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Coordinated care versus standard care in hospital admissions of people with chronic illness: a randomised controlled trial

Chronic, non-communicable diseases including cardiovascular diseases, oral health care, mental disorders and musculoskeletal diseases comprised 85% of the total burden of illness in Australia and New Zealand in the 2008–09 financial year, incurring direct health care costs of $27 billion.1 Respiratory illness, heart disease and diabetes comprised 80% of the total burden of illness and injury and 70% of health expenditure in Australia in 2004.2,3

Fragmentation of health care with poor coordination and communication among care agencies and a lack of continuity of care are noted as problems.4 As a consequence, some consumers rely heavily on local hospital emergency departments (EDs) to provide ongoing care. Although Australian and overseas studies have emphasised coordination problems in the management of chronic care, little is known about what defines well coordinated care, and what comprises an effective program.57

Australian coordinated care experiments between 1997 and 20058 often ended up costing more than standard care, and fewer than half showed an improvement in patient wellbeing.810

Western Sydney’s health services to older people and those with chronic illness were reviewed by the (then) Sydney West Area Health Service’s Service Redesign Unit and PricewaterhouseCoopers in 2007.11 The resulting Care Navigation (CN) framework was intended to help patients with chronic illness access services and providers in a more coordinated and timely way, using alternatives to hospital admission where possible for patients with acute deterioration. Those presenting to the ED would have their care more completely coordinated.

We conducted a randomised controlled trial (RCT) to test the hypotheses that, compared with standard care, CN would:

  • be superior for participants with complex chronic illness, improve quality of life, and reduce emergency re-presentations and hospital readmissions;
  • extend time to first re-presentation and first readmission, and reduce length of stay; and
  • have no effect on the mortality rate.

Methods

The study protocol has been published elsewhere.12 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.

We conducted a pragmatic RCT. Researchers who collected outcome data or performed statistical analyses were blinded to treatment allocation. Patients and CN nurses were not blinded owing to the nature of the intervention.

Eligible patients who presented to Nepean Hospital ED between 17 May 2010 and 25 February 2011 were identified by an algorithm implemented in the ED patient tracking system, and were approached to consent to participate in the trial. The inclusion algorithm identified patients who had three or more unplanned admissions to a Sydney West Area Health Service hospital in any previous 12-month period and were either aged ≥ 70 years or aged ≥ 45 years if they were of Aboriginal or Torres Strait Islander descent; or aged 16–69 years with at least one admission for a respiratory- or cardiology-related condition. Patients were also eligible if a CN nurse determined that a patient would benefit from receiving CN.

Patients were ineligible if they had previously received CN; were medically unable to participate in study activities (questionnaire completion); were admitted to hospital more than one CN business day before randomisation; or did not provide consent.

Randomisation was stratified by age (≥ 70 years; 16–69 years), and participants were randomly allocated 1 : 1 to CN and standard care. The sequence of treatment allocation was determined by block design. A phone-based randomisation service provided by the National Health and Medical Research Council Clinical Trials Centre was used to allocate treatment arms to participants after consent was given. Participants were followed up for 24 months after randomisation.

Intervention

Three nursing roles were allocated: Inbound, Inflight and Outbound. Two full-time nurses were employed to conduct CN through the recruitment period and for 24 months of follow-up. One nurse conducted the Inbound role — managing patients at presentation to the ED, assessing their current health status and risk of readmission, and directing them to the best method of care in the hospital or community. A second nurse carried out the Inflight role — monitoring the progress of patients’ care and minimising delays to discharge from the hospital ward. The second CN nurse also carried out the Outbound role — reviewing patients’ hospital stay, assessing the need for out-of-hospital care facilities and making arrangements for ongoing care after departure from hospital.

CN nurses used an electronic assessment form to identify medical and psychosocial risks of readmission, and to identify patients in the ED who might not require hospital admission if community-based care could be organised instead.

Data collection

Baseline demographics were collected from New South Wales Health’s Health Information Exchange (HIE) system.

The three primary outcomes of the trial were a reflection of the aims of CN: i) number of re-presentations to a Western Sydney or Blue Mountains EDs; ii) number of readmissions to a Western Sydney or Blue Mountains hospital; and iii) quality of life. Re-presentation and readmission data were collected electronically from the HIE database. Participants completed the EQ-5D-3L questionnaire13 at baseline, 12 and 24 months.

Mortality data were obtained from the National Death Index maintained by the Australian Institute of Health and Welfare. HIE data were used to investigate the time that participants spent in and between hospital visits.

Allied health referral data were obtained from the NSW Health Cerner database. Community health service referral data were obtained from the Community Health Information Management Enterprise (CHIME) and provided by Western Sydney/Nepean Blue Mountains Local Health District Community Health, Information Management and Logistical Support. Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data were provided by Medicare Australia Statistics.

Statistical analyses

Primary analyses were intention-to-treat. The main outcomes were analysed using negative binomial models to estimate the incidence rate ratios of re-presentations and readmissions, and change in EQ-5D score from baseline at 24 months using an analysis of covariance (ANCOVA).

Other outcomes were analysed using negative binomial generalised estimating equation models (length of stay in ED, in ward, and total length of hospital stay; time from arrival in ED to first seen by doctor, and to first allied health referral). For the time-to-event outcomes, time to first ED re-presentation and time to first hospital readmission, we used Kaplan–Meier curves and a Cox proportional hazards model to estimate the hazard ratio.

The total follow-up for each patient was used as an offset. All regression models included treatment arm and the stratification variable (age group) as explanatory variables. Further adjusted analyses were conducted for all outcomes, for sex and the number of ED presentations in the 12 months before randomisation (quartiles). Post-hoc subgroup analyses were conducted on the primary outcomes with respect to age strata; number of ED presentations or hospital admissions in the 12 months before randomisation; whether participants were identified as appropriate for CN by clinician flagging; or whether participants had a carer. A two-sided P of 0.05 or less was considered significant. Data were analysed using SAS, version 9.3 (SAS Institute).

Power calculation

We planned to recruit 500 patients over 12 months and expected a 20% loss to follow-up, leaving a final sample size of 400 with 90% power to detect a 20% reduction in hospital readmissions (rate ratio of 0.8), assuming a 5% significance level and a Poisson distribution with an average of 2.5 admissions per patient over 24 months in the control group, compared with 2.0 in the intervention group. A sample of 400 gave 80% power to detect a 15% reduction in hospital readmissions and a clinically significant difference in presentations. It also allowed us to detect a mean difference of 10 points on the EQ-5D scale, with about 80% power at a 5% significance level. This calculation is based on pilot data that estimated standard deviation of EQ-5D scores to be 35 points.6

Besides the quantitative studies of the effect of CN, a process evaluation gave qualitative insights into the process of the provision of care. Extensive interviews with service providers included tracking how the model of care changed over the course of the intervention. These data will be presented in a subsequent publication.

Results

Five hundred patients were recruited to the study between May 2010 and February 2011. Box 1 shows the flow of participants’ progress through the study. Participant baseline demographic information by study arm is presented in Box 2. Randomisation provided an even distribution between study arms for all demographic variables except sex — the CN group had 55% women compared with 45% in the standard care group. Three-quarters of participants were born in Australia, and four of these were reported in the hospital patient database as being Indigenous. Most participants presented to the ED on a weekday, during the daytime, and 88% were admitted to hospital at their randomisation visit.

Primary outcomes

The comparison of outcomes by treatment type is shown in Box 3. The mean number of ED re-presentations during the 24-month follow-up period was not statistically significantly reduced in the CN group (6.28; 95% CI, 5.44–7.26) compared with the standard care group (7.57; 95% CI, 6.55–8.74). This corresponds to a 17% reduction in re-presentation (95% CI, − 1% to 32%; P = 0.07). Similarly, there was no significant reduction in the mean number of hospital readmissions during the follow-up period in the CN group (4.38; 95% CI, 3.79–5.06) compared with the standard care group (5.16; 95% CI, 4.46–5.96). This corresponds to a 15% reduction (95% CI, − 4% to 30%; P = 0.11). Quality of life at 24 months did not differ significantly between the CN and standard care groups, with a mean difference of zero (95% CI, − 0.10 to 0.09; P = 0.93). Further analyses adjusted for sex and ED presentations before randomisation were similar.

CN had no significant treatment effect on any primary outcome in any of the subgroups analysed (results not shown).

Secondary outcomes

CN did not affect the time to first re-presentation after randomisation (hazard ratio, 1.01; 95% CI, 0.84–1.23; P = 0.89; Box 4A), or the time to first readmission (hazard ratio, 0.93; 95% CI, 0.77–1.13; P = 0.47; Box 4B). CN had no effect on the mean number of hours spent in the ED at the randomisation visit (rate ratio, 0.95; 95% CI, 0.82–1.11; P = 0.54) or over the subsequent 24 months (rate ratio, 0.99; 95% CI, 0.90–1.08; P = 0.80; Box 3). CN did not significantly reduce the mean number of days admitted to a ward at the randomisation visit (rate ratio, 1.2; 95%, CI, 0.82–1.76; = 0.36) or over the subsequent 24 months (rate ratio, 0.98; 95% CI, 0.82–1.17; = 0.82; Box 3). CN had no effect on mortality (hazard ratio, 0.92; 95% CI, 0.67–1.26; P = 0.60; Box 4C).

Process outcomes

More than six times the number of patients in the CN group (119/247 [48%]; 95% CI, 42–54) had their medications reviewed by a hospital pharmacist when presenting to hospital than those in the standard care group (19/245 [8%], 95% CI, 5–12); the overall difference was statistically significant (rate ratio, 6.35; 95% CI, 4.03–10.02; P < 0.001). However, there was no difference in the number of prescription medications dispensed over the 24-month follow-up period. CN had no effect on any other inhospital allied health or diagnostic services (results not shown).

Patients in the CN group received more services per year from community health (rate, 13.80; 95% CI, 10.69–17.8) than standard care patients (rate, 7.10; 95% CI, 5.46–9.23); the overall difference was statistically significant (rate ratio, 1.94; 95% CI, 1.35–2.81; P < 0.001). Most of these services were the result of referrals from hospitals (CN rate, 1.00 per year; 95% CI, 0.88–1.13 v standard care rate, 0.38; 95% CI, 0.32–0.45; P < 0.001). CN did not change the number of service payments claimed from the Medicare Benefits Schedule by general practitioners, non-hospital allied health professionals or consultant physicians (results not shown).

Delivery of intervention

CN began in May 2010. Nursing personnel was reduced from two nurses to one nurse on 9 November 2011. The remaining CN nurse reviewed existing risk assessments, updating participants’ requirements where required, but did not carry out any other part of the Inbound CN role due to availability of time and a lack of expertise in ED nursing. CN ceased at Nepean Hospital on 4 April 2012, when the remaining CN nurse left the position. Box 5 depicts the availability of CN nurses along with the number of participants actively in the study in the intervention arm throughout the study period. Per-protocol analyses based on 12 months of follow-up or the period when CN nurses were available demonstrated no difference between standard care and CN in any of the primary or secondary outcomes (results not shown).

Discussion

CN did not improve quality of life or reduce unplanned hospital presentations or admissions despite community health services almost doubling. This study sought to establish whether an energetic hospital care coordination program could enable patients admitted with an exacerbation of chronic illness to receive sufficient assistance in hospital and in the community, to reduce their need for future readmission.

There is a growing body of evidence that outcomes for people living with chronic illness can be improved, and hospital attendances reduced, by redesign of the health care delivery system across primary, secondary and acute sectors to ensure equitable, structured, proactive, coordinated, culturally sensitive care; decision support and clinical information systems that support this care; case management for complex patients; empowerment and support for self-management by patients and their carers; and community mobilisation.4,14,15 The impact of these changes is greatest when multiple, integrated improvements are made in care delivery.16

CN was an attempt to organise these services from a hospital base. However, it was no more effective than the existing processes of care at Nepean Hospital in improving self-reported quality of life, reducing hospital presentations or admissions, reducing the time patients spent in hospital or delaying readmission. CN had no effect on mortality. No intervention effect was detected in any of the subgroups analysed. However, CN did have an impact on the processes of care following discharge. Patients in the intervention group received more services from community health agencies, mainly nursing services.

Patients in the CN group spent the same amount of time in hospital and were referred to inhospital allied health or diagnostic services at the same rates as the standard care group. Delivery of CN was largely within the hospital, with limited arrangements made for ongoing care after departure. While these arrangements presumably reflected the care navigators’ assessment of the participants’ current and expected needs at that time, subsequent changes in their clinical needs would have been managed by health service structures and services that were similar in the two arms of the trial.

Attempts to formally evaluate interventions in health care systems are fraught by changes in the environment of care as staff change, funding sources change, and higher service priorities come to dominate the care scene. CN suffered the effects of all these real-world variations.

While study recruitment achieved the predetermined target of 500 participants and complete data were available for analysis from 492 (98%) at the end of the study, implementation of the intervention varied during the study; in particular, the number of CN nurses reduced from two to one 18 months after recruitment commenced. The second nurse left 4.5 months later, when CN ceased at the hospital, and the final 10 months of the study period had no CN. However, analysis limited to the period when both nurses were available showed no intervention effect on any of the primary or secondary outcomes.

CN during hospital admission with increased referrals for community health services after discharge was too small an intervention in the overall health system to have an impact. Future service development should explore the potential benefits of linking navigated intrahospital care to ongoing, proactive care planning and delivery in the community.

1 Flowchart of participants’ progress through a randomised controlled trial comparing Care Navigation (CN) with standard care for patients with chronic illness, Nepean Hospital, Sydney, May 2010 – February 2013

2 Participant baseline demographic information by study arm

Demographic variable

Care Navigation (n = 247)

Standard care (n = 245)


Age in years at randomisation, mean (SD)

73.3 (12.3)

74.9 (11.8)

Age at randomisation by strata, no. (%)

   

≥ 70 years

171 (69%)

171 (70%)

16–69 years

76 (31%)

74 (30%)

Sex, no. (%)

   

Female

135 (55%)

110 (45%)

Male

112 (45%)

135 (55%)

Country or region of birth, no. (%)

   

Australia

188 (76%)

183 (75%)

Europe

40 (16%)

46 (19%)

Other/not stated

19 (8%)

16 (7%)

Preferred language, no. (%)

   

English

232 (94%)

219 (89%)

Non-English

10 (4%)

13 (5%)

Not stated

5 (2%)

13 (5%)

Marital status, no. (%)

   

Married or de facto

117 (47%)

127 (52%)

Single, widowed, separated or divorced

129 (52%)

116 (47%)

Not stated

1 (< 1%)

2 (1%)

Funding source for services (in addition to Medicare), no. (%)

None

166 (67%)

166 (68%)

Private health insurance

10 (4%)

13 (5%)

Department of Veterans’ Affairs card, all types

21 (9%)

12 (4%)

Compensation

2 (1%)

2 (1%)

Not stated

48 (19%)

52 (21%)

Primary SRG assigned to hospital admissions in the 12 months before randomisation, no. (%)*

Cardiology

85 (34%)

89 (36%)

Surgery

58 (23%)

39 (16%)

Respiratory

38 (15%)

49 (20%)

Other

107 (43%)

97 (40%)

No. of emergency department presentations in the 12 months before randomisation, mean (SD)

1

33 (13)

47 (19)

2–3

92 (37)

88 (36)

4–5

68 (28)

67 (27)

≥ 6

54 (22)

43 (18)

No. of unplanned hospital admissions in the 12 months before randomisation, mean (SD)

0

7 (3)

13 (5)

1

53 (21)

50 (20)

2

53 (21)

45 (18)

3–4

83 (34)

76 (31)

≥ 5

51 (21)

61 (25)

Eligibility criteria used at randomisation visit, no. (%)

Electronic algorithm

181 (73%)

170 (69%)

Clinician flag

66 (27%)

75 (31%)

Unplanned hospital admissions at randomisation, no. (%)

222 (90%)

209 (85%)


SRG = service-related group. * Percentages exceed 100% as some participants with more than one previous admission were listed under more than one primary SRG. † Including gastroenterology; geriatrics; cancer; neurology; renal medicine; rehabilitation; immunology and infectious diseases; endocrinology; non-subspecialty medicine; ear, nose and throat; psychiatry – acute, maintenance, drug and alcohol, unallocated, pain management; renal dialysis; palliative care; gynaecology; or dermatology.

3 Comparison of outcomes of Care Navigation and standard care for the 24 months after randomisation

Outcome

Care Navigation

Standard care

 

RR/HR/MD (95% CI)

P


Primary

         

Mean no. of re-presentations (95% CI)

6.28 (5.44–7.26)

7.57 (6.558.74)

RR, 0.83 (0.68–1.01)

0.07

Mean no. of readmissions (95% CI)

4.38 (3.79–5.06)

5.16 (4.465.96)

RR, 0.85 (0.70–1.04)

0.11

Quality of life 24 months after randomisation —
mean change in EQ-5D scores (95% CI)

0.14 (0.080.21)

0.15 (0.080.22)

MD, 0 (− 0.10 to 0.09)

0.93

Secondary

       

Median time from randomisation to first ED re-presentation, days (IQR)

111 (89143)

103 (72148)

HR, 1.01 (0.84–1.23)

0.89

Median time from randomisation to first hospital readmission, days (IQR)

155 (121205)

144 (102178)

HR, 0.93 (0.77–1.13)

0.47

Median time from randomisation to death, days (IQR)

HR, 0.92 (0.67–1.26)

0.60

Mean length of ED stay, hours (95% CI)

       

To departure-ready

5.73 (5.376.1)

6.81 (5.748.08)

RR, 0.84 (0.69–1.02)

0.08

Actual

10.58 (9.9111.3)

10.71 (10.0311.44)

RR, 0.99 (0.90–1.08)

0.80

Mean length of stay admitted to a ward, days (95% CI)

5.46 (4.866.14)

5.57 (4.766.53)

RR, 0.98 (0.82–1.17)

0.82

Mean length of ED stay at randomisation visit, hours (95% CI)

       

All participants

12.91 (11.5914.39)

13.55 (12.0115.28)

RR, 0.95 (0.82–1.11)

0.54

Participants not admitted to a ward

7 (4.6910.44)

6.52 (5.288.07)

RR, 1.07 (0.65–1.76)

0.78

Participants admitted to a ward

13.61 (12.215.18)

14.74 (13.0116.7)

RR, 0.92 (0.79–1.08)

0.32

Length of stay in a ward at randomisation visit

7.01 (4.5210.87)

5.86 (4.77.31)

RR, 1.2 (0.82–1.76)

0.36


ED = emergency department. HR = hazard ratio. IQR =interquartile range. MD = mean difference. RR = rate ratio. All analyses were adjusted for stratification at randomisation (age: ≥ 70 years; 16–69 years). — = Median survival cannot be obtained as cumulative survival did not fall below 50% during the study period.

4 Kaplan–Meier curves by treatment group in the 24 months after randomisation


A. Time to first emergency department re-presentation. B. Time to first hospital readmission. C. Time to death.

5 Number of participants in the intervention group and the availability of the Care Navigation (CN) nurses throughout the study period

Facilitators and barriers to implementation of a pragmatic clinical trial in Aboriginal health services

The principles of conducting ethically sound health research involving Aboriginal and Torres Strait Islander peoples have been well documented.13 There are, however, many challenges to implementation of these principles and negative experiences have been reported.411 A key element to the National Health and Medical Research Council (NHMRC) guidelines for ethical conduct in Aboriginal and Torres Strait Islander health research is the notion of reciprocity — that the benefits of the research be clearly articulated, negotiated and implemented in such a way that it will build community capacity.1 In the context of clinical trials, this includes ensuring that studies test interventions in the settings in which they will eventually be delivered, rather than contrived environments that are conducive to easier trial implementation. Such trials are often referred to as pragmatic randomised controlled trials (PRCTs).12

The Kanyini Guidelines with the Adherence Polypill (KGAP) study was a PRCT that tested whether a polypill-based strategy would improve prescriber and patient adherence to recommended treatments for cardiovascular disease (CVD).1315 The trial was conducted between 2008 and 2012 across five Australian states in 20 general practices, 11 urban, rural and remote Aboriginal community-controlled health services (ACCHSs) and one government-run Indigenous health service. Participating services were each supported by one to three nominated community pharmacies. Design features that mimicked real-life practice included the prescribing of medicines by treating general practitioners, patient copayment charges for all study and other medicines at standard Pharmaceutical Benefits Scheme rates and the dispensing through community pharmacies. A major challenge to trial implementation was attaining target recruitment rates; only 623 of the target 1000 participants were randomised.16 This led to a longer study duration than anticipated, with concomitant budget pressures.

In this qualitative study, we aimed to identify facilitators and barriers to trial implementation in the ACCHSs and government health service from the perspective of providers and trial participants. The study forms part of a broader trial process evaluation.15

Methods

Fifty-three interviews were conducted with 32 health care providers and 21 Aboriginal and Torres Strait Islander patients at six ACCHSs and the government health service from April to December 2012. (Appendix 1 and Appendix 2). Five ACCHSs that were involved in the trial were unable to participate due to limited capacity at the time when interviews were being conducted. Participants were recruited purposively to yield a maximum variation sample based on location, age, sex, ethnicity, presence of CVD, and medication for patients, and location and profession for providers.

Interviews were conducted at the conclusion of the trial as part of the overall process evaluation and included exploration of experiences regarding trial implementation. Interview guides were developed and iteratively revised to explore themes and issues emerging from earlier interviews. A team of seven researchers, including three Aboriginal researchers, from a range of disciplinary backgrounds (health economics, pharmacy, nursing and public health) who were not involved in the implementation of the trial conducted the interviews. Most interviews were conducted face-to-face, with a small number conducted by telephone for logistic reasons.

Interviews were professionally transcribed and coded by two researchers (H L and L M) using NVivo 9 (QSR International). Twelve transcripts were selected (six patients and six health care providers — pharmacists, GPs, nurses and Aboriginal health workers [AHWs]) and were coded independently by the two researchers. These researchers identified the major themes arising from these 12 interviews and developed an initial coding framework. Insights gained by the research team about the context of the interviews and the local setting were documented and used to aid interpretation. The coding framework was then discussed and refined by a multidisciplinary group comprising the study investigators and the interview team. This included two ACCHS clinicians who were site principal investigators on the trial. The two researchers then coded the remaining interviews and made minor, iterative changes to code definitions.

For this study, we analysed codes specifically relating to issues relating to trial implementation. The randomised controlled trial, including its process evaluation, was approved by seven regional human research ethics committees, including one Aboriginal-specific committee. All participants who contributed data were provided a description of the study by the interviewer and given the opportunity to discuss any concerns before obtaining written consent.

Results

Four principal themes relating to barriers and facilitators for trial implementation were derived. Appendix 3 contains additional quotes that further illustrate the findings.

Health service governance of research

Ensuring community representation in governance of the research was a dominant issue. ACCHSs were invited to participate through initial discussions with senior management and governing boards. Formal memoranda of understanding (MOUs) with the coordinating research institutes were established. Amendments were made to the standard Medicines Australia clinical trial agreement to include intellectual property rights of ACCHSs and the roles and responsibilities associated with data custodianship. The discussions associated with setting up these agreements were critical in establishing mutual roles and responsibilities, data governance, capacity building plans and establishment of funding arrangements. One participant referred to the MOU as being a “landmark document” (GP 23, urban service).

In some instances, these agreements were facilitated by local governance processes. An AHW at an urban ACCHS described how previous negative experiences with external researchers prompted the establishment of a local research committee that would scrutinise external organisations’ research proposals:

In the past, the research that’s been conducted has left some scars … what has helped has been being more organised about having our own research agenda … so if you want to do research [with us then] this is what’s important to us. (AHW 47, urban service)

Motivation to participate

An expectation that the intervention could tangibly address an important health issue was extremely important for both patients and providers:

When you see people that are dying around you that are the same age as you and even younger, it’s all to do with health that they died not taking medication. Maybe if they were given the one pill instead of taking half a dozen they might be still here today. (Patient 4, urban service)

Several participating services had been involved in the Kanyini Vascular Collaboration before the trial and many staff were aware of the treatment gaps documented in the collaboration’s audit of patient records.14 Consequently, there was strong support from health care providers for strategies to address these gaps.

Effectively communicating the need to address these gaps to the community was particularly important. At one urban ACCHS this was done through a community forum and launch of the trial.

A related facilitator of participation was the role played by Aboriginal staff champions. These staff were often the initial point of contact for participants seeking information and were also referred to by other staff. One AHW discussed her role:

At first it was hard to communicate with them. But once it got mentioned once, twice, maybe three times what was in the tablet, what the benefits would be it started sinking into their brains then. (AHW 32, urban service)

Balancing service delivery and research requirements

An important aspect of the research was to incorporate the intervention into usual service delivery. Efforts to streamline the intervention included the prescribing and dispensing of the polypill within existing software platforms, timing pathology tests to coincide with scheduled visits and recruiting community pharmacies that were accessible to the participating sites. Despite these efforts to integrate the intervention into routine care processes, some GPs felt it created “confusion in their management” and “confusion about what they were on when they went into hospital”.

Some providers indicated challenges balancing trial operations with existing workloads. This manifested differently in urban and remote settings. For example, in urban settings, transport services were enlisted to facilitate study visits and access to medicines, potentially leading to limited transport availability for non-trial patients. In remote settings, fly-in fly-out doctors provided services to highly mobile populations. This created substantial challenges for clinic staff to coordinate follow-up study visits. One GP felt that the trial was more suited to urban ACCHSs:

You cannot compare it to an AMS [Aboriginal Medical Service] in Sydney … because we are serving about 200 000 square kilometres at this AMS. … our patients might come into town but they could be based 500 kilometres away … and it’s a very transient place for many of our patients. (GP 40, remote service)

Such logistic challenges inevitably resulted in delays in recruitment and follow-up. To alleviate these challenges the study team committed additional unbudgeted resources to support trial sites.

Research capacity-building challenges

A core study objective was to build health service research capacity through involvement of staff in the clinical trial. Most of those interviewed considered trial participation to be a positive experience, with many staff members describing enhancement of clinical skills, increased awareness of clinical trial processes, and deeper collaborations between the health service and pharmacies.

A key capacity-building initiative was the creation of local Indigenous research fellow (IRF) positions to perform trial coordinator duties. In practice, however, recruitment of suitably trained individuals was challenging and only four positions were filled.

The idea was that we were going to have an [IRF] is a great idea, but it just turned that we didn’t really have anyone that took it on with a passion … [The role] is quite complicated … (GP 3, urban service)

Moreover, like all clinical staff, IRFs frequently had competing responsibilities, and found it difficult to balance their research role with service delivery. This led to staff turnover in the early part of the study, which affected the trial conduct. Overall, most trial sites commented that additional on-site support from research institute staff would have been beneficial. This was easier to provide at those sites located closer to the coordinating research institutes, and those sites tended to manage the trial with fewer challenges.

Discussion

This study examined the often-overlooked views and experiences of patients and health care providers from Aboriginal health services participating in a clinical trial. The key facilitators of participation were the interrelated factors of research governance, patient and provider perception of the need for this research, deployment of effective strategies for communication to the community at large, and enlisting the support of Aboriginal staff champions. These facilitators were tempered by several challenges related to adequate integration of the intervention strategy into routine care processes, large competing demands with routine service delivery, and only partially successful attempts at building local research capacity. These challenges manifested differently due to the highly diverse settings in which the participating services operated.

In Australia, several Indigenous health RCTs have been successfully conducted through established health service–researcher partnerships, particularly in the area of child health.1719 Many have experienced challenges in meeting recruitment targets and implementing the trials as originally conceived. Occasionally, trials have had to be abandoned altogether due to insurmountable constraints.20 Our findings help determine the factors that both hinder and promote successful conduct of such trials. The integration of complex trial protocols that are not supported by senior management into underresourced health service settings is a recipe for implementation failure. Conversely, trials that address a priority health issue, have had strong health service engagement and adequate local support seem more likely to succeed.

The study was an indepth exploration of issues from a sample that was not necessarily representative of all participants and providers in the trial. Fewer interviews were done in remote sites, and staff who had left the service or participants who had withdrawn by the end of the study were not interviewed.

Although this study was based on a PRCT, such a design will not always be feasible nor acceptable. Alternative designs, such as stepped wedge trials and cluster RCTs of health service interventions, have been successfully implemented in collaboration with ACCHSs.21,22 Other designs, such as crossover studies, interrupted time series analyses and propensity score matching, are also practical and often cheaper to implement. Use of automated de-identified data extraction and opt-out consent processes can considerably reduce data collection burden and reduce demands on Aboriginal health services.22 There is also much to be gained from observational studies, in which routinely collected clinical audits can inform the evidence base about effective health service strategies.14,2227

Although community participation in prioritising the research question is of fundamental importance, substantial research infrastructure investment in health services is of equal importance. Aboriginal governance and leadership of the research agenda must be in place, and there are now good examples of how large-scale research can incorporate this from the outset.28,29 Associated with this is clear articulation of the resource implications associated with participation and ensuring there is adequate recognition of this within study budgets. The model for capacity building had mixed success, mainly due to the excessive and competing demands on individuals and limited existing research capacity; novel models to increase research capacity are needed.

There is clearly a need for more interventional studies to build the evidence base of what works in Aboriginal health service settings.23,30 It is important that research funding bodies recognise the factors highlighted in this study in their grant schemes. The overall $5 million (around $8000 per randomised patient) spent on the Kanyini GAP trial was several times higher than the amount originally granted and multiple additional funding applications were required. Although guiding statements on appropriate ethical conduct of research involving Aboriginal and Torres Strait Islander peoples acknowledge these issues, project-specific funding schemes tend not to recognise the importance of long-term investments in research capacity building, beyond what is immediately required to complete the project.1,2 In addition to non-project specific schemes, such as the NHMRC Centres for Research Excellence, project-specific loadings for research conducted in collaboration with already overstretched Aboriginal and Torres Strait Islander health services ought to be considered to support local research capacity building and establishing the governance arrangements needed to ensure community support. Such investments would build the evidence base on models associated with success and strengthen the application of reciprocity in the conduct of Aboriginal and Torres Strait Islander research.

[Department of Error] Department of Error

Yacoub M. Cardiac donation after circulatory death: a time to reflect. Lancet 2015; 385 2554–56—In this Comment, the twelfth bar in the figure should have read Austria. This correction has been made to the online version as of April 27, 2015, and the printed Comment is correct.

Energy drinks deliver deadly jolt

Young people turning to heavily-caffeinated energy drinks to fuel themselves for partying, sport or just to get through the day are putting themselves at heightened risk of heart attacks and chronic heart problems.

In a finding that suggests the marketing and consumption of so-called energy drinks should be much more tightly regulated, a detailed American study of their use has found they are associated with “adverse cardiovascular events”, including sudden and deadly heart attacks, ruptured arteries, heart arrhythmia, tachycardia and elevated blood pressure, particularly among adolescents and young adults.

“By unleashing the new ‘beast’ of energy drinks, we have now seen significant morbidity and mortality in susceptible patients,” the study’s authors said. “Young consumers are at a particularly high risk of complications due to hazardous consumption patterns, including frequent and heavy use.”

The study, Cardiovascular complications of energy drinks, published in the latest edition of the journal Beverages, documented numerous cases where people died or suffered serious cardiovascular problems after consuming energy drinks.

These include a 28-year-old man who collapsed while playing basketball after drinking three cans of energy drink five hours before the match. He was rushed to hospital suffering ventricular tachycardia and died three days later.

In another case, a 25-year-old woman with a pre-existing heart valve problem died from intractable ventricular fibrillation after drinking a 55 millilitre bottle of Race 2005 Energy Blast with Guarana and Ginseng. Subsequent tests found the drink contained caffeine at a concentration of 10 grams a litre – more than 60 times that in cola drinks – and the caffeine in the woman’s bloodstream was concentrated at 19 milligrams a litre, around double the level found in regular coffee drinkers.

The drinks have also been associated with potentially fatal spasms of coronary arteries. One case involved a man, 28, who drank between seven and eight cans of energy drink over a seven-hour period before and during motocross racing. Soon after he stopped he suffered a cardiac arrest, and was found to have had a coronary artery vasospasm doctors believe was precipitated by high levels of caffeine and taurine in his blood.

In addition to heart attacks and arterial spasms, energy drinks have also been associated with surges in blood pressure that can lead to rupture of arteries, and with the impairment blood vessel linings.

The authors said that while some of the cases involved people with pre-existing and underlying cardiac condition, many others did not. They reported the results of a review of 17 cases where people suffered heart attacks or other cardiac “events” after consuming energy drinks and found almost 90 per cent were younger than 30 years of age, and the majority did not have a cardiac abnormality.

While energy drinks advertise high concentrations of caffeine – around 80 milligrams in cans of Red Bull, Monster and Rockstar, and more than 200 milligrams in a 60 millilitre can of 5-Hour Energy compared with around 35 milligrams in a can of cola – researchers said other common ingredients, particularly taurine, which can interfere with the regulation of the cardiovascular system, could also have potentially severe consequences.

The researchers admitted that “confounding variables”, such as strenuous exercise, genetic predispositions and the simultaneous use of alcohol or recreational drugs meant that many deaths could not be attributed to energy drinks alone.

But they said it was clear that consuming energy drinks was associated with “cardiovascular events including death”, and urged much greater attention be paid to their use.

The US Food and Drug Administration reported 18 deaths associated with energy drinks between 2004 and 2012, and the researchers said that because the FDA reporting system typically captured between 1 and 10 per cent of actual adverse events, it was likely there were at least 180 deaths associated with energy drinks during that period.

Given the widespread consumption of energy drinks – Australia’s Food Regulation Standing Committee found that sales of energy drinks in Australia and New Zealand jumped from 34.5 million litres in 2001 to 155.6 million litres in 2010 – the study’s authors have called for greater awareness of the danger they present, particularly for young people, who are typically the biggest consumers.

“Children, young adults and their parents should be aware of the potential hazards of energy drinks,” the authors said. “Physicians should routinely inquire about energy drink consumption in relevant cases, and vulnerable consumers such as young persons should be advised against heavy consumption, especially with concomitant alcohol or drug ingestion.”

The researchers said there was no rigorous scientific evidence that energy drinks boosted energy or improved physical or cognitive performance, and there needed to be public education campaigns to highlight the hazards and dispel the myths about their benefits.

They called for eventual limits on the caffeine content of energy drinks and restrictions on their sale to young people, echoing calls from the AMA and the Country Women’s Association.

The AMA has for several years raised concerns about the health effects of energy drinks and their heavy consumption among young people, including children.

In 2013, the-then AMA President Dr Steve Hambleton demanded that the caffeine content of energy drinks be reduced, or their sale restricted to adults, following evidence linking them to serious effects in young people, including tachycardia and agitation.

In 2009, the death of a young woman was linked to caffeine from energy drinks, and a study published in the Medical Journal of Australia found 297 calls relating to caffeinated energy drinks were made to the NSW Poisons Information Centre between 2004 and 2010, 128 of which resulted in hospitalisation.

Two years ago the Country Women’s Association of New South Wales submitted a petition with 13,600 signatures to Federal Parliament calling for a ban on energy drink sales to everyone younger than 18 years.

Both the AMA and the CWA have highlighted inconsistencies in food standards that limit the amount of caffeine in soft drinks to a maximum of 145 milligrams per kilogram, but impose no similar limit on energy drinks.

Adrian Rollins

Image by Au Kirk on Flickr, used under Creative Commons licence

[Comment] Cardiac donation after circulatory death: a time to reflect

Heart transplantation is a cost-effective method of enhancing quality of life and survival.1 However, this procedure depends on the generosity and support of the community in making organs available for donation. Unfortunately, the availability of organs is a major challenge, resulting in unnecessary death and suffering. Progress in finding new solutions has been patchy, episodic, and very slow—the number of cardiac and multiorgan donations has remained almost constant during the past 20 years and varies widely between countries.

The crux of the matter: Did the ABC’s Catalyst program change statin use in Australia?

On 24 and 31 October 2013, the Australian Broadcasting Corporation (ABC) aired a two-part special edition of the science journalism series, Catalyst, titled Heart of the matter, that was critical of HMG-CoA reductase inhibitors (“statins”). The program questioned the link between high cholesterol levels and cardiovascular disease, and suggested that the benefits of statins had been overstated and the harms downplayed.1 Nearly 1.5 million Australians are estimated to have viewed each part of the program.2

Statins are recommended nationally and internationally both for primary prevention of cardiovascular events in people at increased risk of cardiovascular disease, and for secondary prevention in those with established cardiovascular disease.3,4 They are the most commonly prescribed medicines in Australia,5 used by over 30% of the population aged 50 years and older.6

Considerable media debate and backlash from the medical community followed the Catalyst program, including criticism for misleading patients.2,7 A National Heart Foundation survey of 1094 patients treated with lipid-modifying medications found that 11% of patients who watched Catalyst reported ceasing to take their cholesterol medicines, an additional 12% stopped taking them but restarted, and 12% reported starting to use “natural remedies”.8 Moreover, a survey by the Australian Capital Territory Government of general practitioners and pharmacists found that 58% reported that some of their patients had stopped taking their statins after the Catalyst program.9 Our purpose in this study was to quantify any changes in the dispensing of statins after the airing of the Catalyst program in October 2013.

Methods

We used dispensing records from the Pharmaceutical Benefits Scheme (PBS) — under which all citizens and permanent residents of Australia are entitled to subsidised access to prescribed medicines — from 1 July 2009 to 30 June 2014 for a 10% random sample of people who were dispensed a PBS-listed medicine. The 10% PBS sample is a standard dataset provided by the Australian Government Department of Human Services for analytical use, and is selected based on the last digit of each individual’s randomly assigned unique identifier. This dataset captures all dispensed PBS-listed medicines attracting a government subsidy, which occurs when the price of the medicine is above the PBS copayment threshold. To protect the privacy of people in this dataset, all dates of dispensing are offset randomly by + 14 or − 14 days; the direction of the offset is the same for all records for each individual.

We restricted our analyses to people for whom we had a complete PBS dispensing history for the entire study period. As many commonly dispensed statins fall below the general copayment threshold ($36.90 at 1 January 2014), but above the concessional copayment threshold ($6.00), we included only long-term concessional beneficiaries (ie, individuals dispensed only medicines attracting a concessional copayment during the 5-year study period). Long-term concessional beneficiaries represent about 51% of all people who are dispensed a statin, and consist of older people, those on a low income and the sick and disabled.

Medicines of interest

We included all doses (including combination products) of PBS-listed statins (atorvastatin, fluvastatin, pravastatin, rosuvastatin and simvastatin) and proton pump inhibitors (PPIs; esomeprazole, lansoprazole, omeprazole, pantoprazole and rabeprazole). PPI dispensing was chosen as the comparator as these medicines are commonly dispensed and are used by a similar population as use statins. In addition, we expected that PPI dispensing would be unlikely to be affected by the Catalyst program.

Measures

We defined discontinuation as the absence of any dispensing for a period of at least three times the number of pills last dispensed (assuming one pill per day) plus a 5-day grace period. Nearly all statin dispensing records (99.5%) were for a 30-day supply; therefore, in most cases, a period of 105 days or more without a statin dispensing record was considered a discontinuation. The date of discontinuation was the date that patients would have been expected to refill their prescriptions plus a 5-day grace period (ie, date of last dispensed statin + 35 days).

We classified individuals into four mutually exclusive risk categories. These were based on medicines dispensed for the treatment of cardiovascular disease (World Health Organization Anatomical Therapeutic Chemical [WHO ATC] codes C [excluding statins and topical agents for treating venous disorders] and B01AC), and diabetes (WHO ATC code A10). These risk categories were: (i) dispensed no other cardiac medicines and no diabetes medicines; (ii) dispensed 1–2 other cardiac medicines and no diabetes medicines; (iii) dispensed ≥ 3 other cardiac medicines and no diabetes medicines; or (iv) dispensed at least one diabetes medicine.

Statistical analysis

We used an interrupted time-series analysis to assess the impact of the Catalyst program on dispensing and discontinuation of statins. The date of the first part of the Catalyst program (24 October 2013) was the change point. For each week, we summed the number of dispensing records and the number of individuals who discontinued for statins and for PPIs, overall and stratified by risk category (statins only). We defined a week as starting on Thursday, the day that Catalyst aired. Data were log-transformed to estimate the percentage change.

PBS dispensing data are highly seasonal;10 once an individual or family’s out-of-pocket PBS expenses exceed the PBS Safety Net threshold for a calendar year, all PBS medicines have a reduced copayment until 31 December of that year. To account for this seasonal variability, as well as long-term trends and autocorrelation, we modelled the time series using an autoregressive integrated moving average (ARIMA) approach, using the Box–Jenkins method11 (Appendix). We created individual ARIMA models for overall dispensing (statins and PPIs), for discontinuation (statins and PPIs), and for dispensing and discontinuation within each risk category (statins only).

We estimated the average number of dispensings of statins per week in Australia (including statins falling below the copayment) in the 3 months before the Catalyst program aired using publicly available aggregated dispensing data.12 These data were used to estimate the impact of the Catalyst program on all statin users, not just people included in the 10% PBS sample.

All analyses were performed in SAS, version 9.3 (SAS Institute Inc), and Stata, version 12 (Statacorp).

The study was approved by the New South Wales Population and Health Services Ethics Committee (2013/11/494) and the Department of Human Services External Request Evaluation Committee.

Results

In our sample, 191 833 people were dispensed a statin from 1 July 2009 to 30 June 2014, with a mean of 26 946 statin dispensings weekly (range, 23 505 to 30 465). The average age of statin users in our study sample in 2013 was 72 years (SD, 12 years), and 55% were women. Thirteen per cent of our study population were dispensed no other cardiac or diabetes medicines, 25% were dispensed 1–2 cardiac medicines and no diabetes medicines, 36% were dispensed ≥ 3 cardiac medicines and no diabetes medicines, and 27% were dispensed diabetes medicines (93% of whom were also dispensed at least one cardiac medicine).

The overall trend in dispensing was relatively stable, with the highest dispensing counts in November and December, and lowest in January and February owing to individuals reaching their safety net threshold and preferentially refilling their prescriptions more frequently at the end of the calendar year. Raw dispensing counts and counts adjusted for seasonal variation are presented in Box 1 (A). Rates of discontinuation follow a similar pattern, with an increase at the beginning of every year Box 1 (B).

Dispensing

The week the Catalyst program was aired, we found a significant sustained change of 2.60% fewer statin dispensings per week (Box 2). Given that there are an average of 538 640 statin dispensings per week in Australia,12 this corresponds to an estimated decrease of 14 005 dispensings per week in the Australian population. Assuming that most users are dispensed statins once a month, the equivalent of 60 897 Australians would be affected.

We found a significant reduction in the rate of statin dispensings in all risk categories (Box 2), with 6.0% fewer statin dispensings per week to people with no evidence of taking other cardiac or diabetes medicines and 1.9% fewer dispensings to those with evidence of taking diabetes medicines.

We also identified a decrease in statin dispensing of 1.96% (95% CI, 1.12%–2.79%; P < 0.001) starting the week of 1 March 2012, which coincided with the publication of a news story about the risk of diabetes and dementia associated with statin use;13 the level of statin dispensing returned to expected levels in March 2013.

We found no significant change in the number of PPIs dispensings in the period following the Catalyst program (0.08%; 95% CI, − 1.53% to 1.71%; = 0.92).

Discontinuation

The number of people discontinuing their use of statins increased by 28.8% (P < 0.001) in the week that the Catalyst program aired, and this effect decayed by 9% (P < 0.001) per week, returning to average levels after 18 weeks (Box 3). On average, 1.8% of statin users discontinued using statins each month before the Catalyst program aired; thus, following the Catalyst program, an estimated 28 784 additional Australians discontinued their use of statins. A significant increase in discontinuation was observed regardless of the use of other cardiovascular and diabetes medications (Box 3).

In addition, we observed an increase in discontinuation after the 2012 news story,13 peaking at 29.0% (95% CI, 19.1%–39.6%; P < 0.001) and decaying by 8% each week thereafter, lasting 21 weeks.

There was no apparent change in PPI discontinuation following the airing of the Catalyst program.

Discussion

We found significant and sustained changes in statin dispensing following the airing of the Catalyst program — 2.6% fewer statins were dispensed every week (a total of 504 180 fewer dispensings of statins), which equates to 60 897 Australians having been affected up to 30 June 2014, as a result of increased discontinuation, decreased initiation and/or poor adherence. This includes an estimated 28 784 additional people who discontinued their statins.

On average, among all statin users, the number-needed-to-treat over 5 years to prevent one major vascular event such as a myocardial infarction or stroke ranges from 21 (for those with pre-existing coronary heart disease) to 40 (for those without).14 It is unclear how long the change in statin use that we observed is likely to last. If the 60 897 individuals we estimated to have been affected continue to be non-adherent, this could result in between 1522 and 2900 preventable, and potentially fatal, major vascular events. While statins have been shown to reduce cardiovascular events regardless of an individual’s absolute cardiovascular risk,15 national guidelines recommend their use in those who have had a previous cardiovascular event and in those at moderate or high absolute cardiovascular risk.16 There is evidence that, in Australia, statins are both underused in those at high risk and overused in those at low risk.17 Some of the observed reduction in use may result from patients at low absolute risk of cardiovascular disease ceasing therapy.

However, individuals who did and did not concomitantly take cardiovascular and diabetes medications had reduced their use of statins after the Catalyst program aired. This includes individuals likely to be at high cardiovascular risk, such as those with diabetes; the proven and substantial efficacy of statins in this group means they can least afford to discontinue therapy.18,19

Many elements of the Catalyst program’s contents were inconsistent with the recommendations of key medical advice about statins and cardiovascular disease3 and the ABC has since withdrawn the program, primarily on the grounds that it breached their impartiality standards.20 The program was watched by a substantial proportion of the Australian public and is likely to have influenced their beliefs about the risks and benefits of statins and the relationship between high cholesterol and cardiovascular disease. Belief in the effectiveness of medication and the need for treatment are important predictors of adherence to lipid-lowering medications.21 The National Heart Foundation’s survey of users of lipid-modifying agents found that, compared with those who were unaware of the program, those who watched it were more likely to express concerns about taking their cholesterol medicines, and a desire to stop.8

Our study is not the first to show the impact of adverse media reports on prescribed medicine use in Australia. A 2007 television news program about the association between osteonecrosis of the jaw and bisphosphonate use was associated with 29 633 fewer prescriptions.22 Further, our incidental finding of a reduction in statin dispensing in 2012 that coincided with a news story about the risks of diabetes and dementia demonstrates the long-lasting impact of such publicity; statin dispensing only returned to average levels after a year.

The strength of our study lies in the use of a representative sample of all Australians ever dispensed a PBS-subsidised medicine, and the use of a long-term concessional beneficiary population, ensuring complete capture of statin dispensing. Analyses of time-series data can be problematic because of high levels of autocorrelation, underlying trends, and seasonality. While PBS dispensing data are particularly seasonal, the ARIMA approach is well established and ideally suited for dealing with these problems.11,23 Further, although the lack of change in PPI dispensing supports the idea that the changes in statin dispensing were in response to the Catalyst program, we cannot rule out other factors affecting dispensing behaviour during this study period. We also saw no change in non-statin lipid-lowering medicines (data not shown). We also did not know the exact dates of dispensing, as all dates were offset by 2 weeks, but this would be expected to bias our findings towards the null. Lastly, while we categorised individuals based on other cardiovascular and diabetes medicines they were dispensed, it is not possible to know their true level of cardiovascular risk without additional information, such as blood pressure and smoking status, which was not available in our study.

We estimated the change in the use of statins after the airing of the Catalyst program in all Australians who were prescribed statins based on findings in our study sample. However, as the Australian non-concession population is younger, has higher socioeconomic status, and is in better health than the long-term concession population, they probably have a lower risk of cardiovascular events and a higher risk of not adhering to their statin regimen. Consequently, we may have underestimated the population-level impact on dispensing.

As of mid 2014, there is no indication that the change in dispensing after the Catalyst program has abated. Even though the observed effect was relatively small, the prevalence of statin use in Australia and the established efficacy of these drugs14,22 means that a large number of people are affected, and may suffer unnecessary consequences. The changes in statin use occurred despite warnings in the Catalyst program that its content should not be taken as medical advice, and public criticism of the program. The subsequent retraction of the program may counteract some of the apparent negative impact, but this remains to be seen.

1 Weekly unadjusted and seasonally adjusted (A) dispensing counts and (B) number of people discontinuing use of statins and proton pump inhibitors (PPIs), 1 July 2009 to 30 June 2014

2 Autoregressive integrated moving average (ARIMA) modelling results* of the impact of the Catalyst program on statin dispensing

Population

Mean weekly dispensings 12 weeks before Catalyst

Weekly change in number of dispensings


% (95% CI)

P


Overall

27 536

− 2.60 (− 3.77 to − 1.40)

< 0.001

No cardiac or diabetes medicines

2 549

− 6.03 (− 8.28 to − 3.73)

< 0.001

1–2 cardiac medicines and no diabetes medicines

6 602

− 2.77 (− 4.54 to − 1.06)

0.002

≥ 3 cardiac medicines and no diabetes medicines

10 252

− 2.40 (− 3.34 to − 1.46)

< 0.001

Diabetes medicines

8 133

− 1.94 (− 3.45 to − 0.42)

0.01


* ARIMA specification, (3,1,1)(3,1,1)52.

3 Autoregressive integrated moving average (ARIMA) modelling results of the impact of the Catalyst program on the number of people who discontinued their use of statins

Population

Mean weekly number of discontinuers 12 weeks before Catalyst

Peak change in discontinuation


Weekly
decay

Duration of effect*

Time to peak

% (95% CI)

P


Overall

576

0 weeks

28.8% (15.4% to 43.7%)

< 0.001

9%

18 weeks

No cardiac or diabetes medicines

87

0 weeks

72.2% (27.3% to 133.0%)

< 0.001

16%

16 weeks

1–2 cardiac medicines and no diabetes medicines

143

0 weeks

58.4% (28.6% to 95.1%)

< 0.001

12%

20 weeks

≥ 3 cardiac medicines and no diabetes medicines

186

1 week

30.5% (11.1% to 53.3%)

0.001

13%

14 weeks

Diabetes medicines

161

2 weeks

38.6% (13.3% to 69.6%)

0.002

40%

5 weeks


* The impact was considered to have ended when it decayed to ≤ 5%. † ARIMA specification, (0,1,1)(0,1,1)52. ‡ ARIMA specification, (0,1,2)(0,1,2)52.

[Correspondence] Training children in cardiopulmonary resuscitation worldwide

In Europe and the USA, 700 000 people die after out-of-hospital cardiac arrest and unsuccessful cardiopulmonary resuscitation (CPR) every year,1 about 2000 deaths per day. These estimates apply to many other parts of the world. This cause of death is probably the third most common cause of death in developed countries, after all cancers combined and other cardiovascular causes.1 When professional emergency medical services arrive after cardiac arrest—which can be after 8–12 min or more—the brain has already started to die.

Smartphone app a lifesaver for patients after myocardial infarction

Clinical guidelines recommend that patients complete a cardiac rehabilitation program after experiencing a myocardial infarction, with studies showing that those who do have much better long-term health outcomes.

Despite the benefits, uptake of traditional cardiac rehabilitation programs is poor. Many patients find weekly travel to a health facility to be difficult. This is particularly so for those who work, care for others or live in regional Australia where these services are not available.

To overcome this problem, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Queensland Health have developed a home-based cardiac rehabilitation program delivered via a smartphone app, called the Care Assessment Platform. This home-based program features health and exercise tools, motivational materials and multimedia delivered through the app to educate patients about disease management, and remote mentoring consultations.

A clinical trial conducted through the Australian e-Health Research Centre showed that this delivery model achieved equal or better clinical outcomes compared with a traditional rehabilitation program (Heart 2014; 100: 1770-1779). Patients recovering from myocardial infarctions were almost 30% more likely to take part in rehabilitation at home using the smartphone app, compared with those who had to travel to an outpatient clinic. Patients were also 40% more likely to adhere to the rules of the program and almost 70% more likely to complete it than those in traditional rehabilitation programs.

Most importantly, this delivery model offers a more flexible option. By integrating rehabilitation into patients’ daily lives, they are more likely to complete the program and make the healthy changes to their lifestyle permanent. This overcomes one of the key barriers to patient participation and recovery.

The Care Assessment Platform will soon be offered in several Queensland hospitals. The research team is also looking to adapt the technology for use with other chronic conditions, such as pulmonary disease and diabetes.

Sceptics undermine effective dietary and heart health advice

Recent reports questioning the link between saturated fats and coronary heart disease fail to convince

“Eat less saturated fat and more polyunsaturated fat” has been the central dietary advice for reducing early death from coronary heart disease (CHD) for more than four decades. This advice was based on evidence accumulated over many years, and the decline in developed countries in the numbers of premature deaths caused by CHD is attributable in some measure to its widespread acceptance.

In 2010 and 2014, two studies based on meta-analyses contradicted this longstanding advice,1,2 and their findings have been widely broadcast in the United States and the United Kingdom by the popular media, including the New York Times, Time magazine, New Scientist and the Independent.

The resulting discussion on the place of saturated fat in the diet and the management of cholesterol in the population has the potential to drastically impede further progress in reducing CHD. Why do the conclusions in these two articles depart so markedly from the international consensus that has been trusted until now?3

The first of these articles was the 2010 meta-analysis by Siri-Tarino and colleagues of prospective cohort studies.1 The authors found no significant association between saturated fat consumption and the risk of CHD. It is remarkable that this unconvincing review stimulated extensive interest in the media, whereas a large pooled analysis by experts from 10 universities, published in the same journal a year earlier, had not attracted the same attention. The 2009 article, by Jakobsen and colleagues, had reported that reducing saturated fat in the diet and replacing it with polyunsaturated fatty acids (PUFAs) was associated with a significantly reduced risk of CHD.4

The importance of experimental design

Experts in the US, Australia, the Netherlands, the UK, New Zealand and Norway have strongly criticised the conclusions reached by Siri-Tarino et al. Six major concerns were voiced.

1. With what did study participants replace dietary saturated fats?

Siri-Tarino et al did not consider most of the prospective studies in Jakobsen et al that reported dietary substitution of saturated fats by PUFAs.4

2. The validity of the dietary assessment methods varied between reports

In several studies, for example, participants were asked to recall what they had eaten during a single 24-hour period, an approach that is inadequate for assessing long-term dietary history.

3. A broader variety of dietary cultures should have been included

The optimum approach for evaluating an association between dietary saturated fats and CHD is to study groups from different dietary cultures. An example of this procedure was the Seven Countries Study (7CS), in which research dietitians watched meals being prepared, and collected foods from the local area and organised their transport to the University of Minnesota for chemical analysis of their fatty acid content.5Saturated fat intake in this study ranged between 2.5% and 22% of total energy intake, and its correlation with the number of CHD deaths was highly significant (r = 0.84).5

The media downplayed this finding when reporting the results, suggesting that the director of the 7CS had “cherry-picked” the seven countries. The cohorts described in the 7CS report had, however, been selected on the advice and support of Paul White, President Eisenhower’s cardiologist, and because enthusiastic and reliable local medical researchers, as well as local funding that supplemented US support, were available during the 1950s. Further, a total of 16 cohorts were studied across the seven countries, which also enabled within-country comparisons. Two quite different Finnish cohorts were included, for example, while rural farming and coastal farming villages were compared in Croatia and Japan, as were three dissimilar communities in Serbia.5,6

4. Evidence from metabolic ward studies

The major evidence on the relationship between dietary saturated fat and CHD has been delivered by metabolic ward studies. This type of study has consistently shown since 19567that dietary saturated fats increase plasma levels of total and low-density-lipoprotein (LDL) cholesterol, which are strongly implicated in the pathogenesis of CHD.

5. The evidence from long-term dietary studies

Meta-analysis of the limited number of long-term controlled dietary trials in humans indicates that diets including reduced amounts of saturated fat and an increased PUFA content lead to significantly fewer CHD events.8

6. Historical trends in the epidemiology of CHD

In the early 1960s, experts in Western countries recommended a diet that included less saturated fat and more PUFA for people at risk of CHD, and that advice was soon extended to the entire community. CHD mortality (age-standardised) reached a peak in North America, Australia, Finland and other Western countries around 1965, and has since declined dramatically; in Australian men the rate dropped from 610 per 100 000 in 1965 to 135 per 100 000 in 2000 (the fall was similar in women).3,9 The reduced mortality associated with heart attacks in Australia, the US and in Nordic countries consisted primarily of a reduction in the number of deaths in patients on their way to hospital, indicating that improvements in the management of risk factors, rather than of hospital treatment, explained the change. A cholesterol-lowering diet was a major influence, together with reduced smoking and efficient antihypertensive medication. Cholesterol-lowering statin medications were not introduced into therapy until the 1990s.

Omega-6 fatty acids and heart disease

In the second article that attracted recent media attention, Chowdhury and colleagues analysed four types of evidence from prospective studies or randomised controlled trials concerning the relationship between dietary fat type and CHD events: dietary fatty acid consumption, circulating fatty acid levels, adipose tissue fatty acid levels, and dietary and supplements trials. They concluded that the evidence did not support cardiovascular guidelines that encourage high consumption of PUFAs and low consumption of saturated fats.2 It quickly made headlines around the world, but the findings have been criticised by other experts.10

In my detailed examination of this publication and the associated supplementary material (57 pages in total), in which I focused on the data for omega-6 PUFAs, I also identified a number of problems. In particular, studies were omitted, and the findings of those included were often incorrectly reported. References for these articles are listed in the Appendix.

In their review of prospective studies, Chowdhury et al incorrectly reported the results of two reports (omega-6 PUFAs were protective in the original articles). Seven further published studies were overlooked. In five of these, including two based on large participant numbers,11,12 a negative correlation between omega-6 PUFA intake and the risk of CHD was found. Had all these studies been included in the forest plot in Chowdhury et al, 10 of 15 would have been located on the left-hand side (indicating that omega-6 PUFAs were protective).

In their review of circulating fatty acids and CHD, Chowdhury et al included 10 studies in which levels of linoleic acid, the major PUFA, were measured; two of these were incorrectly placed in the forest plot. Seven studies in the literature were overlooked, including two based on large participant numbers;13,14 linoleic acid tended to be protective in all seven. Overall, this means that 13 of 17 studies should have been placed on the left-hand side of the forest plot (indicating that omega-6 PUFAs were protective).

In their review of adipose tissue fatty acids and CHD, only one study measured linoleic acid, but a further eight reports have been published, of which six found linoleic acid to be protective. A 2007 review included seven articles about adipose tissue fatty acids and CHD,15 none of which were cited by Chowdhury et al.

In their review of dietary and supplements trials, Chowdhury et al made no distinction between these two very different types of investigation. Most of the trials analysed in this section reported simple supplementation with fish oil or capsules containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) capsules. It seems inappropriate, however, for supplements trials to be pooled with dietary trials in which participants consumed both less saturated fats and more PUFAs. In the randomised dietary trials, participants were assisted to make large changes to their diets and to maintain them for several years. In the omega-6 forest plot (figure 14 of Chowdhury et al), the Sydney Diet Heart Study (SDHS) is clearly an outlier; it was not included in other meta-analyses of dietary trials,8and its authors wrote that “comparison of the mean diets of those who died and the survivors revealed only trivial differences”.16 If the SDHS trial is removed, the relative risk for CHD in omega-6 dietary trials is 0.81.

In all, Chowdhury et al omitted or incorrectly reported 25 studies of omega-6 PUFAs and CHD. The protective effect of PUFAs would have been clear if all published studies had been included in their meta-analysis. Changes to established public health guidelines should not be advocated unless all the relevant evidence has been reviewed.

A warning about meta-analyses

Professor Walter Willett (Harvard School of Public Health) told Science Insider that “The controversy should serve as a warning about meta-analyses.” These analyses compile the data of individual studies to reach a clear-cut conclusion. “It looks like a sweeping summary of all the data, so it gets a lot of attention. … But these days meta-analyses are often done by people who are not familiar with a field, who don’t have the primary data or don’t make the effort to get it.” The results of drug trials can be more easily combined because they are similar in design, he explained, but nutritional studies are more diverse. “Often the strengths and weaknesses of individual studies get lost … It’s dangerous.”10

In conclusion, the findings of the two articles discussed here do not justify a change of longstanding cardiovascular dietary guidelines that have contributed to massive reductions of CHD in developed countries.