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Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte
“Hidden populations” are difficult to identify because they have stigmatizing or illegal characteristics. For that reason, determining their size or prevalence in certain contexts is complicated. In those populations, traditional or direct methods, as population surveys, do not usually serve for thi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ministerio de Sanidad, Consumo y Bienestar social
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308831/ https://www.ncbi.nlm.nih.gov/pubmed/31290485 |
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author | Lorenzo-Ortega, Rocío Pulido, José Martínez-Santos, Ana Ruiz-Pérez, Isabel Hoyos, Juan Sordo, Luis |
author_facet | Lorenzo-Ortega, Rocío Pulido, José Martínez-Santos, Ana Ruiz-Pérez, Isabel Hoyos, Juan Sordo, Luis |
author_sort | Lorenzo-Ortega, Rocío |
collection | PubMed |
description | “Hidden populations” are difficult to identify because they have stigmatizing or illegal characteristics. For that reason, determining their size or prevalence in certain contexts is complicated. In those populations, traditional or direct methods, as population surveys, do not usually serve for this purpose, but indirect methods, based on incomplete data sources, can be useful. This work completes the original article published in Revista Española de Salud Pública in 2017: “Indirect methods to estimate hidden populations”. Different methods are exposed, showing their indications and bias. To make an estimation as real as possible it is necessary to evaluate carefully the data available and analyze the risk of bias. |
format | Online Article Text |
id | pubmed-10308831 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Ministerio de Sanidad, Consumo y Bienestar social |
record_format | MEDLINE/PubMed |
spelling | pubmed-103088312023-10-07 Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte Lorenzo-Ortega, Rocío Pulido, José Martínez-Santos, Ana Ruiz-Pérez, Isabel Hoyos, Juan Sordo, Luis Rev Esp Salud Publica Colaboraciones Especiales “Hidden populations” are difficult to identify because they have stigmatizing or illegal characteristics. For that reason, determining their size or prevalence in certain contexts is complicated. In those populations, traditional or direct methods, as population surveys, do not usually serve for this purpose, but indirect methods, based on incomplete data sources, can be useful. This work completes the original article published in Revista Española de Salud Pública in 2017: “Indirect methods to estimate hidden populations”. Different methods are exposed, showing their indications and bias. To make an estimation as real as possible it is necessary to evaluate carefully the data available and analyze the risk of bias. Ministerio de Sanidad, Consumo y Bienestar social 2019-07-10 /pmc/articles/PMC10308831/ /pubmed/31290485 Text en https://creativecommons.org/licenses/by-nc-nd/3.0/Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons |
spellingShingle | Colaboraciones Especiales Lorenzo-Ortega, Rocío Pulido, José Martínez-Santos, Ana Ruiz-Pérez, Isabel Hoyos, Juan Sordo, Luis Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title | Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title_full | Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title_fullStr | Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title_full_unstemmed | Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title_short | Métodos indirectos para la estimación de poblaciones ocultas: Segunda parte |
title_sort | métodos indirectos para la estimación de poblaciones ocultas: segunda parte |
topic | Colaboraciones Especiales |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308831/ https://www.ncbi.nlm.nih.gov/pubmed/31290485 |
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