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BackgroundIn response to the COVID-19 pandemic, general practice in Australia underwent a rapid transition, including the roll-out of population-wide telehealth, with uncertain impacts on GP use and costs.AimTo describe how use and costs of GP services changed in 2020 — following the COVID-19 pandemic and introduction of telehealth — compared with 2019, and how this varied across population subgroups.Design and settingLinked-data analysis of whole-population data for Australia.MethodMulti-Agency Data Integration Project data for ∼19 million individuals from the 2016 census were linked to Medicare data for 2019–2020. Regression models were used to compare age- and sex-adjusted GP use and out-of-pocket costs over time, overall, and by sociodemographic characteristics.ResultsOf the population, 85.5% visited a GP in Q2–Q4 2020, compared with 89.5% in the same period of 2019. The mean number of face-to-face GP services per quarter declined, while telehealth services increased; overall use of GP services in Q4 2020 was similar to, or higher than, that of Q4 2019 for most groups. The proportion of total GP services by telehealth stabilised at 23.5% in Q4 2020. However, individuals aged 3–14 years, ≥70 years, and those with limited English proficiency used fewer GP services in 2020 compared with 2019, with a lower proportion by telehealth, compared with the rest of the population. Mean out-of-pocket costs per service were lower across all subgroups in 2020 compared with 2019.ConclusionThe introduction of widespread telehealth maintained the use of GP services during the COVID-19 pandemic and minimised out-of-pocket costs, but not for all population subgroups.

BackgroundSocioeconomic 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 first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons.MethodsUsing Australian Census (2016) linked to 13-months of Death Registrations data (2016-17), we estimated relative rates (RR) and rate differences (RD, per100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high education (tertiary), for individual causes of death (among those 25-84y) and grouped according to preventability (25-74y), separately by sex and age group, adjusting for age, using negative binomial regression.ResultsAmong 13.9M people contributing 14 452 732 person-years, 84 743 deaths occurred. We observed inequalities in most causes of death for each age-sex group. Among men aged 25-44y, absolute and relative inequalities (low versus high education) were largest for injuries, e.g. transport accidents (RR=10.1 [95%CI: 5.4-18.7], RD=21.1 [15.9-26.3]). Among those aged 45-64y, inequalities were greatest for chronic diseases, e.g. lung cancer (men RR=6.6 [4.9-8.9], RD=55.6 [51.1-60.1]) and ischaemic heart disease (women RR=5.8 [3.7-9.1], RD=19.2 [17.0-21.5]), with similar patterns for people aged 65-84y. 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.ConclusionsAustralian 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.Key messagesUsing linked Australian Census (2016) and Death Registrations data (2016-17), we provide the first estimates of education-related inequalities in cause-specific mortality for Australia, broadly suitable for international comparisons.Among men aged 25-44 years, inequalities were largest for injuries, with mortality rates among those with low education six-to-ten times that of those with high education. Among the mid- and older-age groups, inequalities were largest for chronic diseases, where mortality rates among those with the lowest education were between two- and seven-times those with the highest education.In 2016-17, around half of all deaths for men and one-third of deaths for women aged 25-84 were associated with less than tertiary education. The majority of these excess deaths were attributable to leading causes.The substantial inequalities seen in preventable deaths highlight ongoing opportunities to reduce inequalities in mortality and to improve the overall health of the Australian population.Australian estimates are generally consistent with, but higher than, those for comparable countries and earlier time periods, but further standardisation of methods and reporting would enhance the validity of such comparisons

BackgroundIn line with affordability and equity principles, Medicare—Australia’s universal health care program—has measures to contain out-of-pocket costs (OOPC), particularly for lower income households. This study examined the distribution of OOPC for Medicare-subsidised out-of-hospital services and prescription medicines in Australian households, according to their ability to pay.MethodsOOPC for out-of-hospital services and medicines in 2017-18 were estimated for each household, using 2016 Australian Census data linked to Medicare Benefits Schedule (MBS) and Pharmaceutical Benefit Scheme (PBS) claims. We derived household disposable income by combining income information from the Census linked to income tax and social security data. We quantified OOPC as a proportion of equivalised household disposable income and calculated Kakwani progressivity indices (K).ResultsUsing data from 85% (n=6,830,365) of all Census private households, OOPC as a percentage of equivalised household disposable income decreased from 1.16% in the poorest decile to 0.63% in the richest decile for MBS services, and from 1.35% to 0.35% for PBS medicines. The regressive trend was less pronounced for MBS services (K = −0.06), with percentage OOPC relatively stable between the 2nd and 9th income deciles; while percentage OOPC decreased substantially with increasing income for PBS medicines (K = −0.24).ConclusionOOPC for out-of-hospital Medicare services were mildly regressive while those for prescription medicines were distinctly regressive. Actions to reduce inequity in OOPC, particularly for medicines should be considered.

To quantify Aboriginal and Torres Strait Islander health check claims in Australian adults in relation to sociodemographic and health characteristics, including prior cardiovascular disease (CVD) and CVD risk factors.The study involved analysis of baseline data (2006-2009) from the Sax Institute's 45 and Up Study, involving 1753 Aboriginal and Torres Strait Islander adults in New South Wales, Australia, linked to Medicare Benefits Schedule (MBS) hospital and death data (to December 2015). The outcome was a claim for receiving a Medicare-funded Health Assessment for Aboriginal and Torres Strait Islander People (MBS item 715) in the 2 years before December 2015. Logistic regression was used to estimate odds ratios (ORs) for receiving a health check in relation to sociodemographic and health characteristics.One-third (32%) of participants received at least one Medicare-funded health check in the 2-year period. The probability of receiving a health check was higher for women than men (adjusted OR 1.47; 95% CI 1.18, 1.84), for those with lowest education than for those with highest education (OR 1.58; CI 1.11, 2.24), for those in a regional area (OR 1.56; CI 1.22, 2.01) or remote area (OR 2.38; CI 1.8, 3.16) than for those in major cities, for those with prior CVD than for those without (OR 1.80; CI 1.42, 2.27), for those with CVD risk factors than for those without (adjusted OR between 1.28 and 2.28, depending on risk factor), for those with poor self-rated health than for those with excellent self-rated health (OR 3.15; CI 1.76, 6.65) and for those with more than 10 visits to a general practitioner (GP) per year than for those with 0-2 visits (OR 33.62; CI 13.45, 84.02). Additional adjustment for number of GP visits or self-rated health substantially attenuated ORs for prior CVD and most CVD risk factors. When mutually adjusted, use of GP services and poorer self-rated health remained strongly associated with receiving a health check.Aboriginal and Torres Strait Islander people with the greatest healthcare need and at highest risk of CVD were more likely to receive a health check; however, a significant proportion of those who were eligible had not received this preventive care intervention. Findings indicate that there is greater potential for the use of health checks (MBS item 715) in improving identification and management of Aboriginal and Torres Strait Islander people at high risk of CVD, potentially preventing future CVD events.

Health services are increasingly being reshaped with reference to addressing social determinants of health (SDoH), with social prescribing a prominent example. We examine a project in the Outer Hebrides that reshaped and widened the local health service, framing fuel poverty as a social determinant of health and mobilising a cross-sector support pathway to make meaningful and substantive improvements to islanders' living conditions. The 'Moving Together' project provided support to almost 200 households, ranging from giving advice on home energy, finances and other services, to improving the energy efficiency of their homes. In so doing, the project represents an expansion of the remit of social prescribing, in comparison with the majority of services currently provided under this banner, and can be seen as a more systemic approach that engages with the underlying conditions of a population's health. We present a framework through which to understand and shape initiatives to address fuel poverty through a social prescribing approach.

Objective Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. We estimate education-related inequalities in adult life expectancy in Australia. Design and setting We estimated age-sex-education specific mortality rates using data from 2016 Australian Census linked to 2016-17 Death Registrations and standard life table methodology to calculate life expectancy. Participants 14,565,910 Australian residents aged 25 years and older. Main outcome measure Absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years by sex and education level (5 levels, from Bachelor’s degree or higher [highest] to no secondary or post-secondary qualification [lowest]). Results At each age, those with lower education had shorter life expectancies. For men, the gap (highest vs lowest education) was 9.1 (95%CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. Absolute gaps were smaller for women compared with men but remained substantial: 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences were larger for men and increased with age. Conclusion Education-related inequalities in life expectancy from age 25 years in Australia are substantial such that those with the lowest education have a life expectancy equivalent to the national average 15-20 years ago. These vast gaps indicate large potential for further population health gains. The known Life expectancy in Australia has increased over time and is now among the highest in the world, but national estimates mask within-country inequalities and further potential for health gain. Area-level socioeconomic inequalities in health are well documented in Australia, but life expectancy gaps by individual-level socioeconomic position (e.g. highest level of education), a powerful measure of health inequality, have not been routinely available. The new Newly-available national linked data – 2016 Australian Census linked to Death Registrations (2016-17) – show that at each age (from age 25 years), men and women with lower levels of education, have shorter life expectancies. At age 25 years, the life expectancy gap between those with no educational qualifications and those with a university degree was 9.1 years for men and 5.5 years for women. Given lower life expectancy among men than among women, this represents a much larger relative – as well as absolute – gap for men. The implications Education-related gaps in life expectancy are considerable, larger than the OECD average, and mean that those with the lowest education have a life expectancy equivalent to the national average 15-20 years ago. The gaps indicate large potential for further improvements in population health, and health equity.

IntroductionCardiovascular events are largely preventable though access to timely quality primary health care and use of guideline-recommended medication. However, around half of Australians with cardiovascular disease (CVD) are not receiving best practice treatment. This study aims to identify factors associated with under treatment, using National Health Survey (NHS) linked for the first time to administrative health data. Objectives and ApproachParticipants with self-reported CVD in the NHS 2014-15 were included in the study, with data linked to Medicare (MBS) and pharmaceutical (PBS) data by the Australian Bureau of Statistics through the Multi-Agency Data Integration Project (MADIP). Use of primary and specialist ambulatory care and blood pressure- and lipid-lowering medications and their relation to sociodemographic and health characteristics were quantified using logistic and Poisson regression analyses. Results1100 participants with self-reported CVD were available for analysis, with linkage rates for NHS data to a Person Linkage Spine of 95%. We will present our findings from adjusted regression models (incomplete at time of abstract submission). Conclusion / ImplicationsThe nationally representative linked data developed under this project provides a unique opportunity to quantify and identify points to improve access to best practice CVD care, with the ultimate aim of preventing secondary CVD events in the population. Findings will also inform optimal use of MADIP data by the research community in order to answer questions of national importance and provide robust evidence to drive improvement in health and health care.

IntroductionOfficial Australian estimates of socioeconomic inequalities in cause-specific mortality have been based on area-level socioeconomic measures. Using area-level measures is known to underestimate inequalities. Objectives and ApproachUsing recently released census linked to mortality data, we estimate education-related inequalities in cause-specific mortality for Australia. We used 2016 Australian Census and Death Registration data (2016-17) linked via a Person Linkage Spine (linkage rates: 92% and 97%, respectively) from the Multi-Agency Data Integration Project (MADIP). Education, from the Census, was categorised as low (no secondary school graduation or other qualification), intermediate (secondary graduation with/without other non-tertiary qualifications) and high (tertiary qualification). Cause of death was coded according to the underlying cause of death using the ICD-10. We used negative binomial regression to estimate relative rates (RR) for cause-specific mortality at ages 25-84 years, in the 12-months following Census, comparing low vs high education, separately by sex and 20-year age group, adjusting for age. Results80,317 deaths occurred among 13,856,202 people. For those aged 25-44 years, relative inequalities were large for causes related to injury and smaller for lesspreventable deaths (e.g. for men, suicide RR=5.6, 95%CI: 4.1-7.5 and brain cancer RR=1.3, 0.6-3.1). For those aged 45-64, inequalities were large for causes related to health behaviours and amenable to medical intervention, e.g. lung cancer (men RR= 6.4, 4.7-8.8) and ischaemic heart disease (women RR=5.0, 3.2-7.7), and were small for less preventable causes e.g. brain cancer (women RR=0.9, 0.6-1.3). Patterns among those aged 65-84years were similar to those aged 45-64 years. Conclusion / ImplicationsIn Australia, inequalities in mortality are substantial. Our findings highlight the health burden from inequalities, opportunities for prevention and provide insights on targets to effectively reduce them.

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 ten-view 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 two-view 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.

Introduction: Patients who receive alteplase for acute ischemic strokes are at a high risk for conversion to a hemorrhagic stroke and must be monitored closely through frequent vital signs and neurological checks. Documentation of these measures was found to have a low compliance due to various factors such as a new electronic medical record (EMR) and lack of knowledge related to required documentation. Methods: A team was created to address this issue which included the Emergency Department Nurse Manager, a Clinical Nurse Expert, and the Stroke Program Director. Inservices were provided to the ED and Critical Care nursing staff. Daily review of all alteplase patients and chart audits were performed and real-time feedback was given to the staff and leadership caring for the patients. This real-time feedback allowed for corrections to be made if necessary and served as both an educational and accountability opportunity. The feedback was not limited to just missed opportunities but was also used to praise staff for complete documentation. Results: Concurrent monitoring of blood pressure and neurological checks was completed from September 2018 to June 2019 on all alteplase patients. The overall compliance of documentation for blood pressure checks improved from 88% to 93.1% and the compliance for the neurological checks improved from 81.5% to 85.7%. Conclusions: Providing real time feedback is an effective way to notify staff of both missed opportunities and successes. Overall, when the staff has a better understanding of what is required, the patients are given more comprehensive care.

The appropriate delivery of primary care services, an important policy imperative in many developed nations, is contingent on defining appropriate geographies to which these services are delivered. Primary Care Service Area (PCSA) geographies have been created in some countries to facilitate primary care policy making and have been utilized in a large body of research. In spite of their extensive use across rural and urban settings, the usefulness of PCSAs has not been evaluated. In this study, for the first time we put PCSAs to the test by comparing them to another small area geography - Postal Areas, and by exploring their usefulness in measuring relationships between Primary Care Practitioner supply and use. We find while PCSAs are better than Postal Areas in measuring relationships between General Practitioner supply and visits by patients, this relationship shows some heterogeneity across areas.

The United States Government (USG) is deeply interested in political stability and economic prosperity within each of the sovereign states of Africa. Part of the “whole-ofgovernment approach,” the Department of Defense (DoD) is responsible for lines of effort that place uniformed service members and U.S. Embassy officials at risk. Current USG missions on the African continent typically involve small travelling contact teams, special operators, and diplomatic missions without robust medical support. Beyond Self Aid and Buddy Care (SABC) and unit medics, there is no capacity to support U.S. assets during the golden hour of trauma. Civilian and military assets are integrated with variable success on an ad hoc basis (e.g., Theater Patient Movement Requirement Center coordinates with International SOS, as needed, to arrange medical evacuation by the most effective means possible, usually a civilian aircraft). Those unstable for evacuation are transported to the nearest medical facility where service members and diplomats receive the best care available, which may be different from the standard of care available in other countries. Despite historical precedence and known risks, the USG does not have a robust response capability for medical crisis that involves USG representatives on the African continent. Many proposals exist to push specific medical capabilities closer to U.S. personnel in Africa, but few use evidencebased tactics aligned to strategic end-state objectives. The tactical capabilities proven to save lives and reduce the burden of disease are clear: personnel protective equipment (e.g., body armor and tourniquets), SABC, tactical combat casualty care, casualty evacuation, forward access to trauma surgery, reliable blood bank and laboratory support, evidencebased critical care, and robust medical evacuation. We propose a strategy to assist development of African regional training institutions to teach, train, evaluate, and maintain these specific evidence-based tactical medical capabilities. In this strategy, host nation (HN) training institutions selected based on willingness, regional consensus, and funding legitimacy would draw medics from the region to complete standardized training at a center of excellence, staffed and led by HN medics to serve both the health security objectives of the USG, the training requirements of all partners, and the health care objectives of the HN. The end-state objective directly supports U.S. Africa Command Lines of Effort and a whole-of-government approach to Global Health Engagement. Measurable outcomes based upon this strategy must directly support desired end states, and should include: HN medics graduated from mandatory training, HN medic performance during training exercises, time-distance from U.S. Embassy to accessible Role 2 care, reliable access to accredited blood bank, sustainable information technologies to support unimpeded access to medical subspecialist consultation, incorporation of Millennium Development Goals or the future Sustainable Development Goals, access to 5,000-foot runway and helipad, flexible and integrated AE routes, and HN certified as United Nation/African Union interoperable combat medics with civilian health care credentials. In the initial effort of this Global Health Engagement strategy, HN personnel assigned to military or civilian units responsible for disaster response will receive tactical training based on HN and partner requirements. One training source is the Defense Institute for Medical Operations with extant courses categorized as Health Systems Management, Disaster Management and Consequence Management, Public Health and Force Health Protection, and Patient Transport and Evacuation. Unique courses offer by DoD include the following: SABC, Combat Casualty Care Course, and tactical combat casualty care. Department of State, DoD, and U.S. Agency for International Development (USAID) have the capacity to train Basic Life Support, Advanced Cardiac Life Support, Pre-Hospital Trauma Life Support, Advanced Trauma Life Support, and Advanced Life Support in Obstetrics. Indeed, such training is currently happening in African nations with train-the-trainer follow-up with external instructors. However, African International Health Specialist Team USAFE/AFAFRICA SGXD Unit 3050, Box 170 Ramstein, APO AE 09094-0170. The views expressed are those of the authors and do not reflect the official policy or position of the U.S. Africa Command, the U.S. Air Force, the Department of Defense, or the U.S. Government. doi: 10.7205/MILMED-D-15-00413

Recent Australian policy initiatives regarding primary health care focus on planning services around community needs and delivering these at the local area. As in many other countries, there has also been a growing concern over social inequities in health outcomes. The aims of the analysis presented here were firstly to describe small area variations in hospital admissions for ambulatory care sensitive conditions (ACSC) among children aged 0-4 years between 2003 and 2009 in the state of Victoria, Australia, and secondly to explore the relationship of ACSC hospitalisations with socio-economic disadvantage using a comparative analysis of the Child Social Exclusion (CSE) index and the Composite Score of Deprivation (CSD). This is a cross sectional secondary data analysis, with data sourced from 2003 to 2009 ACSC data from the Victorian State Government Department of Health; the Australian Standard Geographical Classification of remoteness; the Australian 2006 Census of Population and Housing; and AMPCo General Practitioner data from 2010. The relationship between the indexes and child health outcomes was examined through bivariate analysis and visually through a series of maps. The results show there is significant variation in the geographical distribution of the relationship between ACSCs and socio-economic disadvantage, with both indexes capturing important social gradients in child health conditions. However, measures of access, such as geographical accessibility and workforce supply, detect additional small area variation in child health outcomes. This research has important implications for future primary health care policy and planning of services, as these findings confirm that not all areas are the same in terms of health outcomes, and there may be benefit in tailoring mechanisms for identifying areas of need depending on the outcome intended to be affected.

Critical illness frequently involves multi-organ failure and it can be difficult to treat nutritional derangements in these patients. Organ-specific formulas were initially created to meet the specific nutritional needs of critically ill patients. Formulas have been developed to augment pulmonary, pancreas, liver, and renal failure. There is overall minimal evidence evaluating these formulas and many of the studies are small. A few large randomized trials have been done and the results are not supportive of widespread use of these enteral formulas. This review will evaluate the evidence and current guidelines of organ-specific nutritional formulas for the lung, pancreas, liver, and kidney.

To develop a measure of social deprivation that is associated with health care access and health outcomes at a novel geographic level, primary care service area.Secondary analysis of data from the Dartmouth Atlas, AMA Masterfile, National Provider Identifier data, Small Area Health Insurance Estimates, American Community Survey, Area Resource File, and Behavioural Risk Factor Surveillance System. Data were aggregated to primary care service areas (PCSAs).Social deprivation variables were selected from literature review and international examples. Factor analysis was used. Correlation and multivariate analyses were conducted between index, health outcomes, and measures of health care access. The derived index was compared with poverty as a predictor of health outcomes.Variables not available at the PCSA level were estimated at block level, then aggregated to PCSA level.Our social deprivation index is positively associated with poor access and poor health outcomes. This pattern holds in multivariate analyses controlling for other measures of access. A multidimensional measure of deprivation is more strongly associated with health outcomes than a measure of poverty alone.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.

Objective: To examine whether measures of remoteness areas adequately reveal high need populations, measured against socioeconomic disadvantage and physician to population ratios. Design: Exploratory spatial analysis of relationships between remoteness areas, medical workforce supply and the index of relative socioeconomic disadvantage (IRSD). Bivariate analyses examined associations between remoteness areas and IRSD. From this analysis, a composite score of deprivation was constructed combining measures of remoteness areas, physician to population ratios and IRSD, and validated against health outcome measures. These measures included avoidable mortality per 100 000, risk behaviour rate per 1000, diabetes rate per 1000. All analyses were conducted at the statistical local area level and weighted to be population representative. Results: The percentage of small areas and populations within the most socioeconomically disadvantaged quintile rose with increasing remoteness. However, 12.8% of small areas within major cities and 40.7% of outer regional areas were also within the lowest socioeconomic quintile. There was a strong relationship between our composite score of deprivation and avoidable mortality, risk rate, diabetes rate and per cent Indigenous. Regression analysis examined the relationship between each element of the composite score and health outcomes. This revealed that the association between avoidable mortality and remoteness was lost after controlling for per cent Indigenous. Conclusions: Using remoteness areas alone to prioritise 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.

To respond to changing population and workforce needs and expectations, evidence must inform policy investment, implementation and evaluation.

Objectives To estimate undiagnosed diabetes prevalence from general practitioner (GP) practice data and identify areas with high levels of undiagnosed and diagnosed diabetes. Design Data 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. Setting North-West Adelaide, Australia. Participants This 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 measures Total diabetes prevalence, diagnosed and undiagnosed diabetes prevalence at GP practice and Statistical Area Level 1. Results Overall, 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. Conclusions The 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.

Background Good 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. Method We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. Results The 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. Conclusion In 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.