Our HIA projected that fare increases and service cuts to public transportation in the Boston region would have resulted in lost time as more residents sit in traffic; worse air quality; lower levels of physical activity; additional crashes; isolation from basic healthcare resources for hundreds of carless households; increased exposure to high noise levels; and additional greenhouse gas emissions. These estimates were based on the approximate 30,000–49,000 people shifting from public transportation to driving and the fact that current drivers collectively would have spent an additional 18,500–25,100 h per year driving under the proposed scenarios. We found that Scenario 1 would have resulted in approximately 70 new cases of obesity, 10 avoidable deaths, and various morbidity outcomes per year; while Scenario 2 would have produced approximately 120 new cases of obesity and 15 avoidable deaths per year (Table 1
In addition to direct health impacts, the proposed changes would have isolated 550–2200 public transportation-dependent households from basic healthcare resources. Carbon dioxide emissions due to additional personal automobile use and increased congestion would have increased by 52,000–58,000 metric tons per year.
These health impacts would have affected a broad swath of individuals across the region and beyond, including both MBTA riders and the general population. Current drivers in the region would have had longer commutes, those who switched from taking public transportation to driving would have decreased their physical activity, and air pollution levels would have increased across the entire region and across adjacent states.
After monetizing all quantifiable pathways, we estimated that fare increases and service cuts to the MBTA system would have resulted in costs that exceed the $161 million budget shortfall that the proposed scenarios sought to address (Table 2
). The direct economic costs to commuters were comparable in magnitude to the revenue generated by the fare increases and service cuts, and the health-related costs exceeded $100 million per year in both scenarios, largely attributable to car crashes and physical activity reductions. These costs do not take into account indirect economic consequences of higher transportation costs, which may compete directly with the costs of other living necessities. These tradeoffs and their health consequences on health have been detailed in other HIAs [46
Dissemination and Impact Evaluation
Our HIA, including a 20-page report, one-page executive summary, and infographic, was released on 13 March 2012 at the Massachusetts State House, in time for the last public hearing on the proposed scenarios on 14th March. The report identified the MBTA as a health resource and provided a reference for transportation funding advocates and legislators seeking evidence that the proposed changes would carry significant human and financial costs. The HIA was cited at the final public hearing and received over 25 unique press hits, including interviews on the local television news and radio. Additionally, the HIA was recognized by Human Impact Partners 2012 Annual Awards as the “most effective, efficient quantitative analysis” [47
] and was cited in the 2013 Healthy People/Healthy Economy Report Card that called for more extensive funding for informative HIAs [48
In April 2012, the MBTA closed its budget deficit with a third Scenario not previously proposed, which raised fares by 23%—reduced from the proposed 35%–43%—and instituted only modest service cuts. This third Scenario relied on additional sources of revenue from the state to fill the budget deficit.
In the midst of a time-sensitive, controversial transportation policy decision-making process, our HIA provided information to the public and policymakers on the health effects of the proposed changes to MBTA fares and services, as well as their related costs. We leveraged detailed transportation modeling for the two scenarios and connected the outputs with quantitative approaches for multiple pathways linking transportation with health.
Our HIA had multiple unique attributes that contributed to the visibility and utility of the report. To our knowledge, this was the first HIA conducted by a regional planning agency, leveraging relationships with both transportation modelers and Boston-area academic institutions. This provided credibility across all domains of the report, reinforced by the fact that the HIA was independent and self-funded. Having broad domain expertise also facilitated completion of the HIA on a rapid basis, so the findings were timely. Because the HIA was focused on a specific audience (the Massachusetts Legislature) and decision (operations scenarios for the upcoming fiscal year), we were able to tailor the content appropriately. Rather than a lengthy document read only by public health practitioners, the final HIA was 20 pages and was supplemented with a one-pager with an infographic that concisely demonstrated to legislators that the health and economic costs of the fare increases and service cuts exceeded the budget shortfall that the proposed scenarios sought to address. Both the focus and structure of the HIA attracted wide media attention, underscoring the interest in health information connected to everyday activities such as commuting.
This HIA had a number of limitations related in part to its rapid nature. Because we conducted the HIA in a short time frame (eight weeks), we did not hold stakeholder engagement meetings. While there are uncertainties associated with any HIA, we made a number of simplified assumptions given the data available and need for a timely analysis. Where possible, we attempted to make conservative estimates to avoid overstating the benefits, but the magnitude and direction of some key uncertainties is unknown, and the optimal methods for multiple pathways may differ for HIAs with a longer time horizon. We also did not include a discussion on the distribution of health outcomes and did not include a monitoring section, both of which are found in typical HIAs. While we present one assessment approach, HIA practitioners with more time and resources might consider primary data collection, more refined models of exposures and health risks, more detailed geospatial data, additional pathways, and more complete assessment of the distribution of health impacts among susceptible populations. Primary data collection could have provided more informed assumptions to input into our models. For instance, we assumed that carless households rely on public transportation access to access healthcare; however, surveys on how individuals obtain healthcare in the region would provide more applicable data to examine the impact of the service cuts on healthcare access.
Despite these limitations, the MBTA HIA can serve as a model for a rapid quantitative approach to HIA that can be applied to time-sensitive transportation decisions. Having the appropriate team positioned to conduct the analysis is crucial. As a state agency, MAPC was well-situated to collaborate with other agencies to quickly gather necessary health and transportation data. Our cross-disciplinary and cross-sector approach enabled us to incorporate rapid but peer-reviewed and high-quality quantitative approaches that can be replicated in other communities. While the focus on quantitative and monetized outputs may be too narrow for some stakeholders and decisions, the ability to directly compare health and economic consequences with the transportation budget deficit was a key feature of our HIA that increased its visibility and influence.