Human error in the design of a safety-critical system

From the introduction:This thesis is an investigation into some o f the causes and possible remedies to the problem of human error in a complex human-machine system. The system in question is engaged in the design of computer software for the control of railway signalling infrastructure. Error in its operation has the potential to be lethally destructive, a fact that provides not only the system’s epithet but also the primary motivation and significance for its investigation

Environmental concern over time: evidence from the longitudinal analysis of a British cohort study from 1991 to 2008

Objective We examined whether and how levels of environmental concern changed over time in the UK, from 1991 to 2008-2009, as well as how environmental concern relates to socio-economic characteristics across this same time frame. Methods Using item response theory models on the last three sweeps of the British National Child Development Study 1958, we evaluated a measure of environmental concern. Then, using latent growth curve models (LGCM) we estimated the pattern of change for environmental concern across time. Finally, theoretically relevant socio-demographic characteristics were introduced as covariates into the LGCM. Results We found a small but significant downfall of the mean level of environmental concern over time, with individual-level values displaying higher dispersion in 2008-2009 against the previous sweeps of data. We also found that political orientation has significant effects on the outcome and on its changes across time. Conclusions Hypotheses regarding the influence of interest in politics and voting choices on environmental concern are supported. The increasing variance of environmental concern over time warrants further investigation

Micro-macro multilevel analysis of day-to-day lifestyle and carbon emissions in UK multiple occupancy households

Far-reaching changes in daily life present a pressing need to balance energy consumption with environmental impact. Previous research on household carbon emissions generally described its contributors in disposable income, consumption pattern, and household-related lifestyle, whereas they have not fully explored how carbon emissions relate to residents' day-to-day lifestyles. Given that individual lifestyles within a household may be correlated, there is a need to disentangle the clustering effect of household members' lifestyles and their association with household carbon emissions. This study used micro-macro multilevel modelling to examine the structure of individual lifestyles and their impact on household carbon emissions for 8618 multiple occupancy households of 19,816 respondents in the UK Household Longitudinal Study dataset. The results showed that a factor capturing energy-saving lifestyle behaviours significantly reduced housing fuel use emissions and a second capturing transportation and consumption choices cut motor emissions. Interestingly, the contribution of energy-saving lifestyle in cutting down housing-fuel-using emissions becomes more pronounced when household income and household characteristics (e.g., household size, dwelling, house ownership, number of cars, urbanity, employment) were controlled for. Contrarily, the strength of green transportation and consumption lifestyle contributing to lower motor emissions was weakened after controlling for household characteristics. Findings indicated that day-to-day lifestyle not only reflects individual variability in sustainable living but also systematic household variation in carbon emissions. Knowledge of which living patterns are responsible for disproportionately high levels of carbon emissions can enhance effective targeted policy aimed at stimulating sustainable lifestyles and carbon reduction

Environmental concern over time: evidence from the longitudinal analysis of a British cohort study from 1991 to 2008

Objective We examined whether and how levels of environmental concern changed over time in the UK, from 1991 to 2008-2009, as well as how environmental concern relates to socio-economic characteristics across this same time frame. Methods Using item response theory models on the last three sweeps of the British National Child Development Study 1958, we evaluated a measure of environmental concern. Then, using latent growth curve models (LGCM) we estimated the pattern of change for environmental concern across time. Finally, theoretically relevant socio-demographic characteristics were introduced as covariates into the LGCM. Results We found a small but significant downfall of the mean level of environmental concern over time, with individual-level values displaying higher dispersion in 2008-2009 against the previous sweeps of data. We also found that political orientation has significant effects on the outcome and on its changes across time. Conclusions Hypotheses regarding the influence of interest in politics and voting choices on environmental concern are supported. The increasing variance of environmental concern over time warrants further investigation

Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-07-01, epub 2021-07-22Publication status: PublishedFunder: Medical Research Council; Grant(s): MR/R502236/1Funder: Royal Society; Grant(s): 202562/Z/16/Z, INF/R2/180067BackgroundPredicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data.MethodOn a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy.ResultsAll methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days.ConclusionsGiven the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist

Human error in the design of a safety-critical system

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