HPV knowledge, HPV vaccine awareness and initiation among Hispanics: The role of language, information seeking, and information scanning

Background. Among Hispanics, the HPV vaccine has the potential to eliminate disparities in cervical cancer incidence and mortality but only if optimal rates of vaccination are achieved. Media can be an important information source for increasing HPV knowledge and awareness of the vaccine. Very little is known about how media use among Hispanics affects their HPV knowledge and vaccine awareness. Even less is known about what differences exist in media use and information processing among English- and Spanish-speaking Hispanics.^ Aims. Examine the relationships between three health communication variables (media exposure, HPV-specific information scanning and seeking) and three HPV outcomes (knowledge, vaccine awareness and initiation) among English- and Spanish-speaking Hispanics.^ Methods. Cross-sectional data from a survey administered to Hispanic mothers in Dallas, Texas was used for univariate and multivariate logistic regression analyses. Sample used for analysis included 288 mothers of females aged 8-22 recruited from clinics and community events. Dependent variables of interest were HPV knowledge, HPV vaccine awareness and initiation. Independent variables were media exposure, HPV-specific information scanning and seeking. Language was tested as an effect modifier on the relationship between health communication variables and HPV outcomes.^ Results. English-speaking mothers reported more media exposure, HPV-specific information scanning and seeking than Spanish-speakers. Scanning for HPV information was associated with more HPV knowledge (OR = 4.26, 95% CI = 2.41 - 7.51), vaccine awareness (OR = 10.01, 95% CI = 5.43 - 18.47) and vaccine initiation (OR = 2.54, 95% CI = 1.09 - 5.91). Seeking HPV-specific information was associated with more knowledge (OR = 2.27, 95% CI = 1.23 - 4.16), awareness (OR = 6.60, 95% CI = 2.74 - 15.91) and initiation (OR = 4.93, 95% CI = 2.64 - 9.20). Language moderated the effect of information scanning and seeking on vaccine awareness.^ Discussion. Differences in information scanning and seeking behaviors among Hispanic subgroups have the potential to lead to disparities in vaccine awareness.^ Conclusion. Findings from this study underscore health communication differences among Hispanics and emphasize the need to target Spanish language media as well as English language media aimed at Hispanics to improve knowledge and awareness.^

Translating knowledge into action: Information needs of decision-makers in healthcare

Purpose: The purpose of this study was to assess the healthcare information needs of decision-makers in a local US healthcare setting in efforts to promote the translation of knowledge into action. The focus was on the perceptions and preferences of decision-makers regarding usable information in making decisions as to identify strategies to maximize the contribution of healthcare findings to policy and practice. Methods: This study utilized a qualitative data collection and analysis strategy. Data was collected via open-ended key-informant interviews from a sample of 37 public and private-sector healthcare decision-makers in the Houston/Harris County safety net. The sample was comprised of high-level decision-makers, including legislators, executive managers, service providers, and healthcare funders. Decision-makers were asked to identify the types of information, the level of collaboration with outside agencies, useful attributes of information, and the sources, formats/styles, and modes of information preferred in making important decisions and the basis for their preferences. Results: Decision-makers report acquiring information, categorizing information as usable knowledge, and selecting information for use based on the application of four cross-cutting thought processes or cognitive frameworks. In order of apparent preference, these are time orientation, followed by information seeking directionality, selection of validation processes, and centrality of credibility/reliability. In applying the frameworks, decision-makers are influenced by numerous factors associated with their perceptions of the utility of information and the importance of collaboration with outside agencies in making decisions as well as professional and organizational characteristics. Conclusion: An approach based on the elucidated cognitive framework may be valuable in identifying the reported contextual determinants of information use by decision-makers in US healthcare settings. Such an approach can facilitate active producer/user collaborations and promote the production of mutually valued, comprehensible, and usable findings leading to sustainable knowledge translation efforts long-term.^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^

Simulating Data Journalism to Communicate Hydrological Information from Sensor Networks

Presenting relevant information via web-based user friendly interfac- es makes the information more accessible to the general public. This is especial- ly useful for sensor networks that monitor natural environments. Adequately communicating this type of information helps increase awareness about the limited availability of natural resources and promotes their better use with sus- tainable practices. In this paper, I suggest an approach to communicating this information to wide audiences based on simulating data journalism using artifi- cial intelligence techniques. I analyze this approach by describing a pioneer knowledge-based system called VSAIH, which looks for news in hydrological data from a national sensor network in Spain and creates news stories that gen- eral users can understand. VSAIH integrates artificial intelligence techniques, including a model-based data analyzer and a presentation planner. In the paper, I also describe characteristics of the hydrological national sensor network and the technical solutions applied by VSAIH to simulate data journalism.

Publishing Orthology and Diseases Information in the Linked Open Data Cloud

The Linked Data initiative offers a straight method to publish structured data in the World Wide Web and link it to other data, resulting in a world wide network of semantically codified data known as the Linked Open Data cloud. The size of the Linked Open Data cloud, i.e. the amount of data published using Linked Data principles, is growing exponentially, including life sciences data. However, key information for biological research is still missing in the Linked Open Data cloud. For example, the relation between orthologs genes and genetic diseases is absent, even though such information can be used for hypothesis generation regarding human diseases. The OGOLOD system, an extension of the OGO Knowledge Base, publishes orthologs/diseases information using Linked Data. This gives the scientists the ability to query the structured information in connection with other Linked Data and to discover new information related to orthologs and human diseases in the cloud.

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Stable hydrogen and carbon isotope records and concentration data for four long chain n-alkanes from core GIK16160-3 derived from the Zambezi delta in the Mozambique Channel during the last 37,000 years

Hydrogen isotope values (dD) of sedimentary terrestrial leaf wax such as n-alkanes or n-acids have been used to map and understand past changes in rainfall amount in the tropics because dD of precipitation is commonly assumed as the first order controlling factor of leaf wax dD. Plant functional types and their photosynthetic pathways can also affect leaf wax dD but these biological effects are rarely taken into account in paleo studies relying on this rainfall proxy. To investigate how biological effects may influence dD values we here present a 37,000-year old record of dD and stable carbon isotopes (d13C) measured on four n-alkanes (n-C27, n-C29, n-C31, n-C33) from a marine sediment core collected off the Zambezi River mouth. Our paleo d13C records suggest that each individual n-alkanes had different C3/C4 proportional contributions. n-C29 was mostly derived from a C3 dicots (trees, shrubs and forbs) dominant vegetation throughout the entire record. In contrast, the longer chain n-C33 and n-C31 were mostly contributed by C4 grasses during the Glacial period but shifted to a mixture of C4 grasses and C3 dicots during the Holocene. Strong correlations between dD and d13C values of n-C33 (correlation coefficient R2 = 0.75, n = 58) and n-C31 (R2 = 0.48, n = 58) suggest that their dD values were strongly influenced by changes in the relative contributions of C3/C4 plant types in contrast to n-C29 (R2 = 0.07, n = 58). Within regions with variable C3/C4 input, we conclude that dD values of n-C29 are the most reliable and unbiased indicator for past changes in rainfall, and that dD and d13C values of n-C31 and n-C33 are sensitive to C3/C4 vegetation changes. Our results demonstrate that a robust interpretation of palaeohydrological data using n-alkane dD requires additional knowledge of regional vegetation changes from which nalkanes are synthesized, and that the combination of dD and d13C values of multiple n-alkanes can help to differentiate biological effects from those related to the hydrological cycle.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A firm's capability to calibrate supply chain knowledge-antecedents and consequences

This paper proposes a conceptual model for a firm's capability to calibrate supply chain knowledge (CCK). Knowledge calibration is achieved when there is a match between managers' ex ante confidence in the accuracy of held knowledge and the ex post accuracy of that knowledge. Knowledge calibration is closely related to knowledge utility or willingness to use the available ex ante knowledge: a manager uses the ex ante knowledge if he/she is confident in the accuracy of that knowledge, and does not use it or uses it with reservation, when the confidence is low. Thus, knowledge calibration attained through the firm's CCK enables managers to deal with incomplete and uncertain information and enhances quality of decisions. In the supply chain context, although demand- and supply-related knowledge is available, supply chain inefficiencies, such as the bullwhip effect, remain. These issues may be caused not by a lack of knowledge but by a firm's lack of capability to sense potential disagreement between knowledge accuracy and confidence. Therefore, this paper contributes to the understanding of supply chain knowledge utilization by defining CCK and identifying a set of antecedents and consequences of CCK in the supply chain context.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.