Respuestas rápidas

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"/02139111/0000003400000002/v3_202007311754/S0213911119300391/v3_202007311754/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "Brief original article" "titulo" => "Human rights in the Horn of Africa: an index of child and maternal health" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "186" "paginaFinal" => "188" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Karla Aída Zermeño Mejía, Karen Giovanna Añaños Bedriñana, José María Martín Martín, José Antonio Salinas Fernández, José Antonio Rodríguez Martín" "autores" => array:5 [ 0 => array:3 [ "nombre" => "Karla Aída" "apellidos" => "Zermeño Mejía" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 1 => array:3 [ "nombre" => "Karen Giovanna" "apellidos" => "Añaños Bedriñana" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "José María" "apellidos" => "Martín Martín" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 3 => array:3 [ "nombre" => "José Antonio" "apellidos" => "Salinas Fernández" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "d" "identificador" => "aff0020" ] ] ] 4 => array:4 [ "nombre" => "José Antonio" "apellidos" => "Rodríguez Martín" "email" => array:1 [ 0 => "josearm@ugr.es" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:5 [ 0 => array:3 [ "entidad" => "Faculty of Economics and Business, University of Granada, Granada, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Constitutional Law, University of Granada, Granada, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Faculty of Law, Social Sciences and Humanities, Business Area, International University of La Rioja, Logroño, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Department of Spanish and International Economics, University of Granada, Granada, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Department of Applied Economics, University of Granada, Granada, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Los derechos humanos en el Cuerno de África: un índice de salud infantil y materna" ] ] "textoCompleto" => "IntroductionOn 1st January 2016, the world officially began implementation of the action plan based on Sustainable Development Goals (SDGs). Goal three aims to ensure health and well-being for all people of all ages by improving reproductive, maternal and child health.<a class="elsevierStyleCrossRef" href="#bib0075">1</a>Study of the Horn of Africa countries is especially important, as the situation remains disastrous.<a class="elsevierStyleCrossRef" href="#bib0080">2</a> The Horn of Africa region is plagued by a set of complex,<a class="elsevierStyleCrossRef" href="#bib0085">3</a> often interrelated factors including environmental degradation, climate-related disasters such as droughts and floods.<a class="elsevierStyleCrossRefs" href="#bib0090">4,5</a>Multiple factors hinder access to and utilization of health services in the Horn of Africa. These factors include lack of a functional health system, geographical accessibility, financial barriers and limited availability of services.<a class="elsevierStyleCrossRefs" href="#bib0075">1,2</a>In particular, this study aims to construct a synthetic indicator of maternal and child health to enable comparison between five countries in 2017 in the Horn of Africa.The index also allows to study the impact of each variable individually so as to determine disparities in the variables associated with the SDGs for each country. Additionally, the research explores the relative impact of each variable by using the correction factor.MethodThe methodological approaches used in this study are based on the construction of a synthetic index that follows Pena's method (DP2).<a class="elsevierStyleCrossRef" href="#bib0100">6</a> The DP2 provides an ideal solution to the problems involved in devising a synthetic indicator, particularly those related to aggregation and weighting of simple indicators and information duplicity.<a class="elsevierStyleCrossRef" href="#bib0105">7</a>The DP2 measures the distance between the issue studied in each country and a fictitious base reference. We take as reference a theoretical country that obtains the worst values for the variables studied<a class="elsevierStyleCrossRef" href="#bib0105">7</a>.The DP2 from country j is defined as follows:<elsevierMultimedia ident="eq0005"></elsevierMultimedia>where di= |xji– x*i| is the distance between the value of variable i in country j and the reference base. The reference base comprises the results from an imaginary country which reflects the worst possible scenario for all the variables where X*= (x*1, x*2,..., x*n) coincides with the minimum vector. The reference base would therefore be attributed a value of zero in the synthetic indicator.<a class="elsevierStyleCrossRef" href="#bib0105">7</a>n is the number of variables, σi is the standard deviation of variable i, and (1-R2i, i-1,…,1) is a “correction factor”<a class="elsevierStyleCrossRef" href="#bib0100">6</a> that avoids redundancy.<a class="elsevierStyleCrossRefs" href="#bib0080">2,6–9</a> The coefficient of determination, R2i, i−1,…,1, is the determination coefficient in regression Xi over Xi-1, Xi-2,…,X1, which is already included, with R21=0. Put differently, the coefficient measures the part of the variance of each variable explained by the linear regression estimated using the preceding variables.<a class="elsevierStyleCrossRef" href="#bib0080">2</a> The ordering of the variables corresponds to their relative weight measured in terms of linear correlation with the final synthetic indicator.<a class="elsevierStyleCrossRef" href="#bib0105">7</a> The input order of the variables is determined by an algorithm that reaches convergence when the indicator fulfils a number of desirable properties.<a class="elsevierStyleCrossRefs" href="#bib0080">2,6–9</a>It is also possible to establish an order or hierarchy based on the amount of information that each variable contributes to the DP2. To determine this, we construct the Ivanovic Discrimination Coefficient (IDC).<a class="elsevierStyleCrossRefs" href="#bib0120">10–12</a> This indicator finally shows us the amount of information provided by the i-th variable. It can range from 0 (in the event that the values of Xi are identical in all the countries) to 2 (in the event that the variable has total discriminatory power, that is, when the amount of information varies greatly across territories). Thus, the closer the IDC value is to 2, the more useful information it provides to explain the differences in the level of child and maternal health in the countries studied.<a class="elsevierStyleCrossRefs" href="#bib0125">11,12</a>Data were collected from the work of the United Nations Statistical Commission, which created the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs). In particular, we used five variables of child and maternal health associated with the goal 3<a class="elsevierStyleCrossRef" href="#bib0075">1</a> (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>), using as a reference the detailed information contained in a set of variables set out under the SDGs in the Report 2018,<a class="elsevierStyleCrossRef" href="#bib0135">13</a> which provide a more extensive and more reliable set of statistics on the SDG 3 of Horn of Africa. The countries included into the analysis were Ethiopia, Kenya, Somalia, Eritrea and Djibouti. To guarantee fulfilment of the properties of the synthetic indicator, we multiply specific variables whose increase implies a worsening of the child and maternal health by −1.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>The year of analysis is 2017, but for those variables where information was not available for that date, the nearest year was taken as an alternative. This has occurred in the variable “Attended by skilled health personnel, percentage”, whose available information is from 2016.ResultsConstructed from the variables included in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, the result is shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>, which ranks the five countries by level of child and maternal health.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>The resulting classification (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>) shows, first, a distance of almost 5 points between the best-positioned country (Djibouti) and the worst-positioned (Somalia) in 2017. These results indicate a relatively high disparity between the countries analysed.The results show that Djibouti made the greatest progress toward the goals for child and maternal health, with a distance of 4.56 from the baseline (Table 2). It was followed by Kenya (3.46), which accounts for 29% of the total population of the Horn of Africa.Taken together, Somalia and Ethiopia account for nearly 70% of the population of the Horn of Africa. They, in contrast, are the countries with the worst theoretical scenarios (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).If we analyse the results obtained for the variables with the greatest inequality in intercountry values (IDC),<a class="elsevierStyleCrossRef" href="#bib0120">10</a> the most discriminating variable is “Attended by skilled health personnel, percentage” (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). The second-most-discriminating variable is “Maternal mortality ratio per 100,000 live births”.In addition, by means of correction factors, the synthetic indicator DP2 only includes the new information from each variable.<a class="elsevierStyleCrossRef" href="#bib0115">9</a> In particular, the variable “Maternal mortality ratio per 100,000 live births” contains all of its information, so the corresponding correction factor is 100%, as a result of being most closely correlated with it (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).Discussion and conclusionThe DP2 method shows territorial disparities in child and maternal health in the Horn of Africa in 2017. We obtained a difference of 4.56 units between Djibouti and the reference value. Djibouti achieved a higher level of child and maternal health, but it accounts for only 0.5% of the total Horn of Africa population. At the opposite extreme, Somalia registers extremely low values in the set of partial indicators.Priority must be given to interventions to address the variables that have greater power to explain the differences in the values ??between countries relative —primarily the variable “Attended by skilled health personnel, percentage”.The differing values of these variables suggest that progress in maternal health is uneven throughout the Horn of Africa, while fewer territorial differences exist in the variables associated with child health as defined in the SDGs.In summary, delivery of health services is greatly in need of improvement, especially in Somalia and Ethiopia, and there is an urgent need to increase the number of health workers throughout the region to lower maternal and infant mortality.<a class="elsevierStyleCrossRef" href="#bib0140">14</a>In general, the DP2 classification for these countries differs from that made by the Human Development Index (HDI) for countries with low human development in 2017 (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). In this sense, our analysis takes into account a range of SDGs variables, some of which are not included in the HDI.<elsevierMultimedia ident="tb0005"></elsevierMultimedia>Editor in chargeMiguel Ángel Negrín Hernández.Transparency declarationThe corresponding author on behalf of the other authors guarantee the accuracy, transparency and honesty of the data and information contained in the study, that no relevant information has been omitted and that all discrepancies between authors have been adequately resolved and described.Authorship contributionsK.A. Zermeño, J.A. Rodríguez and J.M. Martín conceived the study and supervised all aspects of its performance. J.A. Rodríguez, K. Añaños and J.A. Salinas gathered data, interpreted the results and participated in writing the first draft of the manuscript. K.A. Zermeño, J.M. Martín and K. Añaños were in charge of the bibliographic research. J.A. Salinas and J.M. Martín were in charge of the data analysis. All authors contributed comments and ideas, interpreted the findings and reviewed the drafts of the manuscript. All of the authors approved the final version of the paper." "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:3 [ "identificador" => "xres1368949" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1258334" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1368948" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1258333" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Method" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion and conclusion" ] 8 => array:2 [ "identificador" => "sec0035" "titulo" => "Editor in charge" ] 9 => array:2 [ "identificador" => "sec0040" "titulo" => "Transparency declaration" ] 10 => array:2 [ "identificador" => "sec0045" "titulo" => "Authorship contributions" ] 11 => array:2 [ "identificador" => "xack475390" "titulo" => "Acknowledgements" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2019-06-21" "fechaAceptado" => "2019-11-13" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1258334" "palabras" => array:6 [ 0 => "Africa" 1 => "Child health" 2 => "Health status disparities" 3 => "Maternal health" 4 => "Human rights" 5 => "Sustainable development" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1258333" "palabras" => array:6 [ 0 => "África" 1 => "Salud infantil" 2 => "Disparidades en el estado de la salud" 3 => "Salud materna" 4 => "Derechos humanos" 5 => "Desarrollo sostenible" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "ObjectiveTo construct a territorial measure and classification of child and maternal health in the countries of the Horn of Africa based on the 2030 Agenda for Sustainable Development adopted by all United Nations Member States in 2015. MethodThe design of our index includes the variables child and maternal health defined in the Sustainable Development Goals (SDGs) to enable territorial ranking of the countries. For this purpose, we used Pena's distance method for 2017. ResultsThe results indicate a relatively high territorial disparity in maternal health between the countries of the Horn of Africa according to the differing values of the SDGs variables of child and maternal health. ConclusionsWe propose a territorial classification in the countries of the Horn of Africa. We believe that the most striking differences between countries relate to basic variables of maternal health such as being attended by skilled health personnel." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "ObjetivoElaborar una medida y clasificación territorial de la salud infantil y materna en los países del Cuerno de África, basada en la Agenda 2030 para el Desarrollo Sostenible, que fue adoptada por todos los Estados miembros de las Naciones Unidas en 2015. MétodoEl diseño del índice incluye variables de salud infantil y materna definidas en los Objetivos de Desarrollo Sostenible (ODS), para permitir la clasificación territorial de los países. Para este propósito, utilizamos el método de distancia de Pena para 2017. ResultadosLos resultados revelan una disparidad territorial relativamente alta en salud materna entre los países del Cuerno de África, de acuerdo con los diferentes valores de las variables ODS. ConclusionesProponemos una clasificación territorial en el Cuerno de África. Consideramos que las mayores diferencias entre los países se relacionan con variables básicas de salud materna, como la asistencia de personal de salud cualificado." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:4 [ 0 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Partial indicators \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Ivanovic(IDC) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Correction factor \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Attended by skilled health personnel, percentage(positive sign +) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maternal mortality ratio per 100,000 live births(negative sign −) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.00 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Children under five mortality rate per 1,000 live births(negative sign −) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.33 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Adolescent birth rate, per 1,000 women(negative sign −) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.19 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Prevalence of underweight (% of children under)(negative sign −) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.10 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2351206.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Variables of child and maternal health according to the Ivanovic Discrimination Coefficient (IDC) and to the correction factor." ] ] 1 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "NA: not available." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">IndicatorPena's relative distance(DP2) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total population of the Horn of Africa(%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Human Development Index (HDI)<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Djibouti \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.48 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Kenya \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.59 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Eritrea \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.44 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ethiopia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.46 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Somalia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2351207.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Countries with high HDI (≥0.8), with medium HDI (0.5-<0.8) and with low HDI (HDI <0.5)." ] ] ] "descripcion" => array:1 [ "en" => "Synthetic indicator of child and maternal health in the Horn of Africa, 2017. Countries ordered by Pena's relative distance (DP2) method." ] ] 2 => array:5 [ "identificador" => "eq0005" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "DP2 = ∑i=1n(di/σi)(1 − R2  i,i−1,…,1)" "Fichero" => "STRIPIN_si1.jpeg" "Tamanyo" => 2700 "Alto" => 45 "Ancho" => 262 ] ] 3 => array:5 [ "identificador" => "tb0005" "tipo" => "MULTIMEDIATEXTO" "mostrarFloat" => false "mostrarDisplay" => true "texto" => array:1 [ "textoCompleto" => "What is known about the topic?Several factors must be analysed and monitored on a priority and constant basis in the decision-making process for distribution of international aid to the countries of the Horn of Africa to improve maternal and child health. Research on the evolution of variables associated with maternal and child health in these countries is very limited.What does this study add to the literature?The study provides a complete, up-to-date classification of the Horn of Africa, based on the values of the variables associated with maternal and child health. It also provides information on the variables that best explain the differences between countries. We conclude that the heterogeneous situations of the countries differ from the goals projected by United Nations. The most notable differences relate to the number of births attended by qualified health personnel." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:14 [ 0 => array:3 [ "identificador" => "bib0075" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "United Nations. The Sustainable Development Goals Report 2016. 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"fecha" => "2012" "volumen" => "26" "paginaInicial" => "190" "paginaFinal" => "192" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22023957" "web" => "Medline" ] ] ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0115" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Quality of life in Latin America: a proposal for a synthetic indicator" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "N. Somarriba" 1 => "P. Zarzosa" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "LibroEditado" => array:5 [ "editores" => "G.Tonon" "titulo" => "Indicators of quality of life in Latin America." 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and &#40;1-R<span class="elsevierStyleSup">2</span><span class="elsevierStyleInf">i</span>&#44; <span class="elsevierStyleInf">i-1&#44;&#8230;&#44;1</span>&#41; is a &#8220;correction factor&#8221;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a> that avoids redundancy&#46;<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">2&#44;6&#8211;9</span></a> The coefficient of determination&#44; R<span class="elsevierStyleSup">2</span><span class="elsevierStyleInf">i</span>&#44; <span class="elsevierStyleInf">i&#8722;1&#44;&#8230;&#44;1</span>&#44; is the determination coefficient in regression X<span class="elsevierStyleInf">i</span> over X<span class="elsevierStyleInf">i-1</span>&#44; X<span class="elsevierStyleInf">i-2</span>&#44;&#8230;&#44;X<span class="elsevierStyleInf">1</span>&#44; which is already included&#44; with R<span class="elsevierStyleSup">2</span><span class="elsevierStyleInf">1</span>&#61;0&#46; Put differently&#44; the coefficient measures the part of the variance of each variable explained by the linear regression estimated using the preceding variables&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">2</span></a> The ordering of the variables corresponds to their relative weight measured in terms of linear correlation with the final synthetic indicator&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">7</span></a> The input order of the variables is determined by an algorithm that reaches convergence when the indicator fulfils a number of desirable properties&#46;<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">2&#44;6&#8211;9</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">It is also possible to establish an order or hierarchy based on the amount of information that each variable contributes to the DP<span class="elsevierStyleInf">2</span>&#46; To determine this&#44; we construct the Ivanovic Discrimination Coefficient &#40;IDC&#41;&#46;<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">10&#8211;12</span></a> This indicator finally shows us the amount of information provided by the i-th variable&#46; It can range from 0 &#40;in the event that the values of X<span class="elsevierStyleInf">i</span> are identical in all the countries&#41; to 2 &#40;in the event that the variable has total discriminatory power&#44; that is&#44; when the amount of information varies greatly across territories&#41;&#46; Thus&#44; the closer the IDC value is to 2&#44; the more useful information it provides to explain the differences in the level of child and maternal health in the countries studied&#46;<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">11&#44;12</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Data were collected from the work of the United Nations Statistical Commission&#44; which created the Inter-agency and Expert Group on SDG Indicators &#40;IAEG-SDGs&#41;&#46; In particular&#44; we used five variables of child and maternal health associated with the goal 3<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#44; using as a reference the detailed information contained in a set of variables set out under the SDGs in the Report 2018&#44;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a> which provide a more extensive and more reliable set of statistics on the SDG 3 of Horn of Africa&#46; The countries included into the analysis were Ethiopia&#44; Kenya&#44; Somalia&#44; Eritrea and Djibouti&#46; To guarantee fulfilment of the properties of the synthetic indicator&#44; we multiply specific variables whose increase implies a worsening of the child and maternal health by &#8722;1&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0075" class="elsevierStylePara elsevierViewall">The year of analysis is 2017&#44; but for those variables where information was not available for that date&#44; the nearest year was taken as an alternative&#46; This has occurred in the variable &#8220;Attended by skilled health personnel&#44; percentage&#8221;&#44; whose available information is from 2016&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0080" class="elsevierStylePara elsevierViewall">Constructed from the variables included in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; the result is shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; which ranks the five countries by level of child and maternal health&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The resulting classification &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41; shows&#44; first&#44; a distance of almost 5 points between the best-positioned country &#40;Djibouti&#41; and the worst-positioned &#40;Somalia&#41; in 2017&#46; These results indicate a relatively high disparity between the countries analysed&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">The results show that Djibouti made the greatest progress toward the goals for child and maternal health&#44; with a distance of 4&#46;56 from the baseline &#40;Table&#160;2&#41;&#46; It was followed by Kenya &#40;3&#46;46&#41;&#44; which accounts for 29&#37; of the total population of the Horn of Africa&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">Taken together&#44; Somalia and Ethiopia account for nearly 70&#37; of the population of the Horn of Africa&#46; They&#44; in contrast&#44; are the countries with the worst theoretical scenarios &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">If we analyse the results obtained for the variables with the greatest inequality in intercountry values &#40;IDC&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">10</span></a> the most discriminating variable is &#8220;Attended by skilled health personnel&#44; percentage&#8221; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; The second-most-discriminating variable is &#8220;Maternal mortality ratio per 100&#44;000 live births&#8221;&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">In addition&#44; by means of correction factors&#44; the synthetic indicator DP<span class="elsevierStyleInf">2</span> only includes the new information from each variable&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">9</span></a> In particular&#44; the variable &#8220;Maternal mortality ratio per 100&#44;000 live births&#8221; contains all of its information&#44; so the corresponding correction factor is 100&#37;&#44; as a result of being most closely correlated with it &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Discussion and conclusion</span><p id="par0110" class="elsevierStylePara elsevierViewall">The DP<span class="elsevierStyleInf">2</span> method shows territorial disparities in child and maternal health in the Horn of Africa in 2017&#46; We obtained a difference of 4&#46;56 units between Djibouti and the reference value&#46; Djibouti achieved a higher level of child and maternal health&#44; but it accounts for only 0&#46;5&#37; of the total Horn of Africa population&#46; At the opposite extreme&#44; Somalia registers extremely low values in the set of partial indicators&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">Priority must be given to interventions to address the variables that have greater power to explain the differences in the values &#63;&#63;between countries relative &#8212;primarily the variable &#8220;Attended by skilled health personnel&#44; percentage&#8221;&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">The differing values of these variables suggest that progress in maternal health is uneven throughout the Horn of Africa&#44; while fewer territorial differences exist in the variables associated with child health as defined in the SDGs&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">In summary&#44; delivery of health services is greatly in need of improvement&#44; especially in Somalia and Ethiopia&#44; and there is an urgent need to increase the number of health workers throughout the region to lower maternal and infant mortality&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">14</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">In general&#44; the DP<span class="elsevierStyleInf">2</span> classification for these countries differs from that made by the Human Development Index &#40;HDI&#41; for countries with low human development in 2017 &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; In this sense&#44; our analysis takes into account a range of SDGs variables&#44; some of which are not included in the HDI&#46;<elsevierMultimedia ident="tb0005"></elsevierMultimedia></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Editor in charge</span><p id="par0145" class="elsevierStylePara elsevierViewall">Miguel &#193;ngel Negr&#237;n Hern&#225;ndez&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Transparency declaration</span><p id="par0150" class="elsevierStylePara elsevierViewall">The corresponding author on behalf of the other authors guarantee the accuracy&#44; transparency and honesty of the data and information contained in the study&#44; that no relevant information has been omitted and that all discrepancies between authors have been adequately resolved and described&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Authorship contributions</span><p id="par0155" class="elsevierStylePara elsevierViewall">K&#46;A&#46; Zerme&#241;o&#44; J&#46;A&#46; Rodr&#237;guez and J&#46;M&#46; Mart&#237;n conceived the study and supervised all aspects of its performance&#46; J&#46;A&#46; Rodr&#237;guez&#44; K&#46; A&#241;a&#241;os and J&#46;A&#46; Salinas gathered data&#44; interpreted the results and participated in writing the first draft of the manuscript&#46; K&#46;A&#46; Zerme&#241;o&#44; J&#46;M&#46; Mart&#237;n and K&#46; A&#241;a&#241;os were in charge of the bibliographic research&#46; J&#46;A&#46; Salinas and J&#46;M&#46; Mart&#237;n were in charge of the data analysis&#46; All authors contributed comments and ideas&#44; interpreted the findings and reviewed the drafts of the manuscript&#46; All of the authors approved the final version of the paper&#46;</p></span></span>"
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            1 => "Child health"
            2 => "Health status disparities"
            3 => "Maternal health"
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            0 => "&#193;frica"
            1 => "Salud infantil"
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            4 => "Derechos humanos"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To construct a territorial measure and classification of child and maternal health in the countries of the Horn of Africa based on the 2030 Agenda for Sustainable Development adopted by all United Nations Member States in 2015&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Method</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">The design of our index includes the variables child and maternal health defined in the Sustainable Development Goals &#40;SDGs&#41; to enable territorial ranking of the countries&#46; For this purpose&#44; we used Pena&#39;s distance method for 2017&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The results indicate a relatively high territorial disparity in maternal health between the countries of the Horn of Africa according to the differing values of the SDGs variables of child and maternal health&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">We propose a territorial classification in the countries of the Horn of Africa&#46; We believe that the most striking differences between countries relate to basic variables of maternal health such as being attended by skilled health personnel&#46;</p></span>"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Elaborar una medida y clasificaci&#243;n territorial de la salud infantil y materna en los pa&#237;ses del Cuerno de &#193;frica&#44; basada en la Agenda 2030 para el Desarrollo Sostenible&#44; que fue adoptada por todos los Estados miembros de las Naciones Unidas en 2015&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todo</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">El dise&#241;o del &#237;ndice incluye variables de salud infantil y materna definidas en los Objetivos de Desarrollo Sostenible &#40;ODS&#41;&#44; para permitir la clasificaci&#243;n territorial de los pa&#237;ses&#46; Para este prop&#243;sito&#44; utilizamos el m&#233;todo de distancia de Pena para 2017&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Los resultados revelan una disparidad territorial relativamente alta en salud materna entre los pa&#237;ses del Cuerno de &#193;frica&#44; de acuerdo con los diferentes valores de las variables ODS&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Proponemos una clasificaci&#243;n territorial en el Cuerno de &#193;frica&#46; Consideramos que las mayores diferencias entre los pa&#237;ses se relacionan con variables b&#225;sicas de salud materna&#44; como la asistencia de personal de salud cualificado&#46;</p></span>"
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          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Variables of child and maternal health according to the Ivanovic Discrimination Coefficient &#40;IDC&#41; and to the correction factor&#46;</p>"
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          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Synthetic indicator of child and maternal health in the Horn of Africa&#44; 2017&#46; Countries ordered by Pena&#39;s relative distance &#40;DP<span class="elsevierStyleInf">2</span>&#41; method&#46;</p>"
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                    0 => array:2 [
                      "titulo" => "Common agenda or Europe&#39;s agenda&#63; International protection&#44; human rights and migration from the Horn of Africa"
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                          "etal" => false
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                        "tituloSerie" => "Rev Stat Appli&#46;"
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                  "referenciaCompleta" => "United Nations&#46; The Sustainable Development Goals Report 2018&#46; New York&#58; Oxford University Press&#59; 2018&#46; 40 p&#46;"
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                  "referenciaCompleta" => "UNICEF&#47;WHO&#46; Tracking progress towards universal coverage for women&#39;s&#44; children&#39;s and adolescents&#8217; health&#46; The 2017 Report&#46; Geneva and New York&#58; WHO&#47;UNICEF&#59; 2018&#46; 268 p&#46;"
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