Association of US Households' Disaster Preparedness With Socioeconomic Characteristics, Composition, and Region.

Importance: Since the terrorist attacks on September 11, 2001, the US government has promoted household disaster preparedness, but preparedness remains low. Objective: To identify disparities in disaster preparedness among US households. Design, Setting, and Participants: This cross-sectional study used data from a nationally representative sample of US households from the 2017 American Housing Survey's topical section on preparedness to assess associations of disaster preparedness with households' socioeconomic characteristics, composition, and region. Logistic regressions were used to assess associations of household characteristics with overall preparedness, resource- and action-based preparedness, and specific preparedness items. Data analyses were completed on March 27, 2020. Exposures: Combined household income, head of household's education level, race/ethnicity, marital status of head of household, head of household aged 65 years or older, presence of children or a household member with a disability, and region. Main Outcomes and Measures: Nine actionable preparedness items, such as having an emergency carry-on kit (resource), food and water stockpiles (resource), and alternative communication plans and meeting locations (action). Items were summed for the measures of overall, resource-based, and action-based preparedness, with preparedness defined as meeting at least half of the criteria. Results: Among 16 725 included households, 9103 household heads were men (54.4%), 11 687 were married (69.9%), and 10 749 (66.1%) had some college education or higher. In all, 1969 household heads (11.8%) were black, while 2696 were Hispanic/Latino (16.1%); 3579 household heads (21.4%) were 65 years or older. A total of 7163 households (42.8%) included children, and 3533 households (21.2%) included a person with a disability. Households were more likely to fulfill at least half of the criteria for resource-based preparedness (10 950 households [65.5%]) than for action-based preparedness (6876 households [41.1%]). Wealthy households and those with household heads aged 65 years or older were more likely to fulfill at least half of resource-based items (wealthy households: adjusted odds ratio [aOR] by logged income, 1.18 [95% CI, 1.13-1.22]; household heads age ≥65 years: aOR, 1.42 [95% CI, 1.29-1.55) but less likely to fulfill action-based items (wealthy households: aOR: 0.96 [95% CI, 0.93-0.99]; household heads age ≥65 years: aOR, 0.92 [95% CI, 0.84-0.99]). Households with black household heads were more likely to fulfill items directly related to emergencies (carry-on emergency kit: aOR, 1.26 [95% CI, 1.14-1.39]; alternative communication plan: aOR, 1.55 [95% CI, 1.39-1.72]; alternative meeting location: aOR, 1.18 [95% CI, 1.07-1.31]) but less likely to fulfill resource-based items (at least half of resource items: aOR, 0.89 [95% CI, 0.80-0.99]). Conclusions and Relevance: These findings suggest that types of preparedness vary by household characteristics. Targeted strategies are needed to promote preparedness across communities.

Distributing Local Resources for Public Health Preparedness Grants: A Data-Driven Approach.

OBJECTIVES: To simulate allocations of Public Health Emergency Preparedness funds to counties using alternative metrics of need, minimum allocation amounts, and the proportion earmarked for discretionary considerations. DESIGN: We developed a county-level community resilience index of 57 New York State counties using publicly available indicators, which we incorporated into an interactive spreadsheet of 8 hypothetical allocation formulas with different combinations of population size, the index and its 5 domains, and population density. Simulations were compared with the 2013-2014 fiscal year grant allocation. RESULTS: New York allocated $6.27 million to counties outside New York City, with a median allocation of $78 038, ranging from $50 825 to $556 789. These allocations would vary under different strategies, with the largest changes among sparsely populated counties that currently receive a minimum allocation of $50 825. Allocations were sensitive to changes in minimum allocation, amount earmarked for discretionary allocation, and need indicator. CONCLUSIONS: Population-based approaches are commonly used but ignore important dimensions of need. It is feasible to include robust local community resilience measures in formulas, and interactive spreadsheet models can help stakeholders evaluate the consequences of alternative funding strategies.

Theory and Metrics of Community Resilience: A Systematic Literature Review Based on Public Health Guidelines.

A systematic literature review on quantitative methods to assess community resilience was conducted following Institute of Medicine and Patient-Centered Outcomes Research Institute standards. Community resilience is the ability of a community to bounce back or return to normal after a disaster strikes, yet there is no agreement on what this actually means. All studies reviewed addressed natural disasters, but the methodological approaches can be applied to technological disasters, epidemics, and terrorist attacks. Theoretical frameworks consider the association between vulnerability, resilience, and preparedness, yet these associations vary across frameworks. Because of this complexity, indexes based on composite indicators are the predominant methodological tool used to assess community resilience. Indexes identify similar dimensions but observe resilience at both the individual and geographical levels, reflecting a lack of agreement on what constitutes a community. A consistent, cross-disciplinary metric for community resilience would allow for identifying areas to apply short-term versus long-term interventions. A comparable metric for assessing geographic units in multiple levels and dimensions is an opportunity to identify regional strengths and weaknesses, develop timely targeted policy interventions, improve policy evaluation instruments, and grant allocation formulas design. (Disaster Med Public Health Preparedness. 2017;11:756-763).