Modelled Estimates of Recent Births for English Local Authorities
Updated 29d ago
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Description
England's modelled annual live birth estimates for local authority districts, regions, and ITL2 areas. The Greater London Authority Demography team produces these estimates using GP registration counts to provide more timely data than official ONS figures, which have a 9-12 month publication lag. The dataset includes official ONS estimates from July 1992 to January 2025, interpolated monthly figures, and predicted births up to October 2025 with 95% prediction intervals.
Use Cases
Forecasting local population changes based on predicted annual births.
Analyzing regional birth trends over time using interpolated and actual data.
Comparing modelled birth estimates with official ONS figures to assess prediction accuracy.
Planning public service provision for infant and maternal health based on geospatial birth estimates.
Strengths
Estimates are produced monthly with a latency of 1-2 weeks, offering timeliness over official data.
Covers multiple geographic levels in England, including local authority districts and regions.
Includes 95% prediction intervals for modelled estimates, providing uncertainty quantification.
Methodology and code are publicly available on GitHub for transparency.
Limitations
Row count is unknown, which may limit suitability assessment.
Column-level documentation is absent; field semantics must be inferred after download.
Data are not currently split by sex, limiting gender-based analysis.
Provenance
Source
Greater London Authority Demography team, using NHS Digital patient count data and ONS official estimates.
Collection Method
Modelled estimates generated via correlation between GP-registered infants aged 0 and resident births over the preceding year.
Time Range
Official estimates from July 1992 to January 2025, with predictions up to October 2025.
Freshness
Last updated 2026-06-24 21:01:32.424734
Geography
Local authority districts, regions, country, and ITL2 levels in England.
Data are not split by sex; methodology relies on correlation which may introduce bias.