James Dawber

Referral patterns and proximity to palliative care inpatient services by level of socio-economic disadvantage. A national study using spatial analysis

BackgroundA range of health outcomes at a population level are related to differences in levels of social disadvantage. Understanding the impact of any such differences in palliative care is important. The aim of this study was to assess, by level of socio-economic disadvantage, referral patterns to specialist palliative care and proximity to inpatient services.MethodsAll inpatient and community palliative care services nationally were geocoded (using postcode) to one nationally standardised measure of socio-economic deprivation – Socio-Economic Index for Areas (SEIFA; 2006 census data). Referral to palliative care services and characteristics of referrals were described through data collected routinely at clinical encounters. Inpatient location was measured from each person’s home postcode, and stratified by socio-economic disadvantage.ResultsThis study covered July – December 2009 with data from 10,064 patients. People from the highest SEIFA group (least disadvantaged) were significantly less likely to be referred to a specialist palliative care service, likely to be referred closer to death and to have more episodes of inpatient care for longer time. Physical proximity of a person’s home to inpatient care showed a gradient with increasing distance by decreasing levels of socio-economic advantage.ConclusionThese data suggest that a simple relationship of low socioeconomic status and poor access to a referral-based specialty such as palliative care does not exist. Different patterns of referral and hence different patterns of care emerge.

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.