Mental Health Care Utilization and its Barriers among Iranian Breast Cancer Survivors: A Cross-sectional Study.

Background: Depression and anxiety are common comorbidities complicating the care of breast cancer patients, but many patients do not receive the needed care. We aimed to assess utilization of mental health care and its barriers in breast cancer survivors. Methods: This cross-sectional study was conducted on 311 patients with breast cancer, in Iran, November 2021 to March 2022. Perceived need and utilization of mental health care and barriers to service utilization were measured based on self-report. Depression, Anxiety, and Stress Scale-21 and Multidimensional Scale of Perceived Social Support were used to assess depression, anxiety, and stress as well as social support, respectively. A linear and logistic regression model was used to analyze the data using SPSS version 22. A P-value less than 0.05 was considered statistically significant. Results: 70.1% of the participants perceived need for mental health care, 28.0% of them had used mental health services, and 72% were classified as having unmet needs. The most common perceived barrier to service use was patients' self-adequacy. The prevalence of extremely severe levels of depression, anxiety, and stress was 14.8%, 23.5%, and 10.6%. Also, 48.6%, 78.5%, and 75.6% of patients received a high level of social support from friends, family, and significant others. Conclusion: Findings highlight a substantial unmet need for mental health care and low utilization of mental health services among breast cancer survivors. Given the significant prevalence of depression, anxiety, and stress in this population, it is imperative to address the underutilization of mental health services and to further examine the barriers preventing patients from seeking the care they require.

Determinants of unmet needs for mental health services amongst adolescents in Shiraz, Iran: a cross-sectional study.

Background: Mental disorders are increasingly prevalent among adolescents without appropriate response. There are a variety of reasons for unmet mental health needs, including attitudinal and structural barriers. Accordingly, we investigated perceived mental health needs, using mental health services, and their barriers in adolescents. Method: This cross-sectional study was conducted in 2022 in Shiraz, Iran. Demographic characteristics, the Adolescent Unmet Needs Checklist, and the Young Schema Questionnaire were administered to 348 adolescents aged 13-19 years. Adolescents were classified as having no needs, fully met needs, partially met needs, or wholly unmet needs. Logistic regression analysis was used to determine factors associated with perceived unmet need and refer participants to healthcare centers. Results: 193 (55.5%) adolescents reported perceived need for mental healthcare out of whom, 21.6% reported fully and 21.6% partially unmet needs. Noticeably, only 12.4% of needy participants reported met need. "Reluctance to seek mental healthcare" and "asked but not receiving help" were common barriers to using the services. Conclusion: The present study reveals unmet mental healthcare needs as a significant public health concern among the adolescents. To address this significant concern, reorientation of primary care, removing economic barriers from mental healthcare services, and improving health literacy in the community are recommended.

Delay in Regular Visits of Chronic Disease Patients and Its Associated Factors during the COVID-19 Pandemic.

Background: Chronic patients need regular follow-ups. During the COVID-19 pandemic, these regular visits can be affected. The delay of chronic patients and its contributing factors in their periodic visits during the COVID-19 pandemic are examined here. Methods: This cross-sectional study was conducted between February and June 2021, in Fars, Iran. Two hundred and eighty-six households with at least one chronic patient were recruited. Then, several trained questioners called the studied households and asked about the studied variables. The dependent variable was the number of delays in regular visits during the COVID-19 pandemic. The results were analyzed through Poisson regression by SPSS Statistics version 22 and GraphPad Prism software version 9. A significance level of 0.05 was considered for this study. Results: Out of 286 households 113 (73.4%) fathers, 138 (70.1%) mothers, and 17 (58.6%) children in the households reported delayed referral. In fathers, referring to the health center was significantly associated with a decrease in the number of delays (P=0.033). The higher age of the householder (P=0.005), the higher number of children (P=0.043), and having a family physician (P=0.007) in the mothers' group, also the number of children in households (P=0.001) in the children group were significantly associated with increasing the number of delays. Conclusion: COVID-19 pandemic not only creates direct harmful effects but also adversely affects people in danger of chronic diseases. Delays in follow-ups are taken into account as a major challenge during the COVID-19 pandemic. This issue is not limited to rural or urban residency.

Analyzing delay in referral of pregnant women and children under five years old during the COVID-19 pandemic: Fars Province, Iran.

The researchers assessed the delay in referral of pregnant women and children under five years old during the COVID-19 pandemic that they require regular healthcare. This cross-sectional study was conducted in 2021 in Fars, Iran. A total of 591 households with pregnant women and children under five years old were recruited, then having delay and the number of their delays in regular visits during the COVID-19 outbreak was asked. A total of 153 children under five years old (51.7%) and 93 pregnant women (31.5%) reported delays in referral. In children, higher age and referral to the family physician and private clinic, and in pregnant women, higher gestational age and pregnancy rank and having health problems (e.g., preeclampsia in the current pregnancy) significantly enhanced the number of delays. Due to the vulnerability of these groups, it is necessary to consider active care for them as a priority at the primary care level.

Nonlinear sparse partial least squares: an investigation of the effect of nonlinearity and sparsity on the decoding of intracranial data.

OBJECTIVE: Partial Least Squares (PLS) regression is a suitable linear decoder model for correlated and high dimensional neural data. This algorithm has been widely used in the application of brain-computer interface (BCI) for the decoding of motor parameters. PLS does not consider nonlinear relations between brain signal features. The nonlinear version of PLS that considers a nonlinear relationship between the latent variables has not been proposed for the decoding of intracranial data. This nonlinear model may cause overfitting in some cases due to a larger number of free parameters. In this paper, we develop a new version of nonlinear PLS, namely nonlinear sparse PLS (NLS PLS) and test it in BCI applications. APPROACH: In motor related BCI systems, improving the decoding accuracy of both kinetic and kinematic parameters of movement is crucial. To do this, two BCI datasets were chosen to decode the force amplitude and position of hand trajectory using the nonlinear and sparse versions of PLS algorithm. In our new NLS PLS method, we considered a polynomial relationship between the latent variables and used the lasso penalization in the latent space to avoid overfitting and to improve the decoding accuracy. MAIN RESULTS: Some linear and nonlinear based PLS models and our new proposed method, NLS PLS, were applied to the two datasets. According to our results, significant improvement from the NLS PLS method is confirmed over other methods. Our results show that nonlinear PLS outperforms generic PLS in the force decoding but it has lower accuracy in the hand trajectory decoding because of high dimensional feature space. By using lasso penalization, we presented a sparse nonlinear PLS-based model that outperforms generic PLS in both datasets and improves the coefficient of determination, 34% in the force decoding and 10% in the hand trajectory decoding. SIGNIFICANCE: We constructed a simple PLS-based model that considers a nonlinear relationship between features and it is also robust to overfitting because of using the lasso penalty in the latent space. This model is suitable for a high dimensional and correlated datasets, like intracranial data and can improve the accuracy of estimation.