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Preference: Respiratory Medicine

1039

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.5–7

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.8–10

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; P = 0.36) or over the subsequent 24 months (rate ratio, 0.98; 95% CI, 0.82–1.17; P = 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 (6.55–8.74)

RR, 0.83 (0.68–1.01)

0.07

Mean no. of readmissions (95% CI)

4.38 (3.79–5.06)

5.16 (4.46–5.96)

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 (0.08–0.21)

0.15 (0.08–0.22)

MD, 0 (− 0.10 to 0.09)

0.93

Secondary

        

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

111 (89–143)

103 (72–148)

HR, 1.01 (0.84–1.23)

0.89

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

155 (121–205)

144 (102–178)

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 (5.37–6.1)

6.81 (5.74–8.08)

RR, 0.84 (0.69–1.02)

0.08

Actual

10.58 (9.91–11.3)

10.71 (10.03–11.44)

RR, 0.99 (0.90–1.08)

0.80

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

5.46 (4.86–6.14)

5.57 (4.76–6.53)

RR, 0.98 (0.82–1.17)

0.82

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

        

All participants

12.91 (11.59–14.39)

13.55 (12.01–15.28)

RR, 0.95 (0.82–1.11)

0.54

Participants not admitted to a ward

7 (4.69–10.44)

6.52 (5.28–8.07)

RR, 1.07 (0.65–1.76)

0.78

Participants admitted to a ward

13.61 (12.2–15.18)

14.74 (13.01–16.7)

RR, 0.92 (0.79–1.08)

0.32

Length of stay in a ward at randomisation visit

7.01 (4.52–10.87)

5.86 (4.7–7.31)

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

MERS: worst may be past

The World Health Organisation has indicated that the Middle East Respiratory Syndrome (MERS) outbreak that has so far claimed 24 lives in South Korea may have passed its peak.

While warning that it was critical health authorities closely monitor the situation, the WHO’s Emergency Committee has nonetheless declared that South Korean efforts to track and quarantine infected people had “coincided with a decline in the incidence of cases”.

Since the first case was reported in South Korea last month, 166 people in the North Asian country are confirmed to have been infected with MERS, including 30 currently receiving treatment, while a further 5930 are in quarantine at home or in medical facilities.

Fears that the disease might spread further in the region were fuelled earlier this week when Thai officials reported a visiting businessman from Oman had fallen ill with the disease, and 59 people who had been in contact with have been placed in quarantine.

But the WHO praised South Korean health authorities for rapidly alerting their Chinese counterparts about an infected traveller, who was quickly located and isolated.

The World Health Organisation’s Emergency Committee, which met earlier this week to discuss the outbreak, said it was not yet serious enough to warrant the declaration of a public health emergency, and advised that travel restrictions and airport screening were not necessary.

Nonetheless, the Committee warned the outbreak was “a wake-up call” for governments about the speed with which serious infectious diseases could spread “in a highly mobile world”.

“All countries should always be prepared for the unanticipated possibility of outbreaks of this and other serious infectious diseases,” it said. “The situation highlights the need to strengthen collaboration between health and other key sectors, such as aviation, and to enhance communication processes.”

No cases have been reported in Australia, and a Federal Health Department spokeswoman said the risk of MERS arriving in Australia was considered to be low, at least for the time being.

But health and border protection authorities are on alert for the disease, and the Federal Government is planning to warn Australians travelling overseas, particularly to the Middle East as part of the Hajj pilgrimage, about MERS and what precautions they need to take to minimise the chances of infection.

Though Korean authorities have been praised for the strength of recent actions to control the spread of MERS, serious shortcomings in their initial response have been blamed for helping the outbreak gain momentum.

The WHO Emergency Committee detailed a number of factors that helped the disease spread, including ignorance of MERS among health workers and the broader public; “suboptimal” infection prevention and control measures in hospitals; keeping patients infected with MERS in crowded emergency departments and wards for extended periods; the behaviour of patients in going to several different doctors and hospitals for treatment; and the custom of family and friends staying with their infected loved ones in hospital.

“There are still many gaps in knowledge regarding the transmission of this virus between people, including the potential role of environmental contamination, poor ventilation and other factors,” the Committee said, though adding that there was no evidence of sustained transmission in the community.

Adrian Rollins

Bilateral upper lobe opacification

A 29-year-old Hispanic man presented with a 2-month history of severe progressive dyspnoea, productive cough and 6.8 kg weight loss. His social history was significant: from 1995 to 2005 he had worked in a factory sand-blasting denim while wearing ill-fitting, reportedly ineffective protective clothing. His symptoms had slowly progressed since they commenced in 2008.

Chest auscultation identified bronchial breath sounds with crackles in the upper zones. Chest x-ray showed bilateral upper lung opacification representing extensive pulmonary consolidation and fibrosis (Figure, A), consistent with progressive massive fibrosis, confirmed by computed tomography (Figure, B).

After infectious causes, including tuberculosis and histoplasmosis, were excluded, the patient was diagnosed with chronic silicosis and referred for transplant evaluation.

A: Chest x-ray image of patient. Arrows indicate bilateral upper lung opacification. B: Computed tomography chest image. Mass-like fibrosis (arrows) and displaced trachea (star) are indicated.

[Editorial] MERS—the latest threat to global health security

The spread of Middle East respiratory syndrome (MERS) to South Korea, and now to China, is an important signal of the need for increased vigilance in global health security measures. As reported in Correspondence in this week’s issue, the rapid transmission of MERS in South Korea led to 12 laboratory-confirmed cases over a 2-week period in May, and many more cases since, with relatives, medical staff, and a fellow patient all contracting the disease, which started with one 68-year-old man who had travelled to the Middle East.

[Correspondence] MERS in South Korea and China: a potential outbreak threat?

First reported in September, 2012, human infections with Middle East respiratory syndrome coronavirus (MERS-CoV) can result in severe respiratory disease, characterised by life-threatening pneumonia and renal failure.1 Countries with primary infections of MERS-CoV are located in the Middle East, but cases have been occasionally exported in other countries (figure). Human-to-human infections of MERS-CoV are rare2 and confirmed cases are usually traced back to contact with camels, an intermediate host species for MERS-CoV.

Smoke-free homes and workplaces of a national sample of Aboriginal and Torres Strait Islander people

Second-hand smoke was estimated to cause more than 600 000 deaths globally in 2004, mainly from ischaemic heart disease, respiratory infections, asthma and lung cancer.1 Protecting people from the dangers of second-hand smoke by banning smoking in indoor and other public places is an essential element of effective tobacco control programs.2

Smoking is banned in virtually all enclosed public places in Australia.3 More than 92% of Australian smokers and ex-smokers reported that smoking was not allowed in any indoor area at their workplace in 2010–2011, slightly less than in similar surveys in the United Kingdom and Canada but more than in the United States and European and middle- and low-income countries surveyed.4 In Australia5 and all countries with available trend data, the proportion of the population living in smoke-free homes is increasing; this is not just due to falling smoking prevalence.6

Forty-two per cent of Aboriginal and Torres Strait Islander people aged 15 years or older were daily smokers in 2012–2013, 2.6 times the age-standardised prevalence among other Australians.7 This is a decrease from 45% in 2008 and 49% in 2002, a similar rate of decline as among other Australians.7 In 2008, Aboriginal and Torres Strait Islanders who smoked daily were less likely than other Australians to live in homes where no one usually smoked inside (56% v 68%).5 Aboriginal and Torres Strait Islander smokers with lower household incomes were significantly more likely to live in homes where someone usually smoked inside.5

Here, we provide the first national picture of smoking bans in the workplaces of Aboriginal and Torres Strait Islander people. We also describe whether home smoking bans were always followed and assess the associations between smoke-free workplaces and homes and quitting.

Methods

The Talking About The Smokes (TATS) project surveyed 2522 Aboriginal and Torres Strait Islander people using a quota sampling design in the communities served by 34 Aboriginal community-controlled health services (ACCHSs) and one community in the Torres Strait, and has been described elsewhere.8,9 Briefly, the 35 sites were selected based on the geographic distribution of the Aboriginal and Torres Strait Islander population by state or territory and remoteness. In 30 sites, we aimed to interview 50 smokers or ex-smokers who had quit ≤ 12 months before, and 25 non-smokers, with equal numbers of women and men and in each of two age groups (18–34 and ≥ 35 years). In four major-city sites and the Torres Strait community, the sample sizes were doubled. People were excluded if they were aged less than 18 years, not usual residents of the area, staff of the ACCHS, or deemed unable to complete the survey. In each site, different locally determined methods were used to collect a representative, although not random, sample.

Baseline data were collected from April 2012 to October 2013. Interviews were conducted face to face by trained interviewers, almost all of whom were members of the local Aboriginal and Torres Strait Islander community. The survey was completed on a computer tablet and took 30–60 minutes. The baseline sample closely matched the distribution of age, sex, jurisdiction, remoteness, quit attempts in past year and number of daily cigarettes smoked reported in the 2008 National Aboriginal and Torres Strait Islander Social Survey (NATSISS). There were inconsistent differences in some socioeconomic indicators: our sample had higher proportions of unemployed people, but also higher proportions who had completed Year 12 and who lived in more advantaged areas.8 A single survey of health service activities, including whether there were dedicated tobacco control resources, was completed at each site.

The project was approved by three Aboriginal human research ethics committees (HRECs) and two HRECs with Aboriginal subcommittees: Aboriginal Health & Medical Research Council Ethics Committee, Sydney; Aboriginal Health Research Ethics Committee, Adelaide; Central Australian HREC, Alice Springs; HREC for the Northern Territory Department of Health and Menzies School of Health Research, Darwin; and the Western Australian Aboriginal Health Ethics Committee, Perth.

As the TATS project is part of the International Tobacco Control Policy Evaluation Project (ITC Project), interview questions were closely based on those in other ITC Project studies, especially the Australian ITC surveys.10 We asked questions about whether smoking was allowed inside the home, and whether people smoked inside even if it was not allowed. For those with either an incomplete smoking ban or a complete ban where people still smoked inside the house, we asked if participants were uncomfortable telling elders or community leaders, other visitors or other household members to smoke outside. For participants who were employed, we asked about smoking rules in indoor areas at work. The questions used in this article are listed in Appendix 1.

Results were compared with those from the Australian ITC Project surveys conducted in September 2011 to February 2012 (Wave 8.5, n = 1504) or July 2010 to May 2011 (Wave 8, n = 1513). These surveys were completed by random digit telephone dialling or on the internet, and included those contacted for the first time and those who were recontacted after completing surveys in previous waves. Only smokers were recruited, so these samples only included smokers and ex-smokers who had quit since previous waves. Slightly different definitions of smokers between the TATS project and ITC Project surveys meant that only daily and weekly smoker categories were directly comparable. We focused our comparisons on daily smokers.

Statistical analyses

We calculated the percentages and frequencies of responses to the TATS project questions, but did not include confidence intervals for these as it is not considered statistically acceptable to estimate sampling error in non-probabilistic samples. We compared results for daily smokers with those from Australian ITC Project surveys, which were directly standardised to the distribution of age and sex of Aboriginal and Torres Strait Islander smokers reported in the 2008 NATSISS.

Associations between the outcome variables and sociodemographic and smoking variables were assessed using logistic regression to generate odds ratios (ORs) and P values based on Wald tests. Stata 13 (StataCorp) survey [SVY] commands were used to adjust for the sampling design, using 35 site clusters, and the age–sex quotas as strata.11

Reported percentages and frequencies exclude participants who refused to answer, answered “don’t know”, or for whom the question was not applicable (eg, not employed or no indoor area at work). Less than 1% answered “don’t know” or refused to answer each of the questions analysed in this report, except for questions about being uncomfortable telling others to smoke outside, being treated unfairly, quit attempts and wanting to quit. However, even the least completely answered of these questions, about wanting to quit, had only 79 participants (4.8%) who answered “don’t know” and 11 (0.7%) who refused to answer.

Results

Smoke-free homes

More than half of smokers (56%, 908/1628) and 80% (701/876) of non-smokers reported that smoking was never allowed anywhere in their home. Non-daily smokers (69%; OR, 1.94; 95% CI, 1.45–2.58), ex-smokers (79%; OR 3.36; 95% CI, 2.50–4.51) and never-smokers (80%; OR, 3.58; 95% CI, 2.84–4.52) were significantly more likely to report such bans than were daily smokers (53%) (Box 1). A similar age–sex-standardised percentage of Australian daily smokers (53.4%) reported total home smoking bans in Wave 8.5 of the Australian ITC Project study.

Of the smokers who reported that smoking was never allowed inside, 10% (91/903) said that some people still smoked inside regardless. So, 50% (812/1623) reported an effective total ban, and 28% (450/1623) a partial ban (including a total ban that was not fully effective), while 22% (361/1623) reported that smoking was allowed anywhere inside. Of those with a partial ban, 51% (225/442) reported being uncomfortable telling elders or community leaders (190/439; 43%), visitors (154/443; 35%) or other householders (125/442; 28%) to smoke outside. Of the respondents with no ban, 59% (213/363) reported it would be possible to stop people smoking inside, but 53% of these (114/215) reported that they would have to make some exceptions.

Smokers who were significantly more likely to report an effective total home smoking ban included non-daily smokers, employed people, Torres Strait Islanders and people who were both Torres Strait Islander and Aboriginal (v Aboriginal people), people aged 18–24 years (v those aged 45 years or over), people with children in their home, those who had finished Year 12 or had post-secondary educational qualifications (v those with less than Year 12), and those who did not feel they had been treated unfairly in the past year because they were Aboriginal or Torres Strait Islander (Box 2). There was no significant association between sex, remoteness or area-level disadvantage and having an effective ban.

Smoke-free workplaces

Most employed Aboriginal and Torres Strait Islander daily smokers (406; 88%) reported that smoking was not allowed in any indoor area at work, similar to the standardised estimate in Wave 8 of the Australian ITC Project study (88.5%) (Box 1).

Remoteness and area-level disadvantage were significantly associated with non-smokers not being protected by a workplace indoor smoking ban (Box 3). Smokers working in smoke-free workplaces were more likely to have effective smoking bans at home than those in workplaces where smoking was allowed in some or all indoor areas (287/484, 59% v 22/65, 34%; OR, 2.85; 95% CI, 1.67–4.87).

Association with quit attempts and wanting to quit

Smokers who lived in homes with an effective total smoking ban were significantly more likely than other smokers to have made a quit attempt in the past year, to want to quit and (among smokers who had attempted to quit in the past 5 years) to have made a quit attempt of 1 month or longer (Box 4). In contrast, there were no such significant associations with working in a smoke-free workplace.

Discussion

Smoke-free homes

Previous research has shown that the proportion of smokers who reported living in smoke-free homes was increasing faster among Aboriginal and Torres Strait Islanders than among other Australians, but that a gap remained in 2008.5 Our study demonstrates that this gap now appears to have been closed, reflecting a significant change in behaviour by Aboriginal and Torres Strait Islander smokers.

This does not mean that there is no gap in the proportion of households that are smoke-free or in the proportion of children who live in smoke-free households. Changes to these will probably require smoking prevalence to fall further, along with more smokers choosing to smoke outside. We found that the presence of infants, children and adult non-smokers in the household was associated with having a smoke-free home, consistent with earlier ITC Project research, including Australian surveys.12 Longitudinal research in Darwin also showed that Aboriginal households implemented smoking bans after the birth of a baby.12,13 As in previous research, we found that the most disadvantaged Aboriginal and Torres Strait Islander people were the least likely to live in smoke-free homes, although this association did not hold for remoteness or area-level disadvantage.5

It is encouraging that few people reported any lapses in maintaining their home smoking bans, and more than half of those with no ban reported that a ban would be possible. People more often reported being uncomfortable telling elders or community leaders to smoke outside, rather than other visitors or householders. Local tobacco action workers could work with elders and community leaders to find respectful solutions, so that people do not feel uncomfortable about asking them not to smoke inside. Further research into the barriers to maintaining effective home smoking bans would be useful.

A literature review suggested that comprehensive national tobacco control programs to reduce smoking prevalence are the most effective in increasing the prevalence of smoke-free homes.14 Australia has boosted comprehensive national tobacco control activity in recent years, including programs specifically for Aboriginal and Torres Strait Islander peoples.15 This has been complemented by local tobacco control activity at the participating sites. Local and regional Aboriginal and Torres Strait Islander social marketing campaigns have focused on smoke-free homes (eg, “Smoking can kill those close to you” in the Northern Territory).16 However, the evidence for the impact of such campaigns on the prevalence of smoke-free homes is more modest, as is the evidence for direct counselling of families about smoke-free homes.3,14,17

Other research has demonstrated an increase in smoke-free homes after smoking bans have been implemented in public places, and we have similarly demonstrated an association between smoke-free homes and smoke-free workplaces.4 The previously demonstrated greater concern by Aboriginal people for the effects of smoking on family, especially children, rather than on their own health, further explains the rapid spread of home smoking bans.18 Introducing a home smoking ban is easier than successfully quitting, but the significant association we found between smoke-free homes and quitting suggests that smokers are not making their homes smoke-free as a substitute to quitting.

However, this optimism needs to be tempered by research that shows reported indoor home smoking bans reduce but do not eliminate children’s exposure to environmental tobacco smoke and its toxins.19,20

Smoke-free workplaces

It is good news that almost all Aboriginal and Torres Strait Islander people reported being protected by indoor smoking bans at work, as is reported by other Australians. We are not aware of comparable data to assess trends, but there has been considerable recent attention to promoting and supporting smoke-free policies at Aboriginal organisations. Improvements can still be made in the most disadvantaged and remote areas. Better monitoring and enforcement of existing indoor smoking bans, as well as their extension to outdoor public spaces (where people are close together), is a focus of the current National Tobacco Strategy.15

Association with quit attempts and wanting to quit

Our cross-sectional study is consistent with longitudinal ITC Project research, including Australian surveys, which showed that having a total indoor home smoking ban was associated with both quit intentions and making more and longer quit attempts.12 However, a cross-sectional study using earlier Australian Bureau of Statistics (ABS) Aboriginal and Torres Strait Islander survey data found only a non-significant association with quit attempts, but did find a significant association with successful past cessation.5 Making the home smoke-free might make it easier for a smoker to quit, but it is also likely that this association is in part due to smokers who are most concerned about their smoking making their homes smoke-free as part of the quitting process.

Strengths and limitations

This is a large nationally representative (albeit not random) survey of Aboriginal and Torres Strait Islander people. However, caution is needed as it relies on self-report of smoke-free homes and workplaces without biochemical verification. Due to inaccurate recall or social desirability bias, it is likely that some participants with reportedly effective total smoking bans are still being exposed to second-hand smoke. However, we think marked bias is unlikely as smoking is still very common and normalised in these communities. Our finding that 10% of smokers reported that some smoking occurred in the home despite not being allowed suggests there was minimal bias towards the most socially desirable response (complete adherence to the smoking ban).

Our questions were the same as in the ITC Project comparison survey, but they differed from those used in ABS surveys.5 The ABS asked whether any householders usually smoke inside, whereas we asked whether smoking (by anyone) was ever allowed inside, and whether people smoked in spite of bans. Therefore, our estimates for the percentage of daily smokers living in homes where smoking was either not allowed (53%) or with effective total home smoking bans (48%) were understandably lower than the 2008 ABS estimate for those living in homes where no householder usually smoked inside (56.3%; 95% CI, 52.4%–60.2%).

Analyses of longitudinal data using follow-up surveys to this baseline survey will provide more methodologically sound confirmation of likely causal directions of the observed cross-sectional associations.

In conclusion, we found that the gap has closed between the proportion of Aboriginal and Torres Strait Islander smokers and all Australian smokers who live in homes with smoking bans, and that these bans may help smokers to quit. Aboriginal and Torres Strait Islander non-smokers are also well protected from second-hand smoke at work.

1 Smoking bans in homes and workplaces*

 

Australian ITC Project†

Talking About The Smokes project
 

Daily smokers, % (95% CI)

Daily smokers,
% (frequency)

Non-daily smokers,
% (frequency)

Ex-smokers,
% (frequency)

Never-smokers,
% (frequency)

Home (n)

1010

1377

251

310

568

Total smoking ban

53.4% (47.7%–59.0%)

53% (735)

69% (173)

79% (246)

80% (455)

Partial smoking ban

31.0% (25.7%–36.8%)

23% (313)

18% (46)

15% (46)

14% (80)

No ban

15.7% (11.7%–20.6%)

24% (329)

13% (32)

6% (18)

5% (31)

Work (n)

604

461

89

131

284

Total indoor ban

88.5% (80.9%–93.3%)

88% (406)

89% (79)

95% (124)

93% (263)

Partial indoor ban

4.5% (2.0%–10.0%)

6% (27)

11% (10)

2% (2)

4% (11)

No ban

7.0% (3.3%–14.3%)

6% (28)

0

4% (5)

4% (10)

ITC Project = International Tobacco Control Policy Evaluation Project. * Percentages and frequencies exclude refused responses and “don’t know” responses, or when not applicable. † Australian ITC Project results are from Wave 8.5 (home), conducted September 2011 to February 2012, and Wave 8 (work), conducted July 2010 to May 2011, and were age- and sex-standardised to smokers in the 2008 National Aboriginal and Torres Strait Islander Social Survey.

2 Aboriginal and Torres Strait Islander smokers with effective home smoking bans,* by sociodemographic factors (n = 1643)

Characteristic

% (frequency)†

Odds ratio (95% CI)

P‡

Total

50% (812)

   

Age (years)

      

18–24

56% (193)

1.0

< 0.001

25–34

55% (242)

0.95 (0.71–1.28)

  

35–44

51% (199)

0.79 (0.54–1.16)

  

45–54

38% (102)

0.47 (0.31–0.70)

  

≥ 55

43% (76)

0.58 (0.39–0.86)

  

Sex

     

Female

53% (441)

1.0

0.15

Male

47% (371)

0.81 (0.61–1.08)

  

Number of infants in home

      

None

47% (670)

1.0

< 0.001

One or more

69% (139)

2.49 (1.79–3.48)

  

Number of children in home

      

None

39% (267)

1.0

< 0.001

One or more

58% (540)

2.11 (1.68–2.65)

  

Indigenous status

     

Aboriginal

49% (699)

1.0

0.04

Torres Strait Islander or both

60% (113)

1.61 (1.03–2.52)

  

Labour force status

     

Employed

56% (318)

1.0

0.02

Unemployed

47% (260)

0.69 (0.52–0.91)

  

Not in labour force

47% (232)

0.70 (0.53–0.94)

  

Highest education attained

      

Less than Year 12

44% (371)

1.0

< 0.001

Finished Year 12

57% (246)

1.69 (1.30–2.21)

  

Post-school qualification

56% (193)

1.58 (1.16–2.15)

  

Treated unfairly because Indigenous in past year

      

No

54% (369)

1.0

0.01

Yes

47% (425)

0.75 (0.60–0.93)

  

Smoking status

      

Daily smoker

48% (660)

1.0

0.003

Non-daily smoker

61% (152)

1.68 (1.20–2.34)

  

Remoteness

      

Major cities

52% (220)

1.0

0.66

Inner and outer regional

50% (412)

0.93 (0.68–1.27)

  

Remote and very remote

47% (180)

0.82 (0.53–1.26)

  

Area-level disadvantage

      

1st quintile (most disadvantaged)

51% (325)

1.0

0.30

2nd and 3rd quintiles

51% (348)

1.01 (0.74–1.37)

  

4th and 5th quintiles

45% (139)

0.78 (0.52–1.15)

  

Local health service has dedicated

tobacco control resources

      

No

52% (244)

1.0

0.55

Yes

49% (568)

0.91 (0.67–1.25)

  

* An effective total ban is when smoking is both never allowed and never occurs. † Percentages and frequencies exclude refused responses and “don’t know” responses, or when not applicable. ‡ Wald test for each variable.

3 Aboriginal and Torres Strait Islander employed non-smokers with total indoor smoking bans at work, by sociodemographic factors (n = 417)

Characteristic

% (frequency)*

Odds ratio (95% CI)

P†

Total

93% (387)

    

Age (years)

      

18–24

95% (105)

1.0

0.17

25–34

89% (90)

0.47 (0.17–1.26)

  

35–44

96% (92)

1.31 (0.35–4.92)

  

45–54

96% (67)

1.28 (0.32–5.07)

  

≥ 55

89% (33)

0.47 (0.12–1.81)

  

Sex

     

Female

95% (204)

1.0

0.10

Male

91% (183)

0.50 (0.22–1.14)

  

Indigenous status

      

Aboriginal

94% (349)

1.0

0.43

Torres Strait Islander or both

90% (38)

0.65 (0.23–1.90)

  

Highest education attained

      

Less than Year 12

94% (103)

1.0

0.99

Finished Year 12

94% (118)

1.00 (0.32–3.13)

  

Post-school qualification

93% (165)

0.93 (0.32–2.72)

  

Treated unfairly because Indigenous in past year

      

No

95% (193)

1.0

0.35

Yes

92% (188)

0.67 (0.29–1.55)

  

Smoking status

      

Ex-smoker

95% (124)

1.0

0.43

Never-smoker

93% (263)

0.71 (0.30–1.67)

  

Remoteness

     

Major cities

95% (116)

1.0

0.01

Inner and outer regional

96% (197)

1.13 (0.40–3.18)

  

Remote and very remote

85% (74)

0.29 (0.11–0.80)

  

Area-level disadvantage

     

1st quintile (most disadvantaged)

88% (111)

1.0

0.02

2nd and 3rd quintiles

97% (202)

3.90 (1.50–10.1)

  

4th and 5th quintiles

93% (74)

1.67 (0.61–4.56)

  

* Percentages and frequencies exclude refused responses and “don’t know” responses, or when not applicable. † Wald test for each variable.

4 Quitting-related outcomes of Aboriginal and Torres Strait Islander smokers, by home and work smoking bans

 

Made quit attempt in past year

Want to quit

Quit attempt of 1 month or longer*
 

% (frequency)†

OR (95% CI)

P‡

% (frequency)†

OR (95% CI)

P‡

% (frequency)†

OR (95% CI)

P‡

Home (n)

1594

    

1540

    

970

    

No ban or partial ban

45% (363)

1.0

  

65% (502)

1.0

  

45% (201)

1.0

  

Effective total ban

54% (425)

1.39 (1.10–1.75)

0.006

74% (574)

1.55 (1.22–1.97)

< 0.001

53% (277)

1.38 (1.08–1.77)

0.01

Work (n)

538

    

515

    

352

    

No ban or partial ban

47% (30)

1.0

  

68% (42)

1.0

  

51% (19)

1.0

  

Total ban

52% (246)

1.22 (0.68–2.19)

0.50

76% (344)

1.50 (0.81–2.79)

0.20

59% (186)

1.37 (0.66–2.83)

0.40

OR = odds ratio. * For those with at least one quit attempt in the past 5 years. † Percentages and frequencies exclude refused responses and “don’t know” responses, or when not applicable. ‡ Wald test for each variable.

Asthma control in Australia: a cross-sectional web-based survey in a nationally representative population

Asthma is one of the most common chronic diseases in Australia, affecting 10% of the population1 and is a National Health Priority Area. Despite this, there is a widespread perception that it is no longer a problem in Australia, especially as asthma mortality has fallen by 70% from its peak in the 1980s. Asthma mortality in this country nevertheless remains high by international comparisons, particularly in young people (those aged 5–34 years).1 Further, asthma was the seventh-highest cause of years lived with disability in Australasia in 2010.2

Since 1989, Australia has taken a lead in developing and updating clinical practice guidelines for asthma. In March 2014, the new national guidelines3 were launched, and effective asthma control was affirmed as the key goal of treatment. Consistent with international recommendations,4,5 two domains of asthma control are now assessed: symptom control and the future risk of adverse outcomes, such as flare-ups (exacerbations). Asthma control is also one of the recommended National Asthma Indicators for monitoring asthma in Australia.6

To improve clinical practice and asthma policy, reliable population-based data on asthma control in Australia are needed. While statistics for several asthma indicators are available, including prevalence, general practice encounters, hospitalisations and mortality,1 there is little information on measures of asthma control. A recent review could find no population-level Australian data for validated composite measures of asthma control, such as the Asthma Control Test (ACT). Even the most recent population-based surveys of individual asthma control measures were conducted more than 10 years ago.7

Population-based data would also enable the impact of asthma treatment to be assessed. Asthma-related expenditure in Australia during the 2008–09 financial year was $655 million, of which 50% was spent on prescription pharmaceuticals.8 Preventer medications for asthma, such as inhaled corticosteroids (ICS) alone or in combination with long-acting β2-agonists (LABA), are subsidised by the Pharmaceutical Benefits Scheme (PBS), but analysis of PBS data1 indicates that they are prescribed at much higher doses and in more expensive combination formulations than necessary,3 and are also prescribed together with antibiotics for short-term respiratory conditions.9Further, of adults who are dispensed any preventer medication, only 9%–30% have it dispensed as often as would be consistent with minimal regular use.1 These data indicate that there are substantial quality problems in Australia with respect to both the prescribing and use of preventer medications.

Past gold standard approaches to population studies involved random-digit dialling and postal surveys of randomly selected participants. The declining ownership of telephone landlines in Australian homes, however, and survey participation rates below 30% (eg, in the study by Toelle and colleagues10) have increased the risk of both selection and response biases. Internet access is rapidly increasing across the socioeconomic spectrum, and there is growing interest in well designed, rigorously reported web-based surveys to minimise these problems.11

The aim of our study was to establish the relationship between control of asthma symptoms, medication use and health care utilisation by Australians aged 16 years and over with current asthma.

Methods

Study design and ethics

We undertook a cross-sectional web-based survey of adult Australians with current asthma (details [Checklist for Reporting Results of Internet E-Surveys, CHERRIES] in Appendix 1). Ethics approval was obtained from the Human Research Ethics Committee, Concord Hospital, NSW. All participants provided informed consent.

Inclusion criteria and recruitment

The target population was Australians aged 16 years and over with current asthma. Participants were recruited from an online panel of 224 898 people provided by Survey Sampling International (Melbourne, Australia). To minimise response bias, a three-stage randomised selection process was used: (i) panel members were randomly invited to take a survey, without specifying the topic of the survey; (ii) respondents were shown initial randomly selected profiling questions that included one which asked whether they had ever experienced any of several health conditions, including asthma; and (iii) those responding that they had experienced asthma were asked two questions, similar to those in the Australian Health Survey, to identify those with “current asthma”: “Have you ever been told by a health professional that you have asthma?” and “Have you had symptoms of asthma or taken medication for asthma in the last 12 months?” Those who responded “Yes” to both questions were included in the study sample. Recruitment was stratified by sex, age and state of residence, according to Australian data for people with asthma (2011–12 Australian Health Survey).12 Participants received “points” to a value of about $1.50 from the panel provider.

Questionnaire

The design of the survey instrument was based on information drawn from relevant scientific publications, qualitative research, and professional health care reviews; it was then cognitively tested in five people with current asthma and piloted in 600 panel members with current asthma. Survey topics included basic demographics, asthma history, asthma treatment and frequency of routine and emergency health care utilisation for asthma. Asthma symptom control was assessed with a validated five-item tool (Asthma Control Test; ACT13), used under licence from QualityMetric Incorporated. Symptom control was classified, according to standard cut-off points, as “well controlled” (ACT score of 20–25 points), “not well controlled” (16–19 points) or “very poorly controlled” (5–15 points). Overall health status was assessed with a question from the Australian Health Survey, “In general, would you say your health is….?”, with five response options ranging from “excellent” to “poor”. A standard screening question for assessing health literacy was included: “How confident are you filling out medical forms by yourself?”, with response options ranging from 1 (“not at all”) to 7 (“extremely”); responses of 4 (“somewhat”) or less indicate limited health literacy.14 Self-reported adherence to asthma treatment was assessed by asking “How often do you use your [inhaler name]?”, with eight responses ranging from “every day” to “a few times a year”.

Data analysis

Data were analysed using SPSS 19.0 (SPSS Inc). Analyses were weighted according to Australian asthma population benchmarks by age group, sex and state, based on data for people with current asthma in the 2011–12 Australian Health Survey.12 Results were reported using descriptive statistics, with means and 95% CIs Logistic regression analysis tested the effects of age, sex, Socio-Economic Indexes for Areas (SEIFA) scores,15 smoking status, health literacy, education level, and age at asthma diagnosis on the level of asthma control.

Results

Demographics and medications

The flow chart of participant selection is included in Appendix 2. Of the 80 518 panel members randomly invited to participate, 27 606 accepted and were shown the profiling questions (panel participation rate, 34.3%). Of these, 3033 people with current asthma were selected at recruitment stage (iii), and invited to participate in the survey; 3018 did so (asthma participation rate, 99.5%), and 2686 completed the survey (response rate, 89.0%). The demographic distribution of the sample closely matched national data for people with asthma (Appendix 3).

Box 1 includes the detailed demographic characteristics of the study sample. Of the respondents, 57.1% were women, and 40.4% were aged 50 years and over. A health care concession card was held by 54.7% of participants, and 40.7% lived in areas in the two lowest SEIFA quintiles (greatest socioeconomic disadvantage); 11.5% of participants responded “somewhat” or less to the question about confidence in completing medical forms, consistent with limited health literacy.14 One fifth of participants were current smokers, consistent with national data for people with asthma.1

In the past 12 months, 92.6% of participants reported using a short-acting β2-agonist reliever inhaler, and 60.8% reported using one or more ICS-containing medications, with 49.6% using combination ICS/LABA and 17.1% using ICS-only medications. Of the 1601 participants using ICS or ICS/LABA inhalers, 43.2% reported using them less frequently than 5 days a week, and 30.5% less than weekly (Box 2).

Asthma symptom control and health care utilisation

The mean ACT score of the 2686 participants was 19.2 (range 5–25; 95% CI, 18.9–19.3). Asthma was “well controlled” in 54.4%, “not well controlled” in 22.7%, and “very poorly controlled” in 23.0%. Multivariable analysis identified being male and a history of smoking (daily, less than daily, or in the past), as demographic characteristics that were significantly associated with “very poorly controlled” asthma, but not age group, education level, SEIFA category or age at asthma diagnosis (Appendix 4).

Only half (50.5%) of the 2686 participants reported having seen their general practitioner for a non-urgent asthma review during the previous year, and only 20.4% had discussed their asthma with a pharmacist; 10.6% had consulted a specialist regarding their asthma. Guidelines recommend that every patient with asthma should have a routine review at least yearly. Almost a quarter of participants (23.3%) had visited a general practitioner urgently about asthma at least once during the previous year, and 10.0% had attended a hospital or emergency department one or more times, with, in total, 28.6% reporting an urgent visit. Of the participants with “very poorly controlled” asthma, 44.2% reported one or more urgent GP visits during the previous year, compared with 12.5% of those with “well controlled” asthma (adjusted odds ratio [AOR], 5.98; 95% CI, 4.75–7.54). Similarly, 17.8% of those with “very poorly controlled” asthma had visited an emergency department or hospital, compared with 6.5% of those with “well controlled” asthma (AOR, 2.59; 95% CI, 1.91–3.53).

Preventer use and asthma control

Box 2 and Box 3 classify participants according to asthma symptom control and self-reported frequency of ICS-containing preventer medication use. Participants who reported using both an ICS-only medication and a non-ICS preventer in the past 12 months were excluded from this part of the analysis, as the structure of the questionnaire did not permit the frequency of use of these medications to be individually distinguished (these 32 participants (1.2% of sample) were asked how often they had used these medications, but not to break this down by specific medications).

Four main groups could be identified. Group A (40.0% of participants) had “well controlled asthma” while using a preventer less than 5 days a week or not at all; these patients are considered to have mild asthma.5 Group B (14.6%) had “well controlled” asthma while using a preventer at least 5 days a week. Conversely, group C (19.7%) had uncontrolled symptoms (ie, “not well controlled” or “very poorly controlled”) despite reportedly using their preventer medication at least 5 days a week. Group D (25.7%) had uncontrolled symptoms, and used no preventer medicine at all, or used it infrequently.

Discussion

This study provides the first nationally representative data on asthma control in Australian adults. Participants were recruited from a web-based panel of almost a quarter of a million Australians, using a three-stage randomised selection process to minimise selection and response biases. We identified significant personal and economic burdens associated with asthma. Symptom control was poor in 45% of participants, while 29% had needed urgent health care for their asthma during the previous year. The data indicated significant problems regarding the prescribing of asthma medications: in contrast with guidelines, many more participants had been prescribed expensive combination ICS/LABA inhalers than had been prescribed ICS alone. Adherence to inhaled maintenance therapy was also poor: 43% of preventer medication users reported taking it less than 5 days a week, and 31% used it less than weekly. Of the participants with uncontrolled asthma symptoms, 23% used preventer medication less than 5 days a week, while 34% did not use any preventer. Taken together, these findings indicate that a significant proportion of asthma morbidity and its associated costs in Australia are preventable.

Study strengths and limitations

Rigorous web-based surveys can be valuable for assessing the impact of asthma policy and practice in Australia. Obtaining a representative sample is crucial, and we chose a web-based design being aware of the low response rates associated with surveys that employ random digit dialling and postal questionnaires,16 and high levels of home internet access. For example, 83% of Australian households had home internet access in 2012–2013, including 59% and 77% of those in the lowest and second lowest quintiles of equivalised household income, respectively; 60% of those aged 55 years or over had accessed the internet in the previous 12 months.17

A further strength of our study was that it complied with the requirements of the CHERRIES criteria for reporting e-health surveys (Appendix 1).11 Many earlier internet surveys were advertised on open websites, and investigators could not accurately identify the denominator population (ie, those who had seen the invitation to participate), and were also subject to response bias resulting from topic-specific survey invitations. The potential for selection and responder biases was minimised in our study by the three-stage random selection of participants from a large web-based panel, by the stratification of recruitment and weighting of analyses by age, sex and state according to national data on people with asthma, and by the completion rate of 89%. We do not know whether our findings can be generalised to people without internet access, but comparisons of recruitment methods have found that probability-based internet sampling achieves the best balance of sample composition and accuracy.18,19 The use of a validated asthma control tool also increased the reliability of the findings.

The major limitations of our study were those associated with any asthma survey: the individual diagnoses of asthma cannot be confirmed, medication doses cannot be accurately established, self-reported adherence to treatment schedules may be overestimated, and inhaler technique (an important contributor to poorly controlled asthma20) cannot be assessed. However, our study fills important gaps in our knowledge about asthma in Australia and, if repeated in the future, would enable assessment of trends in asthma treatment outcomes.

Clinical implications of the study

Both poor asthma symptom control and flare-ups are effectively prevented by regular ICS-containing preventer medications, even at low doses. Despite the ready availability of these medications and the fact that they are subsidised by the PBS, we found significant treatment problems relevant to asthma control. It is difficult to assess the appropriateness of preventer prescribing for individual patients without information about past treatment adjustments and currently prescribed doses, but some patterns were nevertheless clear. Australian guidelines emphasise that good asthma control can be achieved in most patients with ICS alone, and only some need combination ICS/LABA medications, which are substantially more expensive for both government and patient.3 However, nearly three times as many participants reported using a combination ICS/LABA medication in the past 12 months as those who used ICS alone.

To elicit the key clinical implications of these data, we intentionally took a broad approach based on clinical information that is emphasised by asthma guidelines and is readily available to general practitioners: asthma symptom control and adherence to prescribed preventer medication. Four groups were identified that have differing implications for clinical practice (Box 3). The 40% of participants with “well controlled” asthma while using preventer medication infrequently or not at all (Group A) would generally be considered to have mild asthma, but may still be at risk of flare-ups,5 so their asthma and its management should be reviewed at least annually.3 For the 14.7% with “well controlled” asthma while using preventer medication at least 5 days a week (Group B), down-titration should be considered once symptoms have been well controlled for 2–3 months, in order to find the minimum effective preventer dose.3 Patients in Group C (almost 20%) had apparently uncontrolled asthma despite regular asthma preventer use; while some respiratory symptoms may be due to concomitant conditions, such as chronic obstructive pulmonary disease, and while patients often overstate their adherence to medication,21 much of the symptom burden in this group is probably due to incorrect inhaler technique.20 Finally, the 25.7% of participants with uncontrolled asthma symptoms while using no preventer treatment or taking it infrequently (Group D) are at significant risk of severe flare-ups, and interventions are needed to initiate preventer medication or to improve adherence to prescribed therapy.

Few previous Australian statistics on asthma control are available for comparison with our findings. No previous population-based studies have used validated asthma control tools, but it was found that 40% of adults in a 2007 non-random sample had poorly controlled asthma as indicated by an Asthma Control Questionnaire score of 1.5 or more.22 The most recent population-based data (from 2002–20077) suggested that asthma symptom control was poor in 12%–37% of adult patients. The level of control, however, may have been overestimated, as only individual control parameters were assessed.23 An urgent visit to a general practitioner because of asthma in the previous year was reported by 23% of the present sample, compared with 14.3% in a 2003 population-based survey.24 The proportion of participants in the present study with suboptimal asthma control according to ACT score (45%) lies between the rates reported by recent population-based studies in the United States (41%)25 and Europe (50%).26

In conclusion, this study provides the first nationally representative data on asthma control and treatment for Australians with current asthma. Substantial problems with respect to prescribing and use of medications were identified. For almost half the participants there was a gap between the potential control of their asthma symptoms and the level currently experienced. These findings challenge the perception that asthma is a “solved” problem in Australia, a view that may contribute to lack of attention to asthma in clinical practice. Our findings reinforce the key recommendations for primary care in the recently published Australian Asthma Handbook,3 including regular and structured assessment to identify patients with poor asthma control; checking for common problems, such as poor adherence to therapy and inhaler technique; and appropriate prescribing of preventer medications to optimise outcomes and minimise costs and risks to the patient and to the community.

1 Demographic characteristics, asthma treatment and health care utilisation for the 2686 respondents with current asthma

Characteristic

Participants

Age group*

  

16–19 years

207 (7.7%)

20–29 years

493 (18.4%)

30–39 years

523 (19.5%)

40–49 years

377 (14.0%)

50–59 years

441 (16.4%)

60–69 years

344 (12.8%)

70 years or over

302 (11.2%)

Sex

  

Female

1534 (57.1%)

Male

1152 (42.9%)

Smoking history

  

Never smoked

1293 (48.1%)

Past smoker

844 (31.4%)

Current smoker

549 (20.4%)

Highest level of education

  

Year 10 or below

458 (17.1%)

Year 11 or 12

518 (19.3%)

Certificate or diploma

908 (33.8%)

University degree

802 (29.9%)

In possession of a government concession card
(Health Care Card, Pensioner Card, Commonwealth Seniors Health Care Card or Veterans (DVA) Card)

1468 (54.7%)

General health status*

  

Excellent

218 (8.1%)

Very good

872 (32.5%)

Good

1050 (39.1%)

Fair

402 (15.0%)

Poor

145 (5.4%)

Medication use in the past 12 months†

  

Short-acting β2-agonist (reliever)

2488 (92.6%)

ICS-only inhaler

459 (17.1%)

ICS/LABA inhaler

1332 (49.6%)

Any ICS-containing inhaler

1634 (60.8%)

LABA-only inhaler and one or more ICS-containing inhalers (not necessarily concurrently)‡

58 (2.2%)

LABA-only inhaler without any ICS in past 12 months‡

17 (0.6%)

Urgent health care for asthma in past 12 months

  

Urgent general practitioner visit for asthma

628 (23.3%)

Hospital or emergency department visit for asthma

269 (10.0%)

Urgent general practitioner visit and/or hospital or emergency department visit for asthma

769 (28.6%)

Spent at least one night in hospital for asthma

98 (3.7%)

Non-urgent visit to general practitioner for review of asthma in past 12 months

1355 (50.5%)

ICS = inhaled corticosteroids; LABA = long-acting β2-agonist. Data were weighted for age, sex and state of residence.

* The disparity between the sum of the numbers in these groups and the total number of participants is the result of rounding weighted data to whole numbers.

† Participants were asked which treatments they had used in the past 12 months, with the images and brand names of relevant medications shown on screen.

‡ Use of LABA without concurrent ICS (either in combination or as separate inhalers) is strongly discouraged by asthma treatment guidelines.

2 Asthma symptom control and frequency of ICS-containing preventer use over the past 12 months by 2654 participants who had not used non-ICS-containing preventer medications*

ICS = inhaled corticosteroids; LABA = long-acting β2-agonist. A = well controlled symptoms, infrequent or no preventer use; B = well controlled symptoms, regular preventer use; C = poorly controlled symptoms, frequent preventer use; D = poorly controlled symptoms, infrequent or no preventer use (see also Box 3 and discussion in text).

* 32 participants who had used an ICS-only and a non-ICS preventer in the past 12 months were excluded from this analysis because the questionnaire did not permit the frequency of use of the ICS to be distinguished. The medications used by the 32 excluded patients were: a leukotriene receptor antagonist (montelukast, 15 patients), cromones (sodium cromoglycate, 12 patients; or nedocromil sodium, 17 patients) and an anti-immunoglobulin E monoclonal antibody (omalizumab, four patients).

† ICS/LABA combinations (budesonide/eformoterol or fluticasone propionate/salmeterol) and/or ICS alone (beclomethasone, budesonide, ciclesonide, fluticasone propionate). For the 157 participants who reported using both an ICS and an ICS/LABA combination in the previous 12 months, the higher of the two reported frequencies was used.

‡ Percentage of total sample of 2654. All other percentages are based on the symptom control group.

§ The disparity between the sum of the numbers in these groups and the total number of participants is the result of rounding weighted data to whole numbers.

3 Implications for clinical practice — four major patient groups by level of asthma symptom control and self-reported frequency of preventer use

[Articles] Immediate delivery versus expectant monitoring for hypertensive disorders of pregnancy between 34 and 37 weeks of gestation (HYPITAT-II): an open-label, randomised controlled trial

For women with non-severe hypertensive disorders at 34–37 weeks of gestation, immediate delivery might reduce the already small risk of adverse maternal outcomes. However, it significantly increases the risk of neonatal respiratory distress syndrome, therefore, routine immediate delivery does not seem justified and a strategy of expectant monitoring until the clinical situation deteriorates can be considered.

The world we live in

Among the great mysteries of human existence, our uncertain relationship with our environment has been a constant source of puzzlement. In the days of the flat earth, when gods and planets needed constant placation and sacrifice lest the food supply fail and fertility fall, surging infections were thought to be a further manifestation of divine displeasure — something that the deities inflicted upon the people (demos) from above (epi) to chasten and punish. Yet the Old Testament book of Leviticus shows that, thousands of years ago, the need to quarantine people with rashes or swellings “like the plague of leprosy” was recognised (Leviticus 13: 2–5), implying that humans understood from early on that they had a measure of control over infective afflictions.

The path from primitive ignorance and fear to the understanding of the microbiological cause of infection is, as the cliche runs, history. Nevertheless, we continue to fear uncontrolled epidemics, despite our heavy investment in technology to hold them at bay.

Battling the threats

At the beginning of 2003, during the early phases of the severe acute respiratory syndrome (SARS) epidemic, I saw lights burning in the windows of Ian Lipkin’s microbiology laboratory at Columbia University, close to where I was working at the time, for 24 hours every day during the race to sequence the genome of the virus responsible. By May, the 29 751-base genome of the Tor2 isolate had been sequenced in British Columbia and published in Science.1 Fortunately, although SARS was a serious illness, as classical epidemiological data were assembled we recognised that it had low infectivity. We had come to know the enemy — quickly and in fastidious detail — yet we still needed traditional methods to prevent its spread.

Infection retains its character of surprise. Who would have guessed the story of Helicobacter pylori and peptic ulcers? As an intern in 1966–1967, peptic ulcer meant antacids, stress and socioeconomic status, vagotomies, pyloroplasties and heroic surgery for life-threatening haematemesis. What other disorders — cancer, coronary disease — may have an infective element in their aetiology? And, like the global financial crisis of 2008, the Ebola epidemic of 2013 caught us off guard. It also reminded us of how critical the social environment and poverty, in particular, are to the formation of modern infective epidemics.

Complex relationships

In recent years, dramatic developments in our exploration of the universe of infection have led us to the human microbiome — the “organ” that has 10 times as many cells as does the whole of the rest of the human body — that inhabits our gut, skin and other surface tissues, and about which new knowledge is coming to us daily. A 2012 Spanish study described a changing microbiome profile in human breast milk over the months after birth that involved over 700 species of microorganisms.2

I had a glimpse of the importance of the human microbiome in 1968 when working at Baiyer River in the western highlands of Papua New Guinea. We were visited by Eben Hipsley, a nutrition scientist from Canberra, who had an interest in understanding how the local Enga people, naturally muscular and fit, kept their metabolism going without eating much more than sweet potato.3 What about essential amino acids? In private conversation, Eben conjectured that their gut flora generated the molecules missing from this people’s natural diet. Today’s experts in this field presumably have a much better idea of Papua New Guinean nutrition, but Hipsley respected what he knew, even then, of the human microbiome. Contemporary experts now agree that while human microbiota do not fix atmospheric nitrogen, they can upgrade dietary nitrogen-containing compounds into essential amino acids.4

Together, we triumph

In terms of infection control, the global response to HIV has been an astounding exercise that combined technology, preventive science, biological insight, social understanding, philanthropy and dogged global political action. This, together with the GAVI Alliance (made up of such heavyweights as the World Health Organization, UNICEF, the World Bank, the Bill & Melinda Gates Foundation and donor countries), the Global Fund to Fight AIDS, Tuberculosis and Malaria, and the elimination of smallpox, should surely give heart to those who doubt the value of medical research and action. Rather than lamenting what we can’t do, these achievements signal what amazing things we can do together when we try.

Respiratory tract infections among children younger than 5 years: current management in Australian general practice

Acute respiratory tract infections (RTIs) are managed at more than 6 million general practice visits each year in Australia.1 RTIs such as the common cold (acute upper respiratory tract infection [URTI]), acute bronchitis/bronchiolitis, acute tonsillitis and pneumonia create a severe health and economic burden.1 They are most prevalent among young children, especially when they attend preschools or day care centres. It is estimated that children younger than 5 years have a cold 23% of the time,2 with 70% of the costs attributed to carers’ lost time at work.3

Current guidelines on the treatment and management of RTIs in children include supportive management such as hydration and rest.4 Over-the-counter (OTC) medications such as analgesics and cough medications may reduce the severity of symptoms, but they do not cure or prevent the illness.

As most RTIs are caused by viruses, antibiotics have limited therapeutic value and should only be prescribed if an RTI is suspected to be bacterial in origin. However, overseas studies suggest high rates of antibiotic prescribing for RTIs among young children.5,6 Contributing factors include physicians’ diagnostic uncertainty, parents’ expectation of receiving antibiotics and physicians’ perception of parents’ satisfaction with the visit.7,8

The current management of RTIs among children in Australia, especially in general practice, is unclear. Much of the published data about the management of this cohort originated from the United Kingdom,9 Canada,5 and the United States.10,11 Therefore, we aimed to explore the current management of RTIs in children under the age of 5 years in Australian general practice using data from the Bettering the Evaluation and Care of Health (BEACH) program.

Methods

We analysed BEACH data collected from April 2007 to March 2012 inclusive. BEACH methods are described elsewhere in detail;12 however, in summary, BEACH is a continuous, paper-based, national study of general practitioner activity in Australia. Every year, as part of a rolling random sample of 1000 GPs, each GP provides information on 100 consecutive GP–patient encounters with consenting, unidentified patients. BEACH collects GP characteristics and, for each encounter: patient characteristics, reasons for encounter, number of problems managed and clinical actions initiated.13,14 Clinical actions may include medication, referral, pathology testing, and non-pharmacological treatment (eg, counselling, giving advice, education or minor surgery). The BEACH program is approved by the University of Sydney Human Research Ethics Committee.

BEACH study statistical analyses in SAS 9.3 (SAS Institute) are adjusted for clustering of encounters around each GP. Statistically significant differences are determined by non-overlapping 95% confidence intervals, equivalent to P < 0.006.

For this study, we identified all GP encounters with patients younger than 5 years (60 months) at the date of encounter. We analysed those encounters where at least one of the following four RTIs (by International Classification of Primary Care, second edition [ICPC-2] code) was recorded as problem managed:

  • upper respiratory infection, acute (“URTI”) [R74];
  • bronchitis/bronchiolitis, acute (“bronchitis”) [R78];
  • tonsillitis, acute (“tonsillitis”) [R76]; and
  • pneumonia [R81].

These RTIs were selected on the basis of their frequency and importance in general practice paediatric management.

The management rate of each of these four RTIs per 100 paediatric encounters was compared in terms of: season (summer [December–February] v winter [June–August]); GP sex; and GP age group (≥ 55 years v < 55 years).

We further examined the use and rate (per 100 of each specified RTI) of six management (“clinical action”) options:

  • antibiotic medications;
  • prescribed or supplied non-antibiotic medications;
  • medications advised for OTC purchase;
  • referrals (to specialists and/or allied health professionals);
  • pathology testing; and
  • counselling (including advice/education) at the encounter.

Results

From April 2007 to March 2012, there were 31 295 encounters (involving 4522 GPs) with children younger than 5 years. Of these children, 53.4% were boys, and 31.1% were aged under 1 year. One or more respiratory infections (ICPC-2 codes R71–R83) were managed at 9261 encounters — 29.6% (95% CI, 28.9%–30.3%) of these GP paediatric encounters.

Of these encounters, at least one of URTI, bronchitis or tonsillitis was recorded at 86.0% (results not shown), and at least one of URTI, bronchitis, tonsillitis or pneumonia was recorded at 88.1%. One or more of these four specified RTIs were managed at 8157 encounters, equating to 26.1% (95% CI, 25.4%–26.7%) of all GP paediatric encounters.

Box 1 presents patient demographics of all paediatric encounters and of those involving at least one of these four specified RTIs. For encounters where at least one of the four specified RTIs was recorded, there was a smaller proportion of patients in the < 1 year and a greater proportion in the 1 to < 4 years age groups and fewer patients new to the practice compared with all paediatric encounters. The characteristics of the two groups were otherwise similar.

The management rate (per 100 paediatric encounters) of each of the four specified RTIs (and the combined total) is shown in Box 2. For all four specified RTIs combined, the management rate was higher among older GPs (≥ 55 years) than among younger, higher among male GPs than female, and higher in winter than summer. URTI was the most frequently managed respiratory infection (18.6), followed by bronchitis (4.2), tonsillitis (2.7) and pneumonia (0.6). The problem management rates of URTI, bronchitis and pneumonia were significantly higher in winter than in summer.

The management rate of URTI and bronchitis was significantly higher among male GPs than among female GPs (Box 2). The rate of tonsillitis management was higher among older GPs than younger GPs (Box 2). There was no significant seasonal difference in the rate at which each management option (“clinical action”) was recorded for each of the specified RTI problems (results not shown).

Box 3 illustrates the mean rate of management options recorded per 100 of each of the four specified RTI problems. The antibiotic prescribing rate for the management of tonsillitis (88.6), was statistically significantly higher than that for pneumonia (65.6), bronchitis (55.2) and URTI (20.2). URTI had the highest rate of OTC medications advised (29.5), compared with tonsillitis (13.0), bronchitis (9.2) and pneumonia (5.2). URTI also had the highest rate of counselling/advice/education (35.6), compared with bronchitis (24.1), pneumonia (21.4) and tonsillitis (13.7). The highest rate of prescribing non-antibiotic medications was for bronchitis (21.6). The highest rate of referrals given (14.1) and pathology tests ordered (9.9) were for pneumonia.

The rate of antibiotic prescribing per 100 URTI problems was higher among male GPs (22.5; 95% CI, 20.6–24.3) than female GPs (17.2; 95% CI, 15.3–19.1) and similarly for prescribed non-antibiotic medication per 100 URTI problems (14.0; 95% CI, 12.3–15.7 v 10.7; 95% CI, 9.1–12.3). The rate of pathology tests ordered per 100 tonsillitis problems was significantly higher among female GPs than among male GPs (3.9; 95% CI, 1.6–6.3 v 0.6 95% CI, 0.0–1.3). The rate of counselling/advice/education per 100 bronchitis problems was significantly higher among female GPs than among male GPs (31.2; 95% CI, 25.9–36.5 v 19.1; 95% CI, 15.7–22.5). No other significant differences were found on this GP sex comparison analysis.

The rate of antibiotic prescribing per 100 URTI problems was significantly higher among older GPs (≥ 55 years) than younger GPs (25.4; 95% CI, 22.8–28.0 v 18.0; 95% CI, 16.4–19.5). The rate of counselling/advice/education per 100 URTI problems was significantly higher among younger GPs than older GPs (38.1; 95% CI, 35.5–40.6 v 30.2; 95% CI, 26.4–33.9). The rate of advising OTC medications per 100 tonsillitis problems was significantly higher among younger GPs than among older GPs (16.2; 95% CI, 12.3–20.0 v 8.0; 95% CI, 4.1–12.0). No other significant differences were found on this GP age group comparison analysis.

Discussion

Our study provided insight into the current management of selected respiratory infections in children younger than 5 years by GPs in Australia.

Our study found that URTI was the most common RTI managed by GPs in this age group. This finding is similar to those reported from Australia, Malaysia and the UK.1,15,16 Studies have shown that parental decisions to consult for a young child with the common cold are influenced by the age of the child, type of symptoms, parents’ education level and their perception of the severity of the symptoms.17 Whereas 60% of parents would visit a GP if their child had a cold,11 parents from a lower income group were 1.5 times more likely to seek advice from health services.18

Despite our analyses showing URTI having the lowest antibiotic prescription rate of the four specified RTIs, guidelines suggest this is beyond clinical requirement. Nonetheless, this result compared favourably with overseas studies,6,19,20 where the reported antibiotic prescription rate for URTI was as high as 42%.6 Similarly, those studies reported the antibiotic prescription rate for bronchitis to be as high as 86%.6,19

Several studies have suggested reasons why antibiotics might be prescribed unnecessarily for (non-bacterial) RTIs.7,15,18,19,21 These include diagnostic uncertainty in children,7 possibly poor medical knowledge of respiratory infections,21 physicians’ perception of parental satisfaction,8 and parents’ misconceptions and expectations regarding the treatment of RTIs, especially the perceived benefits of antibiotics.7,10,11,18 Some of these studies have recommended education about RTIs and antibiotics for parents and carers,10,15 and for physicians to aid decision making and optimal management.22

While URTIs had the lowest antibiotic prescription rate of the four RTIs in our study, they have the highest rate of OTC medications advised. Although physicians, researchers and paediatricians agree that common cold treatments and remedies do not reduce illness duration and offer little benefit,23 GPs might still advise OTC medications (rather than prescribe antibiotics) to address some parents’ expectations that medication will cure the common cold.

For each of the four RTIs, we found that the rate of pathology tests ordered was lower than the rate of antibiotic prescribing. Possible reasons for this include the technical difficulty of pathogen identification in RTIs; the invasive nature of throat swabs in young children; the cost; and the likelihood that management would not be altered by the microbiological results, which are often delayed.22,24,25

There were differences in the management of paediatric RTIs by GP age and sex; male GPs prescribed medication (antibiotics and non-antibiotics) for URTI significantly more frequently than female GPs, and were less likely to provide counselling and education for bronchitis than female GPs. Older GPs prescribed antibiotics for URTI more frequently, but were less likely to provide counselling/advice/education for URTI than younger GPs.

In our study, antibiotic prescribing rates for URTI, bronchitis and tonsillitis were higher than recommended by the current Therapeutic guidelines.4 However, our study was limited by a lack of data on patient comorbidities, which could have influenced GPs’ diagnostic and management decisions. Similarly, the practice of “wait and see” before filling antibiotic prescriptions or buying OTC medications was not recorded, leading to possible overreporting of prescribed and OTC medications.

Nevertheless, the rigour of BEACH data has been well established, and this study gives a detailed estimate of the frequency of and management options for specified paediatric RTIs. Our results open several promising avenues for further research into parents’ and health professionals’ attitudes and practices regarding antibiotic prescribing and OTC medications for managing RTIs in young children. Better understanding of these factors will help maintain favourable management practices.

1 Demographics of general practice patients younger than 5 years, overall and with respiratory infections, 2007–2012

 

All patients (n = 31 295)

Patients with at least one of the four specified respiratory tract infections* (n = 8157)

Demographics

No.

% (95% CI)

No.

% (95% CI)

Sex

        

Male

16 548

53.4% (52.7%–54.0%)

4319

53.3% (52.2%–54.4%)

Female

14 468

46.6% (46.0%–47.3%)

3782

46.7% (45.6%–47.8%)

Missing data†

279

  

56

  

Age group in years

        

< 1

9730

31.1% (30.4%–31.8%)

2056

25.2% (24.2%–26.2%)

1 to < 2

8053

25.7% (25.2%–26.3%)

2233

27.4% (26.4%–28.4%)

2 to < 3

4917

15.7% (15.3%–16.2%)

1559

19.1% (18.2%–20.0%)

3 to < 4

4240

13.5% (13.1%–14.0%)

1251

15.3% (14.5%–16.1%)

4 to < 5

4355

13.9% (13.5%–14.3%)

1058

13.0% (12.2%–13.7%)

Missing data†

0

  

0

  

Indigenous status

        

Aboriginal and/or Torres Strait Islander

699

2.5% (2.1%–2.9%)

160

2.2% (1.7%–2.7%)

Non-Indigenous

27 153

97.5% (97.1%–97.9%)

7121

97.8% (97.3%–98.3%)

Missing data†

3443

  

876

  

HCC status

        

HCC

7380

25.9% (24.8%–27.0%)

2042

27.4% (25.9%–28.9%)

No HCC

21 116

74.1% (73.0%–75.2%)

5416

72.6% (71.1%–74.1%)

Missing data†

2799

  

699

  

Practice status

        

New to practice

4754

15.4% (14.7%–16.0%)

1051

13.0% (12.1%–14.0%)

Seen previously

26 199

84.6% (84.0%–85.3%)

7012

87.0% (86.0%–87.9%)

Missing data†

342

  

94

  

HCC = Health Care Card. * Acute upper respiratory tract infection, acute bronchitis/bronchiolitis, acute tonsillitis and pneumonia. † Missing data were removed from calculations.

2 Respiratory problems among children younger than 5 years per 100 encounters, by general practitioner age and sex, and by season, for the four specified respiratory tract infections, 2007–2012

 

No. of encounters with patients < 5 years

Problems managed per 100 encounters (95% CI)

URTI

Bronchitis

Tonsillitis

Pneumonia

Total

Total

31 295

18.64 (18.05–19.23)

4.15 (3.89–4.42)

2.72 (2.51–2.93)

0.61 (0.51–0.71)

26.13 (25.48–26.78)

GP age group in years*

< 55

21 947

18.51 (17.82–19.20)

3.96 (3.65–4.27)

2.42 (2.19–2.66)

0.61 (0.49–0.73)

25.51 (24.76–26.25)

≥ 55

9195

18.99 (17.84–20.14)

4.64 (4.12–5.17)

3.39 (2.96–3.83)

0.62 (0.43–0.81)

27.65 (26.34–28.96)

GP sex

            

Female

14 410

16.99 (16.18–17.80)

3.71 (3.34–4.09)

2.46 (2.17–2.76)

0.73 (0.56–0.90)

23.89 (22.97–24.81)

Male

16 885

20.05 (19.20–20.90)

4.52 (4.16–4.89)

2.94 (2.65–3.24)

0.52 (0.40–0.63)

28.03 (27.12–28.94)

Season of consultation

Summer

6571

14.35 (13.22–15.48)

2.48 (2.08–2.88)

2.71 (2.24–3.18)

0.41 (0.23–0.59)

19.95 (18.71–21.19)

Winter

8636

21.86 (20.60–23.12)

5.33 (4.72–5.93)

2.84 (2.43–3.25)

0.88 (0.65–1.11)

30.91 (29.47–32.34)

URTI = upper respiratory tract infection. Summer = December–February. Winter = June–August. * Age was missing for 153 GPs; their data were removed from calculations.

3 Mean rate (95% CI) of treatment options recorded per 100 specified respiratory problems treated in general practice among children younger than 5 years, 2007–2012

URTI = upper respiratory tract infection.