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The structure of depressive symptoms using CES-D and ZDS in outpatients in a general hospital in Lima, Peru
Estructura de los síntomas de depresión según el CES-D y la ZDS en pacientes ambulatorios de un hospital general de Lima, Perú
Jair R. Jara-Fernández
Corresponding author
jair.jara.f@upch.pe

Corresponding author.
, Nieves Gutiérrez-Kolotvina, Jhoselyn Milagros Flores-Egocheaga, Paulo Ruíz-Grosso, Johann M. Vega-Dienstmaier
Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Peru
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Depression is a psychiatric disorder that affects many people all over the world&#46; According to the WHO&#44; approximately 350 million worldwide suffer from depression and it is the leading cause of years lost due to disability&#59; 76&#46;4 million years&#44; equivalent to 10&#46;3&#37; of the total burden of diseases&#46; In Peru&#46; the prevalence in 2010 was 4&#46;89&#37; &#40;95&#37; confidence interval &#91;95&#37;CI&#93;&#44; 3&#46;36&#37;&#8211;7&#46;06&#37;&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Depression&#44; considered as a syndrome&#44; is a heterogeneous group of symptoms which gives rise to a variable presentation depending on the onset&#44; severity&#44; course of the disease and the particular set of symptoms&#46; According to the fifth edition of the <span class="elsevierStyleItalic">Diagnostic Statistical Manual of Mental Disorders</span> &#40;DSM-5&#41;&#44; major depression is characterised by the presence of at least five symptoms from a list of nine&#44; which must include sadness or anhedonia&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> The permutation of these nine symptoms leads to a wide variety of possible forms of presentation&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">A study of 3703 patients diagnosed with depression analysed the variation in depressive symptoms in each clinical condition and found a total of 1030 possible patterns&#46; The most common pattern in that study had a frequency of 1&#46;78&#37;&#44; which shows great heterogeneity in the presentation of depression and calls into question the consistency of the diagnosis&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">In current disease classification systems&#44; such as the DSM-5&#44; it is considered that the symptoms and signs that patients report have their origin in a latent cause called a mental disorder&#44; and that these are grouped together&#44; forming discrete entities with clear boundaries between them&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">In psychiatry&#44; we talk about mental disorders&#44; not illnesses&#44; as occurs in other branches of medicine&#44; because illnesses arise from a common pathogenic pathway&#44; while mental disorders are syndromic constellations of empirically linked symptoms&#46; A syndrome consists of multiple inter-related symptoms with a stable and characteristic structure&#44; as well as a particular prognosis&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Therefore&#44; if a syndrome corresponds to a natural entity&#44; there should be some natural limit or discontinuity between mental disorders&#46; The problem with this perspective&#44; which conceives mental disorders as distinct categories&#44; is explaining the high comorbidity of mental disorders or the almost non-existent presence of pathognomonic symptoms&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">Conceiving mental disorders as a common cause underlying the set of symptoms &#40;syndrome&#41; has not been so effective&#44; as it has not been possible to identify any pathogenic pathway or aetiological mechanism for these mental disorders&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">From this theoretical model that understands mental disorders as unknown causes of observable symptoms&#44; statistical models have been developed&#44; such as the latent variable model&#44; in which the symptoms are observable variables of a mental disorder &#40;latent variable&#41;&#46; One example of these models is factor analysis&#44; often used in the development of psychometric scales&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">The network approach proposes that mental disorders are not underlying causes of observable symptoms but rather interconnected symptoms which interact&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> This theoretical approach to mental disorders requires a statistical method that allows models to be developed related to the proposed theoretical framework&#58; network analysis&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">This seeks to complement the limitations of the latent variable model&#46; This approach arises from the following statements&#58; <span class="elsevierStyleItalic">&#40;a&#41;</span> current information does not let us explain the concomitance of symptoms of a certain disorder based on a specific biological or psychological condition&#59; and <span class="elsevierStyleItalic">&#40;b&#41;</span> in psychopathology&#44; the symptoms can be causally related to each other&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> This means that an underlying disorder would not be the common cause of the covariance between symptoms&#44; but rather the interaction between symptoms would be constitutive of the disorder&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0055" class="elsevierStylePara elsevierViewall">The networks are drawn up from correlation matrices of symptoms and made up of nodes&#44; which represent the symptoms&#44; and edges&#44; which represent their association&#46; Edges can&#44; in turn&#44; be weighted or unweighted and directed or undirected&#46; Weighted edges are distinguished from unweighted edges because they quantify the magnitude of the connection between symptoms&#46; The directionality indicates a possible causal relationship between items&#46; The edges can represent positive or negative associations of symptoms and appear as different colours &#40;usually green for positive relationships and red for negative relationships&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0060" class="elsevierStylePara elsevierViewall">Central symptoms represent an important concept within network analysis&#46; These are assessed using measures of centrality &#40;for example&#44; strength&#44; closeness or intermediation&#41; which describe degrees of connectivity of a symptom with others in magnitude and quantity&#46; Due to these characteristics&#44; central symptoms allow both the activation and maintenance of the network in the disease state&#46; The strong connectivity of these symptoms within the network would make them a target for more effective biological or psychosocial interventions&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">Furthermore&#44; the central symptoms of the same disorder could vary depending on the group to which the network analysis is applied&#46; Thus&#44; symptoms such as the desire for death&#44; depressed mood&#44; loss of interest and pessimism constitute the basis of depressive symptoms in old age&#44;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> while in women with postpartum depression&#44; it would be caused by fatigue&#44; sadness and anhedonia&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> In a network analysis study carried out on psychiatric outpatient patients&#44; a structure of depressive symptoms was identified consisting of sadness&#44; loneliness&#44; vulnerability&#44; ideas of death&#44; guilt and worthlessness&#44; and the first three were the most important&#46;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Among the limitations of this type of study are its small sample size and reproducibility&#46; Even though the number of investigations supporting the reproducibility of results in different samples is large&#44; the cross-sectional nature does not allow us to determine causality between the symptoms&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> This study seeks to evaluate a clinical population from the Psychiatry and Internal Medicine clinic of a general hospital and thus assess the reproducibility of the structure found in previous studies on the general population based on the Center for Epidemiologic Studies Depression Scale &#40;CES-D&#41; and the Zung Self-Rating Depression Scale &#40;ZDS&#41;&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Both are consistent instruments for the detection of depressive symptoms in a hospital environment and in the general population&#44; and could be an important contribution to both research and diagnosis of major depressive disorder&#46; It is important to improve the understanding of depressive psychopathology and its symptoms&#44; given the frequency of underdiagnosis of depressive disorders in primary care in developing countries<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> and the still deficient prevention&#44; treatment and rehabilitation of depressive disorders in Latin America&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a></p><p id="par0080" class="elsevierStylePara elsevierViewall">Our study contributes this evidence to the instruments used in Spanish and Spanish-speaking countries&#44; which has been limited until now&#46; The results obtained will enable us to determine the symptom structure of the depressive disorder&#44; visualise this structure and identify the central symptoms according to the CES-D and ZDS&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><p id="par0085" class="elsevierStylePara elsevierViewall">This was a cross-sectional secondary data analysis study&#46; We decided to perform a secondary analysis due to the current limitations in data collection caused by the COVID-19 pandemic&#46; We used the database of a study carried out by Ruiz-Grosso et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> aimed at validating and comparing the psychometric properties of the Spanish versions of the CES-D<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> and ZDS&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> In network analysis&#44; the use of databases from psychometric studies that contain items representative of multiple dimensions of the disorder under evaluation is common&#46; To this extent&#44; the database used has the advantage of including two instruments validated in Spanish with adequate psychometric properties&#46; Furthermore&#44; as it included a clinical population not restricted only to patients with a diagnosis of major depression&#44; it avoided conditioning the variables on a certain scale score&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">The study was carried out at the Hospital Cayetano Heredia &#40;Lima&#44; Peru&#41; from January to December 2006&#46; The study had the informed consent of the participants and the authorisation of the Ethics Committee of the Hospital Cayetano Heredia and the Universidad Peruana Cayetano Heredia&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">A total of 194 records from the aforementioned validation study<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> were used&#46; These records came from patients in the Psychiatry and Internal Medicine outpatient clinic and included 70 patients with a diagnosis of major depression &#40;MD&#41;&#44; 63 with a psychiatric disorder other than major depression &#40;OP&#41;&#44; and 61 without evidence of a psychiatric disorder &#40;NP&#41;&#46; The inclusion criterion in the MD subgroup was the diagnosis of major depression made by a psychiatrist using DSM-IV criteria&#46; The inclusion criterion in the OP subgroup consisted of a diagnosis of other psychiatric disorders &#40;including bipolar disorder&#41; by a psychiatrist using DSM-IV criteria&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">There is no consensus to determine the sample size in network analysis&#46; A sample size of 194 has been considered &#34;acceptable&#34; following the recommendations established for factor analysis studies&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> Simulation studies show that a 20-node network with 200 participants would have a sensitivity around 0&#46;8 and a specificity around 0&#46;9&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> The adequacy of the sample size was confirmed after obtaining results through stability analysis&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Tools</span><p id="par0105" class="elsevierStylePara elsevierViewall">The Center for Epidemiologic Studies-Depression Scale &#40;CES-D&#41; in its Spanish version has 20 Likert-type items and was designed to detect depressive disorders in the general population&#46; Each item on the scale scores from 0 to 3&#46; It has good internal consistency &#40;Cronbach&#39;s alpha&#44; 0&#46;93&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">The <span class="elsevierStyleItalic">Zung Self-rating Depression Scale</span> &#40;ZDS&#41; in its Spanish version is a 20-item Likert-type scale that enables depressive symptoms to be identified at the first level of care&#46; Each item on this scale scores from 1 to 4&#46; It has good internal consistency &#40;&#945;&#8239;&#61;&#8239;0&#46;89&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Statistical analysis</span><p id="par0115" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">R</span> version 4&#46;03 was used for the statistical analysis using the integrated development environment <span class="elsevierStyleItalic">RStudio</span> version 1&#46;4&#46;1103&#46; The database of the original extension study <span class="elsevierStyleItalic">&#42;&#46;dta</span> was imported into <span class="elsevierStyleItalic">RStudio</span>&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Correlation matrices were generated that estimate the association between variables assumed to be continuous but measured on an ordinal scale&#46; The <span class="elsevierStyleItalic">cor&#95;auto</span> function of the <span class="elsevierStyleItalic">qgraph</span> software&#44; version 1&#46;6&#46;9 was used&#44; which automatically attributes the best correlation method for Likert-type ordinal variables&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#44;21</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">A regularised partial correlation network was estimated based on the correlation matrix&#46; The network was built with the <span class="elsevierStyleItalic">estimate Network</span> function from the <span class="elsevierStyleItalic">bootnet</span> package&#46; When considering partial correlations&#44; the connections between nodes represent relationships of conditional independence between nodes&#44; and each relationship between a pair of nodes is given by controlling the effect of the other nodes&#46; The network estimation was carried out using the EBICglasso model&#44; which includes the regularisation technique&#44; glasso &#40;Graphical Least Absolute Shrinkage and Selection Operator&#41; and EBIC &#40;Extended Bayesian Information Criterion&#41;&#46; The glasso algorithm is a regularisation method that allows the connections in the network to be reduced by setting the smallest connections to 0&#46; This helped eliminate spurious relationships and maintain a more dispersed and parsimonious network&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#8211;24</span></a> The EBIC method is a selection criterion that estimates 100 network models with different degrees of dispersion and selects the one with the lowest EBIC according to the hyper-parameter &#947;&#44; which regulates the balance between the inclusion of false positives and the removal of true connections&#46; We chose an intermediate value of &#947;&#8239;&#61;&#8239;0&#46;5&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">The Fruchterman-Reingold algorithm was used to design the network&#44; based on strength&#44; which places the most interconnected symptoms towards the centre of the network and prioritises the visual appearance of the network&#46;<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">24&#44;25</span></a></p><p id="par0135" class="elsevierStylePara elsevierViewall">Centrality measures allow us to know which items have a greater number of connections with other items&#44; as well as stronger connections&#46; Only the strength centrality measure was calculated using the qgraph centrality&#95;auto&#46;<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20&#44;21&#44;24&#44;25</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">An <span class="elsevierStyleItalic">a posteriori</span> stability analysis was carried out&#44; which determines the correlation stability coefficient using the bootnet function of the R bootnet statistical package&#46; This analysis allows the stability of the centrality measures of each network to be established by randomly reducing the sample size with respect to the original sample&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Ethical considerations</span><p id="par0145" class="elsevierStylePara elsevierViewall">For this study&#44; we used a database of 194 records from a 2006 study at Hospital Cayetano Heredia&#44; which had authorisation from the hospital&#39;s Ethics Committee and the Universidad Peruana Cayetano Heredia&#46; This database does not allow participants to be identified&#44; so it was not possible to obtain new informed consent&#44; but it caused them no harm&#46; This study&#39;s protocol is registered in the Sistema Descentralizado de Informaci&#243;n y Seguimiento a la Investigaci&#243;n &#40;SIDISI&#41; &#91;Decentralized Research Information and Monitoring System&#93; and was exempt from review by the Comit&#233; Institucional de &#201;tica en Investigaci&#243;n &#91;Institutional Research Ethics Committee&#93; de la Universidad Peruana Cayetano Heredia &#40;CIEI-UPCH&#41;&#46;</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Results</span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Regularised partial correlation network</span><p id="par0150" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> shows the regularised partial correlation network obtained from the CES-D&#46; The network in general is positively connected and the connections show the different degrees of correlation between the variables&#46; The item pairs with the highest correlation are &#8220;I enjoyed life&#8221; and &#8220;I was happy&#8221; &#40;r&#8239;&#61;&#8239;0&#46;37&#41; and &#8220;I felt that I could not shake off the blues even with help from my family or friends&#8221; and &#8220;I felt depressed&#8221; &#40;r&#8239;&#61;&#8239;0&#46;27&#41;&#46; In the network&#44; only one negative correlation was found between the items &#8220;I felt that I was just as good as other people&#8221; and &#8220;I felt that everything I did was an effort&#8221; &#40;r&#8239;&#61;&#8239;&#8211;0&#46;07&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0155" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> shows the regularised partial correlation network obtained from the ZDS&#46; The network in general is positively connected and the items with the highest correlation are&#58; &#8220;I feel downhearted and blue&#8221; and &#8220;I have crying spells or feel like it&#8221; &#40;r&#8239;&#61;&#8239;0&#46;31&#41;&#44; &#8220;I find living pleasant&#8221; and &#8220;I feel useful and needed&#8221; &#40;r&#8239;&#61;&#8239;0&#46;28&#41;&#46; Two negative correlations were found between &#8220;In the mornings I feel better about my sadness&#8221; and the items &#8220;I feel downhearted and blue&#8221; &#40;r&#8239;&#61;&#8239;&#8211;0&#46;1&#41; and &#8220;I am more irritable or angry than before&#8221; &#40;r&#8239;&#61;&#8239;&#8211;0&#46;1&#41;&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Centrality measures</span><p id="par0160" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3 and 4</a> show the centrality graphs of the two scales&#46; Centrality measures indicate the relative importance of a node within the network&#44; as well as its influence on the activation of other nodes&#46; On the CES-D scale&#44; the item with the highest centrality strength is &#8220;I felt sad&#8221; &#40;Z&#8239;&#61;&#8239;1&#46;63&#41;&#44; while in the ZDS it is &#8220;I feel downhearted and blue&#8221; &#40;Z&#8239;&#61;&#8239;1&#46;92&#41;&#44; followed by &#8220;I find living pleasant&#8221; &#40;Z&#8239;&#61;&#8239;1&#46;73&#41;&#46; The least central item on the CES-D scale is &#8220;I felt that I was just as good as other people&#8221; and on the ZDS&#44; &#8220;In the mornings&#44; I feel better about my sadness&#46;&#8221;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Stability of the network</span><p id="par0165" class="elsevierStylePara elsevierViewall">To evaluate the stability of the network&#44; a bootstrap case-dropping analysis was carried out in which the sample size is randomly reduced&#44; and the centrality measures of the networks with a reduced sample are correlated with the centrality measures of the original network&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> shows that the network obtained from the CES-D scale allows a maximum reduction of up to 36&#46;1&#37; in the sample size to maintain a centrality correlation with the original sample of 0&#46;7 with a 95&#37;CI&#46; <a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> shows that the network obtained from the ZDS scale allows a maximum reduction of up to 20&#46;6&#37; to maintain a centrality correlation with the original sample of 0&#46;7&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><elsevierMultimedia ident="fig0030"></elsevierMultimedia></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Discussion</span><p id="par0170" class="elsevierStylePara elsevierViewall">In this study&#44; a network analysis of partial correlations was performed for each scale &#40;CES-D and ZDS&#41;&#46; Both the network graph of the ZDS scale and that of the CES-D scale &#40;<a class="elsevierStyleCrossRefs" href="#fig0005">Figs&#46; 1 and 2</a>&#41; are positively connected&#44; and the graph of the ZDS scale is more dispersed&#44; that is&#44; with fewer connections&#46; This could be because the CES-D has items that assess the same construct&#44; as in the case of &#8220;I felt sad&#8221;&#44; &#8220;I felt depressed&#8221; and &#8220;I felt that I could not shake off the blues even with help from my family and friends&#8221;&#44; which evaluate negative mood&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">In weighted indirect networks&#44; such as those shown &#40;<a class="elsevierStyleCrossRefs" href="#fig0005">Figs&#46; 1 and 2</a>&#41;&#44; connections can be interpreted as simple paired associations&#44; as potential pathways of causality or as predictors&#46; If you have the A-B connection&#44; the following interpretations can be generated&#58; A is associated with B&#59; it is possible that A&#8594;B&#44; A&#8592;B or that A&#8592;C&#8594;B or that A predicts B or vice versa&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">In the network graph of the CES-D scale &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#44; the strongest connection&#44; without considering those that assess the same construct&#44; was between &#8220;I felt that everything I did was an effort&#8221; and &#8220;I had trouble keeping my mind on what I was doing&#8221;&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">For the sample evaluated and controlling the effect of the other nodes&#44; this connection can be interpreted as a strong association between difficulties in carrying out tasks and concentration problems&#46; Problems with concentration can cause difficulty in completing tasks&#44; or difficulties in completing tasks can cause problems with concentration&#44; or the occurrence of problems with concentration can predict difficulties in completing tasks or vice versa&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">Another of the strongest connections found in the network of the CES-D scale &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41; is between the items &#8220;I felt sad&#8221; and &#8220;I felt lonely&#8221;&#46; This connection can be interpreted as there being a strong association between sadness and loneliness&#59; sadness can cause feelings of loneliness&#59; the feeling of loneliness can cause sadness&#44; or the feeling of loneliness can predict sadness or vice versa&#46;</p><p id="par0195" class="elsevierStylePara elsevierViewall">If these findings are transferred to the clinical setting of a patient with depression&#44; it could be pointed out that intervening to improve the ability to concentrate could lead to requiring less effort for activities&#44; and that intervention in social habits would contribute to improving the mood&#44; which is consistent with the results of cognitive remediation interventions&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a></p><p id="par0200" class="elsevierStylePara elsevierViewall">Previous network analysis studies with the CES-D also showed a strong connection between the items &#8220;I felt sad&#8221; and &#8220;I felt lonely&#8221;&#44; such as the study by Santos et al&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a> in pregnant women between 22 and 24 weeks&#44; and the study by Burger et al&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a> in people going through the loss of a partner due to separation or death&#46; Furthermore&#44; the distribution of symptoms in the network groups the symptoms in accordance with factor analysis studies of the CES-D scale&#44; and coincides in the anhedonia factor &#40;items &#8220;I felt that I was just as good as other people&#8221;&#44; &#8220;I felt optimistic about the future&#8221;&#44; &#8220;I was happy&#8221; and &#8220;I enjoyed life&#8221;&#41; and the negative affect factor &#40;items &#8220;I felt that I could not shake off the blues even with help from my family and friends&#8221;&#44; &#8220;I felt depressed&#8221;&#44; &#8220;I felt lonely&#8221; and &#8220;I felt sad&#8221;&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">In the network graph of the ZDS scale &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#44; the strongest connections&#44; without considering those that assess the same construct&#44; are between &#8220;I feel useful and needed&#8221; and &#8220;I find living pleasant&#8221;&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall">For the sample assessed and controlling the effect of the other nodes&#44; this connection can be interpreted as there being a strong association between the feeling of worthlessness and anhedonia&#59; feeling worthless can lead to not enjoying life&#44; or finding life unpleasant can cause a feeling of worthlessness&#44; or having a feeling of worthlessness can predict anhedonia or vice versa&#46;</p><p id="par0215" class="elsevierStylePara elsevierViewall">Another of the strongest connections found in the CES-D network &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41; is between the items &#8220;I feel useful and needed&#8221; and &#8220;I felt hopeful about the future&#8221;&#46; This connection can be interpreted as a strong association between the feeling of worthlessness and hopelessness&#59; feeling worthless can lead to losing hope in the future&#44; or not having hope in the future can cause feelings of worthlessness&#44; or feeling worthless can predict hopelessness or vice versa&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall">If these findings are transferred to the clinical setting of a patient with depression&#44; it could be pointed out that intervening to make a patient feel more useful could make them enjoy their activities more and give them a more optimistic vision about the future&#44; which coincides with the results of behavioural activation interventions&#46;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a></p><p id="par0225" class="elsevierStylePara elsevierViewall">The items &#8220;I have trouble with digestion and&#47;or constipation&#8221; and &#8220;My heart beats faster than usual&#8221; also have a strong connection as somatic symptoms&#44; which could be explained by the fact that they respond to a common autonomic pathway&#44; and they are also symptoms of the anxiety spectrum in the DSM-III&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> These results are in line with other network analysis studies and the depression-anxiety continuum hypothesis&#46;<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">33&#44;34</span></a></p><p id="par0230" class="elsevierStylePara elsevierViewall">The centrality of the items was evaluated based on the centrality <span class="elsevierStyleItalic">&#40;strength&#41;</span> measure&#44; which denotes the weighted sum of the connections of a node&#44; that is&#44; the correlation coefficients&#46; The strength centrality measure represents the probability that&#44; when a symptom is found&#44; the symptoms connected to that symptom will also be found&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;8</span></a></p><p id="par0235" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> shows the strength of centrality of the items of the CES-D scale&#46; The most central nodes are &#8220;I felt sad&#8221;&#44; &#8220;I felt depressed&#8221;&#44; &#8220;I felt that I could not shake off the blues even with help from my family and friends&#8221; and &#8220;I felt lonely&#8221;&#46; Because the first three assess depressed mood&#44; the most central symptoms of this network would be depressed mood and loneliness&#46; Below these&#44; there are symptoms related to anhedonia&#44; such as &#8220;I could not get &#8216;going&#8217;&#8221; and &#8220;I enjoyed life&#8221;&#44; so the results coincide with the core symptoms of depressive disorder in the DSM-V &#40;sadness and anhedonia&#41;&#44; but also includes loneliness as a central symptom&#44; which does not appear in the diagnostic criteria of the DSM-V or the ICD-10&#44;<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2&#44;35</span></a> although other studies indicate that it could have an important role in the structure of depressive symptoms&#46;<a class="elsevierStyleCrossRefs" href="#bib0140"><span class="elsevierStyleSup">28&#44;36</span></a></p><p id="par0240" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a> shows the strength of centrality of the items of the ZDS scale in graph form&#46; The most central nodes are &#8220;I feel downhearted and blue&#8221; and &#8220;I find living pleasant&#8221;&#44; followed by &#8220;I feel useful and needed&#8221;&#46; These items evaluate sadness&#44; anhedonia and the feeling of worthlessness respectively&#46; These three symptoms are included in the DSM-V criteria&#44; and the first two are core symptoms&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> Furthermore&#44; the item &#8220;Morning is when I feel best about my sadness&#8221;&#44; which represents a symptom of morning blues&#44; is the least connected and furthest in the network&#44; being also less central than the other symptoms&#46; Morning blues was one of the specifier criteria for depression with melancholic features in the DSM-V&#44;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> and also a criterion for melancholia since the DSM-III&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> The low centrality of morning blues coincides with other studies in network analysis&#44; such as those by Briganti et al&#46;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">37</span></a> and Fried et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> which indicates that its presence in psychometric instruments would not be so relevant&#46;</p><p id="par0245" class="elsevierStylePara elsevierViewall">The case-dropping bootstrap technique was used to determine the stability of the network&#46; This technique assesses the stability of the most central items if the sample size was randomly reduced&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> shows the stability of the central symptoms of the CES-D scale&#44; which has regular stability by allowing a reduction in the sample size of 36&#46;1&#37; to maintain a correlation of 0&#46;7 in the central symptoms with those of the original sample&#44; with a 95&#37;CI&#46; <a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> shows the stability of the central symptoms of the ZDS scale&#44; which does not show adequate stability&#44; as it only allows a 20&#46;6&#37; reduction in the sample size&#44; while maintaining a correlation of 0&#46;7 in the central symptoms with those of the original sample&#44; with a 95&#37;CI&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Limitations</span><p id="par0250" class="elsevierStylePara elsevierViewall">The results of this study must be interpreted taking into account the limitations of a cross-sectional study that does not allow causal relationships to be demonstrated&#44; so any connection represents only the strength of association between the symptoms&#46; Furthermore&#44; in network analysis&#44; the choice of the model entails certain limitations that must be considered in relation to the objectives of the study&#44; which in our case aimed to describe only the general structure of symptoms of the CES-D and ZDS&#46;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">39</span></a></p><p id="par0255" class="elsevierStylePara elsevierViewall">Another problem often found in network analysis is that the scales used have more than one item that measures the same construct&#44; so these items would have a strong correlation&#44; and their centrality measures would artificially increase&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a></p><p id="par0260" class="elsevierStylePara elsevierViewall">Finally&#44; sample size significantly affects the performance of network analysis&#46; The network estimation model used in this study is the EBICglasso&#44; a regularisation method that allows the elimination of spurious connections &#40;connections with a tendency to zero&#41;&#44; which facilitates the visualisation of a more parsimonious network&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#44;24</span></a> However&#44; in the most recent literature on network analysis&#44; non-regularised methods have had better performance&#44;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">40</span></a> although this was valid only for large sample sizes&#44; so the EBICglasso method continues to be useful with small samples such as in this study&#46;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">39</span></a></p></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Conclusions</span><p id="par0265" class="elsevierStylePara elsevierViewall">The results indicate that sadness&#44; loneliness&#44; anhedonia and feelings of worthlessness are the most central symptoms of depressive disorder based on the CES-D and ZDS scales&#46; Among these symptoms&#44; there is a greater association between sadness and loneliness and between the feeling of worthlessness and anhedonia&#46;</p><p id="par0270" class="elsevierStylePara elsevierViewall">Somatic symptoms considered comorbid with anxiety&#44; such as tachycardia&#44; digestive discomfort and restlessness&#44; are grouped in the periphery of the network&#44; which suggests they could be considered bridge symptoms with anxiety disorder following the hypothesis of the depression-anxiety continuum&#46;</p><p id="par0275" class="elsevierStylePara elsevierViewall">Finally&#44; morning blues does not represent a central symptom&#44; so its relevance among the diagnostic criteria would be questionable&#46;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Conflicts of interest</span><p id="par0280" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflicts of interest&#46;</p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Background</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Depression represents one of the leading causes of disability due to illness worldwide&#46; Previous studies have demonstrated the significant heterogeneity of the diagnosis of depression&#44; making it necessary to develop new diagnostic approaches&#46; Network analysis is a perspective that considers symptoms as constituents of the psychiatric disorder itself&#46; The objective was to determine the structure of depressive symptoms using the CES-D and ZDS depression scales&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Cross-sectional study of secondary analysis of 194 patients using the CES-D and ZDS scales&#46; Correlation matrices and regularised partial correlation networks were constructed from the database&#46; Centrality measures were estimated&#44; and a network stability analysis was performed&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">On the CES-D scale&#44; the most central item was &#8220;Sad&#8221;&#59; while on the ZDS scale&#44; the most central items were &#8220;Sad&#8221; and &#8220;Live&#8221;&#46; On the CES-D scale&#44; the connection between &#8220;Enjoy&#8221; and &#8220;Happy&#8221; was the strongest&#46; On the ZDS scale&#44; the strongest connection was between the items &#8220;Live&#8221; with &#8220;Useful&#8221;&#46; The item &#8220;Morning&#8221; was the least connected on the ZDS&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The most central symptom from the CES-D scale was sadness&#44; while from the ZDS scale&#44; was sadness and anhedonia&#46;</p></span>"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducci&#243;n</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">La depresi&#243;n es una de las principales causas de discapacidad por enfermedad en el mundo&#46; Estudios previos han demostrado la gran heterogeneidad del diagn&#243;stico de depresi&#243;n&#44; por lo que es necesario desarrollar nuevas aproximaciones diagn&#243;sticas&#46; El an&#225;lisis de redes es una perspectiva que considera los s&#237;ntomas como constituyentes del trastorno psiqui&#225;trico&#46; El objetivo es determinar la estructura de s&#237;ntomas depresivos a partir de las escalas para depresi&#243;n CES-D y ZDS&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todos</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Estudio transversal de an&#225;lisis secundario de 194 pacientes mediante las escalas CES-D y ZDS&#46; A partir de la base de datos se elaboraron matrices de correlaciones y redes de correlaciones parciales regularizadas&#44; se estimaron medidas de centralidad y se realiz&#243; un an&#225;lisis de estabilidad de la red&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">En la escala CES-D&#44; el &#237;tem m&#225;s central fue &#171;Triste&#187;&#44; mientras que en la escala ZDS lo fueron &#171;Triste&#187; y &#171;Vivir&#187;&#46; En la escala CES-D&#44; la conexi&#243;n entre &#171;Disfrutar&#187; y &#171;Contento&#187; fue la m&#225;s fuerte&#46; En la escala ZDS&#44; fue entre los &#237;tems &#171;Vivir&#187; y &#171;&#218;til&#187;&#46; El &#237;tem &#171;Ma&#241;anas&#187; fue el menos conectado de la red ZDS&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">El s&#237;ntoma m&#225;s central de la escala CES-D fue la tristeza&#44; mientras que en la ZDS fueron la tristeza y la anhedonia&#46;</p></span>"
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