Procena anksioznosti i depresivnosti žena sa pozitivnim Papanikolau skrining testom pre i posle dijagnostičkih postupaka

Abstract

SAŽETAK Uvod: Godišnje u Srbiji oko 1300 žena oboli, a oko 500 žena umre od raka grlića materice. Tokom poslednjih decenija, uvođenje skrining programa primenom Papanikolau testa je dovelo do značajnog smanjenja incidencije i mortaliteta od raka grlića materice u razvijenim zemljama. Međutim, određeni broj žena sa pozitivnim Papanikolau skrining testom ne pridržava se preporučenih smernica i ne prolazi dalje dijagnostičke procedure. Razlozi za nepridržavanje praćenja nakon abnormalnog Papanikolau testa su ponekad povećani nivo anksioznosti i depresivnosti. Cilj ovog istraživanja je bio ispitivanje učestalosti javljanja i identifikovanje prediktora anksioznosti i depresivnosti kod žena sa pozitivnim Papanikolau skrining testom pre i posle dijagnostičkih procedura, kao i formiranje prediktivnih modela primenom veštačkih neuronskih mreža. Ispitanice i metodologija: Epidemiološka analitička opservaciona studija o faktorima povezanim sa anksioznošću i depresivnošću sprovedena je tokom 2017. godine u kohorti žena sa pozitivnim Papanikolau skrining testom pre i posle dijagnostičkih procedura (kolposkopije/biopsije/endocervikalne kiretaže). Studija je realizovana u Kliničkom centru Kragujevac. Nakon davanja dobrovoljnog, informisanog pisanog pristanka za učešće u studiji, ispitanice su anketirane u pogledu anksioznosti i depresivnosti neposredno pre sprovođenja dijagnostičkih postupaka i 2-4 nedelje od sprovedenih dijagnostičkih postupaka, odnosno pre prijema definitivnih rezultata. Pored epidemiološkog upitnika, korišćeni su i upitnici „Psihološki distres kod cervikalne displazije‖ („Cervical Dysplasia Distress Questionnaire, CDDQ‖), „Skala depresivnosti Centra za epidemiološke studije‖ („The Center for Epidemiologic Studies Depression scale, CES-D‖), „Bolnička skala anksioznosti i depresivnosti‖ („Hospital Anxiety and Depression Scale, HADS‖) i „Specifična mera procesa i ishoda‖ („Process and Outcome Specific Measure, POSM‖). U analizi podataka su primenjene multivarijantna logistička i linearna regresija, kao i veštačke neuronske mreže.

ABSTRACT Introduction: Every year in Serbia, around 1300 women get cervical cancer and about 500 women die from this disease. During the last decades, implementation of the screening program with the use of Papanicolaou test has led to a significant decline in incidence and mortality from cervical cancer in developed countries. However, a certain number of women with positive Papanicolaou test does not adhere to the recommended guidelines and does not go through further diagnostic procedures. Reasons for non-adherence to follow-up after an abnormal Papanicolaou test result are, sometimes, an increased level of anxiety and depression. The aim of this research was to investigate the frequency and identify predictors of anxiety and depression in women with positive Papanicolaou screening test before and after diagnostic procedures, as well as to build predictive models using artificial neural networks. Participants and methodology: An epidemiological analytical observational study concerning the factors related to anxiety and depression, was carried out during 2017 in a cohort of women with positive Papanicolaou screening test before and after diagnostic procedures (colposcopy/biopsy/endocervical curettage). The study was carried out in Clinical Centre Kragujevac. After providing voluntary, informed written consent for study participation, women completed questionnaires concerning anxiety and depression immediately before the diagnostic procedures and 2- 4 weeks after the performed diagnostic procedures, that is before receiving the final diagnosis. Apart from the epidemiological survey, the following questionnaires were used: ―Cervical Dysplasia Distress Questionnaire, CDDQ”, ―The Center for Epidemiologic Studies Depression scale, CES-D‖, “Hospital Anxiety and Depression Scale, HADS” and ―Process and Outcome Specific Measure, POSM”. Multivariate logistic and linear regression as well as artificial neural networks were applied in the data analysis.