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The distribution of maternity services across rural and remote Australia: does it reflect population… Rolfe, Margaret I; Donoghue, Deborah A; Longman, Jo M; Pilcher, Jennifer; Kildea, Sue; Kruske, Sue; Kornelsen, Jude; Grzybowski, Stefan; Barclay, Lesley; Morgan, Geoffrey G Feb 23, 2017

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RESEARCH ARTICLE Open AccessThe distribution of maternity servicesacross rural and remote Australia: does itreflect population need?Margaret I Rolfe1*, Deborah Anne Donoghue1, Jo M Longman1, Jennifer Pilcher1, Sue Kildea2, Sue Kruske3,Jude Kornelsen4, Stefan Grzybowski4, Lesley Barclay1 and Geoffrey Gerard Morgan1AbstractBackground: Australia has a universal health care system and a comprehensive safety net. Despite this, outcomesfor Australians living in rural and remote areas are worse than those living in cities. This study will examine thecurrent state of equity of access to birthing services for women living in small communities in rural and remoteAustralia from a population perspective and investigates whether services are distributed according to need.Methods: Health facilities in Australia were identified and a service catchment was determined around eachusing a one-hour road travel time from that facility. Catchment exclusions: metropolitan areas, populationsabove 25,000 or below 1,000, and a non-birthing facility within the catchment of one with birthing. Catchments wereattributed with population-based characteristics representing need: population size, births, demographic factors,socio-economic status, and a proxy for isolation - the time to the nearest facility providing a caesarean section (C-section).Facilities were dichotomised by service level – those providing birthing services (birthing) or not (no birthing). Birthingservices were then divided by C-section provision (C-section vs no C-section birthing). Analysis used two-stage univariableand multivariable logistic regression.Results: There were 259 health facilities identified after exclusions. Comparing services with birthing to nobirthing, a population is more likely to have a birthing service if they have more births, (adjusted Odds Ratio (aOR): 1.50for every 10 births, 95% Confidence Interval (CI) [1.33-1.69]), and a service offering C-sections 1 to 2 h driveaway (aOR: 28.7, 95% CI [5.59-148]). Comparing the birthing services categorised by C-section vs no C-section,the likelihood of a facility having a C-section was again positively associated with increasing catchment birthsand with travel time to another service offering C-sections. Both models demonstrated significant associationswith jurisdiction but not socio-economic status.Conclusions: Our investigation of current birthing services in rural and remote Australia identified disparitiesin their distribution. Population factors relating to vulnerability and isolation did not increase the likelihood ofa local birthing facility, and very remote communities were less likely to have any service. In addition, servicesare influenced by jurisdictions.Keywords: Healthcare disparities, Rural health services, Geographic information systems, Catchment area(health), Maternity Hospitals, Health services research* Correspondence: margaret.rolfe@sydney.edu.au1University Centre of Rural Health, University of Sydney, PO Box 3074,Lismore, NSW 2480, AustraliaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Rolfe et al. BMC Health Services Research  (2017) 17:163 DOI 10.1186/s12913-017-2084-8BackgroundEquitable health outcomes are defined by the WorldHealth Organization as “the absence of avoidable orremediable differences among groups of people, whetherthose groups are defined socially, economically, demo-graphically, or geographically” [1]. Achieving such equityin providing health services is an aspirational goal of theWorld Health Organization [2] and the Australianpeople [3, 4].In Australia, 31% (n = 6,879,573) of the population livein rural and remote regions [5]. The population in theseareas have a higher fertility rate and a higher perinatalmortality rate [6, 7]. Their life expectancy is up to 4years lower than those in cities and prevalence of dia-betes, high cholesterol, cancer or ischaemic heart diseasealso increases as remoteness increases [8]. An Australianreview of health services in rural areas reported pooraccess to services, inferior quality of services comparedto metropolitan services and fewer services in rural areas.It also reported that those scarcer services were unevenlydistributed [9]. Australian research has highlighted therole of factors such as isolation, Indigeneity, and socioeco-nomic status (SES) in determining the location and servicelevel of health facilities [10]. The most important influenceon health in Australia is Indigeneity and this is true for allmeasures of health when compared to non-IndigenousAustralians [11]. Aboriginal and Torres Strait Islanderpeoples make up 2.3% of the population, but they have4.5% of all births. This rises to 11.6% of births in rural andremote Australia [12] and 24% in remote or very remotelocations [13].Many factors influence the maternity service level [14]provided in rural and remote health facilities and thereis debate about the appropriate level of service offered insome areas [15–17]. A consortium of the leading mater-nity care and rural organizations in Australia released aNational Consensus Framework for Rural Maternity Ser-vices in 2010 that is built on principles which include“to ensure that rural maternity services are equitable interms of distribution and access” [18]. However, this innot reflected in outcomes [19].The Australian government funds health services viaagreements with its eight jurisdictions. A nation-wideplan, the Australian National Maternity Services Planwas also introduced in 2010 [20]. This plan acknowl-edged the poorer health outcomes for people living inrural and remote Australia, in particular Aboriginal andTorres Strait Islanders, and proposed action to addressspecific issues including to: “examine tools … and de-velop a rigorous methodology to assist in future planningfor maternity care, including in rural and remote com-munities" [19].A review of indices of rural health care found thataccess to services is influenced by such factors as thegeographical placement of the facility, the isolation ofthe community and the socioeconomic vulnerabilityof the catchment population [10, 21, 22]. The RuralBirthing Index (RBI) is one such tool examining ma-ternity services [23]. It was developed in BritishColumbia, Canada using data from focus groups andinterviews to identify population derived indicators ofmaternity service need. Three were identified: thenumber of births to women living in the area; thevulnerability of the community measured by SES; andthe isolation of the service as measured by the timetaken to travel to the nearest facility that can performa C-section at any time [24]. The current study ispart of a larger project to assess the applicability ofthe RBI to the Australian context, and to systematic-ally assess a measure of catchment need across ruraland remote Australia.This paper systematically estimates measures of theneed of the population within the catchment for a facilityacross rural and remote Australia. Equitable distributionof services should include adjustments in the provisionand level of services in response to population characteris-tics. The authors propose that increasing levels of servicewill be associated with increasing numbers of births(need); and other dimensions of need which were in-creasing disadvantage (SES) (vulnerability); increasingproportion of Aboriginal and Torres Strait Islanders inthe population (vulnerability), and decreasing proximityto another facility capable of undertaking an emergencyoperative birth (C-section) (isolation). To do this, theassociation between existing birthing services and thecharacteristics of its populations will be modelled usinggeographically defined service catchments. This is anecological study which aims to examine the associationbetween population-based characteristics of need in-cluding vulnerability and isolation and the provision ofmaternity services across rural and remote Australia.MethodsTo undertake this study, a geographical catchment wasconstructed around each health facility in Australia, andthen population characteristics were assigned to thatcatchment to estimate population based indicators ofmaternity service need. This enabled two stages ofmodelling to be undertaken.Catchments and travel timesA health facility catchment is the surrounding geo-graphical area bounded by a one-hour, road-basedtravel time using a vector-based network analysis ap-proach within a Geographic Information System (GIS)environment similar to Schuuman [24] and providesthe basis for determining population level characteris-tics. Australian road network maps from GeoscienceRolfe et al. BMC Health Services Research  (2017) 17:163 Page 2 of 13Australia GEODATA TOPO 250 K Series 3 (packagedin ARCMAP format) [25] were used to assign roadspeeds as a function of road type and surface [26] asdefined in the Australian Cardiac Aria Study. TheNorthern Territory (NT) has higher speed limits thanother Australian jurisdictions, for example, Australianhighways have a road speed of 100 km except in theNT where it is 135 km/h. Hence all the NT roadspeeds were increased by 25% to better reflect actualspeeds. Defining catchments based on road travel pro-duced irregularly shaped areas. To avoid double counting,catchment populations in adjacent health facilities thatabut, catchments were split to create an equal travel timebetween the adjacent health facilities. Any non-birthingfacilities within one-hour road travel time of an adjacentbirthing facility was excluded to avoid inappropriatelyreducing the size of the catchment population for existingbirthing facilities. All spatial data processing was con-ducted using ESRI ArcGIS 10.0 and 10.1 (ESRI ArcGIS10.0) [27].Data overlays of population characteristicsThe 2006 Australian Bureau of Statistics (ABS) censusprovides a range of socio-demographic data at nestedspatial units including Statistical Local Areas (SLAs;n = 1,426), Census Collector Districts (CCDs, n =38,704, about 220 households in cities which de-creases with increasing rurality) [28] and MeshBlocks(MB = 347,000, around 30 to 60 households) [28].SLA is the most common spatial unit used for rou-tinely reporting detailed population data and birthregistration data for all years, while more limitedpopulation and socio-demographic data is routinelyavailable at the CCD or MB level for Census years(every five years).To determine the number of people living in thecatchment area, and the number of babies born tomothers in the area, routinely available data wereaccessed for birth registrations and for Estimated Resi-dential Populations (ERP). Five year averages were usedfor 2005 to 2010 for each SLA throughout Australia[12]. Usual Residential Population (URP) for the 2006census was available at MB level [29]. URP is considereda less precise estimate of residential population thanERP but is routinely available on a smaller spatial scale.MB data also includes information on non-residentialland use within SLAs [12]. We weighted the SLA levelbirth numbers and ERP by mesh block URP in order tobetter estimate irregularly shaped catchment ERP andbirth numbers while adjusting for non-residential landuse [12]. The number and proportion of women ofchildbearing age (15 to 44 years) was estimated fromURP data, and weighted by the proportion (area) of eachCCD contained in the catchment area.Socioeconomic status (SES) was measured by the ABS2006 Socio-Economic Indexes for Areas Index of Rela-tive Socio Economic Disadvantage (IRSD) [30]. TheIRSD is based on 14 Census questions including house-hold income, housing, Aboriginality and education, andis constructed using the CCD [28]. Low scores (decile 1)indicate the most disadvantage relative to the allAustralia, and the highest score indicates least disadvan-tage (decile 10). To assign a SES score for each catch-ment the IRSD score (mean = 1,000, standard deviation= 100) was aggregated to each catchment and then con-verted to deciles.The Aboriginal and Torres Strait Islander populationwas also estimated using URP data, weighted by thepopulation proportion of each CCD in the catchmentarea. For the modelling, data was categorised into multi-ples of the national rate of Aboriginal and Torres StraitIslander persons (2.5%).Degree of isolation of a facility was defined using roadtravel time in minutes to the next nearest facility thathad the capability and staff to perform emergencycaesarean section (C-section facility) [23] For modelling,it was categorised into intervals of 1 h (Stage 1) and halfhour (Stage 2).Rurality was defined using the ABS Remoteness AreaStructure (RA) [28]. In this study, we refer to RA 1 asMajor Cities; RA 2 (inner regional) and 3 (outer re-gional) as ‘rural’; and RA 4 (remote) and 5 (very remote)as ‘remote’.Health facility locations and maternity service levelsA health facility may or may not provide birthing ser-vices, influenced by a range of factors such as proximityto other birthing services, demand, or staff resourceissues. Health facilities in rural and remote Australiaprovide a range of maternity services with some ruralpopulations having local access to only hospital or com-munity based antenatal and postnatal care. The locationof health facilities in Australia was established usingtheir geocoded location [latitude and longitude] sourcedfrom the ‘My Hospitals’ web site [31] or the airport forseven very remote facilities. After catchments were de-fined for all facilities, exclusions were made if in RA 1‘major cities’, or in large regional centres (catchmentpopulation of more than 25,000) or the sparsely popu-lated catchments of less than 1,000. Facilities offeringbirthing services were identified using jurisdictional listsof hospitals and perinatal reports for 2005-2010, andconfirmed using the My Hospitals’ website [31, 32]. Thelevel of maternity service for each health facility wasprovided or confirmed by representatives from the Ma-ternity Services Inter-Jurisdictional Committee and aredefined using the Australian National Maternity ServiceCapability Framework [14]. For this study, a healthRolfe et al. BMC Health Services Research  (2017) 17:163 Page 3 of 13facility that does not provide birthing but may provideantenatal and/or post-natal care is a ‘non-birthing facility’(Level 1, Table 1) [14]. A summary of the six levels of ma-ternity service is provided in Table 1. The next levelprovides a birthing service, which requires dedicatedequipment and staff to care for the mother and newborn(Level 2, a ‘birthing facility’). Birthing facilities were fur-ther divided by their capacity to perform an emergencycaesarean section (C-section), an operative birth in spe-cialist theatre that occurs after labour has commenced(Levels 3-6). Facilities that cannot provide this service aredefined here as ‘non-C-section birthing’ (Level 2). Womenwith a high-risk pregnancy due to say, medical illnesses,need specialist care (Level 5, 6).The six states of Australia, New South Wales (NSW),Victoria (Vic), Queensland (Qld), South Australia (SA),Western Australia (WA) and Tasmania (TAS) plus theNT are included as covariates in this study. TheAustralian Capital Territory was excluded as it isclassified as metropolitan (RA 1). The catchmentpopulation range of 1,000 to 25,000 excluded all C-section facilities in TAS and left just two in the NT.When small numbers of facilities precluded analysis,the data for TAS and NT was combined with WA,which had a similar (50%) rate of birthing and non-birthing facilities - referred to as WAplus.Statistical modellingA two stage binary logistic regression modelling approachwas used to assess catchment level socio-demographicand service delivery factors associated with maternityservice level for health facilities in Australia. Stage 1 inves-tigated factors related to whether a health facility has abirthing service or not (see Table 1, non-birthing vsbirthing). Stage 2 assessed the subset of facilities witha birthing service (Table 1, no C-section birthing vs C-section). Potential explanatory factors were categorised toensure reasonable distributions for statistical modelling,which could result in different categorisations for eachstage. The number of births were used both as acontinuous measure (births divided by 10) for easeof interpretation, and in categories based on 50 birthincrements. Model fit was assessed using odds ratios(OR) with 95% confidence intervals (95% CI) tomeasure effect size; the chi squared test (chisq) forthe overall model fit and the Wald statistic for sig-nificance of each parameter; the model Nagelkerkepseudo R2 (denoted as RNag2 ) reflected model im-provement. Model discrimination was also assessedby percentage agreement, Area under the Curve(AUC), and the number of discordant facilities foreach Stage. The importance of the covariates in eachlogistic model and the various parameterisation ofthe covariates were assessed based on the improvement tomodel fit, discrimination and the interpretability of thecovariates. Results are reported as adjusted OR (aOR) andtheir corresponding 95% CI.ResultsFacilitiesThere were 259 health facilities identified in communi-ties with one-hour catchment populations of 1,000 to25,000 in rural and remote Australia. No health facilityproviding birthing services had a catchment of less than1,000 people. The upper population limit of 25,000excluded 58 rurally located, referral hospitals providingspecialised services (levels 4-6). Birthing services wereprovided by 108 (42%) health facilities, and 73 (68%) ofthese had the capability to perform a C-section. Figure 1presents the geographical distribution of the health facil-ities included in this study.The histograms in Fig. 2 summarise the annualbirths (averaged for five years) in health facilitycatchments categorised by birthing or non-birthing.The range of births was from one to 350, but nearlyhalf of the catchments had fewer than 50 womengiving birth per year. There was a substantial overlapin the number of births in the catchments of non-Table 1 National maternity service capability framework level of maternity service descriptors and definitions for modelling in bothStage 1 and 2 models [14]Level of maternityserviceService descriptions Stage 1 modelbirthing vs non-birthingStage 2 modelnon C-section birthingvs C-sectionNo level or 1 No local birthing services,May have antenatal and postnatal carenon-birthing2 Local birthing services without C-section,Low risk births beyond 38 weeks’ gestationbirthing Non C-sectionbirthing3 Local birthing services with C-section,Moderate risk births beyond 36 weeks’birthing C-section4 Local birthing services with C-section,Birthing beyond 34 weeks, Special Care Nurserybirthing C-sectionLevels 5 and 6 Local birthing services with C-section,High-risk births, specialist services for mother and babybirthing C-sectionRolfe et al. BMC Health Services Research  (2017) 17:163 Page 4 of 13Fig. 1 Health facilities in rural and remote Australia by level of maternity serviceFig. 2 Annual birth numbers (5 year average) in catchments for non-birthing and birthing facilitiesRolfe et al. BMC Health Services Research  (2017) 17:163 Page 5 of 13birthing facilities and birthing facilities. In fact, eightpercent of non-birthing facilities have more than 100births per year and 10% of birthing facilities haveless than 50 births per year. However, increasingbirth numbers do have a significant association withthe presence of birthing facilities (chisq = 132, df = 4,p < 0.001).Increasing catchment birth numbers (in categories of 50births), have a significant association with a higher propor-tion of facilities offering C-section (chisq = 17.7, df = 4, p =0.003). Non C-section birthing facilities exhibit a bimodalpattern with similar proportions below and above facilitieswith 100 births, whereas only 23% of C-section facilitieshave 100 births or less (Fig. 3).The overall low SES in rural and remote Australia wasrepresented in our study by the catchment IRSD decilescores. The health facilities in our study ranged from 1(lowest or most disadvantaged), to just 7 (highest orleast disadvantaged), (Fig. 4). Non-birthing facilities areevenly distributed over all deciles, but C-section birthingfacilities were concentrated in the low-mid range (2-5;chisq = 27.3, df = 12, p = 0.007), (Fig. 4).Over half (55%) of non-birthing facilities were 1 to 2 hfrom a C-section facility. Of the birthing facilities, 44%were within one-hour of a C-section and 80% were within2 h of a C-section service. Almost all non C-section birth-ing facilities (97%) were within 2 h of a C-section facilityas illustrated in Fig. 5.There were 47 (18%) health facility catchments wheremore than 10% of the people were Aboriginal andTorres Strait Islander. Sixteen provided birthing services(15% of all birthing services), mostly (94%) providing aC-section. However, catchments with more than 10% ofAboriginal and Torres Strait Islander populations weremore likely to (21%) be in non-birthing catchments,Table 2 and Fig. 6.Stage 1 Modelling - birthing facilities vs non-birthingfacilitiesFor Stage 1, descriptive statistics and the tests of associ-ation are presented in Additional file 1: Table S1. The re-sults of the univariable logistic models are presented inAdditional file 2: Table S2. Results are presented as ORand their 95% CI and model fit and predictive accuracyparameters for the explanatory variables.After assessing a range of models with increasingnumbers of predictor variables, the “best” multivariablemodel for Stage 1, is presented in Table 3. The explana-tory parameters are listed in order of the strength oftheir contribution to the model. Birth numbers (dividedby 10) as a continuous input provided a better fit thancategories based on 50 births. This model had 92%agreement between predicted birthing and non-birthingfacility, a large AUC (0.97) and a high RNeg2 (0.79) withonly 23 (out of possible 259) discordant sites. SES wasretained based on an a priori decision to include thispotentially important risk factor.The number of births to women living in the catch-ment was the strongest predictor for a facility having abirthing service, with the likelihood of a facility offeringbirthing increasing by 50% for every 10 births. Traveltime to C-section was also associated with having abirthing facility. The large OR for between 1 to 2 h is anartefact of our exclusion of all non-birthing facilitieswithin the one-hour catchment of a birthing facility.Stage 2: facilities offering birthing with or withoutC-sectionFor the stage 2 model the outcome measure was forall birthing facilities dichotomised as C-section facil-ities capability (levels 3-4; n = 73), compared to no C-section birthing facilities (level 2; n = 35). Descriptivecharacteristics of the facilities as well as results of testsFig. 3 Annual birth numbers (5 year average) in catchments for no C-section birthing and for C-section facilitiesRolfe et al. BMC Health Services Research  (2017) 17:163 Page 6 of 13of association and trend for Stage 2 are presented inAdditional file 3: Table S3. Additional file 4: Table S4provides the summarised results of univariable logisticregression and results are presented as OR and their95% CI and model fit and predictive accuracy parame-ters for the explanatory variables.Jurisdiction was significantly associated with a facilityproviding C-section compared not in the univariableanalysis. Using NSW as the reference, Vic (OR = 0.31,95% CI 0.10-0.98) was less likely to have C-sectionfacilities whereas the likelihood of C-section facilitiesin QLD, SA and WAplus was not different to NSW, soa combination of jurisdictions were used in the multi-variable logistic modelling process. SES was not sig-nificantly associated with the likelihood of a C-sectionfacility. However, the proportion of Aboriginal andTorres Strait Islander persons was significant in boththe continuous and categorical forms with a 15% in-crease in the likelihood of a C-section facility withevery 1% increase in the Aboriginal and Torres StraitIslander population in the catchment.The multivariate logistic model which maximized RNeg2(0.59), AUC (0.91), and percentage agreement (79.6%) andminimised discordant sites (n = 22) is presented in Table 4.Various categorisation of the covariates were assessed andthose shown in Table 4 provided a balance between thebest model fit and interpretability of the covariates. Allcategories of birth numbers were 18 to 90 times morelikely to have a C-section facility compared to a catchmentof less than 50 births. Time to nearest C-section facilityalso retained significance. Where facilities with C-sectionwere more than one to 1.5 h away from an alternative C-section, they were 4-5 times more likely to offer C-sectioncompared to closer facilities. This increased to 15 to 80times more likely to be C-section facilities for longer traveltime. Jurisdictions in ‘NSW, QLD, SA’ were 11 times morelikely to have a C-section facility in rural and remote areascompared to ‘VIC, WAplus‘. Although not significant inthe final model, SES was retained based on an a priori de-cision to include this influential proxy measure for vulner-ability, and did marginally improve the RNeg2 , AUC and thenumber of discordant facilities.Fig. 4 Numbers of facilities for No birthing, no C-section birthing and for C-section birthing by SES decileRolfe et al. BMC Health Services Research  (2017) 17:163 Page 7 of 13DiscussionThis paper has described the association betweenbirthing services in rural and remote Australia andcharacteristics of the catchment populations repre-senting need: birth numbers, vulnerability and isola-tion so as to identify possible disparities in maternityservice distribution.The Australian National Maternity Services Plan ac-knowledged the considerable health inequalities andsocial disadvantage of Aboriginal and Torres Strait Is-lander people and rural and remote communities whichis compounded by the limited provision of quality ma-ternity care and the restricted birthing choices [20]. Thepurpose of this paper is to describe the association be-tween birthing services in rural and remote Australiaand the characteristics of the catchment populations inrelation to equity of access.This study defined catchments using one-hour traveltime for Australian health facilities in rural and remoteareas and then ascertained their level of maternityservice provision. There is some evidence to suggestthat distance is associated with maternal and neonataloutcomes, with Canadian studies indicating thatwomen who have to travel more than one-hour to ac-cess birthing services have worse outcomes [33–35].This study found that the number of births in a catch-ment was the strongest predictor for distinguishingbetween facilities that offered birthing and those thatdid not, and between those that offered C-section andthose that only offered birthing. Our study demon-strated that increasing numbers of births significantlyincreased the likelihood of a higher level of service.However, as illustrated in Figs. 1 and 2 (and Additionalfile 5: Figure S1) there was an overlap in numbers ofbirths in birthing and non-birthing facilities, and thosewith and without C-section.In Australia there are well known poorer outcomes forthose who are financially disadvantaged, who live in thenon-metropolitan areas and who are from minority groups.Of the factors explored in this paper, only increasing birthnumbers were consistently associated with higher levels ofservice. Other aspects of population-based need were notconsistently associated with the distribution of services. Forexample, our proxy measure for isolation, the time it takesto travel by road to the nearest C-section facility revealedan unexpectedly mixed picture. Very remote communitiesare less likely to have any type of birthing service comparedto less remote communities. Rural health facilities 1 to 2 haway from a C-section service are more likely to havebirthing compared to those closer to C-section facilities.Fig. 5 Numbers of facilities for No birthing, no C-section birthing and for C-section birthing by time to nearest C-section serviceRolfe et al. BMC Health Services Research  (2017) 17:163 Page 8 of 13This means that being one hour or less from a C-sectionfacility (i.e., within its catchment area) lessens your likeli-hood of being a C-section facility. This suggests that,controlling for other factors, once a birthing facility in ruraland remote Australia is more than an hour from a C-section facility, health planners are less likely to retainbirthing facilities that are not C-section capable. This isdespite data showing that birthing without a C-section ser-vice produces better outcomes than no services at all [33].Every health facility in our study was in the lowest70% of the standardised SES index (the IRSD) inAustralia, and more than 73% were in the lowest40%. However, SES as a measure of vulnerability didnot predict the level of service, after controlling forother catchment characteristics. Australian and inter-national studies have demonstrated that maternalSES is a powerful determinant of adverse health out-comes for mothers [36] and newborns [37–39].Table 2 Characteristics of facilities with catchment populations of 1,000 to 25,000, by service delivery level, 2005 to 2010Catchment characteristics Non-birthing All birthing Non C-section birthing C-section birthing Totaln = 151 n = 108 n = 35 n = 73 n = 259Freq (%) Freq (%) Freq (%) Freq (%) Freq (%)Births (per year)< 50 116 (77) 11 (10) 9 (26) 2 (3) 127 (49)50-100 23 (15) 23 (21) 8 (23) 15 (21) 46 (18)100-150 8 (5) 22 (20) 4 (11) 18 (25) 30 (12)150-200 4 (3) 8 (7) 7 (20) 1 (1) 25 (10)> 200 0 (0) 9 (8) 7 (20) 2 (3) 25 (10)SES (IRSD deciles)1 most disadvantaged 23 (15) 4 (4) 0 (0) 4 (5) 27 (10)2 13 (9) 17 (16) 5 (14) 12 (16) 30 (12)3 33 (22) 28 (26) 10 (29) 18 (25) 61 (24)4 32 (21) 40 (37) 13 (37) 27 (37) 72 (28)5 29 (19) 15 (14) 4 (11) 11 (15) 44 (17)6, 7 least disadvantaged 21 (14) 4 (4) 3 (9) 1 (1) 25 (10)Travel time≤ 1 h 7 (5) 47 (44) 22 (63) 25 (34) 54 (21)1-2 h 83 (55) 39 (36) 12 (34) 27 (37) 122 (47)2-3 h 27 (18) 5 (5) 0 (0) 5 (7) 32 (12)3-4 h 8 (5) 7 (6) 0 (0) 7 (10) 15 (6)> 4 h 26 (17) 10 (9) 1 (3) 9 (12) 36 (14)Aboriginal & Torres Strait Islander< 2.5% national rate 62 (41) 44 (41) 19 (54) 25 (34) 106 (41)2.5-5% 27 (18) 31 (29) 9 (26) 22 (30) 58 (22)5-10% 31 (21) 17 (16) 6 (17) 11 (15) 48 (19)10-25% 11 (7) 11 (10) 1 (3) 10 (14) 22 (8)> 25% (10 x rate) 20 (13) 5 (5) 0 (0) 5 (7) 25 (10)RemotenessRural RA 2 14 (9) 36 (33) 15 (43) 21 (29) 50 (19)Rural RA 3 73 (48) 55 (51) 18 (51) 37 (51) 128 (49)Remote RA 4 39 (26) 12 (11) 2 (6) 10 (14) 51 (20)Remote RA 5 25 (17) 5 (5) 0 (0) 5 (7) 30 (12)Women aged 15-44mean (SD) 25.3 (5.8) 20.6 (2.6) 20.9 (3.0) 20.5 (2.4) 23.3 (5.3)median 24.5 20.4 20.8 20.3 22.2Rolfe et al. BMC Health Services Research  (2017) 17:163 Page 9 of 13Similarly, Indigeneity is a significant determinant ofperinatal morbidity in Australia [37]. However, theproportion of Aboriginal and Torres Strait Islanderpeople in a catchment, again a measure of vulner-ability, was not associated with the level of birthingservice, suggesting that the current provision ofbirthing services in Australia does not accommodatethe increased vulnerability and risk associated withhigher proportions of Aboriginal and Torres StraitIslanders.The provision of health services in Australia is under-taken by its eight jurisdictions. These data show that forpopulations living in rural or remote areas, the jurisdic-tion of residence has a strong influence on the level ofmaternity service provided. Geography, population dis-tribution, government policy and service delineationscontribute to this phenomenon. The Australian NationalMaternity Services Plan [14, 20] attempted to addressthis problem in providing an evidence-base to assist withmore consistent decisions.LimitationsThere are a number of limitations to this study. The sus-tainability of health care facilities is dependent on manyfactors in rural and remote areas. The existing maternityservice levels were identified by our team in 2010 andany limitations associated with this are discussed inLongman et al. [31, 32]. Since that time, some birthingfacilities have closed, opened or changed their servicelevel either permanently or temporarily. Several of thesefacilities have been identified as ‘discordant sites’ in theregression analyses, perhaps reflecting their changes inservice levels since 2010.Interpretation of the results of Stage 1 models (birthing/no birthing) is complicated by the definition of catchment.The decision to give priority to the catchments forfacilities that currently undertook birthing over non-birthing facilities was to increase the precision of thecatchments. However, a number of non-birthing ser-vices were excluded that may have played a role inbirthing service provision.Fig. 6 Numbers of facilities for no birthing, no C-section birthing and for C-section birthing by percentage of Aboriginal and Torres Strait Islanderpeople in the catchmentRolfe et al. BMC Health Services Research  (2017) 17:163 Page 10 of 13ConclusionThe results of these analyses indicate that the equit-able planning and maintenance of rural and remoteAustralian maternity services assessed at a populationlevel is sub-optimal. This finding is supported bynumerous reports and research demonstrating thatbirthing outcomes consistently show that rural and re-mote Australians have worse outcomes compared tourban families [40, 41]. The Australian National Ma-ternity Services Plan highlighted the potential benefitsof rural maternity service planning tools for ruralcommunities to “develop a rigorous methodology toassist in future planning for maternity care, includingin rural and remote communities” [20]. This researchreinforces the justification of such a methodology.This study found that the provision of maternityservices in rural and remote Australia is not basedsolely on the numbers of births, and provides minimaladjustment for needs of vulnerable and isolated ruraland remote populations. In addition, services are in-fluenced by jurisdictions.Additional filesAdditional file 1: Table S1. Descriptive statistics and the tests ofassociation for Stage 1 Modelling - birthing facilities versus non-birthingfacilities. (DOCX 33 kb)Additional file 2: Table S2. Results of the univariable logistic modelsfor Stage 1 Modelling - birthing facilities versus non-birthing facilities.(DOCX 37 kb)Additional file 3: Table S3. Descriptive characteristics of the facilitiesas well as results of tests of association and trend for Stage 2: facilitiesoffering birthing with or without C-section. (DOCX 35 kb)Additional file 4: Table S4. Summarised results of univariable logisticregression for Stage 2: facilities offering birthing with or without C-section. (DOCX 36 kb)Additional file 5: Figure S1. Annual birth numbers (5 year average) ofcatchments for no birthing, no C-Section and C-Section birthing facilities.(DOCX 41 kb)Abbreviations95% CI: 95% Confidence Interval; ABS: Australian Bureau of Statistics;aOR: Adjusted Odds ratio; AUC: Area under the Curve; CCD: CensusTable 3 Multivariable logistic Stage 1 model for catchments ofbirthing versus non-birthing facilitiesParameters Wald df p Adjusted OR aOR 95% CIaBirth numbers (dividedby 10)45.1 1 0.000 1.50 [1.33-1.69]Travel time to C-section 16.4 4 0.003< 1 h (ref) 1.001-2 h 16.2 1 0.000 28.7 [5.59-148]2-3 h 0.19 1 0.664 1.44 [0.28-7.49]3-4 h 0.45 1 0.504 2.19 [0.22-21.7]4+ hours 0.68 1 0.408 2.15 [0.35-13.1]SES (IRSD deciles) 6.44 3 0.0921 most disadvantaged 4.50 1 0.034 0.07 [0.01-0.82]2-4 (ref) 1.005 0.09 1 0.760 0.80 [0.18-3.45]6-7 less disadvantaged 1.39 1 0.238 0.19 [0.01-3.00]Jurisdiction 23.98 6 0.001NSW (ref) 1.00QLD 0.01 1 0.919 0.92 [0.20-4.20]VIC 11.4 1 0.001 26.9 [3.98-182]SA 10.7 1 0.001 20.5 [3.37-124]WA 1.34 1 0.246 2.81 [0.49-16.1]NT 0.36 1 0.550 0.36 [0.01-10.4]TAS 5.30 1 0.021 0.03 [0.00-0.59]Aboriginal & TorresStrait Islander12.6 4 0.014< 2.5% (ref) 1.002.5-5% 9.70 1 0.002 14.1 [2.67-74. 9]5-10% 0.66 1 0.415 2.03 [0.37-11.1]10-25% 5.99 1 0.014 15.9 [1.74-146]> 25% 3.69 1 0.055 21.2 [0.94-479]Overall Model fit Chisq = 231.0 df = 18 p < 0.001aaOR 95% CI = Adjusted Odds Ratio 95% Confidence IntervalBold indicates significant effects or significant aOR (p < 0.05)Table 4 Multivariable logistic Stage 2 model for catchmentsof no C-section birthing facilities and C-section facilitiesParameters Wald p Adjusted OR aOR 95% CIaCatchment births per annum 0.037< 50 1.0050-100 0.009 46.81 [2.64-830]100-150 0.002 89.06 [5.06-1569]150-200 0.024 18.34 [1.47-229]> 200 0.004 47.17 [3.32-670]Time to nearest C-section facility 0.003< 0.5 h 0.128 4.70 [0.64-34.6]0.5-1 h 1.001-1.5 h 0.051 4.28 [1.00-18]1.5-2 h 0.035 15.18 [1.21-189]> 2 h 0.000 80.26 [6.79-948]SES (IRSD deciles) 0.6276, 7 Less disadvantaged 1.005 0.206 32.91 [0.15-7434]4 0.205 29.47 [0.16-5502]3 0.288 17.52 [0.08-346]1, 2 Most disadvantaged 0.360 11.32 [0.06-2043]Jurisdiction categories 0.001VIC WAplus 1.00NSW QLD SA 0.001 11.34 [2.88-44.7]aaOR 95% CI = Adjusted Odds Ratio 95% Confidence IntervalBold indicates significant effects or significant aOR (p < 0.05)Rolfe et al. BMC Health Services Research  (2017) 17:163 Page 11 of 13Collection Districts; chisq: Chi square statistic; C-section: Caesarean section;df: Degrees of freedom; ERP: Estimated Resident Population; GIS: Geographicinformation systems; h: hours; IRSD: Socio-economic indexes for areas index ofrelative socio economic disadvantage; Km: Kilometres; LRT: LikelihoodRatio Test; MB: Mesh block; NSW: New South Wales; NT: Northern Territory;OR: Odds ratio; Qld: Queensland; RA: Australian Bureau of Statistics RemotenessArea Structure; RBI: Rural birthing index; Ref: Reference category;RNag2 : Nagelkerke pseudo R2; SA: South Australia; SD: Standard deviation;SES: Socioeconomic status; SLA: Statistical Local Areas; TAS: Tasmania;URP: Usual Resident Population; Vic: Victoria; WA: Western Australia;WAPlus: WA, TAS, NT combinedAcknowledgementsThe authors of this paper would like to thank the other members of theAustralian Rural Birth Index [ARBI) Team for their contribution to the projectas a whole and to Dr Arul Earnest for his advice on statistical methods. TheARBI Expert Advisory Group and the Extended Expert Advisory Group generouslycontributed both guidance and insight, and the Maternity Services Inter-Jurisdictional Committee provided expert counsel and financial support.FundingThe National Health and Medical Research Council of Australia funded thisproject as a Special Interest Project grant no 1024868.Availability of data and materialsAlthough much of the data used in this study was available from publiclyavailable sources, the maternity service levels from which the binary outcomeswere generated were provided by jurisdictional health departments with strictconfidentiality requirements. Due to these confidentiality restrictions, the data isnot publicly available.Authors’ contributionsMR collated, analysed and interpreted the data, and wrote the initial manuscript.DD contributed to the study design, data collation and interpretation, and revisedthe manuscript. SG, SKi, GM contributed to the study design, data interpretation,and revising the manuscript. LB, SKr, JK contributed to the study designand revised the manuscript. JP, JL collated data and revised the manuscript. Allauthors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateApproval for this study was obtained from the Human Ethics Committees ofthe Qld Health Office of Health and Medical Research [EC00334); NSW HunterNew England Human Research [EC00403); NT Department of Health andMenzies School of Health [EC00153); Central Australian [HREC-12-96); and WACountry Health Service Board [2013:30).Author details1University Centre of Rural Health, University of Sydney, PO Box 3074,Lismore, NSW 2480, Australia. 2Mater Research Institute, Women’s Health andNewborn Services, Mater Health Service, The University of Queensland (UQ),School of Nursing, Midwifery and Social Work, UQ, Brisbane, QLD 4101,Australia. 3Institute for Urban Indigenous Health, School of Nursing,Midwifery and Social Work, UQ, Bowen Hills, QLD 4006, Australia. 4Centre forRural Health Research, Department of Family Practice, University of BritishColumbia, Vancouver, BC, Canada.Received: 13 August 2016 Accepted: 9 February 2017References1. 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Canberra: AIHW; 2008.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Rolfe et al. BMC Health Services Research  (2017) 17:163 Page 13 of 13

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