Driving, Residential Location, and Employment Outcomes Among Older Adults.

The ability to drive is positively associated with workforce participation among older adults. However, residence in neighborhoods where destinations are easy to reach by public transit could potentially narrow the employment gap between older drivers and non-drivers. This study examines the relationship between driving, residential location characteristics, and employment outcomes among older adults. Findings show that both drivers and non-drivers are more likely to be employed if they live in neighborhoods with high levels of access to jobs via public transit. However, the positive relationship between transit access to jobs and employment outcomes is particularly strong among non-drivers. These findings indicate that although older adult drivers are more likely to work than their non-driving counterparts, the gap in employment outcomes is mitigated by living in dense, transit-rich neighborhoods. Results suggest that policies supporting both automobile access and transit-rich residential environments can facilitate labor force participation among older adults.

Development and application of a dynamic transmission model of health systems' preparedness and response to COVID-19 in twenty-six Latin American and Caribbean countries.

The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?; When are the peaks of cases and deaths expected, according to the different scenarios?; Which occupancy rate will ICU services have along the epidemiological curve?; When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.

The changing accuracy of traffic forecasts.

Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy-traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-021-10182-8.

Toward a Culture of Consequences: Performance-Based Accountability Systems for Public Services.

Performance-based accountability systems (PBASs), which link incentives to measured performance as a means of improving services to the public, have gained popularity. While PBASs can vary widely across sectors, they share three main components: goals, incentives, and measures. Research suggests that PBASs influence provider behaviors, but little is known about PBAS effectiveness at achieving performance goals or about government and agency experiences. This study examines nine PBASs that are drawn from five sectors: child care, education, health care, public health emergency preparedness, and transportation. In the right circumstances, a PBAS can be an effective strategy for improving service delivery. Optimum circumstances include having a widely shared goal, unambiguous observable measures, meaningful incentives for those with control over the relevant inputs and processes, few competing interests, and adequate resources to design, implement, and operate the PBAS. However, these conditions are rarely fully realized, so it is difficult to design and implement PBASs that are uniformly effective. PBASs represent a promising policy option for improving the quality of service-delivery activities in many contexts. The evidence supports continued experimentation with and adoption of this approach in appropriate circumstances. Even so, PBAS design and its prospects for success depend on the context in which it will operate. Also, ongoing system evaluation and monitoring are integral components of a PBAS; they inform refinements that improve system functioning over time. Empirical evidence of the effects of performance-based public management is scarce. This article also describes a framework used to evaluate a PBAS. Such a system identifies individuals or organizations that must change their behavior for the performance of an activity to improve, chooses an implicit or explicit incentive structure to motivate these organizations or individuals to change, and then chooses performance measures tailored to inform the incentive structure appropriately. The study focused on systems in the child care, education, health care, public health emergency preparedness, and transportation sectors, mainly in the United States. Analysts could use this framework to seek empirical information in other sectors and other parts of the world. Additional empirical information could help refine existing PBASs and, more broadly, improve decisions on where to initiate new PBASs, how to implement them, and then how to design, manage, and refine them over time.