Population Health: Data, Trends and Indicators

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

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Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

J ◽

10.3390/j4020015 ◽

2021 ◽

Vol 4 (2) ◽

pp. 182-192

Author(s):

Dianna M. Smith ◽

Alison Heppenstall ◽

Monique Campbell

Keyword(s):

Population Health ◽

Spatial Scales ◽

Local Level ◽

Health Data ◽

Future Planning ◽

Academic Researchers ◽

Quality Health ◽

Spatial Microsimulation ◽

Dynamic Populations ◽

Economic Studies

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

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Planting the S.E.E.D.S of Indigenous Population Health Data Linkage

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1626 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Robyn K Rowe ◽

Jennifer D Walker

Keyword(s):

Population Health ◽

Indigenous Peoples ◽

Health Care Policy ◽

Indigenous Population ◽

Data Linkage ◽

Indigenous Health ◽

Population Data ◽

Health Data ◽

Indigenous Populations ◽

Data Stewardship

IntroductionThe increasing accessibility of data through digitization and linkage has resulted in Indigenous and allied individuals, scholars, practitioners, and data users recognizing a need to advance ways that assert Indigenous sovereignty and governance within data environments. Advances are being talked about around the world for how Indigenous data is collected, used, stored, shared, linked, and analysed. Objectives and ApproachDuring the International Population Data Linkage Network Conference in September of 2018, two sessions were hosted and led by international collaborators that focused on regional Indigenous health data linkage. Notes, discussions, and artistic contributions gathered from the conference led to collaborative efforts to highlight the common approaches to Indigenous data linkage, as discussed internationally. This presentation will share the braided culmination of these discussions and offer S.E.E.D.S as a set of guiding Indigenous data linkage principles. ResultsS.E.E.D.S emerges as a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples’ right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous Peoples and settler states. S.E.E.D.S aims to centre and advance Indigenous-driven population data linkage and research while weaving together common global approaches to Indigenous data linkage. Conclusion / ImplicationsEach of the five elements of S.E.E.D.S interweave and need to be enacted together to create a positive Indigenous data linkage environment. When implemented together, the primary goals of the S.E.E.D.S Principles is to guide positive Indigenous population health data linkage in an effort to create more meaningful research approaches through improved Indigenous-based research processes. The implementation of these principles can, in turn, lead to better measurements of health progress that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous populations.

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Monitoring the quality of maternity care: how well are labour and delivery events reported in population health data?

Paediatric and Perinatal Epidemiology ◽

10.1111/j.1365-3016.2008.00980.x ◽

2009 ◽

Vol 23 (2) ◽

pp. 144-152 ◽

Cited By ~ 92

Author(s):

Christine L. Roberts ◽

Jane C. Bell ◽

Jane B. Ford ◽

Jonathan M. Morris

Keyword(s):

Population Health ◽

Maternity Care ◽

Health Data

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Are women with major depression in pregnancy identifiable in population health data?

BMC Pregnancy and Childbirth ◽

10.1186/1471-2393-13-63 ◽

2013 ◽

Vol 13 (1) ◽

Cited By ~ 8

Author(s):

Lyn Colvin ◽

Linda Slack-Smith ◽

Fiona J Stanley ◽

Carol Bower

Keyword(s):

Major Depression ◽

Population Health ◽

Health Data ◽

Depression In Pregnancy ◽

In Pregnancy

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EpiGraphDB: a database and data mining platform for health data science

Bioinformatics ◽

10.1093/bioinformatics/btaa961 ◽

2020 ◽

Author(s):

Yi Liu ◽

Benjamin Elsworth ◽

Pau Erola ◽

Valeriia Haberland ◽

Gibran Hemani ◽

...

Keyword(s):

Population Health ◽

Data Science ◽

Health Data ◽

Biomedical Literature ◽

Therapeutic Interventions ◽

Supplementary Information ◽

Reproducible Research ◽

Protein Interaction Data ◽

Study Results

Abstract Motivation The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein–protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to ‘triangulate’ evidence from different sources. Availability and implementation The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. Supplementary information Supplementary data are available at Bioinformatics online.

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