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Background: Universal health visiting has been a cornerstone of preventative healthcare for children in the United Kingdom (UK) for over 100 years. In 2016, Scotland introduced a new Universal Health Visiting Pathway (UHVP), involving a greater number of contacts with a particular emphasis on the first year, visits within the home setting, and rigorous developmental assessment conducted by a qualified Health Visitor. To evaluate the UHVP, an outcome indicator framework was developed using routine administrative data. This paper sets out the development of these indicators. Methods: A logic model was produced with stakeholders to define the group of outcomes, before further refining and aligning of the measures through discussions with stakeholders and inspection of data. Power calculations were carried out and initial data described for the chosen indicators. Results: Eighteen indicators were selected across eight outcome areas: parental smoking, breastfeeding, immunisations, dental health, developmental concerns, obesity, accidents and injuries, and child protection interventions. Data quality was mixed. Coverage of reviews was high; over 90% of children received key reviews. Individual item completion was more variable: 92.2% had breastfeeding data at 6–8 weeks, whilst 63.2% had BMI recorded at 27–30 months. Prevalence also varied greatly, from 1.3% of children's names being on the Child Protection register for over six months by age three, to 93.6% having received all immunisations by age two. Conclusions: Home visiting services play a key role in ensuring children and families have the right support to enable the best start in life. As these programmes evolve, it is crucial to understand whether changes lead to improvements in child outcomes. This paper describes a set of indicators using routinely-collected data, lessening additional burden on participants, and reducing response bias which may be apparent in other forms of evaluation. Further research is needed to explore the transferability of this indicator framework to other settings. [ABSTRACT FROM AUTHOR]

Background: The ethnicity data gap pertains to 3 major challenges to address ethnic health inequality: 1) Under-representation of ethnic minorities in research; 2) Poor data quality on ethnicity; 3) Ethnicity data not being meaningfully analysed. These challenges are especially relevant for research involving under-served migrant populations in the UK. We aimed to review how ethnicity is captured, reported, analysed and theorised within policy-relevant research on ethnic health inequities. Methods: We reviewed a selection of the 1% most highly cited population health papers that reported UK data on ethnicity, and extracted how ethnicity was recorded and analysed in relation to health outcomes. We focused on how ethnicity was obtained (i.e. self reported or not), how ethnic groups were categorised, whether justification was provided for any categorisation, and how ethnicity was theorised to be related to health. We held three 1-h-long guided focus groups with 10 young people from Nigeria, Turkistan, Syria, Yemen and Iran. This engagement helped us shape and interpret our findings, and reflect on. 1) How should ethnicity be asked inclusively, and better recorded? 2) Does self-defined ethnicity change over time or context? If so, why? Results: Of the 44 included papers, most (19; 43%) used self-reported ethnicity, categorised in a variety of ways. Of the 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only 8 of 33 papers explicitly theorised how ethnicity related to health. The focus groups agreed that 1) Ethnicity should not be prescribed by others; individuals could be asked to describe their ethnicity in free-text which researchers could synthesise to extract relevant dimensions of ethnicity for their research; 2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change; 3) Migrants and non-migrants' lived experience of ethnicity is not fully inter-changeable, even if they share the same ethnic category. Conclusions: Ethnicity is a multi-dimensional construct, but this is not currently reflected in UK health research studies, where ethnicity is often aggregated and analysed without justification. Researchers should communicate clearly how ethnicity is operationalised for their study, with appropriate justification for clustering and analysis that is meaningfully theorised. We can only start to tackle ethnic health inequity by treating ethnicity as rigorously as any other variables in our research. [ABSTRACT FROM AUTHOR]

The Office for National Statistics (ONS) produce quarterly rolling year House Price Statistics for Small Areas (HPSSAs) for England and Wales. The statistics are produced using administrative data that is obtained without charge from the Land Registry, the government agency responsible for maintaining a record of land ownership in England and Wales. This paper will explain the source data and how it is processed to create the HPSSAs, which are produced for a range of geographies and broken down by house type and whether the property is newly built or existing stock. The method used is flexible and can be adapted to give new cuts of data and applied to new geographies. The paper will then consider how this has allowed ONS to use HPSSAs to support policy and provide new insights. [ABSTRACT FROM AUTHOR]

Introduction: Several forces are contributing to an increase in the number of people living with and surviving colorectal cancer (CRC). However, due to the lack of available data, little is known about the implications of these forces. In recent years, the use of administrative records to inform research has been increasing. The aim of this paper is to investigate the potential contribution that administrative data could have on the health economic research of CRC. Methods: To achieve this aim, we conducted a systematic review of the health economic CRC literature published in the United Kingdom and Europe within the last decade (2009–2019). Results: Thirty‐seven relevant studies were identified and divided into economic evaluations, cost of illness studies and cost consequence analyses. Conclusions: The use of administrative data, including cancer registry, screening and hospital records, within the health economic research of CRC is commonplace. However, we found that this data often come from regional databases, which reduces the generalisability of results. Further, administrative data appear less able to contribute towards understanding the wider and indirect costs associated with the disease. We explore several ways in which various sources of administrative data could enhance future research in this area. [ABSTRACT FROM AUTHOR]

Summary: The paper derives monthly estimates of business sector output in the UK from rolling quarterly value‐added tax based turnover data. The administrative nature of the value‐added tax data implies that their use could ultimately yield a more precise and granular picture of output across the economy. However, they show two particular features which complicate their exploitation: they are overlapping and subject to substantial noise. This motivates our choice of a multivariate unobserved components model for filtering and disaggregating temporally the aggregate figures. After illustrating our method by using one industry as a case‐study, we estimate monthly seasonally adjusted gross output figures for the 75 industries for which the data are available. Our results show material differences from the existing output profile. [ABSTRACT FROM AUTHOR]

The Office for National Statistics (ONS) is exploring models for the future of population statistics. The UK does not have a population register or a set of coherent identifiers across administrative datasets held by government. The current population statistics system is underpinned by the decennial Census, which is expensive and is arguably becoming increasingly unwieldy as a source of data in a rapidly evolving society and with ever increasing demands for more timely, relevant statistics. The system is also highly reliant on a port-based survey to measure migrant flows to and from UK, with the result that the intercensal population size estimates tend to have an increasing element of bias. The ONS is therefore researching how the population statistics system can be transformed within that context. This paper outlines the current plan, focusing on some of the methodological challenges underpinning the transformation. [ABSTRACT FROM AUTHOR]

Summary: Administrative data sets are increasingly used in research because of their excellent coverage and large scale. However, in the UK the use of administrative data on individuals' earnings, and particularly graduates' earnings, is novel. Understanding the strengths and weaknesses of such data is important as they are set to be used extensively for research and to inform policy. Here we compare survey‐based labour earnings data from the UK's Labour Force Survey (LFS) with UK Government administrative sources of individual level earnings data, focusing separately on young (up to age 32 years) graduates and non‐graduates. This type of administrative data set has few sample selection issues and is longitudinal and its large samples mean that the earnings of subpopulations can potentially be studied with low error. Overall we find a similar share of individuals with zero earnings in the LFS and administrative data, but a considerably higher share (conditionally on working) earning below £8000 in the administrative data. The LFS has generally higher earnings right through the distribution, though above the median a large share of the differences can potentially be explained by employee pension contributions. We also find considerably larger gender difference in the survey data. The findings hold for both graduates and non‐graduates. These differences are substantively important and suggest different conclusions about the gender wage gap, the graduate earnings premium and the extent of earnings inequality. [ABSTRACT FROM AUTHOR]

We discuss typical issues in getting access to and using high-quality administrative tax data for research purposes. We discuss research involving both quasi- and field experiments implemented together with the tax authority. We reflect on practical solutions that promote co-creation of knowledge and reduce information asymmetries between researchers and practitioners, based on our experiences of working with the tax authorities in Finland and the UK. We provide examples of how to improve the overall research environment focusing on two successful case studies: the HMRC Datalab in the UK and the remote access to data from Statistics Finland. We propose two key arguments to persuade policymakers elsewhere to follow similar practices: improved data security and equality of access across researchers. [ABSTRACT FROM AUTHOR]

Interpersonal violence comprises a variety of different types of violence that occur between individuals, including violence perpetrated by strangers and acquaintances, intimate partners and family members. Interpersonal violence is a leading cause of death, particularly among young adults. Inconsistencies in definitions and approaches to the measurement of interpersonal violence mean it is difficult to clearly understand its prevalence and the differences and similarities between its different subcategories and contexts. In the UK, specialist services provide support for victim-survivors and also perpetrators of violence. As well as delivering frontline services, specialist services collect data on interpersonal violence, both routinely and for the purpose of research and evaluation. This data has the potential to greatly improve understanding of violence in the UK; however, several issues make this challenging. This review describes and discusses some of the key challenges facing the two types of data collected by specialist services. Key inconsistencies regarding conceptualisation and measurement are identified, along with the implications of these for the synthesis of data, including implications for researchers, service providers, funders and commissioners. Recommendations are proposed to improve practice, the quality of data and, therefore, the understanding of interpersonal violence in the UK. [ABSTRACT FROM AUTHOR]

It is increasingly common for researchers to link survey data to administrative data. If several administrative data sources are of interest, respondents are required to give consent to each of them, meaning that multiple consent questions have to be included in one survey. Existing literature suggests that individual consent varies widely between data sources and over time, but little is known about how respondents process multiple consent requests in a single survey. Using an online access panel in Great Britain, we conducted a set of experiments in two surveys to explore multiple consent requests (covering five domains or data sources). In the first study, we experimentally varied the format of the request, testing three versions: (1) a sequence of pages (with one response per domain), (2) all five requests on the same page (with one response per domain), and (3) a single request (with one joint request covering all five domains). We also varied the order of the domains. We find that average consent rates do not differ by format, but asking a less-sensitive or easier-to-comply request first yields slightly higher average consent rates than asking a more sensitive request first. We repeated the order experiment in a second study, using an independent sample from the same panel, and adding two more order conditions. We find average consent rates are not affected much by order, but the consent to individual domains is affected by order. However, we fail to replicate the pattern of consents found in the first study. We conclude that the order in which multiple consent requests are asked does matter, but in complicated ways that depend on the particular outcomes in which one is interested. Objective knowledge and subjective comprehension of the consent process, and confidence in the decision are largely unaffected by format or order. [ABSTRACT FROM AUTHOR]

Over the last decades, various policies at national and local levels have been implemented to widen participation in higher education (HE) in Scotland and more widely in the UK. Despite this, the acquisition of a HE qualification is still largely determined by the family in which individuals are born. Our study provides new evidence on the extent to which family factors matter by examining sibling data from the Scottish Longitudinal Study, a large‐scale linkage study created using data from administrative and statistical sources. Random effects linear probability models are used to analyse individual and family‐level variance in the chances of obtaining a HE qualification. Our results show that about 40% of the variation in the chances of attaining a university degree is explained by siblings' shared family characteristics and about a third of this share is explained by parental social class, education and housing tenure. A high degree of sibling similarity in the outcome was found across all social‐origin classes. However, while siblings of advantaged families are alike because they both graduated from HE, siblings of disadvantaged families are alike because neither of them did. We suggest that parental compensatory strategies in the former families and economic constraints in the latter families may explain such stark patterns of inequality. Finally, we do not find evidence that the availability of sub‐degrees makes a difference to these patterns. [ABSTRACT FROM AUTHOR]

Background: Adverse childhood experiences (ACEs) are negatively associated with a range of child health outcomes. In this study, we explored associations between five individual ACEs and child mental health diagnoses or symptoms. ACEs included living with someone who had an alcohol-related problem, common mental health disorder or serious mental illness, or experienced victimisation or death of a household member.Methods: We analysed data from a population-level electronic cohort of children in Wales, UK, (N = 191,035) between the years of 1998 and 2012. We used Cox regression with discrete time-varying exposure variables to model time to child mental health diagnosis during the first 15 years of life. Child mental health diagnoses include five categories: (i) externalising symptoms (anti-social behaviour), (ii) internalising symptoms (stress, anxiety, depression), (iii) developmental delay (e.g. learning disability), (iv) other (e.g. eating disorder, personality disorders), and (v) any mental health diagnosis, which was created by combining externalising symptoms, internalising symptoms and other. Our analyses were adjusted for social deprivation and perinatal risk factors.Results: There were strong univariable associations between the five individual ACEs, sociodemographic and perinatal factors (e.g. gestational weight at birth) and an increased risk of child mental health diagnoses. After adjusting for sociodemographic and perinatal aspects, there was a remaining conditional increased risk of any child mental health diagnosis, associated with victimisation (conditional hazard ratio (cHR) 1.90, CI 95% 1.34-2.69), and living with an adult with a common mental health diagnosis (cHR 1.63, CI 95% 1.52-1.75). Coefficients of product terms between ACEs and deprivation were not statistically significant.Conclusion: The increased risk of child mental health diagnosis associated with victimisation, or exposure to common mental health diagnoses, and alcohol problems in the household supports the need for policy measures and intervention strategies for children and their families. [ABSTRACT FROM AUTHOR]

As government administrative data sets are increasingly made available for new (non-administrative) purposes, there is a need to improve access to such resources for voluntary and community organizations, social enterprises and private businesses for statistical analysis and evaluation purposes. The Justice Data Lab set up by the Ministry of Justice in the UK presents an innovative case of how administrative data can be linked to other data held by organizations delivering public services. The establishment of a unit within a secure setting holding evaluation and statistical expertise has enabled providers of programmes aimed at reducing re-offending to obtain evidence on how the impact of their interventions differs from that of a matched comparison group. This article explores the development of the Justice Data Lab, the methodological and other challenges faced, and the experiences of user organizations. The article draws out implications for future development of Data Labs and the use of administrative data for the evaluation of public services. [ABSTRACT FROM PUBLISHER]