bluuebunny/crossref_metadata_2025 huggingface dataset:
Population-based analysis of the effect of a comprehensive, systematic change in an emergency medical service resource allocation plan on 24 hour mortality
Authors: Tallon John , Wei Julie , Zheng Lu , Djurdjev Ognjenka , Papadopoulos George , Slemko Randy , Dick William
Published: September 2018

IntroductionThe British Columbia Emergency Health Services developed a new resource allocation plan (RAP) using an evidenced informed methodology and with further clinical input from EMS physicians, paramedics and allied EMS providers. Population-based analysis was used to determine the effect by comparing 24-hour mortality before and after province-wide implementation of the revised RAP.

Objectives and ApproachThe primary outcome, 24-hour mortality, was obtained through linked provincial health administrative data. All adult cases with evaluable outcome data were included in the analysis. A pre and post methodology was used to evaluate the effect of post-RAP revision (post-RAP-revision) on 24-hour mortality compared to pre-RAP revision (pre-RAP-revision). Multivariable logistic regression was used to adjust for variations in other significant factors associated with 24-hour mortality. The interrupted time series (ITS) estimated any immediate changes in the level or trend of outcome after the start of the revised RAP implementation (fall of 2013), while simultaneously controlling for pre-existing trends.

ResultsThe linked data set included some 1500–2000 children per school census classified as having a CP, representing a prevalence of some 0.3%. Provisionally, results show: prevalence of CP is higher amongst children living in relatively deprived areas; around 60% of CP children have a statement of SEN; the SEN type most commonly recorded for CP children with SEN is ‘Physical and medical difficulties’ and relatively high proportions have profound, multiple or severe learning difficulties; around 30% of CP children are educated in special schools; CP children in main stream (primary, middle and secondary) schools tended to miss more school sessions (~50% more) than other children and lower percentages achieved the expected levels at key stages 2 and 3 and the Level 2 GCSE threshold.

Conclusion/ImplicationsOur results demonstrate that a comprehensive, evidence informed reconstruction of a provincial EMS RAP is feasible. Despite considerable change in crew level response and resource allocation, there was significant decrease in 24 hour mortality in a large pan-provincial population based patient cohort.
Using linked administrative, clinical and primary data to explore the impact of and factors associated with non-adherence to in-hospital medication changes in 30-days post hospital discharge
Authors: Weir Daniala , Motulsky Aude , Lee Todd , Abrahamowicz Michal , Morgan Steve , Buckeridge David , Tamblyn Robyn
Published: September 2018

IntroductionIdentifying strategies to prevent hospital readmissions remains elusive since the reasons for returning to hospital can include a number of interlinked patient, health provider and system level factors. The impact of patient medications are of significant interest since a large proportion of re-admissions are related to adverse drug events.

Objectives and ApproachThe objective was to determine which factors are associated with non-adherence to in-hospital medications and the impact of non-adherence on re-hospitalization, emergency department visits and death in the 30-days post discharge for patients admitted at two tertiary care academic hospitals in Montreal, Quebec between October 2014 and May 2016. Non-adherence to in-hospital changes was measured by comparing patient discharge prescriptions (patient chart) to medications filled in community 30-days post-discharge (dispensing data) and included i) community medications stopped in-hospital and filled post-discharge, ii) community medications modified in-hospital but not filled at the modified daily-dose, and iii) new medications not filled post-discharge.

ResultsAmong 2,895 included patients, mean age was 70 (SD 15) and 58% were males. A median of 4 in-hospital medication changes were made (IQR:3-6) and 54% of patients were non-adherent to at least one change. Multivariable Poisson models suggested that the most important factor associated with the number of new medications not filled post discharge was out of pocket cost; for each additional $10 increase in costs there was a 20% increase in the number of new medications not filled. Multivariable time-varying Cox models suggested that in patients who filled medications post-discharge, selective non-adherence to new and discontinued medications reduced the risk adverse health outcomes in 30-days, while not filling any medications post discharge more than doubled the risk of an adverse event in 30-days.

Conclusion/ImplicationsNot only did the majority of patients not follow all medication changes that were made during hospitalization, the extent to which this occurred significantly impacted the risk of hospital re-admissions and ED visits. Policy and patient level interventions should be developed specifically targeting barriers for adherence to medication changes.
Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living With Frailty?
Authors: Wong Sabrina , Katz Alan , Williamson Tyler , Peterson Sandra , Taylor Carole , McGrail Kim
Published: September 2018

IntroductionFrailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources.

Objectives and ApproachWe construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail.

ResultsPeople were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were > 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail.

Conclusion/ImplicationsIdentifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.
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.
The effect of neighbourhood crime and deprivation on pregnancy outcomes. A quasi-experimental study using routine administrative records
Authors: Clemens Tom , Dibben Chris
Published: September 2018

IntroductionWe know that rates of adverse pregnancy outcome such as low birthweight and preterm births vary both by socio-economic status and across geographical areas. Furthermore, more deprived neighbourhoods show higher rates of adverse outcomes. However, much of the evidence comes from cross-sectional observational studies where causality is difficult to assess.

Objectives and ApproachNeighbourhood crime and a resultant increase in maternal stress is a plausible explanation for the deprivation-birth outcome relationship. Without an experiment, isolating this pathway from other confounding effects is challenging. Because many mothers (within the Scottish maternity dataset) experience multiple pregnancies and varying exposure for the same person, we can assume a quasi-experimental allocation to exposure. We model associations as both within mother (comparing exposures between pregnancies to the same mother) and between mother (comparing different people between more and less deprived areas) partitioning the variation and effects accordingly.

ResultsWe find that both composite neighbourhood deprivation and neighbourhood crime specifically is associated with reduced birthweight. However, the composite neighbourhood deprivation measure is only significant when modelled as a between mother effect. Neighbourhood crime on the other hand is significantly associated with both between mother and within mother effects (though the latter is reduced compared to the former). These results suggest that much of the observed relationship with deprivation in general is therefore confounded by factors associated with the composition of individuals in these areas rather than an area deprivation effect per se. In contrast, the association with neighbourhood crime can be considered in isolation from the composition of individuals and is therefore likely to be an independent “area” effect.

Conclusion/ImplicationsThese results are important because outcomes of pregnancy including birthweight and premature births influence health and social outcomes not just in infancy but across the life course. This study suggests policies targeting neighbourhood crime may help to reduce neighbourhood disparities in pregnancy outcomes and therefore health inequalities in general.
Environmental Risk Factors on Suicide of Children and Young People
Authors: John Ann , Del Pozo Banos Marcos , Lloyd Keith
Published: September 2018

IntroductionThe World Health Organization recognizes suicide as a public health priority. Recent studies have focused on health care contacts for those who complete suicide to highlight opportunities for intervention. In Wales, electronic health records (EHR) are routinely collected, providing an invaluable opportunity for researching suicide risk factors.

Objectives and ApproachWe aim at linking primary and secondary EHRs to identify suicide risk factors for those between 10 and 24 years of age. We linked 7 different demographic and health datasets from the Secure Anonymised Information Linkage Databank UK, and identified a total of 471 cases between 2001 and 2015, 10 matched controls of same gender and age (±1 year), as well as cases’ and controls’ mothers. We measured a number of factors from primary and secondary care including self-harm, mental health issues and drugs and alcohol misuse. We used conditional logistic regression to conduct our analyses.

ResultsPreliminary results suggest that environmental factors extracted from cohabitants and mothers have a statistically significant effect even after adjusting for deprivation. Self-harm, possible maltreatment and alcohol and drugs misuse seem to be strongest factors of those studied. Factors related to mental health have smaller and more complex effects when adjusting for deprivation.

Conclusion/ImplicationsOur preliminary analysis indicate that EHR can be linked to study the effect of the environment on suicide risk. Once completed, we hope this study will help to identify other suicide risk factors, improve our knowledge of the mechanisms underlying suicide and help to identify opportunities for intervention and improve care.
Establishment of a National Homelessness Prevention Programme dataset to enable an anonymised longitudinal dynamic cohort based study
Authors: Lowe Sarah , Johnson Rhodri , Jones Ian , Morrison-Rees Sian
Published: September 2018

IntroductionWelsh Government invests over £120m annually in housing related support to help prevent and tackle homelessness under the ‘Supporting People Programme’.

A 2016 data-linkage Feasibility Study indicated health-service utilisation reductions post-intervention, and led to a four year project to create a national, all-Wales dataset to provide robust statistical results.

Objectives and ApproachEstablish data sharing agreements, acquire and import anonymised individual-level data into the SAIL Databank. Create a research ready dataset, designed to permit annual administrative data updates to form dynamic cohort and control groups.

Create several control group methods: 1) Internal Programme Data; 2) Matched controls; 3) Healthcare-Utilisation Patterns; 4) External Data Sources.

Link to routine health data, obtain and link to other public service data to gain a deeper understanding of the Programme; how it affects use of other public services, and whether it helps people live independently.

Complete statistical analysis using a Generalised Linear Mixed Modelling approach.

ResultsData sharing agreements, data acquisition and standardisation complete for nineteen of twenty-two Unitary Authorities in Wales. Temporal coverage varies by Unitary Authority (2003-2017). 2016 data measures: match rates >85%; 57% female; lead reason for support (top 5) : ‘General’ 20%, ‘Mental Health’ 15%, ‘Older People’ 14%, ‘Domestic Abuse’ 9%, ‘Young People’ 7%.

Various control group methods employed: 1) Internal ‘Programme’ Data – no support taken up; 2) Matched controls; 3) Healthcare-Utilisation Patterns – rejected due to sparse outcome data; 4) External Data Sources being further explored.

Health data-linkage (emergency admissions, emergency department attendance and primary care events) complete. Ongoing discussions to obtain sample social care, and police call data during 2018.

Statistical analysis underway with results planned to be published during the summer of 2018.

Conclusion/ImplicationsDespite many challenges, creation of a national linked dataset for people at risk of homelessness is possible with collaborative working between central government, academic and local government bodies. This ‘Administrative Data Research Centre Wales’ project has created a rich research resource enabling statistical analysis to answer research questions around homelessness.
Self-harm following release from incarceration: Patterns and measurement issues
Authors: Borschmann Rohan , Young Jesse , Kinner Stuart , Spittal Matthew
Published: September 2018

IntroductionDespite an elevated prevalence of self-harm in the incarcerated adult population, little is known about patterns of self-harm following release from prison.

Objectives and ApproachBaseline self-report interviews with 1315 adults immediately prior to release from prison in Queensland, Australia, combined with interrogation of linked health data from >3750 post-release emergency department presentations, >2000 ambulance attendances, and corrections data during periods of re-incarceration.

ResultsApproximately 5% of all contacts with medical emergency services following release from prison resulted from self-harm. These were associated with being Indigenous, having a lifetime history of a mental disorder and having been identified by prison staff as being at risk of self-harm. Agreement between self-reported self-harm and medically-verified episodes of self-harm was poor.

Conclusion/ImplicationsEmergency services contacts resulting from self-harm following release from prison are common and represent an opportunity for tertiary intervention for self-harm. Our findings suggest that a self-reported history of self-harm should not be considered a reliable indicator of prior self-harm, or of future self-harm risk, in incarcerated adults.
Quality of linked data: Linking the National Hospital Care Survey Data to the National Death Index
Authors: Mirel Lisa , Resnick Dean , Campbell Scott , Golden Cordell
Published: September 2018

IntroductionData linkages can produce rich data resources to address a variety of research topics. However, assessing linkage quality can be challenging given that there are many approaches and no clear best practices.

Objectives and ApproachThrough its Data Linkage Program, the National Center for Health Statistics (NCHS) links national survey data with vital and administrative records. A recent linkage of the National Hospital Care Survey data with the National Death Index employed a new linkage methodology, which included a first time approach for validating the results within the linkage algorithm.

ResultsThe new methodology includes two passes: a deterministic linkage, followed by a probabilistic approach based on the Fellegi-Sunter methodology. In the second pass, a key identifier, Social Security Number (SSN), was not used as a linkage variable but instead to determine link accuracy, when available on the patient record. A model was then built to predict link accuracy status according to the computed Fellegi-Sunter total pair weight and then used to estimate it for those patient records without an SSN. Results from this new approach were compared with results from prior linkage methodologies and generated higher match rates and lower error rates. The linkage methodology designed for this study is now being tested on other types of input data such as data from household surveys.

Conclusion/ImplicationsThe linkage approach may be incorporated into additional linkages conducted by NCHS. This talk will describe the input sources for this linkage, the methodology used, the error rate assessment and then discuss conclusions and implications for precision and efficiency.
Enhancing description of hospital-conditions with ICD-11 cluster coding: Better codes for monitoring and prevention
Authors: Southern Danielle , Eastwood Catherine , Quan Hude , Ghali William
Published: September 2018

IntroductionExposure to health care events sometimes has unintended and undesired consequences. Health care and complications arising in the course of care are diverse and complex. Representing them comprehensively in information systems is challenging, and presently beyond the bounds of practicality for routine administrative information systems that include ICD coded data.

Objectives and ApproachThe ICD-11 conceptual model for hospital-acquired conditions has 3 components: 1) harm to patient 2) cause or source of harm and 3) mode or mechanism. A key feature of the Quality and Safety (Q\&S) code-set in ICD-11 is that a cluster of codes is required to represent an event or injury. Use of the term ‘cluster’ is novel in ICD-11 and so is the extent and the requirement for post-coordination. The cluster required to code a Q\&S case has three codes, one for each of the three components of the model given above.

ResultsThe first component, ‘harm’, is represented by an ICD–11 diagnosis code, from any chapter of the classification. Q\&S causes or sources of harm fall into 4 types that capture events caused by substances (drugs and medicaments, etc.), procedures, devices, and a mix of other types of causes (e.g. problems associated with transfusions, incorrect diagnosis, etc.). Q\&S ‘mode or mechanism’ refers to the main way in which the ‘cause’ leads to the ‘harm’ and are specific to the type of ‘cause’ (Table 1).

Table 1 - Examples of corresponding Q\&S Mode or Mechanism

Cause or Source of Harm

Mode or Mechanism

Substance

Overdose, under-dose, wrong substance.

Procedure

Accidental perforation of an organ during a procedure.

Device

Dislodgement. Malfunction.

Other cause

Mismatched blood. Patient dropped during transfer from OR table.

Conclusion/ImplicationsThis new conceptual model for coding healthcare-related harm, dependent on the clustering of codes, has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data, for better monitoring and strategies for prevention.
Linking household level GIS-generated environmental exposure scores with individual level anonymised health data
Authors: Mizen Amy , Rodgers Sarah , Fry Richard , Lyons Ronan
Published: September 2018

IntroductionThe dose-response relationship between exposure to food and BMI, has not been widely investigated. Furthermore, household-level, GIS-generated food environment exposure scores have not previously been linked with individual-level, anonymised BMI data. This study linked GIS-generated residential level environmental exposure scores with historical anonymised, health data held in the SAIL databank.

Objectives and ApproachHousehold level GIS-generated exposure data for a region of about 1 million people were anonymised into SAIL using the ‘split-file’ method. All individuals living in the 633,884 homes at the time of data collection (2009-2010) were flagged using a population register. Separately, a cohort of 1147, 11-13 year old pupils were linked to their health data before joining to their environmental exposures. Two subgroups were established within the linked dataset: individuals living at 4.8km or less from the school they attended were assumed to walk to school (“walkers”) and pupils who lived further than 4.8km were flagged as “non-walkers”.

ResultsA total of 916 pupils (80%) were successfully linked to the population register. The BMIs were collected in 2009-2010, but more recent data is likely to have a greater proportion of successful links (more recently, 97% of individuals and their health data have been linked to their home and exposures in SAIL). Erroneous BMIs were removed (n=33, 2.9%). Anonymised exposure data were linked with the remaining 883 (77%) individuals. The dataset contained 352 males (39.9%) and 531 females (60.1%); of these, 38% were from deprived areas and 62% lived in affluent areas. There were 431 (48.8%) pupils in the “walkers” group and 452 (51.2%) in the “non-walkers” group. In the “walkers” group, 13% were obese compared with 22% of “non-walkers” (chi-squared = 12.3, p <0.05).

Conclusion/ImplicationsWe generated novel regional exposures to combine with historical anonymised health data. Household and individual level linkage of environmental data to health cohorts contributed to the literature to help develop beneficial societal policies. We recommend routine national collections of height and weight for children to allow longitudinal retrospective analyses.
The Impact of Linked Administrative Data on Community Development of Integrated Mental Health Hubs: a Case Study
Authors: Parker Naomi , Salt Valerie , Scott Allison N , Scott Cathie , Lau Jason
Published: September 2018

IntroductionIncreasingly, communities are designing and implementing contextualized approaches to integrated service delivery. Having communities in the lead is critical to successful design and implementation. However, how do researchers work with communities to ensure accurate contextual interpretations of linked administrative data?

Objectives and ApproachPolicyWise for Children & Families has been working with communities to create a framework and assist in the implementation of integrated Community-based Mental Health Service Hubs for youth ages 11-24. The approach used to support these communities included multiple sources of data to highlight the need for youth specific mental health care, integrated service delivery and the opportunity for system transformation. This included, but wasn’t limited to, a qualitative environmental scan and individual-level linked administrative data from PolicyWise’s Child and Youth Data Lab.

ResultsThe goal of presenting the qualitative and quantitative data summaries was to inform community-led design and implementation of the Mental Health Service Hubs. This presentation describes the constructive interplay of community context, qualitative literature and linked administrative data. Community context and the qualitative environmental scan assisted community members in interpreting the administrative data.

Conclusion/ImplicationsThis applied project provides an example of how community members contextualize information from linked administrative data and environmental scan findings to influence program development. The implications of this is that data can reinforce or reshape what communities know about service use and impact how they proceed with planning.
Measuring equity in per capita primary care investment in Ontario: Challenges for data linkage and analysis
Authors: Schultz Sue , Glazier Rick , Green Michael , Kiran Tara , Bayoumi Imaan , Tranmer Joan , Frymire Eliot
Published: September 2018

IntroductionFifteen years ago almost all primary care physicians (PCPs) were paid fee-for-service. Now, many physicians receive other payments as well, including capitation payments, incentives and bonuses and funding for other health professionals. It is challenging to track these changes in primary care payment and understand how they relate to individual patients.

Objectives and ApproachThe objectives of this study were to assess changes in PCP payments from 2002/03 to 2011/12 and examine differences in per capita investment by urban-rural status, recent arrival (proxy for immigrant status) and income quintile. This required a three-step approach: assigning payments to physicians, assigning patients to physicians and then apportioning the payments by patient. Payments were apportioned based on the type of payment and how the data were captured. For example, capitation payments were paid monthly, but without any detail as to which patients they were for, so all capitation payments were summed and apportioned among all rostered patients.

ResultsAll PCPs for whom we had payment data and to whom patients could be assigned were included. Three types of physician-patient 'relationships' were identified: the patient was on the physician's formal roster; the patient was 'virtually' rostered to the physician who provided the plurality of their care; or the patient was part of the physician's overall panel, which includes all patients seen during the year, rostered and not. The type of relationship determined which payment were allocated to each patient. When the $3.5B in payments were apportioned and different populations compared, we found inequities in new primary care investment by income, immigrant status and rurality. For example, we found a disproportionate investment in interdisciplinary teams for non-immigrant Ontarians living in more well-off suburban areas.

Conclusion/ImplicationsEstimating per capita primary care investment is a challenging but worthwhile undertaking. The results of this study suggest that the Government of Ontario should facilitate increased participation in new primary care models by immigrants and people living in major urban centres.