Volume 47, Issue 8, August 2018

Medication non-adherence in a cohort of chronically ill Australians: A case of missed opportunities

Tracey-Lea Laba    Tom Lung    Stephen Jan    Anish Scaria    Tim Usherwood    Jo-anne Brien    Natalie A Plant    Stephen Leeder   
doi: 10.31128/AJGP-10-17-4351   |    Download article
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Background and objectives
This study investigated the effect of management – including home medicines reviews and chronic disease management plans funded through the Medicare Benefits Schedule – on self-reported medication non-adherence.
An observational cohort study including 244 individuals with an exacerbation of chronic illness enrolled into the Care Navigation randomised controlled trial of integrated care. Non-adherence was measured using the Morisky-Greene-Levine self-reported adherence tool.
The cohort comprised an equal number of older men and women with, on average, three chronic diseases, receiving between five and 10 unique medications each month and visiting a general practitioner (GP) more than 50 times in the year prior to completing the questionnaire. Forty per cent reported non-adherence, which was greater in males (relative risk [RR]: 1.73; 95% confidence interval [CI]: 1.25, 2.54) and in patients reporting a recent fall (RR 1.40; 95% CI: 1.02, 1.89). GP-initiated chronic disease management programs did not influence adherence.
Despite almost weekly contact with GPs, two in five patients were non-adherent. Better methods of encouraging adherence are needed.
Multimorbidity is common. It affects not only individual patients but also households and health services.1 Approximately half of patients with multimorbidity do not take medications appropriately, partly because of polypharmacy.2

The range of effective medications for each disease can be confusing, especially when the patient takes medications for several illnesses. Added to this, potential barriers to adherence relate to the delivery, governance and financing of healthcare.3 Optimal care for multimorbidity requires carers in several disciplines.1,4

In Australia, general practice plays a key role in coordinating care.5,6 The Federal Government reimburses patients or general practitioners (GPs) and some allied health practitioners on a fee-for-service basis.7 Reimbursements are available for GPs’ preparation and review of disease management plans and for the review of multidisciplinary care arrangements (‘Team Care Arrangements’), which facilitate subsidised access to non-medical healthcare providers. In addition, payments are available for GPs and pharmacists for comprehensive and collaborative Domiciliary Medication Management Reviews (DMMRs) and Residential Medication Management Reviews (RMMRs). The impact of these services on medication adherence by people with multiple chronic diseases in Australian clinical practice is unclear.

Recently, the Australian Government announced a pilot trial of coordinated care, delivered by multidisciplinary teams in general practice, for patients with chronic and complex conditions.8 Health Care Homes are funded via bundled, rather than fee-for-service, payments. Evidence from similarly designed ‘patient-centred medical homes’ in the USA and UK points to improvements in medication adherence,9 though the evidence base is small.

The current study

We examined the extent of non-adherence and the factors influencing it within a population of chronically ill patients receiving coordinated care in Australia.

Our sample is drawn from the Care Navigation (CN) trial. The CN trial was conducted between 2010 and 2013 in a university-associated hospital in the Nepean Blue Mountains Local Health District (NBMLHD) 25 km west of Sydney. The CN trial examined whether or not a nurse-led program – which coordinated care between hospital services, community health and general practice – could reduce unplanned emergency department presentations and hospital readmissions while simultaneously improving quality of life for patients with recent hospitalisations related to chronic disease.10 NBMLHD encompasses more than 360,000 people with a chronic disease burden similar to that of Australia as a whole.

After an average of 24 months, no significant differences in outcomes were found between the management arms, although the intervention arm made greater use of non-hospital health services.11 Overall, increased hospital use was associated with being cared for at home (rather than in an institution) and with previous emergency department visits.12

In spite of the increasing prevalence of multiple chronic disease medication use, little research has focused specifically on adherence by people with multiple chronic illnesses, especially in Australia.2,13–16 Even fewer studies have examined the impact of coordinated care.9 While patient, disease, socioeconomic and therapy-related factors have typically been implicated as barriers to adherence, the impact of health system–related factors such as access to resources to manage chronic diseases has received very little attention.9 To address these gaps in evidence, and given the interest in delivering coordinated care to people with complex chronic diseases in Australian general practice, we investigated the impact of health system–related factors on medication adherence reported by participants in the CN trial. In particular, we investigated the influence of home medicine reviews and chronic disease management plans funded by the Medicare Benefits Schedule (MBS) alongside patient characteristics on levels of non-adherence.


Our study included all participants with chronic illness admitted to Nepean Hospital and enrolled in the CN trial who completed a self-report medication adherence questionnaire 12 months after randomisation and for whom we had access to administrative claims data.

The inclusion criteria of the CN trial are described elsewhere.10,11 In brief, individuals comprised:

  • patients aged >70 years (or >45 years for Aboriginal and Torres Strait Islander Australians) with three or more hospital admissions during the preceding 12 months
  • ​patients aged >16 years with at least one previous respiratory-related or cardiac-related hospital admission
  • patients determined by a CN nurse as likely to benefit from receiving CN.

Excluded from the CN trial were:

  • previous CN recipients
  • ​those medically unable to participate
  • those admitted to hospital more than one business day prior to randomisation
  • those not providing written informed consent.
For our study, the CN trial population was treated as a single cohort comprising participants from the two arms of the trial. This was because no effect of the intervention was found in the analysis of the trial.11

Of 500 participants in the CN trial, 308 provided detailed demographic and clinical variables (eg age, gender, employment status, comorbidities) by telephone interview 12 months after randomisation. Of these, 299 completed a self-reported adherence questionnaire, becoming eligible for inclusion in this analysis.

We excluded Department of Veterans’ Affairs entitlement holders (n = 9) and anyone not consenting to administrative data linkage (n = 46), because we lacked access to their administrative claims data needed to measure health system factors of interest.

Primary outcome
Using the validated and extensively used Morisky-Greene-Levine questionnaire,17 self-reported non-adherence was assessed 12 months following randomisation. This questionnaire includes four Yes/No questions:
  1. Do you ever forget to take your medications?
  2. Are you careless at times about taking your medications?
  3. When you feel better, do you sometimes stop taking your medications?
  4. If you feel worse when you take your medications, do you sometimes stop taking them?17
As in other research, respondents answering ‘Yes’ to one or more of the questions were classified as non-adherent.13

In addition to the self-reported clinical and demographic characteristics, the health system–related variables measured were:

  • the number of emergency department presentations and hospital admissions in the previous 12 months, recorded in a CN database of electronic medical records
  • ​the number of GP visits and the presence of chronic disease-specific management plans and team care arrangements or DMMRs/RMMRs in the previous 12 months, as coded in the MBS at the time of the study and recorded in individually linked MBS records (refer to Table 1 for specific item codes used).18,19
Finally, the average number of unique medications dispensed monthly for each participant over the previous 12 months was calculated using individually linked Pharmaceutical Benefits Schedule (PBS) administrative records of government-funded medications dispensed outside of public hospitals.
Table 1. Characteristics of the study cohort (frequency and % unless otherwise reported)
Characteristic n = 244 Missing (%)
Care Navigation treatment group 127 (52.0%)
Age (years; mean, SD) 70.4 (11.7)
Male 125 (51.2%)
Financial constraints 68 (27.9%)
Income source: pension/social welfare 200 (83.7%) 2.0
Nursing home resident 4 (2.5%) 2.0
Social isolation 62 (25.4%)
Physical disability 98 (40.2%)
History of falls in the last month 86 (37.2%) 5.3
Visual and/or hearing impairment 197 (80.7%)
Deterioration in health-related quality of life, 12 months* 159 (65.2%)
Number of reported chronic conditions (mean, SD) 3.1 (1.5)
Average number of medications per month    
    <5 86 (35.2%)
    5–10 133 (54.5%)
    >10 25 (10.2%)
Domiciliary or residential medication review in previous 12 months 39 (16.0%)
Number of GP visits, previous 12 months†    
    <50 112 (45.9%)
    >50 132 (54.1%)
Chronic disease management , previous 12 months (GP or practice nurse) 172 (70.5%)
GP mental health treatment plan, previous 12 months 32 (13.1%)
Number of ED visits, previous 12 months (mean, SD) 2.6 (3.2)
Hospital admissions, previous 12 months (mean, SD) 2.0 (2.5)
*Deterioration in EQ-5D utility score between baseline and 12 month visit
Medicare Benefits Schedule Item codes: GP visits: 3, 4, 20, 23, 24, 34–37, 43, 44, 47, 51–54, 57–60, 65, 92, 93, 95, 96, 597, 599, 5000, 5003, 5010, 5020, 5023, 5028, 5040, 5043, 5049, 5060, 5063, 5067; chronic disease management plans and practice nurse chronic disease services, mental health treatment plans, and Team Care Arrangements: 712–14, 716–19, 721, 723, 729, 731, 732, 735, 739, 743, 747, 750, 758, 2700, 2701, 2702, 2710, 2712, 2713, 2715, 2717, 10997; medication reviews: 900, 903
ED, emergency department; GP, general practitioner; ED, emergency department; SD, standard deviation
Table 2. Characteristics of the study cohort by gender (frequency and % unless otherwise reported)
Characteristic Male
(n = 125)
(n = 119)
Care Navigation treatment group 56 (44.8%) 71 (59.7%)
Age (mean, SD) 69 (10.8) 72 (12.5)
Financial constraints 33 (26.4%) 35 (29.4%)
Income source: pension/social welfare 98 (79.7%) 102 (70.6%)§
Nursing home resident 2 (1.6%) 2 (1.7%)§
Social isolation 30 (24.0%) 32 (26.9%)
Physical disability 58 (46.4%) 40 (33.6%)
History of falls in the last month 42 (36.2%) 44 (38.3%)§
Visual and/or hearing impairment 101 (80.8%) 96 (80.7%)
Deterioration in health-related quality of life, 12 months* 48 (38.4%) 37 (31.1%)
Number of reported chronic conditions (mean, SD) 3 (1.5) 3.2 (1.6)
Average number of medications per month    
    <5 46 (36.8%) 40 (33.6%)
    5–10 61 (48.8%) 72 (60.5%)
    >10 18 (14.4%) 7 (5.9%)
Domiciliary or residential medication review in previous 12 months 19 (15.2%) 20 (16.8%)
Number of GP visits, previous 12 months    
    <50 55 (44.0%) 57 (47.9%)
    >50 70 (56.0%) 62 (52.1%)
Chronic disease management, previous 12 months (GP or practice nurse) 84 (67.2%) 88 (73.9%)
GP mental health treatment plan, previous 12 months 14 (11.2%) 18 (15.1%)
Number of EDb visits, previous 12 months (mean, SD) 2.3 (2.8) 2.9 (3.5)
Hospital admissions, previous 12 months (mean, SD) 1.8 (2.2) 2.2 (2.7)
*Deterioration in EQ-5D utility score between baseline and 12 month visit
Medicare Benefit Schedule Item codes: GP visits: 3, 4, 20, 23, 24, 34–37, 43, 44, 47, 51–54, 57–60, 65, 92, 93, 95, 96, 597, 599, 5000, 5003, 5010, 5020, 5023, 5028, 5040, 5043, 5049, 5060, 5063, 5067; chronic disease management plans and practice nurse chronic disease services, mental health treatment plans, and Team Care Arrangements: 712–14, 716–19, 721, 723, 729, 731, 732, 735, 739, 743, 747, 750, 758, 2700, 2701, 2702, 2710, 2712, 2713, 2715, 2717, 10997; medication reviews: 900, 903
Missing: n = 2 income source; n = 1 nursing home resident; n = 9 history of falls
§Missing n = 3 income source; n = 1 nursing home resident; n = 1 history of falls
GP, general practitioner; ED, emergency department
Statistical analysis
Correlations between all covariates were checked using the Pearson correlation coefficient, with >0.4 taken to indicate significant correlation. Continuous explanatory variables were plotted against the primary outcome (ie non-adherence), and those not linearly distributed were collapsed into categorical variables appropriate for the distribution (eg number of medications, number of GP consultations).

The association of covariates with non-adherence (relative to adherence) was analysed using the Poisson regression with robust error variance.20 The Hosmer-Lemeshow purposeful selection of variables strategy was chosen to help guide the selection of a parsimonious model with numerically stable estimates and small standard errors.21,22

Covariates with a univariate P value of 0.25 were selected as candidates for the multivariate analysis and iteratively removed from the model if they were non-significant (a = 0.2) and were not confounders (ie change in parameter estimate <15% compared with full model)

Covariates originally excluded from the multivariate model were individually re-added and retained at the P = 0.15 level. The model was then iteratively reduced as before but only for the variables that were additionally added. Given the aims of the study, the health system­–related variables (emergency department and hospital admissions, GP visits, chronic disease‑specific management plans and team care arrangements, DMMRs/RMMRs) and CN treatment assignment were retained throughout the model-building process and in the final model.

We conducted 1000 bootstrap replications to estimate 95% confidence intervals (CIs) for the coefficients of the iteratively reduced complete case multivariate model. Analyses were performed using SAS, version 9.3.

Written informed consent had been obtained from all participants in the study. Ethics approval was granted by Sydney West Area Health Service Human Research Ethics Committee – Nepean Campus (HREC/09/NEPEAN/55) and ratified by the University of Sydney Research Integrity office (ACTRN12609000554268).


The mean age of participants was 70 years; 51.2% were male, 83.7% were dependent on social welfare and 2.5% resided in nursing homes. Fifty-two per cent received the intervention. Participants reported having, on average, three chronic diseases, and more than one-third reported having fallen in the month prior to interview.

In the 12 months prior to interview, 54.5% had received between five and 10 prescribed medications monthly; 54.1% had, on average, visited a GP more than once weekly; 16.0% had had a government-rebated, GP-initiated DMMR or RMMR; and more than two-thirds had experienced a GP-initiated chronic disease management plan. On average, participants had visited the emergency department three times and had been admitted to hospital twice (Tables 1, 2).

Of the 244 participants, 40.2% (n = 98) self-reported as non-adherent. Table 3 provides the univariate and multivariate rate ratios for non-adherence compared with adherence. After controlling for intervention arm assignment in CN trial and for other health system variables, males were at higher risk of reporting non-adherence (relative risk [RR]: 1.73; 95% CI: 1.25, 2.54). Participants who had reported a fall in the month prior to completing the questionnaire were at higher risk of reporting non-adherence compared with those who had not fallen (RR: 1.40, 95% CI: 1.02, 1.89). We found no other statistically significant associations between non-adherence and other demographic, clinical or therapy-related factors, nor with any of the health system–related variables assessed.

Table 3. Rate ratio of 12-month non-adherence (unadjusted and adjusted, 95% confidence interval; n = 244)
Risk factor Comparison
against reference
n Univariate
rate ratio
P Bootstrapped rate ratio (95% CI) P
CN treatment group CN versus standard care 244 0.82 (0.60, 1.11) 0.193 0.89 (0.62, 1.25) 0.488
Age, increasing   244 0.98 (0.97, 0.99) 0.007 0.99 (0.98, 1.01) 0.188
Male Male versus female 244 1.80 (1.29, 2.50) 0.001 1.73 (1.25, 2.54) 0.003
Financial constraints Yes versus no 244 1.20 (0.87, 1.65) 0.271
Income source Received income vs social welfare 239 1.33 (0.94, 1.90) 0.111
Resident of nursing home Yes versus no 239 0.63 (0.12, 3.47) 0.597
Social isolation Yes versus no 244 1.17 (0.84, 1.63) 0.341
Mental disability Yes versus no 244 1.63 (1.01, 2.62) 0.044
Physical disability Yes versus no 244 1.43 (1.06, 1.94) 0.020 1.25 (0.93, 1.75) 0.154
History of falls in the last month Yes versus no 231 1.24 (0.91, 1.70) 0.181 1.40 (1.02, 1.89) 0.030
Visual and/or hearing impairment Yes versus no 244 1.06 (0.71, 1.58) 0.774
Health-related quality of life deterioration, 12 months* No change versus relative decline 244 0.99 (0.72, 1.37) 0.970
Number of reported chronic conditions   244 0.99 (0.90, 1.10) 0.903
Average number of medications per month 5–10 versus 0–4 244 0.66 (0.48, 0.91) 0.012 0.75 (0.52, 1.06) 0.090
  >10 versus 0–4 244 0.88 (0.54, 1.44) 0.609 0.95 (0.58, 1.48) 0.860
Domiciliary or residential medication review in previous 12 months† Yes versus no 244 0.60 (0.34, 1.04) 0.070 0.68 (0.33, 1.12) 0.171
Number of GP visits in previous 12 months >50 versus ≤50 244 0.75 (0.55, 1.02) 0.067 0.79 (0.57, 1.07) 0.139
Chronic disease management, previous 12 months (GP or practice nurse) Yes versus no 244 1.16 (0.81, 1.65) 0.414 1.21 (0.85, 1.86) 0.358
GP mental health management in previous 12 months Yes versus no 244 1.39 (0.96, 2.01) 0.079 1.35 (0.86, 2.02) 0.146
Number of emergency department visits in the past 12 months   244 1.00 (0.95, 1.05) 0.936 1.00 (0.94, 1.05) 0.895
Number of hospital admissions in the last 12 months   244 0.98 (0.92, 1.05) 0.595
*Deterioration in EQ-5D utility score between baseline and 12 month visit
†Variables that have not been reported are excluded from the adjusted model
CI, confidence interval; CN, Care Navigation; GP, general practitioner


In this Australian cohort of older people with multiple chronic illnesses, who were in frequent contact with their general practice and receive a hospital-based coordinated care program, the extent of self-reported medication non-adherence was 40%. The non-adherence estimate is similar to the range of 20–50% for chronic disease reported in the broader literature,2 but is substantially lower than the 82% non-adherence rate estimated in a Spanish study of a multimorbid cohort recently discharged from hospital, a study that used the same validated self-reported measure of non-adherence.13

Given that our study was conducted among a cohort derived from a clinical trial and that self-reporting measures are potentially subject to social desirability bias (ie providing responses that are viewed favourably),23 it is possible that our figure underestimates the extent of non-adherence within the broader multimorbid population. Nevertheless, only gender and falls history were significantly correlated with non-adherence.

Considering the extensive contact these patients had with a general practice in the preceding 12 months, including chronic disease management programs and services, this high rate of non-adherence is alarming and disappointing. Importantly, the relatively low uptake of specific collaborative medication management services (ie DMMRs/RMMRs), for which the majority of patients could have qualified,24 raises questions about the practical accessibility of such services. This might reflect administrative and time-related barriers, as recently reported by some Australian GPs.25 Alternatively, this finding, alongside the observed lack of GP awareness of medication-specific services,25 could signal an inadequate focus of current chronic disease management services with respect to medication adherence in general practice.

Our findings highlight the fact that surveillance of non-adherence and the use of adherence support services are needed for patients with multimorbidity, particularly men and those who have recently fallen. With the majority of patients in this study visiting a GP almost weekly, general practice seems the optimal, albeit not the only, place for intervention.

Whether further funding for new services is needed or whether such services should be part of currently reimbursed general practice is debateable. On the one hand, emphasis on appropriate use of medications would ideally accompany any prescribing decision during a consultation. However, the reported difficulties of accurately detecting non-adherence in general practice23,26 and the time and cost pressures of the fee-for-service model might be barriers to such routine implementation.

Community pharmacists are trained in medication management. As a part of their professional practice, they are tasked with ensuring the optimal use of dispensed medications. Under the fifth and sixth community pharmacy agreements, they have financial incentives to encourage adherence, including offering in-store medicines use reviews and dose administration aids.27

However, unlike DMMRs/RMMRs, these pharmacist services, not being medical services, do not qualify for reimbursement through MBS (and so could not be captured nor accounted for in our study), do not require the pharmacist to undertake additional accreditation to participate and do not require collaboration with the patient’s GP.

With the remuneration of community pharmacies’ non-dispensing services a contested and divisive area of health policy,28 one solution might be to invest in technology to better connect GP services with the services provided by community pharmacists rather than create and fund ‘new’ adherence services.29,30 This is particularly relevant with respect to pharmacists’ dispensing of generic medications, whose different colours, shapes and sizes could confuse patients. Alternatively, the inclusion of pharmacists in the ‘Health Care Home’, as done in other contexts, could be explored.9,31 In any case, rigorous evaluation of the effectiveness of such services on both adherence and clinical outcomes is needed, with investment decisions guided by suitable cost-effectiveness and financial impact analyses that take into account implementation and scale.

While the association between non-adherence and gender corroborates other findings,32 the apparent correlation of non-adherence with a recent history of falls requires further investigation. Many medications can increase the risk of falls in the elderly.33 Non-adherence might reflect the patient’s appropriate response in the face of a falls-inducing adverse drug reaction.

On the other hand, the pharmacodynamic changes that occur with ageing and the characteristics of medication use – such as duration of use, drug–drug interactions and changes in medication use, including as a result of non-adherence – might be associated with an increased risk of falls.34 Further work considering the appropriateness of therapy, the number and pattern of an individual’s chronic diseases,35 as well as the longer-term impact of a fall on adherence, is needed to unpack this relationship.

The lack of any statistically significant association between the number of prescribed medications and non-adherence was surprising. Although we have allowed for an adequate number of observations per covariate in the multivariate analysis, the small overall sample size may be masking the true difference in outcomes between the different groups. Given the multiple comparisons (covariates) in the final model, we also cannot exclude the possibility of false positive results.

Furthermore, as our sample was a sub-group of participants enrolled in a randomised controlled trial that was conducted in one region of Australia, the generalisability of these findings to the broader population might be limited. In particular, the extent of federally funded non-MBS adherence services provided by community pharmacies might differ between our cohort and the general Australian population. Further, we could only explore the variables that were collected in the CN trial. It is also possible that we have underreported the number of chronic disease management services provided; for example, general practice management plans that were ongoing but not claimed during our study timeframe would not have been captured.

Finally, as there is no ‘gold standard’ measure of adherence, self-reporting can be subject to bias (eg recall bias given no recall period is specified with this instrument). Although the use of a more objective measure of adherence would be ideal, the ease of administration and relatively lower burden on the participants dictated our use of a self-reporting tool.

Implications for general practice

In this study, two in five patients with multiple chronic conditions enrolled in the CN trial reported medication non-adherence. Current GP programs to support chronic disease management, including collaborative home medicines reviews, did not influence adherence.

We recommend greater emphasis being placed, through existing MBS GP items, on addressing non-adherence in such patients. It is important for prescribers to regularly ask patients about their medication adherence and, if necessary, to discuss strategies for promoting this.36 Connecting GPs with the medication management skills of pharmacists might address this striking unmet clinical need.

Competing interests: TLL is an NHMRC Early Career Overseas (Sidney Sax) Research Fellowship recipient (APP1110230). SJ is an NHMRC Principal Research Fellowship recipient (APP1120615).
Provenance and peer review: Commissioned, externally peer reviewed.
Funding: Implementation of the evaluation of the Care Navigation Trial, including recruitment, data management, economic study and statistical analyses, were funded by NHMRC grant 1004393: Care Navigation RCT.
The authors gratefully acknowledge the extensive editorial contributions of Dr Peter Arnold to the development of this paper.
  1. Atun R. Transitioning health systems for multimorbidity. Lancet 2015;386(9995):721–22. doi: 10.1016/S0140-6736(14)62254-6. Search PubMed
  2. Nieuwlaat R, Wilczynski N, Navarro T, et al. Interventions for enhancing medication adherence. Cochrane Database Syst Rev 2014;(11):CD000011. doi: 10.1002/14651858.CD000011.pub4. Search PubMed
  3. World Health Organization. Adherence to long-term therapies: Evidence for action. Geneva: WHO, 2003. Available at [Accessed 9 May 2018]. Search PubMed
  4. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet 2012;380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2. Search PubMed
  5. World Health Organization. WHO Framework on integrated people-centred health services. Geneva: WHO, 2016. Available at [Accessed 9 January 2017]. Search PubMed
  6. NSW Government Health. Integrated care in NSW. Sydney: NSW Government Health, updated 30 October 2017. Available at [Accessed 9 January 2017]. Search PubMed
  7. Department of Health. Chronic disease management – Provider information. Canberra: DoH, updated 9 February 2016. Available at [Accessed 9 January 2017]. Search PubMed
  8. Department of Health. Health Care Homes – Health professionals. Canberra: DoH, updated 4 April 2018. Available at [Accessed 4 August 2017]. Search PubMed
  9. Lauffenburger JC, Shrank WH, Bitton A, et al. Association between patient-centered medical homes and adherence to chronic disease medications: A cohort study. Ann Intern Med. 2017;166(2):81–88. doi: 10.7326/M15-2659. Search PubMed
  10. Plant N, Mallitt KA, Kelly PJ, et al. Implementation and effectiveness of ‘care navigation’, coordinated management for people with complex chronic illness: Rationale and methods of a randomised controlled trial. BMC Health Serv Res 2013;13(1):164. doi: 10.1186/1472-6963-13-164. Search PubMed
  11. Plant NA, Kelly PJ, Leeder SR, et al. Coordinated care versus standard care in hospital admissions of people with chronic illness: A randomised controlled trial. Med J Aust 2015;203(1):33–38. Search PubMed
  12. Mallitt KA, Kelly P, Plant N, et al. Demographic and clinical predictors of unplanned hospital utilisation among chronically ill patients: A prospective cohort study. BMC Health Serv Res 2015;15:136. doi: 10.1186/s12913-015-0789-0. Search PubMed
  13. Jansà M, Hernández C, Vidal M, et al. Multidimensional analysis of treatment adherence in patients with multiple chronic conditions. A cross-sectional study in a tertiary hospital. Patient Educ Couns 2010;81(2):161–68. doi: 10.1016/j.pec.2009.12.012. Search PubMed
  14. Williams A, Manias E, Walker R. Interventions to improve medication adherence in people with multiple chronic conditions: A systematic review. J Adv Nurs 2008;63(2):132–43. doi: 10.1111/j.1365-2648.2008.04656.x. Search PubMed
  15. Laba TL, Lehnbom E, Brien JA, Jan S. Understanding if, how and why non-adherent decisions are made in an Australian community sample: A key to sustaining medication adherence in chronic disease. Res Social Adm Pharm 2015;11(2):154–62. doi: 10.1016/j.sapharm.2014.06.006. Search PubMed
  16. Pages-Puigdemont N, Mangues MA, Masip M, Gabriele G, Fernandez-Maldonado L, Blancafort S et al. Patients' Perspective of Medication Adherence in Chronic Conditions: A Qualitative Study. Adv Ther 2016;33(10):1740–54. doi:10.1007/s12325-016-0394-6. Search PubMed
  17. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986;24(1):67–74. Search PubMed
  18. Department of Health. MBS Online: Medicare Benefits Schedule. Canberra: DoH, 2015. Available at [Accessed 9 January 2017]. Search PubMed
  19. Medicare Local, Sydney North Shore and Beaches. Your desktop guide to MBS item numbers. NSW: Medicare Local, 2013. Search PubMed
  20. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epimedmiol 2004;159(7):702–06. Search PubMed
  21. Hosmer DW, Lemeshow S, Sturdivant RX. Model building strategies and methods for logistic regression. In: Applied logistic regression. 3rd edn. NJ: John Wiley and Sons Inc, 2013. Search PubMed
  22. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008;3:17. doi: 10.1186/1751-0473-3-17. Search PubMed
  23. Nguyen TM, La Caze A, Cottrell N. What are validated self-report adherence scales really measuring?: A systematic review. Br J Clin Pharmacol 2014;77(3):427–45. doi: 10.1111/bcp.12194. Search PubMed
  24. 6th Community Pharmacy Agreement. Home Medicines Review. Canberra: DoH, 2015. Available at [Accessed 9 January 2017]. Search PubMed
  25. Dhillon AK, Hattingh HL, Stafford A, Hoti K. General practitioners’ perceptions on home medicines reviews: A qualitative analysis. BMC Fam Pract 2015;16:16. doi: 10.1186/s12875-015-0227-8. Search PubMed
  26. Sidorkiewicz S, Tran VT, Cousyn C, Perrodeau E, Ravaud P. Discordance between drug adherence as reported by patients and drug importance as assessed by physicians. Ann Fam Med 2016;14(5):415–21. doi: 10.1370/afm.1965. Search PubMed
  27. 6th Community Pharmacy Agreement. Dose Administration Aids. Canberra: DoH, 2015. Available at [Accessed 9 January 2017]. Search PubMed
  28. Department of Health. Review of pharmacy remuneration and regulation. Canberra: DoH, 2016. Available at [Accessed 9 January 2017]. Search PubMed
  29. Freeman C, Rigby D, Aloizos J, Williams I. The practice pharmacist: A natural fit in the general practice team. Aust Prescr 2016;39(6):211–14. Search PubMed
  30. Hayek A, Joshi R, Usherwood T, et al. An integrated general practice and pharmacy-based intervention to promote the use of appropriate preventive medications among individuals at high cardiovascular disease risk: Protocol for a cluster randomized controlled trial. Implement Sci 2016;11(1):129. Search PubMed
  31. Lewis N, Shimp L, Rockafellow S, Tingen J, Choe HM, Marcelino M. The role of the pharmacist in patient-centered medical home practices: Current perspectives. Integr Pharm Res Pract 2014;3:29–38. doi: 10.2147/IPRP.S62670. Search PubMed
  32. Krueger K, Botermann L, Schorr SG, Griese-Mammen N, Laufs U, Schulz M. Age-related medication adherence in patients with chronic heart failure: A systematic literature review. Int J Cardiol 2015;184:728–35. doi: 10.1016/j.ijcard.2015.03.042. Search PubMed
  33. Huang AR, Mallet L, Rochefort CM, Eguale T, Buckeridge DL, Tamblyn R. Medication-related falls in the elderly: Causative factors and preventive strategies. Drugs Aging 2012;29(5):359–76. doi: 10.2165/11599460-000000000-00000. Search PubMed
  34. Chen Y, Zhu LL, Zhou Q. Effects of drug pharmacokinetic/pharmacodynamic properties, characteristics of medication use, and relevant pharmacological interventions on fall risk in elderly patients. Ther Clin Risk Manag 2014;10(1):437–48. doi: 10.2147/TCRM.S63756. Search PubMed
  35. Sibley KM, Voth J, Munce SE, Straus SE, Jaglal SB. Chronic disease and falls in community-dwelling Canadians over 65 years old: A population-based study exploring associations with number and pattern of chronic conditions. BMC Geriatr 2014;14(1):22. doi: 10.1186/1471-2318-14-22. Search PubMed
  36. Usherwood T. Encouraging adherence to long-term medication. Aust Prescr 2017;40(4):147–50. doi: 10.18773/austprescr.2017.050. Search PubMed

chronic diseasecohort studiesgeneral practitionershealth servicesMedicaremedication adherenceresearchsurveys and questionnairestherapeutics

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