatthe inappropriate use of medicines costs the public hospital system approximately $380 millionper year (47).

The savings observed in these studies are indicative of savings that could be expected in theAustralian healthcare system. Taking 20.0% fewer hospital admissions, as a case in point,constitutes potential for tremendous healthcare-related savings – especially when it isconsidered that the total cost per acute myocardial infarction, chest pain or ACS in Australia isestimated to be $281,000, $74,000, and $204,000 respectively (48).

In summary, the evidence highlights the dramatic consequences of non-adherence for bothpatients and health systems. Efforts to assist patients to improve adherence with medicationregimens represents an opportunity to greatly impact population health and improve theefficiency of the health system (49).

3.3 The role of community pharmacy in the management ofmedications

The community pharmacy profession is in the midst of a significant change, shifting the focus ofpharmacists’ away from solely product supply toward the implementation of professionalservices (50). This shift is evident by the increasing literature (36) identifying communitypharmacists as important players in improving medication compliance and the value of CognitivePharmaceutical Services (CPS). Cognitive Pharmaceutical Services, often referred to asPharmaceutical Care, is defined as clinical and professional assistance delivered by thecommunity pharmacist for the purpose of promoting effective and safe medication takingbehaviour (51). CPS focuses on providing patient-centred care to deliver improved healthservice delivery to patients.

There are numerous reasons community pharmacists are well placed to improve patients’medication adherence. Pharmacists are often the last point of contact after patient consultationswith doctors and they can play an important role in reinforcing information and correctingmisunderstandings (52)(53). Pharmacists are also visited on a regular basis when patients needto collect prescription medications (52). For certain chronic conditions pharmacies are bothaccessible and convenient for patients to get to and are often the first point of contact patientswith certain conditions such as asthma (54).

Not only are pharmacists more accessible to patients, they also do not need an appointment tosee them. Pharmacists also have extensive pharmaceutical knowledge and can assist patientswith their medication related concerns in the informal environment of the community pharmacysetting. This atmosphere can facilitate trusting relationships which allow patients to ask morequestions and express their concerns (55).

Background


It is evident that polypharmacy is quite common in the elderly population in globally and here inAustralia. Community pharmacists may therefore be particularly well placed to help patientsadhere to these complex medication regimens by conducting PMPs or setting up the patients onDAAs. Indeed recent research attests to the success of targeted strategies delivered throughcommunity pharmacy in Australia, where a national trial indicated that, over a six month period,an average 16.0% improvement in compliance could be achieved (50).

3.3.1 Strategies for improving medication compliance

This section outlines strategies or interventions which can be used at the community pharmacylevel to help improve medication compliance.

The findings from the literature review suggest a number of strategies which could be integratedinto community pharmacy practice to improve a patient’s medication compliance. Key strategiesidentified for possible use in the community pharmacy setting include (56):

provision of patient education and information

reminders

dose administration aids

follow up

provision of pharmacist education and information

medication profiling and simplification of medication regimens

collaboration between patient, pharmacist and physician

pharmacy remuneration.

Behaviour change

As part of the application of evidence-based and best practice strategies, a communitypharmacy compliance service should be based on relevant behaviour change theories andmodels. Compliance to medication is a variable behaviour, and thus improving poor compliancerelies on changing that behaviour. For example, behaviour change research suggests thatpatient motivation and readiness to change greatly influences the way a patient takes theirmedication. To help patients improve their medication compliance, it is important to identify andunderstand the patient’s readiness or motivation to change their behaviour (57).

Figure 7 illustrates the Stages of Change model, which can be used to identify a patient’sreadiness to change. In this model, patients are shown to move through five stages when tryingto change their behaviour: pre-contemplation; contemplation; preparation; action; andmaintenance.

Background


Figure 7: Stages of Change model

Research has shown that a patient will be more ready to change their behaviour if they feel thechange is important and they have the confidence that they can do it. Figure 8 illustrates howimportance and confidence interact to determine an individual’s readiness to change.

Figure 8: Importance and confidence to change

The research on behaviour change suggests that influencing patients’ medication takingbehaviour relies on community pharmacists delivering a patient-centred and tailored approach tomeet the individual needs of patients. A multi-component intervention, combining numerousstrategies, may therefore be more effective than implementing a single strategy. Thisrecommendation is supported by several literature reviews, including Haynes, Garg andMontage (2005) and McDonald, Garg and Haynes (2002).

Background


Behaviour change in community pharmacies

The successful delivery of a medication compliance professional service relies on the readinessof pharmacists to change their behaviour. In this setting, readiness to change refers to anorganisation’s plan for change and its ability to execute it. This readiness to change begins withthe pharmacist’s perceptions of the benefits of change, the risk of failing to change and thedemands of an externally imposed change. Applying Rollnick et al.’s (1999) model of healthbehaviour change, pharmacists need to assess the need for the change (importance) and havethe resources such as time and skill (confidence) to effectively carry out the change (57).

3.4 Overview of the DAA and PMP service

DAAs and PMPs are tools that pharmacists can tailor in collaboration with their patients basedon individual patient needs and their stage of change. For example, DAAs are a strategy for theaction or maintenance stages as they are used when patients actively seek to improve theirmedication management. On the other hand, PMPs depend on how they are used bypharmacists. For example, it may be that pharmacists use them in the contemplation stage toraise patients’ awareness of their prescribed medications, or alternatively they could be used asan action tool in the first step of improving behaviour.

This section will outline what DAAs and PMPs are and how they can be used to improvemedication adherence.

3.4.1 Dose Administration Aids

Overview of the service

DAAs are devices or systems designed to assist in medication management. The devices orsystems have daily medications divided up into individual dose compartments containing eithersingle dose/s of one medication type or multiple dose/s of one or more medication types. Theregimen is often repeated throughout the day so the packaging of the medications is often sortedchronologically by time of day and day of week (24).

More sophisticated systems than the traditional pill box can be organised through the communitypharmacy as part of a DAA system (24). The Pharmaceutical Society of Australia (PSA) hasoutlined a number of considerations pharmacists need to consider before offering a DAA service.These considerations pertain to factors such as understanding the full requirements of providingthe service, establishing whether or not patients are likely to benefit from the service versustraditional dispensing methods and ensuring the pharmacy is adequately resourced to providethe service effectively (24).

The choice of the community pharmacy derived DAA system needs to be hygienic and providethe patient with the correct medicine/s at the correct dose/s and time/s (24). DAAs are generallyeasy to use with clear and concise instructions as to when the medicines should be taken,outline what all individual medicines are in the pack, and provide protection to the medicationfrom moisture and contamination. The packs also provide a tamper-evident system (such as ablister pack) to alert the patient, carer or pharmacist if there is evidence that the pack has beentampered with or damaged before the dose is due to be taken (24).

Background


However, there are limitations as to the types of medication that are suitable for packaging inDAAs. For example, while solid capsules and tablets are suitable, effervescent, but dispersible,buccal, sublingual preparations, and moisture sensitive medicines, are not (58). Additionally thestability of certain drugs that have been packaged into DAAs are unknown and of concern (3).

Potentially the major problematic issue related to DAA usage is the need for pharmacists toadequately assess the individual needs of patients and determine from these what the mostappropriate type of compliance intervention is (of which DAA supply is just one option) (59). Incontrast, a tendency towards the wholesale administration of DAAs has been reported by anumber of authors who interpret the tendency to reflect that DAA usage may be based largely onthe needs of professionals and carers, rather than the needs of patients (60). A study by Raynoret al. estimated that approximately 100,000 people in the UK have been supplied with DAAs,and that GPs and hospitals generally initiate their usage without a thorough assessment ofpatient need (61)(3).

A final area of concern in the provision of non-automated DAAs is the cost in both time andmoney incurred by pharmacies that provide the service. Nunney and Raynor acknowledged thatfor the pharmacies supplying DAAs there was a lack of payment for the service, which wasprovided without governmental assistance for extra resources or support (59). In addition, thetime-savings experienced by nursing homes using DAAs is often simply transferred to thepharmacy supplying them (62).

Automated dispensing systems

Automated Medication Dispensing Systems (AMDS) are of two primary types. The first typeautomatically packs medications into preformed blister packs, and the other type dispensesmedications into individual plastic sachets. These plastic sachets can contain either a singlemedication (unit-dose packing), or multiple medications (multiple-dose packaging) (3)(63).

AMDS have benefits for both the patient and pharmacist. For the patient, it has been proposedthat the implementation of AMDS may lead to increased safety for patients by eliminating thepossibility of re-dispensing of medications by untrained staff, and by providing a means ofpositive identification of medication, leading to reduced medication errors (3). During a three-month study, it was found that medication errors decreased from 8.0% using blister cards to2.5% using the automated single dose system, and 0% using the multi-dose system (64)(3).

For the pharmacy, most of the automated pharmacy systems on the market today performmedication counting, packaging, labelling and electronic documentation functions (3)(65). As aresult, pharmacy and nursing staff having more time for direct patient-care. One study found thatupon implementing an AMDS, there was a corresponding increase in pharmacists’ time spent onclinical activities from 27.9% to 35.1% (3). Another study reported a time-saving of 21.1 hours ofnursing time per week following the change from blister cards to a multi-dose AMDS in a 47-bednursing unit (a decrease of 20%)(3).

Unfortunately, with automated systems, there are a number of shortcomings that arise as aresult of the heavy reliance on computers in AMDS, including mechanical breakdown of movingparts, and software crashes (3). There are also logistical disadvantages to the pharmacy inrelation to the floor space required to install the AMDS, and staffing issues concerning the skillsand technical training required to operate the machine (3). However, these downsides can bemanaged and the advantages of AMDS will only increase as prescription volumes increase andmore patients seek assistance and advice from pharmacies (16). Another key benefit is that itallows nursing homes to efficiently audit the ‘medication dispensing trail’ in the facility, and mayincrease the level of safety and security of medicine retrieval and storage in nursing homes(3).

Background


3.4.2 Patient Medication Profiling

PMPs are another tool used in the arsenal of medication compliance. Unlike DAAs, they are nota device but more a tool used to educate and inform patients about their medications. LikeDAAs, they can be used alone or in combination with other strategies.

Overview of the service

A PMP consists of a comprehensive summary of all regular medicines consumed by a patient.The summary results in a computerised print out and must contain all medications including,prescription and non-prescription (over-the-counter) medicines, vitamins, supplements and anyother complementary medicines. It also needs to outline information on the brand, dose,directions and prescriber information as well as having the patient’s details including name,address, date of birth and any known allergies or previous adverse drug reactions (52).

The aim of a PMP is to assist the patient in managing their medicines by helping themunderstand their medications better so they know how, when and why to take their medicines.The PMP was also designed to provide a link for inter-professional communication between thepatient and their pharmacist, GP, allied health and community care workers (52). This objectiveis achieved by leaving space on the print out for any comments or additional information that anyof the above health care providers wish to add to the PMP (52).

The PMP is executed at the patient’s local community pharmacy through an interaction with aqualified pharmacist who has been trained on how to conduct a PMP interview which needs totake place in a suitable counselling area (52). During this interview the pharmacist needs toascertain what medications the patient is on, address any identified issues, suggest any otherprofessional services which may benefit the patient if appropriate (such as DAAs and HomeMedicines Review (HMR)), encourage the patient to provide the PMP to their GP and otherhealth professionals during consultations and ensure they keep their PMP up to date.

The PMP lends itself to suggesting other potential strategies/services which may be of benefit tothe patient’s adherence, and so is often not used in isolation. It encourages intercommunicationbetween healthcare professionals and the use of other adherence improving strategies such asDAAs or HMRs. This is consistent with the literature which suggests that programs which aresuccessful at improving adherence are multifaceted (66). This includes aspects of patienteducation, simplified dosing regimens, increasing inter professional communication andincreasing follow up and monitoring amongst other strategies (66).

3.5 Benefits of DAAs & PMPs

DAAs and PMPs are specific strategies among many possible approaches to improvingcompliance – either as sole strategies or in combination with others. They have been shown tohave similar benefits to those reported for other interventions to improve compliance. A recentreview indicated that there is a wide range in reported compliance benefits derived from usingDAAs, ranging from 14.6% to 43.0% (3). The review suggests that the reported differences inbenefit are likely to be due to differences in study protocols rather than inherent variation inefficacy of DAAs(3). The differences included those in the definitions of compliance, inclusioncriteria, number of medications, type of medication, demographics, types of illness, severity ofillness, length of survey, population size and design of the trial (3).

Background


DAAs may also be beneficial for both formal and informal carers (67). DAAs are designed tomake it substantially easier for a carer to either monitor or prepare multiple medications to betaken by the patient (3). If DAAs can contribute either individually or as part of a combination ofstrategies to improve medication adherence then benefits to the patient’s family/carer are alsolikely.

Similarly, the potential impact of PMPs has been considered. Emergency department visits andhospital admissions were collectively reduced by nearly 20.0% for an intervention consisting ofprimarily of education and instructions on medication, but including a PMP undertaken by acommunity pharmacist as part of a baseline interview(25). In-patient health care was reduced bymore than US$2000, and outpatient health care costs were US$886 lower in the interventiongroup (25).

A recent study found that medication compliance significantly improved over a six month trial, byan average improvement of 16.0% (50). This level of improvement supported the study’shypothesis that a significant improvement in compliance could be achieved as a result ofpharmacy intervention strategies, which included, among others, DAAs and PMPs.

Evidently, increased medication adherence can have a significant impact on healthcare costs.As compliance interventions, which include PMPs and DAAs, have been proven to improvecompliance to a significant extent, it can be argued that medication interventions have a directimpact on hospital admissions, admissions to residential aged care and overall costs to thehealth system.

Table 4: Summary of benefits of DAAs and PMPs in various settings (adopted from the Roberts report,2004)

Benefits of DAA Benefits of PMP

Patients able to better manage their medicationsindependently

Helps to prevent hospitalisations

Improves compliance

Prevents under or over-dosing

Reduces wastage or hoarding

Decreases demands on the healthcare system

Reduced medication costs

Reduced risk of medication errors (AMDS)

Increased safety for residents

Easier for nursing staff to distribute and monitor

Increases medication reviews

Time savings for nurses allowing for more directpatient care (AMDS)

Increased time for clinical activities

Facilitates direct patient billing

Decreases drug inventory.

Patients understand what medications they aretaking and why they need to take them

Patients understand when to take their medicationand why it is important to do so

Helps to prevent hospitalisations

Improves compliance

Provides a link for inter-professionalcommunication between the patient and theirpharmacist, GP and/or carer

Increased safety for the patient

Decreases demands on the healthcare system.

Background


3.5.1 People likely to benefit most from the DAA/PMP service

From the literature, it was generally found that medication complexity including the number ofmedications, number of doses and type of medication the patient is on, is a good predictor of apatient who is less likely to adhere to their medication. For example some patients may struggleto adhere to their medication regimen since they may be on more than 15 medicationsconcurrently (40). In fact, the two groups that consume the most medicines are males andfemales aged 65 years and over of which 57.0% to 59.0% take more than five medicines and17.0% to 19.0% take at least ten (68). Similarly, in a study of all drugs dispensed to patientsidentified as being in the Australian Department of Veterans Affairs it was found that 20.0%received more than ten medicines and 6.9% required in excess of 15 (40). In residential agedcare facilities, it was reported that the average resident was on seven medications (40). It hasbeen suggested that this group would benefit from a DAA in order to better enable them to self-manage their medications and maintain their independence (3). Illnesses that require largequantities of solid medication in their treatment, and therefore increase the complexity ofmedication administration, include respiratory patients, patients starting on anticoagulanttherapy, cardiovascular patients, neurology patients, renal patients, HIV/AIDS patients, peoplewith diabetes and asthma (3). So using either a DAA or PMP to simplify medication taking mayhelp to improve compliance for this population sub-group.

Some authors have argued that DAAs may be effective in achieving compliance in patients whomay forget doses (i.e. due to short-term memory loss, carelessness etc.) or are confused by acomplex medication regimen (69). It is these reported problems that DAAs and PMPs, eitherindividually or in conjunction with one another, aim to resolve through simplifying the medicationdosing regimen and educating the patient.

Therefore, in summary, it has been suggested in the literature, that the people likely to benefitmost from a DAA or PMP would be patients who fall within the following criteria (3)(24)(56):

Number of medications equal to or greater than five

Elderly

Complex medication regimens

History of poor compliance

Patients bearing signs of cognitive/physical impairment (with the exception of psychiatricpatients)

Background


4 Objectives and overview of the evaluation

4.1 Objectives of the evaluation

The objective of the DAA and PMP programs are to provide benefit to community patients byimproving adherence and thereby reducing medication related misadventure and increasingquality of life and improving health status.

The purpose of the evaluation was to review existing data and evidence for the DAA and PMPprograms to inform the potential patient and pharmacy benefits in providing these services to theAustralian community, as well as inform any potential value of future government investment.

The specific objectives of the evaluation were to:

analyse the DAA and PMP data to better understand the service provided, who it wasprovided to and the associated costs

identify how the DAA/PMP services interacts with other medication management programsunder the Fourth Community Pharmacy Agreement

undertake a review of local and international evidence to identify the potential benefits topatients and their families and carers of receiving a DAA and/or PMP service

identify the numbers of people nationally who are on multiple medications and areconsidered ‘at-risk’ of adverse medication events and who would benefit fromsuch services

identify rates of admission to hospital due to medication related incidents, including lengthof stay and cost of bed days

identify rates of discharge from hospital to residential aged care for those hospitalised dueto a medication related incident

identify the potential offset (if any) that future investment would offer Government were anaccess scheme to DAA and/or PMP services implemented in the future.

4.2 Evaluation approach

The overall approach to the evaluation was one of program effectiveness evaluation, rather thanan intervention efficacy approach. The purpose of the current evaluation was to consider theoverall benefits of the provision of DAA and PMP services in the Australian CommunityPharmacy setting, and consider the parameters of best investment in delivery of the services.

Background


Perhaps the biggest challenge facing the evaluation was the fact that all of the relevant data,covering DAA/PMP usage, adverse events and medication usage trends, is simply not availablein a single data set. On the other hand, all of this information is available in separate data setswhich can be used in a ‘meta-analytic’ fashion, that is, the information on core sub-groups canbe extrapolated from one data set to another. The rich data collected on the large cohort ofpatients participating in the DAA and PMP programs can be mapped to the trends and outcomeson similar sub groups in the national data sets describing acute patient care and medicationsusage. In this way trends in participation in the DAA and PMP programs, trends in acuteoutcomes and trends in risk factors, and their inter-relationships, can be explored. Accordingly,the evaluation utilised three key sources of data:

1 DAA and PMP service data – provides data concerning trends in participation in DAA andPMP services provided via community pharmacy.

2 Admitted patient care National Minimum Data Set (NMDS) – provides data concerningtrends in adverse medication related outcomes occasioning acute care.

3 PBS data – provides data concerning trends in medication related risk factors

These three data sets together provide an overview of the key dimensions for the evaluation.They form the basis of the analysis for the evaluation. Each is discussed in further detail below.

4.2.2 DAA and PMP data

The DAA and PMP data were collected via the DAA and PMP data and information systemwhich enabled pharmacies to register online for participation in the DAA and/or PMP programs,as well as provide evaluation data online. There were three key sets of data that were collectedfor the programs:

registration data

baseline data

ongoing evaluation data.

The baseline data for both DAA and PMP covered four general areas:

pharmacy hours of operation

pharmacy size

service implementation costs

patient information.

Ongoing evaluation data were collected for both DAA and PMP relating to:

numbers of patients

patient demographics and health status

pharmacy resources spent supporting patients

other activity related to the DAA and PMP programs.

Table 5 provides an overview of the periods for which the DAA and PMP data were collected.

Background


Table 5: DAA and PMP data periods

Data source Period for which data were obtained

DAA data Phase 1:

Baseline

Period 1: 1 July 2008 to 30 September 2008

Period 2: 1 October 2008 to 31 December 2008

Period 3: 1 January 2009 to 31 March 2009

Period 4: 1 April 2009 to 30 June 2009.

Phase 2:

Baseline

Period 1: 1 July 2009 to 31 October 2009

Period 2: 1 November 2009 to 31 January 2010

Period 3: 1 February 2010 to 30 April 2010.

PMP data Phase 1:

Baseline

Period 1: 1 April 2008 to 31 May 2008

Period 2: 1 June 2008 to 30 November 2008

Period 3: 1 December 2008 to 31 May 2009.

Phase 2:

Baseline

Period 1: 1 July 2009 to 31 October 2009

Period 2: 1 November 2009 to 31 January 2010

Period 3: 1 February 2010 to 30 April 2010.

Data considerations

Data considerations regarding the DAA and PMP service data include:

Missing data in Phase 1 of both the DAA and PMP services was substantial, and as aresult there is some concern regarding the reliability and validity of the findings related toPhase 1. Where possible, the results of Phase 1 are presented only for variables whichhave been checked for quality and completeness. When necessary, commentary hasbeen provided on the reliability of the results. Phase 1 data has therefore been limited tocross-sectional descriptive analyses only.

For some variables in Phase 1 missing and withdrawal are confounded. It is unable to bedetermined which data are missing versus those that were not applicable to the pharmacyor patient (e.g. patient exit and reason for exit).

To explore the impact of missing data, all data were classified to indicate completionstatus (i.e. incomplete, partially complete and complete). This allowed for a comparison ofthe characteristics of pharmacies and patients who did and did not complete the program.Results of this comparison showed no substantial differences between completers versusnon-completers (both for pharmacists and patients).

As a result of considerable missing data from the data provided in Phase 1 of the DAA andPMP Programs, combined with some data items not being collected in Phase 2 andconcerns regarding the reliability of costing data, a traditional cost-benefit analysis was notconducted. Instead, a discussion regarding the estimated costs and potential benefits ofboth services has been provided in Section 8. However, please note that the figurespresented in Section 8 should be interpreted with caution due to these data limitations.

Background


In phase 2, no outcome indicators were collected, other than duration of participation andreason for exit (if exit occurred). In addition, as previously discussed, Phase 1 datasuffered from considerable missing data, as well as confounding responses for missingversus a no response. Therefore the outcomes recorded in Phase 1 should be interpretedwith caution. As a result, potential benefits of DAA and PMP services were largelyextrapolated from existing literature.

There were difficulties pooling the data from Phase 1 and Phase 2 for both programs. As aresult the analysis of the two phases have been treated separately, with key similaritiesand differences discussed.

4.2.3 Admitted patient care National Minimum Data Set

The admitted patient care NMDS was obtained for the purpose of providing an overview ofmedication-related incidents in the Australian hospital setting.

Information on hospitalisations in Australia due to medication-related incidents can be obtainedusing a number of different sources. The source of information used in this project to identifymedication related incidents in the hospital setting was the NMDS. This data set contains a coreset of data elements agreed by the National Health Information Management Group formandatory collection and reporting at a national level(70). The admitted patient care NMDSincludes information about patient demographics, diagnosis, admission and discharge.

Data from the Admitted patient care NMDS were obtained for all patients who were hospitalisedfor a medication-related incident. The ICD-10-AM codes which were used to categoriseseparations as medication related incidents were:

poisoning by drugs, medicaments and biological substances (T36 – T50)

drugs, medicaments and biological substances causing adverse effects in therapeuticuse (Y40 – Y59)

accidental poisoning by and exposure to noxious substances (X40 – X49).

Table 6 provides the data elements obtained for this population and indicates the years for whicheach data element was available for analysis.

Background


Table 6: Data elements from the admitted patient care NMDS

Data elements 2001* 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008

Patient demographics:

Patient age (derived)

Patient sex

Patient postcode

Hospital geographical location

Establishment identifier

Clinical:

Diagnosis related group

Principal diagnosis code

Admission:

Mode of admission

Urgency of admission

Discharge:

Mode of separation

Length of stay, in days (derived)

* As 2001 data is for the calendar year, not financial year, no comparisons will be made using 2001 data.

Data considerations

Data considerations regarding the admitted patient care NMDS include:

The data provided for 2001 is for the calendar year, rather than the financial year. As aresult, no comparisons will be made using the 2001 data.

In 2001, the admitted patient care NMDS did not collection information on geographicallocation, and therefore 2001 was not included in the applicable analyses.

Data on hospitalisations from the admitted patient care NMDS may not provide accuratemeasures of the incidence or prevalence of conditions. This is because not all people witha type or degree of illness are treated in hospital and there are multiple admissions forsome chronic conditions.

The ICD-10-AM codes listed above only provide an indication of admissions due tomedication-related incidents.

4.2.4 Pharmaceutical Benefits Scheme data

PBS data were obtained for the purpose of describing medication use amongst the Australianpopulation (ie how many medications are individuals taking), what will happen to medication usein the future and the associated costs. Table 7 provides an overview of the PBS data elementsobtained and the periods for which data were provided.

Background


Table 7: PBS data elements and data periods

Data elementsPeriods for which

PBS data were obtained

Patient identification number

Patient date of birth

Patient gender

Patient postcode

Card type code

Entitlement source code

Patient category

Item code

Date of supply

Drug type code

Pharmacy state

Pharmacy postcode

Number of repeats ordered

Regulation 24 indicator

Commonwealth price to pharmacistfor dispensed quantity

Benefit

Patient contribution

Container fee

Dispensing fee

Pharmacy mark-up

Quantity of PBS item supplied

Scripts

1991: twelve months of data



2004: six months of data (July toDecember)




2008: eleven months of data (nodata for August)

2009: three months of data(January to March)

Data considerations

Data considerations regarding the PBS data include:

Substantial missing data for the date of birth variable in the earlier periods. As a result,analyses requiring the calculation of age were completed using data from 2004 onwards.

Dose Administration Aids


5 Dose Administration Aids evaluation results

Key findings:

Retention of both pharmacies and patients was high in both Phase 1 and Phase 2 of theDAA program, with retention slightly higher in Phase 2 – approximately 82.0% ofpharmacies and 79.0% of patients remained in the program at the end of the April 2010.Furthermore, most pharmacies and patients in Phase 2 were engaged across all datacollection periods of the PMP program.

A broad range of pharmacies participated in the DAA program. The distribution ofparticipating pharmacies across State, PhARIA and SEIFA was representative ofcommunity pharmacies nationally, suggesting that there may be no ‘type’ of pharmacywhich is more likely to opt-in to providing the DAA service. These results also suggest thatthe results from the DAA program are likely to be generalisable to pharmacies nationally.

In Phase 1, very few pharmacies were new to providing the DAA service on entry to theprogram: approximately 99% reported that they had been providing the service for at leastthree months, with the vast majority having provided it for more than 24 months.(Note: these data were not collected for Phase 2.).

The majority of pharmacies in Phase 2 also reported delivering other 4CPA fundedpharmacy services, with the most common being HMRs and PMPs. In fact, the majority ofparticipating pharmacies in Phase 2 also provided these services. (Note: these data werenot collected in Phase 1).

Most participating patients were aged 55 years or older. For both phases, the largestproportion of participants were aged 75 to 84, and the 85 to 94 age category was thesecond most common. These older groups accounted for approximately half of the participants.

Unsurprisingly, the vast majority of participating patients had concession cards, with by farthe most common being a pension card.

The majority of patients in the program received assistance with managing theirmedications and almost half of patients live alone.

The majority of patients had a cardiovascular, nervous system or alimentary system condition.

For patients in both phases, multiple medications were common. In Phase 1 the majoritywere taking more than four medications, and in Phase 2, the majority were taking betweenthree to six medications (76.0%). The most common medications amongst patients in theDAA program were blood pressure medication and lipid modifying agents.

Very few patients in Phase 1 reported any medication related events (3.6%) (Note: thesedata were not collected in Phase 2).

Very few patients were new to the DAA service (less than 5%) and just over half ofpatients were receiving one or more additional services.

The patterns of exit and their reasons were similar in both Phase 1 and Phase 2.Approximately 9,044 exited the program across both phases. The most common reasonfor exit for both Phase 1 and Phase 2 was most commonly death (N= 3,789 across bothphases), or the patient moving. Exit to another care facility was relatively rare.



5.1 Pharmacy results

This section provides a description of the key findings relating to the DAA service delivered bycommunity pharmacists, by addressing a number of important questions related to participatingpharmacies. These questions include:

What was the recruitment of pharmacies to the DAA program?

What were the rates of completion amongst pharmacies in the DAA program?

What were the characteristics of pharmacies who participated in the program?

How do pharmacies who participated compare to pharmacies nationally?

What did the DAA service provided by pharmacies look like?

To address these questions, the DAA service data were analysed and the results for bothPhase 1 and Phase 2, where available, are presented below.

5.1.1 What was the recruitment of pharmacies to the DAA program?

Recruitment of pharmacies to Phase 1 of the DAA program

A total of 4,638 pharmacies registered and/or participated in Phase 1 of the DAA program, ofwhich, 2,523 (54.4%) pharmacies remained in the program at the end of Phase 1. A total of2,487 pharmacies dropped out over the Phase 1 period. Figure 9 provides an overview of therecruitment and retention of pharmacies to Phase 1 of the DAA program.



Figure 9: Recruitment of pharmacies to Phase 1 of the DAA program

Recruitment of pharmacies to Phase 2 of the DAA program

A total of 3,583 pharmacies registered and/or participated in Phase 2 of the DAA program, ofwhich, 3,092 (86.3%) pharmacies remained in the program at the end of Phase 2. A total of855 pharmacies dropped out over the Phase 2 period. Figure 10 provides an overview of therecruitment and retention of pharmacies to Phase 2 of the DAA program.

Overall, retention of pharmacies to the DAA program was higher in Phase 2.



Figure 10: Recruitment of pharmacies to Phase 2 of the DAA program

3,583 pharmacies atbaseline

3,102 pharmacies at period 1

481 pharmaciesdropped out at period 1



146 pharmacies joined/back in program



218 pharmacies joined/back in program

Note: the cut-off date for the inclusion of Phase 2 data in the analysis was 8 June 2010

5.1.2 What were the rates of completion amongst pharmacies in theDAA program?

Rates of completion of pharmacies in Phase 1 of the DAA program

As described in Table 8, of the 3,325 pharmacies with Phase 1 baseline data:

a total of 2,407 (72.4%) pharmacies were classified as ‘completing’ Phase 1 of the DAAprogram (ie they participated in consecutive data collection points until the last datacollection period)

there were 918 pharmacies (17.0%) classified as ‘half-completing’ the program (ie theyparticipated in some data collection points, but not all)

there were 1,313 pharmacies (10.7%) classified as ‘non-completers’ of Phase 1 of theDAA program (ie they registered but provided no data).



Table 8: Rates of completion amongst pharmacies in Phase 1 of the DAA program

Registered Baseline Period 1 Period 2 Period 3 Period 4 Frequency Classification

1313 Non-completing

357 Half-completing

55 Completing

14 Half-completing

48 Completing

11 Half-completing

3 Half-completing

14 Half-completing

91 Completing

143 Half-completing

28 Half-completing

20 Half-completing

46 Half-completing

85 Half-completing

39 Half-completing

158 Half-completing

2,213 Completing

Rates of completion of pharmacies in Phase 2 of the DAA program


a total of 2,944 (82.2%) pharmacies were classified as ‘completing’ Phase 2 of theDAA program

there were 374 pharmacies (10.4%) classified as ‘half-completing’ the program

there were 265 pharmacies (7.4%) classified as ‘non-completers’ of Phase 2 of theDAA program.

Table 9: Rates of completion amongst pharmacies in Phase 2 of the DAA program

Baseline Period 1 Period 2 Period 3 Frequency Classification

265 Non-completing

70 Completing

18 Half-completing

128 Completing

95 Half-completing

148 Half-completing

113 Half-completing

2,746 Completing



5.1.3 What were the characteristics of pharmacies who participated inthe DAA program?

Characteristics of the pharmacies who participated in Phase 1 of theDAA program

Using the classification of pharmacies ‘completing’ the program described in Section 5.1.2, thecharacteristics of those pharmacies who completed the program can be compared to allpharmacies who participated in the program.

Table 10 provides an overview of the characteristics of the pharmacies who participated inPhase 1 of the DAA program, for all pharmacies who participated and for those who wereclassified as completing the program. Overall, there were no key differences in thecharacteristics of pharmacies that completed the program compared to all pharmacies in theprogram in Phase 1 or Phase 2. In addition, the characteristics of pharmacies that participated inPhase 1 and Phase 2 were quite similar.

From Table 10 it can be seen that:

The distribution of participating pharmacies across states was similar for Phase 1 andPhase 2, with the majority of participating pharmacies being from NSW, VIC and QLD.

The distribution of participating pharmacies across PhARIA categories was also similar forPhase 1 and phase 2, with approximately 71.0% of all participating pharmacies classifiedas highly accessible. The distribution largely reflects that evident for communitypharmacies nationally.

In both Phase 1 and Phase 2 there were more pharmacies located in areas with greaterrelative advantage (ie higher socioeconomic status). Approximately 11.0% of pharmaciesin both Phase 1 and Phase 2 were in areas of relative disadvantage.

In both Phase 1 and Phase 2 there was a fairly even distribution of pharmacies across theweekly prescription volume categories, suggesting that a broad range of pharmaciesparticipated in the trial. For example, approximately half of the participating pharmaciesreported prescribing greater than 1000 prescriptions per week.

Dose Administration Aids evaluation results


Table 10: Characteristics of pharmacies for Phase 1 and Phase 2 – for all pharmacies and those who completed the program

Variable description

Phase 1 Phase 2

All pharmacies All completing pharmacies All pharmacies All completing pharmacies

N (%) N (%) N (%) N (%)

State: ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

37

998

19

645

258

99

708

320

241

(1.1%)

(30.0%)

(0.6%)

(19.4%)

(7.8%)

(3.0%)

(21.3%)

(9.6%)

(7.2%)

29

755

17

493

200

74

510

260

69

(1.2%)

(31.4%)

(0.7%)

(20.5%)

(8.3%)

(3.1%)

(21.2%)

(10.8%)

(2.9%)

42

1,164

21

693

294

108

760

394

107

(1.2%)

(32.5%)

(0.6%)

(19.3%)

(8.2%)

(3.0%)

(21.2%)

(11.0%)

(3.0%)

34

983

19

587

252

95

635

339

0

(1.2%)

(33.4%)

(0.6%)

(19.9%)

(8.6%)

(3.2%)

(21.6%)

(11.5%)

(0.0%)

PhARIAcategory:

Category 1-2

Category 3-4

Category 5-6

Missing

2,402

257

78

588

(72.3%)

(7.7%)

(2.4%)

(17.7%)

1,849

201

54

303

(76.8%)

(8.3%)

(2.3%)

(12.6%)

2,486

263

82

752

(69.4%)

(7.4%)

(2.3%)

(21.0%)

2,142

220

69

513

(72.8%)

(7.5%)

(2.3%)

(17.4%)

SEIFA – Index ofrelativeadvantage anddisadvantage:

Category 1-2

Category 3-6

Category 7-10

Missing

344

1,053

1,684

244

(10.4%)

(31.7%)

(50.6%)

(7.3%)

253

798

1,284

72

(10.5%)

(33.2%)

(53.3%)

(3.0%)

398

1,152

1,922

111

(11.1%)

(32.3%)

(53.7%)

(3.1%)

339

971

1,630

4

(11.5%)

(33.0%)

(55.3%)

(0.1%)

Average weeklyprescriptionvolume*:

<400

401-600

601-800

801-1000

1001-1200

1201-1400

≥1401

413

383

364

346

311

240

675

(15.1%)

(14.0%)

(13.4%)

(12.7%)

(11.4%)

(8.8%)

(24.7%)

322

316

310

280

261

193

531

(14.5%)

(14.2%)

(14.0%)

(12.6%)

(11.8%)

(8.7%)

(24.0%)

321

543

546

469

408

324

972

(9.0%)

(15.2%)

(15.2%)

(13.1%)

(11.4%)

(9.0%)

(27.1%)

248

443

436

392

335

274

816

(8.4%)

(15.0%)

(14.8%)

(13.3%)

(11.4%)

(9.3%)

(27.7%)

* As reported by pharmacies in the Period 1 data collection

Dose Administration Aids evaluation results


5.1.4 How do the pharmacies who participated compare topharmacies nationally?

Table 11 provides a comparison of pharmacies who participated in Phase 2 of the DAA programto pharmacies nationally.

Overall, there were no key differences in the characteristics of pharmacies that participated inthe DAA program compared to all pharmacies within Australia. These findings suggest that thesample of pharmacies who participated in the DAA program were representative of pharmaciesnationally, supporting the generalisation of the findings presented in this report topharmacies nationally.

Table 11: Comparison of DAA pharmacies in Phase 2 to all pharmacies nationally

Variable descriptionDAA pharmacies All pharmacies

N (%) N (%)

State (May, 2010): ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

42

1,164

21

693

294

108

760

394

107

(1.2%)

(32.5%)

(0.6%)

(19.3%)

(8.2%)

(3.0%)

(21.2%)

(11.0%)

(3.0%)

65

1,771

31

1,042

420

141

1,211

530

(1.2%)

(34.0%)

(0.6%)

(20.0%)

(8.1%)

(2.7%)

(23.2%)

(10.2%)

PhARIA*: Category 1-2

Category 3-4

Category 5-6

Missing

2,486

263

82

752

(69.4%)

(7.4%)

(2.3%)

(21.0%)

4,363

448

144

120

(86.0%)

(8.8%)

(2.8%)

(2.4%)

SEIFA – Index ofrelative advantage anddisadvantage*:

1-2

3-6

7-10

Missing

398

1,152

1,922

111

(11.1%)

(32.3%)

(53.7%)

(3.1%)

582

1,623

2,863

7

(11.5%)

(32.0%)

(56.4%)

(0.1%)

* These numbers are estimates making up the total number of pharmacies based on the 2006 data from the PGA as opposedto 2003 data from Curtin University of Technology WA, additions in the categories from the 2003 data have been estimated bythe earlier base percentage in each category multiplied by the 2006 figures to estimate the increase in number of pharmaciesin each PhARIA category.



5.1.5 What did the DAA service provided by pharmacies look like?

How long had pharmacies been providing the DAA service?

In Phase 1, pharmacies reported the length of time they had been proving the DAA service.Figure 11 provides an overview of what pharmacies reported at baseline. The majority ofpharmacies (79.8%) reported that they had been providing the service for more than 24 months,with very few pharmacies reporting that they were new to the service, ie they had been providingthe service less than three months (0.7%).

Figure 11: Length of time pharmacies had been proving the DAA service – Phase 1

24 37 128240 187

2655

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<3 mths 3-6 mths 6-12 mths 12-18 mths 18-24 mths >24 mths Missing

Length of time providing DAA service



What was the provision frequency of DAAs?

In Phase 1, pharmacies reported how frequently they provided DAAs to their patients. Figure 12provides an overview of what pharmacies reported at baseline. The majority of pharmacies(75.5%) reported that they provide DAAs to their patients on a weekly basis, 18.6% on afortnightly basis and 3.4% on a monthly basis.

Figure 12: Provision frequency of DAAs – Phase 1

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Weekly Fortnightly Monthly Other Missing

Provision frequency of DAAs



What packaging brands were used for DAAs?

In both Phase 1 and Phase 2, pharmacies reported on the packaging brand that they used forDAAs. In both phases, Manrex Webstercare was the most commonly used brand and was usedby nearly 60.0% of pharmacies, which was followed by QuickPAK for WiniFRED(approximately 13.0%).

Figure 13: Packaging brands used for DAAs – Phase 1 and Phase 2

0

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ail S

equen

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Oth

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Phase 1

Phase 2



What were the packing locations for DAAs?

In both Phase 1 and Phase 2, pharmacies reported on the packing location for DAAs.Pharmacies in Phase 1 and Phase 2 reported that nearly all DAAs were packed in theirpharmacy (approximately 96.0%). Additional information captured in Phase 2 reveals that of theremaining DAAs:

approximately 3.0% were packed in another pharmacy (within in the pharmacy group)

approximately 0.5% were packed by an external provider

approximately 0.2% were packed elsewhere in an approved location.

Were manual or automatic packaging systems used for DAAs?

In Phase 2, pharmacies reported on the packaging system used for DAAs. Ninety-seven percent(97.0%) of pharmacies reported that they used a manual packaging system and 3.0% used anautomatic system.

What type of sealing was used for the DAAs?

In Phase 2, pharmacies reported on the type of sealing used for DAAs. Fifty-nine percent(59.0%) of pharmacies reported that they used hot sealed packs, 36.5% used cold sealed packsand 4.5% used both.

Were non-prescription medications packed in the DAAs?

In Phase 1, pharmacies reported on whether their pharmacy packs medication for communitybased patients that are not prescribed by any medical practitioner. Approximately 31.0% ofpharmacies reported that they packed non-prescribed medications in the DAA, while theremaining 69.0% did not.



What marketing methods were used for DAAs?

In Phase 1, pharmacies reported on the marketing methods that they used to bring DAA patientsonto the program. Figure 14 provides an overview of the methods reported across the fourPhase 1 reporting periods.

Overall the most commonly used marketing method was electronic media, with approximately98.0% of pharmacies reporting that they used it across the four reporting periods. Newsletterswere also commonly used (94.0% of pharmacies reported they used newsletters). Overall, therewas little change in the marketing methods used across Phase 1.

Figure 14: Marketing methods used for DAA – Phase 1

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Direct

approach

Posters Newsletters Electronic

media

Other

Marketing methods

Period 1

Period 2

Period 3

Period 4



What other services were the pharmacies providing?

In Phase 2, pharmacies reported on the other 4CPA funded services that they provided in theirpharmacy, in addition to DAAs. Pharmacies reported that they provided:

no other 4CPA services (6.4%)

one additional 4CPA service (30.2%)

two additional 4CPA services (44.6%)

three additional 4CPA services (16.1%)

four additional 4CPA services (2.7%).

Figure 15 provides an overview of which services they were providing, in addition to DAAs.The most commonly provided service was HMRs (83.3%), followed by PMPs (67.6%), theDiabetes Medication Assistance Service (DMAS) (23.1%) and the Pharmacy AsthmaManagement Service (PAMS) (4.2%).

Figure 15: Other services provided by the pharmacy – Phase 2

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PMP DMAS HMR PAMS

Other services provided in the pharmacy

5.2 Patient results

This section provides a description of the key findings relating to the DAA service received bypatients, by addressing a number of important questions related to participating patients.

These questions include:

What was the recruitment of patients to the DAA program?

What were the rates of completion amongst patients in the DAA program?

What were the characteristics of patients who participated in the program?

How many and what types of medications were patients taking?



What conditions did DAA patients have?

What proportion of patients were classified as being ‘at risk’?

What were the characteristics of the DAA service that patients received?

What other services were the patients receiving?

To address these questions the DAA service data were analysed and results for both Phase 1and Phase 2, where available, are presented below.

5.2.1 What was the recruitment of patients to the DAA service?

Recruitment of patients to Phase 1 of the DAA program

In Phase 1, there were a total of 15,949 patients at baseline receiving the DAA service and12,582 patients at the end of Phase 1 (ie Period 4). A total of 5,194 patients dropped out overthe Phase 1 period. Figure 16 provides an overview of the recruitment and retention of patientsduring to Phase 1.

Figure 16: Recruitment of patients to Phase 1 of the DAA program

15,949 patients at baseline(from 3,218 pharmacies)

13,652 patients at period 1(from 2,735 pharmacies)

2,297 patients droppedout at period 1



594 patients joined/back in program


670 patients droppedout at period 3







Recruitment of patients to Phase 2 of the DAA program

In Phase 1, there were a total of 17,447 patients at baseline receiving the DAA service and15,442 patients at Period 4. A total of 3,841 patients dropped out over the Phase 1 period.Figure 17 provides an overview of the recruitment and retention of patients during Phase 1.

Figure 17: Recruitment of patients to Phase 2 of the DAA program


5.2.2 What were the rates of completion amongst patients in theDAA program?

Rates of completion of patients in Phase 1 of the DAA program

As described in Table 12, of the 15,949 patients with Phase 1 baseline data:

a total of 11,995 (75.2%) patients were classified as ‘completing’ Phase 1 of the DAAprogram (ie they participated in consecutive data collection points until the last datacollection period)

there were 2,802 patients (17.6%) classified as ‘half-completing’ the program (ie theyparticipated in some data collection points, but not all)

there were 1,152 patients (7.2%) classified as ‘non-completers’ of Phase 1 of the DAAprogram (ie they provided baseline but no period data).



Table 12: Rates of completion amongst patients in Phase 1 of the DAA program

Baseline Period 1 Period 2 Period 3 Period 4 Frequency Classification

1152 Non-completing

257 Completing

59 Half-completing

235 Completing

55 Half-completing

20 Half-completing

65 Half-completing

454 Completing

730 Half-completing

143 Half-completing

95 Half-completing

244 Half-completing

415 Half-completing

180 Half-completing

796 Half-completing

11,049 Completing

Rates of completion of patients in Phase 2 of the DAA program

A total of 13,764 (78.9%) patients were classified as ‘completing’ Phase 2 of the DAA program,as described in Table 13. Approximately 10.7% of patients were classified as ‘half-completing’the program and 10.4% were classified as ‘non-completers’ of Phase 2 of the DAA program.

Table 13: Rates of completion amongst patients in Phase 2 of the DAA program


1820 Non-completing

364 Completing

85 Half-completing

653 Completing

489 Half-completing

734 Half-completing

555 Half-completing

12,747 Completing



5.2.3 What were the characteristics of patients who participated?

What age were the patients who were receiving DAAs?

Overall, there were no key differences in the age of patients who completed the programcompared to all patients in the program; therefore the age information presented below isbased on all patients. Figure 18 provides an overview of the age of patients in bothPhase 1 and Phase 2:

Overall, most patients were aged 55 years or older in both Phase 1 (81.7%)and Phase 2 (85.8%)

The largest age category was from ages 75 to 84, with 34.7% of patients inPhase 1 and 34.8% of patients in Phase 2

The 85 to 94 age category was the second largest with 20.2% of patients inPhase 1 and 22.9% of patients in Phase 2.

Figure 18: Age of DAA patients – Phase 1 and Phase 2

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up to

24 yrs

25-34

yrs

35-44

yrs

45-54

yrs

55-64

yrs

65-74

yrs

75-84

yrs

85-94

yrs

95 yrs

and

over

Missing

Age

Phase 1

Phase 2

What were the other demographic characteristics of patientsreceiving DAAs?

Using the classification of patients ‘completing’ the program as described in Section 5.2.2,the characteristics of those patients who completed the program can be compared to all patientswho participated in the program.

Table 14 provides an overview of the characteristics of the patients who participated in Phase 1and 2 of the DAA program – for all patients who participated and for those who were classifiedas completing the program. Overall, there were no key differences in the characteristics of



patients who completed the program compared to all patients in the program in Phase 1 orPhase 2. From Table 14 it can be seen that:

approximately 59.0% of patients, in both Phase 1 and Phase 2, were female

the distribution of participating patients across states was similar for Phase 1 and Phase 2,with the majority of participating patients being from NSW, VIC and QLD

in both Phase 1 and Phase 2 there were more patients located in areas with greaterrelative advantage (ie higher socioeconomic status). Approximately 11.0% of patients inboth Phase 1 and Phase 2 were in areas of relative disadvantage.

Table 14: Demographic characteristics of patients for Phase 1 and 2 of DAA – for all patients and thosewho completed the program


Phase 1 Phase 2

All patientsAll completing

patientsAll patients

All completingpatients

N (%) N (%) N (%) N (%)

Patientgender:

Male

Female

6,529

9,420

(40.9%)

(59.1%)

4,889

7,106

(40.8%)

(59.2%)

7,203

10,244

(41.3%)

(58.7%)

5,646

8,118

(41.0%)

(59.0%)

State: ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

176

4,777

95

3,110

1,232

470

3,376

1,579

1,134

(1.1%)

(30.0%)

(0.6%)

(19.5%)

(7.7%)

(3.0%)

(21.2%)

(9.9%)

(7.1%)

145

3,757

85

2,460

990

370

2,546

1,295

347

(1.2%)

(31.3%)

(0.7%)

(20.5%)

(8.3%)

(3.1%)

(21.2%)

(10.8%)

(2.9%)

205

5,670

105

3,378

1,443

530

3,698

1,925

493

(1.2%)

(32.5%)

(0.6%)

(19.4%)

(8.3%)

(3.0%)

(21.2%)

(11.0%)

(2.8%)

170

4,908

95

2,940

1,260

470

3,175

1,690

0

(1.2%)

(33.4%)

(0.7%)

(20.0%)

(8.6%)

(3.2%)

(21.6%)

(11.5%)

(0.00%)

SEIFA –Index ofrelativeadvantageanddisadvantage:

Category 1-2

Category 3-6

Category 7-10

Missing

1,712

5,474

8,502

261

(10.7%)

(34.3%)

(53.3%)

(1.6%)

1,276

4,115

6,509

95

(10.7%)

(34.4%)

(54.3%)

(0.8%)

1961

5909

9515

62

(11.2%)

(33.9%)

(54.6%)

(0.4%)

1583

4642

7487

52

(11.5%)

(33.7%)

(54.4%)

(0.4%)



What concessions or entitlement cards did patients receivingDAAs have?

For both Phase 1 and Phase 2, the vast majority of patients had concession or entitlement cards(approximately 94.0%). In Phase 2, data was collected on the type of concession or entitlementcard held by the patients. Overall, the majority of patients held a pension card (70.2%), followedby a safety net senior’s card (19.9%) and a Department of Veterans Affairs (DVA) card (12.5%).

Figure 19: Type of concession/entitlement card – Phase 2

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No charge Pension Senior health

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Safety net

senior

Safety net

concession

DVA

Concession card type

Did patients receiving DAAs live on their own or have help managingtheir medications?

In Phase 2, information was collected on whether patients managed their medicationsindependently. Table 15 shows that 70.2% of patients received assistance with managing theirmedications and 42.4% of patients receiving the DAA service were living alone.

Table 15: Other characteristics of DAA patients – Phase 2

Variable descriptionAll patients

N (%)

Assistance with managingmedications:

Receive assistance

Do not receive assistance

12,252

5,195

(70.2%)

(29.8%)

Patients living arrangements: Lives alone

Does not live alone

Missing

6,558

7,665

1,246

(42.4%)

(49.6%)

(8.1%)



5.2.4 How many and what types of medications were patients taking?

How many medications were patients taking?

The number of medications that a patient was taking was collected in both Phase 1 and Phase 2and is presented in Figure 20. From Figure 20 it can be seen that the distribution of the reportednumber of medications a patient was taking varied considerably between Phase 1 and Phase 2.From the analysis of historical PBS data for the Australian population in Section 7.1, thedistribution for Phase 1 is unlikely, and more likely reflects the data quality issues in Phase 1.Therefore the following summary is of Phase 2 data only:

Patients in Phase 2 were more likely to be taking four (22.9%) or five (23.0%) medications

Nearly 15.0% of patients were taking three medications

Approximately 15.0% of patients were taking six medications

Approximately 15.0% of patients were taking more than seven medications

Overall, the majority of patients were taking between three to six medications (75.1%).

Figure 20: Number of medications – Phase 1 and Phase 2

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Number of medications

Phase 1

Phase 2

What number of medications were people taking by age group?

Table 16 provides a summary of the number of medications that each age group were taking, inboth Phase 1 and Phase 2 of the DAA program. From this table it can be seen that patients aged65 and over were taking more medications than the other age groups (median was eightmedications in Phase 1 and five medications in Phase 2).



This is consistent with the PBS analysis presented in Section 7, where the older an individual isthe more medications they are likely to be taking and the more likely it is that the number ofmedications they are taking will increase.

Table 16: Number of medications by age group for DAA – Phase 1 and Phase 2

Age group

Phase 1 (Baseline) Phase 2 (Baseline)

N Mean Median N Mean Median

<55 years 1,790 5.9 5 1988 3.7 4

55 to 64 years 1,504 7.5 7 1706 4.7 5

65 to 74 years 2,576 8.1 8 2960 5.0 5

75 to 84 years 5,540 8.1 8 6070 5.0 5

85 and over 3,405 7.7 8 4230 4.7 5

What types of medications were patients taking?

The type of medication that a patient was taking was collected in Phase 2. Table 17 provides anoverview of the types of medications that patients were taking:

Approximately 84.0% of patients reported that they were taking blood pressure or cardiactherapy medication

Lipid-modifying agents were being taken by 62.5% of the patient cohort

Drug for acid related disorders were being taken by 53.7% of patients

Anti-thrombotic medication was being taken by 51.3% of the cohort.

Table 17: Number of patients on each medication type – Phase 2 of the PMP program

Medication typeNumber of

patients Medication typeNumber of

patients

Blood pressure/cardiac therapy 14,605 83.7% Bone disorder drugs 4,309 24.7%

Lipid-modifying agents 10,906 62.5% Anti-inflammatories/anti-rheumatics 2,904 16.6%

Acid related disorder drugs 9,364 53.7% Laxatives 1,586 9.1%

Anti-thrombotics 8,950 51.3% Hormone supplement/replacement 1,493 8.6%

Other drugs 6,300 36.1% Anti-infectives 661 3.8%

Psychoanaleptic drugs 6,056 34.7% Anti-parkinson drugs 671 3.8%

Drugs for diabetes 4,692 26.9% Anti-diarrhoeals 636 3.6%

Analgesics 4,567 26.2% Anti-neoplastic/immunomodulatingagents

567 3.2%

Psycholeptic drugs 4,344 24.9%



5.2.5 What conditions did DAA patients have?

Using the information discussed in Section 5.2.4, on medications taken by patients, the types ofconditions that patients had were able to be identified. Table 18 provides an overview of thetypes of conditions and the number of patients identified as having that condition. The majority ofpatients had a cardiovascular, nervous system or alimentary system condition.

Table 18: Patient conditions – Phase 2

Condition typeNumber of

patientsMedication type

Number ofpatients

Cardiovascular conditions 15,665 89.8% Diabetes 4,692 26.9%

Nervous system conditions 10,662 61.1% Hormonal conditions 1,493 8.6%

Alimentary tract conditions 10,144 58.1% Bacterial/viral infections 661 3.8%

Musculoskeletal conditions 6,379 36.6% Cancer/haematological conditions/immunedisorders

567 3.2%

Other conditions 6,300 36.1%

5.2.6 What proportion of patients were classified as being ‘at risk’?

Pharmacists participating in the DAA program were encouraged to recruit patients who weremost likely to benefit from the service. As described in the literature (Section 0) potentialcharacteristics of patients ‘at risk’ of an adverse medication related event include patient age,number of medications and assistance or support. Figure 21 illustrates the proportion of patientswho were recruited to Phase 2 of the DAA program with one or more risk factors. From thisfigure it can be seen that in Phase 2:

approximately 64% of recruited patients had two or more risk factors (ie aged 65 and over,had 5 or more medications and/or lived alone)

approximately 21% had all three risk factors

approximately 9% of patients had no risk factors.

Figure 21: Proportion of patients in Phase 2 of the DAA Program with one or more risk factors

DAA Phase 2

Less than 65 yearsn = 3,694 (21.2%)

75 years and oldern = 10,300 (59.0%)

65 to 74 yearsn = 2,960 (17.0%)

≥ 5 meds n = 1,494

(8.6%)

< 5 medsn = 1,233

(7.1%)

< 5 medsn = 2,200(12.6%)

≥ 5 meds n = 1,727

(9.9%)

< 5 medsn = 4,437(25.4%)

≥ 5 meds n = 5,863(33.6%)

Livingalone

n = 641(3.7%)

Not livingalone

n = 1,559(8.9%)

Livingalone

n = 563(3.2%)

Not livingalone

n = 931(5.3%)

Livingalone

n = 547(3.1%)

Not livingalone

n = 686(3.9%)

Livingalone

n = 704(4.0%)

Not livingalone

n = 1,023(5.9%)

Livingalone

n = 2,337(13.4%)

Not livingalone

n = 2,100(12.0%)

Livingalone

n = 3,014(17.3%)

Not livingalone

n = 2,849(16.3%)



5.2.7 What were the characteristics of the DAA service?

Who referred patients to the DAA service?

The source of referral to the DAA service was reported in both Phase 1 and Phase 2.In Phase 1, half of the referrals to the DAA service were provided by the patient’s pharmacist.Nearly 35.0% of referrals were from a General Practitioner (GP), 4.3% from a community healthworker and 10.0% were from another source.

Further detail was collected in Phase 2 on the source of referral and is presented in Figure 22:

in Phase 2, nearly 40.0% of referrals were from the GP

approximately 50.0% of referrals were from a pharmacist and 2.1% from otherpharmacy staff

referrals from carers and family were 12.8% of the total referrals and patient requests were8.1% of referrals.

Figure 22: Source of referral to the DAA service – Phase 2

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Hospital

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Carer/ family Patient

request

Other

Source of referral



How long had patients been receiving the DAA service?

How long the patient had been receiving the DAA service (in the participating pharmacy) wascollected in Phase 2. From Figure 23 it can be seen that:

nearly half of the cohort had been receiving DAAs for more than 24 months (42.2%)

approximately 30.0% of patients had been receiving the service between six to 18 months

only 4.1% of patients were new to the service and 10.6% had been receiving the servicefor less than six months.

Figure 23: How long patients had been receiving the DAA service – Phase 2

New

Less than 6 mths

6-12 mths

13-18 mths

19-24 mths

More than 24 mths

4.1%

10.6%

15.5%

15.9%

11.6%

42.2%

Were patients managing their DAAs independently?

In Phase 1, information was collected on whether patients managed their DAA’s independently.Approximately 81.0% of the patient cohort reported that they did manage theirDAA independently.

Were there changes made to the DAAs?

In both Phase 1 and Phase 2, information was collected on whether changes were made to apatient’s DAA. The reasons for the change were collected in Phase 1 only.

On average, approximately 41.0% of patients required a change to their DAA in Phase 1 andapproximately 38.0% of patients in Phase 2. Of those patients, an average of 1.3 DAAs requiredan immediate repack and 1.1 required a change in the next pack. In Phase 1, for those patientswho responded to the question, the reasons for changes in the DAA were due to:

hospital (approximately 5.0%)

change in medication (approximately 31.0%)

other (approximately 3.0%)

not applicable (61.0%).



What types of medications were packed in the DAAs?

In Phase 2, pharmacies reported on the number of medications which were packed in the DAAand were PBS and non PBS. From Table 19 it can be seen that:

the average number of PBS medications packed in the DAA was seven

the average number of non PBS medication packed in the DAA was one.

Table 19: Number of medications included in the DAA – Phase 2

Medications Number of patients Mean SD Median

PBS/Repat 17,447 6.9 3.1 7.0

Non PBS/Repat 17,447 0.8 1.2 0.0

Did patients experience any medication related events?

Information on whether patients experienced any medication related events was captured inPhase 1. The vast majority of patients reported that they did not have a medication related event(96.4%). Of those who did have a medication related event:

495 patients (3.1%) had one event only

67 patients had two events (0.4%)

20 patients had three or more events (0.1%).

Of those who reported a medication related event:

158 patients reported it was due to a medication underdose

144 patients reported it was due to taking the wrong medication

235 patients reported it was due to another non-specified reason

201 patients reported that they had to visit a health professional as a result of the event

four patients reported having to take sick leave as a result of the event

222 reported that they experienced another non-specified consequence.

What were the reasons for patients exiting the DAA program?

In both Phase 1 and Phase 2, the reasons for patients exiting the DAA program were collected.Figure 24 and Figure 25 present how many patients exited the program and the reasons why,over Phase 1 and Phase 2 of the DAA program. These figures show that the most commonlycited reasons for exiting the program, across all time periods, were:

the patient left the district

the patient died

the patient withdrew from the program.



Figure 24: Patient exit reasons – Phase 1

0

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withdrew

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moved

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Period 2

Period 3

Period 4


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What were the number of deaths amongst patients by age group?

Table 20 provides a summary of the number of deaths categorised by age group, in bothPhase 1 and Phase 2 of the DAA program. From this table it can be seen that patients aged 75and over accounted for the majority of deaths amongst patients in both Phase 1 and Phase 2.Patients under 65 years only accounted for approximately 8.0% to 10.0% of deaths.

Table 20: Number of deaths by age group

Age groupPhase 1 Phase 2

N % N %

<55 years 62 (3.1%) 72 (4.3%)

55 to 64 years 109 (5.5%) 101 (6.0%)

65 to 74 years 259 (13.1%) 235 (14.0%)

75 to 84 years 881 (44.4%) 679 (40.5%)

85 and over 673 (33.9%) 590 (35.2%)

5.2.8 What other services were patients receiving?

In both Phase 1 and Phase 2, information was collected on the number and type of other 4CPAfunded services that patients were receiving. Figure 26 provides an overview of the number ofother services that patients were receiving and it can be seen that:

in Phase 1, 49.0% of patients were receiving a DAA only, 43.4% were receiving oneadditional service and 6.9% of patients were receiving two or more additional services

in Phase 2, 41.0% were receiving a DAA only, 44.4% were receiving one additionalservice and 14.6% of patients were receiving two or more additional services

overall, more patients in Phase 2 were receiving other 4CPA funded services in addition totheir DAA.



Figure 26: Number of other 4CPA funded services received by patients – Phase 1 and Phase 2

0

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7000

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None One Two Three FourNumber of other services

Phase 1

Phase 2

Note: Data on patients receiving four programs not collected in Phase 1.

From Figure 27 it can be seen that of those patients who were receiving additional 4CPAfunded services:

the majority were receiving PMPs (42.8% in Phase 1 and 48.9% in Phase 2)

nearly 14.0% were receiving HMRs in Phase 1 and 24.1% in Phase 2

a small number were receiving DMAS (0.9% in Phase 1 and 1.3% in Phase 2)

there were 0.2% of Phase 2 patients receiving PAMS.

Figure 27: Type of other services received by patients – Phase 1 and Phase 2

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Phase 1

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Note: PAMS data not collected in Phase 1.

Patient Medication Profiling


6 Patient Medication Profiling evaluation results

Key findings:

Retention of both pharmacies and patients to the PMP program was reasonably high andvery high for Phase 2 with over 86.0% of pharmacies and 88.0% of patients remaining inthe program at the end of the April 2010. Furthermore, most pharmacies and patients inPhase 2 were engaged across all periods of the PMP data collection.

As for the DAA program, a broad range of pharmacies participated in the PMP program.The distribution of participating pharmacies across State, PhARIA and SEIFA wasrepresentative of community pharmacies nationally, suggesting that there may be no ‘type’of the pharmacy which is more likely to opt-in to providing the PMP service. These resultstherefore suggest that the results from the PMP program may be generalisable topharmacies nationally.

In Phase 1, very few pharmacies were new to providing the PMP service, with only 8.5%reported that they had been providing the service for less than six months. (Note: Thesedata were not reported for Phase 2).

The majority of pharmacies in Phase 2 reported delivering other 4CPA pharmacy services,particularly DAAs and HMRs (note: these data were not collected in Phase 1).

Most participating patients were aged 55 years or older, with the by far the most commonage category of patients being aged 75 to 84, followed by the age bands on either side ofthis category, that is 65 to 74 and 85 to 94.

The majority of patients reported receiving assistance with managing their medicationsand almost half of patients reported living alone.

The majority of patients had a cardiovascular, nervous system or alimentarysystem condition.

The majority of patients in both phases were taking between three to six medications(67.0%). The most common medications amongst patients in the PMP program wereblood pressure medication and lipids.

Almost one third of patients were new to the PMP service.

However, in line with the nature of the PMP service, the majority of patients (75.0%) werereceiving one or more additional services, and in most instances this was the DAA service.

The patterns of exit and their reasons were similar in both Phase 1 and Phase 2.Approximately 6,863 exited the program across both phases. The most common reasonfor exit for both Phase 1 and Phase 2 was most commonly death (N= 1,722 across bothphases), or the patient moving. Exit to another care facility was relatively rare.



6.1 Pharmacy results

This section provides a description of the key findings relating to the PMP service delivered bycommunity pharmacists, by addressing a number of important questions related to participatingpharmacies. These questions include:

What was the recruitment of pharmacies to the PMP program?

What were the rates of completion amongst pharmacies in the PMP program?

What were the characteristics of pharmacies who participated in the Program?

How do pharmacies who participated compare to pharmacies nationally?

What did the PMP service provided by pharmacies look like?

To address these questions the PMP service data were analysed and results for both Phase 1and Phase 2, where available, are presented below.

6.1.1 What was recruitment of pharmacies to the PMP program?

Recruitment of pharmacies to Phase 1 of the PMP program

A total of 2,967 pharmacies registered and/or participated in Phase 1 of the PMP program, ofwhich, 1,755 pharmacies (59.2%) remained in the program at the end of Phase 1. A total of1,573 pharmacies dropped out over the Phase 1 period. Figure 28 provides an overview of therecruitment and retention of pharmacies to Phase 1 of the PMP program.



Figure 28: Pharmacy recruitment to Phase 1 of the PMP program

Recruitment of pharmacies to Phase 2 of the PMP program

A total of 2,524 pharmacies registered and/or participated in Phase 2 of the PMP program, ofwhich, 2,188 pharmacies (86.7%) remained in the program at the end of Phase 2. A total of585 pharmacies dropped out over the Phase 2 period. Figure 29 provides an overview of therecruitment and retention of pharmacies to Phase 2 of the PMP program. Overall, retention ofpharmacies to the PMP program was higher in Phase 2.



Figure 29: Pharmacy recruitment to Phase 2 of the PMP program


6.1.2 What were the rates of completion amongst pharmacies in thePMP program?

Rates of completion of pharmacies in Phase 1 of the PMP program


a total of 1,708 (57.6%) pharmacies were classified as ‘completing’ Phase 1 of the PMPprogram (ie they participated in every data collection point)

there were 615 pharmacies (20.7%) classified as ‘half-completing’ the program (ie theyparticipated in some data collection points, but not all)

there were 644 pharmacies (21.7%) classified as ‘non-completers’ of Phase 1 of the PMPprogram (ie they registered but provided no period data).



Table 21: Rates of completion amongst pharmacies in Phase 1 of the PMP program

Registered Baseline Period 1 Period 2 Period 3 Frequency Classification

644 Non-completing

246 Half-completing

33 Half-completing

170 Half-completing

47 Half-completing

119 Half-completing

55 Completing

206 Completing

1447 Completing

Rates of completion of pharmacies in Phase 2 of the PMP program


a total of 2,097 (83.1%) pharmacies were classified as ‘completing’ Phase 2 of thePMP program

there were 249 pharmacies (9.9%) classified as ‘half-completing’ the program

there were 178 pharmacies (7.1%) classified as ‘non-completers’ of Phase 2 of thePMP program.

Table 22: Rates of completion amongst pharmacies in Phase 2 of the PMP program


178 Non-completing

11 Half-completing

66 Half-completing

91 Half-completing

81 Half-completing

55 Completing

92 Completing

1950 Completing



6.1.3 What were the characteristics of pharmacies who participated?

Characteristics of the pharmacies who participated in Phase 1 andPhase 2 of the PMP program

Using the classification of pharmacies ‘completing’ the program described in Section 6.1.2,the characteristics of those pharmacies who completed the program can be compared to allpharmacies who participated in the program. From Table 23 it can be seen that:

The distribution of participating pharmacies across states was similar for Phase 1 andPhase 2, with the majority of participating pharmacies being from NSW, VIC and QLD.

The distribution of participating pharmacies across PhARIA categories was also similar forPhase 1 and Phase 2, with approximately 67.5% of participating pharmacies classified ashighly accessible.

In both Phase 1 and Phase 2 there were more pharmacies located in areas with greaterrelative advantage (ie higher socioeconomic status). Broadly in line with the distribution ofpharmacies nationally, approximately 11% of pharmacies in both Phase 1 and Phase 2were in areas of relative disadvantage.

In both Phase 1 and Phase 2 there was a fairly even distribution of pharmacies across theweekly prescription volume categories, suggesting that a broad range of pharmaciesparticipated in the trial. For example, approximately half of the participating pharmaciesreported prescribing greater than 1000 prescriptions per week.

Patient Medication Profiling evaluation results


Table 23: Characteristics of pharmacies who participated in Phase 1 and Phase 2 of PMP– for all pharmacies and those who completed the program


Phase 1 Phase 2

All pharmacies All completing pharmacies All pharmacies All completing pharmacies

N (%) N (%) N (%) N (%)

State: ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

29

716

5

452

181

63

504

219

154

(1.2%)

(30.8%)

(0.2%)

(19.5%)

(7.8%)

(2.7%)

(21.7%)

(9.4%)

(6.6%)

20

555

3

341

143

48

369

176

53

(1.2%)

(32.5%)

(0.2%)

(20.0%)

(8.4%)

(2.8%)

(21.6%)

(10.3%)

(3.1%)

30

834

6

496

203

72

535

282

66

(1.2%)

(33.0%)

(0.2%)

(19.7%)

(8.0%)

(2.9%)

(21.2%)

(11.2%)

(2.6%)

25

707

4

427

174

61

451

248

0

(1.2%)

(33.47%)

(0.2%)

(20.4%)

(8.3%)

(2.9%)

(21.5%)

(11.8%)

(0.0%)

PhARIA category: Category 1-2

Category 3-4

Category 5-6

Missing

1,685

189

54

395

(71.5%)

(8.1%)

(2.3%)

(17.0%)

1,305

146

36

221

(76.4%)

(8.5%)

(2.1%)

(12.9%)

1,755

181

57

531

(69.6%)

(7.1%)

(2.3%)

(21.0%)

1,517

155

45

380

(72.3%)

(7.3%)

(3.2%)

(18.1%)

SEIFA – Index ofrelative advantageand disadvantage:

Category 1-2

Category 3-6

Category 7-10

Missing

248

740

1,179

156

(10.7%)

(31.9%)

(50.8%)

(6.7%)

188

552

91

55

(11.0%)

(32.3%)

(53.5%)

(3.2%)

287

791

1,377

69

(11.4%)

(31.3%)

(54.6%)

(2.7%)

237

671

1,186

3

(11.3%)

(32.0%)

(56.6%)

(0.1%)

Average weeklyprescriptionvolume*:

<400

401-600

601-800

801-1000

1001-1200

1201-1400

≥1401

247

257

292

202

214

145

426

(13.9%)

(14.4%)

(16.4%)

(11.4%)

(12.0%)

(8.2%)

(23.9%)

205

211

245

168

173

118

327

(14.1%)

(14.5%)

(16.9%)

(11.6%)

(11.9%)

(8.2%)

(22.6%)

220

378

400

333

288

232

673

(8.7%)

(15.0%)

(15.8%)

(13.2%)

(11.4%)

(9.2%)

(26.6%)

176

313

316

280

241

202

569

(8.4%)

(14.9%)

(15.1%)

(13.4%)

(11.5%)

(9.6%)

(27.1%)

* As reported by pharmacies in the Period 1 and Phase 2 data collection

Patient Medication Profiling evaluation results


6.1.4 How do the pharmacies who participated compare topharmacies nationally?

Table 24 provides a comparison of pharmacies who participated in Phase 2 of the PMP programto pharmacies nationally. Overall, there were no key differences in the characteristics of thepharmacies that participated in the PMP program compared to all pharmacies within Australia.These findings suggest that the sample of pharmacies who participated in the PMP programwere representative of pharmacies nationally, supporting the generalisation of the findingspresented in this report to pharmacies nationally.

Table 24: Comparison of PMP pharmacies in Phase 2 to all pharmacies nationally

Variable descriptionPMP pharmacies All pharmacies

N (%) N (%)

State (May, 2010): ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

30

834

6

496

203

72

535

282

66

(1.2%)

(33.0%)

(0.2%)

(19.7%)

(8.0%)

(2.9%)

(21.2%)

(11.2%)

(2.6%)

65

1,771

31

1,042

420

141

1,211

530

(1.2%)

(34.0%)

(0.6%)

(20.0%)

(8.1%)

(2.7%)

(23.2%)

(10.2%)

SEIFA – Index of relativeadvantage anddisadvantage*

Category 1-2

Category 3-6

Category 7-10

Missing

287

791

1,377

69

(11.4%)

(31.3%)

(54.6%)

(2.7%)

582

1,623

2,863

7

(11.5%)

(32.0%)

(56.4%)

(0.1%)

PhARIA*: Category 1-2

Category 3-4

Category 5-6

Missing

1,755

181

57

531

(69.6%)

(7.1%)

(2.3%)

(21.0%)

4,363

448

144

120

(86.0%)

(8.8%)

(2.8%)

(2.4%)

* These numbers are estimates making up the total number of pharmacies based on the 2006 data from the PGA as opposedto 2003 data from Curtin University of Technology WA, additions in the categories from the 2003 data have been estimated bythe earlier base percentage in each category multiplied by the 2006 figures to estimate the increase in number of pharmaciesin each PhARIA category.



6.1.5 What did the service provided by pharmacies look like?

How long had pharmacies been providing the PMP service?

In Phase 1, pharmacies reported the length of time they had been proving the PMP service.Figure 30 provides an overview of what pharmacies reported at baseline. This graphreveals that:

just under half of the pharmacies (44.0%) reported that they had been providing the PMPservice for approximately six to 18 months

another 20.0% of pharmacies reported that they had been providing the PMP service forover 24 months

very few pharmacies reported that they were new to the service, ie they had beenproviding the service less than three months (6.9%).

Figure 30: Length of time pharmacies had been proving the PMP service – Phase 1

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<3 mths 3-6 mths 6-12 mths 12-18 mths 18-24 mths >24 mths Missing

Length of time providing PMP service



What software was used for PMPs?

In Phase 2, pharmacies reported on the software brand that they used for PMPs. In Phase 2,Manrex Webstercare was the most commonly used software brand, used by 45.0% ofpharmacies. This was followed by Fred Pak (approximately 20.0%).

Figure 31: Packaging brands used for PMPs – Phase 2*

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Acq

uariu

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Min

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Oth

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* Other includes Phillips and Phillips, Pheonix, Healthsoft and Meditek



What marketing methods were used for PMPs?

In Phase 1, pharmacies reported on the marketing methods that they used to bring PMP patientsonto the program. Figure 32 provides an overview of the methods reported across the fourPhase 1 reporting periods.

Overall, the most commonly used marketing method was electronic media, with approximately99.0% of pharmacies reporting that they used it across the four reporting periods. Newsletterswere also commonly used, with approximately 95.0% of pharmacies reporting that they usedthem. Overall, there was little change in the marketing methods used across Phase 1.

Figure 32: Marketing methods used for PMP – Phase 1

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Direct approach Posters Newsletters Electronic media Other

Period 1

Period 2

Period 3

What other services were the pharmacies providing?

In Phase 2, pharmacies reported on the other 4CPA funded services that they provide withintheir pharmacy. The number of other services that pharmacies reported they provided are:

no other 4CPA funded services (1.4%)

one additional 4CPA funded service only (13.4%)

two additional 4CPA funded services (60.3%)

three additional 4CPA funded services (21.6%)

four additional 4CPA funded services (3.3%).



Figure 33 provides an overview of which 4CPA funded services they were providing, in additionto PMP. The most commonly provided service was DAAs (96.8%), followed by HMR (83.5%)and DMAS (26.4%). Interestingly, compared with pharmacies participating in the DAA program,the pharmacies who participated in the PMP program were more likely to be providing otherservices within their pharmacy.

Figure 33: Other 4CPA funded services provided by the pharmacy – Phase 2

0

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DAA DMAS HMR PAMS

Other services provided in the pharmacy

6.2 Patient results

This section provides a description of the key findings relating to the PMP service received bypatients, by addressing a number of important questions related to participating patients. Thesequestions include:

What was the recruitment of patients to the PMP program?

What were the rates of completion amongst patients in the PMP program?

What were the characteristics of patients who participated in the Program?

How many and what types of medications were patients taking?

What conditions did PMP patients have?

What proportion of patients were classified as being ‘at risk’?

What were the characteristics of the PMP service that patients received?

What other services were the patients receiving?



To address these questions the PMP service data were analysed and results for both Phase 1and Phase 2, where available, are presented below.

6.2.1 What was the recruitment of patients to the PMP program?

Recruitment of patients to Phase 1 of the PMP program

In Phase 1, there were a total of 11,250 patients at baseline receiving the PMP service and8,750 patients at Period 3. A total of 4,246 patients dropped out over the Phase 1 period.Figure 34 provides an overview of the recruitment and retention of patients during to Phase 1.

Figure 34: Patient recruitment to Phase 1 of the PMP program



Recruitment of patients to Phase 2 of the PMP program

In Phase 2, there were a total of 12,289 patients at baseline receiving the PMP service and10,910 patients at Period 3. A total of 2,617 patients dropped out over the Phase 2 period.Figure 35 provides an overview of the recruitment and retention of patients during to Phase 2.

Figure 35: Patient recruitment to Phase 2 of the PMP program

Note: the cut-off date for the inclusion of Phase 2 data in the analysis was 8 June 2010.

6.2.2 What were the rates of completion amongst patients in thePMP program?

Rates of completion of patients in Phase 1 of the PMP program


a total of 8,524 (75.8%) patients were classified as ‘completing’ Phase 1 of the PMPprogram (ie they participated in consecutive data collection points until the last datacollection period)


there were 945 patients (8.4%) classified as ‘non-completers’ of Phase 1 of the PMPprogram (ie they provided no data).



Table 25: Rates of completion amongst patients in Phase 1 of the PMP program


945 Non-completing

158 Half-completing

808 Half-completing

222 Half-completing

593 Half-completing

286 Completing

1,076 Completing

7162 Completing

Rates of completion of patients in Phase 2 of the PMP program


a total of 9,797 (79.7%) patients were classified as ‘completing’ Phase 2 of the PMPprogram (ie they participated in consecutive data collection points until the last datacollection period)


there were 1,249 patients (10.2%) classified as ‘non-completers’ of Phase 2 of the PMPprogram (ie they provided no data).

Table 26: Rates of completion amongst patients in Phase 2 of the PMP program


1,249 Non-completing

52 Half-completing

324 Half-completing

456 Half-completing

411 Half-completing

265 Completing

465 Completing

9,067 Completing

6.2.3 What were the characteristics of patients who participated?

What age were the patients who were receiving PMPs?

Overall, there were no key differences in the age of patients who completed the programcompared to all patients in the program; therefore the age information presented below is based onall patients.



Figure 36 provides an overview of the age of patients in both Phase 1 and Phase 2:

Overall, most patients were aged 55 years or older in both Phase 1 (82.5%) andPhase 2 (87.0%)

The largest age category was from ages 75 to 84, with 33.0% of patients in Phase 1 and34.0% of patients in Phase 2

The 65 to 74 and 85 to 94 age categories were the second largest with 18.0% of patientsin Phase 1 and 20.0% in Phase 2.

Figure 36: Age of PMP patients – Phase 1 and Phase 2

0

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3000

3500

4000

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up to 24

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25-34

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35-44

yrs

45-54

yrs

55-64

yrs

65-74

yrs

75-84

yrs

85-94

yrs

95 yrs

and

over

Missing

Phase1

Phase 2

What were the demographic characteristics of patients receiving PMPs?

Using the classification of patients ‘completing’ the program described in Section 16.2.2 thecharacteristics of those patients who completed the program can be compared to all patientswho participated in the program.

Table 27 provides an overview of the characteristics of the patients who participated in Phase 1and Phase 2 of the PMP program, for all patients who participated and for those who wereclassified as completing the program. Overall, there were no key differences in thecharacteristics of patients that completed the program compared to all patients in the programin Phase 1. From Table 27 it can be seen that:

about 58.0% of patients, in both Phase 1 and Phase 2, were female

the distribution of participating patients across states was similar for Phase 1 and Phase 2,with the majority of participating pharmacies being from NSW, VIC and QLD

in both Phase 1 and Phase 2 there were more patients located in areas with greaterrelative advantage (ie higher socioeconomic status). Approximately 11.0% of patients inboth Phase 1 and Phase 2 were in areas of relative disadvantage.



Table 27: Patient demographics for Phase 1 and Phase 2 of PMP – for all patients and completers


Phase 1 Phase 2

All patientsAll completing

patientsAll patients

All completingpatients

N (%) N (%) N (%) N (%)

Patientgender:

Male

Female

4,694

6,556

(41.7%)

(58.3%)

3,532

4,992

(41.4%)

(58.6%)

5,168

7,121

(42.1%)

(57.9%)

4,092

5,705

(41.8%)

(58.2%)

State: ACT

NSW

NT

QLD

SA

TAS

VIC

WA

Missing

140

3,473

24

2,196

882

309

2,459

1,042

725

(1.2%)

(30.9%)

(0.2%)

(19.5%)

(7.8%)

(2.8%)

(21.9%)

(9.26%)

(6.44%)

100

2,764

15

1,705

712

240

1,845

885

258

(1.2%)

(32.4%)

(0.2%)

(20.0%)

(8.4%)

(2.8%)

(21.6%)

(10.4%)

(3.0%)

140

4,086

23

2,394

1,006

351

2,582

1,385

322

(1.1%)

(33.3%)

(0.2%)

(19.5%)

(8.2%)

(2.9%)

(21.0%)

(11.3%)

(2.6%)

125

3,531

20

2,125

867

300

2,251

1,235

0

(1.2%)

(33.8%)

(0.2%)

(20.3%)

(8.3%)

(2.9%)

(21.5%)

(11.8%)

(0.00%)

SEIFA –Index ofrelativeadvantageanddisadvantagecategory:

Category 1-2

Category 3-6

Category 7-10

Missing

1,238

3,244

6,054

179

(11.0%)

(34.4%)

(53.0%)

(1.6%)

963

2,886

4,601

74

(11.3%)

(33.8%)

(54.0%)

(0.9%)

1,411

4,076

9,771

31

(11.5%)

(33.1%)

(55.1%)

(0.3%)

1,109

3,232

5,428

28

(11.3%)

(33.1%)

(53.2%)

(0.3%)



What concessions and entitlements did patients receiving PMPs have?

For both Phase 1 and Phase 2, the vast majority of patients held concession or entitlement cards(approximately 92.0%). In Phase 2, data was collected on the type of concession/entitlementcard held by the patients. Overall, the majority of patients held a pension card (69.0%), followedby a safety net senior’s card (21.3%) and a DVA card (11.1%).

Figure 37: Type of concession/entitlement card – Phase 2

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Nu

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er

ofp

atie

nts

Pension Senior health

card

Health care

card

Safety net

senior

Safety net

concession

DVA repat card

Concession card type

Did patients receiving PMPs have help managing their medications?

In Phase 2, information was collected on whether patients managed theirmedications independently. Table 28 shows that 73% of patients receive assistance withmanaging their medications. This table also shows that just under half of patients receiving thePMP service were living alone.

Table 28: Other characteristics of patients who participated in Phase 2 of the PMP program

Variable descriptionAll patients

N (%)

Assistance with managing medications: Receive assistance

Do not receive assistance

8,940

3,349

(72.7%)

(27.3%)

Patients living arrangement: Lives alone

Does not live alone

5,315

6,974

(43.3%)

(56.7%)



6.2.4 How many and what types of medications were patients taking?

How many medications were patients taking?

The number of medications that a patient was taking was collected in both Phase 1 and Phase 2and is presented in Figure 38. From Figure 38 it can be seen that the distribution of the reportednumber of medications a patient was taking varied considerably between Phase 1 and Phase 2.From the analysis of historical PBS data for the Australian population (Section 7.1), thedistribution for Phase 1 is unlikely, and more likely reflects the data quality issues in Phase 1.Therefore the following summary is of Phase 2 data only:

Patients in Phase 2 were more likely to be taking four (18.5%) or five (20.8%) medications

Just over 11.0% of patients were taking three medications

Approximately 17.0% of patients were taking six medications

Approximately 12.0% of patients were taking more than seven medications

Overall, the majority of patients were taking between four to seven medications (67.1%).

Figure 38: Number of medications for all patients – Phase 1 and Phase 2

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What were the numbers of medications that people were taking byage group?

Table 29 provides a summary of the number of medications that each age group were taking, inboth Phase 1 and Phase 2 of the PMP program. From this table it can be seen that patientsaged 65 and over were taking more medications than the other age groups (median was eightmedications in Phase 1 and five medications in Phase 2).

This is consistent with the PBS analysis presented in Section 7.1, where the older an individualis the more medications they are likely to be taking and the more likely it is that the number ofmedications they are taking will increase.

Table 29: Number of medications by age group for PMP – Phase 1 and Phase 2

Age groupPhase 1 (Baseline) Phase 2 (Baseline)

N Mean Median N Mean Median

<55 years 1242 6.2 6 1295 4.2 4

55 to 64 years 1338 7.5 7 1493 5.0 5

65 to 74 years 2052 8.2 8 2407 5.5 5

75 to 84 years 3734 8.6 8 4207 5.7 5

85 and over 2159 8.1 8 2565 5.4 5

What types of medications were patients taking?

The type of medication that a patient was taking was collected in Phase 2. Table 30 provides anoverview of the types of medications that patients were taking, in order of most common types.

The most common medication amongst patients in the PMP program was blood pressuremedication (approximately 85.0%).

The next most common medication was lipids, taken by almost 65.0% of the patientcohort.

About half of patients were taking acids and anti-thrombosis medications.



Table 30: Number of patients on each medication type – Phase 2

Medication typeNumber of


Number ofpatients

Blood pressure/cardiac therapy 10,404 84.7% Obstructive airway disease drugs 1,714 13.9%

Lipid-modifying agents 7,969 64.8% Ear/eye preparations 1,698 13.8%

Acid related disorder drugs 6,779 55.2% Laxatives 1,445 11.8%

Anti-thrombotics 6,247 50.8% Hormone supplement/replacement 1,209 9.8%

Analgesics 4,586 37.3% Anti-infectives 748 6.1%

Psychoanaleptic drugs 3,941 32.1% Anti-psoriatics/corticosteroidpreparations

719 5.9%

Other drugs 3,815 31.0% Anti-diarrhoeals 632 5.1%

Drugs for diabetes 3,562 29.0% Anti-neoplastic/immunomodulatingagents

438 3.6%

Psycholeptic drugs 3,150 25.6% Anti-parkinson drugs 423 3.4%

Bone disorder drugs 3,074 25.0% Nasal preparations 262 2.1%

Anti-inflammatories/anti-rheumatics

2,506 20.4% Emollients and protectives 244 2.0%

6.2.5 What conditions did PMP patients have?

Using the information discussed in Section 6.2.4 on type of medications taken by patients, thetypes of conditions that patients had were able to be identified. Table 31 provides an overview ofthe types of conditions and the number of patients identified as having that condition. Themajority of patients had a cardiovascular, nervous system or alimentary system condition.

Table 31: Patient conditions – Phase 2

Condition typeNumber of


Number ofpatients

Cardiovascular conditions 11,163 90.8% Respiratory conditions 1,895 15.4%

Nervous system conditions 8,012 65.2% Ear/eye conditions 1,698 13.8%

Alimentary tract conditions 7,460 60.7% Hormonal conditions 1,209 9.8%

Musculoskeletal conditions 4,800 39.1% Dermatological conditions 883 7.2%

Other conditions 3,815 31.0% Bacterial/viral infections 748 6.1%

Diabetes 3,562 29.0% Cancer/haematologicalconditions/immune disorders

438 3.6%



6.2.6 What proportion of patients were classified as being ‘at risk’?

Pharmacists participating in the PMP program were encouraged to recruit patients who weremost likely to benefit from the service. As described in the literature (Section 0) potentialcharacteristics of patients ‘at risk’ of an adverse medication related event include patient age,number of medications and assistance or support. Figure 39 illustrates the proportion of patientswho were recruited to Phase 2 of the PMP program with one or more risk factors. From thisfigure it can be seen that in Phase 2:

approximately 67.0% of recruited patients had two or more risk factors (ie aged 65 andover, had five or more medications or lived alone)

approximately 24.0% of patients had all three risk factors

approximately 8.5% of patients had no risk factors.

Figure 39: Proportion of patients in Phase 2 of the PMP Program with one or more risk factors

PMP Phase 2

Less than 65 yearsn = 2,788 (22.7%)

75 years and oldern = 6,772 (55.1%)

65 to 74 yearsn = 2,407 (19.3%)

≥ 5 meds n = 1,370(11.2%)

< 5 medsn = 833(6.5%)

< 5 medsn = 1,418(11.5%)

≥ 5 meds n = 1,574(12.8%)

< 5 medsn = 2,108(17.1%)

≥ 5 meds n = 4,664(38.0%)

Livingalone

n = 386(3.1%)

Not livingalone

n = 1,032(8.4%)

Livingalone

n = 440(3.6%)

Not livingalone

n = 930(7.6%)

Livingalone

n = 326(2.7%)

Not livingalone

n = 507(4.1%)

Livingalone

n = 567(4.6%)

Not livingalone

n = 1,007(8.2%)

Livingalone

n = 1,095(8.9%)

Not livingalone

n = 1,013(8.2%)

Livingalone

n = 2,343(19.1%)

Not livingalone

n = 2,321(18.9%)

6.2.7 What were the characteristics of the PMP service thatpatients received?

Who referred the patients to the PMP service?

The source of referral to the PMP service was reported in both Phase 1 and Phase 2.In Phase 1, most referrals to the PMP program were provided by thepatient’s pharmacist (87.0%). The rest of the referrals mainly came from the patient’s GP(approximately 8.0%). Further detail was collected in Phase 2, and is presented in Figure 40.

The majority of patients (9,000 patients or 73.2%) were referred to the PMP service bythe pharmacist

GPs were the next most likely to refer patients to the service (11.9%), followed by a careror family member (5.5%) and the patient themselves (3.0%).



Figure 40: Referral to the PMP Service – Phase 2

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What types of medications were included on the PMP?

In Phase 2, pharmacies reported on the number of medications which were included on thePMP. From Table 32 it can be seen that:

the average number of PBS medications included on the PMP was eight

the average number of non PBS medication included on the PMP was one.

Table 32: Number of medications included on the PMP – Phase 2

Medications Number of patients Mean SD Median

PBS/Repat 12,289 8.2 3.7 8.0

Non PBS/Repat 12, 289 1.0 1.4 0

How long have patients been receiving the PMP service?

In Phase 2, pharmacies reported on the length of time the patients had been receiving the PMPservice. From Figure 41 it can be seen that:

almost a third of patients were new to the PMP service (30.4%)

another third of patients had been receiving the PMP service for less than12 months (35.6%)

only 8.3% of patients had been receiving the service for more than 24 months.



Figure 41: Length of time patient has been receiving PMP service – Phase 2

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Did patients experience any medication related events?

Information on whether patients experienced any medication related events was captured inPhase 1. The vast majority of patients reported that they did not have a medication related event(96.6%). Of those who did have a medication related event:

324 patients (2.9%) had one event only

54 patients had two events (0.5%)

Five patients had three or more events (0.1%).

Of those who patients who had a reported medication related event:

115 were due to a medication underdose

93 were due to a medication overdose

92 were due to taking the wrong medication

147 were due to another non-specified reason.

It was also reported that as a result of the event:

175 patients had to visit a health professional

147 patients were hospitalised

Five patients reported having to take sick leave

110 experienced another non-specified consequence.



What were the reasons for patients exiting the PMP program?

In both Phase 1 and Phase 2, the reasons for patients exiting the PMP program were collected.Due to data quality issue, this section will only report on Phase 2 data.

Figure 42 presents how many patients exited the program in Phase 2 and the reasons why.This figure shows that the most commonly cited reasons for exiting the program across all timeperiods were that they patient left the district or the patient died. This was followed bypatient withdrawal.


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What were the number of deaths amongst patients by age group?

Table 33 provides a summary of the number of deaths categorised by age group, in bothPhase 1 and Phase 2 of the PMP program. From this table it can be seen that patients aged75 and over accounted for the majority of deaths amongst patients in both Phase 1 and Phase 2.Patients under 65 years only accounted for approximately 9.0% to 11.0% of deaths.

Table 33: Number of deaths by age group

Age groupPhase 1 Phase 2

N % N %

<55 years 17 (3.0%) 30 (2.7%)

55 to 64 years 47 (8.2%) 69 (6.2%)

65 to 74 years 76 (13.2%) 145 (12.9%)

75 to 84 years 226 (39.3%) 434 (38.7%)

85 and over 209 (36.4%) 444 (39.6%)



6.2.8 What other services were patients receiving?

In both Phase 1 and Phase 2, information was collected on the number and type of otherservices that patients were receiving:

Overall, 26.0% of patients were only receiving the PMP service in both Phase 1 and 2

More patients were receiving only one other service in Phase 1 (64.6%) than inPhase 2 (54.9%)

In Phase 2, 18.8% of patients were receiving two other services compared to 9.4%in Phase 1

Less than 1.0% of patients were receiving three or more other services in both Phase 1and Phase 2.

From Figure 43 it can be seen that of those patients who were receiving additional services:

the majority were receiving DAAs (71.0% in both Phase 1 and Phase 2)

there was a two-fold increase in the proportion of patients who received an HMR inaddition to the PMP service from Phase 1 (12.0%) to Phase 2 (22.0%)

only a small number were also receiving a DMAS (approximately 1.0% in both Phase 1and Phase 2)

there were 0.2% of Phase 2 patients receiving PAMS.

Figure 43: Other services patients received – Phase 1 and Phase 2

71% 71%

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Population at risk


7 Who is the population at risk?

Key findings:

The average age of Australians taking medications has increased from 55.5 years of agein 2004 to 58.2 years in 2009. In 2013, the average age of Australians taking medicationswill potentially increase to 62 years. Individuals aged 65 years and over equate toapproximately 40.0% of the Australian population who take medications.

The average number of medications being taken by Australians has remained relativelysteady between 2004 and 2009, with an average of 3.4 medications being taken perperson in 2009. In 2013, the average number of medications being taken will potentiallyincrease to 4.4 medications per person.

Approximately 20.0% of the Australian population taking medications are taking five ormore medications, of which, approximately 16.0% are taking six or more medications.

For those aged 75 and over, the average number of medications being takenhas increased over time. In particular, individuals aged 85 and over take moremedications than any other age group and in 2009 they were taking, on average, 5.7medications.

The average monthly PBS benefit paid per person by Medicare Australia has increasedfrom $96 in 2004 to $131 in 2009, which will potentially increase to $204 in 2013.

While there have been increases in the monthly PBS benefits paid for all age groupsbetween 2004 and 2009, the biggest increase has been for individuals aged 85 and over.On average those aged 75-84 are paid the highest monthly benefits.

Between 2003/2004 and 2007/2008, separations due to medication-relatedincidents increased by 22.3%. In 2007/2008 there were 133,369 separations due tomedication-related incidents.

In 2007/2008, approximately 45.0% of all medication-related admissions were for patientsaged 65 or over, approximately 58.0% of medication-related admissions were female andthe majority were assigned as emergency. Over 95,000 separations (71.0%) occurred inmajor cities. Just over 1.0% of medication-related admissions occurred in remote or veryremote locations.

In 2007/2008, the average length of stay (LOS) (including same-day separations) inhospitals for medication-related admissions was 8.3 days. The average LOS formedication-related admissions were substantially higher than for all patient admissions,8.3 days compared with 3.3 days respectively.

About 80.0% of separations for medication-related admissions resulted in patients goinghome, 9.0% of patients were discharged to another acute hospital, 2.0% were dischargedto a residential aged care (RAC) facility and about 2.5% of patients died.

The proportion of patients discharged to residential aged care and who died following amedication-related admission were slightly higher than for all patient admissions.

Results suggest that the proportion of patients who go home following amedication-related admission was slightly lower than for all patient admissions.

Population at risk


7.1 Who are the Australian population taking medicationsand what are the associated costs?

This section provides an overview of the key findings related to the age of Australians takingmedications, the numbers of medications taken by the Australian population and the associatedbenefit amount paid by Medicare Australia. This section aims to address the following questions:

What is the average age of Australians taking medications and what will happen to themover time?

What is the average number of medications taken by Australians and what will happen tothem over time?

What is the average PBS benefit amount paid by Medicare Australia per month and whatwill happen over time?

To address these questions data from the PBS was analysed. An overview of the PBS data isoutlined in this section.

7.1.1 What is the average age of Australians taking medications andwhat will happen to them over time?

The average age of Australians taking medications

Figure 44 provides an overview of the average age of Australians taking medications betweenJuly 2004 and March 2009. From Figure 44 it can be seen that in July 2004 the average age ofAustralians taking medication was 55.5 years. The average age has increased over time to58.2 years in March 2009.

Population at risk


Figure 44: The average age of Australians taking medications between July 2004 and March 2009A

ge

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Figure 45 provides an overview of the proportion of Australians taking medications by age group(between July 2004 and March 2009), from which it can be seen that:

Not surprisingly, those aged 55 years and less are the largest age grouptaking medications

However, those aged 65 years and over equate to approximately 40.0% of the Australianpopulation who take medications

Of those aged 65 years and older, the 65-74 age category is slightly larger than the75-84 and 85 and over age categories.

Population at risk


Figure 45: Proportion of Australians by age group who take medications between July 2004 and March2009

Age <55 55-<65 65-<75 75-<85 >=85

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The average age of Australians taking medications in the future

Figure 46 aims to provide an indication of what will happen to the average age of Australianstaking medications in the future.

The average age of Australians taking medications was 58.2 years in March 2009. Figure 46indicates that the average age of Australians taking medications will have increased to 62 yearsof age (95%CI= (59.06, 64.75)) by the end of 2013.

Population at risk


Figure 46: The projected mean age of Australians taking medications to November 2013

7.1.2 What is the average number of medications taken by Australiansand what will happen to them over time?

The average number of medications being taken by Australians

Figure 47 provides an overview of the average number of medications being taken byAustralians between July 2004 and March 2009. From Figure 47 it can be seen that the averagenumber of medications has remained relatively steady, with an average of approximately threemedications being taken by Australians in July 2004 and 3.4 medications being taken inMarch 2009.

Population at risk


Figure 47: The average number of medications being taken by Australians between July 2004 and March2009

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Figure 48 provides an overview of the proportion of Australians who take medications by numberof medications (between July 2004 and March 2009), from which it can be seen that:

Not surprisingly, the largest proportion of Australians taking medications (approximately40.0%) were taking one medication only

However, about 20.0% of the population taking medications are taking five or moremedications. Of this cohort, approximately 16.0% are taking six or more medications.

Population at risk


Figure 48: Proportion of Australians by number of medications between July 2004 and March 2009

Medications 1 2 3 4 5 >=6

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Figure 49 aims to provide an indication of what will happen to the average number ofmedications being taken by Australians in the future.

The average number of medications being taken by Australians in March 2009 was3.4 medications. Figure 49 indicates that the average number of medications being taken byAustralians will have increased to 4.4 medications (95%CI= (2.59, 6.30)) by the end of 2013.

Population at risk


Figure 49: The projected mean number of medications being taken by Australians to November 2013

The average number of medications being taken by age group

Figure 50 provides an overview of the average number of medications being taken by theAustralian population, by age group, between July 2004 and March 2009. From this graph it canbe seen that:

those aged 85 years and over take more medications than any other age group.On average, those aged 85 years and over were taking approximately 4.9 medications inJuly 2004 and this had increased to 5.7 medications in March 2009. This increase inmedications is larger than any other age group

Australians aged between 75 and 84 were taking, on average, 4.6 medications inJuly 2004 and this increased to 5.1 medications in March 2009

Population at risk


the number of medications being taken by those aged less than 55 years (approximatelytwo medications), those aged 55 to 64 (approximately three medications) and those aged65 to 74 (approximately four medications) changed very little between July 2004 andMarch 2009

overall, Figure 50 highlights that the older a person is, the more medications they are likelyto be taking and the number of medications are more likely to increase over time.

Figure 50: Average number of medications taken by age group between July 2004 and March 2009

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Projected number of medications taken by age group

Figure 51 provides an overview of the projected number of medications taken by age group,based on current trends, up to 2019. From this graph it can be seen that:

Those aged 75 years and older may potentially be taking nearly double the number ofmedications in 2019.

In 2019, it is estimated that individuals aged 65 to 75 years will be taking, on average, sixmedications.

Population at risk


Figure 51: Predicted number of medications taken by age group

7.1.3 What is the average PBS benefit amount paid by MedicareAustralia per month and what will happen over time?

The data presented in this section are based on the PBS data item ‘Benefit’. This is the PBSbenefit amount paid by Medicare Australia for the individual item. It is the gross price less thecalculated patient contribution at the time of supply.

The average monthly PBS benefit paid per person by Medicare Australia

Figure 52 provides an overview of the average monthly PBS benefit, paid per person, byMedicare Australia between July 2004 and March 2009. From Figure 52 it can be seen that inJuly 2004 the average monthly PBS benefit paid per person was $96. The average monthly PBSbenefit paid per person had increased to $131 in March 2009.

Population at risk


Figure 52: The average monthly PBS benefit paid per person by Medicare Australia between July 2004and March 2009 ($AUD)

Benefitcosts

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Figure 53 aims to provide an indication of what will happen to the average monthly PBS benefitpaid per person by Medicare Australia in the future.

The average monthly PBS benefit paid per person in March 2009 was $131. Figure 53 indicatesthat the average monthly PBS benefit paid per person will have increased to $204(95%CI= (131.98, 276.89)) by the end of 2013.

Population at risk


Figure 53: The projected mean monthly PBS benefit paid per person by Medicare Australia toNovember 2013

The average monthly PBS benefit by age group

Figure 54 provides an overview of the average monthly PBS benefit (paid per person) byMedicare Australia, by age group, between July 2004 and March 2009. From this graph it can beseen that:

those aged 75-84 were, on average, paid the highest monthly PBS benefits per person($135 per month in 2004 and $164 in 2009)

this was followed by those aged 85 and over ($120 per month in 2004 and $160 in 2009)and those aged 65-74 ($124 per month in 2004 and $144 in 2009)

the biggest increase in monthly PBS benefits paid per month, between July 2004 andMarch 2009, was for those aged 85 and over (approximately $40)

overall, there were increases in the monthly PBS benefits paid for all age groups betweenJuly 2004 and March 2009.

Population at risk


Figure 54: Average monthly PBS benefit paid by age groupA

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7.2 Medication-related incidents in Australian hospitals

This section provides a summary of key findings relating to medication-related incidents in theAustralian hospital setting by addressing four important questions:

What is the extent of medication-related incidents in the hospital setting?

What are the characteristics of patients who are admitted due tomedication-related incidents?

How long do patients stay in hospital for medication-related admissions?

What happens to patients who are hospitalised due to medication-related incidents?

To address these questions regarding hospitalisations in Australia due to medication-relatedincidents, the Admitted patient care data was analysed. Results are also compared with AIHWnational data on hospitalisations.

Population at risk


7.2.1 What is the extent of medication-related incidents in thehospital setting?

Admitted patient care data for medication-related incidents

The numbers of admitted patient separations due to medication-related incidents are provided inTable 34. This table breaks down the total number of medication-related separations by public,private and other hospitals.

Table 34: Number of separations due to medication-related incidents by hospital type

YearPublic hospitalseparations

Private hospitalseparations

Other separations**Totalseparations

2001* 86,478 (83.5%) 16,285 (15.7%) 847 (0.9%) 103,610

2003/2004 97,088 (83.07%) 19,134 (16.37%) 649 (0.6%) 116,871

2004/2005 104,109 (83.3%) 20,421 (16.3%) 447 (0.4%) 124,977

2005/2006 109,234 (83.8%) 20,600 (15.8%) 487 (0.4%) 130,321

2006/2007 114,862 (85%) 19,804 (14.7%) 429 (0.3%) 135,095

2007/2008 113,692 (85.2%) 18,955 (14.2%) 722 (0.5%) 133,369

* 2001 data is descriptive only.

** Other includes public psychiatric, private free-standing day hospital and NSW non-acute (public) [used up to 2002/2003 only]

Overall, these results show:

In 2007/2008 there were 133,369 separations due to medication-related incidents.Of these, 85.2% were in public hospital compared to 14.2% in private hospital

The proportion of medication-related separations in public versus private hospitalsremained fairly stable over time

Between 2003/2004 and 2007/2008, separations due to medication-relatedincidents increased by 22.3%. The increase in medication-related separations isillustrated in Figure 55.

Population at risk


Figure 55: Number of separations to hospital due to medication-related incidents

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Year

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Separations - Public

Comparison to national hospital statistics

To provide a more complete picture, hospital separations for the subgroup was compared withhospital separations for the national population.

Subgroup – refers to separations due to medication-related incidents derived for thisproject from the admitted patient care data.

National population – refers to all hospital separations, as reported by the AIHW in theAustralian Hospital Statistics reports.

Table 35 provides a comparison of numbers of admitted patient separations for both thesubgroup and the national population.

Table 35: Comparison of separations between subgroup and national population

Total separations forsubgroup

Total separations for nationalpopulation

Percentage of totalseparations

2003/2004 116,871 6,841,192 1.71%

2004/2005 124,977 7,018,850 1.78%

2005/2006 130,321 7,311,983 1.78%

2006/2007 135,095 7,603,000 1.78%

2007/2008 133,369 7,874,000 1.69%

Population at risk



Approximately 1.8% of all hospital admissions may be medication-related

The proportion of hospitalisations due to medication-related incidents has remained fairlyconstant over time

These figures are in-line with existing research, previously described in Section 0,which suggests that approximately two percent of all hospital admissions may bemedication-related(71).

7.2.2 What are the characteristics of patients who are admitted due tomedication-related incidents?

High rates of medication-related admissions amongst older Australians

Results from the Admitted patient care data for medication-related admissions revealed that in2007/2008, the mean age of patients admitted due to medication-related incidents was 56 yearsold. Between 2003/2004 and 2007/2008, the mean age increased by 1.6%, from 54.8 to55.7 years respectively.

Table 36 compares data for the number of medication-related separations in both 2003/2004and 2007/2008 by age categories.

Table 36: Number of medication-related separations, by age group for 2003/2004 and 2007/2008

2003/2004 2007/2008

N (%) N (%)

< 24 16,645 (14.2%) 18,652 (14.0%)

25 to 34 11,698 (10.0%) 11,938 (9.0%)

35 to 44 12,271 (10.5%) 13,547 (10.2%)

45 to 54 12,248 (10.5%) 14,009 (10.5%)

55 to 64 13,969 (12.0%) 16,166 (12.1%)

65 to 74 17,759 (15.2%) 20,029 (15.0%)

75 to 84 22,610 (19.3%) 26,344 (19.8%)

> 85 9,671 (8.3%) 12,684 (9.5%)

Population at risk



In 2007/2008, approximately 45.0% of all medication-related admissions were for patientsaged 65 or over

The age distribution for patients admitted to hospital due to medication-relatedincidents has remained fairly constant over time, with a slight increase amongst patientsaged 75 and above

There also appears to be a high rate of medication-related admissions amongst childrenand people under the age of 24 (approximately 14.0%).

When compared to all hospital admissions (national population), results suggest that patientsadmitted to hospital due to medication-related incidents (subgroup) have a slightly older agedistribution. Comparisons for 2007/2008 are illustrated in Figure 56. Research suggests this maypartly be explained by the fact that older people are more likely to be taking one or moremedications. Therefore, where appropriate, this section will comment on age adjusted rates.

Figure 56: Age distribution for medication-related admissions versus all admissions, 2007/2008

0

2

4

6

8

10

12

14

16

18

20

Pro

po

rtio

no

fa

dm

iss

ion

s

< 24 25-34 35-44 45-54 55-64 65-74 75-84 > 85

Age (in years)

Sample population

National population

Population at risk


Gender differences in medication-related admissions

Results from the Admitted patient care data for medication-related admissions revealed thatfemales account for more medication-related admissions than males. Table 37 provides data forthe number of medication-related separations by patient gender.

Table 37: Number of mediction-related separations, by patient gender

Male Female

N (%) N (%)

2003/2004 49,228 (42.1%) 67,642 (57.9%)

2004/2005 52,297 (41.8%) 72,676 (58.2%)

2005/2006 54,998 (42.2%) 75,321 (57.8%)

2006/2007 56,886 (42.1%) 78,206 (57.9%)

2007/2008 56,475 (42.3%) 76, 891 (57.7%)

Overall, these results show that in 2007/2008:

Approximately 58.0% of medication-related admissions were female (57.0% afterage-adjustment)

Females outnumbered males in medication-related admissions in all age groups

The gender distribution for patients admitted to hospital due to medication-relatedincidents has remained fairly constant over time

The proportion of females admitted due to medication-related incidents was higher than forall patient admissions, 57.2% (age-adjusted rate) compared with 52.7% respectively.

Impact of location on medication-related admissions

Results from the Admitted patient care data for medication-related admissions revealed that themajority of medication-related admissions occurred in major cities (71.0%). Table 38 comparesdata for the number of medication-related separations in both 2003/2004 and 2007/2008 bylocation. It is important to note that this section is related to the location of hospitals and not withthe location of health need (ie patient location).

Table 38: Number of medication-related separations, by location for 2003/2004 and 2007/2008

2003/2004 2007/2008

N (%) N (%)

Major cities 84,006 (71.9%) 95,230 (71.4%)

Inner regional 22,854 (19.6%) 27,140 (20.3%)

Outer regional 8,623 (7.4%) 9,461 (7.1%)

Remote 961 (0.8%) 1,076 (0.8%)

Very remote 427 (0.4%) 462 (0.3%)

Population at risk


Overall, these results show that:

Of the 133,369 medication-related admissions in 2007/2008, over 95,000 separations(71.0%) occurred in major cities

Just over 1.0% of medication-related admissions in 2007/08 occurred in remote or veryremote locations

The proportion of medication-related admissions in major cities was slightly higher than forall patient admissions, 71.4% (age-adjusted rate) compared with 67.1% respectively.Similarly, the proportion of medication-related admissions in rural or remote areas wasslightly lower than for all patient admissions, 1.2% (age-adjusted rate) compared with2.7% respectively. This is shown in Figure 57.

Figure 57: Comparison of location distraction between subgroup and national population, 2007/2008

Remote to

very remote

3%

Outer

regional

10%

Regional

20%

Major cities

67%

Medication-related admissions All admissions

Major cities

71%

Regional

20%

Remote to

very remote

1%

Outerregional

7%

Major cities

71%

Regional

20%

Remote to

very remote

1%

Outerregional

7%

Population at risk


7.2.3 How long do patients stay in hospital for medication-related admissions?

Medication-related admissions often occur on an emergency basis

Table 39 provides a comparison of the urgency of admission assigned for both medication-related admissions (subgroup) and all admissions (national population).

Table 39: Comparison of urgency of admission between subgroup and national population, 2007/2008

Medication-related admissions (subgroup) All admissions (national population)

Emergency 96,542 (72.4%) 2,152,384 (27.3%)

Elective 26,658 (20.0%) 4,553,773 (57.8%)

Other 10,169 (7.6%) 1,167,843 (14.8%)

Overall, this table shows:

In 2007/08, the majority of medication-related admissions were assigned as emergency

The proportion of medication-related admissions assigned as emergency was substantiallyhigher than for all patient admissions, 72.3% (age-adjusted rate) compared with27.0% respectively.

Longer length of stay for medication-related admissions

The average lengths of stay (LOS), in days, for patients admitted to hospital due tomedication-related incidents are provided in Table 40. This table also compares the averageLOS for medication-related incidents with statistics for the national population.

Table 40: Average length of stay for medication-related admissions

Year Average LOS (subgroup) Average LOS (national population)

2003/04 8.4 days 3.4 days

2004/05 8.2 days 3.4 days

2005/06 8.4 days 3.3 days

2006/07 8.7 days 3.3 days

2007/08 8.3 days 3.3 days


In 2007/08, the average LOS (including same-day separations) in hospitals formedication-related admissions was 8.3 days

The average LOS for medication-related admissions remained relatively stable over time,decreasing by only 0.1% from 2003/04

The average LOS for medication-related admissions were substantially higher than for allpatient admissions, 8.3 days compared with 3.3 days respectively (2007/08).

Population at risk


However, research suggests that length of stay for medication-related incidents variesdepending on age, with older Australians (ie 65 and older) being more likely to remain in hospitalfor longer periods of time. Using the admitted patient care data, the average length of stay wascalculated for different age groups.

The results presented in Figure 58 support existing research and suggest that length of stay dueto medication-related admissions increases with age. For patients aged 65 or older, the averagelength of stay increases to over ten days.

Figure 58: Mean length of stay for medication-related hospitalisations, by age group

4.8

5.86

7.2

9.1

1010.5

11.6

0

2

4

6

8

10

12

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an

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S(d

ay

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< 24 25-34 35-44 45-54 55-64 65-74 75-84 > 85

Age group (Years)

7.2.4 What happens to patients who are hospitalised due tomedication-related incidents?

Table 41 provides data for the mode of separation following a medication-related admission.

Table 41: Mode of separation for medication-related admissions

YearOther (eg

went home

Discharge toanother acute

hospital

Dischargeto RAC

Discharge/transferto psychiatric orother healthcareaccommodation

Statisticaldischarge (inc.from leave, at

own risk)

Death

2003/04 94,228

(80.6%)

10,182

(8.7%)

2,442

(2.1%)

1,418

(1.2%)

5,575

(4.7%)

3,024

(2.6%)

2004/05 100,998

(80.8%)

10,750

(8.6%)

2,800

(2.2%)

1,396

(1.2%)

5,787

(4.6%)

3,148

(2.5%)

2005/06 104,465

(80.2%)

11,980

(9.2%)

3,003

(2.3%)

1,433

(1.1%)

6,119

(4.5%)

3,316

(2.5%)

2006/07 108,334

(80.2%)

12,557

(9.3%)

3,190

(2.4%)

1,488

(1.1%)

6,110

(4.4%)

3,413

(2.5%)

2007/08 107,154

(80.3%)

12,278

(9.2%)

3,057

(2.3%)

1,487

(1.1%)

6,233

(4.5%)

3,156

(2.4%)

Population at risk



About 80.0% of separations for medication-related admissions were included in the ‘other’category. This suggests that the majority of patients go home following an episode of carefor medication-related incidents

About 9.0% of patients are discharged to another acute hospital following a medication-related admission

About 2.0% of patients are discharged to a residential aged care (RAC) facility following amedication-related admission

About 2.5% of patients die following a medication-related admission.

Higher proportion enter residential aged care following medication-related admission

The proportion of patients discharged to residential aged care following a medication-relatedadmission was slightly higher than for all patient admissions, 1.7% (age-adjusted rate) comparedwith 0.8% respectively.

Intuitively, the proportion of patients entering residential aged care following a medication-relatedadmission increased with age. Figure 59 illustrates the age-adjusted rates of discharge to RACfollowing a medication-related admission.

Figure 59: Age-adjusted rates for discharge to RAC following medication-related admission, 2007/2008

0

2

4

6

8

10

12

<24 25-34 35-44 45-54 55-64 65-74 75-84 85+

Age group

Ag

ead

juste

d%

Population at risk


Higher proportion of deaths following medication-related admission

The proportion of patients who die following a medication-related admission was slightly higherthan for all patient admissions, 2.1% (age-adjusted rate) compared with 0.9% respectively.

Intuitively, the proportion of patients who died following a medication-related admissionincreased with age. Figure 60 illustrates the age-adjusted rates of death following amedication-related admission.

Figure 60: Age-adjusted rates for death following medication-related admission, 2007/2008

0

1

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4

5

6

<24 25-34 35-44 45-54 55-64 65-74 75-84 85+

Age group

Ag

ea

dju

ste

d%

Population at risk


Lower proportion return home following medication-related admissions

Results suggest that the proportion of patients who go home following a medication-relatedadmission was slightly lower than for all patient admissions, 81.8% (age-adjusted rate)compared with 92.2% respectively.

Intuitively, the proportion of patients who died following a medication-related admissionincreased with age. Figure 61 illustrates the age-adjusted rates of ‘other’ following a medication-related admission.

Figure 61: Age-adjusted rates for ‘other’ following medication-related admission, 2007/2008

60

65

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75

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85

90

<24 25-34 35-44 45-54 55-64 65-74 75-84 85+

Age group

Ag

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Population most likely to benefit


8 Who is the population that benefits most?

Key findings:

Existing literature suggests that the potential benefits of DAA and PMP services can bemaximised by targeting patients who will benefit most. While these are not acomprehensive criteria list, the key sources of data reveal four characteristics of patientswho may be considered ‘at-risk’ of adverse medication-related events:

– Patient age, ie 65 years or older

– Number of medications used, ie five or more medications

– Nature of medication problem, ie nervous system, diabetes and cardiovascularconditions which require complex medication regimens

– Assistance and support available, ie patients who do not have access to socialsupport or are living alone.

8.1 Overview

As described in Section 0, existing literature supports DAAs and PMPs as effective strategiesfor improving medication management amongst patients in the community. Such researchsuggests that the benefits of these strategies can be maximised by targeting patients who willbenefit most.

Utilising the four key sources of data (ie existing literature, DAA and PMP service data, admittedpatient care data and PBS data), this section identifies potential characteristics of patients ‘atrisk’ of adverse medication events and who should be targeted for such services. The fourpotential characteristics of ‘at risk’ patients discussed are:

Patient age

Number of medications used

Nature of medication problem

Assistance and support available.



8.2 Patient age

According to the literature, one of the strongest associations with poor medication managementis patient age, with the risk of medication non-adherence increasing with age (72)(73). This maybe due to the fact that elderly patients’ conditions are more likely to have greater chronicity,severity and co-morbidity(3). Given the increased risk of poor medication management andsubsequent adverse medication events, it has been argued that the effectiveness of DAA orPMP services can be maximised by targeting older patients.

Pharmacists were instructed to recruit patients based on their professional judgement as to whois likely to benefit from the services. The results of the DAA and PMP service data, presented inSection 5 and Section 5, suggest that targeting patients based on age was a key strategy usedby participating pharmacies. In both the DAA and PMP programs, the majority of patientsrecruited were aged 65 years and over. For the DAA programs approximately 76.0% of patientswere aged 65 and over, with the majority of these patients falling between the ages of 75 to 84.These results were similar for the PMP program. Together, these results suggest that elderlypatients were seen to be most likely to benefit from the DAA and PMP service.

The benefits of targeting patients aged 65 and over are also supported by the results of theadmitted patient care data for medication-related admissions. As described in Section 7.2, in2007/08 almost half of all medication-related admissions were for patients aged 65 and over.Furthermore, the comparison of medication-related admissions to all hospital admissionssuggested that patients with medication-related incidents have a slightly older age distribution.

The admitted patient care data also revealed that the mean length of stay for medication-relatedadmissions increases with age. In 2007/08, the average length of stay increased to over tendays for patients aged 65 or older.

8.3 Number of medications used

Research has also found a strong correlation between the number of medications a patient istaking and poor medication management(72). Such research suggests that the complexityrelated to taking multiple medications increases the risk of patients being non-adherent. Giventhe increased risk of poor medication adherence and adverse events, research suggests that theeffectiveness of services such as DAA or PMP may be increased by targeting patients onmultiple medications.

Majority of this research on medication complexity, suggest that patients on five or moremedications are likely to most benefit from strategies such as DAAs or PMPs. For exampleHugtenburg et al(74) targeted their medication passport service (equivalent to a PMP) to patientson five or more medications. Similarly, Lee(75) found that patients amongst 200 community-based patients on at least four chronic medications, DAAs significantly improved medicationadherence. Finally, the PSA guidelines for DAAs also recommend the selection of patients onfive or more medications(24).

The results of the DAA and PMP service data appear to be aligned with the literature, suggestingthat patients were targeted based on the number of medications they were taking. In both theDAA and PMP programs, almost half of patients recruited in Phase 2 were taking between five toseven medications. A further 20.0% of patients were on four medications. Such results suggestthat pharmacists saw patients on multiple medications (i.e. five or more) as being most likely tobenefit from the service.



The benefits of targeting patients with five or more medications are also aligned withthe results of the PBS data. As described in Section 7.1, 20.0% of all medication-taking patientsare taking five or more medications. Furthermore, the results from the PBS data projections(see Section 7.1) show that the average number of medications being taken by Australians isincreasing. These results indicate that the average number of medications being taken byAustralians is likely to increase from 3.4 medications in March 2009 to 4.4 medications by theend of 2013. Therefore, the number of people taking five or medications is also likely to increase.

8.4 Nature of medication problem

Disease or medication types have also been shown in the literature to influence a patient’scapacity to manage their medications. In a review of the literature, Roberts(3) identified anumber of patient sub-groups, based on disease and medication type, who would be most likelyto benefit from services such as DAAs. Such disease types include cardiovascular,musculoskeletal, diabetes, asthma, and nervous system disease. Such disease and illnessrequire large quantities of medication in their treatment, increasing the complexity of themedication regimen.

Research also shows that, over the last two decades, of those medications listed on the PBS,cardiovascular system medications have consistently been the most common at almost 70million services in 2009. The second and third most common medications are nervous systemand alimentary tract medications at approximately 40 million and 30 million respectively(16).

The results of the DAA and PMP service data appear to be aligned with the literature, suggestingthat the most common medication amongst patients recruited to both services wascardiovascular system medications. Approximately 90.0% of patients receiving the DAA andPMP service were on cardiovascular medications. Similar to above, the second most commonmedication amongst participating patients were nervous system medications, with just over60.0% of patients in both the DAA and PMP programs taking such medications. Finally, the thirdmost common type of medication was alimentary tract drugs, taken by just under 60.0% ofparticipating patients.

8.5 Assistance and support available

Existing literature has also shown an association between social support and adherence,whereby patients who receive little to no support or assistance are more at risk of poormedication management. Factors such as living alone, being single or divorced and havingaccess to social support have all been associated with increased risk of adverse medicationevents. Given the increased risk, research suggests that medication management services suchas DAA or PMP should be targeted to patients who do not have access to support or assistance,or who live alone.

The results of the DAA service data, presented in Section 5, show that just under half of patientsreported living alone, and almost a third of patients did not receive assistance with managingtheir medications. The results were similar for the PMP program (see Section 6).

Research has also suggested that targeted strategies, such as the DAA and PMP services,increase the capacity of patients to independently manage their medications, which in turnallows them to remain in their own home for longer and delay the need for admission to RACfacilities.

The benefits of targeting patients who do not have access to assistance or live alone are alsosupported by the results of the admitted patient care data for medication-related admissions. As



described in Section 7.2, in 2007/08 there were a higher proportion of patients who entered RACfollowing a medication-related admission, than for all patient admissions. Furthermore, results ofthe admitted patient care data reveal that the proportion of patients who return home following amedication-related admission was lower than for all patient admission.

8.5.1 Summary of ‘at risk’ patients

Overall, the results of the data analysis suggest four potential characteristics of patients at risk ofadverse medication events. Specifically, patients who should be considered at risk:

are aged 65 years and older

are on five or more medications

have conditions which require complex medication regimens (eg cardiovascular, nervoussystem, diabetes)

do not have access to social support or are living at home.

Costs and benefits


9 Costs and benefits

Key findings:

Based on pharmacy staff time involved in delivering the DAA and PMP services, resultsshow that the average weekly cost for delivering the DAA and PMP service was $565.89and $122.10 respectively.

Breaking costs down to the patient level, the average patient cost per week of the DAAand PMP services was $17.25 and $25.44.

Whilst the service delivery costs of DAA and PMPs are calculated separately, results ofthe DAA and PMP service data reveal that, in reality, many pharmacies offer multipleprograms and many patients receive multiple services. Additionally, the literature showsthat medication non-adherence is best addressed via multiple strategies. The implicationof these findings, therefore, is that the unique costs and subsequent benefits of DAAs andPMPs should not be considered in isolation.

Well over half of pharmacies (63.0%) charged their patients less than $5.00 per pack forthe DAA service, with most of the remaining pharmacies charging between $5.00 and$10.00 per DAA. Just over half of pharmacies charged their patients between $5.00 and$10.00 for the PMP service, with most of the remaining pharmacies charging less than$5.00 per PMP.

Findings and outcomes of existing research were used to extrapolate and quantify thebenefits of the DAA and PMP services. This literature suggests that significantimprovements in medication adherence can be achieved as a result of DAA and PMPservices, potentially ranging from 10.9% to 43.0% improvements.

Such improvements in medication adherence have also been shown to result insubstantial health and economic benefits, including reduced hospitalisations, reducedmortality and morbidity and reduced transfer to residential aged care. For example,applying the results of Murray et al (2007), a 10.0% improvement in adherence couldreduce medication-related hospital admissions (approximately 133,400 in 2007/09) by25,340.

Results from the PBS data projections revealed that there is a potential growth in the ‘atrisk’ population (ie older people, multiple medications). Therefore, services such as DAAsand PMPs may also help to minimise the impact of the potentially increasing demand onhospitals and RAC facilities.

9.1 Costs of delivering the DAA and PMP services

Data were collected in both Phase 1 and Phase 2 on the time spent by pharmacy staff indelivering the DAA and PMP programs. The data presented in this section are based on Phase 2baseline data only; this is due to the quality issues associated with the Phase 1 data.

This section provides an overview of the costs of delivering the DAA (Section 9.1.1) and PMP(Section 9.1.2) services to all community based patients.

Costs and benefits


9.1.1 Unique DAA service delivery costs

What time was spent by pharmacy staff in delivering the DAA service toall community based patients?

Table 42 provides a summary of the average number of staff hours, per week, spent by variouspharmacy staff members in providing the DAA service to all community based patients – as self-reported by pharmacies. In summary:

Pharmacy technicians spent, on average, the most time providing DAA services(9.6 hours per week)

Pharmacists spent, on average, 8.1 hours per week providing DAA services

Pharmacies reported spending, on average, a total of 23.4 hours per week providing DAAservices to community based patients.

Table 42: Time spent by pharmacy staff in delivering the DAA service – Phase 2

PositionHours per week

Mean SD Median

Pharmacist 8.1 11.2 5

Pharmacy technician 9.6 25.1 2

Pharmacy assistant 4.3 18.3 1

Other staff 1.4 5.4 0

Pharmacy total time 23.4 46.2 13

What are the hourly costs of staff time?

The hourly costs by staff were estimated using the current state award rates for each category ofpharmacy staff. The highest level wage rate was used for each category of staff and averagedacross all states and territories. These rates include a 25% on-cost to cover annual holidays,public holidays, sick leave, long-service leave and superannuation contributions.

Table 43: Hourly staff costs

Staff Hourly rate Staff Hourly rate

Pharmacist $27.93 Pharmacy Assistant $22.20

Pharmacy Technician $22.20 Other staff $22.20

What were the average weekly costs of providing the DAA service to allcommunity based patients?

Using the information on time spent by pharmacy staff in delivering the DAA service in Table 42and the hourly staff costs in Table 43, the average weekly cost of providing the DAA service toall community based patients was calculated. As outlined in Table 44, the total cost of pharmacytime in delivering the DAA service was $565.89 per week.

Costs and benefits


Table 44: Average weekly cost of providing DAA service – Phase 2

Position Average cost per week

Pharmacist $226.23

Pharmacy technician $213.12

Pharmacy assistant $95.46

Other staff $31.08

Total cost of pharmacy time $565.89

Pharmacies reported that the average number of community based patients being providedDAAs in their pharmacy, across Phase 2, was 32.8 patients per week. This means that theaverage patient cost per week, of providing the DAA service to a community based patient, was$17.25 per patient.

9.1.2 Unique PMP service delivery costs

What time was spent by pharmacy staff in delivering the PMP service toall community based patients?

Table 45 provides a summary of the average number of staff hours, per week, spent by variouspharmacy staff members in providing the PMP service to all community based patients – asself-reported by pharmacies. In summary:

Pharmacists spent, on average, the most time providing PMP services(3.1 hours per week)

Pharmacy technicians spent, on average, 0.8 hours per week providing PMP services

Pharmacies reported spending, on average, a total of 4.8 hours per week providing PMPservices to community based patients.

Table 45: Time spent by pharmacy staff in delivering the PMP service – Phase 2

Position

Hours per week

Mean SD Median

Pharmacist 3.1 5.3 2

Pharmacy technician 0.8 3.4 0

Pharmacy assistant 0.6 3.4 0

Other staff 0.2 1.1 0

Pharmacy total time 4.8 9.9 2

What are the hourly costs of staff time?

The hourly costs of staff time as per reported previously for DAA in Table 43.

Costs and benefits


What were the average weekly costs of providing the PMP service to allcommunity based patients?

Using the information on time spent by pharmacy staff in delivering the PMP service in Table 45and the hourly staff costs in Table 43, the average weekly cost of providing the PMP service toall community based patients was calculated. As outlined in Table 46, the total cost of pharmacytime in delivering the PMP service was $122.10 per week.

Table 46: Average weekly cost of providing PMP service – Phase 2

Position Average cost per week

Pharmacist $86.58

Pharmacy technician $17.76

Pharmacy assistant $13.32

Other staff $4.44

Total cost of pharmacy time $122.10

Pharmacies reported that the average number of community based patients being providedPMPs in their pharmacy, across Phase 2, was 4.8 patients per week. This means that theaverage patient cost per week, of providing the PMP service to a community based patient, was$25.44 per patient.

9.2 Impact of multiple patient services

It is important to recognise the nature of the services being costed. The service delivery costs ofthe DAA and PMP services, calculated in Section 9.1, consider the services separately.However, results from the DAA and PMP service data suggest that, in reality, many pharmaciesoffer multiple programs and many patients receive multiple services. Results show that:

most pharmacies (94.0%) participating in Phase 2 of the DAA program were providingadditional 4CPA funded services (eg PMP, HMR, DMAS)

about half of patients participating in Phase 2 of the DAA program were receiving both aDAA and a PMP

almost all pharmacies (99.0%) participating in Phase 2 of the PMP program wereproviding additional 4CPA funded services (eg DAA, HMR, DMAS)

the majority of patients (71.0%) participating in Phase 2 of the PMP program werereceiving both a DAA and PMP.

The implications of these results are that it is difficult to consider the unique costs of eitherservice in isolation. Instead, consideration of the incremental costs and benefits of patientsreceiving multiple services is needed. For example, for patients receiving both the DAA and PMPservice, the cost could be estimated as the service delivery cost of a DAA per patient per week($17.25) plus the service delivery cost of a PMP per patient per week ($25.44). Thus theestimated weekly cost for patients receiving both services would be approximately $43.

The importance of considering the impact of multiple patient services has also been highlightedin the literature. Few studies have investigated the impact of DAAs or PMPs in isolation, withmost studies examining the effect of strategies in combinations. As a result, the unique impact ofDAAs or PMPs on health outcomes cannot be estimated in isolation. According to existingstudies, non-adherence with medication regimens is a complex and multi-causal problem(35)

Costs and benefits


and interventions with multiple components are seen to yield the biggest improvements inadherence. The following section therefore examines the potential clinical and financial benefitsof DAA and/or PMP services often in combination with other adherence strategies.

9.3 Costs to patients for the DAA and PMP services

9.3.1 What did pharmacists charge their patients for the DAA service?

Costs to patients for the DAA service

In Phase 1, pharmacies reported on whether there was a charge for the DAA service in theirpharmacy and how much that charge was. Approximately 93.0% of pharmacies reported thatthey do charge for the DAA service. Figure 62 provides an overview of the cost of the DAAservice to patients. The majority of pharmacies (63.0%) charged less than $5.00 per DAA, whileapproximately 30.0% charged between $5.00 and $10.00. Very few pharmacies charged nothing(0.1%) or more than $10.00 (0.1%).

It is important to note that the amount charged to patients was only collected in Phase 1.

Figure 62: Price charged for DAA – Phase 1

0

500

1000

1500

2000

2500

Nu

mb

er

of

ph

arm

ac

ies

No charge <$5 $5-$10 $10-$15 Missing

Charge for DAA

Characteristics of the pharmacy which influenced amount charged topatients

Table 47 breaks down the number of pharmacies by prescription volume (indicator of pharmacysize) and number of patients who received a DAA service during a week period (indicator ofpharmacy activity). This table suggests that pharmacies:

Costs and benefits


with a low prescription volume had a tendency to provide DAAs to fewer patients

with higher prescription volume had a tendency to provide DAAs to more patients.

Table 47: Patient receiving DAA service by prescription volume – number of pharmacies

Number of patients receiving DAA service during the last week

Low (<10) Med (11-41) High (>41)

Scriptvolume

Low (<800) 1490 (61.5%) 2371 (43.9%) 431 (16.9%)

Medium (801-1200) 547 (22.6%) 1491 (27.6%) 520 (20.3%)

High (>1200) 385 (15.9%) 1542 (28.5%) 1606 (62.8%)

Costs and benefits


A test of independence was conducted to look at the effect that pharmacy size and pharmacyactivity have on the amount charged to patients for the DAA service. The results are provided inTable 48. This table suggests that:

there was no significant relationship between prescription volume and the amount chargedto patients for the DAA service

there was a significant relationship between the number of patients receiving the DAAservice and the amount charged to patients for the DAA service

together, prescription volume and number of patients receiving the DAA service wassignificantly related to the amount charged to patients for the DAA service.

Table 48: Test of independence – pharmacy characteristics and provision price

Pharmacy characteristics p-value

Prescription volume (size) x provision price 0.1873

Number of patients receiving service (activity) x provision price <0.0001

(Script x number of patients receiving service) x provision price <0.0001

Table 49 provides an overview of the amount charged to patients from pharmacies in each of thenine size/activity categories. This table indicates that pharmacies that provided more DAAservices (in a one week period) were consistently more likely to charge their patients less, thanpharmacies that provide fewer DAA services (in a one week period). The table also suggeststhat as increasing script volume accompanies higher levels of DAA service provision,pharmacists were likely to charge less. In other words, from these data, it appears that DAAactivity is the primary driver of amount charged by pharmacies, with lower charges as activityincreases, and that this is particularly evident as script volume also increases.

Table 49: Prescription volume x patients receiving DAA service – Impact on DAA provision price

DAA Provision price

Script volume Pts receivingDAA service

No charge < $5 $5 – $10 $10 – $15

Low Low 0 (0%) 897 (65%) 479 (35%) 4 (1%)

Low Med 0 (0%) 1574 (69%) 694 (31%) 0 (0%)

Low High 4 (1%) 278 (71%) 104 (27%) 4 (1%)

Med Low 4 (1%) 320 (62%) 190 (37%) 0 (0%)

Med Med 0 (0%) 943 (66%) 480 (34%) 0 (0%)

Med High 0 (0%) 349 (71%) 146 (29%) 0 (0%)

High Low 0 (0%) 196 (54%) 163 (45%) 4 (1%)

High Med 2 (1%) 1004 (67%) 488 (33%) 0 (0%)

High High 0 (0%) 1069 (69%) 478 (31%) 0 (0%)

Costs and benefits


Influence of pharmacy location on amount charged to patients

Table 50 breaks down the number of pharmacies by PhARIA category (indicator of remoteness)and provision price. This table suggests that there were no meaningful differences in the amountcharged to patients for a DAA based on pharmacy location (remoteness).

Table 50: Impact of pharmacy location on DAA provision price

DAA Provision price

PhARIA category No charge < $5 $5 – $10 > $10 Missing

1-2 1 (0%) 1,526 (64%) 722 (30%) 3 (0%) 150 (6%)

3-6 1 (0%) 204 (61%) 122 (36%) 0 (0%) 8 (2%)

Missing 1 (0%) 373 (63%) 140 (24%) 0 (0%) 74 (13%)

9.3.2 What did pharmacists charge their patients for the PMP service?

Costs to patients for the PMP service

In Phase 1, pharmacies reported on how much they charged their patients for the PMP service.Just over a third of the pharmacies (38.5%) reported that they did charge their patients for thePMP service. Of the pharmacies who indicated that they charged their patients for the PMP,almost all pharmacies reported charging their patients less than $10. It is important to note thatthe amount charged to patients was only collected in Phase 1, which had considerable missingdata; all findings related to provision price should be interpreted with caution.

Figure 63: Amount charged to patients for PMP – Phase 1*

Chargedpatient38.5%

Did notcharge patient

42.7%

Missing18.8%

0.5%

56.5%

43.0%

%

>$10

$5 - $10

<$5

Amount

0.5%

56.5%

43.0%

%

>$10

$5 - $10

<$5

Amount

* NB: There was a high number of missing responses to these two questions, so results should be interpreted with caution.

Costs and benefits


Characteristics of the pharmacy which influenced amount charged topatients

Table 51 breaks down the number of pharmacies by prescription volume and number of patientswho received a PMP service during a week period. From this table, there does not appear to bea correlation between script volume and the number of patients receiving PMP services, with arelatively even distribution across categories.

Table 51: Patient receiving PMP service by prescription volume – number of pharmacies

Number of patients receiving PMP service during the last week

Low (<1) Med (2-5) High (>6)

Scriptvolume

Low (<800) 776 (39.8%) 963 (47.3%) 308 (48.7%)

Medium (801-1200) 506 (25.9%) 441 (21.7%) 152 (24.0%)

High (>1200) 668 (34.3%) 632 (31.0%) 173 (27.3%)

A test of independence was conducted to look at the effect that pharmacy size and pharmacyactivity have on the amount charged to patients for the PMP service. The results are provided inTable 52. This table suggests that:

there was a significant relationship between prescription volume and the amount chargedto patients for the PMP service

there was a significant relationship between the number of patients receiving the PMPservice and the amount charged to patients for the PMP service

together, prescription volume and number of patients receiving the PMP service wassignificantly related to the amount charged to patients for the PMP service.

Table 52: Test of independence – pharmacy characteristics

Pharmacy characteristics p-value

Prescription volume (size) x provision price 0.0089

Number of patients receiving service (activity) x provision price <0.0001

Script x number of patients receiving service x provision price <0.0001

Table 53 provides an overview of the amount charged to patients from pharmacies in each of thenine size/activity categories. These data suggest that pharmacies that provided more PMPservices (in a one week period) were consistently more likely to charge their patients more thanpharmacies who provide fewer PMP services. The data further suggest that, as increasing scriptvolume accompanies higher levels of PMP service provision, pharmacies are likely to chargetheir patients at lower levels. From these data, it appears that PMP activity is the primary driverof amount charged by pharmacies, with lower charges as activity increases, and that this isparticularly evident as script volume also increases.

Costs and benefits


Table 53: Prescription volume x patients receiving service – Impact on pharmacy PMP provision price

PMP Provision price

Script volume Pts receivingPMP service

No charge < $5 $5 – $10 $10 – $15 >$15

Low Low 6 (1%) 176 (38%) 275 (60%) 0 (0%) 3 (1%)

Low Med 13 (2%) 218 (40%) 310 (57%) 3 (1%) 3 (1%)

Low High 8 (6%) 64 (44%) 72 (50%) 0 (0%) 0 (0%)

Med Low 9 (3%) 105 (37%) 170 (59%) 0 (0%) 2 (1%)

Med Med 0 (0%) 103 (49%) 106 (51%) 0 (0%) 0 (0%)

Med High 1 (2%) 40 (65%) 21 (34%) 0 (0%) 0 (0%)

High Low 9 (2%) 149 (40%) 215 (58%) 0 (0 %) 0 (0%)

High Med 20 (6%) 129 (41%) 166 (53%) 0 (0%) 0 (0%)

High High 4 (5%) 44 (54%) 30 (37%) 3 (4%) 0 (0%)

Influence of pharmacy location on amount charged to patients

Table 54 breaks down the number of pharmacies by PhARIA category (indicator of remoteness)and provision price. This table suggests that there were no meaningful differences in the amountcharged to patients for a PMP based on pharmacy location (remoteness).

Table 54: Impact of pharmacy location on PMP provision price

PMP Provision price

PhARIA category No charge < $5 $5 – $10 > $10 Missing

1-2 17 (1%) 301 (18%) 406 24%) 4 (1%) 957 (56%)

3-6 6 (2%) 48 (20%) 59 (24%) 0 (0%) 130 (54%)

Missing 1 (0%) 58 (15%) 70 (18%) 1 (0%) 265 (67%)

Costs and benefits


9.4 Potential benefits of the DAA and PMP services

This section examines the potential clinical and financial benefits of the DAA and PMP services,including improved health outcomes and reduced health care costs. To be able to quantify thebenefits of DAA and PMP services, this section draws on current literature and results of theDAA and PMP service data which associates such services with improved compliance, which, inturn, improve health outcomes and health care costs. Based on this literature, a number ofimportant findings can be extrapolated to the current study.

9.4.1 Improved medication adherence as a result of DAA and/or PMP

It is well known from the literature that poor adherence to long-term medication regimensseverely compromises the effectiveness of treatment, impacting on quality of life, mortality andhealth care costs. Although the impact of DAA and PMPs on medication adherence and healthoutcomes could not accurately be assessed using the DAA and PMP service data, existingliterature on medication adherence can be used. This literature supports the role of communitypharmacists in improving medication adherence amongst patients. Specifically, a handful ofstudies have specifically investigated the potential impact that DAA and PMP services can haveon patients’ adherence to medications. These studies are summarised in Table 55.

Table 55: Studies demonstrating improved adherence as a result of DAA and PMP strategies

Study Design Intervention Results

Roberts, etal (2004)

Review of literature on the impactof DAAs on medication adherenceand other outcomes

Literature review ofDAAs

Improvements in compliancerange from 14.6% to 43%.

Lee, at al(2006)(75)

Six-month randomised controlledtrial amongst 200 community-based patients aged 65 years orolder and on at least four chronicmedications.

DAA with educationand follow-up

Adherence in the interventiongroup increased by 35.5% (95%CI 31.2% – 38.5%; p <0.001)

Murray etal(2007)(25)

Nine-month randomised controlledtrial amongst 314 patients aged 50years or older with heart failure.

PMP with educationand follow-up

There was a 10.9% (95% CI, 5.0to 16.7%) increase in medicationadherence compared to thecontrol group.

PwC(2010)

Six-month trial amongst 732patients.

Combination ofstrategies includingDAA and PMPs

There was a 16% increase inadherence (p<0.0001)

Costs and benefits


Overall, this table shows that significant improvements in adherence can be achieved as a resultof DAA and PMP services. These studies show that improvements in compliance as a result ofDAAs and PMPs range from 10.9% to 43%. The variation is likely to be a result of differences intype of medication, demographics of the sample and design of the trial. Despite these variations,research supports DAAs and PMPs as effective strategies for improving patient adherence.

9.4.2 Impact on health outcomes

Interventions within the community pharmacy setting, which are effective in promotingcompliance to long-term medications, have also been shown to result in substantial health andeconomic benefits, through primary prevention of risk factors and secondary prevention ofadverse health outcomes (WHO, 2003). Previous studies investigating the cost-effectivenessand cost-benefits of adherence strategies, such as DAAs and PMPs, have consistently foundsignificant health improvements and cost savings that are attributable to relatively low-costinterventions delivered by health professionals. These can be broken down into three mainbenefits; reduced hospitalisations, reduced mortality and morbidity, and reduced transferto RAC.

Reduced hospitalisations

The case study provided in Table 56 outlines the potential reduction in hospital admissions forpatients receiving a DAA or PMP service. This case study is based on a research study byMurray et al (2007), which looks at improved medication adherence and health outcomes forpeople on heart failure medication. As approximately 90% of patients participating in the DAAand/or PMP program were taking medications for cardiovascular conditions, this study providesa useful framework for extrapolating the impact of improved adherence as a result of DAAs andPMPs on hospitalisations and health care costs.

Table 56: Case study one

Reduced hospitalisations and annual health care costs (Murray et al, 2007)

In a recent study, Murray et al (2007) conducted a three and a half year randomised, controlled trial whichinvestigated whether pharmacy interventions improves adherence to medications and health outcomes forpatients on heart failure medications.

Results of this study showed a 10.9% difference in adherence between the control group and the interventiongroup who received a multi-level intervention (which consisted of a PMP-like service). This improvement inmedication adherence for patients receiving the intervention was associated with:

a 19.4% reduction in emergency department visits and hospital admissions

a $3,165 reduction in annual direct health care costs per patient

As DAA and/or PMP services have been shown to significantly improve adherence, these findings can beextrapolated to the DAA and PMP services, and suggests that the number of patients being admitted to hospitaldue to poor medication management or adverse medication incidents could be reduced by up to 19%. Applied tothe 133,369 medication-related hospital admission in 2007/08 (as described in Section 7.2), a 10% improvementin adherence could reduce hospital admissions by 25,340.

Reduced mortality and morbidity

The case study provided in Table 57 outlines the potential reduction in mortality for patientsreceiving a DAA or PMP service. This case study is based on a research study by Wei et al(2007), which also looks at improved medication adherence and health outcomes for people onmedication for cardiovascular conditions (specifically coronary heart disease). Therefore, thisstudy provides a useful framework for extrapolating the impact of improved adherence as aresult of DAAs and PMPs on mortality.

Costs and benefits


Table 57: Case study two

Reduced mortality (Wei et al, 2004)

In a recent study, Wei et al (2004) conducted a community-based observational study to estimate the effect ofadherence to beta-blockers on subsequent mortality. The demographics of participating patients in Wei’s studywas very similar to the patients who participated in the DAA and PMP programs, with the majority of patientsbeing aged 55 or over (approximately 87%). This allows for the extrapolation of potential reductions in mortalitybased on comparable sample populations.

Results of the study showed that adherence to beta-blocker treatment (defined 80% or greater) was significantlyassociated with a lower relative risk of mortality with unexposed patients (hazard ratio 0.49, 95% CI 0.30-0.80,p = 0.01). Within the high-risk subgroup of patients (eg older patients), the adjusted relative risk of mortality was0.40 (95% CI 0.17-0.93, p = 0.03).

Based on these results, it can be estimated that the potential reduction in the risk of mortality for patientsreceiving the DAA and/or PMP who reach 80% adherence or greater would be approximately 40%.

Reduced transfer to RAC

Improved adherence to medications as a result of the DAA and PMP services is also likely tohave a positive impact on patients staying in their own homes for longer. Research suggests thatup to 23% of nursing home admissions are the result of medication non-adherence in the elderly(Lakey, Gray and Borson, 2009). Although no known studies have been conducted which wouldallow for extrapolation, research suggests that increasing the adherence of medication regimensin the elderly population, through the DAA and PMP services, may contribute to the reduction ofpremature admission to RAC.

Addressing the potential growth in the ‘at risk’ population

Results from the PBS data projections (see Section 7.1) revealed that there is a potentialgrowth in the ‘at risk’ population (ie older people, multiple medications). For example, PBS dataprojections indicate that the average age of Australians taking medications is likely to increasefrom 58.2 years in March 2009 to 62 years by the end of 2013. Additionally, results indicate thatthe average number of medications being taken by Australians is likely to increase from3.4 medications in March 2009 to 4.4 medications by the end of 2013.

The implications of these projections are that more Australians are likely to become ‘at risk’ ofadverse medication-related events, which subsequently will increase the demand on hospitals(due to medication-related admissions) and RAC facilities. Therefore, services such as DAAsand PMPs may also help to minimise the impact of the potentially increasing demand onhospitals and RAC facilities.

Key findings


10 Key findings

This section provides a discussion of the key findings of the evaluation, along with the limitationsof the study and considerations for Government.

10.1 With the expansion of morbidity and an increasingnumber of medications being taken by Australians,medication non-adherence will increasingly become anissue in the health landscape of the future

The current health landscape reflects an ageing population, increasing burden ofchronic disease and increasing pressure and financial burden on the health system

The current Australian health landscape is well documented. It reflects an ageing population,increasing prevalence and burden of chronic disease, and decreasing mortality rates amongstmore common diseases. The combination of these three factors has meant that there is a rapidlygrowing need for the long-term management of many health conditions, which is resulting inincreasing pressure and financial burden for the Australian health system.

Since 1971, Australia’s population over 65 years of age has increased from 1.1 million (8.3% ofpopulation) to 2.9 million (13.3% of population) and Australia’s median age has increased by 5.1years over the past two decades (1). In 2007–08, an estimated 75% of Australians had at leastone long-term condition (1). Overall, cancer, cardiovascular disease, diabetes and renal failureaccount for approximately 45% of the total burden of disease and injury in Australia (3).Moreover, the prevalence of certain conditions is rapidly increasing: diabetes and end-stagekidney disease have increased substantially over the past 25 years (diabetes has more thandoubled; end-stage kidney disease has tripled) (2).

The aging population and increasing prevalence of chronic disease and chronic disease riskfactors (e.g. smoking and obesity) are placing unprecedented burden on the health system. Theexpansion of morbidity in Australia is integrally linked to the importance of medications as part ofthe suite of tools for better management of chronic and complex care needs. A key feature of theAustralian health landscape, therefore, is the significant increase in the demand for medicationsunder the PBS.

In 2006, there were approximately 179 million community PBS prescriptions (1). This representsa 44% increase in the number of community PBS prescriptions since 1996, which isconsiderably larger than the growth in the Australian population (11%) and the growth in thepopulation of Australians aged 65 years and over (18%) (2) (1).

Over the last two decades, of those medications listed on the PBS, cardiovascular system drugshave consistently been the most common, at almost 70 million services in 2009, and these drugsare now nearly double the volume of the second leading medication type. Nervous system andgeneral anti-infectives for systemic use have been consistently the second and third mostcommon medications at approximately 40 million and 30 million services respectively (13).

In terms of aggregate benefits paid by the government, cardiovascular system drugs accountedfor the highest overall value – reaching over $2 billion in 2009 (13). This is followed by anti-neoplastic and immunomodulating agents ($1.3 billion), nervous system drugs ($1.3 billion) and

Key findings


alimentary tract and metabolism drugs (~$1 billion). Strong growth is evident in expenditure inthese therapeutic areas, particularly for anti-neoplastic and immunomodulating agents, whichhas almost tripled since 2003 (13).

Medication use is on the rise in Australia and effective management of medicationswill become increasingly important

The trends in the use of prescription medications are set to continue to increase dramatically inthe near future. In 2009, PBS data revealed that Australians on medications were taking anaverage of 3.4 medications daily; this will potentially increase to 4.4 medications in 2013,indicating that the use of multiple medications is becoming increasingly prominent in the healthlandscape of the future. The average monthly PBS benefit paid per person by Medicare Australiahas increased from $96 in 2004, to $131 in 2009 and will potentially increase to $204 in 2013,reflecting that medications will also be an increasing cost to the Australian government.

With the increase in the contribution of medication to the management of chronic conditionscomes the need for the effective management of medications. While the use of medications is onthe rise, poor adherence to medication regimens remains a significant challenge – with farreaching impacts on health outcomes and health care costs. Existing literature suggests thatapproximately 50% of patients do not take their medications as prescribed by their health careprofessional. Medication non-adherence, first and foremost, compromises patient outcomes,including decreasing the effectiveness of treatments, increases the risk of medicationmisadventures, increases the need for unnecessary escalation of therapy and reduces quality oflife.

In addition to the adverse outcomes for patients, the economic costs of medication non-adherence are high. This is particularly evident in terms of avoidable hospital admissions tohospitals which are estimated to cost the Australian health system $660 million per year (11).Between 2003/04 and 2007/08, hospital separations due to medication-related incidentsincreased by 22.3% and the average LOS for medication-related admissions (8.3 days) wassubstantially higher than for all patient admissions (3.3 days).

Overall, these findings reflect the changing health landscape in terms of the increasing burden ofchronic and complex conditions and the increasing use of medications. As such, the effectivemanagement of medications has a central role to play in both reduced health burden for theAustralian population and reduced financial burden on the Australian health system.

10.2 Medication adherence strategies to support effectivemedication management are complex: the role of DAAsand PMPs as part of a suite of services

Existing literature supports the effectiveness of using a combination of services or strategies toimprove medication adherence, and, in particular, strategies that target the particular source ofnon-compliance. Any single strategy used in a blanket fashion, and/or in isolation is likely toresult in only limited improvements in adherence. Programs such as DAAs and PMPs, whichfocus on medication adherence in the community setting, are becoming increasingly importantnot only to reduce the incidence of adverse effects on patients but to relieve the growingpressure placed on the Australian health system. There is a significant body of evidencesupporting the view that DAA’s and PMP’s are more beneficial when provided as part of a suiteof services targeted at effective medication management.

Key findings


The results of the DAA and PMP program data were in line with the literature, reflecting that inthe ‘real world’ it is more common that patients receive multiple services, rather than a singleservice in isolation. This was demonstrated by the fact that nearly all of the participatingpharmacies offered multiple 4CPA funded services in their pharmacies and a large proportion ofpatients were receiving more than one 4CPA funded services.

In Phase 2, nearly 60.0% of DAA patients and 75.0% of PMP patients were receiving multipleservices. For those PMP patients who were receiving multiple services, the majority were alsoreceiving DAAs. This is not surprising given that PMPs are often used as a diagnostic tool whichleads to the identification of the need for other services, such as DAAs and HMRs.

Thus the study findings reflect the evidence. The benefits of a suite of services that are targetedto the needs of the individual patient and the various factors influencing adherence are likely tobe far greater than the provision of an individual service. Identification and targeting of those atrisk of non-adherence is likely to benefit from a multifaceted approach, rather than a ‘one-size-fits-all’ approach.

There are a number of patient risk factors that can be considered in the effective targeting ofpatients for services such as DAAs and PMPs

Existing research indicates that there are risk factors for non-adherence and patientscan be targeted for services such as DAAs and PMPs

The use of DAAs and PMPs as effective strategies for improving medication managementamongst patients in the community is well documented. These strategies enable individuals tobetter self-manage their medications and maintain their independence. Such research suggeststhat the benefits of these strategies can be maximised by targeting those patients who areconsidered to be at-risk of medication non-adherence and related adverse events and, therefore,will benefit most from the service.

Broadly, the evidence suggests that medication complexity including the number of medications,the number of doses and the type of medication, can be reliable predictors of risk of medicationnon-adherence and its adverse outcomes. Risk factors identified in the literature include takingfive or more medications daily, being elderly, poor compliance, cognitive / physical impairmentand having complex medication regimens. It has been argued that DAAs and PMPs may beeffective in achieving compliance in patients who may forget doses or are confused by acomplex medication regimen. Conditions which typically require large quantities of medication intheir treatment, and, therefore increase the complexity of medication administration, includerespiratory conditions, conditions requiring anticoagulant therapy, cardiovascular conditions,neurological conditions, renal conditions, HIV/AIDS, diabetes and asthma (56).

Evaluation results also indicate that there are potential benefits based on thetargeting patients with key risk factors

The current evaluation utilised multiple sources of data (existing literature, DAA and PMP servicedata, admitted patient care data and PBS data) to identify the characteristics of those patientswho might be considered to be at-risk of medication non-adherence. Together, these data wereused to confirm the key risk factors for non-adherence in the Australian medication takingpopulation. These are outlined below.

Individuals on five or more medications

It is well documented that there is a strong correlation between the number of medications anindividual is taking and the risk of non-adherence due to the complexity associated with taking

Key findings


multiple medications. Of the patients recruited to Phase 2 of the DAA and PMP programs,almost half of the patients were taking between five to seven medications, suggesting thatpharmacists saw patients on multiple medications (ie five or more) as being the most likely tobenefit from the services, in line with the findings from the literature.

The PBS data indicated that approximately 20.0% of all Australians who take medications takefive or more, with the average number of medications being taken by older Australianscontinuing to increase over time. This increase could be quite dramatic for those aged 75 yearsand older. In 2009, individuals in this age group were taking on average approximately fivemedications each; projections of the PBS data indicate that this age group may potentially betaking nearly double the number of medications in 2019.

Moreover, the proportion of Australians who are likely to be taking five or more medications isalso expanding. While the increase is not likely to be as dramatic, those individuals aged 65 to75 years are also estimated to be taking more medications over the next ten years. In 2009, theywere taking an average four medications and, based on current trends, it is estimated that theywill be taking, on average, six medications by 2019.

Thus, the proportion of the population taking more than five medications is expanding. Thoseaged 75 and over have long been recognised as being at risk of non-adherence due to thenumbers of medications being taken by them. Older Australians are clearly likely to remain themost in need of services such as DAAs and PMPs, as the average number of medications takenby those in these older age groups increases significantly in coming years. However, theexpansion of the ‘at-risk’ category (ie those taking five or more medications) to those aged 65 to75 is also predicted over the next ten years, suggesting that this younger group will also fallwithin the ambit of adherence targeting.

Individuals aged 65 years and over

Existing literature suggests that one of the strongest associations with poor medicationmanagement is patient age, with the risk of medication non-adherence increasing with age.

The admitted patient care data for medication-related admissions revealed that in 2007/08almost half of all medication-related admissions were for patients aged 65 years and over andthat the average length of stay increased to over 10 days for this age group. The PBS dataindicates that those aged 65 years and over equate to approximately 40.0% of the Australianpopulation who take medications, with the average age of Australians taking medicationscontinuing to increase with the ageing population and as individuals live longer.

Despite there being no specified patient enrolment criteria, the majority of patients recruited toboth the DAA and PMP programs were aged 65 years and over, with the majority of thesepatients falling between the ages of 75 to 84. Again, while those aged 75 years and over weretargeted by pharmacists in the DAA and PMP programs, these data suggest that pharmacists inthe program recognised the benefits that might accrue with the provision of DAAs and PMPs tothose in the emerging ‘at risk’ group, those aged 65 years and over.

Individuals who do not have access to social support or live alone

A significant body of evidence supports the association between social support and adherence.Patients who receive little support or assistance have been shown to be at greater risk of non-adherence. Factors such as living alone, being single or divorced and not having access tosocial support have all been associated with an increased risk of adverse medication events.Results of the DAA and PMP program showed that just under half of patients reported livingalone, and almost a third of all patients did not receive assistance with managing theirmedications. Thus this risk factor for non-adherence was well represented among the patientsrecruited to the program.

Key findings


The flow-on broader benefits of medication management for those living alone and/or withoutsupport through services such as DAAs and PMPs are also clear. Research suggests thatservices such as DAAs and PMPs increase the capacity of patients to independently managetheir medications, which allows them to remain in their own home for longer and delay the needfor admission to residential aged care facilities. This is supported by the admitted patient caredata for medication-related admissions which showed that in 2007/08 there was a higherproportion of patients entering aged care facilities following a medication-related acute careadmission, than for all patient admissions, and the proportion of patients who return homefollowing a medication-related acute care admission was lower than for all patient admissions.These data suggest that broader benefits of services such as DAAs and PMPs may bemaximised through targeting individuals who do not have access to support services and live ontheir own.

Nature of the condition and complex medication regimens

Disease or medication types have been shown in the literature to influence a patient’s capacityto manage their medications. Disease types include cardiovascular, musculoskeletal, diabetes,asthma, and nervous system diseases. The common denominator suggested by the literature forthe added risk of non-adherence for these diseases, is that they tend to involve greatercomplexity of the medication regimen.

Of those medications listed on the PBS, cardiovascular system medications have consistentlybeen the most common at almost 70 million services in 2009. The second and third mostcommon medications are nervous system and alimentary tract medications at approximately 40million and 30 million respectively (13). Results of the DAA and PMP data are aligned with theliterature; approximately 90.0% of patients were taking cardiovascular medications, just over60.0% were taking nervous system medications and fewer than 60.0% were taking alimentarytract drugs.

These results suggest that consideration of a patient’s condition and medication regime whentargeting patients for DAA and PMP is warranted based on the literature and possible in practicalterms. Considering the cohort of patients participating in the program, it is clear that pharmacistsrecruited to the program in line with the current evidence base.

Pharmacists recruited appropriately, with no enforced recruitment criteria

Pharmacists who participated in the DAA and PMP programs were encouraged to recruitpatients to the service who they thought were most likely to benefit. However, there were nospecified patient criteria for participation. Despite this, the vast majority of patients who weresuccessfully recruited to the services were individuals who could be considered to be at risk (iethey had multiple risk factors). Less than ten percent of patients recruited to the services wereidentified as having no risk factors.

On the other hand, pharmacists participating in the DAA and PMP programs were able toprovide important qualitative information, based on the experience in the program, about thecontra indications for DAA and PMP services. They clearly described the characteristics ofpatients who were not suited to the effective use of DAAs and PMPs. These characteristicsincluded advanced age (greater than 90 years), dementia, continual changes to medications,increasing non-adherence, and particular medications such as patients on warfarin (DAA). Toenable pharmacies to effectively target those patients who are most likely to benefit from suchservices, consideration of patient characteristics suggesting unsuitability for the program are alsoof practical use.

Key findings


10.3 A broad range of pharmacies participated in the DAA andPMP programs and the reported costs associated withdelivering the programs were highly varied

Pharmacies recruited to the programs were broadly representative of communitypharmacies nationally

A broad range of pharmacies participated in both the DAA and PMP programs. The distributionof participating pharmacies across State, PhARIA and SEIFA was representative of communitypharmacies nationally, suggesting that there may be no ‘type’ of pharmacy which is more likelyto opt-in to providing the DAA and PMP services. The broadly representative nature of theparticipating pharmacy cohort also suggests that the evaluation results are likely to begeneralisable to pharmacies nationally.

The costs associated with providing the DAA and PMP services are largely driven bythe level of staff involved in providing the service

Based on pharmacy staff time involved in delivering the DAA and PMP services, results showedthat the average weekly cost for delivering the DAA and PMP service was $565.89 and $122.10respectively. The cost per patient per week of the DAA service was $17.25 and $25.44 for thePMP service, with the difference in cost for the two services attributable to the level of staffinvolved in providing the service.

It is important to recognise the nature of the services being costed. The service delivery costs ofthe DAA and PMP services described above consider the services separately. However, resultsfrom the DAA and PMP service data suggest that, in reality, many pharmacies offer multipleprograms and many patients receive multiple services. The implication of this is that it is difficultto consider the unique costs of either service in isolation. Instead, consideration of theincremental costs and benefits, with patients receiving multiple services, requires furtherexploration.

Qualitative data provided by pharmacists indicated that the provision of the DAA and PMPservices for some pharmacies were either cost neutral at best, or, for some, not cost effective. Itwas generally those pharmacies who provided a large number of DAA’s where cost benefit wasderived from the service, due to the increased business and volume of scripts. It is noteworthythat the qualitative data collected during the program indicated that, while the services may havehad little cost benefit for many of the participating pharmacies, this was outweighed by theperceived benefits for their patients (eg provides a better form of care) and the other perceivedbenefits for the pharmacy (eg improved patient loyalty and pharmacy reputation).

The costs associated with receiving the DAA and PMP service for patients washighly varied

Approximately 93.0% of pharmacies reported that they charge patients for the DAA servicecompared with 38.5% for PMPs.

The costs to patients for the DAA and PMP services varied, with the costs of PMPs being slightlyhigher than DAAs. For DAA’s, the majority of pharmacies (63.0%) charged less than $5.00, whileapproximately 30.0% charged between $5.00 and $10.00. For PMP’s 43.0% of pharmaciescharged less than $5.00 and 56.5% charged between $5.00 and $10.00

Key findings


10.4 Appropriate reimbursement and incentivisation for theDAA and PMP services in community pharmacy is likelyto reflect a combination of factors

Thus the DAA and PMP program data indicated that costs were variable, and that while mostpharmacies charged for the services, the charges to patients were variable as well. Yet, somepharmacies reported that there was little cost benefit in providing the programs, indicating thatthe charges did not reflect particular reimbursement models alone, but perhaps a range ofinteractive factors, including non-financial benefits described above. These findings raise thequestion then, of what might be an appropriate model for reimbursement or incentive for theprovision of such services fin community pharmacies.

To further consider the factors that might be part of a reimbursement model, the evaluationexamined the characteristics of the pharmacy which may influence the costs charged to patientsfor the DAA and PMP services. These characteristics included prescription volume (as anindicator of pharmacy size), number of patients who received a DAA service during a one weekperiod (as an indicator of pharmacy activity) and pharmacy location (as an indicator ofremoteness).

Pharmacy size and activity

Overall, the provision of the DAA service was significantly more common among, pharmacieswith a higher prescription volume. This was not the case for PMPs, where no correlations werefound between script volume and the number of patients receiving PMPs. When there was arelationship between activity level (number of services – either PMP or DAA – in a week) andpharmacy volume (number of scripts per week), the pattern of findings was reasonably similar,however;

there was no significant relationship between prescription volume and the amount chargedto patients for the DAA or PMP service

there was a significant relationship between the number of patients receiving the DAA orPMP service and the amount charged to patients for the service

together, prescription volume and number of patients receiving the DAA or PMP servicewas significantly related to the amount charged to patients for the service.

The program data indicated that the levels of DAA and PMP activity were the primary drivers ofamount charged by pharmacies, with higher activity levels being associated with lower charges.Moreover, that impact of service activity level is particularly evident as script volume alsoincreases. Taken together, these data suggest that where pharmacies were in a higher band ofservice provision and script volume they were likely to charge lower rates to their patients.

Pharmacy location

The evaluation examined the impact of PhARIA category (an indicator of remoteness) and theDAA/PMP provision price. The data suggested that there were no meaningful differences in theamount charged to patients for a DAA or PMP based on pharmacy location (remoteness).

Key findings


10.5 Limitations of the evaluation and areas for furtherinvestigation

Limitations of the evaluation

The main limitation of the evaluation emanates from the DAA and PMP program data. The DAAand PMP data were collected via the DAA and PMP data and information system which enabledpharmacies to register online for participation in the programs, as well as provide evaluation dataonline. Despite attempts to set up a system that would allow comprehensive and complete datacollection, there were a number of limitations with the system that had an impact on overallquality and usability of the data in the evaluation. Some of the key limitations of these datainclude:

missing data were significant in Phase 1

there was a lack of comparable data across phases, particularly at the patient level

there were limited data on the health outcomes of patients

for some key variables it was unable to be determined which data were missing and thosethat were not applicable, undermining any aggregated analysis

the data were not available to undertake a robust cost benefit analysis.

Due to the limitations of the DAA and PMP program data, strategies for maximal use of thesedata were explored, including the use of corroborative data from existing research, the PBS andthe admitted patient care NMDS. These data sets were used in a ‘meta-analytic’ fashion, that is,the information on core sub-groups were extrapolated from one data set to another. The largecohort of patients participating in the DAA and PMP programs were referenced to the trends andoutcomes on similar sub groups in the national data sets describing acute patient care andmedications usage. Despite the limitations associated with the DAA and PMP program data, thekey findings reported here were supported by other reliable sources of data. Thus, the keyfindings and implications of the evaluation are also well supported.

A second important shortcoming of the evaluation was the limited availability of data on thetransfer to residential aged care due to medication related events. One key premise of the DAAand PMP programs, as with other medication management programs, is the support it providesto independence and self care. Premature transfer to aged care, due to medication managementissues, is therefore a central indicator for such programs. On the other hand, it needs to berecognised that the identification of a causal link would have been a complex task. A number offactors are likely to contribute to transfer to residential aged care. However, effective medicationmanagement is thought to play a key role for these patients and consideration should be given tohow to best collect data to describe the extent of its contribution to premature residential agedcare admission.

Further considerations for Government

The existing literature, and this evaluation, supports the use of strategies such as DAAs andPMPs to assist patients in the management of their medications and to improve medicationmanagement and adherence. Equally importantly the evaluation demonstrated the viability ofoffering such services in community pharmacy. Unquestionably, the program indicated that thereis an appetite in community pharmacy to provide the service, pharmacists are able to recruitpatients who fit evidence based criteria for being at risk of non adherence, and there is a highlevel of retention of patients once they are recruited. A range of data attest to the benefits of

Key findings


recruiting those at risk of medication non adherence and retaining them in appropriate regimesdesigned to assist medication management.

Perhaps the key area that warrants further investigation concerns the opportunity afforded interms of longer term impact (and return on investment) of such strategies and services, offeredas a package of care in the community pharmacy setting. This opportunity reflects a range oftrends including increasing recognition of the multiplicative impact of a suite of strategies tomanage non-adherence, the likelihood that an individual patient’s needs might change over time,and, that with appropriate fostering of pharmacist skills the community pharmacy setting maywell provide the opportunity to tailor strategies as patient needs change.

This evaluation demonstrated that it was very difficult to separate out unique costs and benefitsattributable to strategies such as DAAs and PMPs. Current evidence suggests that pursuingincreasing accuracy and specificity is in fact not likely to be as helpful to the larger agenda ofmedication non adherence as careful documentation of exposure of patients to systematic,responsive and long term strategies to assist in their medication management. Accordingly, asthe mediation management agenda inevitably grows in significance and as a focus ofGovernment investment, consideration might best be given to definition of outcomes andbenefits of the investment, and better measurement frameworks to inform future investment.

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