bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes
Authors: Gou Wanglong , Ling Chu-wen , He Yan , Jiang Zengliang , Fu Yuanqing , Xu Fengzhe , Miao Zelei , Sun Ting-yu , Lin Jie-sheng , Zhu Hui-lian , Zhou Hongwei , Chen Yu-ming , Zheng Ju-Sheng 
Published: December 2020

OBJECTIVE
To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features.

RESEARCH DESIGN AND METHODS
We used an interpretable machine learning framework to identify the type 2 diabetes–related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS–type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832).

RESULTS
The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23–1.33), 1.23 (1.13–1.34), and 1.12 (1.06–1.18) across three cohorts. The MRS was positively associated with future glucose increment (P &lt; 0.05) and was correlated with a variety of gut microbiota–derived blood metabolites. Animal study further confirmed the MRS–type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome–type 2 diabetes relationship.

CONCLUSIONS
Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.
Independent Observational Analysis of Ulcer Outcomes for SINBAD and University of Texas Ulcer Scoring Systems
Authors: Leese Graham P. , Soto-Pedre Enrique , Schofield Christopher 
Published: December 2020

OBJECTIVEThe aim of this study was to compare the University of Texas (UT) and Site, Ischemia, Neuropathy, Bacterial Infection, and Depth (SINBAD) foot ulcer scores in predicting ulcer outcome within a routine diabetes foot clinic.RESEARCH DESIGN AND METHODSFrom 2006 to 2018, data were collected from all patients attending an outpatient diabetes foot clinic with an active ulcer not healed within 4 weeks. UT and SINBAD were compared in predicting ulcer outcome. A unified numerical score for UT was constructed and compared with UT grade (depth) and stage scores. Outcomes included death, a healed ulcer, or a nonhealed ulcer, which included major or minor amputation and nonhealing chronic ulcers.RESULTSOutcomes were available from 1,645 ulcer outcomes in 1,068 patients (mean [SD] age 65.4 [4] years, 72% male), of which 1,108 (67%) healed. With exclusion of death as an adverse outcome, the c-statistic (area under operator curve) was 0.67 (95% CI 0.65–0.71) for UT grade/depth and 0.64 (0.61–0.67) for UT stage. The new unified UT score had an improved c-statistic of 0.71 (0.68–0.74). The c-statistic was 0.72 (0.69–0.75) for SINBAD. There was a stepwise decrease in the proportion of ulcers healed for each increased score on ulcer grading for both grading schemes.CONCLUSIONSThis large and independent observational comparison, in a real-world clinical setting, demonstrated that the UT and SINBAD diabetes foot ulcer grading schemes had similar prognostic ability for predicting foot ulcer outcomes. We have devised and validated a unified numerical scoring system for UT.
Participation in Boys & Girls Clubs Of Winnipeg is Associated With Health, Social and Education Outcomes Among Metis Children
Authors: Enns Jennifer , Brownell Marni , Nickel Nathan , Sanguins Julianne , Sarkar Joykrishna , Burland Elaine , Chartier Mariette , Chateau Dan , Katz Alan 
Published: December 2020

IntroductionThe Boys & Girls Clubs of Winnipeg (BGCW) provide out-of-school programs to children and youth living in low-income neighbourhoods in Winnipeg, Canada. These programs aim to set participants on a path to success by supporting healthy behaviours, academic achievement and social skills. Metis children and youth make up a substantial proportion of BGCW participants, but there are few empirical data available on how the programs influence health, social and education outcomes in this population.
Objectives and ApproachWe linked BGCW attendance records for all Manitoba children born 1987-2010 to the individual-level administrative health, social and education data in the population-based Data Repository at the Manitoba Centre for Health Policy in Winnipeg. The comparison group of non-participants was matched on age and neighbourhood of residence. We narrowed the cohort to Metis participants by linking to datasets containing Metis identifiers. Education outcomes were examined using a generalized additive model, and health and justice system outcomes with a time-to-event model, adjusted for socioeconomic status, mother's mental health and age at first birth, and involvement in the child welfare system.
Results1,664 Metis children and youth participated in BGCW from 2005-2016. The comparison group comprised 9,248 Metis children and youth. Greater BGCW participation was associated with higher likelihood of performing well in grade 3 numeracy (boys and girls) and grade 7 student engagement (boys only), but not other education outcomes (grade 3 reading, grade 7 mathematics, grade 7/8 reading/writing, or graduating high school). Greater BGCW participation was also associated with lower risk of becoming involved with the justice system (i.e., lower risk of being charged with a crime) and with lower risk of teen pregnancy.
Conclusion / ImplicationsParticipation in BGCW programs may contribute to improved health, justice and education outcomes for Metis children and youth in Winnipeg.
Maternal Depression in Early Childhood and Developmental Vulnerability at School Entry
Authors: Wall-Wieler Elizabeth , Roos Leslie L. , Gotlib Ian 
Published: December 2020

IntroductionStudies on the relationship between exposure to maternal depression in early childhood and childhood development have been limited by small samples, lack of information on timing of maternal depression, and use of a composite measure of childhood development.
Objectives and ApproachWe linked multiple Manitoba datasets to examine the relationship between exposure to maternal depression in early childhood and childhood development at school entry across five domains, and age at exposure to maternal depression on developmental outcomes using a population-based cohort (n = 52,103). Maternal depression was defined using physician visits, hospitalizations, and pharmaceutical data, while developmental vulnerability was assessed using the well-validated Early Development Instrument. Relative risk of developmental vulnerability was assessed using log-binomial regression models, adjusted for maternal and childhood characteristics at the birth of the child.
ResultsChildren exposed to maternal depression before age 5 had a 17% higher risk of having at least one developmental vulnerability at school entry than children not exposed to such depression before age 5. Exposure to maternal depression before age 5 was most strongly associated with social competence (aRR = 1.28, 95% CI 1.20, 1.38), physical health and well-being (aRR = 1.28, 95% CI 1.20, 1.36), and emotional maturity (aRR = 1.27, 95% CI 1.18, 1.37). For most developmental domains, exposure to maternal depression before age 1 and between ages 4 and 5 had the greatest association with developmental vulnerability.
Conclusion / ImplicationsOur findings that children exposed to maternal depression were at higher risk of developmental vulnerability at school entry is consistent with previous studies. However, we found that the association between exposure to maternal depression and development varied across developmental domains, and the relationship varied depending on the age of exposure to maternal depression. Ongoing analyses of discordant cousins will shed more light on the causal nature of this relationship.
Challenges in Data Linkage – Experiences from An Upper Gastrointestinal Cancer Data Linkage Study
Authors: Khan Nadia N , Ioannou Liane , Pilgrim Charles , Earnest Arul , Maharaj Ashika , Croagh Daniel , Liew Danny , Atwood David , Holland Jennifer , Philip Jennifer , Emery Jon , Ijzerman Maarten , Brown Wendy , Zalcberg John , Evans Sue 
Published: December 2020

IntroductionNearly one quarter of a million Australian workers experience a work injury annually and make a benefit claim through one of the nation’s eleven workers’ compensation (WC) systems. The total cost to Australian society has most recently been estimated at $61.8 billion or 4.1% of GDP. The disaggregation of legislative responsibility between jurisdictions has contributed to a lack of common data standards, and thus minimal understanding of the efficiency or effectiveness of service provision in the Australian WC sector.
Objectives and ApproachThis project developed a new multi-jurisdictional work disability database including detailed information on work disability duration, health and social care service provision. Service level payment data contained in structured WC insurance claims datasets held by five large WC jurisdictions with >60% coverage of the Australian labour force was collected for all cases of work-related low back pain, fractures and limb soft tissue disease over between 2010 and 2015. Database development involved creation and coding of harmonised service-level indicators for individual episodes of healthcare provision and weekly periods of wage replacement.
ResultsA total of 253,000 cases and 10.7 million service episodes are included in the database. Initial exploratory analyses focused on the frequency, prevalence, timing, intensity and continuity of General Practitioner (GP) services to each of the injury groups. Regression modelling examined occupational, injury, demographic and jurisdictional factors affecting GP service use outcomes. As anticipated, service patterns varied by injury type, age, gender and occupational group. Significant differences in service use between WC jurisdictions were observed.
Conclusion / ImplicationsThis exploratory study demonstrates the feasibility of developing a population-based service level database for monitoring health service delivery to injured Australian workers. Future studies will examine the impact of jurisdictional policy differences on service delivery, and the relationship between service delivery and outcomes such as disability duration.
Tracking the Development of Multimorbidity In the Australian Longitudinal Study on Women’s Health
Authors: Dobson Annette , Anderson Amy , Cavenagh Dominic , Egan Nick , Fitzgerald David , Forder Peta , Hockey Richard , Loos Colleen , Loxton Deborah , Waller Michael , Xu Zhiwei , Mishra Gita , Byles Julie 
Published: December 2020

IntroductionWith population ageing the prevalence of multi-morbidity (the co-occurrence of two or more chronic medical conditions) is increasing.
Objectives and ApproachOur goal was to use data linkage to obtain clinically validated data on the incidence of a range of common chronic conditions developed by participants in the Australian Longitudinal Study on Women’s Health (57,000 women) and hence to track the cumulative incidence of multi-morbidity over time.
ResultsThe major data sources differed for different conditions, e.g. the Pharmaceutical Benefits Scheme was important for identifying musculoskeletal conditions, whereas hospital admission data was crucial for identifying stroke. The most common combinations of conditions differed for women at different ages, e.g., mental health, musculoskeletal and respiratory conditions were most common for women born in 1989-95, whereas heart disease was a prominent part of multi-morbidity for women born in 1921-26. Among these older women about 50% had 3 or more chronic conditions in 2002 (when they were aged 76-81) and this increased to over 80% by 2015 (when they were 89-93). For comparison, among women born in 1973-78 fewer than 5% had 2 or more chronic conditions in 2002 (when they were aged 24-29) and this increased to about 15% in 2016 (when they were 38-43).
Conclusion / ImplicationsRecord linkage from multiple data sources, repeated over a long time span, is a powerful method for studying the development of multi-morbidity patterns in cohorts.
Identifying Incident Cancer Cases in Medicine Dispensing Claims: A Validation Study Using Australia’s Pharmaceutical Scheme (PBS) Data
Authors: Daniels Benjamin , Tervonen Hanna E , Pearson Sallie-Anne 
Published: December 2020

IntroductionDispensing claims are used commonly as proxy measures in pharmacoepidemiological studies, however, their validity is often untested.
Objectives and ApproachWe quantified the level of ascertainment and potential biases arising when using dispensing claims to identify incident cancer cases in cohort studies. We used Department of Veterans’ Affairs client data linked with the New South Wales (NSW) Cancer Registry and Repatriation and Pharmaceutical Benefits Scheme data. We included clients aged ≥65 residing in NSW between July 2004 and December 2012. We matched clients with a cancer diagnosis to clients without a diagnosis based on demographic characteristics and available observation time. We used dispensing claims for anticancer medicines dispensed between July 2004 and December 2013 as a proxy for cancer diagnosis and calculated sensitivity, specificity, positive predictive values and negative predictive values compared with cancer registry data (gold standard), overall and by cancer site. We illustrated the potential for misclassification by the proxy in a cohort of people initiating opioid therapy.
ResultsWe identified 15,679 incident cancer diagnoses in 14,112 clients from the cancer registry and 62,663 clients without a diagnosis. The proxy’s sensitivity was 30% for all cancers and ranged from 10-67% for specific cancers. Specificity was >90% for all cancers. The proxy correctly identified 26% of people with a cancer diagnosis who initiated opioid therapy, failed to identify 74% those with a cancer diagnosis, and was most robust for clients with breast cancer (61% were correctly identified).
Conclusion / ImplicationsUse of anticancer medicine dispensings for identifying people with incident cancer diagnosis is a poor proxy. Excluding people with evidence of anticancer medicine dispensing from cohort studies may remove a disproportionate number of women with breast cancer. Researchers excluding or otherwise using anticancer medicine dispensing to identify people with cancer in pharmacoepidemiological studies should acknowledge the potential biases introduced to their findings.
Predicting Care Homes with Limited Residential Information
Authors: Hollinghurst Joe , Akbari Ashley , Rodgers Sarah E , Lyons Ronan A , Watkins Alan , Fry Richard 
Published: December 2020

IntroductionResearch involving care homes is often difficult due to a lack of data and ethical issues. Wales (United Kingdom) contains approximately 1.3million residences, of these 717 are officially recorded as care homes for older people.
Objectives and ApproachOur objective was to develop a predictive methodology for identifying care homes in administrative data.
We used two data sources within the Secure Anonymised Information Linkage Databank to conduct our study. The Welsh Demographic Service Dataset (WDSD) contains all residences in Wales and demographic details of their occupants. An anonymised dataset of deterministically matched care home addresses was used to determine which of the residences in the WDSD were care homes.
We used details in the WDSD to determine the average age of the occupants, the number of people who moved into the residence in a year, and the number of people who died in a year. We were interested in care homes for older people and restricted all the residences in the WDSD to only those with an average age of 50+ years. We applied logistic regression to determine a probabilistic match for care homes based on the above characteristics. We determined an optimal cut-point for the probability of a residence being a care home based on the sensitivity and specificity.
ResultsRestricting the WDSD to have an average age of occupants of 50+ created a dataset of 3,939 residences, containing 562 care homes. After applying the logistic model to predict the care homes, we found an optimal probability cut-point which resulted in 548 true positives, 105 false positives, 14 false negatives, and 3,272 true negatives.
ImplicationsIdentification of care homes in an anonymised databank using only demographic data allows research into healthcare pathways for this hard to reach and under-researched population.
Provincial Overdose Cohort: Population Data Linkage During an Overdose Crisis
Authors: Xavier Chloé , Zhao Bin , Gan Wen Qi , Slaunwhite Amanda 
Published: December 2020

IntroductionIn 2016, the Provincial Overdose Cohort (ODC) was created following the declaration of the public health emergency in British Columbia (BC), Canada. The ODC is a set of longitudinal and linked administrative data which identifies illicit drug-related overdose events, including death, ambulance, emergency room, hospital, physician, and prescription drug records.
Objectives and ApproachThe ODC was developed to better understand factors associated with overdose in order to support response activities, prevent overdose deaths, and identify trends and opportunities for interventions. Person-level linkages were conducted using provincial health insurance and health history data; socio-economic information, mental and physical illness diagnoses, and corrections history were also appended. The ODC currently includes people who have had a drug-related overdose between January 1 st 2015 and December 31 st 2017 as well as a 20% random sample of the general population.
ResultsThe ODC contains 36,576 overdose episodes and 23,161 people who have a drug-related overdose between January 1 st , 2015 and December 31 st 2017. Of the 23,161 people, 3,604 (15.6%) had a fatal overdose and 19,557 (84.4%) had a non-fatal overdose. 49.9% of people in BC who had an overdose were 20-39 years of age and 67.4% were males. From 2015 to 2017, the proportion of people experiencing 3 or more overdoses a year increased from 3.6% to 8.7%, respectively. There were an increasing number of fatal and non-fatal drug-related overdoses in BC during this time period.
Conclusion / ImplicationsLarge population data linkages can be invaluable tools during a public health emergency. Collaborative partnerships and a shared data governance across jurisdictions was central in building the ODC and understanding how various social determinants of health impact risk of overdose among people who use drugs.
British Columbia’s Health Data Platform: Unleashing the Power of a Data Environment Commons for Health and Health System Improvement
Authors: Wong Shirley , Schuckel Victoria , Thompson Simon , Ford David , Lyons Ronan , Hier Richard 
Published: December 2020

IntroductionThere is no power for change greater than a community discovering what it cares about.1
The Health Data Platform (HDP) will democratize British Columbia’s (population of approximately 4.6 million) health sector data by creating common enabling infrastructure that supports cross-organization analytics and research used by both decision makers and cademics. HDP will provide streamlined, proportionate processes that provide timelier access to data with increased transparency for the data consumer and provide shared data related services that elevate best practices by enabling consistency across data contributors, while maintaining continued stewardship of their data. HDP will be built in collaboration with Swansea University following an agile pragmatic approach starting with a minimum viable product.
Objectives and ApproachBuild a data sharing environment that harnesses the data and the understanding and expertise about health data across academe, decision makers, and clinicians in the province by:

Enabling a common harmonized approach across the sector on:

Data stewardship
Data access
Data security and privacy
Data management
Data standards

To:

Enhance data consumer data access experience
Increase process consistency and transparency
Reduce burden of liberating data from a data source
Build trust in the data and what it is telling us and therefore the decisions made
Increase data accessibility safely and responsibly

Working within the jurisdiction’s existing legislation, the Five Safes Privacy and Security Framework will be implemented, tailored to address the requirements of data contributors.
ResultsThe minimum viable product will provide the necessary enabling infrastructure including governance to enable timelier access, safely to administrative data to a limited set of data consumers. The MVP will be expanded with another release planned for early 2021.
Conclusion / ImplicationsCollaboration with Swansea University has enabled BC to accelerate its journey to increasing timelier access to data, safely and increasing the maturity of analytics by creating the enabling infrastructure that promotes collaboration and sharing of data and data approaches.
1 Margaret Wheatley
Does Identifying Frailty from ICD-10 Coded Data on Hospital Admission Improve Prediction of Adverse Outcomes in Older Surgical Patients
Authors: Harvey Lara , Toson Barbara , Harris Ian , Gandy Robert , Close Jacqueline 
Published: December 2020

IntroductionAs the population ages, increasing numbers of older adults are undergoing surgery. Outcomes for older people are known to be worse than younger people following surgical procedures, and identifying which patients stand to benefit from surgery can be challenging.
Frailty is recognised as a major contributor to poor outcomes, however assessing frailty clinically is time-consuming and not routinely undertaken. Using data available from electronic medical records can potentially provide the opportunity to routinely screen for frailty electronically at time of admission.
Objectives and ApproachThis population-based external validation study aimed to: 1. assess the performance of the Hospital Frailty Risk Score (HFRS) in the prediction of adverse outcomes (mortality, prolonged length of stay (LOS) and 28-day readmission), 2. to determine optimal age-groups and lookback periods and 3. compare HFRS performance against the Charlson Comorbidity Index (CCI).
Hospital and death data for individuals (n=487,197) aged >50 years admitted under a surgical specialty to all public/private hospitals in NSW, Australia, 2013-2017 were linked. Logistic regression models were tested for each outcome of interest. Area under receiving operator curve (AUC) and Akaike information criterion (AIC) were assessed for each model.
ResultsFor prediction of 30-day, all models performed better than age and sex alone; however adjusting for CCI (AUC 0.76) provided marginally better prediction than adjusting for HFRS (AUC 0.75). Models consistently performed better in the younger age-group (50-65), providing excellent discrimination (AUC 0.82). In contrast, all models had poor ability to predict prolonged-LOS (AUC range 0.62- 0.63) or readmission (AUC range 0.62-0.65). Using a 5-year lookback period did not improve model discrimination over using a 2-year period.
Conclusion / ImplicationsAdjusting for frailty using the HFRS did not improve prediction of 30-mortality over that achieved by the CCI. Neither HFRS nor CCI were useful for predicting prolonged-LOS r 28-day unplanned readmission.