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      The Association of Energy Poverty with Health and Wellbeing in Children in a Mediterranean City

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          Abstract

          Children have been identified as being particularly vulnerable to energy poverty (EP), but little empirical research has addressed the effect of EP on children’s health and wellbeing, especially in southern Europe. In this work we aimed to provide an in-depth description of the distribution of EP by sociodemographic, socioeconomic and housing characteristics, as well as to analyse the association between EP and health and wellbeing in children in Barcelona. We performed a cross-sectional study using data from the Barcelona Health Survey for 2016 ( n = 481 children under 15 years). We analysed the association between EP and health outcomes through prevalence differences and prevalence ratios (PR) and their 95% confidence interval (CI), using Poisson regression models with robust variance. In Barcelona, 10.6% of children were living in EP and large inequalities were found by sociodemographic, socioeconomic and housing characteristics. EP was strongly associated with poor health in children (PR (95% CI): 7.70 (2.86, 20.72)). Living in EP was also associated with poor mental health (PR (95% CI): 2.46 (1.21, 4.99)) and with more cases of asthma (PR (95% CI): 4.19 (1.47, 11.90)) and overweight (PR (95% CI): 1.50 (1.05, 2.15)) in children. It is urgent to develop specific measures to avoid such serious and unfair health effects on children.

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          The classification of child psychopathology: a review and analysis of empirical efforts.

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            Estimating predicted probabilities from logistic regression: different methods correspond to different target populations.

            We review three common methods to estimate predicted probabilities following confounder-adjusted logistic regression: marginal standardization (predicted probabilities summed to a weighted average reflecting the confounder distribution in the target population); prediction at the modes (conditional predicted probabilities calculated by setting each confounder to its modal value); and prediction at the means (predicted probabilities calculated by setting each confounder to its mean value). That each method corresponds to a different target population is underappreciated in practice. Specifically, prediction at the means is often incorrectly interpreted as estimating average probabilities for the overall study population, and furthermore yields nonsensical estimates in the presence of dichotomous confounders. Default commands in popular statistical software packages often lead to inadvertent misapplication of prediction at the means. Using an applied example, we demonstrate discrepancies in predicted probabilities across these methods, discuss implications for interpretation and provide syntax for SAS and Stata. Marginal standardization allows inference to the total population from which data are drawn. Prediction at the modes or means allows inference only to the relevant stratum of observations. With dichotomous confounders, prediction at the means corresponds to a stratum that does not include any real-life observations. Marginal standardization is the appropriate method when making inference to the overall population. Other methods should be used with caution, and prediction at the means should not be used with binary confounders. Stata, but not SAS, incorporates simple methods for marginal standardization. © The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
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              Fuel poverty and human health: A review of recent evidence

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                02 June 2021
                June 2021
                : 18
                : 11
                : 5961
                Affiliations
                [1 ]Agència de Salut Pública de Barcelona, 08023 Barcelona, Spain; cborrell@ 123456aspb.cat (C.B.); mgotsens@ 123456aspb.cat (M.G.); mjlopez@ 123456aspb.cat (M.J.L.); lpalenci@ 123456aspb.cat (L.P.); lartazco@ 123456aspb.cat (L.A.); mmari@ 123456aspb.cat (M.M.-D.)
                [2 ]Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain
                [3 ]Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08005 Barcelona, Spain
                [4 ]CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
                [5 ]Centre for Regional Economic Social Research, Sheffield Hallam University, Sheffield S1 2LX, UK; irene.gonzalez@ 123456esf-cat.org
                Author notes
                [* ]Correspondence: lolivera@ 123456aspb.cat
                Author information
                https://orcid.org/0000-0003-0033-7829
                https://orcid.org/0000-0002-1170-2505
                https://orcid.org/0000-0003-2952-2024
                https://orcid.org/0000-0003-4402-2239
                https://orcid.org/0000-0002-3726-9893
                https://orcid.org/0000-0002-6300-5111
                https://orcid.org/0000-0003-4345-4988
                Article
                ijerph-18-05961
                10.3390/ijerph18115961
                8199602
                34199387
                2752e6bd-d58b-46ef-b413-7d3c661f6af3
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 20 April 2021
                : 30 May 2021
                Categories
                Article

                Public health
                energy poverty,fuel poverty,health,health inequalities,social determinants of health,children,southern europe,urban

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