Constructing synthetic populations in the age of big data.
Disclosure risk
Synthetic population
Journal
Population health metrics
ISSN: 1478-7954
Titre abrégé: Popul Health Metr
Pays: England
ID NLM: 101178411
Informations de publication
Date de publication:
31 Oct 2023
31 Oct 2023
Historique:
received:
15
03
2022
accepted:
19
10
2023
medline:
2
11
2023
pubmed:
1
11
2023
entrez:
1
11
2023
Statut:
epublish
Résumé
To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population. Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features. The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features. By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues.
Sections du résumé
BACKGROUND
BACKGROUND
To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population.
METHODS
METHODS
Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features.
RESULTS
RESULTS
The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features.
CONCLUSIONS
CONCLUSIONS
By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues.
Identifiants
pubmed: 37907904
doi: 10.1186/s12963-023-00319-5
pii: 10.1186/s12963-023-00319-5
pmc: PMC10617102
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19Informations de copyright
© 2023. The Author(s).
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