Stress and anxiety during pregnancy and length of gestation: a federated study using data from five Canadian and European birth cohorts.

While its etiology is not fully elucidated, preterm birth represents a major public health concern as it is the leading cause of child mortality and morbidity. Stress is one of the most common perinatal conditions and may increase the risk of preterm birth. In this paper we aimed to investigate the association of maternal perceived stress and anxiety with length of gestation. We used harmonized data from five birth cohorts from Canada, France, and Norway. A total of 5297 pregnancies of singletons were included in the analysis of perceived stress and gestational duration, and 55,775 pregnancies for anxiety. Federated analyses were performed through the DataSHIELD platform using Cox regression models within intervals of gestational age. The models were fit for each cohort separately, and the cohort-specific results were combined using random effects study-level meta-analysis. Moderate and high levels of perceived stress during pregnancy were associated with a shorter length of gestation in the very/moderately preterm interval [moderate: hazard ratio (HR) 1.92 (95%CI 0.83, 4.48); high: 2.04 (95%CI 0.77, 5.37)], albeit not statistically significant. No association was found for the other intervals. Anxiety was associated with gestational duration in the very/moderately preterm interval [1.66 (95%CI 1.32, 2.08)], and in the early term interval [1.15 (95%CI 1.08, 1.23)]. Our findings suggest that perceived stress and anxiety are associated with an increased risk of earlier birth, but only in the earliest gestational ages. We also found an association in the early term period for anxiety, but the result was only driven by the largest cohort, which collected information the latest in pregnancy. This raised a potential issue of reverse causality as anxiety later in pregnancy could be due to concerns about early signs of a possible preterm birth.

Addressing Thalassaemia Management from Patients' Perspectives: An International Collaborative Assessment.

Background and Objectives: The effective management of chronic diseases, particularly hereditary and rare diseases and thalassaemia, is an important indicator of the quality of healthcare systems. We aimed to assess healthcare services in different countries for thalassaemia patients by using publicly available health indicators and by surveying thalassaemia patients and their caregivers. Materials and Methods: We reviewed official worldwide databases from the WHO, World Bank, and scientific resources, and we used a structured patient-tailored self-completed questionnaire to survey thalassaemia patients and their caregivers in 2023. Results: A total of 2082 participants were surveyed (mean age, 27 years; males, 42%). About 1 in 4 respondents did not complete high-school education, while 24% had a bachelor's degree. About a third of respondents were married and were in either full- or part-time employment. The vast majority (~80%) had initiated transfusion therapy between 1 and 4 years of age. Only 42% reported no delays in receiving blood transfusion, while 47% reported occasional delays and 8% serious delays. About half of patients reported being very satisfied (11%) or satisfied (38%) with the quality of services provided, while 1 in 3 patients reported being unsatisfied or very unsatisfied, and that their access to treatment was difficult or very difficult due to traveling expenses and the high cost of treatment. Conclusions: Important improvements in the care of thalassaemia patients have been documented during the past few decades. Nevertheless, additional focus is required through national healthcare systems to effectively address the many unmet needs revealed by our recent survey, as well as to achieve satisfactory patient outcomes.

Sleep duration in preschool age and later behavioral and cognitive outcomes: an individual participant data meta-analysis in five European cohorts.

Short sleep duration has been linked to adverse behavioral and cognitive outcomes in schoolchildren, but few studies examined this relation in preschoolers. We aimed to investigate the association between parent-reported sleep duration at 3.5 years and behavioral and cognitive outcomes at 5 years in European children. We used harmonized data from five cohorts of the European Union Child Cohort Network: ALSPAC, SWS (UK); EDEN, ELFE (France); INMA (Spain). Associations were estimated through DataSHIELD using adjusted generalized linear regression models fitted separately for each cohort and pooled with random-effects meta-analysis. Behavior was measured with the Strengths and Difficulties Questionnaire. Language and non-verbal intelligence were assessed by the Wechsler Preschool and Primary Scale of Intelligence or the McCarthy Scales of Children's Abilities. Behavioral and cognitive analyses included 11,920 and 2981 children, respectively (34.0%/13.4% of the original sample). In meta-analysis, longer mean sleep duration per day at 3.5 years was associated with lower mean internalizing and externalizing behavior percentile scores at 5 years (adjusted mean difference: - 1.27, 95% CI [- 2.22, - 0.32] / - 2.39, 95% CI [- 3.04, - 1.75]). Sleep duration and language or non-verbal intelligence showed trends of inverse associations, however, with imprecise estimates (adjusted mean difference: - 0.28, 95% CI [- 0.83, 0.27] / - 0.42, 95% CI [- 0.99, 0.15]). This individual participant data meta-analysis suggests that longer sleep duration in preschool age may be important for children's later behavior and highlight the need for larger samples for robust analyses of cognitive outcomes. Findings could be influenced by confounding or reverse causality and require replication.

Associations of Maternal Educational Level, Proximity to Greenspace During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood: A Proof-of-Concept Federated Analysis in 18 Birth Cohorts.

International sharing of cohort data for research is important and challenging. We explored the feasibility of multi-cohort federated analyses by examining associations between three pregnancy exposures (maternal education, exposure to green vegetation and gestational diabetes) with offspring BMI from infancy to 17 years. We used data from 18 cohorts (n=206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13 and 14-17 years. Associations were estimated using linear regression via one-stage IPD meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z-score comparing low with high education age 2-3 years = 0.03 [95% CI 0.00, 0.05], 4-7 years = 0.16 [95% CI 0.14, 0.17], 8-13 years = 0.24 [95% CI 0.22, 0.26]). Gestational diabetes was positively associated with BMI from 8 years (BMI z-score difference = 0.18 [CI 0.12, 0.25]) but not at younger ages; however associations attenuated towards the null when restricted to cohorts which measured GDM via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age one but not at older ages. Opportunities of cross-cohort federated analyses are discussed.

Lifestyle patterns in European preschoolers: Associations with socio-demographic factors and body mass index.

BACKGROUND: Energy balance-related behaviours (EBRBs), that is, dietary intake, screen, outdoor play and sleep, tend to combine into 'lifestyle patterns', with potential synergistic influences on health. To date, studies addressing this theme mainly focused on school children and rarely accounted for sleep, with a cross-country perspective. OBJECTIVES: We aimed at comparing lifestyle patterns among preschool-aged children across Europe, their associations with socio-demographic factors and their links with body mass index (BMI). METHODS: Harmonized data on 2-5-year-olds participating in nine European birth cohorts from the EU Child Cohort Network were used (EBRBs, socio-demographics and anthropometrics). Principal component analysis and multivariable linear and logistic regressions were performed. RESULTS: The most consistent pattern identified across cohorts was defined by at least three of the following EBRBs: discretionary consumption, high screen time, low outdoor play time and low sleep duration. Consistently, children from low-income households and born to mothers with low education level had higher scores on this pattern compared to their socioeconomically advantaged counterparts. Furthermore, it was associated with higher BMI z-scores in the Spanish and Italian cohorts (ß = 0.06, 95% CI = [0.02; 0.10], both studies). CONCLUSION: These findings may be valuable in informing early multi-behavioural interventions aimed at reducing social inequalities in health at a European scale.

Leadership and Intention to Leave Among Public Health Sector Physicians in Cyprus: A National Cross-Sectional Study.

Physician shortage is a major concern in many health care systems globally, while healthcare leadership constitutes one of the most vital factors within human resource management. Our study examined the relationship between managers' leadership styles and physicians' intent to leave their current position. In this cross-sectional national survey, questionnaires were distributed to all physicians working in the public health sector of Cyprus. Most demographic characteristics evaluated by chi-square or Mann-Whitney test, were statistically significantly different between those who intended to leave their job and those who did not. The results of our study demonstrated that transformational leadership has a positive influence on retention of physicians in public hospitals, while non leadership infers a negative influence. Developing leadership skills in physician supervisors is of a great importance for organizations to make a large impact on health professionals' retention and overall performance.

Correction: Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

[This corrects the article DOI: 10.1371/journal.pmed.1004036.].

Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

BACKGROUND: Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. METHODS AND FINDINGS: We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. CONCLUSIONS: This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.

Exposure to natural environments during pregnancy and birth outcomes in 11 European birth cohorts.

Research suggests that maternal exposure to natural environments (i.e., green and blue spaces) promotes healthy fetal growth. However, the available evidence is heterogeneous across regions, with very few studies on the effects of blue spaces. This study evaluated associations between maternal exposure to natural environments and birth outcomes in 11 birth cohorts across nine European countries. This study, part of the LifeCycle project, was based on a total sample size of 69,683 newborns with harmonised data. For each participant, we calculated seven indicators of residential exposure to natural environments: surrounding greenspace in 100m, 300m, and 500m using Normalised Difference Vegetation Index (NDVI) buffers, distance to the nearest green space, accessibility to green space, distance to the nearest blue space, and accessibility to blue space. Measures of birth weight and small for gestational age (SGA) were extracted from hospital records. We used pooled linear and logistic regression models to estimate associations between exposure to the natural environment and birth outcomes, controlling for the relevant covariates. We evaluated the potential effect modification by socioeconomic status (SES) and region of Europe and the influence of ambient air pollution on the associations. In the pooled analyses, residential surrounding greenspace in 100m, 300m, and 500m buffer was associated with increased birth weight and lower odds for SGA. Higher residential distance to green space was associated with lower birth weight and higher odds for SGA. We observed close to null associations for accessibility to green space and exposure to blue space. We found stronger estimated magnitudes for those participants with lower educational levels, from more deprived areas, and living in the northern European region. Our associations did not change notably after adjustment for air pollution. These findings may support implementing policies to promote natural environments in our cities, starting in more deprived areas.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD.

OBJECTIVE: Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. RESULTS: We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.

Mortality in Cyprus Over the Period 2016-2021.

Background Mortality in the general population is one of the most robust measures used to examine epidemiological trends over time and especially over periods of public health crises such as the current coronavirus disease 2019 (COVID-19) pandemic. Methodology In this study, we analyzed information reported by the Cyprus Ministry of Health to the European Statistical Office (Eurostat), which includes weekly all-cause mortality over the period 2016-2021. In addition, we used data collected by the European Centre for Disease Prevention and Control regarding daily reported COVID-19 cases and COVID-19-related deaths. Results Based on our data analysis, we observed a substantial increase of 9.7% in all-cause mortality in Cyprus in 2021 compared to 2020, with an overall mortality increase of 16.5% in 2021 compared to the mean mortality of the previous five years. Particularly, we documented a sharp increase over the third and the fourth quarters of the year 2021. Conclusions The substantial increase in mortality in Cyprus in 2021 is not entirely explained by COVID-19 deaths and is parallel to the concurrent vaccination campaign. This concerning observation should be comprehensively investigated by the National and European public health authorities to identify and address the underlying causes.

Associations of early-life pet ownership with asthma and allergic sensitization: A meta-analysis of more than 77,000 children from the EU Child Cohort Network.

BACKGROUND: Studies examining associations of early-life cat and dog ownership with childhood asthma have reported inconsistent results. Several factors could explain these inconsistencies, including type of pet, timing, and degree of exposure. OBJECTIVE: Our aim was to study associations of early-life cat and dog ownership with asthma in school-aged children, including the role of type (cat vs dog), timing (never, prenatal, or early childhood), and degree of ownership (number of pets owned), and the role of allergic sensitization. METHODS: We used harmonized data from 77,434 mother-child dyads from 9 birth cohorts in the European Union Child Cohort Network when the child was 5 to 11 years old. Associations were examined through the DataSHIELD platform by using adjusted logistic regression models, which were fitted separately for each cohort and combined by using random effects meta-analysis. RESULTS: The prevalence of early-life cat and dog ownership ranged from 12% to 45% and 7% to 47%, respectively, and the prevalence of asthma ranged from 2% to 20%. There was no overall association between either cat or dog ownership and asthma (odds ratio [OR] = 0.97 [95% CI = 0.87-1.09] and 0.92 [95% CI = 0.85-1.01], respectively). Timing and degree of ownership did not strongly influence associations. Cat and dog ownership were also not associated with cat- and dog-specific allergic sensitization (OR = 0.92 [95% CI = 0.75-1.13] and 0.93 [95% CI = 0.57-1.54], respectively). However, cat- and dog-specific allergic sensitization was strongly associated with school-age asthma (OR = 6.69 [95% CI = 4.91-9.10] and 5.98 [95% CI = 3.14-11.36], respectively). There was also some indication of an interaction between ownership and sensitization, suggesting that ownership may exacerbate the risks associated with pet-specific sensitization but offer some protection against asthma in the absence of sensitization. CONCLUSION: Our findings do not support early-life cat and dog ownership in themselves increasing the risk of school-age asthma, but they do suggest that ownership may potentially exacerbate the risks associated with cat- and dog-specific allergic sensitization.

A deterministic approach for protecting privacy in sensitive personal data.

BACKGROUND: Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. METHODS: In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. RESULTS: We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. CONCLUSIONS: The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview.

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.

The network limits of infectious disease control via occupation-based targeting.

Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.

Dietary Macronutrient Composition in Relation to Circulating HDL and Non-HDL Cholesterol: A Federated Individual-Level Analysis of Cross-Sectional Data from Adolescents and Adults in 8 European Studies.

BACKGROUND: Associations between increased dietary fat and decreased carbohydrate intake with circulating HDL and non-HDL cholesterol have not been conclusively determined. OBJECTIVE: We assessed these relations in 8 European observational human studies participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) using harmonized data. METHODS: Dietary macronutrient intake was recorded using study-specific dietary assessment tools. Main outcome measures were lipoprotein cholesterol concentrations: HDL cholesterol (mg/dL) and non-HDL cholesterol (mg/dL). A cross-sectional analysis on 5919 participants (54% female) aged 13-80 y was undertaken using the statistical platform DataSHIELD that allows remote/federated nondisclosive analysis of individual-level data. Generalized linear models (GLM) were fitted to assess associations between replacing 5% of energy from carbohydrates with equivalent energy from total fats, SFAs, MUFAs, or PUFAs with circulating HDL cholesterol and non-HDL cholesterol. GLM were adjusted for study source, age, sex, smoking status, alcohol intake and BMI. RESULTS: The replacement of 5% of energy from carbohydrates with total fats or MUFAs was statistically significantly associated with 0.67 mg/dL (95% CI: 0.40, 0.94) or 0.99 mg/dL (95% CI: 0.37, 1.60) higher HDL cholesterol, respectively, but not with non-HDL cholesterol concentrations. The replacement of 5% of energy from carbohydrates with SFAs or PUFAs was not associated with HDL cholesterol, but SFAs were statistically significantly associated with 1.94 mg/dL (95% CI: 0.08, 3.79) higher non-HDL cholesterol, and PUFAs with -3.91 mg/dL (95% CI: -6.98, -0.84) lower non-HDL cholesterol concentrations. A statistically significant interaction by sex for the association of replacing carbohydrates with MUFAs and non-HDL cholesterol was observed, showing a statistically significant inverse association in males and no statistically significant association in females. We observed no statistically significant interaction by age. CONCLUSIONS: The replacement of dietary carbohydrates with fats had favorable effects on lipoprotein cholesterol concentrations in European adolescents and adults when fats were consumed as MUFAs or PUFAs but not as SFAs.

Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD.

Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture ("resources") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (https://isglobal-brge.github.io/resource_bookdown).

Privacy preserving data visualizations.

Data visualizations are a valuable tool used during both statistical analysis and the interpretation of results as they graphically reveal useful information about the structure, properties and relationships between variables, which may otherwise be concealed in tabulated data. In disciplines like medicine and the social sciences, where collected data include sensitive information about study participants, the sharing and publication of individual-level records is controlled by data protection laws and ethico-legal norms. Thus, as data visualizations - such as graphs and plots - may be linked to other released information and used to identify study participants and their personal attributes, their creation is often prohibited by the terms of data use. These restrictions are enforced to reduce the risk of breaching data subject confidentiality, however they limit analysts from displaying useful descriptive plots for their research features and findings. Here we propose the use of anonymization techniques to generate privacy-preserving visualizations that retain the statistical properties of the underlying data while still adhering to strict data disclosure rules. We demonstrate the use of (i) the well-known k-anonymization process which preserves privacy by reducing the granularity of the data using suppression and generalization, (ii) a novel deterministic approach that replaces individual-level observations with the centroids of each k nearest neighbours, and (iii) a probabilistic procedure that perturbs individual attributes with the addition of random stochastic noise. We apply the proposed methods to generate privacy-preserving data visualizations for exploratory data analysis and inferential regression plot diagnostics, and we discuss their strengths and limitations.

Hand-Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network.

The risk for a global transmission of flu-type viruses is strengthened by the physical contact between humans and accelerated through individual mobility patterns. The Air Transportation System plays a critical role in such transmissions because it is responsible for fast and long-range human travel, while its building components-the airports-are crowded, confined areas with usually poor hygiene. Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) consider hand hygiene as the most efficient and cost-effective way to limit disease propagation. Results from clinical studies reveal the effect of hand washing on individual transmissibility of infectious diseases. However, its potential as a mitigation strategy against the global risk for a pandemic has not been fully explored. Here, we use epidemiological modeling and data-driven simulations to elucidate the role of individual engagement with hand hygiene inside airports in conjunction with human travel on the global spread of epidemics. We find that, by increasing travelers engagement with hand hygiene at all airports, a potential pandemic can be inhibited by 24% to 69%. In addition, we identify 10 airports at the core of a cost-optimal deployment of the hand-washing mitigation strategy. Increasing hand-washing rate at only those 10 influential locations, the risk of a pandemic could potentially drop by up to 37%. Our results provide evidence for the effectiveness of hand hygiene in airports on the global spread of infections that could shape the way public-health policy is implemented with respect to the overall objective of mitigating potential population health crises.

Synthetic ALSPAC longitudinal datasets for the Big Data VR project.

Three synthetic datasets - of observation size 15,000, 155,000 and 1,555,000 participants, respectively - were created by simulating eleven cardiac and anthropometric variables from nine collection ages of the ALSAPC birth cohort study. The synthetic datasets retain similar data properties to the ALSPAC study data they are simulated from (co-variance matrices, as well as the mean and variance values of the variables) without including the original data itself or disclosing participant information.  In this instance, the three synthetic datasets have been utilised in an academia-industry collaboration to build a prototype virtual reality data analysis software, but they could have a broader use in method and software development projects where sensitive data cannot be freely shared.