ASU Scholarship Showcase

Background: Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.

Objectives: This quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season.
Methods: Using Poisson regression models that controlled for temporal confounders, we assessed daily temperature–health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different “trigger points” at which heat-health intervention measures might be activated.

Results: We found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22°C) across multiple health events and diagnoses.

Conclusion: Consideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades.

Background: Vulnerability mapping based on vulnerability indices is a pragmatic approach for highlighting the areas in a city where people are at the greatest risk of harm from heat, but the manner in which vulnerability is conceptualized influences the results.

Objectives: We tested a generic national heat-vulnerability index, based on a 10-variable indicator framework, using data on heat-related hospitalizations in Phoenix, Arizona. We also identified potential local risk factors not included in the generic indicators.

Methods: To evaluate the accuracy of the generic index in a city-specific context, we used factor scores, derived from a factor analysis using census tract–level characteristics, as independent variables, and heat hospitalizations (with census tracts categorized as zero-, moderate-, or high-incidence) as dependent variables in a multinomial logistic regression model. We also compared the geographical differences between a vulnerability map derived from the generic index and one derived from actual heat-related hospitalizations at the census-tract scale.

Results: We found that the national-indicator framework correctly classified just over half (54%) of census tracts in Phoenix. Compared with all census tracts, high-vulnerability tracts that were misclassified by the index as zero-vulnerability tracts had higher average income and higher proportions of residents with a duration of residency < 5 years.

Conclusion: The generic indicators of vulnerability are useful, but they are sensitive to scale, measurement, and context. Decision makers need to consider the characteristics of their cities to determine how closely vulnerability maps based on generic indicators reflect actual risk of harm.

Background: Extreme heat is a leading weather-related cause of mortality in the United States, but little guidance is available regarding how temperature variable selection impacts heat–mortality relationships.
Objectives: We examined how the strength of the relationship between daily heat-related mortality and temperature varies as a function of temperature observation time, lag, and calculation method.
Methods: Long time series of daily mortality counts and hourly temperature for seven U.S. cities with different climates were examined using a generalized additive model. The temperature effect was modeled separately for each hour of the day (with up to 3-day lags) along with different methods of calculating daily maximum, minimum, and mean temperature. We estimated the temperature effect on mortality for each variable by comparing the 99th versus 85th temperature percentiles, as determined from the annual time series.

Results: In three northern cities (Boston, MA; Philadelphia, PA; and Seattle, WA) that appeared to have the greatest sensitivity to heat, hourly estimates were consistent with a diurnal pattern in the heat-mortality response, with strongest associations for afternoon or maximum temperature at lag 0 (day of death) or afternoon and evening of lag 1 (day before death). In warmer, southern cities, stronger associations were found with morning temperatures, but overall the relationships were weaker. The strongest temperature–mortality relationships were associated with maximum temperature, although mean temperature results were comparable.

Conclusions: There were systematic and substantial differences in the association between temperature and mortality based on the time and type of temperature observation. Because the strongest hourly temperature–mortality relationships were not always found at times typically associated with daily maximum temperatures, temperature variables should be selected independently for each study location. In general, heat-mortality was more closely coupled to afternoon and maximum temperatures in most cities we examined, particularly those typically prone to heat-related mortality.

Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.

Background: Studies show that ex-prisoners often experience more health problems than the general population; unfortunately, these issues follow them upon their release from prison. As such, it is possible re-entry rates signal the need for neighborhood-based health care organizations (HCOs). We ask: are incarceration and re-entry rates associated with the availability of HCOs?

Methods: Using 2008 Central Business Pattern data, 2008 prison admissions and release data, and 2000 and 2010 census data, we test whether prison admission and release rates impact the availability of HCOs net of neighborhood characteristics in Arkansas using Logit-Poisson hurdle models with county fixed effects.

Results: We find that the incarceration and re-entry rates – together known as coercive mobility -- are related to whether a neighborhood has one or more HCOs, but not to the number of HCOs in a neighborhood.

Conclusion: Future public policies should aim to locate health care organizations in areas where there is significant churning of individuals in and out of prison.

The planetary boundary framework constitutes an opportunity for decision makers to define climate policy through the lens of adaptive governance. Here, we use the DICE model to analyze the set of adaptive climate policies that comply with the two planetary boundaries related to climate change: (1) staying below a CO₂ concentration of 550 ppm until 2100 and (2) returning to 350 ppm in 2100. Our results enable decision makers to assess the following milestones: (1) a minimum of 33% reduction of CO₂ emissions by 2055 in order to stay below 550 ppm by 2100 (this milestone goes up to 46% in the case of delayed policies); and (2) carbon neutrality and the effective implementation of innovative geoengineering technologies (10% negative emissions) before 2060 in order to return to 350 ppm in 2100, under the assumption of getting out of the baseline scenario without delay. Finally, we emphasize the need to use adaptive path-based approach instead of single point target for climate policy design.

The context in which many self-governed commons systems operate will likely be significantly altered as globalization processes play out over the next few decades. Such dramatic changes will induce some systems to fail and subsequently to be transformed, rather than merely adapt. Despite this possibility, research on globalization-induced transformations of social-ecological systems (SESs) is still underdeveloped. We seek to help fill this gap by exploring some patterns of transformation in SESs and the question of what factors help explain the persistence of cooperation in the use of common-pool resources through transformative change. Through the analysis of 89 forest commons in South Korea that experienced such transformations, we found that there are two broad types of transformation, cooperative and noncooperative. We also found that two system-level properties, transaction costs associated group size and network diversity, may affect the direction of transformation. SESs with smaller group sizes and higher network diversity may better organize cooperative transformations when the existing system becomes untenable.

It has become common for sustainability science and resilience theory to be considered as complementary approaches. Occasionally the terms have been used interchangeably. Although these two approaches share some working principles and objectives, they also are based on some distinct assumptions about the operation of systems and how we can best guide these systems into the future. Each approach would benefit from some scholars keeping sustainability science and resilience theory separate and focusing on further developing their distinctiveness and other scholars continuing to explore them in combination. Three areas of research in which following different procedures might be beneficial are whether to prioritize outcomes or system dynamics, how best to take advantage of community input, and increasing the use of knowledge of the past as a laboratory for potential innovations.

The objective of articulating sustainability visions through modeling is to enhance the outcomes and process of visioning in order to successfully move the system toward a desired state. Models emphasize approaches to develop visions that are viable and resilient and are crafted to adhere to sustainability principles. This approach is largely assembled from visioning processes (resulting in descriptions of desirable future states generated from stakeholder values and preferences) and participatory modeling processes (resulting in systems-based representations of future states co-produced by experts and stakeholders). Vision modeling is distinct from normative scenarios and backcasting processes in that the structure and function of the future desirable state is explicitly articulated as a systems model. Crafting, representing and evaluating the future desirable state as a systems model in participatory settings is intended to support compliance with sustainability visioning quality criteria (visionary, sustainable, systemic, coherent, plausible, tangible, relevant, nuanced, motivational and shared) in order to develop rigorous and operationalizable visions. We provide two empirical examples to demonstrate the incorporation of vision modeling in research practice and education settings. In both settings, vision modeling was used to develop, represent, simulate and evaluate future desirable states. This allowed participants to better identify, explore and scrutinize sustainability solutions.

In traditional public good experiments participants receive an endowment from the experimenter that can be invested in a public good or kept in a private account. In this paper we present an experimental environment where participants can invest time during five days to contribute to a public good. Participants can make contributions to a linear public good by logging into a web application and performing virtual actions. We compared four treatments, with different group sizes and information of (relative) performance of other groups. We find that information feedback about performance of other groups has a small positive effect if we control for various attributes of the groups. Moreover, we find a significant effect of the contributions of others in the group in the previous day on the number of points earned in the current day. Our results confirm that people participate more when participants in their group participate more, and are influenced by information about the relative performance of other groups.