The Corporate Manslaughter and Corporate Homicide Act 2007 or the Health and Safety (Offences) Act 2008: corporate killing and the law

This thesis examines the regulatory and legislative approach taken in the United Kingdom to deal with deaths arising from work related activities and, in particular, deaths that can be directly attributed to the behaviour of corporations and other organisations. Workplace health and safety has traditionally been seen in the United Kingdom as a regulatory function which can be traced to the very earliest days of the Industrial Revolution. With an emphasis on preventing workplace accidents and ill-health through guidance, advice and support, the health and safety legislation and enforcement regime which had evolved over the best part of two centuries was considered inadequate to effectively punish corporations considered responsible for deaths caused by their activities following a series of disasters in the late twentieth and early twenty-first centuries. To address this apparent inadequacy, the Corporate Manslaughter and Corporate Homicide Act 2007 was introduced creating the offence of corporate manslaughter and corporate homicide. Based on a gross breach of a relevant duty of care resulting in the death of a person, the Act effectively changed what had previously considered a matter of regulation, an approach that had obvious weaknesses and shortcomings, to one of crime and criminal law. Whether this is the best approach to dealing with deaths caused by an organisation is challenged in this thesis and the apparent distinction between ‘criminal’ and ‘regulatory’ offences is also examined. It was found that an amended Health and Safety at Work etc. Act 1974 to include a specific offence of corporate killing, in conjunction with the Health and Safety (Offences) Act 2008 would almost certainly have resulted in a more effective approach to dealing with organisations responsible for causing deaths as consequence of their activities. It was also found that there was no substantive difference between ‘regulatory’ and ‘criminal’ law other than the stigma associated with the latter, and that distinction would almost certainly disappear, at least in the context of worker safety, as a consequence of the penalties available following the introduction of the Health and Safety (Offences) Act 2008.

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.