Protective Effects of Smoke-free Legislation on Birth Outcomes in England: A Regression Discontinuity Design.

BACKGROUND: Environmental tobacco smoke has an adverse association with preterm birth and birth weight. England introduced a new law to make virtually all enclosed public places and workplaces smoke free on July 1, 2007. We investigated the effect of smoke-free legislation on birth outcomes in England using Hospital Episode Statistics (HES) maternity data. METHODS: We used regression discontinuity, a quasi-experimental study design, which can facilitate valid causal inference, to analyze short-term effects of smoke-free legislation on birth weight, low birth weight, gestational age, preterm birth, and small for gestational age. RESULTS: We analyzed 1,800,906 pregnancies resulting in singleton live-births in England between 1 January 2005 and 31 December 2009. In the 1 to 5 months following the introduction of the smoke-free legislation, for those entering their third trimester, the risk of low birth weight decreased by between 8% (95% confidence interval [CI]: 4%, 12%) and 14% (95% CI: 5%, 23%), very low birth weight between 28% (95% CI: 19%, 36%) and 32% (95% CI: 21%, 41%), preterm birth between 4% (95% CI: 1%, 8%) and 9% (95% CI: 2%, 16%), and small for gestational age between 5% (95% CI: 2%, 8%) and 9% (95% CI: 2%, 15%). The estimated impact of the smoke-free legislation varied by maternal age, deprivation, ethnicity, and region. CONCLUSIONS: The introduction of smoke-free legislation in England had an immediate estimated beneficial impact on birth outcomes overall, although we did not observe improvements across all age, ethnic, or deprivation groups.See video abstract at http://links.lww.com/EDE/B85.

Analysis of hospital admissions due to accidental non-fire-related carbon monoxide poisoning in England, between 2001 and 2010.

BACKGROUND: Accidental non-fire-related (ANFR) carbon monoxide (CO) poisoning is a cause of fatalities and hospital admissions. This is the first study that describes the characteristics of ANFR CO hospital admissions in England. METHODS: Hospital Episode Statistics (HES) inpatient data for England between 2001 and 2010 were used. ANFR CO poisoning admissions were defined as any mention of ICD-10 code T58: toxic effect of CO and X47: accidental poisoning by gases or vapours, excluding ICD-10 codes potentially related to fires (X00-X09, T20-T32 and Y26). RESULTS: There were 2463 ANFR CO admissions over the 10-year period (annual rate: 0.49/100 000); these comprised just under half (48.7%) of all non-fire-related (accidental and non-accidental) CO admissions. There was seasonal variability, with more admissions in colder winter months. Higher admission rates were observed in the north of England. Just over half (53%) of ANFR admissions were male, and the highest rates of ANFR admissions were in those aged >80 years. CONCLUSION: The burden of ANFR CO poisoning is preventable. The results of this study suggest an appreciable burden of CO and highlight differences that may aid targeting of public health interventions.

Automation of cleaning and reconstructing residential address histories to assign environmental exposures in longitudinal studies.

BACKGROUND: We have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10 µm (PM10) concentrations. METHODS: ALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility. RESULTS: Average PM10 exposure was 32.6 µg/m3 [standard deviation (S.D.) 3.0 µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6 µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3 µg/m3 to 12.4 µg/m3 (up to 26% difference) during pregnancy and -7.22 µg/m3 to 7.64 µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy. CONCLUSIONS: ALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification.

Data capture in bioinformatics: requirements and experiences with Pedro.

BACKGROUND: The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing - both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort. RESULTS: This paper reports on experience gained in the deployment of the Pedro data capture tool in a range of representative bioinformatics applications. The paper makes explicit the requirements that have recurred when capturing data in different contexts, indicates how these requirements are addressed in Pedro, and describes case studies that illustrate where the requirements have arisen in practice. CONCLUSION: Data capture is a fundamental activity for bioinformatics; all biological data resources build on some form of data capture activity, and many require a blend of import, analysis and annotation. Recurring requirements in data capture suggest that model-driven architectures can be used to construct data capture infrastructures that can be rapidly configured to meet the needs of individual use cases. We have described how one such model-driven infrastructure, namely Pedro, has been deployed in representative case studies, and discussed the extent to which the model-driven approach has been effective in practice.

Local- and regional-scale air pollution modelling (PM10) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15 years: Avon Longitudinal Study of Parents And Children (ALSPAC).

We established air pollution modelling to study particle (PM10) exposures during pregnancy and infancy (1990-1993) through childhood and adolescence up to age ~15 years (1991-2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM10 with a yearly-varying concentration for all background sources. We modelled PM10 (µg/m3) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5th to 95th centile: 24.4-37.3 µg/m3) and mostly related to temporal variability in regional/long-range PM10. PM10 exposures fell on average by 11.6 µg/m3 from first year of life (mean concentration = 31.2 µg/m3) to age ~15 (mean = 19.6 µg/m3), and 5.4 µg/m3 between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM10 exposures (5th to 95th centile) were relatively low: 25.4-30.0 µg/m3 to age ~8 years and 20.7-23.9 µg/m3 from age ~8 to age ~15 years. The contribution of local sources to total PM10 was 18.5%-19.5% during pregnancy and infancy, and 14.4%-17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM10 exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies.

A systematic approach to modeling, capturing, and disseminating proteomics experimental data.

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

Model-driven user interfaces for bioinformatics data resources: regenerating the wheel as an alternative to reinventing it.

BACKGROUND: The proliferation of data repositories in bioinformatics has resulted in the development of numerous interfaces that allow scientists to browse, search and analyse the data that they contain. Interfaces typically support repository access by means of web pages, but other means are also used, such as desktop applications and command line tools. Interfaces often duplicate functionality amongst each other, and this implies that associated development activities are repeated in different laboratories. Interfaces developed by public laboratories are often created with limited developer resources. In such environments, reducing the time spent on creating user interfaces allows for a better deployment of resources for specialised tasks, such as data integration or analysis. Laboratories maintaining data resources are challenged to reconcile requirements for software that is reliable, functional and flexible with limitations on software development resources. RESULTS: This paper proposes a model-driven approach for the partial generation of user interfaces for searching and browsing bioinformatics data repositories. Inspired by the Model Driven Architecture (MDA) of the Object Management Group (OMG), we have developed a system that generates interfaces designed for use with bioinformatics resources. This approach helps laboratory domain experts decrease the amount of time they have to spend dealing with the repetitive aspects of user interface development. As a result, the amount of time they can spend on gathering requirements and helping develop specialised features increases. The resulting system is known as Pierre, and has been validated through its application to use cases in the life sciences, including the PEDRoDB proteomics database and the e-Fungi data warehouse. CONCLUSION: MDAs focus on generating software from models that describe aspects of service capabilities, and can be applied to support rapid development of repository interfaces in bioinformatics. The Pierre MDA is capable of supporting common database access requirements with a variety of auto-generated interfaces and across a variety of repositories. With Pierre, four kinds of interfaces are generated: web, stand-alone application, text-menu, and command line. The kinds of repositories with which Pierre interfaces have been used are relational, XML and object databases.

Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology.

BACKGROUND: In England there are four national routinely collected data sets on births: Office for National Statistics (ONS) births based on birth registrations; Hospital Episode Statistics (HES) deliveries (mothers' information); HES births (babies' information); and NHS Numbers for Babies (NN4B) based on ONS births plus gestational age and ethnicity information. This study describes and compares these data, with the aim of recommending the most appropriate data set(s) for use in epidemiological research and surveillance. METHODS: We assessed the completeness and quality of the data sets in relation to use in epidemiological research and surveillance and produced detailed descriptive statistics on common reproductive outcomes for each data set including temporal and spatial trends. RESULTS: ONS births is a high quality complete data set but lacks interpretive and clinical information. HES deliveries showed good agreement with ONS births but HES births showed larger amounts of missing or unavailable data. Both HES data sets had improved quality from 2003 onwards, but showed some local spatial variability. NN4B showed excellent agreement with ONS and HES deliveries for the years available (2006-2010). Annual number of births increased by 17.6% comparing 2002 with 2010 (ONS births). Approximately 6% of births were of low birth weight (2.6% term low birth weight) and 0.5% were stillbirths. CONCLUSIONS: Routinely collected data on births provide a valuable resource for researchers. ONS and NN4B offer the most complete and accurate record of births. Where more detailed clinical information is required, HES deliveries offers a high quality data set that captures the majority of English births.

PEDRo: a database for storing, searching and disseminating experimental proteomics data.

BACKGROUND: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. RESULTS: This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. CONCLUSIONS: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

Pedro: a configurable data entry tool for XML.

UNLABELLED: Pedro is a Java application that dynamically generates data entry forms for data models expressed in XML Schema, producing XML data files that validate against this schema. The software uses an intuitive tree-based navigation system, can supply context-sensitive help to users and features a sophisticated interface for populating data fields with terms from controlled vocabularies. The software also has the ability to import records from tab delimited text files and features various validation routines. AVAILABILITY: The application, source code, example models from several domains and tutorials can be downloaded from http://pedro.man.ac.uk/.

EST-based gene discovery in pig: virtual expression patterns and comparative mapping to human.

A molecular understanding of porcine reproduction is of biological interest and economic importance. Our Midwest Consortium has produced cDNA libraries containing the majority of genes expressed in major female reproductive tissues, and we have deposited into public databases 21,499 expressed sequence tag (EST) gene sequences from the 3' end of clones from these libraries. These sequences represent 10,574 different genes, based on sequence comparison among these data, and comparison with existing porcine ESTs and genes indicate as many as 4652 of these EST clusters are novel. In silico analysis identified sequences that are expressed in specific pig tissues or organs and confirmed the broad expression in pig for many genes ubiquitously expressed in human tissues. Furthermore, we have developed computer software to identify sequence similarity of these pig genes with their human counterparts, and to extract the mapping information of these human homologues from genome databases. We demonstrate the utility of this software for comparative mapping by localizing 61 genes on the porcine physical map for Chromosomes (Chrs) 5, 10, and 14.