Health, out-of-home activities and digital inclusion in later life: Implications for emerging mobility services

This paper focuses on the question of how digital technologies are differentially embedded in day-to-day practices and associated with mobility and health of older citizens. The motivation is to look for evidence that helps us anticipate opportunities and barriers of digital technologies and innovative transport services in enhancing independent living, social inclusion, health and well-being in ageing societies. Using the English Longitudinal Study of Ageing (ELSA), we identify six groups with different day-to-day leisure practices and find that the use of digital technology (Internet use, smart phones) is associated with higher frequencies of out-of-home activities. Barriers associated with lower levels of engagement include health-related restrictions, the same barriers that also prevent patronage of public transport. Although digital technology use has increased during the COVID-19 pandemic, lack of skills, access to equipment and health problems contribute to a continued Digital Divide. We conclude that the digitalisation of both public and transport services is unlikely to support independent living for all by itself and could indeed exacerbate existing inequalities. Instead, addressing issues of exclusion among less active, mobility-restricted groups require targeted service designs that respond to differential health and skills-related barriers in using digital technologies

Names-based ethnicity enhancement of hospital admissions in England, 1999–2013

Background: Accurate recording of ethnicity in electronic healthcare records is important for the monitoring of health inequalities. Yet until the late 1990s, ethnicity information was absent from more than half of records of patients who received inpatient care in England. In this study, we report on the usefulness of a names-based ethnicity classification, Ethnicity Estimator (EE), for addressing this gap in the hospital records. / Materials and methods: Data on inpatient hospital admissions were obtained from Hospital Episode Statistics (HES) between April 1999 and March 2014. The data were enhanced with ethnicity coding of participants’ surnames using the EE software. Only data on the first episode for each patient each year were included. / Results: A total of 111,231,653 patient-years were recorded between April 1999 and March 2014. The completeness of ethnicity records improved from 59.5 % in 1999 to 90.5 % in 2013 (financial year). Biggest improvement was seen in the White British group, which increased from 55.4 % in 1999 to 73.9 % in 2013. The correct prediction of NHS-reported ethnicity varied by ethnic group (2013 figures): White British (89.8 %), Pakistani (81.7 %), Indian (74.6 %), Chinese (72.9 %), Bangladeshi (63.4 %), Black African (57.3 %), White Other (50.5 %), White Irish (45.0 %). For other ethnic groups the prediction success was low to none. Prediction success was above 70 % in most areas outside London but fell below 40 % in parts of London. / Conclusion: Studies of ethnic inequalities in hospital inpatient care in England are limited by incomplete data on patient ethnicity collected in the 1990s and 2000s. The prediction success of a names-based ethnicity classification tool has been quantified in HES for the first time and the results can be used to inform decisions around the optimal analysis of ethnic groups using this data source

British surname origins, population structure and health outcomes – an observational study of hospital admissions

Population structure is a confounder on pathways linking genotypes to health outcomes. This study examines whether the historical, geographical origins of British surnames are associated with health outcomes today. We coded hospital admissions of over 30 million patients in England between 1999 and 2013 to their British surname origin and divided their diagnoses into 125 major disease categories (of which 94 were complete-case). A base population was constructed with patients’ first admission of any kind. Age- and sex-standardised odds ratios were calculated with logistic regression using patients with ubiquitous English surnames such as “Smith” as reference (alpha = .05; Benjamini–Hochberg false discovery rate (FDR) = .05). The results were scanned for “signals”, where a branch of related surname origins all had significantly higher or lower risk. Age- and sex-standardised admission (alpha = .05) was calculated for each signal across area deprivation and surname origin density quintiles. Signals included three branches of English surnames (disorders of teeth and jaw, fractures, upper gastrointestinal disorders). Although the signal with fractures was considered unusual overall, 2 out of the 9 origins in the branch would only be significant at a FDR > .05: OR 0.92 (95% confidence interval 0.86–0.98) and 0.70 (0.55–0.90). The risk was only different in the quintile with the highest density of that group. Differential risk remained when studied across quintiles of area deprivation. The study shows that surname origins are associated with diverse health outcomes and thus act as markers of population structure over and above area deprivation

Regional surnames and genetic structure in Great Britain

Following the increasing availability of DNA-sequenced data, the genetic structure of populations can now be inferred and studied in unprecedented detail. Across social science, this innovation is shaping new bio-social research agendas, attracting substantial investment in the collection of genetic, biological and social data for large population samples. Yet genetic samples are special because the precise populations that they represent are uncertain and ill-defined. Unlike most social surveys, a genetic sample's representativeness of the population cannot be established by conventional procedures of statistical inference, and the implications for population-wide generalisations about bio-social phenomena are little understood. In this paper, we seek to address these problems by linking surname data to a censored and geographically uneven sample of DNA scans, collected for the People of the British Isles study. Based on a combination of global and local spatial correspondence measures, we identify eight regions in Great Britain that are most likely to represent the geography of genetic structure of Great Britain's long-settled population. We discuss the implications of this regionalisation for bio-social investigations. We conclude that, as the often highly selective collection of DNA and biomarkers becomes a more common practice, geography is crucial to understanding variation in genetic information within diverse populations

Family Name Origins and Intergenerational Demographic Change in Great Britain

We develop bespoke geospatial routines to typify 88,457 surnames by their likely ancestral geographic origins within Great Britain. Linking this taxonomy to both historic and contemporary population data sets, we characterize regional populations using surnames that indicate whether their bearers are likely to be long-settled. We extend this approach in a case study application, in which we summarize intergenerational change in local populations across Great Britain over a period of 120 years. We also analyze much shorter term demographic dynamics and chart likely recent migratory flows within the country. Our research demonstrates the value of family names in characterizing long-term population change at regional and local scales. We find evidence of selective migratory flows in both time periods alongside increasing demographic diversity and distinctiveness between regions in Great Britain

The social and spatial context of urban health inequalities: towards an interpretive geodemographic framework

Geodemographics, the technique of classifying small areas by the aggregate characteristics of their residents, is a promising method to study health inequalities and prepare the development of preventive policy. Yet, current approaches do not account for the complexity and contingency of health pathways, which are found to be differentially activated in different groups of populations at different ecological levels. Building on the strength of geodemographics to integrate diverse data and link them ecologically, I suggest an interpretive framework, which characterises population vulnerability to health disadvantage at the level of regions, neighbourhoods and individuals. At the regional level, I explore vulnerability in terms of population structure in Great Britain and UK metropolitan areas in order to assess probable geographies of specific cultural or biological factors that may shape vulnerability. Based on indicators derived from hospital admission data and the UK Census, I identify different specific expressions of vulnerability at the neighbourhood level (which I call health environments), reflecting generic social causes of health advantage and disadvantage as well as specific forms of vulnerability. A comparison of metropolitan areas further reveals specific, local guises of vulnerability across England's cities. At the individual level, I discover from social survey data different groups in society (health milieus), which are characterised by distinct activity patterns, subjective orientations, attitudes and everyday life routines. I model the geographical distribution of health milieus probabilistically for London. Geographical linkage of these layers of information results in suggestions for an alternative urban policy programme to reduce population vulnerability through an emphasis on multi-sectoral and preventive action. The thus advanced geodemographic framework provides a conceptually focussed view of health, socially and spatially contextualised at multiple ecological levels, that contributes to interpreting health inequalities in social science and addressing their root causes through strategic policy

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks

Ethnicity estimation using family naming practices

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society