Academic nursing leadership in a rural setting: Different game,same standards

The call for transformation of nursing education and practice continues to be a national priority. The American Association of Colleges of Nursing recommends enhanced partnerships between academic nursing and academic health centers to advance nursing and healthcare. For academic leaders in rural settings, the context of health and healthcare means that these partnerships are vastly different from academic health centers. The purpose of this article is to describe the context of nursing education and practice in regions that are predominantly rural. The challenges and opportunities for rural academic leaders as they respond to calls for transforming the education of the future nursing workforce are described from the perspective of resources, recruitment and retention of faculty, clinical education and faculty practice, scholarship, and fundraising. Meeting rural health workforce needs is a national imperative and a priority of academic nursing leaders in rural settings.     

HIV Infection Among People Who Inject Drugs in the United States: Geographically Explained Variance Across Racial and Ethnic Groups

Objectives. We explored how variance in HIV infection is distributed across multiple geographical scales among people who inject drugs (PWID) in the United States, overall and within racial/ethnic groups.Methods. People who inject drugs (n = 9077) were recruited via respondent-driven sampling from 19 metropolitan statistical areas (MSAs) for the Centers for Disease Control and Prevention’s 2009 National HIV Behavioral Surveillance system. We used multilevel modeling to determine the percentage of variance in HIV infection explained by zip codes, counties, and MSAs where PWID lived, overall and for specific racial/ethnic groups.Results. Collectively, zip codes, counties, and MSAs explained 29% of variance in HIV infection. Within specific racial/ethnic groups, all 3 scales explained variance in HIV infection among non-Hispanic/Latino White PWID (4.3%, 0.2%, and 7.5%, respectively), MSAs explained variance among Hispanic/Latino PWID (10.1%), and counties explained variance among non-Hispanic/Latino Black PWID (6.9%).Conclusions. Exposure to potential determinants of HIV infection at zip codes, counties, and MSAs may vary for different racial/ethnic groups of PWID, and may reveal opportunities to identify and ameliorate intraracial inequities in exposure to determinants of HIV infection at these geographical scales.Since the mid-1990s, there has been an increase in studies evaluating whether features of the social, economic, physical, and political environment (i.e., place characteristics) affect health. This focus on place characteristics is evident in the development of theories conceptualizing place characteristics as health determinants,1–3 in the use of geospatial and systematic social observation methods to measure place characteristics,4–10 in the application of multilevel modeling to assess the potential impacts of place characteristics,11–18 and in the recognition that interventions should not solely encourage individual behavior change but also modify environmental features.3,16,19Literature emerging from this field of research demonstrates that place characteristics operationalized at different geographical scales influence psychosocial processes and individual behaviors that increase vulnerability to several health outcomes. With rare exception,20–24 however, studies of place and health typically assess the potential influence of place characteristics at a single geographical scale and do not simultaneously evaluate characteristics of other geographical scales. For example, several studies, including our own,25,26 sample participants from a single metropolitan statistical area (MSA) to assess the relationships of census tract characteristics to health, without sampling participants from multiple MSAs to simultaneously assess the relationships of tract-, county-, and MSA-level characteristics to health.25–32 The decision to focus on characteristics of a single geographical scale may arise because of data availability, cost constraints, or feasibility.Studies of place and health that focus on a single geographical scale, however, may misspecify relationships and hinder the exploration of causal pathways in 2 ways. First, studies that focus on features measured at a single geographical scale may overlook potential health determinants that are operationalized at other geographical scales. For instance, research assessing the relationships of features of neighborhoods (e.g., economic deprivation, racial/ethnic composition, policing practices, and “crackdowns”) cannot determine the influence of policies, laws, and governmental expenditures that are operationalized at county, MSA, and state levels, and shape neighborhood environments. Second, studies of features of a single geographical scale cannot determine whether relationships between characteristics operating at one geographical scale are confounded, mediated, or modified by characteristics of other geographic scales.3,16,33 The possibility that at least 1 of these mechanisms can occur has been demonstrated in research conducted by Warner and Gomez, which suggests that, among Black women diagnosed with breast cancer, residing in census blocks with high concentrations of Black residents is more protective against mortality in more racially segregated metropolitan areas than less racially segregated metropolitan areas.34In addition, research assessing the association of place-based factors with health outcomes rarely highlights the extent to which variance in health outcomes is explained by place and place-based factors. Determining whether health outcomes vary geographically can generate hypotheses about inequities in exposure to potential place-based determinants of health, and thereby inform how interventions and social policies are developed and spatially concentrated.35The present study illustrates the generative possibilities of extending research beyond a single geographical scale by achieving 2 primary aims. The study’s first aim is to determine the share of total variance in HIV infection that is apportioned to zip codes, counties, and MSAs among people who inject drugs (PWID). In the United States, PWID account for 22% of people living with HIV,36 and a growing body of literature demonstrates that features of neighborhoods such as census-tract racial composition and block-level social or physical disorder are associated with HIV-related outcomes among PWID,37,38 as are features of MSAs, including drug-related law enforcement, income inequality, residential segregation, and health service access.39–41 Revealing the geographical scale to which variance in HIV infection is apportioned among PWID can stimulate hypotheses about inequities in exposure to place-based determinants of HIV and inform the development and tailoring of place-based interventions. For example, finding high MSA-level variance in HIV infection may support analyses of whether MSA-level variations in health care service access predict variance in HIV serostatus and, if they do, support interventions to increase health care access in low-access MSAs. In contrast, if little to no variance in HIV infection among PWID is apportioned to MSAs, PWID may encounter a relatively uniform exposure to health care service access.Previous studies have found that variance in some health outcomes vary across racial/ethnic groups.42,43 The second aim of this study therefore tests the hypothesis that variance in HIV infection will differ within each of 3 racial/ethnic groups of PWID: non-Hispanic/Latino Whites, non-Hispanic/Latino Blacks, and Hispanics/Latinos.     

A joint model for interval‐censored functional decline trajectories under informative observation

Multi‐state models are useful for modelling disease progression where the state space of the process is used to represent the discrete disease status of subjects. Often, the disease process is only observed at clinical visits, and the schedule of these visits can depend on the disease status of patients. In such situations, the frequency and timing of observations may depend on transition times that are themselves unobserved in an interval‐censored setting. There is a potential for bias if we model a disease process with informative observation times as a non‐informative observation scheme with pre‐specified examination times. In this paper, we develop a joint model for the disease and observation processes to ensure valid inference because the follow‐up process may itself contain information about the disease process. The transitions for each subject are modelled using a Markov process, where bivariate subject‐specific random effects are used to link the disease and observation models. Inference is based on a Bayesian framework, and we apply our joint model to the analysis of a large study examining functional decline trajectories of palliative care patients. Copyright © 2015 John Wiley & Sons, Ltd.