bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of spatial cohesion in suburban areas based on physical characteristics of buildings
Authors: Zierke Piotr , Kołata Joanna
Published: December 2020

Gwałtowna ekspansja budownictwa mieszkalnego na terenach pod-miejskich powoduje, że obszary te są obecnie miejscem szczególnie narażonym na degradacjęprzestrzenną, zanik tradycyjnych krajobrazów wiejskich, a także rozmaite problemy będące następstwem tych zjawisk. Brak spójności prze-strzennej tych terenów jest jednym z elementów pogłębiających nieład przestrzenny. Przedmiotem prezentowanych badań są fizyczne cechy form budynków, które poprzez swoją rolę w kształto-waniu kontekstu przestrzennego wpływają na ład przestrzenny. Autorzy badania postawili sobie za cel opracowanie metody monitoringu przestrzeni opierającej się na charakterystyce cech fizycz-nych budynków zlokalizowanych na danym ob-szarze. Wyniki badania mają dostarczyć w przy-stępny sposób dogłębnej informacji o zabudowie poszczególnych terenów oraz pozwolić na lepsze poznanie miejscowych form budynków uczest-nikom procesu budowlanego, a także podnieśćświadomość architektoniczną mieszkańców
Post-intervention Effects of Varying Treatment Arms on Glycemic Failure and Beta-Cell Function in the TODAY Study
Authors: Gandica Rachelle , Braffett Barbara H. , Katz Lorraine E. Levitt , White Neil H. , Tryggestad Jeanie B. , Geffner Mitchell E. , Caprio Sonia , McKay Siripoom , Nadeau Kristen J. , Arslanian Silva , Group The TODAY Study
Published: December 2020

Objective: The Treatment Options for type 2
Diabetes in Adolescents and Youth (TODAY) trial demonstrated that glycemic
failure rates were significantly lower in youth randomized to metformin plus
rosiglitazone treatment compared to metformin alone or metformin plus intensive
lifestyle intervention. At end of study, rosiglitazone was permanently
discontinued, and routine diabetes care resumed. Herein, we report post-intervention
glycemic failure rates in TODAY participants over an additional 36 months of
follow-up for the three original treatment arms and describe insulin
sensitivity and beta-cell function outcomes.

Research Design and Methods: A total of 699 participants were
randomized during TODAY, of whom 572 enrolled in the TODAY2 observational
follow-up. Glycemic failure was defined as HbA1c ≥8% over a 6-month period or
inability to wean from temporary insulin therapy within 3 months after acute
metabolic decompensation during TODAY or a sustained HbA1c ≥8% over two
consecutive visits during TODAY2. Oral glucose tolerance tests were conducted and
insulin sensitivity, insulinogenic index, and oral disposition index (oDI) were
calculated.

Results: During the 36 months of
TODAY2, glycemic failure rates did not differ among participants by original
treatment group assignment. Insulin
sensitivity and beta-cell function deteriorated rapidly during the 36 months of
TODAY2 routine diabetes care, but did not differ by treatment group assignment.

Conclusions: The added benefit of preventing
glycemic failure by using rosiglitazone as a second agent in youth-onset type 2
diabetes did not persist after its discontinuation. More work is needed to address
this rapid progression to avoid long-term diabetes complications.
Analysis of The Cycling Trend During the Pandemic of COVID 19 Towards Small and Medium Enterprises (UMKM) Income
Authors: Ramdani Irham
Published: December 2020

The Corona pandemic has take down the vital parts of the economic sector. The implementation of the Large Scale Social Restriction (PSBB) has forced people to stay at home. Meanwhile, they also obliged to maintain their well being and health to prevent themselves infected by the Corona Virus Disease (COVID 19). On of the many ways to prevent the virus attack is by doing exercises, most of the society choose cycling as their exercise during the pandemic. This condition leads to the increase of the demands of bicycle and its spare parts although their prices is also increasing. This phenomena could increase the income of the Small and Medium Enterprises. The aim of this research is  to analyze the impact of cycling trend during the pandemic of COVID 19 towards the income of small-medium enterprise, specifically  the ones in the domain of bicycle. This research will use the qualitative descriptive approach. The interview data will be collected and categorized based on the source of the information, and will be presented in narrative voice showing the increase of small and medium bicycle enterprises’s income, so we can conclude that there is an impact of the cycling trend during the pandemic of COVID 19 towards the increase of the small medium bicycle enterprise, whether it is online, offline, the selling of bicycle, the spare parts, or the accessories.
Creation of First Nations Health Profiles Through Data Linkage in Manitoba
Authors: Ramayanam Shravan
Published: December 2020

BackgroundFirst Nation peoples (FNs) were not able to identify themselves within administrative datasets due to lack of FNs identifiers, which perpetuates a pan-indigenous approach in advocacy and evaluation capabilities. Linking databases improves the quality and accuracy of FNs health data and offsets the burden of survey fatigue in communities. Creating community profiles helps FNs in identifying health status priorities for communities, Tribal Council and other geographically defined areas.

MethodsA resolution was passed in September 2017 to link Indian Status Registry (ISR) file with Manitoba Health Registry, with First Nations Health and Social Secretariat of Manitoba (FNHSSM) and Health Information Research Governance Committee (HIRGC) oversight to create a Key Linked file which has First Nations specific information. Encrypted Personal Health Information Numbers (PHINs) were added to the Key Linked file to create a Manitoba First Nations Research file which is linkable to other databases. Information Sharing Agreements (ISA) have been developed with federal and provincial governments to mandate the processes for data linkage.

ResultsA resolution was passed in September 2017 to link Indian Status Registry (ISR) file with Manitoba Health Registry, with First Nations Health and Social Secretariat of Manitoba (FNHSSM) and Health Information Research Governance Committee (HIRGC) oversight to create a Key Linked file which has First Nations specific information. Encrypted Personal Health Information Numbers (PHINs) were added to the Key Linked file to create a Manitoba First Nations Research file which is linkable to other databases. Information Sharing Agreements (ISA) have been developed with federal and provincial governments to mandate the processes for data linkage.

ConclusionData Linkage is a key process to assert self-determination, strengthen FNs data governance and achieve Data Sovereignty. Linking databases creates opportunities for FNs to access accurate data that will assist their Nations to lead their own health research and program evaluation that are driven by their own needs and priorities.
Trends in diabetes medications in Canada, England, Scotland and Australia: a repeated cross-sectional analysis (2012-2017)
Authors: Kalia Sumeet
Published: December 2020

BackgroundWe studied the uptake of new classes of glucose lowering medications, such as Dipeptidyl peptidase-4 inhibitors (DPP4s) and Sodium-glucose cotransporter 2 inhibitors (SGLT2s) amongst patients living with type 2 diabetes. We compared this in Australia, Canada, England and Scotland, and explored whether these new drugs are supplementing or replacing older classes of medications.

Research Design and MethodsWe used primary care Electronic Medical Data on prescriptions (Canada, UK) and dispensing data (Australia) from 2012 to 2017. We included persons aged 40 years or over on at least one glucose lowering medication in each year of interest; we excluded those on insulin only. We determined proportions of patients in each nation on each class of medication, as well as on combinations of classes.

ResultsIn 2017, data from 28,063 patients in Canada, 106,000 in Australia, 88,953 in England and 15,603 in Scotland were included. The proportion of patients on metformin increased by 3.4% in Australia (95% CI: 3.24% to 3.55%) and decreased in the other nations. Canada had the greatest decrease, at 4.7% (95% CI: -5.05% to -4.34%). Sulfonylurea use decreased in most nations, while DPP4s increased in all. By 2017, between 10.1% and 15.3% of patients were on a SGLT2 and the use of either a DPP4 or SGLT2 combined with metformin approached or exceeded the use of sulfonylureas with metformin.

ConclusionsNewer, more expensive medications are replacing sulfonylureas and, to a lesser degree, metformin. The effects of these trends on health outcomes and overall costs should be examined.
Higher Ultraviolet Radiation During Early Life Reduces Risk of Childhood Type 1 Diabetes in Boys
Authors: Miller Kate
Published: December 2020

IntroductionThere is increasing evidence that environmental exposures may be important in the pathogenesis of type 1 diabetes (T1D). Ultraviolet radiation (UVR) is of interest in relation to the development of T1D because of its immunoregulatory actions. Ecological studies testing the correlation between levels of UVR and T1D have shown a significant inverse relationship for both incidence and prevalence.

Objectives and Approach We used large linked datasets to test ambient UVR during early life against T1D risk at the individual level. We conducted a nested case-control study using linked data from state-wide administrative datasets and NASA satellites. Cases (n=1819) were all children born in Western Australia from 1980-2014 with a diagnosis of T1D on the population-based Western Australian Children’s Diabetes Database between 0-16 years of age. Controls (n=27 259) were randomly selected from all live births in Western Australia and matched to cases on sex and date of birth.

Daily UVR data from NASA satellites, that were date-and location-specific for each individual, were used to estimate total UVR dose for each trimester of pregnancy and the first year of life.

ResultsConditional logistic regression showed that T1D risk was 44% lower in boys of mothers with UVR levels in the highest quartile (compared to the lowest quartile) during their third trimester of pregnancy (p=0.04). Higher UVR in the first year of life was also associated with a significantly lower risk of T1D in later childhood among boys. Among girls, there was no evidence of an association between total UVR dose and T1D risk.

ConclusionHigher UVR in the third trimester and first year of life appears to interact with sex-specific factors to lower T1D risk among boys (but not girls) in Western Australia.
Effect Measure Modification of Multimorbidity On the Association Between Regularity of General Practitioner Contact and Potentially Avoidable Hospitalisations
Authors: Moorin Rachael
Published: December 2020

IntroductionRegular contact with a general practitioner (GP) has been shown to lower the risk of potentially avoidable hospitalisations (PAHs) independently of continuity of provider and frequency of contact. Multimorbidity affects between 55 and 98% people aged 65+ years and continues to place pressure on healthcare systems globally. However, little is known about its impact on the relationship between continuity of primary care and PAHs.

Objectives and ApproachA retrospective, longitudinal cohort study using survey data linked to routinely-collected administrative health data from the 45 and Up Study conducted in New South Wales, Australia was used to investigate the effect measure modification by multimorbidity on the relationship between regularity of GP contact and PAHs.

Multimorbidity was assessed using the Rx-Risk comorbidity score, which captures the number of condition groups, assigned based on medicine dispensing records, using a 5-year look-back period. PAHs were: (i) any unplanned hospitalisations, (ii) chronic ambulatory care sensitive conditions (ACSC) hospitalisations or (iii) unplanned ACSC hospitalisations. Multivariable logistic regression and population attributable fractions (PAF) were used to examine effect measure modification by multimorbidity.

ResultsHigher GP regularity was significantly associated with a reduction in the probability of each PAH type. This reduction diminished with increasing multimorbidity with the effect measure modification most apparent for chronic ACSC and unplanned chronic ACSC hospitalisations. The PAF of moving to the highest quintile of regularity significantly reduced with increasing multimorbidity. For example, a reduction in the PAF of unplanned ACSC hospitalisations of 31.1% was observed in those with a RX Risk score of >10 (17.8%) compared with those with no multimorbidity (48.9%).

Conclusion / ImplicationsWeakening of the relationship between GP visit regularity and PAHs with increasing levels of multimorbidity suggests a need to focus on improving primary care support to prevent PAHs for patients with multimorbidity.
Life expectancy and health care utilization of individuals with dementia in Ontario, Canada: A population-level study
Authors: Fisher Stacey , Manuel Douglas G , Spruin Sarah , Anderson Geoffrey , Taljaard Monica , Tanuseputro Peter
Published: December 2020

IntroductionIt is important for dementia patients, their families and caregivers, and health planners to have an understanding of life expectancy and the likely care experience of patients from dementia diagnosis to end-of-life.

Objectives and ApproachUsing administrative health data of all dementia patients in Ontario, Canada (2014 to 2017) and period life table methodology, life expectancy and average health care use and cost for individuals from dementia diagnosis to death was examined. Dementia was ascertained by a validated case ascertainment definition and information from long-term care, home care and complex continuing care assessments. Formal care was categorized as inpatient, outpatient, home care or long-term care.

ResultsLife expectancy at dementia diagnosis was 8.7 years and 9.8 years for men and women diagnosed prior to age 75, of which 3.7 years (42%) and 4.7 years (49%) was spent receiving formal care, respectively. Life expectancy was 4.4 and 5.2 years for men and women diagnosed after age 75, of which 2.2 years (50%) and 3.1 years (60%) was spent receiving care, respectively. Women received proportionally more long-term and home care compared to men, while men received more inpatient and outpatient care. In the year prior to dementia diagnosis, individuals received formal care for 20 days per 100 person-days. Those at the end-of-life received care for 79 days per 100 person-days, including 55 long-term care days and 7.8 inpatient care days. Average direct health care costs from diagnosis to end-of-life was $230,000 and was higer for women and those diagnosed before age 75.

ConclusionThe burden of formal care for those with dementia is substantial. The results of this study will be used by physicians to inform conversations with patients, their families and caregivers around what to expect after a dementia diagnosis, and by health care planners for population health planning.
Unifying the UK’S Data for Respiratory Science: BREATHE, The Health Data Research UK (HDR UK) Respiratory Health Data Research Hub
Authors: Orton Chris , Ford David , Sheikh Aziz , Norrie John , Fletcher Monica , Quint Jennifer , Tobin Martin , Hall Ian
Published: December 2020

IntroductionThe BREATHE Health Data Research Hub is a consortium of five academic institutions and several industry partners seeking to facilitate and accelerate respiratory science initiatives and outcomes. Unlocking organisational, jurisdictional, and scientific challenges, such as differing and inherent complexities with data standards, incongruous governance, and disparate data access mechanisms for over 100 diverse UK datasets are key aims.

Objectives and ApproachCentral to the data effort is the UK Secure eResearch Platform (SeRP UK), and its flagship tenancy, the SAIL Databank. Onboarding datasets, making them remotely available to the respiratory research community, is a key approach. Datasets targeted range from population cohort studies, to respiratory trials data, routine healthcare datasets, and specialist ‘omics data. Partnerships with national safe havens and providers such as eDRIS and NHS Digital will enable BREATHE to expedite and improve wider sharing of datasets for the respiratory science.

Data improvements focus on datasets from primary, secondary, and tertiary care from national healthcare systems, ‘respiratorising’ these datasets and increasing utility for academic and industry respiratory scientists. Incorporating dataset metadata and access permutations into national cataloguing systems at HDR UK, standardising metadata, and interoperability for in-scope datasets form a concerted data quality improvement effort.

ResultsFacilitating data sharing through initiatives such as BREATHE will increase visibility and accessibility for datasets within respiratory science, whilst addressing national cultural and governance issues to data sharing. BREATHE data sharing processes will allow for team science to be undertaken in a highly collaborative manner and allow for best practise in data collection and sharing to flow to nationwide datasets in respiratory science.

Conclusion / ImplicationsCollaborative hubs with scientific domain expertise can be created and leveraged to accelerate data sharing and data science within the scientific area. These collaborative efforts can however be translated to other disease-specific efforts, and indeed disease agnostic platform solutions.
Self-Reported Mental Health Versus Psychotropic Medication Record as A Predictor of Suicide: A Record Linkage Study
Authors: Onyeka Ifeoma N , Maguire Aideen , O'Reilly Dermot
Published: December 2020

IntroductionBoth rates of self-reported mental ill-health and the number of individuals in receipt of psychotropic medications are increasing. Research suggests that poor mental health is a major predictor of death by suicide, but measuring individual level mental ill-health can be difficult.

Objectives and ApproachThis study assessed if self-reported mental health, psychotropic medication record, or both in combination better predict risk of death by suicide. Data from the 2011 Northern Ireland Census on all individuals living in the community aged 18-74 years (n=1,098,967) were linked to the Enhanced Prescribing Database (EPD) and death registrations until end of 2015. Mental health status was ascertained through a single-item self-reported mental health question from the Census and record of psychotropic medication in the EPD. Deaths by suicide were identified using ICD-10 codes. Logistic regression models examined the association between measures of mental health and death by suicide, with adjustment for individual attributes known to be associated with mental ill-health.

ResultsOf the 1,098,967 individuals, 857 died by suicide during the study period; 0.05% of those with no mental ill-health, 0.21% who self-reported poor mental health, 0.16% of those in receipt of psychotropic medication and 0.38% of those with both. In unadjusted regression models, having both self-report and psychotropic medication record was the strongest predictor of suicide (OR=8.23, 95%CI: 6.97–9.72). Upon adjustment, having both remained the strongest suicide predictor (OR=6.13, 95%CI: 4.94–7.61), followed by psychotropic medication record only (OR=4.00, 95%CI: 3.28–4.88) and self-report only (OR=2.88, 95%CI: 2.16–3.84).

Conclusion/ImplicationsIndividuals with both self-reported poor mental health and receipt of psychotropic medication had the highest likelihood of death by suicide. Of the two measures individually, receipt of psychotropic medication was associated with a higher likelihood of suicide mortality. Understanding suicide risk is vitally important for the effective targeting of interventions.
The Association of Sibling Fracture History with Major Osteoporotic Fractures in Individuals from A Population-Based Cohort
Authors: Hamad Amani F , Yan Lin , Leslie William D , Morin Suzanne N , Yang Shuman , Walld Randy , Roos Leslie L , Lix Lisa M
Published: December 2020

IntroductionMajor osteoporotic fractures (MOF) are associated with significant morbidity and healthcare system burden.

Objectives and ApproachWe aimed to determine whether sibling fracture history is associated with MOF risk amongst individuals from a population-based cohort using objectively-ascertained measures of fracture history. This retrospective cohort study used administrative databases from the province of Manitoba, Canada, which has a universal healthcare system. The cohort included individuals aged 40 years and older between 1997 and 2015 with linkage to at least one sibling. The exposure was MOF diagnosis occurring at age 40 years or older in a randomly selected sibling. The outcome was incident clinically-diagnosed MOF (hip, wrist, humerus or spine) identified in hospital and physician records using established case definitions. A multivariable Cox proportional hazards regression was used to test the association of sibling fracture history with the risk of MOF in individuals after adjustment for known fracture risk factors.

ResultsThe cohort included 217,519 individuals; 92% were linked to full siblings (i.e., same mother/father) and 49% were females. During a median follow-up of 11 years (IQR 5 -15), 7274 (3.3%) incident MOF cases were identified. Sibling MOF history was associated with increased risk of MOF (HR 1.71, 95% CI 1.48–1.97). The risk was elevated in both men (HR 1.63, 95% CI 1.29-2.06) and women (HR 1.78, 95% CI 1.48-2.13) but was higher among sisters (HR 2.08, 95% CI 1.65-2.61) compared to brothers (HR 1.67, 95% CI 1.20-2.32). In a secondary analysis of sibling fracture site, the highest risk was observed with diagnosis of wrist followed by spine fractures (HR 1.86, 95% CI 1.57-2.21 and HR 1.46, 95% CI 1.08-1.98, respectively).

ConclusionSibling fracture history is associated with increased MOF risk in individuals and should be considered as a candidate risk factor for improving fracture risk prediction.
Estimating the Proportion of Antibiotics Attributable to Common Paediatric Respiratory Viruses: An Example Leveraging Unique Population-Based Prescribing and Laboratory Data
Authors: Fitzpatrick Tiffany , Malcolm William , McMenamin Jim , Reynolds Arlene , Guttmann Astrid , Hardelid Pia
Published: December 2020

IntroductionInappropriate antibiotic prescribing, such as that for viral illness, is common in primary care. This is of growing interest given concerns around antimicrobial resistance and harms associated with unnecessary treatment; however, current data limitations have hindered population-based estimates of the proportion of community-prescribed antibiotics attributable to common respiratory viruses.

Objectives and ApproachTo estimate the proportion of antibiotics prescribed in primary care to young children attributable to common respiratory viruses, including respiratory syncytial virus (RSV), influenza, human metapneumovirus (HuMPV) and parainfluenza. We leveraged two unique sources of comprehensive, linked population-based administrative data on dispensed antibiotic prescriptions and laboratory tests for respiratory viruses for all Scottish children (<5 years). We fit time series negative binomial models to predict weekly antibiotic prescribing rates from positive viral tests rates for the period April 1, 2009 through Dec 27, 2017. Using linked demographic and hospitalization data, we stratified our analysis by age, presence of high-risk chronic medical conditions, and antibiotic class.

ResultsWe included data on over 6 million antibiotic prescriptions among nearly 800,000 children. An estimated 6.9% (95% CI: 5.6,8.3), 2.4% (1.7,3.1), and 2.3% (0.8,3.9) of prescribed antibiotics were attributable to RSV, influenza and HuMPV, respectively. RSV was consistently associated with the highest proportion of antibiotics prescribed across all analyses but particularly among children without chronic conditions [4.30% (3.19, 5.41)] and for amoxicillin [8.10% (6.43, 9.76)] and macrolide prescriptions [7.65% (6.14, 9.16)].

Conclusion / ImplicationsNearly 14% of antibiotics prescribed to Scottish children in this study were attributable to common viral pathogens such as RSV for which antibiotics are not recommended. This highlights clear targets for antibiotic stewardship programs and suggests antibiotic prescribing could be reduced once an RSV vaccine is introduced.
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.