International Journal of Population Data Science最新文献

{"title":"Unplanned health service use among people living with cancer and mental health conditions: A retrospective matched case-comparison study in Australia.","authors":"Ramya Walsan, Rebecca Mitchell, Reema Harrison","doi":"10.23889/ijpds.v11i5.3513","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3513","url":null,"abstract":"<p><p>People living with mental health conditions experience persistent inequities across the cancer pathway, yet their patterns of unplanned service use remain poorly described. This study examined predictors of unplanned health service use among people with cancer, with and without mental health comorbidities, in Australia. A retrospective matched case-comparison study using data from three Australian states, linking non-admitted patient (NAP) oncology records to cancer registry, hospital admission, emergency department (ED) presentation, outpatient mental health, and mortality data. Adults aged ≥18 years living with cancer who had NAP oncology encounter between 2018-2021 were included. Individuals with mental health conditions were matched 1:1 to those without a mental health diagnosis using propensity scores. Negative binomial regression, adjusted for sociodemographic and clinical factors, examined unplanned ED presentations or hospitalisations within 12 months. Of 99,230 people living with cancer, 10,442 (10.5%) had a recorded mental health condition. After matching, individuals with mental health conditions had a 36% higher risk of unplanned ED use (IRR 95% CI 1.32-1.41), 31% higher risk of unplanned hospitalisations (IRR 95% CI 1.26-1.36) and 46% higher risk of cancer-related hospitalisations (IRR 95% CI 1.39-1.53) compared to those without. Virtual care use, digestive and lung cancers (vs breast) and physical comorbidities were also associated with increased unplanned use. Substantial inequities in unplanned acute care exist for people with cancer and mental health conditions. Strengthening integration between oncology and mental health services and refining virtual care delivery may reduce avoidable acute care use and support more equitable outcomes.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3513"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Severe maternal morbidity and mortality at the intersection of race and insurance status: a national serial cross-sectional study of United States births.","authors":"Amelia Srajer, Amity Quinn, Paul Ronksley, Erin Brennand, Amy Metcalfe","doi":"10.23889/ijpds.v11i5.3566","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3566","url":null,"abstract":"<p><strong>Objective: </strong>Medicaid, a public insurance program for low-income individuals, covers over 40% of United States births. It disproportionately serves racial minority groups who already face elevated obstetrical risks. Coverage gaps compared to private insurance may further exacerbate these adverse outcomes. Our objective was to assess severe maternal morbidity (SMM) and in-hospital mortality at the intersection of race and insurance status.</p><p><strong>Study design: </strong>We analyzed obstetric deliveries from 2015-2022 using the National Inpatient Sample. Ten interaction terms compared Medicaid versus private insurance across White, Black, Hispanic, Asian, and Native American groups. SMM complications were defined using ICD-10 codes. Demographics were compared using bivariate tests [p<0.05 and standardized differences>0.2]. Multivariable logistic regression models estimated odds ratios and 95% confidence intervals of SMM and mortality, adjusted for preexisting/gestational comorbidities, year, caesarean section, and hospital region.</p><p><strong>Results: </strong>Among 23.5 million weighted deliveries, 43.8% were insured by Medicaid. Medicaid coverage was higher among Black (66.7%), Hispanic (66.6%) and Native American (67.6%) individuals compared to White (32.3%) and Asian (27.9%) patients. Adjusted odds of SMM were higher among all intersectional groups compared to White patients with private insurance (all p<0.001). Within each racial category, SMM odds were significantly greater for the Medicaid versus private groups. However, increased odds of mortality were primarily observed for those with Medicaid rather than by race.</p><p><strong>Conclusion: </strong>Racial disparities in SMM are amplified for those with public insurance. However, mortality risk is primarily driven by insurance rather than race. Policy reform ensuring equitable coverage is critical to mitigate obstetrical inequities.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3566"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Missing Paternal Linkage in Administrative Data - A Candidate Father Approach.","authors":"Amani Hamad, Lin Yan, Barret Monchka, Oleguer Plana-Ripoll, Olawale Ayilara, Lisa Lix","doi":"10.23889/ijpds.v11i5.3651","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3651","url":null,"abstract":"<p><strong>Objective: </strong>Paternal linkages are often missing in administrative data. We propose and evaluate a method to identify candidate fathers using residence and demographic information.</p><p><strong>Approach: </strong>We conducted a population-based cohort study of Manitoba adults born 1977-2017 with maternal linkages. We modelled the probability of missing paternal linkage using key covariates for births post-1980, when missingness was highest. We applied this model to births pre-1980, when missingness was lowest, to exclude those with the highest predicted probability of missing linkage, at missingness levels of 10-50%. We identified candidate fathers in the same postal code area; up to three candidates were randomly selected using parental age-gap weights. We measured the percentage of candidate father sets containing the true father and estimated hazard ratios (HR) and 95% confidence intervals for paternal transfer of mental disorder and ischemic heart disease (IHD) for true versus candidate fathers.</p><p><strong>Results: </strong>The cohort included 140,001 births; 25.8% lacked paternal linkages, and 26.8% were born before 1980. Only 27-28% of candidate sets included the true father across missingness levels. For mental disorders, the true-father HR was 1.27 (1.23-1.31); candidate-father HRs ranged from 1.17 (1.13-1.22) at 50% to 1.24 (1.20-1.29) at 10% missingness. For IHD, the true-father HR was 1.94 (1.35-2.79); candidate-father HRs ranged from 1.64 (0.92-2.89) to 1.91 (1.32-2.75).</p><p><strong>Conclusions: </strong>Our method had low accuracy in identifying true fathers but produced similar risk estimates for true and candidate fathers, especially at lower missingness levels, suggesting utility for intergenerational research with incomplete paternal linkage.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3651"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kids' Environment and Health Cohort (KEHC): Linking Longitudinal Health and Environmental Histories for 11 Million Children Born in England (2006-2022).","authors":"Beth Allen, Esther Lewis, Sarah Cummins, Pia Hardelid, Joana Pereira Da Cruz, Sarah Barrett, Nathan O'Connor","doi":"10.23889/ijpds.v11i5.3753","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3753","url":null,"abstract":"<p><p>Children are uniquely vulnerable to environmental conditions, yet there is no national infrastructure in England that links environmental exposures to paediatric health outcomes at population scale. The Kids' Environment and Health Cohort (KEHC) is developing a transformative administrative data resource to address this gap. KEHC has established a national spine of over 11 million children born between 2006-2022 using official birth registrations, deterministically linked to 53,445 death registrations. Ongoing linkage to Hospital Episode Statistics (HES), Emergency Care Dataset (ECDS) and the Maternity Services Dataset (MSDS) will provide longitudinal information on childhood health trajectories and maternal health before and after birth. To capture environmental conditions from birth to adolescence, KEHC will link a diverse range of environmental datasets to children's address histories from the Patient Register (PR) and Personal Demographic Service (PDS). Environmental measures include annual mean concentrations of air pollutants, daily maximum temperature, green space availability, and housing indicators such as energy performance index and overcrowding. Environmental datasets will be deterministically linked using address strings and Unique Property Reference Numbers (UPRNs) derived from the Address Index Matching Service (AIMS), with clerical review to quantity the linkage error. This presentation will describe progress to date, outline methodological challenges and reflect on key lessons learned. The resulting cohort provides an unprecedented opportunity for studying environmental influences on child health and enabling the translation of population data science into evidence-based policy.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3753"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Including seldom-heard voices in a novel, circular and reflective data learning model: the Citizen Panel at UK Longitudinal Linkage Collaboration (UK LLC).","authors":"Robin Flaig, Lidis Garbovan","doi":"10.23889/ijpds.v11i5.3637","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3637","url":null,"abstract":"<p><strong>Background: </strong>The UK LLC Citizen Panel is a research and public engagement project which is the first proof of concept of an innovative model of learning in data research governance, proposed by Understanding Patient Data (UDPD).</p><p><strong>Methods: </strong>The Citizen Panel was designed in two stages, co-designed by a Steering Group and comprising of 15 members, with 50% participants from LPS collaborating with UK LLC and 50% public members from seldom-heard voices in longitudinal research. The Panel operated in two rounds, held six online meetings and two hybrid workshops.</p><p><strong>Results: </strong>The Citizen Panel conducted an end-to-end assessment (audit) of the UK LLC application process and provided two sets of innovative recommendations to UK LLC. This model enhanced scrutiny and audit of the UK LLC data access process, evolved the UPD data learning model for research governance and shifted the UPD idea towards a circular and reflective model of work, built on multiple feedback loops and embedded in co-design, mutual learning, dialogue and deliberation with public members from minoritized groups often under-represented in longitudinal research.</p><p><strong>Conclusions: </strong>The UK LLC Citizen Panel provided novel and important insights on the UK LLC data access process and decision making. The Panel could expand its impact beyond UK LLC to improve the UK's data infrastructure landscape and a model for national and international research organisations. The UK LLC Citizen Panel model is fundamental to good ethical practice and building trust with publics, inclusion of underserved and underrepresented groups and for broader dialogue between citizens and science.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3637"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Let's talk AI: Understanding public attitudes towards use of AI with sensitive data.","authors":"Elizabeth Waind, Katie Porter, Shayda Kashef","doi":"10.23889/ijpds.v11i5.3669","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3669","url":null,"abstract":"<p><p>The development and use of Artificial Intelligence (AI) with sensitive data in Trusted Research Environments (TREs) is a complex and rapidly evolving area of work. To ensure the use of AI with sensitive data meets public expectations, a deeper understanding of public attitudes is essential. In late 2025, as part of the UK Research and Innovation DARE UK (Data and Analytics Research Environments UK) programme, we conducted a pilot public deliberation with 22 individuals in England with limited prior knowledge of data research and AI. A series of three workshops with the same group tested approaches to knowledge development for this emerging and complex topic, and explore hopes and concerns about AI. Discussions explored training and development of AI models in TREs, and use of AI capabilities to support data analysis and risk assessment. Participants recognised the potential of AI to improve efficiency and accuracy when drawing conclusions from data, delivering public benefit. While existing TRE safeguards reassured participants about personal privacy, fears of data and AI misuse persisted. Trust, transparency, oversight and regulation emerged as critical themes, and participants emphasised that AI should remain a tool, not a decision-maker. Participants called for diverse and representative AI training data to mitigate bias, and greater efforts to increase public awareness. These insights will inform a larger, UK-wide public deliberation on the same topic in 2026, in partnership with ADR UK (Administrative Data Research UK) and others. We look forward to sharing the interim findings of this wider work in July 2026.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3669"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing trust and accountability through community governance of immigration data: embedding community perspectives.","authors":"Elise Leong-Sit, Laura Ferreira-Legere, Astrid Guttmann, J Michael Paterson, Sujitha Ratnasingham, Baiju Shah, Dominique Legacy","doi":"10.23889/ijpds.v11i5.3571","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3571","url":null,"abstract":"<p><strong>Objective: </strong>Outside recognized Indigenous Data Governance Principles, there is a growing movement towards community governance of other demographic data. Community governance seeks to recenter decision-making about how data are used and reported with those who are the subject of the data, but broad understanding on how to operationalize this principle is lacking. We sought to develop a community-guided governance approach for the use of immigration data at ICES, a health data institute which holds administrative health and demographic data for the province of Ontario, Canada.</p><p><strong>Approach: </strong>Community perspectives to guide our approach were gathered using semi-structured focus groups. 36 individuals were recruited using purposive and snowball sampling based on their lived or professional expertise in immigration.</p><p><strong>Results: </strong>Key recommendations emerged from the discussions, including 1) clearly delineating which research projects would require community engagement; 2) emphasizing the need for reciprocity in community relationships; 3) building researchers' capacity in critical reflexivity and data contextualization; 4) developing an organizational change management strategy to socialize expectations; and 5) establishing an intersectionally diverse Community Governance Advisory Table to guide and oversee the approach.</p><p><strong>Conclusion: </strong>These discussions highlighted tangible operational considerations, including the importance of engagement with immigrant communities and of change management when implementing structural-level change to organizational processes. We will employ a participatory approach to further develop and operationalize community members' recommendations.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3571"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perinatal Mental Illness in Those With and Without Pre-existing Diagnosis of ADHD: A Population-Based Study.","authors":"Amreen Babujee, Hilary Brown, Jonathan Zipursky, Joel Ray, Eyal Cohen, Natasha Saunders, Alene Toulany, Tara Gomes, Lucy Barker, Cindy-Lee Dennis","doi":"10.23889/ijpds.v11i5.3512","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3512","url":null,"abstract":"<p><p>Attention Deficit Hyperactivity Disorder (ADHD) affects 1 in 50 women of reproductive age. The perinatal period is a time of heightened vulnerability to sleeplessness, stress and mental illness. Pre-pregnancy ADHD and adverse perinatal psychiatric outcomes is understudied. This population-based cohort study used health administrative data in Ontario, Canada, 2013-2023. Compared were 22,995 mothers with vs. 1,028,038 mothers without pre-pregnancy ADHD. The main outcome was a non-ADHD-related mental healthcare system contact from conception up to 365 days postpartum. Secondary outcomes included contacts for specific psychiatric disorder subtypes (anxiety, bipolar, depressive, psychotic, substance/alcohol use disorders), and for severe psychiatric illness (psychiatric ED visits, hospitalizations, intentional self-injury). Relative risks (aRR) and 95% CI were adjusted for maternal age, parity, immigration status, income quintile, rurality, preconception mental illness, infant sex, and year of delivery. Pre-pregnancy maternal ADHD was associated with elevated aRR for mental health contact for any psychiatric disorder (1.35, 95% CI 1.33-1.38), all psychiatric disorder sub-types with aRRs ranging from 1.34 to 2.37, psychiatric ED visits (1.98, 95% CI 1.88-2.08) or hospitalizations (2.26, 95% CI 2.06-2.48), and intentional self-injury (2.16, 95% CI 1.88-2.49). Pre-pregnancy maternal ADHD is a risk factor for various forms of perinatal mental healthcare contact, highlighting the need for targeted supports and services to enhance prevention and early identification in this vulnerable group.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3512"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Privacy, a key pillar in realizing ethical Open Science in resource constrained settings: Balancing Privacy, Protection and Accessibility.","authors":"Bonface Ingumba","doi":"10.23889/ijpds.v11i5.3465","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3465","url":null,"abstract":"<p><p>Researchers in resource-constrained settings continue to face ethical challenges in collecting, using, and sharing data within Trusted Research and Data Environments (TRDEs). While open science promotes transparency, equity, and collaboration, it also heightens privacy concerns amid the rapid spread of artificial intelligence, big data, and machine learning. The African Population and Health Research Center (APHRC) is leading efforts to strengthen ethical data governance in Africa, focusing on how to protect privacy without restricting fair access to data. Growing risks such as data protectionism and restrictive monetization threaten cross-border collaboration and deepen inequalities across low- and middle-income countries (LMICs). APHRC's approach balances individual rights with collective benefits by treating privacy as both a safeguard and an enabler of open science. Guided by the principle that data is a public good, the Center aligns global frameworks-such as the AU Data Policy Framework, GDPR, and UNESCO's Open Science Recommendation, with regional instruments and practical innovations. These include the APHRC Central Data Catalogue, a secure repository governed by the Data Sharing Policy; improved metadata systems aligned with FAIR principles; a Data Governance Curriculum on the Virtual Learning Academy; the DASSA Platform supporting federated learning; and a low-code anonymization tool that simplifies privacy practices. Through these initiatives, APHRC demonstrates enhanced data visibility, capacity building, and trust while embedding methods such as k-anonymization, differential privacy and federated learning. This experience shows that context-sensitive governance rooted in CARE and FAIR principles can advance ethical, inclusive, and globally interoperable open science.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3465"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Country Comparison of Medication Adherence After Stroke Using Standardised Methods: A Pilot Distributed Network Analysis for INSPIRE-STROKE.","authors":"Lachlan L Dalli, Monique F Kilkenny, Anna Chu, Jenni Ilomaki, Cynthia A Jackevicius, Moira K Kapral, Manav V Vyas, Muideen T Olaiya, Dominique A Cadilhac, Adam C Livori","doi":"10.23889/ijpds.v11i5.3707","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3707","url":null,"abstract":"<p><strong>Background: </strong>Post-stroke medication adherence can be assessed using dispensing data; however, variations in methods hinder reliable comparisons across regions. We developed and implemented standardised methods to compare post-stroke medication adherence between countries.</p><p><strong>Methods: </strong>Australia and Canada, with population-based data and universal healthcare systems, were selected for this INSPIRE-STROKE proof-of-concept distributed network analysis (www.inspire-stroke.bigdatacvd.com). Metadata were shared to harmonise data variables and structure. Patients admitted with incident ischaemic stroke and atrial fibrillation (Victoria, Australia [July 2014-June 2017]; Ontario, Canada; [July 2014-March 2022]), aged 66-105 years, who survived ≥90 days post-discharge were included. The proportion of days covered (PDC) of direct oral anticoagulants (DOACs) was measured from initiation to 1-year post-discharge. Adherence was measured in 8 simulations, using standardised code, to adjust for different PDC measurement parameters from the TEN-SPIDERS framework (www.tenspiders.adherencelab.com). Aggregated outputs were compared between countries.</p><p><strong>Results: </strong>Among 2471 Australians (50% female; 36% aged 85+ years) and 16622 Canadians (51% female; 34% aged 85+ years), 50% and 65% initiated a DOAC within 90 days post-stroke, respectively. Among initiators, median 1-year DOAC PDC was 86.8% (interquartile range [IQR]: 75.9%-92.6%) in Australia vs 96.2% (IQR: 89.6%-98.6%) in Canada. Adjustment for pre-supply, early refills, and switching increased median PDC by +10.3% in Australia and +3.5% in Canada. Further adjustment for in-hospital stays had negligible impacts on PDC in both countries.</p><p><strong>Conclusions: </strong>Standardised methods were successfully implemented to facilitate cross-country comparisons of post-stroke medication adherence. Use of standardised methods in other countries will enable reliable global surveillance of post-stroke medication adherence.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3707"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}