bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frailty and Initiation of Cardio-Protective Medicines Following Incident Acute Coronary Syndrome in Older People: A Linked Data Study
Authors: Lopez Derrick , Sanfilippo Frank , Nedkoff Lee , Briffa Tom , Preen David 
Published: December 2020

IntroductionBeta-blockers, renin-angiotensin system inhibitors and statins are evidence-based pharmacotherapies for preventing recurrent acute coronary syndrome (ACS), however their use in older people may depend on clinical factors such as comorbidity and frailty. Deriving comorbidity information from linked administrative data is well established. The recent development of the Hospital Frailty Risk Score (HFRS) now allows ascertainment of frailty from linked administrative data.
Objectives and ApproachWe determined the ability of the HFRS to discriminate between groups of older patients initiated on these three cardio-protective medicines following hospitalisation for an incident ACS. We used a 15-year look back to identify incident ACS cases between 2005 and 2008 from Western Australian hospital data and linked them to national pharmaceutical dispensing records. The study was limited to patients aged ≥65 years who had not received these medicines in the last two years and were discharged alive from their index ACS admission. Separate competing risk regression models assessed the association between HFRS and initiation of each medicine, controlling for comorbidities and other variables available from the linked datasets.
ResultsOverall, increasing levels of HFRS were associated with lower subdistribution hazards of initiation of each medicine examined, however these varied by age group and sex. For example, the subdistribution hazard ratios (SHR) for beta-blocker initiation among men in the oldest age group (≥85 years) were 0.65 (95% CI: 0.44-0.95) and 0.51 (95% CI: 0.29-0.90) in the intermediate and high HFRS groups respectively compared to the low frailty group. The corresponding SHR for women were 1.01 (95% CI: 0.76-1.35) and 0.89 (95% CI: 0.63-1.27), respectively.
Conclusion / ImplicationsThe HFRS applied to linked administrative data discriminates between groups of older people who received cardio-protective medicines following an incident ACS. There is potential for it to be used in other chronic disease conditions.
Linking Health and Social Data to Assess the Performance of High Dimensional Propensity Scores
Authors: Hamm Naomi , Singal Deepa , Dahl Matthew , Chateau Dan , Brownell Marni 
Published: December 2020

IntroductionHigh dimensional propensity scores (HDPS) aim to account for unmeasured confounding. However, it is unclear to what extent HDPS are able to attain this.
Objectives and ApproachThis study aimed to test how well HDPS can account for confounding due to social determinants of health when using only health data. A retrospective cohort study was used to examine the effect of exposure to prescription opioids in utero on childhood outcomes (ADHD, school readiness, NICU admission, and hospitalization within the first year of life). Administrative health and social data were linked at the individual level and HDPS for each outcome were calculated using the mothers’ health data. Exposed and unexposed mother-child dyads were then matched. Standardized differences of mothers’ social factors (history of teen birth, lowest income quintile, ever received income assistance (i.e., welfare), ever lived in social housing, history with child protection services, residential mobility, and contact with the justice system) were compared before and after matching to determine to what degree the HDPS could account for differences in social determinants of health. Additional HDPS analyses were performed with social factors included in the HDPS with the health data.
ResultsBefore matching, standardized differences between exposed and unexposed groups for the social factors ranged between 0.40-0.75. Income assistance and lowest income quintile consistently had the greatest and smallest standardized difference for all outcomes, respectively. After matching, using health data only, standardized differences decreased considerably, ranging from 0.05-0.27. When including social factors into the HDPS, the addition of income assistance produced the smallest standardized differences with a range of 0.01-0.13 for all outcomes.
ConclusionsUsing the HDPS with health data only can reduce confounding due to social factors. If data are available, including income assistance in the HDPS may further reduce confounding for all social determinants of health.
Encoding Diagnostic Codes for Privacy-Preserving Record Linkage
Authors: Schnell Rainer , Borgs Christian 
Published: December 2020

IntroductionDiagnostic codes, such as the ICD-10, may be considered as sensitive information. If such codes have to be encoded using current methods for data linkage, all hierarchical information given by the code positions will be lost. We present a technique (HPBFs) for preserving the hierarchical information of the codes while protecting privacy. The new method modifies a widely used Privacy-preserving Record Linkage (PPRL) technique based on Bloom filters for the use with hierarchical codes.
Objectives and ApproachAssessing the similarities of hierarchical codes requires considering the code positions of two codes in a given diagnostic hierarchy. The hierarchical similarities of the original diagnostic code pairs should correspond closely to the similarity of the encoded pairs of the same code.
Furthermore, to assess the hierarchy-preserving properties of an encoding, the impact on similarity measures from differing code positions at all levels of the code hierarchy can be evaluated. A full match of codes should yield a higher similarity than partial matches.
Finally, the new method is tested against ad-hoc solutions as an addition to a standard PPRL setup. This is done using real-world mortality data with a known link status of two databases.
ResultsIn all applications for encoded ICD codes where either categorical discrimination, relational similarity or linkage quality in a PPRL setting is required, HPBFs outperform other known methods. Lower mean differences and smaller confidence intervals between clear-text codes and encrypted code pairs were observed, indicating better preservation of hierarchical similarities. Finally, using these techniques allows for much better hierarchical discrimination for partial matches.
ConclusionThe new technique yields better linkage results than all other known methods to encrypt hierarchical codes. In all tests, comparing categorical discrimination, relational similarity and PPRL linkage quality, HPBFs outperformed methods currently used.
Allocating Unique Property Reference Numbers to Patient Addresses Using A Deterministic Address-Matching Algorithm: Evaluation of Accuracy, Match Rate and Bias
Authors: Harper Gill , Boomla Kambiz , Robson John , Stables David , Ahmed Zaheer , Fry Richard , Dezateux Carol 
Published: December 2020

IntroductionRepresenting patient-registered addresses as pseudonymised Unique Property Reference Numbers (UPRNs) enables linkage of environmental and household information to electronic health records (EHRs). However, the accuracy and potential biases in address-matching algorithm results applied to patient addresses is unknown.
Objectives and ApproachTo investigate accuracy, match rate, and biases in assigning UPRNs to general practitioner (GP)-registered patient addresses for a geographically-defined UK population, using a bespoke deterministic address-matching algorithm comprising 213 rules applied in rank order of minimising false-positives, developed for the Discovery Data Service.
We ran this algorithm to match 906,220 adult patient GP-registered addresses (48% female, 47% non-White, 89% 20-64) sampled in mid-2018 from 159 GP practices in four London boroughs to Ordnance Survey’s AddressBase Premium database.
We evaluated the error rates using a gold-standard dataset. We used binary logistic regression to estimate the likelihood (Odds Ratio [OR]; 95% Confidence Intervals [CI]) of no UPRN match according to and adjusting for patient age, sex, ethnic background, deprivation, residential mobility and multiple GP registrations.
Results96% of patient addresses were successfully assigned a UPRN. Algorithm sensitivity, specificity, positive and negative predictive-values and F-measure were, respectively: 0.993, 0.019, 0.914, 0.204, and 0.9516.
After mutual adjustment, UPRN assignment was less likely for: men (OR: 0.87; 95%CI: 0.83,0.91); adolescents and the elderly (15-19 years: 0.57;0.43,0.77; ≥90 years: 0.39;0.18,0.84); those from Chinese ethnic backgrounds (0.87;0.8,0.91), living in the least deprived areas (0.25;0.21,0.31), or with two or more distinct UPRNs across multiple registrations (0.37;0.28,0.49); and more likely for: those from Bangladeshi ethnic backgrounds (1.79;1.61,2.00), registered before 2018 (5.10;4.42,5.87), or with multiple GP registrations (2.36;1.82,3.05).
Conclusion / ImplicationsThe Discovery open-source algorithm achieves a high accurate match rate and quantifies the demographic groups that may be under-represented among those successfully matched. This is the first time that bias in matching rates for an address-matching algorithm has been evaluated using patient-registered addresses.
Use of Machine Learning and Linked Population Health Data to Develop Predictive Risk Algorithms for Population Health Decision-Making
Authors: Fisher Stacey , Pagalan Lief , Hurst Mack , O’Neill Meghan , Diemert Lori , Rosella Laura 
Published: December 2020

IntroductionData from population health surveys, administrative health records and environmental monitoring are increasingly being linked at the individual level. As these data become available to health researchers, there is an increasing need for methods which can make sense of large, noisy and heterogeneous data and can model complex relationships. Using these data, machine learning methods have the potential to produce population health risk algorithms with better performance than those developed with traditional statistical approaches.
Objectives and ApproachThe objective of this work is to explore the use of machine learning methods for the development, validation and implementation of predictive risk algorithms designed specifically for population health planning purposes. Algorithms to predict risk of dementia and avoidable hospitalizations are in development using the Canadian Community Health Survey, geographic sociodemographic information, administrative health care utilization data and vital statistics. Methods being explored include naïve Bayes, gradient boosting, support vector machines and neural networks.
ResultsRisk algorithms for population health should generally prioritize calibration over discrimination due to implications for resource allocation decisions. Approaches to minimize the risk of overfitting should be used and reweighting of unbalanced data avoided as it distorts the population-level nature of the data. It is important to be aware of propagating underlying bias in the data or exacerbating existing health inequities, which can be evaluated in part through assessment of calibration across relevant population subgroups. Approaches that consider multi-level data structures are needed to appropriately incorporate neighbourhood-level measures with individual-level information. To maximize population health impact and acceptability, model transparency and interpretability should be prioritized.
ConclusionThere is tremendous potential for machine learning approaches to leverage large volumes of linked population data to produce predictive risk algorithms that will inform population health decision-making. Future work will explore use of complex environmental remote sensing and built environment data.
The Association Between Mode of Delivery and Later Educational Outcomes
Authors: Li Eileen , Slykerman Rebecca , Milne Barry 
Published: December 2020

IntroductionCaesarean section (C-section) is becoming increasingly prevalent worldwide. It can be a life-saving intervention when medical complications arise, but may cause adverse consequences for the mothers when it is not medically necessary. The upwards trend of C-section is becoming a general concern as it might be associated with a wide range of child outcomes such as immune diseases, respiratory diseases and developmental problems. One underlying mechanism of such association is through gut microbiota. Mode of delivery is a signification factor which determines the gut bacterial environment in early days of life. Gut microbiota can impact cognitive development via microbiota gut brain axis.
Objectives and ApproachThis study investigated the association between mode of delivery and later educational outcomes of the children, using linked data from New Zealand Integrated Data Infrastructure. The participants consisted of children born in New Zealand between 1Jan1996 and 31Dec1998. All birth information were retrieved from Department of Internal Affairs and were linked to Ministry of Health data to obtain delivery methods from mothers’ diagnosis records. Once delivery modes were identified, the data was then linked to records from Ministry of Education to obtain children’s educational outcomes at secondary school. Three outcome variables were chosen: University Entrance, Highest Endorsement Level and National Certificate of Educational Achievement Level 2 Percentile Score.
ResultsThe results have shown that C-section and assisted vaginal delivery were associated with better secondary school educational achievement, comparing to unassisted vaginal delivery. The results persisted after adjusting for sociodemographic factors such as household income, maternal education and deprivation index; infant factors such as birth weight, gestational age and ethnicity. However, when within family variation was further controlled for in the sibling fixed effects analysis, C-section was no longer associated with improved educational achievement.
Conclusion / ImplicationsThis indicated that the impact of delivery mode might be negligible on later educational achievement.
Linking Sensitive Data – Applications, Techniques, and Challenges
Authors: Christen Peter , Ranbaduge Thilina , Schnell Rainer 
Published: December 2020

IntroductionThe linking of sensitive databases containing personal identifying information across organisations is an increasingly important task in application domains ranging from health and social science research to national censuses. Various techniques have been developed to facilitate the linking of sensitive databases while at the same time preserving the privacy of individuals represented in these databases.
Objectives and approachWe present several case studies where the privacy-preserving linking of sensitive databases is crucial, and then discuss the advantages and limitations of existing algorithms and techniques to link sensitive databases. We discuss privacy techniques such as Bloom filter encoding, hashing, and secure multi-party computation, from the point of view of a linkage practitioner. We highlight those aspects that are important when selecting or implementing a privacy-preserving linkage technique within practical applications.
ResultsConceptually, linkage techniques can be evaluated across three main dimensions linkage quality, scalability to linking large or multiple databases, and the privacy protection provided by a technique. From a practical perspective, however, several other dimensions are crucial, including the availability of software or ease of implementation, technical knowledge available in an organisation, and the suitability of techniques for a given linkage scenario. Our analysis of a diverse range of linkage techniques has shown that currently no technique provides an adequate solution along all conceptual as well as all practical dimensions.
ConclusionsMore research is required to develop novel techniques that facilitate the privacy-preserving linkage of large sensitive databases across organisations; including new encoding methods and cryptanalysis attacks (where until now most attacks have neglected the attack vectors that likely occur in practice), and novel evaluation measures to assess the privacy provided by linkage techniques. We encourage practitioners to be aware of the identified limitations – as well as the opportunities – of existing privacy-preserving linkage techniques and carefully assess the technical and organisational requirements of such techniques within their institution.
Trajectories of Hospital Readmission After A Fall Injury for Older Adults Living in Aged Care or The Community
Authors: Mitchell Rebecca , Draper Brian , Close Jacqueline , Harvey Lara , Brodaty Henry , Do Vu , Driscoll Timothy , Braithwaite Jeffrey 
Published: December 2020

IntroductionFall injuries are one of the leading causes of hospitalisation for adults aged ≥65 years. Distinguishing key characteristics of older adults who are either living in aged care or in the community who have multiple hospital readmissions after a fall injury may inform targeted approaches to the prevention of hospital readmissions.
Objectives and ApproachTo examine trajectories of hospital readmission of older adults living in aged care or the community after a fall injury hospitalisation and to identify factors predictive of trajectory group membership. A group-based trajectory analysis of hospital readmissions of adults aged ≥65 years who had a fall injury hospitalisation during 2008-09 in New South Wales, Australia was conducted. Linked hospitalisation and aged care data were examined for a 5 year period to 2013. Group-based trajectory models were derived based on number of subsequent readmissions following the index admission. Multinominal logistic regression examined predictors of trajectory group membership.
ResultsThere were 24,729 fall injury hospitalisations; 78.8% of fallers were living in the community and 21.2% in aged care. Five distinct trajectory groups were identified for community-living (i.e. Moderate-declining, Chronic, Low-constant, Low-declining, and High users) and four trajectory groups for aged care residents (i.e. Low, Moderate-declining, Moderate-chronic, and High users). Key predictors of trajectory group membership for both community-living and aged care residents were age group, number of comorbidities, and dementia status. For aged care residents, depression, assistance with activities of daily living, and number of subsequent fall injury admissions were also predictors of group membership, with time to move to an aged care facility a predictor of group membership for community-living.
Conclusion / ImplicationsIdentifying trajectories of ongoing hospital use informs targeting of strategies to reduce hospital admissions and design of services to allow community-living individuals to remain as long as possible within their own residence.
Using Linked Data to Examine the Epidemiology of All-Cause and Cause-Specific Mortality Following Release from Incarceration in Eleven Countries
Authors: Borschmann Rohan , Keen Claire , Young Jesse T , Love Alexander D , Spittal Matthew , Preen David , Larney Sarah , Pirkis Jane , Rosen David , Kinner Stuart A 
Published: December 2020

IntroductionMore than 30 million adults are released from incarceration globally each year. Many experience complex physical and mental health problems, and are at markedly increased risk of preventable mortality. Despite this, evidence regarding the global epidemiology of mortality following release from incarceration is insufficient to inform the development of targeted, evidence-based responses. Many previous studies have suffered from inadequate power and poor precision, and even large studies have limited capacity to disaggregate data by specific causes of death, sub-populations or time since release to answer questions of clinical and public health relevance.
Objectives and ApproachWe aimed to comprehensively document the incidence, timing, causes and risk factors for mortality in adults released from incarceration. We created the Mortality After Release from Incarceration Consortium (MARIC), a multi-disciplinary collaboration representing 29 cohorts of adults who have experienced incarceration from 11 countries. Findings across cohorts will be analysed using a two-step, individual participant data meta-analysis methodology.
ResultsUsing linked data from the 29 individual cohorts, the combined sample includes 1,337,993 individuals (89% male), with 75,795 deaths recorded over 9,191,393 person-years of follow-up. Preliminary analyses indicate a marked elevation in mortality risk following release from incarceration, with this risk beginning on the day of release. At the time of writing, more detailed analyses are underway regarding all-cause and cause-specific deaths – along with risk and protective factors – and findings will be presented at the IPDLN conference in October.
Conclusion / ImplicationsThe MARIC consortium represents an important advancement in the field, bringing international attention to this problem. It will provide internationally relevant evidence to guide policymakers and clinicians in reducing preventable deaths in this marginalised population.
Higher Education Completion Rates for Aboriginal and Torres Strait Islander Students Using A Multi-Agency Matched Population Cohort
Authors: Ladbrook Megan , Hendrickson Luke 
Published: December 2020

Using the Multi-Agency Data Integration Project (MADIP), which combines health, tax, welfare and demographic data with student data, our analysis looked at the relationship between income support and Aboriginal and Torres Strait Islander university completion rates.
IntroductionDomestic undergraduate university completion rates of Aboriginal and Torres Strait Islander students are significantly lower (40%) than non-Indigenous students (66%). Few prior studies have used population level matched data from multiple agencies to analyse the determinants of Australian Indigenous completion rates.
Objectives and ApproachWe aimed to quantify the major determinants driving the completion rates of Indigenous students in Australian undergraduate university courses compared with domestic non-Indigenous students. We used the Higher Education Information Management System (HEIMS) linked to the MADIP creating approximately 555, 000 records. A Random forest tree was constructed to determine the most important indicators for outcome of interest which were then used for matching and statistical analysis. Summary statistics and a binomial logit was used on the matched sample to confirm significance.
ResultsWe found that Indigenous students are more likely to start university belonging to around three equity groups such as having a lower socio-economic status background, older commencement age and being the first member of their family to attend university. However, Indigenous status remains a significant contributor to lower completion rates after controlling for a wide range of equity groups. One factor that has a positive influence on Indigenous university completion rates is access to study assistance. Completion rates for Indigenous students who were not members of other equity groups on income support was 70 per cent compared to 57 per cent for similar students on no income support.
Conclusion / ImplicationsThese linked datasets provide the opportunity to better evaluate the drivers of completion rates of Australian Indigenous students to inform and evaluate policy reforms.