Interrogating the technical, economic and cultural challenges of delivering the PassivHaus standard in the UK.

A peer-reviewed eBook, which is based on a collaborative research project coordinated by Dr. Henrik Schoenefeldt at the Centre for Architecture and Sustainable Environment at the University of Kent between May 2013 and June 2014. This project investigated how architectural practice and the building industry are adapting in order to successfully deliver Passivhaus standard buildings in the UK. Through detailed case studies the project explored the learning process underlying the delivery of fourteen buildings, certified between 2009 and 2013. Largely founded on the study of the original project correspondence and semi-structured interviews with clients, architects, town planners, contractors and manufacturers, these case studies have illuminated the more immediate technical as well as the broader cultural challenges. The peer-reviewers of this book stressed that the findings included in the book are valuable to students, practitioners and academic researchers in the field of low-energy design. It was launched during the PassivHaus Project Conference, held at the Bulb Innovation Centre on the 27th June 2014

Policy without politics: technocratic control of climate change adaptation policy making in Nepal

As developing countries around the world formulate policies to address climate change, concerns remain as to whether the voices of those most exposed to climate risk are represented in those policies. Developing countries face significant challenges for contextualizing global-scale scientific research into national political dynamics and downscaling global frameworks to sub-national levels, where the most affected are presumed to live. This article critiques the ways in which the politics of representation and climate science are framed and pursued in the process of climate policy development, and contributes to an understanding of the relative effectiveness of globally framed, generic policy mechanisms in vulnerable and politically volatile contexts. Based on this analysis, it also outlines opportunities for the possibility of improving climate policy processes to contest technocratic framing and generic international adaptation solutions.Policy relevanceNepal's position as one of the countries most at risk from climate change in the Himalayas has spurred significant international support to craft climate policy responses over the past few years. Focusing on the National Adaptation Programme of Action (NAPA) and the Climate Change Policy, this article examines the extent to which internationally and scientifically framed climate policy in Nepal recognizes the unfolding political mobilizations around the demand for a representative state and equitable adaptation to climate risks. This is particularly important in Nepal, where political unrest in the post-conflict transition after the end of the civil war in 2006 has focused around struggles over representation for those historically on the political margins. Arguing that vulnerability to climate risk is produced in conjunction with social and political conditions, and that not everyone in the same locality is equally vulnerable, we demonstrate the multi-faceted nature of the politics of representation for climate policy making in Nepal. However, so far, this policy making has primarily been shaped through a technocratic framing that avoids political contestations and downplays the demand for inclusive and deliberative processes. Based on this analysis, we identify the need for a flexible, contextually grounded, and multi-scalar approach to political representation while also emphasizing the need for downscaling climate science that can inform policy development and implementation to achieve fair and effective adaptation to climate change

Bumps and Babies Longitudinal Study (BABBLES): An independent evaluation of the Baby Buddy app

Introduction: Developments in information and communication technologies have enabled and supported the development and expansion of electronic health in the last decade. This has increased the possibility of self-management and care of health issues.Objectives: To assess the effectiveness of on maternal self-efficacy and mental wellbeing three months post-birth in a sample of mothers recruited during the antenatal period. In addition, to explore when, why and how mothers use the app and consider any benefits the app may offer them in relation to their parenting, health, relationships or communication with their child, friends, family members or health professionals.Design: A mixed methods approach, including a longitudinal cohort study, a qualitative study and detailed analysis and synthesis of data from the Baby Buddy app about the way in which mothers accessed and used the app content.Setting: The study was conducted in five geographically separate sites in England: Coventry, Lewisham, Bradford, Blackpool and Leicester. These areas were chosen as they were geographically, ethnically and socio-economically diverse and where the Baby Buddy app was reported to be well-embedded, both formally and informally, into the maternity and child health pathways by the relevant healthcare staff.Participants: Pregnant women who were aged 16 years and over, had no previous live child, were between 12-16 weeks and six days gestation and booked with the maternity services in each of the five study sites were invited to take part.Interventions: Self-reported use of the Baby Buddy app at one of the three data collection time-points: 12-16+6 weeks gestation, 35 weeks gestation and three months post-birth.Outcome measures: The primary outcome measure was parental self-efficacy at three months post-birth using the Tool to measure Parenting Self-Efficacy (TOPSE). The main secondary outcome was maternal mental well-being at three months post-birth using the Warwick and Edinburgh Mental Wellbeing Scale (WEMWBS).Results: Recruitment took place between September 2016 and February 2017. A total of 488 participants provided valid data at baseline (12-16 weeks gestation), 296 participants also provided valid data at 3 months post-birth, 114 (38.5%) of whom reported that they had used the Baby Buddy app at one or more of the data collection time-points (‘app user’). Seventeen first-time mothers participated in the qualitative arm via telephone interviews (n=9) and a focus group (n=8). Twenty healthcare professionals participated in interviews (n=5) and two focus groups (n=15). Consent was gained from 98 participants who gave permission for their in-app4data to be made accessible but just 61 participants could be identified from the database provided, of whom 51 were included in the analyses.At recruitment there were no differences between Baby Buddy app users and non-app users in respect to: age, IMD, ethnicity, highest education, employment, relationship status. Baby Buddy app users were more likely to use pregnancy or parenting apps (80.7% vs 69.6%, p=.035), more likely to have been introduced to the app by a healthcare professional (p=.005) and have a lower median score for perceived social support (81 vs 83, p=.034) than non-app users. The Baby Buddy app did not illicit a statistically significant change in TOPSE scores from baseline to 3 months post-birth (adjusted OR 1.12, 95%CI 0.59 to 2.13, p=.730). Finding out about the Baby Buddy app from a healthcare professional appeared to grant no additional benefit to app users compared to all other participants in terms of self-efficacy at three months post-birth (adjusted OR 1.16, 95%CI 0.60 to 2.23, p=.666).Apps were popular; Baby Buddy app users were more likely to use other pregnancy-related apps than non-Baby Buddy users and the most frequent source from which Baby Buddy app users found out about the app was a midwife. A post-hoc analysis found that Baby Buddy app users were more likely to breastfeed than non-Baby Buddy app users. This was a consistent pattern for both exclusive breastfeeding and any breast feeding: there was a 9% increase in exclusive breastfeeding at any time up to 3 months post-birth in Baby Buddy app users and a 12% increase in any breastfeeding up to three months post-birth, compared to non-app users. Whilst this is an important finding, this needs to be used with care due to the post-hoc element of the analysis.First-time mothers who participated in the qualitative arm of the study found that the Baby Buddy app worked well due to its accessibility and that the information was concise and easy to find. They liked that it followed the progress of pregnancy with appropriately-timed information and that different aspects could be accessed as and when needed. The app was designed to be an adjunct to service delivery not a replacement for healthcare. The importance of this was demonstrated by many first-time mothers reporting that they preferred in-practice support from a healthcare professional.The qualitative data indicated that the four preconditions of normalisation process theory: implementation, adoption, translation and stabilisation were met in regard to healthcare professionals’ use of the Baby Buddy app. This suggests that the healthcare professionals were actively integrating the Baby Buddy app into clinical practice with other professionals and first-time mothers, therefore embedding the Baby Buddy app into their service delivery.The in-app data from the sub-sample of participants (n=51) suggest that there was a difference in the amount of time participants spent accessing elements of the app; the median time spent using the app per session was 8.3 minutes (SD 5.8 minutes). The most popular features that5were used were ‘Today’s Information’, videos, ‘Bump/Baby Booth’, ‘Ask Me’ and ‘What does that mean?’. Participants used the app most often between 9-10am with another peak in the evening around 8-9pm. There were also a broad range of topics and issues that the participants searched for, of which the most searched words included: ‘labour’, ‘form’, ‘birth’, ‘pregnant’ and ‘developing’. In the sub-sample for whom we had in-app data, there was a large range for the number of times the app was used, from 0-593 times. The median number of times the app was opened was 146.5 but the data were positively skewed (LQ 52.5 – UQ 329). This indicates that the data are bunched towards the smaller number of times opened. Within this sub-sample, 21.6% of the engaged type of user used the app up to 25 times and 47% of this type of user used the app more than 100 times. This contrasts with the highly engaged type of user where 43% used the app 25 or less times and just 9.8% of this proactive type of user used it more than 100 times.We found no statistically significant difference in the TOPSE or the WEMWBS scores between the type of user who was engaged with the app and non-app users (adjusted OR 0.69, 95%CI 0.22 to 2.16, p=.519 and adjusted OR 1.54, 95%CI 0.57 to 4.16, p=.329, respectively). Similarly, we found no statistically significant difference between the type of users who were highly engaged users and non-app users (TOPSE: adjusted OR 0.48, 95%CI 0.14t o 1.68, p=.251; WEMWBS: adjusted OR 1.40, 95%CI 0.52 to 3.76, p=.509).Strengths and limitations: The primary objective was to explore the impact of the Baby Buddy app on parental self-efficacy and the Tool for Parenting Self-Efficacy (TOPSE website, Kendall, Bloomfield and Nash 2009), a validated measure, was selected to measure the primary outcome. The retention rate of 60.7% from baseline to three months post-birth demonstrates the difficulty of engaging new mothers during this demanding period of their lives. Nevertheless, in the initial and final samples, app users and non-users remained generally comparable and relevant confounders were adjusted for. Mothers were invited to take part in interviews and/or focus groups, the latter of which were held in a baby-friendly, welcoming environment for women and babies. Telephone interviews were offered for greater convenience for the women. Analysing the in-app data, we were able to compare outcomes for both the high versus low or non-user app groups and for those mothers who were the type of highly engaged users versus those who were a less engaged type. This was for a relatively small number of mothers but was a new method of analysing the in-app data.The Baby Buddy app was publicly available, meaning randomisation was not possible and therefore participants were only asked about their specific use of the app after the 35 weeks gestation data collection point to avoid directed app use. The participants were a self-selected group, especially those for whom we had in-app data and this is reflected in the higher than the national average for women who were degree holders (58.6% in final sample versus 42% nationally). The overall TOPSE scores were high at baseline which meant there was little room6for improvement. Nevertheless, there was no difference between the Baby Buddy app users and those participants who did not use the app.Conclusions: First-time mothers in the study found the app accessible and the information concise. The quantitative results, including those from the in-app data, found no evidence of impact from the Baby Buddy app on the primary outcome of parental self-efficacy or mental well-being (secondary outcome) at three months post-birth. The participant mothers had lower social support scale scores, which might suggest that the app attracted mothers who had a smaller social support network. Both mothers and healthcare professionals valued the fact that the Baby Buddy app was professionally endorsed which encouraged the women to trust the contents and the healthcare professionals to use it in their everyday practice. The most frequent source from which Baby Buddy app users found out about the app was a midwife, which suggests that the embedding of the app into service delivery by Best Beginnings was beneficial. A post-hoc finding was that women who used the Baby Buddy app were significantly more likely to exclusively breastfeed, or ever breastfeed, than those not using the app. The Baby Buddy app has gone some way to help to ‘Make Every Contact Count’ for both first-time mothers and healthcare professionals

The relationship between left ventricular wall thickness, myocardial shortening and ejection fraction in hypertensive heart disease:insights from cardiac magnetic resonance: LVH independently augments EF in hypertension

Hypertensive heart disease is often associated with a preserved left ventricular ejection fraction despite impaired myocardial shortening. The authors investigated this paradox in 55 hypertensive patients (52±13 years, 58% male) and 32 age‐ and sex‐matched normotensive control patients (49±11 years, 56% male) who underwent cardiac magnetic resonance imaging at 1.5T. Long‐axis shortening (R=0.62), midwall fractional shortening (R=0.68), and radial strain (R=0.48) all decreased (P<.001) as end‐diastolic wall thickness increased. However, absolute wall thickening (defined as end‐systolic minus end‐diastolic wall thickness) was maintained, despite the reduced myocardial shortening. Absolute wall thickening correlated with ejection fraction (R=0.70, P<.0001). In multiple linear regression analysis, increasing wall thickness by 1 mm independently increased ejection fraction by 3.43 percentage points (adjusted β‐coefficient: 3.43 [2.60–4.26], P<.0001). Increasing end‐diastolic wall thickness augments ejection fraction through preservation of absolute wall thickening. Left ventricular ejection fraction should not be used in patients with hypertensive heart disease without correction for degree of hypertrophy

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19.

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions

Cosmological distance indicators

We review three distance measurement techniques beyond the local universe: (1) gravitational lens time delays, (2) baryon acoustic oscillation (BAO), and (3) HI intensity mapping. We describe the principles and theory behind each method, the ingredients needed for measuring such distances, the current observational results, and future prospects. Time delays from strongly lensed quasars currently provide constraints on $H_0$ with < 4% uncertainty, and with 1% within reach from ongoing surveys and efforts. Recent exciting discoveries of strongly lensed supernovae hold great promise for time-delay cosmography. BAO features have been detected in redshift surveys up to z <~ 0.8 with galaxies and z ~ 2 with Ly-$\alpha$ forest, providing precise distance measurements and $H_0$ with < 2% uncertainty in flat $\Lambda$CDM. Future BAO surveys will probe the distance scale with percent-level precision. HI intensity mapping has great potential to map BAO distances at z ~ 0.8 and beyond with precisions of a few percent. The next years ahead will be exciting as various cosmological probes reach 1% uncertainty in determining $H_0$, to assess the current tension in $H_0$ measurements that could indicate new physics.Comment: Review article accepted for publication in Space Science Reviews (Springer), 45 pages, 10 figures. Chapter of a special collection resulting from the May 2016 ISSI-BJ workshop on Astronomical Distance Determination in the Space Ag

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19

BackgroundIn 2020, the UK enacted an intensive, nationwide lockdown on March 23 to mitigate transmission of COVID-19. As restrictions began to ease, resurgences in transmission were targeted by geographically-limited interventions of various stringencies. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to inform interventions targeted at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence.MethodsWe use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time.FindingsWe found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance journeys central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas.InterpretationWe propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions.Putting Research Into ContextEvidence before this studyLarge-scale intensive interventions in response to the COVID-19 pandemic have been implemented globally, significantly affecting human movement patterns. Mobility data show spatially-explicit network structure, but it is not clear how that structure changed in response to national or locally-targeted interventions.Added value of this studyWe used daily mobility data aggregated from Facebook users to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. We identified changes in human behaviour in response to interventions and identified the community structure inherent in these networks. This approach to understanding changes in the travel network can help quantify the extent of strongly connected communities of interaction and their relationship to the extent of spatially-explicit interventions.Implications of all the available evidenceWe show that spatial mobility data available in near real-time can give information on connectivity that can be used to understand the impact of geographically-targeted interventions and in the future, to inform spatially-targeted intervention strategies.Data SharingData used in this study are available from the Facebook Data for Good Partner Program by application. Code and supplementary information for this paper are available online (https://github.com/hamishgibbs/facebook_mobility_uk), alongside publication.</jats:sec

Adaptation interventions and their effect on vulnerability in developing countries: Help, hindrance or irrelevance?

This paper critically reviews the outcomes of internationally-funded interventions aimed at climate change adaptation and vulnerability reduction. It highlights how some interventions inadvertently reinforce, redistribute or create new sources of vulnerability. Four mechanisms drive these maladaptive outcomes: (i) shallow understanding of the vulnerability context; (ii) inequitable stakeholder participation in both design and implementation; (iii) a retrofitting of adaptation into existing development agendas; and (iv) a lack of critical engagement with how ‘adaptation success’ is defined. Emerging literature shows potential avenues for overcoming the current failure of adaptation interventions to reduce vulnerability: first, shifting the terms of engagement between adaptation practitioners and the local populations participating in adaptation interventions; and second, expanding the understanding of ‘local’ vulnerability to encompass global contexts and drivers of vulnerability. An important lesson from past adaptation interventions is that within current adaptation cum development paradigms, inequitable terms of engagement with ‘vulnerable’ populations are reproduced and the multi-scalar processes driving vulnerability remain largely ignored. In particular, instead of designing projects to change the practices of marginalised populations, learning processes within organisations and with marginalised populations must be placed at the centre of adaptation objectives. We pose the question of whether scholarship and practice need to take a post-adaptation turn akin to post-development, by seeking a pluralism of ideas about adaptation while critically interrogating how these ideas form part of the politics of adaptation and potentially the processes (re)producing vulnerability. We caution that unless the politics of framing and of scale are explicitly tackled, transformational interventions risk having even more adverse effects on marginalised populations than current adaptation

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from &lt;5% of those younger than 20 years to &gt;66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from &lt;1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research