bluuebunny/crossref_metadata_2025_split huggingface dataset:
Population Data Science: The science of data about people
Authors: McGrail Kim , Jones Kerina 
Published: September 2018

IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward.
Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper.
ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement.
Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.
A comparison of child mortality from potentially preventable causes in England and Sweden using birth cohorts from linked administrative datasets
Authors: Zylbersztejn Ania , Gilbert Ruth , Hjern Anders , Hardelid Pia 
Published: September 2018

IntroductionChild mortality is nearly twice as high in England as in Sweden. A comparison of mortality from potentially preventable causes could inform health system responses. This study focused on respiratory tract infection (RTI)-related deaths, amenable to healthcare interventions, and sudden unexpected deaths in infancy (SUDI), amenable to public health interventions.
Objectives and ApproachWe developed nationally-representative birth cohorts of singleton live births in 2003-2012 using a hospital admissions database in England and the Medical Birth Register in Sweden. Children were followed-up from 31st day of life until their fifth birthday via linkage to hospital admission and mortality records. We compared child mortality using Cox proportional hazards models to estimate hazard ratios (HR) for England versus Sweden for RTI-related mortality at 31-364 days and 1-4 years, and for SUDI mortality at 31-364 days. Models were adjusted for birth characteristics (gestational age, birthweight, sex, congenital anomalies), and socio-economic factors (maternal age and socio-economic status).
ResultsOf 3,928,483 children in England, there were 807 RTI-related deaths at 31-364 days (17% of all deaths in the age range), 691 deaths at 1-4 years (31%), and 1,166 SUDIs (24%) in England. Corresponding figures for 1,012,682 children in Sweden were 136 (18%), 118 (25%) and 189 (24%). Unadjusted HRs for RTI-related deaths in England versus Sweden were 1.50 (95% confidence interval: 1.25-1.80) at 31-364 days. Adjustment for birth characteristics reduced the HR to 1.16 (0.97-1.39), and for socio-economic factors to 1.11 (0.92-1.33). Corresponding figures for RTI-related mortality at 1-4 years were 1.58 (1.30-1.92), 1.32 (1.09-1.61) and 1.30 (1.07-1.59), respectively. Unadjusted HRs for SUDIs reduced from 1.59 (1.36-1.85) to 1.40 (1.20-1.63) after adjusting for birth characteristics, and to 1.19 (1.02-1.39) after adjusting for socio-economic factors.
Conclusion/ImplicationsHigher prevalence of adverse birth characteristics (such as prematurity, low birthweight, congenital anomalies) contributed to increased risks of RTI-related and SUDI mortality in England relative to Sweden. Therefore, preventive strategies should focus on maternal health and socio-economic circumstances before and during pregnancy to reduce RTI-related and SUDI mortality in England.
Association between blue and green space availability with mental health and wellbeing
Authors: Song Jiao , Fry Richard , Mizen Amy , Akbari Ashley , Wheeler Benedict , White James , White Mathew , Lovell Rebecca , Parker Chrissie , Berridge Damon , Stratton Gareth , Nieuwenhuijsen Mark , Lyons Ronan , Rodgers Sarah 
Published: September 2018

IntroductionGreen-blue spaces (GBS), such as parks, woodlands, and beaches, may be beneficial for population mental health and wellbeing. However, there are few longitudinal studies on the association between GBS and mental health and wellbeing, and few that incorporate network analysis as opposed to simple Euclidian proximity.
Objectives and ApproachWe are examining the association between the availability of GBS with wellbeing and common mental health disorders. We will use geographic information systems (GIS) to create quarterly household level GBS availability data using digital map and satellite data (2008-2018) for over 1 million homes in Wales, United Kingdom. We will link GBS availability to individual level mental health (1.7 million people with General Practitioner (GP) data) and data from the National Survey for Wales (n = 24,000) on wellbeing (Warwick Edinburgh Mental Wellbeing Scale (WEMWBS)) using the Secure Anonymised Information Linkage (SAIL) databank.
ResultsWe created an historic dataset of GBS availability using road network and path data to create quarterly household level GBS exposures (2008-2018). We tested Residential Anonymised Linking Fields (RALFs) and accurately linked 97\% of individuals and their health data to their home and GBS exposure. The 1.65 million exposure-health data pairs, updated quarterly, will enable a longitudinal panel study to be built. Using GP recorded data on treatments, diagnoses, symptoms and prescriptions for mental health problems we identified 35,000 people had a common mental health disorder in 2016, and 24,000 people answered the National Survey for Wales questions about their wellbeing and use of GBS. We will explore how house moves, and visits to GBS change the association between GBS availability and outcomes.
Conclusion/ImplicationsThis study fills the gap in the evidence base around environmental planning policy to shape living environments to benefit health. It will inform the planning and management of GBS in urban and rural environments and contribute to international work on impacts of the built environment on mental health and wellbeing.
Linking midwives and hospital morbidity data to investigate the effect of interpregnancy interval on gestational diabetes: a 35-year cohort study in Western Australia
Authors: Gebremedhin Amanuel , Regan Annette , Pereira Gavin , Malacova Eva 
Published: September 2018

IntroductionInterpregnancy interval (IPI) is a potentially modifiable risk factor for preganncy outcomes, and short and long IPI may be associated with increased rik of pregnancy complications. Record linkage provides the only practiacble means to investigate IPI effects, which requires large generalisable sample sizes and long follow-up time.
Objectives and ApproachThis study examines the effect of IPI on gestational diabetes in Western Australia, with the aim to inform the evidence-base for IPI recommendations in high-income countries. A longitudinal population-based retrospective cohort study was conducted using de-identified, probabilistically-linked records for all births in Western Australia from 1980 to 2015 (inclusive) from the state’s Midwives Notification System and the WA Hospital Morbidity Data Collection. Logistic regression model was used to estimate the odds of gestational diabetes by IPI category. Analyses included all women with at least two consecutive singleton live births at 20-44 weeks of gestation.
ResultsA cohort of 320,616 women were included in the study. Of these, 13,680 (4\%) had an IPI > 120 months (AOR:1.53, 95\% CI 1.38-1.70) as compared to 18-23 months.
Conclusion/ImplicationsOur findings show that both short and long IPIs may be associated with increased risk of gestational diabetes in a high-income setting. In this study, data linkage improved ascertainment of the outcome measure. Results suggest 18-23 months following a previous livebirth may be optimal for avoiding complications in future pregnancies.
Development and validation of data quality rules in administrative health data using association rule mining
Authors: Peng Mingkai , Lee Sangmin (Sarah) , Kelly Maureen , Linton Cassandra , Penchoff Margaret , Sabljakovic Sanjin , Quan Hude 
Published: September 2018

IntroductionData quality assessment is a challenging facet for researches using coded administrative health data. Our previous study had demonstrated the potentials of association rule mining to assess data quality. The objective of this study is to develop and validate a set of coding association rules for data quality assessment.
Objectives and ApproachWe used the Canadian reabstracted hospital discharge abstract data (DAD) with clinical diagnosis coded in International Classification of Disease – 10th revision, Canada (ICD-10-CA) codes for rule development. The DAD data were divided into 5 age groups. Association rule mining were conducted on reabstracted DAD in each age group to extract ICD-10 coding association rules at the three and four digits levels. The rule strength was assessed using support and confidence. The rules will be reviewed by a panel of 5 physicians and 2 coding specialists to assess their appropriateness from clinical and coding perspectives using a modified Delphi rating
ResultsIn total, 975 rules at the three digits level and 822 rules at the four digits level were learned from the data. Half of the rules were in the age group of ≥65 and no rules were found in the age group of 5 to 19. The interquartile range of rule confidences were 0.112 to 0.425 in the three digits level and 0.073 to 0.222 in the four digits level. Two-thirds of rules had the diagnosis codes related to endocrine and metabolic disorders and diseases of circulatory, respiratory and genitourinary systems. The panel review will be conducted in early April and will have the final set of rules available before the conference.
Conclusion/ImplicationsThis study developed a set of validated ICD-10 coding association rules and creates a useful tool to cost-effectively assess data quality in routinely collected administrative health data.
Meeting the challenge of data linkage for special populations
Authors: Hills Brent 
Published: September 2018

IntroductionA file’s overall linkage rate may hide significant bias in unlinked records. Newborns, for example, may represent a small percentage of a file but a large proportion of unlinked records. This creates challenges for researchers focused on the affected populations. Decreasing bias is a clear goal for data linkage science.
Objectives and ApproachA standard spine-based linkage process with a typical set of identifiers was used to link perinatal records in British Columbia. This included multiple iterations and some manual linkage, stopping when there were only minor improvements. Despite high overall linkage rates, poor rates existed for babies who were stillborn or died near birth. Given research interests in stillbirth, there was a desire to use a creative approach to improve linkage for that sub-population. The solution was to expand the scope of the spine linkage to include birth and stillbirth records from other files, e.g. hospital discharge and Vital Statistics Registrations.
ResultsOriginal linkage methods produce overall baby linkage rates of 98.1% but only 3.9% of records discharged to death/stillbirth were linked. Revised methods provide a small change in the overall rate to 99.4% but increase the discharge to death/stillbirth linkage rate to 90.3%. All numbers exclude terminations.
Additions to our base representation of a linked entity enabled the association of records for babies either stillborn or never registered for health insurance coverage with the Ministry of Health. Additional variables addressed high rates of missing identifiers, while new linkage processing to Vital Statistics data addressed gaps within multiple newborn related datasets.
Conclusion/ImplicationsThe complexity of adding BC Perinatal Data as a linked dataset to Population Data BC holdings was underappreciated. There are always adjustments in linkage approach across different data sets, but the linkage of babies with adverse outcomes required considerably more change, including moving beyond our usual spine linkage approach.
Linking big data for cardiovascular health surveillance – opportunities and challenges using the CANHEART cohort
Authors: Chu Anna , Hennessy Deirdre , Johnston Sharon , Udell Jacob , Ko Dennis , Khan Anam , Jia Jing , Tu Jack 
Published: September 2018

IntroductionOur increasing ability to link large population-based health administrative datasets to create ‘big data’ cohorts offers unique opportunities to conduct health and health services surveillance at lower costs than traditional methods using surveys or primary data collection. However, comparability of findings from big data with traditional methods is unknown.
Objectives and ApproachIn the CArdiovascular HEalth in Ambulatory Care Research Team (CANHEART) ‘big data’ initiative, we linked 19 population-based health databases to obtain baseline and 5-year follow-up health information on a cohort of 9.8 million adult residents of Ontario, Canada as of January 2008. We compared cardiovascular risk factor prevalence with results from 3500 participants in the 2007-09 Canadian Health Measures Survey (CHMS), a traditional population health surveillance survey. Additionally, we determined cardiovascular preventative care use and clinical event rates by sex and age. Planned linkages to new data sources will enable continued cohort surveillance of population health-related and care indicators.
ResultsCholesterol and glucose levels determined from the CANHEART cohort were comparable to the CHMS, whereas blood pressure values and obesity rates were substantially higher. Overall, receipt of cardiovascular preventive care in the CANHEART cohort was high, with 85.7% of males and 91.8% of females having blood pressure assessments, and 67.8% of males and 79.4% of females having weight assessments. Cholesterol and diabetes screening rates among those recommended for screening were over 75%. Incidence of myocardial infarction, stroke or cardiovascular death was 51% higher among males than females (3.8 and 2.5 events per 1000 person-years, respectively). Challenges encountered in analyzing data included treatment of repeated and time-varying measures, selection of valid diagnostic and physician billing codes, changing coding practices and handling of missing and outlying data.
Conclusion/ImplicationsComparability of cardiovascular risk factor prevalence using linked administrative data with survey methods varies by indicator. Selection biases amongst survey participants and different measurement methods could explain discrepancies. The added ability to examine health care indicators longitudinally and by subgroup supports use of linked population-based data to enhance health surveillance.
Automatic detection of cohorts in the haystack of academic publications
Authors: Tingay Karen , Anastasiou Athanasios 
Published: September 2018

IntroductionDiscovering suitable datasets is an important part of health research, particularly for projects using linked and/or cohort data, but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for research teams to locate real world datasets that are most relevant to their project objectives.
Objectives and ApproachTo assist researchers in this, a bibliographic data analysis platform, bibInsight, is being developed which aims to identify potentially useful datasets, among other information, from large numbers of publications.
A search dataset on the broad topic of "dementia" was obtained from PubMed and abstracts mentioning cohort names were identified. These helped in determining the context within which cohort data are mentioned. Terms were also informed by specialist knowledge from the European Medical Information Framework (EMIF) Alzheimer's Disease project, and were used to train a one-class classifier that identified their structure.
ResultsThe initial PubMed search resulted in 129,040 articles, with 1961 identified cohorts. From these, the classifier was able to identify 1,588 as containing cohort information, and 373 as not, a precision of approximately 80\%.
The terms identified by the classifier included "annual", "longitudinal", and "prospective", suggesting re-contact.
Further analysis of these 1,588 abstracts can find those most relevant to specific topics, co-authorship collaborations (suggesting data sharing agreements are already in place), and mention of linkage to other datasets. These can be done through keyword extraction, longitudinal data mining methods, linking author affiliations to institutions through the Global Resource Information Database (GRID), and graph databases, all of which have been built into the bibInsight platform.
Conclusion/Implications`This approach shows promise in facilitating research by making literature searches more efficient. A key issue is working with unstructured text data which requires additional standardisation for analysis. Data reuse is a growing area, and publications can assist this, but better reporting would be beneficial.
A longitudinal, historical model of households using linked Welsh administrative data
Authors: Tingay Karen , Anastasiou Athanasios , Musselwhite Charles 
Published: September 2018

IntroductionRoutine data sources are increasingly being explored as an alternative to surveys for governmental censuses. Censuses are useful, but only capture one snapshot in time. For research, changes in state over time can be important. This is particularly true for households, where changes in both composition and location are routine.
Objectives and ApproachThe Secure Anonymised Information Linkage (SAIL) databank at Swansea University holds anonymised address data tied to Welsh General Practice (GP) registrations. The dataset has the individual’s unique identifier (ALF), address identifier (RALF), and moving in and moving out dates. This can be securely linked to other demographic and health data.
Using this data, a finite, deterministic household model was developed. This model implements a set of rules for establishing, advancing, and terminating households. The rules cover different types of households, including transient and multi-generational, but not communal residences such as student halls or residential care homes.
ResultsThe model was implemented in Python, and operates over finite moving in/out transactions undertaken by actors (ALFs) on specific properties (RALFs). The roles also help to diminish data issues, such as infants moving into an address before their parents.
The model creates and updates histories of households for each RALF. This allows researchers to identify unstable versus stable households, or to look at possible environmental health factors over time.
The script also provides a running count of the total number of residents at each RALF at any given time. This makes it useful for time-specific research, or for linking to censuses.
Conclusion/ImplicationsThe ability to have a longitudinal record of households can provide additional detail to research which can be costly to obtain otherwise. This method shows promise as a useful tool for linked population data research. Further validation against surveys, and work to identify and model communal residences is needed.
Combining cross-sectoral, prospective data linkage with other data sources to examine health outcomes for socially excluded populations: A case study from Australia
Authors: Kinner Stuart , Preen David B , Young Jesse , Cumming Craig , Borschmann Rohan 
Published: September 2018

IntroductionStudies of the health of prisoners have proliferated in the past decade. However, relatively few studies have examined health outcomes after release from custody. As such, although >30 million people are released from prison globally each year, the health and health service experiences of this population remain poorly understood.
Objectives and ApproachWell-powered longitudinal studies are essential for answering these questions but are expensive and difficult to conduct. Internationally, few such studies have been undertaken, and most suffer from either substantial and biased attrition, or recruitment bias. Cross-sectoral linkage of health and justice data circumvents some of these problems but presents its own ethical and methodological challenges. Large longitudinal studies, combining rich survey and clinical data with prospective data linkage, provide unique opportunities to examine and understand health outcomes for these highly marginalised individuals.
ResultsThis presentation will describe the process and challenges of establishing the world’s largest (N=2,702) prospective study of adults released from prison: the Health After Release from Prison (HARP) cohort study. The cohort spans two Australian states and includes 538 women and 1,002 Indigenous Australians. An overview of the data collected through face-to-face surveys, medical record review, and prospective linkage with State-based health and correctional records, national Medicare and pharmaceutical records, and the National Death Index, will be provided. Some examples of how these data have been used to answer novel public health questions will be presented. The strengths and limitations of the cohort, and the applicability of this research design for other inclusion health populations, will be discussed.
Conclusion/ImplicationsGiven their extreme vulnerability, understanding the health of people who cycle through prisons is important to addressing health inequalities at the population level. Novel research designs that combine prospective data linkage with other data sources provide new opportunities to examine the health of socially excluded populations.