External validation of the Health Care Homes hospital admission risk stratification tool in the Aboriginal Australian population of the Northern Territory

Aust Health Rev. 2023 Oct;47(5):521-534. doi: 10.1071/AH23017.

Abstract

Objective This study aimed to externally validate the Commonwealth's Health Care Homes (HCH) algorithm for Aboriginal Australians living in the Northern Territory (NT). Methods 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. Results 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. Conclusion 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.

Publication types

  • Validation Study

MeSH terms

  • Australian Aboriginal and Torres Strait Islander Peoples*
  • Delivery of Health Care
  • Hospitalization*
  • Hospitals
  • Humans
  • Northern Territory / epidemiology
  • Retrospective Studies
  • Risk Assessment