The Impact of Local Government Mosquito Control Programs on Ross River Virus Disease in Queensland, Australia

Home , Ross River (Queensland), Ross River fever, Ross River virus

Author Tomerini, Deanna Majella

Published 2008

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/1658

Downloaded from http://hdl.handle.net/10072/366893

Griffith Research Online https://research-repository.griffith.edu.au

The impact of local government mosquito control programs on Ross River virus disease in Queensland, Australia

Deanna Majella Tomerini BSc AES (Hons 1) (Griffith), GCertHigherEd (Griffith)

Griffith School of Environment Griffith University

A thesis submitted in fulfillment of the requirements of the degree of Doctor of Philosophy

February 2007

Abstract

In this study, I have investigated the relationship between mosquito control and mosquito-borne disease rates within Queensland, Australia. The thesis considers the most prevalent arbovirus causing human disease in Australia (Ross River virus) and estimates, how much Ross River virus disease is avoided through local government mosquito control in Queensland, and then compares the monetary value of avoided

Ross River virus disease with the financial costs of local government mosquito control.

A survey to collect information about mosquito control costs and practices was designed and implemented in each of the 125 local governments in Queensland. This survey collated previously dispersed information; because, although local governments in Queensland are legally obliged to perform mosquito control for disease prevention and nuisance reduction, there is no formal or regular reporting of mosquito control costs and practices to the State.

A substantive conclusion from this research is that mosquito control has resulted in lower Ross River virus disease notifications in some local government areas. Ross

River virus disease notifications are consistently lower in local government areas that implement mosquito control programs that pre-empt mosquito outbreaks using routine surveillance and then reduce mosquito abundance using mosquito control.

Furthermore, there is evidence that local governments using extensive freshwater mosquito control, in addition to saltwater mosquito control, have relatively lower annual Ross River virus disease rates and lower standard deviations of the annual

Ross River virus disease rates (indicating the freshwater mosquito control is important in suppressing outbreaks of Ross River virus disease). In contrast,

i mosquito control practices in the inland local government areas tend to be reactive to community complaints of mosquito abundance causing nuisance, and generally include ad-hoc mosquito control treatments. There is no evidence that reactive, ad- hoc mosquito control programs result in reduced Ross River virus disease notifications.

The numbers of avoided Ross River virus notifications were estimated for the local governments that are located in the south eastern coastal region of Queensland. It has been estimated that an annual average of 2206 Ross River virus disease notifications have been avoided through effective mosquito control; and, for each actual notification of Ross River virus disease in the southern coastal local governments, two notifications have been avoided.

The survey revealed that in excess of $10 million was spend by local governments implementing mosquito control in Queensland in 2004. The majority of this expenditure occurs in the more densely populated local governments located in the southern coastal strip of the state.

A comparison of the financial costs of mosquito control and the financial value of avoided disease produced a cost-benefit ratio of 0.37, meaning that on average, 37% of the costs of mosquito control are directly recouped through the value of avoided

Ross River virus disease. In years when the risk of Ross River virus outbreaks is relatively low, due to below average rainfall, the costs of mosquito control exceed the value of avoided Ross River virus notifications—but in years where the risk of an epidemic of Ross River virus is high, effective mosquito control practices can avoid an epidemic of Ross River virus disease, and in this situation the financial value of avoided disease exceeds the costs of the mosquito control program.

Contents

Chapter 1: The research problem ...... 1

1.1. Introduction...... 1

1.2. Mosquitoes and disease ...... 2 1.2.1. Malaria...... 2 1.2.2. Filariasis...... 3 1.2.3. The arboviruses...... 3

1.3. Mosquito-borne disease in Australia...... 6 1.3.1. Malaria in Australia...... 6 1.3.2. Filariasis in Australia...... 6 1.3.3. The arboviruses in Australia...... 6

1.4. Interventions against mosquito-borne disease ...... 10 1.4.1. Vaccines against mosquito-borne disease...... 10 1.4.2. Mosquito control strategies...... 10

1.5. Resource implications of mosquito control: Costs and benefits...... 11 1.5.1. The costs of mosquito control...... 11 1.5.2. The benefits of mosquito control...... 11 1.5.3. Responsibilities for mosquito control...... 11

1.6. The research problem: Is mosquito control cost effective in reducing mosquitoes, nuisance and disease?...... 18 1.6.1. Broad research questions ...... 18 1.6.2. Refining the research questions within temporal and spatial limits ...... 18 1.6.3. Answerable research questions...... 20

1.7. Overview of this thesis...... 20 Chapter 2: Literature review ...... 21

2.1. Mosquito control strategies...... 21 2.1.1. Biological control of mosquitoes...... 21 2.1.2. Chemical control of mosquitoes ...... 22 2.1.3. Physical control of mosquitoes...... 24 2.1.4. Personal protection against mosquito bites...... 25

2.2. Literature review: Ross River virus...... 26 2.2.1. The clinical symptoms of Ross River virus disease ...... 27 2.2.2. Ross River virus transmission...... 28 2.2.3. Describing the spatial and temporal patterns of Ross River virus disease 34 2.2.4. Explaining the spatial and temporal patterns of Ross River virus disease 34 2.2.5. The costs associated with Ross River virus disease ...... 37 2.2.6. Major findings from the literature on Ross River virus...... 39

iii

2.3. Literature review: Economic analysis of mosquito control ...... 40 2.3.1. Economic evaluation of mosquito control programs at local government level ...... 40 2.3.2. Economic evaluations of malaria prevention and treatment...... 44 2.3.3. Review articles of economic evaluations of health interventions.. 45 2.3.4. Identifying the costs and benefits of mosquito control...... 47 2.3.5. Major findings from the literature on the economic evaluation of mosquito control...... 52 Chapter 3: Research methodology ...... 53

3.1. Methods for Research Question 1: ‘How much Ross River virus disease is avoided through local government mosquito control in Queensland?’...... 53 3.1.1. Identifying local governments with mosquito control programs that are effective at reducing Ross River virus disease ...... 53 3.1.2. Estimating avoided Ross River virus notifications...... 59

3.2. Methods for Research Question 2: ‘Does the monetary value of avoided Ross River virus disease exceed the financial cost of local government mosquito control programs in Queensland?’ ...... 60 3.2.1. Estimating the cost of mosquito control ...... 60 3.2.2. Estimating the value of avoided Ross River virus disease ...... 61 3.2.3. Calculating cost-benefit ratios ...... 61

3.3. Data collection...... 62 3.3.1. Ross River virus notification data...... 62 3.3.2. Mosquito control data ...... 62

3.4. Overview of methods ...... 73 Chapter 4: Results and discussion...... 74

4.1. Survey implementation and response rates...... 74 4.1.1. Factors affecting response rates...... 75 4.1.2. Summary of survey results ...... 76

4.2. Results for Research Question 1: ‘How much Ross River virus disease is avoided through local government mosquito control in Queensland?’...... 81 4.2.1. Between-group differences...... 84 4.2.2. Within-group differences...... 85 4.2.3. Conclusions from the analysis of within-group differences ...... 127 4.2.4. Estimating avoided Ross River virus notifications...... 127

4.3. Results for research question 2: ‘How does the monetary value of avoided Ross River virus disease compare with the financial cost of local government mosquito control programs in Queensland?’ ...... 136 Chapter 5: Conclusions...... 140

5.1. Substantive conclusions from this research...... 140

5.2. Methodological conclusions ...... 142

5.3. Broader relevance of the research...... 143

5.4. Recommendations for further research...... 143 Chapter 6: References...... 145

Chapter 7: Appendixes ...... 158

7.1. Survey contact details...... 158

7.2. Summary of responses to the survey of mosquito control costs and practices ...... 165

7.3. Ross River virus disease rates for local governments in Queensland .... 173

Figures

Figure 2.1: Generalised transmission cycles for arboviruses ...... 29

Figure 3.1: Conceptual model of Ross River virus infection ...... 54

Figure 3.2: Australian climate zones based on seasonal rainfall ...... 56

Figure 3.3: Australian climate zones based on temperature and humidity...... 57

Figure 3.4: Australian climate zones based on the Koeppen classification...... 57

Figure 3.5: Schematic overview of the tailored design perspective ...... 64

Figure 3.6: Covering letter sent with survey...... 66

Figure 3.7: Survey forms: Survey of mosquito control in Queensland local governments 1993–2004...... 68

Figure 3.8: Reminder postcard...... 72

Figure 4.1: Classification of local governments ...... 82

Figure 4.2: Group 1 Southern Queensland coastal subtropical local governments... 86

Figure 4.3: Central Queensland coastal subtropical local governments...... 93

Figure 4.4: Northern Queensland coastal subtropical local governments ...... 98

Figure 4.5: Far northern Queensland coastal subtropical local governments...... 104

Figure 4.6: Far Northern Queensland inland subtropical local governments...... 106

Figure 4.7: Central Queensland inland subtropical local governments...... 109

Figure 4.8: Northern Queensland inland grassland local governments ...... 112

Figure 4.9: Southern Queensland inland grassland local governments...... 115

Figure 4.10: Southern Queensland inland subtropical local governments ...... 118

Figure 4.11: Southern Queensland inland temperate local governments ...... 121

Figure 4.12: Southern Queensland inland desert local governments...... 125

Figure 4.13: Mean annual avoided Ross River virus disease rates 1993-2004 for Group 1 local governments...... 131

Figure 4.14: Annual average cost-benefit ratios for Group 1 local governments... 137

Tables

Table 1.1: The arboviruses most commonly associated with human disease...... 4

Table 1.2: Comparison of legislative responsibilities for mosquito control in Australia...... 13

Table 2.1: Some common mosquito species in Queensland...... 30

Table 2.2: Identification of the resources consumed by mosquito control...... 49

Table 2.3: Identification of the benefits of mosquito control ...... 51

Table 3.1: An overview of the research methods ...... 73

Table 4.1: Estimated cost of mosquito control to local governments in Queensland (1993–2004)...... 80

Table 4.2: ANOVA results investigating differences in Ross River virus disease rates between model groups ...... 84

Table 4.3: Population, mosquito control expenditure and Ross River virus disease statistics for Group 1 local governments ...... 87

Table 4.4: ANOVA results for within-group differences: Group 1 local governments ...... 88

Table 4.5: Approach to mosquito control in Group 1 local governments ...... 90

Table 4.6: Population, mosquito control expenditure and Ross River virus disease statistics for Group 2 local governments ...... 94

Table 4.7: ANOVA results for within-group differences: Group 2 local governments ...... 94

Table 4.8: Approach to mosquito control in Group 2 local governments ...... 96

Table 4.9: Population, mosquito control expenditure and Ross River virus disease statistics for Group 3 local governments ...... 99

Table 4.10: ANOVA results for within-group differences: Group 3 local governments...... 100

Table 4.11: Approach to mosquito control in Group 3 local governments ...... 101

Table 4.12: Population, mosquito control expenditure and Ross River virus disease statistics for Group 4 local governments ...... 104

Table 4.13 ANOVA results for within group differences: Group 4 local governments ...... 105

Table 4.14: Population, mosquito control expenditure and Ross River virus disease statistics for Group 5 local governments ...... 107

vii

Table 4.15: ANOVA results for within-group differences: Group 5 local governments...... 108

Table 4.16: Population, mosquito control expenditure and Ross River virus disease statistics for Group 6 local governments ...... 110

Table 4.17: ANOVA results for within-group differences: Group 6 local governments...... 111

Table 4.18: Population, mosquito control expenditure and Ross River virus disease statistics for Group 7 local governments ...... 113

Table 4.19: ANOVA results for within-group differences:...... 114

Table 4.20: Population, mosquito control expenditure and Ross River virus disease statistics for Group 8 local governments ...... 116

Table 4.21: ANOVA results for within-group differences: Group 8 local governments...... 117

Table 4.22: Population, mosquito control expenditure and Ross River virus disease statistics for Group 9 local governments ...... 119

Table 4.23: ANOVA results for within-group differences: Group 9 local governments...... 120

Table 4.24: Population, mosquito control expenditure and Ross River virus disease statistics for Group 10 local governments...... 122

Table 4.25: ANOVA results for within-group differences: Group 10 local governments...... 123

Table 4.26: Population, mosquito control expenditure and Ross River virus disease statistics for Group 11 local governments ...... 125

Table 4.27: ANOVA results for within-group differences:...... 126

Table 4.28: Estimated avoided Ross River virus notifications in Group 1 local governments...... 130

Table 4.29: Ross River virus disease rates in Miriam Vale Shire Council...... 132

Table 4.30: Comparison of mosquito control costs and the value of avoided Ross River virus disease for Group 1 local governments...... 137

viii

Graphs

Graph 1.1: Total arbovirus notifications in Australia: 1993–2005...... 9

Graph 4.1: Untransformed distribution of annual Ross River virus rates for local governments in Queensland 1993–2004...... 83

Graph 4.2: Logarithmic transformed distribution of annual Ross River virus rates for local governments in Queensland 1993–2004...... 83

Graph 4.3: The mean annual Ross River virus disease rates and the 95% confidence interval for the 11 model groups in Queensland...... 84

Graph 4.4: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 1 local governments...... 89

Graph 4.5: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 2 local governments...... 95

Graph 4.6: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 3 local governments...... 100

Graph 4.7: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 4 local governments...... 105

Graph 4.8: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 5 local governments...... 108

Graph 4.9: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 6 local governments...... 111

Graph 4.10: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 7 local governments...... 114

Graph 4.11: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 8 local governments...... 117

Graph 4.12: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 9 local governments...... 120

Graph 4.13: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 10 local governments...... 124

Graph 4.14: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 11 local governments...... 126

Graph 4.15: Monthly rainfall in Miriam Vale Shire in 1993: This rainfall pattern was associated with a low rate of Ross River virus disease...... 133

Graph 4.16: Monthly rainfall in Miriam Vale Shire during 1996: This rainfall pattern was associated with a high rate of Ross River virus disease...... 133

Graph 4.17: Monthly rainfall in Brisbane City during 1993...... 134

Graph 4.18: Monthly rainfall in Brisbane City in 1996...... 135

Acknowledgments

I am grateful to Professor Pat Dale who has been the primary supervisor of this research. Pat is an exemplary academic scholar, and a great colleague – I look forward to on-going research collaborations. I also thank my associate supervisors,

Dr. Neil Sipe and Dr. Peter Daniels who have both provided valuable guidance, particularly during the early stages of my research.

My thanks must also go to the staff of the Communicable Diseases Unit of

Queensland Health who have supported this research as an Industry Partner and have provided both research funding and in-kind support. In particular, I thank George

Hapgood and David Gould for being important contacts within Queensland Health; and Craig Davis for assistance in obtaining disease notification data.

To the mosquito control professionals within local governments in Queensland – thank you for contributing to this research by completing the survey of mosquito control costs and practices that has formed a major data source of this research.

Thanks to Sue Jarvis for professional editorial assistance; to Lynne Bradshaw for trouble-shooting word-processing problems; and to fellow PhD students and staff at

Griffith for their much valued collegiality.

Finally, to my family, Michael and Layla – thanks for your understanding, your support and your love.

Statement of originality

This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.

…………………………… …../…../…..

Deanna Majella Tomerini BSc AES (Hons 1) (Griffith), GCertHigherEd (Griffith)

xii

Abbreviations

BFV Barmah Forest virus

BOM Bureau of Meteorology

CLAG Contiguous Local Authority Group

GIS Geographic Information System

JE Japanese encephalitis

LGA Local Government Authority

LGAQ Local Government Association of Queensland

MARC Mosquito and Arbovirus Research Committee

MCAA Mosquito Control Association of Australia

MVE Murray Valley encephalitis

NEMMO North East Moreton Mosquito Organisation

NPHP National Public Health Partnership

QIMR Queensland Institute of Medical Research

RRV Ross River virus

SCMCC Sunshine Coast Mosquito Control Committee

WHO World Health Organisation

xiii

Chapter 1: The research problem

1.1. Introduction

In this study, I have investigated the relationship between mosquito control and mosquito-borne disease rates. The research has been sponsored by Queensland

Health — a state government health department in Queensland, Australia. The initial question posed by Queensland Health was: ‘Is mosquito control cost effective at reducing mosquitoes, nuisance and disease?’ (Australian Research Council Linkage

Project LP0211583). To fit the research within the scope of a PhD thesis, this question has been framed within the geographical limits of Queensland, Australia and only considers the most prevalent mosquito-borne disease in Queensland. Hence the broad question has been refined in this thesis to specifically investigate whether local government mosquito control programs in Queensland result in lower Ross

River virus notifications. Furthermore, the research presents a limited cost-benefit analysis by comparing the financial cost of local government mosquito-control programs to the financial value of avoided Ross River virus.

The remainder of Chapter 1 presents the background to this research problem. I will first describe the three types of mosquito-borne diseases, the incidence of these diseases, and the range of health interventions used against these diseases. Second, I will outline how the legislative responsibilities for these health interventions in

Queensland have resulted in unresolved issues about who should pay the costs of mosquito control and look at how this, in conjunction with an incomplete knowledge of the effectiveness of mosquito control, has led to the need for this research.

1.2. Mosquitoes and disease

Mosquitoes transmit three categories of human diseases, namely the malarial diseases, human filariasis and the arboviral diseases (MCAA 2002). These diseases are characterised by differing causative agents, human disease manifestations, pathogen ecologies and disease epidemiologies.

1.2.1. Malaria

Malaria is a parasitic disease caused by a protozoan pathogen. There are many hundreds of species of malaria parasites, and four of these are the causative agents of human malaria (Plasmodium vivax, Plasmodium malariae, Plasmodium ovale and

Plasmodium falciparum). All mosquito vectors of malaria belong to the Anopheles genus of mosquitoes. Human malaria is an anthroponotic disease — that is, there are no non-human vertebrate hosts involved in the transmission cycle of human malaria

(Burkot & Graves 2004).

Malaria is the most widespread mosquito-borne disease at a global level, and is responsible for high rates of mortality and morbidity. With an estimated 350-

500 million clinical cases of malaria appearing annually, it dominates the global burden of mosquito-borne disease. This burden of malaria is not distributed evenly, however, with 60% of cases and 80% of deaths occurring in sub-Saharan Africa

(Burkot & Graves 2004). Malaria is a major constraint on economic development, and estimating the direct and indirect economic impacts of Malaria has been one focus of recent research efforts by the World Health Organisation (WHO) through the Roll Back Malaria initiative (WHO 1998).

1.2.2. Filariasis

The human disease lymphatic filariasis is caused by three species of filarial nematode able to be transmitted to humans by mosquitoes. A comprehensive discussion of the life cycle, ecology and epidemiology of the filarial nematodes can be found in Lok et al. (2004). The most geographically widespread of the filarial nematodes is Wuchereria bancrofti Bancroft, and worldwide there are an estimated

115 million people infected with bancroftian filariasis.

1.2.3. The arboviruses

The arboviral diseases (arthropod-borne viruses) are caused by viral pathogens.

There are more than 500 viruses able to be transmitted to vertebrate hosts by arthropods, and in excess of 100 of these viruses are able to cause infection in humans (Eldridge et al. 2004). These arbovirus infections in humans generally manifest as a systemic febrile illness, haemorrhagic fever or meningoencephalitis

(Gubler 2001). The arboviruses most commonly associated with human disease have been summarised by Gubler (2001) and are shown in Table 1.1, along with information about the ecology, geographic distribution, vertebrate hosts and human illness associated with each arbovirus.

The majority of arbovirus species are included in five virus families, namely

Togaviridae, Flaviviridae, Bunyaviridae, Reoviridae and Rhabdoviridae (Monath

1988), with mosquito-borne arboviruses belonging to the Togaviridae, Flaviviridae and Bunyaviridae families. Most of the arboviruses are zoonoses, and are maintained within wild animal and avian populations. Mosquitoes are the arthropods of most importance in the transmission of arboviruses between natural vertebrate hosts and humans (Gubler 2001).

Table 1.1: The arboviruses most commonly associated with human disease

Disease Vertebrate in Geographic Family/virus Vector host Ecologyb humansc distribution Epidemics

TOGAVIRIDAE

humans, Chikungungaa mosquitoes primates U, S, R SFI Africa, Asia yes

marsupials Australia, Ross Rivera mosquitoes R, S, U SFI yes South Pacific

birds Mayaroa mosquitoes R, S, U SFI South America yes

O’nyong-nyonga mosquitoes ? R SFI Africa yes

Asia, Africa, Australia, Sindbis mosquitoes birds R SFI yes Europe, Americas

Barmah Foresta mosquitoes ? R SFI Australia yes

Eastern equine mosquitoes birds R SFI, ME Americas yes encephalitis

Western equine mosquitoes birds, rabbits R SFI, ME Americas yes encephalitis

Venezuelan equine mosquitoes rodents R SFI, ME Americas yes encephalitisa

FLAVIVIDAE

humans, worldwide in Dengue 1–4a mosquitoes U, S, R SFI, HF yes primates tropics

humans, Africa, South Yellow Fevera mosquitoes R, S, U SFI, HF yes primates America

Japanese mosquitoes birds, pigs R, S SFI, ME Asia, Pacific yes encephalitis

Murray Valley mosquitoes birds R SFI, ME Australia yes encephalitis

Rocio mosquitoes birds R SFI, ME South America yes aArboviruses that produce significant human viremia. b U = urban; S = suburban; R = rural; underline designates the most important ecology. cSFI = systemic febrile illness; ME = meningoencephalitis; HF = haemorrhagic fever. Yellow shading indicates arboviruses occurring in Australia.

Table 1.1: The more important arboviruses causing human disease (cont.)

Disease Vertebrate in Geographic Family/virus Vector host Ecologyb humansc distribution Epidemics

FLAVIVIDAE. Cont.

St Louis mosquitoes birds R, S, U SFI, ME Americas yes encephalitis

Africa, Asia, West Nilea mosquitoes birds R, S, U SFI, ME Europe, North yes America

primates, Kyasanar Forest SFI, HF, India, Saudi ticks rodents, R yes disaeasea ME Arabia camels

Omsk haemorrhagic ticks rodents R SFI, HF Asia no fever

Tick-borne Europe, Asia, ticks birds, rodents R, S SFI, ME no encephalitis North America

BUNYAVIRIDAE

Europe, Africa, Sandfly fevera midges ? R SFI yes Asia

Rift Valley SFI, HF, Africa, Middle mosquitoes ? R yes fevera ME East

La Crosse mosquitoes rodents R, S SFI, ME North America yes encephalitis

California North America, mosquitoes rodents R SFI, ME yes encephalitis Europe, Asia

Crimean-Congo Europe, Asia, haemorrhagic ticks rodents R SFI, HF yes Africa fevera

Central and Oropouchea midges ? R,S,U SFI yes South America aArboviruses that produce significant human viraemia. b U = urban; S = suburban; R = rural; underline designates the most important ecology. cSFI = systemic febrile illness,; ME = meningoencephalitis; HF = haemorrhagic fever. Yellow shading indicates arboviruses occurring in Australia. Source: Gubler (2001)

1.3. Mosquito-borne disease in Australia

1.3.1. Malaria in Australia

Malaria was considered endemic in Australia until 1981, when it was declared to be eradicated. Malaria is still a risk in Australia because several recognised malaria vectors still exist here, particularly in northern Australia. There are still cases of malaria in Australia each year; however, these cases are rarely locally acquired and are usually travel-related or have been diagnosed in refugee arrivals (Russell 1998c).

1.3.2. Filariasis in Australia

The filarial nematode associated with the human disease lymphatic filariasis was once present in northern Australia, but has now been eradicated (Lok et al. 2004).

The disease commonly known as dog heartworm is also caused by a mosquito-borne filarial nematode known as Dirofilaria immitis (Leady). There are several mosquito species in Australia that are suspected to be competent vectors of Dirofilaria immitis

— hence dog heartworm is of importance in veterinary medicine in Australia (Lok et al. 2004).

1.3.3. The arboviruses in Australia

Arboviruses that have been implicated in human disease in Australia include Ross

River virus, Sindbis, Kunjin, Barmah Forest virus, the dengue viruses, Japanese encephalitis and Murray Valley encephalitis (Russell 1995). The 13 arboviruses that cause human disease in Australia are all transmitted by mosquitoes (Russell &

Dwyer 2000). These important arboviruses in Australia are highlighted in yellow in

Table 1.1.

The research about arboviruses in Australia has been described in key reviews by

Doherty (1974), Kay and Aaskov (1989), Mackenzie et al. (1994), Russell (1995,

1998a, 1998b), Russell and Dwyer (2000) and Russell and Kay (2004). Russell

(1995) provides a description of a number of the arboviruses that cause human disease in Australia. The impetus for Russell’s 1995 review article was that much of the ground-breaking research related to arbovirus vectors was occurring within disparate surveillance programs (often being run by local governments and state health departments) and this research was not formally being published. Russell identified a need to collate this information and to provide a nationwide synthesis of the current knowledge of arboviruses related to human disease in Australia. The arboviruses that are discussed in this 1995 review are the alphaviruses Barmah

Forest, Ross River and Sindbis, the bunyaviruses Gan Gan and Trubanaman, and the flaviviruses Alfuy, dengue, Edge Hill, Kokobera, Kunjin, Murray Valley encephalitis and Stratford. This synthesis of information allowed Russell (1995) to conclude that there is spatial and temporal variability in the epidemiology of many of these arboviruses. The review also allowed confirmation that the mosquito species that represent the greatest importance as vectors of endemic virus activity are the saltmarsh mosquitoes Aedes vigilax (Skuse) and Aedes camptorhynchus (Thomson), and the freshwater mosquito Culex annulitrostris Skuse, while other species probably play an important role in enzootic and epidemic virus activity. Importantly, this collation of unpublished data showed that there were many cases of disease with similar symptoms to known arboviruses, but for which the causative agent could not be isolated, indicating that there may be unidentified arboviruses causing human infection.

A subsequent review article published by Russell and Dwyer (2000) focused particularly on those arboviruses that cause widespread morbidity or mortality. The alphaviruses Ross River and Barmah Forest can cause severe morbidity characterised

7 by debilitating polyarthritis and fatigue, while the flaviviruses Murray Valley encephalitis, Kunjin, Japanese encephalitis and dengue can cause life-threatening illness. Japanese encephalitis was first reported in Australia in 1995 and was not discussed in the previous review by Russell (1995). Russell and Kay (2004) have summarised the evolution of knowledge on mosquito-borne disease in Australia from the period 1972–2004. Theses authors have described how an epidemic of Murray

Valley encephalitis and Kunjin in 1974 led to a rapid increase in knowledge, how the

1980s saw the recognition of Ross River virus as a nationwide problem, and then the

1990 s saw the recognition of Barmah Forest virus. Other concerns were the less prevalent, but more clinically dangerous, dengue and Japanese encephalitis. Recent years have seen a focus on understanding the ecology of the viruses and their vectors, along with host ecology (Russell & Kay 2004).

These arboviruses are notifiable diseases in Australia, and disease incidence data are collected by state health departments. Notification data enable a retrospective spatial and temporal analysis of disease trends and allow disease incidence and prevalence to be monitored. The distribution of arbovirus notifications by Australian states and territories for the years 1993–2005 is shown in Graph 1.1.

Ross River virus is the most common arbovirus causing human disease in Australia

(Russell 2002). Generally, Ross River virus has occurred in all Australian states and territories in all years from 1993 - 2005. The highest incidence rate of Ross River virus notifications is experienced by the Northern Territory, while the highest absolute number of notifications is experienced in Queensland.

) 70000

60000

50000

40000

30000

20000

10000

Number of notifications (1993 - 2005 - (1993 Number notifications of 0 Australian New South Northern Queensland South Tasmania Victoria Western Total Capital Wales Territory Australia Australia Territory State or Te rritory

Dengue Barmah Forest virus Ross River virus Total arbovirus

Graph 1.1: Total arbovirus notifications in Australia: 1993–2005

Barmah Forest virus has occurred in all years from 1993 – 2005 in New South

Wales, the Northern Territory, Queensland, Victoria and Western Australia. Barmah

Forest virus notifications are lower than for Ross River virus, and data for Barmah

Forest virus have only been recorded since 1995 (previous to 1995, Barmah Forest virus was reported as ‘other alpha virus’). Barmah Forest virus is less prevalent in the southern jurisdictions, South Australia, Tasmania and the Australian Capital

Territory. Barmah Forest virus follows similar spatial and temporal patterns to Ross

River virus, with high Ross River virus years often coinciding with high Barmah

Forest virus years.

Dengue fever has occurred in all jurisdictions at some stage, but higher notifications have occurred in Queensland where the dengue mosquito (Aedes aegypti) is endemic. Temporal variability of dengue does not follow the same pattern as Ross

River virus and Barmah Forest virus.

1.4. Interventions against mosquito-borne disease

There are several health interventions against mosquito-borne disease, including the use of and development of vaccines, personal protection against mosquito bites, biological control, chemical control and physical control.

1.4.1. Vaccines against mosquito-borne disease

Vaccines currently do exist for the virus that causes Japanese encephalitis, but there are no effective vaccines for Ross River virus infection or Barmah Forest virus infection, and development of these vaccines is restricted by several factors. A preclinical evaluation of a vaccine against Ross River virus was performed by Aaskov et al. (1997); however, this has not resulted in a commercially available human vaccine.

Suhrbier and La Linn (2004) identify the major restrictions on vaccine development for Ross River virus and similar arboviruses as being, first, the high cost of vaccine development, second, the risk of vaccine-induced illness, and third, the risk of the vaccine exacerbating a naturally acquired disease. It is unlikely that a disease will attract the commercial investment needed for vaccine development if the global burden of a disease is comparatively low, the spatial extent of the disease is limited, and the disease is self-limiting and not associated with high mortality (Suhrbier and

La Linn 2004). Rulli et al. (2005) also comment on the issue of vaccine development for Ross River virus, offering similar conclusions regarding the financial commitment required by a pharmaceutical company to develop and gain approval for the use of vaccine.

1.4.2. Mosquito control strategies

In the absence of effective vaccines against mosquito-borne disease, the most common intervention is publicly funded mosquito control programs. Such programs

10 often include a combination of biological control (using mosquito antagonists such as predator fish), chemical control (using organophosphates, insect growth regulators, bacterial endotoxins and synthetic pyrethroids), physical control (using water management, runnelling and weed management), community education (often focused on home-owners and personal protection) and development control (that discourages human habitation near mosquito sites) (MCAA 2002).

1.5. Resource implications of mosquito control: Costs and benefits

1.5.1. The costs of mosquito control

The costs associated with mosquito control include direct costs related to implementing a program, such as labour, capital costs for equipment and ongoing costs of consumable items such as chemicals. In addition there are indirect costs such as environmental harm, pesticide resistance and the effect of chemicals on non-target organisms.

1.5.2. The benefits of mosquito control

The benefits of mosquito control include disease reduction in humans, livestock and pets, protecting amenity, protecting tourism and real estate values, and increasing worker productivity (Williams 1986).

1.5.3. Responsibilities for mosquito control

In Australia, mosquito control issues are addressed in either public health legislation or the legislation that sets out the powers and responsibilities of local governments, or both (National Public Health Partnership (NPHP) 2000, 2002). The arrangements vary across states and territories, and the laws often do not address resource

11 allocation issues (NSW Health 1998). Table 1.2 summarises the legislative responsibilities for mosquito control in each state and territory in Australia.

Table 1.2: Comparison of legislative responsibilities for mosquito control in Australia

Jurisdiction Relevant legislation Role of local government Role of the state Implementation and funding arrangements

New South Local Government Act There is no specific reference to mosquito Administration and Only a few councils have mosquito Wales 1993 control in the legislation. enforcement of public health control programs (Tweed, Griffith, Port legislation are shared with Stephens), and the individual local Public Health Act 1991 Administration and enforcement of public local government. governments meet the costs (NSW health legislation are shared with Health 1998). Department of Health (NPHP 2002). State can intervene if local governments fail to carry out State funds NSW arbovirus surveillance ‘Role of local government in relation to functions and responsibilities and monitoring program (NSW Health public health is not clear.’ (NPHP 2002: 35). (NPHP 2002). 1998).

Northern Local Government Act Local government responsibilities generally Chief Health Officer can give Medical Entomology group of NT Territory 1993 confined to animal control and waste powers and responsibilities to health implement control programs in management. local government (NPHP major towns in collaboration with local Public Health Act 1952 2002). councils (NSW Health 1998). (amended 2005). Have the power to carry out ‘preventative services’ under Schedule 2 of the Act, but Mosquito control carried out Two-to-one funding from Health to Public Health (General these services are not defined. by Medical Entomology local councils (NSW Health 1998). Sanitation, Mosquito Branch of Department of Prevention Rat Exclusions Mosquito breeding also identified in Health and Community Federal funding to eradicate dengue and Prevention) Nuisance Regulation – but local Services (NSW Health 1998). mosquito from Tenant Creek Regulations 1998 governments are not obliged to enforce. (Department of Health and Community Services 2003).

Table 1.2: Comparison of legislative responsibilities for mosquito control in Australia (cont.)

Jurisdiction Relevant legislation Role of local government Role of the state Implementation and funding arrangements

Queensland Local Government Act Local governments have a ‘duty to prevent Emergency powers Local government meets the cost of the 1993 disease’ (s 34A) and a ‘general power’ to mosquito control programs within their abate nuisance (s 77) (Health Act 1937). Advisory role jurisdictions (LGAQ 2000). Health Act 1937 Local governments shall superintend Information dissemination role State resources some dengue control Health Regulation 1996 mosquito prevention and destruction (Health and emergency responses (Arbovirus Part 8 Mosquito Regulation 1996). Taskforce 2003). Prevention and Destruction The state funds entomology and epidemiology staff (LGAQ 2000, In transition to: Arbovirus Taskforce 2003). Public Health Act 2005

South Local Government Act Local government has a general duty to State has advisory role and State does larviciding when required Australia 1999 promote proper standards of public and information dissemination role and meets the costs (NSW Health environmental health in its area, must take (NPHP 2002). 1998). Public and Environmental reasonable steps to prevent notifiable Health Act 1987 diseases within its area, and has the power Public Environmental Health Local councils undertake no mosquito to abate nuisances (NPHP 2002). Council has operational role control programs for arbovirus control (NPHP 2002). (NSW Health 1998). Local government has annual reporting obligations to the minister (NPHP 2002). State may delegate (and withdraw) powers or functions No specific reference to mosquito control in to local government (NPHP the legislation. 2002).

Table 1.2: Comparison of legislative responsibilities for mosquito control in Australia (cont.)

Jurisdiction Relevant legislation Role of local government Role of the state Implementation and funding arrangements

Tasmania Public Health Act 1997 Public Health Act 1997 requires councils to Policy, oversight and No extensive mosquito control ‘develop and implement strategies to emergency role. programs. promote and improve public health’. State has the primary responsibility for notifiable No explicit reference to mosquito control. disease surveillance and control.

Victoria Health Act 1958 Section 29A of the Act outlines the role of Emergency powers Local government mosquito control local governments ‘to seek to prevent programs subsidised by Department of diseases, prolong life and promote public Information dissemination role Human Services on a dollar for dollar health through organised programs’. basis (Department of Human Services Councils have a duty under the Act to Consultation role 1998). remedy all nuisances (Health Act 1958).

No specific reference to mosquito control in the Act.

Western Local Government Act Local government is authorised and directed Emergency powers Local governments implement mosquito Australia 1995 to make local laws to prevent the spread of control programs. infectious disease. Local government have Information dissemination role Health Act 1911 the power to make local laws to abate State subsidises local governments on a nuisances including destruction of Advisory role dollar for dollar basis for larvicide and mosquitoes (NPHP 2002). habitat modification work. Local The state has the same powers government resources personnel, as local government to adulticide and hardware (NSW Health administer the Act and acts 1998). where there is no local government (NPHP 2002).

Queensland public health legislation is unique in comparison to the public health legislation of other state governments because the key public health legislation, the

Health Act 1937 assigns a ‘duty’ to local governments to control notifiable disease and the Health Regulation 1996 specifically establishes the role of local government in mosquito control. As local governments in Queensland are legally required to perform these mosquito control functions, they are also responsible for the costs of the mosquito control programs. Currently, the Queensland state government does not directly fund mosquito control on Crown land and is not legally obliged to do so

(Arbovirus Taskforce 1993).

Mosquito control consumes a significant amount of local government resources — for example, an informal survey performed by Queensland Health in 2002 showed that the total mosquito control budget for local governments in Queensland was approximately $9 million for one year (personal communication with George

Hapgood of Queensland Health, 2002) and that mosquito control budgets for local governments vary greatly, ranging from no money being spent on mosquito control to multi-million dollar budgets.

Mosquito control has almost a century of history in Queensland with some large- scale control activities commencing in 1912 (Arbovirus Taskforce 1993). This early control work was performed by local governments with guidance from the state health department. It was not until the late 1970s, that the first regional committees to coordinate saltmarsh control across local government boundaries were formed in southeast Queensland. The current mosquito control structure in Queensland involves local governments being responsible for their own areas, and regional committees formed by neighbouring local authorities to coordinate trans-boundary control. This work is funded by the member local governments. Queensland Health

16 has an advisory role, a disaster management role and enforces the legislation. There are also formal research arrangements, including the Mosquito and Arbovirus

Research Committee (MARC), Queensland Institute of Medical Research (QIMR) and Griffith University (Arbovirus Taskforce 1993). A protocol that guides the partnership between the State, and the Local Government Association of Queensland for the management and control of mosquitoes was established in 2001 (State of

Queensland and Local Government Association of Queensland, 2001).

There are two major contentious issues between the state government and local governments in Queensland relating to the allocation of resources for mosquito control. The first unresolved issue between the two levels of government is who should pay the costs of controlling mosquitoes on Crown land (LGAQ 2000,

Arbovirus Taskforce 1993). The Local Government Association of Queensland has calculated that 25% of local government mosquito control funds are spent on controlling mosquitoes on Crown land (LGAQ 2000). It argues that the cost of vector-borne disease is upwards of $10 million per year (without considering the value of lost tourism), and as the benefits of avoided disease are received by the wider community, the state should assist local governments in meeting some of the costs of mosquito control. The second unresolved issue concerns the variability in standards of mosquito control across the state. The Local Government Association of

Queensland argue that the state should set and enforce a minimum standard of mosquito control for all local governments (LGAQ 2000).

These unresolved issues underpin the research problem addressed in this thesis.

Furthermore, because mosquito control agencies are continually under pressure to show public policy-makers and ratepayers how mosquito control budgets are spent and to justify the continuation of their programs (Hansen 2003), it is reasonable to

17 expect that issues relating to resource allocation can be addressed. While the discipline of economics has developed methods that allow resource allocation decisions to be evaluated by comparing resources consumed and resources saved, these methods have never been applied to mosquito control in any comprehensive manner — that is, an evaluation of local government health interventions against mosquito-borne disease in Australia has never been performed.

1.6. The research problem: Is mosquito control cost effective in reducing mosquitoes, nuisance and disease?

1.6.1. Broad research questions

There are two broad research questions posed by this thesis:

1. What is the actual impact of mosquito control on disease rates?

2. Do the benefits of avoided mosquito-borne disease exceed the costs of

mosquito control?

While the economic evaluation approach allows valid decisions to be made, this can only be valid if the question to be answered is well defined, the viewpoint of the analysis is disclosed, and the boundaries of the evaluation are identified (Drummond et al. 1997). Section 1.6.2 outlines the viewpoint of this analysis and defines the temporal and spatial boundaries of this research.

1.6.2. Refining the research questions within temporal and spatial limits

The temporal scale of this research is limited by the availability of disease notification data and mosquito control information. The spatial scale of this research is defined by the research sponsor, Queensland Health. The geographic unit of

18 interest is the local government area as this is the administrative division that performs mosquito control. The range of arboviruses that can be investigated is also constrained. Ross River virus is the obvious choice of the mosquito-borne diseases to consider in this research because it is endemic in Queensland, it is the most prevalent arbovirus and there is some information available about the cost of having the disease. In addition, notification data for Ross River virus disease have been collected since 1975, though the disease surveillance system in Queensland was changed towards the end of 1990, moving to a predominantly laboratory-based notification system rather than the previous clinician-based notification system

(Arbovirus Taskforce 1993). Notification data for the years 1993 to 2004 have been used in this research. Every area of the state has had some notifications of Ross

River virus disease in the years between 1993 and 2004.

The second most prevalent arbovirus is Barmah Forest virus, but notification data for this disease are only reliable after 1995 (prior to this, Barmah Forest virus was not identified from other arboviruses) and there is no information about the cost of having Barmah Forest virus disease. The choice of dengue is not appropriate for this research as it presents in limited areas of the state and the state government (rather than only local governments) is highly involved in dengue control (Queensland

Health 2000). Other arboviruses such as Japanese encephalitis, Murray Valley encephalitis and Kunjin virus are clinically more severe, but they are less prevalent and not the arboviruses targeted by local government mosquito control in

Queensland.

The benefits of mosquito control considered in this research are limited to considering avoided disease.

Setting these limits on time, space, scale and disease type allows the broad questions to be refined into two answerable questions that are addressed in this thesis.

1.6.3. Answerable research questions

1. How much Ross River virus disease is avoided through local government

mosquito control in Queensland?

2. How does the monetary value of avoided Ross River virus disease compare

with the financial costs of local government mosquito control programs in

Queensland?

1.7. Overview of this thesis

Chapter 1 has described the starting point of this research and outlined the research questions.

Chapter 2 is a review of relevant literature including a description of the methods used in mosquito control, a review of the Ross River virus literature and a review of the literature relevant to the economic evaluation of mosquito control.

Chapter 3 sets out the research methodology and outlines how these methods have been informed by the literature presented in Chapter 2.

Chapter 4 presents the results of the data-collection process and the statistical analysis of this data. This chapter provides the answers to the research questions outlined in Chapter 1.

Chapter 5 draws conclusions from the research and identifies the wider relevance of these conclusions.

Chapter 2: Literature review

This chapter comprises a critical review of literature relevant to the evaluation of mosquito control on Ross River virus disease rates. This literature review chapter is presented in three sections. Section 2.1 describes the strategies used in mosquito control, Section 2.2 describes Ross River virus disease, ecology and epidemiology, while Section 2.3 synthesises the literature relevant to the economic evaluation of mosquito control. These reviews have informed the methods described in Chapter 3.

2.1. Mosquito control strategies

Mosquito control strategies were introduced briefly in Section 1.4.2. This research required collection of information about mosquito control in local governments. It was therefore necessary to obtain an understanding of the range of mosquito control practices used and the relevant terminology. Strategies for the biological, chemical and physical control of mosquitoes are described below.

2.1.1. Biological control of mosquitoes

Biological control includes control types that involve the use of predators, parasites, pathogens and competitors. Mosquito antagonists used in biological control include vertebrate predators (fish, amphibians, birds and bats), invertebrate predators

(hydras, flatworms, snails, leeches, spiders, mites, crustaceans and insects), parasites

(nematodes) and pathogens (fungi, protozoa and bacteria). Biological control agents can be introduced by inoculation which aims to establish the mosquito antagonist in the habitat, or inundation which involves the release of a large number of antagonists which will have a significant and immediate effect on the mosquito population.

Inundation strategies are used when the mosquito antagonist is unlikely to establish and reproduce in the habitat (MCAA 2002, Becker et al. 2003).

Biological control through the use of a predator mosquito fish was one of the earliest widespread strategies used in the early 1900s (Becker et al. 2003). Mosquito control using predator fish is best suited as an inundation strategy where the control agent is likely to become established in the environment. Most small fish are larvivorous — that is, they will eat mosquito larvae. The translocation of fish may require approval, as the use of exotic fish can be problematic if the fish species may become a pest.

Inundation with local species of predator fish can be an effective means of mosquito control in water storages and backyard ponds (MCAA 2002).

2.1.2. Chemical control of mosquitoes

The chemical groups used in mosquito control include chlorinated hydrocarbons, organophosphates, carbamates, synthetic pyrethroids and insect growth regulators.

Each of these chemical groups has a specific mode of action on mosquito larvae or adults. Their historical use in mosquito control is varied, with some chemical groups no longer used (MCAA 2002; Becker et al. 2003).

Chlorinated hydrocarbons have a toxic action on the nervous system of mosquitoes, causing tremors, hyperexcitability and paralysis and death. A widely used chlorinated hydrocarbon was DDT, which was introduced in 1939. The extensive use of DDT in the period from the 1940s to the 1970s led to resistance and impact on non-target organisms. DDT is now banned in most parts of the world (Becker et al.

2003).

Organophosphates disrupt nervous system functioning, leading to twitching and then paralysis. Organophosphates were introduced in 1950 and have been used in

22 mosquito adulticiding. This group of chemicals has broad-spectrum effects on non- target organisms (Becker et al. 2003).

Carbamates have a similar mode of action to organophosphates, causing hyperexcitation of the nervous system, convulsions, paralysis and then death. Insects can recover if the dose used is too low, so resistance can be a problem. Carbamates have been used in mosquito adulticiding, but in a similar way to organophosphates they have broad-spectrum effects on not-target organisms (Becker et al. 2003).

Synthetic pyrethroids are highly active synthetic insecticides that have the same mode of action as natural pyrethrin. They are neurotoxic to insects, causing a lethal knockdown effect (Becker et al. 2003).

Insect growth regulators disrupt the normal growth and development of insects.

Chitin synthesis inhibitors interfere with new cuticle formation and disrupt the moulting stage of development. Juvenile Hormone Analogues disrupt the level of juvenile hormone at certain stages in the life cycle and adversely affects metamorphosis from larvae to adult (Becker et al. 2003).

A biorational chemical control is the use of a synthetic larvicidal bacteria Bacillus thuringiensis var israeliensis (commonly known as bti). This synthetic bacteria produces proteins during sporulation which are highly toxic to mosquito larvae and cause mortality (Becker et al. 2003). The bacillis larvicides can be mass-produced with relative ease, they are efficient, environmentally safe, easy to handle, stable when stored, cost-effective and the risk of resistance is lower than with chemical insecticides (MCAA 2002).

The application method of chemical agents for mosquito control is dependant on the type of area to be treated, the size of the area, accessibility, mosquito species, life- cycle stage (larval instar or adult), weather conditions, presence of non-target species and the formulation of the chemical agent being used. Formulation of the agent may be a liquid that requires the generation of droplets, or solid (usually pesticide on sand or corn-cob) involving the use of spreaders to distribute the granules. Application techniques and equipment can be categorised according to whether the agent is applied from the ground or from the air (ground-based or aerial). An extensive description of this equipment is provided in the Australian Mosquito Control Manual

(MCAA 2002).

2.1.3. Physical control of mosquitoes

Physical control of mosquitoes can be categorised as source reduction, water management, or modification of the water–air interface. (Becker et al. 2003).

Source reduction aims to reduce mosquito habitat through elimination of water sources and requires consideration of both private land (household water containers such as plant pots, rainwater tanks and pet bowls) and public land (including parks and water/sewerage treatment plants). Simple source reduction can be achieved by community education, while good design of roads, drains and stormwater systems is necessary for source reduction on a wider scale (MCAA 2002; Becker et al. 2003).

Water management involves controlling the depth and flow of water so that shallow temporary pools of water that encourage mosquitos are not created. Water management should encourage water flow so that mosquito larvae are flushed out into marine waters, or predators can move into ponds. One such water management technique developed in Queensland for saltmarsh habitat is runnelling, which

24 involves constructing spoon shaped shallow channels that facilitate tidal flushing of the saltmarsh (Dale et al. 1993).

Water management might also involve the removal of vegetation from water body edges, thereby removing the calm waters that are suitable for oviposition and larvae.

Modification of the water–air interface involves the use of oil, surface films, and polystyrene beads to disrupt the respiration process of mosquito larvae. Oil has a detrimental ecological effect and surface films can impact non-target organisms

(Becker et al. 2003). Methods involving modification of the water–air interface are not commonly used in Australia.

2.1.4. Personal protection against mosquito bites

Personal protection against mosquito bites can involve the use of impregnated bed nets, personal repellents, mosquito coils, vapourising mats or liquid vapouriser.

Simple forms of personal protection such as covering up and staying indoors at peak biting times are also effective (MCAA 2002; Becker et al. 2003). Personal protection is considered to be the most effective way to avoid mosquito bites and subsequently lower the risk of being infected with a mosquito-borne agent (Suhrbier & La Linn

2004). The use of bed nets impregnated with insecticides is a fundamental strategy in health interventions against malaria in developing countries (WHO 1998).

2.2. Literature review: Ross River virus

This second section of the literature review considered the research that has investigated Ross River virus. This was necessary because initial investigations had shown that Ross River virus is a complex arbovirus that is influenced by several variables (other than mosquito control), and it is necessary to understand the Ross

River virus system in order to develop a method to quantify the effect of mosquito control.

Ross River virus is the most prevalent arbovirus in Australia. Knowledge of Ross

River virus disease can be traced back to 1928, when symptoms of Ross River virus disease were described in a submission to the Medical Journal of Australia by J. R.

Nimmo, who reported ‘an unusual epidemic’ at Narrandera in New South Wales and described the clinical symptoms which retrospectively have been classified as probable Ross River virus (Harley et al. 2001). Several similar descriptions of epidemic polyarthritis were reported in the Medical Journal of Australia in subsequent years (Harley et al. 2001). Significant breakthroughs in the understanding of Ross River virus have included: isolation of the virus from mosquitoes; serological evidence of infection with alphavirus; discovery of the alphavirus T48

Ross River virus; isolation from overseas patients; and isolation from an Australian patient (Harley et al. 2001).

There have been several published reviews that have specifically focused on Ross

River virus. Kay and Aaskov (1988) have contributed a chapter about Ross River virus in the publication The Arboviruses: Epidemiology and Ecology (Monath 1989), which provides a comprehensive overview of the knowledge about Ross River virus up to 1989. Russell (1994) has described disease trends and vector ecology, while

Harley et al. (2001) have provided an interdisciplinary synthesis of knowledge about

Ross River virus disease and epidemiology. Russell (2002) has provided an update of knowledge about the ecology and distribution of Ross River virus.

2.2.1. The clinical symptoms of Ross River virus disease

The human disease manifestations of Ross River virus infection are consistent with other alphaviruses. A person infected with Ross River virus is likely to first experience symptoms seven to nine days after being bitten by the infectious mosquito, and will experience some or all of the symptoms: febrile illness, polyarthritis and rash (Gubler 2001). The arthritic manifestations affecting multiple joints were the reason that early epidemics of the syndrome caused by human infection with Ross River virus were referred to as ‘epidemic polyarthritis’ (Harley et al. 2001). The disease is commonly referred to as ‘Ross River fever’; however, the medically accepted name for the syndrome caused by this arbovirus is Ross River virus disease (abbreviated as Ross River virus). The term ‘Ross River virus disease’ is more appropriate than the term ‘Ross River fever’ as many cases of Ross River virus infection are not associated with fever symptoms. This thesis will use the terms defined by Harley et al. (2001), who have proposed to use the term ‘Ross River virus disease’ to refer to symptomatic infections (where the disease is diagnosed and notified) and the term ‘Ross River virus infection’ to refer to both symptomatic and asymptomatic infections.

Harley et al. (2002) provides an extensive review of the disease associated with Ross

River virus and has investigated the typical duration and progressive resolution of symptoms. Ross River virus disease has had a reputation for being a lifelong affliction characterised by recurrent arthritis and fatigue, but consensus now is that the disease is self-limiting (six months) and other chronic symptoms (commonly

27 depression and rheumatoid arthritis) are associated with co-morbidity (Harley et al.

2002). This conclusion has important implications for the valuation of morbidity associated with Ross River virus disease.

There are also subclinical manifestations of Ross River virus infection. In some cases, people show serologically that they have been infected with Ross River virus, but have not experienced noticeable or severe symptoms. For this reason, notification data should be used with the caveat that notifications do not represent the full extent of prevalence of infection. Some research has attempted to estimate the clinical to subclinical ratio of Ross River virus infection. The clinical to subclinical infection ratios were estimated by Choi (2002) to range between 1:2 and 1:65 for different models of a Ross River virus outbreak in Southwestern Australia during 1995-1996.

Harley et al. (2001) have summarised research that has estimated asymptomatic to symptomatic case ratio and shows that there is a large variability in the estimates ranging from 0.3:1 to 50:1. Harley et al. (2001) have considered the methodology used for each estimation and have concluded that the most reliable estimates are

1.2:1 and 3:1. Aaskov et al. (1998) suggest that the rate of subclinical to clinical infections during epidemics is lower than for non-epidemic periods.

2.2.2. Ross River virus transmission

The transmission of an arbovirus such as Ross River virus can occur as horizontal transmission or vertical transmission, and can occur via mechanical or biological processes. Horizontal transmission refers to transmission to a vertebrate by a bite, while vertical transmission refers to transmission to the vector’s progeny.

Mechanical transmission refers to the case where the vector need not actually be infected with the disease to pass it on, but acts as a vehicle for transferring the virus during successive feeding on a viraemic host and a non-viraemic host. In contrast,

28 biological transmission requires the vector to be infected so that the virus actually replicates and multiplies within the vector before it is transmitted (Turrell 1988).

Hardy (1988) has stated that there is overwhelming evidence that biological transmission, rather than mechanical transmission, is responsible for the long-term maintenance of arbovirus cycles. The transmission dynamics of an arbovirus will be a function of vector activity, host activity and viral activity; therefore, climatic conditions and habitat suitability for virus, vector and host are all important in arbovirus ecology (Turrell 1988). Three generalised cycles of transmission of arboviruses are shown in Figure 2.1.

Source: Turrell (1988)

Figure 2.1: Generalised transmission cycles for arboviruses

The cycle of transmission labelled A in Figure 2.1 is indicative of an anthroponotic disease such as malaria. In contrast, Ross River virus could be transmitted in either

of the cycles labelled B and C. Cycle B represents the endemic cycle involving one

type of host and vector, while Cycle C represents the epidemic cycle which may

involve several types of intermediate hosts and vectors. The epidemic Cycle C would

be associated with higher human infection rates than the endemic Cycle B.

Vector competence of mosquito species to amplify and transmit Ross River virus

There are 35 common species of mosquitoes in Queensland; however, the ability of

mosquitoes to carry and transmit viruses differs between species of mosquitoes. This

ability to carry and transmit a virus is referred to as the vector competence of a

species (Russell 1995). Table 2.1 shows some common mosquito species that are

important Ross River virus vectors in Queensland. Current consensus is that the

important vector of Ross River virus in coastal saltmarsh habitat is Ae. vigilax, while

Cx. annulitrostris is important in freshwater habitat. Other vectors, such as

Verrallina funerea (Theobald) may be important in specific locations.

Table 2.1: Some common mosquito species in Queensland

Mosquito Distribution Preferred habitat Concern or risk Control activity species

Coquillettidia Widespread Permanent and semi- May be a serious pest in linealis (Skuse) permanent waterholes the vicinity of extensive with aquatic plants. freshwater wetlands. BF, Larvae attach to plant RR isolated. tissues below the water surface.

Culex Widespread Fresh water wetlands, Most important mosquito Some control in annulirostris usually with vegetation. disease vector in southeast, and after Skuse Breeding can be prolific Australia. Vector of BF, floods. in low-lying areas that JE, Kunjin, MVE, RR hold water for a few and heartworm of dogs. weeks after heavy rain.

Source: Adapted from LGAQ (2002).

Table 2.1: Some common mosquito species in Queensland (cont.)

Mosquito Distribution Preferred habitat Concern or risk Control activity species Culex Widespread Commonly found in Night-biting occasional quinquefasciatus polluted water close to pest. BF, RR isolated. Say human habitation. Vector of heartworm in Breeding can be prolific dogs. in waterways polluted by sewage or organic matter.

Culex sitiens Coastal saline Commonly found in Occasional pest where Controlled in some Wiedemann and brackish pools formed when residential areas are southeast high tides flood the close to breeding sites. Queensland coastal upper limits of marine RR isolated. areas. wetlands. Heavy breeding can occur where natural drainage in tidal wetlands is blocked.

Mansonsia Widespread, Permanent and May be a serious pest in uniformis mainly coastal semipermanent swamps the vicinity of freshwater (Theobald) and water courses. A wetlands. RR isolated. high degree of pollution seems necessary to attract them to potential habitat.

Aedes alternans Widespread Saline and fresh RR isolated, rarely (Westwood) wetland. abundant.

Aedes Inland and Temporary ground BF, MVE, RR isolated. normanensis northern areas pools. (Taylor)

Aedes Widespread, Domestic and natural Significant domestic Control depends on notoscriptus urban and containers. pest. Vector of BF, RR, householders. (Skuse) rural heartworm in dogs.

Aedes vigilax Most of Temporary pools in Major pest species. Widely controlled in (Skuse) coastline saltmarshes flooded Vector of BF, RR and southeast. during higher tides or heartworm in dogs. by rain.

Verallina Coastal Slightly brackish and Vector of RR, BF Target in hot spots in funerea fresh water pools that isolated. southeast. (Theobald) are often shaded. Can be a significant pest Commonly found in residential areas breeding in tea-tree and adjacent to breeding other wetlands sites. adjoining tidal areas. Source: Adapted from LGAQ (2002).

Vertebrate hosts of Ross River virus

A summary of the research that has investigated intermediate hosts for Ross River virus is provided by Russell (2002) and Harley et al. (2001). Harley et al. (2001) have compared Ross River virus antibody prevalence surveys in potential non-human vertebrates and have shown that Ross River virus has been isolated from a range of placental mammals (including domestic animals), marsupials and birds. Much of the evidence relating to intermediate hosts of Ross River virus has been based on the investigation of antibody prevalence, though in some cases detection of viraemia has been used (Harley et al. 2001).

It is believed that Ross River virus is maintained in enzootic cycles by non-migratory native macropods, including kangaroos and wallabies (family: Macropodidae). These macropods are thought to be particularly important in cases where Ross River virus has occurred in epidemic proportions in rural areas. Urban Ross River virus transmission is likely to be maintained through amplification within marsupial animals, including the common brushtail possum, Trichosurus vulpecular Kerr.

Humans probably play a role in transmission cycles during epidemics (Russell 2002;

Harley et al. 2001) particularly when virus titers amongst the human population are high. The movement of humans between geographic areas also inadvertently distributes the virus (Russell 1995).

Evidence indicates that horses can amplify the virus and can suffer clinical symptoms of Ross River virus disease (Azoulas et al. 2003). Flying foxes (family:

Pteropodidae) are also suspected, but evidence is insufficient to confirm their ability to be efficient amplifiers (Ryan 1997; Constantine 2003).

Modelling Ross River virus transmission

The transmission dynamics of Ross River virus have been modelled by Choi et al.

(2002) and Glass (2005).

Choi et al (2002) have modelled the transmission dynamics of a Ross River virus epidemic which occurred in Southwestern Australia during the 1995 to 1996 season.

The objectives of this research by Choi et al. (2002) were, first, to estimate the clinical to subclinical ratio of Ross River virus infection during this epidemic, and second, to test the sensitivity of the incidence of human Ross River virus disease to changes in transmission rates between hosts and vectors, changes in the extrinsic incubation period of the arbovirus and changes in the mortality rates of the mosquito vectors. Choi et al (2002) acknowledge that the mathematical models they have developed proceeded from a number of assumptions about immunity, viraemia, interactions between hosts and vectors, and the density of hosts and vectors; hence they acknowledge that the model may not provide accurate results and requires further development of the models to include environmental conditions such as rainfall and temperature to improve the accuracy of the results (Choi et al. 2002).

Glass (2005) has developed a theoretical model of the mechanisms that allow Ross

River virus to survive in host and vector populations. The parameters modelled by

Glass (2005) represented marsupial hosts (such as kangaroos and wallabies) and two vector types (Ae. vigilax and Cx. annulitrostris). The transmission dynamics of Ross

River virus within a human population was not included in the model by Glass

(2005).

2.2.3. Describing the spatial and temporal patterns of Ross River virus disease

Descriptions of the spatial and temporal patterns of arboviruses in Australian states and territories have been provided by Russell (1995, 2002). The spatial and temporal patterns of Ross River virus disease notifications in Queensland have been statistically analysed by Gatton et al. (2004, 2005), who have considered the seasonal incidence of Ross River virus for local governments during the period 1991 to 2001.

Gatton et al. (2004, 2005) have developed a statistical methodology to identify unusual activity from endemic transmission and then consider spatial autocorrelation to look at the distribution of outbreaks across Queensland. These authors show that rates are highly variable and are spatially autocorrelated at the local government level — meaning that neighbouring local governments often experience outbreaks at the same time.

2.2.4. Explaining the spatial and temporal patterns of Ross River virus disease

There have also been attempts to explain the spatial and temporal variability in Ross

River virus disease rates by consideration of weather and climate conditions, mosquito abundance and proximity of residences to vegetation.

The relationship between Ross River virus, weather and climate

There is an increasing amount of research that investigates the influence of climate on Ross River virus disease incidence. Woodruff et al. (2000) have identified that there are two approaches to modelling Ross River virus and weather relationships.

The first approach is to use local conditions (such as rainfall and temperature), while the second approach uses large-scale climate variables (such as the Southern

Oscillation Index (SOI)).

Woodruff et al. (2000) have described the influence of climate on the mosquito vectors of Ross River virus and have concluded that temperature, humidity and water

(rainfall and tides) are the three important variables that influence the life-cycle of the vectors and the replication of the virus. These authors conclude that, ideally, a combination of local-scale and large-scale approaches are needed to predict Ross

River virus outbreak seasons at a regional level. When considering local conditions, model stability is at risk due to the complexity of the relationships and they emphasise that there is a need to keep the models simple to be useful.

The influence of local weather conditions on Ross River virus disease has been investigated by Woodruff et al. (2002), Tong and Hu (2001; 2002), Tong et al. (2002,

2005) and Whelan et al. (2003). The methods used between studies vary — for example, Woodruff et al. 2002 have used logistic regression to investigate 13 variables relating to monthly weather and to test which variables best predicted the epidemic or non-epidemic binary outcome for two regions in southern Australia. In contrast, Tong and Hu (2001; 2002) and Tong et al. (2002; 2005) used correlation and regression to look at the relationship between monthly Ross River notifications and climate variability (temperature, relative humidity, rainfall and sea level) for major cities in Queensland. Whelan et al. (2003) investigated the correlation between rainfall, mosquito abundance and Ross River virus disease rates in the Alice Springs and Tennant Creek regions of the Northern Territory. While the studies often showed a statistical association between climatic factors and Ross River virus transmission, the relative importance of these climatic factors seems to vary with geographical area

— for example, climatic variation was more strongly correlated with Ross River virus transmission in coastal cities compared with inland cities.

The influence of large-scale climate fluctuations has been investigated by Done et al.

(2002), who have considered the relationship between Quasi-Biennial Oscillation

(QBO) and the aggregated Ross River virus notifications for Queensland for the years 1991 to 1997, concluding that there is a cyclical increase in Ross River virus notifications approximately every two years and the peaks in Ross River virus cases have consistently coincided with one particular phase of the QBO.

The most comprehensive analysis of both the environmental and human factors associated with Ross River virus outbreaks in Australia is a retrospective review of

57 reports of Ross River virus outbreaks from 1886 to 1998 that has been compiled by Kelly-Hope et al. (2004a). This has considered monthly rainfall, temperature, SOI values, La Nina episodes and sea level (for coastal localities) associated with the

Ross River virus outbreaks. In relation to local weather variables, Kelly-Hope et al.

(2004a, 2004b) concluded that rainfall was the single most important risk factor, that the role of temperature varied between tropical and arid regions, and that sea levels are correlated with coastal outbreaks. In relation to broad-scale climatic fluctuations,

Kelly-Hope et al. (2004c) concluded that the SOI and La Nina may be useful predictive tools, but these relationships were stronger in the southeast temperate regions. This comprehensive analysis supports the ideas of Russell (2002) that there are different epidemiologies of Ross River virus throughout Australia, and this highlights the importance of choosing an appropriate scale when investigating the environmental variables of arboviral disease.

Ross River virus and mosquito abundance

In recent years, researchers at the Queensland Institute of Medical Research have been considering the relationship between mosquito abundance, mosquito biting complaints and Ross River virus disease notifications for several local government

36 areas in Queensland (Ryan et al. 1999; Ryan et al. 2003; Ryan et al. 2004). The mapping of mosquito abundance and disease rates has shown that there is considerable heterogeneity in the spatial and temporal pattern of mosquito abundance and Ross River virus disease, even within one individual local government area.

Ryan et al. (1999) compared notifications of Ross River virus disease notifications to seasonal light trap indices for several mosquito species in the Maroochy Shire in

Queensland, and reported that the species Cx. annulitrostris and Ve. funerea were positively correlated with Ross River virus disease incidence; however, they did not find an association between Ae. vigilax or Ae. procax and Ross River virus disease incidence. Mosquito abundance data have also been used to create isodensity maps of species distributions by using interpolation techniques to estimate mosquito numbers between the traps (Ryan et al. 2001; Ryan et al. 2003; Jeffery et al. 2002).

Ross River virus and vegetation

There is only one published research paper that has specifically considered the spatial relationship between Ross River virus disease notifications and vegetation.

Muhar et al. (2000) mapped Ross River virus notifications at suburb level in

Brisbane from 1991 to 1996 and then used principal component factor analysis and regression to show a positive relationship between the rate of Ross River virus notification and the proportion of wetland and bushland within each suburb.

2.2.5. The costs associated with Ross River virus disease

Generally, there is a lack of data that adequately describes the economic impact of arboviral diseases, and the true costs of Ross River virus to an individual or to society are not known. Some of the costs associated with having Ross River virus disease are borne directly by the individual as medical costs and loss of income,

37 while other costs are more widely distributed, such as loss of worker productivity, and the impact on the value of tourism and real estate.

Where estimates of the costs of specific outbreaks have been made, usually only direct medical costs, diagnostic costs and direct loss of income have been estimated, and the estimated costs vary from $1018 to $2508 per case. The cost of a Ross River virus outbreak in Queensland during 1983–84 was conservatively $3 million, which equates to $2508 for each of the 1196 laboratory-confirmed cases (LGAQ 2000).

Mylonas et al. (2002) estimated that each diagnosed case of Ross River virus resulted in a direct cost to the community of $1018 per patient (this cost estimate included the cost of negative tests given that 23 patients are tested to detect one case of Ross

River virus). Harley (2001) has assumed that each case consults a general practitioner twice, and half of the cases take one week off work. Harley also estimates the direct medical costs and lost earnings of Ross River virus cases at between $2.7 and $5.6 million in an average year (4745 cases) — this equates to a per case cost of between $569 and $1180, and does not include the cost of medication. Ratnayake (2005) has used market and non-market approaches to valuation of the cost of having Ross River virus disease, and has estimated an average cost per case of $1070 (AUD 2002) which is equivalent to $1097 (AUD

2004). This included health care costs of $293, lost production costs of $527 and health-related quality of life costs of $250. Ratnayake’s estimate of the cost of having Ross River virus disease and has been chosen as the most reliable estimate for this research.

There is no published research that has investigated the economic impact of Ross

River virus disease on either general amenity, tourism or property values.

2.2.6. Major findings from the literature on Ross River virus

The findings from the review of literature on Ross River virus that are relevant to this research are summarised below.

The important mosquito vector species in Queensland are the freshwater species Cx. annulitrostris and the saltmarsh species Ae. vigilax in coastal areas. More research is needed to confirm the relative importance of vector species and to develop efficient systems to identify potential mosquito breeding habitat.

The important intermediate hosts of Ross River virus in rural areas are kangaroos and wallabies, while the common brushtail possum has been implicated as an intermediate host in urban areas. It is also speculated that horses may be involved in

Ross River virus amplification in rural and semi-rural areas, and that humans may play a role as amplifying hosts during urban Ross River virus epidemics.

Climate and weather variables can be used to predict Ross River virus disease activity; however, questions of spatial scale are important, and it seems that local/regional data give more reliable and useful predictions and that rainfall is the most reliable predictor of Ross River virus outbreaks. Using large-scale climatic variables to predict Ross River virus activity is only reliable in those regions that have a distinct and noticeable relationship to the large-scale climatic variables being used.

2.3. Literature review: Economic analysis of mosquito control

This section will identify, summarise and synthesise the existing body of work about the economic analysis of mosquito control. Research relevant to the economic evaluation of mosquito control programs can be categorised into three major groupings:

1. economic evaluations of mosquito control programs at local government or

equivalent level;

2. economic evaluations of malaria prevention and treatment; and

3. review articles of economic evaluations of health interventions.

2.3.1. Economic evaluation of mosquito control programs at local government level

Economic evaluation provides ‘an organised consideration of the factors involved in a decision to commit resources to one use instead of another’ (Drummond et al.

1997:7). Economic evaluation provides a means of comparing the costs and consequences of an action and, importantly, allows for informed public policy decisions.

Cost-benefit analysis and cost-effectiveness analysis are two commonly used economic evaluation techniques. Cost-benefit analysis is an approach that measures and compares the costs and outcomes (benefits) of a program, intervention or action.

An important aspect of cost-benefit analysis is that both costs and benefits are valued in monetary terms. In contrast, cost-effectiveness analysis is a narrower approach that considers costs but not outcomes. In essence, cost-effectiveness analysis allows a

40 decision to be made with respect to the cheapest way to achieve a defined outcome

(Drummond et al. 1997).

There are only a few authors who have specifically investigated the economic issues associated with mosquito control programs. This literature is discussed below and as much as possible has been presented in a chronological order to show how methods of economic evaluation of mosquito control have developed over time.

Prior to the mid-1970s, there was no published literature that explicitly considered the economics of mosquito control at the local government level. Research published by Carlson and colleagues (Carlson et al. 1976) represents a significant early attempt to quantify the economics of mosquito control. This research was essentially a cost- effectiveness analysis of temporary and permanent mosquito control measures for 30 mosquito control districts in the United States. One of the key conclusions of this study was that temporary measures such as chemical controls are more cost-effective than permanent measures such as ditching. These conclusions were subsequently debated in the literature and criticised as being suspect due to the methods and data used (Hansen et al. 1976; Langham and Lanier 1981).

In the late 1970s to mid-1980s, there was an expansion of cost-effectiveness studies, mostly comparing temporary and permanent control methods. Sarhan et al. (1980) report an economic evaluation of mosquito control programs in the Kern Mosquito

Abatement District in California for the period 1955 to 1974. Their research first used statistical models to estimate the effect of control methods and environmental variables on mosquito populations, and second used linear programming techniques to model the most economically efficient mosquito control method to achieve a defined decrease in mosquito populations. Their study compared the efficiency of

41 one method against another, given an assumption about the amount of reduction in mosquito numbers required. Their study made the assumption that a reduction in mosquito numbers would be related to a reduction in disease rates and a reduction in nuisance biting, but did not attempt to quantify these relationships, or test these assumptions. A key conclusion from Sarhan et al. (1980) is that chemical controls might be too extensively used given the alternatives, and that source control was generally more economically efficient than chemical control, but the effect of permanent controls is generally under-estimated in the long term. This study showed that finding an efficient balance of pesticide controls and source controls will help to preserve pesticide effectiveness (decrease pesticide resistance). A series of publications by Shisler and others also compared permanent and temporary control methods (Shisler et al. 1979; Shisler and Harker 1981; Shisler and Schulze 1981;

Shisler and Schulze 1985). These authors found that, while there had been some attempts to evaluate the economics of mosquito control, these attempts were described as complex and theoretical, and were not practical for implementation at the local level. These authors summarise findings from several studies that have compared permanent and temporary controls, and have concluded that the economic criteria for evaluating effectiveness of a control program will vary substantially between control districts due to the changing nature of the control problem. Hence economic evaluation results cannot directly be transferred from one area to another.

The mid-1980s saw the focus of research move from cost-effectiveness analysis of control methods to broader economic analysis that also considered outcomes of mosquito control. Lichtenberg and Getz (1985) quantified economic aspects of mosquito control in rice fields in California using cost-benefit analysis. The costs of both chemical methods and integrated pest management (IPM) approaches of

42 controlling mosquitoes were considered. The benefits of mosquito control were defined as costs avoided (including medical costs, mortality costs and lost time and wages) due to the reduction in encephalitis incidence. In contrast to earlier research, both the costs and benefits of the programs were valued in monetary terms.

Importantly, Lichtenberg and Getz (1985) discuss the relationship between encephalitis incidence and mosquito population levels, showing how the risk of contracting the disease can be calculated from the number of trapped mosquitoes.

Another example of a cost-benefit analysis of mosquito control was published by

Ofiara and Allison (1986a), and involved the comparison of two mosquito control programs — one that used source control and one that used chemical control. Ofiara and Allison (1986a) have provided a thorough description of the cost-benefit methodology, including an explanation of the willingness-to-pay methodology that they used to evaluate the benefits of the programs. Ofiara and Allison (1986b) have linked the economic evaluation of mosquito control to economic theory and provide the reader with an overview of the theory behind economic demand and the concept of measuring benefits. These authors have shown how the benefits of mosquito control can be defined as a public good and, as such, are not easily measured using the methods developed to value market goods; furthermore, they explain why the valuation of public goods (non-market goods) is difficult and provide a discussion of the appropriateness of willingness-to-pay measures and then demonstrate the application of this methodology within two mosquito control commissions. Using a similar methodology to Ofiara and Allison (1986a, 1986b), John et al. (1987) used the willingness-to-pay approach to value the benefits of mosquito control in Jefferson

County, Texas. John et al. (1987) have also provided a summary of past work in

43 economic evaluation of mosquito control, including an overview of the work presented by Carlson and DeBord (1976).

More recently, there has been recognition of the need to evaluate other aspects of mosquito control programs such as surveillance. Scott et al. (2001) have considered the cost effectiveness of three surveillance methods, and compared sentinel chickens, virus isolation from trapped mosquitoes, and antibody detection in wild birds. They have concluded that surveillance of wild bird populations was the most expensive method. This is the only published study that has considered the cost of surveillance.

2.3.2. Economic evaluations of malaria prevention and treatment

The majority of economic evaluations that have considered mosquito control have been concerned with malaria prevention, and there is an expanding field of literature that is concerned with the economic aspects of malaria. A significant number of these studies have been concerned with cost-effectiveness analysis of methods of prevention, diagnosis and treatment of malaria. Cost-effectiveness of prevention includes the comparison of insecticide-treated bed nets, vector control, and use of anti-malarial drugs. Mills (1992) has considered the economics of malaria control for

Nepal, while malaria control in Africa has been considered by Goodman et al.

(1999); Goodman and Mills (1999); Goodman et al. (2001a) and Goodman, Mnzava et al. (2001). Reviews of the literature concerned with economic analysis of insecticide-treated bed nets in Africa have been published by Guyatt and Snow

(2002) and Mills (1998). Methods of diagnosis and treatment have also been considered by Pang and Piovesan-Alves (2001), and by Goodman et al. (2001b).

Walker (2000) has presented a cost comparison of insecticides used for malaria control. Utzinger et al. (2001) have considered the cost-effectiveness of environmental management for malaria control while Utzinger et al. (2002) have

44 performed a retrospective analysis of integrated malaria control in Zambia between

1930 and 1950. A description and analysis of the economic impact of malaria on households in Sri Lanka has been published by Attanayake et al. (2000). McCarthy et al. (1999) have provided an overview of the link between malaria and the economic growth of countries.

Phillips et al. (1993) have published a guide for the economic evaluation of vector control programs for the reduction of malaria. This guide provides accessible information for local government programs as the theoretical economic information has been distilled to a level that is suitable for a non-economist.

The methodologies used in the economic evaluations of interventions against malaria have evolved from general economic theory, and hence can provide guidance for economic evaluation of vector control programs at local government level in

Australia. Caution must, however, be exercised when considering the transferability of these methods, keeping in mind that malaria research is most often carried out in developing countries and that malaria is a parasitic disease, while in Australia we are mainly concerned with controlling mosquitoes for the prevention of arboviral disease, and are concerned with different vector species and, importantly, different vector habitat.

2.3.3. Review articles of economic evaluations of health interventions

The field of health economics offers some generic guidance for the economic evaluation of communicable disease interventions such as vector control programs.

In particular, researchers at the London School of Hygiene and Tropical Medicine have reviewed the literature regarding communicable disease interventions in developing countries (Walker and Fox-Rushby 2000a, 2000b). Researchers from the

World Health Organisation (WHO) have also contributed a summary of the critical issues in economic evaluation of interventions against communicable diseases

(Hutubessy et al. 2001).

Walker and Fox-Rushby (2000a, 2000b) have published two literature reviews pertaining to the economic evaluation of health programs. They have considered economic evaluations of communicable disease interventions in developing countries and economic evaluations of parasitic diseases. Walker and Fox-Rushby (2000a,

2000b) reviewed the published literature using criteria developed by Drummond et al. (1997). The review criteria included technical aspects concerned with cost

(perspective, identification, sources of data, clarity of measurement and valuation), technical aspects concerned with outcomes (choice of outcome, sources of data and valuation methods) and other technical aspects concerning time, choice of summary measure, sensitivity analysis, affordability and generalizability of results. In relation to economic evaluations of communicable disease interventions, Walker and Fox-

Rushby (2000a) considered the literature from 1984–97 and found 107 published economic evaluations in 54 journals. This literature tended to be dispersed across many disciplines, with the majority of studies being cost-effectiveness analyses with a smaller number of cost-benefit analyses.

Significant conclusions from these reviews by Walker and Fox-Rushby (2000a,

2000b) were that many of the papers did not acknowledge the source of their funding, or disclose the perspective, of the study. Also, they found an imbalance between disease burden and research, indicating that the diseases that contribute the most to disease burden are not receiving a proportionate amount of research focus.

Studies tended to have a narrow perspective, often from the viewpoint of the health care provider, and are often also narrow with respect to the timeframe adopted

(which is often annual). They further concluded that there were several common sources of bias, including bias in measuring costs (such as not including all capital costs) and bias in measuring benefits, as well as bias in the choice of pricing for labour and foreign exchange. Many studies showed a lack of transparency and a lack of critical examination of findings, did not perform sensitivity analysis, or did not consider generalisability of results to other areas.

Hutubessy et al. (2001) reviewed economic evaluations of health programs that were aimed at controlling or reducing communicable diseases and identified the critical issues that hinder the wider use of results by decision-makers. These authors showed how the number of economic evaluations in the area of communicable disease interventions has increased markedly in the academic literature in the last couple of decades. They also categorised these interventions according to the disease and the economic status of the country. Interestingly, all studies involving the economic evaluation of interventions against the mosquito-borne diseases of malaria (28 studies) and Japanese encephalitis (one study) have been performed in developing countries. They reported that there are no published economic evaluations of dengue.

2.3.4. Identifying the costs and benefits of mosquito control

The overview of mosquito control techniques in Section 2.1 has informed the list of costs described in Table 2.2. This table summarises the resource requirements of mosquito control, who incurs these costs, whether they are market or non-market goods, and the method of economic valuation applicable. It shows that many of the costs incurred directly by the mosquito control agencies are financial costs that can be classified as market goods and valued according to the price paid for them. In contrast, there are other costs of mosquito control, such as environmental harm, that are non-market goods incurred by society, and not easily valued in monetary terms.

In comparison, Table 2.3 summarises the resource savings or benefits of mosquito control, who benefits from these savings, and whether these benefits are market or non-market goods. Some of the benefits of mosquito control accrue as financial savings to the individual and to the health system, and are classed as market goods.

There are other benefits, such as the protection of general amenity, that are non- market goods and are difficult to value.

Table 2.2: Identification of the resources consumed by mosquito control

Mosquito control component Resource Costs are incurred by: Market or non-market requirements goods?

Surveillance

Mosquito-borne disease rates Maintenance of State and federal health Market goods notification system agencies Laboratory testing

Mosquito habitat and Labour, equipment Mosquito control agency Market goods mosquito abundance (e.g. mosquito traps and vehicles)

Regional and local weather Subscription to Mosquito control agency Market goods weather/tide data

Sentinel surveillance Maintenance of flocks, Mosquito control agency Market goods serological testing, labour, record keeping State and federal health and reporting system agencies

Community opinion Record-keeping system, Mosquito control agency Market goods (complaints) labour

Control using pesticides

Ground-based larviciding and Equipment Mosquito control agency Market goods adulticiding Labour Chemical costs Administration of program Staff training and certification

Environmental harm Chemical resistance Society Market and non-market Health effects on goods humans Effect on non-target organisms/ecosystem

Control using community education

Media releases Labour Mosquito control agency Market goods Website? Brochures Production costs of Distribution of mosquito Response to questions printed material control staff time spent on community education Maintenance of may be difficult to electronic material quantify.

Table 2.2: Identification of the resources consumed by mosquito control (cont.)

Mosquito control component Resource Costs are incurred by: Market or non-market requirements goods?

Control using habitat modification

Establishment and Equipment, labour, Mosquito control agency Market goods implementation of habitat permit costs. modification program Maintenance of Mosquito control agency Market goods modified sites

Environmental harm Effect on non-target Society Non-market goods organisms

Control using personal protection Repellents, mosquito Private citizens Market goods traps, bed nets, etc. Dispersed and difficult to quantify.

Evaluation of overall mosquito Labour Mosquito control agency Market goods control program Record keeping system Reporting system

Collaboration with other agencies Labour Mosquito control agency Market goods State and federal health agencies

Research Within agency Labour Mosquito control agency Market goods Equipment Consumables State and federal health agencies

Contribution to research Monetary contribution Market goods organisations In-kind contribution Labour component can be valued as a market good

Table 2.3: Identification of the benefits of mosquito control

Who accrues the Market or non-market Benefit benefits goods

Disease reduction in humans Avoided medical costs of Individual citizens Market goods diagnosis Government health agencies

Avoided medical costs of Individual citizens Market goods treatment Government Health agencies

Avoided loss of income Individual citizens Market goods

Avoided pain and suffering Individual citizens Non-market goods

Disease reduction in Avoided veterinary costs Individual citizens Market goods livestock and pets Livestock industry

Avoided loss of production in Livestock industry Market goods livestock

Avoided pain and suffering Livestock and pets Non-market goods

Protecting amenity Avoided negative impact on Society Non-market goods general amenity

Protecting tourism Avoided negative impact on Tourism industry Market goods tourist visitations

Protecting real estate values Avoided negative impact on Individual citizens Market goods real-estate value Real estate industry

Increasing worker Avoided negative impact on Commercial operators Market goods productivity worker productivity

2.3.5. Major findings from the literature on the economic evaluation of mosquito control

The key conclusions from this literature review are:

• There has been only a small amount of applied research into the costs and

benefits of mosquito control.

• Cost-effectiveness is the most common evaluation technique used (does

not question overall value of the program) and most cost-effectiveness

analysis has been concerned with the comparison of chemical and source

controls — but these results are not easily generalized.

• There have been few attempts to evaluate or quantify the reduction in

mosquito numbers, the reduction in disease rates, the reduction in

nuisance biting, or the impact of indirect control methods (community

education or planning tools).

Chapter 3: Research methodology

3.1. Methods for Research Question 1: ‘How much Ross River virus disease is avoided through local government mosquito control in Queensland?’

The methodology developed to answer Research Question 1 proceeded by first identifying the local governments with mosquito control strategies that are effective at reducing Ross River virus disease and, second, estimating the number of Ross

River virus disease cases that had been avoided in these local government areas.

3.1.1. Identifying local governments with mosquito control programs that are effective at reducing Ross River virus disease

The literature review has shown that the incidence of Ross River virus disease in each local government area in Queensland is a function of vector types, virus presence, host types and the level of immunity within the host and human populations. Rainfall, temperature and humidity affect the reproduction rates of vectors and hosts, mosquito control will constrain the vector populations, and the use of personal protection will decrease the risk to the human population of Ross River virus disease. Conceptually, these interactions between virus, vectors, hosts and humans are shown in Figure 3.1.

The literature shows that there is a positive correlation between Ross River virus disease and rainfall, and that this relationship is stronger for coastal locations than inland locations. In Queensland, annual rainfall increases with distance north, and consequently Ross River virus rates generally increase with distance north. This

53 relationship occurs for both coastal and inland locations, but is more pronounced for coastal locations.

Key factors contributing to Ross River virus infection rates

Vector type Host type Susceptible and and human abundance abundance abundance

Confounding factors

Coastal or Coastal or Seroprevalence inland location inland location

Rainfall Rainfall Lifestyle risk factors

Temperature Temperature Personal protection

Humidity Humidity

Mosquito Host population control Immunity

Data availability

Mosquito control These variables are These variables are These variables information is fixed effects that can fixed effects that can cannot be controlled collected by the be controlled by be controlled by for due to lack of mosquito control grouping local grouping local understanding and agencies (local governments with governments with information. government). similar geography. similar climate.

Figure 3.1: Conceptual model of Ross River virus infection

The model shown in Figure 3.1 indicates that estimating the Ross River virus infection rate in any locality requires accurate knowledge of each of the components in the system, such as climate, vector type and abundance, the amount of suitable vector habitat, and the immunity of the host and human populations. Complete modelling of the Ross River virus disease system would require extensive data, and

54 collection of this data would require a great deal of time and extensive resources. In many cases, the relationships between variables are not fully understood, and, given the state of research and current literature, it is not possible to fully and accurately model this system at present.

How, then, can we evaluate the effectiveness of mosquito control programs in reducing Ross River virus disease?

This research proceeds with a method that by-passes (not ignores) the complex range of data needs in the overall system. Although not all of the variables shown in Figure

3.1 can be measured, some can be treated as fixed effects by grouping local governments with similar characteristics. To illustrate this approach, the boxes within Figure 3.1 are coloured to indicate which variables can be treated as fixed effects, which variables can be measured and which variables are not able to be easily controlled.

The premise of this approach is that if one were to compare local governments with similar geography and climate, one would expect to see similar Ross River virus disease rates. This premise is supported by the research on Ross River virus that indicates the epidemiologies of Ross River virus disease vary with climate (rainfall, temperature and humidity) and geography (presence of vector species) (Russell

2002). If local governments have similar climates and geography, but significantly different Ross River virus rates, the presence or absence of mosquito control may explain these differences in disease rates.

The approach used to group the local governments in Queensland is described below.

Grouping local governments with similar geography and climate

The proximity of a local government area to the coast will influence the types of mosquitoes that will live there. For example, Ae. vigilax breeds in coastal saltmarsh, and therefore this species is not normally found inland; therefore, grouping local governments according to their coastal or inland location will allow some control over the vector types that are likely to be present. Grouping local governments according to climatic factors will allow one to control for the influence of rainfall, humidity and temperature. There are three climate classifications applied by the

Bureau of Meteorology for Australia that were considered for this research (BOM

2007). The first classification is based on seasonal rainfall (Figure 3.2), the second classification is based on temperature and humidity (Figure 3.3) and the third classification is based on temperature, humidity, rainfall and dominant vegetation

(Figure 3.4).

Figure 3.2: Australian climate zones based on seasonal rainfall Source: BOM (2007)

Figure 3.3: Australian climate zones based on temperature and humidity Source: BOM (2007)

Figure 3.4: Australian climate zones based on the Koeppen classification Source: BOM (2007)

This third classification, known as the Koeppen climatic classification (Stern et al. n.d), has been chosen as the most appropriate classification for the purpose of this research because it considers three aspects of climate (temperature, humidity and rainfall) — all important variables in Ross River virus ecology.

Between-group comparisons

The local governments in Queensland were grouped according to geographic location and Koeppen climatic classifications (using overlay functions with MapInfo software), and between-group comparisons were performed (using ANOVA functions within SPSS software) to investigate the distribution of Ross River virus disease across the state and to test whether the relationships between Ross River virus, geography and climate described in the literature do hold true for this data set.

One should expect to see higher Ross River virus disease rates in Northern

Queensland, and a stronger relationship between rainfall and Ross River virus disease in coastal locations.

Prior to any statistical analysis being performed, the distribution of the yearly Ross

River virus disease rates data was plotted to investigate whether the data were normally distributed. A one-way analysis of variance (ANOVA) was performed to ascertain whether there were statistically significant differences in the Ross River virus disease rates between the model groups. Where statistically significant differences occurred between groups, an extended t-test was performed to identify the differences.

Within-groups comparisons

For each of the model groups, within-group comparisons of the yearly Ross River virus rates were performed using a one-way analysis of variance (ANOVA). Where

58 the ANOVA revealed that there were differences in Ross River virus disease rates between local governments, an extended t-test was performed to identify which local governments were different. The presence (or absence) of mosquito control in these local governments was then investigated as a possible explanation of the significantly different Ross River virus disease rates.

3.1.2. Estimating avoided Ross River virus notifications

Where statistical analysis indicated that a local government had lower Ross River virus disease rates than other local governments in its group, and that local government had a mosquito control program, it was necessary to establish what the

Ross River virus rate would have been in the absence of the mosquito control program. This was needed to calculate, first, avoided Ross River virus disease and, second, the value of the resources subsequently saved by avoiding disease.

Once again, if the Ross River virus disease system (Figure 3.1) was fully understood, statistical analysis (such as regression) could be used to estimate the influence of each variable on Ross River virus rates and to quantify the effect of mosquito control. In the absence of a complete understanding of the Ross River virus disease system, an alternative, but logical, approach was to compare local government areas with similar fixed effects, but differing mosquito control programs.

Each local government that had Ross River virus disease and mosquito control was matched to a local government with comparative geography and climate, but no mosquito control program.

The potential Ross River virus disease rate was defined as the average of the yearly

Ross River virus rates that had actually occurred in the local government area that did no mosquito control. An estimate of the number of Ross River virus disease

59 notifications that had been avoided in each of the local government areas with effective mosquito control programs was then made by calculating the difference between the potential disease rate (without control) and the actual disease rate (with control) and applying this difference in rates to the population of the local government.

3.2. Methods for Research Question 2: ‘Does the monetary value of avoided Ross River virus disease exceed the financial cost of local government mosquito control programs in Queensland?’

This final component of the research involved the calculation of cost-benefit ratios to compare the financial costs of mosquito control to the financial benefits of avoided

Ross River virus disease.

3.2.1. Estimating the cost of mosquito control

The costs of mosquito control were collected from local governments using the hard- copy survey (described in Section 3.3.2). Where the cost of control was not provided by the local government, but it was known by the researcher that some control was undertaken, cost data were estimated using the most appropriate of the following methods:

1. Estimate the cost as the same as the most recent year for which information is

available. This method was used where the survey indicated similar practices

had been used across years.

2. Estimate the cost as an average of all known years. This method was used

where similar spending had occurred in known years.

3. Estimate the cost based on information provided in the survey about the

practices used or about changes to the program.

4. Estimate the cost from other available surveys, such as the survey undertaken

by the Local Government Association of Queensland in 1999 and the survey

undertaken by Queensland Health in 2002.

5. Assumed no mosquito control is performed and expenditure is zero.

The method used for estimation of each missing cost datum is recorded in Appendix

7.3.

3.2.2. Estimating the value of avoided Ross River virus disease

The research that has estimated the direct cost of having Ross River virus disease has been reviewed in Section 2.2.5 of this thesis. The most recent and reliable estimate of the cost of having Ross River virus disease is $1097 (AUD 2004) estimated by

Ratnayake (2005). The value of avoided Ross River virus notifications within local governments was estimated by multiplying the estimated number of avoided Ross

River virus cases for each year by the value $1097.

3.2.3. Calculating cost-benefit ratios

The final step in the analysis was to calculate the cost-benefit ratio for local governments that had avoided Ross River virus disease and to investigate the spatial and temporal patterns of these cost-benefit ratios. The cost benefit ratios were calculated as a ratio of the financial cost of mosquito control to the financial value of avoided disease. If a cost-benefit ratio exceeds one, the value of benefits of avoided

Ross River virus exceeds the cost of mosquito control. In contrast, if a cost-benefit

61 ratio is less than one, the cost of mosquito control exceeds the value of the benefits of avoided Ross River virus disease.

3.3. Data collection

The two data requirements for this research were Ross River virus notification data and local government mosquito control information.

3.3.1. Ross River virus notification data

The notifications of arboviral diseases in Queensland for the period 1992–2004 were obtained from Queensland Health. The Ross River virus notification data included the onset date, notification date, sex, age group, street name, locality, postcode and local government of each notified disease case. For reasons of confidentiality, the data set did not include the name or address of the person.

Microsoft Access software was used to calculate age- and sex-adjusted Ross River virus notification rates per 10 000 people for each local government area in

Queensland. Geoda software was used to apply empirical Bayes smoothing to the

Ross River virus data to adjust for the small populations of some local governments.

These Ross River virus disease rates were calculated for each financial year from

1993 to 2004.

3.3.2. Mosquito control data

The information required from local governments concerned the amount of money spent on mosquito control and the practices used in mosquito control. This information could only be obtained directly from each local government, as there is no centralised reporting of mosquito control budgets to the state government in

Queensland. A hard-copy mail survey was chosen as the most appropriate technique

62 to collect this information. The survey needed to collect detailed information about mosquito control costs and practices and required that the survey respondents investigate records from previous years, as several years of data were required from each local government to allow comparison with longitudinal patterns of disease rates. Further to this, the nature of mosquito control requires that mosquito control personnel’s day-to-day activities are varied, sometimes including field operations, and hence a hard-copy mail survey that could be completed by several staff members was envisaged to obtain better response rates then a one-off face-to-face or telephone interview or an electronic survey.

The physical distance to some of the local governments in Queensland meant that it was not possible for the researcher to visit each local government in person; in fact, where the survey indicated that no mosquito control was undertaken, travelling to such local government areas would not have been an effective or efficient use of the research resources.

The survey of mosquito control costs and practices in local government in

Queensland was based on the tailored design method described by Dillman (2000).

Dillman (2000) emphasises that the response rates and quality of response to a survey process will be a direct consequence of the conceptual approach to the survey, the visual design of the survey questionnaire, the implementation process and the sampling selection process. The tailored design method is underpinned by social exchange theory, which advocates that an effective survey process should ‘provide rewards for responding’, ‘reduce the perceived costs of responding’ and ‘promote trust in beneficial outcomes from the survey’. A schematic overview of the tailored design approach is shown in Figure 3.5.

These issues associated with establishing trust, increasing rewards and reducing social costs have been taken into account in the development of the survey of mosquito control costs and practices. To establish trust, the introductory letter named

Griffith University and Queensland Health as legitimate authorities conducting the research, a branded Griffith University pen was included with the survey forms, and a relationship for the exchange of information was created by asking whether the respondent wanted to receive a summary of survey results.

Source: Dillman (2000: 27)

Figure 3.5: Schematic overview of the tailored design perspective

To increase rewards, a covering letter acknowledged the importance of mosquito control for public health, thanked the respondent in advance, and offered the opportunity for the respondent to provide advice by including extra space for comments. To reduce social costs, the questionnaire was designed to be concise, and avoided inconvenience by giving opportunities to provide either detailed information or general information.

Dillman (2000) emphasises that conducting a survey of business or other organisations (such as local government), as opposed to a survey of individuals or households, requires additional considerations such as identifying the most appropriate respondent within the organisation. The most appropriate mosquito control staff member within each local government was identified: in some cases the researcher had prior knowledge, and in other cases a phone call to the local government was needed.

Dillman (2000) also recommends using an on-site cognitive interview to pilot test the survey instrument and the implementation approach. A pilot survey was conducted with a mosquito control professional, George Santagiuliana of Redland Shire

Council. This process was aimed at testing the design of the questions and confirming the appropriateness of the terminology used in the survey.

The survey was implemented as a process of five contacts, involving identifying the survey respondent, a pre-notice contact by phone or email, mailing the survey forms, sending a reminder postcard and sending a thank-you email. In cases where local governments did not respond to the reminder postcard, an additional reminder phone call was made. The covering letter, survey forms and reminder postcard are shown in

Figure 3.6, Figure 3.7 and Figure 3.8 respectively.

Figure 3.6: Covering letter sent with survey

Figure 3.6: Covering letter sent with survey (cont.)

Figure 3.7: Survey forms: Survey of mosquito control in Queensland local governments 1993–2004

Figure 3.7: Survey forms: Survey of mosquito control in Queensland local governments 1993–2004 (cont.)

Figure 3.8: Reminder postcard

The contact information for each local government surveyed is shown in Appendix

7.1 and the response rates to the survey and a summary of the survey responses are provided in Chapter 4.

3.4. Overview of methods

An overview of the methods developed to answer the research questions is shown in

Table 3.2. Each research question is described in terms of background information required, research methods and research outcomes.

Table 3.1: An overview of the research methods

Research questions Background knowledge Research methods Research outputs (Chapter 1) needed (Chapter 2) (Chapter 3) (Chapters 4 and 5)

Research question 1: An understanding of Calculate disease rates for Ross River virus rates for How much Ross River Mosquito control techniques each local government. local governments in virus disease is avoided and terminology. Queensland. through mosquito control Classify local in local governments in An understanding of the governments according to A conceptual model of Queensland? ecology and epidemiology of variables that influence Ross River virus disease. Ross River virus needed to Ross River virus disease. identify the variables that influence Ross River virus Survey local governments disease rates. to collect data about the Knowledge of mosquito financial costs of control costs and practices mosquito control and for each local government practices used. in Queensland 1993-2004.

Use ANOVA to test for A statistical comparison of statistical differences in Ross River virus rates Ross River virus rates between local governments between local with and without mosquito governments. control.

An estimate of the number Calculate avoided Ross of Ross River virus cases River virus notifications avoided through mosquito in local governments with control. effective mosquito control programs.

Research Question 2: An understanding of the Compare financial costs Cost-benefit ratios for local How does the monetary economic evaluation of of control (collected from governments with effective value of avoided Ross mosquito control. survey) to financial value mosquito control programs. River virus disease of avoided disease compare with the financial Estimates of the cost of (estimate of avoided cost of local government having Ross River virus disease from Research mosquito control programs disease (i.e. the value of one Question 1 multiplied by in Queensland? avoided case of Ross River the financial value of one virus disease). case of avoided disease)

Chapter 4: Results and discussion

This chapter first presents a summary of the responses to the survey of mosquito control costs and practices in Queensland, and second addresses the research questions using the methods outlined in Chapter 3.

4.1. Survey implementation and response rates

The implementation of the survey began on 1 March 2005 and proceeded over a six- month period, with most of the survey implementation being complete in the first three months. A summary of the survey implementation information is provided in

Appendix 1.

Each of the 125 local governments in Queensland was surveyed and 83 of these responded. There were 53 local governments that returned the survey within the requested time. Reminders were posted to 70 local governments and 30 more surveys were returned following the reminder postcard. The use of the reminder postcard increased the survey response rate from 43% to 66% of local governments.

There were 42 local governments that did not respond to the survey, and it is known from other sources that nine of these do some mosquito control. The local governments that do mosquito control, but did not respond to the survey, are

Atherton Shire Council, Boonah Shire Council, Bowen Shire Council, Cloncurry

Shire Council, Hinchinbrook Shire Council, Longreach Shire Council, Redcliffe City

Council, Rockhampton City Council and Waggamba Shire Council. The remaining

33 local governments that did not respond to the survey probably do no mosquito control, as many of them are in remote rural areas with small populations.

Most of the coastal local governments responded to the survey, with the exception of

Bowen Shire Council and Redcliffe City Council. The local governments that responded to the survey represent the majority of the Queensland population.

4.1.1. Factors affecting response rates

Factors that negatively affected the response rates to this survey relate to the transient nature of environmental health officers in rural and remote local governments, lack of formal record-keeping systems, changes in record-keeping systems over time, institutional knowledge and changes in mosquito control personnel during the survey process.

Many rural and remote local governments do not employ a full-time environmental health officer, and mosquito control work is contracted to a consultant environmental health officer who is often also contracted to neighbouring local governments. In cases where mosquito control is done on a complaint-driven basis, record-keeping is often minimal.

The survey requested several years of information, and hence required record- searching, meaning that the survey was time-consuming to complete. In many local governments, record-keeping systems have changed over time and a combination of hard-copy and digital records may have needed to be consulted.

In many local governments, institutional knowledge of mosquito control programs has been lost when a specific staff member has ceased working for that local government. In many cases, this meant that the staff member completing the survey could only provide information for the time period during which they had been employed by the local government.

In a few local governments, mosquito control staff changed during the period when the survey was being implemented and partially completed surveys were passed to new staff to complete, or were lost. In two instances, new survey forms were required.

4.1.2. Summary of survey results

The survey results provide two important types of information: first, they show the number of local governments that were able to provide a response to each question; and second, they reveal what that response was. The first type of information is important when assessing the reliability and validity of inferences made from these survey responses. The second type of information is the data required to compare and contrast local governments. A summary of responses to the survey is provided below and in Appendix 2.

Response to Question 1

Question 1 asked whether the respondent would like to receive a summary of the results of the survey. Most of the local governments that responded to the survey answered yes to this question: 72 out of 83 responses (87%). The high ‘yes’ response to this question indicates that there is a genuine interest in issues relating to mosquito control at the local government level in Queensland.

Response to Question 2

Question 2 asked ‘Which section of council undertakes mosquito control in your local government area?’ In most local governments, mosquito control activities are located within a broad Health, Environmental Health, or Community Health section.

Some larger local governments have mosquito control activities undertaken by a

76 distinct Vector Control or Pest Control unit, but these units may also be responsible for activities other than mosquito control.

Response to Question 3

Question 3 asked ‘How many employees are involved in mosquito control activities in your local government area?’ The survey showed that there were at least 150 local government employees involved in mosquito control in Queensland in 2005. It is difficult to estimate the number of full-time equivalent (FTE) positions, as many of these staff may be involved in a range of environmental or pest control tasks.

These employee figures do not include contractors, consultant environmental health officers, consultant mosquito professionals or Queensland Health staff. In addition, responses to the survey indicated that, in some local governments, the number of staff involved in mosquito control varies from year to year depending on rainfall and disease outbreaks.

Response to Questions 4 and 5

Question 4 of the survey asked whether the local government had documents that outlined its mosquito control strategy. A total of 32 of the 83 local governments that responded to the survey did have documentation that outlined their mosquito control strategy. Question 5 asked for copies of the documents and 20 of the local governments provided a copy of their mosquito control documentation with the survey.

Documentation types included mosquito control strategies that relate specifically to the local government and that outlined the surveillance, control and evaluation procedures of the program, community education brochures and fact sheets (some web-based), memoranda of understanding for contiguous local government groups,

77 local government policies, dengue management plans, service agreements between council departments and general documents such as the Australian Mosquito Control

Manual (MCAA 2002) and the Mosquito Management Code of Practice for

Queensland (LGAQ 2002).

The documentation provided by each local government provides information about the nature of the mosquito control program and allows each program to be classified according to the triggers for treatment (pre-emptive or complaint-driven), evaluation measures used, species targeted (freshwater or saltwater), resource requirements and future outlook for the program. Examination of the documentation elicited information that was not easily obtained from a survey questionnaire.

Response to Question 6

Question 6 asked ‘How are mosquito breeding sites identified?’ There were 71 responses to this question. While four local governments use complaints as the means to identify mosquito habitat, the majority of local governments use a combination of methods to identify mosquito habitat. A combination of local knowledge and complaints is used by 18 local governments, a combination of local knowledge, ground surveys and house-to-house inspections are used by 24 local governments, and 17 local governments use a combination of local knowledge, historical data and aerial photography to identify mosquito habitat. Some local governments only identify mosquito sites around the towns within their jurisdiction, and eight local governments responded that they did not identify mosquito habitat.

Response to Questions 7 to 10

Questions 7 to 10 asked for information about maps of the mosquito sites in their jurisdiction. Twenty local governments have maps of mosquito breeding habitat; four

78 local governments responded that their maps were hard-copy, hand-coloured maps;

12 local governments have GIS versions of their mosquito sites and use MapInfo,

Arcview, Arcgis, Latitude or Easimaps GIS software. Thirteen local governments provided copies of maps with the survey either in hard copy or digital form.

Response to Question 11

Question 11 asked for information about the physical characteristics of known mosquito sites in the local government area. The responses to this question can be used to assess the reliability of mosquito habitat information. The majority of local governments in Queensland do not have reliable estimates of mosquito habitat within their jurisdictions.

Responses to Question 12

Question 12 asked local governments to indicate mosquito control treatment methods which they had used in the years 1993–2004. Some 53% of local governments had done some control in at least one year between 1993 and 2004. Of the local governments that responded to the survey, 10% had used aerial larviciding, 39% had used ground-based larviciding, 19% had used ground-based adulticiding, 3.5% had used habitat modification and 28% had used community education. Most local governments had used a combination of treatment methods.

Response to Question 13

Eighty of the 83 responding local governments were able to provide cost information for some of the years between 1993 and 2004. Generally, councils were able to provide more reliable information for recent years, and some local governments were able to provide information for all years between 1993 and 2004. Of the 83 local governments that responded to the survey, 24 did not spend any money on mosquito

79 control in the years 1993–2004. Thirty-eight of the remaining 59 were able to supply a breakdown of costs for one or more years.

The estimated total cost of mosquito control to local governments in Queensland during the years 1993 to 2004 is summarised in Table 4.1. The costs have been adjusted to 2004 values using implicit price deflators for state and local governments obtained from the Australian Bureau of Statistics.

Table 4.1: Estimated cost of mosquito control to local governments in Queensland (1993–2004)

Number of Number of local local Cost of Total cost of governments governments mosquito mosquito where Number of local where cost of control control mosquito governments that mosquito (data (including control costs spent zero dollars control was obtained estimation Financial was provided on mosquito estimated by from the of missing year by the survey control the researcher survey) values).

AUD 2004 AUD 2004 1993 37 26 62 $961 614 $6 977 520 1994 37 26 62 $1 066 430 $7 041 597 1995 38 26 61 $1 175 808 $7 471 829 1996 42 27 56 $1,105,165 $7 424 644 1997 43 27 55 $1 702 026 $7 945 537 1998 47 27 51 $4 583 907 $8 199 411 1999 65 27 33 $7 013 881 $8 935 059 2000 67 25 33 $8 920 273 $9 193 567 2001 61 25 39 $6 303 249 $9 389 889 2002 67 24 34 $7 507 190 $9 723 282 2003 70 24 31 $8 463 838 $10 357 990 2004 80 24 21 $9 496 958 $10 707 417

4.2. Results for Research Question 1: ‘How much Ross River virus disease is avoided through local government mosquito control in Queensland?’

The classification of Queensland local governments according to their proximity to the coast and the Koeppen climate classification resulted in the 11 groups shown in

Figure 4.1. These groups are named according to their geographical location, coastal location and climate classification as follows:

1. Southern Queensland coastal subtropical local governments

2. Central Queensland coastal subtropical local governments

3. Northern Queensland coastal tropical local governments

4. Far Northern Queensland coastal tropical local governments

5. Far Northern Queensland inland subtropical local governments

6. Central Queensland inland subtropical local governments

7. Northern Queensland inland grassland local governments

8. Southern Queensland inland grassland local governments

9. Southern Queensland inland subtropical local governments

10. Southern Queensland inland temperate local governments

11. Southern Queensland inland desert local governments

Classification names CQ Coastal Subtropical CQ Inland Subtropical FNQ Coastal Tropical FNQ Inland SubTropical NQ Coastal Tropical NQ Inland Grassland SQ Coastal SubTropical SQ Inland Desert SQ Inland Grassland SQ Inland Subtropical SQ Inland Temperate FNQ Coastal Tropical

FNQ Inland SubTropical

NQ Coastal Tropical

NQ Inland Grassland

CQ Coastal SQ Inland Desert Subtropical CQ Inland Subtropical

SQ Inland SQ Inland Grassland Subtropical SQ Coastal SubTropical

SQ Inland Temperate

Figure 4.1: Classification of local governments

The Ross River virus notification data were plotted to investigate whether they were normally distributed (Graph 4.1) and it was found that a logarithmic transformation of the Ross River virus disease rate data (Graph 4.2) resulted in a better fit to the normal curve; hence all statistical analysis has been performed on the logarithmic transformation of the data.

Graph 4.1: Untransformed distribution of annual Ross River virus rates for local governments in Queensland 1993–2004

Graph 4.2: Logarithmic transformed distribution of annual Ross River virus rates for local governments in Queensland 1993–2004

4.2.1. Between-group differences

An ANOVA to compare the between-group differences was performed to investigate the differences in Ross River virus disease rates across the state (Table 4.2). This

ANOVA revealed that there were statistically significant differences between groups and an extended t-test was then performed to identify the differences.

Table 4.2: ANOVA results investigating differences in Ross River virus disease rates between model groups

Sum of Mean Squares df Square F Sig. Between groups 20.367 10 2.037 22.350 .000 Within groups 135.691 1489 .091 Total 156.058 1499

The mean and the 95% confidence interval for the Ross River virus disease rates for each of the 11 local government groups are shown in Graph 4.3.

Graph 4.3: The mean annual Ross River virus disease rates and the 95% confidence interval for the 11 model groups in Queensland

Group 1 represents the southernmost coastal local governments, Group 2 represents centrally located coastal local governments and Group 3 represents northernmost coastal local governments. The relationship between Ross River virus disease, geography and climate is very pronounced for these coastal local government groups, and clearly confirms that Ross River virus disease rates increase with distance north.

Group 4 represents local governments located in the coastal regions of Far North

Queensland; however, due to the unreliable nature of notification data from these local governments, no reliable conclusions can be drawn.

Local government Groups 6, 7, 8, 9 and 11 are inland local governments and the relationships between Ross River virus disease, geography and climate are less pronounced. There are no significant differences in Ross River virus disease rates between these model groups.

Groups 5 and 10 represent inland local governments that experience a milder climate

(lower temperature and humidity) because they are located in regions of higher altitude. The local governments in Group 5 and Group 10 show relatively lower Ross

River virus disease rates compared with other inland model groups, and this difference is statistically significant for Group 10 local governments.

4.2.2. Within-group differences

Each of the 11 model groups is discussed below in terms of their location within

Queensland, the number of local governments in the group, the percentage of the

Queensland population (2004), patterns of human settlement and general climate characteristics. As outlined in Section 3.1.1, statistical testing of the differences in

Ross River virus rates between local governments was performed using ANOVA and extended t-test. Where statistically significant differences occurred, the nature of

85 mosquito control performed by local governments within the group was examined to indicate the likely influence of mosquito control on disease rates.

Group 1: Southern Queensland coastal subtropical local governments

Group 1 local governments are located in the coastal region of Southern Queensland

(shown in Figure 4.2). This group of local governments represents approximately

60% of the Queensland population and includes the major urban localities of

Brisbane (the state capital), the Sunshine Coast and the Gold Coast.

Noosa (S)

Maroochy (S)

Caloundra (C)

Caboolture (S)

Pine Rivers (S)

Brisbane (C) Mean annual Ross River virus disease rate per 10 000 Redland (S) Logan (C) 0 to 5 5 to 10 10 to 15 15 to 20 20 to 25 25 to 30 Gold Coast (C)

Figure 4.2: Group 1 Southern Queensland coastal subtropical local governments

The southern coastal region of Queensland has a subtropical climate and receives predominantly summer rainfall. Local governments located in the northern area of this group (Noosa Shire Council, Maroochy Shire Council, Caloundra City Council and Caboolture Shire Council) receive, on average, more than 1200 mm of rainfall

per year, while the local governments located in the southern area of the group

(Redcliffe City Council, Pine Rivers Shire Council, Brisbane City Council, Redland

Shire Council, Logan City Council and Gold Coast City Council) receive slightly

less average annual rainfall of between 600–1200 mm.

Table 4.3 lists the local governments that form Group 1, along with the population

(2004), mosquito control expenditure (2004) and the average and standard deviation

of the annual Ross River virus disease rates. The yearly population, mosquito control

expenditure and Ross River virus notification rates for each year from 1993 to 2004

are provided in Appendix 7.3.

Table 4.3: Population, mosquito control expenditure and Ross River virus disease statistics for Group 1 local governments

Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates Local (2004) (1993–2004) (1993–2004) government Population (2004) $ AUD per 10 000 per 10 000 Brisbane (C) 941 208 3 210 743 3.57 2.81 Caboolture (S) 126 169 712 200 7.27 4.10 Caloundra (C) 85 469 245 917 6.65 4.06 Gold Coast (C) 453 199 1 449 661 2.93 2.07 Logan (C) 172 299 317 734 3.39 3.09 Maroochy (S) 138 142 300 000 8.07 4.45 Noosa (S) 45 725 125 191 11.52 8.04 Pine Rivers (S) 138 404 304 962 5.76 4.31 Redcliffe (C) 52 043 480 000 4.69 2.80 Redland (S) 127 777 710 000 4.75 3.78 (C) City Council,

(S) Shire Council

Redcliffe City Council was the only local government within Group 1 that did not

respond to the survey of mosquito control costs and practices; however, some

information about mosquito control in Redcliffe City Council was obtained from the

Local Government Association of Queensland (LGAQ 2000) and Queensland

Health. There are reliable estimates of saltwater breeding habitats for most of these local governments; however, estimates of freshwater breeding habitats are less reliable.

The results of an ANOVA comparing the mean Ross River virus rates between the local governments within Group 1 revealed that there are significant differences in the mean Ross River virus rates between at least two of the local governments (Table

4.4). An extended t-test was then performed to identify these differences. The mean and the 95% confidence interval for the Ross River virus disease rates for each of the

Group 1 local government groups are shown in Graph 4.4.

Table 4.4: ANOVA results for within-group differences: Group 1 local governments

Sum of Mean Squares df Square F Sig. Between groups 2.797 9 .311 4.906 .000 Within groups 6.969 110 .063 Total 9.766 119

When considering long-term average yearly Ross River virus disease rates, the local governments within Group 1 cluster into two sub-groups, one to the north and one to the south of the region. There is no statistically significant difference in Ross River virus disease rates between the southern located local governments of Brisbane City

Council, Gold Coast City Council, Logan City Council, Redcliffe City Council and

Redland Shire Council. These local governments also have relatively lower standard deviations of the average Ross River virus rate. Likewise, there is no statistically significant difference between the northern located local governments of Caboolture

Shire Council, Caloundra City Council, Maroochy Shire Council and Noosa Shire

Council. However, these northern local governments have significantly higher Ross

River virus disease rates than the local governments located in the southern region of

Group 1.

Graph 4.4: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 1 local governments

The variation in Ross River virus disease rates within this group of local governments was considered in relation to the type of mosquito control undertaken, and this analysis revealed that mosquito control may account for this variation. While all of the local governments within Group 1 perform some mosquito control, there is a wide range of budgets and program types. A summary of the approach to mosquito control in each of the Group 1 local governments is provided in Table 4.5.

Each of the 10 local governments in Group 1 undertakes extensive aerial larviciding activities that focus on the control of the saltmarsh species Ae. vigilax. The local governments that have significantly lower Ross River virus disease rates also all have freshwater mosquito control strategies incorporated into their programs. For example, Brisbane City Council, Gold Coast City Council and Logan City Council have extensive freshwater control programs in addition to saltwater mosquito control.

Table 4.5: Approach to mosquito control in Group 1 local governments

Local government Program details

Brisbane (C) Extensive freshwater and saltwater control with routine surveillance all year. Aerial larviciding and ground-based larviciding in all years from 1993–2004. Member of North East Moreton Mosquito Organisation (NEMMO).

Caboolture (S) Freshwater sites surveyed monthly and treated quarterly. All saltwater sites surveyed after heavy rainfall or high tides. Aerial larviciding, ground-based larviciding and ground- based adulticiding in all years from 1993–2004. Member of NEMMO.

Caloundra (C) Temporary freshwater sites surveyed after rainfall. Permanent freshwater sites not currently identified. Saltwater sites surveyed weekly/fortnightly from August to May. Aerial larviciding in all years from 1993–2004. Ground-based larviciding increased in recent years. Use development control near mosquito breeding areas. Member of Sunshine Coast Mosquito Control Committee (SCMCC).

Gold Coast (C) Extensive freshwater and saltwater control with routine surveillance all year. Aerial larviciding and ground-based larviciding, ground-based adulticiding in all years from 1993– 2004. Some habitat modification implemented in 2001 and 2004. Member of Contiguous Local Authority Group (CLAG).

Logan (C) Extensive freshwater and saltwater control with routine surveillance all year. Aerial larviciding and ground-based larviciding, in all years from 1993–2004. Member of CLAG.

Table 4.5: Approach to mosquito control in Group 1 local governments (cont.)

Local government Program details Maroochy (S) Focus on saltwater control. Aerial larviciding and ground- based larviciding, in all years from 1993–2004. Some habitat modification from 1993–2000. A review of the program in 2002 recommended expansion of surveillance and treatments. Member of SCMCC.

Noosa (S) Focus on saltwater control. Aerial larviciding, ground-based larviciding and ground-based adulticiding in all years from 1993–2004. Some habitat modification in 1993 and 1998. Extensive areas of freshwater breeding habitat identified, but not treated. Member of SCMCC.

Pine Rivers (S) Focus on saltwater control. Freshwater program developed in 2000 (Wrights 2000). Aerial larviciding, ground-based larviciding and ground-based adulticiding from 2000–2004 (prior years unknown). Member of NEMMO.

Redcliffe (C) Limited information due to non-response to survey. Focus on saltwater control. Control program reviewed in 2002 with increase in freshwater control recommended. Member of NEMMO.

Redland (S) Extensive saltwater control with many saltwater sites located on Moreton Bay islands. Some freshwater control on the mainland. Aerial larviciding in all years from 1993–2004. Ground-based larviciding and habitat modification from 1996–2004. Member of CLAG.

Gold Coast City Council has had the lowest average Ross River virus notification rate for the years from 1993 to 2004, and this is significantly lower than Caboolture

Shire Council, Caloundra City Council, Maroochy Shire Council, Noosa Shire

Council and Pine Rivers Shire Council (all of which are located in the northern region of this group). Pine Rivers Shire Council has had lower Ross River virus rates than northern local governments, but higher rates than southern local governments,

91 and this could be due to the influence of extensive control practices in neighbouring local government areas.

Consideration of the mosquito control practices used in these local government areas indicates that Ross River virus notification rates are consistently lower in those local government areas that undertake both extensive freshwater and saltwater control, compared with programs including saltwater control only. The lower standard deviations in the Ross River virus disease rates shown in the local governments that incorporate extensive freshwater control indicate that freshwater control may be playing an important role in preventing Ross River virus epidemics in these local government areas.

Group 2 Central Queensland coastal subtropical local governments.

Group 2 includes 10 local government areas that are located on the east coast of central Queensland (shown in Figure 4.3), and represented approximately 8% of the

Queensland population in 2004. Major urban centres located in this region include

Rockhampton, Gladstone, Hervey Bay and Bundaberg. Much of this region is rural, with grazing being a major industry.

The central coastal region of Queensland has a subtropical climate and a summer seasonal rainfall classification, with all local governments in the group receiving average annual rainfall between 600 and 1200 mm. A small northern portion of

Livingstone Shire Council receives more than 1200 mm annually.

Eight of the local governments in Group 2 responded to the survey. Calliope Shire

Council did not respond; however, it is known from the survey from Gladstone City

Council (which neighbours Calliope Shire) that no mosquito control is undertaken and that mosquito control undertaken by Gladstone City may have cross-boundary

92 effects into Calliope Shire. Rockhampton City Council did not respond to the survey and it is known from other sources (MCAA survey and Queensland Health Survey) that control is performed. Some information about Rockhampton City Council’s mosquito control practices and costs has been obtained from these alternative sources.

Livingstone (S)

Rockhampton (C)

FitzroyFitzroy (S)(S) Gladstone (C)

Calliope (S)

Miriam Vale (S)

Mean annual Ross River virus disease rate Bundaberg (C) per 10 000 Burnett (S)

0 to 5 5 to 10 Hervey Bay (C) 10 to 15 15 to 20 Maryborough (C) 20 to 25 25 to 30

Figure 4.3: Central Queensland coastal subtropical local governments

Table 4.6 lists the local governments that form Group 2, along with the population

(2004), mosquito control expenditure (2004), and the average and standard deviation of the annual Ross River virus disease rates. The yearly population, mosquito control expenditure and Ross River virus notification rates for each year from 1993 to 2004 are provided in Appendix 7.3.

Table 4.6: Population, mosquito control expenditure and Ross River virus disease statistics for Group 2 local governments

Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local Population government (2004) AUD 2004 per 10 000 per 10 000

Bundaberg (C) 45 378 10 000 7.25 4.97 Burnett (S) 25 635 13 310 3.50 2.43 Calliope (S) 15 963 0 13.94 8.42 Fitzroy (S) 10 265 4 285 10.21 6.30 Gladstone (C) 28 380 150 000 11.40 5.67 Hervey Bay (C) 48 153 398 453 5.40 2.53 Livingstone (S) 27 760 20 000 12.22 6.73 Maryborough (C) 25 231 8 950 7.58 4.02 Miriam Vale (S) 5 008 0 16.93 14.54 Rockhampton (C) 59 134 132 710 12.87 9.85 (C) City Council, (S) Shire Council

Comparing the mean Ross River virus rates between the local governments within

Group 2 revealed significant differences in the mean Ross River virus rates between

at least two of the local governments. The results of the ANOVA are shown in Table

4.7.

Table 4.7: ANOVA results for within-group differences: Group 2 local governments

Sum of df Mean F Sig. Squares Square Between groups 3.372 9 .375 6.449 .000 Within groups 6.392 110 .058 Total 9.764 119

An extended t-test was performed to ascertain which local governments were

significantly different. The mean and the 95% confidence interval for the Ross River

94 virus disease rates for each of the Group 2 local governments are plotted in Graph

4.5.

Graph 4.5: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 2 local governments

The variation in Ross River virus disease rates within Group 2 was then considered in relation to the type of mosquito control undertaken in each local government. A summary of the approach to mosquito control in each of the Group 2 local governments is provided in Table 4.8.

Eight of the 10 local governments in Group 2 have a mosquito control program. The smallest program is that of Fitzroy Shire Council with expenditure of $4285 in 2004, and the largest program is Hervey Bay City Council, which had expenditure of

$398 453 in 2004. Calliope Shire Council and Miriam Vale Shire Council do not perform any mosquito control in their jurisdictions and their Ross River virus disease

95 rates are higher than all other local governments in Group 2; and, are significantly higher than those for Bundaberg City Council, Burnett Shire Council, Hervey Bay

City Council and Maryborough City Council, all of which do control.

Table 4.8: Approach to mosquito control in Group 2 local governments

Local government Program details

Bundaberg (C) Freshwater control, sites surveyed after rain.

Burnett (S) Freshwater and saltwater control, sites surveyed twice weekly (Sept–April).

Calliope (S) No control.

Fitzroy (S) Freshwater control is reactive to complaints, saltwater control undertaken by Capricorn Mosquito Management Committee (aerial larviciding began in 2002).

Gladstone (C) Focus on saltwater control at Port development sites. Aerial larviciding began in 2003.

Hervey Bay (C) Freshwater sites surveyed fortnightly all year, saltwater sites surveyed fortnightly in August to May. Aerial larviciding has occurred since 1995.

Livingstone (S) Limited freshwater control, saltwater control undertaken by Capricorn Mosquito Management Committee.

Maryborough (C) Freshwater and saltwater sites routinely surveyed from October to March. All treatments are ground-based larviciding.

Miriam Vale (S) No control.

Rockhampton (C) Limited information due to non-response to survey. Saltwater control is undertaken by Capricorn Mosquito Management Committee.

Hervey Bay City Council has the most extensive mosquito control program, and has had the lowest Ross River virus disease rates. Hervey Bay City Council also has the lowest standard deviation of annual Ross River virus rates, indicating stability in disease rates across years. Interestingly, Burnett Shire Council also has comparatively low Ross River virus disease rates — in fact, much lower than neighbouring local governments that have much higher mosquito control expenditure. The low Ross River virus disease rates in Burnett Shire Council may be due, first, to the council’s focus on control of both freshwater and saltwater mosquito species and, second, to its routine surveillance of breeding sites from September to

April, as opposed to surveillance regimes that are triggered by significant rainfall events (Bundaberg City Council) or complaints (Fitzroy Shire Council). It is also possible that Burnett Shire Council receives some protection from the large-scale mosquito control program in the neighbouring Hervey Bay City Council.

Consideration of the mosquito control practices used in Group 2 local governments indicate that Ross River virus notification rates are significantly lower for those local governments that have extensive mosquito control compared with those that do little or no mosquito control. Local governments that undertake both freshwater and saltwater control (compared to programs including saltwater control only) show lower mean and standard deviations in Ross River virus disease rates, indicating that freshwater control may be playing an important role in preventing Ross River virus epidemics in these local government areas.

There is also evidence (from Burnett Shire Council) that small-scale mosquito control programs can be effective at reducing disease if they incorporate routine surveillance to pre-empt mosquito breeding problems (and then trigger a treatment)

97 rather than relying on complaints of mosquito nuisance to indicate that mosquito abundance has increased.

Group 3 Northern Queensland coastal tropical local governments

Group 3 is located on the east coast of northern Queensland (shown in Figure 4.4) and includes 12 local governments that represented 12% of the Queensland population in 2004.

Douglas (S)

Cairns (C) JohnstoneJohnstone (S)(S)

Cardwell (S)

Hinchinbrook (S)

Townsville (C) Thuringowa (C)

Mean annual Ross River virus disease rate Burdekin (S) per 10 000 Whitsunday (S)

0 to 5 5 to 10 10 to 15 15 to 20 Bowen (S) 20 to 25 25 to 30 Mackay (C)

SarinaSarina (S)(S)

Figure 4.4: Northern Queensland coastal subtropical local governments

Group 3 is characterised by a tropical climate in the north and subtropical climate in the south. Dominant rainfall occurs in summer, with the northern regions receiving in excess of 1200 mm annually and the southern regions receiving 600-1200 mm annually. Table 4.9 lists the local governments that form Group 3, along with the population (2004), mosquito control expenditure (2004) and the mean and standard deviation of Ross River virus notification rates per 10 000 people. The yearly

population, mosquito control expenditure and Ross River virus notification rates for

each year from 1993 to 2004 are provided in Appendix 7.3.

Table 4.9: Population, mosquito control expenditure and Ross River virus disease statistics for Group 3 local governments

Bowen (S) 12 325 7 000 11.91 6.30 Burdekin (S) 18 381 216 000 11.61 6.12 Cairns (C) 114 665 461 161 9.95 6.92 Cardwell (S) 10 437 13 000 24.25 14.99 Douglas (S) 8 805 47 577 29.20 12.00 Hinchinbrook (S) 12 139 29 079 11.36 6.97 Johnstone (S) 19 091 0 13.02 4.86 Mackay (C) 79 350 434 000 10.29 8.31 Sarina (S) 9 950 11 157 10.99 7.61 Thuringowa (C) 57 249 47 976 16.92 12.35 Townsville (C) 95 817 340 000 20.10 13.49 Whitsunday (S) 14 731 30 000 11.74 7.73 (C) City Council, (S) Shire Council

Bowen Shire Council, Hinchinbrook Shire Council and Johnstone Shire Council did

not respond to the survey of mosquito control costs and practices; however, some

information about mosquito control in these local governments was obtained from

the Local Government Association of Queensland (LGAQ 2000) and Queensland

Health (2002).

The results of an ANOVA comparing the mean Ross River virus rates between local

governments in Group 3 have shown that there are significant differences in mean

Ross River virus rates between at least two of the local governments in this group

(Table 4.10). The means and 95% confidence interval for Group 3 local governments are shown in Graph 4.6.

Table 4.10: ANOVA results for within-group differences: Group 3 local governments

Sum of df Mean F Sig. Squares Square Between groups 2.999 11 .273 4.150 .000 Within groups 8.670 132 .066 Total 11.668 143

Graph 4.6: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 3 local governments

The variation in Ross River virus disease rates between Group 3 local governments was considered in relation to the nature of mosquito control undertaken. A summary

100 of the approach to mosquito control in each of the Group 3 local governments is provided in Table 4.11.

Table 4.11: Approach to mosquito control in Group 3 local governments

Local government Program details Bowen (S) Limited information due to non-response to survey.

Burdekin (S) Freshwater and saltwater control with routine surveillance, aerial larviciding and ground-based larviciding from 2000 to present. Respondent was unsure of mosquito control undertaken prior to 2000.

Cairns (C) Freshwater and saltwater control with routine surveillance of permanent breeding sites. Ground-based larviciding and adulticiding from 1993–2004. Aerial larviciding performed after cyclone in 2000. Dengue vectors are a focus of control.

Cardwell (S) Ground-based larviciding and adulticiding from 1995–2004. No mosquito control performed in 73% of the local government as the land is Wet Tropics World Heritage Area, National Park or protected mangrove habitat.

Douglas (S) Main focus is eradication of potential dengue mosquito breeding sites.

Hinchinbrook (S) Limited information due to non-response to survey.

Johnstone (S) Limited information due to non-response to survey.

Mackay (C) Freshwater and saltwater control with routine surveillance of permanent breeding sites. Ground-based larviciding and adulticiding from 1993–2004. Aerial larviciding performed since 2004.

Sarina (S) Complaint driven freshwater and saltwater control. Inspections of premises for dengue control when needed.

Thuringowa (C) Saltwater control with aerial larviciding since 1998. No freshwater control. Inspections of premises for dengue control when needed.

Townsville (C) Freshwater and saltwater control with routine surveillance during summer. Records prior to 2002 are incomplete.

Whitsunday (S) Freshwater and saltwater control with routine surveillance during summer. Ground-based larviciding from 1993–2004 and ground- based adulticiding from 2000–2004. Spending increased from $3000 to $30 000 in 2003.

101

There is high variability between Group 3 local governments in both the mean and the standard deviation of the Ross River virus disease rates. Many of the local governments in this group do have mosquito control programs; however, there is a wide range of expenditure and approaches to mosquito control. The relationship between mosquito control and Ross River virus disease rates in Group 3 is complicated by several confounding factors.

Many of the local governments in Group 3 need to focus on the control of the dengue vector (Ae. aegypti) in addition to Ross River virus vectors, and as the dengue mosquito is a freshwater container-breeding mosquito, considerable resources are consumed by the need to perform inspections of premises, but this would have little impact on the Ross River virus vectors.

Cardwell Shire Council and Douglas Shire Council have a portion of their jurisdictions listed under the World Heritage Wet Tropics rainforest area, and these areas are subject to monsoonal climates with no dry season, conditions that may result in year-long transmission of Ross River virus.

Several of the Group 3 local governments have expanded their mosquito control programs in recent years. Whitsunday Shire Council increased mosquito control expenditure tenfold from 2002 to 2003, and Mackay City Council implemented aerial larviciding in 2004; however, Ross River virus disease rates would need to be evaluated in future years to examine the effect of these expanded mosquito control strategies on long-term disease rates.

The evidence from Group 3 indicates that the variability in Ross River virus disease rates is complicated by many factors and the influence of mosquito control practices is not clear.

102

Group 4 Far northern Queensland coastal tropical local governments.

Group 4 consists of six local governments that are located in the Far Northern

Coastal region of Queensland (Figure 4.5). This group of local governments is characterised by dispersed human settlement patterns and contained less than 1% of the state’s population in 2004.

The region is characterised by several climate groups, including equatorial at the northern tip of the Cape, with tropical savanna and grassland in the lower gulf region. The climate is characterised by a marked wet summer and dry winter, the northern region of Group 4 receives annual average rainfall in excess of 1200 mm and the southern region receives annual average rainfall of 650–1200 mm.

Due to the remote nature of the Group 4 local governments, it is likely that the Ross

River virus disease rates under-estimate the true disease incidence — long distances to travel to diagnostic facilities and knowledge that Ross River virus infection is self- limiting may result in many people not seeking confirmation of their condition via laboratory testing. For example, while Aurukun Shire Council shows low adjusted

Ross River virus rates (no notifications), it is likely that this is inaccurate and that the larger local government of Cook Shire (where more medical facilities are available) may be allocated notifications from the remote areas. In addition, these local governments incorporate some island communities and it is known that small, isolated and non-transient populations have shown higher immunity to Ross River virus.

Table 4.12 lists the local governments that form Group 4, along with the population

(2004), mosquito control expenditure (2004) and the average and standard deviation of the annual Ross River virus disease rates. The population, mosquito control

103

expenditure and Ross River virus notification rates for each year from 1993 to 2004

are provided in Appendix 7.3.

Torres (S)

Aurukun (S)

Cook (S)

Mornington (S) Mean annual Ross River virus disease rate per 10 000

0 to 5 5 to 10 10 to 15 15 to 20 20 to 25 25 to 30 Carpentaria (S) Burke (S)

Figure 4.5: Far northern Queensland coastal subtropical local governments

Table 4.12: Population, mosquito control expenditure and Ross River virus disease statistics for Group 4 local governments

Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local Population Government (2004) AUD 2004 per 10 000 per 10 000

Aurukun (S) 1 168 0 4.31 2.20 Burke (S) 485 6 761 9.32 6.99 Carpentaria (S) 2 395 0 8.55 5.65 Cook (S) 3 961 5 000 16.21 8.68 Mornington (S) 1 042 0 6.83 3.88 Torres (S) 3 798 0 4.03 2.59 (C) City Council, (S) Shire Council

104

The results of an ANOVA comparing the mean Ross River virus rates between local governments in Group 4 showed that there are significant differences in mean Ross

River virus rates between at least two of these local governments (Table 4.13). The mean and the 95% confidence interval for the Ross River virus disease rates for each of the Group 4 local governments are shown in Graph 4.7.

Table 4.13 ANOVA results for within group differences: Group 4 local governments

Sum of df Mean F Sig. Squares Square Between groups 2.286 5 .457 7.800 .000 Within groups 3.869 66 .059 Total 6.155 71

Graph 4.7: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 4 local governments

105

Aurukun Shire Council, Carpentaria Shire Council, Mornington Shire Council and

Torres Shire Council did not respond to the survey, and it has been assumed that no mosquito control is performed in these local governments. Burke Shire Council and

Cook Shire Council both have small complaint-driven mosquito control programs.

The differences in Ross River virus disease rates between the local governments in

Group 4 are likely to result from unreliable Ross River virus notification data rather than being real differences in the Ross River virus infection rates. No clear conclusions can be made about the effect of mosquito control on Ross River virus disease rates in the Group 4 local governments.

Group 5 Far northern Queensland inland subtropical local governments

Group 5 consists of four local governments located in the inland area of far northern

Queensland (Figure 4.6) and these local governments represented 1% of the state population in 2004.

Atherton (S) Mareeba (S)

Eacham (S) Mean annual Ross River virus disease rate Herberton (S) per 10 000

0 to 5 5 to 10 10 to 15 15 to 20 20 to 25 25 to 30

Figure 4.6: Far Northern Queensland inland subtropical local governments

106

This region is characterised by relatively higher altitudes and a subtropical climate

with areas of no dry season in the east of the group and areas of dry winter in the

west. Annual average rainfall is 650 – 1200 mm and this rainfall is dominant in

summer.

Atherton Shire Council did not respond to the survey of mosquito control costs and

practices; however, some information about mosquito control undertaken by

Atherton Shire Council was obtained from the Local Government Association of

Queensland (LGAQ 2002) and Queensland Health (2002). Three local governments

within this group have small complaint-driven mosquito control programs with

budgets ranging from $1000 to $4000.

Table 4.14 lists the local governments that form Group 5, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

Ross River virus notification rates per 10 000 people. The yearly population,

mosquito control expenditure and Ross River virus notification rates for each year

from 1993 to 2004 are provided in Appendix 7.3.

Table 4.14: Population, mosquito control expenditure and Ross River virus disease statistics for Group 5 local governments

Atherton (S) 10 876 1 750 5.68 3.82 Eacham (S) 6 227 0 4.92 4.17 Herberton (S) 5 477 1 000 10.18 7.92 Mareeba (S) 18 518 4 000 8.76 4.75 (C) City Council, (S) Shire Council

107

The ANOVA to compare Ross River virus disease rates between the local governments in Group 5 has shown that there are no significant differences in the mean Ross River virus rates between these local governments (Table 4.15). The mean and the 95% confidence interval for each of the Group 5 local governments are plotted in Graph 4.8.

Table 4.15: ANOVA results for within-group differences: Group 5 local governments

Sum of df Mean F Sig. Squares Square Between groups .548 3 .183 2.423 .078 Within groups 3.318 44 .075 Total 3.866 47

Graph 4.8: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 5 local governments

108

The similarities in Ross River virus disease rates between the local governments in

Group 5 show that there is no evidence that small complaint-driven mosquito control programs have an impact on Ross River virus disease rates.

Group 6 Central Queensland inland subtropical local governments

Group 6 consists of 14 local governments located in the inland region of central

Queensland, characterised by a climate that is subtropical with moderately dry winter and predominant rainfall in summer (350–650 mm annually). The group represented

2% of the 2004 population, and most of these areas are rural with dispersed populations and hence reporting of Ross River virus is likely to be an under-estimate of actual disease.

Mirani (S)

Nebo (S)

Broadsound (S)

Peak Downs (S)

Mount Morgan (S)

Duaringa (S)

Bauhinia (S) Banana (S)

Mean annual Ross River virus disease rate per 10 000

Taroom (S)

0 to 5 5 to 10 Bungil (S) 10 to 15 15 to 20 Bendemere (S) 20 to 25 25 to 30

Warroo (S)

Figure 4.7: Central Queensland inland subtropical local governments

Table 4.16 lists the local governments that form Group 6, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

109

Ross River virus notification rates per 10 000 people. The yearly population,

mosquito control expenditure and Ross River virus notification rates for each year

from 1993 to 2004 are provided in Appendix 7.3.

Table 4.16: Population, mosquito control expenditure and Ross River virus disease statistics for Group 6 local governments

Banana (S) 14 179 2 123 13.42 8.61 Bauhinia (S) 2 212 0 11.67 8.46 Bendemere (S) 994 0 11.64 12.52 Broadsound (S) 6 453 0 9.47 5.11 Bungil (S) 1 947 0 8.20 8.14 Duaringa (S) 6 584 0 8.46 2.45 Emerald (S) 13 419 0 11.31 6.18 Mirani (S) 5 256 4 500 12.78 7.05 Mount Morgan (S) 3 050 0 8.40 6.93 Nebo (S) 2 142 1400 11.52 10.23 Peak Downs (S) 3 142 0 11.93 10.14 Roma (T) 6 741 2 000 14.19 9.89 Taroom (S) 2 546 0 12.65 10.47 Warroo (S) 1 060 0 11.47 7.78 (C) City Council, (S) Shire Council

The ANOVA to compare Ross River virus between the local governments in

Group 6 has shown that there are no significant differences in mean Ross River virus

rates between these local governments (Table 4.17). The mean and the 95%

confidence interval for each of the Group 6 local governments are plotted in Graph

4.9.

110

Table 4.17: ANOVA results for within-group differences: Group 6 local governments

Sum of df Mean F Sig. Squares Square Between groups .998 13 .077 .943 .511 Within groups 12.544 154 .081 Total 13.542 167

Graph 4.9: Mean annual Ross River virus rates 1993–2004 (showing 95% confidence interval) for Group 6 local governments

The survey revealed that there are four of local governments within Group 6 that do

some mosquito control. All of these mosquito control programs are complaint driven

and budgets range from $1000 to $4500. Conclusions for Group 6 are similar to

those for Group 5 — that is, there is no evidence that small-scale complaint-driven

mosquito control programs impact on Ross River virus disease rates.

111

Group 7 Northern Queensland inland grassland local governments

Group 7 includes nine local governments located in the northern inland region of

Queensland. The group contained 1.1% of the Queensland population in 2004 and, due to remoteness of these populations and long distances to medical services, Ross

River virus notification rates are likely to be under-estimated. The Koeppen classification for this region is ‘grassland’, which is characterised by hot conditions and winter drought. Rainfall is dominant in summer, but is only 350–650 mm annually.

Croydon (S) Etheridge (S)

Charters Towers (C) McKinlay (S) Mount Isa (C) Richmond (S)

Dalrymple (S) Flinders (S) Mean annual Ross River virus disease rate per 10 000 0 to 5 Cloncurry (S) 5 to 10 10 to 15 15 to 20 20 to 25 25 to 30

Figure 4.8: Northern Queensland inland grassland local governments

Table 4.18 lists the local governments that form Group 7, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

Ross River virus notification rates per 10 000 people. The yearly population,

112

mosquito control expenditure and Ross River virus notification rates for each year

from 1993 to 2004 are provided in Appendix 7.3.

The survey revealed that six of the nine local governments in Group 7 do some

mosquito control. All of these mosquito control programs are complaint driven, and

budgets range from $1000 to $5000.

Table 4.18: Population, mosquito control expenditure and Ross River virus disease statistics for Group 7 local governments

Charters Towers (C) 8 790 5 000 12.83 12.64 Cloncurry (S) 3 811 5 000 9.82 4.70 Croydon (S) 287 4 500 6.36 2.57 Dalrymple (S) 3 425 0 6.12 5.10 Etheridge (S) 971 0 10.61 7.37 Flinders (S) 1 998 1 000 11.65 10.25 McKinlay (S) 1 014 1 050 12.31 9.12 Mount Isa (C) 20 508 0 6.10 2.88 Richmond (S) 1 109 1 000 10.65 10.00 (C) City Council, (S) Shire Council

The ANOVA to compare Ross River virus between the local governments in

Group 7 has shown that there are no significant differences in mean Ross River virus

rates between these local governments (Table 4.19). The mean and the 95%

confidence interval for each of the Group 7 local governments are plotted in Graph

4.10.

113

Table 4.19: ANOVA results for within-group differences: Group 7 local governments

Sum of df Mean F Sig. Squares Square Between groups .909 8 .114 1.495 .169 Within groups 7.520 99 .076 Total 8.428 107

Graph 4.10: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 7 local governments

Conclusions from Group 7 are similar to those for Group 5 and Group 6 — that is, there is no evidence that small complaint-driven mosquito control programs impact on Ross River virus disease rates.

Group 8 Southern Queensland Inland Grassland local governments

Group 8 consists of 14 local governments located in the southern inland region of

Queensland. These local governments collectively contained less than 1% of the

114

2004 Queensland population, and much of the population is rural-based. The climate classification of this region is predominantly grassland and arid. It is generally hot and persistently dry, with annual rainfall of 350–650 mm dominant in summer.

Belyando (S) Aramac (S)

Longreach (S) JerichoJericho (S)(S)

IlfracombeIlfracombe (S)(S)Barcaldine (S)

IsisfordIsisford (S)(S) IsisfordIsisford (S)(S) Blackall (S)

Tambo (S)

Quilpie (S) Murweh (S) Booringa (S)

Mean annual Ross River virus disease rate per 10 000

0 to 5 5 to 10 10 to 15 15 to 20 Paroo (S)(S) 20 to 25 25 to 30 Balonne (S)

Figure 4.9: Southern Queensland inland grassland local governments

Table 4.20 lists the local governments that form Group 8, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

Ross River virus notification rates per 10 000 people. The yearly population, mosquito control expenditure and Ross River virus notification rates for each year from 1993 to 2004 are provided in Appendix 7.3.

The survey revealed that eight of the 14 local governments in Group 8 do some mosquito control. All of these mosquito control programs are complaint driven, and mosquito control is limited to controlling freshwater species in areas around major

115

towns. Mosquito control budgets for Group 8 local governments ranged from $200 to

$6100.

Table 4.20: Population, mosquito control expenditure and Ross River virus disease statistics for Group 8 local governments

Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local government Population (2004) AUD 2004 per 10 000 per 10 000 Aramac (S) 707 0 5.97 3.53 Balonne (S) 5 582 6 093 13.15 9.80 Barcaldine (S) 1 686 5 000 11.86 11.91 Belyando (S) 10 508 0 8.32 4.68 Blackall (S) 1 653 2 400 9.45 7.52 Booringa (S) 1 857 1 500 10.17 7.77 Ilfracombe (S) 368 0 7.14 5.15 Isisford (S) 302 0 11.93 8.55 Jericho (S) 1 097 0 10.65 8.13 Longreach (S) 3 974 3 000 10.61 7.82 Murweh (S) 5 004 0 10.30 7.02 Paroo (S) 2 156 500 11.30 6.25 Quilpie (S) 1 067 200 9.41 4.23 Tambo (S) 624 5 500 10.89 11.94 (C) City Council, (S) Shire Council

The ANOVA to compare Ross River virus between the local governments in

Group 8 has shown that there are no significant differences in the mean Ross River

virus rates between these local governments (Table 4.21). The mean and the 95%

confidence interval for each of the Group 5 local governments are plotted in Graph

4.11.

116

Table 4.21: ANOVA results for within-group differences: Group 8 local governments

Sum of Squares df Mean F Sig. Square Between groups .844 13 .065 .916 .538 Within groups 10.906 154 .071 Total 11.750 167

Graph 4.11: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 8 local governments

Conclusions from Group 8 are similar to those for Group 5, Group 6 and Group 7 — that is, there is no evidence that small complaint-driven mosquito control programs impact on Ross River virus disease rates.

117

Group 9 Southern Queensland inland subtropical local governments

Group 9 local governments are located in the southern inland subtropical region of

Queensland. There are 19 local governments in Group 9 and this represents 2.4% of the Queensland population. The climate is subtropical and generally there is no dry season, with annual average rainfall of 350-650mm that is dominant in summer.

Monto (S) Kolan (S)

Perry (S) Biggenden (S) Eidsvold (S)

Gayndah (S) Woocoo (S) Mundubbera (S) Tiaro (S) Kilkivan (S)

Wondai (S) Cooloola (S) Chinchilla (S) Murgon (S) Murilla (S) Murgon (S)

Mean annual Ross River virus disease rate per 10 000 Tara (S)

0 to 5 5 to 10 10 to 15 15 to 20 20 to 25 Waggamba (S) 25 to 30 Goondiwindi (T)

Figure 4.10: Southern Queensland inland subtropical local governments

Table 4.22 lists the local governments that form Group 9, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

The survey revealed that eight of the 19 local governments in Group 9 do some mosquito control. The local governments in Group 9 do not have an estimate of the

118

amount of breeding habitat in their area. The focus of mosquito control is freshwater

mosquito species and identified sites are mostly temporary freshwater sites following

rainfall. All of these mosquito control programs are complaint driven mosquito

control budgets for Group 9 local governments ranged from $100 to $7400.

Table 4.22: Population, mosquito control expenditure and Ross River virus disease statistics for Group 9 local governments

Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local government Population (2004) AUD 2004 per 10 000 per 10 000 Biggenden (S) 1 528 0 8.41 8.23 Chinchilla (S) 6 114 0 9.84 8.71 Cooloola (S) 35 372 5 000 7.72 4.65 Eidsvold (S) 928 7 400 11.3 12.38 Gayndah (S) 2 923 0 9.52 9.11 Goondiwindi (T) 5 006 0 20.03 26.39 Isis 6 050 5 000 9.00 9.35 Kilkivan (S) 3 269 1 350 6.38 6.35 Kolan (S) 4 519 250 7.99 8.08 Monto (S) 2 455 0 16.06 13.82 Mundubbera (S) 2 369 0 10.07 8.98 Murgon (S) 3 717 0 6.27 3.40 Murilla (S) 2 720 100 16.92 19.33 Perry (S) 436 0 6.94 7.48 Tara (S) 3 950 0 20.81 25.69 Tiaro (S) 4 921 0 8.14 5.22 Waggamba (S) 2 994 1 500 14.42 14.19 Wondai (S) 4 329 1 000 8.68 8.20 Woocoo (S) 3 148 0 5.16 2.73 (C) City Council, (S) Shire Council (T) Town

The ANOVA to compare Ross River virus between the local governments in

Group 9 has shown that there are no significant differences in the mean Ross River

virus rates between these local governments (Table 4.23). The mean and the 95%

119 confidence interval for each of the Group 5 local governments are plotted in Graph

4.12.

Table 4.23: ANOVA results for within-group differences: Group 9 local governments

Sum of df Mean F Sig. Squares Square Between groups 2.748 18 .153 1.334 .169 Within groups 23.914 209 .114 Total 26.662 227

Graph 4.12: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 9 local governments

Conclusions from Group 9 are similar to those for Groups 5–8 — that is, there is no evidence that small complaint-driven mosquito control programs impact on Ross

River virus disease rates.

120

Group 10 Southern Queensland inland temperate local governments

Group 10 local governments are located in the south-eastern inland area of

Queensland (shown in Figure 4.11). These local governments are located in a region of temperate climate characterised by relatively higher altitude, lower temperature and lower humidity compared with neighbouring groups. This region receives an average annual rainfall of 650 – 1200 mm and experiences wet summers and dry winters. There are 22 local government areas in this group and this represents 12% of the Queensland population. The population is rural-based, with Toowoomba being the largest population centre. In addition, there are several other smaller townships of

Warwick, Stanthorpe, Beaudesert and Ipswich.

Kingaroy (S)

Nanango (S) Kilcoy (S) Esk (S) Wambo (S)

Dalby (T) Rosalie (S)Crow's Nest (S)

JondaryanJondaryan (S)(S) Toowoomba (C) Laidley (S) IpswichIpswich (C)(C) PittsworthPittsworth (S) Gatton (S) Cambooya (S) Millmerran (S) Clifton (S) Boonah (S) Beaudesert (S)

Warwick (S) Mean annual Ross River virus disease rate Warwick (S) per 10 000

0 to 5 5 to 10 10 to 15 InglewoodInglewood (S)(S) StanthorpeStanthorpe (S)(S) 15 to 20 StanthorpeStanthorpe (S)(S) 20 to 25 25 to 30

Figure 4.11: Southern Queensland inland temperate local governments

Table 4.24 lists the 22 local governments that form Group 10, along with the population (2004), mosquito control expenditure (2004) and the mean and standard

121

deviation of Ross River virus notification rates per 10 000 people. The yearly

population, mosquito control expenditure and Ross River virus notification rates for

each year from 1993 to 2004 are provided in Appendix 7.3. The years of 1996 and

2004 saw epidemic levels of Ross River virus notifications in almost every local

government area in Group 10. Freshwater mosquito species are the Ross River virus

vectors of concern in these local governments, and generally high Ross River virus

years correlate with above-average rainfall.

Table 4.24: Population, mosquito control expenditure and Ross River virus disease statistics for Group 10 local governments. Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local government Population (2004) AUD 2004 per 10 000 per 10 000 Beaudesert (S) 58 697 10 000 5.70 6.15 Boonah (S) 8 525 4 000 8.63 10.45 Cambooya (S) 5 529 0 4.43 3.04 Clifton (S) 2 487 0 5.96 5.64 Crow’s Nest (S) 11 635 0 3.47 3.44 Dalby (T) 10 159 5 823 8.50 6.64 Esk (S) 15 144 0 11.20 15.05 Gatton (S) 16 131 0 9.00 11.52 Inglewood (S) 2 629 0 8.89 10.21 Ipswich (C) 133 185 196 000 5.72 7.70 Jondaryan (S) 13 630 1 250 5.01 5.61 Kilcoy (S) 3 459 0 5.22 3.27 Kingaroy (S) 12 223 0 7.03 6.92 Laidley (S) 13 296 0 10.33 14.23 Millmerran (S) 3 333 0 9.47 15.83 Nanango (S) 8 674 0 10.66 13.55 Pittsworth (S) 4 861 0 4.92 5.25 Rosalie (S) 8 944 0 5.83 7.85 Stanthorpe (S) 10 529 650 4.27 5.21 Toowoomba (C) 93 448 14 000 2.94 2.64 Wambo (S) 5 268 0 4.75 4.59 Warwick (S) 21 440 5 500 4.59 6.20 (C) City Council, (S) Shire Council (T) Town

122

Fourteen of the local governments in Group 10 do not do any mosquito control, and

four local governments have mosquito control programs that are generally

complaint-driven mosquito control responses. Ipswich City Council has the highest

mosquito control expenditure. Toowoomba has second highest control budget,

though the Toowoomba program is largely complaint driven with adulticiding being

a key part of the program. The Warwick program is also complaint driven, and

ground-based larviciding is used. Stanthorpe has a complaint-driven program with a

very small budget ($500) and Jondaryan has between three and 10 sites that are

surveyed after rainfall.

The ANOVA to compare Ross River virus between the local governments in

Group 10 has shown that there are no significant differences in the mean Ross River

virus rates between these local governments (Table 4.25). The mean and the 95%

confidence interval for each of the Group 10 local governments are plotted in Graph

4.13.

Table 4.25: ANOVA results for within-group differences: Group 10 local governments

Sum of Squares df Mean Square F Sig.

Between groups 3.165 21 .151 1.340 .150 Within groups 27.211 242 .112 Total 30.376 263

The local governments that do control for mosquitoes are not significantly lower in

Ross River virus notification levels than those that do not. The conclusions indicate

the reactive control programs (those which rely on adulticiding) do not impact on

disease rates. Ipswich has lower Ross River virus rates than most other local

governments, but the differences are not statistically significant.

123

Graph 4.13: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 10 local governments.

Group 11 Southern Queensland inland desert local governments

Group 11 contains five local government areas that are located in the south-western inland region of Queensland (shown in Figure 4.12). These local government areas are predominantly arid land with consistently low rainfall (less than 350 mm) relative to the rest of the state.

These local governments all have relatively small populations that are widely dispersed on rural grazing properties. The Ross River virus notification rates for these local governments are shown in Table 4.26. Due to the remote nature of these local government areas, it is likely that these disease rates under-estimate the true disease incidence.

124

Winton (S) Boulia (S)

Diamantina (S)

Barcoo (S)

Mean annual Ross River virus disease rate per 10 000

0 to 5 5 to 10 10 to 15 Bulloo (S) 15 to 20 20 to 25 25 to 30

Figure 4.12: Southern Queensland inland desert local governments

Table 4.26 lists the local governments that form Group 11, along with the population

(2004), mosquito control expenditure (2004) and the mean and standard deviation of

Ross River virus notification rates per 10 000 people. The yearly population,

mosquito control expenditure and Ross River virus notification rates for each year

from 1993 to 2004 are provided in Appendix 7.3.

Table 4.26: Population, mosquito control expenditure and Ross River virus disease statistics for Group 11 local governments Standard Mosquito Mean Ross deviation of control River virus Ross River expenditure rates virus rates (2004) (1993–2004) (1993–2004) Local government Population (2004) AUD 2004 per 10 000 per 10 000 Barcoo (S) 456 0 8.85 6.76 Boulia (S) 536 0 9.95 9.47 Bulloo (S) 466 0 6.35 4.43 Diamantina (S) 301 0 6.83 3.41 Winton (S) 1 527 0 9.49 5.96 (C) City Council, (S) Shire Council

125

The ANOVA to compare Ross River virus between the local governments in

Group 11 has shown that there are no significant differences in the mean Ross River virus rates between these local governments (Table 4.27).

Table 4.27: ANOVA results for within-group differences: Group 11 local governments

Sum of df Mean F Sig. Squares Square Between groups .169 4 .042 .657 .624 Within groups 3.542 55 .064 Total 3.711 59

The mean and the 95% confidence interval for each of the Group 11 local governments are plotted in Graph 4.14.

Graph 4.14: Mean annual Ross River virus disease rates 1993–2004 (showing 95% confidence interval) for Group 11 local governments

126

There is no mosquito control undertaken by these five local governments, and all other variables (climate) are similar for all local governments within the group. This finding that there is no significant difference in the mean Ross River virus rates between local governments in this group supports the assumption underpins this research –that local governments with similar geography and climate should exhibit similar disease rates in the absence of mosquito control programs.

4.2.3. Conclusions from the analysis of within-group differences

The relationship between mosquito control and avoided Ross River virus disease is most pronounced in Group 1 local governments; hence, Group 1 local governments have been subject to further analysis that estimates avoided Ross River virus notifications.

The relationship between mosquito control and Ross River virus disease rates in

Group 2 and Group 3 local governments are not as clear and, though Ross River virus rates are generally higher in those local governments that do not do control, there is still great variability in the rates for the local governments that do have mosquito control programs. The influence of confounding factors, such as local climate, lifestyle, population immunity and host dynamics, need to be better understood in order to explain this variability.

4.2.4. Estimating avoided Ross River virus notifications

Avoided Ross River virus disease has been estimated for each of the Group 1 local governments. Group 1 incorporates the majority of the state population, and the local governments in this group have provided high-quality, detailed responses to the survey. The Group 1 local governments are also located in a relatively small area of

127 the state, thereby increasing the validity of the assumptions about fixed effects of climate and geography.

In order to establish the avoided Ross River virus disease rates in Group 1 local governments, a local government with comparable geography and climate — but with no mosquito control measures — needed to be identified. Each of the local governments in Group 1 has performed mosquito control in most years between 1992 and 2004. The closest comparable local governments that did not perform mosquito control are located within the Group 2 model grouping which is located in the central coast of Queensland (refer to Figure 4.1). There were two local governments that had performed no mosquito control in the years from 1992 – 2004 in Group 2, Calliope

Shire Council and Miriam Vale Shire Council. It was revealed during the survey of

Gladstone City Council (which neighbours Calliope Shire) that Gladstone City undertakes cross-boundary mosquito control into Calliope Shire; and, that Ross River virus disease rates in Calliope Shire Council may be influenced by mosquito control by Gladstone City Council. In comparison, Miriam Vale Shire Council does not undertake any mosquito control, and is comparable to the Group 1 local governments in terms of its subtropical climate classification (Refer Figure 3.4), its annual average rainfall of 600-1200 mm, rainfall seasonality (Refer Figure 3.2) and the major mosquito species present (Ae. vigilax and Cx. annulirostris). The human settlement patterns in Miriam Vale Shire include a concentration of population in the township of Miriam Vale and dispersed rural dwellings. While Group 1 includes major urban centres, it has is a range of settlement patterns with some similarity to those in

Miriam Vale Shire (for example rural areas of Noosa Shire). Hence, Miriam Vale

Shire was selected as the only local government comparable to Group 1 local governments for use in avoided disease calculations.

128

The average Ross River virus disease notification rate in Miriam Vale Shire Council for the years from 1993 to 2004 was 16.93 notifications per 10 000 people. In comparison, the average Ross River virus disease notification rate in the Group 1 local governments for the years from 1993 to 2004 was 5.85 notifications per 10 000 people. The difference between the Ross River virus notification rates in

Miriam Vale Shire and the Group 1 local governments can be used to estimate that on average, 11.08 Ross River virus notifications per 10 000 people have been avoided each year in the Group 1 local governments. Based on this comparison, for each actual notification of Ross River virus disease in the Group 1 local governments, approximately two notifications have been avoided.

The spatial variation in estimated avoided Ross River virus disease

There is spatial variability in the Group 1 in respect to the average annual avoided

Ross River virus notifications within each local government. The estimated avoided

Ross River virus notifications for each Group 1 local government are shown in Table

4.28.

Gold Coast City Council has the highest rate of avoided Ross River virus notifications and Noosa Shire Council has the lowest rate of avoided Ross River virus notifications. The spatial pattern of the average annual avoided Ross River virus notifications for the Group 1 local government areas is shown in Figure 4.13 and shows that there are lower rates of avoided Ross River virus notifications in the local government areas located in the northern region of Group 1 compared to those local government areas located in the southern region.

129

Table 4.28: Estimated avoided Ross River virus notifications in Group 1 local governments

Average yearly Average yearly Average yearly Average yearly Ross River avoided Ross Ross River virus avoided Ross Average yearly virus River virus rate River virus rate population notifications notifications Local government (1993–2004) (1993 – 2004) (1993 – 2004) (1993 – 2004) (1993 – 2004)

per 10 000 per 10 000 Brisbane (C) 3.57 13.36 848 002 303 1 133 Caboolture (S) 7.27 9.66 106 540 77 103 Caloundra (C) 6.65 10.28 70 473 47 72 Gold Coast (C) 2.93 14.00 375 575 110 526 Logan (C) 3.39 13.54 164 045 56 222 Maroochy (S) 8.07 8.86 114 797 93 102 Noosa (S) 11.52 5.41 38 162 44 21 Pine Rivers (S) 5.76 11.17 114 476 66 128 Redcliffe (C) 4.69 12.24 49 931 23 61 Redland (S) 4.75 12.18 109 418 52 133 Total 5.85 11.08 1 991 418 871 2 206 (C) City Council, (S)

Shire Council

The differences in average avoided Ross River virus notification rates between the

southern and northern local governments in Group 1 correspond to the extent and

efficiencies of the local government mosquito control discussed in Section 4.2.1 —

that is, the local governments with mosquito control programs that pre-empt

mosquito outbreaks using routine surveillance, and incorporate both freshwater and

saltwater mosquito control, demonstrate higher rates of avoided Ross River virus

disease.

130

Noosa (S)

Maroochy (S)

Caloundra (C)

Caboolture (S)

Redcliffe (C)

Pine Rivers (S)

Brisbane (C) Mean annual avoided Ross River virus disease rate per 10 000 Redland (S) Logan (C) 0 to 5 5 to 7 7 to 9 9 to 11 11 to 13 13 to 15 Gold Coast (C)

Figure 4.13: Mean annual avoided Ross River virus disease rates 1993-2004 for Group 1 local governments.

Temporal variation in estimated avoided Ross River virus disease

The avoided Ross River virus disease notifications shown in Table 4.28 are based on averages of the annual Ross River virus rates for each of the local governments and do not illustrate that there is also temporal variation in the Ross River virus rates within each local government. Establishing the potential Ross River virus rates without mosquito control in each local government is difficult and requires knowledge of factors such as rainfall, temperature, humidity, immunity and host abundance as illustrated in Figure 3.1. For example, the temporal variation of annual

Ross River virus disease rates for Miriam Vale Shire Council is shown in Table 4.29.

131

Table 4.29: Ross River virus disease rates in Miriam Vale Shire Council 1993 – 2004.

Ross River virus notifications Year per 10 000 1993 5.42 1994 17.41 1995 25.58 1996 58.62 1997 11.84 1998 17.89 1999 7.24 2000 9.27 2001 4.28 2002 12.34 2003 19.82 2004 13.46

The extremely low and high occurrences of Ross River virus notifications in

Miriam Vale Shire in 1993 and 1996 can be used to establish upper and lower boundaries of avoided Ross River virus disease. The lowest annual Ross River virus disease rate in Miriam Vale Shire (5.42 notifications per 10 000 people) occurred in

1993 and corresponded with below average rainfall in most months of that year. The monthly rainfall as a ratio of the long-term average monthly rainfall for Miriam Vale

Shire in 1993 is shown in Graph 4.15.

The highest annual Ross River virus disease rate in Miriam Vale Shire (58.62 notifications per 10 000 people) occurred in 1996 and corresponded with above average rainfall in consecutive spring and summer months. The monthly rainfall as a ratio of the long-term average monthly rainfall for Miriam Vale Shire in 1996 is plotted in Graph 4.16.

132

1 July August September October November December January February March April May June Rainfall as a ratio of the average long-term monthly rainfall monthly long-term the average of as a ratio Rainfall

0 Month

Graph 4.15: Monthly rainfall in Miriam Vale Shire in 1993: This rainfall pattern was associated with a low rate of Ross River virus disease.

1 July August September October November December January February March April May June Rainfall as a ratio of the average long-term monthly rainfall monthly long-term average the of as a ratio Rainfall

0 Month

Graph 4.16: Monthly rainfall in Miriam Vale Shire during 1996: This rainfall pattern was associated with a high rate of Ross River virus disease.

133

Brisbane City also experienced below average monthly rainfall during 1993

(compare Graph 4.17 to Graph 4.15) and hence, in the absence of mosquito control, the Ross River virus disease notification rate might have been similar to Miriam Vale

Shire which experienced 5.42 notifications per 10 000 people. However, Brisbane

City experienced only 1.8 Ross River virus notifications per 10 000 people – a difference of 3.62 notifications per 10 000 people which would have resulted in 278 avoided notifications based on the 1993 population of Brisbane City (768 638 people).

0 Month

Graph 4.17: Monthly rainfall in Brisbane City during 1993.

In 1996 Brisbane City also experienced above average monthly rainfall during spring and summer (compare Graph 4.18 to Graph 4.16) and hence, in the absence of mosquito control, the Ross River virus disease notification rate might have been similar to Miriam Vale Shire which experienced 58.62 notifications per 10 000

134 people. However, Brisbane experienced only 9.97 Ross River virus notifications per

10 000 people – a difference of 48.65 notifications per 10 000 people which would have resulted in 3945 avoided notifications based on the 1996 population of Brisbane

City (810 878 people).

0 Month

Graph 4.18: Monthly rainfall in Brisbane City in 1996.

135

The value of avoided Ross River virus disease has been calculated by multiplying the average yearly avoided Ross River virus notifications for each of the Group 1 local governments by $1097 (AUD 2004) which is the value of one case of Ross River virus disease as estimated by Ratnayake (2005).

The average annual avoided Ross River virus disease for Group 1 is 2206 notifications and this is valued at $2 420 525 per annum. The average annual expenditure on mosquito control for Group 1 is $6 502 655. Hence, the long-term cost-benefit ratio for Group 1 is 0.37, meaning that on average 37% of the costs of mosquito control are directly recouped through the value of avoided Ross River virus disease.

Spatial variation in cost-benefit ratios

The mosquito control expenditure, value of avoided Ross River virus disease and the ratio of costs and benefits for each of the Group 1 local governments is shown in

Table 4.30. These cost-benefit ratios vary spatially within Group 1, ranging from

0.17 for Redcliffe City Council to 0.69 for Pine Rivers Shire Council. The annual average cost-benefit ratios for the Group 1 local governments are shown in Figure

4.14.

136

Table 4.30: Comparison of mosquito control costs and the value of avoided Ross River virus disease for Group 1 local governments

Average yearly Average avoided Ross mosquito Value of Cost-benefit Local River virus control costs avoided Ross ratio government notifications (1993 – 2004) River virus

AUD 2004 AUD 2004 Brisbane (C) 1133 3 048 756 1 242 825 0.41 Caboolture (S) 103 386 632 112 900 0.29 Caloundra (C) 72 142 425 79 474 0.56 Gold Coast (C) 526 1 100 073 576 808 0.52 Logan (C) 222 361 870 243 663 0.67 Maroochy (S) 102 234 255 111 576 0.48 Noosa (S) 21 73 316 22 648 0.31 Pine Rivers (S) 128 204 684 140 273 0.69 Redcliffe (C) 61 385 762 67 044 0.17 Redland (S) 133 564 883 146 198 0.26 Total 2206 6 502 655 2 420 525 0.37 (C) City Council,

(S) Shire Council

Noosa (S)

Maroochy (S)

Caloundra (C)

Caboolture (S)

PinePine RiversRivers (S)(S)

Brisbane (C) The cost of mosquito control compared to the value of avoided Ross River virus disease Redland (S) 0 to 0.2 Logan (C) 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 0.8 to 1 Gold Coast (C)

Figure 4.14: Annual average cost-benefit ratios for Group 1 local governments

137

Temporal variation in cost-benefit ratios

The cost-benefit ratios shown in Table 4.30 are based on averages of the annual avoided Ross River virus notifications rates, and averages of the annual spending on mosquito control by each of the local governments and do not illustrate that there is also temporal variation in these cost-benefit ratios within each local government.

The examples of below and above average rainfall can again be used to establish an upper and lower boundary of cost benefit ratios. In 1993 Brisbane City avoided some

278 Ross River virus notifications as a result of mosquito control. This equates to a saving of $297 724, total mosquito control expenditure was $2 853107 and the cost- benefit ratio is 0.10. In 1996 Brisbane City avoided some 3945 Ross River virus notifications as a result of mosquito control. This equates to a saving of $4 221 066 and a cost-benefit ratio of 1.54.

This approach of investigating the upper and lower boundaries of avoided disease leads to an important finding – that is, in years where environmental conditions support the occurrence of extreme outbreaks of Ross River virus disease, effective mosquito control can prevent such an epidemic; and, the monetary value of avoided

Ross River virus disease actually exceeds the cost of mosquito control (the cost- benefit ratio in this case is greater than one).

In contrast, in years when rainfall is low or below average, local governments with mosquito control have comparatively lower Ross River virus rates; however, the monetary value of avoided Ross River virus disease does not exceed the cost of the mosquito control program (the cost-benefit ratio in this case is less than one).

The survey of mosquito control costs and practices in Queensland revealed that the expenditure on mosquito control by local governments does vary from year to year as

138 a result of environmental conditions; however, many of the costs involved in the mosquito control programs are fixed costs relating to staff and equipment and hence, the variation in mosquito control costs is not proportional to the risk of Ross River virus disease outbreaks. The yearly expenditure on mosquito control by each local government in Queensland for the years 1993 to 2004 is shown in Appendix 7.3. The example, of Brisbane City illustrates that while the risk of Ross River virus disease in

1993 was much lower than 1996 (due to differing rainfall conditions), the expenditure on mosquito control is not substantially different between the two years

($2853107 in 1993 compared to $2741585 in 1996).

139

Chapter 5: Conclusions

The chapter presents both substantive and methodological conclusions from this research. The wider relevance of this research and options for future research are also discussed.

5.1. Substantive conclusions from this research

The rates of Ross River virus vary across the state of Queensland — some of this variation is controlled by climate and geography — but statistical analysis used in this research indicates that some of this variation may be a consequence of local government mosquito control programs.

It has been estimated during this research that in excess of $10 million is spent each year by local governments implementing mosquito control programs in Queensland.

The majority of this expenditure occurs in the more densely populated local governments located on the southern coastal strip of Queensland. A large portion of most mosquito control budgets is consumed by fixed expenses such as employee costs, equipment costs and surveillance programs.

The relationship between mosquito control and reduced Ross River virus disease rates is more pronounced in the southern coastal local government areas than in the northern coastal local government areas. The disease rates in northern regions are complicated by less reliable notification data, competition of mosquito control resources for dengue control and a relatively longer virus transmission season.

The statistical analysis of Ross River virus rates and mosquito control for coastal local governments supports the conclusions that pre-emptive mosquito programs incorporating both saltwater and freshwater control strategies show the most

140 consistent evidence of reduced Ross River virus disease rates. This highlights the importance of freshwater control in relation to avoiding epidemics. It is likely that all southern coastal local governments have avoided some Ross River virus notifications in all years investigated, and in some years have avoided potential epidemics. In years where the potential for Ross River virus is relatively low, due to below average rain, the costs of control exceed the value of avoided Ross River virus notifications – but in years where epidemics have been avoided, the financial benefits of avoided disease outweigh the costs of the programs.

Where mosquito control is performed in inland local governments in Queensland, the programs tend to have small expenditures and treatments are mostly reactive to complaints. There is no statistical evidence that these low-funded, complaint-driven programs have an impact on Ross River virus disease rates.

The evidence shows that contiguous local government arrangements for trans- boundary mosquito control may have positive outcomes. In simple terms, local governments are influenced by what their neighbours do; this is demonstrated in situations, where some local governments that do not carry out extensive organised mosquito control show relatively low disease rates; and, it is probable that they receive some protection from large-scale programs in contiguous local government areas.

This research shows that some local governments have avoided Ross River virus cases, and hence it is reasonable to assume that other arboviruses, such as Barmah

Forest virus, are also avoided. These have not been quantified here, but could be estimated using similar methods.

141

The research has not attempted to value all of the benefits of mosquito control programs, but has looked at the most prevalent arboviral disease in Queensland. On average, about one-third of mosquito control expenses are directly recouped by reduced Ross River virus disease notifications, and many subclinical infections are also avoided. The value of reduced mosquito populations and avoided Ross River virus disease to tourism and real estate has not been estimated, though it is possible that this value may actually exceed the benefits of avoided medical costs.

5.2. Methodological conclusions

The conclusions in relation to the methods used in this research relate predominantly to the data collection phase of the research. The survey approach used to collect data from local governments was appropriate, but there are several ways in which the process could be improved. Improvements would include asking more direct questions about mosquito sites — for example, how much mosquito habitat is present in a local government. Many of the local governments that responded to the survey indicated that they did not have a reliable estimate of mosquito habitat within their jurisdiction; therefore, it was not possible to calculate the proportion of mosquito habitat within a local government that was treated. There is great variation in the geographical size of local governments and the lack of detailed information on mosquito habitat was a major limitation on statistical analysis. Having such mosquito habitat information for each local government would allow comparisons between local governments to be made in common units such as dollars spent per areal unit of mosquito habitat. It is likely that the best way to obtain mosquito habitat information would be to use a remote sensing approach, but this was beyond the scope of this research. This is an option for future research as it is likely to be of interest to the local governments which also require this information.

142

This research is not a complete cost-benefit analysis because the value of avoided nuisance was not estimated and the value of tourism was not estimated —methods to value these do not currently exist and developing them is beyond the scope of this research. In addition, it was difficult to establish Ross River virus rates in the absence of control for Southeast Queensland, as most local governments undertake some forms of control, and it is not possible at this stage to quantify the accuracy of the avoided disease estimates.

5.3. Broader relevance of the research

This research has developed a survey instrument to collect information about local government mosquito control programs that would need only slight alterations to be applicable to other states or countries with similar arbovirus diseases and mosquito control approaches. The conclusions regarding the importance of freshwater control for preventing epidemics are directly transferable to other areas and other arboviral diseases.

The research has developed a simple method to compare arbovirus rates between local governments with and without control while considering some environmental factors through using climate and geography to group local governments. Climate information is easily obtained and arbovirus notifications are available for all

Australian states, so application of this method would be possible for other

Australian states.

5.4. Recommendations for further research

In relation to better answering the research questions posed in this thesis, future research needs to include developing methods to collect reliable information about

143 mosquito breeding habitat. There is also a need for a better understanding of the environmental factors that influence Ross River virus disease rates (including hosts, vectors and climate).

In relation to expanding this research into a more complete cost-benefit analysis, the requirements for further research include the quantification of the impact of mosquito control on nuisance, tourist visitation and value, real estate value and general amenity. For example, there is a need to understand the influence of disease epidemics on tourist visitation and how tourists perceive the risk of disease. This would contribute towards an understanding of the value of avoided Ross River virus disease on the tourism industry and would have a direct relevance to Queensland and beyond.

In relation to better understanding the influence of mosquito control on disease, there is a need for a detailed study of the efficiencies and effectiveness of mosquito control methods in reducing mosquito abundance, including a comparison of control types

(short-term and long-term) and combinations of control types used. Such a study might consider the effect of development control (to separate residences from mosquito breeding habitat) on disease rates, or whether combinations of control types (such as community education, habitat modification and chemical controls) have increased effects.

In relation to the future of local government mosquito control, there is now a move towards local governments attempting to consider public health issues related to mosquitoes in their development assessment decisions. This presents an opportunity for a research project that investigates the relationship between the mosquito control section and the urban planning section within local governments.

144

Chapter 6: References

Aaskov, J., Williams, L., & Yu, S. (1997). A candidate Ross River virus vaccine:

preclinical evaluation. Vaccine, 15, 1396–1404.

Aaskov, J. G., Chen, J.-Y., Hanh, N. T. H., & Dennington, P. M. (1998).

Surveillance for Ross River virus infection using blood donors. American

Journal of Tropical Medicine and Hygiene, 58, 726–730.

Arbovirus Taskforce (1993). Report of the Arbovirus Taskforce. Brisbane:

Queensland Health.

Attanayake, N., Fox-Rushby, J., & Mills, A. (2000). Household costs of malaria

morbidity: A study in Matale district, Sri Lanka. Tropical Medicine &

International Health, 5, 595–606.

Becker, N., Petric, D., Boase, C., Lane, J., Zgomba, M., Dahl, C., et al. (2003).

Mosquitoes and their control. New York: Kluwer Academic/Plenum

Publishers.

Bureau of Meteorology. (2007). Australian climate zones, http://www.bom.gov.au

/lam/climate/levelthree/ausclim/koeppen2.htm (accessed 7 February 2007)

Burkot, T.R., & Graves, P.M. (2004). Malaria, babesiosis, theileriosis and related

diseases. In B.F. Eldridge & J.D. Edman (Eds.) Medical entomology: A

textbook on public health and veterinary problems caused by arthropods

(pp.187-230). London: Kluwer Academic.

Carlson, G. A., & DeBord, D. V. (1976). Public mosquito abatement. Journal of

Environmental Economics and Management, 3, 142–153.

145

Choi, Y. H., Comiskey, C., Lindsay, M. D. A., Cross, J. A., & Anderson, M. (2002).

Modelling the transmission dynamics of Ross River virus in Southwestern

Australia. IMA Journal of Mathematics Applied in Medicine and Biology, 19,

61–74.

Dale P.E.R., Dale P.T., Hulsman K. and Kay B.H. (1993). Runnelling to Control

Saltmarsh Mosquitoes: long-term efficacy and environmental

impacts, Journal of the American Mosquito Control Association, 9,

174-181.

Department of Health (2004). Business Plan 2004–2005: Population Health. Perth:

Western Australian Department of Health.

Department of Health and Community Services (2003). Annual Report 2003/2004.

Darwin: Department of Health and Community Services, Northern Territory.

Department of Human Services (Loddon Mallee Region) (1998). Forging

partnerships for arbovirus protection: Loddon Mallee regional arbovirus

strategic plan 1998–2003. Melbourne: Department of Human Services,

Victoria.

Dillman, D.A., (2000) Mail and Internet surveys: The tailored design method. New

York: John Wiley & Sons, Inc.

Doherty, R.L., (1974). Arthropod-borne viruses in Australia and their relation to

infection and disease, Progress in Medical Virology, 17, 136–192.

Done, S., Holbrook, N., & Beggs, P. (2002). The Quasi-Biennial Oscillation and

Ross River virus incidence in Queensland, Australia. International Journal of

Biometeorology, 46, 202–207.

146

Drummond, M. F., O’Brien, B., Stoddart, G. L., & Torrance, G. W. (1997). Methods

for the economic evaluation of health care programs (2nd ed.). Melbourne:

Oxford University Press.

Eldridge, B.F., Scott, T.W., Day, J.F. & Tabachnick, W.J. (2004). Arbovirus

Diseases. In B.F. Eldridge & J.D. Edman (Eds.) Medical entomology: A

textbook on public health and veterinary problems caused by arthropods

(pp.415-460). London: Kluwer Academic.

Gatton, M. L., Kay, B. H., & Ryan, P. A. (2005). Environmental predictors of Ross

River virus disease outbreaks in Queensland, Australia. American Journal of

Tropical Medicine and Hygiene, 72, 792–799.

Gatton, M. L., Kelly-Hope, L. A., Kay, B. H., & Ryan, P. A. (2004). Spatial-

temporal analysis of Ross River virus disease patterns in Queensland,

Australia. American Journal of Tropical Medicine and Hygiene, 75, 629–635.

Glass, K. (2005). Ecological mechanisms that promote arbovirus survival: a

mathematical model of Ross River virus transmission. Transactions of the

Royal Society of Tropical Medicine and Hygiene, 99, 252-260.

Goodman, C. A., Coleman, P. G., & Mills, A. J. (1999). Cost-effectiveness of

malaria control in sub-Saharan Africa. The Lancet, 354, 368–375.

Goodman, C. A., Coleman, P. G., & Mills, A. J. (2001a). Changing the first line drug

for malaria treatment: Cost-effectiveness analysis with highly uncertain inter-

temporal trade-offs. Health Economics, 10, 731–749.

147

Goodman, C. A., Coleman, P. G., & Mills, A. J. (2001b). The cost-effectiveness of

antenatal malaria prevention in sub-Saharan Africa. American Journal of

Tropical Medicine and Hygiene, 64, 45–56.

Goodman, C. A., & Mills, A. J. (1999). The evidence base on the cost-effectiveness

of malaria control measures in Africa. Health Policy and Planning, 14, 301–

312.

Goodman, C. A., Mnzava, A. E. P., Dlamini, S. S., Sharp, B. L., Mthembu, D. J., &

Gumede, J. K. (2001). Comparison of the cost and cost-effectiveness of

insecticide-treated bed nets and residual house-spraying in KwaZulu-Natal,

South Africa. Tropical Medicine & International Health, 6, 280–295.

Gubler, D. J. (2001). Human arbovirus infections worldwide. West Nile Virus:

Detection, Surveillance, and Control, 951, 13–24.

Guyatt, H. L., & Snow, R. W. (2002). The cost of not treating bed nets. Trends in

Parasitology, 18, 12–16.

Hansen, JA (2003) The economics of mosquito control. The 28th Annual Mid

Atlantic Mosquito Control Association Meeting, Portsmouth, Virginia.

Hansen, JA, Lessor, FH, Lombardi, RW, Shisler, JK and Slavin, P (1976) The

economics of marsh water management - a New Jersey view, Proceedings of

the New Jersey Mosquito Control Association, 63, 77-81.

Harley, D., Bossingham, D., Purdie, D. M., Pandeya, N., & Sleigh, A. C. (2002).

Ross River virus disease in tropical Queensland: evolution of rheumatic

manifestations in an inception cohort followed for six months. Medical

Journal of Australia, 177, 352–355.

148

Harley, D., Sleigh, A., & Ritchie, S. (2001). Ross River virus transmission, infection,

and disease: a cross-disciplinary review. Clinical Microbiology Reviews, 14,

909–932.

Hutubessy, R. C. W., Bendib, L. M., & Evans, D. B. (2001). Critical issues in the

economic evaluation of interventions against communicable diseases. Acta

Tropica, 78, 191–206.

Jeffery, J. A. L., Ryan, P., A., Lyons, S. A., Thomas, P. T., & Kay, B. H. (2002).

Spatial distribution of vectors of Ross River virus and Barmah Forest virus on

Russell Island, Moreton Bay, Queensland. Australian Journal of Entomology,

41, 329–339.

John, K. H., Stoll, J. R., & Olson, J. K. (1987). An economic assessment of the

benefits of mosquito abatement in an organised mosquito control district.

Journal of the American Mosquito Control Association, 3(1), 8–14.

Kay, B.H. & Aaskov, J.G. (1989). Ross River virus (epidemic polyarthritis). In T.

Monath (ed.), The arboviruses: epidemiology and ecology, Volume IV, Boco

Raton, FL: CRC Press, 93–112.

Kelly-Hope, L. A., Purdie, D. M., & Kay, B. H. (2004a). Ross River virus disease in

Australia, 1886–1998, with analysis of risk factors association with

outbreaks. Journal of Medical Entomology, 41, 133–150.

Kelly-Hope, L. A., Purdie, D. M., & Kay, B. H. (2004b). Differences in climatic

factors between Ross River virus disease outbreak and nonoutbreak years.

Journal of Medical Entomology, 41, 1116–1122.

149

Kelly-Hope, L. A., Purdie, D. M., & Kay, B. H. (2004c). El Nino Southern

Oscillation and Ross River Virus Outbreaks in Australia. Vector-Borne and

Zoonotic Diseases, 4, 210–213.

Langham, M. R., & Lanier, R. (1981). Public mosquito abatement. Journal of

Environmental Economics and Management, 8, 97–99.

LGAQ (2000). Submission on mosquito management in Queensland. Brisbane: Local

Government Association of Queensland.

LGAQ (2002). Mosquito Management Code of Practice for Queensland. Brisbane:

Local Government Association of Queensland.

Lichtenberg, E. R., & Getz, W. (1985). Economics of rice-field mosquito control in

California. Bioscience, 35, 292–297.

Lok, J.B., Walker, E.D. & Scoles, G.A. (2004). Filariasis. In B.F. Eldridge & J.D.

Edman (Eds.) Medical entomology: A textbook on public health and

veterinary problems caused by arthropods (pp.299-376). London: Kluwer

Academic.

Mackenzie, J.S., Lindsay, M.D., Coelen, R.J., Broom, A.K., Hall, R.A. & Smith,

D.W. (1994). Arboviruses causing human disease in the Australasian

zoogeographic region. Archives of Virology, 136, 447-461.

MCAA (2002). Australian mosquito control manual (2nd ed.). Brisbane: Mosquito

Control Association of Australia.

McCarthy, F. D., Wolf, H., & Wu, Y. (1999). The growth costs of malaria. National

Bureau of Economic Research Working Paper w7541.

150

Mills, A. (1992). The economic evaluation of malaria control technologies: The case

of Nepal. Social Science & Medicine, 34, 965–972.

Monath, T. P. (ed.) (1988). The Arboviruses: epidemiology and ecology (Vol. 1–5).

Boca Raton, FL: CRC Press.

Muhar, A., Dale, P. E. R., Thalib, L., & Arito, E. (2000). The spatial distribution of

Ross River virus infections in Brisbane: significance of residential location

and relationships with vegetation. Environmental Health and Preventative

Medicine, 4, 184–189.

Mylonas, A. D., Brown, A. M., Carthew, T. L., McGrath, B., Purdie, D. M.,

Pandeya, N., et al. (2002). Natural history of Ross River virus-induced

epidemic polyarthritis. Medical Journal of Australia, 177, 356–360.

National Public Health Partnership (2000). The role of local government in public

health (Consultation Paper). Melbourne: National Public Health Partnership.

National Public Health Partnership (2002). The role of local government in public

health regulation (Report). Melbourne: National Public Health Partnership.

NSW Health (1998). NSW arbovirus disease control strategy: A green paper for

public comment. Sydney: NSW Health Department.

Ofiara, D. D., & Allison, J. R. (1986a). A comparison of alternative mosquito

abatement methods using benefit-cost analysis. Journal of the American

Mosquito Control Association, 2, 522–528.

151

Ofiara, D. D., & Allison, J. R. (1986b). On assessing the benefits of public mosquito

control practices. Journal of the American Mosquito Control Association, 2,

280–288.

Pang, L. W., & Piovesan-Alves, F. (2001). Economic advantage of a community-

based malaria management program in the Brazilian Amazon. American

Journal of Tropical Medicine and Hygiene, 65, 883–886.

Phillips, M., Mills, A., & Dye, C. (1993). Guidelines for cost-effectiveness analysis

of vector control. Geneva: World Health Organisation.

Queensland Health (2000). Dengue Fever Management Plan for North Queensland

2000–2005. Brisbane: Queensland Health.

Ratnayake, J. (2005). The valuation of social and economic cost of mosquito-

transmitted Ross River virus. PhD thesis. Brisbane: Griffith University.

Rulli, N. E., Suhrbier, A., Hueston, L., Heise, M. T., Tupanceska, D., Zaid, A., et al.

(2005). Ross River virus: Molecular and cellular aspects of disease

pathogenesis. Pharmacology & Therapeutics, 107, 329–342.

Russell, R. C. (1994). Ross River virus: Disease trends and vector ecology in

Australia. Bulletin of the Society of Vector Ecology, 19, 73–81.

Russell, R. C. (1995). Arboviruses and their vectors in Australia: An update on the

ecology and epidemiology of some mosquito-borne arboviruses. Review of

Medical and Veterinary Entomology, 83, 141–158.

152

Russell, R. C. (1998a). Mosquito-borne arboviruses in Australia: The current scene

and implications of climate change for human health. International Journal

for Parasitology, 28, 955–969.

Russell, R. C. (1998b). Vectors vs humans in Australia — who is on top down

under? An update on vectorborne disease and research on vectors in

Australia. Journal of Vector Ecology, 23, 1–46.

Russell, R. C. (2002). Ross River virus: Ecology and distribution. Annual Review of

Entomology, 47, 1–31.

Russell, R. C., & Dwyer, D. E. (2000). Arboviruses associated with human disease in

Australia. Microbes and Infection, 2, 1693–1704.

Russell, R. C., & Kay, B. H. (2004). Medical entomology: Changes in the spectrum

of mosquito-borne disease in Australia and other vector threats and risks,

1972–2004. Australian Journal of Entomology, 43, 271–282.

Ryan, P., Alsemgeest, D., Thomas, P., Lyons, S., & Kay, B. (2003). Development of

a zonal risk assessment approach to the management of arbovirus disease by

local government. Brisbane. MARC Annual Report 2002–2003.

Ryan, P. A., Do, K. A., & Kay, B. H. (1999). Spatial and temporal analysis of Ross

River virus disease patterns at Maroochy Shire, Australia: Association

between human morbidity and mosquito (Diptera:Culicidae) abundance.

Journal of Medical Entomology, 36, 515–521.

Ryan, P. A., Lyons, S. A., Alsemgeest, D., Thomas, P., & Kay, B. H. (2004). Spatial

statistical analysis of adult mosquito (Diptera:Culicidae) counts: An example

153

using light trap data, in Redland Shire, Southeastern Queensland, Australia.

Journal of Medical Entomology, 41, 1143–1156.

Sarhan, M. E., Howitt, R. E., Moore, C. V., & Mitchell, C. J. (1980). Economic

evaluation of mosquito control programs. California Agriculture, November-

December 1980, 22–24.

Scott, T. W., Wright, S. A., Eldridge, B. F., & Brown, D. A. (2001). Cost

effectiveness of three arbovirus surveillance methods in Northern California.

Journal of the American Mosquito Control Association, 17, 118–123.

Shisler, J. K., & Harker, W. (1981). Source reduction as an economical approach to

mosquito management. Mosquito News, 41, 419–423.

Shisler, J. K., Lesser, F., & Candeletti, T. (1979). An approach to the evaluation of

temporary versus permanent measures in salt marsh mosquito control

operations. Mosquito News, 39, 776–780.

Shisler, J. K., & Schulze, T. L. (1981). Comparison of costs for mosquito control on

New Jersey disposal sites. Mosquito News, 41, 465–469.

Shisler, J. K., & Schulze, T. L. (1985). Methods for evaluation of costs associated

with permanent and temporary control methods for salt marsh mosquito

abatement. Journal of the American Mosquito Control Association, 1, 164–

168.

State of Queensland, & Local Government Association of Queensland (2001). A

Protocol guiding the partnership between the state of Queensland, and the

Local Government Association of Queensland for the management and

control of mosquitoes in Queensland. Brisbane: Queensland Government.

154

Stern, H., de Hoedt, G and Ernst, J. (no date). The objective classification of

Australian Climates. http://www.bom.gov.au/climate/environ/other/koppen

_explain.shtml (accessed 7 February, 2007).

Suhrbier, A., & La Linn, M. (2004). Clinical and pathologic aspects of arthritis due

to Ross River virus and other alphaviruses. Current Opinion in

Rheumatology, 16, 374–379.

Sutherst, R. W., Ingram, J. S. I., & Scherm, H. (1998). Global change and vector-

borne diseases. Parasitology Today, 14, 297–299.

Tong, S., Donald, K., & McMichael, A. J. (2002). Climate variability and Ross River

virus transmission. Journal of Epidemiology and Community Health, 56,

617–621.

Tong, S., & Hu, W. (2001). Climate variation and incidence of Ross River Virus in

Cairns, Australia: A time-series analysis. Environmental Health Perspectives,

109, 1271–1273.

Tong, S., & Hu, W. (2002). Different responses of Ross River virus to climate

variability between coastline and inland cities in Queensland, Australia.

Occup Environ Med, 59, 739–744.

Tong, S., Hu, W., Nicholls, N., Dale, P., MacKenzie, J. S., Patz, J., et al. (2005).

Climatic, high tide and vector variables and the transmission of Ross River

virus. Internal Medicine Journal, 35, 677–680.

Turrell, M. J. (1988). Horizontal and vertical transmission of viruses by insects and

tick vectors. In T. P. Monath (ed.), The arboviruses: Epidemiology and

ecology (Vol. 1). Boca Raton, FL: CRC Press.

155

Utzinger, J., Tozan, Y., Doumani, F., & Singer, B. H. (2002). The economic payoffs

of integrated malaria control in the Zambian copperbelt between 1930 and

1950. Tropical Medicine & International Health, 7, 657–677.

Utzinger, J., Tozan, Y., & Singer, B. H. (2001). Efficacy and cost-effectiveness of

environmental management for malaria control. Tropical Medicine &

International Health, 6, 677–687.

Walker, D., & Fox-Rushby, J. (2000a). Economic evaluation of parasitic diseases: A

critique of the internal and external validity of published studies. Tropical

Medicine & International Health, 5, 237–249.

Walker, D., & Fox-Rushby, J. A. (2000b). Economic evaluation of communicable

disease interventions in developing countries: A critical review of the

published literature. Health Economics, 9, 681–698.

Walker, K. (2000). Cost-comparison of DDT and alternative insecticides for malaria

control. Medical and Veterinary Entomology, 14, 345–354.

Whelan, P., I., Jacups, S., P., Melville, L., Broom, A., Currie, B., J., Krause, V., L.,

et al. (2003). Rainfall and vector mosquito numbers as risk indicators for

mosquito-borne disease in Central Australia. Communicable Disease

Intelligence, 27, 110–116.

Williams, L. A. J. (1986). The benefits of mosquito control. Journal of the Florida

Anti-Mosquito Association, 32–36.

Woodruff, R. E., Guest, C. S., & Garner, M. G. (2000). Summarising the mosquito:

Generic parameters for prediction of arbovirus epidemics. Paper presented at

156

the Arbovirus Research in Australia conference, South Stradbroke Island,

Queensland, Australia.

Woodruff, R. E., Guest, C. S., Garner, M. G., Becker, N., Lindesay, J., Carvan, T., et

al. (2002). Predicting Ross River virus epidemics from regional weather data.

Epidemiology, 13, 384–393.

World Health Organisation (1998) Economic costs of malaria, www.rbm.who.int/

cmc_upload/0/000/015/363/RBMInfosheet_10.htm (accessed 29 December

2006).

Wrights, A. E. (2000). Freshwater mosquito control at Pine Rivers Shire Council,

Queensland. Paper presented at the Arbovirus Research in Australia

conference, South Stradbroke Island, Queensland, Australia.

157

Chapter 7: Appendixes

7.1. Survey contact details

Local government Respondent name Position

Aramac Shire Council Vince Corbin CEO

Atherton Shire Council Stuart Innes Manager, Environmental Services

Aurukun Shire Council Not known CEO

Balonne Shire Council Steve Mizen Manager, Environmental Services

Banana Shire Council Jan Proposch Senior Environment Officer

Barcaldine Shire Rob Haywood Environmental Health Officer Council

Barcoo Shire Council Kerry Graham Environmental Health Officer

Bauhinia Shire Council Kevin Nofkee Environmental Health Officer

Beaudesert Shire Michael Bond Manager, Environmental Council Activities

Belyando Shire Council Tony Goldsworthy Environmental Health Officer

Bendemere Shire Michelle Ramsay Environmental Health Officer Council

Biggenden Shire Karen Aspinall Deputy Council

Blackall Shire Council David Souter Environmental Health Officer

Boonah Shire Council Mark Pattemore Director, Planning and Community Services

Booringa Shire Council Kay Crosby Environmental Health Officer

Boulia Shire Council Kerry Graham Environmental Health Officer

Bowen Shire Council Jon Gibbons Director, Health and Environmental Services

158

Local government Respondent name Position

Brisbane City Council Mike Muller Medical Entomologist, Vegetation and Pest Services

Broadsound Shire Wendy Sandford Environmental Health Officer Council

Bulloo Shire Council Robert Powell Environmental Health Officer

Bundaberg City Greg Savage Manager, Health and Council Environmental Services Department

Bungil Shire Council Michelle Ramsay Environmental Health Officer

Burdekin Shire Council Bronwyn Williams Manager, Environmental Health Services

Burke Shire Council Russell Cunningham Shire Ranger

Burnett Shire Council Greg Wolff Manager, Environmental Health

Caboolture Shire Matthew Lawrence Environmental Health Officer Council

Cairns City Council Gil Farrows Environmental Health Officer

Calliope Shire Council Darryl Saw Local Law and Noxious Weeds Coordinator

Caloundra Shire Vern Butterworth Acting Manager, Regulatory Council Services

Cambooya Shire Mike Lisle Manager, Development Council Services

Cardwell Shire Council Doug Green Manager, Environmental Health Services

Carpentaria Shire Dennis Kerr Director of Engineering Council

Charters Towers City Mick Langburne Health and Environment Council Officer

Chinchilla Shire Ken French Environmental Health Officer Council

159

Local government Respondent name Position

Clifton Shire Council Vince Stephens Environmental Health Officer

Cloncurry Shire Tiffany Coffison Cadet Environmental Health Council Officer

Cook Shire Council Chris Vakas Environmental Health Officer

Cooloola Shire Council Michael Grant Acting Manager, Community Services Department

Crows Nest Shire Allan Young Director, Corporate Services Council

Croydon Shire Council Bill Kerwin CEO

Dalby Town Council Todd Summerville Planning and Environment Officer

Dalrymple Shire Kent Dungavell Environmental Health Officer Council

Diamantina Shire Robert Powell Environmental Health Officer Council

Douglas Shire Council Paul Hoye Manager, Environmental Health Services

Duaringa Shire Council Warren Bolton Director, Planning and Environment

Eacham Shire Council Matthew Hyde Environmental Health Officer

Eidsvold Shire Council Gerry Brennan Environmental Health Officer

Emerald Shire Council Cameron Fraser Environmental Health Officer

Esk Shire Council Doug Phipps Environmental Health Officer

Etheridge Shire Allan Smith Rural Lands Officer Council

Fitzroy Shire Council Philip Steer Manager, Environmental Services

Flinders Shire Council Rose Longley Environmental Health Officer

Gatton Shire Council Marcus Moloney Environmental Health Officer

160

Local government Respondent name Position

Gayndah Shire Council Michael Cuvalo Environmental Health Officer

Gladstone City Council Ron Doherty Manager, Environmental Health

Gold Coast City David Allaway Senior Environmental Health Council Officer, Pest Management Unit

Goondiwindi Town Mahomad Saleem Environmental Health Officer Council

Herberton Shire Luke Taylor Environmental Health Officer Council

Hervey Bay City Ian Fanning Principal Scientist Council

Hinchinbrook Shire Matthew Buckman Technical Officer Council

Ilfracombe Shire Peter Chay Administration Officer Council

Inglewood Shire David Angell Environmental Health Officer Council

Ipswich Shire Council Chris Thomas Vermin and Vector Control Officer

Isis Shire Council Steve Rapkins Manager, Environmental Services

Isisford Shire Council Robert Bauer CEO

Jericho Shire Council Rob Haywood Environmental Health Officer

Johnstone Shire Kirsty Lamperd Environmental Health Officer Council

Jondaryan Shire Kevern Hay Manager, Environmental Council Services

Kilcoy Shire Council John Hrobelko Health and Building

Kilkivan Shire Council Jeff Miles Manager, Community Services

Kingaroy Shire Council Megan Jackson Environmental Health Officer

161

Local government Respondent name Position

Kolan Shire Council Eric Dyke Manager, Pest Management and Area Control

Laidley Shire Council Steven Brennan Senior Environmental Health Officer

Livingstone Shire Joanne Tucker Administrative Assistant to the Council EHO

Logan City Council Environmental Health Officer

Longreach Shire Bob O’Brien CEO Council

Mackay City Council Peter Grant Vector Control Foreman

Mareeba Shire Council Andrew Foster Acting Parks and Gardens Supervisor

Maroochy Shire Greg Doyle Supervisor, Pest Management Council

Maryborough City Glenn Wiley Manager, Environmental Council Services

McKinlay Shire Tim Vollmer Environmental Health Officer Council

Millmerran Shire Peter McCashney Environmental Health Officer Council

Mirani Shire Council Debra Adams Environmental Health Officer

Miriam Vale Shire Laurie French Public Health Officer Council

Monto Shire Council Gerry Brennan Environmental Health Officer

Mornington Shire Graeme Pearson CEO Council

Mount Isa City Council Keith Stevens C/- Sanitation Department

Mount Morgan Shire Wolfgang Zadravec Environmental Health Officer Council

162

Local government Respondent name Position

Mundubbera Shire Michael Cuvalo Environmental Health Officer Council

Murgon Shire Council Gavin Crawford Manager, Community Services

Murilla Shire Council David Angell Environmental Health Officer

Murweh Shire Council Chris Blanch CEO

Nanango Shire Council Angela Black Health Officer

Nebo Shire Council Shannon Gorman Environmental Health Officer

Noosa Shire Council Greg Mulder Vector Control Officer

Paroo Shire Council Brad Wheeler Environmental Health Officer

Peak Downs Shire Rebecca Shepherd Environmental Health Officer Council

Perry Shire Council Russell Lyons Environmental Health Officer

Pine Rivers Shire Robyn Moffat Environmental Health Officer Council

Pittsworth Shire Vince Stephens Environmental Health Officer Council

Quilpie Shire Council Kerry Graham Environmental Health Officer

Redcliffe City Council Clay Perel Coordinator, Animal and Pest Management

Redland Shire Council George Santagiuliana Team Leader, Mosquito Management

Richmond Shire Rose Longley Environmental Health Officer Council

Rockhampton City Barry Harper Environmental Health Council Coordinator

Roma Town Council Jason Gilmore Environment Officer

Rosalie Shire Council Environmental Health Community Services Officer

163

Local government Respondent name Position

Sarina Shire Council Shannon Gorman Environmental Health Officer

Stanthorpe Shire Mark Hughes Environmental Health Officer Council

Tambo Shire Council Kerry Graham Environmental Health Officer

Tara Shire Council John McLennon Environmental Health Officer/Building Surveyor

Taroom Shire Council Michelle Ramsay Environmental Health Officer

Thuringowa City Gary Ewart Manager, Environmental Council Services

Tiaro Shire Council CEO

Toowoomba City Mike Gerlach Manager, Environment and Council Health Branch

Torres Shire Council Environmental Health Officer

Townsville City Council Darren Alseemgeest Coordinator for Preventative Programs and Administration

Waggamba Shire Mohamed Salem Environmental Health Officer Council

Wambo Shire Council Fred Vicary Environmental Health Officer

Waroo Shire Council Michelle Ramsay Environmental Health Officer

Warwick Shire Council Nicole Hutchings Environmental Health Officer

Whitsunday Shire Troy Huckstepp Technical Officer, Weed and Council Pest Control

Winton Shire Council Bob Hoogland CEO

Wondai Shire Council Alisha Norris Environmental Health Officer

Woocoo Shire Council Joe Hill CEO

164

7.2. Summary of responses to the survey of mosquito control costs and practices

Aramac (S) Did not respond to survey Atherton (S) Did not respond to survey Aurukun (S) Did not respond to survey Balonne (S) Health, Planning and 3 (part) n/a complaints, local no Environment knowledge, house Department inspections

Banana (S) Environment and 2 (part) n/a local knowledge Yes – hand Community Services coloured hard copy maps

Barcaldine (S) Environment Section 1 (part) n/a complaints, local no knowledge

Barcoo (S) Did not respond to survey Bauhinia (S) Did not respond to survey Beaudesert (S) Department of 0.1 Integrated Mosquito local knowledge, yes (refer Development and the Control Strategy, Fact complaints Appendix of Environment, Sheet Integrated Community Health and Mosquito Control Compliance (control by Strategy) GCCC)

Belyando (S) Did not respond to survey Bendemere (S) Did not respond to survey Biggenden (S) No Control 0 n/a not identified no

Blackall (S) Environmental 1 (part) n/a local knowledge No Services/Animal and Pest Control

Boonah (S) Did not respond to survey Booringa (S) Water and Sewerage 4 (part) Procedure in control of mapped Yes - hand- Services mosquitos coloured hard copy maps

Boulia (S) Did not respond to survey Bowen (S) Did not respond to survey

165

Which section of How are Maps of Local council How mosquito mosquito Government undertakes many Mosquito control breeding sites breeding Name mosquito control employees documents? identified? habitat? Brisbane (C) Vegetation and Pest 20 Mosquito Management Known breeding sites Maps of aerial Services and Control Program for treatment sites Mosquito and Pest Services Customer Service Agreement between Pollution Prevention Health and Safety, and Vegetation and Pest Services

Broadsound (S) Environmental Health 1 (part) n/a local knowledge, no ponding after rain

Bulloo (S) No Control 0 n/a not identified no Bundaberg (C) Health and 0.1 n/a local knowledge, no Environment Services historical data, aerial photography

Bungil (S) Did not respond to survey Burdekin (S) Environmental Health 3 Mosquito Management local knowledge, yes Services Code of Practice for ground surveying, Queensland aerial surveying (GIS mapping 2003/2004), aerial surveying (no GIS mapping 2004/2005)

Burke (S) Shire Ranger 1(part) Community Education local knowledge no Document

Burnett (S) Environmental Health 2 (part) n/a local knowledge, aerial no surveys

Caboolture (S) Environmental Health, 1 No yet complete local knowledge, yes (saltmarsh Compliance Services satellite photography, sites) orthophotography, aerial surveys, ground surveys, complaints

166

Which section of How are Maps of Local council How mosquito mosquito Government undertakes many Mosquito control breeding sites breeding Name mosquito control employees documents? identified? habitat? Cairns (C) Vector Control Unit, 7 Vector Control Policy, local knowledge, aerial Yes – Mapinfo Environment Fish Breeding Program, photography, maps, GIS format Assessment, City Light Trapping Program, survey Assessment Mosquito Management Development Control Strategy, Adulticiding Routine, Complaints Investigation Procedure, Inspection Procedure, Refusal of Entry Procedure

Calliope (S) Did not respond to survey Caloundra (C) Environmental Health, 1.75 Mosquito Management ground surveys, aerial Yes – Arcgis Regulatory Services Control Plan 1991 photography format Unit

Cambooya (S) Did not respond to survey Cardwell (S) Health and 0.1 local knowledge no Environment

Carpentaria (S) Did not respond to survey Charters Towers (C) Environmental Health 0.1 Queensland Health local knowledge no Guidelines for Dengue outbreaks

Chinchilla (S) Health and Works 0 n/a not identified no Clifton (S) Health Branch 1 (part) n/a not identified no Cloncurry (S) Did not respond to survey Cook (S) Environmental Health 1 (part) Currently being developed local knowledge, no Section, Planning and ground surveys and Development sampling Department

Cooloola (S) Health and Community as required n/a complaints no Services Department

Crow's Nest (S) No mosquito control nil n/a complaints no program

Croydon (S) Health Department 2 (part) n/a local knowledge no

Dalby (T) Community Services 0.05 n/a local knowledge no

Dalrymple (S) Did not respond to survey

167

Douglas (S) Environmental Health 4 (part) Mosquito Management House to house no Department (by Paul Hoye, Manager survey, complaints EH Services), Dengue Fever Management Plan (QH)

Duaringa (S) No Control 0 n/a n/a n/a Eacham (S) No Control 0 n/a n/a n/a Eidsvold (S) Environmental Health 0.1 n/a local knowledge, no complaints, informal surveys

Emerald (S) Did not respond to survey Esk (S) Did not respond to survey Etheridge (S) No Control 0 n/a n/a n/a Fitzroy (S) Environmental Servies, 0.1 MOU with Capricorn Complaints, ground yes (saltmarsh Corporate, Community Mosquito Management survey sites) and Environmental Committee Services latitude

Flinders (S) Environmental Health 1 (part) site procedures local knowledge, no ground survey after rain

Gatton (S) No Control 0 n/a not identified n/a Gayndah (S) Environmental Health 0 n/a not identified n/a

Gladstone (C) Environmental Health 2 plans local knowledge, aerial no photography

Gold Coast (C) Pest Management Unit, 28 Mosquito Control local knowledge, aerial Yes – MapInfo Health Regulatory and Strategy, Habitat surveys format Lifeguard Services, Modification Strategy Community Services

Goondiwindi (T) Did not respond to survey Herberton (S) Health and 2 (part) n/a local knowledge, no Environment complaints

168

Which section of How are Maps of Local council How mosquito mosquito Government undertakes many Mosquito control breeding sites breeding Name mosquito control employees documents? identified? habitat? Hervey Bay (C) Vector Control Unit, 4 Review of the mosquito local knowledge, aerial Yes – MapInfo City Health and control strategy for photography, routine format Protection Hervey Bay surveillance, complaints

Hinchinbrook (S) Did not respond to survey Ilfracombe (S) No Control 0 n/a local knowledge - no no sites identified

Inglewood (S) Did not respond to survey Ipswich (C) Health Department 1 Internal Mosquito Control local knowledge Yes – Easimap Program Review format

Isis (S) Environmental Services 0.1 Framework for a local knowledge no Mosquito Control Code of Practice

Isisford (S) Did not respond to survey Jericho (S) Did not respond to survey Johnstone (S) Did not respond to survey Jondaryan (S) Environmental Services 2 (part) n/a local knowledge no

Kilcoy (S) No Control 0 n/a not identified no Kilkivan (S) Community Services 0.1 n/a local knowledge no

Kingaroy (S) Health and Building 0 n/a local knowledge, no Department complaints

Kolan (S) Community Services 1 (part) n/a local knowledge no

Laidley (S) Did not respond to survey Livingstone (S) Public and 1 (part) Policy No H1.14 local knowledge Yes – MapInfo Environmental Health Capricorn Mosquito format (involves contractor and Management Policy regional group)

Logan (C) Vector Control, Animal and Pest Services

Longreach (S) Did not respond to survey Mackay (C) Health and Regulatory 5 Vector Management Plan local knowledge, aerial yes Services photography

Mareeba (S) Environmental Health not provided n/a local knowledge no Services

169

Which section of How are Maps of Local council How mosquito mosquito Government undertakes many Mosquito control breeding sites breeding Name mosquito control employees documents? identified? habitat? Maroochy (S) Pest Management Unit, 2 full + 1 part Mosquito Management local knowledge, aerial yes Regulatory Services Code of Practice for photography Branch Queensland, Benchmarking of Mosquito Control Program

Maryborough (C) Environmental Health 1 n/a local knowledge, aerial no photography

McKinlay (S) Environmental Health 1.5 Health Regulations local knowledge, no complaints

Millmerran (S) No Control 0 n/a local knowledge no Mirani (S) Health and 1 (part) n/a local knowledge, no Environment complaints

Miriam Vale (S) No Control 0 n/a not identified no Monto (S) Did not respond to survey Mornington (S) Did not respond to survey Mount Isa (C) Did not respond to survey Mount Morgan (S) Health Department 0 n/a complaints no

Mundubbera (S) Environmental Health 0 n/a not identified no

Murgon (S) Environmental Health 0 n/a not identified no

Murilla (S) Environmental Health 1 (part) n/a local knowledge, no reports from residents

Murweh (S) Did not respond to survey Nanango (S) Community Services 1 (part) Mosquito Management ground surveys no Code of Practice for Queensland

Nebo (S) Environmental Health 1 (part) n/a local knowledge, no complaints, approved inspection programs

Noosa (S) Environmental Health 0.4 Aerial Treatment Control local knowledge, aerial Yes – hard copy Procedures, Mosquitoes: photography what council is doing and why?

Paroo (S) Health Department 1 (part) n/a local knowledge no

Peak Downs (S) Did not respond to survey

170

Pine Rivers (S) Community Response, 3.5 Aerial Contract and local knowledge, aerial yes Division of Lifestyle Specifications, NEMMO surveys, ground and Environment TOR, Operational Plan, survey, infrared maps, WPH&S Safe Plan Survey GPS data

Pittsworth (S) Environmental Health 1 (part) n/a not identified no

Quilpie (S) Health and 1 (part) n/a local knowledge, y Environmental Services general observation

Redcliffe (C) Did not respond to survey Redland (S) Health and 5 Mosquito and Biting local knowledge, aerial yes Environment Midge Management Plan photography, aerial 2003 -2007, Operating surveys, ground based Procedures surveys, adult mosquito surveillance

Richmond (S) Environmental Health 2 (part) Richmond Shire Dengue local knowledge, yes Officer, Parks and Fever Management Plan inspection Gardens, Plumbers and 1996 Outside Contractor

Rockhampton (C) Did not respond to survey Roma (T) Health and 2 (part) n/a local knowledge no Environment

Rosalie (S) Did not respond to survey Sarina (S) Environment and 3 (part) Mosquito Management complaints, surveys, no Health Services Code of Practice for Qld local knowledge, 2002, Dengue Fever approved inspection Management Plan for programs, knowledge North Qld 2000-2005 of mosquito biology

Stanthorpe (S) Health Department 0.05 n/a local knowledge, no complaints

Tambo (S) Local Laws/Stock 1 (part) n/a local knowledge, no Routes approved inspection programs, periodic assessment of unallocated lands

171

Which section of How are Maps of Local council How mosquito mosquito Government undertakes many Mosquito control breeding sites breeding Name mosquito control employees documents? identified? habitat? Tara (S) Did not respond to survey Taroom (S) Did not respond to survey Thuringowa (C) Environmental Health 1/8 FTE Environmental Services local knowledge, aerial no Memorandums photography, topographic maps, site survey/inspections

Tiaro (S) No Control 0 n/a not identified n/a Toowoomba (C) Environment and 3 (part) Policy Regarding ULV local knowledge, no Health Treatment complaints

Torres (S) Did not respond to survey Townsville (C) Environmental Health 1.8 Townsville City Council local knowledge, aerial Yes – Arcview Services - Preventive Mosquito Management survey, ground survey, format Programs Plan complaints

Waggamba (S) Did not respond to survey Wambo (S) Did not respond to survey Warroo (S) Did not respond to survey Warwick (S) Environmental Services 0.1 n/a local knowledge, no complaints

Whitsunday (S) Catchment 2 Whitsunday Shire Council local knowledge, Yes – MapInfo Management Services Draft Mosquito flooding maps, aerial format in cooperation with the Management Plan 2004- photography Environmental Health 2006 Department

Winton (S) Did not respond to survey Wondai (S) Environmental Health 1 (part) n/a local knowledge no Officer

Woocoo (S) Did not respond to survey

172

7.3. Ross River virus disease rates for local governments in Queensland

Age and sex Mosquito adjusted and control Local Number of Ross Crude Ross smoothed Ross Mosquito expenditure government River virus River virus River virus control data estimation name Year Population notifications notifications notifications expenditure method

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1)

Aramac (S) 1993 917 0 0.00 2.92 0 5 Aramac (S) 1994 888 0 0.00 7.70 0 5 Aramac (S) 1995 863 0 0.00 3.12 0 5 Aramac (S) 1996 856 0 0.00 8.77 0 5 Aramac (S) 1997 841 1 11.89 12.51 0 5 Aramac (S) 1998 823 0 0.00 3.02 0 5 Aramac (S) 1999 806 1 12.41 7.41 0 5 Aramac (S) 2000 796 3 37.69 2.39 0 5 Aramac (S) 2001 729 0 0.00 10.65 0 5 Aramac (S) 2002 742 1 13.48 1.88 0 5 Aramac (S) 2003 729 0 0.00 7.09 0 5 Aramac (S) 2004 707 0 0.00 4.24 0 5 Atherton (S) 1993 9430 5 5.30 2.65 1141 2 Atherton (S) 1994 9554 7 7.33 9.21 1134 2 Atherton (S) 1995 9755 4 4.10 6.20 1122 2 Atherton (S) 1996 10024 8 7.98 8.86 1097 2 Atherton (S) 1997 10130 5 4.94 8.06 1101 2 Atherton (S) 1998 10276 14 13.62 14.12 1094 2 Atherton (S) 1999 10344 1 0.97 2.90 2655 4 Atherton (S) 2000 10539 6 5.69 5.81 1043 4 Atherton (S) 2001 10554 4 3.79 4.38 1030 2 Atherton (S) 2002 10662 1 0.94 1.13 1051 4 Atherton (S) 2003 10786 1 0.93 2.11 1010 2 Atherton (S) 2004 10876 6 5.52 2.72 1000 2 Aurukun (S) 1993 812 0 0.00 3.13 0 5 Aurukun (S) 1994 819 0 0.00 7.88 0 5 Aurukun (S) 1995 823 0 0.00 3.18 0 5 Aurukun (S) 1996 836 0 0.00 8.86 0 5 Aurukun (S) 1997 844 0 0.00 3.59 0 5 Aurukun (S) 1998 854 0 0.00 2.97 0 5 Aurukun (S) 1999 864 0 0.00 6.00 0 5 Aurukun (S) 2000 887 0 0.00 2.29 0 5 Aurukun (S) 2001 1096 0 0.00 4.01 0 5 Aurukun (S) 2002 1143 0 0.00 1.62 0 5 Aurukun (S) 2003 1146 0 0.00 4.52 0 5 Aurukun (S) 2004 1168 1 8.56 3.62 0 5 Balonne (S) 1993 5177 4 7.73 10.19 1594 1 Balonne (S) 1994 5008 6 11.98 14.52 1584 1 Balonne (S) 1995 4867 5 10.27 6.79 1568 survey

173

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Balonne (S) 1996 4821 20 41.49 37.23 2086 survey Balonne (S) 1997 4806 14 29.13 26.59 3815 survey Balonne (S) 1998 4846 6 12.38 3.91 3861 survey Balonne (S) 1999 4853 8 16.48 13.86 8305 survey Balonne (S) 2000 4898 8 16.33 11.78 4097 survey Balonne (S) 2001 5574 6 10.76 8.44 4908 survey Balonne (S) 2002 5597 3 5.36 6.75 2048 survey Balonne (S) 2003 4606 2 4.34 3.56 1967 survey Balonne (S) 2004 5582 11 19.71 14.21 6093 survey Banana (S) 1993 14459 19 13.14 7.77 5489 2 Banana (S) 1994 14185 28 19.74 24.50 5452 2 Banana (S) 1995 13851 15 10.83 10.83 5398 2 Banana (S) 1996 13797 40 28.99 23.75 5275 2 Banana (S) 1997 13693 38 27.75 30.27 5298 2 Banana (S) 1998 13630 32 23.48 19.44 5263 2 Banana (S) 1999 13480 12 8.90 9.86 6042 survey Banana (S) 2000 13419 10 7.45 6.37 7811 survey Banana (S) 2001 14359 8 5.57 5.98 3168 survey Banana (S) 2002 14288 16 11.20 8.72 5046 survey Banana (S) 2003 14228 7 4.92 6.83 4671 survey Banana (S) 2004 14179 8 5.64 6.77 2123 survey Barcaldine (S) 1993 1768 0 0.00 6.09 5706 2 Barcaldine (S) 1994 1779 0 0.00 5.98 5668 2 Barcaldine (S) 1995 1783 7 39.26 30.41 5611 2 Barcaldine (S) 1996 1783 4 22.43 21.25 5483 2 Barcaldine (S) 1997 1751 8 45.69 38.95 5507 2 Barcaldine (S) 1998 1747 1 5.72 5.69 5471 2 Barcaldine (S) 1999 1732 0 0.00 5.31 5310 2 Barcaldine (S) 2000 1713 3 17.51 11.40 5217 2 Barcaldine (S) 2001 1719 0 0.00 3.65 5148 survey Barcaldine (S) 2002 1725 0 0.00 1.36 5127 survey Barcaldine (S) 2003 1703 1 5.87 6.50 5050 survey Barcaldine (S) 2004 1686 2 11.86 5.70 5000 survey Barcoo (S) 1993 475 0 0.00 4.06 0 5 Barcoo (S) 1994 470 1 21.28 11.71 0 5 Barcoo (S) 1995 464 1 21.55 7.31 0 5 Barcoo (S) 1996 461 3 65.08 19.88 0 5 Barcoo (S) 1997 454 1 22.03 23.99 0 5 Barcoo (S) 1998 457 1 21.88 9.79 0 5 Barcoo (S) 1999 464 0 0.00 6.39 0 5 Barcoo (S) 2000 456 0 0.00 2.90 0 5 Barcoo (S) 2001 456 0 0.00 4.46 0 5 Barcoo (S) 2002 456 0 0.00 2.11 0 5 Barcoo (S) 2003 444 0 0.00 5.53 0 5 Barcoo (S) 2004 456 1 21.93 8.08 0 5 Bauhinia (S) 1993 2290 1 4.37 1.56 0 5

174

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Bauhinia (S) 1994 2252 3 13.32 12.10 0 5 Bauhinia (S) 1995 2216 1 4.51 8.14 0 5 Bauhinia (S) 1996 2209 8 36.22 27.13 0 5 Bauhinia (S) 1997 2188 6 27.42 29.10 0 5 Bauhinia (S) 1998 2159 6 27.79 11.43 0 5 Bauhinia (S) 1999 2147 3 13.97 14.51 0 5 Bauhinia (S) 2000 2140 4 18.69 8.16 0 5 Bauhinia (S) 2001 2224 1 4.50 8.17 0 5 Bauhinia (S) 2002 2227 2 8.98 6.48 0 5 Bauhinia (S) 2003 2217 0 0.00 3.53 0 5 Bauhinia (S) 2004 2212 2 9.04 9.78 0 5 Beaudesert (S) 1993 41159 7 1.70 1.42 13638 2 Beaudesert (S) 1994 43431 44 10.13 9.85 13546 2 Beaudesert (S) 1995 45281 4 0.88 1.30 13411 2 Beaudesert (S) 1996 47000 106 22.55 22.07 13105 2 Beaudesert (S) 1997 48333 6 1.24 1.60 13162 2 Beaudesert (S) 1998 49509 9 1.82 1.34 13076 2 Beaudesert (S) 1999 50590 30 5.93 5.94 14444 survey Beaudesert (S) 2000 51992 8 1.54 1.82 14190 survey Beaudesert (S) 2001 53342 26 4.87 4.64 15443 survey Beaudesert (S) 2002 54958 4 0.73 1.02 10766 survey Beaudesert (S) 2003 56897 60 10.55 9.27 9090 survey Beaudesert (S) 2004 58697 47 8.01 8.16 10000 survey Belyando (S) 1993 11370 6 5.28 5.93 0 5 Belyando (S) 1994 11064 13 11.75 12.35 0 5 Belyando (S) 1995 10817 13 12.02 10.95 0 5 Belyando (S) 1996 10743 22 20.48 18.31 0 5 Belyando (S) 1997 10794 16 14.82 13.44 0 5 Belyando (S) 1998 10806 11 10.18 8.15 0 5 Belyando (S) 1999 10639 2 1.88 6.61 0 5 Belyando (S) 2000 10581 8 7.56 4.95 0 5 Belyando (S) 2001 9935 3 3.02 5.65 0 5 Belyando (S) 2002 10212 7 6.85 7.23 0 5 Belyando (S) 2003 10487 5 4.77 5.31 0 5 Belyando (S) 2004 10508 0 0.00 0.91 0 5 Bendemere (S) 1993 1092 1 9.16 6.55 0 survey Bendemere (S) 1994 1064 2 18.80 16.80 0 survey Bendemere (S) 1995 1046 0 0.00 2.87 0 survey Bendemere (S) 1996 1036 6 57.92 48.99 0 survey Bendemere (S) 1997 1021 2 19.59 10.76 0 survey Bendemere (S) 1998 993 1 10.07 7.53 0 survey Bendemere (S) 1999 975 2 20.51 8.97 0 survey Bendemere (S) 2000 961 3 31.22 2.20 0 survey Bendemere (S) 2001 992 2 20.16 14.22 0 survey Bendemere (S) 2002 995 2 20.10 9.86 0 survey Bendemere (S) 2003 989 1 10.11 4.71 0 survey

175

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Bendemere (S) 2004 994 0 0.00 6.25 0 survey Biggenden (S) 1993 1657 0 0.00 1.99 0 survey Biggenden (S) 1994 1679 4 23.82 16.88 0 survey Biggenden (S) 1995 1651 0 0.00 2.27 0 survey Biggenden (S) 1996 1630 6 36.81 31.14 0 survey Biggenden (S) 1997 1601 0 0.00 7.85 0 survey Biggenden (S) 1998 1566 2 12.77 6.70 0 survey Biggenden (S) 1999 1540 1 6.49 7.91 0 survey Biggenden (S) 2000 1505 1 6.64 1.75 0 survey Biggenden (S) 2001 1547 0 0.00 4.68 0 survey Biggenden (S) 2002 1535 2 13.03 6.99 0 survey Biggenden (S) 2003 1544 1 6.48 8.11 0 survey Biggenden (S) 2004 1528 1 6.54 4.66 0 survey Blackall (S) 1993 2027 2 9.87 5.82 2511 survey Blackall (S) 1994 1961 3 15.30 16.44 3061 survey Blackall (S) 1995 1891 1 5.29 5.89 2806 survey Blackall (S) 1996 1849 2 10.82 11.45 2851 survey Blackall (S) 1997 1800 8 44.44 30.57 2313 survey Blackall (S) 1998 1785 1 5.60 5.55 2298 survey Blackall (S) 1999 1749 2 11.44 9.39 2868 survey Blackall (S) 2000 1720 1 5.81 4.66 2504 survey Blackall (S) 2001 1812 0 0.00 3.60 2265 survey Blackall (S) 2002 1775 2 11.27 7.42 2051 survey Blackall (S) 2003 1701 1 5.88 6.37 2828 survey Blackall (S) 2004 1653 1 6.05 6.19 2400 survey Boonah (S) 1993 6760 1 1.48 1.76 4565 2 Boonah (S) 1994 6789 5 7.36 7.26 4534 2 Boonah (S) 1995 8048 1 1.24 2.69 4489 2 Boonah (S) 1996 8130 29 35.67 34.11 4387 2 Boonah (S) 1997 6940 2 2.88 3.29 4406 2 Boonah (S) 1998 6970 0 0.00 1.48 4377 4 Boonah (S) 1999 8219 8 9.73 8.62 4248 4 Boonah (S) 2000 8219 4 4.87 3.80 4174 2 Boonah (S) 2001 8345 2 2.40 2.98 4118 2 Boonah (S) 2002 8361 3 3.59 3.83 4102 4 Boonah (S) 2003 8507 7 8.23 7.59 4040 2 Boonah (S) 2004 8525 21 24.63 26.17 4000 2 Booringa (S) 1993 2049 1 4.88 4.94 685 survey Booringa (S) 1994 2012 1 4.97 9.70 453 2 Booringa (S) 1995 1956 2 10.22 7.90 224 survey Booringa (S) 1996 1929 8 41.47 30.11 219 2 Booringa (S) 1997 1891 4 21.15 19.09 220 survey Booringa (S) 1998 1859 2 10.76 12.57 328 2 Booringa (S) 1999 1831 0 0.00 5.24 425 survey Booringa (S) 2000 1802 0 0.00 1.57 1513 2 Booringa (S) 2001 1904 4 21.01 9.89 1493 2

176

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Booringa (S) 2002 1872 1 5.34 4.39 2563 survey Booringa (S) 2003 1872 1 5.34 6.06 1919 2 Booringa (S) 2004 1857 3 16.16 10.57 1300 survey Boulia (S) 1993 528 3 56.82 37.52 0 5 Boulia (S) 1994 520 0 0.00 10.84 0 5 Boulia (S) 1995 507 0 0.00 3.75 0 5 Boulia (S) 1996 512 0 0.00 10.53 0 5 Boulia (S) 1997 518 1 19.31 15.04 0 5 Boulia (S) 1998 528 0 0.00 3.59 0 5 Boulia (S) 1999 520 0 0.00 6.33 0 5 Boulia (S) 2000 517 1 19.34 8.55 0 5 Boulia (S) 2001 559 1 17.89 6.39 0 5 Boulia (S) 2002 547 1 18.28 2.03 0 5 Boulia (S) 2003 543 1 18.42 10.33 0 5 Boulia (S) 2004 536 0 0.00 4.53 0 5 Bowen (S) 1993 13192 35 26.53 18.67 7989 1 Bowen (S) 1994 13079 17 13.00 19.59 7935 1 Bowen (S) 1995 12924 15 11.61 16.78 7856 1 Bowen (S) 1996 12892 20 15.51 14.40 7676 1 Bowen (S) 1997 12833 28 21.82 21.86 7710 1 Bowen (S) 1998 12659 11 8.69 8.05 7660 1 Bowen (S) 1999 12541 7 5.58 6.03 7434 1 Bowen (S) 2000 12440 18 14.47 11.78 7304 4 Bowen (S) 2001 12170 10 8.22 9.30 7207 1 Bowen (S) 2002 12278 9 7.33 8.55 7178 1 Bowen (S) 2003 12306 7 5.69 5.80 7070 1 Bowen (S) 2004 12325 4 3.25 2.08 7000 1 Brisbane (C) 1993 768638 157 2.04 1.80 2853107 2 Brisbane (C) 1994 778833 567 7.28 7.31 2833895 2 Brisbane (C) 1995 795125 105 1.32 1.90 2805556 2 Brisbane (C) 1996 810878 849 10.47 9.97 2741585 2 Brisbane (C) 1997 818415 180 2.20 2.30 2753544 2 Brisbane (C) 1998 834032 119 1.43 1.31 2847374 survey Brisbane (C) 1999 853249 502 5.88 5.60 3217206 survey Brisbane (C) 2000 867802 81 0.93 1.01 3380600 survey Brisbane (C) 2001 881845 324 3.67 3.65 3335218 survey Brisbane (C) 2002 902073 39 0.43 0.61 3347815 survey Brisbane (C) 2003 923929 363 3.93 3.63 3258423 survey Brisbane (C) 2004 941208 342 3.63 3.76 3210743 survey Broadsound (S) 1993 8131 6 7.38 8.39 0 survey Broadsound (S) 1994 7837 4 5.10 7.20 0 survey Broadsound (S) 1995 7614 12 15.76 12.91 0 survey Broadsound (S) 1996 7509 14 18.64 14.01 0 survey Broadsound (S) 1997 7434 14 18.83 20.36 0 survey Broadsound (S) 1998 7295 12 16.45 14.90 0 survey Broadsound (S) 1999 7122 2 2.81 4.47 0 survey

177

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Broadsound (S) 2000 6991 8 11.44 9.04 0 survey Broadsound (S) 2001 6447 0 0.00 6.44 0 survey Broadsound (S) 2002 6449 6 9.30 7.98 0 survey Broadsound (S) 2003 6455 3 4.65 5.16 0 survey Broadsound (S) 2004 6453 1 1.55 2.74 0 survey Bulloo (S) 1993 552 0 0.00 3.80 0 survey Bulloo (S) 1994 532 0 0.00 8.70 0 survey Bulloo (S) 1995 522 0 0.00 3.72 0 survey Bulloo (S) 1996 509 0 0.00 10.55 0 survey Bulloo (S) 1997 508 2 39.37 17.80 0 survey Bulloo (S) 1998 497 0 0.00 3.66 0 survey Bulloo (S) 1999 498 1 20.08 8.37 0 survey Bulloo (S) 2000 491 0 0.00 2.84 0 survey Bulloo (S) 2001 447 0 0.00 4.47 0 survey Bulloo (S) 2002 458 0 0.00 2.11 0 survey Bulloo (S) 2003 459 0 0.00 5.51 0 survey Bulloo (S) 2004 466 0 0.00 4.66 0 survey Bundaberg (C) 1993 41873 25 5.97 5.20 9130 survey Bundaberg (C) 1994 42200 50 11.85 15.91 9068 survey Bundaberg (C) 1995 42547 24 5.64 5.59 9539 survey Bundaberg (C) 1996 43161 76 17.61 17.75 9321 survey Bundaberg (C) 1997 43465 34 7.82 8.38 9362 survey Bundaberg (C) 1998 43611 35 8.03 8.22 9848 survey Bundaberg (C) 1999 43723 17 3.89 5.89 9558 survey Bundaberg (C) 2000 43899 14 3.19 2.40 9390 survey Bundaberg (C) 2001 44138 12 2.72 3.69 9781 survey Bundaberg (C) 2002 44625 10 2.24 2.03 9741 survey Bundaberg (C) 2003 44940 37 8.23 8.00 9595 survey Bundaberg (C) 2004 45378 12 2.64 3.93 10000 survey Bungil (S) 1993 2041 2 9.80 5.49 0 5 Bungil (S) 1994 2011 4 19.89 16.84 0 5 Bungil (S) 1995 1997 1 5.01 4.68 0 5 Bungil (S) 1996 1985 5 25.19 23.24 0 5 Bungil (S) 1997 1959 5 25.52 22.94 0 5 Bungil (S) 1998 1953 1 5.12 1.88 0 5 Bungil (S) 1999 1926 2 10.38 8.93 0 5 Bungil (S) 2000 1892 1 5.29 1.53 0 5 Bungil (S) 2001 1939 0 0.00 4.94 0 5 Bungil (S) 2002 1939 0 0.00 1.28 0 5 Bungil (S) 2003 1968 0 0.00 3.72 0 5 Bungil (S) 2004 1947 0 0.00 2.90 0 5 Burdekin (S) 1993 18761 32 17.06 18.54 91299 2 Burdekin (S) 1994 18581 18 9.69 13.52 90685 2 Burdekin (S) 1995 18531 30 16.19 16.66 89778 2 Burdekin (S) 1996 18696 31 16.58 14.92 87731 2 Burdekin (S) 1997 18759 33 17.59 19.91 88113 2

178

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Burdekin (S) 1998 18655 10 5.36 5.44 87541 2 Burdekin (S) 1999 18553 22 11.86 8.38 94837 4 Burdekin (S) 2000 18488 28 15.14 18.62 148743 4 Burdekin (S) 2001 18261 18 9.86 7.46 189409 2 Burdekin (S) 2002 18360 9 4.90 6.44 262497 4 Burdekin (S) 2003 18407 13 7.06 7.71 218160 survey Burdekin (S) 2004 18381 2 1.09 1.71 216000 survey Burke (S) 1993 1286 0 0.00 2.37 7141 2 Burke (S) 1994 1237 0 0.00 6.92 7093 2 Burke (S) 1995 1191 1 8.40 5.48 7022 2 Burke (S) 1996 1163 0 0.00 7.63 6862 2 Burke (S) 1997 1130 2 17.70 16.17 6892 2 Burke (S) 1998 1104 6 54.35 27.95 6847 2 Burke (S) 1999 1091 2 18.33 5.80 9298 survey Burke (S) 2000 1135 0 0.00 10.79 6968 survey Burke (S) 2001 499 2 40.08 7.75 7126 survey Burke (S) 2002 500 0 0.00 2.07 3631 survey Burke (S) 2003 500 2 40.00 10.35 4933 survey Burke (S) 2004 485 1 20.62 8.53 6761 survey Burnett (S) 1993 17452 0 0.00 0.26 9872 survey Burnett (S) 1994 18684 0 0.00 1.14 10197 survey Burnett (S) 1995 19927 2 1.00 0.75 10498 survey Burnett (S) 1996 20746 17 8.19 8.20 10669 survey Burnett (S) 1997 21429 9 4.20 4.26 11144 survey Burnett (S) 1998 22089 16 7.24 5.74 11514 survey Burnett (S) 1999 22635 6 2.65 5.06 11621 survey Burnett (S) 2000 23228 9 3.87 2.99 11873 survey Burnett (S) 2001 23656 7 2.96 2.83 12184 survey Burnett (S) 2002 24200 2 0.83 1.34 12619 survey Burnett (S) 2003 24945 14 5.61 5.94 12927 survey Burnett (S) 2004 25635 10 3.90 3.50 13310 survey Caboolture (S) 1993 83289 38 4.56 5.95 228249 2 Caboolture (S) 1994 90124 99 10.98 12.84 226712 2 Caboolture (S) 1995 95730 19 1.98 2.34 336667 2 Caboolture (S) 1996 100313 120 11.96 12.11 328990 2 Caboolture (S) 1997 103743 58 5.59 5.56 330425 2 Caboolture (S) 1998 106906 48 4.49 3.77 328277 2 Caboolture (S) 1999 109518 137 12.51 12.81 318718 4 Caboolture (S) 2000 111639 42 3.76 3.82 323555 4 Caboolture (S) 2001 113833 75 6.59 7.87 369278 2 Caboolture (S) 2002 116475 18 1.55 1.94 417586 survey Caboolture (S) 2003 120738 141 11.68 11.43 718921 survey Caboolture (S) 2004 126169 78 6.18 6.81 712200 survey Cairns (C) 1993 91710 290 31.62 29.42 342373 1 Cairns (C) 1994 95721 80 8.36 11.52 340067 1 Cairns (C) 1995 100373 159 15.84 15.09 448889 survey

179

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Cairns (C) 1996 104014 91 8.75 8.43 438654 survey Cairns (C) 1997 106546 136 12.76 11.78 440567 survey Cairns (C) 1998 108802 94 8.64 8.07 437703 survey Cairns (C) 1999 110610 55 4.97 4.79 424816 survey Cairns (C) 2000 112351 85 7.57 6.65 417355 survey Cairns (C) 2001 107788 49 4.55 4.85 411825 survey Cairns (C) 2002 109280 56 5.12 4.91 441647 survey Cairns (C) 2003 112093 63 5.62 5.61 459406 survey Cairns (C) 2004 114665 96 8.37 8.33 461161 survey Calliope (S) 1993 11907 33 27.71 9.87 0 5 Calliope (S) 1994 12449 16 12.85 28.80 0 5 Calliope (S) 1995 12907 10 7.75 6.23 0 5 Calliope (S) 1996 13368 40 29.92 31.15 0 5 Calliope (S) 1997 13725 15 10.93 11.13 0 5 Calliope (S) 1998 14019 29 20.69 18.63 0 5 Calliope (S) 1999 14240 11 7.72 5.80 0 5 Calliope (S) 2000 14463 13 8.99 10.00 0 5 Calliope (S) 2001 14826 15 10.12 9.16 0 5 Calliope (S) 2002 15143 15 9.91 8.90 0 5 Calliope (S) 2003 15549 34 21.87 17.44 0 5 Calliope (S) 2004 15963 11 6.89 10.15 0 5 Caloundra (C) 1993 57749 35 6.06 4.04 80111 survey Caloundra (C) 1994 60534 53 8.76 10.18 121587 survey Caloundra (C) 1995 63316 17 2.68 3.73 83363 survey Caloundra (C) 1996 65236 81 12.42 13.70 114840 survey Caloundra (C) 1997 67073 31 4.62 4.94 115163 survey Caloundra (C) 1998 68668 24 3.50 3.59 116983 survey Caloundra (C) 1999 70330 73 10.38 10.85 95148 survey Caloundra (C) 2000 72051 21 2.91 3.27 122598 survey Caloundra (C) 2001 75328 41 5.44 5.49 201377 survey Caloundra (C) 2002 77890 15 1.93 1.97 183613 survey Caloundra (C) 2003 82042 112 13.65 12.76 211214 survey Caloundra (C) 2004 85469 33 3.86 5.25 263106 survey Cambooya (S) 1993 3513 0 0.00 1.11 0 5 Cambooya (S) 1994 3749 1 2.67 4.00 0 5 Cambooya (S) 1995 4022 1 2.49 5.71 0 5 Cambooya (S) 1996 4199 2 4.76 6.84 0 5 Cambooya (S) 1997 4295 1 2.33 2.94 0 5 Cambooya (S) 1998 4406 1 2.27 2.83 0 5 Cambooya (S) 1999 4512 1 2.22 3.88 0 5 Cambooya (S) 2000 4620 2 4.33 2.38 0 5 Cambooya (S) 2001 5062 1 1.98 5.42 0 5 Cambooya (S) 2002 5247 1 1.91 1.96 0 5 Cambooya (S) 2003 5402 1 1.85 3.49 0 5 Cambooya (S) 2004 5529 8 14.47 12.56 0 5 Cardwell (S) 1993 7836 42 53.60 57.52 11412 1

180

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Cardwell (S) 1994 7985 34 42.58 39.50 11336 1 Cardwell (S) 1995 8164 22 26.95 21.58 11222 1 Cardwell (S) 1996 8472 26 30.69 30.06 10966 survey Cardwell (S) 1997 8676 39 44.95 38.78 11014 survey Cardwell (S) 1998 8821 24 27.21 26.46 12037 survey Cardwell (S) 1999 8965 13 14.50 10.33 11682 survey Cardwell (S) 2000 9277 17 18.32 14.86 12521 survey Cardwell (S) 2001 9907 6 6.06 8.89 12355 survey Cardwell (S) 2002 10092 24 23.78 19.31 13330 survey Cardwell (S) 2003 10296 11 10.68 8.20 13130 survey Cardwell (S) 2004 10437 18 17.25 15.56 13000 survey Carpentaria (S) 1993 3415 1 2.93 1.13 0 5 Carpentaria (S) 1994 3471 1 2.88 8.31 0 5 Carpentaria (S) 1995 3506 0 0.00 1.38 0 5 Carpentaria (S) 1996 3518 4 11.37 12.05 0 5 Carpentaria (S) 1997 3558 3 8.43 10.03 0 5 Carpentaria (S) 1998 3638 1 2.75 3.82 0 5 Carpentaria (S) 1999 3738 3 8.03 5.40 0 5 Carpentaria (S) 2000 3802 5 13.15 14.99 0 5 Carpentaria (S) 2001 2270 5 22.03 10.14 0 5 Carpentaria (S) 2002 2342 4 17.08 10.53 0 5 Carpentaria (S) 2003 2389 8 33.49 20.14 0 5 Carpentaria (S) 2004 2395 1 4.18 4.65 0 5 Charters Towers 1993 9165 29 31.64 36.41 5706 1 (C) Charters Towers 1994 9121 11 12.06 16.09 5668 1 (C) Charters Towers 1995 9019 17 18.85 6.81 5611 1 (C) Charters Towers 1996 8984 26 28.94 40.25 5483 1 (C) Charters Towers 1997 8961 12 13.39 13.77 5507 1 (C) Charters Towers 1998 8932 4 4.48 4.70 5471 1 (C) Charters Towers 1999 8883 3 3.38 4.78 26020 4 (C) Charters Towers 2000 8820 14 15.87 9.96 28171 4 (C) Charters Towers 2001 8713 3 3.44 8.90 5148 1 (C) Charters Towers 2002 8751 3 3.43 2.57 5127 survey (C) Charters Towers 2003 8777 5 5.70 7.68 5050 survey (C) Charters Towers 2004 8790 1 1.14 2.02 5000 survey (C) Chinchilla (S) 1993 5932 5 8.43 10.56 0 survey Chinchilla (S) 1994 5875 5 8.51 11.34 0 survey Chinchilla (S) 1995 5837 4 6.85 4.06 0 survey Chinchilla (S) 1996 5818 21 36.09 34.01 0 survey Chinchilla (S) 1997 5791 8 13.81 17.15 0 survey

181

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Chinchilla (S) 1998 5773 4 6.93 5.50 0 survey Chinchilla (S) 1999 5728 1 1.75 4.59 1062 4 Chinchilla (S) 2000 5682 1 1.76 3.33 1043 4 Chinchilla (S) 2001 6028 1 1.66 3.20 0 survey Chinchilla (S) 2002 6038 5 8.28 4.98 0 survey Chinchilla (S) 2003 6058 3 4.95 8.34 0 survey Chinchilla (S) 2004 6114 7 11.45 10.98 0 survey Clifton (S) 1993 2456 0 0.00 5.29 0 survey Clifton (S) 1994 2417 2 8.27 7.68 0 survey Clifton (S) 1995 2410 0 0.00 4.18 0 survey Clifton (S) 1996 2409 5 20.76 20.40 0 survey Clifton (S) 1997 2393 1 4.18 1.86 0 survey Clifton (S) 1998 2388 0 0.00 4.70 0 survey Clifton (S) 1999 2379 0 0.00 4.89 0 survey Clifton (S) 2000 2370 0 0.00 1.32 0 survey Clifton (S) 2001 2443 0 0.00 3.30 0 survey Clifton (S) 2002 2468 0 0.00 1.12 0 survey Clifton (S) 2003 2485 0 0.00 3.35 0 survey Clifton (S) 2004 2487 5 20.10 13.45 0 survey Cloncurry (S) 1993 3120 6 19.23 12.59 5706 1 Cloncurry (S) 1994 3140 3 9.55 14.32 5668 1 Cloncurry (S) 1995 3126 1 3.20 3.98 5611 1 Cloncurry (S) 1996 3170 1 3.15 7.06 5483 1 Cloncurry (S) 1997 3259 6 18.41 10.64 5507 1 Cloncurry (S) 1998 3354 3 8.94 16.35 5471 1 Cloncurry (S) 1999 3375 4 11.85 9.85 5310 1 Cloncurry (S) 2000 3430 3 8.75 6.47 5217 4 Cloncurry (S) 2001 3800 15 39.47 17.49 5148 1 Cloncurry (S) 2002 3838 1 2.61 4.03 5127 1 Cloncurry (S) 2003 3844 4 10.41 10.31 5050 1 Cloncurry (S) 2004 3811 1 2.62 4.74 5000 1 Cook (S) 1993 7468 14 18.75 15.68 2282 2 Cook (S) 1994 7489 17 22.70 19.91 2267 2 Cook (S) 1995 7548 15 19.87 15.78 2244 2 Cook (S) 1996 7776 11 14.15 15.35 2193 2 Cook (S) 1997 7923 13 16.41 14.81 2203 2 Cook (S) 1998 8133 9 11.07 10.28 2189 2 Cook (S) 1999 8282 5 6.04 6.66 2124 2 Cook (S) 2000 8319 13 15.63 10.28 2087 2 Cook (S) 2001 5701 6 10.52 8.29 2059 2 Cook (S) 2002 5811 9 15.49 12.88 2051 2 Cook (S) 2003 3780 18 47.62 26.85 2020 2 Cook (S) 2004 3961 23 58.07 37.76 5000 survey Cooloola (S) 1993 29698 6 2.02 1.82 5706 2 Cooloola (S) 1994 30583 28 9.16 8.91 5668 2 Cooloola (S) 1995 31233 14 4.48 5.25 5611 2

182

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Cooloola (S) 1996 31844 47 14.76 16.77 5483 2 Cooloola (S) 1997 32343 30 9.28 8.67 5507 2 Cooloola (S) 1998 32650 21 6.43 6.50 5471 2 Cooloola (S) 1999 32925 31 9.42 10.17 5310 2 Cooloola (S) 2000 33240 12 3.61 4.27 5217 2 Cooloola (S) 2001 33413 15 4.49 5.10 5148 survey Cooloola (S) 2002 33775 3 0.89 1.12 5127 survey Cooloola (S) 2003 34502 52 15.07 13.82 5050 survey Cooloola (S) 2004 35372 31 8.76 10.20 5000 survey Crow's Nest (S) 1993 7648 1 1.31 1.69 0 survey Crow's Nest (S) 1994 8148 1 1.23 3.24 0 survey Crow's Nest (S) 1995 8510 0 0.00 0.67 0 survey Crow's Nest (S) 1996 8801 5 5.68 7.07 0 survey Crow's Nest (S) 1997 9059 3 3.31 5.55 0 survey Crow's Nest (S) 1998 9233 2 2.17 1.47 0 survey Crow's Nest (S) 1999 9542 1 1.05 3.81 0 survey Crow's Nest (S) 2000 9917 2 2.02 1.21 0 survey Crow's Nest (S) 2001 10224 0 0.00 2.25 0 survey Crow's Nest (S) 2002 10564 0 0.00 0.39 0 survey Crow's Nest (S) 2003 11078 2 1.81 1.89 0 survey Crow's Nest (S) 2004 11635 13 11.17 12.36 0 survey Croydon (S) 1993 254 0 0.00 5.03 7581 2 Croydon (S) 1994 265 0 0.00 9.64 7530 2 Croydon (S) 1995 272 0 0.00 4.33 7455 2 Croydon (S) 1996 287 0 0.00 12.13 7285 2 Croydon (S) 1997 296 0 0.00 5.34 7317 2 Croydon (S) 1998 305 0 0.00 4.19 8754 survey Croydon (S) 1999 308 0 0.00 6.55 8496 survey Croydon (S) 2000 305 0 0.00 3.21 8347 survey Croydon (S) 2001 288 0 0.00 4.60 7207 survey Croydon (S) 2002 287 1 34.84 7.59 6665 survey Croydon (S) 2003 290 0 0.00 5.82 4545 survey Croydon (S) 2004 287 1 34.84 7.86 4500 survey Dalby (T) 1993 9893 3 3.03 3.28 3424 2 Dalby (T) 1994 9837 15 15.25 14.24 3401 2 Dalby (T) 1995 9781 6 6.13 6.58 3367 2 Dalby (T) 1996 9795 25 25.52 26.46 3290 2 Dalby (T) 1997 9802 6 6.12 6.63 3304 2 Dalby (T) 1998 9762 10 10.24 6.64 3283 2 Dalby (T) 1999 9753 0 0.00 5.78 3817 survey Dalby (T) 2000 9739 7 7.19 5.07 3187 survey Dalby (T) 2001 10074 2 1.99 4.28 4670 survey Dalby (T) 2002 10110 6 5.93 4.82 4066 survey Dalby (T) 2003 10083 3 2.98 4.60 4963 survey Dalby (T) 2004 10159 17 16.73 13.63 5823 survey Dalrymple (S) 1993 3425 4 11.68 15.15 0 5

183

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Dalrymple (S) 1994 3402 2 5.88 8.49 0 5 Dalrymple (S) 1995 3377 2 5.92 1.42 0 5 Dalrymple (S) 1996 3377 3 8.88 16.25 0 5 Dalrymple (S) 1997 3375 3 8.89 8.17 0 5 Dalrymple (S) 1998 3396 1 2.94 3.51 0 5 Dalrymple (S) 1999 3400 1 2.94 5.42 0 5 Dalrymple (S) 2000 3402 1 2.94 3.19 0 5 Dalrymple (S) 2001 3430 3 8.75 5.98 0 5 Dalrymple (S) 2002 3419 0 0.00 0.92 0 5 Dalrymple (S) 2003 3458 0 0.00 2.82 0 5 Dalrymple (S) 2004 3425 0 0.00 2.10 0 5 Diamantina (S) 1993 276 0 0.00 4.91 0 survey Diamantina (S) 1994 287 0 0.00 9.55 0 survey Diamantina (S) 1995 301 0 0.00 4.25 0 survey Diamantina (S) 1996 322 0 0.00 11.85 0 survey Diamantina (S) 1997 332 1 30.12 12.52 0 survey Diamantina (S) 1998 332 0 0.00 4.11 0 survey Diamantina (S) 1999 342 0 0.00 6.51 0 survey Diamantina (S) 2000 335 1 29.85 10.68 0 survey Diamantina (S) 2001 326 0 0.00 4.57 0 survey Diamantina (S) 2002 316 0 0.00 2.25 0 survey Diamantina (S) 2003 310 0 0.00 5.78 0 survey Diamantina (S) 2004 301 0 0.00 5.01 0 survey Douglas (S) 1993 6535 31 47.44 43.50 1712 2 Douglas (S) 1994 6903 36 52.15 45.64 1700 2 Douglas (S) 1995 7250 42 57.93 43.92 1683 2 Douglas (S) 1996 7574 28 36.97 32.81 1656 survey Douglas (S) 1997 7838 42 53.59 43.28 1308 survey Douglas (S) 1998 8056 34 42.20 25.35 903 survey Douglas (S) 1999 8188 16 19.54 18.15 75458 survey Douglas (S) 2000 8432 11 13.05 14.51 18367 survey Douglas (S) 2001 8174 25 30.58 19.59 25172 survey Douglas (S) 2002 8478 27 31.85 24.35 15728 survey Douglas (S) 2003 8714 22 25.25 15.89 33012 survey Douglas (S) 2004 8805 22 24.99 23.48 47577 survey Duaringa (S) 1993 9894 7 7.07 5.70 0 survey Duaringa (S) 1994 9563 11 11.50 11.54 0 survey Duaringa (S) 1995 9253 12 12.97 9.91 0 survey Duaringa (S) 1996 9086 6 6.60 8.59 0 survey Duaringa (S) 1997 8983 11 12.25 13.51 0 survey Duaringa (S) 1998 8793 10 11.37 9.07 0 survey Duaringa (S) 1999 8659 3 3.46 5.96 0 survey Duaringa (S) 2000 8472 10 11.80 7.01 0 survey Duaringa (S) 2001 6505 4 6.15 8.00 0 survey Duaringa (S) 2002 6429 5 7.78 8.46 0 survey Duaringa (S) 2003 6493 7 10.78 8.77 0 survey

184

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Duaringa (S) 2004 6584 3 4.56 4.97 0 survey Eacham (S) 1993 5913 8 13.53 14.73 0 survey Eacham (S) 1994 5997 4 6.67 10.38 0 survey Eacham (S) 1995 6058 2 3.30 3.39 0 survey Eacham (S) 1996 6117 0 0.00 2.46 0 survey Eacham (S) 1997 6193 1 1.61 2.08 0 survey Eacham (S) 1998 6235 6 9.62 9.10 0 survey Eacham (S) 1999 6260 2 3.19 3.31 0 survey Eacham (S) 2000 6260 0 0.00 2.82 0 survey Eacham (S) 2001 6142 2 3.26 4.17 0 survey Eacham (S) 2002 6194 1 1.61 1.64 0 survey Eacham (S) 2003 6237 0 0.00 1.94 0 survey Eacham (S) 2004 6227 1 1.61 2.99 0 survey Eidsvold (S) 1993 997 0 0.00 10.99 2282 2 Eidsvold (S) 1994 975 2 20.51 13.38 2267 2 Eidsvold (S) 1995 959 0 0.00 2.98 2244 2 Eidsvold (S) 1996 957 9 94.04 47.54 2193 2 Eidsvold (S) 1997 943 3 31.81 15.89 2203 2 Eidsvold (S) 1998 928 2 21.55 14.95 2189 2 Eidsvold (S) 1999 912 1 10.96 8.05 2124 2 Eidsvold (S) 2000 899 1 11.12 7.66 2087 2 Eidsvold (S) 2001 932 0 0.00 4.12 2780 survey Eidsvold (S) 2002 941 0 0.00 1.74 5537 survey Eidsvold (S) 2003 941 0 0.00 4.78 6464 2 Eidsvold (S) 2004 928 0 0.00 3.92 7400 survey Emerald (S) 1993 11230 7 6.23 4.57 0 5 Emerald (S) 1994 11494 12 10.44 10.29 0 5 Emerald (S) 1995 12138 33 27.19 23.07 0 5 Emerald (S) 1996 12487 16 12.81 14.84 0 5 Emerald (S) 1997 12772 27 21.14 19.54 0 5 Emerald (S) 1998 13019 26 19.97 17.53 0 5 Emerald (S) 1999 13092 9 6.87 9.61 0 5 Emerald (S) 2000 13162 18 13.68 9.90 0 5 Emerald (S) 2001 12976 8 6.17 6.89 0 5 Emerald (S) 2002 13170 14 10.63 10.28 0 5 Emerald (S) 2003 13291 5 3.76 4.16 0 5 Emerald (S) 2004 13419 8 5.96 5.01 0 5 Esk (S) 1993 13241 2 1.51 0.92 0 5 Esk (S) 1994 13432 22 16.38 14.60 0 5 Esk (S) 1995 13587 1 0.74 1.72 0 5 Esk (S) 1996 13801 75 54.34 53.96 0 5 Esk (S) 1997 13942 5 3.59 3.73 0 5 Esk (S) 1998 14089 4 2.84 2.72 0 5 Esk (S) 1999 14200 28 19.72 17.22 0 5 Esk (S) 2000 14281 7 4.90 2.82 0 5 Esk (S) 2001 14711 5 3.40 3.82 0 5

185

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Esk (S) 2002 14807 3 2.03 3.96 0 5 Esk (S) 2003 15005 13 8.66 7.61 0 5 Esk (S) 2004 15144 33 21.79 21.29 0 5 Etheridge (S) 1993 973 1 10.28 14.98 0 survey Etheridge (S) 1994 951 1 10.52 11.24 0 survey Etheridge (S) 1995 930 1 10.75 28.13 0 survey Etheridge (S) 1996 924 1 10.82 12.22 0 survey Etheridge (S) 1997 913 0 0.00 3.44 0 survey Etheridge (S) 1998 896 2 22.32 18.63 0 survey Etheridge (S) 1999 890 1 11.24 7.64 0 survey Etheridge (S) 2000 890 0 0.00 2.28 0 survey Etheridge (S) 2001 983 1 10.17 5.27 0 survey Etheridge (S) 2002 987 1 10.13 7.35 0 survey Etheridge (S) 2003 987 0 0.00 4.72 0 survey Etheridge (S) 2004 971 2 20.60 11.38 0 survey Fitzroy (S) 1993 8954 3 3.35 4.12 0 1 Fitzroy (S) 1994 9464 15 15.85 14.79 0 1 Fitzroy (S) 1995 9731 10 10.28 9.56 0 1 Fitzroy (S) 1996 9806 26 26.51 24.19 0 1 Fitzroy (S) 1997 9908 13 13.12 11.62 0 1 Fitzroy (S) 1998 9881 25 25.30 19.41 0 1 Fitzroy (S) 1999 9926 4 4.03 8.66 0 1 Fitzroy (S) 2000 9975 6 6.02 5.86 754 survey Fitzroy (S) 2001 9975 4 4.01 3.97 1493 survey Fitzroy (S) 2002 9996 7 7.00 7.39 4140 survey Fitzroy (S) 2003 10167 10 9.84 6.22 3052 survey Fitzroy (S) 2004 10265 3 2.92 6.69 4285 survey Flinders (S) 1993 2446 10 40.88 38.42 0 5 Flinders (S) 1994 2340 2 8.55 9.52 0 5 Flinders (S) 1995 2244 1 4.46 4.30 0 5 Flinders (S) 1996 2195 3 13.67 14.46 0 5 Flinders (S) 1997 2149 5 23.27 22.91 0 5 Flinders (S) 1998 2086 1 4.79 4.71 0 5 Flinders (S) 1999 2061 4 19.41 9.43 0 5 Flinders (S) 2000 2022 3 14.84 14.80 0 5 Flinders (S) 2001 2052 3 14.62 9.09 0 5 Flinders (S) 2002 2037 1 4.91 3.71 0 5 Flinders (S) 2003 2056 1 4.86 5.56 0 5 Flinders (S) 2004 1998 0 0.00 2.86 0 5 Gatton (S) 1993 14654 3 2.05 4.38 0 survey Gatton (S) 1994 14894 12 8.06 8.13 0 survey Gatton (S) 1995 14876 2 1.34 2.27 0 survey Gatton (S) 1996 14940 68 45.52 43.06 0 survey Gatton (S) 1997 15048 3 1.99 2.74 0 survey Gatton (S) 1998 15075 5 3.32 4.19 0 survey Gatton (S) 1999 15182 13 8.56 5.64 0 survey

186

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Gatton (S) 2000 15299 6 3.92 5.63 0 survey Gatton (S) 2001 15429 6 3.89 4.46 0 survey Gatton (S) 2002 15894 4 2.52 3.46 0 survey Gatton (S) 2003 16010 10 6.25 5.75 0 survey Gatton (S) 2004 16131 31 19.22 18.22 0 survey Gayndah (S) 1993 2905 2 6.88 1.30 0 survey Gayndah (S) 1994 2915 3 10.29 13.60 0 survey Gayndah (S) 1995 2876 2 6.95 6.29 0 survey Gayndah (S) 1996 2861 6 20.97 12.03 0 survey Gayndah (S) 1997 2824 1 3.54 13.32 0 survey Gayndah (S) 1998 2780 13 46.76 35.33 0 survey Gayndah (S) 1999 2750 1 3.64 7.50 0 survey Gayndah (S) 2000 2708 1 3.69 3.74 0 survey Gayndah (S) 2001 2876 0 0.00 3.12 0 survey Gayndah (S) 2002 2864 2 6.98 6.01 0 survey Gayndah (S) 2003 2897 0 0.00 3.10 0 survey Gayndah (S) 2004 2923 3 10.26 8.94 0 survey Gladstone (C) 1993 25337 34 13.42 5.99 114124 2 Gladstone (C) 1994 25669 32 12.47 19.09 113356 2 Gladstone (C) 1995 26094 16 6.13 6.88 112222 2 Gladstone (C) 1996 26459 66 24.94 23.21 109663 2 Gladstone (C) 1997 26763 34 12.70 13.19 110142 2 Gladstone (C) 1998 27079 51 18.83 14.37 109426 2 Gladstone (C) 1999 27303 20 7.33 9.64 121073 4 Gladstone (C) 2000 27612 23 8.33 6.05 171116 4 Gladstone (C) 2001 26719 13 4.87 7.20 154434 2 Gladstone (C) 2002 26983 28 10.38 10.88 153807 2 Gladstone (C) 2003 27688 45 16.25 14.69 151500 2 Gladstone (C) 2004 28380 16 5.64 5.62 150000 survey Gold Coast (C) 1993 298438 78 2.61 2.38 912994 2 Gold Coast (C) 1994 312628 215 6.88 7.03 906846 2 Gold Coast (C) 1995 331319 48 1.45 1.79 897778 2 Gold Coast (C) 1996 344277 213 6.19 6.20 877307 2 Gold Coast (C) 1997 356496 53 1.49 1.47 1101418 2 Gold Coast (C) 1998 366988 38 1.04 0.89 1094258 2 Gold Coast (C) 1999 378755 155 4.09 4.11 1062040 2 Gold Coast (C) 2000 391610 33 0.84 0.83 1098365 survey Gold Coast (C) 2001 409258 130 3.18 3.26 1147401 survey Gold Coast (C) 2002 423508 25 0.59 0.64 1142225 survey Gold Coast (C) 2003 440425 173 3.93 3.81 1510585 survey Gold Coast (C) 2004 453199 120 2.65 2.75 1449661 survey Goondiwindi (T) 1993 4454 0 0.00 0.90 0 5 Goondiwindi (T) 1994 4390 0 0.00 3.61 0 5 Goondiwindi (T) 1995 4393 0 0.00 1.16 0 5 Goondiwindi (T) 1996 4361 54 123.82 88.28 0 5 Goondiwindi (T) 1997 4396 10 22.75 20.11 0 5

187

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Goondiwindi (T) 1998 4472 8 17.89 9.21 0 5 Goondiwindi (T) 1999 4503 6 13.32 12.72 0 5 Goondiwindi (T) 2000 4566 13 28.47 17.39 0 5 Goondiwindi (T) 2001 4777 4 8.37 9.30 0 5 Goondiwindi (T) 2002 4873 10 20.52 16.15 0 5 Goondiwindi (T) 2003 4942 1 2.02 3.68 0 5 Goondiwindi (T) 2004 5006 40 79.90 57.90 0 5 Herberton (S) 1993 4882 6 12.29 17.00 1141 survey Herberton (S) 1994 4939 5 10.12 12.70 1134 survey Herberton (S) 1995 5090 12 23.58 23.81 1122 survey Herberton (S) 1996 5221 7 13.41 15.39 1097 survey Herberton (S) 1997 5338 2 3.75 4.65 1101 survey Herberton (S) 1998 5427 4 7.37 6.05 1094 survey Herberton (S) 1999 5479 1 1.83 4.41 1062 survey Herberton (S) 2000 5570 0 0.00 0.69 1043 survey Herberton (S) 2001 5309 0 0.00 2.40 1030 survey Herberton (S) 2002 5380 3 5.58 4.61 1025 survey Herberton (S) 2003 5469 13 23.77 22.68 1010 survey Herberton (S) 2004 5477 4 7.30 7.77 1000 survey Hervey Bay (C) 1993 31650 13 4.11 2.85 0 survey Hervey Bay (C) 1994 34356 21 6.11 9.42 0 survey Hervey Bay (C) 1995 36361 9 2.48 2.87 112222 2 Hervey Bay (C) 1996 37850 28 7.40 7.90 120630 2 Hervey Bay (C) 1997 38992 22 5.64 5.68 126663 2 Hervey Bay (C) 1998 40210 28 6.96 8.30 131311 2 Hervey Bay (C) 1999 41134 17 4.13 4.30 198601 4 Hervey Bay (C) 2000 42002 14 3.33 2.38 262593 survey Hervey Bay (C) 2001 42229 17 4.03 4.02 327154 survey Hervey Bay (C) 2002 43305 14 3.23 3.55 381943 survey Hervey Bay (C) 2003 45579 40 8.78 8.67 373321 survey Hervey Bay (C) 2004 48153 21 4.36 4.88 398453 survey Hinchinbrook (S) 1993 15303 13 8.50 14.60 22825 2 Hinchinbrook (S) 1994 15263 28 18.35 18.30 22671 2 Hinchinbrook (S) 1995 15282 11 7.20 6.85 22444 2 Hinchinbrook (S) 1996 15406 30 19.47 24.37 21933 2 Hinchinbrook (S) 1997 15458 21 13.59 13.84 22028 2 Hinchinbrook (S) 1998 15475 20 12.92 12.08 21885 2 Hinchinbrook (S) 1999 15475 9 5.82 5.46 32796 4 Hinchinbrook (S) 2000 15550 32 20.58 19.84 28462 4 Hinchinbrook (S) 2001 12339 8 6.48 8.45 28828 2 Hinchinbrook (S) 2002 12257 2 1.63 2.34 28711 2 Hinchinbrook (S) 2003 12271 3 2.44 3.57 28280 2 Hinchinbrook (S) 2004 12139 13 10.71 6.63 28000 2 Ilfracombe (S) 1993 349 0 0.00 4.56 0 survey Ilfracombe (S) 1994 348 1 28.74 13.34 0 survey Ilfracombe (S) 1995 339 0 0.00 4.15 0 survey

188

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Ilfracombe (S) 1996 328 0 0.00 11.80 0 survey Ilfracombe (S) 1997 325 2 61.54 19.50 0 survey Ilfracombe (S) 1998 319 0 0.00 4.14 0 survey Ilfracombe (S) 1999 315 1 31.75 7.70 0 survey Ilfracombe (S) 2000 310 0 0.00 3.20 0 survey Ilfracombe (S) 2001 360 0 0.00 4.54 0 survey Ilfracombe (S) 2002 370 0 0.00 2.19 0 survey Ilfracombe (S) 2003 375 0 0.00 5.66 0 survey Ilfracombe (S) 2004 368 0 0.00 4.86 0 survey Inglewood (S) 1993 2953 0 0.00 1.28 0 5 Inglewood (S) 1994 2913 2 6.87 6.84 0 5 Inglewood (S) 1995 2844 0 0.00 3.58 0 5 Inglewood (S) 1996 2821 12 42.54 38.50 0 5 Inglewood (S) 1997 2761 0 0.00 4.37 0 5 Inglewood (S) 1998 2707 8 29.55 12.04 0 5 Inglewood (S) 1999 2690 2 7.43 12.60 0 5 Inglewood (S) 2000 2662 3 11.27 11.97 0 5 Inglewood (S) 2001 2660 0 0.00 3.21 0 5 Inglewood (S) 2002 2655 0 0.00 1.08 0 5 Inglewood (S) 2003 2696 0 0.00 3.22 0 5 Inglewood (S) 2004 2629 4 15.21 8.03 0 5 Ipswich (C) 1993 124533 23 1.85 2.11 223684 survey Ipswich (C) 1994 127227 79 6.21 6.19 222177 survey Ipswich (C) 1995 122084 26 2.13 2.89 219956 survey Ipswich (C) 1996 123528 359 29.06 29.20 214940 survey Ipswich (C) 1997 131030 25 1.91 2.18 215878 survey Ipswich (C) 1998 131732 37 2.81 2.98 214475 survey Ipswich (C) 1999 125614 93 7.40 7.34 208160 survey Ipswich (C) 2000 127103 12 0.94 1.28 204504 survey Ipswich (C) 2001 126289 29 2.30 2.81 201794 survey Ipswich (C) 2002 128596 10 0.78 0.83 200975 survey Ipswich (C) 2003 131507 51 3.88 3.80 197960 survey Ipswich (C) 2004 133185 92 6.91 7.01 196000 survey Isis (S) 1993 4954 3 6.06 2.26 3424 1 Isis (S) 1994 5238 7 13.36 14.25 3401 1 Isis (S) 1995 5486 5 9.11 4.98 3367 1 Isis (S) 1996 5711 20 35.02 35.90 3290 1 Isis (S) 1997 5787 5 8.64 4.92 3304 1 Isis (S) 1998 5849 8 13.68 13.65 3283 1 Isis (S) 1999 5898 4 6.78 8.10 3186 1 Isis (S) 2000 5924 1 1.69 1.29 3130 survey Isis (S) 2001 5763 3 5.21 4.31 3089 survey Isis (S) 2002 5815 3 5.16 5.21 4102 survey Isis (S) 2003 5919 9 15.21 7.48 5050 survey Isis (S) 2004 6050 3 4.96 5.64 5000 survey Isisford (S) 1993 323 2 61.92 27.77 0 5

189

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Isisford (S) 1994 321 0 0.00 9.42 0 5 Isisford (S) 1995 312 0 0.00 4.22 0 5 Isisford (S) 1996 302 3 99.34 26.75 0 5 Isisford (S) 1997 288 0 0.00 13.32 0 5 Isisford (S) 1998 285 1 35.09 4.25 0 5 Isisford (S) 1999 278 0 0.00 9.81 0 5 Isisford (S) 2000 271 1 36.90 7.41 0 5 Isisford (S) 2001 311 4 128.62 5.99 0 5 Isisford (S) 2002 302 2 66.23 21.10 0 5 Isisford (S) 2003 301 0 0.00 8.18 0 5 Isisford (S) 2004 302 0 0.00 5.00 0 5 Jericho (S) 1993 1090 0 0.00 2.63 0 5 Jericho (S) 1994 1071 0 0.00 7.27 0 5 Jericho (S) 1995 1055 5 47.39 24.79 0 5 Jericho (S) 1996 1036 3 28.96 24.15 0 5 Jericho (S) 1997 1030 4 38.83 20.63 0 5 Jericho (S) 1998 1013 0 0.00 6.95 0 5 Jericho (S) 1999 1006 0 0.00 5.88 0 5 Jericho (S) 2000 997 1 10.03 2.17 0 5 Jericho (S) 2001 1089 1 9.18 7.34 0 5 Jericho (S) 2002 1096 2 18.25 3.93 0 5 Jericho (S) 2003 1069 2 18.71 9.46 0 5 Jericho (S) 2004 1097 2 18.23 12.57 0 5 Johnstone (S) 1993 18280 30 16.41 16.20 0 5 Johnstone (S) 1994 18657 32 17.15 21.19 0 5 Johnstone (S) 1995 18862 31 16.44 16.86 0 5 Johnstone (S) 1996 19339 15 7.76 8.68 0 5 Johnstone (S) 1997 19605 38 19.38 19.34 0 5 Johnstone (S) 1998 19737 21 10.64 10.88 0 5 Johnstone (S) 1999 19821 23 11.60 10.11 0 5 Johnstone (S) 2000 19968 22 11.02 12.36 0 5 Johnstone (S) 2001 19022 12 6.31 6.86 0 5 Johnstone (S) 2002 19024 36 18.92 16.88 0 5 Johnstone (S) 2003 19110 15 7.85 7.82 0 5 Johnstone (S) 2004 19091 16 8.38 9.04 0 5 Jondaryan (S) 1993 11218 0 0.00 0.39 1427 2 Jondaryan (S) 1994 11227 3 2.67 4.04 1417 2 Jondaryan (S) 1995 11363 0 0.00 0.52 1403 survey Jondaryan (S) 1996 11407 24 21.04 20.74 1371 survey Jondaryan (S) 1997 11582 4 3.45 3.09 1377 survey Jondaryan (S) 1998 11744 2 1.70 3.40 1368 survey Jondaryan (S) 1999 11863 6 5.06 5.52 1328 survey Jondaryan (S) 2000 12082 4 3.31 2.06 1304 survey Jondaryan (S) 2001 12867 5 3.89 4.66 1287 survey Jondaryan (S) 2002 13177 2 1.52 2.24 1282 survey Jondaryan (S) 2003 13507 3 2.22 3.07 1263 survey

190

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Jondaryan (S) 2004 13630 17 12.47 10.41 1250 survey Kilcoy (S) 1993 3105 0 0.00 1.23 0 survey Kilcoy (S) 1994 3148 2 6.35 8.56 0 survey Kilcoy (S) 1995 3182 0 0.00 1.48 0 survey Kilcoy (S) 1996 3194 4 12.52 9.09 0 survey Kilcoy (S) 1997 3209 1 3.12 3.90 0 survey Kilcoy (S) 1998 3194 0 0.00 4.19 0 survey Kilcoy (S) 1999 3193 1 3.13 5.80 0 survey Kilcoy (S) 2000 3189 2 6.27 6.33 0 survey Kilcoy (S) 2001 3306 0 0.00 2.97 0 survey Kilcoy (S) 2002 3386 1 2.95 0.93 0 survey Kilcoy (S) 2003 3417 3 8.78 10.51 0 survey Kilcoy (S) 2004 3459 3 8.67 7.60 0 survey Kilkivan (S) 1993 3000 1 3.33 1.26 1541 1 Kilkivan (S) 1994 3091 1 3.24 8.68 1530 1 Kilkivan (S) 1995 3200 1 3.13 3.70 1515 1 Kilkivan (S) 1996 3220 8 24.84 25.28 1480 1 Kilkivan (S) 1997 3230 1 3.10 5.20 1487 1 Kilkivan (S) 1998 3250 1 3.08 4.72 1477 1 Kilkivan (S) 1999 3256 0 0.00 4.42 1434 1 Kilkivan (S) 2000 3259 3 9.21 4.77 1409 1 Kilkivan (S) 2001 3210 0 0.00 3.98 1390 1 Kilkivan (S) 2002 3217 0 0.00 0.96 1384 1 Kilkivan (S) 2003 3253 4 12.30 7.74 1364 1 Kilkivan (S) 2004 3269 1 3.06 5.81 1350 survey Kingaroy (S) 1993 11140 8 7.18 5.59 0 survey Kingaroy (S) 1994 11309 4 3.54 7.82 0 survey Kingaroy (S) 1995 11355 2 1.76 1.38 0 survey Kingaroy (S) 1996 11371 26 22.87 23.96 0 survey Kingaroy (S) 1997 11401 4 3.51 3.04 0 survey Kingaroy (S) 1998 11417 13 11.39 10.70 0 survey Kingaroy (S) 1999 11400 3 2.63 5.41 0 survey Kingaroy (S) 2000 11406 2 1.75 0.37 0 survey Kingaroy (S) 2001 11735 1 0.85 3.23 0 survey Kingaroy (S) 2002 11912 3 2.52 1.75 0 survey Kingaroy (S) 2003 12076 5 4.14 4.95 0 survey Kingaroy (S) 2004 12223 20 16.36 16.14 0 survey Kolan (S) 1993 3571 2 5.60 1.09 228 1 Kolan (S) 1994 3821 0 0.00 7.59 227 1 Kolan (S) 1995 4067 3 7.38 7.89 224 1 Kolan (S) 1996 4317 16 37.06 28.98 219 1 Kolan (S) 1997 4496 3 6.67 8.55 220 1 Kolan (S) 1998 4670 9 19.27 18.90 219 1 Kolan (S) 1999 4754 1 2.10 4.57 212 survey Kolan (S) 2000 4763 2 4.20 3.25 209 survey Kolan (S) 2001 4621 1 2.16 2.57 206 survey

191

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Kolan (S) 2002 4639 2 4.31 5.62 205 survey Kolan (S) 2003 4518 1 2.21 3.68 253 survey Kolan (S) 2004 4519 1 2.21 3.24 250 survey Laidley (S) 1993 10090 3 2.97 3.29 0 5 Laidley (S) 1994 11053 13 11.76 12.30 0 5 Laidley (S) 1995 11826 1 0.85 3.18 0 5 Laidley (S) 1996 12399 70 56.46 52.82 0 5 Laidley (S) 1997 12555 5 3.98 3.43 0 5 Laidley (S) 1998 12698 6 4.73 4.52 0 5 Laidley (S) 1999 12805 14 10.93 10.02 0 5 Laidley (S) 2000 12884 5 3.88 4.20 0 5 Laidley (S) 2001 13031 4 3.07 4.23 0 5 Laidley (S) 2002 13053 3 2.30 2.57 0 5 Laidley (S) 2003 13143 7 5.33 4.69 0 5 Laidley (S) 2004 13296 24 18.05 18.67 0 5 Livingstone (S) 1993 19774 22 11.13 11.25 5706 2 Livingstone (S) 1994 20902 33 15.79 18.32 5668 2 Livingstone (S) 1995 21928 25 11.40 11.50 5611 2 Livingstone (S) 1996 22777 31 13.61 12.14 5483 2 Livingstone (S) 1997 23514 53 22.54 21.64 5507 2 Livingstone (S) 1998 24161 57 23.59 25.13 10943 survey Livingstone (S) 1999 24762 16 6.46 6.56 29419 survey Livingstone (S) 2000 25094 18 7.17 9.16 28902 survey Livingstone (S) 2001 25938 21 8.10 8.04 20591 1 Livingstone (S) 2002 26423 21 7.95 5.69 20508 1 Livingstone (S) 2003 27157 40 14.73 14.77 20200 1 Livingstone (S) 2004 27760 4 1.44 2.40 20000 survey Logan (C) 1993 152454 21 1.38 1.37 342373 2 Logan (C) 1994 155910 137 8.79 9.31 340067 2 Logan (C) 1995 159732 9 0.56 0.87 336667 2 Logan (C) 1996 161904 147 9.08 9.10 328990 2 Logan (C) 1997 163807 11 0.67 0.82 428055 survey Logan (C) 1998 164797 26 1.58 1.64 421226 survey Logan (C) 1999 165519 73 4.41 4.38 405215 survey Logan (C) 2000 166731 15 0.90 1.09 312716 survey Logan (C) 2001 166508 54 3.24 3.55 367680 survey Logan (C) 2002 168422 7 0.42 0.48 386941 survey Logan (C) 2003 170459 81 4.75 4.88 347760 survey Logan (C) 2004 172299 51 2.96 3.15 324755 survey Longreach (S) 1993 3944 0 0.00 3.65 3424 1 Longreach (S) 1994 3919 2 5.10 7.38 3401 1 Longreach (S) 1995 3841 4 10.41 9.34 3367 1 Longreach (S) 1996 3815 5 13.11 11.96 3290 1 Longreach (S) 1997 3795 15 39.53 28.62 3304 1 Longreach (S) 1998 3777 7 18.53 13.01 3283 1 Longreach (S) 1999 3767 3 7.96 9.13 3186 1

192

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Longreach (S) 2000 3780 13 34.39 23.28 3130 4 Longreach (S) 2001 3919 0 0.00 5.01 3089 1 Longreach (S) 2002 3997 2 5.00 4.31 3076 1 Longreach (S) 2003 3973 2 5.03 5.85 3030 1 Longreach (S) 2004 3974 2 5.03 5.73 3000 1 Mackay (C) 1993 65576 209 31.87 32.84 228249 2 Mackay (C) 1994 67289 47 6.98 9.21 226712 2 Mackay (C) 1995 69022 82 11.88 11.37 224444 2 Mackay (C) 1996 71023 126 17.74 17.45 219327 2 Mackay (C) 1997 72457 94 12.97 13.76 220284 2 Mackay (C) 1998 73619 68 9.24 8.24 218852 2 Mackay (C) 1999 75050 31 4.13 4.99 289499 4 Mackay (C) 2000 76473 63 8.24 8.00 323542 4 Mackay (C) 2001 75541 30 3.97 4.38 319164 2 Mackay (C) 2002 76697 50 6.52 6.85 318586 4 Mackay (C) 2003 77884 33 4.24 3.75 323200 2 Mackay (C) 2004 79350 22 2.77 2.64 434000 survey Mareeba (S) 1993 17538 21 11.97 12.26 4565 survey Mareeba (S) 1994 17675 24 13.58 14.66 4534 survey Mareeba (S) 1995 17810 5 2.81 3.02 4489 survey Mareeba (S) 1996 18079 16 8.85 8.66 4387 survey Mareeba (S) 1997 18238 22 12.06 12.59 4406 survey Mareeba (S) 1998 18487 36 19.47 18.32 4377 survey Mareeba (S) 1999 18619 10 5.37 6.57 4248 survey Mareeba (S) 2000 18736 11 5.87 5.34 4174 survey Mareeba (S) 2001 18341 11 6.00 6.11 4118 survey Mareeba (S) 2002 18441 6 3.25 3.32 4102 survey Mareeba (S) 2003 18518 15 8.10 8.47 4040 survey Mareeba (S) 2004 18518 14 7.56 5.84 4000 survey Maroochy (S) 1993 90839 82 9.03 6.96 171186 survey Maroochy (S) 1994 96504 87 9.02 11.19 198373 survey Maroochy (S) 1995 101644 52 5.12 6.29 213222 survey Maroochy (S) 1996 105831 138 13.04 13.06 208360 survey Maroochy (S) 1997 109477 91 8.31 8.01 220284 survey Maroochy (S) 1998 113001 51 4.51 4.47 218852 survey Maroochy (S) 1999 115968 176 15.18 14.60 244269 survey Maroochy (S) 2000 119143 32 2.69 3.15 239979 survey Maroochy (S) 2001 124562 55 4.42 4.96 257390 survey Maroochy (S) 2002 128671 25 1.94 1.91 256345 survey Maroochy (S) 2003 133784 226 16.89 15.16 282800 survey Maroochy (S) 2004 138142 76 5.50 7.06 300000 survey Maryborough (C) 1993 24393 16 6.56 6.13 11412 2 Maryborough (C) 1994 24382 25 10.25 14.06 11336 2 Maryborough (C) 1995 24375 10 4.10 4.49 11222 2 Maryborough (C) 1996 24484 44 17.97 17.25 14410 survey Maryborough (C) 1997 24551 11 4.48 5.51 18878 survey

193

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Maryborough (C) 1998 24655 17 6.90 6.64 15298 survey Maryborough (C) 1999 24639 15 6.09 6.80 23259 survey Maryborough (C) 2000 24620 17 6.90 4.07 19574 survey Maryborough (C) 2001 24766 11 4.44 5.33 19098 survey Maryborough (C) 2002 24900 10 4.02 4.91 16765 survey Maryborough (C) 2003 25140 23 9.15 8.16 20049 survey Maryborough (C) 2004 25231 17 6.74 7.64 8950 survey McKinlay (S) 1993 1178 1 8.49 8.80 1141 2 McKinlay (S) 1994 1150 0 0.00 7.10 1134 2 McKinlay (S) 1995 1140 2 17.54 7.86 1122 2 McKinlay (S) 1996 1133 4 35.30 23.35 1097 2 McKinlay (S) 1997 1151 4 34.75 29.07 1101 2 McKinlay (S) 1998 1135 1 8.81 27.17 1094 2 McKinlay (S) 1999 1119 1 8.94 7.62 1062 2 McKinlay (S) 2000 1107 3 27.10 7.82 1043 2 McKinlay (S) 2001 1044 4 38.31 13.05 1030 2 McKinlay (S) 2002 1049 0 0.00 1.68 1025 2 McKinlay (S) 2003 1024 2 19.53 10.45 1061 survey McKinlay (S) 2004 1014 0 0.00 3.81 1050 survey Millmerran (S) 1993 3058 4 13.08 4.42 0 survey Millmerran (S) 1994 2978 3 10.07 15.74 0 survey Millmerran (S) 1995 2922 0 0.00 1.58 0 survey Millmerran (S) 1996 2901 21 72.39 57.06 0 survey Millmerran (S) 1997 2875 1 3.48 3.17 0 survey Millmerran (S) 1998 2858 0 0.00 1.44 0 survey Millmerran (S) 1999 2849 0 0.00 4.62 0 survey Millmerran (S) 2000 2838 0 0.00 1.16 0 survey Millmerran (S) 2001 3447 2 5.80 5.28 0 survey Millmerran (S) 2002 3424 0 0.00 0.92 0 survey Millmerran (S) 2003 3306 0 0.00 2.89 0 survey Millmerran (S) 2004 3333 6 18.00 15.38 0 survey Mirani (S) 1993 4851 14 28.86 28.62 571 1 Mirani (S) 1994 4865 7 14.39 15.01 567 1 Mirani (S) 1995 4881 12 24.59 17.88 561 1 Mirani (S) 1996 4978 7 14.06 12.47 548 1 Mirani (S) 1997 5036 8 15.89 16.61 551 1 Mirani (S) 1998 5128 8 15.60 15.13 547 1 Mirani (S) 1999 5186 3 5.78 5.90 531 4 Mirani (S) 2000 5281 8 15.15 13.65 4695 survey Mirani (S) 2001 5241 4 7.63 7.32 4633 survey Mirani (S) 2002 5264 7 13.30 13.35 4614 survey Mirani (S) 2003 5250 2 3.81 4.46 4545 survey Mirani (S) 2004 5256 1 1.90 2.91 4500 survey Miriam Vale (S) 1993 3148 4 12.71 5.42 0 survey Miriam Vale (S) 1994 3390 5 14.75 17.41 0 survey Miriam Vale (S) 1995 3643 11 30.19 25.58 0 survey

194

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Miriam Vale (S) 1996 3893 28 71.92 58.62 0 survey Miriam Vale (S) 1997 4097 6 14.64 11.84 0 survey Miriam Vale (S) 1998 4251 8 18.82 17.89 0 survey Miriam Vale (S) 1999 4456 4 8.98 7.24 0 survey Miriam Vale (S) 2000 4596 5 10.88 9.27 0 survey Miriam Vale (S) 2001 4392 3 6.83 4.28 0 survey Miriam Vale (S) 2002 4526 5 11.05 12.34 0 survey Miriam Vale (S) 2003 4722 14 29.65 19.82 0 survey Miriam Vale (S) 2004 5008 11 21.96 13.46 0 survey Monto (S) 1993 3003 6 19.98 21.31 0 5 Monto (S) 1994 2947 9 30.54 27.26 0 5 Monto (S) 1995 2905 5 17.21 18.94 0 5 Monto (S) 1996 2903 20 68.89 52.77 0 5 Monto (S) 1997 2848 3 10.53 16.15 0 5 Monto (S) 1998 2771 6 21.65 17.68 0 5 Monto (S) 1999 2704 3 11.09 8.76 0 5 Monto (S) 2000 2647 4 15.11 10.15 0 5 Monto (S) 2001 2531 0 0.00 4.76 0 5 Monto (S) 2002 2492 1 4.01 3.63 0 5 Monto (S) 2003 2458 2 8.14 6.63 0 5 Monto (S) 2004 2455 1 4.07 4.66 0 5 Mornington (S) 1993 882 0 0.00 2.99 0 5 Mornington (S) 1994 959 0 0.00 7.53 0 5 Mornington (S) 1995 1042 2 19.19 11.95 0 5 Mornington (S) 1996 1124 1 8.90 11.84 0 5 Mornington (S) 1997 1165 1 8.58 9.84 0 5 Mornington (S) 1998 1191 2 16.79 11.21 0 5 Mornington (S) 1999 1193 2 16.76 8.75 0 5 Mornington (S) 2000 1251 1 7.99 1.94 0 5 Mornington (S) 2001 1006 0 0.00 4.07 0 5 Mornington (S) 2002 1005 1 9.95 1.70 0 5 Mornington (S) 2003 1042 0 0.00 6.39 0 5 Mornington (S) 2004 1042 0 0.00 3.77 0 5 Mount Isa (C) 1993 23568 16 6.79 5.42 0 5 Mount Isa (C) 1994 23026 10 4.34 5.96 0 5 Mount Isa (C) 1995 22463 12 5.34 5.07 0 5 Mount Isa (C) 1996 22218 11 4.95 4.88 0 5 Mount Isa (C) 1997 22031 22 9.99 8.76 0 5 Mount Isa (C) 1998 21875 12 5.49 5.79 0 5 Mount Isa (C) 1999 21701 15 6.91 6.21 0 5 Mount Isa (C) 2000 21676 12 5.54 5.39 0 5 Mount Isa (C) 2001 20990 37 17.63 14.09 0 5 Mount Isa (C) 2002 20628 9 4.36 4.26 0 5 Mount Isa (C) 2003 20492 9 4.39 4.62 0 5 Mount Isa (C) 2004 20508 5 2.44 2.73 0 5 Mount Morgan (S) 1993 3194 5 15.65 12.60 0 survey

195

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Mount Morgan (S) 1994 3131 6 19.16 23.96 0 survey Mount Morgan (S) 1995 3015 1 3.32 3.44 0 survey Mount Morgan (S) 1996 2963 6 20.25 17.40 0 survey Mount Morgan (S) 1997 2901 3 10.34 6.80 0 survey Mount Morgan (S) 1998 2822 0 0.00 4.09 0 survey Mount Morgan (S) 1999 2753 3 10.90 5.52 0 survey Mount Morgan (S) 2000 2693 1 3.71 11.94 0 survey Mount Morgan (S) 2001 2942 0 0.00 3.10 0 survey Mount Morgan (S) 2002 2959 1 3.38 1.01 0 survey Mount Morgan (S) 2003 2987 1 3.35 8.69 0 survey Mount Morgan (S) 2004 3050 0 0.00 2.26 0 survey Mundubbera (S) 1993 2326 0 0.00 1.55 0 survey Mundubbera (S) 1994 2364 3 12.69 11.72 0 survey Mundubbera (S) 1995 2369 2 8.44 1.82 0 survey Mundubbera (S) 1996 2411 7 29.03 28.53 0 survey Mundubbera (S) 1997 2420 4 16.53 16.09 0 survey Mundubbera (S) 1998 2437 8 32.83 23.29 0 survey Mundubbera (S) 1999 2438 1 4.10 4.85 0 survey Mundubbera (S) 2000 2443 0 0.00 3.52 0 survey Mundubbera (S) 2001 2277 2 8.78 6.00 0 survey Mundubbera (S) 2002 2297 1 4.35 1.17 0 survey Mundubbera (S) 2003 2332 2 8.58 7.64 0 survey Mundubbera (S) 2004 2369 4 16.88 14.65 0 survey Murgon (S) 1993 4648 3 6.45 3.30 0 survey Murgon (S) 1994 4598 3 6.52 11.81 0 survey Murgon (S) 1995 4612 0 0.00 1.12 0 survey Murgon (S) 1996 4625 4 8.65 11.11 0 survey Murgon (S) 1997 4587 2 4.36 5.08 0 survey Murgon (S) 1998 4564 3 6.57 4.93 0 survey Murgon (S) 1999 4552 5 10.98 9.53 0 survey Murgon (S) 2000 4529 2 4.42 3.12 0 survey Murgon (S) 2001 3618 3 8.29 8.46 0 survey Murgon (S) 2002 3650 3 8.22 4.60 0 survey Murgon (S) 2003 3684 1 2.71 4.32 0 survey Murgon (S) 2004 3717 3 8.07 7.86 0 survey Murilla (S) 1993 2932 2 6.82 9.93 0 survey Murilla (S) 1994 2884 8 27.74 21.36 0 survey Murilla (S) 1995 2815 2 7.10 5.92 0 survey Murilla (S) 1996 2797 25 89.38 67.94 0 survey Murilla (S) 1997 2759 13 47.12 43.31 0 survey Murilla (S) 1998 2723 5 18.36 9.69 0 survey Murilla (S) 1999 2695 4 14.84 10.38 0 survey Murilla (S) 2000 2658 2 7.52 7.00 104 survey Murilla (S) 2001 2715 1 3.68 5.91 103 survey Murilla (S) 2002 2738 1 3.65 3.71 103 survey Murilla (S) 2003 2723 3 11.02 7.61 101 survey

196

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Murilla (S) 2004 2720 2 7.35 10.25 100 survey Murweh (S) 1993 5451 3 5.50 3.91 0 5 Murweh (S) 1994 5274 4 7.58 8.31 0 5 Murweh (S) 1995 5074 4 7.88 7.75 0 5 Murweh (S) 1996 4973 11 22.12 20.93 0 5 Murweh (S) 1997 4902 15 30.60 27.25 0 5 Murweh (S) 1998 4835 2 4.14 6.54 0 5 Murweh (S) 1999 4758 6 12.61 9.52 0 5 Murweh (S) 2000 4797 6 12.51 4.23 0 5 Murweh (S) 2001 5018 7 13.95 10.80 0 5 Murweh (S) 2002 5016 4 7.97 7.73 0 5 Murweh (S) 2003 5018 2 3.99 4.80 0 5 Murweh (S) 2004 5004 9 17.99 11.80 0 5 Nanango (S) 1993 7594 1 1.32 1.36 0 survey Nanango (S) 1994 7869 8 10.17 8.97 0 survey Nanango (S) 1995 7982 0 0.00 2.06 0 survey Nanango (S) 1996 8063 36 44.65 45.74 0 survey Nanango (S) 1997 8151 1 1.23 1.90 0 survey Nanango (S) 1998 8158 5 6.13 6.57 0 survey Nanango (S) 1999 8178 9 11.01 9.03 0 survey Nanango (S) 2000 8245 2 2.43 1.21 0 survey Nanango (S) 2001 8502 8 9.41 5.17 0 survey Nanango (S) 2002 8516 5 5.87 9.07 0 survey Nanango (S) 2003 8605 8 9.30 6.40 0 survey Nanango (S) 2004 8674 30 34.59 30.41 0 survey Nebo (S) 1993 2416 4 16.56 18.37 1141 2 Nebo (S) 1994 2346 1 4.26 7.91 1134 2 Nebo (S) 1995 2266 1 4.41 4.35 1122 2 Nebo (S) 1996 2253 6 26.63 28.87 1097 2 Nebo (S) 1997 2250 7 31.11 33.04 1101 2 Nebo (S) 1998 2190 3 13.70 5.03 1094 2 Nebo (S) 1999 2145 0 0.00 6.13 1062 2 Nebo (S) 2000 2141 0 0.00 1.41 1043 2 Nebo (S) 2001 2090 2 9.57 4.65 1030 2 Nebo (S) 2002 2090 4 19.14 14.72 1025 2 Nebo (S) 2003 2157 1 4.64 8.12 1010 2 Nebo (S) 2004 2142 1 4.67 5.65 1400 survey Noosa (S) 1993 28180 21 7.45 6.90 65679 survey Noosa (S) 1994 30379 28 9.22 11.13 67617 survey Noosa (S) 1995 32982 22 6.67 6.09 70363 survey Noosa (S) 1996 34962 65 18.59 19.12 66237 survey Noosa (S) 1997 36498 63 17.26 15.35 67517 survey Noosa (S) 1998 38165 25 6.55 5.98 64561 survey Noosa (S) 1999 39692 60 15.12 14.43 68675 survey Noosa (S) 2000 41230 14 3.40 3.72 63759 survey Noosa (S) 2001 42030 31 7.38 6.99 66168 survey

197

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Noosa (S) 2002 43427 13 2.99 3.46 75222 survey Noosa (S) 2003 44678 166 37.15 31.48 78803 survey Noosa (S) 2004 45725 51 11.15 13.58 125191 survey Paroo (S) 1993 2545 3 11.79 4.38 571 2 Paroo (S) 1994 2461 2 8.13 12.14 567 2 Paroo (S) 1995 2350 6 25.53 20.46 561 2 Paroo (S) 1996 2296 5 21.78 21.18 548 2 Paroo (S) 1997 2258 4 17.71 18.54 551 2 Paroo (S) 1998 2215 2 9.03 5.47 547 2 Paroo (S) 1999 2182 1 4.58 8.02 531 2 Paroo (S) 2000 2144 6 27.99 13.97 522 2 Paroo (S) 2001 2199 0 0.00 6.48 515 2 Paroo (S) 2002 2173 3 13.81 6.17 513 2 Paroo (S) 2003 2171 1 4.61 5.01 505 survey Paroo (S) 2004 2156 5 23.19 13.79 500 survey Peak Downs (S) 1993 3531 2 5.66 5.46 0 5 Peak Downs (S) 1994 3388 7 20.66 14.00 0 5 Peak Downs (S) 1995 3226 5 15.50 15.99 0 5 Peak Downs (S) 1996 3111 8 25.72 18.20 0 5 Peak Downs (S) 1997 3055 16 52.37 39.67 0 5 Peak Downs (S) 1998 2996 2 6.68 14.24 0 5 Peak Downs (S) 1999 2952 2 6.78 5.95 0 5 Peak Downs (S) 2000 2919 4 13.70 9.03 0 5 Peak Downs (S) 2001 3129 3 9.59 8.34 0 5 Peak Downs (S) 2002 3241 3 9.26 7.19 0 5 Peak Downs (S) 2003 3140 0 0.00 2.97 0 5 Peak Downs (S) 2004 3142 0 0.00 2.22 0 5 Perry (S) 1993 378 0 0.00 4.43 0 survey Perry (S) 1994 366 0 0.00 9.26 0 survey Perry (S) 1995 372 1 26.88 4.06 0 survey Perry (S) 1996 370 2 54.05 30.02 0 survey Perry (S) 1997 369 0 0.00 5.02 0 survey Perry (S) 1998 363 0 0.00 4.01 0 survey Perry (S) 1999 352 0 0.00 6.50 0 survey Perry (S) 2000 352 0 0.00 3.11 0 survey Perry (S) 2001 420 0 0.00 4.49 0 survey Perry (S) 2002 436 0 0.00 2.13 0 survey Perry (S) 2003 442 0 0.00 5.54 0 survey Perry (S) 2004 436 0 0.00 4.72 0 survey Pine Rivers (S) 1993 96888 35 3.61 3.91 171186 2 Pine Rivers (S) 1994 99721 103 10.33 10.86 170034 2 Pine Rivers (S) 1995 102734 24 2.34 2.89 168333 2 Pine Rivers (S) 1996 105649 154 14.58 14.41 164495 2 Pine Rivers (S) 1997 108153 19 1.76 2.03 165213 2 Pine Rivers (S) 1998 110965 32 2.88 2.60 164139 2 Pine Rivers (S) 1999 113616 125 11.00 10.73 305868 4

198

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Pine Rivers (S) 2000 116180 19 1.64 1.95 141379 4 Pine Rivers (S) 2001 121579 62 5.10 5.21 202824 2 Pine Rivers (S) 2002 126684 5 0.39 0.84 264359 survey Pine Rivers (S) 2003 133141 110 8.26 7.72 233414 survey Pine Rivers (S) 2004 138404 84 6.07 6.01 304962 survey Pittsworth (S) 1993 4426 0 0.00 3.19 0 survey Pittsworth (S) 1994 4462 3 6.72 8.06 0 survey Pittsworth (S) 1995 4457 0 0.00 1.15 0 survey Pittsworth (S) 1996 4448 9 20.23 20.06 0 survey Pittsworth (S) 1997 4436 0 0.00 3.55 0 survey Pittsworth (S) 1998 4391 1 2.28 2.87 0 survey Pittsworth (S) 1999 4378 3 6.85 6.82 0 survey Pittsworth (S) 2000 4394 1 2.28 0.84 0 survey Pittsworth (S) 2001 4623 0 0.00 3.55 0 survey Pittsworth (S) 2002 4707 0 0.00 0.74 0 survey Pittsworth (S) 2003 4776 1 2.09 3.61 0 survey Pittsworth (S) 2004 4861 3 6.17 4.62 0 survey Quilpie (S) 1993 1344 2 14.88 10.99 0 survey Quilpie (S) 1994 1314 1 7.61 9.01 0 survey Quilpie (S) 1995 1296 2 15.43 13.91 0 survey Quilpie (S) 1996 1278 3 23.47 15.91 0 survey Quilpie (S) 1997 1266 2 15.80 14.48 0 survey Quilpie (S) 1998 1258 0 0.00 2.45 0 survey Quilpie (S) 1999 1258 2 15.90 8.65 0 survey Quilpie (S) 2000 1246 2 16.05 9.76 0 survey Quilpie (S) 2001 1100 1 9.09 5.23 0 survey Quilpie (S) 2002 1088 2 18.38 11.74 205 survey Quilpie (S) 2003 1079 0 0.00 4.60 505 survey Quilpie (S) 2004 1067 1 9.37 6.17 200 survey Redcliffe (C) 1993 49196 20 4.07 3.01 228249 2 Redcliffe (C) 1994 49199 48 9.76 10.41 226712 2 Redcliffe (C) 1995 49070 8 1.63 2.69 224444 2 Redcliffe (C) 1996 49363 31 6.28 6.13 328990 2 Redcliffe (C) 1997 49461 18 3.64 3.87 330425 2 Redcliffe (C) 1998 49548 18 3.63 2.67 437703 2 Redcliffe (C) 1999 49627 42 8.46 8.32 424816 2 Redcliffe (C) 2000 50045 20 4.00 4.83 500826 4 Redcliffe (C) 2001 49641 14 2.82 2.92 411825 2 Redcliffe (C) 2002 50463 3 0.59 0.79 410152 4 Redcliffe (C) 2003 51522 38 7.38 7.31 505000 2 Redcliffe (C) 2004 52043 14 2.69 3.35 600000 Redland (S) 1993 91347 12 1.31 1.20 342373 survey Redland (S) 1994 95225 130 13.65 13.39 354464 survey Redland (S) 1995 98740 18 1.82 2.87 555242 survey Redland (S) 1996 102371 80 7.81 7.46 439785 survey Redland (S) 1997 105183 21 2.00 1.76 588212 survey

199

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Redland (S) 1998 107547 23 2.14 2.28 596371 survey Redland (S) 1999 110641 90 8.13 7.99 597945 survey Redland (S) 2000 114234 20 1.75 2.38 634018 survey Redland (S) 2001 116446 97 8.33 7.99 643881 survey Redland (S) 2002 119542 13 1.09 1.35 672509 survey Redland (S) 2003 123963 69 5.57 5.72 643793 survey Redland (S) 2004 127777 33 2.58 2.58 710000 survey Richmond (S) 1993 1139 2 17.56 12.17 228 2 Richmond (S) 1994 1125 0 0.00 7.15 227 2 Richmond (S) 1995 1103 4 36.26 2.80 224 2 Richmond (S) 1996 1094 2 18.28 33.15 219 2 Richmond (S) 1997 1073 3 27.96 18.26 220 2 Richmond (S) 1998 1045 1 9.57 7.60 219 2 Richmond (S) 1999 1023 1 9.78 5.86 212 2 Richmond (S) 2000 1014 5 49.31 25.72 209 2 Richmond (S) 2001 1115 0 0.00 5.27 206 2 Richmond (S) 2002 1117 0 0.00 1.64 205 2 Richmond (S) 2003 1127 0 0.00 4.54 202 2 Richmond (S) 2004 1109 0 0.00 3.69 280 survey Rockhampton (C) 1993 59334 81 13.65 10.51 114124 2 Rockhampton (C) 1994 59388 114 19.20 24.13 113356 2 Rockhampton (C) 1995 59178 130 21.97 20.96 112222 2 Rockhampton (C) 1996 59325 196 33.04 33.82 109663 2 Rockhampton (C) 1997 59133 113 19.11 19.19 110142 2 Rockhampton (C) 1998 59117 77 13.03 13.98 109426 2 Rockhampton (C) 1999 58948 31 5.26 7.14 143163 4 Rockhampton (C) 2000 58635 20 3.41 3.43 136287 4 Rockhampton (C) 2001 58401 17 2.91 2.79 133843 2 Rockhampton (C) 2002 58881 30 5.10 5.91 133935 4 Rockhampton (C) 2003 58663 63 10.74 10.22 141400 2 Rockhampton (C) 2004 59134 9 1.52 2.36 140000 2 Roma (T) 1993 6812 6 8.81 7.46 2282 2 Roma (T) 1994 6754 15 22.21 21.23 2267 2 Roma (T) 1995 6632 5 7.54 7.11 2244 2 Roma (T) 1996 6551 27 41.22 35.13 2193 2 Roma (T) 1997 6472 19 29.36 30.48 2203 2 Roma (T) 1998 6423 8 12.46 8.95 2189 2 Roma (T) 1999 6379 4 6.27 7.36 4885 4 Roma (T) 2000 6381 12 18.81 7.84 2087 survey Roma (T) 2001 6687 3 4.49 11.83 2059 survey Roma (T) 2002 6691 13 19.43 15.47 2051 survey Roma (T) 2003 6715 2 2.98 3.99 2020 survey Roma (T) 2004 6741 10 14.83 13.46 2000 survey Rosalie (S) 1993 8074 1 1.24 1.46 0 5 Rosalie (S) 1994 8331 0 0.00 2.26 0 5 Rosalie (S) 1995 8306 2 2.41 2.67 0 5

200

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Rosalie (S) 1996 8280 22 26.57 28.46 0 5 Rosalie (S) 1997 8320 4 4.81 5.21 0 5 Rosalie (S) 1998 8327 1 1.20 1.64 0 5 Rosalie (S) 1999 8320 2 2.40 4.18 0 4 Rosalie (S) 2000 8383 1 1.19 1.42 0 4 Rosalie (S) 2001 8703 1 1.15 1.82 0 5 Rosalie (S) 2002 8768 1 1.14 3.03 0 5 Rosalie (S) 2003 8858 3 3.39 4.39 0 5 Rosalie (S) 2004 8944 12 13.42 13.39 0 5 Sarina (S) 1993 8474 23 27.14 22.25 0 1 Sarina (S) 1994 8676 8 9.22 11.20 0 1 Sarina (S) 1995 8938 16 17.90 20.03 0 1 Sarina (S) 1996 9328 27 28.95 24.38 0 survey Sarina (S) 1997 9545 11 11.52 15.16 0 survey Sarina (S) 1998 9755 5 5.13 5.24 2626 survey Sarina (S) 1999 9880 4 4.05 5.15 531 survey Sarina (S) 2000 10024 10 9.98 8.39 1461 survey Sarina (S) 2001 9779 3 3.07 4.21 566 survey Sarina (S) 2002 9834 10 10.17 8.42 1928 survey Sarina (S) 2003 9886 4 4.05 3.69 679 survey Sarina (S) 2004 9950 3 3.02 3.82 11157 survey Stanthorpe (S) 1993 10199 0 0.00 0.43 571 2 Stanthorpe (S) 1994 10163 2 1.97 2.74 567 2 Stanthorpe (S) 1995 10127 2 1.97 3.29 561 2 Stanthorpe (S) 1996 10022 17 16.96 16.94 548 2 Stanthorpe (S) 1997 9969 1 1.00 1.32 551 2 Stanthorpe (S) 1998 9923 1 1.01 1.34 547 2 Stanthorpe (S) 1999 9885 1 1.01 3.43 531 4 Stanthorpe (S) 2000 9908 0 0.00 0.42 522 4 Stanthorpe (S) 2001 10338 4 3.87 4.67 515 survey Stanthorpe (S) 2002 10473 0 0.00 0.39 513 survey Stanthorpe (S) 2003 10569 3 2.84 3.52 505 survey Stanthorpe (S) 2004 10529 15 14.25 12.73 650 survey Tambo (S) 1993 663 0 0.00 3.48 2282 2 Tambo (S) 1994 637 1 15.70 12.48 2267 2 Tambo (S) 1995 617 0 0.00 3.53 2244 2 Tambo (S) 1996 602 1 16.61 10.01 2193 2 Tambo (S) 1997 592 6 101.35 46.07 3304 2 Tambo (S) 1998 585 2 34.19 17.23 3283 2 Tambo (S) 1999 577 0 0.00 6.27 3186 2 Tambo (S) 2000 578 1 17.30 10.59 5217 2 Tambo (S) 2001 611 0 0.00 4.34 5148 2 Tambo (S) 2002 611 0 0.00 1.98 5127 2 Tambo (S) 2003 604 0 0.00 5.26 5555 survey Tambo (S) 2004 624 1 16.03 9.40 5500 survey Tara (S) 1993 3822 1 2.62 3.26 0 5

201

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Tara (S) 1994 3791 29 76.50 47.55 0 5 Tara (S) 1995 3724 2 5.37 2.76 0 5 Tara (S) 1996 3666 35 95.47 79.62 0 5 Tara (S) 1997 3602 25 69.41 56.75 0 5 Tara (S) 1998 3555 3 8.44 13.38 0 5 Tara (S) 1999 3545 3 8.46 7.32 0 5 Tara (S) 2000 3498 3 8.58 3.23 0 5 Tara (S) 2001 3857 2 5.19 5.72 0 5 Tara (S) 2002 3911 5 12.78 13.31 0 5 Tara (S) 2003 3997 3 7.51 5.06 0 5 Tara (S) 2004 3950 5 12.66 11.75 0 5 Taroom (S) 1993 3069 4 13.03 10.95 0 5 Taroom (S) 1994 2953 6 20.32 23.17 0 5 Taroom (S) 1995 2836 6 21.16 13.96 0 5 Taroom (S) 1996 2782 9 32.35 33.98 0 5 Taroom (S) 1997 2728 3 11.00 9.75 0 5 Taroom (S) 1998 2663 13 48.82 29.39 0 5 Taroom (S) 1999 2613 1 3.83 6.72 0 5 Taroom (S) 2000 2544 1 3.93 3.80 0 5 Taroom (S) 2001 2663 2 7.51 5.15 0 5 Taroom (S) 2002 2616 1 3.82 5.93 0 5 Taroom (S) 2003 2617 1 3.82 4.57 0 5 Taroom (S) 2004 2546 2 7.86 4.46 0 5 Thuringowa (C) 1993 39514 78 19.74 22.38 49088 survey Thuringowa (C) 1994 41670 58 13.92 15.21 71958 survey Thuringowa (C) 1995 43754 55 12.57 9.77 47133 2 Thuringowa (C) 1996 45254 74 16.35 18.59 46059 2 Thuringowa (C) 1997 46283 169 36.51 34.01 46260 2 Thuringowa (C) 1998 47661 219 45.95 42.91 45959 Thuringowa (C) 1999 49173 45 9.15 7.49 45257 4 Thuringowa (C) 2000 51077 127 24.86 26.77 50083 4 Thuringowa (C) 2001 52532 29 5.52 5.65 30887 2 Thuringowa (C) 2002 54279 34 6.26 6.55 27583 survey Thuringowa (C) 2003 55758 46 8.25 7.35 56740 survey Thuringowa (C) 2004 57249 33 5.76 6.35 47976 survey Tiaro (S) 1993 3659 3 8.20 9.65 0 survey Tiaro (S) 1994 3896 4 10.27 8.75 0 survey Tiaro (S) 1995 4137 3 7.25 5.80 0 survey Tiaro (S) 1996 4300 10 23.26 19.99 0 survey Tiaro (S) 1997 4438 2 4.51 2.40 0 survey Tiaro (S) 1998 4554 4 8.78 13.39 0 survey Tiaro (S) 1999 4622 6 12.98 9.78 0 4 Tiaro (S) 2000 4729 2 4.23 0.79 0 4 Tiaro (S) 2001 4652 1 2.15 6.63 0 survey Tiaro (S) 2002 4749 1 2.11 2.64 0 survey Tiaro (S) 2003 4848 5 10.31 8.67 0 survey

202

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Tiaro (S) 2004 4921 7 14.22 9.23 0 survey Toowoomba (C) 1993 85070 26 3.06 2.97 228 1 Toowoomba (C) 1994 85305 27 3.17 3.96 227 1 Toowoomba (C) 1995 85335 9 1.05 1.16 224 1 Toowoomba (C) 1996 86020 91 10.58 10.66 15353 2 Toowoomba (C) 1997 86059 13 1.51 2.73 15420 2 Toowoomba (C) 1998 86345 33 3.82 2.37 23527 survey Toowoomba (C) 1999 86549 17 1.96 3.68 14869 survey Toowoomba (C) 2000 87102 12 1.38 0.67 14607 survey Toowoomba (C) 2001 89455 12 1.34 1.91 14414 survey Toowoomba (C) 2002 90610 7 0.77 1.35 14355 survey Toowoomba (C) 2003 92074 9 0.98 1.22 14140 survey Toowoomba (C) 2004 93448 24 2.57 2.57 14000 survey Torres (S) 1993 8392 4 4.77 5.67 0 5 Torres (S) 1994 8436 4 4.74 6.61 0 5 Torres (S) 1995 8535 1 1.17 1.54 0 5 Torres (S) 1996 8654 1 1.16 2.99 0 5 Torres (S) 1997 8869 3 3.38 3.38 0 5 Torres (S) 1998 8985 0 0.00 0.56 0 5 Torres (S) 1999 9177 0 0.00 2.67 0 5 Torres (S) 2000 9444 2 2.12 2.42 0 5 Torres (S) 2001 3590 1 2.79 4.63 0 5 Torres (S) 2002 3723 2 5.37 2.28 0 5 Torres (S) 2003 3786 5 13.21 9.85 0 5 Torres (S) 2004 3798 1 2.63 5.75 0 5 Townsville (C) 1993 84422 362 42.88 41.19 228249 2 Townsville (C) 1994 84496 245 29.00 30.67 226712 2 Townsville (C) 1995 84701 239 28.22 22.95 224444 2 Townsville (C) 1996 84850 289 34.06 33.06 274159 2 Townsville (C) 1997 84944 297 34.96 36.43 275354 2 Townsville (C) 1998 85289 160 18.76 16.57 328277 2 Townsville (C) 1999 86218 66 7.66 7.24 307992 4 Townsville (C) 2000 87629 261 29.78 27.84 366229 4 Townsville (C) 2001 90093 47 5.22 6.63 350051 2 Townsville (C) 2002 91892 63 6.86 6.60 389645 4 Townsville (C) 2003 93881 78 8.31 7.68 343400 2 Townsville (C) 2004 95817 43 4.49 4.38 340000 survey Waggamba (S) 1993 2783 2 7.19 1.34 1141 2 Waggamba (S) 1994 2737 23 84.03 48.66 1134 2 Waggamba (S) 1995 2696 2 7.42 12.77 1122 2 Waggamba (S) 1996 2678 10 37.34 29.10 1097 2 Waggamba (S) 1997 2678 7 26.14 24.17 1101 2 Waggamba (S) 1998 2685 1 3.72 1.51 1094 2 Waggamba (S) 1999 2670 1 3.75 5.92 1593 4 Waggamba (S) 2000 2701 5 18.51 9.55 1565 4 Waggamba (S) 2001 2950 1 3.39 6.22 1544 2

203

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Waggamba (S) 2002 2986 3 10.05 8.42 1538 2 Waggamba (S) 2003 2997 1 3.34 3.05 1515 2 Waggamba (S) 2004 2994 9 30.06 22.38 1500 2 Wambo (S) 1993 5282 2 3.79 2.52 0 5 Wambo (S) 1994 5317 2 3.76 7.47 0 5 Wambo (S) 1995 5354 1 1.87 2.25 0 5 Wambo (S) 1996 5348 9 16.83 17.51 0 5 Wambo (S) 1997 5288 1 1.89 2.54 0 5 Wambo (S) 1998 5257 2 3.80 7.87 0 5 Wambo (S) 1999 5220 0 0.00 3.63 0 5 Wambo (S) 2000 5178 1 1.93 0.73 0 5 Wambo (S) 2001 5236 1 1.91 3.88 0 5 Wambo (S) 2002 5244 0 0.00 0.68 0 5 Wambo (S) 2003 5249 1 1.91 3.94 0 5 Wambo (S) 2004 5268 2 3.80 4.03 0 5 Warroo (S) 1993 1127 1 8.87 2.58 0 5 Warroo (S) 1994 1078 1 9.28 13.70 0 5 Warroo (S) 1995 1021 2 19.59 10.60 0 5 Warroo (S) 1996 987 3 30.40 19.51 0 5 Warroo (S) 1997 963 2 20.77 16.20 0 5 Warroo (S) 1998 932 1 10.73 17.13 0 5 Warroo (S) 1999 927 1 10.79 7.50 0 5 Warroo (S) 2000 904 1 11.06 2.27 0 5 Warroo (S) 2001 1056 0 0.00 6.00 0 5 Warroo (S) 2002 1071 1 9.34 1.66 0 5 Warroo (S) 2003 1071 2 18.67 13.90 0 5 Warroo (S) 2004 1060 5 47.17 26.63 0 5 Warwick (S) 1993 20088 1 0.50 1.25 5706 2 Warwick (S) 1994 20364 4 1.96 3.05 5668 2 Warwick (S) 1995 20308 0 0.00 0.30 5611 2 Warwick (S) 1996 20378 39 19.14 18.24 5483 2 Warwick (S) 1997 20364 5 2.46 3.57 5507 2 Warwick (S) 1998 20402 7 3.43 3.00 5471 2 Warwick (S) 1999 20459 3 1.47 3.09 5310 2 Warwick (S) 2000 20550 6 2.92 2.22 5217 2 Warwick (S) 2001 21200 7 3.30 4.84 5148 2 Warwick (S) 2002 21298 1 0.47 0.21 5127 2 Warwick (S) 2003 21377 2 0.94 1.51 7070 survey Warwick (S) 2004 21440 38 17.72 17.46 5500 survey Whitsunday (S) 1993 10554 25 23.69 25.82 3424 2 Whitsunday (S) 1994 10934 12 10.97 11.24 3401 2 Whitsunday (S) 1995 11514 30 26.06 22.26 3367 2 Whitsunday (S) 1996 12165 29 23.84 21.95 3290 2 Whitsunday (S) 1997 12555 19 15.13 14.89 3304 2 Whitsunday (S) 1998 12959 12 9.26 7.78 3283 2 Whitsunday (S) 1999 13249 12 9.06 6.87 3186 survey

204

per 10 000 per 10 000 AUD 2004 (Refer 3.2.1) Whitsunday (S) 2000 13503 8 5.92 6.22 2087 survey Whitsunday (S) 2001 13552 9 6.64 6.44 3089 survey Whitsunday (S) 2002 13965 19 13.61 10.29 3076 survey Whitsunday (S) 2003 14381 4 2.78 4.05 30300 survey Whitsunday (S) 2004 14731 6 4.07 3.08 30000 survey Winton (S) 1993 1712 2 11.68 7.49 0 5 Winton (S) 1994 1681 2 11.90 13.66 0 5 Winton (S) 1995 1651 2 12.11 9.40 0 5 Winton (S) 1996 1635 0 0.00 6.36 0 5 Winton (S) 1997 1624 3 18.47 8.49 0 5 Winton (S) 1998 1596 4 25.06 14.75 0 5 Winton (S) 1999 1575 3 19.05 12.60 0 5 Winton (S) 2000 1556 5 32.13 23.16 0 5 Winton (S) 2001 1596 4 25.06 9.27 0 5 Winton (S) 2002 1595 0 0.00 1.41 0 5 Winton (S) 2003 1587 0 0.00 4.05 0 5 Winton (S) 2004 1527 0 0.00 3.25 0 5 Wondai (S) 1993 4108 1 2.43 5.26 571 2 Wondai (S) 1994 4123 3 7.28 6.37 567 2 Wondai (S) 1995 4108 0 0.00 4.90 561 2 Wondai (S) 1996 4094 11 26.87 25.91 548 2 Wondai (S) 1997 4091 2 4.89 3.61 551 2 Wondai (S) 1998 4051 11 27.15 23.80 547 2 Wondai (S) 1999 4031 1 2.48 6.36 531 2 Wondai (S) 2000 3992 0 0.00 0.90 522 2 Wondai (S) 2001 4217 5 11.86 8.15 515 2 Wondai (S) 2002 4251 0 0.00 0.79 513 2 Wondai (S) 2003 4281 3 7.01 5.32 505 2 Wondai (S) 2004 4329 6 13.86 12.83 500 2 Woocoo (S) 1993 2677 0 0.00 1.38 0 5 Woocoo (S) 1994 2811 1 3.56 6.35 0 5 Woocoo (S) 1995 2875 1 3.48 3.48 0 5 Woocoo (S) 1996 2902 0 0.00 4.40 0 5 Woocoo (S) 1997 2951 2 6.78 7.20 0 5 Woocoo (S) 1998 2965 3 10.12 11.07 0 5 Woocoo (S) 1999 3011 0 0.00 4.54 0 5 Woocoo (S) 2000 3050 0 0.00 1.10 0 5 Woocoo (S) 2001 3028 3 9.91 4.43 0 5 Woocoo (S) 2002 3048 0 0.00 4.48 0 5 Woocoo (S) 2003 3097 2 6.46 6.28 0 5 Woocoo (S) 2004 3148 3 9.53 7.26 0 5

205