Towards an Integrated Global Transport and Health Assessment Tool (TIGTHAT). (June 2017) - Woodcock, James; Tainio, Marko; Herick de Sa, Thiago; de Nazelle, Audrey; Goel, Rahul; Gouveia, Nelson; Tiwari, Geetam; Verma, Asish; Miranda, Jaime; Brage, Soren
Abstract : Background: Urban land transport has positive (physical activity) and negative (road traffic injuries and air pollutants) side effects. Studies in high income cities have found substantial population health benefits from mode shift to active travel, with physical activity dominating the impact. However, our earlier work in Brazil, India, and Malaysia identifies a more varied picture, with a larger burden and trickier trade-offs. To investigate risks and benefits in low and medium income countries (LMICs), we will conduct in year 2017-19 international project, TIGTHAT, which will develop methods to estimate these trade-offs. Methods: In this project we lay the basis for a globally applicable stochastic synthesis engine with a user friendly interface, to support evidence based decision making on transport and health. A key challenge is data comparability and quality. Our international, multidisciplinary team will assess what data is available and develop approaches for mapping (calibrating) available to desired data, while representing propagated uncertainty. The main objectives of TIGTHAT are: Health Impact modelling (physical activity, air pollution, and road traffic injuries) of scenarios for two new cities in India (Bengaluru and Visakhapatnam) and developing and reanalysing for two already analysed cities (Sao Paulo, Brazil, and Delhi, India). The objective is both to generate results about the size of impacts, but more importantly to understand how choice of data and uncertainty affects the results. Test the feasibility of Google Street View to estimate traffic mode share in multiple cities, and to develop an ecological model to predict population distributions of travel time mode share and total physical activity. Appraise urban traffic injury data in a range of settings globally, identify patterns, develop generalizable rules for combining data, and validate by testing them in selected cities Develop and test a generalizable approach to estimate changes in transport related air pollution (fine particulate matter, PM2.5) Identify, evaluate, and compare personal travel survey data for multiple settings and how estimates for travel physical activity could differ due to diverse survey methods. Results: This project lays the foundation for an analytic modelling framework to estimate multiple outcomes with respect to a major determinant of population health. In this presentation we will describe the work plan and position it within the wider scientific and policy challenge. Preliminary results for Delhi and Sao Paulo will be presented. Conclusions: We will organise a group exercise to develop a shared understanding of data issues relating to modelling impacts in LMICs.