Métodos indirectos para la estimación de poblaciones ocultas: segunda parte : e201907033

Rocío Lorenzo-Ortega; José Pulido; Ana Martínez-Santos; Isabel Ruiz-Pérez; Juan Hoyos; Luis Sordo

Authors

Rocío Lorenzo-Ortega Servicio de Medicina Preventiva. Hospital Virgen de la Victoria. Málaga. España.
José Pulido Centros de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP). Madrid. España. / Escuela Nacional de Sanidad. Instituto de Salud Carlos III. Madrid. España. / Departamento de Salud Pública y Materno-Infantil. Facultad de Medicina. Universidad Complutense de Madrid. España.
Ana Martínez-Santos School of Health Sciences. Salford University. Manchester. Reino Unido.
Isabel Ruiz-Pérez Centros de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP). Madrid. España. / Escuela Andaluza de Salud Pública. Granada. España.
Juan Hoyos Centros de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP). Madrid. España. / Escuela Nacional de Sanidad. Instituto de Salud Carlos III. Madrid. España. / Departamento de Salud Pública y Materno-Infantil. Facultad de Medicina. Universidad Complutense de Madrid. España.
Luis Sordo Centros de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP). Madrid. España. / Departamento de Salud Pública y Materno-Infantil. Facultad de Medicina. Universidad Complutense de Madrid. España.

Keywords:

Hidden populations, Epidemiologic study, Epidemiological monitoring, Data collection

Abstract

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.

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