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Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. It further explores the role of synthetic populations and modelling in identifying at-risk populations and delivering targeted aid. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Combining land scan data, time-use survey analyses, interview data, and ArcGIS, the resulting synthetic population was analysed via classification and regression tree (CART) analysis to identify hot-spots of need, both ethnically and geographically. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. In addition, the use of a synthetic population can generate information about ‘hot spots’ of need, based on geography, ethnicity, and type of aid required. Conclusions: These data demonstrate the challenges that remain to the Roma population in Romania, and also point to the myriad of ways in which all rural Romanians, regardless of ethnicity, are encountering hardship. This study highlights an approach that combines traditional survey data with more wide-reaching geographically based data and CART analysis to determine ‘hot spot’ areas of need in a given population. With the appropriate inputs, this tool can be extrapolated to any population in any country.

Abstract Introduction: The Anti-fibrinolytics Trialists Collaboration aims to increase knowledge about the effectiveness and safety of anti-fibrinolytic treatment by conducting individual patient data (IPD) meta-analyses of randomised trials. This article presents the statistical analysis plan for an IPD meta-analysis of the effects of anti-fibrinolytics for acute intracranial haemorrhage. Methods: The protocol for the IPD meta-analysis has been registered with PROSPERO (CRD42019128260). We will conduct an individual patient data meta-analysis of randomised controlled trials with 500 patients or more assessing the effects of anti-fibrinolytics in acute intracranial haemorrhage. The primary outcomes will be 1) death from stroke or head injury within 30 days of randomisation, and 2) death from stroke or head injury, or dependency within 90 days of randomisation. The primary outcomes will be limited to patients treated within three hours of injury or stroke onset. We will report treatment effects using odds ratios and 95% confidence intervals. We use logistic regression models to examine how the effect of anti-fibrinolytics vary by time to treatment, severity of intracranial bleeding, and age. We will also examine the effect of anti-fibrinolytics on secondary outcomes including death, dependency, vascular occlusive events, seizures, and neurological outcomes. Secondary outcomes will be assessed in all patients irrespective of time of treatment. All analyses will be conducted on an intention-to-treat basis. Conclusions: This IPD meta-analysis will examine important clinical questions about the effects of anti-fibrinolytic treatment in patients with intracranial haemorrhage that cannot be answered using aggregate data. With IPD we can examine how effects vary by time to treatment, bleeding severity, and age, to gain better understanding of the balance of benefit and harms on which to base recommendations for practice.

Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. Conclusions: These data demonstrate the challenges that remain to the Roma population in Romania, and also point to the myriad of ways in which all rural Romanians, regardless of ethnicity, are encountering hardship. This study highlights the areas in which improvements can be made to ensure the Roma, and indeed all Romanian citizens, have access to and confidence in sanitation services, clean water, and adequate healthcare treatment.

Invasions by pest insects pose a significant threat to agriculture worldwide. In the case of Ceratitis capitata incursions on the US mainland, where it is not officially established, repeated detections are followed by quarantines and treatments to eliminate the invading population. However, it is difficult to accurately set quarantine duration because non-detection may not mean the pest is eliminated. Most programs extend quarantine lengths past the last fly detection by calculating the amount of time required for 3 generations to elapse under a thermal unit accumulation development model (“degree day”). A newer approach is to use an Agent-Based Simulation (ABS) to explicitly simulate population demographics and elimination. Here, predicted quarantine lengths for 11 sites in the continental United States are evaluated using both approaches. Results indicate a strong seasonality in quarantine length, with longer predictions in the second half of the year compared with the first; this pattern is more extreme in degree day predictions compared with ABS. Geographically, quarantine lengths increased with latitude, though this was less pronounced under the ABS. Variation in quarantine lengths for particular times and places was dramatically larger for degree day than ABS, generally spiking in the middle of the year for degree day and peaking in second half of the year for ABS. Analysis of 34 C. capitata quarantines from 1975 to 2017 in California shows that, for all but two, quarantines were started in the second half of the year, when degree day quarantine lengths are longest and have the highest uncertainty. For a set of hypothetical outbreaks based on these historical quarantines, the ABS produced significantly shorter quarantines than degree day calculations. Overall, ABS quarantine lengths were more consistent than degree day predictions, avoided unrealistically long values, and captured effects of rare events such as cold snaps.

In recent years, the number of mathematical modelling studies has increased steeply. Many of the questions addressed in these studies are relevant to the development of World Health Organization (WHO) guidelines, but modelling studies are rarely formally included as part of the body of evidence. An expert consultation hosted by WHO, a survey of modellers and users of modelling studies, and literature reviews informed the development of recommendations on when and how to incorporate the results of modelling studies into WHO guidelines. In this article, we argue that modelling studies should routinely be considered in the process of developing WHO guidelines, but particularly in the evaluation of public health programmes, long-term effectiveness or comparative effectiveness. There should be a systematic and transparent approach to identifying relevant published models, and to commissioning new models. We believe that the inclusion of evidence from modelling studies into the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process is possible and desirable, with relatively few adaptations. No single “one-size-fits-all” approach is appropriate to assess the quality of modelling studies. The concept of the ‘credibility’ of the model, which takes the conceptualization of the problem, model structure, input data, different dimensions of uncertainty, as well as transparency and validation into account, is more appropriate than ‘risk of bias’.

There seems to be a general agreement amongst practitioners of economic evaluations, including Health Technology Assessment, that the explicit statement of a perspective is a necessary element in designing and reporting research. Moreover, there seems also to be a general presumption that the ideal perspective is “societal”. In this paper we endorse the first principle but dissent from the second. A review of recommended perspectives is presented. The societal perspective is frequently not the one recommended. The societal perspective is shown to be less comprehensive than is commonly supposed, is inappropriate in many contexts and, in any case, is in general not a perspective to be determined independently of the context of a decision problem. Moreover, the selection of a perspective, societal or otherwise, is not the prerogative of analysts.

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Background: The relationship between adiposity at birth and in childhood, and telomere length is yet to be determined. We aimed to systematically review and meta-analyse the results of studies assessing associations between neonatal and later childhood adiposity, and telomere length. Methods: We searched Medline, EMBASE and PubMed for studies reporting associations between adiposity measured in the neonatal period or later childhood/adolescence, and leucocyte telomere length, measured at any age via quantitative polymerase chain reaction, or terminal restriction fragment analysis, either cross-sectionally, or longitudinally. Papers published before April 2017 were included. Results: Out of 230 abstracts assessed, 23 papers (32 estimates) were retained, from which 19 estimates were meta-analysed (15 cross-sectional, four longitudinal). Of the 15 cross-sectional estimates, seven reported on neonates: four used binary exposures of small-for-gestational-age vs. appropriate-for-gestational age (or appropriate- and large-for-gestational age), and three studied birth weight continuously. Eight estimates reported on later childhood or adolescent measures; five estimates were from studies of binary exposures (overweight/obese vs. non-obese children), and three studies used continuous measures of body mass index. All four longitudinal estimates were of neonatal adiposity, with two estimates for small-for-gestational-age vs. appropriate-for-gestational age neonates, and two estimates of birth weight studied continuously, in relation to adult telomere (49-61 years). There was no strong evidence of an association between neonatal or later childhood/adolescent adiposity, and telomere length. However, between study heterogeneity was high, and there were few combinable studies. Conclusions: Our systematic review and meta-analysis found no strong evidence of an association between neonatal or later childhood or adolescent adiposity and telomere length.

Invasions by pest insects pose a significant threat to agriculture worldwide. In the case of Ceratitis capitata incursions on the US mainland, where it is not officially established, repeated detections are followed by quarantines and treatments to eliminate the invading population. However, it is difficult to accurately set quarantine duration because non-detection may not mean the pest is eliminated. Most programs extend quarantine lengths past the last fly detection by calculating the amount of time required for 3 generations to elapse under a thermal unit accumulation development model (“degree day”). A newer approach is to use an Agent-Based Simulation (ABS) to explicitly simulate population demographics and elimination. Here, predicted quarantine lengths for 11 sites in the continental United States are evaluated using both approaches. Results indicate a strong seasonality in quarantine length, with longer predictions in the second half of the year compared with the first; this pattern is more extreme in degree day predictions compared with ABS. Geographically, quarantine lengths increased with latitude, though this was less pronounced under the ABS. Variation in quarantine lengths for particular times and places was dramatically larger for degree day than ABS, generally spiking in the middle of the year for degree day and peaking in second half of the year for ABS. Analysis of 34 C. capitata quarantines from 1975 to 2017 in California shows that, for all but two, quarantines were started in the second half of the year, when degree day quarantine lengths are longest and have the highest uncertainty. For a set of hypothetical outbreaks based on these historical quarantines, the ABS produced significantly shorter quarantines than degree day calculations. Overall, ABS quarantine lengths were more consistent than degree day predictions, avoided unrealistically long values, and captured effects of rare events such as cold snaps.

Introduction: The Antifibrinolytic Trialists Collaboration aims to increase knowledge about the effectiveness and safety of antifibrinolytic treatment by conducting individual patient data (IPD) meta-analyses of randomised trials. This article presents the statistical analysis plan for an IPD meta-analysis of the effects of antifibrinolytics for acute intracranial haemorrhage. Methods: The protocol for the IPD meta-analysis has been registered with PROSPERO (CRD42016052155). We will conduct an individual patient data meta-analysis of randomised controlled trials with 1000 patients or more assessing the effects of antifibrinolytics in acute intracranial haemorrhage. We will assess the effect on two co-primary outcomes: 1) Death in hospital within 30 days of randomisation, and 2) Death or dependency at final follow-up within 90 days of randomisation. The co-primary outcomes will be limited to patients treated within three hours of injury or stroke onset. We will report treatment effects using odds ratios and 95% confidence intervals. We use logistic regression models to examine how the effect of antifibrinolytics vary by time to treatment, severity of intracranial bleeding, and age. We will also examine the effect of antifibrinolytics on secondary outcomes including death, dependency, vascular occlusive events, seizures, and neurological outcomes. Secondary outcomes will be assessed in all patients irrespective of time of treatment. All analyses will be conducted on an intention-to-treat basis. Conclusions: This IPD meta-analysis will examine important clinical questions about the effects of antifibrinolytic treatment in patients with intracranial haemorrhage that cannot be answered using aggregate data. With IPD we can examine how effects vary by time to treatment, bleeding severity, and age, to gain better understanding of the balance of benefit and harms on which to base recommendations for practice.

Introduction: The Antifibrinolytic Trialists Collaboration aims to increase knowledge about the effectiveness and safety of antifibrinolytic treatment by conducting individual patient data (IPD) meta-analyses of randomised trials. This article presents the statistical analysis plan for an IPD meta-analysis of the effects of antifibrinolytics for acute intracranial haemorrhage. Methods: The protocol for the IPD meta-analysis has been registered with PROSPERO (CRD42016052155). We will conduct an individual patient data meta-analysis of randomised controlled trials with 1000 patients or more assessing the effects of antifibrinolytics in acute intracranial haemorrhage. We will assess the effect on two co-primary outcomes: 1) death in hospital at end of trial follow-up, and 2) death in hospital or dependency at end of trial follow-up. The co-primary outcomes will be limited to patients treated within three hours of injury or stroke onset. We will report treatment effects using odds ratios and 95% confidence intervals. We use logistic regression models to examine how the effect of antifibrinolytics vary by time to treatment, severity of intracranial bleeding, and age. We will also examine the effect of antifibrinolytics on secondary outcomes including death, dependency, vascular occlusive events, seizures, and neurological outcomes. Secondary outcomes will be assessed in all patients irrespective of time of treatment. All analyses will be conducted on an intention-to-treat basis. Conclusions: This IPD meta-analysis will examine important clinical questions about the effects of antifibrinolytic treatment in patients with intracranial haemorrhage that cannot be answered using aggregate data. With IPD we can examine how effects vary by time to treatment, bleeding severity, and age, to gain better understanding of the balance of benefit and harms on which to base recommendations for practice.

Vaccination against seasonal influenza viruses is the most effective way to prevent infection. A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. The high evolutionary rate, antigenic shift and antigenic drift of influenza viruses, represents the main obstacle for correct prediction of the vaccine effectiveness for an upcoming flu season. Conventional structural and phylogenetic approaches for assessment of vaccine effectiveness have had a limited success in prediction of vaccine efficacy in the past. Recently, a novel bioinformatics approach for assessment of effectiveness of seasonal influenza vaccine was proposed. Here, this approach was used for prediction of the vaccine effectiveness for the influenza season 2017/18 in US.

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Background: The objective of this study was to investigate the study design characteristics of Post-Authorisation Studies (PAS) requested by the European Medicines Agency which were recorded on the European Union (EU) PAS Register held by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Methods: We undertook a cross-sectional descriptive analysis of all studies registered on the EU PAS Register as of 18 th October 2016. Results: We identified a total of 314 studies on the EU PAS Register, including 81 (26%) finalised, 160 (51%) ongoing and 73 (23%) planned. Of those studies identified, 205 (65%) included risk assessment in their scope, 133 (42%) included drug utilisation and 94 (30%) included effectiveness evaluation. Just over half of the studies (175; 56%) used primary data capture, 135 (43%) used secondary data and 4 (1%) used a hybrid design combining both approaches. Risk assessment and effectiveness studies were more likely to use primary data capture (60% and 85% respectively as compared to 39% and 14% respectively for secondary). The converse was true for drug utilisation studies where 59% were secondary vs. 39% for primary. For type 2 diabetes mellitus, database studies were more commonly used (80% vs 3% chart review, 3% hybrid and 13% primary data capture study designs) whereas for studies in oncology, primary data capture were more likely to be used (85% vs 4% chart review, and 11% database study designs). Conclusions: Results of this analysis show that PAS design varies according to study objectives and therapeutic area.

Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addition, ad hoc categorization of BMI values prevents the estimation and analysis of the underlying continuous BMI distribution and leads to information loss. As an alternative to multinomial regression following ad hoc categorization, we propose a continuous outcome logistic regression model for the estimation of a continuous BMI distribution. Parameters of interest, such as odds ratios for specific categories, can be extracted from this model post hoc in a general way. A continuous BMI logistic regression that describes BMI distributions avoids the necessity of ad hoc and post hoc category choice and simplifies between-study comparisons and pooling of studies for joint analyses. The method was evaluated empirically using data from the Swiss Health Survey.

Invasions by pest insects pose a significant threat to agriculture worldwide. In the case of Ceratitis capitata incursions on the US mainland, where it is not officially established, repeated detections are followed by quarantines and treatments to eliminate the invading population. However, it is difficult to accurately set quarantine duration because non-detection may not mean the pest is eliminated. Most programs extend quarantine lengths past the last fly detection by calculating the amount of time required for 3 generations to elapse under a thermal unit accumulation development model (“degree day”). A newer approach is to use an Agent-Based Simulation (ABS) to explicitly simulate population demographics and elimination. Here, predicted quarantine lengths for 11 sites in the continental United States are evaluated using both approaches. Results indicate a strong seasonality in quarantine length, with longer predictions in the second half of the year compared with the first; this pattern is more extreme in degree day predictions compared with ABS. Geographically, quarantine lengths increased with latitude, though this was less pronounced under the ABS. Variation in quarantine lengths for particular times and places was dramatically larger for degree day than ABS, generally spiking in the middle of the year for degree day and peaking in second half of the year for ABS. Analysis of 34 C. capitata quarantines from 1975 to 2017 in California shows that, for all but two, quarantines were started in the second half of the year, when degree day quarantine lengths are longest and have the highest uncertainty. For a set of hypothetical outbreaks based on these historical quarantines, the ABS produced significantly shorter quarantines than degree day calculations. Overall, ABS quarantine lengths were more consistent than degree day predictions, avoided unrealistically long values, and captured effects of rare events such as cold snaps.

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.

Background: Tuberculosis (TB) disease burden is determined by both infection and progression rate to disease. Progression rate varies by immune status, with prior infection in high burdened settings significantly reducing the progression to disease from subsequent reinfections and completion of successful treatment associated with increased risk of subsequent TB disease. Novel studies of TB vaccines are now underway targeting high risk individuals who have completed successful combination TB chemotherapy for active TB. Methods: In our study, we explored the impact of effective contact rate (β) and post-treatment immune status on population TB burden using a mathematical model incorporating five immunological states; susceptible, newly infected, reinfected, active TB and treated TB. Results: We found that the number of newly infected individuals increased with increasing values of β< 10yr -1, but declined when β> 10yr -1. Corresponding numbers of reinfected individuals increased with increasing values of β irrespective of post-treatment immune status. Furthermore, we noted that the number of active TB cases decreased by 7 - 17% when treated individuals moved to either newly infected or reinfected immune states, respectively, rather than to the fully susceptible state at values of β< 10yr -1. The corresponding declines in TB burden were only 2 - 7% at values of β> 10yr -1. Results show that TB prevalence in high burden settings is primarily driven by effective contact rates, which are significantly modified by pre- and post-treatment immune factors. Conclusions: The observation that impact of post-treatment immune status modification on population burden may be diminished in very high burdened settings will be important for vaccine design.

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” ( Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. Conclusions: These data demonstrate the challenges that remain to the Roma population in Romania, and also point to the myriad of ways in which all rural Romanians, regardless of ethnicity, are encountering hardship. This study highlights the areas in which improvements can be made to ensure the Roma, and indeed all Romanian citizens, have access to and confidence in sanitation services, clean water, and adequate healthcare treatment.

In recent years, the number of mathematical modelling studies has increased steeply. Many of the questions addressed in these studies are relevant to the development of World Health Organization (WHO) guidelines, but modelling studies are rarely formally included as part of the body of evidence. An expert consultation hosted by WHO, a survey of modellers and users of modelling studies, and literature reviews informed the development of recommendations on when and how to incorporate the results of modelling studies into WHO guidelines. In this article, we argue that modelling studies should routinely be considered in the process of developing WHO guidelines, but particularly in the evaluation of public health programmes, long-term effectiveness or comparative effectiveness. There should be a systematic and transparent approach to identifying relevant published models, and to commissioning new models. We believe that the inclusion of evidence from modelling studies into the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process is possible and desirable, with relatively few adaptations. No single “one-size-fits-all” approach is appropriate to assess the quality of modelling studies. The concept of the ‘credibility’ of the model, which takes the conceptualization of the problem, model structure, input data, different dimensions of uncertainty, as well as transparency and validation into account, is more appropriate than ‘risk of bias’.

Background: The objective of this study was to investigate the study design characteristics of Post-Authorisation Studies (PAS) requested by the European Medicines Agency which were recorded on the European Union (EU) PAS Register held by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Methods: We undertook a cross-sectional descriptive analysis of all studies registered on the EU PAS Register as of 18 th October 2016. Results: We identified a total of 314 studies on the EU PAS Register, including 81 (26%) finalised, 160 (51%) ongoing and 73 (23%) planned. Of those studies identified, 205 (65%) included risk assessment in their scope, 133 (42%) included drug utilisation and 94 (30%) included effectiveness evaluation. Just over half of the studies (175; 56%) used primary data capture, 135 (43%) used secondary data and 4 (1%) used a hybrid design combining both approaches. Risk assessment and effectiveness studies were more likely to use primary data capture (60% and 85% respectively as compared to 39% and 14% respectively for secondary). The converse was true for drug utilisation studies where 59% were secondary vs. 39% for primary. For type 2 diabetes mellitus, database studies were more commonly used (80% vs 3% chart review, 3% hybrid and 13% primary data capture study designs) whereas for studies in oncology, primary data capture were more likely to be used (85% vs 4% chart review, and 11% database study designs). Conclusions: Results of this analysis show that study objectives and therapeutic area influence PAS design in terms of type of data capture used.

Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether ‘alerting’ alone improves care quality. We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the “Streams-AKI” app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines. We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George’s, University of London (Population Health Research Institute).

Background: The aim was to examine the validity of a new measure, Te Waka Kuaka, in assessing the cultural needs of Māori with traumatic brain injury (TBI). Methods: Māori from around Aotearoa, New Zealand were recruited. 319 people with a history of TBI, their whānau (extended family members), friends, work associates, and interested community members participated. All completed the 46-item measure. Rasch analysis of the data was undertaken. Results: All four subscales; Wā (time), Wāhi (place), Tangata (people) and Wairua practices (activities that strengthen spiritual connection) were unidimensional. Ten items were deleted because they did not fit the model, due to statistically significant disordered thresholds, non-uniform differential item functioning (DIF) and local dependence. Five items were re-scored in the fourth subscale resulting in ordered thresholds. Conclusions: Rasch analysis facilitated a robust validation process of Te Waka Kuaka.

Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether ‘alerting’ alone improves care quality. We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the “Streams-AKI” app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines. We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George’s, University of London (Population Health Research Institute).

The Internet has enabled online social interaction for scientists beyond physical meetings and conferences. Yet despite these innovations in communication, dissemination of methods is often relegated to just academic publishing. Further, these methods remain static, with subsequent advances published elsewhere and unlinked. For communities undergoing fast-paced innovation, researchers need new capabilities to share, obtain feedback, and publish methods at the forefront of scientific development. For example, a renaissance in virology is now underway given the new metagenomic methods to sequence viral DNA directly from an environment. Metagenomics makes it possible to “see” natural viral communities that could not be previously studied through culturing methods. Yet, the knowledge of specialized techniques for the production and analysis of viral metagenomes remains in a subset of labs. This problem is common to any community using and developing emerging technologies and techniques. We developed new capabilities to create virtual communities in protocols.io, an open access platform, for disseminating protocols and knowledge at the forefront of scientific development. To demonstrate these capabilities, we present a virology community forum called VERVENet. These new features allow virology researchers to share protocols and their annotations and optimizations, connect with the broader virtual community to share knowledge, job postings, conference announcements through a common online forum, and discover the current literature through personalized recommendations to promote discussion of cutting edge research. Virtual communities in protocols.io enhance a researcher’s ability to: discuss and share protocols, connect with fellow community members, and learn about new and innovative research in the field. The web-based software for developing virtual communities is free to use on protocols.io. Data are available through public APIs at protocols.io.

Self-reported dietary intake is assessed by methods of real-time recording (food diaries and the duplicate portion method) and methods of recall (dietary histories, food frequency questionnaires, and 24-hour dietary recalls). Being less labor intensive, recall methods are more frequently employed in nutritional epidemiological investigations. However, sources of error, which include the participants’ inability to fully and accurately recall their intakes as well as limitations inherent in the food composition databases applied to convert the reported food consumption to energy and nutrient intakes, may limit the validity of the generated information. The use of dietary biomarkers is often recommended to overcome such errors and better capture intra-individual variability in intake; nevertheless, it has its own challenges. To address measurement error associated with dietary questionnaires, large epidemiological investigations often integrate sub-studies for the validation and calibration of the questionnaires and/or administer a combination of different assessment methods (e.g. administration of different questionnaires and assessment of biomarker levels). Recent advances in the omics field could enrich the list of reliable nutrition biomarkers, whereas new approaches employing web-based and smart phone applications could reduce respondent burden and, possibly, reporting bias. Novel technologies are increasingly integrated with traditional methods, but some sources of error still remain. In the analyses, food and nutrient intakes always need to be adjusted for total daily energy intake to account for errors related to reporting.

Background: Point-of-care tests (POCTs) for influenza have been criticised for their diagnostic accuracy, with clinical use limited by low sensitivity. These criticisms are based on diagnostic-accuracy studies that often use the questionable assumption of an infallible gold standard. Bayesian latent class modelling can estimate diagnostic performance without this assumption. Methods: Data extracted from published diagnostic-accuracy studies comparing the QuickVue® influenza A+B influenza POCT to reverse-transcriptase polymerase chain reaction (RT-PCR) in two different populations were re-analysed. Classical and Bayesian latent class methods were applied using the Modelling for Infectious diseases CEntre (MICE) web-based application. Results: Under classical analyses the estimated sensitivity and specificity of the QuickVue® were 66.9% (95% confidence interval (CI) 61.4-71.9) and 97.8% (95% CI 95.7-98.9), respectively. Bayesian latent class models estimated sensitivity of 97.8% (95% credible interval (CrI) 82.1-100) and specificity of 98.5% (95% CrI 96.5-100). Conclusions: Data from studies comparing the QuickVue® point-of-care test to RT-PCR are compatible with better diagnostic performance than previously reported.

ECOUTER ( Employing COncept ual schema for policy and Translation E in Research – French for ‘to listen’ – is a new stakeholder engagement method incorporating existing evidence to help participants draw upon their own knowledge of cognate issues and interact on a topic of shared concern. The results of an ECOUTER can form the basis of recommendations for research, governance, practice and/or policy. This paper describes the development of a digital methodology for the ECOUTER engagement process based on currently available mind mapping freeware software. The implementation of an ECOUTER process tailored to applications within health studies are outlined for both online and face-to-face scenarios. Limitations of the present digital methodology are discussed, highlighting the requirement of a purpose built software for ECOUTER research purposes.

Background: Periodic general health examinations (GHEs) are gradually becoming more popular as they employ subclinical screenings, as a means of early detection. This study considers the effect of information technology (IT), health communications and the public’s attitude towards GHEs in Vietnam. Methods: A total of 2,068 valid observations were obtained from a survey in Hanoi and its surrounding areas. Results: In total, 42.12% of participants stated that they were willing to use IT applications to recognise illness symptoms, and nearly 2/3 of them rated the healthcare quality at average level or below. Discussion: The data, which was processed by the BCL model, showed that IT applications (apps) reduce hesitation toward GHEs; however, older people seem to have less confidence in using these apps. Health communications and government’s subsidy also increased the likelihood of people attending periodic GHEs. The probability of early check-ups where there is a cash subsidy could reach approximately 80%.

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning. Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio (IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains. Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤ 0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions. Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addition, ad hoc categorization of BMI values prevents the estimation and analysis of the underlying continuous BMI distribution and leads to information loss. As an alternative to multinomial regression following ad hoc categorization, we propose a continuous outcome logistic regression model for the estimation of a continuous BMI distribution. Parameters of interest, such as odds ratios for specific categories, can be extracted from this model post hoc in a general way. A continuous BMI logistic regression that describes BMI distributions avoids the necessity of ad hoc and post hoc category choice and simplifies between-study comparisons and pooling of studies for joint analyses. The method was evaluated empirically using data from the Swiss Health Survey.

Background: The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites. Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites (Kilifi county and Rachuonyo South district) and one Gambian site (Kombo coastal districts) to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and distance in space and time for parasite pairs. Results: Using 107, 177 and 82 SNPs that were successfully genotyped in 133, 1602, and 1034 parasite isolates from The Gambia, Kilifi and Rachuonyo South district, respectively, we show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes. Genetic relatedness of sample pairs is predicted by relatedness in space and time. Conclusions: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.

This article provides the first detailed demonstration of the research value of the Electronic Health Record (EHR) linked to research data in Generation Scotland Scottish Family Health Study (GS:SFHS) participants, together with how to access this data. The structured, coded variables in the routine biochemistry, prescribing and morbidity records, in particular, represent highly valuable phenotypic data for a genomics research resource. Access to a wealth of other specialized datasets, including cancer, mental health and maternity inpatient information, is also possible through the same straightforward and transparent application process. The EHR linked dataset is a key component of GS:SFHS, a biobank conceived in 1999 for the purpose of studying the genetics of health areas of current and projected public health importance. Over 24,000 adults were recruited from 2006 to 2011, with broad and enduring written informed consent for biomedical research. Consent was obtained from 23,603 participants for GS:SFHS study data to be linked to their Scottish National Health Service (NHS) records, using their Community Health Index number. This identifying number is used for NHS Scotland procedures (registrations, attendances, samples, prescribing and investigations) and allows healthcare records for individuals to be linked across time and location. Here, we describe the NHS EHR dataset on the sub-cohort of 20,032 GS:SFHS participants with consent and mechanism for record linkage plus extensive genetic data. Together with existing study phenotypes, including family history and environmental exposures, such as smoking, the EHR is a rich resource of real world data that can be used in research to characterise the health trajectory of participants, available at low cost and a high degree of timeliness, matched to DNA, urine and serum samples and genome-wide genetic information.

Background: In a recent paper, a novel W-test for pairwise epistasis testing was proposed that appeared, in computer simulations, to have higher power than competing alternatives. Application to genome-wide bipolar data detected significant epistasis between SNPs in genes of relevant biological function. Network analysis indicated that the implicated genes formed two separate interaction networks, each containing genes highly related to autism and neurodegenerative disorders. Methods: Here we investigate further the properties and performance of the W-test via theoretical evaluation, computer simulations and application to real data. Results: We demonstrate that, for common variants, the W-test is closely related to several existing tests of association allowing for interaction, including logistic regression on 8 degrees of freedom, although logistic regression can show inflated type I error for low minor allele frequencies, whereas the W-test shows good/conservative type I error control. Although in some situations the W-test can show higher power, logistic regression is not limited to tests on 8 degrees of freedom but can instead be taylored to impose greater structure on the assumed alternative hypothesis, offering a power advantage when the imposed structure matches the true structure. Conclusions: The W-test is a potentially useful method for testing for association - without necessarily implying interaction - between genetic variants disease, particularly when one or more of the genetic variants are rare. For common variants, the advantages of the W-test are less clear, and, indeed, there are situations where existing methods perform better. In our investigations, we further uncover a number of problems with the practical implementation and application of the W-test (to bipolar disorder) previously described, apparently due to inadequate use of standard data quality-control procedures. This observation leads us to urge caution in interpretation of the previously-presented results, most of which we consider are highly likely to be artefacts.

In many low- and middle-income countries (LMICs), a poor link between antibiotic policies and practices exists. Numerous contextual factors may influence the degree of antibiotic access, appropriateness of antibiotic provision, and actual use in communities. Therefore, improving appropriateness of antibiotic use in different communities in LMICs probably requires interventions tailored to the setting of interest, accounting for cultural context. Here we present the ABACUS study (AntiBiotic ACcess and USe), which employs a unique approach and infrastructure, enabling quantitative validation, contextualization of determinants, and cross-continent comparisons of antibiotic access and use. The community infrastructure for this study is the INDEPTH-Network (International Network for the Demographic Evaluation of Populations and Their Health in Developing Countries), which facilitates health and population research through an established health and demographic surveillance system. After an initial round of formative qualitative research with community members and antibiotic suppliers in three African and three Asian countries, household surveys will assess the appropriateness of antibiotic access, provision and use. Results from this sample will be validated against a systematically conducted inventory of suppliers. All potential antibiotic suppliers will be mapped and characterized. Subsequently, their supply of antibiotics to the community will be measured through customer exit interviews, which tend to be more reliable than bulk purchase or sales data. Discrepancies identified between reported and observed antibiotic practices will be investigated in further qualitative interviews. Amartya Sen’s Capability Approach will be employed to identify the conversion factors that determine whether or not, and the extent to which appropriate provision of antibiotics may lead to appropriate access and use of antibiotics. Currently, the study is ongoing and expected to conclude by 2019. ABACUS will provide important new insights into antibiotic practices in LMICs to inform social interventions aimed at promoting optimal antibiotic use, thereby preserving antibiotic effectiveness.

Background The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites. Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites and one Gambian site to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs. Results We show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes. Genetic relatedness of sample pairs is predicted by relatedness in space and time. Conclusions Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.

Size varies. Small things are typically more frequent than large things. The logarithm of frequency often declines linearly with the logarithm of size. That power law relation forms one of the common patterns of nature. Why does the complexity of nature reduce to such a simple pattern? Why do things as different as tree size and enzyme rate follow similarly simple patterns? Here I analyze such patterns by their invariant properties. For example, a common pattern should not change when adding a constant value to all observations. That shift is essentially the renumbering of the points on a ruler without changing the metric information provided by the ruler. A ruler is shift invariant only when its scale is properly calibrated to the pattern being measured. Stretch invariance corresponds to the conservation of the total amount of something, such as the total biomass and consequently the average size. Rotational invariance corresponds to pattern that does not depend on the order in which underlying processes occur, for example, a scale that additively combines the component processes leading to observed values. I use tree size as an example to illustrate how the key invariances shape pattern. A simple interpretation of common pattern follows. That simple interpretation connects the normal distribution to a wide variety of other common patterns through the transformations of scale set by the fundamental invariances.

Size varies. Small things are typically more frequent than large things. The logarithm of frequency often declines linearly with the logarithm of size. That power law relation forms one of the common patterns of nature. Why does the complexity of nature reduce to such a simple pattern? Why do things as different as tree size and enzyme rate follow similarly simple patterns? Here I analyze such patterns by their invariant properties. For example, a common pattern should not change when adding a constant value to all observations. That shift is essentially the renumbering of the points on a ruler without changing the metric information provided by the ruler. A ruler is shift invariant only when its scale is properly calibrated to the pattern being measured. Stretch invariance corresponds to the conservation of the total amount of something, such as the total biomass and consequently the average size. Rotational invariance corresponds to pattern that does not depend on the order in which underlying processes occur, for example, a scale that additively combines the component processes leading to observed values. I use tree size as an example to illustrate how the key invariances shape pattern. A simple interpretation of common pattern follows. That simple interpretation connects the normal distribution to a wide variety of other common patterns through the transformations of scale set by the fundamental invariances.

High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or OTU composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, whether parametric or nonparametric. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests and nonparametric testing using community networks and the ggnetwork package.

Understanding costs and ensuring that we demonstrate value in healthcare is a foundational presumption as we transform the way we deliver and pay for healthcare in the U.S. With a focus on population health and payment reforms underway, there is increased pressure to examine cost-effectiveness in healthcare delivery. Cost-effectiveness analysis (CEA) is a type of economic analysis comparing the costs and effects (i.e. health outcomes) of two or more treatment options. The result is expressed as a ratio where the denominator is the gain in health from a measure (e.g. years of life or quality-adjusted years of life) and the numerator is the incremental cost associated with that health gain. For higher cost interventions, the lower the ratio of costs to effects, the higher the value. While CEA is not new, the approach continues to be refined with enhanced statistical techniques and standardized methods. This article describes the CEA approach and also contrasts it to optional approaches, in order for readers to fully appreciate caveats and concerns. CEA as an economic evaluation tool can be easily misused owing to inappropriate assumptions, over reliance, and misapplication. Twelve issues to be considered in using CEA results to drive healthcare delivery decision-making are summarized. Appropriately recognizing both the strengths and the limitations of CEA is necessary for informed resource allocation in achieving the maximum value for healthcare services provided.

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short tutorial, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose reporting practices.

To date, there have been no studies that investigate the independent use of the photoplethysmogram (PPG) signal to determine heart rate variability (HRV). However, researchers have demonstrated that PPG signals offer an alternative way of measuring HRV when electrocardiogram (ECG) and PPG signals are collected simultaneously. Based on these findings, we take the use of PPGs to the next step and investigate a different approach to show the potential independent use of short 20-second PPG signals collected from healthy subjects after exercise in a hot environment to measure HRV. Our hypothesis is that if the PPG--HRV indices are negatively correlated with age, then short PPG signals are appropriate measurements for extracting HRV parameters. The PPGs of 27 healthy male volunteers at rest and after exercise were used to determine the HRV indices: standard deviation of heartbeat interval (SDNN) and the root-mean square of the difference of successive heartbeats (RMSSD). The results indicate that the use of the $aa$ interval, derived from the acceleration of PPG signals, is promising in determining the HRV statistical indices SDNN and RMSSD over 20-second PPG recordings. Moreover, the post-exercise SDNN index shows a negative correlation with age. There tends to be a decrease of the PPG--SDNN index with increasing age, whether at rest or after exercise. This new outcome validates the negative relationship between HRV in general with age, and consequently provides another evidence that short PPG signals have the potential to be used in heart rate analysis without the need to measure lengthy sequences of either ECG or PPG signals.

Background: Gait is a powerful tool to identify ageing and track disease progression. Yet, its high resolution measurement via traditional instruments remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearables where the synergy between devices and smart algorithms has provided the potential of ‘a gait lab on a chip’. Methods: Commercially available wearables for gait quantification remain expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive assessment required within intervention or epidemiological studies. However, the increasing demand for low-cost diagnostics has fuelled the shift in how health-related resources are distributed. As such we adopt open platform technology and validated research methodologies to harmonise engineering solutions to satisfy current epidemiological needs. Results: We provide an introduction to conduct a routine instrumented gait assessment with a discrete, low-cost, accelerometer-based wearable. We show that the capture and interpretation of raw gait signals with a common scripting language can be straightforward and suitable for use within modern studies. We highlight the best approaches and hope that this will help compliment any analytical tool-kit as part of future cohort assessments. Conclusions: Deployment of wearables can allow accurate gait assessment in accordance with advocated methods of data collection as there is a strong demand for sensitive outcomes derived from pragmatic tools. This tutorial shows that instrumentation of gait using a single open source wearable is pragmatic due to low-cost and translational analytical methods to derive sensitive outcomes.

The Internet has enabled online social interaction for scientists that previously happened only in physical meetings and conferences. Yet despite these innovations in communication, dissemination of methods is often relegated to the slow process of academic publishing. Further, these methods remain static, with subsequent advances published elsewhere and unlinked. For communities undergoing fast-paced innovation, researchers need new capabilities to share, obtain feedback, and publish methods at the forefront of scientific development. For example, a renaissance in virology is now underway given the new metagenomic methods to sequence viral DNA directly from an environment. Metagenomics makes it possible to “see” natural viral communities that could not be previously studied through culturing methods. Yet, the knowledge of specialized techniques for the production and analysis of viral metagenomes remains in a subset of labs. This problem is common to any community using and developing emerging technologies and techniques. We developed new capabilities to create virtual communities in protocols.io, an open access platform, for disseminating protocols and knowledge at the forefront of scientific development. To demonstrate these capabilities, we present a virology community forum called VERVENet. These new features allow virology researchers to share protocols and their annotations and optimizations, connect with the broader virtual community to share knowledge, job postings, conference announcements through a common online forum, and discover the current literature through personalized recommendations to promote discussion of cutting edge research. Virtual communities in protocols.io enhance a researcher’s ability to: discuss and share protocols, connect with fellow community members, and learn about new and innovative research in the field. The web-based software for developing virtual communities is free to use on protocols.io. Data are available through public APIs at protocols.io.

Background: Internationally, wheelchair users are an emerging demographic phenomenon, due to their increased prevalence and rapidly increasing life-span. While having significant healthcare implications, basic robust epidemiological information about wheelchair users is often lacking due, in part, to this population’s ‘hidden’ nature. Increasingly popular in epidemiological research, Respondent Driven Sampling (RDS) provides a mechanism for generating unbiased population-based estimates for hard-to-reach populations, overcoming biases inherent within other sampling methods. This paper reports the first published study to employ RDS amongst wheelchair users. Methods: Between October 2015 and January 2016, a short, successfully piloted, internet-based national survey was initiated. Twenty seeds from diverse organisations were invited to complete the survey then circulate it to peers within their networks following a well-defined protocol. A predetermined reminder protocol was triggered when seeds or their peers failed to respond. All participants were entered into a draw for an iPad. Results: Overall, 19 people participated (nine women); 12 initial seeds, followed by seven second-wave participants arising from four seeds . Completion time for the survey ranged between 7 and 36 minutes. Despite repeated reminders, no further people were recruited. Discussion: While New Zealand wheelchair user numbers are unknown, an estimated 14% of people have physical impairments that limited mobility. The 19 respondents generated from adopting the RDS methodology here thus represents a negligible fraction of wheelchair users in New Zealand, and an insufficient number to ensure equilibrium required for unbiased analyses. While successful in other hard-to-reach populations, applying RDS methodology to wheelchair users requires further consideration. Formative research exploring areas of network characteristics, acceptability of RDS, appropriate incentive options, and seed selection amongst wheelchair users is needed.

In nematodes, environmental or physiological perturbations alter death’s scaling of time. In human cancer, genetic perturbations alter death’s curvature of time. Those changes in scale and curvature follow the constraining contours of death’s invariant geometry. I show that the constraints arise from a fundamental extension to the theories of randomness, invariance and scale. A generalized Gompertz law follows. The constraints imposed by the invariant Gompertz geometry explain the tendency of perturbations to stretch or bend death’s scaling of time. Variability in death rate arises from a combination of constraining universal laws and particular biological processes.

Size varies. Small things are typically more frequent than large things. The logarithm of frequency often declines linearly with the logarithm of size. That power law relation forms one of the common patterns of nature. Why does the complexity of nature reduce to such a simple pattern? Why do things as different as tree size and enzyme rate follow similarly simple patterns? Here I analyze such patterns by their invariant properties. For example, a common pattern should not change when adding a constant value to all observations. That shift is essentially the renumbering of the points on a ruler without changing the metric information provided by the ruler. A ruler is shift invariant only when its scale is properly calibrated to the pattern being measured. Stretch invariance corresponds to the conservation of the total amount of something, such as the total biomass and consequently the average size. Rotational invariance corresponds to pattern that does not depend on the order in which underlying processes occur, for example, a scale that additively combines the component processes leading to observed values. I use tree size as an example to illustrate how the key invariances shape pattern. A simple interpretation of common pattern follows. That simple interpretation connects the normal distribution to a wide variety of other common patterns through the transformations of scale set by the fundamental invariances.

Metrics for assessing adoption of good development practices are a useful way to ensure that software is sustainable, reusable and functional. Sustainability means that the software used today will be available - and continue to be improved and supported - in the future. We report here an initial set of metrics that measure good practices in software development. This initiative differs from previously developed efforts in being a community-driven grassroots approach where experts from different organisations propose good software practices that have reasonable potential to be adopted by the communities they represent. We not only focus our efforts on understanding and prioritising good practices, we assess their feasibility for implementation and publish them here.