Table 4 Health impact planning support systems
Paper | Schoner et al. [39] Bringing health into transportation and land use scenario planning: Creating a National Public Health Assessment Model (N-PHAM) | Boulange et al. [40] Improving planning analysis and decision making: The development and application of a Walkability Planning Support System | Ulmer et al. [24] Application of an evidence-based tool to evaluate health impacts of changes to the built environment |
|---|---|---|---|
Health PSS tool/system | National Public Health Assessment Model (N-PHAM) | Walkability Planning Support System | Coalitions Linking Action and Science for Prevention (CLASP) |
Country | USA | Australia | Canada |
Description | Provides baseline built and natural environmental conditions and pre-calculated health outcomes/levels Health Module “engine” contains computed equations describing the association between built environment features and health outcomes N-PHAM calculates/forecasts new outcome values based on the provided custom inputs/user changes to the built and natural environmental measures | Simulates changes in the built environment and modelling the impact these would have on transport walking behaviours: Measure the walkability of an area Test potential impacts of future policies and scenarios by allowing users to create and manipulate a virtual representation of an urban precinct Assess selected health impacts of these planning decisions/scenario for the community | Designed to predict physical activity levels, health-related indicators and GHG emissions associated with proposed land use and transportation developments |
Software and hardware characteristics | Web-based API plug-in Integrates with multiple existing scenario planning platforms and software applications, allowing users of available scenario planning tools (CommunityViz, Envision Tomorrow, UrbanFootprint) to choose an area of interest represented by Census block groups which return baseline input and outcome values for each block group, as well as aggregated values for the study area These data are then available to the tool user to map and analyse data in ways specific to the respective tool | ArcGIS + CommunityViz 5.1 Commercially available software package owned and administered by City Explained Inc It is customisable and is an extension of ESRI’s ArcGIS Displayed on a touch-enabled 46-inch MapTable that can support up to 10 people around its screen | ArcGIS + CommunityViz 5.1 Commercially available software package owned and administered by City Explained Inc It is customisable and is an extension of ESRI's ArcGIS |
Functions and user interface | Spatial data visualisation Dynamic interface for sketch-planning + editing of spatial layers Maps, charts Health impact analysis and modelling | Spatial data visualisation Dynamic interface for sketch-planning + editing of spatial layers Maps, charts Real-time Health impact analysis and modelling | Spatial data visualisation Dynamic interface for sketch-planning + editing of spatial layers Maps, charts Real-time Health impact analysis and modelling |
Urban magnitude/scale of the project/scenario application/s | Precinct Census block groups Region/state National | Precinct Suburb | Precinct Suburb Postal codes |
Built environmental/urban design layers | Gross population density Gross employment density Jobs within a 45-min transit commute, distance decay, walk network and GTFS schedule travel time Employment entropy index using a 5-tier employment classification scheme Retail jobs within a 5-tier employment classification scheme % of CBG employment within 1/4 mile of a fixed guideway transit stop Network density—facility miles of pedestrian-orientated links per square mile Street intersection density, weighted auto-orientated intersections eliminated % of land cover developed as open space % of land area covered by tree canopy % land cover = forest % land cover = natural % of land cover = developed open space or natural space | Land use mix (commercial, education, industrial, parkland, residential) Dwelling density Housing diversity score Local living destination score—convenience (convenience store, newsagent or petrol station); supermarket; speciality food destination (fruit and vegetable, meat, fish or poultry store); post office; bank; pharmacy; general practice/medical centre; dentist; community centre; childcare facility; library; Closest train station (< / > 800 m); Closest bus stop (< / > 400 m) Street network—intersection density | Length of roads; bicycle and sidewalk facilities; Distance to nearest major arterial, school and transit stop/station; Accessibility to major regional destinations; several density vectors, including net-residential, intersection, schools, transit stop and type of each food location (sit down and fast food, grocery and convenience stores); Land use—an entropy-based measure of the mix, retail floor-to-land area and park area |
Health behaviour or outcomes or impacts have been used/estimated as coefficients for the health impact scenario model | Transportation walking (binary participation + continuous duration) Leisure walking (binary participation + continuous duration) Transportation biking (binary participation + continuous duration) Auto travel/sedentary time (binary participation + continuous duration) Recreational physical activity (binary participation + continuous duration) Body mass index (continuous) Overweight (binary) Obese (binary) Kessler-6 mental health—moderate (binary) Fair or poor general health (binary) | Transportation walking | Walking and biking for exercise; Walking and biking to work/school Body mass index Daily energy expenditure Blood pressure; Walk/bike trips/day, Transit trips/day, Automobile trips/day, Kilometres of travel/day Estimated vehicular emissions of CO2/day |
Scale at which the health outcome data is collected/modelled | Census block | Meshblock (the smallest geographic region on the Australian Statistical Geography Standard) | Postal code |
What predictive/statistical modelling technique was used to estimate the health impacts? | Likelihood of participating in the activity = binary health outcomes = binary logistic regression | Multi-level, multivariate logistic regression To estimate the probability that an individual participates in transport-walking, the formula takes in the values for each built environmental variable multiplied by the corresponding regression coefficients and summed with the constants | Multivariate regression models were used to predict the value of each health outcome/behaviour based on each participant's built environment and demographic/socioeconomic characteristics Four different types of regression model were used, depending on the type and distribution of the outcome variable: linear, log-linear, binary logistic and two-stage (zero-inflated). In each case, a base model was first built to include any statistically significant (p < 0.05) demographic/socioeconomic variables |
Population demographic/s | Adults 18–64 Older adults 65 + | Adults > 18 years | Adults > 18 years |
Target application/planning-related task or stage | Baseline analysis Scenario testing | Baseline analysis Scenario testing | Baseline analysis Scenario testing |
Intended users | Planners Community Policy makers | Planners Community Policy makers | Planners Academics Policymakers |
Does it support individual or group decision making? | Individual Group | Individual Group | Individual Group |