Address inaccuracy in health registration data: determinants and impacts

Access to demographic data that are complete, accurate and up-to-date is fundamental to many aspects of public health, government and academic work and for accurate interpretation of other databases. Health registration data are the prime source of demographic information for health and social care systems; for example, as an indicator of need, as a source of denominators to convert number of events into rates, or in the case of the residential address information as the basis for generating the call-recall invitation letters that are used for most screening programs (e.g. breast, colo-rectal and AAA screening). However, list inflation (ghosts, duplicates or emigrants) and a degree of address inaccuracy are recognised caveats with the health registration data and a recent NILS-related study on breast screening suggests that improved address accuracy might be a fast and efficient means of increasing screening uptake rates in cities and amongst deprived populations. In NI these data are collated by the BSO who uniquely in the UK also have access to data relating to prescribing, dental registrations and use of A&E services. These can be used to supplement the standard demographic and address information by (i) indicating patients who are alive and resident in NI and (ii) providing an independent source of probably improved address information. This study will use the NI Unique Property Reference Number (UPRN), rather than the addresses per se which are difficult to work with, to compare the addresses registered in the BSO with those addresses in the enumerated 2011 census. Assuming that the census is a more accurate source of address information for individuals, a comparison of the health registration addresses with those recorded at the census, the aim of the proposed study will be to (i) characterise the amount and distributions of these differences, (ii) to see what proportion of those who do not attend for screening did not actually receive an invitation letter because the addresses were incorrect, (iii) to determine how much of the social gradient (and urban/rural differences) in screening uptake are due to address inaccuracies, (iv) a comparison of timing of address changes at the BSO will provide information on the delays in updating of addresses.

People and places: Understanding geographical accuracy in administrative data from the census and healthcare systems

Administrative systems such as health care registration are of increasing importance in providing information for statistical, research, and policy purposes. There is thus a pressing need to understand better the detailed relationship between population characteristics as recorded in such systems and conventional censuses. This paper explores these issues using the unique Northern Ireland Longitudinal Study (NILS). It takes the 2001 Census enumeration as a benchmark and analyses the social, demographic and spatial patterns of mismatch with the health register at individual level. Descriptive comparison is followed by multivariate and multilevel analyses which show that approximately 25% of individuals are reported to be in different addresses and that age, rurality, education, and housing type are all important factors. This level of mismatch appears to be maintained over time, as earlier migrants who update their address details are replaced by others who have not yet done so. In some cases, apparent mismatches seem likely to reflect complex multi-address living arrangements rather than data error.

Do spectral gamma ray data really reflect humid-arid palaeoclimates? A test from the Palaeogene Interbasaltic weathered horizons at the Giant’s Causeway, N.Ireland

Since 1999, the rapid, inexpensive and non-destructive use of Th/K and Th/U ratios from spectral gamma ray measurements have been used as a proxy for changes in palaeo-hinterland weathering. This model is tested here by analysis of in situ palaeoweathering horizons where clay mineral contents are well-known. A residual palaeoweathered horizon of Palaeogene laterite (developed on basalt) has been logged at 14 locations across N. Ireland using spectral gamma ray detectors. The results are compared to published elemental and mineralogical data. While the model of K and U loss during the early stages of weathering to smectite and kaolinite is supported, the formation (during progressively more advanced weathering) of gibbsite and iron oxides has reversed the predicted pattern and caused U and Th retention in the weathering profile. The severity (duration, humidity) of weathering and palaeoweathering may be estimated using Th/K ratios as a proxy. The use of Th/U ratios is more problematic should detrital gibbsite (or similar clays) or iron oxides be detected. Mineralogical analysis is needed in order to evaluate the hosts for K, U and Th: nonetheless, the spectral gamma ray machine offers a real-time, inexpensive and effective tool for the preliminary or conjunctive assessment of degrees of weathering or palaeoweathering.

Is it time for standards and a common data exchange format for digital image analysis?

Digital image analysis is at a crossroads. While the technology has made great strides over the past few decades, there is an urgent need for image analysis to inform the next wave of large scale tissue biomarker discovery studies in cancer. Drawing parallels from the growth of next generation sequencing, this presentation will consider the case for a common language or standard format for storing and communicating digital image analysis data. In this context, image analysis data comprises more than simply an image with markups and attached key-value pair metrics. The desire to objectively benchmark competing platforms or a push for data to be deposited to public repositories much like genomics data may drive the need for a standard that also encompasses granular, cell-by-cell data.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.

Quantification of the smoking-associated cancer risk with rate advancement periods: meta-analysis of individual participant data from cohorts of the CHANCES consortium

BACKGROUND: Smoking is the most important individual risk factor for many cancer sites but its association with breast and prostate cancer is not entirely clear. Rate advancement periods (RAPs) may enhance communication of smoking related risk to the general population. Thus, we estimated RAPs for the association of smoking exposure (smoking status, time since smoking cessation, smoking intensity, and duration) with total and site-specific (lung, breast, colorectal, prostate, gastric, head and neck, and pancreatic) cancer incidence and mortality.

METHODS: This is a meta-analysis of 19 population-based prospective cohort studies with individual participant data for 897,021 European and American adults. For each cohort we calculated hazard ratios (HRs) for the association of smoking exposure with cancer outcomes using Cox regression adjusted for a common set of the most important potential confounding variables. RAPs (in years) were calculated as the ratio of the logarithms of the HRs for a given smoking exposure variable and age. Meta-analyses were employed to summarize cohort-specific HRs and RAPs.

RESULTS: Overall, 140,205 subjects had a first incident cancer, and 53,164 died from cancer, during an average follow-up of 12 years. Current smoking advanced the overall risk of developing and dying from cancer by eight and ten years, respectively, compared with never smokers. The greatest advancements in cancer risk and mortality were seen for lung cancer and the least for breast cancer. Smoking cessation was statistically significantly associated with delays in the risk of cancer development and mortality compared with continued smoking.

CONCLUSIONS: This investigation shows that smoking, even among older adults, considerably advances, and cessation delays, the risk of developing and dying from cancer. These findings may be helpful in more effectively communicating the harmful effects of smoking and the beneficial effect of smoking cessation.

Using administrative data to understand mental health in Northern Ireland:Results from two exemplar projects

Mental health is unevenly distributed in the Northern Ireland population. Administrative data on psychotropic medication prescribing is increasingly being used in research into population mental health. This paper illustrates how these data indicate concentrations of poor mental health in Northern Ireland, e.g. within deprived neighbourhoods, at interfaces, among older persons admitted to care homes and among persons bereaved through sudden death or suicide. This briefing also aims to widen the debate about mental health from a disorder/service paradigm to a whole population approach.

Facilitating a culture of responsible and effective sharing of cancer genome data

Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.

Replicability of data collected for empirical estimation of relative pollen productivity

The effects of repeated survey and fieldwork timing on data derived from a recently proposed standard field methodology for empirical estimation of Relative Pollen Productivity have been tested. Seasonal variations in vegetation and associated pollen assemblages were studied in three contrasting cultural habitat types; semi-natural ancient woodlands, lowland heaths, and unimproved, traditionally managed hay meadows. Results show that in woodlands and heathlands the standard method generates vegetation data with a reasonable degree of similarity throughout the field season, though in some instances additional recording of woodland canopy cover should be undertaken, and differences were greater for woodland understorey taxa than for arboreal taxa. Large differences in vegetation cover were observed over the field season in the grassland community, and matching the phenological timing of surveys within and between studies is clearly important if RPP estimates from these sites are to be comparable. Pollen assemblages from closely co-located moss polsters collected on different visits are shown to be variable in all communities, to a greater degree than can be explained by the sampling error associated with pollen counting, and further study of moss polsters as pollen traps is recommended.

Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.

Integration of cad/ tool path data for 5-axis step-nc machining of free form / irrregular contoured surfaces

This research paper presents the work on feature recognition, tool path data generation and integration with STEP-NC (AP-238 format) for features having Free form / Irregular Contoured Surface(s) (FICS). Initially, the FICS features are modelled / imported in UG CAD package and a closeness index is generated. This is done by comparing the FICS features with basic B-Splines / Bezier curves / surfaces. Then blending functions are caculated by adopting convolution theorem. Based on the blending functions, contour offsett tool paths are generated and simulated for 5 axis milling environment. Finally, the tool path (CL) data is integrated with STEP-NC (AP-238) format. The tool path algorithm and STEP- NC data is tested with various industrial parts through an automated UFUNC plugin.