International Journal of Population Data Science最新文献

Comorbidity measures, such as the Charlson Comorbidity Index, are commonly used in risk adjustment models to account for variability in disease burden. This narrative synthesis describes and critiques available comorbidity indices and offers implementation guidance to researchers based on a critical review of existing literature. First, common comorbidity measures are described. Instruments derived using case definitions, grouping of International Classification of Diseases (ICD) codes, and mapping of dispensed medications to chronic conditions are presented. Comorbidity indices that combine diagnostic and medication data are also introduced. No single option consistently outperforms the rest. Next, important considerations when applying a comorbidity index are described. It is crucial to respect temporality and exclude health events that arise after the study index date. Researchers must also weigh the interpretability of using a weighted sum against the flexibility of using a large set of binary variables. When modelling long-term outcomes, there are benefits to applying a one-year look-back window and augmenting data via linkage. For short-term outcomes, certain chronic conditions may exhibit a protective association; however, not all indices capture these relationships. Implementation of these findings will improve the interpretability of comorbidity measures and the quality of future studies.

Objective: There is increasing demand for access to general practice health records for secondary purposes, including research. However, the extent to which the public supports such use is unclear. We sought to explore informed Australians' perspectives on conditions under which the use of general practice data for research would be acceptable.

Methods: We conducted two community juries in July and August 2023 with 20 participants, selected for diversity, in each jury. Jurors worked for 36 hours, in a combination of online and face-to-face sessions, over 6 days. They listened to expert presentations, discussed, and challenged experts, deliberated, and developed their own recommendations.

Results: Both juries, in principle, supported sharing general practice data for research purposes. They made 24 (Sydney jury) and 19 (Melbourne jury) recommendations related to consent, information provision, public benefit, data security, governance and costs.

Conclusions: The outcomes of the deliberative process suggest that an informed group of Australian citizens are willing to share general practice data for research provided strict conditions are met.

Implications for public health: Adopting the recommendations from the juries will require a range of policy and regulatory responses including legislative changes.

Introduction: Transparency in the use of data for research benefits the public and researchers by fostering trust and enabling efficient data sharing. Public support for access to their data for research depends on robust data security, the absence of conflicting interests, and a clear demonstration of public benefit, all of which must be evident through transparent practices. A lack of clarity in data access processes can delay research, highlighting the need for clear and streamlined approval procedures. To maintain what is often referred to as a 'social license to operate', organisations must meet and uphold societal expectations, with transparency being a key dimension of that responsibility.

Objective: To develop and foster adoption of a set of transparency standards for the data science community, supporting trustworthy and streamlined data use for health and socio-economic research and planning.

Methods: A multi-stakeholder deliberation was undertaken, informed by two reviews of existing data access procedures across participating organisations. Stakeholders included healthcare and research organisations, data custodians, regulators, industry representatives, academic experts, and members of the public.

Results: The review and deliberation identified missed opportunities to inform and involve the public in data access procedures, along with inconsistencies in data access processes and supporting materials across the organisations. In response, we developed the Transparency Standards, comprising 28 recommended actions grouped into four themes: provision of clear data access guidance; clear website navigation designed to meet the needs of public and research users; regular review and iterative improvement of processes; and reporting of data access outcomes and information security findings. A targeted funding call facilitated the adoption of standards in 19 organisations, resulting in reusable transparency materials and transferable knowledge to support wider implementation.

Conclusion: The Transparency Standards support data custodians in strengthening openness and accountability in data access processes, helping to build public trust while simplifying procedures for researchers. Their broad adoption demonstrates a shared commitment to the ethical use of data. However, varying levels of implementation point to the need for continued investment to sustain progress and respond to public and researcher expectations.

Introduction: Electronic medical records (EMR) are an essential tool in modern healthcare, providing a centralised source of patient information. Longitudinal analysis of EMRs can identify opportunities for targeted interventions to improve health outcomes for children. However, the research value of EMRs is contingent on data quality and completeness.

Methods: This retrospective cohort study used deidentified EMRs from all Aboriginal children born in 2015 who attended an Aboriginal-controlled health service in Central Australia over a 5-year period. The purpose of this study was to demonstrate the utility of EMRs in longitudinal research via presentation of three case study example analyses, and to evaluate the quality of the extracted dataset.

Results: EMRs of 319 Aboriginal children (48.9% girls, 51.1% boys) were included in the analysis. These children visited the service an average of 19.9 times (min 2 - max 102). Attendance rates for routine well-child check-ups were highest at 0 to 8 weeks and 4 years of age (37.3% and 40.1% respectively). Among 12-month-olds with recorded haemoglobin levels, 43% were anaemic. Weight-for-age medians were comparable to World Health Organization (WHO) growth standards until 12 months age, thereafter Aboriginal girls tended to weigh more overtime. Data completeness varied: key variables (date of birth, sex and Aboriginal status) were 100% complete, while others like anthropometrics (up to 62.1%), birth weight (54.2%), gestational age (50.2%), and haemoglobin results (up to 34.1%) were less complete. Average accuracy (99.2%) and consistency of available data (100%) were high. However, crucial data on risk factors, maternal health, and family functioning were either not collected by the service, not provided to the service from external sources, or stored in inaccessible free-text fields.

Conclusions: Missing data were the greatest limiting factor for reporting on the health and development of these children. To reap the benefit of utilising EMRs for longitudinal research, the service should continue encouraging families to attend their child's routine health assessments in the first years of life. Setting key data variables as mandatory at each visit may also help increase data completeness over time.

Background: Researchers can apply to UK Longitudinal Linkage Collaboration (UK LLC) to access Longitudinal Population Study (LPS) data linked to health, non-health administrative and geo-environmental data. This paper describes the protocol for the "UK LLC Citizen Panel": a new method of incorporating a diverse public in decisions about the acceptability and suitability of the UK LLC data access process. The UK LLC Citizen Panel aims to embed public feedback and perceptions into UK LLC's data access process design and decision-making.

Methods: The UK LLC Citizen Panel will be created through a two-stage co-design process. Stage 1: UK LLC will identify and invite a public Steering Group to co-design the work of the Citizen Panel. The Steering Group will include public contributors from UK LLC's Public Involvement Programme and participant representatives from partner LPS. Stage 2: the UK LLC Citizen Panel will comprise participants of partner LPS and seldom-heard groups, primarily recruited via third sector organisations. The Panel will review the data access process during several online and in-person meetings. Findings will be analysed using thematic analysis and disseminated to UK LLC partner organisations, third sector organisations working with minority communities and young people under-represented in longitudinal studies, and networks of the Universities of Edinburgh and Bristol.

Discussion: The UK LLC Citizen Panel is a novel methodological approach that aims to consider a diverse public view of the use of a Trusted Research Environment to provide access to LPS data linked to health, non-health administrative and geo-environmental data. This diversity complements the existing public involvement in decision-making in all UK LLC data access and enables populations that are rarely heard in such decision-making to participate in and review the UK LLC data access process.

Introduction: Firearm injuries are a significant public health issue in Canada, yet the broader consequences, particularly non-fatal injuries, remain under examined in research and policy discussions. These injuries impose long-term physical, psychological, and social burdens on survivors and create substantial economic costs. While firearm-related injury data are collected across health, justice, and policing sectors, the lack of integration between these datasets hampers a comprehensive understanding of the issue.

Objectives: This study aims to explore opportunities for linking national, provincial, and municipal datasets on firearm-related injuries in Canada, focusing on data from healthcare, legal, and firearm-specific domains.

Methods: A comprehensive search for publications related to firearms of Medline, Scopus, and Web of Science and grey literature up to February 2025 identified several relevant datasets, including health records, death registries, and crime databases.

Results: We found that while valuable information exists, the datasets are siloed, limiting the ability to analyse firearm injuries holistically. Gaps in data, such as the psychological impact of firearm injuries and specific details on firearm ownership, further constrain research. Despite these challenges, linking healthcare, justice, and firearm data could offer critical insights into the epidemiology of firearm injuries, their long-term effects, and associated risk factors.

Conclusions: Overcoming operational constraints related to privacy, data quality, and funding will be essential for advancing this research and informing evidence-based interventions to reduce firearm-related harm. Drawing from successful data integration initiatives in other jurisdictions, such as Sweden and Australia, this study advocates for the development of a cross-sectoral data linkage strategy to enhance firearm injury prevention and policy development in Canada.

Introduction: The prioritisation of acute cases of coronavirus during the pandemic caused significant disruption to non-urgent healthcare services, creating a backlog of undiagnosed and untreated individuals with long-term conditions. Previous research has explored the impact of the pandemic on long-term conditions in Wales, but not the geographic variation or underlying area-level characteristics associated with these changes.

Objectives: We created the SAIL long-term conditions e-cohort (SLTC cohort) within the Secure Anonymised Information Linkage (SAIL) Databank to describe changes in healthcare service use of individuals living with long-term conditions during the COVID-19 pandemic, and to facilitate future investigations into the underlying reasons for these changes.

Methods: Individuals were included in the cohort if they interacted with health services with a long-term condition between January 2017 and December 2022. Interactions were identified using primary and secondary care datasets within the SAIL Databank. We linked this interaction level data with individual, residence, and area-level demographic data. We calculated area-level age-sex-standardised rates of interactions, based on an individual's address at the time of interaction, for the 3 years pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). Percentage changes in rates between these time periods were calculated, and we investigated the underlying area-level characteristics associated with these differences.

Results: The SLTC cohort contains 1,277,532 individuals. Age-sex standardised interaction rates varied by Welsh Index of Multiple Deprivation (WIMD) quintiles and Rural-Urban Classification. Areas in the most deprived WIMD quintile had the greatest median percentage decrease (23.5%) in primary care rates of interactions from pre- to during-COVID-19, and the least deprived overall WIMD quintile had the smallest (16.9%). Areas classified as 'Urban city & town in a sparse setting' had the greatest decrease in primary care interactions (29.7%), and 'Rural village' areas had the smallest decrease (17.1%). Secondary care rates of interactions showed less variation in rates of interactions between the two time periods.

Conclusion: We have created a cohort that links area-level characteristics and measures of healthcare resource use, in a study period that covers pre- and during-COVID-19, which will allow researchers to investigate geographic variation of changes in healthcare resource use over this time period and the underlying influences. This cohort can also be further linked to other area-level characteristics of interest, such as travel times to general practices, or access to green space measures.

Introduction: Aging while living with HIV poses new challenges in clinical management, mainly due to the onset of multiple chronic comorbidities. Population-specific risk prediction indices considering comorbidities and other risk factors are essential to comprehensively characterise disease burden among PLWH. We developed and validated a mortality risk prediction index (MRPi) to predict the risk of one-year all-cause mortality among people living with HIV (PLWH).

Methods: Participants were ≥18 years and had initiated antiretroviral therapy (ART) between 01/2001 and 12/2018, in British Columbia, Canada. The index date was randomly selected between one-year post-ART initiation and the end of the follow-up. Participants were followed for at least one year from the index date until 12/2019, the last contact date, or the date of death (all-cause), whichever came first. The MRPi included 18 physical/mental comorbidities, demographic and clinical variables, and ranged from 0 (no risk) to 100 (highest risk).

Results: The final model demonstrated the highest discrimination (c-statistic 0.8355, 95% CI: 0.8187-0.8523 in the training dataset and 0.7965, 95% CI: 0.7664-0.8266 in the test dataset). The comorbidities with the highest weights in the MRPi were substance use disorders, metastatic solid tumors and non-AIDs defining cancers. For example, for an MRPi of 30, the predicted one-year all-cause mortality was 0.2%, while an MRPi of 50 had a predicted mortality of 2.3%.

Conclusions: The MRPi provides a promising tool to assess the risk of short-term mortality among PLWH in the modern ART era that can inform clinical practice and health policy decisions.

Background: Linking digital footprint data into longitudinal population studies (LPS) presents an opportunity to enrich our understanding of how digitally captured behaviours relate to health traits and disease. However, this linkage introduces significant methodological challenges that require systematic exploration.

Objectives: To develop a robust framework for successful digital footprint linkage into LPS, informed by discussions from a workshop from the Digital Footprints Conference 2024.

Methods: We propose a structured, four-stage framework to facilitate successful linkage of digital footprint data into LPS: (1) understand participant expectations and acceptability; (2) collect and link the data; (3) evaluate properties of the data; and (4) ensure secure and ethical access for research. This framework addresses the key methodological challenges identified at each stage, discussed through the lens of two LPS case studies: the Avon Longitudinal Study of Parents and Children and Generation Scotland.

Results: Key methodological challenges identified include privacy and confidentiality concerns, reliance on third-party platforms, data quality issues like missing data and measurement error. We also emphasize the role of trusted research environments and synthetic datasets in enabling secure, privacy-sensitive data sharing for research.

Conclusions: While the linkage digital footprint data to LPS remains in early stages, our framework provides a methodological foundation for overcoming current challenges. Through iterative refinement of these methods there is significant potential to advance population-level insights into health and wellbeing.

Introduction: The COVID-19 pandemic produced social inequities in health outcomes between and within nations. Reported inequitable COVID-19 outcomes for ethnic minorities and indigenous peoples are likely to be associated in part because of poorer socioeconomic circumstances experienced by these populations. Understanding these associations within national populations is vital for future pandemic management.

Objective: This study explores the social inequity of COVID-19 outcomes within New Zealand over the first 3 years of the pandemic. We aimed to identify policy amenable socioeconomic factors associated with COVID-19 outcomes while adjusting for relevant individual factors and household structure. We also aimed to examine whether ethnic group differences are smaller when accounting for these socioeconomic factors and household structure.

Methods: Administrative individual-level data for the New Zealand population was analysed to assess COVID-19 health outcomes during 2020 - 2023. The association between individual (e.g. age, ethnicity, disability status), household structure (e.g. household composition) and socioeconomic (e.g. crowding, housing quality, deprivation) factors and four COVID-19 health outcomes - infection, hospitalisation, mortality, and vaccination status was assessed.

Results: Indigenous peoples and ethnic minorities experienced worse outcomes across most COVID-19 outcomes. Adjusting for household structure and socioeconomic factors reduced but did not eliminate these inequities between ethnic groups. Housing issues including high housing mobility, poor quality housing, and household crowding were associated with worse outcomes, as were disability status, no primary health care enrolment, lower household income and older age. The size of these effects also differed for different health outcomes.

Conclusions: Ethnic inequity was persistent and likely partly explained by policy-modifiable social factors, despite the relatively minor population health impacts of COVID-19 in New Zealand. We also demonstrate how a range of socioeconomic determinants predict COVID-19 outcomes in different ways.