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BackgroundThe 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.MethodsThe 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.ResultsPrevalence 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.ConclusionsThis 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.

Based on the available evidence, a predominance of team nursing within the comparisons is suggestive of its popularity. Patient outcomes, nurse satisfaction, absenteeism and role clarity/confusion did not differ across model comparisons. Little benefit was found within primary nursing comparisons and the cost effectiveness of team nursing over other models remains debatable. Nonetheless, team nursing does present a better model for inexperienced staff to develop, a key aspect in units where skill mix or experience is diverse.

BackgroundPrevalence 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.MethodsBaseline 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.ResultsMost 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.ConclusionsSelf-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.

This study compared nurse outcomes between the shared care in nursing (SCN) and patient allocation (PA) models of care. A quasi-experimental design was used. Job satisfaction, stress and aspects of role were measured at baseline and 6 months after the implementation of the SCN model using validated instruments. Nurses in the PA (n = 51) and SCN (n = 74) units were comparable at baseline. Nurses from both groups were satisfied with their job and experienced clarity in their role despite some levels of pressure. 'Satisfaction with co-workers' in the SCN group decreased, emphasizing the challenges of supervising staff. Matched pair sample sizes at follow-up were small. The SCN represents an innovative model of care delivery that is responsive to increasing proportions of enrolled nurses and assistants in nursing within wards. Both models have been found to be supportive of nursing staff. Although difficulties with follow-up data were experienced, this study represents the first Australian quasi-experimental research, comparing two models with validated measures. New tasks such as negotiating with co-workers might create some new challenges for nurses. Hospital administrators should consider the repertoire of care delivery models available.