An Urban Trail Network and Cardiovascular Disease: A Natural Experiment

Overview

Associations between the built environment and health behaviours are robust, however (1) it remains unclear if the behaviours they elicit lead to meaningful improvements in health outcomes, at the population level and (2) little experimental evidence exists supporting these associations. The primary objective of this study is to capitalize on an urban natural experiment to determine if changing the built environment to support physical activity will (1) reduce the burden of CVD within a population and (2) if it's a cost-effective population intervention. An interrupted time series analysis will be performed over a period of 19 years to determine if the expansion of an urban trail network is associated with reductions in major advserse cardiovascular events (MACE) and CVD-related risk factors within a large urban centre in Canada.

Two different time series methods will be used to estimate the effect of an urban trail expansion (i.e. "intervention") that occured in WInnipeg, Manitoba Canada, between 2010 and 2012. The study is designed to determine if a reduction in Major Adverse Cardiovascular Events (MACE) was observed in neighbourhoods that received the intervention relative to trends among the control neighbourhoods that did not receive the intervention. First, a multi-group segmented regression of interrupted time series data will be used to assess the effect of the intervention on CVD incidence, both immediately (change in level) and over time (change in trend) by creating indicator variables . The level will be the base rate of CVD-related end-points at the beginning of the pre-intervention period (2000) and the value immediately following each change point at which successive segments join until 2010. The trend is the rate of change in MACE end-points (in other words, the slope) during a segment. Autoregressive errors will be modeled to account for correlated outcomes. Second, an autoregressive integrated moving average (ARIMA) model will be fitted for the CVD incidence time series by using the standard approach to identification, estimation, and checking. A trend and periodic seasonal terms will be applied to the entire study period (November 2000 to October 2019). A separate ARIMA model will also be built for the pre-intervention period to forecast CVD evolution of the treated neighbourhoods. The number of CVD end-points prevented by the intervention will be estimated by calculating the difference between the predicted number and the observed number of cases. Should there by difficulty fitting an ARIMA model to a relatively small dataset, exponential smoothing models or the Holt Winters Algorithm will be used. Although they require larger sample sizes, they are ideal for this project as (1) they permit a variety of different types of intervention effect to be modeled explicitly, and (2) they are well suited to forecasting future trends.