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Objectives Greenspace is one of the important factors that can promote an active lifestyle. Thus, greener surroundings may be a motivating factor for people with newly diagnosed diabetes to engage in more physical activity. Given that diagnosis of type 2 diabetes (T2D) may serve as a window opportunity for behavioural modification, we hypothesise that the association between neighbourhood greenspace and physical activity among people with newly diagnosed T2D may be greater than those not diagnosed with T2D. The aim of this study was to investigate the association between access to greenspace and changes in physical activity and sedentary behaviour, and whether these associations differed by T2D. Design Prospective cohort. Setting New South Wales, Australia. Methods We used self-reported information from the New South Wales 45 and Up Study (baseline) and a follow-up study. Information on sitting, walking and moderate to vigorous physical activity was used as outcomes. The proportion of greenspace within 500 m, 1 km and 2 km road network buffers around participant’s residential address was generated as a proxy measure for access to greenspace. The association between the access to greenspace and the outcomes were explored among the newly diagnosed T2D group and those without T2D. Results Among New T2D, although no significant changes were found in the amount of walking with the percentage of greenspace, increasing trends were apparent. There was no significant association between the percentage of greenspace and changes in amount of moderate to vigorous physical activity (MVPA). Among No T2D, there were no significant associations between the amount of MVPA and walking, and percentage of greenspace. For changes in sitting time, there were no significant associations with percentage of greenspace regardless of buffer size. Conclusions In this study, there was no association between access to greenspace at baseline and change in walking, MVPA and sitting time, regardless of T2D status. [ABSTRACT FROM AUTHOR]

Background The increased prevalence of diabetes and its significant impact on use of health care services, particularly hospitals, is a concern for health planners. This paper explores the risk factors for all-cause hospitalisation and the excess risk due to diabetes in a large sample of older Australians. Methods The study population was 263,482 participants in the 45 and Up Study. The data assessed were linked records of hospital admissions in the 12 months following completion of a baseline questionnaire. All cause and ambulatory care sensitive admission rates and length of stay were examined. The associations between demographic characteristics, socioeconomic status, lifestyle factors, and health and wellbeing and risk of hospitalisation were explored using zero inflated Poisson (ZIP) regression models adjusting for age and gender. The ratios of adjusted relative rates and 95% confidence intervals were calculated to determine the excess risk due to diabetes. Results Prevalence of diabetes was 9.0% (n = 23,779). Age adjusted admission rates for all-cause hospitalisation were 631.3 and 454.8 per 1,000 participant years and the mean length of stay was 8.2 and 7.1 days respectively for participants with and without diabetes. In people with and without diabetes, the risk of hospitalisation was associated with age, gender, household income, smoking, BMI, physical activity, and health and wellbeing. However, the increased risk of hospitalisation was attenuated for participants with diabetes who were older, obese, or had hypertension or hyperlipidaemia and enhanced for those participants with diabetes who were male, on low income, current smokers or who had anxiety or depression. Conclusions This study is one of the few studies published to explore the impact of diabetes on hospitalisation in a large non-clinical population, the 45 and Up Study. The attenuation of risk associated with some factors is likely to be due to correlation between diabetes and factors such as age and obesity. The increased risk in association with other factors such as gender and low income in participants with diabetes is likely to be due to their synergistic influence on health status and the way services are accessed. [ABSTRACT FROM AUTHOR]

Background Prevalence studies usually depend on self-report of disease status in survey data or administrative data collections and may over- or under-estimate disease prevalence. The establishment of a linked data collection provided an opportunity to explore the accuracy and completeness of capture of information about diabetes in survey and administrative data collections. Methods Baseline questionnaire data at recruitment to the 45 and Up Study was obtained for 266,848 adults aged 45 years and over sampled from New South Wales, Australia in 2006-2009, and linked to administrative data about hospitalisation from the Admitted Patient Data Collection (APDC) for 2000-2009, claims for medical services (MBS) and pharmaceuticals (PBS) from Medicare Australia data for 2004-2009. Diabetes status was determined from response to a question 'Has a doctor EVER told you that you have diabetes' (n = 23,981) and augmented by examination of free text fields about diagnosis (n = 119) or use of insulin (n = 58). These data were used to identify the sub-group with type 1 diabetes. We explored the agreement between self-report of diabetes, identification of diabetes diagnostic codes in APDC data, claims for glycosylated haemoglobin (HbA1c) in MBS data, and claims for dispensed medication (oral hyperglycaemic agents and insulin) in PBS data. Results Most participants with diabetes were identified in APDC data if admitted to hospital (79.3%), in MBS data with at least one claim for HbA1c testing (84.7%; 73.4% if 2 tests claimed) or in PBS data through claim for diabetes medication (71.4%). Using these alternate data collections as an imperfect 'gold standard' we calculated sensitivities of 83.7% for APDC, 63.9% (80.5% for two tests) for MBS, and 96.6% for PBS data and specificities of 97.7%, 98.4% and 97.1% respectively. The lower sensitivity for HbA1c may reflect the use of this test to screen for diabetes suggesting that it is less useful in identifying people with diabetes without additional information. Kappa values were 0.80, 0.70 and 0.80 for APDC, MBS and PBS respectively reflecting the large population sample under consideration. Compared to APDC, there was poor agreement about identifying type 1 diabetes status. Conclusions Self-report of diagnosis augmented with free text data indicating diabetes as a chronic condition and/or use of insulin among medications used was able to identify participants with diabetes with high sensitivity and specificity compared to available administrative data collections. [ABSTRACT FROM AUTHOR]

Background: Although primary health care (PHC) is a key component of all health care systems, services are not always readily available, accessible or affordable. This systematic review examines effective strategies to enhance access to best practice processes of PHC in three domains: chronic disease management, prevention and episodic care. Methods: An extensive search of bibliographic data bases to identify peer and non-peer reviewed literature was undertaken. Identified papers were screened to identify and classify intervention studies that measured the impact of strategies (singly or in combination) on change in use or the reach of services in defined population groups (evaluated interventions). Results: The search identified 3,148 citations of which 121 were intervention studies and 75 were evaluated interventions. Evaluated interventions were found in all three domains: prevention (n = 45), episodic care (n = 19), and chronic disease management (n = 11). They were undertaken in a number of countries including Australia (n = 25), USA (n = 25), and UK (n = 15). Study quality was ranked as high (31% of studies), medium (61%) and low (8%). The 75 evaluated interventions tested a range of strategies either singly (n = 46 studies) or as a combination of two (n = 20) or more strategies (n = 9). Strategies targeted both health providers and patients and were categorised to five groups: practice re-organisation (n = 43 studies), patient support (n = 29), provision of new services (n = 19), workforce development (n = 11), and financial incentives (n = 9). Strategies varied by domain, reflecting the complexity of care needs and processes. Of the 75 evaluated interventions, 55 reported positive findings with interventions using a combination of strategies more likely to report positive results. Conclusions: This review suggests that multiple, linked strategies targeting different levels of the health care system are most likely to improve access to best practice PHC. The proposed changes in the structure of PHC in Australia may provide opportunities to investigate the factors that influence access to best practice PHC and to develop and implement effective, evidence based strategies to address these. [ABSTRACT FROM AUTHOR]