A qualitative study of the relationship between clinician attributes, organization, and patient characteristics on implementation of a disease management program

The purpose of this study was to examine the challenges of integrating an asthma disease management (DM) program into a primary care setting from the perspective of primary care practitioners. A second goal was to examine whether barriers differed between urban-based and nonurban-based practices. Using a qualitative design, data were gathered using focus groups in primary care pediatric practices. A purposeful sample included an equal number of urban and nonurban practices. Participants represented all levels in the practice setting. Important themes that emerged from the data were coded and categorized. A total of 151 individuals, including physicians, advanced practice clinicians, registered nurses, other medical staff, and nonmedical staff participated in 16 focus groups that included 8 urban and 8 nonurban practices. Content analyses identified 4 primary factors influencing the implementation of a DM program in a primary care setting. They were related to providers, the organization, patients, and characteristics of the DM program. This study illustrates the complexity of the primary care environment and the challenge of changing practice in these settings. The results of this study identified areas in a primary care setting that influence the adoption of a DM program. These findings can assist in identifying effective strategies to change clinical behavior in primary care practices. © 2008 Mary Ann Liebert, Inc.

Organizational Attributes of Practices Successful at a Disease Management Program

Objective: To assess the contribution of organizational factors to implementation of 3 asthma quality measures: enrollment in a disease management program, development of a written treatment plan, and prescription of severity-appropriate anti-inflammatory therapy. Study design: A total of 138 pediatric clinicians and 247 office staff in 13 urban clinics and 23 nonurban private practices completed questionnaires about their practice's organizational characteristics (eg, leadership, communication, perceived effectiveness, job satisfaction). Results: 94% of the clinicians and 92% of the office staff completed questionnaires. When adjusted for confounders, greater practice activity and perceived effectiveness in meeting family needs were associated with higher rates of enrollment in the Easy Breathing program, whereas higher scores for 3 organizational characteristics-communication timeliness, decision authority, and job satisfaction-were associated with both higher enrollment and a greater number of written treatment plans. None of the organizational characteristics was associated with greater use of anti-inflammatory therapy. Conclusions: Three organizational characteristics predicted 2 quality asthma measures: use of a disease management program and creation of a written asthma treatment plan. If these organizational characteristics were amenable to change, then our findings could help focus interventions in areas of effective and acceptable organizational change. © 2009 Mosby, Inc. All rights reserved.

Role of Predalors, Habitat Attributes, and Spatial Autocorrelation on the Distribution of Eggs in the Northern Spectacled Salamander (Salamandrina perspicillata)

We predicted that the probability of egg occurrence of salamander Salamandrina perspicillata depended on stream features and predation by native crayfish Austropotamobius fulcisianus and the introduced trout Salmo trutta. We assessed the presence of S. perspicillata at 54 sites within a natural reserve of southern Tuscany, Italy. Generalized linear models with binomial errors were constructed using egg presence/absence and altitude, stream mean size and slope, electrical conductivity, water pH and temperature, and a predation factor, defined according to the presence/absence of crayfish and trout. Some competing models also included an autocovariate term, which estimated how much the response variable at any one sampling point reflected response values at surrounding points. The resulting models were compared using Akaike's information criterion. Model selection led to a subset of 14 models with Delta AIC(c) <7 (i.e., models ranging from substantial support to considerably less support), and all but one of these included an effect of predation. Models with the autocovariate term had considerably more support than those without the term. According to multimodel inference, the presence of trout and crayfish reduced the probability of egg occurrence from a mean level of 0.90 (SE limits: 0.98-0.55) to 0.12 (SE limits: 0.34-0.04). The presence of crayfish alone had no detectable effects (SE limits: 0.86-0.39). The results suggest that introduced trout have a detrimental effect on the reproductive output of S. perspicillata and confirm the fundamental importance of distinguishing the roles of endogenous and exogenous forces that act on population distribution.

Is Use of Optional Attributes and Associations in Conceptual Modeling Always Problematic? Theory and Empirical Tests

Prior research has argued that use of optional properties in conceptual models results in loss of information about the semantics of the domains represented by the models. Empirical research undertaken to date supports this argument. Nevertheless, no systematic analysis has been done of whether use of optional properties is always problematic. Furthermore, prior empirical research might have deliberately or unwittingly employed models where use of optionality always causes problems. Accordingly, we examine analytically whether use of optional properties is always problematic. We employ our analytical results to inform the design of an experiment where we systematically examined the impact of optionality on users’ ability to understand domains represented by different types of conceptual models. We found evidence that use of optionality undermines users’ ability to understand the domain represented by a model but that this effect weakens when use of mandatory properties to replace optional properties leads to more-complex models.

The marginal willingness-to-pay for attributes of a hypothetical HIV vaccine

This paper estimates the marginal willingness-to-pay for attributes of a hypothetical HIV vaccine using discrete choice modeling. We use primary data from 326 respondents from Bangkok and Chiang Mai, Thailand, in 2008–2009, selected using purposive, venue-based sampling across two strata. Participants completed a structured questionnaire and full rank discrete choice modeling task administered using computer-assisted personal interviewing. The choice experiment was used to rank eight hypothetical HIV vaccine scenarios, with each scenario comprising seven attributes (including cost) each of which had two levels. The data were analyzed in two alternative specifications: (1) best-worst; and (2) full-rank, using logit likelihood functions estimated with custom routines in Gauss matrix programming language. In the full-rank specification, all vaccine attributes are significant predictors of probability of vaccine choice. The biomedical attributes of the hypothetical HIV vaccine (efficacy, absence of VISP, absence of side effects, and duration of effect) are the most important attributes for HIV vaccine choice. On average respondents are more than twice as likely to accept a vaccine with 99% efficacy, than a vaccine with 50% efficacy. This translates to a willingness to pay US$383 more for a high efficacy vaccine compared with the low efficacy vaccine. Knowledge of the relative importance of determinants of HIV vaccine acceptability is important to ensure the success of future vaccination programs. Future acceptability studies of hypothetical HIV vaccines should use more finely grained biomedical attributes, and could also improve the external validity of results by including more levels of the cost attribute.

Changes in quality and safety attributes throughout long term storage of tomato juice processed by high pressure.

Tomato is the second most widely grown vegetable crop across the globe and it is one of widely cultivated crops in Sri Lanka. However, tomato industry in Sri Lanka facing a problem of high postharvest loss (54%) during the glut coupled with heavy revenue loss to the country by importing processed products. The aim of this work is to develop shelf-stable tomato product with maximum quality characteristics using high pressure processing (HPP). Tomato juice with altered and unaltered pH was processed using HPP at 600 MPa for 1 min after blanching (90 oC/2 min). As a control tomato juice was subjected to thermal processing (TP) at 95 oC /20 min. Processed samples were stored under 20oC and 28oC for 9 month period and analysed for total viable count (TVC) and instrumental colour (L, a, b) value at 0,1,2 3, and 4 week and 2, 3, 6 and 9 months interval. The raw juice sample had initial 6.69 log10 CFU/ml and both TP and HPP caused a more than 4.69 log10 reduction in the TVC of juice and microbial numbers remained low throughout the storage period even at 3 months after storage irrespective of the storage temperature. Both TP and HPP treated samples had the redness ⤘a value’ of 14.44-17.15 just after processing and showed non-significant reduction with storage in all the treatments after 3 months. The storage study results and discussed in relation to the end goal and compared with the literature.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.