Decision framework and risk metrics

As stated in the introductory chapter, the computational model is evaluated within the context of a decision framework, which consists of three possible routes of action: 1) risk results mandate action to reduce risk; 2) risks deemed acceptable and benefits clearly outweigh risks, and 3) more analysis is required to inform decision-making for immediate action.

To support the decision framework, metrics and criteria for judgment are elaborated. The metrics reflect measures of factors of risk and benefit for specific population targets temporal patterns of activity and exposure. The criteria for judgment concern the level of acceptability of risk and sufficiency of benefit. Both are described in this section.

The choice of metrics applied in this work is guided by the desire to assess the competing risks and benefits associated with physical exertion in a changing urban environment. To

characterize exposure to air pollution while accounting for energy expenditure, the outcome measured for an individual is the inhalation dose of the modeled air pollutants, PM10 and ozone. A measure of daily inhalation dose is developed, to allow a real evaluation of the effects of the choice of mode on overall inhalation dose. Indeed, not only are the relative contribution of travel times interesting for the analysis, but also because changes in transportation modes and activity location choices impacts the duration of the activity, a measure of inhalation dose outside of the travel activity (a form of “opportunity inhalation”) is necessary to compare overall effects22_{. For}

22 _{Note that duration differences between activity diaries and modeled travel times are taken or added to} sleep time in the model. Sleep is in most cases the activity generating lowest inhalation dose because of low activity rates.

measures of healthy physical activity however, it is not necessary to track energy expenditures throughout the day (except for the purpose of estimating inhalation dose). Energy expenditure is considered health-promoting when it is above a certain threshold – such as levels associated with walking - so a mark of physical exertion during active travel solely is sufficient for comparing incremental healthy effects of the built environment.

Thus, the metrics chosen for risks and benefits associated with each built environment scenario are for an individual in a day (hereafter referred to as the risk/benefit factors):

- Inhalation dose of PM10 throughout the day, (µg/day) - Inhalation dose of ozone throughout the day, (µg/day)

- Energy expenditure during active travel in a day (kilocalories/day)

Both in the interest of assessing the variability in exposures and the more chronic effects of exposures, these factors are extended to yearly measures of risks and benefits by simulating 365 days of activities for an individual. For this individual, the outcomes of interest may then compare the distribution of the risk factors throughout the year for the different built environment scenarios. Selected individual metrics thus take the form:

- Change in the fraction of days above thresholds of each risk/benefit factor (graph fraction of days above certain threshold as a factor of inhalation dose)

- Difference in the distribution of each risk/benefit factor (tested using a 1-tailed Wilcoxon matched-pairs signed-rank test)

- Difference in various percentile values of risk/benefit factors

Next, as the goal of this work is to assess impacts of built environment on the entire population and not just on an individual, the individual metrics are applied to a whole population, and the final metrics considered, comparing 2 different built environment scenarios are:

- Difference in the distribution of each risk/benefit factor for the entire population (tested using a 1-sided Wilcoxon matched-pairs signed-rank test)

- Change in the intersubject variability distribution of individuals’ fraction of days above different thresholds (comparison using Wilcoxon test)

- Distribution of the change in fractions of days above certain thresholds for each individual - Change in the distribution of 95th _{and 99}th _{percentile values of risk factors and 30}th _{and 50}th

percentiles of the benefit factor (the intent of the policy is to generate more physical activity, and the 30th _{percentile value provides an estimate of the minimum amount of energy}

individuals expended 70% of the days in a year) (Wilcoxon test)

Different percentiles of outcomes are considered to portray both the variability and uncertainty associated with the outcomes (Cullen and Frey 1999). When possible, the measure of change is tested against the hypothesis of no change using statistical procedures, applying the classic 95 percent probability estimate and considering the p-values to provide a measure of uncertainty. The Wilcoxon matched-pairs signed-rank test is appropriate for assessing differences in built environment scenarios, as the outputs from each scenario for each individual are

dependent (hence matched-pairs) and a non-parametric method is necessary for data that is not necessarily normally distributed (McGrew and Monrow 2000). In addition, uncertainty in the variability estimates is assessed qualitatively and semi-quantitatively by comparing different approaches to characterize the variability in the risk/benefit estimates, and results of sensitivity analyses on several model inputs.

The threshold used for the active travel measure is the recommended level of daily physical activity: 150kcal. For inhalation dose, no safe or unsafe thresholds have been determined in the literature (Bell et al. 2006), so a reference level is constructed in reference to NAAQS standards: an individual with a simplified activity pattern is simulated for days where the concentration in the air reaches the standard levels (see section 5.1.4).

With regards to the decision framework, an argument can be made that any deliberate move by local governments that has a potential of compromising residents’ health by increasing inhalation of toxic air, albeit with the intention and the outcome of otherwise improving health

through encouraging active lifestyles, warrants attention on their part to minimize deleterious exposures. Therefore, a finding that the distribution of individuals experiences a 95% probability of increased fraction of days above the inhalation dose theshold due to changes in the built environment would provoke path 1 of the decision framework (action on the part of decision makers). In addition, considering the hazards of acute exposures at the high end of the

distribution, more than a 10% increase in inhalation in 5% of person-days above the thresholds would also lead to the 1st _{route of action in the decision framework. Finally, both because effects} may occur at much lower levels than the NAAQS standards (no safe thesholds are known), and also because the simulation showing a high increase on a low pollution day could also possibly have occured on high pollution day in another simulation, one more trigger for policy making in route 1 would be the doubling of pollution intake on 5% of the days for any individual. The level of action recommended however would be commensurate to the degree of risk estimated, depending both on the magnitude and uncertainty associated with the risk. The policy discussion section tackles this issue.

Another facet of this decision path is the possibility of increased hazards as defined above, concomitant to clear benefits in terms of health-promoting lifestyles. One measure of benefits could be characterized as a significant shift to the right in the population distribution of daily energy expenditure due to active travel, as ascertained by a Wilcoxon test with 0.05 probability. Another is to test an increase in population distribution of 30th percentile value of individual’s daily expenditure value (indicating the minimum level of activity undertaken 70% of the days). Reaching 150 kcal/day for 30% of the population would be a clear indication of benefits of the policy. In the case of clear benefits of the built environment policies

accompanying increased hazards, actions considered would not only address limiting hazardous exposure but also expanding opportunities for active travel. The policy discussion section addresses these avenues, while also considering uncertainty and magnitude of benefits.

A consistent finding of no difference in inhalation dose for the various metrics and for the several modeling tested approaches, and ascertainment of clear benefits of the policy as described above effectuates the second path of the decision framework. In this course of action, decision- makers focus their attention to expanding policies of pedestrian-oriented environments.

A low uncertainty threshold is implemented to trigger action plan 1 (i.e. chance of detrimental effect with high risk uncertainty still brings about action), therefore decision path 3 is not exclusive of path 1, so that the course of conduct may be revised as uncertainty is reduced. The case of no significant finding of increased harmful exposures (path 2), naturally does not alleviate the need for more research for similar policies implemented in different conditions, particularly in areas with greater air pollution concerns. However, such a finding would indicate an acceptable level of risk for conditions portrayed in this case study. If no benefits of the policy can be demonstrated in this computational model, the recommendation from this study is still to develop further research on risks and benefits of pedestrian-oriented environment, as many more benefits and a few other risks than those quantified in the computational model have been identified, as reviewed in Chapter 2.