Christine Dobbs

On moving targets and magic bullets: Can the UK lead the way with responsible data linkage for health research?

Highlights • We explore key elements of good governance in health linkage.• Adaptive reflexive governance models are essential.• Two examples illustrate how we can achieve standardisation of practice.• Distinct elements of governance compiled in a composite fashion tend to challenges.

Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research.

In parallel with the advances in big data-driven clinical research, the data safe haven concept has evolved over the last decade. It has led to the development of a framework to support the secure handling of health care information used for clinical research that balances compliance with legal and regulatory controls and ethical requirements while engaging with the public as a partner in its governance. We describe the evolution of 4 separately developed clinical research platforms into services throughout the United Kingdom-wide Farr Institute and their common deployment features in practice. The Farr Institute is a case study from which we propose a common definition of data safe havens as trusted platforms for clinical academic research. We use this common definition to discuss the challenges and dilemmas faced by the clinical academic research community, to help promote a consistent understanding of them and how they might best be handled in practice. We conclude by questioning whether the common definition represents a safe and trustworthy model for conducting clinical research that can stand the test of time and ongoing technical advances while paying heed to evolving public and professional concerns.

A support network typology for application in older populations with a preponderance of multigenerational households.

ABSTRACT This paper considers the support networks of older people in populations with a preponderance of multigenerational households and examines the most vulnerable network types in terms of loneliness and isolation. Current common typologies of support networks may not be sensitive to differences within and between different cultures. This paper uses cross-sectional data drawn from 590 elders (Gujaratis, Punjabis and Sylhetis) living in the United Kingdom and South Asia. Six variables were used in K-means cluster analysis to establish a new network typology. Two logistic regression models using loneliness and isolation as dependent variables assessed the contribution of the new network type to wellbeing. Four support networks were identified: ‘Multigenerational Households: Older Integrated Networks’, ‘Multigenerational Households: Younger Family Networks’, ‘Family and Friends Integrated Networks’ and ‘Non-kin Restricted Networks’. Older South Asians with ‘Non-kin Restricted Networks’ were more likely to be lonely and isolated compared to others. Using network typologies developed with individualistically oriented cultures, distributions are skewed towards more robust network types and could underestimate the support needs of older people from familistic cultures, who may be isolated and lonely and with limited informal sources of help. The new typology identifies different network types within multigenerational households, identifies a greater proportion of older people with vulnerable networks and could positively contribute to service planning.

Dangers from within?: Looking inwards at the role of maladministration as the leading cause of health data breaches in the UK

Despite the continuing rise of data breaches in the United Kingdom’s health sector there remains little evidence or understanding of the key causal factors leading to the misuse of health data and therefore uncertainty remains as to the best means of prevention and mitigation. Furthermore, in light of the forthcoming General Data Protection Regulation, the stakes are higher and pressure will continue to increase for organisations to adopt more robust approaches to information governance. This chapter builds upon the authors’ 2014 report commissioned by the United Kingdom’s Nuffield Council on Bioethics and Wellcome Trust’s Expert Advisory Group on Data Access, which uncovered evidence of harm from the processing of health and biomedical data. One of the review’s key findings was identifying maladministration (characterised as the epitome of poor information governance practices) as the number one cause for data breach incidents. The chapter uses a case study approach to extend the work and provide novel analysis of maladministration and its role as a leading cause of data breaches. Through these analyses we examine the extent of avoidability of such incidents and the crucial role of good governance in the prevention of data breaches. The findings suggest a refocus of attention on insider behaviours is required, as opposed to, but not excluding, the dominant conceptualisations of data misuse characterised by more publicised (and sensationalised) incidents involving third-party hackers.

Social support networks of older migrants in England and Wales: The role of collectivist culture

ABSTRACT This article tests the fit of a social support network typology developed for collectivist cultures to six migrant populations living in England and Wales. We examine the predictive utility of the typology to identify networks most vulnerable to poor quality of life and loneliness. Variables representing network size, and the proportion of the network classified by gender, age, kin and proximity, were used in confirmatory and exploratory latent profile analysis to fit models to the data (N = 815; Black African, Black Caribbean, Indian, Pakistani, Bangladeshi and Chinese). Multinomial logistic regression examined associations between demographic variables and network types. Linear regression examined associations between network types and wellbeing outcomes. A four-profile model was selected. Multigenerational Household: Younger Family networks were most robust with lowest levels of loneliness and greatest quality of life. Restricted Non-kin networks were least robust. Multigenerational Household: Younger Family networks were most prevalent for all but the Black Caribbean migrants. The typology is able to differentiate between networks with multigenerational households and can help identify vulnerable networks. There are implications for forecasting formal services and variation in networks between cultures. The use of a culturally appropriate typology could impact on the credibility of gerontological research.

The other side of the coin: harm due to the non-use of health-related data

ABSTRACTObjectivesIt is widely acknowledged that breaches and misuses of health-related data can have serious implications and consequently they often carry penalties. However, harm due to the omission of health data usage, or data non use, is a subject that lacks attention. A better understanding of this other side of the coin is required before it can be addressed effectively. ApproachThis article uses an international case study approach to explore why data non use is difficult to ascertain, the sources and types of health-related data non-use, its implications for citizens and society and some of the reasons it occurs. It does this by focussing on issues with clinical care records, research data and governance frameworks and associated examples of non-use. ResultsThe non-use of health-related data is a complex issue with multiple sources and reasons contributing to it. Instances of data non-use can be associated with harm, but taken together they describe a trail of data non-use, and this may complicate and compound its impacts. Actual evidence of data non-use is sparse and harm due to data non use is difficult to prove. But although it can be nebulous, it is a real problem with largely unquantifiable consequences. There is ample indirect evidence that health data non-use is implicated in the deaths of many thousands of people and potentially £billions in financial burdens to societies.ConclusionThe most effective initiatives to address specific contexts of data non-use will be those that are cognisant of the multiple aspects to this complex issue, in order to move towards socially responsible reuse of data becoming the norm to save lives and resources.