Life, death, and statins: association of statin prescriptions and survival in older general practice patients.

AIMS: This study serves as an exemplar to demonstrate the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. Collection of these data, the subsequent analysis, and the preparation of practice-specific reports were performed using a bespoke distributed data collection and analysis software tool. BACKGROUND: Statins are a very commonly prescribed medication, yet there is a paucity of evidence for their benefits in older patients. We examine the relationship between statin prescriptions for general practice patients over 75 and all-cause mortality. METHODS: We carried out a retrospective cohort study using survival analysis applied to data extracted from the electronic health records of five Australian general practices. FINDINGS: The data from 8025 patients were analysed. The median duration of follow-up was 6.48 years. Overall, 52 015 patient-years of data were examined, and the outcome of death from any cause was measured in 1657 patients (21%), with the remainder being censored. Adjusted all-cause mortality was similar for participants not prescribed statins versus those who were (HR 1.05, 95% CI 0.92-1.20, P = 0.46), except for patients with diabetes for whom all-cause mortality was increased (HR = 1.29, 95% CI: 1.00-1.68, P = 0.05). In contrast, adjusted all-cause mortality was significantly lower for patients deprescribed statins compared to those who were prescribed statins (HR 0.81, 95% CI 0.70-0.93, P < 0.001), including among females (HR = 0.75, 95% CI: 0.61-0.91, P < 0.001) and participants treated for secondary prevention (HR = 0.72, 95% CI: 0.60-0.86, P < 0.001). This study demonstrated the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. We found no evidence of increased mortality due to statin-deprescribing decisions in primary care.

Increasing use of intraoperative cholangiogram in Australia: is it evidence-based?

BACKGROUND: The role of routine intraoperative cholangiograms (IOCs) for prevention of bile duct injury (BDI) is contentious. There are recent reports of limited utility of IOC in preventing BDI. In Australia, IOCs are used more frequently than internationally. This study aimed to evaluate the rate of IOC use in Australia and explore potential changes in practice in light of evolving evidence for the utility of IOC. METHODS: Data were collated using service item numbers in Medicare Benefits Scheme records on the Australian Government Medicare website, for services claimed between 1 January 2001 and 31 December 2019. These data were used to analyse trends in rates of IOC, cholecystectomy and BDI repair. Data were age-standardized to account for changes in the population over time. RESULTS: The number of IOCs claimed increased by 31.8% and cholecystectomies by 7.0% over the study period. Age-standardized service rates per 100 000 persons increased by 5.5 and 32.6, respectively. Rates of IOC per 100 000 cholecystectomies steadily increased across the study period, while BDI repair rates remained low and erratic. CONCLUSION: Increasing use of IOC over the last 20 years reflects a trend towards routine rather than selective IOC; however, there is little discernible change in the number of BDIs requiring repair procedures. This suggests that routine IOC use to prevent or minimize BDI is unwarranted. Further investigation is required into the selective IOC use in high-risk patients rather than mandatory use in all patients.

Geographic variation in cardiometabolic risk factor prevalence explained by area-level disadvantage in the Illawarra-Shoalhaven region of the NSW, Australia.

Cardiometabolic risk factors (CMRFs) demonstrate significant geographic variation in their distribution. The study aims to quantify the general contextual effect of the areas on CMRFs; and the geographic variation explained by area-level socioeconomic disadvantage. A cross sectional design and multilevel logistic regression methods were adopted. Data included objectively measured routine pathology test data between years 2012 and 2017 on: fasting blood sugar level; glycated haemoglobin; total cholesterol; high density lipoprotein; urinary albumin creatinine ratio; estimated glomerular filtration rate; and body mass index. The 2011 Australian census based Index of Relative Socioeconomic Disadvantage (IRSD) were the area-level study variables, analysed at its smallest geographic unit of reporting. A total of 1,132,029 CMRF test results from 256,525 individuals were analysed. After adjusting for individual-level covariates, all CMRFs significantly associated with IRSD and the probability of higher risk CMRFs increases with greater area-level disadvantage. Though the specific contribution of IRSD in the geographic variation of CMRF ranged between 57.8 and 14.71%, the general contextual effect of areas were found minimal (ICCs 0.6-3.4%). The results support universal interventions proportional to the need and disadvantage level of populations for the prevention and control of CMRFs, rather than any area specific interventions as the contextual effects were found minimal in the study region.

Role of Area-Level Access to Primary Care on the Geographic Variation of Cardiometabolic Risk Factor Distribution: A Multilevel Analysis of the Adult Residents in the Illawarra-Shoalhaven Region of NSW, Australia.

BACKGROUND: Access to primary care is important for the identification, control and management of cardiometabolic risk factors (CMRFs). This study investigated whether differences in geographic access to primary care explained area-level variation in CMRFs. METHODS: Multilevel logistic regression models were used to derive the association between area-level access to primary care and seven discrete CMRFs after adjusting for individual and area-level co-variates. Two-step floating catchment area method was used to calculate the geographic access to primary care for the small areas within the study region. RESULTS: Geographic access to primary care was inversely associated with low high density lipoprotein (OR 0.94, CI 0.91-0.96) and obesity (OR 0.91, CI 0.88-0.93), after adjusting for age, sex and area-level disadvantage. The intra-cluster correlation coefficient (ICCs) of all the fully adjusted models ranged between 0.4-1.8%, indicating low general contextual effects of the areas on CMRF distribution. The area-level variation in CMRFs explained by primary care access was ≤10.5%. CONCLUSION: The findings of the study support proportionate universal interventions for the prevention and control of CMRFs, rather than any area specific interventions based on their primary care access, as the contextual influence of areas on all the analysed CMRFs were found to be minimal. The findings also call for future research that includes other aspects of primary care access, such as road-network access, financial affordability and individual-level acceptance of the services in order to gain an overall picture of the area-level contributing role of primary care on CMRFs in the study region.

Neighborhood Environment and Type 2 Diabetes Comorbidity in Serious Mental Illness.

Aim: The aim of this study was to examine the association between neighborhood characteristics and type 2 diabetes (T2D) comorbidity in serious mental illness (SMI). We investigated associations of neighborhood-level crime, accessibility to health care services, availability of green spaces, neighborhood obesity, and fast food availability with SMI-T2D comorbidity. Method: A series of multilevel logistic regression models accounting for neighborhood-level clustering were used to examine the associations between 5 neighborhood variables and SMI-T2D comorbidity, sequentially adjusting for individual-level variables and neighborhood-level socioeconomic disadvantage. Results: Individuals with SMI residing in areas with higher crime rates per 1000 population had 2.5 times increased odds of reporting T2D comorbidity compared to the individuals with SMI residing in lower crime rate areas after controlling for individual and areal level factors (95% CI 0.91-6.74). There was no evidence of association between SMI-T2D comorbidity and other neighborhood variables investigated. Conclusion: Public health strategies to reduce SMI-T2D comorbidity might benefit by targeting on individuals with SMI living in high-crime neighborhoods. Future research incorporating longitudinal designs and/or mediation analysis are warranted to fully elucidate the mechanisms of association between neighborhoods and SMI-T2D comorbidity.

Australian general practitioners' attitudes to the extraction of research data from electronic health records.

BACKGROUND AND OBJECTIVES: General practice electronic health records (EHRs) are a rich source of primary care data that can be used for important research. The aim of this qualitative study was to analyse the attitudes of Australian general practitioners (GPs) to the use of data extracted from primary care EHRs for clinical research. METHOD: Semi-structured interviews were conducted with 13 Australian GPs. Interviews were recorded, transcribed and thematically analysed. RESULTS: Two main themes emerged. The data custodian role encompassed GPs' determination to maintain privacy, their trust of organisations involved and benefits of research outcomes. The theme of protecting practice resources included concerns about unrecompensed staff time and potential risks to data and systems. DISCUSSION: This study highlights that while Australian GPs have concerns about the use of data stored on their EHRs, they also recognise the benefits of using this data for research purposes. Addressing these concerns could help to ensure that researchers have access to this valuable and rich data resource.

Exploring the geography of serious mental illness and type 2 diabetes comorbidity in Illawarra-Shoalhaven, Australia (2010 -2017).

OBJECTIVES: The primary aim of this study was to describe the geography of serious mental illness (SMI)-type 2 diabetes comorbidity (T2D) in the Illawarra-Shoalhaven region of NSW, Australia. The Secondary objective was to determine the geographic concordance if any, between the comorbidity and the single diagnosis of SMI and diabetes. METHODS: Spatial analytical techniques were applied to clinical data to explore the above objectives. The geographic variation in comorbidity was determined by Moran's I at the global level and the local clusters of significance were determined by Local Moran's I and spatial scan statistic. Choropleth hotspot maps and spatial scan statistics were generated to assess the geographic convergence of SMI, diabetes and their comorbidity. Additionally, we used bivariate LISA (Local Indicators of Spatial Association) and multivariate spatial scan to identify coincident areas with higher rates of both SMI and T2D. RESULTS: The study identified significant geographic variation in the distribution of SMI-T2D comorbidity in Illawarra Shoalhaven. Consistently higher burden of comorbidity was observed in some urban suburbs surrounding the major metropolitan city. Comparison of comorbidity hotspots with the hotspots of single diagnosis SMI and T2D further revealed a geographic concordance of high-risk areas again in the urban areas outside the major metropolitan city. CONCLUSION: The identified comorbidity hotspots in our study may serve as a basis for future prioritisation and targeted interventions. Further investigation is required to determine whether contextual environmental factors, such as neighbourhood socioeconomic disadvantage, may be explanatory. IMPLICATIONS FOR PUBLIC HEALTH: Ours is the first study to explore the geographic variations in the distribution of SMI and T2D comorbidity. Findings highlight the importance of considering the role of neighbourhood environments in influencing the T2D risk in people with SMI.

Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia.

INTRODUCTION: Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches. METHOD: A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA). RESULTS: A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area. CONCLUSION: The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

Examining the Association between Neighbourhood Socioeconomic Disadvantage and Type 2 Diabetes Comorbidity in Serious Mental Illness.

This study examined the association between neighbourhood socioeconomic disadvantage and serious mental illness (SMI)-type 2 diabetes (T2D) comorbidity in an Australian population using routinely collected clinical data. We hypothesised that neighbourhood socioeconomic disadvantage is positively associated with T2D comorbidity in SMI. The analysis considered 3816 individuals with an SMI living in the Illawarra and Shoalhaven regions of NSW, Australia, between 2010 and 2017. Multilevel logistic regression models accounting for suburb (neighbourhood) level clustering were used to assess the association between neighbourhood disadvantage and SMI -T2D comorbidity. Models were adjusted for age, sex, and country of birth. Compared with the most advantaged neighbourhoods, residents in the most disadvantaged neighbourhoods had 3.2 times greater odds of having SMI-T2D comorbidity even after controlling for confounding factors (OR 3.20, 95% CI 1.42-7.20). The analysis also revealed significant geographic variation in the distribution of SMI -T2D comorbidity in our sample (Median Odds Ratio = 1.35) Neighbourhood socioeconomic disadvantage accounted for approximately 17.3% of this geographic variation. These findings indicate a potentially important role for geographically targeted initiatives designed to enhance prevention and management of SMI-T2D comorbidity in disadvantaged communities.

The Use of Primary Care Electronic Health Records for Research: Lipid Medications and Mortality in Elderly Patients.

General practice electronic health record (EHR) data have significant potential for clinical research. This study demonstrates the feasibility of utilising longitudinal EHR data analysis to address clinically relevant outcomes and uses the relationship between lipid medication prescription and all-cause mortality in the elderly as an exemplar for the validity of this methodology. EHR data were analysed to describe the association of lipid medication use, non-use or cessation with all-cause mortality in patients aged ≥75 years. Survival analysis with Cox regression was used to calculate hazard ratios, which were adjusted for confounders. There was no significant difference in all-cause mortality among patients according to their use, non-use, or cessation of lipid medications. The outcomes of this study correlate well with the results of other research works. This single-practice study demonstrates the feasibility and potential of analysing EHR data to address important clinical issues such as the relationship between all-cause mortality and lipid medication prescription in the elderly.

Area-Level Walkability and the Geographic Distribution of High Body Mass in Sydney, Australia: A Spatial Analysis Using the 45 and Up Study.

Improving the walkability of built environments to promote healthy lifestyles and reduce high body mass is increasingly considered in regional development plans. Walkability indexes have the potential to inform, benchmark and monitor these plans if they are associated with variation in body mass outcomes at spatial scales used for health and urban planning. We assessed relationships between area-level walkability and prevalence and geographic variation in overweight and obesity using an Australian population-based cohort comprising 92,157 Sydney respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. Individual-level data on overweight and obesity were aggregated to 2006 Australian postal areas and analysed as a function of area-level Sydney Walkability Index quartiles using conditional auto regression spatial models adjusted for demographic, social, economic, health and socioeconomic factors. Both overweight and obesity were highly clustered with higher-than-expected prevalence concentrated in the urban sprawl region of western Sydney, and lower-than-expected prevalence in central and eastern Sydney. In fully adjusted spatial models, prevalence of overweight and obesity was 6% and 11% lower in medium-high versus low, and 10% and 15% lower in high versus low walkability postcodes, respectively. Postal area walkability explained approximately 20% and 9% of the excess spatial variation in overweight and obesity that remained after accounting for other individual- and area-level factors. These findings provide support for the potential of area-level walkability indexes to inform, benchmark and monitor regional plans aimed at targeted approaches to reducing population-levels of high body mass through environmental interventions. Future research should consider potential confounding due to neighbourhood self-selection on area-level walkability relations.

Increasing rate of para-oesophageal hiatus hernia surgical repair within Australia.

BACKGROUND: Symptomatic para-oesophageal hiatus (PEH) hernias are treated by surgical intervention, and are associated with older age (>50 years) and higher body mass index (>25 kg/m2 ). Both risk factors are increasing within the Australian population. Given these trends, this study aimed to determine if the rate of PEH repair is increasing within Australia. METHODS: The study used publically available Medicare Benefits Scheme service data for operations claimed under the item number 31468 (PEH hernia, repair of, with complete reduction of hernia, resection of sac and repair of hiatus, with or without fundoplication) between 1 January 2001 and 31 December 2016. Directly age-standardized rates per 100 000 population were calculated using the 2001 Australian standard population and compared using Poisson regression models. RESULTS: Repair of PEH significantly increased (P < 0.0001) in Australia during this period, with the average number of services increasing by 14.6% per annum. Average rates of repair increased significantly (P < 0.001) with increasing age up to 75 years after which they significantly reduced (P < 0.001) in each successive age group. Western Australia had the greatest increase in annual claims and Northern Territory had the least, but the state-specific average claim rate over the whole period was highest in Queensland and lowest in Northern Territory. CONCLUSION: Operations claimed under Medicare Benefits Scheme item number 31468 have significantly increased in Australia since January 2001. Reasons for this rise are likely multifactorial, and may indicate increasing PEH incidence, increased diagnosis and investigation or increased surgical capability to manage the issue laparoscopically with reduced peri-operative morbidity.

Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature.

INTRODUCTION: A growing number of publications report variation in the distribution of cardiometabolic risk factors (CMRFs) at different geographic scales. A review of these variations may help inform policy and health service organisation. AIM: To review studies reporting variation in the geographic distribution of CMRFs and its association with various proxy measures of area-level socioeconomic disadvantage (ASED) among the adult ( ≥ 18 years) population across the world. METHODS: A systematic search for published articles was conducted in four databases (MEDLINE (Ovid), PubMed, Scopus and Web of Science) considering the interdisciplinary nature of the review question. Population-based cross-sectional and cohort studies on geographic variations of one or more biological proxies of CMRFs with/without an analysed contextual association with ASED were included. Two independent reviewers screened the studies and PRISMA guidelines were followed in the study selection and reporting. RESULT: A total of 265 studies were retrieved and screened, resulting in 24 eligible studies. The review revealed reports of variation in the distribution of CMRFs, at varying geographic scales, in multiple countries. In addition, consistent associations between ASED and higher prevalence of CMRFs were demonstrated. The reports were mainly from industrialised nations and small area geographic units were frequently used. CONCLUSION: Geographic variation in cardiometabolic risk exists across multiple spatial scales and is positively associated with ASED. This association is independent of individual-level factors and provides an imperative for area-based approaches to informing policy and health service organisation. The study protocol is registered in International prospective register of systematic reviews (Register No: CRD42018115294) PROSPERO 2018.

Cross-sectional study of area-level disadvantage and glycaemic-related risk in community health service users in the Southern.IML Research (SIMLR) cohort.

Objectives The aim of the present study was to determine the association between area-level socioeconomic disadvantage and glycaemic-related risk in health service users in the Illawarra-Shoalhaven region of New South Wales, Australia. Methods HbA1c values recorded between 2010 and 2012 for non-pregnant individuals aged ≥18 years were extracted from the Southern.IML Research (SIMLR) database. Individuals were assigned quintiles of the Socioeconomic Indices for Australia (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) according to their Statistical Area 1 of residence. Glycaemic risk categories were defined as HbA1c 5.0-5.99% (lowest risk), 6.0-7.49% (intermediate risk) and ≥7.5% (highest risk). Logistic regression models were fit with glycaemic risk category as the outcome variable and IRSD as the study variable, adjusting for age and sex. Results Data from 29064 individuals were analysed. Higher disadvantage was associated with belonging to a higher glycaemic risk category in the fully adjusted model (most disadvantaged vs least disadvantaged quintile; odds ratio 1.74, 95% confidence interval 1.58, 1.93; P<0.001). Conclusion In this geocoded clinical dataset, area-level socioeconomic disadvantage was a significant correlate of increased glycaemic-related risk. Geocoded clinical data can inform more targeted use of health service resources, with the potential for improved health care equity and cost-effectiveness. What is known about the topic? The rapid increase in the prevalence of Type 2 diabetes (T2D), both globally and nationally within Australia, is a major concern for the community and public health agencies. Individual socioeconomic disadvantage is a known risk factor for abnormal glucose metabolism (AGM), including T2D. Although small-area-level socioeconomic disadvantage is a known correlate of AGM in Australia, less is known of the association of area-level disadvantage and glycaemic-related risk in individuals with AGM. What does this paper add? This study demonstrates a robust association between small-area-level socioeconomic disadvantage and glycaemic-related risk in regional New South Wales. The study demonstrates that it is feasible to use geocoded, routinely collected clinical data to identify communities at increased health risk. What are the implications for practitioners? The identification of at-risk populations is an essential step towards targeted public health policy and programs aimed at reducing the burden of AGM, its complications and the associated economic costs. Collaboration between primary care and public health in the collection and use of data described in the present study has the potential to enhance the effectiveness of both sectors.

Serious Mental Illness, Neighborhood Disadvantage, and Type 2 Diabetes Risk: A Systematic Review of the Literature.

AIM OF THE STUDY: This review aims to systematically synthesize the body of literature examining the association between neighborhood socioeconomic disadvantage and serious mental illness (SMI)-type 2 diabetes (T2D) co-occurrence. METHODS: We conducted an electronic search of four databases: PubMed, Scopus, Medline, and Web of Science. Studies were considered eligible if they were published in English, peer reviewed, quantitative, and focused on the association between neighborhood disadvantage and SMI-T2D comorbidity. Study conduct and reporting complied with PRISMA guidelines, and the protocol is made available at PROSPERO (CRD42017083483). RESULTS: The one eligible study identified reported a higher burden of T2D in persons with SMI but provided only a tentative support for the association between neighborhood disadvantage and SMI-T2D co-occurrence. CONCLUSION: Research into neighborhood effects on SMI-T2D comorbidity is still in its infancy and the available evidence inconclusive. This points to an urgent need for attention to the knowledge gap in this important area of public health. Further research is needed to understand the health resource implications of the association between neighborhood deprivation and SMI-T2D comorbidity and the casual pathways linking them.

Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia.

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed "disease maps" for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial distress.

Survival analysis using primary care electronic health record data: A systematic review of the literature.

PURPOSE: An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. METHOD: An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. RESULTS: A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority ( n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half ( n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. CONCLUSION: Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

The contribution of area-level walkability to geographic variation in physical activity: a spatial analysis of 95,837 participants from the 45 and Up Study living in Sydney, Australia.

BACKGROUND: Individual-level studies support a positive relation between walkable built environments and participation in moderate-intensity walking. However, the utility of this evidence for population-level planning is less clear as it is derived at much finer spatial scales than those used for regional programming. The aims of this study were to: evaluate if individual-level relations between walkability and walking to improve health manifest at population-level spatial scales; assess the specificity of area-level walkability for walking relative to other moderate and vigorous physical activity (MVPA); describe geographic variation in walking and other MVPA; and quantify the contribution of walkability to this variation. METHODS: Data on sufficient walking, sufficient MVPA, and high MVPA to improve health were analyzed for 95,837 Sydney respondents to the baseline survey of the 45 and Up Study between January 2006 and April 2010. We used conditional autoregressive models to create smoothed MVPA "disease maps" and assess relations between sufficient MVPA to improve health and area-level walkability adjusted for individual-level demographic, socioeconomic, and health factors, and area-level relative socioeconomic disadvantage. RESULTS: Within-cohort prevalence of meeting recommendations for sufficient walking, sufficient MVPA, and high MVPA were 31.7 (95% CI 31.4-32.0), 69.4 (95% CI 69.1-69.7), and 56.1 (95% CI 55.8-56.4) percent. Prevalence of sufficient walking was increased by 1.20 (95% CrI 1.12-1.29) and 1.07 (95% CrI 1.01-1.13) for high and medium-high versus low walkability postal areas, and for sufficient MVPA by 1.05 (95% CrI 1.01-1.08) for high versus low walkability postal areas. Walkability was not related to high MVPA. Postal area walkability explained 65.8 and 47.4 percent of residual geographic variation in sufficient walking and sufficient MVPA not attributable to individual-level factors. CONCLUSIONS: Walkability is associated with area-level prevalence and geographic variation in sufficient walking and sufficient MVPA to improve health in Sydney, Australia. Our study supports the use of walkability indexes at multiple spatial scales for informing population-level action to increase physical activity and the utility of spatial analysis for walkability research and planning.