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Vesicular stomatitis (VS) is a vector‐borne viral disease that causes lesions in livestock, premises, county and state quarantines, and important economic losses. We investigated vector–habitat characteristics for vectors associated with VS in regions of recurrent disease within the western United States (US) that consistently lead to the environment where vector, host, and pathogen populations intersect to enable pathogen transmission. We analyzed the habitats of previously identified insect vectors, including black flies (BFs) (Simulium vittatum complex), biting midges (BMs) (Culicoides variipennis complex, which includes Culicoides sonorensis), and sand flies (SFs) (Lutzomyia shannoni) in six regions of interest (ROIs) containing hotspots of VS ranging from Texas (TX) to Wyoming. This analysis broadened the understanding of (1) how regions of reoccurring VS differ from the broader western US, (2) how geographically separated regions and hotspots are similar across time, and (3) how vector–environment habitat a priori knowledge relates to observed hotspots. Analysis of watershed factors (livestock densities, land‐cover proportions, stream and lake densities, and irrigation methods) indicated a complex system separating areas with high, recurring VS from the broader western US. Although no single characteristic separated the six ROIs from other areas, we found two distinct emerging groups (northern ROI and TX). Hotspots, estimated from monthly VS concentrations, evolved northward throughout the year and most hotspots were closer to flowing water and agricultural land than the broader ROI. All ROIs contained environmental conditions suitable for multiple vectors at some point in the year, but BFs had the highest suitability scores, whereas BM scores were lower and varied annually with higher suitability in summer. SFs had the lowest suitability score in all ROIs, consistent with their low likelihood of being vectors. BM habitat patches were often orders of magnitude smaller than BF patches, and hotspot patches reinforce the likelihood that BF may be the most critical vector in northern ROI, whereas both BM and BF have similar likelihood in southern ROI. Given limited existing vector data, this analysis provides an alternate pathway for using habitat information to associate likely vectors responsible for transmission. Results could support early warning and mitigation efforts to reduce the incidence of VS. [ABSTRACT FROM AUTHOR]

The article offers information related to the growth of freestanding emergency departments (ED) in the U.S. It mentions that growth has been spurred by a variety of factors including overcrowded hospital-based EDs, expanding demands for emergency care, a desire by health care systems to create satellite locations to attract patients, health care entrepreneurship, and evolving state rules that have made it easier in some localities to open them.

Objectives While the Affordable Care Act seeks to reduce emergency department ( ED) visits for outpatient-treatable conditions, it remains unclear whether Medicaid patients or the uninsured have adequate access to follow-up care. The goal of this study was to determine the availability of follow-up orthopedic care by insurance status. Methods Using simulated patient methodology, all 102 eligible general orthopedic practices in Dallas-Fort Worth, Texas, were contacted twice by a caller requesting follow-up for an ankle fracture diagnosed in a local ED using a standardized script that differed by insurance status. Practices were randomly assigned to paired private and uninsured or Medicaid and uninsured scenarios. Results We completed 204 calls: 59 private, 43 Medicaid, and 102 uninsured. Appointment success rate was 83.1% for privately insured (95% confidence interval [ CI] = 73.2% to 92.9%), 81.4% for uninsured (95% CI = 73.7% to 89.1%), and 14.0% for Medicaid callers (95% CI = 3.2% to 24.7%). Controlling for paired calls to the same practice, an uninsured caller had 5.7 times higher odds (95% CI = 2.74 to 11.71) of receiving an appointment than a Medicaid caller (p < 0.001), but the same odds as a privately insured caller (odds ratio = 1.0, 95% CI = 0.19 to 5.37, p = 1.0). Uninsured patients had to bring a median of $350 (interquartile range = $250 to $400) to their appointment to be seen, and only two uninsured patients were able to obtain an appointment for $100 or less up front. In comparison, typical total payments collected for privately insured patients were $236 and for Medicaid patients $128. When asked where else they could go, 49 (48%) uninsured callers and one Medicaid caller (2%) were directed to local public hospital EDs as alternative sources of care. Of the practices that appeared on Medicaid's published list of orthopedic providers accepting new patients, 15 told callers that they did not accept Medicaid, 11 did not treat ankles, nine listed nonworking phone numbers, and only three actually scheduled an appointment for the Medicaid caller. Conclusions Less than one in seven Medicaid patients could obtain orthopedic follow-up after an ED visit for a fracture, and prices quoted to the uninsured were 30% higher than typical negotiated rates paid by the privately insured. High up-front costs for uninsured patients and low appointment availability for Medicaid patients may leave these patients with no other option than the ED for necessary care. [ABSTRACT FROM AUTHOR]

Objective: Rank county health using a Bayesian factor analysis model.Data Sources: Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009).Study Design: Our model builds on the existing county health rankings (CHRs) by using data-derived weights to compute ranks from mortality and morbidity variables, and by quantifying uncertainty based on population, spatial correlation, and missing data. We apply our model to Wisconsin, which has comprehensive data, and Texas, which has substantial missing information.Data Collection Methods: The data were downloaded from www.countyhealthrankings.org.Principal Findings: Our estimated rankings are more similar to the CHRs for Wisconsin than Texas, as the data-derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHRs and our ranks are 0.89 for Wisconsin and 0.65 for Texas. Uncertainty is especially severe for Texas given the state's substantial missing data.Conclusions: The reliability of comprehensive CHRs varies from state to state. We advise focusing on the counties that remain among the least healthy after incorporating alternate weighting methods and accounting for uncertainty. Our results also highlight the need for broader geographic coverage in health data. [ABSTRACT FROM AUTHOR]