Consequences of Commuting Patterns and the Structure of Food Retail Markets for SNAP Redemption: Implications for Food Access

Persistent food insecurity and hunger increase the risk of illness, psychological dysfunction and lower educational achievement. Even though these burdens affect society at large, they are most acutely felt by the individuals and households living in poverty. To address the costs of hunger and food insecurity, policies have been designed, many by urban planners, to increase access to healthy food. Because low-income populations are assumed to shop at the nearest store that sells food, most policies have focused on opening new supermarkets in “food deserts.” However, there is little evidence that such assumptions are true or that nearby supermarkets make a difference. This dissertation presents a 7-year panel model for 207 counties in Texas as a tool to test the consequences of commuting patterns and the structure of the retail grocery market on food dollars spent by people living in poverty. The model uses longitudinal data from the Supplemental Nutrition Assistance Program (SNAP). The model uses publicly available geocoded data on SNAP benefits and redemptions, retail locations, and commuting patterns. The model explicitly examines the consequences of commuting patterns and retail markets, both local and in surrounding counties, for SNAP redemption. Results show that commuting patterns and the grocery retail market are important factors for predicting SNAP redemptions. Specifically, workers that commute out of a county have a negative effect on the amount of SNAP dollars redeemed in a county, and workers that commute into a county have the opposite effect. Large SNAP retailers, such as super stores or chain stores, have the largest positive effect. The number of supermarkets in a neighboring county does not affect the net SNAP dollars redeemed within a county, but the number of neighboring super stores or chain stores does. SNAP redemptions decrease significantly when counties do not have large retailers and when counties have more outbound workers than inbound workers. The factors identified in this research that influence redemption patterns may have implications for policies that attempt to enhance SNAP redemptions. In the broader picture, such policies may have a significant impact on food access for people living in poverty

Disaster impacts on cost and utilization of Medicare

Abstract Background To estimate changes in the cost and utilization of Medicare among beneficiaries over age 65 who have been impacted by a natural disaster, we merged publically available county-level Medicare claims for the years 2008–2012 with Federal Emergency Management Agency (FEMA) data related to disasters in each U.S. County from 2007 to 2012. Methods Fixed-effects generalized linear models were used to calculate change in per capita costs standardized by region and utilization per 1000 beneficiaries at the county level. Aggregate county demographic characteristics of Medicare participants were included as predictors of change in county-level utilization and cost. FEMA data was used to determine counties that experienced no, some, high, and extreme hazard exposure. FEMA data was merged with claims data to create a balanced panel dataset from 2008 to 2012. Results In general, both cost and utilization of Medicare services were higher in counties with more hazard exposure. However, utilization of home health services was lower in counties with more hazard exposure. Conclusions Additional research using individual-level data is needed to address limitations and determine the impacts of the substitution of services (e.g., inpatient rehabilitation for home health) that may be occurring in disaster affected areas during the post-disaster period

A Stochastic Approach to Model Household Re-occupancy in A Community Following A Natural Hazard

additionally, based on available social science studies, we further define a set of time-dependent conditional re-occupancy probability functions (CRPFs) that give the probability of a household re-occupying its pre-event dwelling units at any time conditional on the change in households JFS and its income level. Finally, the time-variant household-level re-occupancy probability is derived by solving the DTMC with partially absorbing boundary conditions described by the CRPFs. The community-level HRO is then obtained through aggregating the household-level re-occupancy state across the community over the recovery time horizon. The model will be further calibrated by data collected in ongoing field studies with an ultimate goal of supporting further researches on community resilience planning.The re-occupancy of displaced households in a community following a hazard event is a complex social process driven collectively by the functionality states of community building portfolios and supporting lifelines. This study presents a novel approach for household re-occupancy (HRO) modeling using discrete state, Discrete Time Markov Chain (DTMC). Our hypothesis is that the reoccupancy state of a displaced household at a post-event time is collectively determined by the joint functionality status (JFS) (of its related school(s), workplace(s) and pre-event dwelling unit) and by the resourcefulness of the household largely determined by its income level. Accordingly, we construct a one-step transition probability matrix of the DTMC modeling the households JFS as a function of the functionality states of its school(s), workplace(s) and pre-event dwelling unitThis research was supported by the National Key R&D Program of China (Grant No. 2016YFC0800200) and by the US National Institute of Standards and Technology (NIST) under Cooperative Agreement No. 70NANB15H044

Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households, social institutions, and local economy. Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis, it is necessary but not sufficient for community resilience planning. While there have been several studies on hurricane risk and recovery assessment at the building- and community-level, few studies have focused on the nexus of coupled physical and social disruptions, particularly when characterizing recovery in the face of coastal multi-hazards. Therefore, this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards (e.g., wind, storm surge, wave) by considering the physical damage and functionality of the built environment along with the population dynamics over time. Specifically, high-resolution fragility models of buildings, and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment. Beyond simulating recovery by tracking infrastructure network performance metrics, such as access to essential facilities, this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery. The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities, along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter. Furthermore, the methodology presented here provides a foundation for resilience-informed decisions for coastal communities

Disaster impacts on cost and utilization of Medicare

Abstract Background To estimate changes in the cost and utilization of Medicare among beneficiaries over age 65 who have been impacted by a natural disaster, we merged publically available county-level Medicare claims for the years 2008–2012 with Federal Emergency Management Agency (FEMA) data related to disasters in each U.S. County from 2007 to 2012. Methods Fixed-effects generalized linear models were used to calculate change in per capita costs standardized by region and utilization per 1000 beneficiaries at the county level. Aggregate county demographic characteristics of Medicare participants were included as predictors of change in county-level utilization and cost. FEMA data was used to determine counties that experienced no, some, high, and extreme hazard exposure. FEMA data was merged with claims data to create a balanced panel dataset from 2008 to 2012. Results In general, both cost and utilization of Medicare services were higher in counties with more hazard exposure. However, utilization of home health services was lower in counties with more hazard exposure. Conclusions Additional research using individual-level data is needed to address limitations and determine the impacts of the substitution of services (e.g., inpatient rehabilitation for home health) that may be occurring in disaster affected areas during the post-disaster period

Estimating long-term K-12 student homelessness after a catastrophic flood disaster

Despite efforts to end homelessness in the United States, student homelessness is gradually growing over the past decade. Homelessness creates physical and psychological disadvantages for students and often disrupts school access. Research suggests that students who experience prolonged dislocation and school disruption after a disaster are primarily from low-income households and under-resourced areas. This study develops a framework to predict post-disaster trajectories for kindergarten through high school (K-12) students faced with a major disaster; the framework includes an estimation on the households with children who recover and those who experience long-term homelessness. Using the National Center for Education Statistics school attendance boundaries, residential housing inventory, and U.S. Census data, the framework first identifies students within school boundaries and links schools to students to housing. The framework then estimates dislocation induced by the disaster scenario and tracks the stage of post-disaster housing for each dislocated student. The recovery of dislocated students is predicted using a multi-state Markov chain model, which captures the sequences that households transition through the four stages of post-disaster housing (i.e., emergency shelter, temporary shelter, temporary housing, and permanent housing) based on the social vulnerability of the household. Finally, the framework predicts the number of students experiencing long-term homelessness and maps the students back to their pre-disaster school. The proposed framework is exemplified for the case of Hurricane Matthew-induced flooding in Lumberton, North Carolina. Findings highlight the disparate outcomes households with children face after major disasters and can be used to aid decision-making to reduce future disaster impacts on students

Flood Performance and Dislocation Assessment for Lumberton Homes after Hurricane Matthew

In order to better understand community resilience following a disaster, a multidisciplinary research team from the Center of Excellence (CoE) for Risk-Based Community Resilience Planning and the National Institute of Standards and Technology (NIST) jointly conducted a series of longitudinal field studies in the U.S. city of Lumberton, North Carolina following major flooding from Hurricane Matthew (2016). Damage surveys on structures and interviews with households were conducted during the first field study to explore physical, economic, and social impacts of major riverine flooding on this small, tri-racial community. This paper is focused on damage to housing and subsequent household dislocation. Empirical damage fragilities were developed for residential buildings using a comprehensive set of engineering damage inspection data collected by the team. Multi-variate models were developed to assess the consequences of physical damage to housing units for household dislocation, including socio-demographic factors. The goal was not to develop the definitive model of household dislocation, but rather to show how engineering and social science data can be combined to better understand the broader social impacts of disasters – in this case, household dislocation. This study may help inform assessments of flood damage and dislocation patterns for other U.S. communities as a function of construction, social, and economic makeup.This conferences presentation is published as Deniz, Derya; Sutley, Elaina J.; van de Lindt, John W.; Peacock, Walter Gillis; Rosenheim, Nathanael; Gu, Donghwan; Mitrani-Reiser, Judith; Dillard, Maria; Koliou, Maria; Hamideh, Sara. Flood Performance and Dislocation Assessment for Lumberton Homes after Hurricane Matthew. at the 13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019. Posted with permission. </p

Flood Performance and Dislocation Assessment for Lumberton Homes after Hurricane Matthew

In order to better understand community resilience following a disaster, a multidisciplinary research team from the Center of Excellence (CoE) for Risk-Based Community Resilience Planning and the National Institute of Standards and Technology (NIST) jointly conducted a series of longitudinal field studies in the U.S. city of Lumberton, North Carolina following major flooding from Hurricane Matthew (2016). Damage surveys on structures and interviews with households were conducted during the first field study to explore physical, economic, and social impacts of major riverine flooding on this small, tri-racial community. This paper is focused on damage to housing and subsequent household dislocation. Empirical damage fragilities were developed for residential buildings using a comprehensive set of engineering damage inspection data collected by the team. Multi-variate models were developed to assess the consequences of physical damage to housing units for household dislocation, including socio-demographic factors. The goal was not to develop the definitive model of household dislocation, but rather to show how engineering and social science data can be combined to better understand the broader social impacts of disasters – in this case, household dislocation. This study may help inform assessments of flood damage and dislocation patterns for other U.S. communities as a function of construction, social, and economic makeup

Validating Interdependent Community Resilience Modeling using Hindcasting

The resilience of communities prone to natural hazards can be enhanced through the use of risk-informed decision-making tools. These tools can provide community decision-makers key information, allowing them to consider an array of mitigation and/or recovery strategies. To comprehensively assess community resilience, all sectors that have an influence, including physical infrastructure (buildings, bridges, electric power networks, etc.) and the socio-economic systems should be considered. For this purpose, the Center for Risk-Based Community Resilience Planning (hereon referred to as the Center), headquartered at Colorado State University in Fort Collins, Colorado, USA, developed an Interdependent Networked COmmunity Resilience modeling Environment (IN-CORE) capable of simulating the effects of different natural hazards including tornadoes, earthquakes, tsunamis, among others, on physical and socio-economic sectors of a community while accounting for interdependencies between the various sectors. However, such a complex computational environment must be validated with each model being verified as a single component or sub-system. Within the Center, models are verified for accuracy as they are developed, but the combination of all the models must be verified for accuracy and then validated to ensure that it provides the desired output with the accuracy needed for risk-informed decisions. The community of Joplin Missouri in the United States was hit by an EF-5 tornado on May 22, 2011. In this paper, the city of Joplin is modeled in IN-CORE to estimate the building and electrical power network damage, economic disruption and recovery, infrastructure repair and recovery through several metrics, as well as population dislocation. Results are compared with best estimates obtained from collected post-event data, interpreted existing government documentation, and archived literature related to Joplin.Funding for this study was provided as part of Cooperative Agreement 70NANB15H044 between the National Institute of Standards and Technology (NIST) and Colorado State University. The content expressed in this paper are the views of the authors and do not necessarily represent the opinions or views of NIST or the U.S Department of Commerce. Researchers who helped with gathering data during the field trip to the city of Joplin are acknowledged