Saving lives and money in the suburbs - PowerPoint Presentation

Saving lives and money in the suburbsWhat are the health and health economic impacts of active transport and car use in Melbourne? Margaret Beavis MBBS FRACGP MPH

Background-Physical activityPhysical inactivity (PI) accounts for 9% of premature mortality worldwide- equivalent to global deaths from tobacco (Lee et al. 2012). In Australia only 33% of men and 29% of women reach Australian recommendations of 30 minutes daily of moderate to vigorous exercise (at least 150 minutes per week) (Australian Bureau of Statistics 2011a). Ischaemic heart disease and stroke were the two leading causes of death in Australia in 2009 (Australian Bureau of Statistics 2011b ).

Physical Activity- a public health best buy? (WHO 2011)Ischaemic Heart Disease 20%Stroke 25-30% Type 2 Diabetes 30% Bowel Cancer 30% Breast Cancer 20% Depression 20-30% Dementia 20-30%Hip fracture/falls 30-36%US Physical Activity Guidelines Advisory Committee 2008 Risk Reduction

But wait- there’s moreHelps prevent overweight and obesityReduces blood pressureImproves cholesterol levels Prevents kidney disease

And more….Improves mood, job satisfaction and reduces absenteeismIs as effective as standard antidepressants in treating mild/moderate depressionReduces risk of premature death 20-25%

Works for people who are over weight or obese, and for the elderlyHas other benefits- increased community connectedness,lower emissions, less traffic, saves money (transport and health)

Why Physical Activity Matters Time spent on Physical Activity and All-Cause Mortality risk reduction (USPAGC2008).

BackgroundMelbourne largest population growth of all Australian capital cities every year for the last ten yearsmost on the outer suburban fringes (Australian Bureau of Statistics 2011). PT infrastructure has not kept up with new developments, resulting in many households being largely car dependent (Thom 2011).

Walking and cycling for transport (Loader 2010)

Type 2 Diabetes CASEY 2001

Type 2 Diabetes CASEY 2011

Aims:To assess travel related physical activity: -by mode of transport -by residential location To establish the factors associated with gaining 30 minutes daily of physical activity from transport To estimate the health and economic benefits of proposed changes in transport use and residential location.

MethodologyPA and Active Transport literature reviewVISTA07-08 Travel Data29,840 people in Melbourne recorded their travel for one day spread over a year May 2007 to June 2008 The VicHealth Deakin Health Economics Model Australian population, 2008 reference year Very detailed modelling drawing on large burden of disease study (Begg et al. 2007) and many government and non government data sets Assessed PA 30 minutes daily (Min 150 min/ wk ) (Cadilhac et al. 2009)

Mode of transport Average Active Transport Time (any walking or cycling) Survey Data (minutes) All of Melbourne Weighted (minutes) Private Vehicle User 10.3 10.0 Public Transport User (average) 36.6 35.2 PTU no vehicle use 40.1 41.0 PTU with vehicle use 31.7 30.7 Walker/Cyclist (no vehicle/PT use) 36.9 38.3 VISTA07-08

Mode of transport Adequate Physical Activity- at least 30 minutes/day Mode of Transport Percentage of Individuals Achieving Adequate Physical Activity Vehicle Transport User (n=20,631) 12.6% (n= 2,602) Public Transport User (n= 3,059) 60.3% (n=1,846) Walker (n=3,540) 58.3% (n=3,875) Cyclist (n= 550) 80.2% (n=441) Total Travelling on Survey day (n=23,522) VISTA07-08 Note: numbers travelling add up to more than 23,522 as many people used more than one mode.

Urban Sub-Region and Adequate Physical Activity (Adequate PA - at least 30 minutes) Sub-Region* Percentage of Individuals Achieving Adequate Physical Activity (at least 30 minutes/day) Inner City (n=1,267) 33.3% (n=494) Inner Suburban (n=3,726) 23.5% (n=1,063) Middle Suburban (n=9,247) 15.4% (n=1,827) Outer Suburban (n= 9,284) 9.5% (n=1,135) Total Travelling on Survey day (n=23,522) *Subregions used by VISTA07-08, created with ABS statistical local area boundaries and distance from the centre of Melbourne

Socio-demographic Correlates of Adequate Physical Activity from Transport VISTA 07-08 Logistic Regression Multi-Level Nested OR 95% c.i SEX a Female 0.98 0.87-1.11 AGE b 5-14 0.95 0.38-2.34 15-29 1.35 0.51-3.57 30-44 1.32 0.49-3.57 45-64 1.13 0.42-3.04 65-79 1.06 0.37-3.00 80+ ^ 0.34 0.11-1.07 MAIN ACTIVITY c Part-time Work 1.07 0.86-1.32 Casual Work 1.33 0.98-1.81 Primary School 0.79 0.47-1.33 Secondary School* 3.18 2.16-4.69 Full-time TAFE/Uni* 2.34 1.69-3.24 Part-time TAFE/Uni* 1.90 1.17-3.08 Other Education 1.49 0.65-3.38 Not Yet at School 0.63 0.24-1.70 Keeping House 0.91 0.58-1.42 Unemployed 0.69 0.40-1.19 Retired 0.98 0.62-1.54 Other 1.17 0.55-2.49

HOMESUBREGION d Inner Suburban* 0.60 0.45-0.80 Middle Suburban* 0.25 0.19-0.32 Outer Suburban* 0.15 0.12-0.20 INCOME e $526-$1000 1.24 0.93-1.65 $1001-$1550^ 1.19 0.89-1.59 $1551-$2475^ 1.35 1.01-1.80 >$2475* 1.63 1.23-2.17 OCCUPATION f Manager 0.93 0.61-1.43 Professional^ 1.31 0.92-1.88 Technical Tradesman 0.73 0.49-1.10 Community Personal 1.01 0.69-1.47 Clerical Administration^ 1.27 0.88-1.84 Sales* 0.57 0.39-0.82 Drivers* 0.40 0.23-0.71 Labourers* 0.63 0.40-0.99 ACCESS TO VEHICLE g * 0.230.17-0.31RUNNING COSTS PAID BY WORK h *0.630.50-0.79 Socio-demographic variables from original data (n=29,840).Travel Times weighted for non reporting and include double vehicle walk time. c.i. = Confidence Intervalsareference category = Men, breference category = 0-4yo, creference category = Full Time Work, dreference category = Inner City , ereference category = $0-525, f reference category = Not in Workforce, g reference category =No Vehicle Access, h reference category = Privately paid vehicle costsNote: “Running Costs Paid by Work” were analysed omitting the 1200 respondents did not have vehicle access

Economic ModellingThe health and economic benefits of reducing disease risk factors (Cadilhac et al 2009)By transport mode By Urban subregion- Inner city, Inner suburban Middle suburban and Outer suburban

Economic modelling: Limitationsmodelling is limited to prevention of only 5 diseases in the 2008 population, when we know PA helps many more diseases and also can be used for treatment. Productivity is measured using the Friction Cost Approach (from a societal perspective, assuming workers are replaced in 3 months) again making estimates conservative. Modelled changes in transport mode and urban location of new developments are not consistent with current infrastructure in Melbourne.

Change in Transport Mode HEALTH OUTCOMES Where APA levels associated with Private Vehicle Users (VTU) is baseline. Outcomes per annum for Melbourne’s population of 3.9 Million in 2008. Mortality Disease Incidence DALY’s Leisure Time lost (days- ‘000s) (95% confidence interval) Loss of Home Based Production (days-‘000s) (95% confidence interval) 10% VTU change to Public Transport =3.2% increase in population APA 86 286 1,114 14.1 (13.4 -14.8) 8.1 (7.0-9.2) 10% VTU change to Walking =3.3% increase in population APA 89 295 1,148 14.6 (13.9 –15.3) 8.3 (7.2-8.2) 10% VTU change to Cycling =4.7% increase in population APA 127 421 1,635 20.7 (19.7-21.8) 11.9 (10.2-13.5) 20% VTU change to Public Transport =6.6% increase in population APA 178 591 2,297 29.1 (27.7-30.6) 16.7 (14.3-19.0) Note: Disabilty Adjusted Life Years (DALYs), incidence of disease and mortality were calculated for all age groups. Leisure and home based production were calculated for persons aged 15+ years. Confidence intervals were derived from VicHealth data (who used survey standard errors, survey proportions or data from the literature).They are narrower than if calculated using the smaller population in this analysis.

Financial Outcomes Of Change of Transport Mode Friction Cost Approach for valuing workforce production gains/(losses). This was applied to the lifetime of Melbourne’s 2008 adult population. Production Gains/ (Losses) ($’000) (95% confidence interval) Leisure Based Production ($’000) (95% confidence interval) Home Based Production ($‘000) (95% confidence interval) Health Sector Costs ($‘000) 10% VTU change to Public Transport =3.2% increase in population APA 528 (293-828) 3,553 (2,674-4,618) 3,195 (2,722-3,668) 3,917 10% VTU change to Walking =3.3% increase in population APA 541 (301-848) 3,663 (2,757-4,761) 3,295 (2,807-3,783) 4,040 10% VTU change to Cycling =4.7% increase in population APA 770 (428-1207) 5,217 (3,927-6,780) 4,693 (3998-5,388) 5,753 20% VTU change to Public Transport =6.6% increase in population APA 1,083 (602 – 1,698) 7,326 (5,514-9,521) 6,591 (5,616-7,566) 8,079

Financial Outcomes Of Change of Transport Mode Friction Cost Approach for valuing workforce production gains/(losses). This was applied to the lifetime of Melbourne’s 2008 adult population. Production Gains/ (Losses) ($’000) (95% confidence interval) Leisure Based Production ($’000) (95% confidence interval) Home Based Production ($‘000) (95% confidence interval) Health Sector Costs ($‘000) 10% VTU change to Public Transport =3.2% increase in population APA 528 (293-828) 3,553 (2,674-4,618) 3,195 (2,722-3,668) 3,917 10% VTU change to Walking =3.3% increase in population APA 541 (301-848) 3,663 (2,757-4,761) 3,295 (2,807-3,783) 4,040 10% VTU change to Cycling =4.7% increase in population APA 770 (428-1207) 5,217 (3,927-6,780) 4,693 (3998-5,388) 5,753 20% VTU change to Public Transport =6.6% increase in population APA 1,083 (602 – 1,698) 7,326 (5,514-9,521) 6,591 (5,616-7,566) 8,079

B) Suburban Region Travel Patterns HEALTH OUTCOMES Travel patterns in outer suburban areas are the baseline case Outcomes per annum for Outer Sub-region population Mortality Disease Incidence DALY’s Leisure Time lost (days- ‘000s) (95% confidence interval) Loss of Home Based Production (days-‘000s) (95% confidence interval) 3% increase in APA 34 114 442 5.6 (5.3 – 5.9) 3.2 (2.8 -3.7) Middle Suburban =5.9% increase in APA 67 224 870 11.0 (10.5 – 11.6) 6.3 (5.2 – 7.2) Inner Suburban =14 % increase in APA 160 531 2,065 26.2 (24.9 - 27.5) 15.0 (12.9 – 29.0) Inner City =23.8% increase in APA 272 903 3,511 44.6 (42.4 – 46.8) 25.5 (21.9 – 29.0)

FINANCIAL OUTCOMES Travel patterns in outer suburban areas are the baseline case Friction Cost Approach for valuing workforce production gains /(losses). This was applied to the lifetime of the 2008 adult population living in Melbourne’s outer suburban areas Production Gains/ (Losses) ($’000) (95% confidence interval) Leisure Based Production ($’000) (95% confidence interval) Home Based Production ($‘000) (95% confidence interval) Health Sector Costs ($‘000) 3% increase in APA $ 209 $1,412 $1,270 $1,543 Middle Suburban =5.9% increase in APA $ 413 $2,776 $2,497 $3,034 Inner Suburban =14 % increase in APA $ 972 $6,587 $5,926 $7,201 Inner City =23.8% increase in APA $ 1,661 $11,198 $10,074 $12,241 Note: Production gains are calculated for persons aged 15-64 years. Leisure and home based production were calculated for persons aged 15+ years. Values are 2008 values using a 3% discount rate. Confidence intervals were derived from VicHealth data (who used survey standard errors, survey proportions or data from the literature). They are narrower than if calculated using the smaller population in this analysis.

FINANCIAL OUTCOMES Travel patterns in outer suburban areas are the baseline case Friction Cost Approach for valuing workforce production gains /(losses). This was applied to the lifetime of the 2008 adult population living in Melbourne’s outer suburban areas Production Gains/ (Losses) ($’000) (95% confidence interval) Leisure Based Production ($’000) (95% confidence interval) Home Based Production ($‘000) (95% confidence interval) Health Sector Costs ($‘000) 3% increase in APA $ 209 $1,412 $1,270 $1,543 Middle Suburban =5.9% increase in APA $ 413 $2,776 $2,497 $3,034 Inner Suburban =14 % increase in APA $ 972 $6,587 $5,926 $7,201 Inner City =23.8% increase in APA $ 1,661 $11,198 $10,074 $12,241 Note: Production gains are calculated for persons aged 15-64 years. Leisure and home based production were calculated for persons aged 15+ years. Values are 2008 values using a 3% discount rate. Confidence intervals were derived from VicHealth data (who used survey standard errors, survey proportions or data from the literature). They are narrower than if calculated using the smaller population in this analysis.

ConclusionIncidental Physical Activity associated with travel is an important health issueHealthEconomicFinancial impactsTransport availability, walkability and destination density known key determinants Major health (and economic) benefits need to be factored into transport planning

AcknowledgementsMr Stephen RoddisVictorian Department of TransportDr Ann Magnus Deakin Health Economics Dr Sandy Muspratt Statistician Professor M arj Moodie Deakin Health Economics

Thankyou!

Limitations 1GeneralSelf selection- do more active people choose to use active transport more?Publication bias in PA literature not a major issue (United States Physical Activity Guidelines Advisory Committee 2008, Warburton et al 2010).More research is needed in non-Caucasian populations and in under developed and developing nations (Bauman et al 2012). VISTA07-08 46% response rate may create biases No measure of leisure or occupational PA Vehicle walk time not clearly specified for the return journey Self- reporting of PA may cause bias (GPS pilot survey helps, but still an issue)Cross-sectional survey – no evidence of cause/ effect“Lumpy” data (Tendency to report in 5 minute blocks- affects short times most)

Limitations 2Analysis of CorrelatesTravel times weighted for under reporting had potential to alter variables associated with PA of at least 30 minutes. However identical analysis using raw data changed little.Economic modellingA major weakness of this study is the limitation of modelling to prevention of only 5 diseases in the 2008 population, making estimates conservative. Productivity is measured using the Friction Cost Approach (from a societal perspective, assuming workers are replaced in 3 months) again making estimates conservative. Proposed changes in transport mode and urban location of new developments are not consistent with current infrastructure in Melbourne.

ConclusionPhysical activity associated with travel is an important health issue, with economic and financial impacts.Transport availability, walkability and destination density are key determinants. Increased public transport/cycling/walking infrastructure, and planning new developments in suitable sites in existing urban areas is important to improve health and economic outcomes. More research is needed looking at : Better PA measurement- both with survey designs and objective measures Fine grain research looking in more detail at local environments and health/economic outcomesEconomic models including all health impacts (prevention and treatment) of all relevant diseases How to best influence PA levels at a population level- including policy, urban design, infrastructure and social marketing approaches Capacity building- how to develop cross-sectoral approaches and a suitable workforce Ongoing monitoring, evaluation and review of strategies over time.