Government policy, not consumer behaviour, is driving rising Medicare costs

By Professor Stephen Duckett, Director, Health Program, Grattan Institute

This article first appeared in The Conversation on 2 December, 2015, and can be viewed at: https://theconversation.com/governmentpolicynotconsumerbehaviour…

Announcing the ill-fated 2014 budget initiative to introduce a consumer co-payment for general practice visits, the-then Health Minister, Peter Dutton, lamented that annual Commonwealth health costs had increased from $8 billion to $19 billion over a decade.

He described the increase as “unsustainable”, and used it to justify the Budget’s bitter pill.

The implication of his announcement was that consumers were driving the increase in costs, and that action to change consumer behaviour was necessary to rein them in.

The growth numbers were presented as part of the government’s then mantra of a “debt and deficit disaster”, and massaged to create maximum shock and awe. The minister’s numbers did not adjust either for population growth or inflation.

Nonetheless, a more legitimate set of growth numbers would still show Medicare Benefits Schedule (MBS) payments growing at an annual rate of 2.3 per cent in real per head terms, faster than growth in Government expenditure overall (1.8 per cent).

But this still leaves open the question of whether consumer behaviour is driving rising costs, or whether there may be other causes.

A report released in late November by the Parliamentary Budget Office shows that Government policy has driven a significant proportion of the growth in MBS costs. In fact, of the $325 real increase in MBS spending per head since 1993-94, all but $74 has been the result of explicit government decisions.

MBS spending per head is the product of the rebate for each MBS item and the per head use of those items. Both elements of this calculation have been tinkered with as part of policy change over the last two decades.

A significant proportion of the growth in Medicare costs has been driven by Government policies such as items for new services and larger rebates.

Governments have increased rebates for some items faster than inflation. This has been done, for example, to encourage an increased rate of bulk billing.

New item numbers have also been added as part of major policy reviews. (Each MBS service involves one or more item numbers and an associated description. For example, an ordinary consultation with a general practitioner is item number 24.) The single largest cost impact ($51 per head) came from changes to diagnostic imaging items, including new items for magnetic resonance imaging (MRI).

But implementation of policies to expand magnetic resonance imaging and reform diagnostic imaging items more generally has been poor. It is questionable whether consumers are getting value for money from this investment. Also, some diagnostic imaging tests appear to be overused.

Policies designed to increase bulk billing accounted for an extra $70 per head: increasing the GP rebate from 85 per cent of the schedule fee to 100 per cent accounted for $42 per head; targeted increases in the rebate to increase bulk billing rates accounted for the rest.

When did Medicare spending soar?

In the decade to 2003-044, Medicare spending grew by $53 per head. Just over half of that was attributable to the addition of new diagnostic imaging items to the schedule. In the next decade, spending grew at five times that rate – by $272 per head.

Most of the growth was due to decisions taken when Tony Abbott was Health Minister, between 2003 and 2007. In fact, almost half (47 per cent) of the growth in Medicare spending over the last two decades is the result of policy decisions taken when he was running the health portfolio.

The changes were introduced over the years for a mix of policy and political reasons.

The decline in bulk billing was associated with public dissatisfaction with Medicare and was clearly having political impacts. This led to new bulk billing incentives and increases to the rebates for general practitioner fees.

The increasing prevalence of chronic diseases, such as diabetes and heart disease, led to new assessment and care planning items.

A decline in the proportion of GPs providing after-hours care led to new items to redress that as well.

General practitioners got more rebate income (in real terms) for seeing the same number of patients, so it was actually changes initiated by Government that led to the increase in spending.

What does this mean for Medicare reform?

Two main lessons can be drawn from the Parliamentary Budget Office report.

First, the Government must be clear about what is driving growth in expenditure. The co-payment proposal sank like a lead balloon partly because it was seen as inefficient and unfair, but also because the public didn’t have any ownership of the “problem” the changes sought to address. The way the problem was initially presented was wrong, causing confusion between Medicare services (which include diagnostic tests) and GP visits. The vast majority of the population, who have few visits, refused to accept that per head use was going up.

Second, the report shows how much governments have relied on tinkering with the Medicare Benefits Schedule to drive system change in the last decade. “Here a new item, there a new item, everywhere a new item”, became the Canberra policy song sheet.

Health Minister Sussan Ley wiped the slate clean when she was appointed in December, setting up a raft of reviews to look at everything from primary care to disinvestment.

Importantly, reviews must consider whether the Medicare Schedule is still “fit for purpose” in the context of the increase in chronic disease and the impact this is having on clinical practice.

It must be hoped new policies developed in response will be both more sophisticated and less profligate than we have seen over recent decades.

Health gets a guernsey in Paris

The right to health has been explicitly recognised in the agreement negotiated at the United Nations Paris climate change talks, boosting hopes of an increasing focus on the health effects of global warming.

In its preamble, the Paris Agreement directed that, when taking action on climate change, signatories should “respect, promote and consider their respective obligations on…the right to health”.

Director of the World Health Organisation’s Department of Public Health, Environmental and Social Determinants of Health, Dr Maria Neira, hailed the declaration as a “breakthrough” in recognising the health effects of climate change.

“This agreement is a critical step forward for the health of people everywhere,” Dr Neira said. “The fact that health is explicitly recognised in the text reflects the growing recognition of the inextricable linkage between health and climate.”

Dr Neira said that health considerations were essential to effective plans to adapt to climate change and mitigate its effects, and “better health will be an outcome of effective policies”.

Under the Paris deal, countries have expressed an “ambition” to limit global warming to less than 2 degrees Celsius, the point at which science suggests climate change becomes untenably dangerous.

While avoiding setting an explicit target, the signatory countries, including Australia, committed to “pursuing efforts to limit the temperature increase to 1.5 degrees Celsius”.

Attempts to orchestrate concerted global climate change action have in the past been frustrated by arguments over who should bear the greatest responsibility for causing climate change and, as a consequence, who carries the greatest obligation to ameliorate its effects.

Developing countries have argued that industrialised nations have become rich on fossil fuel-based economic activity and should bear the greater share of the burden in adopting to its consequences.

But developed countries have countered that any progress they make in curbing greenhouse gas emissions should not be simply offset by an increase in emissions from emerging economies.

The Paris agreement has sought to break the impasse by detailing a framework of “differentiated responsibilities” for climate action. Developed countries are expected to take the lead in reducing greenhouse gas emissions, but developing nations are also expected to contribute.

To help drive the global response, it is expected that by 2020, countries will contribute $US100 billion a year to a global fund to help finance emission reduction and climate change adaptation measures.

Though the agreement does not include any enforcement mechanism, countries are required to provide an update on their climate change action each five years, and each successive update has to be at least as strong as the current one, leading to what the framers of the document will be a “ratcheting up” of measures over time.

The promising outcome to the Paris meeting followed a call by the AMA and other peak medical groups worldwide for more concerted action to prepare for and mitigate the health effects of climate change.

In an updated Position Statement on Climate Change and Human Health released last year, the AMA highlighted multiple health threats including increasingly frequent and severe storms, droughts, floods and bushfires, pressure on food and water supplies, rising vector-borne diseases and climate-related illnesses and the mass displacement of people.

AMA President Professor Brian Owler said significant health and social effects of climate change were already evident, and would only become more severe over time.

“Nations must start now to plan and prepare,” Professor Owler said. “If we do not get policies in place now, we will be doing the next generation a great disservice. It would be intergenerational theft of the worst kind – we would be robbing our kids of their future.”

The AMA’s Position Statement on Climate Change and Human Health can be viewed at: position-statement/ama-position-statement-climate-change-and-human-health-2004-revised-2015

Adrian Rollins

Infectious bacteria found in sticky situation

Sticky fingers are unavoidable when indulging in sugar coated sweets, but scientists have discovered that some infectious disease causing-bacteria use this sticky situation to their advantage.

Pathogenic bacteria has been found to initiate infection in a rather unique way – it uses its surface sugars to attach bacteria directly to sugars on the surface of human cells.

Researchers have found that four different types of bacteria pathogens: Campylobacter jejuni, Salmonella typhimurium, Shigella flexneri and Haemophilius influenzae, use this method to spread infection.

University of Adelaide researchers found that the Shingella flexneri bacteria, which causes millions of episodes of dysentery each year, use sugars of their surface lipopolysaccharide molecules to stick to human gut cells.

There is no Shingella vaccine currently available despite decades of research worldwide, and the bacteria can be resistant to antibiotics. The researchers hope their new understanding of how the bacteria spreads will advance progress towards a vaccine and other ways to block the sugars.

Lead researcher Associate Professor Renato Morona said that “as a result of the discovery we now have a better understanding of how bacteria initiate infections and how many current vaccines work”.

“It’s been known for a long time that sugars on the surface of bacteria can be involved in bacteria sticking to cells, to promote infections,” Associate Professor Morona told Adelaide Advertiser.

“What hasn’t been realised is that these sugars are often sticking to is sugars on the surface of cells.”

Associate Professor Morona said that while bacteria were known to use sugars to attach proteins, any sugar-to-sugar interaction was considered either impossible, weak, or irrelevant.

“The discovery is fundamental knowledge that is broadly applicable to many other bacteria and microbes, and could have other translational outcomes such as probes for studying human cells, and development of better infant milk formula,” Associate Professor Morona said.

The research was supported by the National Health and Medical Research Council. The team has received a four-year grant to explore the potential of their discovery.

The University of Adelaide in collaboration with Griffith University published the research in the Proceedings of the National Academy of Sciences journal.

Kirsty Waterford

[Comment] Universal access to medicines

Medicines account for 20–60% of health spending in low-income and middle-income countries, whereas in high-income countries the proportion is 18% or lower.1 Up to 90% of low-income populations purchase medicines through out-of-pocket payments, making medicines the largest household expenditure item after food.1 Strategies to make medicines more available and affordable are therefore crucial in increasing their use in low-income and middle-income countries, in which the burden of non-communicable diseases, in addition to awareness of the benefits of prevention and treatment, are increasing.

[Correspondence] Future inequalities in life expectancy in England and Wales – Authors’ reply

The observed closing of the life expectancy gap between men and women in our study of life expectancy trends in England and Wales,1 and the projections that this closing will continue, are consistent with data in other populations with small mortality differences between men and women.2,3 Investigators of the most recent of such reports concluded that “the current excess of female life expectancy in adulthood is a relatively new demographic phenomenon”.2 The divergence and reconvergence of male–female mortality is well known to be largely due to different levels and trends in deaths from external causes (injuries) and from disorders such as lung cancer and cardiovascular diseases, for which risk factors (eg, smoking) have different trends in men and women.

[Comment] China Diabetes Society 2016: a call for papers

Two decades ago, it seemed almost inconceivable that China would be heading towards an epidemic of obesity and type 2 diabetes; HIV/AIDS and other communicable diseases were much greater concerns. Rapid economic growth and investment in health systems have led to growing income, rapidly declining infectious disease rates, and increasing life expectancy.1 This good news story, however, carries with it the baggage of an increasing burden of obesity and diabetes. In 1994, it was estimated that the prevalence of diabetes was 2·5%.

[Comment] Offline: Chronic diseases—the social justice issue of our time

It would be normal to be anxious at a meeting about chronic diseases. Even overwhelmed. Non-communicable diseases (NCDs) are many—cardiovascular, cancer, diabetes, respiratory, liver, renal, neurological. The list goes on. And then there are risk factors: tobacco, diet, physical inactivity, high blood pressure, air pollution. The context only adds to the complexity—rapid globalisation, urbanisation, an ageing society. If you were a minister of health amid this extraordinary diversity of challenges, where would you begin? Last week, WHO held a “dialogue” to discuss the Global Coordination Mechanism on the Prevention and Control of Non-communicable Diseases.

Health neglected in climate talks

More than half of governments around the world are yet to develop national plans to protect their citizens from the health effects of climate change despite increasing warnings it will cause more extreme weather, spread disease and put pressure on food and water supplies.

As leaders from around the world attending the United Nations Climate Change Conference in Paris reaffirmed their commitment to provide $139 billion a year by 2020 to the UN’s Green Climate Fund and other climate initiatives, an international survey of 35 countries, including Australia, has found a general lack of focus and urgency around the looming threat of climate change to health, with most governments doing little work on likely effects and how to mitigate them.

The survey results underline calls from the AMA, the World Medical Association and other national medical organisations for the health effects of climate change to be made a priority.

AMA President Professor Brian Owler said that while much of the Paris talks were about carbon emission targets, there should be equal emphasis on equipping health systems to cope with the extra burden of problems created by climate change.

“Climate change will dramatically alter the patterns and rate of spread of diseases, rainfall distribution, availability of drinking water and drought,” Professor Owler said. “The incidence of conditions such as malaria, diarrhoea and cardio-respiratory problems is likely to rise.”

The AMA President’s comments came as a survey coordinated by the World Federation of Public Health Associations (WFPHA) found almost 80 per cent of governments are yet to comprehensively assess the threat climate change poses to the health of their citizens, two-thirds had done little to identify vulnerable populations and infrastructure or examine their capacity to cope, and less than half had developed a national plan.

The result underlines the importance of repeated AMA calls for the Federal Government to do much more to prepare for the effects of climate change, which Professor Owler said were “inevitable”.

Earlier this year the AMA released an updated Position Statement on Climate Change and Human Health that warned of multiple risks including increasingly frequent and severe extreme weather events, deleterious effects on food production, increased pressure on scarce water resources, the displacement of people and an increase in health threats such as vector-borne diseases and climate-related illnesses.

“There are already significant health and social effects of climate change and extreme weather events, and these effects will worsen over time if we do not take action now,” Professor Owler said.

“Nations must start now to plan and prepare. If we do not get policies in place now, we will be doing the next generation a great disservice.

“It would be intergenerational theft of the worst kind — we would be robbing our kids of their future.”

In May, the AMA and the Australian Academy of Science jointly launched the Climate change challenges to health: Risks and opportunities report that detailed the likely health effects of climate change and called for the establishment of a National Centre of Disease Control to provide a national and coordinated approach to threat.

The WFPHA said the results of its survey should serve as a wake-up call for governments to do much more.

“The specifics of these responses provide insight into the lack of focus of national governments around the world on climate and health,” the Federation said.

Disturbingly, the survey found that Australia was one of the laggards in addressing the health effects of climate change, having done little to assess vulnerabilities and long-term impacts, develop an early warning system or adaptation responses, and yet to establish a health surveillance plan.

On many of these measures, the nation was lagging behind countries like the United States, Sweden, Taiwan, New Zealand and even Russia and China.

Climate and Health Alliance Executive Director Fiona Armstrong, who helped coordinate the survey, said the results showed the Federal Government needed to place far greater emphasis on human health in its approach to climate change.

“As a wealthy country…whose population is particularly vulnerable to the health impacts of climate change, it is very disappointing to see this lack of leadership from policymakers in Australia,” Ms Armstrong said.

Public Health Association of Australia Chief Executive Officer Mike Moore said the increasing number and ferocity of bushfires and storms underlined the urgent need for action.

“It is time to ensure that health-related climate issues are part of our national planning and budgeting if we are to pre-empt many avoidable illnesses and injuries,” Mr Moore said.

Read the AMA’s Position Statement on Climate Change and Human Health.

Adrian Rollins

[Series] Turning off the tap: stopping tuberculosis transmission through active case-finding and prompt effective treatment

To halt the global tuberculosis epidemic, transmission must be stopped to prevent new infections and new cases. Identification of individuals with tuberculosis and prompt initiation of effective treatment to rapidly render them non-infectious is crucial to this task. However, in settings of high tuberculosis burden, active case-finding is often not implemented, resulting in long delays in diagnosis and treatment. A range of strategies to find cases and ensure prompt and correct treatment have been shown to be effective in high tuberculosis-burden settings.

Risk factors and burden of acute Q fever in older adults in New South Wales: a prospective cohort study

Q fever is a highly infectious zoonotic disease caused by the bacterium Coxiella burnetii.1–3 The main reservoirs for this bacterium are domestic and wild animals, and it can be excreted in their urine, faeces, milk and products of conception, and can survive in harsh environmental conditions.1 Transmission to humans occurs mainly through direct contact with infected animal products or by inhalation of contaminated dust or aerosols.4 In humans, Q fever manifests as an acute flu-like illness or, less frequently, with pneumonia or hepatitis; infection is often asymptomatic.1 Chronic Q fever, most frequently presenting as endocarditis, occurs in about 5% of symptomatic cases.1 Q fever fatigue syndrome is the most frequently reported sequela of acute infection (10%–20% of cases).5

A Q fever vaccine is available in Australia and is recommended for those at high occupational risk of infection.6,7 During 2001–2006, the federal government funded the National Q Fever Management Program (NQFMP) in various states, including New South Wales; under this program, people at high risk were screened and vaccinated, including abattoir workers, sheep shearers, and sheep, dairy and beef cattle farmers and their farm workers. Uptake of the vaccine was almost 100% among abattoir workers and about 43% among farmers; the program significantly reduced the number of notified cases of Q fever in abattoir workers.7 National notification rates suggest there was some decline in the incidence of Q fever during 2006–2009 — from 2.0 to 1.4 notified cases per 100 000 population — but this was followed by a gradual return to 2.0 cases per 100 000 population by 2014; the highest reported rates were among adults aged 45–69 years.8

Most epidemiological studies have been retrospective and focused on specific occupational groups,9,10 and there are only limited data on factors associated with Q fever risk outside these populations. We therefore examined the risk and acute burden of Q fever in a population-based prospective study of Australian adults aged 45 years and over living in NSW.

Methods

Participants

We used data for participants recruited in NSW during 2006–2009 for a prospective study of adults aged 45 years and over (the Sax Institute’s 45 and Up Study); the recruitment procedures have been published elsewhere.11 In brief, NSW residents aged 45 years or over were randomly selected from the Australian Medicare database and invited to participate. The 45 and Up Study oversampled residents in rural and remote areas, and those aged 80 years and over. At recruitment, participants completed a baseline questionnaire that provided information on their sociodemographic factors, behaviour and health.12

Participants consented to long-term follow-up and linkage of their data.11 For the study described in this article, participants were linked to the NSW Notifiable Conditions Information Management System (NCIMS), the NSW Admitted Patient Data Collection (APDC) and the NSW Registry of Births, Deaths and Marriages (RBDM). The NSW Centre for Health Record Linkage (CHeReL) performed the linkage independently of the study investigators, using probabilistic matching.

The NCIMS database records all notifications of Q fever in NSW residents; it includes information on the date of onset and details of laboratory confirmation, including the type of specimen used. Notifications of Q fever require laboratory definitive evidence or laboratory suggestive evidence together with clinically compatible disease (Box 1).13 The APDC records information about all admissions to hospitals in NSW, including the date of admission and discharge, the primary diagnosis, and up to 49 secondary diagnoses affecting treatment or length of stay, coded according to the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM). The RBDM records the date of death of NSW residents.14 For this study, the data from the NCIMS and RBDM were complete to 31 December 2012, and the APDC data were complete to 30 June 2012.

All participants provided written informed consent. This study was approved by the NSW Population Health Research Ethics Committee (approval number, 2010/12/292) and the University of New South Wales Human Research Ethics Committee.

Outcome definitions

The study outcomes were incident Q fever diagnoses (cases) and the proportion of these patients who were admitted to hospital. We defined participants as having an incident Q fever diagnosis if they had a linked record of notified Q fever in the NCIMS database after recruitment. Cases of Q fever with linked hospital records between 6 weeks before and after the Q fever notification date were examined, and classified as follows:

primary Q fever: at least one hospitalisation for which the ICD-10-AM code A78 was recorded as the primary diagnosis
secondary Q fever: at least one record including A78 as a secondary diagnosis
Q fever-related: no A78 codes but one of the following primary diagnoses recorded: A49.9 (bacterial infection, unspecified), B17.9 (acute viral hepatitis, unspecified), B34.9 (viral infection, unspecified), J18.9 (pneumonia, unspecified organism), or R50.9 (fever, unspecified);15,16 and
presumed unrelated: none of the above recorded.

The 6-week window was chosen because most cases of acute illness resolve within 6 weeks of onset.17 The number of deaths among notified Q fever patients within 6 weeks of the recorded onset of disease were determined.

Statistical analyses

Analyses excluded those with a record of Q fever notification before study recruitment. Person-years at risk were calculated from the date of study recruitment to the date of Q fever onset or death, or 31 December 2012, whichever occurred first. Hospitalisation analyses were restricted to cases with a diagnosis date on or before 20 May 2012; ie, 6 weeks before the last date for which we had complete hospital records. This restriction was imposed to ensure that all hospitalisation events within 6 weeks of the onset of Q fever were captured.

The incidence of notified Q fever cases was estimated according to age (stratified as 45–54 years, 55–64 years and 65 years or older); sex; area and type of residence (a composite variable that includes both area of residence — major city, inner regional or outer regional/remote/very remote, according to the Accessibility/Remoteness Index of Australia [ARIA+] — and accommodation type — living on a farm or not); smoking history (never or ever smoked); and number of hours spent outdoors each day (less than 4, 4 to less than 8, 8 hours or more).

We used Cox proportional hazard models to estimate unadjusted (univariate) hazard ratios (HR) for Q fever according to these characteristics. Variables associated with Q fever (P < 0.1) were included in a multivariable model, with the final model determined using a backward elimination method. Variables for which P < 0.05 were retained in the final model. Missing categories were only included in the multivariable model and reported if the proportion of missing cases was greater than 5%.

We also examined the proportion of notified patients who were hospitalised, their concurrent diagnoses on admission, and, for those with a Q fever-coded hospitalisation, the median length of stay. Kruskal–Wallis tests were used to compare the median number of hours spent outdoors each day according to area and type of residence. P < 0.05 was defined as statistically significant. All analyses were performed with Stata 12 (StataCorp).

Results

After excluding 202 participants with notified Q fever before recruitment, our analysis included 266 906 participants who were followed up for 1 254 650 person-years (mean follow-up time, 4.7 ± 1.0 years per person). The mean recruitment age was 62.7 ± 11.2 years, and 53.6% were women. There were 45 participants with a linked Q fever notification during follow-up (for 44 there was positive serological evidence; for one, the diagnosis method was unknown).

In our study population, the incidence of notified Q fever was 3.6 (95% CI, 2.7–4.8) per 100 000 person-years. The relationship of incidence with various sociodemographic characteristics is shown in Box 2. In unadjusted models, age (P = 0.01), sex (P = 0.03), area and type of residence (P < 0.001 for trend), and time spent outdoors each day (P < 0.001 for trend) were significantly associated with Q fever notification, while smoking was not (P = 0.8). Only age (P = 0.03), sex (P = 0.02), and area and type of residence (P < 0.001 for trend) remained significant in the multivariable model. There was a gradient of increasing risk according to geographic area and residence on a farm. Those living on a farm in outer regional/remote areas were at greatest risk, followed by those living on a farm in inner regional areas, with those not living on farms least at risk (Box 2). The relative risk of Q fever for those aged 65 years or over was significantly lower than for younger participants, and was also lower for women than men (Box 2). The amount of time spent outdoors each day was related to the area and type of residence, ranging from 2.6 hours for living in a major city to 4.6 hours for those living on a farm in outer regional/remote areas (Kruskal–Wallis test, P < 0.001). However, differences in time outdoors did not remain significant (P = 0.4 for trend) after adjustment for area and type of residence in the multivariable model.

Of 45 incident notifications, we had complete follow-up of hospital records for 39 patients. Of these, 17 (44%) were hospitalised at least once (for any cause) within 6 weeks of the recorded disease onset date (before or after onset). The hospitalisation was coded as being for Q fever in 15 cases (seven patients with primary Q fever or secondary Q fever, eight as Q fever-related). The median length of stay for patients with these diagnoses was 4 days (interquartile range, 3–9 days). There were no deaths or intensive care unit stays recorded for the notified cases.

According to the APDC database, 11 participants had been hospitalised with primary Q fever or secondary Q fever, but four of these were not recorded as Q fever cases in the NCIMS database.

Discussion

This is the first population-based prospective study of the risk and burden of acute Q fever in a general adult population in Australia. We found that a clear increase in the risk of notified Q fever in adults was associated with living on a farm and with geographic remoteness. Those living on farms in outer regional and remote areas were at highest risk, and the hazard was lowest for those living in major cities. Risks were also greater for those under 65 years of age and for men, but risk was not increased for smokers or associated with greater time spent outdoors. Fifteen of 39 notified Q fever cases (38%) were hospitalised with a diagnosis consistent with Q fever.

In this study, we observed an incidence of notified Q fever of 3.6 per 100 000 person-years, with the highest rate among those aged 55–64 years (5.4 per 100 000 person-years). This is broadly consistent with Q fever notification rates for the total NSW population aged 45 years or over reported during 2009–2012 (2.9 per 100 000 persons, with the highest average annual rates for those aged 55–64 years: 4.1 per 100 000 persons).7,8,18 The slightly higher disease burden in our study is not surprising, as the 45 and Up Study oversampled the residents of rural and remote NSW, where Q fever notification rates are much higher than in urban centres.

We estimated that the notified Q fever risk was about five times higher for adults living on a farm in inner regional areas and about 12 times higher for those living on a farm in outer regional and remote areas than for those in inner regional areas not living on a farm. This finding is consistent with other reports that found farmers to be at greater risk of Q fever,18,19 and suggests that immunisation coverage in this group is inadequate. Even though the NQFMP provided free vaccination to farmers, uptake was estimated to be only about 43%, and in NSW the vaccination program ended in 2004.7 After allowing for workforce turnover, it is likely that an even lower proportion of current farmers have been vaccinated. An alternative explanation would be that vaccine-induced immunity has waned, but there is good evidence that the vaccine is highly effective, with immunity lasting for at least 5 years and probably for life.20

Massey and colleagues19 have suggested that demographic factors other than occupation should be identified to better define risk groups, as a fifth of notified Q fever cases from rural areas did not report occupational exposure to Q fever. Similarly, the recent major Q fever outbreak in the Netherlands found that people living near farms, but not specifically working on one, were also at increased risk of disease.21–23 We did not have information on the occupations of participants in our study, but our finding of increased Q fever risk for those living in more remote areas but not living on a farm are consistent with the results of these other studies. Taken together, they support calls for medical practitioners in regional and remote Australia to routinely consider Q fever in their differential diagnosis of acute flu-like illnesses, even for patients not living on farms.24

We also examined other factors potentially relevant to Q fever risk. Time spent outdoors was not significant in our multivariable model, as any effect was almost completely explained by the area and type of residence variable. There was no indication of an increased risk for smokers. A significant fraction (44%) of notified Q fever cases had been hospitalised. This is within the higher range of hospitalisation estimates reported by an extensive review.1 Studies suggest that up to 20% of those with Q fever will develop chronic conditions, such as endocarditis or chronic fatigue syndrome, that also require health care outside of hospitals, and which also entail losses of productivity and quality of life.14,25–27 This lends further weight to calls for improving disease prevention efforts.

We identified 15 cases of Q fever for which a hospitalisation code consistent with Q fever was recorded, but only seven were specifically coded as Q fever (ICD-10-AM, A78). This suggests that limiting analysis to hospital admissions specifically coded as primary or secondary Q fever diagnoses is likely to substantially underestimate the true burden of Q fever-related morbidity. We also identified four participants linked to hospitalisations coded as Q fever, but for which there was no record of Q fever in the NCIMS database. It is possible that these were clinically compatible cases that did not meet the case definition of confirmed Q fever because of negative diagnostic test results, and were therefore not notified, or it may indicate under-reporting of genuine cases.

To our knowledge, our study is the first using prospectively ascertained events to examine the risk and burden of Q fever in older adults in a general population of Australian residents. Our study encompassed a time period during which no major Q fever outbreaks were reported, and thus more accurately assesses the risk and burden of endemic Q fever. Potential limitations include the fact that we used notification data to identify Q fever cases, and such data usually underestimate the number of infections; they may also depend on the propensity of physicians to consider the diagnosis, which may differ according to the characteristics of their patients. In addition, we had no data on the occupations or the vaccination status of participants. The numbers of Q fever cases were relatively small, leading to wide confidence intervals for the risk estimates. Similarly, the small numbers meant that we could not stratify the “ever smoked” category into current and past smokers. Finally, the study cohort was probably healthier than the overall NSW population of the same age range, as indicated by a lower rate of smoking.11

In conclusion, our results support current recommendations for Q fever vaccination of farmers and add to the existing body of evidence that suggests targeting a broader, geographically based population in regional and remote regions is required to reduce the burden of Q fever in Australia.

Box 1 –
Australian national notifiable diseases case definitions — Q fever13

Confirmed case

A confirmed case requires either:

1. Laboratory definitive evidence

2. Laboratory suggestive evidence AND clinical evidence.

Laboratory definitive evidence

1. Detection of Coxiella burnetii by nucleic acid testing

2. Seroconversion or significant increase in antibody level to Phase II antigen in paired sera tested in parallel in absence of recent Q fever vaccination

3. Detection of C. burnetii by culture (note this practice should be strongly discouraged except where appropriate facilities and training exist.)

Laboratory suggestive evidence

Detection of specific IgM in the absence of recent Q fever vaccination.

Clinical evidence

A clinically compatible disease

Box 2 –
Incidence of and hazard ratios for notified Q fever in NSW according to various sociodemographic characteristics, 2006–2012

	Cases	Population	Person-years	Incidence per 100 000 person-years (95% CI)	HR* (95% CI)	Adjusted HR^† (95% CI)

All participants	45	266 906	1 254 650	3.6 (2.7–4.8)
Age group
45–54 years	16	78 756	377 770	4.2 (2.6–6.9)	1.00	1.00
55–64 years	22	85 654	408 515	5.4 (3.5–8.2)	1.27 (0.67–2.42)	1.20 (0.63–2.29)
≥ 65 years	7	102 496	468 365	1.5 (0.7–3.1)	0.35 (0.14–0.85)	0.39 (0.16–0.96)
Sex
Men	28	123 766	579 608	4.8 (3.3–7.0)	1.00	1.00
Women	17	143 140	675 042	2.5 (1.6–4.0)	0.52 (0.28–0.95)	0.48 (0.26–0.88)
Smoking
Never	27	152 427	718 838	3.7 (2.6–5.5)	1.00	na
Ever	18	113 052	529 243	3.4 (2.1–5.4)	0.90 (0.50–1.64)	na
Area and type of residence
Major city	^‡	120 267	562 377	0.2 (0.1–1.3)	0.07 (0.01–0.55)	0.07 (0.01–0.54)
Inner region; not on farm	10	84 699	398 756	2.5 (1.3–4.7)	1.00	1.00
Outer region/remote; not on farm	11	42 006	198 012	5.5 (3.1–10.0)	2.21 (0.94–5.21)	2.21 (0.94–5.21)
Inner region; on farm	6	9 082	43 511	13.8 (6.2–30.6)	5.51 (2.00–15.15)	4.95 (1.79–13.65)
Outer region/remote; on farm	17	10 657	51 090	33.3 (20.7–53.5)	13.28 (6.08–29.01)	11.98 (5.47–26.21)
Time spent outdoors^§
< 4 hours/day	19	172 874	814 719	2.3 (1.5–3.6)	1.00	1.00
4–7 hours/day	14	57 363	269 247	5.2 (3.1–8.8)	2.23 (1.12–4.45)	1.21 (0.58–2.51)
≥ 8 hours/day	6	16 432	76 995	7.8 (3.5–17.3)	3.35 (1.34–8.38)	1.20 (0.45–3.19)
Missing data	6	20 237	93 689	6.40 (2.9–14.2)	2.74 (1.09–6.86)	1.93 (0.75–4.93)

HR = hazard ratio; na = not applicable. *Unadjusted results. †Variables in final model: age group, sex, area and type of residence. ‡Number of cases not displayed due to small numbers. §Adjusted for age group, sex, and area and type of residence.