Health Datasets Research Articles

Associations between sensory impairment and functional limitations among older Chinese adults: mediating roles of social isolation and cognition

BackgroundThe high prevalence of sensory impairment and functional limitations in older adults is a significant concern, yet there is limited understanding of the relationship between these two conditions. Therefore, the objective of this study was to investigate the pathways connecting sensory impairment and functional limitations by examining serial multiple mediating effects of social isolation and cognition in older adults.MethodsUsing the China Health and Retirement Longitudinal Study dataset, a sample of 4871 older adults was selected. The study variables included sensory impairment, functional limitations, social isolation and cognition, and other covariates. A hierarchical multiple linear regression model was used to assess the association between sensory impairment and functional limitations. Mediation analysis was conducted to explore the sequential multiple mediating effects of social isolation and cognitive function in the relationship between sensory impairment and functional limitations.ResultsOur findings revealed a significant and positive association between sensory impairment and functional limitations (B = 0.615, 95% CI: 0.397, 0.834). After adjusting for social isolation and cognitive function, the impact of sensory impairment on functional limitations accounted for 87.19% of the total effect. Additionally, approximately 12.81% of the significant relationship between dual sensory impairment and functional limitations was mediated by social isolation and cognitive function. A serial multiple mediating pathway (sensory impairment → social isolation → cognition → functional limitations) was identified, with a coefficient of 0.013 (95% CI: 0.006, 0.020).ConclusionsOur study provides evidence for the mediating effects of social isolation and cognition in the relationship between sensory impairment and functional limitations. Given the prevalence of functional limitations among older adults with sensory impairment, it is crucial to consider social isolation and cognitive function in efforts to reduce the burden of disability care. Future validation of these findings through longitudinal studies is necessary.

Impact of symptom-to-balloon time in patients with non-ST-segment elevation myocardial infarction and complex lesions.

Considering the limited data regarding clinical outcomes of patients with non-ST-segment on the ECG elevation myocardial infarction (NSTEMI), this study compared the outcomes of patients undergoing percutaneous coronary intervention with newer-generation drug-eluting stents stratified by the presence/absence of complex lesions and symptom-to-balloon time (SBT; <48 h or ≥48 h). We enrolled 4373 patients with NSTEMI from the Korea Acute Myocardial Infarction Registry-National Institute of Health dataset and stratified them into the complex group (2106 patients; SBT < 48 h, n = 1365; SBT ≥48 h, n = 741) and the noncomplex group (2267 patients; SBT < 48 h, n = 1573; SBT ≥48 h, n = 694). The primary outcome was the 3-year all-cause mortality rate. The secondary outcomes were any major adverse cardiac events (MACE), including cardiac death (CD), recurrent myocardial infarction, and stroke. The incidence of all-cause mortality (adjusted hazard ratio, 0.656; P = 0.009), CD (P = 0.037), and MACE (P = 0.047) in the complex group and of stroke in the noncomplex group (P = 0.020) were significantly lower in patients with SBT < 48 h than in those with SBT ≥48 h. Among patients with SBT < 48 h, the stroke incidence (P = 0.019) was higher in the complex group than in the noncomplex group, while among patients with SBT ≥48 h, the MACE incidence (P = 0.011) was higher in the former than in the latter. SBT reduction effectively decreased the 3-year mortality in patients with NSTEMI in the complex group compared with the noncomplex group.

Development of a predictive model for the U-shaped relationship between the triglyceride glycemic index and depression using machine learning (NHANES 2009–2018)

BackgroundAt present, the relationship between depression and the triglyceride glycemic (TyG) index remains a topic of debate. This study sought to elucidate the relationship between depression and the TyG index to create a predictive model that would help doctors diagnose patients. MethodsWe conducted a cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) dataset, which comprises data from 2009 to 2018. The analysis involved 11,222 adults with a Patient Health Questionnaire-9 (PHQ-9) score of 5 or higher, indicating the presence of depression. As part of the analysis, multiple regression models were used to test whether a linear relationship existed between the TyG index and depression. A threshold effects analysis was used to generate smoothed curves and detect nonlinear correlations. Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to identify the key risk factors associated with depression. The factors identified were then used to construct the risk prediction nomogram. Finally, Receiver Operating Characteristic (ROC) curves were used to evaluate the discriminative performance of the model. ResultsMultivariable linear regression analysis indicated a strong positive correlation between depression and the TyG index (β: 0.38, 95 % CI: 0.16–0.60, p = 0.0008). A U-shaped relationship with an inflection point was observed at a TyG index of 8.16. The nomogram model, constructed using risk factors identified by LASSO, exhibited a significant predictive value (AUC = 0.888). ConclusionsThe results of this investigation point to a U-shaped association between depression risk and the TyG index among Americans. Those with a TyG index of over 8.16 are significantly more likely to develop depression. These results suggest a possible causal relationship and emphasize the importance of monitoring the TyG index in depression risk assessment.

The role of body mass index in the association between serum vitamin D and non-alcoholic fatty liver disease: A mediation analysis

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a classic disease of liver injury, and studies have confirmed that nutrients can influence NAFLD. Findings on serum vitamin D (VD) and NAFLD are conflicting and the mechanisms are not yet clear. This study aimed to determine the association between the two and the mediating role mediated by body mass index (BMI). MethodsA cross-sectional study (N = 2609) was conducted using the 2017–2018 National Health and Nutrition Examination Survey (NHANES) dataset. Weighted multivariate logistic regression models estimated OR and 95 % CIs between serum VD and NAFLD. The mediation analysis was performed for BMI. ResultsSerum VD was statistically significant in subjects with and without NAFLD (P = 0.026). Adequate serum VD was found to be a protective factor for NAFLD after adjusting for covariates (OR (95 %CI) of 0.717 (0.553,0.929)). After mediation analysis, it was found that BMI mediated 34.07 % of the effect of serum VD on NAFLD. ConclusionThere is an association between serum vitamin D and NAFLD and sufficient VD was protective factor for NAFLD. BMI partly mediated the association between serum VD and NAFLD.

Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand

BackgroundIn the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aimed to apply ML methods on extensive national-level health and social administrative datasets to assess the utility of these for predicting future diabetes complications, including by ethnicity.MethodsFive ML models were used to predict CVD events among all people with known diabetes in the population of New Zealand, utilizing nationwide individual-level administrative data.ResultsThe Xgboost ML model had the best predictive power for predicting CVD events three years into the future among the population with diabetes (N = 145,600). The optimization procedure also found limited improvement in prediction by ethnicity (using area under the receiver operating curve, [AUC]). The results indicated no trade-off between model predictive performance and equity gap of prediction by ethnicity (that is improving model prediction and reducing performance gaps by ethnicity can be achieved simultaneously). The list of variables of importance was different among different models/ethnic groups, for example: age, deprivation (neighborhood-level), having had a hospitalization event, and the number of years living with diabetes.Discussion and conclusionsWe provide further evidence that ML with administrative health data can be used for meaningful future prediction of health outcomes. As such, it could be utilized to inform health planning and healthcare resource allocation for diabetes management and the prevention of CVD events. Our results may suggest limited scope for developing prediction models by ethnic group and that the major ways to reduce inequitable health outcomes is probably via improved delivery of prevention and management to those groups with diabetes at highest need.

Estimating the effect of inclisiran on hypercholesterolemia and primary prevention of cardiovascular disease: the NHANES 1999–2018 study

BackgroundHypercholesterolemia has been identified as an independent predictor of cardiovascular disease (CVD). Inclisiran, an innovative small interfering RNA agent, is anticipated to result in a notable reduction of approximately 50% in low-density lipoprotein cholesterol (LDL-C) levels. Given its transformative impact, this study scrutinized the eligibility of the US population for inclisiran treatment and evaluated its potential effects on hypercholesterolemia and the primary prevention of CVD.MethodsThis study applied the eligibility criteria from the ORION 10 and 11 trials to the 1999–2018 National Health and Nutrition Examination Survey (NHANES) dataset to estimate the size of the eligible population for atherosclerotic cardiovascular disease (ASCVD) and ASCVD-risk equivalents. Utilizing the reduction in LDL-C levels from ORION 10, this study predicted the impact of inclisiran on LDL-C levels among ASCVD patients. Similarly, leveraging the changes in lipid levels from ORION 11, this study predicted inclisiran’s effect on the 10-year change in CVD risk and preventable CVD events in the ASCVD-risk equivalents population, employing the Framingham CVD Risk Score.ResultsThe study identified 579 ASCVD patients (5 million) and 382 ASCVD-risk equivalents (2.66 million) who met the eligibility criteria from ORION 10 and 11. Among the ASCVD population, 3.5 million (70.2%) would achieve a ≥ 50% reduction in LDL-C levels after treatment. Furthermore, 4.6 million (91.3%) would achieve LDL-C levels < 70 mg/dL, and 3.8 million (75%) would achieve LDL-C levels < 55 mg/dL after treatment. For the ASCVD-risk equivalents population, the estimated 10-year CVD risk would decrease from 25.3 to 17.7%, an absolute reduction of 7.6% and a relative reduction of 30% following inclisiran treatment, potentially preventing 202,353 CVD events over a decade, including 138,084 coronary heart disease cases, 37,351 strokes, and 23,894 congestive heart failure cases.ConclusionsInclisiran has the potential to substantially reduce the prevalence of hypercholesterolemia and prevent nearly 200,000 CVD events in eligible US adults.

National stakeholder consultation on how to measure care home residents' quality of life.

The Developing research resources And minimum data set for Care Homes' Adoption and use or DACHA study aims to create a prototype minimum data set combining residents' information recorded by care homes with their data held in health and social care data sets. The DACHA minimum data set will contain information on quality of life. Internationally and in the UK, there is no consensus on collecting information on quality of life in a standardised format equivalent to the consensus for health measures. This paper describes an online consultation with stakeholder representatives about how to measure the quality of life of residents in UK older-adult care homes, for inclusion in the DACHA minimum data set. We drew on principles of the Delphi technique, identifying participants knowledgeable about living, working in and visiting care homes, and preference scoring. We used a bespoke online research engagement platform (Thiscovery, www.thiscovery.org, Cambridge, UK) to engage the participants. Participants included care home staff and managers, old age specialists (clinical/research), commissioners/providers/regulators, primary care professionals, relatives/family carers of care home residents. The consultation is complementary to DACHA's research and patient and public involvement and engagement activities, which have involved people living in care homes; thus, care home residents were not included in this consultation. The first round asked 30 participants to rank the most important principles and domains to consider when measuring quality of life in care homes. Responses to round 1 informed the selection of quality of life measures that round 2 (September 2022) participants were asked to report their familiarity with and confidence in a range of outcome measures all of which met the criteria identified as important in round 1. Recruitment was extended in round 2, and 72 individuals participated. Based on the rankings and the qualitative feedback in round 2, we included four of the shortlisted quality of life outcome measures in DACHA's prototype minimum data set for care homes. The qualitative feedback suggested a shared understanding across the different representative groups about the strengths and limitations of the selected measures. This work makes an important contribution, understanding the opportunities that quality of life measures pose for different stakeholder groups as regular users of care home resident data. In future DACHA work, interviews and focus groups will collect further data about the perceptions of care home staff who completed measures during the pilot study and about the usefulness of the data collected via these measures. The quality-of-life section of the DACHA minimum data set can contribute to informing similar care home data sets internationally. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR127234.

Effects of community water fluoridation on child dental caries in remote Northern Territory, Australia: a difference-in-difference analysis.

Community water fluoridation (CWF) is a cost-effective intervention to reduce dental caries at population level. This Australian study used a difference-in-difference (DiD) analysis to measure dental caries in children exposed to CWF in the Northern Territory (NT), Australia. Oral health data obtained from the NT Department of Health contained 64 399 person-year observations from 2008 to 2020, totalling 24 546 children aged 1-17 years. Drinking water quality data for fluoride levels, held by the Power and Water Corporation, were obtained for 50 remote communities and linked to the oral health dataset. The DiD analysis used a treatment group and two control groups to compare the effects of CWF on dental caries outcomes in children, measured using the decayed, missing and filled teeth (dmft/DMFT) index. The treatment group consisted of records from children residing in five remote NT communities that implemented CWF in 2014.The control 1 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies at levels at or above the Department of Health policy threshold of 0.5 mg/L. The control 2 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies below the level recommended by the Department of Health policy (<0.5 mg/L). The data were grouped into time periods prior to the inception of CWF in five remote communities in 2014 (pre-intervention) and after 2014 (post-intervention). Our results demonstrated that dental caries was significantly decreased for children in the treatment group following the implementation of CWF at a greater magnitude than both control groups for the same time period. Overall, children assigned to the treatment group exhibited a decline in the number of teeth affected by caries by an average of 0.28 (p=0.001). Notably, children of ages 7-10 years and 11-17 years experienced significantly greater post-intervention declines in average dmft/DMFT, by 0.32 (p=0.051) and 0.40 (p=0.012) fewer affected teeth respectively. While dental caries disproportionately impacts Aboriginal children in remote and very remote NT, it is clear that CWF produces population-level reductions in overall dental caries for these populations. Additionally, our study demonstrates the application of the DiD method in a public health policy evaluation. Our findings suggest that the longstanding policy position of the NT Department of Health on CWF has supported improvements in oral health among child populations that experience high levels of dental caries in remote NT communities.

Abstract B170: Advancing cancer disparities research through the integration of new data: Opportunities from the National Institutes of Health All of Us Research Program

Abstract The NIH All of Us Research Program is building one of the largest, richest, and most diverse health datasets of its kind. The program aims to promote health equity across multiple health conditions including cancer by making a comprehensive and diverse dataset available to over 10,000 vetted and registered researchers on its Researcher Workbench (RW). The RW offers access to various data types including genomics, electronic health records, physical measurements, wearable data, and survey data on social determinants of health (SDOH) and self- reported cancer diagnoses. The latest data release (Q3 2024), also known as the Curated Data Repository (CDR Version 8) includes data from over 630,000 participants, with more than 80% from populations underrepresented in biomedical research (UBR). Racial, ethnic minoritized populations and other underrepresented groups are more likely to experience health-related disparities and inequities. While previous data releases included aggregate categories, new data released in CDR Version 8 considers the interplay of self-identified categories of demographic descriptors and allows the examination of comorbidities, while emphasizing the importance of identifying appropriate comparator groups in research and considering the heterogeneity that exists within and across groups. It includes a 40% increase in array data, a 67% increase in srWGS data, and a 150% increase in long-read sequences, as well as data from expanded EHR sources, DHT devices, a new Life Functioning Survey, and racial and ethnic subcategories for more than 610,000 participants. This vast and diverse dataset release enables the examination of the intersecting impact of SDOH on cancer-related outcomes both within and across racial and ethnic populations, including populations experiencing health-related disparities and inequities. The extensive data release is facilitated by a two-pronged approach: A robust community and participant engagement model and the All of Us Health Equity strategic plan (HESP). The program employs a socioecological model for participant recruitment and retention, specifically targeting UBR groups. This multi-level approach encompasses activities ranging from targeted outreach and awareness campaigns to establishing trust and fostering collaborative partnerships with communities and participants. HESP complements these efforts by promoting the integration of equity considerations across the research lifecycle through the promotion of data equity and data justice. This community engagement and equity integrated approach guarantees that participants who reflect the demographic diversity of cancer patients in the United States and territories are actively engaged in shaping and benefiting from cancer disparities research. This session will update the scientific audience on recent data expansions available to researchers, provide examples of how that data can be used to examine disparities in cancer outcomes and prevention, and demonstrate the importance of community engagement and health equity in advancing research. Citation Format: Karriem S Watson, JoAnn Smith, Nandita Rahman, Ryan Lupi, Rubin Baskir, Romuladus Azuine, Janeth Sanchez. Advancing cancer disparities research through the integration of new data: Opportunities from the National Institutes of Health All of Us Research Program [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B170.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Vitamin C and suicidal ideation: A cross-sectional and Mendelian randomization study

BackgroundStudies have shown that a history of suicide attempts is associated with low levels of antioxidant vitamins. However, the specific relationship between vitamin C and suicidal ideation remains poorly understood. The purpose of this study is to investigate the effect of vitamin C on the development of suicidal ideation and to explore further the causal relationship using Mendelian randomization (MR). MethodsUsing the 2005–2018 National Health and Nutrition Examination Survey (NHANES) dataset, a comprehensive, cross-sectional, stratified survey of 28,623 participants aged 20 years and older was conducted. Suicidal ideation was assessed using item 9 of the Patient Health Questionnaire-9 (PHQ-9) item. The relationship between vitamin C and suicidal ideation was revealed by weighted multiple linear regression, smooth curve fitting, hierarchical analysis, and interaction test. To determine causality, MR was performed on vitamin C and suicidal ideation. ResultsIn cross-sectional studies, all models showed a negative association between vitamin C and suicidal ideation, with a 37 % reduction in the risk of suicidal ideation for each unit increase in vitamin C in the highest quartile in the fully adjusted model. The results of the subgroup analysis showed that the association between vitamin C and suicidal ideation was stable across gender, age, education, smoking status, and marital status. In the MR study, our bivariate model showed no significant causal relationship between vitamin C and the development of suicidal ideation. ConclusionsDietary vitamin C intake was negatively associated with the development of suicidal ideation. However, genetic evidence does not support causation of these associations.

Evolving the HDRUK Phenotype Library: Phenotype Creation and Editing

ObjectiveThe HDRUK Phenotype Library (phenotypes.healthdatagateway.org) shares definitions used to measure concepts of interest (such as diagnoses or treatments) within health datasets. It already holds more than 1000 phenotypes, with researchers able to contribute their work via an API. We aimed to create a more user-friendly method of contributing to the Library. ApproachWe designed a phenotype creation workflow enabling users to create and submit new content via web interface. Goals included clarity and ease of use for a broad range of users. ResultsA home page shows researchers’ own content. Researchers can create new phenotypes using a web form, entering metadata such as name, authors, description, and publications. Code lists are defined via one or more rules, including search terms or referring to another phenotype, or by CSV upload. Users can make their phenotypes accessible to a research group or to all authenticated users, as well as publish content on the web. Publication requests are reviewed to ensure content is complete and appropriate. Editing, with full history and version control, is also supported. ConclusionsWe implemented and released the new features. We are currently engaging researchers to get feedback and invite content submission. ImplicationsThe benefit of tools to support research transparency and repeatability is only realized when they are adopted. We hope that a GUI to support phenotype creation will broaden the Library’s user base help it serve as an enabler of higher-quality, more efficient research across the worldwide health data research community.

The Networked Data Lab: lessons from four years of analytical collaboration on common problems in health on social care

ObjectiveThe Networked Data Lab (NDL) is a UK-wide network of analytical teams embedded in the health and care system. Each year, we support five local partners to acquire and link new health and social care datasets on a different topic. The NDL provides insights from this linked data to help UK policymakers improve health and care, leveraging public, patient, and stakeholder engagement to prioritise topics of shared local interest. Approach Now in its fourth year, the NDL has commissioned a Learning Study (LS) to evaluate progress towards its aims. This employs a theory-based approach to test and assess key assumptions about programme impact through a Theory of Change. For the study, semi-structured interviews with analysts and stakeholders at the five NDL sites were conducted to identify factors influencing the programme’s impact. ResultsDrawing on interim LS findings, we will describe the types of impact the programme has achieved, and some of the key barriers and enablers to analytical collaboration across geographic and organisational boundaries. Themes include senior sponsorship; stakeholder input into problem definition; information governance and tools and skills. Themes will be illustrated with case studies drawn from the different NDL topic areas, including children and young people’s mental health and unpaid carers. Conclusions and ImplicationsThe Networked Data Lab shows that data linkages can provide unique insights that local service planners can act on, but highlights a complex set of conditions that need to be met to enable impactful analytics using real-world health and care data.

Prenatal and early life maternal substance use prevalence among Australian children born 2007-2018: a data linkage study using health, death, and child protection data

ObjectiveQuantify the scale and type of maternal substance use from conception to the child’s second birthday (First 1000 days) to inform screening and support services that may reduce associated harm or risk for children. ApproachWe used mother and child records from whole-population health, death, and child protection datasets to ascertain maternal substance use during the First 1000 days for children born in NSW, Australia, from 2008-2017. The primary outcome - maternal substance use - included use of illicit substances, alcohol, opioid-agonist treatment, organic compounds, solvents, and misuse of prescription medicines. ICD-10 and SNOMED-CT diagnosis codes were used. ResultsThe birth cohort included 970,470 children and 625,856 mothers. 32,000 children (3.4%) had a record of maternal substance use during the First 1000 days of life, including 13,647 (1.4%) with alcohol and 23,485 (2.4%) other drug use. Ascertainment was highest from child protection records (26,045 children), followed by mother’s (12,956 children) then children’s hospital records (3,826 children). 18,672 (1.9%) children had a record of carer substance use only in child protection records. Combining data increased the prevalence estimates; adding child protection records increased the prevalence estimate to 3.4%, compared with 1.2% in health and death records alone. ConclusionMore than 3 in every 100 Australian children had a record of maternal substance use in administrative data during the First 1000 days of life in this decade-long study. In addition to health and death data, child protection data offers public health insights into the scale of maternal substance use among whole-population cohorts of children.

The Networked Data Lab: using linked data to understand intermediate care services in the United Kingdom

Intermediate care (IC) is short-term care aimed at maximising patients’ independence. Across the UK, scaling up IC is included in post-pandemic recovery plans. This could reduce pressures on acute services through timely discharge or preventing admissions. The issues IC seeks to address are common across high-income countries. The Networked Data Lab is a network of analytical teams embedded in the UK health and care system. Each year, we support five local partners with acquisition, linkage and analysis of health and social care datasets. Our 2023-24 project is focused on IC. Patients and the public (PP) played an integral role in throughout our project. We established a panel of individuals with lived experience of IC to assist with the research questions, interpretation, and communication of results. Our partners all consult PP in their areas to inform their analyses. Our partners’ analyses of linked patient-level data on IC will provide information about who is receiving IC, changes over time and outcomes for patients. We are also analysing national linked data for England on intermediate care following hospital discharge. Preliminary results include: The age and health needs of people receiving bed-based intermediate care has increased over time (Grampian, Scotland) Intermediate care following a hospital admission is more common for emergency admissions than elective admissions (Leeds, England) Large variation in intermediate care delivery between neighbouring boroughs (North West London, England) Full results are expected in April 2024. Our work highlights the importance of linking data to answer important health and care questions.

The CanPath-HDRN Canada Collaboration: Enabling Multi-jurisdictional Research in Canada

ObjectiveTo highlight the partnership between the Canadian Partnership for Tomorrow’s Health (CanPath) and Health Data Research Network (HDRN Canada), which enables researchers to link CanPath’s health and lifestyle survey data to health records and related data across multiple regions. BackgroundCanPath is a population health study of over 350,000 Canadians from seven regional cohorts across all ten provinces, making it one of the world's largest population cohorts. HDRN Canada is a network of member organizations, including provincial, territorial, and pan-Canadian data centres. ApproachThis partnership leveraged collective expertise and resources to facilitate the linkage of CanPath’s harmonized data to multi-regional health and health-related administrative data held at HDRN Canada’ s data centres. Researchers can access these data for multi-regional projects through HDRN Canada’s Data Access Support Hub, which provides a single access portal. ResultsResearchers were able to successfully link CanPath’s survey data to provincial administrative health data. The collaboration’s streamlined process for data access enhances efficiency and facilitates pan-Canadian population health research. ConclusionThis collaboration demonstrates the feasibility and value of linking population health datasets while demonstrating the challenges and opportunities associated with accessing national administrative health data within a federated health data system. ImplicationsThe partnership between CanPath and HDRN Canada has significant implications for advancing population health research in Canada. By providing researchers with access to linked data from diverse sources, the partnership enables comprehensive investigations into health determinants, disease patterns, and clinical outcomes. This enhances the scope and depth of population health research in Canada, thereby leading to a better understanding of the most pressing health challenges.

Improving reproducibility and validity in measures of chronic disease incidence using large-scale linked data in Australia.

Objective and ApproachOur study aimed to improve methods for identifying incident chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes in a young cohort from the Antecedents of Renal Disease in Aboriginal Children (ARDAC) study. We linked 20 state and federal administrative health datasets, including hospital records, Medicare claims, pharmaceutical data, and the National Diabetes Services Scheme (NDSS) data for the first time. This approach facilitated the development of new algorithms for disease detection. Notably, we will make the R code for these algorithms publicly available. ResultsThe analysis, involving 3,758 ARDAC study participants, created clinically robust definitions of CKD, CVD, and diabetes (by subtype) in the Australian linked data context; and revealed that all other data sources inaccurately estimated the incidence of diabetes compared to the NDSS data. ConclusionsThis key finding illustrates the discrepancy in diabetes incidence estimates and highlights the value of integrating multiple data sources for the investigation of chronic disease. ImplicationsHundreds of published studies use data linkage to investigate CVD, CKD, and diabetes in the Australian context. Our novel robust case definitions, and the identified poor estimation of diabetes incidence among young people, underscores the need to critically evaluate linked data sources used to inform health policy and research. By sharing the developed R code, we support ongoing efforts to improve transparency and replicability in health research. Our study refines chronic disease definition methods and contributes to more effective public health strategies, with particular focus on improving outcomes for Indigenous communities.

Towards a "Biography" of Population Health Data about Substance Use Disorders: Qualitative Approaches to Communicating Context

BackgroundUnderstanding contexts of data production is necessary for using data accurately and ethically. “Biographies of data"" have developed as a complement to existing metadata as a way to critically examine and communicate social structures and power dynamics encoded in data. This project is focused on developing a “biography” of diagnostic codes, used to identify substance use disorders (SUD) in population health datasets. People with SUD regularly experience stigma and exclusion, particularly from healthcare settings. MethodsQualitative semi-structured interviews were conducted in 2024 with ""producers"" and “managers” of administrative data, including physicians and health information professionals, in British Columbia, Canada. The interviews explored processes involved with coding and billing, and the meaning of diagnostic codes from the perspective of those involved. This includes considerations of the population, social, and geographic factors, and implications of coding as inputs to decision-making. Reflexive thematic analysis was used to identify broad themes and report findings. ResultsAlthough data collection and analysis is ongoing, emerging findings have revealed structural, institutional, and social-level processes that influence coding decisions, as well as potential implications on research, policy making, and knowledge production about SUD. ConclusionAddressing the complexities of SUD and the overdose crisis will require a variety of data sources and approaches, and linked population health and administrative data will continue to be one important resource. Through better understanding the contexts and processes that inform the production and management of SUD-related population data, we will shed light on the ethical and social implications of their use.

General practitioners’ perspectives on data linkage in Australian general practice

ObjectiveIn the rapidly evolving landscape of digital health, data sharing using primary health care records has gained prominence. Linking primary care data with other administrative health datasets has the potential to provide a whole-of-life perspective on the patient journey and improve health outcomes. This study aimed to gain insight into Australian General Practitioners' (GPs) perspectives on the use of general practice health records for research and data linkage. ApproachThe study employed a mixed-methods approach. We conducted semi-structured interviews with GPs who had little experience of data sharing, focusing on understanding the challenges and opportunities that increased data sharing poses. We also conducted a three-round modified Delphi study with GPs with experience of and interest in research and data sharing to refine recommendations for data sharing using general practice data. ResultsIn this paper we describe the findings from both arms of the study and compare their recommendations to contribute to a nuanced understanding of general practitioners’ views on data linkage and sharing in general practice. Conclusions and ImplicationsBridging the knowledge gap between novices and expert practitioners, the findings will inform policies, practices, and initiatives to address the ethical, legal and social implications of data sharing in general practice settings in Australia.

Improving the Clinical Utility of Platelet Count for Cancer Detection in Primary Care: A Cohort Study in England, Canada, and Australia.

The platelet count, a component of the full blood count, has been identified as a useful diagnostic marker for cancer in primary care. The reference range for the platelet count is 150 to 400 or 450 × 109/L; this range does not account for natural variation in platelet count by age and sex. This study used three primary care cohorts from England, Canada, and Australia. Patients aged 40 years and over with a full blood count were included and stratified by age (in 10-year bands), sex, (male/female), and platelet count group. Cancer incidence within one year of the test date was estimated from linked registry data. In all three countries, there was a clear upwards trend in cancer incidence with increasing platelet count for both sexes and at all age groups. Lung and colorectal were the most common sites. These results have important implications for the international application of this work; analysis of local health datasets will be crucial to determining appropriate thresholds. Appropriate upper thresholds will depend on local populations, healthcare needs, and priorities. Further research is needed to assess the likely impact of new recommendations on the healthcare system, on cancer outcomes, and patient benefit.