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An Urban Trail Network and Cardiovascular Disease: A Natural Experiment

U

University of Manitoba

Status

Unknown

Conditions

Diabetes Mellitus
Cardiovascular Diseases
Cardiovascular Risk Factor

Treatments

Other: Urban Trail

Study type

Interventional

Funder types

Other

Identifiers

NCT04057417
HS20928

Details and patient eligibility

About

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.

Full description

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.

Enrollment

225 estimated patients

Sex

All

Ages

30 to 65 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • The entire population of Winnipeg

Exclusion criteria

  • Individuals < 30 years of age and >65 years of age.

Trial design

Primary purpose

Prevention

Allocation

Non-Randomized

Interventional model

Parallel Assignment

Masking

None (Open label)

225 participants in 2 patient groups

Urban Trail Expansion
Experimental group
Description:
Neighbourhoods within 400m to 800m of a neely built greenway, defined as a multi-use concrete/asphalt trail that was \>4km in length)
Treatment:
Other: Urban Trail
Control
No Intervention group
Description:
Neighbourhoods that are located beyond 400 to 800m of a newly built greenway

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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