The development of social class sensitive proxies for infant mortality at the PCT level: An appraisal of candiate indicators for the commission for health improvement

The main aim of the work is to identify social class-sensitive proxies for infant mortality at Primary Care Trust level that could be used in the CHI performance ratings process for PCTs in 2003/4

Automated census record linking: a machine learning approach

Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data genealogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, assimilation, discrimination, and the returns to education. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for downstream analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be replicated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to the well known problem of record linkage, but with a focus on the sorts of costs and benefits of working with historical data. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census to their adult-selves in the 1940 Federal Census. In addition, I provide guidance on a number of practical questions, including how large the training data needs to be relative to the sample.This research has been supported by the NSF-IGERT Multidisciplinary Program in Inequality & Social Policy at Harvard University (Grant No. 0333403)

The Road Map Project: 2014 Results Report

The Road Map Project's annual report card shows data on 29 indicators of student success, which are important measures related to student achievement from cradle through college. Data in the report are often disaggregated by district, student race/ethnicity or income level to illustrate the region's challenges and progress.The Road Map Project is a region-wide collective impact effort aiming to dramatically improve education results in South King County and South Seattle, the county's areas of greatest need. The project's goal is to double the number of students who are on track to graduate from college or earn a career credential by 2020, and to close opportunity gaps. Seven school districts -- Auburn, Kent, Federal Way, Highline, Renton, Seattle (south-end only) and Tukwila -- are among the hundreds of partners working together toward the Road Map Project's 2020 goal. The 2014 results report includes a special focus on whether the region is on track to reach the goal

Analysis of pavement condition survey data for effective implementation of a network level pavement management program for Kazakhstan

Pavement roads and transportation systems are crucial assets for promoting political stability, as well as economic and sustainable growth in developing countries. However, pavement maintenance backlogs and the high capital costs of road rehabilitation require the use of pavement evaluation tools to assure the best value of the investment. This research presents a methodology for analyzing the collected pavement data for the implementation of a network level pavement management program in Kazakhstan. This methodology, which could also be suitable in other developing countries’ road networks, focuses on the survey data processing to determine cost-effective maintenance treatments for each road section. The proposed methodology aims to support a decision-making process for the application of a strategic level business planning analysis, by extracting information from the survey data

Chi-square-based scoring function for categorization of MEDLINE citations

Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Results: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine learning algorithms (support vector machines, decision trees, na\"ive Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine learning algorithms. Conclusions: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.Comment: 34 pages, 2 figure

Financial development and economic growth: Evidence from ten new EU members

This paper reviews the main features of the banking and financial sector in ten new EU members, and then examines the relationship between financial development and economic growth in these countries by estimating a dynamic panel model over the period 1994-2007. The evidence suggests that the stock and credit markets are still underdeveloped in these economies, and that their contribution to economic growth is limited owing to a lack of financial depth. By contrast, a more efficient banking sector is found to have accelerated growth. Furthermore, Granger causality test indicate that causality runs from financial development to economic growth, but not in the opposite direction

To bin or not to bin? Deselecting print back-runs available electronically at Imperial College London Library

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