Determinants of Patient Delay in Seeking Diagnosis and Treatment among Moroccan Women with Cervical Cancer.
Fatima OuasmaniZaki HanchiBouchra Haddou RahouRachid BekkaliSamir AhidAbdelhalem MesfiouiPublished in: Obstetrics and gynecology international (2016)
Introduction. This study sought to investigate potential determinants of patient delay among Moroccan women with cervical cancer. Methods. A cross-sectional study was conducted from June 2014 to June 2015 at the National Institute of Oncology in Rabat. Data were collected using questionnaire among patients with cervical cancer locally advanced or metastatic (stages IIA-IVB). Medical records were abstracted to complete clinical information. An interval longer than 90 days between discovery of initial symptoms and presentation to a provider was defined as a patient delay. Results. Four hundred and one patients with cervical cancer enrolled in this study. The mean age was 52.4 years (SD = 11.5). 53.6% were illiterate. Abnormal vaginal bleeding was identified for 65.8% of patients. 60.1% were diagnosed at stages IIA-IIB. 55.4% were found having patient delay. The regression analyses showed the association between literacy (p < 0.001), distance of the place of the first consultation (p = 0.031), abnormal vaginal bleeding as an earlier symptom (p < 0.001), stage at diagnosis (p < 0.03), knowledge of symptoms (p < 0.001), knowledge of causes (p = 0.008), and practice of gynecological exam during the last three years (p = 0.018) and the patient delay. Conclusion. Educational messages should aim at increasing awareness of cervical cancer, assisting women in symptom recognition, and encouraging earlier presentation.
Keyphrases
- case report
- healthcare
- squamous cell carcinoma
- palliative care
- locally advanced
- end stage renal disease
- mental health
- chronic kidney disease
- small molecule
- radiation therapy
- type diabetes
- physical activity
- newly diagnosed
- rectal cancer
- patient reported
- risk assessment
- polycystic ovary syndrome
- neoadjuvant chemotherapy
- high throughput
- depressive symptoms
- insulin resistance
- electronic health record
- prognostic factors
- machine learning