Public health and emergency management agencies play a critical role in addressing the needs of vulnerable populations in preparation for and in response to emergencies. Identifying and leveraging community assets is a way to address such needs. This study focuses on the development of a process and tools to engage community leaders in sharing their knowledge about their community characteristics and assets useful for emergency planning.

We conducted interviews with community leaders across five study sites with the goal of understanding what type of local knowledge community leaders are able to share in regard to emergency preparedness. Based on the interview results we developed and tested a mobile application as a mobile friendly directory of community assets.

We identified two main types of local knowledge about community assets for emergency preparedness: communication-based and trust-based local knowledge. We created an application to facilitate the sharing of such knowledge. Community leaders were able to share local knowledge across four areas: communication-based assets, trust-based assets, spatial-based assets and personal-preparedness assets.

Community leaders’ engagement in preparedness efforts is important to identify community assets that can be leveraged to address the needs of the most vulnerable segments of a community.

When Hurricane Harvey struck the coastline of Texas in 2017, it caused 88 fatalities and over US $125 billion in damage, along with increased emergency department visits in Houston and in cities receiving hurricane evacuees, such as the Dallas-Fort Worth metroplex (DFW).

This study explored demographic indicators of vulnerability for patients from the Hurricane Harvey impact area who sought medical care in Houston and in DFW. The objectives were to characterize the vulnerability of affected populations presenting locally, as well as those presenting away from home, and to determine whether more vulnerable communities were more likely to seek medical care locally or elsewhere.

We used syndromic surveillance data alongside the Centers for Disease Control and Prevention Social Vulnerability Index to calculate the percentage of patients seeking care locally by zip code tabulation area. We used this variable to fit a spatial lag regression model, controlling for population density and flood extent.

Communities with more patients presenting for medical care locally were significantly clustered and tended to have greater socioeconomic vulnerability, lower household composition vulnerability, and more extensive flooding.

These findings suggest that populations remaining in place during a natural disaster event may have needs related to income, education, and employment, while evacuees may have more needs related to age, disability, and single-parent household status.

There is a paucity of scientific analysis that has examined spatial heterogeneities in the socioeconomic vulnerabilities related to coronavirus disease 2019 (COVID-19) risk and potential mitigation strategies at the sub-national level in India. The present study examined the demographic, socioeconomic, and health system-related vulnerabilities shaping COVID-19 risk across 36 states and union territories in India.

Using secondary data from the Ministry of Health and Family Welfare (MoHFW), Government of India; Census of India, 2011; National Family Health Survey, 2015-16; and various rounds of the National Sample Survey, we examined socioeconomic vulnerabilities associated with COVID-19 risk at the sub-national level in India from March 16, 2020, to May 3, 2020. Descriptive statistics, principal component analysis, and the negative binomial regression model were used to examine the predictors of COVID-19 risk in India.

There persist substantial heterogeneities in the COVID-19 risk across states and union territories in India. The underlying demographic, socioeconomic, and health infrastructure characteristics drive the vulnerabilities related to COVID-19 in India.

This study emphasizes that concerted socially inclusive policy action and sustained livelihood/economic support for the most vulnerable population groups is critical to mitigate the impact of the COVID-19 pandemic in India.

To determine accessibility of the primary healthcare system for patients with stroke recently discharged from hospital.

This project mapped retrospective patient location data and the location of primary healthcare services in the same region. Patient location data were from all patients with stroke (N = 1595: January 2011–January 2017) discharged from one metropolitan hospital to the local Primary Health Network. Geographic Information System technology was used to map the patient discharge locations and the spatial distribution of primary healthcare services (general practitioner, pharmacy, allied health) across the region. Road network data were used to measure the level of access from each patient’s discharge location to the services.

Access to primary healthcare services was variable. Areas with larger proportions of patients with stroke did not necessarily have good service access. With an increase in travel time, the number of services accessible to patients also increased. However, the spatial variation of access to services remained largely unchanged.

Access to primary healthcare services for patients with stroke varies spatially, with a trend towards relatively low levels of accessibility for many patients. There is an urgent need for future planning to consider geographical access to primary healthcare services for patients with stroke.

To demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness.

We used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression.

We found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island.

Predictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities. (Disaster Med Public Health Preparedness. 2016;10:261–273)