Cross-sectional examination of 24-hour movement behaviours among 3- and 4-year-old children in urban and rural settings in low-income, middle-income and high-income countries: the SUNRISE study protocol.

INTRODUCTION: 24-hour movement behaviours (physical activity, sedentary behaviour and sleep) during the early years are associated with health and developmental outcomes, prompting the WHO to develop Global guidelines for physical activity, sedentary behaviour and sleep for children under 5 years of age. Prevalence data on 24-hour movement behaviours is lacking, particularly in low-income and middle-income countries (LMICs). This paper describes the development of the SUNRISE International Study of Movement Behaviours in the Early Years protocol, designed to address this gap. METHODS AND ANALYSIS: SUNRISE is the first international cross-sectional study that aims to determine the proportion of 3- and 4-year-old children who meet the WHO Global guidelines. The study will assess if proportions differ by gender, urban/rural location and/or socioeconomic status. Executive function, motor skills and adiposity will be assessed and potential correlates of 24-hour movement behaviours examined. Pilot research from 24 countries (14 LMICs) informed the study design and protocol. Data are collected locally by research staff from partnering institutions who are trained throughout the research process. Piloting of all measures to determine protocol acceptability and feasibility was interrupted by COVID-19 but is nearing completion. At the time of publication 41 countries are participating in the SUNRISE study. ETHICS AND DISSEMINATION: The SUNRISE protocol has received ethics approved from the University of Wollongong, Australia, and in each country by the applicable ethics committees. Approval is also sought from any relevant government departments or organisations. The results will inform global efforts to prevent childhood obesity and ensure young children reach their health and developmental potential. Findings on the correlates of movement behaviours can guide future interventions to improve the movement behaviours in culturally specific ways. Study findings will be disseminated via publications, conference presentations and may contribute to the development of local guidelines and public health interventions.

Planning for subacute care: predicting demand using acute activity data.

Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals. Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre. Results The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs. Conclusions The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning. What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age-sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated 'rehabilitation-sensitive'. This improved methodology has been used in several Australian jurisdictions. What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the methodology based on rehabilitation-sensitive AR-DRGs has been extended by updating them to AR-DRG Version 7.0, quantifying the level of 'sensitivity' and incorporating the patient's age to improve the prediction of demand for subacute services. What are the implications for practitioners? The predictive model takes the form of tables of probabilities that patients will require rehabilitation or GEM care after an acute episode and can be applied to acute in-patient administrative datasets in any Australian jurisdiction or local area. The use of patient-level characteristics will enable service planners to improve their forecasting of demand for these services. Clinicians and jurisdictional representatives consulted during the project regarded the model favourably and believed that it was an improvement on currently available methods.

Emergency department waiting times: Do the raw data tell the whole story?

OBJECTIVE: To determine whether there are real differences in emergency department (ED) performance between Australian states and territories. METHODS: Cross-sectional analysis of 2009-10 attendances at an ED contributing to the Australian non-admitted patient ED care database. The main outcome measure was difference in waiting time across triage categories. RESULTS: There were more than 5.8 million ED attendances. Raw ED waiting times varied by a range of factors including jurisdiction, triage category, geographic location and hospital peer group. All variables were significant in a model designed to test the effect of jurisdiction on ED waiting times, including triage category, hospital peer group, patient socioeconomic status and patient remoteness. When the interaction between triage category and jurisdiction entered the model, it was found to have a significant effect on ED waiting times (P<0.001) and triage was also significant (P<0.001). Jurisdiction was no longer statistically significant (P=0.248 using all triage categories and 0.063 using only Australian Triage Scale 2 and 3). CONCLUSIONS: Although the Council of Australian Governments has adopted raw measures for its key ED performance indicators, raw waiting time statistics are misleading. There are no consistent differences in ED waiting times between states and territories after other factors are accounted for. WHAT IS KNOWN ABOUT THE TOPIC? The length of time patients wait to be treated after presenting at an ED is routinely used to measure ED performance. In national health agreements with the federal government, each state and territory in Australia is expected to meet waiting time performance targets for the five ED triage categories. The raw data indicate differences in performance between states and territories. WHAT DOES THIS PAPER ADD? Measuring ED performance using raw data gives misleading results. There are no consistent differences in ED waiting times between the states and territories after other factors are taken into account. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS? Judgements regarding differences in performance across states and territories for triage waiting times need to take into account the mix of patients and the mix of hospitals.

The health of people in Australian immigration detention centres.

OBJECTIVE: To determine the health status of people in Australian immigration detention centres and the effect of time in, and reason for, detention. DESIGN, SETTING AND SUBJECTS: An analysis of the health records of 720 of the 7375 people in detention in the financial year 1 July 2005-30 June 2006, with oversampling of those detained for > 3 months. MAIN OUTCOME MEASURES: Health encounters and health condition categories; estimated incidence rates of new health conditions, new mental health conditions, and new injuries for each cohort (defined by time in, and reason for, detention). RESULTS: People in detention had an estimated 1.2 (95% CI, 1.18-1.27) health encounters per person-week. Those detained for > 24 months had particularly poor health, both mental and physical. Asylum seekers had more health problems than other people in detention. The main health problems varied depending on the length of time in detention, but included dental, mental health, and musculoskeletal problems, and lacerations. Both time in, and reason for, detention were significantly related to the rate of new mental health problems (P = 0.018 and P < 0.001, respectively). The relationship between these variables and the incidence rates of physical health problems was more complex. CONCLUSION: People in immigration detention are frequent users of health services, and there is a clear association between time in detention and rates of mental illness. Government policies internationally should be informed by evidence from studies of the health of this marginalised and often traumatised group.

Current funding and financing issues in the Australian hospice and palliative care sector.

This article overviews current funding and financing issues in the Australian hospice and palliative care sector. Within Australia, the major responsibilities for managing the health care system are shared between two levels of government. Funding arrangements vary according to the type of care. The delivery of palliative care services is a State/Territory responsibility. Recently, almost all States/Territories have developed overarching frameworks to guide the development of palliative care policies, including funding and service delivery structures. Palliative care services in Australia comprise a mix of specialist providers, generalist providers, and support services in the public, nongovernment, and private sectors. The National Palliative Care Strategy is a joint strategy of the Commonwealth and States that commenced in 2002 and includes a number of major issues. Following a national study in 1996, the Australian National Subacute and Nonacute Patient (AN-SNAP) system was endorsed as the national casemix classification for subacute and nonacute care. Funding for palliative care services varies depending on the type of service and the setting in which it is provided. There is no national model for funding inpatient or community services, which is a State/Territory responsibility. A summary of funding arrangements is provided in this article. Palliative care continues to evolve at a rapid rate in Australia. Increasingly flexible evidence-based models of care delivery are emerging. This article argues that it will be critical for equally flexible funding and financing models to be developed. Furthermore, it is critical that palliative care patients can be identified, classified, and costed. Casemix classifications such as AN-SNAP represent an important starting point but further work is required.

The development of Version 2 of the AN-SNAP casemix classification system.

This paper presents the results of a recent review of the Australian National Sub-acute and Non-acute Patient (AN-SNAP) classification system. The AN-SNAP system was developed by the Centre for Health Service Development, University of Wollongong in 1997. The review was conducted between August 2005 and September 2006. Four clinical sub-committees comprising more than 50 clinicians from sub-acute services across New South Wales as well as representatives from Queensland and the Australian Capital Territory were established to develop a set of proposals to be considered for incorporation into Version 2 of the classification. It is proposed that the final AN-SNAP Version 2 classification will be available for implementation from 1 July 2007.

An Australian casemix classification for palliative care: technical development and results.

OBJECTIVES: To develop a palliative care casemix classification for use in all settings including hospital, hospice and home-based care. SAMPLE: 3866 palliative care patients who, in a three-month period, had 4596 episodes of care provided by 58 palliative care services in Australia and New Zealand. METHOD: A detailed clinical and service utilization profile was collected on each patient with staff time and other resources measured on a daily basis. Each day of care was costed using actual cost data from each study site. Regression tree analysis was used to group episodes of care with similar costs and clinical characteristics. RESULTS: In the resulting classification, the Australian National Sub-acute and Non-acute Patient (AN-SNAP) Classification Version 1, the branch for classifying inpatient palliative care episodes (including hospice care) has 11 classes and explains 20.98% of the variance in inpatient palliative care phase costs using trimmed data. There are 22 classes in the ambulatory palliative care branch that explains 17.14% variation in ambulatory phase cost using trimmed data. DISCUSSION: The term 'subacute' is used in Australia to describe health care in which the goal--a change in functional status or improvement in quality of life--is a better predictor of the need for, and the cost of, care than the patient's underlying diagnosis. The results suggest that phase of care (stage of illness) is the best predictor of the cost of Australian palliative care. Other predictors of cost are functional status and age. In the ambulatory setting, symptom severity and the model of palliative care are also predictive of cost. These variables are used in the AN-SNAP Version 1 classification to create 33 palliative care classes. The classification has clinical meaning but the overall statistical performance is only moderate. The structure of the classification allows for it to be improved over time as models of palliative care service delivery develop.

An Australian casemix classification for palliative care: lessons and policy implications of a national study.

OBJECTIVES: To provide a nontechnical discussion of the development of a palliative care casemix classification and some policy implications of its implementation. SAMPLE: 3866 palliative care patients who, in a three month period, had 4596 episodes of care provided by 58 palliative care services in Australia and New Zealand. METHOD: A detailed clinical and service utilization profile was collected on each patient with staff time and other resources measured on a daily basis. A statistical summary of the clinical variables was compiled as the first stage of the analysis. RESULTS: Palliative care phase was found to be a good predictor of resource use, with patients fairly evenly distributed across the five categories. Clients treated in an inpatient setting had poorer function and higher symptom severity scores than those treated in an ambulatory setting, a result that is not surprising in this Australian setting. DISCUSSION: Implementation of the resultant AN-SNAP classification has been proceeding since 1998 in some Australian jurisdictions. The development and implementation of a classification such as AN-SNAP provides the possibility of having a consistent approach to collecting palliative care data in Australia as well as a growing body of experience on how to progressively improve the classification over time.

Developing a New Zealand casemix classification for mental health services.

This study aimed to develop a casemix classification of characteristics of New Zealand mental health services users. Over a six month period, patient information, staff time and service costs were collected from 8 district health boards. This information was analysed seeking the classification of service user characteristics which best predicted the cost drivers of the services provided. A classification emerged which explained more than two thirds of the variance in service user costs. It can be used to inform service management and funding, but it is premature to have it determine funding.

Health services research and development in practice: an Australian experience.

While there is a growing literature on how health services research can inform health policy decisions, the practical challenge is for health services researchers to develop an effective interface with health policy-making processes and to produce outputs that lead to outcomes. The experience of the Centre for Health Service Development at the University of Wollongong, Australia, is used to illustrate the issues so commonly described in the literature and to reflect on our experience of trying to remain viable while producing relevant and valid research. A case study in a specific policy area - namely, the development of case-mix classifications and information systems to inform policy and funding in the subacute and non-acute hospital and community care sectors - is used as a practical example of the research-policy interface.

Urinary incontinence in subacute care--a retrospective analysis of clinical outcomes and costs.

OBJECTIVE: To investigate the effect of incontinence on clinical outcomes and costs for patients in subacute care. DESIGN: Retrospective analysis of data collected over a 3-month period in 1996. SETTING: 54 medical facilities in Australia and New Zealand providing subacute care in an inpatient setting. PATIENTS: 6773 episodes of care provided to 6455 rehabilitation and geriatric evaluation and management patients. MAIN OUTCOME MEASURES: Urinary continence status, treatment outcomes, length of stay, discharge destination, and nursing and allied healthcare costs. RESULTS: Discharge destination differed between incontinent and continent patients (57% compared with 82%, respectively, discharged home, and 29% compared with 12%, respectively, discharged to a nursing home or to further care). There was a difference in cost between patients who were continent and those who were incontinent throughout their episode of care (rehabilitation: $185.60 [95% CI, $181-$190] per day for incontinent and $156.82 [95% CI, $153-$160] for continent patients; and geriatric evaluation and management: $164.62 [95% CI, $157-$172] for incontinent and $121.40 [95% CI, $114-$129] for continent patients). However, multilevel analyses showed that, after allowing for age and level of functional independence, the contribution of continence status to the cost of care depended on the functional independence of the patient (cognitive function for orthopaedic patients [P < 0.01] and motor function for stroke patients [P = 0.04]). CONCLUSION: The relationship between continence status and cost of care is complex. However, the cost differences found in our study need to be considered in payment systems, allocation of staff levels on wards and in development of casemix classifications.