Hamid Moayyed1, Bridget Kelly2, Xiaoqi Feng1,2,3, Victoria Flood4,5. 1. School of Health and Society, Faculty of Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia. 2. Early Start Research Institute, Faculty of Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia. 3. Menzies Centre for Health Policy, University of Sydney, Sydney, New South Wales, Australia. 4. Faculty of Health Sciences, University of Sydney, Sydney, New South Wales, Australia. 5. St Vincent's Hospital Research Committee, Sydney, New South Wales, Australia.
Abstract
AIM: To obtain expert consensus to develop and evaluate a rating system on the relative healthiness of Australian suburbs' food outlet types. METHODS: Twenty-four food outlet types and 10 local suburbs were identified from previous mapping studies and based on a scan of suburbs across one large Australian geographical region. Initial food outlet 'scores' for relative healthiness were proposed based on available literature, classified into five categories, from 'most' to 'least' healthy. In round 1 of a modified Delphi survey, participants, Australian public health and nutrition experts, were given each outlet type's definition and the proposed scores and invited to modify the scores based on their perceived 'healthiness'. In round 2, participants were able to revise or adjust their scores. RESULTS: Median scores for food outlet types from both rounds highly correlated with the originally proposed scores (two-tailed Pearson's correlation coefficient 0.97 and 0.96, respectively, P = 0.01), and scores from round 1 highly correlated with those from round 2 (Pearson's coefficient 0.998, P = 0.01). Round 2 scores were used to calculate suburbs' overall food environment score, healthiness score, unhealthiness score and a ratio of unhealthiness to healthiness scores. There was strong positive correlation between suburbs' ratio of unhealthiness to healthiness scores and a previously recognised scoring ratio, Retail Food Environment Index (Spearman's rho 0.847, P < 0.01). CONCLUSIONS: The study generated experts' consensus about relative healthiness of food outlet types found in Australian neighbourhoods. Proposed scores can be used to assess and compare healthiness of community food environments and to explore their associations with area characteristics, population's diet and health outcomes.
AIM: To obtain expert consensus to develop and evaluate a rating system on the relative healthiness of Australian suburbs' food outlet types. METHODS: Twenty-four food outlet types and 10 local suburbs were identified from previous mapping studies and based on a scan of suburbs across one large Australian geographical region. Initial food outlet 'scores' for relative healthiness were proposed based on available literature, classified into five categories, from 'most' to 'least' healthy. In round 1 of a modified Delphi survey, participants, Australian public health and nutrition experts, were given each outlet type's definition and the proposed scores and invited to modify the scores based on their perceived 'healthiness'. In round 2, participants were able to revise or adjust their scores. RESULTS: Median scores for food outlet types from both rounds highly correlated with the originally proposed scores (two-tailed Pearson's correlation coefficient 0.97 and 0.96, respectively, P = 0.01), and scores from round 1 highly correlated with those from round 2 (Pearson's coefficient 0.998, P = 0.01). Round 2 scores were used to calculate suburbs' overall food environment score, healthiness score, unhealthiness score and a ratio of unhealthiness to healthiness scores. There was strong positive correlation between suburbs' ratio of unhealthiness to healthiness scores and a previously recognised scoring ratio, Retail Food Environment Index (Spearman's rho 0.847, P < 0.01). CONCLUSIONS: The study generated experts' consensus about relative healthiness of food outlet types found in Australian neighbourhoods. Proposed scores can be used to assess and compare healthiness of community food environments and to explore their associations with area characteristics, population's diet and health outcomes.
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