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Challenges in researching CVD inequalities between Aboriginal and non-Aboriginal Australians - examples of trusted approaches that do not always work

Session Poster Session III - Friday 08:30 - 12:30

Speaker Associate Professor Judith Katzenellenbogen

Event : ESC Preventive Cardiology (Formerly EuroPrevent) 2015

  • Topic : preventive cardiology
  • Sub-topic : Lipids
  • Session type : Poster Session

Authors : JM Katzenellenbogen (Perth,AU), D Derrick Lopez (Perth,AU), P Bradshaw (Perth,AU), FM Sanfilippo (Perth,AU), THK Teng (Perth,AU), MW Knuiman (Perth,AU), MST Hobbs (Perth,AU), SC Thompson (Perth,AU)

JM Katzenellenbogen1 , D Derrick Lopez1 , P Bradshaw1 , FM Sanfilippo2 , THK Teng1 , MW Knuiman2 , MST Hobbs2 , SC Thompson1 , 1The University of Western Australia, Western Australian Centre for Rural Health - Perth - Australia , 2The University of Western Australia, School of Population Health - Perth - Australia ,


Purpose:  Using 3 separate studies, we illustrate how age-standardisation, Global Registry of Acute Coronary Events (GRACE) risk scores and multiple regression were unable to adequately elucidate inequalities in  cardiac disease and care between Aboriginal and non-Aboriginal Western Australians.

Methods:  Study 1, a cohort incidence study, used linked hospital admissions and death data to determine disparities in age-standardised incidence of myocardial infarction (MI) between the two populations aged 25-74. Study 2 assessed the performance of the GRACE risk score for predicting mortality in Aboriginal acute coronary syndrome (ACS) cases using clinical data from medical notes and linked mortality records. In Study 3, linked data and multiple regression modelling were used to control for demographic, co-morbidity and admission factors, to investigate disparities in transfers to urban hospitals for rural Aboriginal MI patients. The impact of these factors on rate ratios was assessed through progressive adjustment of covariates in regression models (Table).

Results: Study 1: The age-standardised Aboriginal to non-Aboriginal incidence rate ratio for MI (25-74 years) was 4.4, yet the rate ratio was 16.4 for 25-4 years, reducing with age to 2.7 for 65-74 years. 
Study 2: The mean GRACE scores and crude mortality were lower for Aboriginal ACS patients, with evidence of under-estimation of the risk of death to 6 months from hospital discharge. 
Study 3: see Table.

Conclusions: Age-standardisation diluted the inequality in MI incidence. The dominance of age in GRACE models means that risk may be underestimated given the younger age of Aboriginal cases. Adjustment through progressive regression models explains disparities in access to services, leading to interpretations of ‘no difference’ when the inequalities lie in the dissimilar distributions of risk factors. Researchers should use statistical tools critically when studying inequalities in cardiac health and care between main and disadvantaged populations.

Model 1: age group + sex RR= 0.88 (0.83-0.94) P=<0.001
Model 2: Model 1 + residential area, SES, type of MI RR= 0.89 (0.83-0.95) P=0.001
Model 3: Model 2 + 5-year history of diabetes, heart failure, COPD** or CKD*** RR= 0.96 (0.90-1.03) P =0.256
Model 4: Model 3 + private health insurance RR= 0.99 (0.93-1.06) P=0.804
*Adjusted Aboriginal to non-Aboriginal risk ratios **chronic obstructive pulmonary disease *** chronic kidney disease

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