Residents of rural jurisdictions face significant health challenges, including some of the highest rates of risky health behaviors and worst health outcomes of any group in the country. Rural communities are served by smaller local health departments (LHDs) that are more understaffed and underfunded than their suburban and urban peers. As a result of history and current need, rural LHDs are more likely than their urban peers to be providers of direct health services, leading to relatively lower levels of population-focused activities. This review examines the double disparity faced by rural LHDs and their constituents: pervasively poorer health behaviors and outcomes and a historical lack of investment by local, state, and federal public health entities.

We present here a new method for literature reviews that utilizes citation network analysis from academic databases. Special attention is paid to the construction of the citation network in order to ensure that the sample contains key publications in the field. Keyword searches in databases can be misleading because keywords are not used consistently across publications. Snowball sampling is onerous because the sample grows at an exponential rate. Constrained snowball sampling (limiting the percentage of articles collected at each level) is presented as a feasible and effective alternative that allows for robust citation analysis using a small fraction of the data. The methodology is outlined here. Free software for the R environment is also available.

Objectives. We identified the levels of joint action that led to collaboration between hospitals and local health departments (LHDs) using the hospital’s community health needs assessments (CHNAs). Methods. In 2014, we conducted a content analysis of Missouri nonprofit hospitals (n = 34) CHNAs, and identified hospitals based on previously reported collaboration with LHDs. We coded the content according to the level of joint action. A comparison sample (n = 50) of Missouri nonprofit hospitals provided the basic comparative information on hospital characteristics. Results. Among the hospitals identified by LHDs, 20.6% were “networking,” 20.6% were “coordinating,” 38.2% were “cooperating,” and 2.9% were “collaborating.” Almost 18% of study hospitals had no identifiable level of joint action with LHDs based on their CHNAs. In addition, comparison hospitals were more often part of a larger system (74%) compared with study hospitals (52.9%). Conclusions. The results of our study helped develop a better understanding of levels of joint action from a hospital perspective. Our results might assist hospitals and LHDs in making more informed decisions about efficient deployment of resources for assessment processes and implementation plans.

Objectives. To more clearly articulate, and more graphically demonstrate, the impact of poverty on various health outcomes and social conditions by comparing the poorest counties to the richest counties in the United States and to other countries in the world. Methods. We used 5-year averages for median household income to form the 3141 US counties into 50 new “states”—each representing 2% of the counties in the United States (62 or 63 counties each). We compared the poorest and wealthiest “states.” Results. We documented dramatic and statistically significant differences in life expectancy, smoking rates, obesity rates, and almost every other measure of health and well-being between the wealthiest and poorest “states” in the country. The populations of more than half the countries in the world have a longer life expectancy than do US persons living in the poorest “state.” Conclusions. This analysis graphically demonstrates the true impact of the extreme socioeconomic disparities that exist in the United States. These differences can be obscured when one looks only at state data, and suggest that practitioners and policymakers should increasingly focus interventions to address the needs of the poorest citizens in the United States.