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      Métodos indirectos para la estimación de poblaciones ocultas: segunda parte Translated title: Indirect methods to estimate hidden population: 2nd part

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          Abstract

          RESUMEN Las poblaciones ocultas, aquellas difíciles de identificar por tener características estigmatizadoras o ilegales, suelen dar problemas a la hora de determinar su tamaño o prevalencia en determinados contextos. Los métodos tradicionales o directos, como las encuestas poblacionales, no suelen servir para este cometido. Los métodos indirectos, que parten de fuentes de datos incompletas para estimar la prevalencia real de la población, sí pueden ser útiles. Este trabajo completa el artículo original publicado en 2017 por Revista Española de Salud Pública sobre métodos indirectos para la estimación de poblaciones ocultas. Se exponen cuatro métodos diferentes, cada uno de los cuales tiene distintas indicaciones dependiendo de los datos de los que dispongamos y diferentes sesgos que deben valorarse detenidamente para realizar una estimación lo más cercana posible a la realidad.

          Translated abstract

          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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          Counting hard-to-count populations: the network scale-up method for public health

          Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation.
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            Biases in Random Route Surveys

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              Capture-recapture models including covariate effects.

              Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries.
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                Author and article information

                Journal
                resp
                Revista Española de Salud Pública
                Rev. Esp. Salud Publica
                Ministerio de Sanidad, Consumo y Bienestar social (Madrid, Madrid, Spain )
                1135-5727
                2173-9110
                2019
                : 93
                : e201907033
                Affiliations
                [1] Málaga orgnameHospital Virgen de la Victoria orgdiv1Servicio de Medicina Preventiva España
                [3] Madrid orgnameCentros de Investigación Biomédica en Red en Epidemiología y Salud Pública España
                [6] Manchester orgnameSalford University orgdiv1School of Health Sciences Reino Unido
                [2] Madrid Andalucía orgnameUniversidad de Málaga orgdiv1Facultad de Medicina orgdiv2Departamento de Medicina Preventiva, Salud pública e Historia de la ciencia Spain
                [5] Madrid orgnameUniversidad Complutense de Madrid orgdiv1Facultad de Medicina orgdiv2Departamento de Salud Pública y Materno-Infantil Spain
                [4] Madrid orgnameInstituto de Salud Carlos III orgdiv1Escuela Nacional de Sanidad España
                Article
                S1135-57272019000100304 S1135-5727(19)09300000304
                aa9bbd36-c289-43a6-ae5e-6310b8d32f3b

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International License.

                History
                : 10 August 2018
                : 03 June 2019
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 20, Pages: 0
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                SciELO Public Health

                Categories
                Colaboraciones Especiales

                Epide-miologic study,Epidemiological monitoring,Data collection,Hidden populations,Recogida de datos,Poblaciones ocultas,Métodos epidemiológicos,Vigilancia epidemiológica

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