External validation of the Health Care Homes hospital admission risk stratification tool in the Aboriginal Australian population of the Northern Territory
Laura Goddard
A B , Emma Field B , Judy Moran C , Julie Franzon A , Yuejen Zhao C and Paul Burgess C *
A Northern Territory Primary Health Network, Darwin, NT, Australia.
B National Centre for Epidemiology and Population Health, Australia National University, Canberra, ACT, Australia.
C Health Statistics and Informatics, Northern Territory Department of Health, Darwin, NT, Australia.
Australian Health Review 47(5) 521-534 https://doi.org/10.1071/AH23017
Submitted: 10 April 2022 Accepted: 7 August 2023 Published: 12 September 2023
Abstract
This study aimed to externally validate the Commonwealth’s Health Care Homes (HCH) algorithm for Aboriginal Australians living in the Northern Territory (NT).
A retrospective cohort study design using linked primary health care (PHC) and hospital data was used to analyse the performance of the HCH algorithm in predicting the risk of hospitalisation for the NT study population. The study population consisted of Aboriginal Australians residing in the NT who have visited a PHC clinic at one of the 54 NT Government clinics at least once between 1 January 2013 and 31 December 2017. Predictors of hospitalisation included demographics, patient observations, medications, diagnoses, pathology results and previous hospitalisation.
There were a total of 3256 (28.5%) emergency attendances or preventable hospitalisations during the study period. The HCH algorithm had an area under the receiver operating characteristic curve (AUC) of 0.58 for the NT remote Aboriginal population, compared with 0.66 in the Victorian cohort. A refitted model including ‘previous hospitalisation’ had an AUC of 0.72, demonstrating better discrimination than the HCH algorithm. Calibration was also improved in the refitted model, with an intercept of 0.00 and a slope of 1.00, compared with an intercept of 1.29 and a slope of 0.55 in the HCH algorithm.
The HCH algorithm performed poorly on the NT cohort compared with the Victorian cohort, due to differences in population demographics and burden of disease. A population-specific hospitalisation risk algorithm is required for the NT.
Keywords: chronic disease management, external validation, health policy, indigenous health, performance and evaluation, predictive risk model, primary health care, sensitivity analysis.
References
2 Australian Bureau of Statistics. Estimated resident Aboriginal and Torres Strait Islander and Non-Indigenous population, States and Territories, Remoteness Areas - 30 June 2016. Canberra: Australian Bureau of Statistics; 2016. Available at https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3238.0.55.001June%202016?OpenDocument
3 Vos T, Barker B, Begg S, et al. Burden of disease and injury in Aboriginal and Torres Strait Islander Peoples: the Indigenous health gap. Int J Epidemiol 2009; 38(2): 470-477.
| Crossref | Google Scholar |
5 Zhao Y, You J, Wright J, et al. Health inequity in the Northern Territory, Australia. Int J Equity Health 2013; 12(1): 79-87.
| Crossref | Google Scholar |
6 Griffiths K, Coleman C, Lee V, et al. How colonisation determines social justice and Indigenous health: a review of the literature. J Popul Res 2016; 33(1): 9-30.
| Crossref | Google Scholar |
7 Zhao Y, Thomas SL, Guthridge SL, et al. Better health outcomes at lower costs: the benefits of primary care utilisation for chronic disease management in remote Indigenous communities in Australia’s Northern Territory. BMC Health Serv Res 2014; 14(1): 463-472.
| Crossref | Google Scholar |
8 Gador-Whyte AP, Wakerman J, Campbell D, et al. Cost of best-practice primary care management of chronic disease in a remote Aboriginal community. Med J Aust 2014; 200(11): 663-666.
| Crossref | Google Scholar |
9 Wakerman J, Sparrow L, Thomas SL, et al. Equitable resourcing of primary health care in remote communities in Australia’s Northern Territory: a pilot study. BMC Fam Pract 2017; 18: 75.
| Crossref | Google Scholar |
10 Zhao Y, Wright J, Guthridge S, et al. The relationship between number of primary health care visits and hospitalisations: evidence from linked clinic and hospital data for remote Indigenous Australians. BMC Health Serv Res 2013; 13: 466.
| Crossref | Google Scholar |
11 Thomas SL, Zhao Y, Guthridge SL, et al. The cost-effectiveness of primary care for Indigenous Australians with diabetes living in remote Northern Territory communities. Med J Aust 2014; 200(11): 658-662.
| Crossref | Google Scholar |
12 Thomas SL, Wakerman J, Humphreys JS. Ensuring equity of access to primary health care in rural and remote Australia – what core services should be locally available? Int J Equity Health 2015; 14(1): 111-119.
| Crossref | Google Scholar |
13 Ahn E, Kim J, Rahman K, et al. Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia. BMJ Open 2018; 8(9): e021323.
| Crossref | Google Scholar |
14 Hippisley-Cox J, Coupland C. Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions score. BMJ Open 2013; 3: e003482.
| Crossref | Google Scholar |
15 Khanna S, Rolls DA, Boyle J, et al. A risk stratification tool for hospitalisation in Australia using primary care data. Sci Rep 2019; 9(1): 5011.
| Crossref | Google Scholar |
16 Walter M, Kukutai T. Artificial Intelligence and Indigenous Data Sovereignty. Input paper for the Horizon Scanning Project “The Effective and Ethical Development of Artificial Intelligence: An Opportunity to Improve Our Wellbeing” on behalf of the Australian Council of Learned Academies. 2018. Available at https://acola.org/wp-content/uploads/2019/07/acola-ai-input-paper_indigenous-data-sovereignty_walter-kukutai.pdf
17 Australian Institute of Health and Welfare (AIHW). National Healthcare Agreement: PI 18–Selected potentially preventable hospitalisations, 2019. Canberra: Australian Institute of Health and Welfare; 2019. Available at https://meteor.aihw.gov.au/content/index.phtml/itemId/698904 [accessed 9 March 2020].
18 National Centre for Classification in Health. International statistical classification of diseases and related health problems, 10th revision, Australian modification (ICD-10-AM). Sydney: National Centre for Classification in Health, University of Sydney; 2019. Available at https://icd.who.int/browse10/2019/en [accessed 15 June 2020].
19 Wallace E, Stuart E, Vaughan N, et al. Risk Prediction models to predict emergency hospital admission in community-dwelling adults: A systematic review. Med Care 2014; 52(8): 751-765.
| Crossref | Google Scholar |
20 Van Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med 2019; 17(1): 230.
| Crossref | Google Scholar |
22 Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015; 162(1): 55-63.
| Crossref | Google Scholar |
24 Davy C, Bleasel J, Liu H, et al. Effectiveness of chronic care models: opportunities for improving healthcare practice and health outcomes: a systematic review. BMC Health Serv Res 2015; 15(1): 194.
| Crossref | Google Scholar |
25 Howard R, Sanders R, Lydall-Smith SM. The implementation of Restoring Health: a chronic disease model of care to decrease acute health care utilization. Chron Respir Dis 2008; 5(3): 133-141.
| Crossref | Google Scholar |
26 Sack C, Phan VA, Grafton R, et al. A chronic care model significantly decreases costs and healthcare utilisation in patients with inflammatory bowel disease. J Crohns Colitis 2012; 6(3): 302-310.
| Crossref | Google Scholar |
