bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking surveillance and climate data to combat malaria
Authors: Sironi Alberto , Barreto Marcos , Bertoldo Juracy , Conceição Daniel , Sampaio Vanderson 
Published: September 2018

IntroductionMalaria is an infectious disease that affected nearly 215 million individuals in 2015. In Brazil, there are various information systems targeted to store data from disease notification, including malaria surveillance. However, these databases are identified and difficult to be accessed by researchers due to privacy restrictions.
Objectives and ApproachOur goal is to integrate data from two different malaria surveillance systems, as well climate, hydrographics and socioeconomic data, to support ecological and cohort-based analyzes on malaria recurrence, parasitic classification assisted by machine learning methods and epidemics forecasting.
Our approach so far was to generate data sets organized by municipality of residence and by municipality of infection and the disposal of these data sets with information aggregated on monthly and annual basis. We expect these databases can be freely used by any researcher intending to conduct studies on malaria using governmental data.
ResultsOur current results comprise data sets with information aggregated by municipality of residence, for all municipalities within the Amazonian region (n=772), and by municipalities of infection with active transmission (n=613). For both case, we added variables referring to demographic, socioeconomic, climatological and hydrological data for the period 2010 to 2015 in an annual and monthly form.
We also performed a machine learning-based classification to group notifications according to the type of parasite into P. vivax, P. falciparum and mixed cases. Our goal is to identify similar and different characteristics among such groups that can be used to correctly assess recurrence, as well support epidemic forecasting.
Conclusion/ImplicationsFrom the databases we created, it will be possible to implement an indexing structure with related metadata, as well publicize these databases to allow for free access by researchers. Currently, we are also running different predictive analytics methods (including visualisation) targeted to generate a forecast model for malaria epidemics.
Survival outcomes of a population-based cohort of adolescent and young adult (AYA), acute lymphoblastic leukemia (ALL) patients in Ontario, Canada: The IMPACT Cohort
Authors: Lau Cindy , Pole Jason , Sutradhar Rinku , Baxter Nancy , Nathan Paul , Gupta Sumit 
Published: September 2018

IntroductionAdolescents and young adults (AYA) are an understudied population in cancer research. The Initiative to Maximize Progress in Adolescent and Young Adult Cancer Therapy (IMPACT) cohort includes Ontario individuals aged 15-21 years, diagnosed with a malignancy during 1992-2011. This cohort contains a rich source of patient, disease, and treatment data.
Objectives and ApproachThe IMPACT cohort was created using chart review and linkages to population-based health services databases. The cohort is comprised of AYA with a primary diagnosis of acute leukemia, Hodgkin lymphoma, non-Hodgkin lymphoma, sarcoma, or testicular cancer. For the current study, we focused on acute lymphoblastic leukemia (ALL) patients, and examined survival outcomes of those treated at pediatric vs adult centers, with adult vs pediatric protocols. We assessed 5-year event-free survival (first of relapse, progression, secondary malignant neoplasm, or death) and overall survival.
ResultsThe IMPACT cohort contains 2,963 patients, of which 152/271 ALL patients were treated at adult centers. The 5-year event-free survival (EFS ± standard error) among those treated at a pediatric vs. adult center was 72%±4% vs. 56%±4% (p = 0.03), respectively. The 5-year overall survival (OS) was 82%±4% vs. 64%±4% (p
Conclusion/ImplicationsALL patients treated with a pediatric protocol at a pediatric center experienced better, crude survival outcomes compared to those at an adult center. The IMPACT cohort offers the opportunity to study a myriad of questions on different cancer groups, with the ultimate goal of improving outcomes among the AYA population.
Developmental vulnerability at age five among children who enter and progress through the child protection system in New South Wales, Australia: a cross-sectoral data linkage study
Authors: Falster Kathleen , Hanly Mark , Pilkington Rhiannon , Chilvers Marilyn , Whittaker Elizabeth , Lynch John 
Published: September 2018

IntroductionA recent independent review of the child protection system in New South Wales (NSW), Australia, highlighted the need for whole-of-government reform to improve outcomes for children at risk of, or experiencing, maltreatment. Population-level evidence on outcomes of children who enter and progress through the child protection system is currently lacking.
Objectives and ApproachWe aimed to quantify developmental vulnerability at age five among children who enter and progress through the child protection system during early childhood to demonstrate the value of cross-sectoral data linkage to inform and evaluate policy at a population-level. We used Australian Early Development Census (AEDC) data linked to cross-sectoral population datasets in NSW, including birth registrations, perinatal, and child protectionnotification and out-of-home care (OOHC) placement data. Linked AEDC data, collected in 2009 and 2012, areavailable for 153,670 NSW children. Socio-demographic and perinatal characteristics available in the linked data were used to characterise the population.
Results21,179 (13.9%) children had ≥1 ‘screened in’ notification, 4927 (3.2%) had ≥1 substantiated abuse and neglect notification, and 2177 (1.4%) had ≥1 OOHC placement before their fifth birthday. Indicators of disadvantage and adverse birth outcomes were more common among children who progressed to higher levels of the child protection system. The proportion developmentally vulnerable on ≥1 domains of the AEDC increased for children who entered and progressed through the child protection system; from 21% of children with no contact with child protection before age five, to 39% of children with ≥1 ‘screened in’ notification, 50% with ≥1 substantiated notification, and 54% with ≥1 OOHC placement before their fifth birthday. Comparison of findings from other Australian jurisdictions with similar data will be discussed.
Conclusion/ImplicationsThis study demonstrates there is scope to improve developmental outcomes through targeted interventions among children who become known to child protection during early childhood in NSW. Moreover, it illustrates that cross-sectoral data linkage can be used to inform and evaluate policy reforms to drive better outcomes for vulnerable children.
Incidence, recurrence and predictors associated with recurrence of low birth weight in Northern Tanzania
Authors: Mahande Michael Johnson , Obure Joseph , Mvunta Miriam Hamis 
Published: September 2018

IntroductionLow birth weight (LBW) is an important indicator of newborn survival and is associated with higher risk of infant mortality, morbidity and long term health consequences later in life. Little has been explored on the recurrence of LBW and associated risk factors in developing countries including Tanzania.
Objectives and ApproachThis study aimed to determine incidence, recurrence and associated risk factors for in Northern Tanzania.
MethodsThis was a retrospective cohort study using maternally-linked data from Kilimanjaro Christian Medical Centre (KCMC) medical birth registry. A total of 48,008 births from 8,417 women who delivered live born between 2000 and 2014 were followed for subsequent deliveries. Recurrence risk with 95% Confidence Interval for LBW and associated risk factors were estimated in a multivariate log binomial model while accounting for correlation between births of the same mother.
ResultsIncidence of LBW was 7.9%. The recurrence rate of LBW was 24.9% compared with 5.9% for those who had normal birth weight babies in first pregnancy. This corresponded to a relative risk of 3.7 (95% CI: 3.10 – 4.52). Some maternal conditions in the first pregnancy were associated with increased risk of LBW in the subsequent pregnancy. These include; HIV positive status (RR 2.0; 95% CI 1.26 – 3.21), preterm birth (RR 1.2; 95% CI 1.03 – 1.63) and preeclampsia (RR 1.8; 95% CI 1.26 – 2.45). Only preeclampsia in the first pregnancy was associated with increased risk of recurrent of LBW (RR: 1.6 95% CI 1.01-2.54).
Conclusion/ImplicationsAlthough the incidence of LBW is low in the study population, but the recurrence risk is high. Preeclampsia in the first pregnancy was associated with recurrent LBW. Early prenatal identification of women at risk of preeclampsia to address modifiable risk factors and counsel mothers on persisting risk factors for recurrence
Supporting Applied, Multidisciplinary Analytic Skills: An Innovative Training Platform for Researcher Capacity Building
Authors: Greenwood Ann 
Published: September 2018

OverviewPopulation Health Research is inherently interdisciplinary, multifaceted and firmly rooted in the evolving connections between place, time and related socioeconomic processes. To excel in this rapidly diversifying field, individuals require a broad range of multidisciplinary skills gained through applied, collaborative and real world learning environments.
ObjectiveTo serve the emerging multidisciplinary skills and capacity building needs of Population Health Researchers within a rapidly diversifying field.
ApproachEstablishment of an innovative research training platform that supports skill development in an accessible, collaborative and applied learning environment. Designed in collaboration with a multi-university research platform, this fully online practice-based training environment both mirrors and supports many of the day to day skill development needs of health and social science researchers.
ResultsNow in its 6th year of delivery, the multidisciplinary focus of this specialized training platform is successfully addressing the skill development needs of a diverse cross section of health research professionals. Trainees are bringing a wealth of experience and knowledge to share with their online colleagues, supporting a rich, practice based education and skills development environment. Those enrolled in the program possess backgrounds ranging from Population and Public Health, Epidemiology, Statistics and Sociology to Medicine to Psychology, Geography, Biostatistics and International Health. Graduates of the program are gaining skills that benefit their research and careers. Lessons learned with respect to the development of the training platform and capacity building skills achieved by program graduates will be outlined.
Conclusion/ImplicationsProviding timely, practical, hands-on analytic skills training is critical to building the capacity of new and mid-career researchers and health professionals. Direct application of these new skills is an essential outcome and best measure of success. We are listening to our trainees and learning as we grow. Learn more here.
School readiness of maltreated children: Timing, type, and chronicity of maltreatment
Authors: Bell Megan , Bayliss Donna , Glauert Rebecca , Ohan Jeneva 
Published: September 2018

IntroductionChildren who have been maltreated during early childhood may experience a difficult transition into schooling, due to maladaptive development of the abilities that are important for positive school adaptation. An understanding of how different dimensions of maltreatment relate to children’s school readiness is important for informing appropriate supports for maltreated
Objectives and ApproachIn this study, the Australian Early Development Census scores of 19,203 children were linked to information on child maltreatment allegations (substantiated and unsubstantiated), including the type of alleged maltreatment, the timing of the allegation (infancy-toddlerhood or preschool), and the total number of allegations (chronicity).
ResultsChildren with a maltreatment allegation had increased odds of poor school readiness in cognitive and non-cognitive domains. Substantiated maltreatment was associated with poor social and emotional development in children, regardless of maltreatment type, timing, or chronicity. For children with unsubstantiated maltreatment allegations, developmental outcomes according to the type of alleged maltreatment were more heterogeneous; however, these children were also at risk of poor school readiness irrespective of the timing and/or chronicity of the alleged maltreatment.
Conclusion/ImplicationsThe findings suggest that all children with maltreatment allegations are at risk for poor school readiness; these children may benefit from additional support to increase the chance of a successful school transition. Interventions should commence prior to the start of school to mitigate early developmental difficulties.
Factors predicting out-of-school suspensions for young children
Authors: Bell Megan , Bayliss Donna , Glauert Rebecca , Ohan Jeneva 
Published: September 2018

IntroductionDespite limited evidence supporting the effectiveness of out-of-school suspension for rectifying behavioural issues, the practice continues. Certain demographic factors (male, ethnic minority, low SES) predict suspensions; however, developmental and family factors can also play a role. Knowledge of these factors may inform alternative practices aimed at limiting the practice of suspension.
Objectives and ApproachThis study investigated the relationship between out-of-school suspensions and demographic, developmental, and family factors. Children suspended in early schooling may be particularly at risk of poor school outcomes; thus, our sample includes children in the first years of school. Linked administrative data were obtained for 14,269 children enrolled in grade 3 at public schools in Western Australia. Multilevel logistic regressions were run, grouped by school, with out-of-school suspensions predicted by: child and parent demographic characteristics; a measure of children’s school-entry development (the Australian Early Development Census); and indicators of family risk (parental psychiatric hospitalizations, parental criminal offending, child maltreatment).
ResultsApproximately 2% of children had been suspended at least once by grade 3. Aboriginal children, boys, and children attending schools with high levels of socioeconomic disadvantage had significantly increased odds of being suspended from school (2 times, 6 times, and 3 times increase, respectively). Furthermore, children considered socially and emotionally vulnerable at school-entry were around 3 times more likely than their peers to be suspended in the first few years of school. Parental psychiatric hospitalizations, parental criminal offending, and child maltreatment all significantly predicted out-of-school suspensions. Odds increased exponentially with each additional family risk factor experienced in early childhood. Children experiencing all three family risk factors were almost 7 times more likely than their peers to be suspended at least once by grade 3.
Conclusion/ImplicationsOur results provide further justification for the implementation of alternatives to out-of-school suspension, as children who would most likely benefit from a stable, nurturing school environment were significantly more likely to be suspended. Schools need to be better supported by inter-agency collaboration to manage the complex needs of vulnerable children.
Public engagement with cohort participants in Scotland and India: How do participants want to shape mental health research?
Authors: Chapko Dorota , Butler Jessica , Black Corri , Johnston Marjorie , Fall Caroline , Kumaran Kalyanaraman , Krishnaveni Ghattu V , Murali Murali 
Published: September 2018

IntroductionWe now have the opportunity to disentangle the complexities of lifespan brain health through linking rich birth cohorts data to novel information utilizing health informatics techniques. Wellcome Trust evidenced that efforts to manage and link digital health data require exploring the attitudes of public and data guardians towards this initiative.
Objectives and ApproachOur teams in Aberdeen, Mysore and Mumbai have established general population cohorts that have provided evidence on the early-life origins of later-life diseases. In 2016, we engaged with the participants of the Aberdeen Children of the 1950s (62-68,N~8,400) in an event attended by 350 study members. We are also engaging with members and data guardians of Mysore Parthenon Cohort (20-21y,N~500), Mysore Birth Records Cohort (51-83y,N~750), and Mumbai Maternal Nutrition Cohort (5-11y,N~1,600). This qualitative project utilises focus groups and individual interviews. The goal is to understand their attitudes and perceived public benefits towards future novel data collection, data platforms and biorepositories.
ResultsThe ACONF event attendees were very willing to volunteer for further studies. The highest support was for ‘provide a blood sample’ (92%) and the lowest for ‘take an online survey’ (83%). They were satisfied with data governance but had incomplete understanding of linkage possibilities with their data. Through the ongoing activities in Mysore and in Mumbai, we aim to achieve the following: 1) Develop awareness of health informatics through public engagement with participants and researchers; 2) Explore attitudes and potential barriers to creation of secure data linkage between imaging, laboratory, health data and outcomes to facilitate future linkage studies; 3) Understand what is required for interoperable, secure data storage and plan future biorepository as a resource for researchers that will build over time in India.
Conclusion/ImplicationsThe proposed activities will inform preparation of a large scale grant to investigate the hypothesis that early life environment affects future risk of mental illness and cognitive ability globally. They will also begin to create a platform of enduring value for future cross-cultural population research.
Association rule mining to identify potential under-coding of conditions in the problem list in primary care electronic medical records
Authors: Weaver Colin , Garies Stephanie , Williamson Tyler , McBrien Kerry , Peng Mingkai 
Published: September 2018

IntroductionThe problem list of a patient’s primary care electronic medical record (EMR) generally reflects their important medical conditions. We will use association rule mining to assess between-provider and between-clinic variation in the coding of select conditions in the EMR problem list, in order to identify possible under-coding outliers.
Objectives and ApproachEMR data from participating clinics in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) will be used, with a focus on three commonly-occurring conditions (hypertension, diabetes, and depression). Association rule mining will be used to develop association rules between these conditions and other clinical information available in the EMR, such as other diagnoses in the problem list, billing codes, medications, and laboratory results (e.g., a rule of “diabetic medication→diabetes” indicates that patients prescribed a diabetic medication are likely to have diabetes in the problem list). Under-coding outliers at the provider and clinic levels will be identified by comparing rule enforcement.
ResultsResults from this work in progress will be presented at the conference. An estimated 270 clinics, 1340 providers, and 1.8 million patients will be included from the CPCSSN database. Rule ‘confidence’ will be used to identify outliers; the confidence of a rule X→Y is the proportion of individuals with X who also have Y (Pr(Y|X)). For example, we may find that on average 80% of patients prescribed a diabetic medication will also have a diagnosis of diabetes in the problem list (average confidence of 80%), but an outlier clinic may have a confidence of 40%; this low rule confidence may indicate under-coding of diabetes in the problem list. Confounding by patient demographics (e.g., age, sex, urban/rural) will be assessed and adjusted for, if necessary.
Conclusion/ImplicationsThis work examines a novel method to identify potential under-coding in the EMR problem list. Providers/clinics could use this information to update patients’ problem list or inform quality improvement interventions. Researchers using primary care EMR data need to be aware of potential under-coding and take steps to mitigate the effects.
Interactive Data Visualization of Patient Experience and Inpatient Datasets using Tableau Desktop
Authors: Kemp Kyle , Santana Maria , Quan Hude 
Published: September 2018

IntroductionData visualization is a valuable means for reporting and interpreting large datasets. It allows users to present key messages from the data in a simple way. Although many industries have adopted and embedded data visualization within their analytic teams, healthcare has only recently begun to realize this potential.
Objectives and ApproachThe objective of this interactive presentation is to introduce Tableau (visualization software) and to provide quick, impactful examples of its use. Visualizations will be created using a free version of Tableau Desktop; available to students and academics. Two blinded datasets encompassing hospital discharges in Alberta from April 2014 to March 2016 will be used. The first dataset will contain approximately 50,000 patient experience surveys, as completed by patients within 6 weeks of their hospital discharge. The second dataset will contain inpatient records from the Discharge Abstract Database (DAD). Visualizations will be created using the individual and combined datasets.
ResultsFollowing a brief description of each dataset and its respective elements, a variety of interactive visualizations will be created in real-time. From the patient experience dataset, we will be able to quickly determine which hospitals have the highest overall rating from their patients. We will then display the results from all survey questions from a single hospital; allowing for a determination of areas where care is delivered well, and to provide opportunities for improvements. From the DAD, we will highlight hospital length of stay, and its relation with gender, age group, geography, and clinical condition. In the final portion of the presentation, both datasets will be linked to examine the relationships between survey responses, patient demographics, and clinical characteristics.
Conclusion/ImplicationsData visualization has great potential in healthcare. From this presentation, attendees will receive an introduction to its use using practical, real-world examples. The dynamic visualizations in this presentation will be created in mere minutes; a small fraction of the time typically spent by analysts to create static, paper-based reports.