How quickly should we titrate antihypertensive medication? Systematic review modelling blood pressure response from trial data.

Context There are no evidence syntheses available to guide clinicians on when to titrate antihypertensive medication after initiation. Objective To model the blood pressure (BP) response after initiating antihypertensive medication. Data sources electronic databases including Medline, Embase, Cochrane Register and reference lists up to December 2009. Study selection Trials that initiated antihypertensive medication as single therapy in hypertensive patients who were either drug naive or had a placebo washout from previous drugs. Data extraction Office BP measurements at a minimum of two weekly intervals for a minimum of 4 weeks. An asymptotic approach model of BP response was assumed and non-linear mixed effects modelling used to calculate model parameters. Results and conclusions Eighteen trials that recruited 4168 patients met inclusion criteria. The time to reach 50% of the maximum estimated BP lowering effect was 1 week (systolic 0.91 weeks, 95% CI 0.74 to 1.10; diastolic 0.95, 0.75 to 1.15). Models incorporating drug class as a source of variability did not improve fit of the data. Incorporating the presence of a titration schedule improved model fit for both systolic and diastolic pressure. Titration increased both the predicted maximum effect and the time taken to reach 50% of the maximum (systolic 1.2 vs 0.7 weeks; diastolic 1.4 vs 0.7 weeks). Conclusions Estimates of the maximum efficacy of antihypertensive agents can be made early after starting therapy. This knowledge will guide clinicians in deciding when a newly started antihypertensive agent is likely to be effective or not at controlling BP.

Traumatic Brain Injury in Iowa Inpatient Hospital Data, 2003-2005

According to the Centers for Disease Control and Prevention, unintentional injury is the fifth leading cause of death for all age groups and the first leading cause of death for people from 1 to 44 years of age in the United States, while homicide remains the 2nd leading cause of death for 15 to 24 years old (CDC, 2006). In 2004, there were approximately 144,000 deaths due to unintentional injuries in the US; 53% of which represent people over 45 years of age (CDC, 2004). With 20,322 suicidal deaths and 13,170 homicidal deaths, intentional injury deaths affect mostly people under 45 years old. On average, there are 1,150 unintentional deaths per year in Iowa. In 2004, 37% of unintentional deaths were due to motor vehicle accidents (MTVCC) occurring across all age ranges and 30% were due to falls involving persons over 65 years of age 82% of the time (IDPH Health Stat Div., 2004). The most debilitating outcome of injury is traumatic brain injury, which is characterized by the irreversibility of its damages, long-term effects on quality of life, and healthcare costs. The latest data available from the CDC estimated that, nationally, 50,000 traumatic brain injured (TBI) people die each year; three times as many are hospitalized and more than twenty times as many are released from emergency room (ER) departments (CDC, 2006). Besides the TBI registry, brain injury data is also captured through three other data sources: 1) death certificates; 2) hospital inpatient data; and, 3) hospital outpatient data. The inpatient and outpatient hospital data are managed by the Iowa Hospital Association, which provides to Iowa Department of Public Health the hospital data without personal identifiers. (The hospitals send reports to the Agency of Health Care Research and Quality, which developed the Health Care Utilization Project and its product, the National Inpatient Sample).

GSVA: gene set variation analysis for microarray and RNA-seq data

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

Subclinical thyroid dysfunction and the risk for fractures: a systematic review and meta-analysis.

BACKGROUND: Data on the association between subclinical thyroid dysfunction and fractures conflict. PURPOSE: To assess the risk for hip and nonspine fractures associated with subclinical thyroid dysfunction among prospective cohorts. DATA SOURCES: Search of MEDLINE and EMBASE (1946 to 16 March 2014) and reference lists of retrieved articles without language restriction. STUDY SELECTION: Two physicians screened and identified prospective cohorts that measured thyroid function and followed participants to assess fracture outcomes. DATA EXTRACTION: One reviewer extracted data using a standardized protocol, and another verified data. Both reviewers independently assessed methodological quality of the studies. DATA SYNTHESIS: The 7 population-based cohorts of heterogeneous quality included 50,245 participants with 1966 hip and 3281 nonspine fractures. In random-effects models that included the 5 higher-quality studies, the pooled adjusted hazard ratios (HRs) of participants with subclinical hyperthyroidism versus euthyrodism were 1.38 (95% CI, 0.92 to 2.07) for hip fractures and 1.20 (CI, 0.83 to 1.72) for nonspine fractures without statistical heterogeneity (P = 0.82 and 0.52, respectively; I2= 0%). Pooled estimates for the 7 cohorts were 1.26 (CI, 0.96 to 1.65) for hip fractures and 1.16 (CI, 0.95 to 1.42) for nonspine fractures. When thyroxine recipients were excluded, the HRs for participants with subclinical hyperthyroidism were 2.16 (CI, 0.87 to 5.37) for hip fractures and 1.43 (CI, 0.73 to 2.78) for nonspine fractures. For participants with subclinical hypothyroidism, HRs from higher-quality studies were 1.12 (CI, 0.83 to 1.51) for hip fractures and 1.04 (CI, 0.76 to 1.42) for nonspine fractures (P for heterogeneity = 0.69 and 0.88, respectively; I2 = 0%). LIMITATIONS: Selective reporting cannot be excluded. Adjustment for potential common confounders varied and was not adequately done across all studies. CONCLUSION: Subclinical hyperthyroidism might be associated with an increased risk for hip and nonspine fractures, but additional large, high-quality studies are needed. PRIMARY FUNDING SOURCE: Swiss National Science Foundation.

Improved Employment Data for Transportation Planning, 1998

Transportation planners typically use census data or small sample surveys to help estimate work trips in metropolitan areas. Census data are cheap to use but are only collected every 10 years and may not provide the answers that a planner is seeking. On the other hand, small sample survey data are fresh but can be very expensive to collect. This project involved using database and geographic information systems (GIS) technology to relate several administrative data sources that are not usually employed by transportation planners. These data sources included data collected by state agencies for unemployment insurance purposes and for drivers licensing. Together, these data sources could allow better estimates of the following information for a metropolitan area or planning region: · Locations of employers (work sites); · Locations of employees; · Travel flows between employees’ homes and their work locations. The required new employment database was created for a large, multi-county region in central Iowa. When evaluated against the estimates of a metropolitan planning organization, the new database did allow for a one to four percent improvement in estimates over the traditional approach. While this does not sound highly significant, the approach using improved employment data to synthesize home-based work (HBW) trip tables was particularly beneficial in improving estimated traffic on high-capacity routes. These are precisely the routes that transportation planners are most interested in modeling accurately. Therefore, the concept of using improved employment data for transportation planning was considered valuable and worthy of follow-up research.

Thyroid antibody status, subclinical hypothyroidism, and the risk of coronary heart disease: an individual participant data analysis.

CONTEXT: Subclinical hypothyroidism has been associated with increased risk of coronary heart disease (CHD), particularly with thyrotropin levels of 10.0 mIU/L or greater. The measurement of thyroid antibodies helps predict the progression to overt hypothyroidism, but it is unclear whether thyroid autoimmunity independently affects CHD risk. OBJECTIVE: The objective of the study was to compare the CHD risk of subclinical hypothyroidism with and without thyroid peroxidase antibodies (TPOAbs). DATA SOURCES AND STUDY SELECTION: A MEDLINE and EMBASE search from 1950 to 2011 was conducted for prospective cohorts, reporting baseline thyroid function, antibodies, and CHD outcomes. DATA EXTRACTION: Individual data of 38 274 participants from six cohorts for CHD mortality followed up for 460 333 person-years and 33 394 participants from four cohorts for CHD events. DATA SYNTHESIS: Among 38 274 adults (median age 55 y, 63% women), 1691 (4.4%) had subclinical hypothyroidism, of whom 775 (45.8%) had positive TPOAbs. During follow-up, 1436 participants died of CHD and 3285 had CHD events. Compared with euthyroid individuals, age- and gender-adjusted risks of CHD mortality in subclinical hypothyroidism were similar among individuals with and without TPOAbs [hazard ratio (HR) 1.15, 95% confidence interval (CI) 0.87-1.53 vs HR 1.26, CI 1.01-1.58, P for interaction = .62], as were risks of CHD events (HR 1.16, CI 0.87-1.56 vs HR 1.26, CI 1.02-1.56, P for interaction = .65). Risks of CHD mortality and events increased with higher thyrotropin, but within each stratum, risks did not differ by TPOAb status. CONCLUSIONS: CHD risk associated with subclinical hypothyroidism did not differ by TPOAb status, suggesting that biomarkers of thyroid autoimmunity do not add independent prognostic information for CHD outcomes.

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Subclinical Hypothyroidism and the Risk of Stroke Events and Fatal Stroke: An Individual Participant Data Analysis.

OBJECTIVE: The objective was to determine the risk of stroke associated with subclinical hypothyroidism. DATA SOURCES AND STUDY SELECTION: Published prospective cohort studies were identified through a systematic search through November 2013 without restrictions in several databases. Unpublished studies were identified through the Thyroid Studies Collaboration. We collected individual participant data on thyroid function and stroke outcome. Euthyroidism was defined as TSH levels of 0.45-4.49 mIU/L, and subclinical hypothyroidism was defined as TSH levels of 4.5-19.9 mIU/L with normal T4 levels. DATA EXTRACTION AND SYNTHESIS: We collected individual participant data on 47 573 adults (3451 subclinical hypothyroidism) from 17 cohorts and followed up from 1972-2014 (489 192 person-years). Age- and sex-adjusted pooled hazard ratios (HRs) for participants with subclinical hypothyroidism compared to euthyroidism were 1.05 (95% confidence interval [CI], 0.91-1.21) for stroke events (combined fatal and nonfatal stroke) and 1.07 (95% CI, 0.80-1.42) for fatal stroke. Stratified by age, the HR for stroke events was 3.32 (95% CI, 1.25-8.80) for individuals aged 18-49 years. There was an increased risk of fatal stroke in the age groups 18-49 and 50-64 years, with a HR of 4.22 (95% CI, 1.08-16.55) and 2.86 (95% CI, 1.31-6.26), respectively (p trend 0.04). We found no increased risk for those 65-79 years old (HR, 1.00; 95% CI, 0.86-1.18) or ≥ 80 years old (HR, 1.31; 95% CI, 0.79-2.18). There was a pattern of increased risk of fatal stroke with higher TSH concentrations. CONCLUSIONS: Although no overall effect of subclinical hypothyroidism on stroke could be demonstrated, an increased risk in subjects younger than 65 years and those with higher TSH concentrations was observed.

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Limits of Meritocracy. How Fields of Study and Gender Segregation Affect Social Stratification in Finland

This dissertation critically reviews the idea of meritocracy from both a theoretical and an empirical perspective. Based on a discussion of classical texts of social philosophy and sociology, it is argued that meritocracy as a concept for social stratification is best compatible with the sociological tradition of status attainment research: both frame social inequality in primarily individualistic terms, centring on the role of ascribed (e.g., gender, social background) and achieved (e.g., educational qualifications) characteristics for determining individuals’ socioeconomic rewards. This theoretical argument introduces the research problem at the core of this dissertation: to what extent can the individualistic conception of social stratification be maintained empirically? Fields of study and their interaction with educational attainment levels play a prominent role in the analysis of this question. Drawing on sociological versions of segmented labour market theory, it is assumed that fields of study may channel individuals into heterogeneous political-economic contexts on the labour market, which potentially modify the socioeconomic benefit individuals derive from their qualification levels. The focus on fields of study may also highlight economic differentials between men and women that derive from the persisting segregation of men’s and women’s occupational and educational specializations rather than direct gender discrimination on the labour market. The quantitative analyses in this dissertation consist of three research articles, which are based primarily on Finnish data, but occasionally extend the view to other European countries. The data sources include register-based macro- and microdata as well as survey data. Article I examines the extent and the patterns of gender segregation within the Finnish educational system between 1981 and 2005. The results show that differences between men’s and women’s field specializations have for the most part remained stable during this period, with particularly high levels of gender segregation observed at lower educational levels. The focus in Article II rests on the effects of gender-segregated fields of study on higher education graduates’ occupational status. It is shown that fields of study matter for accessing professional jobs and avoiding low-skilled positions in Finland: at the early career stage, particularly polytechnic graduates from female-dominated fields are less likely to work in professional positions. Finnish university graduates from male-dominated fields were more likely than their peers with different specializations to work as professionals, yet they also faced a greater risk of being sorted into lowskilled jobs if they failed to make use of this advantage. Article III proceeded to analyse the joint impact of educational qualification levels and fields of study on young adults’ median earnings in Finland between 1985 and 2005. The results show that qualification levels do not confer a consistent benefit in the process of earnings stratification. Advanced qualifications raise median earnings most clearly among individuals specializing in the same field of study. When comparing individuals with different field specializations, on the other hand, higher-level qualifications do not necessarily lead to higher median earnings. Overall, the findings of this dissertation reveal a heterogeneous effect of education for achieving social positions, which challenges individual-centred, meritocratic accounts of social stratification and underlines the problematic lack of structural and institutional dimensions in the dominant account of social status attainment.

Integration of marketing research data in new product development. Case study: Food industry company

The aim of this master’s thesis is to provide a real life example of how marketing research data is used by different functions in the NPD process. In order to achieve this goal, a case study in a company was implemented where gathering, analysis, distribution and synthesis of marketing research data in NPD were studied. The main research question was formulated as follows: How is marketing research data integrated and used by different company functions in the NPD process? The theory part of the master’s thesis was focused on the discussion of the marketing function role in NPD, use of marketing research particularly in the food industry, as well as issues related to the marketing/R&D interface during the NPD process. The empirical part of the master’s thesis was based on qualitative explanatory case study research. Individual in-depth interviews with company representatives, company documents and online research were used for data collection and analyzed through triangulation method. The empirical findings advocate that the most important marketing data sources at the concept generation stage of NPD are: global trends monitoring, retailing audit and consumers insights. These data sets are crucial for establishing the potential of the product on the market and defining the desired features for the new product to be developed. The findings also suggest the example of successful crossfunctional communication during the NPD process with formal and informal communication patterns. General managerial recommendations are given on the integration in NPD of a strategy, process, continuous improvement, and motivated cross-functional product development teams.

Governance of Healthcare Data: International Standards and Recommendations

In the global phenomenon, the aging population becomes a critical issue. Data and information concerning elderly citizens are increasing and are not well organized. In addition, these unstructured data and information cause the problems for decision makers. Since we live in a digital world, Information Technology is considered to be a tool in order to solve problems. Data, information, and knowledge are crucial components to facilitate success in IT service system. Therefore, it is necessary to study how to organize or to govern data from various sources related elderly citizens. The research is conducted due to the fact that there is no internationally accepted holistic framework for governance of data. The research limits the scope to study on the healthcare domain; however, the results can be applied to the other areas. The research starts with an ongoing research of Dahlberg and Nokkala (2015) as a theory. It explains the classification of existing data sources and their characteristics with the focus on managerial perspectives. Then the studies of existing frameworks at international and national level organizations have been performed to show the current frameworks, which have been used and are useful in compiling data on elderly citizens. The international organizations in this research are selected based on their reputations and the reliability to obtain information. The selected countries at national level provide different point of views between two countries. Australia is a forerunner in IT governance while Thailand is the country which the author has familiar knowledge of the current situation. Considered the discussions of frameworks at international and national organizations level illustrate the main characteristics of each framework. At international organization level gives precedence to the interoperability of exchanging data and information between different parties. Whereas at national level shows the importance of the acknowledgement of using frameworks throughout the country in order to make the frameworks to be effective. After the studies of both international and national organization levels, the thesis shows the summarized tables to answer the fitness to the proposed framework by Dahlberg and Nokkala whether the framework help to consolidate data from various sources with different formats, hierarchies, structures, velocities, and other attributes of data storages. In addition, suggestions and recommendations will be proposed for the future research.

Getting Started With Open Data

This lecture introduces an array of data sources that can be used to create new applications and visualisations, many examples of which are given. Additionally, there are a number of slides on open data standards, freedom of information requests and how to affect the future of open data.

WAIS Seminar: Research Discussion: Open Data at the Hampshire Hub

This is a research discussion about the Hampshire Hub - see http://protohub.net/. The aim is to find out more about the project, and discuss future collaboration and sharing of ideas. Mark Braggins (Hampshire Hub Partnership) will introduce the Hampshire Hub programme, setting out its main objectives, work done to-date, next steps including the Hampshire data store (which will use the PublishMyData linked data platform), and opportunities for University of Southampton to engage with the programme , including the forthcoming Hampshire Hackathons Bill Roberts (Swirrl) will give an overview of the PublishMyData platform, and how it will help deliver the objectives of the Hampshire Hub. He will detail some of the new functionality being added to the platform Steve Peters (DCLG Open Data Communities) will focus on developing a web of data that blends and combines local and national data sources around localities, and common topics/themes. This will include observations on the potential employing emerging new, big data sources to help deliver more effective, better targeted public services. Steve will illustrate this with practical examples of DCLG’s work to publish its own data in a SPARQL end-point, so that it can be used over the web alongside related 3rd party sources. He will share examples of some of the practical challenges, particularly around querying and re-using geographic LinkedData in a federated world of SPARQL end-point.

A data-driven approach to disease control

As our world becomes increasingly interconnected, diseases can spread at a faster and faster rate. Recent years have seen large-scale influenza, cholera and ebola outbreaks and failing to react in a timely manner to outbreaks leads to a larger spread and longer persistence of the outbreak. Furthermore, diseases like malaria, polio and dengue fever have been eliminated in some parts of the world but continue to put a substantial burden on countries in which these diseases are still endemic. To reduce the disease burden and eventually move towards countrywide elimination of diseases such as malaria, understanding human mobility is crucial for both planning interventions as well as estimation of the prevalence of the disease. In this talk, I will discuss how various data sources can be used to estimate human movements, population distributions and disease prevalence as well as the relevance of this information for intervention planning. Particularly anonymised mobile phone data has been shown to be a valuable source of information for countries with unreliable population density and migration data and I will present several studies where mobile phone data has been used to derive these measures.