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A model-driven approach to qualitatively assessing the added value of community coalitions.

ABSTRACT: Community-based coalitions are commonly formed to plan and to carry out public health interventions. The literature includes evaluations of coalition structure, composition, and functioning; evaluations of community-level changes achieved through coalition activities; and the association between coalition characteristics and various indicators of success. Little information is available on the comparative advantage or "added value" of conducting public health interventions through coalitions as opposed to less structured collaborative mechanisms. This paper describes a qualitative, iterative process carried out with site representatives of the Controlling Asthma in American Cities Project (CAACP) to identify outcomes directly attributable to coalitions. The process yielded 2 complementary sets of results. The first were criteria that articulated and limited the concept of "added value of coalitions". The criteria included consensus definitions, an organizing figure, a logic model, and inclusion/exclusion criteria. The second set of results identified site-specific activities that met the definitional criteria and were, by agreement, examples of CAACP coalitions' added value. Beyond the specific findings relevant to the added value of coalitions in this project, the use of a social ecological model to identify the components of added value and the placement of those components within a logic model specific to coalitions should provide useful tools for those planning and assessing coalition-based projects.

SUBMITTER: Herman EJ

PROVIDER: S-EPMC3042058 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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A model-driven approach to qualitatively assessing the added value of community coalitions.

Herman Elizabeth Jane EJ Keller Adrienne A Davis Adam A Ehrensberger Ryan R Telleen Sharon S Kurz Richard R Nesvold Jill Heins JH Findley Sally S Bryant-Stephens Tyra T Benson Mindy M Fierro Leslie L

Journal of urban health : bulletin of the New York Academy of Medicine 20110201

Community-based coalitions are commonly formed to plan and to carry out public health interventions. The literature includes evaluations of coalition structure, composition, and functioning; evaluations of community-level changes achieved through coalition activities; and the association between coalition characteristics and various indicators of success. Little information is available on the comparative advantage or "added value" of conducting public health interventions through coalitions as ...[more]

PMID: 21337059

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Project description:BackgroundValue sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors.ObjectivesThe aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets.MethodsWe developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors.ResultsThirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets-while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients.ConclusionThe proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

Project description:ObjectivesTo evaluate the diagnostic performance and reliability of MRI descriptors used for the detection of Ménière's disease (MD) on delayed post-gadolinium MRI. To determine which combination of descriptors should be optimally applied and whether analysis of the vestibular aqueduct (VA) contributes to the diagnosis.Materials and methodsThis retrospective single centre case-control study evaluated delayed post-gadolinium MRI of patients with Ménièriform symptoms examined consecutively between Dec 2017 and March 2023. Two observers evaluated 17 MRI descriptors of MD and quantified perilymphatic enhancement (PLE) in the cochlea. Definite MD ears according to the 2015 Barany Society criteria were compared to control ears. Cohen's kappa and diagnostic odds ratio (DORs) were calculated for each descriptor. Forward stepwise logistic regression determined which combination of MRI descriptors would best predict MD ears, and the area under the receiver operating characteristic curve for this model was measured.ResultsA total of 227 patients (mean age 48.3 ± 14.6, 99 men) with 96 definite MD and 78 control ears were evaluated. The presence of saccular abnormality (absent, as large as or confluent with the utricle) performed best with a DOR of 292.6 (95% confidence interval (CI), 38.305-2235.058). All VA descriptors demonstrated excellent reliability and with DORs of 7.761 (95% CI, 3.517-17.125) to 18.1 (95% CI, 8.445-39.170). Combining these saccular abnormalities with asymmetric cochlear PLE and an incompletely visualised VA correctly classified 90.2% of cases (sensitivity 84.4%, specificity 97.4%, AUC 0.938).ConclusionEither absent, enlarged or confluent saccules are the best predictors of MD. Incomplete visualisation of the VA adds value to the diagnosis.Clinical relevance statementA number of different MRI descriptors have been proposed for the diagnosis of Ménière's disease, but by establishing the optimally performing MRI features and highlighting new useful descriptors, there is an opportunity to improve the diagnostic performance of Ménière's disease imaging.Key points• A comprehensive range of existing and novel vestibular aqueduct delayed post-gadolinium MRI descriptors were compared for their diagnostic performance in Ménière's disease. • Saccular abnormality (absent, confluent with or larger than the utricle) is a reliable descriptor and is the optimal individual MRI predictor of Ménière's disease. • The presence of this saccule descriptor or asymmetric perilymphatic enhancement and incomplete vestibular aqueduct visualisation will optimise the MRI diagnosis of Ménière's disease.

Dataset Information

A model-driven approach to qualitatively assessing the added value of community coalitions.

Publications

A model-driven approach to qualitatively assessing the added value of community coalitions.

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