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Table 1 Key model input parameters

From: Better transport accessibility, better health: a health economic impact assessment study for Melbourne, Australia

Parameters Data source and assumptions
Mean number of PT trips per day, by sex, age group and SNAMUTS category Sampled from a lognormal distribution [13]
(Additional file 2)
Mean distance (km), combined PT access/egress by sex and SNAMUTS category Sampled from a lognormal distribution [13]
(Additional file 2)
Mean distance trips by private motor vehicle (km) by sex and SNAMUTS category Sampled from a lognormal distribution [13]
(Additional file 2)
Marginal MET walking from house to car or bus, from car or bus to go places, from car or bus to and from worksite Sampled from a lognormal distribution, MET value of 2.5 (walking from house to car or bus, from car or bus to and from worksite) with standard deviation 0.75. Sensitivity analysis MET value 4 (walking to work or class), standard deviation 1.6. Values adjusted for inactivity [16], marginal MET 1.5 (sensitivity analysis marginal MET 3).
Total population estimates (population numbers, mortality rates, BMI distribution, PA levels) Australian Bureau of Statistics [25, 34]
Disease epidemiology, disability weights Salomon et al. 2012 [35]
Relative risks, total years of life lived with disability Relative risk uncertainty SE(logRR), Institute for Health Metrics and Evaluation [24], Murray et al. 2013 [36]
Relative risks of PA-related diseases by risk categories Relative risk uncertainty SE(logRR), Danaei et al. 2009 [37]
Disease healthcare costs Australian Institute of Health and Welfare [26]
Health Price Index Australian Institute of Health and Welfare [27]
Transport-related mortality Australian Road Deaths Database [38]
Transport-related serious injury Henley et al. 2012 [39]
  1. Table notes: BMI body mass index, Km kilometres, MET metabolic equivalent task, PA physical activity, PT public transport, RR relative risk, SE standard error, SNAMUTS Spatial Network Analysis for MultiModal Urban Transport Systems