{"doi":"10.31406/relap2024.v.18e202408","title":"Methods for Calculating Life Expectancy at Birth for Small Areas: A Systematic Review","abstract":"<jats:p>Estimating life expectancy at birth for small areas is challenging because of the reduced sample size, which includes small populations and death counts, resulting in high variability and instability in mortality rates. Data deficiencies and imprecision further complicate the task. To address these issues, researchers have used smoothing techniques and data adjustments. In recent decades, significant advances have led to the development of various demographic and/or statistical methods in this field. This systematic review aims to present the state of the art of methods for estimating specific mortality rates in small areas, with and without data deficiencies. Following the PRISMA protocol, 47 articles were selected. Most of the studies focused on the variability of the estimates, while a few considered the quality of the data before and/or after the smoothing of mortality rates, even in contexts known for poor information quality and underreporting.</jats:p>","journal":"Revista Latinoamericana de Población","year":2025,"id":646565,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1684179,"name":"Valéria Andrade Silva","orcid":"0000-0003-3316-7459","position":1,"is_corresponding":false},{"id":1684180,"name":"Caio César Gonçalves","orcid":"0000-0002-3366-7560","position":2,"is_corresponding":false},{"id":1684181,"name":"Igor Augusto Tadeu De Souza","orcid":"0009-0003-2969-6179","position":3,"is_corresponding":false},{"id":1684178,"name":"Denise Helena França Marques","orcid":"0000-0001-7002-4794","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Methods for Calculating Life Expectancy at Birth for Small Areas: A Systematic Review","abstract":"<jats:p>Estimating life expectancy at birth for small areas is challenging because of the reduced sample size, which includes small populations and death counts, resulting in high variability and instability in mortality rates. Data deficiencies and imprecision further complicate the task. To address these issues, researchers have used smoothing techniques and data adjustments. In recent decades, significant advances have led to the development of various demographic and/or statistical methods in this field. This systematic review aims to present the state of the art of methods for estimating specific mortality rates in small areas, with and without data deficiencies. Following the PRISMA protocol, 47 articles were selected. Most of the studies focused on the variability of the estimates, while a few considered the quality of the data before and/or after the smoothing of mortality rates, even in contexts known for poor information quality and underreporting.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4407165336","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.0132626,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://revistarelap.org/index.php/relap/article/download/313/873","host_type":"journal"},{"url":"https://revistarelap.org/index.php/relap/article/download/313/873","host_type":"publisher"},{"url":"https://revistarelap.org/index.php/relap/article/download/313/874","host_type":"publisher"},{"url":"https://doi.org/10.31406/relap2024.v.18e202408","host_type":"journal"},{"url":"https://repositorio.fjp.mg.gov.br/handle/123456789/4687","host_type":"repository"},{"url":"https://doaj.org/article/acb8477c28504925843112aee4613606","host_type":"repository"},{"url":"https://repositorio.fjp.mg.gov.br/bitstreams/555c1d77-3cfd-479f-adaa-f93d11ded2c3/download","host_type":"repository"}],"fields_of_study":["Insurance, Mortality, Demography, Risk Management","Global Health Care Issues","Health disparities and outcomes"],"mesh_terms":[],"keywords":["Life expectancy","Expectancy theory","Psychology","Demography","Sociology","Social psychology","Population"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T13:51:37.090671Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}