Effect of 20 mph speed limits on traffic injuries in Edinburgh, UK: a natural experiment and modelling study.

INTRODUCTION: There is limited research evaluating 20 mph speed limit interventions, and long-term assessments are seldom conducted either globally or within the UK. This study evaluated the impact of the phased 20 mph speed limit implementation on road traffic collisions and casualties in the City of Edinburgh, UK over approximately 3 years post implementation. METHODS: We used four sets of complementary analyses for collision and casualty rates. First, we compared rates for road segments changing to 20 mph against those at 30 mph. Second, we compared rates for the seven implementation zones in the city against paired control zones. Third, we investigated citywide casualty rate trends using generalised additive model. Finally, we used simulation modelling to predict casualty rate changes based on changes in observed speeds. RESULTS: We found a 10% (95% CI -19% to 0%) greater reduction in casualties (8% for collisions) for streets that changed to 20 mph compared with those staying at 30 mph. However, the reduction was similar, 8% (95% CI -22% to 5%) for casualties (10% collisions), in streets that were already at 20 mph. In the implementation zones, we found a 20% (95% CI -22% to -8%) citywide reduction in casualties (22% for collisions) compared with control zones; this compared with a predicted 10% (95% CI -18% to -2%) reduction in injuries based on the changes in speed and traffic volume. Citywide casualties dropped 17% (95% CI 13% to 22%) 3 years post implementation, accounting for trend. CONCLUSION: Our results indicate that the introduction of 20 mph limits resulted in a reduction in collisions and casualties 3 years post implementation. However, the effect exceeded expectations from changes in speed alone, possibly due to a wider network effect.

Analysis of the impact of COVID-19 on Scotland's care-homes from March 2020 to October 2021: national linked data cohort analysis.

BACKGROUND: The impact of the COVID-19 pandemic on long-term care residents remains of wide interest, but most analyses focus on the initial wave of infections. OBJECTIVE: To examine change over time in: (i) The size, duration, classification and pattern of care-home outbreaks of COVID-19 and associated mortality and (ii) characteristics associated with an outbreak. DESIGN: Retrospective observational cohort study using routinely-collected data. SETTING: All adult care-homes in Scotland (1,092 homes, 41,299 places). METHODS: Analysis was undertaken at care-home level, over three periods. Period (P)1 01/03/2020-31/08/2020; P2 01/09/2020-31/05/2021 and P3 01/06/2021-31/10/2021. Outcomes were the presence and characteristics of outbreaks and mortality within the care-home. Cluster analysis was used to compare the pattern of outbreaks. Logistic regression examined care-home characteristics associated with outbreaks. RESULTS: In total 296 (27.1%) care-homes had one outbreak, 220 (20.1%) had two, 91 (8.3%) had three, and 68 (6.2%) had four or more. There were 1,313 outbreaks involving residents: 431 outbreaks in P1, 559 in P2 and 323 in P3. The COVID-19 mortality rate per 1,000 beds fell from 45.8 in P1, to 29.3 in P2, and 3.5 in P3. Larger care-homes were much more likely to have an outbreak, but associations between size and outbreaks were weaker in later periods. CONCLUSIONS: COVID-19 mitigation measures appear to have been beneficial, although the impact on residents remained severe until early 2021. Care-home residents, staff, relatives and providers are critical groups for consideration and involvement in future pandemic planning.

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University.

The impact of the COVID-19 pandemic on University students has been a topic of fiery debate and of public health research. This study demonstrates the use of a combination of spatiotemporal epidemiological models to describe the trends in COVID-19 positive cases on spatial, temporal and spatiotemporal scales. In addition, this study proposes new epidemiological metrics to describe the connectivity between observed positivity; an analogous metric to the R number in conventional epidemiology. The proposed indices, Rspatial, Rspatiotemporal and Rscaling will aim to improve the characterisation of the spread of infectious disease beyond that of the COVID-19 framework and as a result inform relevant public health policy. Apart from demonstrating the application of the novel epidemiological indices, the key findings in this study are: firstly, there were some Intermediate Zones in Edinburgh with noticeably high levels of COVID-19 positivity, and that the first outbreak during the study period was observed in Dalry and Fountainbridge. Secondly, the estimation of the distance over which the COVID-19 counts at the halls of residence are spatially correlated (or related to each other) was found to be 0.19km (0.13km to 0.27km) and is denoted by the index, Rspatial. This estimate is useful for public health policy in this setting, especially with contact tracing. Thirdly, the study indicates that the association between the surrounding community level of COVID-19 positivity (Intermediate Zones in Edinburgh) and that of the University of Edinburgh's halls of residence was not statistically significant. Fourthly, this study reveals that relatively high levels of COVID-19 positivity were observed for halls for which higher COVID-19 fines were issued (Spearman's correlation coefficient = 0.34), and separately, for halls which were non-ensuite relatively to those which were not (Spearman's correlation coefficient = 0.16). Finally, Intermediate Zones with the highest positivity were associated with student residences that experienced relatively high COVID-19 positivity (Spearman's correlation coefficient = 0.27).

"Are We Working (Too) Comfortably?": Understanding the Nature of and Factors Associated with Sedentary Behaviour When Working in the Home Environment.

Home working has increased due to COVID-19, but little is known about how this change has impacted the health risk behaviour of elevated sedentary time. The aim of this cross-sectional exploratory study was to assess occupational sitting behaviour when working at home, and use the Capability Opportunity Motivation-Behaviour (COM-B) model to identify influences on this behaviour. University staff (n = 267; 69% female; 92% white) who were predominantly working from home completed a questionnaire to assess sitting time, sitting breaks, demographic and occupational characteristics, and a 7-item COM-B questionnaire and open-ended questions to assess influences on time spent sitting whilst working from home. Data were analysed descriptively, a repeated measures ANOVA was used to determine differences in the COM-B items, and binary logistic regression was used to examine predictors of sitting time. Staff spent on average 89.5% (SD = 17.1) of their time sitting whilst working at home, and took an average of 1.36 (1.38) sitting breaks per hour. There were significant and meaningful differences in the influence of the COM factors on ability and willingness to reduce sitting behaviour (p < .0001; ηp 2 = .38), and the open-ended responses added further context. The included variables accounted for 20.7% of variance in sitting behaviour, with age, sitting breaks, motivation-automatic, and opportunity-physical contributing significantly. Working from home leads to elevated levels of sitting, and the COM-B provides a useful model to identify key influences on ability and willingness to reduce sitting. Strategies incorporating regular breaks, habit formation/reversal, and restructuring the physical environment may be beneficial.

Investigating the impact of a 20 miles per hour speed limit intervention on road traffic collisions, casualties, speed and volume in Belfast, UK: 3 year follow-up outcomes of a natural experiment.

BACKGROUND: Evidence regarding the effectiveness of 20 miles per hour (mph) speed limit interventions is limited, and rarely have long-term outcomes been assessed. We investigate the effect of a 20 mph speed limit intervention on road traffic collisions, casualties, speed and volume at 1 and 3 years post-implementation. METHODS: An observational, repeated cross-sectional design was implemented, using routinely collected data for road traffic collisions, casualties, speed and volume. We evaluated difference-in-differences in collisions and casualties (intervention vs control) across three different time series and traffic speed and volume pre-implementation, at 1 and 3 years post-implementation. RESULTS: Small reductions in road traffic collisions were observed at year 1 (3%; p=0.82) and year 3 post-implementation (15%; p=0.31) at the intervention site. Difference-in-differences analyses showed no statistically significant differences between the intervention and control sites over time for road traffic collisions. There were 16% (p=0.18) and 22% (p=0.06) reductions in casualty rates at years 1 and 3 post-implementation, respectively, at the intervention site. Results showed little change in mean traffic speed at year 1 (0.2 mph, 95% CI -0.3 to 2.4, p=0.14) and year 3 post-implementation (0.8, 95% CI -1.5 to 2.5, p=0.17). For traffic volume, a decrease in 57 vehicles per week was observed at year 1 (95% CI -162 to -14, p<0.00) and 71 vehicles at year 3 (95% CI -213 to 1, p=0.05) post-implementation. CONCLUSION: A 20 mph speed limit intervention implemented at city centre scale had little impact on long-term outcomes including road traffic collisions, casualties and speed, except for a reduction in traffic volume. Policymakers considering implementing 20 mph speed limit interventions should consider the fidelity, context and scale of implementation.

The relationship between women's empowerment and household food and nutrition security in Pakistan.

Women's empowerment is considered to play a crucial role in food and nutrition security. We aimed to explore the relationship between women's empowerment and food and nutrition security, in rural Pakistan. METHODS: To estimate women's empowerment, we developed a Rural Women Composite Empowerment Index incorporating nine domains. For indicators of food and nutritional data we used data of 1879 rural households from Pakistan Rural Household Panel Survey (PRHPS). Food insecurity was measured through a caloric intake approach; nutrition insecurity was measured through recommended nutrient intake (RNI). Using the Rural Women's Composite Empowerment Index (RWCEI), we employed multi-level mixed-effect regression analysis. RESULTS: The domains of traveling safely (21%), time allocated to tasks (20%), and (lack of) domestic violence (19%) were the most significant domains in defining empowerment of rural woman. The prevalence of food and nutrition insecurity were 33% and 50% respectively. Regression analysis found a positive and significant relationship between women's empowerment and food and nutrition security-the proportion of household who were food and nutritionally secure in empowered households was 70% and 98% respectively. CONCLUSIONS: Developing programmes and policies to improve the range of domains of women's empowerment requires a focussed policy agenda, bringing together policy makers from a number of different sectors including education, economy, communications, technology and agriculture. Women's empowerment has the potential to make positive changes not only in food and nutrition security, but in all aspects of family health and wellbeing.

How does the community COVID-19 level of risk impact on that of a care home?

OBJECTIVES: To model the risk of COVID-19 mortality in British care homes conditional on the community level risk. METHODS: A two stage modeling process ("doubly latent") which includes a Besag York Mollie model (BYM) and a Log Gaussian Cox Process. The BYM is adopted so as to estimate the community level risks. These are incorporated in the Log Gaussian Cox Process to estimate the impact of these risks on that in care homes. RESULTS: For an increase in the risk at the community level, the number of COVID-19 related deaths in the associated care home would be increased by exp (0.833), 2. This is based on a simulated dataset. In the context of COVID-19 related deaths, this study has illustrated the estimation of the risk to care homes in the presence of background community risk. This approach will be useful in facilitating the identification of the most vulnerable care homes and in predicting risk to new care homes. CONCLUSIONS: The modeling of two latent processes have been shown to be successfully facilitated by the use of the BYM and Log Gaussian Cox Process Models. Community COVID-19 risks impact on that of the care homes embedded in these communities.

Evaluating the citywide Edinburgh 20mph speed limit intervention effects on traffic speed and volume: A pre-post observational evaluation.

OBJECTIVES: Traffic speed is important to public health as it is a major contributory factor to collision risk and casualty severity. 20mph (32km/h) speed limit interventions are an increasingly common approach to address this transport and health challenge, but a more developed evidence base is needed to understand their effects. This study describes the changes in traffic speed and traffic volume in the City of Edinburgh, pre- and 12 months post-implementation of phased city-wide 20mph speed limits from 2016-2018. METHODS: The City of Edinburgh Council collected speed and volume data across one full week (24 hours a day) pre- and post-20mph speed limits for 66 streets. The pre- and post-speed limit intervention data were compared using measures of central tendency, dispersion, and basic t-tests. The changes were assessed at different aggregations and evaluated for statistical significance (alpha = 0.05). A mixed effects model was used to model speed reduction, in the presence of key variables such as baseline traffic speed and time of day. RESULTS: City-wide, a statistically significant reduction in mean speed of 1.34mph (95% CI 0.95 to 1.72) was observed at 12 months post-implementation, representing a 5.7% reduction. Reductions in speed were observed throughout the day and across the week, and larger reductions in speed were observed on roads with higher initial speeds. Mean 7-day volume of traffic was found to be lower by 86 vehicles (95% CI: -112 to 286) representing a reduction of 2.4% across the city of Edinburgh (p = 0.39) but with the direction of effect uncertain. CONCLUSIONS: The implementation of the city-wide 20mph speed limit intervention was associated with meaningful reductions in traffic speeds but not volume. The reduction observed in road traffic speed may act as a mechanism to lessen the frequency and severity of collisions and casualties, increase road safety, and improve liveability.

Use of natural experimental studies to evaluate 20mph speed limits in two major UK cities.

INTRODUCTION: Reductions in traffic speed can potentially offer multiple health and public health benefits. In 2016, implementation of 20mph (30kph) speed limit interventions began in Edinburgh (city-wide) and Belfast (city centre). The aims of this paper are to describe 1) the broad theoretical approach and design of two natural experimental studies to evaluate the 20mph speed limits in Edinburgh and Belfast and 2) how these studies allowed us to test and explore theoretical mechanisms of 20mph speed limit interventions. METHODS: The evaluation consisted of several work packages, each with different research foci, including the political decision-making processes that led to the schemes, their implementation processes, outcomes (including traffic speed, perceptions of safety, and casualties) and cost effectiveness. We used a combination of routinely and locally collected quantitative data and primary quantitative and qualitative data. RESULTS: The evaluation identified many contextual factors influencing the likelihood of 20mph speed limits reaching the political agenda. There were substantial differences between the two sites in several aspects related to implementation. Reductions in speed resulted in significant reductions in collisions and casualties, particularly in Edinburgh, which had higher average speed at baseline. The monetary value of collisions and casualties prevented are likely to exceed the costs of the intervention and thus the overall balance of costs and benefits is likely to be favourable. CONCLUSIONS: Innovative study designs, including natural experiments, are important for assessing the impact of 'real world' public health interventions. Using multiple methods, this project enabled a deeper understanding of not only the effects of the intervention but the factors that explain how and why the intervention and the effects did or did not occur. Importantly it has shown that 20mph speed limits can lead to reductions in speed, collisions and casualties, and are therefore an effective public health intervention.

A qualitative exploration of the mechanisms, pathways and public health outcomes of a city centre 20mph speed limit intervention: The case of Belfast, United Kingdom.

Twenty miles per hour (mph) speed limits can impact the health of the public (e.g., road safety, active travel). However, a better understanding of how individuals experience 20mph limits is required, to ensure interventions are cognisant of perceptions and potential un/intended outcomes. Focus groups (n = 9, 60 participants) to explore the Belfast 20mph intervention highlighted divergent perspectives and experiences including: 12 mechanisms (e.g., limited awareness), 15 pathways (e.g., reduced driving speed→improved liveability) and 10 public health outcomes (e.g., increased cyclist safety). Future interventions should consider un/intended outcomes and implement strategies to enhance effectiveness and mitigate harms (e.g., through training, enforcement).

Bayesian Spatial NBDA for Diffusion Data with Home-Base Coordinates.

Network-based diffusion analysis (NBDA) is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction) from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area) can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN). This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses.

Perching but not foraging networks predict the spread of novel foraging skills in starlings.

The directed social learning hypothesis suggests that information does not spread evenly through animal groups, but rather individual characteristics and patterns of physical proximity guide the social transmission of information along specific pathways. Network-based diffusion analysis (NBDA) allows researchers to test whether information spreads following a social network. However, the explanatory power of different social networks has rarely been compared, and current models do not easily accommodate random effects (e.g. allowing for individuals within groups to correlate in their asocial solving rates). We tested whether the spread of two novel foraging skills through captive starling groups was affected by individual- and group-level random and fixed effects (i.e. sex, age, body condition, dominance rank and demonstrator status) and perching or foraging networks. We extended NBDA to include random effects and conducted model discrimination in a Bayesian context. We found that social learning increased the rate at which birds acquired the novel foraging task solutions by 6.67 times, and acquiring one of the two novel foraging task solutions facilitated the asocial acquisition of the other. Surprisingly, the spread of task solutions followed the perching rather than the foraging social network. Upon acquiring a task solution, foraging performance was facilitated by the presence of group mates. Our results highlight the importance of considering more than one social network when predicting the spread of information through animal groups. This article is part of a Special Issue entitled: Cognition in the wild.