Source Attribution of Food-Borne Zoonoses in New Zealand: A Modified Hald Model

A Bayesian approach was developed by Hald et al .^{( 1 )} to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens. Our modified Hald model has several advantages over the original approach, which include the introduction of uncertainty in the estimates of source prevalence and an improved strategy for identifiability. We have applied our modified model to the two major food-borne zoonoses in New Zealand, namely, campylobacteriosis and salmonellosis. Major challenges were the data quality for salmonellosis and the inclusion of environmental sources of campylobacteriosis. We conclude that by modifying the Hald model we have improved its identifiability, made it more applicable to countries with less intensive surveillance, and feasible for other pathogens, in particular with respect to the inclusion of nonfood sources. The wider application and better understanding of this approach is of particular importance due to the value of the model for decision making and risk management.     

Characterizing the Risk of Infection from Mycobacterium tuberculosis in Commercial Passenger Aircraft Using Quantitative Microbial Risk Assessment

Quantitative microbial risk assessment was used to predict the likelihood and spatial organization of Mycobacterium tuberculosis ( Mtb ) transmission in a commercial aircraft. Passenger exposure was predicted via a multizone Markov model in four scenarios: seated or moving infectious passengers and with or without filtration of recirculated cabin air. The traditional exponential ( k  = 1) and a new exponential ( k  = 0.0218) dose-response function were used to compute infection risk. Emission variability was included by Monte Carlo simulation. Infection risks were higher nearer and aft of the source; steady state airborne concentration levels were not attained. Expected incidence was low to moderate, with the central 95% ranging from 10⁻⁶ to 10⁻¹ per 169 passengers in the four scenarios. Emission rates used were low compared to measurements from active TB patients in wards, thus a "superspreader" emitting 44 quanta/h could produce 6.2 cases or more under these scenarios. Use of respiratory protection by the infectious source and/or susceptible passengers reduced infection incidence up to one order of magnitude.     

Allowing Family to be Family: End-of-Life Care in Veterans Affairs Medical Foster Homes

The Medical Foster Home program is a unique long-term care program coordinated by the Veterans Health Administration. The program pairs Veterans with private, 24-hour a day community-based caregivers who often care for Veterans until the end of life. This qualitative study explored the experiences of care coordination for Medical Foster Home Veterans at the end of life with eight Veterans’ family members, five Medical Foster Home caregivers, and seven Veterans Health Administration Home-Based Primary Care team members. A case study, qualitative content analysis identified these themes addressing care coordination and impact of the Medical Foster Home model on those involved: (a) Medical Foster Home program supports Veterans’ families; (b) Medical Foster Home program supports the caregiver as family; (c) Veterans’ needs are met socially and culturally at the end of life; and (d) the changing needs of Veterans, families, and caregivers at Veterans’ end of life are addressed. Insights into how to best support Medical Foster Home caregivers caring for Veterans at the end of life were gained including the need for more and better respite options and how caregivers are compensated in the month of the Veteran’s death, as well as suggestions to navigate end-of-life care coordination with multiple stakeholders involved.     

Green,Yellow, Red: Implementing a New Workflow for Collection Management at College of Charleston Libraries

Abstract

This article summarizes the North Carolina Serials Conference presentation “Green, Yellow, Red: Implementing a New Workflow for Collection Management at College of Charleston Libraries,” a case study evaluating a new workflow created by the Collection and Content Services Department at the College of Charleston Libraries to streamline the renewals process after significant departmental and staff changes. The librarians involved in creating the workflow found that they could not only use it to organize their renewals processes, but also to open communication with faculty regarding underutilized resources.     

SMALL‐AREA ESTIMATION OF POVERTY: THE AID INDUSTRY STANDARD AND ITS ALTERNATIVES

Small‐area estimation of poverty‐related variables is an increasingly important analytical tool in targeting the delivery of food and other aid in developing countries. We compare two methods for the estimation of small‐area means and proportions, namely empirical Bayes and composite estimation, with what has become the international standard method of Elbers, Lanjouw & Lanjouw (2003) . In addition to differences among the sets of estimates and associated estimated standard errors, we discuss data requirements, design and model selection issues and computational complexity. The Elbers, Lanjouw and Lanjouw (ELL) method is found to produce broadly similar estimates but to have much smaller estimated standard errors than the other methods. The question of whether these standard error estimates are downwardly biased is discussed. Although the question cannot yet be answered in full, as a precautionary measure it is strongly recommended that the ELL model be modified to include a small‐area‐level error component in addition to the cluster‐level and household‐level errors it currently contains. This recommendation is particularly important because the allocation of billions of dollars of aid funding is being determined and monitored via ELL. Under current aid distribution mechanisms, any downward bias in estimates of standard error may lead to allocations that are suboptimal because distinctions are made between estimated poverty levels at the small‐area level that are not significantly different statistically.     

Bayesian D-optimal supersaturated designs

We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E(s²)$E(s^2)$ criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.