Predicting pharmacy naloxone stocking and dispensing following a statewide standing order, Indiana 2016

BACKGROUND: While naloxone, the overdose reversal medication, has been available for decades, factors associated with its availability through pharmacies remain unclear. Studies suggest that policy and pharmacist beliefs may impact availability. Indiana passed a standing order law for naloxone in 2015 to increase access to naloxone. OBJECTIVE: To identify factors associated with community pharmacy naloxone stocking and dispensing following the enactment of a statewide naloxone standing order. METHODS: A 2016 cross-sectional census of Indiana community pharmacists was conducted following a naloxone standing order. Community, pharmacy, and pharmacist characteristics, and pharmacist attitudes about naloxone dispensing, access, and perceptions of the standing order were measured. Modified Poisson and binary logistic regression models attempted to predict naloxone stocking and dispensing, respectively. RESULTS: Over half (58.1%) of pharmacies stocked naloxone, yet 23.6% of pharmacists dispensed it. Most (72.5%) pharmacists believed the standing order would increase naloxone stocking, and 66.5% believed it would increase dispensing. Chain pharmacies were 3.2 times as likely to stock naloxone. Naloxone stocking was 1.6 times as likely in pharmacies with more than one full-time pharmacist. Pharmacies where pharmacists received naloxone continuing education in the past two years were 1.3 times as likely to stock naloxone. The attempted dispensing model yielded no improvement over the constant-only model. CONCLUSIONS: Pharmacies with larger capacity took advantage of the naloxone standing order. Predictors of pharmacist naloxone dispensing should continue to be explored to maximize naloxone access

Validation of EPIC for Two Watersheds in Southwest Iowa

The Erosion Productivity Impact Calculator (EPIC) model is validated using long-term data collected for two southwest Iowa watersheds that have been cropped in continuous corn under two different tillage systems. The annual hydrologic balance was calibrated during 1988-94 by adjusting the runoff curve numbers and residue effects on soil evaporation. Model validation was performed for 1976-87 using both summary statistics and parametric and nonparametric statistical tests. Overall, results show that EPIC was able to replicate the long-term relative differences between the two tillage systems

Effects of Training on Social Work, Nursing and Medical Trainees' Knowledge, Attitudes and Beliefs Related to Screening and Brief Intervention for Alcohol Use

Indiana University's Schools of Social Work, Nursing and Medicine formed a consortium to advance education for Screening Brief Intervention and Referral to Treatment (SBIRT). Trainees participated in SBIRT training and completed data collection before, immediately after, and 30 days after a face-to-face training. The study explored participants' perceptions about the training and the likelihood of implementing SBI in practice, including attitudes and beliefs that may be predictive of SBIRT utilization in clinical practice. Results show the training targeting SBI and MI behaviors may improve participants' self-reported competence with SBI. This improvement was consistent and strong in all programs. The study results also provided a preliminary indication that the training affected participants' perception of time utilization and compensation for performing SBI

Interplay between spatially explicit sediment sourcing, hierarchical river-network structure, and in-channel bed material sediment transport and storage dynamics

Understanding how sediment moves along source to sink pathways through watersheds„from hillslopes to channels and in and out of floodplains„is a fundamental problem in geomorphology. We contribute to advancing this understanding by modeling the transport and in-channel storage dynamics of bed material sediment on a river network over a 600æyear time period. Specifically, we present spatiotemporal changes in bed sediment thickness along an entire river network to elucidate how river networks organize and process sediment supply. We apply our model to sand transport in the agricultural Greater Blue Earth River Basin in Minnesota. By casting the arrival of sediment to links of the network as a Poisson process, we derive analytically (under supply-limited conditions) the time-averaged probability distribution function of bed sediment thickness for each link of the river network for any spatial distribution of inputs. Under transport-limited conditions, the analytical assumptions of the Poisson arrival process are violated (due to in-channel storage dynamics) where we find large fluctuations and periodicity in the time series of bed sediment thickness. The time series of bed sediment thickness is the result of dynamics on a network in propagating, altering, and amalgamating sediment inputs in sometimes unexpected ways. One key insight gleaned from the model is that there can be a small fraction of reaches with relatively low-transport capacity within a nonequilibrium river network acting as ñbottlenecksî that control sediment to downstream reaches, whereby fluctuations in bed elevation can dissociate from signals in sediment supply. ©2017. American Geophysical Union. All Rights Reserved

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems

The important questions about agriculture, climate, and sustainability have become increasingly complex and require a coordinated, multifaceted approach for developing new knowledge and understanding. A multistate, transdisciplinary project was begun in 2011 to study the potential for both mitigation and adaptation of corn-based cropping systems to climate variations. The team is measuring the baseline as well as change of the system\u27s carbon (C), nitrogen (N), and water footprints, crop productivity, and pest pressure in response to existing and novel production practices. Nine states and 11 institutions are participating in the project, necessitating a well thought out approach to coordinating field data collection procedures at 35 research sites. In addition, the collected data must be brought together in a way that can be stored and used by persons not originally involved in the data collection, necessitating robust procedures for linking metadata with the data and clearly delineated rules for use and publication of data from the overall project. In order to improve the ability to compare data across sites and begin to make inferences about soil and cropping system responses to climate across the region, detailed research protocols were developed to standardize the types of measurements taken and the specific details such as depth, time, method, numbers of samples, and minimum data set required from each site. This process required significant time, debate, and commitment of all the investigators involved with field data collection and was also informed by the data needed to run the simulation models and life cycle analyses. Although individual research teams are collecting additional measurements beyond those stated in the standardized protocols, the written protocols are used by the team for the base measurements to be compared across the region. A centralized database was constructed to meet the needs of current researchers on this project as well as for future use for data synthesis and modeling for agricultural, ecosystem, and climate sciences

Data management for prospective research studies using SAS® software

<p>Abstract</p> <p>Background</p> <p>Maintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created.</p> <p>Methods</p> <p>Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. The data management procedures are described.</p> <p>Results</p> <p>Study data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated.</p> <p>Conclusion</p> <p>Data management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.</p