bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of linked data to assess the impact of out-of-hospital deaths on 30-day mortality indicators
Authors: Li Mingyang (Xiaoqiang) , Gu Jing , Yin Ling , Hu Mahbubul , Gurevich Yana 
Published: September 2018

IntroductionPublicly reported 30-day mortality indicators in Canada usually only take into account in-hospital deaths recorded in clinical administrative databases. Studies show that the percentage out-of-hospital deaths may account for 24% to 53% of all 30-day mortality, depending on the indicator, however, such assessments have not been done in Canada.
Objectives and ApproachThe objective of this study was to compare 30-day mortality rates calculated using clinical administrative data only (in-hospital deaths) with rates calculated combining administrative data and Canadian Vital Statistics Death Database (CVSD) that captures both in- and out-of-hospital deaths. We considered mortality following acute myocardial infarction (AMI), stroke and major surgery. Episodes of care were created through linkage of Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS). Mortality information on deaths outside of acute care hospitals was obtained from DAD/NACRS-CVSD linked files created by Statistics Canada. Data from Quebec and Yukon were not included in the analysis.
ResultsThe overall 30-day AMI mortality rate calculated using both DAD and DAD-CSVD linked file was 7.4% compared to 6.7% 30-day in-hospital mortality rate calculated using DAD only. Mortality rates after stroke were 15.8% and 14.0% and after major surgery 1.8% and 1.6%, respectively. The impact of adding out-of-hospitals deaths to rate calculations varied by province and rurality. Adding death data from the DAD-CVSD linked file accounted for 10% of 30-day AMI mortality, 11% of 30-day stroke mortality and 12% of 30-day mortality after major surgery, based on 2011 data. However, depending on the indicator, 7% to 9% of the deaths within 30 days recorded in DAD were not found in DAD-CVSD linked file due to limitations of the linkage methodology.
Conclusion/ImplicationsAn impact of including out-of-hospital deaths in the 30-day mortality rates appears to be less in Canada (~10%) than shown in other studies. However, while the DAD/NACRS-CVSD linked files provide valuable supplemental information, linkage methodology limitations suggest that they should be used in conjunction with mortality information available in DAD.
Machine learning: how much does it improve the prediction of unplanned hospital admissions?
Authors: Weaver Colin , McBrien Kerry , Sajobi Tolu , Ronksley Paul E , Lethebe Brendan , Williamson Tyler 
Published: September 2018

IntroductionRisk prediction models can be used to inform decision-making in clinical settings. With large and detailed electronic medical record data, machine learning may improve predictions. The objective of this work is to determine the feasibility and accuracy of machine learning versus logistic regression to predict unplanned hospital admissions.
Objectives and ApproachData from primary care electronic medical records for community-dwelling adults in Alberta, Canada available from the Canadian Primary Care Sentinel Surveillance Network will be linked to acute care administrative health data held by Alberta Health Services. Two regression methods (forward stepwise logistic, LASSO logistic) will be compared with three machine learning methods (classification tree, random forest, gradient boosted trees). Prior primary and acute care use will be used to predict three outcomes: ≥1 unplanned admission within 1 year, ≥1 unplanned admission within 90 days, and ≥1 unplanned admission within 1 year due to an ambulatory care sensitive condition.
ResultsThe results of this work in progress will be presented at the conference. 41,142 patients will have their primary and acute care data linked. We anticipate that the machine learning methods will improve predictive performance but will be more challenging for clinicians and patients to understand, including why a given patient is predicted to be at higher risk. The primary comparison of machine learning and regression methods will be based on positive predictive values corresponding to the top 5% predicted risk threshold, and estimated via 10-fold cross-validation.
Conclusion/ImplicationsThis project aims to help researchers decide which statistical methods to use for risk prediction models. When considering machine learning methods the best approach may be to try multiple methods, compare their predictive accuracy and interpretability, and then choose a final method.
Patient-Physician Relational Continuity and Health System Utilization Among Albertans
Authors: Patil Tanmay , Shahid Rizwan , Patel Alka , Oddie Scott 
Published: September 2018

IntroductionThe Office of the Auditor General report on Chronic Disease Management in 2014 identified that in Alberta there are no processes to identify individuals with chronic diseases who do not have or cannot find a Primary Health Care (PHC) physician, and provide them with ongoing care.
Objectives and ApproachLinked provincial administrative data (Practitioner Claims, National Ambulatory Care Reporting System, Discharge Abstract Database, Clinical Risk Groupers) to identify Albertans with chronic disease who do not have any contact or have low relational continuity with primary care physicians, and examine their healthcare utilization (Hospitalizations, Ambulatory Care Sensitive Conditions, 30-day Hospital Readmissions, Emergency Department visits, and Family practice Sensitive conditions). Usual Provider Continuity index was used to measure relational continuity. Used GIS software to combine findings from secondary data analysis and produced an interactive Online Mapping Application. Ongoing spatial and regression analysis will examine relationship between relational continuity and healthcare utilization.
ResultsThe majority of individuals in Alberta with no or low primary care visits were men (62.6%), did not have a chronic disease (94.9%), and belonged to the healthy or healthcare non-user status. Albertans were grouped based on patient-physician relational continuity, and it was seen that the likelihood of emergency department visits and 30-day readmissions declined with increase in continuity, however similar results were not seen in case of hospitalizations. To promote utilization of findings an Online Mapping Application was used to present population groups and their characteristics. Univariate and multivariate analysis will be conducted to examine relation between relational continuity and healthcare utilization, especially usage that could be avoided through regular contact with primary care, while controlling for socio-demographic and health related factors.
Conclusion/ImplicationsThe results emphasize that the focus should not be on those with no or low number of primary care visits. Strategies focused on linking patients to PHC services and fostering patient-provider relationship are critical to meet the health needs of Albertans and create efficiencies in health system utilization.
Education Outcomes Associated with Full Day Kindergarten Among First Nations Children: A retrospective administrative database cohort study
Authors: Nickel Nathan , Brownell Marni , Taylor Carole , Sarkar Joykrishna , Chartier Mariette , Burland Elaine , Enns Jennifer 
Published: September 2018

IntroductionThe quality of early education children receive influences their developmental trajectories, with long-term effects extending into adulthood. First Nations children face many structural barriers to academic success. Few studies have examined the impact of education programs on removing these barriers to support better outcomes amongst First Nations children.
Objectives and ApproachWe examined educational outcomes associated with full-day (FDK) versus half-day (HDK) kindergarten among First Nations children using data from the Manitoba Population Research Data Repository. We linked children’s education records with the Manitoba Health Registry and the First Nations Registry to identify all First Nations children who attended kindergarten in a Winnipeg school division (1998-2011). Children enrolled FDK were age- and sex- matched to children enrolled in HDK. Propensity scores used to adjust for confounding. Outcomes included academic achievements in grades 3, 7, and 8 and high school graduation. We used generalized linear models to test for differences in education.
ResultsWe identified 324 First Nations children enrolled in FDK and 595 matches in HDK in the study period. Among these, 37% FDK and 31% HDK students met or approached numeracy expectations in grade 3; and 30% FDK and 33% HDK met or approached numeracy expectations in grade 7. For reading expectations, 37% FDK and 33% HDK met or approached grade 3 reading expectations; in grade 8, roughly half of each group met or approached expectations for reading and writing. High school graduation rate for First Nations children in both FDK and HDK children was 60%. We found no differences in education outcomes when we tested for differences between HDK and FDK children.
Conclusion/ImplicationsEducation outcomes did not differ between First Nations children enrolled in FDK vs. HDK programs. Kindergarten programs may be insufficient to overcome structural barriers to academic success that these children face. Culturally appropriate education strategies may be needed to support improved outcomes amongst this population.
Improving name comparison similarity scores to reduce number of records for clerical review
Authors: Palfy Miro , Vasquez Stacy Ann , Garcia Alexandre Franco 
Published: September 2018

IntroductionMany well established string comparators are currently used in data linkage. Jaro-Winkler distance is SA NT DataLink’s metric of choice for comparing personal names. However, due to Jaro-Winkler’s lower specificity we investigated if output scores could be transformed to produce scores more closely matching those assigned manually.
Objectives and ApproachOur objective was to reduce the need for clerical review by modifying the Jaro-Winkler distance metric output scores. Clerical reviewers assigned similarity scores to pairs of first or last names from a database of approximately 2,000 random cases. By plotting the Jaro-Winkler scores against those assigned by the reviewers, a distinct radical function shape was observed. We then transformed the Jaro-Winkler scores by applying a power function where we gradually changed the exponent until we obtained the best fit with our clerically assigned scores. From the next linkage, two separate outputs were created (original and modified) and the results compared.
ResultsTo assess the best fit we calculated the sum of squared errors for each of tested exponent values ranging from 1.1 to 6.0 (with 0.1 steps). The minimum sum of squared errors was achieved with exponent value of 4.6. We performed a probabilistic linkage for one decade of the Birth Registry records looking for familial links. Two separate linkage runs were conducted and clerically reviewed. In the second run, names were compared using the modified Jaro-Winkler comparator. This resulted in a reduced number of false positives. Though the lower-end threshold of the clerically reviewed “grey area” had to be lowered, the overall range was narrower resulting in less record pairs for clerical review.
Conclusion/ImplicationsBy transforming the Jaro-Winkler scores, we reduced the number of records requiring clerical review. While only three linkage variables were affected, the resultant outcome was encouraging enough to consider exploring other possibilities for replicating clerical review knowledge in other comparators and metrics to reduce the demands for clerical review.
Regular General Practitioner contact - analysis of methods for measurement using administrative data
Authors: Youens David , Moorin Rachael , Preen David B , Harris Mark , Robinson Suzanne 
Published: September 2018

IntroductionThere is a small body of literature examining the relationship between “regularity” of contact with General Practitioners (GPs), i.e. the pattern of visits over time, and health outcomes. Methods previously used to measure regularity may be conflated with the number of GP visits (frequency) which may impact on effect estimates.
Objectives and ApproachTwo published regularity measures, one derived from the variance in the days between GP visits and the second a categorical indicator, were assessed alongside a new measure designed to be uncorrelated with frequency. A cohort at risk of diabetes-related hospitalisation was identified from primary care and hospitalisation data. Associations between regularity and frequency were assessed for each measure using negative binomial regression. Hospitalisation outcomes were regressed on regularity scores using negative binomial models, with and without frequency included, to assess whether associations between regularity and frequency biased estimates. Simulated data tested each measure’s responsiveness to changes in GP visit patterns.
ResultsThe new regularity measure showed a substantially weaker association between regularity and frequency than the two previously published scores. According to the new measure, more regular GP contact was associated with a reduction in the rate of hospitalisation and this association was unchanged by the inclusion of frequency as a covariate. Under the existing measures regular contact was also associated with reduced hospitalisation, but the association differed depending on whether frequency was included in the model, suggesting that associations between regularity and frequency may confound relationships with health outcomes if uncontrolled for. Simulated data suggested that the measures responded differently to changes in visitation pattern with the existing categorical indicator being the least responsive.
Conclusion/ImplicationsDespite a large body of literature on provider continuity, little research has examined regularity of GP contact. This is the first work to compare measures of regularity and represents an important methodological advancement. Researchers should consider regularity of contact as a dimension of continuity of care when designing studies.
Reconciling Parent-Child Relationships across US Administrative Datasets
Authors: O'Hara Amy , Genadek Katie , Medalia Carla , Alexander Trent 
Published: September 2018

IntroductionPopulation data capture children, parents, relatives, and others moving in and out of households. The U.S. has seen falling marriage rates, and increases in multigenerational households and complex families, young children living with grandparents, and adult children living with parents. Robust parent-child linkages are critical to understand these demographic shifts.
Objectives and ApproachWe construct and validate parent-child linkages over a century to observe how U.S. households are changing over time. The three largest person-based datafiles in the U.S. are the decennial censuses, the Social Security Administration transaction file, and individual tax returns from the Internal Revenue Service. These sources operationalize relationships differently, capture data at various frequencies, and gather the data for unique purposes. We use probabilistic matching to observe and reconcile parent-child relationships across these sources. The data include a variety of personal identifiers including name, date of birth, parents’ names, address, and place of birth that support matching and validation.
ResultsWe find that understanding the content, consistency, and coverage of the files before matching is critical for high quality linkages. The representativeness of the parent-child relationship file improves over time, with the weakest coverage for the Greatest Generation and the strongest coverage for Millennials. Coverage varies by source: tax data underrepresent non-white children and have duplicate records for SSNs, while names and dates of birth are missing from Census data. Multiple match rates differ among demographic groups and over time. In the matching process, the blocking variables rely on common variables across the population datasets. Our approach provides robust entity resolution for women, despite married-maiden name changes. We describe challenges due to data problems in old census records and validation changes in social security data.
Conclusion/ImplicationsWe conduct a successful reconciliation of parent-child relationships in U.S. population level files. The project supports operational and research uses, such as the 2020 Census. We will extend this work using graph matching and will expand the method to validate other relationship links including spouses and siblings.
Using linked data to evaluate enhanced primary care policies for chronic diseases using stroke as a case study
Authors: Andrew Nadine , Kim Joosup , Cadilhac Dominique , Sundararajan Vijaya , Thrift Amanda 
Published: September 2018

IntroductionThe global burden of chronic diseases is large and increasing. In response, governments are investing substantial funds in innovative models of primary care, characterised by multidisciplinary care and self-management support for people with chronic conditions. Currently, large scale population-based evaluations of the effectiveness of these policies are lacking.
Objectives and ApproachWe aim to evaluate the effectiveness and cost-effectiveness of enhanced primary care policies for chronic diseases funded through Medicare Australia using stroke as a case study. Person-level linkages from the following will be used: Australian Stroke Clinical Registry (AuSCR) to define the cohort; Australian government-held Medicare claims data to identify receipt or not of enhanced primary care items; state government-held hospital data to define outcomes; and Australian government-held pharmaceutical and aged care claims data to define covariates. In Australia, unique identifiers are not used therefore, personal-identifiers will be submitted to data linkage units and content records merged using a Project-ID.
ResultsIdentifiers from ~25,000 AuSCR registrants (2012-2016), from Victoria and Queensland will be submitted for linkage. The index event is the first event recorded in the AuSCR. Data applications to state health departments and the Australian Institute of Health and Welfare have commenced. To obtain detailed information on patient’s primary care experience 1,500 randomly selected AuSCR registrants are being sent surveys. Multivariable analyses using a competing risks Poisson regression model for multiple events and adjusted by a propensity score, will be used to test for differences in the rates of hospital presentations. We have power (a >0.05) to detect a ≥6% difference in the number of hospital contacts between those who did and did not receive enhanced primary care. An economic evaluation will also be undertaken.
Conclusion/ImplicationsThis is the largest stroke data linkage study in Australia. The breadth of data will provide a comprehensive evaluation of the effectiveness of enhanced primary care policies within “real world” healthcare provision. Methods will advance the use of population data linkage in healthcare evaluation where unique identifiers are unavailable.
Gana Burrai: applying population data linkage to more accurately measure Aboriginal maternal and infant outcomes in the Goulburn Murray Region
Authors: Freemantle Jane , Ferguson Karyn , Boyle Douglas 
Published: September 2018

IntroductionThe Australian 2016 census reported Aboriginal population of Goulburn Murray Region (GMR) as being 3% of the total GMR population is considered a significant under ascertainment. This is supported by the Freemantle et al population data linkage project that reported an underestimation of 51% of Aboriginal births in Hume region.
Objectives and ApproachThe main objective of the Gana Burrai project was to undertake a Proof of Concept (POC) project to address the critical issue the under-ascertainment of data describing Aboriginal maternal and child health outcomes within the GMR. The PoC explored the feasibility of linking a number of population administrative datasets that included information describing Aboriginal babies born and mothers and fathers living in, the GMR. The PoC also enabled an evaluation of the cultural, organisational, practical and technical issues critical to the development of this comprehensive population data set. The linkage was undertaken using GRHANITETM privacy protecting software.
ResultsPopulation data from seven administrative datasets describing birth years 2005-2015, representing 111862 records were successfully extracted. These data represented 37,338 patients. Following the linkage process, 28,016 patient records were successfully linked. The ever-Aboriginal concept was applied to the linked data. After linkage, there was a significant under ascertainment of Aboriginal births in records maintained by the Shire of Campaspe (16%), less so by Shire of Greater Shepparton (50%); Goulburn Valley Heath (92%), and Echuca Regional Health (86%) demonstrated a more complete ascertainment. However, the linkage showed a strong correlation of patient Indigenous identity collected by the shires’/hospitals’ and the mothers’/fathers’ chance of having a record at the Aboriginal Community Controlled Health Organisations. Selected birth outcomes were also analysed and will be reported.
Conclusion/Implications1st Nations Governance combined with a privacy-protecting linkage model underpinned PoC ensuring strong community trust, engagement and control. It is 1st time such extensive data-linkage has been achieved demonstrating feasibility in developing a comprehensive dataset with the potential for evidence-based, targeted population health initiatives to be developed, implemented and evaluated.