Related Experiment Video
Updated: Jul 4, 2026
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
15.5K
Administrative data linkage to Census 2021 in Wales, UK: A cross-sectional study examining completeness and
Jane Lyons1,2, Rhodri D Johnson1,2, Michael Edwards1
1Population Data Science, Swansea University Medical School, Swansea, SA2 8PP.
International Journal of Population Data Science
|April 2, 2026
Summary
Census 2021 data linkage in Wales shows certain groups, including males, older adults, and ethnic minorities, are less represented. This highlights potential biases in population-wide health studies using linked data.
Area of Science:
- Public Health Research
- Epidemiology
- Data Linkage Methodology
Background:
- Population representativeness is crucial for public health and epidemiological studies.
- Ensuring data linkage accuracy is vital for reliable population-wide research.
- Administrative datasets and census data are key resources for health research.
Purpose of the Study:
- To assess the representativeness of Census 2021 data linkage within the Secure Anonymised Information Linkage (SAIL) Databank for Wales.
- To compare Census 2021 data with the Welsh Demographic Service Dataset (WDSD).
- To identify characteristics of individuals successfully linked and those not linked, and to understand subgroup representation in population-wide studies.
Main Methods:
- An observational, population-wide cross-sectional comparison study.
- Utilized administrative demographic data (WDSD) and decennial survey data (Census 2021) within SAIL.
- Employed logistic regression models to analyze linkage variations by demographic and household characteristics.
Main Results:
- A central cohort of 2,440,191 individuals was present in both WDSD and Census 2021.
- Higher odds of non-linkage were observed for males, individuals aged 75+, Asian ethnicity, recent migrants, LGBTQ+ individuals, separated/divorced/widowed individuals, and those in rental accommodation.
- Specific demographic and household characteristics are associated with disproportionate representation in linked data.
Conclusions:
- Census 2021 data linkage in SAIL may not fully represent the population of Wales.
- Certain subgroups are underrepresented, potentially impacting the validity of population-wide studies.
- Understanding these disparities is essential for improving data linkage strategies in public health research.
Related Concept Videos
01:50Cross-Sectional Research
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
01:08Data Collection by Observations
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:26Principles of Disease Surveillance
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
01:25Selected Data About Geographic Locations
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...