Reactive Attachment Disorder in infants in foster care and associated mental health and cognitive functioning

Background: Reactive attachment disorder (RAD) has been described as one of the least researched and most poorly understood psychiatric disorders (Chaffin et al., 2006). Despite this, given what is known about maltreatment and attachment, it is likely that RAD has profound consequences for child development. Very little is known about the prevalence and stability of RAD symptoms over time. Until recently it has been difficult to investigate the presence of RAD due to limited measures for informing a diagnosis. However this study utilised a new observational tool Method: A cross sectional study design with a one-year follow-up explored RAD symptoms in maltreated infants in Scotland (n=55, age range= 16-62 months) and associated mental health and cognitive functioning. The study utilised the Rating of Inhibited Attachment Behavior Scale (Corval, et al., unpublished 2014) that has recently been developed by experts in the field along side The Disturbances of Attachment Interview (Smyke & Zeanah, 1999). Children were recruited as part of the BeST trial, whereby all infants who came in to the care of the local authority in Glasgow due to child protection concerns were invited to participate. The study sample was representative of the larger pool of data in terms of age, gender, mental health and cognitive functioning. Results: The sample was found to be representative of the population of maltreated children from which it was derived. Prevalence of RAD was found to be 7.3% (n=3, 95% CI [0.43 – 14.17]) at T1, when children are first placed in to foster care. At T2, following one year in improved care conditions, 4.3% (n=2, 95% CI [below 0 – 10.16]) met a borderline RAD diagnosis. Levels of observed RAD symptoms decreased significantly at T2 in comparison to T1 but carer reported symptoms of RAD did not. Children whose RAD symptoms did not improve were found to be significantly older and showed less prosocial behaviour. RAD was associated with some mental health and cognitive difficulties. Lower Verbal IQ and unexpectedly, prosocial behaviour were found to predict RAD symptoms. Conclusions: The preliminary findings have added to the developing understanding of RAD symptoms and associated difficulties however further exploration of RAD in larger samples would be invaluable.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

Tackling health inequalities in primary care: an exploration of GPs’ experience at the frontline

In Scotland, life expectancy and health outcomes are strongly tied to socioeconomic status. Specifically, socioeconomically deprived areas suffer disproportionately from high levels of premature multimorbidity and mortality. To tackle these inequalities in health, challenges in the most deprived areas must be addressed. One avenue that merits attention is the potential role of general medical practitioners (GPs) in helping to address health inequalities, particularly due to their long-term presence in deprived communities, their role in improving patient and population health, and their potential advocacy role on behalf of their patients. GPs can be seen as what Lipsky calls ‘street-level bureaucrats’ due to their considerable autonomy in the decisions they make surrounding individual patient needs, yet practising under the bureaucratic structure of the NHS. While previous research has examined the applicability of Lipsky’s framework to the role of GPs, there has been very little research exploring how GPs negotiate between the multiple identities in their work, how GPs ‘socially construct’ their patients, how GPs view their potential role as ‘advocate’, and what this means in terms of the contribution of GPs to addressing existing inequalities in health. Using semi-structured interviews, this study explored the experience and views of 24 GPs working in some of Scotland’s most deprived practices to understand how they might combat this growing health divide via the mitigation (and potential prevention) of existing health inequalities. Participants were selected based on several criteria including practice deprivation level and their individual involvement in the Deep End project, which is an informal network comprising the 100 most deprived general practices in Scotland. The research focused on understanding GPs’ perceptions of their work including its broader implications, within their practice, the communities within which they practise, and the health system as a whole. The concept of street-level bureaucracy proved to be useful in understanding GPs’ frontline work and how they negotiate dilemmas. However, this research demonstrated the need to look beyond Lipsky’s framework in order to understand how GPs reconcile their multiple identities, including advocate and manager. As a result, the term ‘street-level professional’ is offered to capture more fully the multiple identities which GPs inhabit and to explain how GPs’ elite status positions them to engage in political and policy advocacy. This study also provides evidence that GPs’ social constructions of patients are linked not only to how GPs conceptualise the causes of health inequalities, but also to how they view their role in tackling them. In line with this, the interviews established that many GPs felt they could make a difference through advocacy efforts at individual, community and policy/political levels. Furthermore, the study draws attention to the importance of practitioner-led groups—such as the Deep End project—in supporting GPs’ efforts and providing a platform for their advocacy. Within this study, a range of GPs’ views have been explored based on the sample. While it is unclear how common these views are amongst GPs in general, the study revealed that there is considerable scope for ‘political GPs’ who choose to exercise discretion in their communities and beyond. Consequently, GPs working in deprived areas should be encouraged to use their professional status and political clout not only to strengthen local communities, but also to advocate for policy change that might potentially affect the degree of disadvantage of their patients, and levels of social and health inequalities more generally.