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Exploring risk factors for depression: a network analysis
Explorando los factores de riesgo de la depresión: un análisis de red
Jonatan Baños-Chaparro
Corresponding author
banos.jhc@gmail.com

Corresponding author.
MSc. Universidad Norbert Wiener Lima, 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 common modern-day psychological problem in the general population that contributes significantly to the global disease burden&#44; affecting approximately 280 million people worldwide&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> The prevalence in Latin American and Caribbean countries places depression as the main disability problem&#44; with an average of 7&#46;8&#37; in the 15&#8722;50-year-old age group&#44; and Peru being the third most affected country &#40;8&#46;6&#37;&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> The figures are similar for Colombia&#44; which ranks fifth &#40;8&#46;2&#37;&#41;&#44; and studies based on national mental health protocols highlight depression as the most common psychological problem&#44; considering a lifetime prevalence of 4&#46;3&#37;&#46;<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3&#44;4</span></a> Depressive symptoms have repercussions on health&#44; social and economic conditions&#46; One systematic review and meta-analysis points out that depression is associated with higher direct costs &#40;hospitalisation&#44; emergency room visits&#44; outpatient treatment and medication&#41; and indirect costs &#40;reduced&#47;loss of productivity&#41; in adolescents&#44; adults and older adults&#44; and these are of the same magnitude in people with comorbid depression&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> Given that depressive symptoms have a strong likelihood of recurrence and the financial costs involved in the intervention process are high&#44; it seems obvious that preventing depression would be better than having to treat it clinically&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">The evidence tells us that there is no single conclusive cause or set of causes for depression&#44; and that&#44; on the contrary&#44; depressive symptoms can stem from the interaction of other psychological problems&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> The determining of risk factors is of prime importance when it comes to identifying vulnerable people and optimising prevention&#46; Risk factors complicate the course of a health problem and hinder access to optimal coping mechanisms&#44; thus increasing the likelihood of negative outcomes when people face adversity&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> A new way of exploring psychopathology&#8212;in this case the risk factors for a particular psychological problem &#40;depression&#41;&#8212;is through network modelling&#44; according to which psychopathology is a complex system arising from symptoms that interact with each other in a network&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In the last decade&#44; network models have been popular in psychological and psychiatric research for analysing emotional disorders &#40;mental disorders&#41;&#44; as they make it possible to extend the interaction at the level of symptoms and to discover patterns of conditional associations among the elements of a phenomenon&#46; For example&#44; network theory postulates that a psychological problem&#44; such as depression&#44; arises not from the presence of a latent cause&#44; but from a process in which conditions or symptoms are related to each other&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> From this perspective&#44; network models based on covariance estimation and statistical interactions in a Pairwise Markov Random Field &#40;PMRF&#41; can provide information on symptom relationships or on which symptoms connect to other clusters &#40;network structure&#41;&#44; clustering of physical &#40;chronic diseases&#41; and psychological symptoms&#44; descriptive measures of the network &#40;centrality&#44; topology and network comparison&#41; and stability measures of network inference&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#44;11</span></a> They are also particularly useful when analysing the links between different domains &#40;comorbid variables or risk factors&#41;&#44; as they help identify bridge variables in the development of a psychopathological phenomenon&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;8</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">A narrative review based on 56 studies on network models of depression found that depression often coexists with symptoms of other emotional disorders&#44; such as anxiety&#44; post-traumatic stress&#44; obsessive-compulsive disorder&#44; prolonged grief&#44; autism and alcohol use&#44; etc&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> A six-year prospective network analysis reported that insomnia is a risk factor that foreshadows depression&#44; and this is due to the difficulty in initiating sleep&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> Another study among adults in the general population of the United Kingdom identified that depression was related to anxiety&#44; insomnia and post-traumatic stress&#46;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> Meanwhile&#44; another study in US military veterans reported that depression was associated with suicidal ideation&#44; difficulty sleeping&#44; anger and post-traumatic stress&#44; and that depression was the second central variable in the network structure&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> Although recent studies have investigated risk factors for depression&#44; it is unclear whether insomnia&#44; suicidal ideation and anxiety&#44; included in a single model and not in isolation&#44; or the incorporation of other psychological variables that influence the network structure&#44; are determining risk factors for depressive symptoms&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Therefore&#44; our primary objective with this study was to analyse a network model in order to explore the associations between depression&#44; insomnia&#44; suicidal ideation and anxiety&#46; In addition&#44; a predictive path diagram was created to identify which variables were directly related to depression &#40;risk factors&#41; and the central variables of the network structure&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Participants</span><p id="par0030" class="elsevierStylePara elsevierViewall">The study included 567 Peruvian adults&#44; selected through convenience sampling&#46; We used the following inclusion criteria&#58; a&#41; age from 18 to 60 years&#59; b&#41; place of residence in Metropolitan Lima&#59; and c&#41; agreeing to take part by giving informed consent&#46; People who did not meet the criteria mentioned above were excluded from the research&#46; For example&#44; people were excluded if they did not give their informed consent &#40;n&#8239;&#61;&#8239;2&#41;&#44; if they were outside the established age range &#40;n&#8239;&#61;&#8239;33&#41; or if they resided in another part of Peru &#40;n&#8239;&#61;&#8239;108&#41;&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">Of the sample&#44; 426 were female &#40;75&#46;1&#37;&#41; and 141 were male &#40;24&#46;9&#37;&#41;&#46; The average age was 29 years with a standard deviation of 10&#46;2 years&#46; Also&#44; the majority were single &#40;80&#46;6&#37;&#41;&#44; a smaller percentage were married &#40;15&#46;3&#37;&#41;&#44; and some said they were divorced &#40;4&#46;1&#37;&#41;&#46; Regarding academic level&#44; 72&#37; had university studies&#44; 16&#46;9&#37; had attended a technical college&#44; 10&#46;9&#37; had secondary studies and one person &#40;0&#46;2&#37;&#41; reported only having had primary education&#46; Lastly&#44; with respect to employment status&#44; 53&#46;6&#37; stated that they had a job&#44; while 46&#46;4&#37; reported that they were unemployed&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Tools</span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Patient Health Questionnaire-9 &#40;PHQ-9&#41;</span><p id="par0040" class="elsevierStylePara elsevierViewall">The PHQ-9 evaluates depressive symptoms during the last two weeks and has adequate sources of evidence of validity in the Peruvian population&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> It consists of 9 items with 4 response options&#44; where high scores indicate greater depressive symptoms&#46; In this study&#44; an acceptable reliability was obtained with the coefficient omega &#40;&#969;&#41;&#8239;&#61;&#8239;0&#46;89&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Athens Insomnia Scale &#40;AIS&#41;</span><p id="par0045" class="elsevierStylePara elsevierViewall">The AIS consists of 5 items that assess insomnia symptoms over the past month with a frequency of 3 times a week&#46; It presents psychometric evidence in Peruvian adults and the response system uses a Likert-type scale&#44; where high scores indicate a greater severity of insomnia problems&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> In this study&#44; we obtained an acceptable reliability of &#969;&#8239;&#61;&#8239;0&#46;83&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Frequency of Suicidal Ideation Inventory &#40;FSII&#41;</span><p id="par0050" class="elsevierStylePara elsevierViewall">The FSII is a brief tool that evaluates the frequency of suicidal ideation in the last 12 months and is adapted to the Peruvian population&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> The scale consists of 5 items&#44; using a Likert-type response system and a total score range from 5 and 25 points&#44; with high scores being an indicator of greater severity of suicidal ideation&#46; The FSII has adequate reliability of &#969;&#8239;&#61;&#8239;0&#46;96&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Generalised Anxiety Disorder-7 &#40;GAD-7&#41;</span><p id="par0055" class="elsevierStylePara elsevierViewall">GAD-7 is a questionnaire consisting of 7 items with 4 response options&#44; which assesses the severity of generalised anxiety symptoms during the past two weeks&#46; The GAD-7 has been analysed psychometrically in the Peruvian population and high scores are an indicator of greater generalised anxiety&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> In this study&#44; we obtained an acceptable reliability of &#969;&#8239;&#61;&#8239;0&#46;91&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Procedure</span><p id="par0060" class="elsevierStylePara elsevierViewall">Given that the state of emergency and restriction measures to counter SARS-CoV-2 are still in place in Peru&#44; the data collection was performed online using a Google&#174; form&#46; The link was posted on the Facebook&#174; and WhatsApp&#174; social media platforms throughout the month of October 2021&#46; The description of the questionnaire indicated the aim of the research&#44; the confidentiality of the responses&#44; the usefulness of the information for academic purposes&#44; and the anonymity thereof&#46; Informed consent for voluntary participation was also requested&#46; Those who accepted had access to demographic profiling questions and questionnaires&#46; Otherwise&#44; they would not have access to any questions in the questionnaire and their participation would end&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Statistical analysis</span><p id="par0065" class="elsevierStylePara elsevierViewall">Initially&#44; descriptive analyses were performed of the demographic variables and of the total scores for each variable of the tools used&#46; The absolute frequency of the participants&#44; measures of central tendency and dispersion were analysed&#46; Subsequently&#44; a Gaussian graphical model &#40;GGM&#41; of an undirected&#44; weighted network based on regularised partial correlations was estimated&#46; To check for any spurious associations&#44; the Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; was used&#44; with a recommended tuning parameter of &#947;&#8239;&#61;&#8239;0&#46;50 and applying the Spearman correlation method&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> The structure consists of four nodes&#44; which are the total scores for each variable &#40;depression&#44; insomnia&#44; suicidal ideation and anxiety&#41;&#46; A connection between two nodes indicates a partial correlation between two variables after conditioning all the other variables in the network structure&#46; Blue edges indicate positive partial correlations and red edges indicate negative correlations&#46; The broader and more saturated the edge&#44; the stronger the partial correlation&#46; The network structure was visualised using the Fruchterman-Reingold algorithm&#44; which locates the strongest correlations in the centre and the weakest correlations at the periphery&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">To examine which variables are directly related to depression&#44; a predictive path diagram was estimated&#46; This type of diagram places a node of interest &#40;in this case depression&#41; as the source node on the left-hand side and then lists the nodes that have a connection to depression on the right&#44; creating a vertical network of connections to the source node&#44; the nodes that are connected to the nodes connected to the source node&#44; and so on&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> The shortest predictive path can be defined as the path that connects the destination node to the source node&#44; whilst controlling for all other variables in the network&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">To quantify which node is more influential in the network structure&#44; the expected influence was analysed as a measure of centrality&#46; In the centrality analysis&#44; we also analysed predictability&#44; which provides evidence of the relevance of each node in the network model&#44; by calculating the explained variance &#40;R<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a>&#41;&#44; which varied from 0 to 1&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> In the network model graph&#44; predictability can be seen as the blue area or circle around each node&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">Finally&#44; to verify that the results were reliable&#44; we performed the following two analyses&#46; The first was related to the accuracy of the network model&#44; analysing the accuracy of the structure using the non-parametric bootstrapping method based on 1&#44;000 samples to calculate the 95&#37; confidence intervals &#40;CI&#41; of the network edges&#46; The second analysis examined the stability of the expected influence centrality measurement by sampling different bootstrap-based data subsets&#46; In relation to this&#44; the correlation stability &#40;CS&#41; coefficient indicates what proportion of participants can be eliminated in order to retain a correlation of at least 0&#46;70&#44; with 95&#37; probability&#44; based on the original sample with the generation of a subset of samples&#46; The CS should not be less than 0&#46;25 and should preferably be greater than 0&#46;50&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0085" class="elsevierStylePara elsevierViewall">All analyses were performed using R statistical software&#44; version 4&#46;1&#46;1&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> The networks were constructed and visualised using the qgraph package version 1&#46;9 and mgm version 1&#46;2&#44;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#44;23</span></a> with the bootnet package version 1&#46;5&#44;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> being used for precision and stability&#44; the psych package version 2&#46;1&#46;9 and MBESS version 4&#46;8&#46;1&#44;<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">24&#44;25</span></a> for the descriptive analysis and reliability of each tool&#44; cowplot package version 1&#46;1&#46;1 for the combination of figures&#44; and the joycon package version 0&#46;1&#46;0<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> for colour selection&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Ethical considerations</span><p id="par0090" class="elsevierStylePara elsevierViewall">The research was carried out based on the guidelines of the Declaration of Helsinki and the code of ethics of the Colegio de Psic&#243;logos del Per&#250; &#40;CPsP&#41; &#91;College of Psychologists of Peru&#93;&#44; described in chapter 3&#44; dedicated to research&#46;<a class="elsevierStyleCrossRefs" href="#bib0135"><span class="elsevierStyleSup">27&#44;28</span></a> Neither the names of the participants nor any other aspect likely to identify them were requested&#46; The study did not involve any risk to their physical or mental integrity&#44; and all participants gave their informed consent&#46;</p></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Results</span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Descriptive analysis and network structure</span><p id="par0095" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows that the mean was between 3&#46;99 &#40;insomnia&#41; and 7&#46;50 &#40;suicidal ideation&#41;&#46; The largest standard deviation was found in depression and the smallest in insomnia&#46; The average predictability was 59&#37;&#44; while the highest predictability was found in depression &#40;76&#37;&#41;&#46; Predictability is shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#44; represented as the blue area or circle around each node&#46; The highest value of expected influence is located in depression &#40;1&#46;34&#41;&#44; followed by anxiety symptoms &#40;0&#46;74&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0100" class="elsevierStylePara elsevierViewall">The network model of depression and risk factors in Peruvian adults is shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#46; It can be seen that all the node correlations were positive&#44; and stronger correlations were found between depression and anxiety symptoms &#40;r&#8239;&#61;&#8239;0&#46;60&#41;&#44; and between depression and suicidal ideation &#40;r&#8239;&#61;&#8239;0&#46;46&#41;&#46; In the predictive path diagram&#44; depression is directly related to all the variables&#44; showing a strong connection with symptoms of anxiety and suicidal ideation&#44; but to a lesser extent with insomnia problems&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Network model accuracy and stability of centrality</span><p id="par0105" class="elsevierStylePara elsevierViewall">Overall&#44; the CIs were quite narrow and mostly non-overlapping&#44; indicating reliable results&#46; The expected influence centrality estimate was stable &#40;CS&#8239;&#61;&#8239;0&#46;75&#41;&#46; This suggests that 75&#37; of the data could be removed to retain 95&#37; certainty &#40;a correlation of 0&#46;70 with the original data set&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Discussion</span><p id="par0110" class="elsevierStylePara elsevierViewall">This study used a network approach to explore the associations and predictive path diagram between depression&#44; insomnia&#44; suicidal ideation and anxiety in a sample of Peruvian adults from the general population&#46; In particular&#44; it was shown that the psychological variables included in the general network model and predictive path were positively connected&#46; The results were robust to tests for accuracy and stability&#46; The contributions&#44; clinical implications and limitations are discussed below&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">In the general network model&#44; depression was found to be positively associated with insomnia&#44; suicidal ideation and anxiety&#44; although the association was strongest with anxiety&#46; These findings are consistent with other previous instances of network research that included depression and reported its association with various psychological problems&#44; including the variables proposed in this study&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> An important aspect in the results is that anxiety showed the strongest association with depression&#46; Comorbidity between symptoms of anxiety and depression has been widely documented in network analyses and reviews on network models based on 56 studies conducted between 2014 and 2020&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#44;8</span></a> This indicates that people who experience anxiety symptoms are likely to have depressive symptoms&#46; In fact&#44; a study based on Peruvian adults from the general population found that &#8220;uncontrollable worry&#8221; was associated with &#8220;anhedonia&#8221;&#44; while &#8220;nervousness&#8221; was associated with &#8220;sad mood&#8221;&#44; highlighting the concurrence of both psychological problems&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a></p><p id="par0120" class="elsevierStylePara elsevierViewall">The expected influence centrality may play an important role in finding symptoms that activate or maintain psychopathological networks&#44; as well as providing potential targets for intervention&#46; In the network structure&#44; depression was the node with the highest centrality&#44; indicating that depression plays an important role in activating and maintaining the network model&#46; In other words&#44; interventions targeting depression could alleviate problems with insomnia&#44; suicidal ideation and anxiety&#46; This result is consistent with the predictability analyses&#44; where depression also obtained the highest value&#46; A recent study in military veterans in the USA also identified depression as a central variable in a network structure of post-traumatic stress&#44; anger&#44; suicidal ideation and sleeping difficulties&#44;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> while another study in the Spanish population reported depression as a central node in terms of strength&#46;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">In the predictive path diagram&#44; depression was directly related to insomnia&#44; suicidal ideation and anxiety&#46; The fact that depression is connected to multiple psychological variables reflects that it is a complex phenomenon&#46; The connection between depression and anxiety had the strongest correlation coefficient of all the direct connections in the structure&#46; This result is consistent with the literature on the high comorbidity between anxiety and depression&#59; even incorporating other emotional disorders&#44; the connection is still maintained&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;13</span></a> Among adults in the general population&#44; studies have shown that loss of pleasure&#44; worry and depressed mood are the most common bridge symptoms between anxiety and depression&#44;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a> although&#44; in patient populations&#44; agitation or psychomotor retardation were found to be the bridge explaining this comorbidity&#46;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a> The connection between depression and suicidal ideation also has a strong direct relationship&#46; A network analysis of depressive symptoms in Peruvian adults identified that thoughts of death correlated with psychomotor problems&#44; feelings of worthlessness&#44; changes in appetite and depressed mood&#44; and were the central symptom for developing a depressive episode&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> A direct connection between depression and insomnia was also found&#46; Another network study reported that insomnia correlates with depression and this is likely due to the difficulty in initiating sleep&#44; specifically in the sleep-wake cycle&#44; either for sleep onset or at any time thereafter during the night&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#44;13</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">Overall&#44; the study results suggest that insomnia&#44; suicidal ideation and anxiety are significant risk factors for triggering depressive symptoms&#46; From the perspective of complex adaptive systems&#44; emotional disorders such as depression arise from attractor states that cause an individual to become &#34;stuck&#34; in a particular state &#40;depressed state&#41; at a tipping point in the system&#46; However&#44; the state of attractors can change over time and place the individual in stable alternative attractors &#40;healthy state&#41;&#44; this being called &#34;phase transition&#34;&#44; to explain the transitions between one attractor and another&#46;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">33</span></a> Therefore&#44; depression can be understood as the transition between a depressed and a healthy attractor&#46; However&#44; attractor states arise and are maintained from interactions between various processes &#40;for example&#44; biological&#44; psychological&#44; social&#41;&#44; which are constantly evolving&#46;<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a> Network models provide relevant information on the study of psychopathology&#44; where the phenomena are multifactorial&#46; To prevent rather than treat depression&#44; it is necessary to identify the risk factors&#46; Identifying insomnia&#44; suicidal ideation and anxiety as risk factors allows the intervention pathway to be established&#46; For example&#44; a systematic review and meta-analysis showed that cognitive-behavioural therapy is very effective for depression and anxiety&#44;<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">35</span></a> and the same results have been found for insomnia and suicidal ideation&#46;<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">36&#44;37</span></a> Furthermore&#44; these interventions have also been effective in remote health services &#40;telehealth&#41; and relapse prevention&#44; as they provide individuals with adequate psychological resources &#40;for example&#44; problem solving&#44; coping strategies&#44; etc&#46;&#41; to improve their lifestyle&#46;<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">35&#8211;37</span></a></p><p id="par0135" class="elsevierStylePara elsevierViewall">Our research findings therefore reflect the importance of strengthening mental health prevention and monitoring programmes in the general population&#44; especially in adults living in Metropolitan Lima&#46; In primary prevention measures&#44; it would be important to incorporate the assessment of anxiety&#44; insomnia and suicidal ideation in mental health screenings&#46; In view of the state of emergency and the restrictions caused by the pandemic&#44; it would be best to use technological tools for screening&#46; As the scientific literature and the results of the study suggest that these symptoms are considerable risk factors for depression&#44; when mild cases are identified&#44; online sessions should be scheduled or phone calls with available psychologists&#44; whilst moderate or severe cases should immediately be referred to a mental health centre for in-person care&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#44;29</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">Another strategy would be coordination between Lima&#39;s local district councils and community mental health centres to organise specific virtual health campaigns&#44; in order to provide self-care guidelines in situations of anxiety&#44; education on sleep hygiene&#44; and information on health centres&#44; organisations and communication and support numbers if people are presenting with suicidal ideation&#46; All of this should be done with the purpose of offering education&#44; guidance and sufficient tools to handle adverse situations individually&#46; This would help to decongest the healthcare system&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">Despite the contributions made by this study&#44; the results should be interpreted whilst taking certain limitations into consideration&#46; A first limitation is the sampling method&#58; as recruitment was carried out online and through social media platforms&#44; the participants present a particular characteristic of interaction with this type of platform with no difficulties in responding to an online survey&#44; which will not be representative of the general population&#46; Secondly&#44; the current analysis was based on cross-sectional data and it is not therefore possible to infer causality&#44; such as the direction of the connections&#46; Future studies could seek to confirm the exploratory results with the estimation of a directed acyclic graph &#40;DAG&#41;&#44; which makes it possible to incorporate information on the likely direction of the conditional dependence relationships between the variables&#46; Thirdly&#44; psychological variables were assessed from self-report scores&#44; which may limit the capture of a clinical phenomenon&#46; Fourthly&#44; there was greater participation by females compared to males&#44; which may bias the results&#46; Furthermore&#44; the age group considered does not allow us to generalise the conclusions to other stages of life such as adolescence&#44; childhood and older adults&#46; Finally&#44; the network structure of risk factors for depression could be expanded by considering other psychological variables&#44; in order to provide more information about its complexity&#46;</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Conclusions</span><p id="par0150" class="elsevierStylePara elsevierViewall">Overall&#44; the results of the study suggest that insomnia&#44; suicidal ideation and anxiety are significant risk factors for triggering a depressive episode&#46; Centrality and predictability measures indicated that depression is the most central variable in the network structure&#44; followed by anxiety&#46; Identifying and intervening early on these risk factors in adults in the general population could help prevent the onset of depressive symptoms&#46;</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Ethical responsibilities</span><p id="par0155" class="elsevierStylePara elsevierViewall">No experimental procedure was performed in the study that would harm or put participants at risk&#46; The research was carried out based on the guidelines of the Declaration of Helsinki and the code of ethics of the Colegio de Psic&#243;logos del Per&#250; &#40;CPsP&#41; &#91;College of Psychologists of Peru&#93;&#44; described in chapter 3&#44; dedicated to research&#46; Neither the names of the participants nor any other aspect likely to identify them were requested&#46; The study did not involve any risk to their physical or mental integrity&#44; and all participants gave their informed consent&#46;</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Funding</span><p id="par0160" class="elsevierStylePara elsevierViewall">No funding was received for conducting this study&#46;</p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Depression is a frequent psychological problem in the general population&#46; There are no single conclusive causes for its development&#59; on the contrary&#44; it arises from the interaction of other emotional disorders&#46; Determining risk factors is a primary objective to identify vulnerable individuals and optimize prevention&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objective</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">To analyze risk factors of the depression through network analysis in Peruvian adults from the general population&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Methods</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Cross-sectional study with a quantitative approach&#46; A total of 567 Peruvian adults who answered several instruments assessing depressive symptoms&#44; insomnia&#44; suicidal ideation and anxiety participated&#46; An undirected network model with all psychological variables and a predictive path diagram was estimated to identify risk factors for depression&#46; Measures of centrality&#44; precision and stability were also analyzed&#46; Results&#58; The network structure showed that depression&#44; insomnia&#44; suicidal ideation&#44; and anxiety were mutually associated&#46; In terms of expected influence and predictability&#44; depression obtained the highest value&#44; followed by anxiety&#46; In the prediction plot&#44; all psychological variables were directly connected with depression&#44; with anxiety having the highest connection&#46; The tests of accuracy and stability &#40;CS&#8239;&#61;&#8239;0&#44;75&#41;&#44; were robust&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The results of the study suggest that problems with insomnia&#44; suicidal ideation&#44; and anxiety&#44; are considerable risk factors for depression&#46; Identifying and intervening early on those risk factors in adults in the general population could help to prevent the development of depressive symptoms&#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="spar0040" class="elsevierStyleSimplePara elsevierViewall">La depresi&#243;n es un problema psicol&#243;gico frecuente en la poblaci&#243;n general&#46; No existen causas &#250;nicas concluyentes para su desarrollo&#44; por el contrario&#44; surge de la interacci&#243;n de otros des&#243;rdenes emocionales&#46; Determinar los factores de riesgo es un objetivo primordial para identificar a las personas vulnerables y optimizar la prevenci&#243;n&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Objetivo</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Analizar los factores de riesgo de la depresi&#243;n mediante el an&#225;lisis de redes en adultos peruanos de poblaci&#243;n general&#46; M&#233;todos&#58; Estudio transversal con enfoque cuantitativo&#46; Participaron 567 adultos peruanos que contestaron diversos instrumentos que evaluaban s&#237;ntomas depresivos&#44; insomnio&#44; ideaci&#243;n suicida y ansiedad&#46; Se estim&#243; un modelo de red no dirigido con todas las variables psicol&#243;gicas y un diagrama de ruta predictiva para identificar los factores de riesgo de la depresi&#243;n&#46; Las medidas de centralidad&#44; precisi&#243;n y estabilidad tambi&#233;n fueron analizadas&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">La estructura de red demostr&#243; que la depresi&#243;n&#44; insomnio&#44; ideaci&#243;n suicida y ansiedad se asociaron mutuamente&#46; En t&#233;rminos de influencia esperada y predictibilidad&#44; la depresi&#243;n obtuvo el valor m&#225;s alto&#44; seguido de la ansiedad&#46; En el diagrama de predicci&#243;n&#44; todas las variables psicol&#243;gicas estaban directamente conectadas con la depresi&#243;n&#44; siendo la ansiedad con la mayor conexi&#243;n&#46; Las pruebas de precisi&#243;n y estabilidad &#40;CS&#8239;&#61;&#8239;0&#44;75&#41;&#44; fueron robustas&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Los resultados del estudio sugieren que los problemas de insomnio&#44; ideaci&#243;n suicida y ansiedad&#44; son factores de riesgo considerables para la depresi&#243;n&#46; Identificar e intervenir anticipadamente aquellos factores de riesgo en adultos de poblaci&#243;n general&#44; podr&#237;a ayudar a evitar el desarrollo de los s&#237;ntomas depresivos&#46;</p></span>"
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                  \t\t\t\t">2&#46;97&nbsp;\t\t\t\t\t\t\n
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                            2 => "M&#46;N&#46; Rodriguez"
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                            3 => "K&#46;M&#46; Keyes"
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Original language: English
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