1 Introduction

Cervical cancer (CC) is one of the most common gynecological cancers among women globally, the current rate of CC worldwide is calculated at approximately 660.000 new cases in 2022/23 with 350.000 deaths mainly occurring in low- and middle-income countries such as sub-Saharan Africa (SSA), Central America and South-East Asia [1, 2]. Numerous studies have defined the plethora of CC and BC clinical symptoms allowing to describe a specific psychological and psychiatric profile [1, 2]. According to recent data, the prevalence of anxiety and depression is high among women with breast cancer (BC) and CC, harming the quality of life [3,4,5,6]. The variegate spectrum of depression and anxiety disorders, which includes panic disorder (PD) and agoraphobia, obsessive–compulsive disorder (OCD), phobic disorder, and post-traumatic stress disorder (PTSD) among peri- and post-menopausal women has been estimated at approximately 41.8%. Of all women, 23.2% were premenopausal and 56.9% were postmenopausal. Selective serotonin reuptake inhibitors (SSRIs) are the antidepressant drugs most commonly prescribed to women (71.0%) affected by these conditions, particularly in the 45 to 65 age group. With the discovery of new psychotropic medications, specific diagnosis within this spectrum is essential because each of these disorders responds to specific pharmacotherapy. The approach to anxiety and depression should also recognize are often comorbid conditions (χ2 P < 0.05) [7, 8].

Many are the studies focusing on the peri and post-menopausal vasomotor symptoms but fewer on peri and post-menopausal depression [5, 6]. Peri- and post-menopause are accompanied by important physiological and psychological changes, in which depression and anxiety are probably the most important features. Menopause itself does not cause cancer, but during this period the risk of develo** cancer increases with a higher incidence among women aged 60–65 years and older [9, 10]. There is an age-mediated inflamed microenvironment, which enhances autoimmune and inflammatory responses with decreased protective immune capacities. The peri- and post-menopause period is widely recognized as a period characterized by a higher proliferation rate of harmful microorganisms in the vaginal microenvironment [11,12,13]. Studies conducted on middle-aged women have shown a strong correlation between hormones, eating disorders, intestinal and vaginal dysbiosis, sleep disorders, anxiety, depression, and inflammatory patterns. Improvements in dysbiosis following a low-glycemic index diet, and better sleep quality were seen as favorably associated with improved inflammation levels with positive effects on depression and anxiety [11,12,13,14,15,16]. Ever since, the main therapeutic answer was the use of antidepressant medication considered crucial in containing the intensity of symptoms, since these drugs have even been found useful in inflammatory processes, at least in the short term [17,18,19,20,21,22]. Overall outcomes confirmed that the median age at the time of the index Pap test and human papillomavirus (HPV) test was 37 years (range 30–59 years) in the premenopausal cohort and 60 years (range 42–74 years) in the postmenopausal cohort [19,20,21,22,23]. HPV prevalence was 22.6% among all women, 41.6% among premenopausal women, and 11.5% among postmenopausal women. Of note, the results confirmed the presence of invasive tumors with a ratio four times higher in the perimenopausal group and two times higher in the postmenopausal group, as to say the higher the dysbiosis, the higher the presence of harmful microorganisms and the higher the chances of develo** malignancies [23,24,25,

$$f\left( t \right) = \frac{L}{{\left( {1 + e^{{ - kt + kt_{0} }} } \right)}}$$

t = the time (x axis); f(CC%) = percentage of cases (y-axis); L = the upper limit representing a theoretical maximum % of CC, carrying capacity; k = growth coefficient; t0 = start at which the trend begins to be observed; Note that we only used a growth factor k, it is expected that further studies can improve the model by adding more variables.

The advantage of the proposed score function is that the constant difference rate can be obtained from the growth logistic model. For example, there are two different individuals and the covariates are × 1 and × 2 respectively (Fig. 4) [150, 151]. This means that if the covariates between two individuals differ by one unit, which could be the use of SSRIs, then their log difference ratios differ by the coefficient β, which can also be used as a further explanation of the equation. The suggested equation is as follows

$$\log \left\{ {\frac{{F\left( {t|x_{1} } \right)/\left( {1 - F\left( {t|x_{1} } \right)} \right)}}{{F\left( {t|x_{2} } \right)/\left( {1 - F\left( {t|x_{2} } \right)} \right)}}} \right\} = \beta^{\prime } \left( {x_{2} - x_{1} } \right).$$

Although a wide range of pre-existing risk factors must be taken into account, it is difficult to fully control the effects of antidepressant drugs, partly due to considerable variability in recording the effects of severity in long-term therapy. However, these logarithms represent a theoretical model that allow us to obtain a number as close as possible to the number of women who could be affected by CC, considering the general population and the different conditions related to peri- and post-menopause, depression and use of SSRIs [152].