Magnitude of psychological distress and associated factors among war survivor women in Northern, Ethiopia, 2022: a community-based cross-sectional survey.

BACKGROUND: Civilian war and internal conflicts increase the incidences of mental health conditions among war survivors. It is crucial to assess war-related psychological consequences in war-affected areas in Ethiopia to intervene in the future. Thus, this study aimed to determine the magnitude of psychological distress and associated factors of psychological distress among war survivor women in Northern, Ethiopia. METHODS: A community-based cross-sectional survey was conducted, and 1596 war survivor women were recruited to participate using a face-to-face interviews with a census sampling technique from May 1-30, 2022. The psychological distress was assessed using a Kessler psychological distress scale (K10). Bi-variable and multi-variable logistic regression analyses were used, and variables with a p-value less than 0.05 in the multivariable analyses were considered statistically significant. RESULT: In this study, the response rate was 100% and the prevalence of psychological distress was 44.90% at a 95% CI: (42.40, 47.40). Psychological distress was significantly associated with the education of ability to read and write (AOR = 2.92; 95% CI: 2.12, 4.01), primary education and above (AOR = 3.08; 95% CI: 2.09, 4.54), housewife (AOR = 5.07; 95%CI: 2.64, 9.74), farmer (AOR = 8.92; 95%CI: 4.03, 19.70), emotional violence (AOR = 1.52; 95%CI: 1.05, 2.18), physical violence (AOR = 3.85; 95%CI: 2.37, 6.26) and sexual violence (AOR = 3.25; 95%CI: 1.98, 5.33) whereas being separate was protective for psychological distress (AOR = 0.38; 95%CI: 0.16, 0.92). CONCLUSION: The prevalence of psychological distress was found to be high. Therefore, women who are housewives, married, farmers, educated, and who have experienced violence must be the focus of governmental and private collaborative interventions to prevent war-related psychological morbidity and mortality.

Assessing Acute and Chronic Risks of Human Exposure to Arsenic: A Cross-Sectional Study in Ethiopia Employing Body Biomarkers.

Background: Arsenic, a widely recognized and highly toxic carcinogen, is regarded as one of the most hazardous metalloids globally. However, the precise assessment of acute and chronic human exposure to arsenic and its contributing factors remains unclear in Ethiopia. Objective: The primary goal of this study was to assess the levels of acute and chronic arsenic exposure, as well as the contributing factors, using urine and nail biomarkers. Methods: A community-based analytical cross-sectional study design was employed for this study. Agilent 7900 series inductively coupled plasma mass spectrometry was used to measure the concentrations of arsenic in urine and nail samples. We performed a multiple linear regression analysis to assess the relationships between multiple predictors and outcome variables. Results: The concentration of arsenic in the urine samples ranged from undetectable (<0.01) to 126.13, with a mean and median concentration of 16.02 and 13.5 µg/L, respectively. However, the mean and median concentration of arsenic in the nails was 1.01, ranging from undetectable (<0.01 µg/g) to 2.54 µg/g. Furthermore, Pearson's correlation coefficient analysis showed a significant positive correlation between arsenic concentrations in urine and nail samples (r = 0.432, P < .001). Also, a positive correlation was observed between urinary (r = 0.21, P = .007) and nail (r = 0.14, P = .044) arsenic concentrations and the arsenic concentration in groundwater. Groundwater sources and smoking cigarettes were significantly associated with acute arsenic exposure. In contrast, groundwater sources, cigarette smoking, and the frequency of showers were significantly associated with chronic arsenic exposure. Conclusions: The study's findings unveiled the widespread occurrence of both acute and chronic arsenic exposure in the study area. Consequently, it is crucial to prioritize the residents in the study area and take further measures to prevent both acute and chronic arsenic exposure.

Examining carcinogenic and noncarcinogenic health risks related to arsenic exposure in Ethiopia: A longitudinal study.

Background: The carcinogenic properties of arsenic make it one of the most hazardous chemicals globally. Nevertheless, the exact level of human exposure to arsenic and the associated risks of cancer and non-cancer effects through different pathways in Ethiopia are still uncertain. Objective: The primary aim of this study was to evaluate the risk of both cancer and non-cancer outcomes among children and adults who have been exposed to arsenic through drinking water in the Adami Tulu Jido Kombolcha district of Ethiopia. Methods: For this study, a longitudinal study design was employed. A total of 45 groundwater sources were sampled using the census sampling method. The concentrations of total arsenic were measured using Agilent 7900 series inductively coupled plasma mass spectrometry. Carcinogenic and noncarcinogenic risk assessments were conducted by calculating lifetime cancer risk and hazard quotients. Microsoft Office Excel was utilized to calculate human health risk indices, and descriptive statistical analysis were performed using SPSS software. Results: Our findings revealed that during the dry season, the mean arsenic concentration in the groundwater samples was 11.15 ± 9.38 µg/L, while during the rainy season, it was 10.67 ± 8.16 µg/L. The total cancer risk for children, resulting from oral ingestion and skin contact, was 1.15 × 10-2 and 1.07 × 10-2 during the dry and rainy seasons, respectively. For adults, the total cancer risk from oral ingestion and skin contact during the dry and rainy seasons was 4.95 × 10-3 and 4.59 × 10-3, respectively. Furthermore, the total hazard quotients for children via oral ingestion and skin absorption were 25.9 and 24.0 during the dry and rainy seasons, respectively. For adults, the total hazard quotients from ingestion and dermal contact during the dry and rainy seasons were 11 and 10, respectively. Conclusions: The findings indicate that the risks of cancer and non-cancer effects resulting from arsenic exposure through ingestion and dermal exposure were found to exceed the acceptable thresholds in both seasons. These results emphasize the urgent need for focused attention on the study population in the study area due to the high likelihood of experiencing adverse health outcomes.

Iron folic acid supplementation adherence level and its associated factors among pregnant women in Ethiopia: a multilevel complex data analysis of 2019 Ethiopian mini demographic and health survey data.

Background: Iron and folic acid deficiency is a worldwide public health concern, particularly in low and middle-income countries. In Ethiopia, adherence to iron and folic acid supplements is still very low. Despite the fact that a number of studies on IFA supplementation have been conducted in Ethiopia, they do not indicate a nationwide problem and do not use advanced models to demonstrate clustering effects. The purpose of this study was to assess the level of non-adherence to iron folic acid supplementation and predictors among pregnant women in Ethiopia. Objective: To assess iron folic acid supplementation adherence level and its associated factors among pregnant women in Ethiopia using data from the 2019 Mini-Ethiopian demographic health survey. Methods: The Mini Ethiopian Demographic and Health Survey 2019 data were obtained from the official database website of the Demographic and Health Survey program (http://dhsprogram.com). The analysis included a sample of 2,356 weighted study participants. A multivariable multilevel mixed-effects logistic regression model was used Variables with p-values less than 5%) was reported as statistically significant variables in the multivariable analysis. Results: The proportion of mothers who did not adhere to iron and folic acid supplements was 81.03% (95 %CI, 79.39, and 82.56). Birth interval less than 2 years [AOR: 2.03; 95% CI: 1.12, 3.66], women ever born less than six children [AOR: 1.99; 95% CI: 1.09, 3.64], starting ANC visit during first trimester [AOR: 2.74; 95 %CI: 1.03, 7.30], region [AOR = 0.24; 95% CI: 0.10], and having a high no ANC visit in the community [AOR = 1.77; 95% CI: 1.08, 2.88] were statistically significant factors. There was Intra-Custer Correlation (ICC = 17.72%), indicating that 17.72% variability in non-adherence levels was due to clusters. Conclusion and recommendation: In Ethiopia, nearly four out of every five pregnant women did not receive iron folic acid supplementation for the recommended periods. Birth intervals, number of children, timing of ANC visits, region, and community level no ANC service were significant factors for non-adherence IFAS. As a result, the community, govern metal and non-governmental sectors enacting on health should focus on reducing non-adherence through tailored interventions on factors that influence it.

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.

Introduction: Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods: The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results: The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion: This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.