Publications

In this issue of Academic Medicine, Kitterman and colleagues report the results of an evaluation of the prevalence and cost of low-enrolling studies (zero or one participant enrolled) conducted at Oregon Health & Science University (OHSU). They found that one-third of all studies terminated between 2005 and 2009 at OHSU had low enrollment and that these low-enrolling studies cost the institution almost $1 million annually. The recruitment of research participants is critical to conducting clinical and translational research. Failure to recruit research participants has a negative financial impact, but, more importantly, underenrolled studies do not contribute to scientific or clinical knowledge. In this commentary, the authors describe four areas in which academic health centers (AHCs) could invest more effort and resources to improve the recruitment of research participants. First, more planning and resources should be put into determining the feasibility of participant recruitment. Second, studies that are underenrolling should be terminated early to prevent unethical research, to save financial and other resources, and to allow these resources to be applied to successful research. Third, AHCs should professionalize, centralize, and automate participant recruitment. Fourth, AHCs should take a leadership role in partnering with the public to improve participation in clinical research. Participant recruitment must be improved if clinical and translational research is to meet its promise of improving health.

The change in prevalence and total Veterans Affairs (VA) spending were estimated for 16 chronic condition categories between 2000 and 2008. The drivers of changes in spending also were examined. Chronic conditions were identified through diagnoses in encounter records, and treatment costs per patient were estimated using VA cost data and regression models. The estimated differences in total VA spending between 2000 and 2008 and the contributions of population increase, differences in prevalence, and differences in treatment costs were evaluated. Most of the spending increases during the study period were driven by the increase in the VA patient population from 3.3 million in 2000 to 4.9 million in 2008. Spending on renal failure increased the most, by more than $1.5 billion, primarily because of higher prevalence. Higher treatment costs did not contribute much to higher spending; lower costs per patient for several conditions may have helped to slow spending for diabetes, chronic obstructive pulmonary disease, heart conditions, renal failure, dementia, and stroke. Lowering treatment costs per patient for common conditions can help slow spending for chronic conditions, but most of the increase in spending in the study period was the result of more patients seeking care from VA providers and the higher prevalence of conditions among patients. As the VA patient population continues to age and to develop more co-morbidities, and as returning veterans seek care for service-related problems, higher spending on chronic conditions will become a more prominent issue for the VA health care system.

Conservation biologists face many challenges in assessing health, immune status and infectious diseases in protected species. These challenges include unpredictable sample populations, diverse genetic and environmental backgrounds of the animals, as well as the practical, legal and ethical issues involved in experimentation. The use of whole genome scale transcriptomics with animal samples obtained in a minimally invasive manner is an approach that shows promise for health assessment. In this study we assessed the utility of a microarray to identify changes in gene expression predictive of health status by interrogating blood samples from California sea lions (Zalophus californianus) in rehabilitation. A custom microarray was developed from the commercially available dog microarray (Canis familiaris) by selecting probes that demonstrated reliable cross-hybridization with RNA in sea lion blood. This custom microarray was used for the analysis of RNA from 73 sea lion blood samples, from animals with a broad spectrum of health changes. Both traditional classifying techniques and newer artificial neural network approaches correctly classified sea lions with respect to health status, primarily distinguishing between leptospirosis infection and domoic acid exposure. Real time PCR validation for a small set of genes, followed by sequencing, showed good correlation with array results and high identity (96-98%) between the dog and sea lion sequences. This approach to health status classification shows promise for disease identification in a clinical setting, and assessment of health status of wildlife.