Hospital proximity and mortality in australia

Leung, Andrew Article Hospital proximity and mortality in australia Risks Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Leung, Andrew (2019) : Hospital proximity and mortality in australia, Risks, ISSN 2227-9091, MDPI, Basel, Vol. 7, Iss. 3, pp. 1-24, https://doi.org/10.3390/risks7030081 This Version is available at: https://hdl.handle.net/10419/257919 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ risks Article Hospital Proximity and Mortality in Australia Andrew Leung Independent Researcher, Richmond 3121, Australia; [email protected] Received: 20 May 2019; Accepted: 1 July 2019; Published: 17 July 2019   Abstract: It is intuitive that proximity to hospitals can only improve the chances of survival from a range of medical conditions. This study examines the empirical evidence for this assertion, based on Australian data. While hospital proximity might serve as a proxy for other factors, such as indigenity, income, wealth or geography, the evidence suggests that proximity provides the most direct link to these factors. In addition, as it turns out, a very statistically significant one that transcends economies. Keywords: hospital proximity; Australian regional mortality; heteroscedasticity JEL Classification: I1; I11; R11 1. Introduction The case of Australia provides an appropriate setting for a proximity study as it is very diverse in terms of hospital locations and population densities, not unlike the United States or Canada, but less densely populated than most parts of Europe or Asia. Whilst there are some studies of the effect of hospital proximity on the outcome of particular conditions (for example, the effect of hospital proximity on young road traffic victims in the UK Bentham (1986)), Nicholl discusses the effect of proximity based on ambulance data, and claims that age, sex and illness are not significant factors. However, that study was for the UK, where distances are much smaller than in Australia. Nonetheless, this paper is broadly consistent with those quantitative findings, allowing for the greater distances involved and the emergency of the situation. Infant mortality Karra et al. (2017) has also been studied across a range of countries, which are also broadly in line with this paper. This study thus provides a quantitative first step with the general conclusion that proximity affects an increase in mortality according to region; it may provide insight into the optimum location of hospitals, and perhaps the facilities that should be provided to deal with certain conditions. The geographic and spatial dimensions of mortality have long been recognized, particularly with respect to particular conditions such as cancer and heart disease Haining (2017). There are many variables that affect regional mortality, such as ethnicity, income, familiarity as well as local amenity. In such studies, the issue of heteroscedasticity in statistical models is particularly serious Fung et al. (2017), especially when historical analysis and forecasting are involved. This paper adopts a simple approach by avoiding all the factors that might be incidentally associated with region. It is based on an Australian census, conducted every three years. Whilst regional data are provided as part of the survey, the only relevance to this paper is the proximity to hospitals that the population enjoys (or suffers), and not any other incidental factor that location might provide. It is possible that proximity is a proxy for income and wealth variables—which are either unavailable or inaccessible. Thus, we are not focused on mortality trends; indeed, the results of this paper highlight the pitfalls of doing so. In summary, this paper is broadly consistent with the proximity studies cited above, given their limited scope. First, it is relevant to the planning and location of hospitals. Second, it throws a different light on insurance pricing and design. Risks 2019,7, 81; doi:10.3390/risks7030081 www.mdpi.com/journal/risks Risks 2019,7, 81 2 of 24 2. Data Data were provided by the Australian Bureau of Statistics (ABS) from the inter-census period 2005–2007 Australian Bureau of Statistics (2010), which was used to construct the Australian Life Tables relating to that period. Population data related to the mid-census year 2006 and mortality data to the inter-census period; these were reasonably consistent with the data published in the Australian Life Tables 2005–07 (ALT) Australian Government Actuary (2009). It is acknowledged that more recent data are available, but, given the difficulty in acquiring that data, this should not detract from the principles involved in this study. It should be noted that mortality relates to a three-year period, so that the mortality rates in this study need to be divided by 3 to provide annualized rates. The statistics were subdivided by age, sex and statistical division (SD). The SD is a concept used by the ABS to denote geographical location: there are 61 SDs in total, covering all states, territories and dependencies in Australia. In this paper, the terms SD and ‘region’ are used interchangeably. The origin of SDs is purely historical and political, based on the establishment of the various Australian states and their settlement since colonization. Thus, they do not afford a truly objective basis for studying mortality, a theme to which we return in this paper. Of the 61 SDs, mortality data were provided for only 59 SDs. No data were provided for ‘Other Territories’, which cover the outlying islands. Canberra and the Australian Capital Territory were amalgamated into a single SD for the purpose of this study as no separate data was provided for the latter. The SDs are defined by the ABS in terms of geographical polygons, giving the longitude and latitude of their vertices. This information is available in ESRI shapefile format, which facilitates the calculation of distances and the plotting of charts, as is set out below 1 . In this study, all positions, distances and areas are expressed in terms of degrees of latitude and longitude, with 1 ◦ in spherical coordinates being approximately 60 nautical miles. Though age was a relevant factor, mortality statistics were provided only to the age of 85. All statistics after that age were aggregated at age 85. The reason is that deaths after that age in individual SDs are few in number and would be identifiable, so it appears that privacy considerations prevail. The ABS also provided population data for 1755 cities and towns across Australia in the mid census year (the smallest town having a population of only 24), together with their geographical coordinates. Again, this should not detract from the principles involved in this study. Hospital data were provided by the Australian Institute of Health and Welfare Australian Institute of Health and Welfare (2017), an agency of the Australian government. Hospitals were categorized by the number of beds and geographical location. For the purpose of this study, only 347 of these hospitals, both private and public, have been included, as the number of 50 beds is considered to be the minimum for providing emergency care in critical conditions (such as accidents, cardiac arrest and stroke). A depiction of the various town/city locations, and those of hospitals, is set out below in Figure 1. 1For example, with the use of standard Matlab functions. Risks 2019,7, 81 3 of 24 Figure 1. Towns and hospitals. 2.1. Notation Denote: •xage next birthday, for x=1, 2, . . . n=86, •rregion, for r=1, 2 . . . m=59, •crcity within region r, •hhospital, •qxage dependent mortality for age x, •Dr,xnumber of deaths at age xin region r, •Xr,xnumber of exposed lives at age xin region r, •ρ(cr)population of city crcontained in region r, •γ(cr)the distance (in degrees) from crto the nearest hospital h. Since not all the population in a given SD resides in a city or town, we assume that the rural population is distributed uniformly within the region and assess the average nearest distance to a hospital accordingly. The technicalities are set out in Appendix A. Overall statistics in terms of geography, population, urbanization and hospitalization are set out in the following table. The towns in the ‘Other Territories’ are excluded from Table 1below. Risks 2019,7, 81 4 of 24 Table 1. Regional statistics. SD Statistical Area Male Female No. of Ruralization No. of Average Hospital Division Population Population Towns * Hospitals Distance 1 Sydney 1.180 2,112,861 2,169,127 54 6% 61 0.165 2 Hunter 2.779 306,840 310,712 73 13% 10 0.603 3 Illawarra 0.819 205,886 208,818 46 11% 11 0.235 4 Richmond-Tweed 0.949 113,390 116,673 36 29% 5 0.322 5 Mid-North Coast 2.397 146,638 150,213 72 28% 7 0.324 6 Northern (NSW) 9.323 89,820 90,387 39 33% 3 1.054 7 North Western 18.868 58,670 57,641 27 32% 1 2.164 8 Central West 6.105 89,913 88,718 38 28% 3 0.800 9 South Eastern 5.138 104,421 102,955 46 40% 4 0.551 10 Murrumbidgee 6.297 77,592 76,454 36 26% 4 0.715 11 Murray 8.868 58,048 57,389 31 26% 3 0.896 12 Far West 13.887 11,421 11,500 5 12% 1 1.793 13 Melbourne 0.788 1,848,781 1,894,234 44 6% 71 0.131 14 Barwon 0.921 133,285 136,406 20 32% 4 0.419 15 Western District 2.344 50,956 51,549 25 34% 4 0.372 16 Central Highlands 1.219 72,583 74,984 21 23% 3 0.450 17 Wimmera 3.413 24,938 25,215 19 32% 1 0.639 18 Mallee 3.953 45,699 46,029 24 32% 2 0.942 19 Loddon 1.463 86,199 88,719 31 27% 4 0.393 20 Goulburn 2.752 101,682 100,753 52 34% 4 0.398 21 Ovens-Murray 1.773 47,753 48,358 29 29% 2 0.510 22 East Gippsland 3.238 41,661 41,255 24 37% 2 0.621 23 Gippsland 1.125 81,531 83,970 44 31% 3 0.303 24 Brisbane 0.543 900,397 919,365 24 5% 25 0.133 25 Gold Coast 0.122 256,603 261,575 3 22% 4 0.115 26 Sunshine Coast 0.283 144,723 150,361 19 26% 8 0.143 27 West Moreton 1.086 36,611 36,070 26 54% 0 0.382 28 Wide Bay-Burnett 4.360 134,483 135,056 48 49% 7 0.762 29 Darling Downs 7.057 112,598 114,543 33 28% 5 1.075 30 South West 29.092 13,580 12,786 12 37% 1 2.991 31 Fitzroy 10.440 102,989 97,396 33 26% 4 1.410 32 Central West 35.074 5922 5640 8 33% 0 3.701 33 Mackay 7.869 83,572 76,228 32 73% 2 1.528 34 Northern (QLD) 6.909 106,422 103,480 27 17% 2 1.407 Risks 2019,7, 81 5 of 24 Table 1. Cont. SD Statistical Area Male Female No. of Ruralization No. of Average Hospital Division Population Population Towns * Hospitals Distance 35 Far North 23.008 125,711 121,584 60 26% 4 2.971 36 North West 26.434 17,680 15,533 10 18% 1 2.355 37 Adelaide 0.180 559,635 586,177 14 5% 22 0.073 38 Outer Adelaide 1.153 64,537 64,233 43 41% 0 0.649 39 Yorke and Lower North 1.988 23,031 22,463 28 40% 0 0.765 40 Murray Lands 4.742 35,435 34,048 22 44% 1 0.956 41 South East 2.157 32,733 31,759 15 32% 2 0.523 42 Eyre 6.963 17,994 16,834 12 36% 0 2.805 43 Northern (SA) 75.032 40,688 38,321 26 19% 3 3.766 44 Perth 0.514 758,396 760,352 24 9% 17 0.147 45 South West 2.783 111,608 108,400 32 18% 4 0.651 46 Lower Great Southern 3.804 28,455 27,314 12 32% 1 1.048 47 Upper Great Southern 4.430 9761 9017 13 45% 0 1.772 48 Midlands 10.451 27,918 25,446 33 46% 0 1.680 49 South Eastern 71.362 29,501 25,832 10 36% 1 3.923 50 Central 54.304 32,009 29,355 18 84% 2 3.789 51 Pilbara 44.214 24,665 19,424 14 14% 1 3.980 52 Kimberley 35.757 16,732 15,196 14 26% 0 5.628 53 Greater Hobart 0.150 99,799 105,682 19 14% 4 0.143 54 Southern 2.651 18,720 17,411 23 62% 0 0.641 55 Northern (Tas) 2.150 68,556 70,146 34 24% 3 0.591 56 Mersey-Lyell 2.434 54,481 55,156 25 28% 2 0.817 57 Darwin 0.259 60,551 53,811 6 11% 2 0.330 58 Northern Territory - Bal 115.970 48,764 47,501 51 44% 2 2.871 59 Canberra 0.080 165,151 168,688 2 3% 4 0.087 Total 695.403 10,280,979 10,414,242 1661 347 Risks 2019,7, 81 6 of 24 It is evident that some SDs are poorly serviced by hospitals—for example, the Northern Territory and Kimberley SDs. These may coincide with the areas of the highest indigenous population. We do not speculate on the reasons for this, whether it be the economics of low population density or the outcome of government health policy. However, a rural population is included with the assessment of the each SD, giving a notional rural population in ρ(cr)and its associated hospital proximity γ(cr). 2.2. A General Model We hypothesize that deaths D can be explained by natural mortality qx and the town/city populations within each region, along with the distances of those cities from hospitals. Thus, we investigate a model of the form Dr,x=qxXr,x+αxBr+σr,xεr,x, where •αxis the strength of age-based hospital proximity effects; •Br=∑ cr ρ(cr)γ(cr), the population-weighted distance to the nearest hospital; •σr,xis a variance term (see below); •εr,xis a normal error term with constant variance. In general, the suffixes rand xare omitted where the meaning is clear from context. The variance σ2 in the above model allows for heteroscedasticity. It is well known that both the Poisson and binomial models may be approximated by a normal distribution, where the population is high in relation to the mortality rate. This is a result of the Central Limit Theorem. Thus, a ‘continuous’ Poisson distribution may be adopted Ilienko (2013).2 Under the Poisson model for mortality, we could take σ2=qX , or under the binomial σ2=q(1−q)X . Alternatively, we could regard the proximity effect as part of the overall mortality, and take σ2=qxX+αxBr . In general, we avoid the binomial model in favor of the Poisson, which is simpler and leads to much of the same results. Under a negative binomial approach, σ2=qX (1+rqX) for some constant r> 0. All of these approaches, which are of increasing complexity, are examined below, in order to assess whether the errors ε may be found to be normal and have constant (hopefully unit) variance. As with most statistical mortality models Venter (2001), the method of estimating the parameters in the model is taken as that of maximum likelihood (ML). In this instance, the likelihood is L=∏ r,x 1 √2πσ2e−(D−qX−αB)2 2σ2 and thus the log likelihood is, up to an additive constant3, H=−1 2"∑ r,x [D−qX −αB]2 σ2+∑ r,x ln σ2/X#. 2A continuity adjustment may be used for practical applications. 3The constants are 2πand X. Risks 2019,7, 81 7 of 24 An appropriate formula for model comparison is the Akaike Information Criterion AIC Brockett (1991)4 that allows for the number of parameters (whether in q , α , or σ2) to be estimated, and favours models with the lowest level of AIC =#q,α,σ2−H. 3. The Model with Heteroscedasticity Though it is possible to examine models with homoscedastic errors, i.e., σ2 is constant across ages and regions, we do not do so as the assumption is entirely unrealistic, as it would not take account of population size. Thus, some form of heteroscedasticity needs to be assumed. It is instructive to consider models with and without proximity effects, in order to gauge their overall significance. The truism is that neglecting heteroscedasticity produces unbiased but inefficient estimators. Thus, mortality estimates may suffer unwarranted volatility. 3.1. The Model without Proximity or Regionality The raw ALT rates may be derived rigorously by considering a model of the form D=qxX+σε, where σ2=λ2X , with λ being a constant across all and ages and regions. The estimators for this model are the naive ratios that ignore regional data completely ˆ qalt x= ∑ rD ∑ rX. However, the estimates of the variance parameter λare less intuitive ˆ λ2=1 mn ∑ r,xD−ˆ qalt xX2=0.0805 and with AIC =n+1+mn 2h1+ln ˆ λ2i. This type of model has several shortcomings. First, the variance term λX , whilst allowing the greatest errors for the greatest populations, does not allow for ages where mortality is lowest (say near birth) or highest. Second, it does not discriminate between regions of different age structure and/or mortality. The AIC statistics are poor, which is not surprising as the model does not give any credence to regional data. 3.2. The Model with Regionality but Not Proximity It is clear that the general model above encapsulates the case where proximity effects are absent, i.e., Br= 0. This may provide a first approximation to the parameter q in the general model. It is of interest in its own right, as it allows comparison with the graduated rates set out in ALT. 4 Strictly speaking, this is half the usual definition of AIC . It also excludes the term resulting from ∑ln (X) , which is constant across all models. Risks 2019,7, 81 8 of 24 In this case, we take σ2=qX , and the estimators for q from maximizing the log likelihood can be derived analytically: ˆ q=rm2+4∑ rX.∑ rD2/X−m 2∑ rX, (1) where m= 59 is the number of SDs. This is a result of the more general model in Section 4, where ρr= 0 and Br=0. A comparison of these estimated mortality rates, which allow for heteroscedasticity, and both the raw and graduated rates set out in ALT2005-07 are in Figure 2as follows. Figure 2. Regional mortality rates. It is apparent that, by allowing for heteroscedasticity, which favors the regions with the lowest mortality rates, and thus volatility of deaths, the estimated rates are generally lower than those derived in ALT without such considerations, and with lower inter-age volatility. The graduated rates Australian Government Actuary (2009) were reached using cubic splines and human judgement, so it is not possible to ascribe an AIC . However they would not differ significantly from that for the raw rates. 3.2.1. A Regionalized Model Another model assumes that age and regional effects can be separated: D=(qx+γr)X+σε again with σ2=(qx+γr)X and with overall mortality e qr,x=qx+γr . This is referred to as a ‘Regionalized Model’. It has a high AIC and is the nearest competitor to models with proximity, as it allows for regional effects to manifest directly. This may be depicted in Figure 3below. Under this regionalized model, it is possible to identify the SDs with the highest overall mortality, being those with the highest γr. This is depicted graphically in Figure 4as follows. It comes as no surprise that the SDs with the highest mortality are also those with the highest level of indigenity and sparsest population. It also appears that females do not receive the same level of medical care as males in male dominated regions, where mining or farming predominate. However, it would be highly ethically challenging to price insurance on such as a basis, as this would be arbitrary and discontinuous (for example, how would borderline populations be treated?). To this end, we examine the relevance of an objective variable, which is continuous and largely determined by the population itself, namely proximity to hospitals and medical care. This is the antithesis to regionality. Risks 2019,7, 81 15 of 24 From these first derivatives, we may calculate the second derivatives. It is clear that the off diagonal terms in ∂2H ∂2α,∂2H ∂2qand ∂2H ∂α∂qare zero. Hence, the only non-zero terms are: ∂2H ∂α∂q=−1 q2[χ−αϕ], (A2) ∂2H ∂2α=−ϕ q, ∂2H ∂2q=−1 q3hψ+ϕα2−2χαi+m 2q2. (A3) Equating the first derivatives to zero for a minimum in H we get α=χ−βq ϕ and substituting in Label (A1) −ξq2+ψ+ϕχ ϕ−β ϕq2 −2χχ ϕ−β ϕq−mq =0, which simplifies to q2ξ−β2 ϕ+mq −ψ−χ2 ϕ=0 and hence an exact ML estimator for qis: ˆ q=rm2+4ξ−β2 ϕψ−χ2 ϕ−m 2ξ−β2 ϕ. (A4) We take the positive root above since β2≤ξϕ and χ2≤ψϕ from Cauchy’s inequality. In the case that B=0, we have β=χ=ϕ=0, and the expression for ˆ qreduces to that in Equation (1). The Hessian matrix, ∇2H , which is used to assess the variance of the estimators, is then given by evaluating the second derivatives at the value for ˆ q: ∂2H ∂α∂q=−β ˆ q, ∂2H ∂2q=−1 ˆ q3ψ+β2ˆ q2−χ2 ϕ+m 2ˆ q2 −∇2H=  ϕ ˆ q β ˆ q β ˆ q1 ˆ q3hψ+β2ˆ q2−χ2 ϕ−m 2ˆ qi . For convenience of notation, note that ϕ ˆ qrefers to the diagonal matrix with ϕ ˆ qas its diagonal, etc. Since ∇2Hcan be partitioned into four diagonal matrices, its inverse can be determined exactly: −∇2H−1="ϕ ˆ q∆−β ˆ q∆ −β ˆ q∆ˆ q#, where ∆=ϕ ˆ q1 ˆ q3hψ+β2ˆ q2−χ2 ϕ−m 2ˆ qi−β2 ˆ q2=ϕ ˆ q4hψ−χ2 ϕ−m 2ˆ qi . The Fisher information matrix −∇2H−1may then be used to assess error bounds for the parameter estimates in Section 4. Risks 2019,7, 81 16 of 24 Appendix C Table A1. Proximity Effects—Males. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 1 Sydney 1.9% 1.1% 0.8% 5.4% 3.6% 2.2% 1.9% 2.6% 2.4% 1.5% 0.7% 0.8% 0.6% 0.5% 0.3% 0.2% 0.1% 0.0% 0.5% 2 Hunter 13.4% 7.7% 5.5% 28.9% 23.9% 18.2% 15.1% 18.5% 16.2% 9.7% 4.8% 4.8% 3.5% 3.1% 1.9% 1.1% 0.6% −0.1% 2.7% 3 Illawarra 6.1% 3.1% 2.2% 13.9% 11.4% 8.5% 6.8% 8.6% 7.0% 4.1% 2.0% 2.0% 1.4% 1.1% 0.7% 0.4% 0.2% −0.1% 1.1% 4 Richmond-Tweed 6.2% 3.1% 2.1% 13.4% 13.3% 10.0% 7.6% 8.7% 6.9% 3.6% 1.7% 1.7% 1.4% 1.1% 0.6% 0.3% 0.2% 0.0% 0.9% 5 Mid-North-Coast 17.9% 9.3% 6.2% 34.7% 37.2% 29.4% 22.9% 25.3% 20.2% 11.5% 5.4% 5.3% 3.7% 3.0% 1.8% 1.0% 0.6% −0.1% 2.8% 6 Northern (NSW) 19.2% 11.4% 8.3% 40.2% 37.9% 30.3% 25.1% 28.9% 25.2% 15.5% 7.7% 7.5% 5.5% 4.7% 3.0% 2.0% 1.2% −0.3% 4.8% 7 North-Western 31.1% 19.9% 15.1% 60.1% 58.0% 46.9% 40.2% 44.7% 40.0% 27.0% 15.1% 14.7% 10.9% 9.3% 6.3% 3.8% 2.7% −0.7% 9.9% 8 Central-West 14.6% 8.3% 6.1% 31.3% 29.1% 22.1% 18.0% 21.8% 18.9% 11.3% 5.5% 5.4% 4.1% 3.5% 2.2% 1.4% 0.8% −0.2% 3.4% 9 South-Eastern 17.2% 9.8% 6.8% 36.3% 35.3% 26.4% 20.6% 23.0% 19.4% 11.5% 5.7% 5.5% 4.1% 3.5% 2.3% 1.4% 0.8% −0.2% 3.6% 10 Murrumbidgee 14.8% 8.1% 6.0% 30.3% 27.4% 21.2% 18.0% 21.8% 18.8% 11.5% 5.9% 6.1% 4.8% 3.8% 2.4% 1.4% 0.8% −0.2% 3.7% 11 Murray 23.1% 13.8% 9.5% 44.7% 42.8% 32.0% 27.5% 32.0% 27.8% 16.9% 8.4% 8.5% 6.5% 5.3% 3.4% 1.9% 1.1% −0.3% 5.1% 12 Far-West 50.6% 37.0% 28.8% 74.7% 73.0% 64.9% 57.8% 62.9% 56.9% 40.6% 23.7% 22.7% 19.6% 16.8% 10.1% 5.4% 4.0% −1.2% 15.5% 13 Melbourne 1.3% 0.8% 0.6% 3.5% 2.3% 1.5% 1.3% 1.7% 1.6% 1.0% 0.5% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 14 Barwon 6.3% 3.4% 2.3% 14.6% 12.4% 8.8% 6.9% 8.6% 7.5% 4.4% 2.1% 2.1% 1.6% 1.4% 0.8% 0.5% 0.2% −0.1% 1.2% 15 Western-District 27.0% 14.1% 9.7% 46.0% 44.7% 35.7% 29.2% 33.2% 28.4% 18.3% 9.1% 9.3% 7.4% 6.3% 3.8% 2.2% 1.2% −0.3% 5.5% 16 Centra6.1% 3.2% 2.2% 13.2% 11.4% 8.9% 7.1% 8.6% 7.2% 4.3% 1.9% 2.0% 1.5% 1.3% 0.8% 0.5% 0.3% −0.1% 1.2% 17 Wimmera 30.6% 16.9% 11.8% 51.7% 51.4% 41.5% 34.8% 37.4% 31.5% 19.3% 10.3% 9.9% 7.8% 6.1% 3.5% 2.0% 1.2% −0.3% 5.3% 18 Mallee 35.2% 21.5% 16.0% 60.6% 59.0% 47.0% 41.9% 45.4% 41.5% 27.9% 15.6% 15.8% 12.3% 10.0% 6.6% 3.5% 2.0% −0.5% 9.1% 19 Loddon 7.1% 3.5% 2.4% 15.5% 14.3% 11.0% 8.5% 9.8% 8.2% 4.4% 2.1% 2.1% 1.6% 1.4% 0.9% 0.5% 0.3% −0.1% 1.3% 20 Goulburn 9.5% 5.1% 3.5% 22.3% 22.1% 16.3% 12.2% 13.7% 11.9% 6.9% 3.3% 3.3% 2.4% 2.1% 1.3% 0.8% 0.4% −0.1% 2.0% 21 Ovens-Murray 21.2% 12.3% 8.3% 39.1% 38.7% 30.4% 24.6% 28.2% 24.0% 15.0% 7.4% 7.6% 6.0% 5.1% 3.5% 1.9% 1.1% −0.3% 4.9% 22 East-Gippsland 20.6% 11.5% 7.8% 39.9% 38.7% 29.3% 25.2% 27.8% 23.9% 13.7% 6.3% 6.0% 4.4% 3.5% 2.3% 1.3% 0.8% −0.2% 3.6% 23 Gippsland 8.5% 4.1% 2.8% 17.4% 16.9% 12.7% 9.9% 11.2% 9.3% 5.3% 2.5% 2.4% 1.8% 1.5% 0.9% 0.5% 0.3% −0.1% 1.4% 24 Brisbane 3.2% 1.9% 1.4% 8.3% 5.8% 4.0% 3.3% 4.5% 4.2% 2.6% 1.2% 1.2% 1.0% 1.0% 0.7% 0.4% 0.2% 0.0% 0.9% 25 Gold-Coast 1.7% 1.0% 0.7% 4.4% 3.1% 2.0% 1.7% 2.3% 2.1% 1.2% 0.6% 0.6% 0.4% 0.4% 0.2% 0.1% 0.1% 0.0% 0.3% 26 Sunshine-Coast 3.3% 1.7% 1.2% 8.3% 7.5% 5.2% 3.8% 4.7% 3.9% 2.1% 1.0% 1.0% 0.7% 0.6% 0.4% 0.2% 0.1% 0.0% 0.5% 27 West-Moreton 9.3% 4.9% 3.3% 22.7% 22.8% 16.9% 12.3% 13.5% 10.9% 6.6% 3.1% 2.9% 2.1% 1.8% 1.3% 0.9% 0.5% −0.1% 2.1% 28 Wide-Bay-Burnett 16.3% 8.6% 5.8% 35.4% 34.1% 25.8% 20.4% 22.9% 19.5% 11.3% 5.4% 4.8% 3.2% 2.6% 1.8% 1.1% 0.7% −0.2% 2.9% 29 Darling 12.5% 7.3% 5.1% 29.3% 24.8% 19.4% 16.0% 19.4% 17.4% 10.4% 5.1% 5.2% 3.8% 3.3% 2.1% 1.3% 0.7% −0.2% 3.1% 30 South-West 43.7% 31.1% 28.2% 76.0% 70.9% 59.1% 52.7% 59.4% 55.7% 41.4% 27.0% 26.5% 22.3% 20.2% 13.3% 9.0% 7.4% −1.4% 20.2% Risks 2019,7, 81 17 of 24 Table A1. Cont. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 31 Fitzroy 17.5% 10.6% 7.6% 39.0% 32.7% 24.6% 21.1% 25.3% 22.2% 14.0% 7.5% 8.1% 6.6% 6.0% 4.0% 2.5% 1.6% −0.4% 5.9% 32 Central-West 53.8% 42.4% 39.3% 81.1% 75.7% 67.7% 62.4% 67.5% 64.4% 51.1% 30.5% 34.2% 25.3% 22.2% 14.6% 9.7% 6.7% −1.7% 22.3% 33 Mackay 35.1% 22.7% 17.3% 62.7% 53.4% 41.2% 36.5% 42.0% 39.0% 26.8% 15.1% 16.4% 13.8% 13.5% 10.1% 6.3% 4.0% −1.1% 13.6% 34 Northern (QLD) 40.5% 26.9% 20.2% 64.5% 55.5% 46.7% 42.5% 49.8% 47.7% 34.3% 20.3% 20.8% 17.9% 16.2% 11.9% 7.5% 4.4% −1.1% 15.8% 35 Far24.7% 15.8% 11.7% 53.1% 46.8% 34.4% 28.1% 33.2% 30.4% 20.3% 10.8% 11.1% 9.2% 8.7% 6.1% 4.2% 2.5% −0.6% 8.9% 36 North-West 56.3% 43.2% 39.3% 82.9% 75.0% 64.9% 61.3% 69.3% 68.2% 57.1% 40.6% 41.7% 35.7% 39.7% 29.9% 24.9% 19.5% −6.4% 40.2% 37 Adelaide 0.6% 0.4% 0.2% 1.5% 1.1% 0.7% 0.6% 0.8% 0.7% 0.4% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.0% 0.1% 38 Outer-Adelaide 14.0% 7.3% 5.1% 28.5% 29.2% 22.0% 16.4% 17.6% 14.9% 8.5% 4.3% 4.2% 3.2% 2.7% 1.8% 1.1% 0.6% −0.1% 2.6% 39 Yorke 24.4% 13.0% 9.4% 46.0% 45.2% 35.8% 30.3% 31.1% 25.0% 15.6% 7.1% 6.7% 4.4% 3.6% 2.3% 1.3% 0.8% −0.2% 3.6% 40 Murray-Lands 25.3% 14.7% 10.4% 47.7% 45.6% 34.6% 28.3% 30.4% 27.9% 17.4% 8.8% 8.7% 6.4% 5.6% 3.5% 2.1% 1.2% −0.3% 5.4% 41 South-East 36.7% 22.6% 16.4% 61.2% 56.0% 44.6% 39.6% 42.3% 39.1% 26.6% 14.7% 15.4% 12.9% 11.4% 7.6% 4.1% 2.5% −0.6% 10.3% 42 Eyre 46.8% 29.2% 22.4% 70.5% 66.1% 55.6% 49.3% 52.8% 50.1% 34.9% 19.7% 20.6% 16.0% 14.7% 10.3% 5.7% 3.3% −0.8% 13.6% 43 Northern SA 44.6% 28.7% 22.8% 69.4% 66.6% 54.8% 48.4% 52.9% 47.8% 34.9% 20.8% 21.0% 16.4% 13.8% 9.9% 6.3% 4.2% −1.0% 14.6% 44 Perth 1.5% 0.9% 0.6% 3.6% 2.6% 1.8% 1.6% 2.0% 1.8% 1.1% 0.5% 0.5% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.4% 45 South-West 23.4% 13.1% 9.0% 44.6% 43.5% 32.7% 27.0% 29.7% 26.0% 16.4% 8.5% 8.4% 6.3% 5.1% 3.3% 2.0% 1.2% −0.3% 5.2% 46 Lower 42.2% 26.7% 20.6% 66.6% 66.6% 54.3% 48.9% 52.5% 45.8% 32.7% 17.9% 18.1% 15.1% 12.4% 8.3% 5.2% 3.0% −0.8% 12.4% 47 Upper-Great-Southern 29.9% 18.7% 15.6% 63.3% 57.2% 43.0% 34.6% 38.5% 36.4% 23.0% 12.0% 12.2% 9.6% 9.0% 6.6% 4.0% 2.9% −0.5% 9.0% 48 Midlands 29.2% 16.6% 12.6% 57.5% 54.6% 43.3% 34.2% 36.7% 30.8% 20.4% 10.7% 10.0% 7.7% 6.5% 4.6% 3.1% 2.1% −0.6% 7.7% 49 South-Eastern 53.4% 38.5% 33.2% 80.2% 72.3% 60.4% 55.2% 61.2% 59.8% 48.3% 32.6% 34.5% 31.9% 32.2% 27.6% 22.1% 17.3% −4.3% 34.5% 50 Central 52.4% 36.0% 29.9% 77.6% 75.3% 64.3% 56.8% 61.4% 57.9% 44.3% 27.1% 27.7% 23.5% 20.2% 15.3% 9.3% 7.2% −2.3% 22.5% 51 Pilbara 68.9% 56.9% 53.9% 90.3% 85.6% 74.7% 68.4% 75.1% 74.7% 63.8% 48.9% 55.5% 60.9% 69.1% 74.3% 62.7% 58.9% −41.0% 65.2% 52 Kimberley 62.9% 48.5% 45.9% 86.7% 80.2% 70.0% 66.1% 72.7% 73.3% 61.7% 44.6% 51.4% 47.6% 47.0% 42.2% 41.2% 32.4% −9.5% 50.6% 53 Greater-Hobart 2.3% 1.4% 1.0% 5.8% 4.5% 3.4% 2.8% 3.5% 3.0% 1.7% 0.8% 0.8% 0.6% 0.6% 0.4% 0.2% 0.1% 0.0% 0.5% 54 Southern 13.7% 8.0% 5.7% 33.6% 33.3% 24.3% 17.3% 18.7% 16.1% 8.9% 4.1% 3.8% 2.7% 2.6% 1.8% 1.3% 0.8% −0.3% 3.2% 55 Northern (TAS) 6.6% 3.6% 2.5% 15.8% 13.4% 10.1% 8.1% 9.3% 8.1% 4.5% 2.2% 2.1% 1.6% 1.4% 0.9% 0.5% 0.3% −0.1% 1.3% 56 Mersey-Lyell 20.0% 12.0% 8.5% 42.4% 41.4% 30.8% 25.4% 28.3% 24.1% 15.4% 7.6% 7.7% 5.6% 4.7% 3.1% 2.0% 1.1% −0.3% 4.7% 57 Darwin 3.0% 2.0% 1.4% 9.8% 6.4% 3.9% 3.2% 4.4% 4.0% 2.5% 1.2% 1.3% 1.2% 1.3% 1.2% 0.9% 0.7% −0.2% 1.6% 58 Northern-Territory 55.2% 40.1% 34.7% 79.9% 74.6% 65.0% 61.1% 67.8% 67.3% 56.1% 39.4% 43.5% 41.0% 44.8% 47.1% 36.9% 32.7% −15.1% 48.2% 59 Canberra 0.7% 0.4% 0.3% 1.8% 1.1% 0.8% 0.7% 1.0% 0.9% 0.5% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.2% Total 10.4% 6.2% 4.4% 24.1% 18.7% 13.1% 11.0% 13.9% 12.6% 7.7% 3.9% 3.9% 3.1% 2.7% 1.8% 1.0% 0.6% −0.1% 2.5% Risks 2019,7, 81 18 of 24 Table A2. Proximity Effects—Females. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 1 Sydney 1.5% 0.1% 1.1% 2.4% 1.4% 1.8% 1.9% 2.1% 2.2% 1.5% 0.9% 0.9% 0.6% 0.7% 0.5% 0.2% 0.1% 0.0% 0.3% 2 Hunter 11.2% 0.8% 6.9% 14.8% 11.0% 15.3% 15.8% 15.6% 15.1% 10.0% 6.4% 6.0% 3.8% 4.1% 2.6% 1.2% 0.5% 0.0% 1.8% 3 Illawarra 4.8% 0.3% 2.8% 6.5% 4.9% 7.1% 7.1% 6.9% 6.3% 4.1% 2.6% 2.5% 1.5% 1.5% 1.0% 0.5% 0.2% 0.0% 0.7% 4 Richmond-Tweed 5.1% 0.3% 2.7% 6.4% 5.9% 8.1% 7.6% 6.8% 6.1% 3.7% 2.2% 2.3% 1.5% 1.5% 0.9% 0.4% 0.2% 0.0% 0.6% 5 Mid-North-Coast 15.9% 1.0% 7.9% 18.4% 19.4% 24.3% 22.9% 20.3% 18.0% 11.4% 7.3% 6.8% 4.0% 4.2% 2.7% 1.4% 0.6% 0.0% 2.0% 6 Northern 16.2% 1.2% 10.2% 21.8% 18.6% 25.0% 25.3% 24.5% 23.2% 15.8% 10.5% 9.8% 6.2% 6.4% 4.5% 2.3% 1.0% −0.1% 3.2% 7 North-Western 26.4% 2.2% 18.5% 38.7% 32.7% 38.7% 40.2% 38.3% 38.0% 27.9% 19.6% 18.6% 12.4% 13.2% 8.9% 5.0% 2.2% −0.2% 7.0% 8 Central-West 12.3% 0.8% 7.3% 16.1% 13.6% 18.9% 18.9% 18.3% 17.2% 11.9% 7.5% 7.0% 4.4% 4.8% 3.1% 1.6% 0.7% −0.1% 2.2% 9 South-Eastern 14.7% 1.0% 8.2% 19.4% 17.7% 21.9% 20.9% 18.4% 17.8% 11.9% 7.6% 7.1% 4.4% 5.0% 3.5% 1.8% 0.8% −0.1% 2.6% 10 Murrumbidgee 11.7% 0.8% 7.3% 16.0% 12.8% 17.6% 18.3% 18.4% 17.6% 12.1% 7.9% 7.9% 5.1% 5.1% 3.4% 1.7% 0.7% −0.1% 2.4% 11 Murray 19.4% 1.5% 12.0% 25.4% 21.0% 27.5% 28.5% 26.8% 26.1% 17.2% 11.4% 11.1% 7.1% 7.2% 4.9% 2.4% 1.0% −0.1% 3.5% 12 Far-West 46.1% 5.0% 33.3% 57.5% 51.9% 58.4% 58.4% 57.9% 55.1% 41.3% 30.8% 29.8% 20.8% 21.0% 13.3% 6.5% 3.6% −0.2% 9.9% 13 Melbourne 1.1% 0.1% 0.7% 1.6% 0.9% 1.2% 1.3% 1.3% 1.5% 1.0% 0.6% 0.6% 0.4% 0.5% 0.3% 0.1% 0.1% 0.0% 0.2% 14 Barwon 5.3% 0.3% 3.1% 6.9% 5.2% 7.2% 7.3% 7.0% 6.8% 4.4% 2.7% 2.6% 1.7% 1.8% 1.1% 0.5% 0.2% 0.0% 0.8% 15 Western-District 20.7% 1.5% 12.6% 26.0% 24.9% 30.1% 30.6% 28.2% 26.3% 18.5% 12.3% 12.1% 8.0% 8.2% 5.3% 2.6% 1.0% −0.1% 3.5% 16 Centra5.1% 0.3% 2.9% 6.1% 4.7% 7.1% 7.4% 6.7% 6.6% 4.3% 2.6% 2.6% 1.7% 1.9% 1.2% 0.5% 0.2% 0.0% 0.8% 17 Wimmera 25.1% 1.8% 14.3% 32.3% 31.5% 34.3% 35.0% 31.9% 30.5% 20.8% 13.8% 13.3% 8.1% 8.3% 5.3% 2.4% 1.0% −0.1% 3.4% 18 Mallee 31.9% 2.5% 19.0% 38.6% 35.9% 41.0% 42.2% 40.3% 39.2% 29.1% 20.7% 19.8% 13.2% 13.4% 8.9% 4.3% 1.8% −0.2% 6.2% 19 Loddon 5.6% 0.3% 3.0% 7.1% 5.8% 8.9% 8.7% 7.7% 7.1% 4.6% 2.8% 2.7% 1.8% 2.0% 1.3% 0.6% 0.3% 0.0% 0.9% 20 Goulburn 8.4% 0.5% 4.5% 11.0% 10.0% 12.5% 12.2% 11.0% 10.6% 7.2% 4.4% 4.2% 2.7% 3.0% 1.9% 1.0% 0.4% 0.0% 1.3% 21 Ovens-Murray 17.9% 1.2% 10.5% 22.6% 20.5% 25.6% 25.3% 23.3% 22.1% 14.9% 9.9% 9.6% 6.4% 7.2% 4.7% 2.3% 1.0% −0.1% 3.2% 22 East-Gippsland 17.3% 1.2% 9.5% 21.0% 20.6% 25.7% 25.6% 23.3% 21.4% 13.6% 8.2% 7.6% 4.9% 5.0% 3.5% 1.8% 0.8% −0.1% 2.6% 23 Gippsland 6.5% 0.4% 3.5% 8.4% 7.0% 10.0% 10.0% 9.2% 8.5% 5.4% 3.3% 3.1% 1.9% 2.1% 1.4% 0.7% 0.3% 0.0% 1.0% 24 Brisbane 2.7% 0.2% 1.7% 3.7% 2.2% 3.2% 3.5% 3.6% 3.8% 2.6% 1.6% 1.6% 1.1% 1.4% 0.9% 0.4% 0.2% 0.0% 0.6% 25 Gold-Coast 1.4% 0.1% 0.9% 2.0% 1.2% 1.7% 1.8% 1.8% 1.8% 1.2% 0.7% 0.7% 0.4% 0.5% 0.4% 0.2% 0.1% 0.0% 0.2% 26 Sunshine-Coast 2.9% 0.2% 1.5% 3.8% 3.1% 4.3% 4.0% 3.6% 3.2% 2.1% 1.3% 1.2% 0.7% 0.8% 0.6% 0.3% 0.1% 0.0% 0.4% 27 West-Moreton 8.4% 0.4% 4.0% 10.4% 9.8% 12.9% 12.1% 10.5% 9.8% 6.7% 4.2% 3.7% 2.4% 2.8% 2.1% 1.2% 0.5% 0.0% 1.6% 28 Wide-Bay-Burnett 13.5% 0.8% 7.4% 18.6% 16.2% 20.3% 20.3% 18.0% 16.8% 11.3% 6.9% 6.0% 3.6% 4.0% 2.9% 1.6% 0.7% −0.1% 2.3% 29 Darling 10.2% 0.7% 6.4% 14.7% 11.3% 16.0% 16.5% 15.9% 15.8% 10.5% 6.8% 6.5% 4.1% 4.6% 3.0% 1.6% 0.7% 0.0% 2.1% 30 South-West 40.3% 4.0% 33.5% 61.5% 44.6% 52.3% 52.4% 52.0% 52.3% 42.8% 35.2% 33.8% 25.1% 24.8% 19.6% 11.1% 5.7% −0.5% 15.5% Risks 2019,7, 81 19 of 24 Table A2. Cont. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 31 Fitzroy 14.5% 1.0% 9.0% 20.6% 15.5% 20.2% 21.0% 21.3% 20.5% 14.8% 10.3% 10.8% 7.4% 8.3% 5.7% 3.1% 1.4% −0.1% 4.2% 32 Central-West 53.3% 5.8% 43.1% 65.3% 48.7% 56.6% 62.6% 61.7% 59.5% 51.7% 39.3% 41.1% 29.4% 30.9% 24.6% 14.7% 7.3% −0.7% 20.0% 33 Mackay 29.4% 2.5% 19.6% 39.6% 29.7% 36.1% 37.4% 37.0% 36.5% 27.6% 20.1% 21.0% 15.7% 19.0% 13.4% 7.7% 3.7% −0.3% 10.1% 34 Northern 33.9% 3.3% 23.9% 42.4% 32.1% 40.8% 43.5% 44.2% 44.7% 34.8% 25.8% 26.0% 19.3% 21.8% 15.8% 8.6% 3.9% −0.3% 11.6% 35 Far20.3% 1.6% 14.1% 30.9% 24.0% 27.5% 28.0% 27.8% 28.0% 20.5% 14.2% 14.8% 10.5% 13.0% 9.9% 5.4% 2.4% −0.2% 7.0% 36 North-West 47.4% 5.9% 43.1% 68.0% 51.2% 56.4% 60.8% 65.2% 66.9% 58.4% 49.9% 51.4% 41.7% 53.5% 41.9% 28.1% 18.0% −1.7% 34.9% 37 Adelaide 0.5% 0.0% 0.3% 0.7% 0.4% 0.6% 0.7% 0.7% 0.7% 0.4% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.0% 0.1% 38 Outer-Adelaide 12.1% 0.7% 6.4% 15.1% 14.3% 18.3% 16.1% 14.0% 13.2% 9.0% 5.7% 5.2% 3.3% 3.9% 2.7% 1.3% 0.6% 0.0% 1.8% 39 Yorke 20.6% 1.4% 11.0% 27.3% 26.9% 31.2% 30.7% 24.8% 23.2% 15.5% 9.7% 8.5% 4.7% 5.3% 3.4% 1.8% 0.8% −0.1% 2.5% 40 Murray-Lands 21.0% 1.5% 12.0% 28.9% 25.0% 29.6% 29.8% 26.7% 25.8% 18.4% 11.7% 10.5% 7.4% 7.5% 5.2% 2.7% 1.0% −0.1% 3.6% 41 South-East 29.6% 2.4% 19.6% 40.2% 34.0% 39.0% 40.6% 37.5% 38.0% 27.6% 19.4% 20.2% 13.4% 14.0% 10.0% 5.0% 2.2% −0.2% 6.8% 42 Eyre 40.1% 3.5% 26.8% 50.9% 44.8% 48.9% 49.8% 47.9% 47.6% 35.3% 26.0% 25.2% 16.8% 20.2% 14.6% 6.6% 3.0% −0.2% 9.4% 43 Northern 38.8% 3.5% 25.8% 49.7% 42.0% 47.8% 48.9% 47.4% 47.5% 37.3% 26.5% 25.5% 17.2% 19.1% 13.6% 7.2% 3.5% −0.3% 10.3% 44 Perth 1.2% 0.1% 0.8% 1.6% 1.0% 1.5% 1.6% 1.6% 1.6% 1.1% 0.6% 0.7% 0.5% 0.5% 0.3% 0.2% 0.1% 0.0% 0.2% 45 South-West 19.6% 1.4% 11.4% 25.1% 22.9% 27.8% 26.8% 24.5% 23.6% 16.6% 11.1% 10.4% 6.7% 7.1% 5.0% 2.6% 1.3% −0.1% 4.0% 46 Lower 35.1% 3.2% 24.1% 46.4% 44.4% 48.5% 48.8% 44.9% 43.6% 32.0% 23.1% 22.9% 15.8% 16.7% 11.9% 6.4% 2.9% −0.2% 9.0% 47 Upper-Great-Southern 24.0% 1.9% 17.8% 41.0% 32.6% 36.5% 39.1% 34.5% 36.8% 23.4% 16.4% 15.6% 11.6% 11.3% 9.0% 5.4% 1.9% −0.1% 6.0% 48 Midlands 23.2% 1.7% 15.3% 38.6% 32.0% 35.8% 33.4% 30.7% 30.0% 21.1% 13.2% 12.4% 8.3% 9.4% 7.7% 4.5% 2.1% −0.2% 6.0% 49 South-Eastern 43.8% 4.9% 36.5% 59.8% 49.3% 53.6% 55.1% 57.1% 58.5% 51.2% 40.7% 44.3% 37.1% 42.9% 35.2% 23.5% 12.2% −1.1% 27.3% 50 Central 44.9% 4.5% 32.7% 58.5% 52.5% 56.0% 57.6% 55.8% 55.1% 45.0% 34.4% 35.0% 25.7% 28.7% 21.2% 14.2% 7.5% −0.7% 18.8% 51 Pilbara 57.5% 9.1% 55.8% 77.9% 65.6% 67.7% 68.3% 71.5% 74.6% 68.5% 60.3% 68.1% 69.1% 80.3% 79.1% 73.3% 52.3% −12.9% 64.7% 52 Kimberley 53.0% 7.3% 50.2% 71.1% 56.9% 62.5% 66.6% 68.5% 71.3% 63.8% 54.2% 60.3% 53.4% 63.7% 61.8% 47.9% 45.7% −3.8% 50.3% 53 Greater-Hobart 2.0% 0.1% 1.3% 2.6% 1.8% 2.7% 2.9% 2.8% 2.7% 1.7% 1.0% 1.0% 0.7% 0.8% 0.5% 0.2% 0.1% 0.0% 0.3% 54 Southern 10.9% 0.7% 6.8% 17.5% 15.9% 18.6% 16.8% 14.8% 14.0% 8.8% 5.4% 4.6% 3.2% 4.0% 3.1% 1.9% 0.9% −0.1% 2.5% 55 Northern 5.5% 0.4% 3.2% 7.6% 5.4% 8.0% 8.2% 7.6% 7.5% 4.7% 2.9% 2.7% 1.7% 1.9% 1.3% 0.6% 0.3% 0.0% 0.9% 56 Mersey-Lyell 18.0% 1.3% 10.7% 23.8% 20.5% 24.9% 25.6% 23.3% 23.0% 15.7% 10.1% 9.5% 6.1% 6.7% 4.5% 2.4% 0.9% −0.1% 3.2% 57 Darwin 2.2% 0.2% 1.7% 4.2% 2.6% 2.9% 3.2% 3.4% 3.7% 2.5% 1.6% 1.8% 1.5% 2.3% 2.2% 1.3% 0.7% −0.1% 1.5% 58 Northern-Territory 48.8% 5.8% 39.0% 61.5% 49.5% 56.5% 60.4% 62.6% 66.1% 56.8% 46.6% 53.0% 46.2% 56.7% 53.5% 43.2% 31.2% −3.7% 44.3% 59 Canberra 0.6% 0.0% 0.4% 0.8% 0.4% 0.6% 0.7% 0.8% 0.8% 0.5% 0.3% 0.3% 0.2% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% Total 8.3% 0.6% 5.3% 11.5% 7.7% 10.2% 10.9% 10.9% 11.1% 7.5% 4.8% 4.7% 3.2% 3.6% 2.3% 1.1% 0.5% 0.0% 1.6% Risks 2019,7, 81 20 of 24 Appendix D Table A3. Proximity Effects per 10 km—Males. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 1 Sydney 0.6% 0.4% 0.3% 1.8% 1.2% 0.7% 0.6% 0.8% 0.8% 0.5% 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 2 Hunter 1.2% 0.7% 0.5% 2.6% 2.1% 1.6% 1.4% 1.7% 1.5% 0.9% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 3 Illawarra 1.4% 0.7% 0.5% 3.2% 2.6% 2.0% 1.6% 2.0% 1.6% 0.9% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 4 Richmond-Tweed 1.0% 0.5% 0.3% 2.2% 2.2% 1.7% 1.3% 1.4% 1.2% 0.6% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 5 Mid-North Coast 3.0% 1.6% 1.0% 5.8% 6.2% 4.9% 3.8% 4.2% 3.4% 1.9% 0.9% 0.9% 0.6% 0.5% 0.3% 0.2% 0.1% 0.0% 0.5% 6 Northern (NSW) 1.0% 0.6% 0.4% 2.1% 1.9% 1.5% 1.3% 1.5% 1.3% 0.8% 0.4% 0.4% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.2% 7 North Western 0.8% 0.5% 0.4% 1.5% 1.4% 1.2% 1.0% 1.1% 1.0% 0.7% 0.4% 0.4% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.2% 8 Central West 1.0% 0.6% 0.4% 2.1% 2.0% 1.5% 1.2% 1.5% 1.3% 0.8% 0.4% 0.4% 0.3% 0.2% 0.1% 0.1% 0.1% 0.0% 0.2% 9 South Eastern 1.7% 1.0% 0.7% 3.6% 3.5% 2.6% 2.0% 2.3% 1.9% 1.1% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.4% 10 Murrumbidgee 1.1% 0.6% 0.5% 2.3% 2.1% 1.6% 1.4% 1.6% 1.4% 0.9% 0.4% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 11 Murray 1.4% 0.8% 0.6% 2.7% 2.6% 1.9% 1.7% 1.9% 1.7% 1.0% 0.5% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 12 Far West 1.5% 1.1% 0.9% 2.3% 2.2% 2.0% 1.7% 1.9% 1.7% 1.2% 0.7% 0.7% 0.6% 0.5% 0.3% 0.2% 0.1% 0.0% 0.5% 13 Melbourne 0.5% 0.3% 0.2% 1.4% 1.0% 0.6% 0.5% 0.7% 0.6% 0.4% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.0% 0.1% 14 Barwon 0.8% 0.4% 0.3% 1.9% 1.6% 1.1% 0.9% 1.1% 1.0% 0.6% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 15 Western District 3.9% 2.0% 1.4% 6.7% 6.5% 5.2% 4.2% 4.8% 4.1% 2.7% 1.3% 1.3% 1.1% 0.9% 0.5% 0.3% 0.2% 0.0% 0.8% 16 Central Highlands 0.7% 0.4% 0.3% 1.6% 1.4% 1.1% 0.9% 1.0% 0.9% 0.5% 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 17 Wimmera 2.6% 1.4% 1.0% 4.4% 4.3% 3.5% 2.9% 3.2% 2.7% 1.6% 0.9% 0.8% 0.7% 0.5% 0.3% 0.2% 0.1% 0.0% 0.5% 18 Mallee 2.0% 1.2% 0.9% 3.5% 3.4% 2.7% 2.4% 2.6% 2.4% 1.6% 0.9% 0.9% 0.7% 0.6% 0.4% 0.2% 0.1% 0.0% 0.5% 19 Loddon 1.0% 0.5% 0.3% 2.1% 2.0% 1.5% 1.2% 1.3% 1.1% 0.6% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 20 Goulburn 1.3% 0.7% 0.5% 3.0% 3.0% 2.2% 1.6% 1.9% 1.6% 0.9% 0.5% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 21 Ovens-Murray 2.2% 1.3% 0.9% 4.1% 4.1% 3.2% 2.6% 3.0% 2.5% 1.6% 0.8% 0.8% 0.6% 0.5% 0.4% 0.2% 0.1% 0.0% 0.5% 22 East Gippsland 1.8% 1.0% 0.7% 3.5% 3.4% 2.5% 2.2% 2.4% 2.1% 1.2% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 23 Gippsland 1.5% 0.7% 0.5% 3.1% 3.0% 2.3% 1.8% 2.0% 1.7% 0.9% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 24 Brisbane 1.3% 0.8% 0.6% 3.4% 2.3% 1.6% 1.4% 1.8% 1.7% 1.0% 0.5% 0.5% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.4% 25 Gold Coast 0.8% 0.5% 0.3% 2.1% 1.4% 1.0% 0.8% 1.1% 1.0% 0.6% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 26 Sunshine Coast 1.3% 0.7% 0.4% 3.1% 2.9% 2.0% 1.5% 1.8% 1.5% 0.8% 0.4% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 27 West Moreton 1.3% 0.7% 0.5% 3.2% 3.2% 2.4% 1.7% 1.9% 1.5% 0.9% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 28 Wide Bay-Burnett 1.2% 0.6% 0.4% 2.5% 2.4% 1.8% 1.4% 1.6% 1.4% 0.8% 0.4% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 29 Darling Downs 0.6% 0.4% 0.3% 1.5% 1.2% 1.0% 0.8% 1.0% 0.9% 0.5% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 30 South West 0.8% 0.6% 0.5% 1.4% 1.3% 1.1% 1.0% 1.1% 1.0% 0.7% 0.5% 0.5% 0.4% 0.4% 0.2% 0.2% 0.1% 0.0% 0.4% Risks 2019,7, 81 21 of 24 Table A3. Cont. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 31 Fitzroy 0.7% 0.4% 0.3% 1.5% 1.3% 0.9% 0.8% 1.0% 0.9% 0.5% 0.3% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.2% 32 Central West 0.8% 0.6% 0.6% 1.2% 1.1% 1.0% 0.9% 1.0% 0.9% 0.7% 0.4% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 33 Mackay 1.2% 0.8% 0.6% 2.2% 1.9% 1.5% 1.3% 1.5% 1.4% 0.9% 0.5% 0.6% 0.5% 0.5% 0.4% 0.2% 0.1% 0.0% 0.5% 34 Northern 1.6% 1.0% 0.8% 2.5% 2.1% 1.8% 1.6% 1.9% 1.8% 1.3% 0.8% 0.8% 0.7% 0.6% 0.5% 0.3% 0.2% 0.0% 0.6% 35 Far North 0.4% 0.3% 0.2% 1.0% 0.8% 0.6% 0.5% 0.6% 0.6% 0.4% 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 36 North West 1.3% 1.0% 0.9% 1.9% 1.7% 1.5% 1.4% 1.6% 1.6% 1.3% 0.9% 1.0% 0.8% 0.9% 0.7% 0.6% 0.4% −0.1% 0.9% 37 Adelaide 0.5% 0.3% 0.2% 1.1% 0.8% 0.6% 0.5% 0.6% 0.5% 0.3% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.1% 38 Outer Adelaide 1.2% 0.6% 0.4% 2.4% 2.4% 1.8% 1.4% 1.5% 1.2% 0.7% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 39 Yorke and Lower North 1.7% 0.9% 0.7% 3.2% 3.2% 2.5% 2.1% 2.2% 1.8% 1.1% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 40 Murray Lands 1.4% 0.8% 0.6% 2.7% 2.6% 2.0% 1.6% 1.7% 1.6% 1.0% 0.5% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 41 South East 3.8% 2.3% 1.7% 6.3% 5.8% 4.6% 4.1% 4.4% 4.0% 2.7% 1.5% 1.6% 1.3% 1.2% 0.8% 0.4% 0.3% −0.1% 1.1% 42 Eyre 0.9% 0.6% 0.4% 1.4% 1.3% 1.1% 0.9% 1.0% 1.0% 0.7% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 43 Northern (QLD) 0.6% 0.4% 0.3% 1.0% 1.0% 0.8% 0.7% 0.8% 0.7% 0.5% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.1% 0.0% 0.2% 44 Perth 0.6% 0.3% 0.2% 1.3% 1.0% 0.7% 0.6% 0.7% 0.7% 0.4% 0.2% 0.2% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 0.1% 45 South West 1.9% 1.1% 0.7% 3.7% 3.6% 2.7% 2.2% 2.5% 2.2% 1.4% 0.7% 0.7% 0.5% 0.4% 0.3% 0.2% 0.1% 0.0% 0.4% 46 Lower Great Southern 2.2% 1.4% 1.1% 3.4% 3.4% 2.8% 2.5% 2.7% 2.4% 1.7% 0.9% 0.9% 0.8% 0.6% 0.4% 0.3% 0.2% 0.0% 0.6% 47 Upper Great Southern 0.9% 0.6% 0.5% 1.9% 1.7% 1.3% 1.1% 1.2% 1.1% 0.7% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 48 Midlands 0.9% 0.5% 0.4% 1.8% 1.8% 1.4% 1.1% 1.2% 1.0% 0.7% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.1% 0.0% 0.2% 49 South Eastern 0.7% 0.5% 0.5% 1.1% 1.0% 0.8% 0.8% 0.8% 0.8% 0.7% 0.4% 0.5% 0.4% 0.4% 0.4% 0.3% 0.2% −0.1% 0.5% 50 Central 0.7% 0.5% 0.4% 1.1% 1.1% 0.9% 0.8% 0.9% 0.8% 0.6% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 51 Pilbara 0.9% 0.8% 0.7% 1.2% 1.2% 1.0% 0.9% 1.0% 1.0% 0.9% 0.7% 0.8% 0.8% 0.9% 1.0% 0.9% 0.8% −0.6% 0.9% 52 Kimberley 0.6% 0.5% 0.4% 0.8% 0.8% 0.7% 0.6% 0.7% 0.7% 0.6% 0.4% 0.5% 0.5% 0.5% 0.4% 0.4% 0.3% −0.1% 0.5% 53 Greater Hobart 0.9% 0.5% 0.4% 2.2% 1.7% 1.3% 1.1% 1.3% 1.2% 0.6% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.2% 54 Southern 1.2% 0.7% 0.5% 2.8% 2.8% 2.0% 1.5% 1.6% 1.4% 0.8% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.3% 55 Northern (TAS) 0.6% 0.3% 0.2% 1.4% 1.2% 0.9% 0.7% 0.9% 0.7% 0.4% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.0% 0.1% 56 Mersey-Lyell 1.3% 0.8% 0.6% 2.8% 2.7% 2.0% 1.7% 1.9% 1.6% 1.0% 0.5% 0.5% 0.4% 0.3% 0.2% 0.1% 0.1% 0.0% 0.3% 57 Darwin 0.5% 0.3% 0.2% 1.6% 1.0% 0.6% 0.5% 0.7% 0.7% 0.4% 0.2% 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.3% 58 Northern Territory - Bal 1.0% 0.8% 0.7% 1.5% 1.4% 1.2% 1.1% 1.3% 1.3% 1.1% 0.7% 0.8% 0.8% 0.8% 0.9% 0.7% 0.6% −0.3% 0.9% 59 Canberra 0.4% 0.3% 0.2% 1.1% 0.7% 0.5% 0.4% 0.6% 0.6% 0.3% 0.2% 0.2% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.1% Risks 2019,7, 81 22 of 24 Table A4. Proximity Effects per 10 km—Females. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 1 Sydney 0.5% 0.0% 0.4% 0.8% 0.5% 0.6% 0.6% 0.7% 0.7% 0.5% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.1% 2 Hunter 1.0% 0.1% 0.6% 1.3% 1.0% 1.4% 1.4% 1.4% 1.4% 0.9% 0.6% 0.5% 0.3% 0.4% 0.2% 0.1% 0.0% 0.0% 0.2% 3 Illawarra 1.1% 0.1% 0.6% 1.5% 1.1% 1.6% 1.6% 1.6% 1.5% 1.0% 0.6% 0.6% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.2% 4 Richmond-Tweed 0.9% 0.1% 0.4% 1.1% 1.0% 1.3% 1.3% 1.1% 1.0% 0.6% 0.4% 0.4% 0.2% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% 5 Mid-North Coast 2.7% 0.2% 1.3% 3.1% 3.2% 4.1% 3.8% 3.4% 3.0% 1.9% 1.2% 1.1% 0.7% 0.7% 0.4% 0.2% 0.1% 0.0% 0.3% 6 Northern 0.8% 0.1% 0.5% 1.1% 1.0% 1.3% 1.3% 1.3% 1.2% 0.8% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 7 North Western 0.7% 0.1% 0.5% 1.0% 0.8% 1.0% 1.0% 1.0% 0.9% 0.7% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 8 Central West 0.8% 0.1% 0.5% 1.1% 0.9% 1.3% 1.3% 1.2% 1.2% 0.8% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% 9 South Eastern 1.4% 0.1% 0.8% 1.9% 1.7% 2.1% 2.1% 1.8% 1.7% 1.2% 0.7% 0.7% 0.4% 0.5% 0.3% 0.2% 0.1% 0.0% 0.3% 10 Murrumbidgee 0.9% 0.1% 0.6% 1.2% 1.0% 1.3% 1.4% 1.4% 1.3% 0.9% 0.6% 0.6% 0.4% 0.4% 0.3% 0.1% 0.1% 0.0% 0.2% 11 Murray 1.2% 0.1% 0.7% 1.5% 1.3% 1.7% 1.7% 1.6% 1.6% 1.0% 0.7% 0.7% 0.4% 0.4% 0.3% 0.1% 0.1% 0.0% 0.2% 12 Far West 1.4% 0.2% 1.0% 1.7% 1.6% 1.8% 1.8% 1.7% 1.7% 1.2% 0.9% 0.9% 0.6% 0.6% 0.4% 0.2% 0.1% 0.0% 0.3% 13 Melbourne 0.4% 0.0% 0.3% 0.6% 0.4% 0.5% 0.5% 0.6% 0.6% 0.4% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 14 Barwon 0.7% 0.0% 0.4% 0.9% 0.7% 0.9% 0.9% 0.9% 0.9% 0.6% 0.4% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 15 Western District 3.0% 0.2% 1.8% 3.8% 3.6% 4.4% 4.4% 4.1% 3.8% 2.7% 1.8% 1.8% 1.2% 1.2% 0.8% 0.4% 0.1% 0.0% 0.5% 16 Central Highlands 0.6% 0.0% 0.3% 0.7% 0.6% 0.9% 0.9% 0.8% 0.8% 0.5% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 17 Wimmera 2.1% 0.2% 1.2% 2.7% 2.7% 2.9% 3.0% 2.7% 2.6% 1.8% 1.2% 1.1% 0.7% 0.7% 0.5% 0.2% 0.1% 0.0% 0.3% 18 Mallee 1.8% 0.1% 1.1% 2.2% 2.1% 2.4% 2.4% 2.3% 2.2% 1.7% 1.2% 1.1% 0.8% 0.8% 0.5% 0.2% 0.1% 0.0% 0.4% 19 Loddon 0.8% 0.0% 0.4% 1.0% 0.8% 1.2% 1.2% 1.1% 1.0% 0.6% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% 20 Goulburn 1.1% 0.1% 0.6% 1.5% 1.4% 1.7% 1.7% 1.5% 1.4% 1.0% 0.6% 0.6% 0.4% 0.4% 0.3% 0.1% 0.1% 0.0% 0.2% 21 Ovens-Murray 1.9% 0.1% 1.1% 2.4% 2.2% 2.7% 2.7% 2.5% 2.3% 1.6% 1.0% 1.0% 0.7% 0.8% 0.5% 0.2% 0.1% 0.0% 0.3% 22 East Gippsland 1.5% 0.1% 0.8% 1.8% 1.8% 2.2% 2.2% 2.0% 1.9% 1.2% 0.7% 0.7% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.2% 23 Gippsland 1.2% 0.1% 0.6% 1.5% 1.2% 1.8% 1.8% 1.6% 1.5% 1.0% 0.6% 0.6% 0.3% 0.4% 0.2% 0.1% 0.1% 0.0% 0.2% 24 Brisbane 1.1% 0.1% 0.7% 1.5% 0.9% 1.3% 1.4% 1.5% 1.5% 1.0% 0.7% 0.6% 0.5% 0.6% 0.4% 0.2% 0.1% 0.0% 0.2% 25 Gold Coast 0.7% 0.0% 0.4% 0.9% 0.6% 0.8% 0.8% 0.9% 0.9% 0.6% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.1% 26 Sunshine Coast 1.1% 0.1% 0.6% 1.4% 1.2% 1.6% 1.5% 1.4% 1.2% 0.8% 0.5% 0.5% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.2% 27 West Moreton 1.2% 0.1% 0.6% 1.5% 1.4% 1.8% 1.7% 1.5% 1.4% 1.0% 0.6% 0.5% 0.3% 0.4% 0.3% 0.2% 0.1% 0.0% 0.2% 28 Wide Bay-Burnett 1.0% 0.1% 0.5% 1.3% 1.1% 1.4% 1.4% 1.3% 1.2% 0.8% 0.5% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 29 Darling Downs 0.5% 0.0% 0.3% 0.7% 0.6% 0.8% 0.8% 0.8% 0.8% 0.5% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.1% 30 South West 0.7% 0.1% 0.6% 1.1% 0.8% 0.9% 0.9% 0.9% 0.9% 0.8% 0.6% 0.6% 0.5% 0.4% 0.4% 0.2% 0.1% 0.0% 0.3% Risks 2019,7, 81 23 of 24 Table A4. Cont. SD Statistical Division 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71–75 76–80 81–85 ≥86 Total 31 Fitzroy 0.6% 0.0% 0.3% 0.8% 0.6% 0.8% 0.8% 0.8% 0.8% 0.6% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 32 Central West 0.8% 0.1% 0.6% 1.0% 0.7% 0.8% 0.9% 0.9% 0.9% 0.8% 0.6% 0.6% 0.4% 0.5% 0.4% 0.2% 0.1% 0.0% 0.3% 33 Mackay 1.0% 0.1% 0.7% 1.4% 1.1% 1.3% 1.3% 1.3% 1.3% 1.0% 0.7% 0.7% 0.6% 0.7% 0.5% 0.3% 0.1% 0.0% 0.4% 34 Northern 1.3% 0.1% 0.9% 1.6% 1.2% 1.6% 1.7% 1.7% 1.7% 1.3% 1.0% 1.0% 0.7% 0.8% 0.6% 0.3% 0.2% 0.0% 0.4% 35 Far North 0.4% 0.0% 0.3% 0.6% 0.4% 0.5% 0.5% 0.5% 0.5% 0.4% 0.3% 0.3% 0.2% 0.2% 0.2% 0.1% 0.0% 0.0% 0.1% 36 North West 1.1% 0.1% 1.0% 1.6% 1.2% 1.3% 1.4% 1.5% 1.5% 1.3% 1.1% 1.2% 1.0% 1.2% 1.0% 0.6% 0.4% 0.0% 0.8% 37 Adelaide 0.4% 0.0% 0.2% 0.5% 0.3% 0.5% 0.5% 0.5% 0.5% 0.3% 0.2% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.1% 38 Outer Adelaide 1.0% 0.1% 0.5% 1.3% 1.2% 1.5% 1.3% 1.2% 1.1% 0.8% 0.5% 0.4% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.2% 39 Yorke and Lower North 1.5% 0.1% 0.8% 1.9% 1.9% 2.2% 2.2% 1.8% 1.6% 1.1% 0.7% 0.6% 0.3% 0.4% 0.2% 0.1% 0.1% 0.0% 0.2% 40 Murray Lands 1.2% 0.1% 0.7% 1.6% 1.4% 1.7% 1.7% 1.5% 1.5% 1.0% 0.7% 0.6% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.2% 41 South East 3.0% 0.3% 2.0% 4.1% 3.5% 4.0% 4.2% 3.9% 3.9% 2.8% 2.0% 2.1% 1.4% 1.4% 1.0% 0.5% 0.2% 0.0% 0.7% 42 Eyre 0.8% 0.1% 0.5% 1.0% 0.9% 0.9% 1.0% 0.9% 0.9% 0.7% 0.5% 0.5% 0.3% 0.4% 0.3% 0.1% 0.1% 0.0% 0.2% 43 Northern 0.6% 0.1% 0.4% 0.7% 0.6% 0.7% 0.7% 0.7% 0.7% 0.5% 0.4% 0.4% 0.2% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% 44 Perth 0.5% 0.0% 0.3% 0.6% 0.4% 0.5% 0.6% 0.6% 0.6% 0.4% 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 45 South West 1.6% 0.1% 0.9% 2.1% 1.9% 2.3% 2.2% 2.0% 2.0% 1.4% 0.9% 0.9% 0.6% 0.6% 0.4% 0.2% 0.1% 0.0% 0.3% 46 Lower Great Southern 1.8% 0.2% 1.2% 2.4% 2.3% 2.5% 2.5% 2.3% 2.2% 1.6% 1.2% 1.2% 0.8% 0.9% 0.6% 0.3% 0.1% 0.0% 0.5% 47 Upper Great Southern 0.7% 0.1% 0.5% 1.3% 1.0% 1.1% 1.2% 1.1% 1.1% 0.7% 0.5% 0.5% 0.4% 0.3% 0.3% 0.2% 0.1% 0.0% 0.2% 48 Midlands 0.7% 0.1% 0.5% 1.2% 1.0% 1.2% 1.1% 1.0% 1.0% 0.7% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.1% 0.0% 0.2% 49 South Eastern 0.6% 0.1% 0.5% 0.8% 0.7% 0.7% 0.8% 0.8% 0.8% 0.7% 0.6% 0.6% 0.5% 0.6% 0.5% 0.3% 0.2% 0.0% 0.4% 50 Central 0.6% 0.1% 0.5% 0.8% 0.7% 0.8% 0.8% 0.8% 0.8% 0.6% 0.5% 0.5% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.3% 51 Pilbara 0.8% 0.1% 0.8% 1.1% 0.9% 0.9% 0.9% 1.0% 1.0% 0.9% 0.8% 0.9% 0.9% 1.1% 1.1% 1.0% 0.7% −0.2% 0.9% 52 Kimberley 0.5% 0.1% 0.5% 0.7% 0.5% 0.6% 0.6% 0.7% 0.7% 0.6% 0.5% 0.6% 0.5% 0.6% 0.6% 0.5% 0.4% 0.0% 0.5% 53 Greater Hobart 0.8% 0.1% 0.5% 1.0% 0.7% 1.0% 1.1% 1.1% 1.0% 0.6% 0.4% 0.4% 0.3% 0.3% 0.2% 0.1% 0.0% 0.0% 0.1% 54 Southern 0.9% 0.1% 0.6% 1.5% 1.3% 1.6% 1.4% 1.2% 1.2% 0.7% 0.5% 0.4% 0.3% 0.3% 0.3% 0.2% 0.1% 0.0% 0.2% 55 Northern 0.5% 0.0% 0.3% 0.7% 0.5% 0.7% 0.7% 0.7% 0.7% 0.4% 0.3% 0.3% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 56 Mersey-Lyell 1.2% 0.1% 0.7% 1.6% 1.4% 1.6% 1.7% 1.5% 1.5% 1.0% 0.7% 0.6% 0.4% 0.4% 0.3% 0.2% 0.1% 0.0% 0.2% 57 Darwin 0.4% 0.0% 0.3% 0.7% 0.4% 0.5% 0.5% 0.6% 0.6% 0.4% 0.3% 0.3% 0.3% 0.4% 0.4% 0.2% 0.1% 0.0% 0.2% 58 Northern Territory - Bal 0.9% 0.1% 0.7% 1.2% 0.9% 1.1% 1.1% 1.2% 1.2% 1.1% 0.9% 1.0% 0.9% 1.1% 1.0% 0.8% 0.6% −0.1% 0.8% 59 Canberra 0.4% 0.0% 0.2% 0.5% 0.3% 0.4% 0.5% 0.5% 0.5% 0.3% 0.2% 0.2% 0.1% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% Risks 2019,7, 81 24 of 24 References Australian Bureau of Statistics. 2010. ERP by Sex by Single Year of Age Deaths by Sex by Single Year of Age (Private Communication ABS). Canberra: Australian Bureau of Statistics. Australian Government Actuary. 2009. Australian Life Tables 2005–2007. Available online: http://www. aga.gov.au/publications/life_tables_2005-07/downloads/Australian_Life_Tables_2005-07.pdf (accessed on 13 July 2019). Australian Institute of Health and Welfare. 2017. Hospital Contact Details. November 21. Available online: http://www.aihw.gov.au/ (accessed on 6 December 2017). Bentham, Graham. 1986. Proximity to hospital and mortality from motor vehicle traffic accidents. Social Science Medicine 23: 1021–26. [CrossRef] Brockett, Patrick L. 1991. Information theoretic approach for actuarial science. Transactions of the Society of Actuaries 43: 173–35. Fung, Man Chung, Gareth W. Peters, and Pavel V. Shevchenko. 2017. A unified approach to mortality modelling using state-space framework: Characterisation, identification, estimation and forecasting. Annals of Actuarial Science 11: 343–389. [CrossRef] Haining, Robert. 2017. Estimation with heteroscedastic and correlated errors: A spatial analysis of intra-urban mortality data. The Journal of the RSAI 70: 223–41. Ilienko, Andrii. 2013. Continuous counterparts of poisson and binomial distributions and their properties. Annales Universitatis Scientiarium Budapestinensis Sect. Comp. 39: 137–47. Karra, Mahesh, Günther Fink, and David Canning. 2017. Facility distance and child mortality: A multicountry study of health facility access, service utilization, and child health outcomes. International Journal of Epidemiology 46: 817–26. [CrossRef] [PubMed] Kemp, Adrienne W., and C.D. Kemp. 2005. Encyclopedia of Biostatistics. Hoboken: John Wiley and Sons. Nicholl, Jon, James West, Steve Goodacre, and Janette Turner. 2007. The relationship between distance to hospital and patient mortality in emergencies: An observational study. Emergency Medicine Journal 24: 665–68. [CrossRef] [PubMed] Venter, Gary G. 2001. Mortality Trend Models. Casualty Actuarial Society E-Forum. Available online: https: //pdfs.semanticscholar.org/7d00/d6ba5d1cbc74d78d07816be12bbcd8f26f92.pdf (accessed on 13 July 2019). c 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).