Síntomas depresivos en adultos peruanos: un análisis de redes
Palabras clave:
depresión, síntomas afectivos, salud mental, adulto, análisis de redes
Resumen
Introducción: las personas que experimentan síntomas depresivos generalmente informan problemas emocionales y físicos que complican su estado de salud. Los esfuerzos de prevención son necesarios para un tratamiento temprano y resulta relevante identificar qué síntomas son más importantes para las estrategias de prevención e intervención en población general.
Objetivo: analizar la interrelación y síntomas depresivos centrales en una población general de adultos peruanos mediante el análisis de redes.
Metodología: participaron 275 adultos peruanos que fueron evaluados con el Patient Health Questionnaire-9. Se estimó una estructura de red transversal de correlación parcial regularizada. Específicamente se analizó el índice de centralidad de fuerza, predictibilidad, estabilidad y pruebas de diferencias de los pesos de bordes y fuerza. Este estudio respetó los aspectos éticos, como la confidencialidad de la información, el anonimato y el consentimiento informado.
Resultados: se evidenció que todos los nodos de la estructura de red se asociaron positivamente, donde los pensamientos de muerte se relacionaron con los problemas psicomotores, sentimientos de inutilidad y cambios en el apetito. La predictibilidad media fue de 30% y los síntomas depresivos más centrales fueron los pensamientos de muerte y las dificultades de concentración. La estimación de la fuerza fue estable (0,59) y las pruebas post-hoc mostraron diferencias significativas en los pesos de los bordes y fuerza.
Conclusiones: los hallazgos del estudio sugieren que los pensamientos de muerte y las dificultades de concentración desempeñan un papel importante en la estructura de red en población general de adultos peruanos. Por lo tanto, estos síntomas podrían ayudar a los profesionales de la salud a identificar a las personas más vulnerables a desarrollar síntomas depresivos y, en consecuencia, pueden ser objetivos importantes de pronóstico, intervención y estrategias de prevención en la atención primaria de salud mental.
Citas
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doi: https://doi.org/10.12688/f1000research.27422.1
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doi: 10.3758/s13428-017-0910-x
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2. Jiang Z, Liu Y, Zhang J, Lamis DA. The role of depressive symptoms in suicide attempt in rural China. J Nerv Ment Dis. 2019; 207(7):561-68. doi: 10.1097/NMD.0000000000001006
3. Organización Mundial de la Salud. Depresión [Internet]. La Organización; 2020 [actualizado 30 de enero 2020; citado 9 Mar 2021]. Disponible en: https://bit.ly/3l2wG
4. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet /Internet/. 2018 /cited 2020 Dec 3/; 392(10159):1789-858. Available from: https://pubmed.ncbi.nlm.nih.gov/30496104/. doi:10.1016/S0140-6736(18)32279-7
5. Organización Panamericana de la Salud. La carga de los trastornos mentales en la región de las Américas, 2018 [Internet]. Washington, DC: OPA, OMS; 2018 [citado 9 Mar 2021]. Disponible en: https://bit.ly/3togBzR
6. Instituto Nacional de Salud Mental. Estudio epidemiológico de salud mental en Lima Metropolitana y Callao Replicación 2012. Informe general. Anales de Salud Mental [Internet]. 2013 [citado 9 Mar 2021]; 29(Supl.1): 1-397. Disponible en: https://bit.ly/3coN1D7
7. Villarreal-Zegarra D, Cabrera-Alva M, Carrillo-Larco RM, Bernabe-Ortiz A. Trends in the prevalence and treatment of depressive symtoms in Peru: a population-based study. BMJ Open /Internet/. 2020 /cited 2021 Jan 15/; 10(7):e036777. Available from: https://pubmed.ncbi.nlm.nih.gov/32690526/ doi: 10.1136/bmjopen-2020-036777
8. Niles AN, Dour HJ, Stanton AL, Roy-Byrne PP, Stein MB, Sullivan G, et al. Anxiety and depressive symptoms and medical illness among adults with anxiety disorders. J Psychosom Res /Internet/. 2015 /cited 2020 Dec 15/; 78(2):109-15. Available from: https://pubmed.ncbi.nlm.nih.gov/25510186/. doi: 10.1016/j.jpsychores.2014.11.018
9. Assari S, Lankarani MM. Depressive symptoms are associated with more hopelessness among white than black older adults. Front Public Health /Internet/. 2016 /cited 2020 Dec 15/; 4:82. Available from: https://pubmed.ncbi.nlm.nih.gov/27200335/.
doi: 10.3389/fpubh.2016.00082
10. Noh JW, Kwon YD, Park J, Kim J. Body mass index and depressive symptoms in middle aged and older adults. BMC Public Health /Internet/. 2015 /cited 2020 Dec 15/; 15:310. Available from: https://pubmed.ncbi.nlm.nih.gov/25884564/.
doi: 10.1186/s12889-015-1663-z
11. Hartung TJ, Fried EI, Mehnert A, Hinz A, Vehling S. Frequency and network analysis of depressive symptoms in patients with cancer compared to the general population. J Affect Disord. 2019; 256:295-301. doi: 10.1016/j.jad.2019.06.009
12. Baños-Chaparro J. Uso de redes sociales y sintomatología depresiva en estudiantes universitarios. Eureka /Internet/. 2020 /citado 12 enero 2021/; 17(2):293-308. Disponible en: https://psicoeureka.com.py/sites/default/files/articulos/eureka-17-2-13_0.pdf
13. Hoebel J, Maske UE, Zeeb H, Lampert T. Social inequalities and depressive symptoms in adults: The role of objective and subjective socioeconomic status. PLoS One /Internet/. 2017 /cited 2021 Jan 15/; 12(1):e0169764. Available from: https://pubmed.ncbi.nlm.nih.gov/28107456/ doi: 10.1371/journal.pone.0169764
14. Vázquez Valverde C, Roca Morales P, Poyato Vega N, Pascual Nicolás T. Teoría de redes en psicopatología: Una alternativa radical a los sistemas diagnósticos. En: Fernández Ballesteros R, Baños Rivera R. Progresos y crisis en psicología clínica: tratamientos basados en la evidencia y crisis del diagnóstico. España: Pirámide; 2017. p. 65-89.
15. Hakulinen Ch, Fried EI, Pulkki-Raback L, Virtanen M, Suvisaari J, Elovainio M. Network structure of depression symptomology in participants with and without depressive disorder: the population- based health 2000-2011 study. Soc Psychiatry Psychiatr Epidemiol /Internet/. 2020 /cited 2021 Jan 15/; 55(10):1273-82. Available from: https://pubmed.ncbi.nlm.nih.gov/32047972/. doi: 10.1007/s00127-020-01843-7
16. Boschloo L, van Borkulo CD, Borsboom D, Schoevers RA. A prospective study on how symptoms in a network predict the onset of depression. Psychother Psychosom /Internet/. 2016 /cited 2020 Dec 18/; 85(3):183-4. Available from: https://pubmed.ncbi.nlm.nih.gov/27043457/.
doi: 10.1159/000442001
17. Ramos-Vera C, Baños-Chaparro J, Oluwaseun Ogundokun R. The network structure of depressive symptomatology in Peruvian adults with arterial hypertension [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research /Internet/. 2021 /cited 2021 Mar 2/; 10:19. Available from: https://f1000researchdata.s3.amazonaws.com/manuscripts/55636/6c077f37-5107-4160-b157-d1a30a881fdb_27422_-_roseline_ogundokun_v2.pdf?doi=10.12688/f1000research.27422.2&numberOfBrowsableCollections=27&numberOfBrowsableInstitutionalCollections=4&numberOfBrowsableGateways=25.
doi: https://doi.org/10.12688/f1000research.27422.1
18. Villarreal-Zegarra D, Copez-Lonzoy A, Bernabé-Ortiz A, Melendez-Torres GJ, Bazo-Alvarez JC. Valid group comparisons can be made with the Patient Health Questionnaire (PHQ-9): A measurement invariance study across groups by demographic characteristics. PLoS One /Internet/. 2019 /cited 2020 Dec 4/; 14(9):e0221717. Available from: https://pubmed.ncbi.nlm.nih.gov/31498796/.
doi: 10.1371/journal.pone.0221717
19. Epskamp S, Waldorp LJ, Mõttus R, Borsboom D. The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behav Res /Internet/. 2018 /cited 2020 Nov 25/; 53(4):453-80. Available from: https://pubmed.ncbi.nlm.nih.gov/29658809/.
doi: 10.1080/00273171.2018.1454823
20. Drton M, Foygel R, Sullivant S. Global identifiability of linear structural equation models. Ann Stat /Internet/. 2011 /cited 2020 Nov 25/; 39(2):865-86. Available from: https://arxiv.org/abs/1003.1146. doi: 10.1214/10-AOS859
21. Haslbeck JMB, Waldorp LJ. How well do network models predict observations? On the importance of predictability in network models. Behav Res Methods /Internet/. 2018 /cited 2020 Dec 4/; 50(2):853-61. Available from: https://pubmed.ncbi.nlm.nih.gov/28718088/.
doi: 10.3758/s13428-017-0910-x
22. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods /Internet/. 2018 /cited 2020 Dec 4/; 50(1):195-212. Available from: https://pubmed.ncbi.nlm.nih.gov/28342071/. 10.3758/s13428-017-0862-1
23. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data. J Stat Softw /Internet/. 2012 /cited 2020 Dec 10/; 48(4):1-18. Available from: https://www.jstatsoft.org/article/view/v048i04. doi: 10.18637/jss.v048.i04
24. Haslbeck JMB, Waldorp LJ. mgm: Estimating time-varyinf mixed graphical models in high-dimensional data. J Stat Softw /Internet/. 2020 /cited 2020 Dec 10/; 93(8):1-46. Available from: https://www.jstatsoft.org/article/view/v093i08. doi: 10.18637/jss.v093.i08
25. Kelley K. The MBESS R Package versión 4.8.0 [Internet]. 2020 [cited 2021 Mar 10]. Available from: https://bit.ly/3g7Dkcg
26. Revelle WR. Psych: Procedures for personality and psychological research. Evanston: Northwestern University; 2017.
27. Belvederi Murri M, Amore M, Respino M, Alexopoulos GS. The symptom network structure of depressive symptoms in late-life: Results from a European population study. Mol Psychiatry. 2020; 25(7):1447-56. doi: 10.1038/s41380-018-0232-0
28. Ferreira Gould MS, Coronel Diaz G, Rivarola Vargas MA. Impacto sobre la salud mental durante la pandemia COVID 19 en Paraguay. Rev virtual Soc Parag Med Int /Internet/. 2021 /citado 3 Mar 2021/; 8(1):61-8. Disponible en: https://www.revistaspmi.org.py/index.php/rvspmi/article/view/202
