This geographic index has utility for identifying areas in need of assistance and is timely for revision of 35-year-old provider shortage and geographic underservice designation criteria used to allocate federal resources.

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ObjectivesTo estimate undiagnosed diabetes prevalence from general practitioner (GP) practice data and identify areas with high levels of undiagnosed and diagnosed diabetes.DesignData from the North-West Adelaide Health Survey (NWAHS) were used to develop a model which predicts total diabetes at a small area. This model was then applied to cross-sectional data from general practices to predict the total level of expected diabetes. The difference between total expected and already diagnosed diabetes was defined as undiagnosed diabetes prevalence and was estimated for each small area. The patterns of diagnosed and undiagnosed diabetes were mapped to highlight the areas of high prevalence.SettingNorth-West Adelaide, Australia.ParticipantsThis study used two population samples—one from the de-identified GP practice data (n=9327 active patients, aged 18 years and over) and another from NWAHS (n=4056, aged 18 years and over).Main outcome measuresTotal diabetes prevalence, diagnosed and undiagnosed diabetes prevalence at GP practice and Statistical Area Level 1.ResultsOverall, it was estimated that there was one case of undiagnosed diabetes for every 3–4 diagnosed cases among the 9327 active patients analysed. The highest prevalence of diagnosed diabetes was seen in areas of lower socioeconomic status. However, the prevalence of undiagnosed diabetes was substantially higher in the least disadvantaged areas.ConclusionsThe method can be used to estimate population prevalence of diabetes from general practices wherever these data are available. This approach both flags the possibility that undiagnosed diabetes may be a problem of less disadvantaged social groups, and provides a tool to identify areas with high levels of unmet need for diabetes care which would enable policy makers to apply geographic targeting of effective interventions.

Using remoteness areas alone to prioritize workforce incentive programs and training requirements has significant limitations. Including measures of socioeconomic disadvantage and workforce supply would better target health inequities and improve resource allocation in Australia.

BackgroundGood quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere.Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness.MethodWe compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness.ResultsThe datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets.ConclusionIn the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets.

Detection and accurate classification of traumatic tarsal fractures are important for identifying cases requiring surgical intervention. The aim of this prospective, experimental, methods comparison study was to directly compare the accuracy, sensitivity, and specificity of tarsal computed tomography (CT), ten-view and two-view digital radiographs for detecting traumatic fractures of the canine tarsus. The working hypothesis was that tarsal fractures would be detected with higher accuracy, sensitivity, and specificity using CT imaging compared to radiography, and a tenview would be superior to a two-view radiographic study. Ten cadaver hind limbs of medium to large dogs received a CT scan and ten-view radiographic study before and after induction of fractures with a hydraulic press. All bones included in the radiographic images were assessed for fractures by two observers and gross dissection was used as the gold standard. The two-view radiographic study (dorsoplantar, lateromedial) was created from the ten-view study and reviewed 2 years later. All limbs sustained fractures, the most common locations were the talus and calcaneus (n = 7). The sensitivity of CT was greater than ten-view radiographic study (77% vs. 57%), while the specificity was similar (97% vs. 98%). The sensitivity and specificity of the ten-view and twoview radiograph studies were similar (57% vs. 55%; both 98%). Computed tomography images were reassessed postdissection to determine if failure to identify fractures resulted from observer error. Overall, CT was better than radiography for detecting fractures of the canine tarsus, however there was little improvement with ten-view compared to two-view radiographic studies.

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Background Socioeconomic inequalities in mortality are evident in all high-income countries, and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide the first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons. Methods We used Australian Census (2016) linked to 13 months of Death Registrations (2016–17). We estimated relative rates (RR) and rate differences (RD, per 100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high (tertiary) education for individual causes of death (among those aged 25–84 years) and grouped according to preventability (25–74 years), separately by sex and age group, adjusting for age, using negative binomial regression. Results Among 13.9 M people contributing 14 452 732 person-years, 84 743 deaths occurred. All-cause mortality rates among men and women aged 25–84 years with low education were 2.76 [95% confidence interval (CI): 2.61–2.91] and 2.13 (2.01–2.26) times the rates of those with high education, respectively. We observed inequalities in most causes of death in each age-sex group. Among men aged 25–44 years, relative and absolute inequalities were largest for injuries, e.g. transport accidents [RR = 10.1 (5.4–18.7), RD = 21.2 (14.5–27.9)]). Among those aged 45–64 years, inequalities were greatest for chronic diseases, e.g. lung cancer [men RR = 6.6 (4.9–8.9), RD = 57.7 (49.7–65.8)] and ischaemic heart disease [women RR = 5.8 (3.7–9.1), RD = 20.2 (15.8–24.6)], with similar patterns for people aged 65–84 years. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men. Conclusions Australian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.