The Aboriginal and Torres Strait Islander Casemix Study

Synopsis

• With increasing implementation of casemix-based funding for hospitals, quantitative data were needed to confirm the clinical impression that treating Aboriginal (compared with non-Aboriginal) inpatients consumes significantly more resources.
  
• Utilisation data, collected over a three-month period in 10 hospitals, were used to determine a cost per inpatient episode, which was grouped according to AN-DRG-3 to give a cost per AN-DRG for Aboriginal and Torres Strait Islander (ATSI) patients and non-ATSI patients.
  
• ATSI patients had consistently longer average length of stay and significant variation in relative frequency of admissions, compared with non-ATSI patients, with higher prevalences of infectious diseases. Degenerative and neoplastic conditions were more common in non-ATSI patients.
  
• There were significant differences in casemix-adjusted costs per patient episode (ATSI, $1856; non-ATSI, $1558; P < 0.001).
  
• Our study has quantified differential resource consumption between two Australian populations, and highlights the need for recognition of some hospitals' atypical populations and special funding requirements.

Introduction

There is substantial evidence in the medical literature of poor health outcomes for Aboriginal and Torres Strait Islander (ATSI) people despite high hospital utilisation rates.¹ Among the reforms designed to improve health outcomes, casemix classification (Australian national diagnosis-related groups, AN-DRGs) for hospital inpatients could, on the contrary, have deleterious effects if its limitations were not appreciated.

The principle underpinning casemix systems -- that clinically similar patients consuming similar resources can be grouped into a DRG which will have an equal spread of patients consuming more and less resources -- means that a hospital with an atypical population will be inappropriately funded. Health service providers who treat patients from remote Aboriginal communities believe that treating Aboriginal patients is considerably more expensive for a range of reasons (severity of disease at presentation, comorbidities, and social factors relating to culture, education and remote location), but there are few data quantifying their resource consumption during inpatient care.

With increasing implementation of casemix, quantitative data were urgently needed, so that hospitals caring for such populations would receive appropriate funding. The first study attempting to quantify differential resource consumption of Aboriginal and non-Aboriginal patients² had considerable methodological problems, resulting in the data being of limited use.

In 1993 the Australian Casemix Clinical Committee recommended to the (then) Commonwealth Department of Human Services and Health that a multicentre study be conducted to quantify differences in resource consumption patterns between ATSI and non-ATSI inpatients in rural and remote settings.

Methods In view of the complexity of the project, a representative steering committee was established to define the scope and provide clinical oversight for the proposed research.

After an analysis of Australia-wide hospital morbidity data, including utilisation rates by ATSI patients, a sampling framework was developed. Ten hospitals of more than 30 beds from Western Australia (Kalgoorlie), Northern Territory (Royal Darwin, Katherine and Alice Springs), South Australia (Port Augusta) and Queensland (Cairns, Mount Isa, Cunnamulla, St George and Innisfail) agreed to participate as study sites.

External consultants (Brewerton and Associates, Adelaide) were appointed to facilitate data collection and analysis within the guidelines established by the steering committee.

Collection and review of data, and consultation
Data were collected from each site over a three-month period. Six sites commenced collection on 1 July 1995. The remaining four sites began one month later. For each patient in the study, a range of detailed utilisation data was obtained. Specific proformas were developed to collect details on nursing time, medical time, diagnostic services (pathology and imaging) and therapeutic services (theatre, pharmaceuticals, allied health). Additional information on diagnosis, procedures and morbidity was obtained from the hospitals' information systems.

The utilisation data were used to determine a cost per inpatient episode. The costed patient data were grouped according to AN-DRG-3 to produce a cost per AN-DRG for the two populations.

Traditional costing studies, which use cost information extracted from the hospital's general ledger and allocated to DRG classes, would not have provided costing information to the required level. Therefore, we used national unit prices to complete the cost allocation process (Box 1). This also overcame the lack of sophistication of many of the hospitals' cost reporting, and avoided the need to make accrual adjustments to hospitals' general ledgers for the three-month period. The national unit prices were based on national and State labour force data, and recently completed national casemix costing and service weight studies and analyses undertaken to generate AN-DRG-3 cost weights.³ This approach also removed idiosyncratic local cost variations and enhanced the reliability of the results. Thus, for the purposes of our study, costs such as those for a unit of nursing time, and individual radiology and pathology tests, were the same for all hospitals.

Patients were classified according to AN-DRG-3. To ensure satisfactory coding standards, a random audit of medical records was undertaken in each hospital before the commencement of data collection.

Interim results were compiled and presented at a workshop in Alice Springs in April 1996. Attendees included health service providers from the study hospitals and State Health Departments, as well as representatives from consumer groups, such as the National Aboriginal Community Controlled Health Organisation (NAACHO) and the Office of Aboriginal and Torres Strait Islanders (OATSI). As a result of this meeting the data were further refined, allowing for more clinically accurate and culturally appropriate interpretation. A final report was presented to the Commonwealth Department of Health and Family Services in April 1997.⁴

Ethical approval
Participating hospitals were required to consider the ethical implications of the research project, and, in particular, issues of confidentiality. The hospital data and the study report were not to include any information identifying individual patients or communities. At the conclusion of the study, hospitals were provided with their own data in addition to that of the total cohort. No hospital had access to another hospital's data unless by private arrangement.

Statistical analysis
Collation of data was facilitated by a specially designed application using dBase as the programming tool. SPSS (SPSS Inc, Chicago, Illinois, USA) and standard spreadsheet packages were used for the analyses, which were based on t tests, as comparisons were between two populations with large sample sizes. For both populations, only those AN-DRGs with a sample size exceeding 20 separations were analysed.

Results The study collected clinical and demographic data on 31 222 inpatient episodes and utilisation data relating to 128 813 occupied bed-days. These data were trimmed to remove incomplete episodes during the study period. A total of 27 768 separations were analysed in detail (Box 2).

It was not possible to standardise the data by sex and age for the total study population, as population data for the hospitals' catchment areas were not available. Standardised data for the Northern Territory (not presented here) revealed higher admission rates for male and female ATSI patients compared with non-ATSI patients in all age groups.

While ATSI separations represented 33.9% of the total cohort they represented 66% (115/174) of patients and 67.6% (3157/4673) of separations of those assigned AN-DRG 572, Admit for renal dialysis. Because of the impact this caseload would have had on cost analysis (eg, one population would have a disproportionate number of "day-only" admissions), data relating to dialysis were excluded from relevant sections of the analysis. The average length of stay in the ATSI population was two days shorter when AN-DRG 572 was included in the analysis, but in the non-ATSI cohort it was only 0.3 days shorter.

Differences in DRGs
The ATSI population was distributed across 426 of a possible 667 (64%) DRGs. In contrast, the non-ATSI population was distributed across 547 DRGs (82%). Box 3 highlights the consistently longer average length of stay of ATSI patients, as well as a significant variation in relative frequency of admissions. For example, DRGs for gastroenteritis and respiratory infections contain more ATSI patients, despite there being twice as many non-ATSI patients in the cohort. In contrast, DRGs for gastroscopy and colonoscopy have a higher proportion of non-ATSI patients. Dental extractions and restorations recorded low separation rates in ATSI patients. The following DRGs were not encountered in ATSI patients during the data collection period: Other major joint and limb reattachment procedures without comorbidities and complications; Major shoulder or elbow procedures, age < 60; and Hip and femur procedures except major joint, age > 54 without comorbidities and complications.

Boxes 4 and 5 show the top 20 DRGs by volume for ATSI and non-ATSI patients, respectively. Of note is the prevalence of infectious diseases in the ATSI population compared with the non-ATSI population, whereas the non-ATSI population has a high prevalence of degenerative diseases and DRGs related to neoplastic conditions.

Cost differences
The unadjusted average cost of an ATSI inpatient episode was $1627 compared with $1545 for non-ATSI inpatient episodes (this difference was not significant). The casemix-adjusted costs, however, showed significant differences (P < 0.001) per episode at $1856 and $1558 for ATSI and non-ATSI patients, respectively (Box 6).

Box 7 shows the breakdown of total and average costs and confirms that the cost differential is a result of increased utilisation of most services. Theatre and pathology services are the only areas where costs are higher for non-ATSI patients. Further analysis of the data showed that operating room expenses were higher for ATSI patients. However, the average cost is lower because a significantly smaller number of ATSI patients had operations.

The data also confirm that ATSI patients have longer lengths of stay and higher costs in most Major Diagnostic Categories (MDCs) (Box 8). An unexpected observation was the shorter length of stay and cost for this population in MDC 19 (Mental Diseases and Disorders), and MDC 20 (Alcohol/Drug Use and Alcohol/Drug Induced Organic Mental Disorders).

Discussion The study confirmed the clinical perception that caring for ATSI inpatients consumed greater resources for the same DRG than caring for non-ATSI inpatients, and demonstrated a 39% overall differential cost. For some DRGs (eg, those including paediatric infectious diseases) the increase in resource consumption was considerable in ATSI patients. In MDCs 19 and 20, non-ATSI patients used slightly more resources.

The greater costs in ATSI patients are believed to be related to disease severity on admission as well as comorbidities and complicating factors. Data obtained during the project support this.

Other studies have also found that resource utilisation for ATSI patients is lower for mental disorders.⁵ Easier reintegration of ATSI patients into their community may facilitate shorter lengths of stay. Social networks and supports may also favour outpatient psychiatric care. The same may be true for DRGs associated with alcohol abuse, although given the known prevalence and impact of substance abuse in ATSI patients, we may also be identifying a need for further review of the models of healthcare delivery to ATSI patients.

Actual needs were not addressed by our study. We only measured the current state of healthcare provision, which is largely a result of historical funding arrangements. However, many clinicians would argue that current health services for underprivileged groups are inadequate.

This is the first study to quantify differential resource consumption between two Australian populations. It highlights the need to recognise potentially confounding factors when a casemix classification funding system is implemented. The Northern Territory, South Australia and New South Wales have recognised the disparity and incorporated funding adjustments for ATSI patients.

As with ATSI patients in remote and rural hospitals, other socially disadvantaged groups including Aboriginals in urban settings and immigrant subpopulations may also have a cost and utilisation profile different from the "typical" Australian population. Hospitals caring for a significant proportion of such patients may equally need recognition for their "atypical" population. Appropriate funding of such hospitals can be either through funding adjustments or by an improved classification system.

Future versions of AN-DRGs are likely to make greater use of complicating clinical factors (CCFs), which could include indicators of social disadvantage. Notwithstanding these efforts, hospitals caring for atypical populations remain vulnerable because their relatively small number of patients lack statistical importance when national figures are reviewed.

One of the great challenges of casemix implementation is to provide the basis by which hospitals can be funded appropriately for appropriate care. If this challenge is not met it is the sickest patients from the most disadvantaged subpopulations who will suffer. The Aboriginal and Torres Strait Islander Casemix Study has demonstrated a genuine risk in this regard.

Acknowledgements This study was funded by the Commonwealth Department of Health and Family Services and sponsored by the Australian Casemix Clinical Committee and received constant support from all members and the then Chair, Professor John Hickie. We would like to acknowledge the cooperation of staff at the study hospitals and State and Territory Health Departments and the assistance of the University of Adelaide Statistics Department. Countless individuals were also major contributors to the study, including Art Huston and Jenni Bowen (Brewerton and Associates) and Alan Browne and Josie Lanza (Commonwealth Department of Health and Family Services). The study also owes its success to the other members of the Steering Committee, Dr Mark Salmon, Mr Peter Woodley, Ms Marian Kickett, Mr Garnett Brady and Dr Chris Wagner. References

1. Plant AJ, Condon JR, Durling G. Northern Territory health outcomes, morbidity and mortality 1979-1991. Darwin: Northern Territory Department of Health and Community Services, 1995.
2. Harkin K. Incremental resource consumption by Aboriginal inpatients: a research project conducted at Alice Springs Hospital from 1 October to 31 May,1992. Report to the Department of Human Services and Health. Darwin: NT Dept of Health and Community Services, 1994.
3. Casemix Development Program. Report on the development of AN-DRG Version 3 Cost weights. Canberra: Commonwealth Department of Human Services and Health, 1995.
4. Commonwealth Department of Health and Family Services. Report on National Aboriginal and Torres Strait Islander Casemix Study. Adelaide: Brewerton and Associates Pty Ltd, April 1997.
5. Jablensky A. The epidemiology of schizophrenia. Curr Opin Psych 1993; 6: 43-52.

Authors' details Royal Darwin Hospital, Darwin, NT.
Dale A Fisher,* FRACP, DTM&H, Physician and Senior Lecturer.

Classification and Payments Branch, Department of Health and Family Services, Canberra, ACT.
Jo M Murray,* BSc(Med), Acting Assistant Secretary.

Princess Alexandra Hospital, Brisbane, QLD.
Michael I Cleary,* FACEM, MHA, Executive Director of Medical Services.

Brewerton and Associates, Adelaide, SA.
Rita E Brewerton, BSc(MaSc)Hons, Director.

Reprints will not be available from the authors.
Correspondence: Dr D A Fisher, Royal Darwin Hospital, PO Box 41326, Casuarina, NT 0811.
E-mail: dale.fisherATnt.gov.au

*Steering Committee members (other members are listed in the Acknowledgements above).