Evidence map›Paper›PMID 39695188›Full record

ArticleScientific reports2024

Using population register data and capture-recapture models to estimate over-coverage in Sweden.

Bruno Santos, Eleonora Mussino, Sven Drefahl, Eleni Matechou

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bruno SantosCEAUL - Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
Eleonora MussinoStockholm University Demography Unit -SUDA, Stockholm University, Stockholm, Sweden. eleonora.mussino@sociology.su.se.
Sven DrefahlStockholm University Demography Unit -SUDA, Stockholm University, Stockholm, Sweden.
Eleni MatechouUniversity of Kent, School of Mathematics Statistics and Actuarial Science, Canterbury, UK.

Funding

Vetenskapsrådet 2021-00875
6 · The paper itself

Abstract

Over-coverage occurs when individuals who reside in a country leave or pass away, and this demographic event is not recorded in population registers, leading to population size overestimation. This problem can have important policy and decision-making consequences. With the increased reliance on incomplete but overlapping official registers for documenting whole populations or subgroups of populations, there is a need for more sophisticated modelling techniques that reliably estimate population size, and hence over-coverage, from such registers. Previous approaches have considered multiple systems estimation (MSE) for monitoring over-coverage, but MSE does not naturally extend to cases where individuals are followed over time. In this paper, motivated by the case study of Sweden, we develop a capture-recapture (CR) modelling framework for population registers that allows us to estimate the population size each year, the probability of presence for each individual in the population, conditional on their records, each year and to quantify the effect of demographic characteristics on the probability of emigration and re-immigration, amongst other parameters. Our results suggest that the CR approach, which accounts for the whole time series for each individual, gives a more realistic estimate of the population size compared to existing, deterministic approaches, especially when considering the subgroup of newly arrived individuals, and that it provides new insights on individual behaviour in terms of migration patterns than existing MSE approaches.

Indexed as

Population DensityRegistriesEmigration and ImmigrationFemaleHumansMaleModels, StatisticalPopulation DynamicsSwedenCapture-recapture modelsOver-coverageRegister data

Identifiers

PMID39695188
PMCPMC11655540

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.