International Journal of Population Data Science最新文献

Introduction: Public service leaders face increasing challenges using data effectively due to program silos, limited resources, and the increasing complexity of data. To address these challenges, Iowa's Integrated Data System for Decision-Making (I2D2) partnered with state and local leaders in early childhood to curate key indicators and develop population-level data tools and training to promote policy and practice improvements.

Methods: We relied on a mixed-methods, participatory approach to understand early childhood data and reporting requirements and how state and local leaders leverage data to meet these requirements and inform decisions. We conducted a Data Landscape Overview consisting of interviews, surveys, document review, and meetings with state and local leaders. Public deliberation facilitated iterative feedback and collective decision-making through stakeholder discussions.

Results: Our participatory approach resulted in three actions to improve data collection and use within Iowa's early childhood system: curating a set of early childhood indicators; developing training and strategic planning tools for effective data use; and building the Iowa Data Drive (IDD), an interactive data portal for accessing key early childhood indicators and population-level insights.

Conclusions: A robust IDS can promote systems change when grounded in strong partnerships, phased implementation, and a commitment to clear communication. By centering local voices and fostering trust, we developed indicators and tools that support data-informed decisions and improved services for young children and their families.

Introduction: Mental health and substance use (MH/SU) problems are highly prevalent among the prison population. However, early and preventative post-imprisonment care appears to be insufficient to meet the MH/SU needs of people released. This is demonstrated by elevated rates of MH/SU-related emergency care and deaths attributable to alcohol, drugs and suicide. Studies examining post-imprisonment healthcare contacts across community, outpatient, inpatient and emergency services for MH/SU are required to address this issue. This protocol paper describes the outcome of data linkage and details our plans for data cleaning and analysis.

Methods: The RELEASE study will follow a retrospective observational cohort design. This is the first study using national individual-level linked administrative health and prison data from Scotland. We report the results of creating the cohort, and outline proposed methods for data preparation and analysis. Within the cohort, the exposed group comprises everyone released from prison in 2015, and the unexposed group consists of a random sample of the general population matched (1:5 ratio) on age, sex, postcode and postcode-derived index of multiple deprivation, and with no prison exposure in the preceding 5 years. Health data (community prescribing, outpatient visits, specialist substance use, psychiatric inpatient, general inpatient, out-of-hours general practice, 24-hour National Health Service [NHS] helpline, ambulance, and emergency services), deaths data, and prison data (admissions, releases, demographic data) were linked to the cohort using unique identifiers. Service contacts associated with MH/SU will be quantified and compared across the two groups using regression modelling, controlling for potential confounding variables, reimprisonment and deaths.

Conclusion: RELEASE is a comprehensive study with potential to inform post-imprisonment MH/SU service delivery, whilst the dataset holds significant potential for exploring other health conditions and outcomes. This research will allow for an unprecedented understanding of post-imprisonment service use patterns in Scotland, and RELEASE will make a significant public health contribution given the overrepresentation of people released in costly emergency care contact and death rates.

Synthetic data is emerging as a key area of development for supporting research that involves secure forms of administrative and health data, both in the United Kingdom and globally. In practice, key challenges in the generation and adoption of synthetic data are closely tied to the need for agreed and consistent terminology for describing it. The absence of standardised language hinders the setting of quality standards, establishment of governance and guidelines and effective sharing of knowledge and best practices. This has implications for research that uses synthetic healthcare and administrative data, particularly when such data are generated from protected personal data. This commentary paper reviews existing literature on synthetic data to explore how key terms are currently defined in practice, with a focus on privacy-preserving use cases. Our analysis reveals that terms describing properties of synthetic data are often lacking and inconsistent, largely due to the breadth of synthetic data types, contexts and use cases. Context-specific terminology with nuanced meanings complicates efforts for the development of universally agreed definitions, particularly for privacy-preserving synthetic data that captures characteristics from protected data sources. To address this, we propose broad definitions for key terms including synthetic data, utility, utility measure and fidelity. We conclude by offering a set of recommendations emphasising the need for consensus on terminology and encouraging clearer descriptions in future literature that specify both the intended use of the data and the measures used to describe it.

Data transparency lays the groundwork for the ethical use of administrative data. This is particularly true for linked administrative data within integrated data systems (IDS). Data dictionaries, resources that maintain the metadata of the information housed in an IDS, offer a tool to ensure transparency throughout the data life cycle. The FAIR Principles, which assert that data be Findable, Accessible, Interoperable, and Reusable provide a useful framework by which to measure the effectiveness of data dictionaries in the IDS context. This paper uses the FAIR Principles to discuss the ways in which data dictionaries serve as tools in the ethical and transparent use of integrated data as well as the challenges that remain. Linked administrative data is a valuable source of information for programmatic and academic research. Data dictionaries facilitate the ethical handling of this sensitive information and maintain a commitment to transparency in data inquiry and research.

Introduction: Studies of epidemiology and health system use that use linked admitted patient data benefit from understanding the patient journey, particularly when it spans multiple records within or across multiple datasets.

Objectives: To develop a flexible method for grouping together administrative admitted patient records into periods of hospital care that follow patients from admission to discharge.

Methods: We describe a flexible and generalisable graph theoretic algorithm for grouping patient records into periods of hospital care. The algorithm can account for a variety of complex hospitalisation patterns involving multiple transfers and overlapping records. An R package, journeyer, that implements this algorithm, is included in the Supplementary Material.

Results: This algorithm was applied to the New South Wales Admitted Patient Data Collection, finding 21,405,451 periods of hospital care from 22,794,746 hospital records. The parameters and decisions required for this algorithm were assessed and found appropriate for this dataset, but we offer some advice for generalisation to other datasets.

Conclusions: Our method assists in preparing data for epidemiological research in New South Wales and can be generalised to inpatient data in other jurisdictions. The method can be extended to include ambulance and emergency department data.

Introduction: Ethnicity in Australia's non-Indigenous population is not collected routinely in health data but the proxy of ancestry is collected in the Census.

Objectives: We aimed to develop an approach to using ancestry data to examine health inequalities by ethnicity in Australia's non-Indigenous population. We then applied this to the example of all-cause mortality.

Methods: We established an expert and community panel to inform our approach to categorising ancestry data. This included shifting those identifying as 'Australian' or 'New Zealander' from the Oceanian to the European continental category; prioritising ethnic minority identities over national identities in those with two ancestries; and examining outcomes using the smallest ethnicity categories possible. We examined how results compared to existing approaches based on country of birth or ancestry (without our modifications) in the detection of mortality inequalities using 2016 Australian Census data linked to death registrations for 2016-2021 in 20.3 million people.

Results: We found important differences in mortality inequalities observed in Māori and Pasifika populations in Australia based on the method used. Ancestry data was able to demonstrate significantly higher mortality that was not observed when using country of birth in Māori females (747 vs 507 per 100,000 person-years), Melanesian and Papuan males (1684 vs 617 per 100,000 person-years) and Polynesian males and females (928 vs 724 in males and 693 vs 569 per 100,000 person-years in females). The size of the inequalities observed was larger using our expert and community informed approach compared to existing approaches (e.g. Polynesian males 928 vs 853 per 100,000 person-years).

Conclusions: We demonstrated an approach to using ancestry data from the Australian Census that improved identification of mortality inequalities in Māori and Pasifika ethnic groups. Inequalities were either hidden or underestimated when country of birth or the standard approach to ancestry data was used.

Introduction: Data linkage methodologies are increasingly being utilised across research, but there is currently no evidence on the extent and nature of studies that have used linked reproductive health data. The objective of this scoping review is to identify UK studies that use reproductive health data linkage, to improve our understanding of how data linkage could be used for policy, practice, and research in reproductive health.

Methods: We conducted a scoping review using a systematic search in five databases: MEDLINE, EMBASE, CINAHL, MIDIRS, and PSYCINFO to identify literature published in English between January 2000 - April 2024. Following duplication removal, piloting, and screening of titles/abstracts, screening of full texts was conducted. Publications using reproductive health data linkage among UK participants of reproductive age were included. Data was extracted from included articles to capture details relating to study characteristics and what, how, and why data was linked.

Findings: Of the 7,291 identified studies, 272 studies were included in the review. Most studies using data linkage answered questions around reproductive cancer and maternal and child health, whilst only a few studies focused on abortion, contraception, menopause, and preconception health. Several nationally agreed reproductive health indicators did not appear in any included study. Information on sample sociodemographic characteristics, such as ethnicity and deprivation, was often unreported, limiting the identification of health inequalities. Many different datasets were linked (n = 155) with routine health data sources, such as hospital episode statistics (HES), being the most frequently linked.

Interpretation: There is a growing body of research using linked UK reproductive health data, with gaps in which reproductive health domains are covered and which sample characteristics are reported. Further efforts to create a comprehensive, linked reproductive health data resource with robust linkage methods would enable us to fill data gaps, examine inequalities, and explore reproductive health trajectories.

Funding: National Institute for Health and Care Research (NIHR) Policy Research Unit in Reproductive Health.

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.