Funding Priorities: Data-Driven Approach for Prioritizing Community Health Needs in Vulnerable Communities.

The Patient Protection and Affordable Care Act of 2010 mandated nonprofit hospitals to complete community health needs assessments (CHNAs) every 3 years to identify priority health needs for the community they serve. The CHNA must include input from the community in the determination of health needs. Large variation exists across CHNAs on methods used in the integration of quantitative and qualitative data both in the determination and prioritization of health needs and those needs chosen by the hospital for community benefit funding. An important part of the CHNA is the prioritization of the needs identified, as it can influence hospital community benefit funding decisions. This article describes a method for clearly integrating qualitative and quantitative data in the CHNA process offering a best practice strategy for conducting CHNAs. The method uses an approach based on flexible, objective decision points that can be used to both generate a list of significant health needs and a prioritization of those needs based on community input, influencing funding priorities of the hospital. The method provides a standard approach useful across multiple hospital CHNAs in both rural and urban settings, and in collaborative-based CHNAs (local public health departments and hospitals) as well.

Community clusters of tsunami vulnerability in the US Pacific Northwest.

Many coastal communities throughout the world are threatened by local (or near-field) tsunamis that could inundate low-lying areas in a matter of minutes after generation. Although the hazard and sustainability literature often frames vulnerability conceptually as a multidimensional issue involving exposure, sensitivity, and resilience to a hazard, assessments often focus on one element or do not recognize the hazard context. We introduce an analytical framework for describing variations in population vulnerability to tsunami hazards that integrates (i) geospatial approaches to identify the number and characteristics of people in hazard zones, (ii) anisotropic path distance models to estimate evacuation travel times to safety, and (iii) cluster analysis to classify communities with similar vulnerability. We demonstrate this approach by classifying 49 incorporated cities, 7 tribal reservations, and 17 counties from northern California to northern Washington that are directly threatened by tsunami waves associated with a Cascadia subduction zone earthquake. Results suggest three primary community groups: (i) relatively low numbers of exposed populations with varied demographic sensitivities, (ii) high numbers of exposed populations but sufficient time to evacuate before wave arrival, and (iii) moderate numbers of exposed populations but insufficient time to evacuate. Results can be used to enhance general hazard-awareness efforts with targeted interventions, such as education and outreach tailored to local demographics, evacuation training, and/or vertical evacuation refuges.

Getting more for your money: designing community needs assessments to build collaboration and capacity in hospital system community benefit work.

Most community health needs assessments (CHNAs) are unilateral in nature and fail to include a community-based participatory research (CBPR) approach, limiting them in their scope. Nonprofit hospitals are required to conduct CHNAs every 3 years to determine where community prevention dollars should be spent. In 2010, a CBPR CHNA approach was conducted with four hospital systems in Northern California. Merging concepts from organization development, the approach included (a) goal determination, (b) use of a guiding framework, (c) creation of a container in which to interact, (d) established feedback loops, and (e) intentional trust-building exercises. The approach was to build lasting relationships between hospital systems that would extend beyond the CHNA. Results using this approach revealed that members representing all four hospital systems (a) began to meet regularly after the CHNA was completed, (b) increased collaboration with other community organizations, (c) expanded their level of intraorganization partnerships, (d) enjoyed the process, (e) felt that their professional knowledge expanded, and (f) felt connected professionally and personally with other hospital representatives. As a result, other joint projects are underway. The results of this study indicate that using CBPR to design a CHNA can build sustained collaborative relationships between study participants that continue.

A sensitivity analysis of the social vulnerability index.

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003), examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty-four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.