A comparison of dietary estimates from the National Aboriginal and Torres Strait Islander Health Survey to food and beverage purchase data

Emma McMahon, Thomas Wycherley, Kerin O'Dea, Julie Brimblecombe

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12 Citations (Scopus)


Objective: We compared self-reported dietary intake from the very remote sample of the National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (VR-NATSINPAS; n=1,363) to one year of food and beverage purchases from 20 very remote Indigenous Australian communities (servicing ∼8,500 individuals). Methods: Differences in food (% energy from food groups) and nutrients were analysed using t-test with unequal variance. Results: Per-capita energy estimates were not significantly different between the surveys (899 MJ/person/day [95% confidence interval −152,1950] p=0.094). Self-reported intakes of sugar, cereal products/dishes, beverages, fats/oils, milk products/dishes and confectionery were significantly lower than that purchased, while intakes of meat, vegetables, cereal-based dishes, fish, fruit and eggs were significantly higher (p<0.05). Conclusion: Differences between methods are consistent with differential reporting bias seen in self-reported dietary data. Implications for public health: The NATSINPAS provides valuable, much-needed information about dietary intake; however, self-reported data is prone to energy under-reporting and reporting bias. Purchase data can be used to track population-level food and nutrient availability in this population longitudinally; however, further evidence is needed on approaches to estimate wastage and foods sourced outside the store. There is potential for these data to complement each other to inform nutrition policies and programs in this population.

Original languageEnglish
Pages (from-to)598-603
Number of pages6
JournalAustralian and New Zealand Journal of Public Health
Issue number6
Publication statusPublished - 1 Dec 2017
Externally publishedYes


  • 24-hour recall
  • dietary assessment
  • Indigenous Australians
  • population nutrition
  • store sales data

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