bluuebunny/crossref_metadata_2025_split huggingface dataset:
Population-Wide Data Linkage to Reduce Premature Mortality in Women Who Use Mental Health Services In NSW
Authors: Wilson Elizabeth , Tervonen Hanna , Currow David , Sara Grant 
Published: December 2020

IntroductionCancer deaths are a major contributor to premature mortality in people with mental health conditions. Some cancers occur more often in people with mental health conditions because of increased risk factors. However, most premature cancer mortality in people with mental health conditions arises from increased cancer case fatality rates due to health care related factors. While there is substantial evidence that a problem exists, further evidence is needed to support effective action and the translation of research findings into better policy, services and care.
Objectives and ApproachThe NSW Mental Health Living Longer program involves population-wide data linkage that combines records from nine NSW data collections. Our collection includes over 120 million records for more than nine million people. This presentation focuses on the use of linked data to develop indicators to support reporting on premature breast and cervical cancer mortality for women living with mental illness. These indicators will be used to identify variation in care, assess areas for targeted intervention, and evaluate the effectiveness of research translation into more effective care.
ResultsThis work is ongoing and will be finalised by August 2020. We will use regression techniques to examine predictors of participation in breast and cervical cancer screening for women who use mental health services in NSW. These results will be used to assess geographical variation in risk-adjusted screening participation rates. We will also present methods and results for measuring incidence and stage at presentation, as well as 5 year survival for women who use mental health services in NSW.
Conclusion / ImplicationsIf cancer survival is a key measure of the effectiveness of healthcare systems, then reduced survival in people with mental health problems reflects less effective health care. Improving screening and treatment services is likely to be the most important strategy for reducing the cancer mortality gap for women with mental illness.
Identifying In-Hospital Complications Using Lookback Periods In Australian Health Administrative Datasets
Authors: Palamuthusingam Dharmenaan , Ratnayake Gishan , Hawley Carmel M , Pascoe Elaine M , Johnson David W , Fahim Magid 
Published: December 2020

IntroductionThe condition onset flag (COF) variable was introduced into hospitalization coding practice in 2008 to distinguish ICD coded diagnoses as either preexisting, new or status unknown. For datasets with multiple hospitalizations per participant, the COF enables identification of diagnoses that may have arisen in between admissions thereby enriching datasets considerably. However, datasets preceding 2008 lack equivalent depth, potentially leading to data waste.
Objectives and Approach The aim of this study was to determine if a lookback period could accurately identify new onset conditions in datasets coded prior to 2008. Outcomes identified in an index admission and not present in the lookback period were classified as a new condition. Three different lookback periods entailing the preceding 3, 2, and immediately previous admission were investigated. Accuracy was determined by applying this procedure to datasets in which COF were assigned by coders and comparing agreement between the lookback procedure and the COF. Codes examined were myocardial infarction, pulmonary embolism and pneumonia. Sensitivity analyses excluded indexed admissions where a lookback period was not possible. True positives were those conditions correctly identified by the lookback periods when compared to that classified using the COF. The crude incidence rates were also determined.
Results248,042 hospital records from 12,355 patients on chronic kidney replacement therapy were analysed. Lookback Period 1 provided the highest true positive rates for all 3 complications, ranging from 83% to 92%. Sensitivity analyses excluding patients with no previous separations, demonstrated a true positive percentage from 98% to 100%. Lookback period 3 true positive rates were the lowest (77.2-78.3%). The crude incidence calculated for first-time myocardial infarction using Lookback Period 1 was 25 events per 1000 patient years, consistent with current literature. Rates calculated using the COF variable was more than 3-fold lower. This finding was similar when evaluating other clinical outcomes.
Conclusion / ImplicationsThe Lookback Period 1 method accurately identifies in-hospital complications compared with the current method using the COF variable and is a means of identifying new hospital complications in health administrative datasets predating the availability of COF, thereby enriching existing datasets and minimising data waste.
Maternal and Neonatal Outcomes Associated with Psychostimulant Use in Pregnancy
Authors: Camacho Ximena , Havard Alys , Zoega Helga , Wilson Margaret , Gomes Tara , Vigod Simone , Pearson Sallie , Henry David 
Published: December 2020

IntroductionRecent evidence from the USA and Nordic countries suggests a possible association between psychostimulant use during gestation and adverse pregnancy and birth outcomes.
Objectives and ApproachWe employed a distributed cohort analysis using linked administrative data for women who gave birth in New South Wales (NSW; Australia) and Ontario (Canada), whereby a common protocol was implemented separately in each jurisdiction. The study population comprised women who were hospitalized for a singleton delivery over an 8 (NSW) and 4 (Ontario) year period, respectively, with the NSW cohort restricted to social security beneficiaries. Psychostimulant exposure was defined as at least one dispensing of methylphenidate, amphetamine, dextroamphetamine or lisdexamfetamine during pregnancy. We examined the risk of maternal and neonatal outcomes among psychostimulant exposed mothers compared with unexposed mothers.
ResultsThere were 140,356 eligible deliveries in NSW and 449,499 in Ontario during the respective study periods. Fewer than 1% of these pregnancies were exposed to psychostimulants during gestation, although use was higher in Ontario (0.30% vs 0.11% in NSW). Preliminary unadjusted analyses indicated possible associations between psychostimulant use in pregnancy and higher risks of pre-term birth (relative risk [RR] 1.7, 95% confidence interval [CI] 1.4-2.0 (Ontario); RR 1.8, 95% CI 1.2-2.6 (NSW)) and pre-eclampsia (RR 2.0, 95% CI 1.5-2.6 (Ontario); RR 2.0, 95% CI 1.2-3.5 (NSW)). Similarly, psychostimulant use was associated with higher risks of low birthweight (RR 1.6, 95% CI 1.3-1.9 (Ontario); RR 2.0, 95% CI 1.3-3.0 (NSW)) and admission to neonatal intensive care (RR 2.1, 95% CI 1.9-2.3 (Ontario); RR 1.5, 95% CI 1.1-1.9 (NSW)).
Conclusion / ImplicationsUnadjusted analyses indicate an increased risk of adverse maternal and birth outcomes associated with psychostimulant exposure during pregnancy, potentially representing a placental effect. We are currently refining the analyses, employing propensity score methods to adjust for confounding.
High Rate of Fatal Overdose After Release from Prison In BC, Canada: A Data Linkage Study
Authors: Kinner Stuart A , Gan Wenqi , Slaunwhite Amanda 
Published: December 2020

IntroductionThe province of BC, Canada is in the grips of a sustained overdose epidemic. People released from prison are at increased risk of fatal drug overdose, but the impact of the overdose epidemic on mortality after release from prison in BC is poorly understood. Few studies have been able to examine risk factors for overdose death in this population.
Objectives and ApproachWe aimed to (a) measure risk of overdose-related and all-cause death in different time periods after release from prison; and (b) identify risk factors for overdose-related and all-cause death. In a random 20% sample of the population of BC, Canada, we identified those released from prison 2015-2017 and examined linked health and correctional records for this cohort.
ResultsOf 6106 persons released from prison 2015-2017, 77 (1.3%) died from any cause and 48 (0.8%) died from overdose 2015-2017. The incidence of all-cause death was 16.1 (95%CI 13.7-18.8) per 1000 person years, and the incidence of overdose death was 11.2 (95%CI 9.2-13.5) per 1000 person years. Risk factors for overdose death included a history of 3 or more incarcerations (HR=3.00, 95%CI 1.67-5.39), co-occurring substance use disorder and mental illness (HR=4.73, 95%CI 2.94-7.62), chronic physical morbidity (HR=3.10, 95%CI 1.97-4.88), and being dispensed benzodiazepines (HR=3.31, 95%CI 2.27-4.84) or opioids for pain (HR=6.77, 95%CI 3.86-11.89). The incidence of fatal overdose was significantly higher in the first two weeks post-release than at any other time during follow-up.
ConclusionPeople released from prison in BC are at markedly increased risk of preventable death, mainly due to overdose. As such, people transitioning from prison to the community should be a key target population for overdose prevention efforts. To be maximally effective, these efforts must go beyond provision of methadone and naloxone on release, to consider physical and mental health comorbidities, and psychosocial disadvantage.
Is Wellbeing Associated with Time Spent in Nature?
Authors: Rodgers Sarah E , Rowney Francis , Thompson Daniel , Mizen Amy , White Mathew , Lovell Rebecca , Fry Richard , Watkins Alan , Wheeler Benedict , Akbari Ashley , Stratton Gareth , Lyons Ronan , White RonanJames , Nieuwenhuijsen Mark , Geary Rebecca , Rodgers Sarah 
Published: December 2020

IntroductionGreen and blue spaces (GBS), such as parks, woodlands, rivers, and beaches, are thought to be important for mental health and wellbeing. Our longitudinal cohort contains objective household-level environment data linked at the invidual level to routinely recorded mental health data, augmented with cross sectional self-reported health behaviours, including leisure visits to the outdoors.
Objectives and ApproachOur overall approach will evaluate if residential proximity to GBS is associated with mental health and wellbeing, and if any associations aremodified by visits to outdoors spaces following individual-level data linkage. Here, we examined cross-sectional survey data on time spent visiting nature outdoors. Wellbeing outcomes were assessed using self-reported scores. Data were analysed using generalised additive models in the SAIL Databank.
ResultsUsing a sample of National Survey for Wales respondents (2016/17, n=3,481), over 40% of adults in Wales reported spending less than 30 minutes outdoors each week. Weekly time outdoors was positively associated with wellbeing (p=0.007) and life satisfaction (p=0.03) having adjusted for potential confounders including, age, rurality, loneliness, employment status. Confidence intervals varied along the fitted GAM model. Models using a second wave of survey data (n≈7,000), anonymously record-linked to residential environment and health data will explore these associations further.
ConclusionA previous study based in England (White et al. 2019) found an upper wellbeing benefit threshold of 2 hours per week for time spent in nature. This was not apparent in our preliminary models, but may be revealed in further analyses. We will next incorporate longitudinal mental health and environmental data for 2 million adults living in Wales, UK. Linking to ambient and accessible residential GBS, while taking into account changes due to migration and actual visits, will allow us to provide valuable guidance to local government, who are often responsible for provisioning and maintaining outdoor facilities.
Creating A Validation Dataset to Test Record Linkage Algorithms in A Province-Wide South African Health Information Exchange
Authors: Mutemaringa Themba , Heekes Alexa , Smith Mariette , Tiffin Nicki , Boulle Andrew 
Published: December 2020

IntroductionIncreasing use of digital medical records creates disparate data resources for the same health care client population; and harnessing the benefits of real-time health data requires effective data linkage. A South African Health Information Exchange (HIE) collates and links routine health data from multiple sources, running daily updates through an automated ETL process. Many existing deterministic and probabilistic algorithms link person-level data using demographic identifiers, and can be combined in an optimised methodological pipeline. The performance of such pipelines must be validated against known matched pairs. The HIE uses current algorithms for record linkage, but methods that rely on similar spelling, name frequency and phonetic matching have been optimised for non-African names, and are not as effective.
ObjectivesWe assessed common problems arising in the linkage process in the HIE, using this information to compile a curated representative African validation database for optimising existing and new linkage pipelines.
ResultsUsing current linkage algorithms, we have identified the proportion of duplicates in the last five years, ranging from 25% in 2015 and stabilising at 10% by 2019. Common causes of duplicates across the whole database include mismatch in first name (37%), surname (17%), date of birth (13%), sex (8%) and South African Identification Number (0.2%). Complications from new-born naming and records of twins affect >8% of all records, and temporary health identifiers assigned at birth, during emergency response, and during poor connectivity of facilities to the provincial patient master index affect 2% of records.
ConclusionsBased on these data, we have constructed a South African-specific, representative validation dataset that contains linkage pairs that represent placeholder phrases for newborns prior to naming (e.g. “baby of”), language variations; twins; character insertions, substitution and omissions in names with similar spellings; frequencies of names in the general population; and similar-sounding names.
Planting the S.E.E.D.S of Indigenous Population Health Data Linkage
Authors: Rowe Robyn K , Walker Jennifer D 
Published: December 2020

IntroductionThe increasing accessibility of data through digitization and linkage has resulted in Indigenous and allied individuals, scholars, practitioners, and data users recognizing a need to advance ways that assert Indigenous sovereignty and governance within data environments. Advances are being talked about around the world for how Indigenous data is collected, used, stored, shared, linked, and analysed.
Objectives and ApproachDuring the International Population Data Linkage Network Conference in September of 2018, two sessions were hosted and led by international collaborators that focused on regional Indigenous health data linkage. Notes, discussions, and artistic contributions gathered from the conference led to collaborative efforts to highlight the common approaches to Indigenous data linkage, as discussed internationally. This presentation will share the braided culmination of these discussions and offer S.E.E.D.S as a set of guiding Indigenous data linkage principles.
ResultsS.E.E.D.S emerges as a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples’ right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous Peoples and settler states. S.E.E.D.S aims to centre and advance Indigenous-driven population data linkage and research while weaving together common global approaches to Indigenous data linkage.
Conclusion / ImplicationsEach of the five elements of S.E.E.D.S interweave and need to be enacted together to create a positive Indigenous data linkage environment. When implemented together, the primary goals of the S.E.E.D.S Principles is to guide positive Indigenous population health data linkage in an effort to create more meaningful research approaches through improved Indigenous-based research processes. The implementation of these principles can, in turn, lead to better measurements of health progress that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous populations.
A Comparison of The Health Impacts of Individual Level and Area Based Welsh Government Fuel Poverty Schemes
Authors: Morrison-Rees Sian , Lowe Sarah 
Published: December 2020

IntroductionLiving in a cold and/or damp house is known to increase the risk of morbidity, mortality and excess winter deaths. To reduce fuel poverty in Wales, the Welsh Government developed schemes to provide energy efficiency improvements to those most likely affected by fuel poverty.
We explored the relative impacts on health of an individual-level scheme, Warm Homes Nest, and an area-based scheme, Arbed.
Objectives and ApproachOverall aim: to evaluate the health impacts of Welsh Government funded schemes designed to reduce fuel poverty. Presented objective: to investigate the relative impact of the individual-level and area-based schemes on the health of recipients. A longitudinal dataset was created using the anonymised residence that received improvements linked to residents’ health measures using routine health records held in the SAIL Databank at Swansea University.
We used difference-in-difference (DID) estimations to compare any changes in recipient health before and after intervention with any concurrent change in health in those yet to receive the intervention.
ResultsAn analysis of the Warm Homes Nest Scheme, published in 2017 and presented at the IPDLN 2018 conference found a positive impact of the scheme on the health of recipients.
This presentation will describe the further analysis comparing the area-based Arbed scheme with both the recipients of the individual-level Nest scheme and groups in comparable need that had not yet received the intervention. We will present results focussing on the relative impacts of the two schemes on respiratory health, infection prescribing and mental health.
Conclusion / ImplicationsProviding home energy efficiency interventions has the potential to benefit population health, however there is a scarcity of evidence comparing different methods of implementing schemes. Our findings will inform more effectively focussed home energy efficiency schemes and potentially thus improve the health and wellbeing of people living in Wales.
Evaluating Machine-Learning Models for Predicting Hospital Transfers in Administrative Data: A Study of Admissions for Myocardial Infarction
Authors: Lopez Derrick , Lu Juan , Sanfilippo Frank , Briffa Tom , Hung Joe , Nedkoff Lee 
Published: December 2020

IntroductionHospital administrative data is a valuable source to measure myocardial infarction (MI) rates. However, admission counts are susceptible to over-inflation if the patient is transferred multiple times during a single episode of care, and variables denoting transfers may not be reliable. To obtain an accurate number of events, hospital transfers need to be correctly identified.
Objectives and ApproachWe assessed multivariable logistic regression and various machine-learning models to predict transfers in hospital administrative data. Using Western Australian linked hospital data, we identified records from 2000-2016 with a principal discharge diagnosis of MI. Our standard method to compare against was a 24-hour look-back to identify a transfer using just admission and separation dates from the current and previous records for the same patient. Multivariable logistic regression and decision trees with various boosting algorithms were used to predict if a single record was a transfer, using variables recorded in the admission (e.g. age, sex, type of hospital, admitted from, emergency/elective admission). The performance of each model was calculated using metrics including area under the curve (AUC).
ResultsRecords in the training, validation and testing samples had similar characteristics: mean age=68.9 years, 66% were male and 58% admitted to tertiary hospitals. Gradient Boosting Decision Tree (AUC=0.887, 95%CI: 0.886-0.887) outperformed multivariable logistic regression (AUC=0.875; 95% CI: 0.869-0.881) and random forest models (AUC=0.859; 95% CI: 0.853-0.865).
Conclusion / ImplicationsMultivariable logistic regression and machine-learning models are able to identify transfers in a single record from existing variables. They can be used in unlinked hospital administrative data where records belonging to the same patient cannot be identified.
Academic Achievement Among Children with Congenital Heart Defects in Arkansas
Authors: Maity Suman , El-hassan Nahed , Goudie Anthony , Kaiser Jeffrey R , Nembhard Wendy N 
Published: December 2020

Children with congenital heart defects (CHD) have impaired cognitive development.
The objective was to determine if children with CHDs differed in academic performance during elementary and middle school years from children without CHDs. Data from the state birth defects registry, Arkansas Reproductive Health Monitoring System (ARHMS), birth certificate records, and achievement test-scores (grades 3-8) from the Arkansas Department of Education were linked using unique identifiers for children born between 2000-2011 in Arkansas. Cases were identified using 6-digit British Paediatric Association codes and matched controls were assigned from birth certificate data. Proficiency (yes/no) on achievement tests was determined using standard thresholds per grade. Data were stratified based on sex, gestational age, and birth weight. Odds ratios (OR) and 95% confidence intervals (CI) were calculated from multivariate logistic regression adjusting for maternal education level and age, race/ethnicity, and infant sex.
A total of 3,730 children with CHDs and 7,385 children without CHDs were evaluated. Children with CHD were less likely to be proficient in early elementary school grades for mathematics (3rd: OR=0.72, 95% CI: 0.59, 0.87; 4th: OR=0.77, 95% CI: 0.62, 0.95) and literacy (3rd: OR=0.75, 95% CI: 0.63, 0.89; 4th: OR=0.72, 95% CI: 0.58, 0.90). The trend of being less proficient associated with CHD remained mostly consistent with sex, gestational age, and birth weight. In multivariate analysis, CHD was negatively associated with being proficient in mathematics and literacy. Maternal education (1-3 years beyond high school) was positively associated with test proficiency. Additionally, non-Hispanic (NH) black children had lower odds of being proficient compared to NH white children.
Educational performance was negatively associated with CHD in early elementary grades; there was no difference in later grades. However, larger sample sizes in later grades are necessary for reliable estimates. Maternal education and race/ethnicity were significant factors associated with childhood educational performance.