Background: This paper investigated mental health literacy level and information seeking behavior, and mental health-related information sources in limited-resource settings, in the case of Ethiopian university students. Methods: A cross-sectional, self-administered web-based survey was conducted among Dilla University students, from 1 January to 29 February 2022, with a total of 780 respondents. We presented descriptive statistics using mean, standard deviations, and proportions. Bivariate and multivariate logistic regression were employed to identify factors associated with mental health literacy and information seeking behavior of students. Further, path analysis was also employed. Results: The result showed 71 (9.1%) respondents had a diagnosed history of mental illness. Overall, 397 (50.9%) respondents were identified as having adequate mental health literacy and 420 (53.8%) sought mental health information. In multivariate analysis, mental health literacy was significantly associated with: being female (AOR = 2.8; 95% CI (1.5– 5.4)), higher digital health literacy (AOR=2.8; 95% CI (1.5– 5.4), seeking mental health-related information (AOR=1.6; 95% CI: (1.1– 2.5)), having family with mental illness, and students in health-related programs (AOR = 2.1; 95% CI (1.0– 4.2)). Furthermore, health-related programs, level of mental health literacy, exposure to mental health problems in the family, and were associated with information seeking behavior regarding mental health. Further, path analysis revealed significant positive associations of information seeking behavior and digital health literacy with mental health literacy. Conclusion: The result indicated the status of university students’ mental health literacy level and information seeking behavior were low and inadequate. This study suggests the need to improve students’ digital health competencies by designing mental health literacy programs by collaboration of different stakeholders, and mental health literacy programs need to optimize access to internet and online resources in the university settings.

Introduction: Dietary diversity has a significant impact on children’s nutritional health. For developing and implementing interventions, it is critical to understand the regional distribution of dietary diversity and underlying factors. However, the application of spatial techniques in dietary studies has not been well documented. The study’s goal was to look into the regional variances and factors that influence children’s dietary diversity. Further, we have discussed the spatial correlation of dietary diversity with nutritional status. Methods: Data from the National Demographic and Health Survey were used during analyses. This work evaluated the overall dietary diversity of children aged 6– 23 months based on the 2017 WHO and UNICEF classification of minimum dietary diversity (MDD). The Local Anselin Moran’s I was estimated to look into the regional variation of dietary diversity and hotspot and cold spot areas. Further, multivariate multilevel logistic regression was used for factor analyses. Results: Overall, only 13.3% (95% CI: 10.2– 14.7%) of children in 2011 and 24% (95% CI: 15.5– 26.5%) in 2016 achieved MDD. We identified statistically significant clusters of high inadequate dietary diversity (hotspots) in the districts of northern Ethiopia, notably in the Amhara, Tigray, and Afar regions, and clusters of low inadequate dietary diversity (cold spots) in the country’s central and western regions. In both studies, the frequency of dietary diversity was significantly higher among older children, those who had media exposure, and mothers and fathers who had received formal education. Conclusion: According to our findings, the MDD of children in Ethiopia, as measured by WHO dietary assessment, slightly increased from 2011 to 2016. The dietary diversity of children was distributed non-randomly in different districts across regions of Ethiopia. Localized intervention and preventative methods to improve dietary patterns and culture can be developed using existing socio-demographic factors and districts with a larger distribution of inadequate dietary diversity.

Introduction: Vitamin A has been one of the most important micronutrients which are necessary for the health of the children. In developing countries, the supplementation of vitamins under a regular schedule had different constraints. Awareness, access, and resource limitations were usually the problem. In the current study, we analyzed the data from the demographic health survey (EDHS) 2016 to uncover the spatial distribution, predictors, and to provide additional information for policymaking and interventions. Methods: In this analysis, we applied intra-community correlation to measure the random effect; global Moran’s I to test the nature of variance in the null model; proportional change in variance to check the variance of null and neighborhood in subsequent models. We used STATA 15 for prediction; ArcGIS 10.7 for the spatial distribution of vitamin A supplementation; SaTscan 9.6.1 to specify location of clustering were the applied soft wares. After confirming that the traditional logistic regression cannot explore the variances, we applied multilevel logistic regression to examine predictors where p-value <0.25 was used to include variables into the model and p-value<0.05 was used to declare associations. We presented the result using means, standard deviations, numbers, and proportions or percent, and AOR with 95% CI. Result: The vitamin A coverage was 4,029.22 (44.90%) in Ethiopia in 2016. The distribution followed some spatial geo-locations where Afar, Somali were severely affected (RR = 1.46, P-value < 0.001), some pockets of Addis Ababa (RR = 1.47, p-value <0.001), and the poor distribution also affected all other regions partially. Place of delivery 1.2(1–1.34), primary and secondary education 1.3 (1–1.6), media exposure 1.2(1.1–1.4), having work 1.4(1.2–1.5), and all visits of ANC were positively influenced the distribution. Conclusion: The distribution of vitamin A coverage was not random as per the EDHS 2016 data. Regions like Afar, Somali, and some pocket areas in Addis inquires immediate interventions. Pastoralist, agrarian, and city administrations were all involved from severe to the lesser coverage in order. Since factors like Place of delivery, education, ANC, media exposure, and having work were showed positive associations, interventions considering awareness, access, and availability of service need more attention than ever.

Childhood acute malnutrition, in the form of wasting defined by a severe weight loss as a result of acute food shortage and/or illness. It is a critical public health problem that needs urgent attention in developing countries, like Ethiopia. Despite its variation between localities, the risk factors and its geospatial variation were not addressed enough across the various corner of the country. Therefore, the current study was undertaken to assess spatial variation and factors associated with acute malnutrition among under-five children in Ethiopia. A total weighted sample of 4 955 under-five children were included from the 2019 Demographic and Health Survey. Getis-Ord spatial statistical tool used to identify the hot and cold spot areas of severe and acute malnutrition. A multilevel multivariable logistic regression model using was used to examine predictors of acute malnutrition. In the multivariable multilevel analysis, Adjusted Odds Ratio with 95% CI was used to declare significant determinants of acute malnutrition among children. Among 4 955 under-five children, 7% of them were wasted and 1% of them were severely wasted in Ethiopia during the 2019 national demographic survey. The distribution was followed some spatial geo-locations where most parts of Somali were severely affected (RR = 1.46, P37 value <0.001), and the distribution affected few areas of Afar, Gambella, and Benishangul Gumz regions. Factors that significantly associated with childhood wasting were: gender(male)1.9 (1.3–2.7), age (above 36 months) 0.5 (0.2–0.9), wealth index(richest) 0.5 (0.2–0.8), and water source (unimproved source) 1.5 (1.0–2.3). Our finding implies, the distribution of childhood wasting was not random. Regions like Afar, Somali, and pocket areas in Gambella and SNNP should be considered as priority areas nutritional interventions for reducing acute malnutrition. The established socio-demographic and economic characteristics can be also used to develop strategies.

Introduction: Determinants of the magnitude of abortion among women of diverse social and economic status, particularly in Africa poorly understood because of the missing information in most countries. In this study, we addressed abortion and its determinants among youth women of 15–24 ages to provide clear direction for policymaking in Ethiopia. Methods: We examined the 2016 Ethiopian demographic health survey data downloaded from the EDHS website after obtaining permission on abortion among 15–24 age women. We applied bivariate and multilevel binary logistic regression. Community and Individual level abortion predictors passed through a three-level binary logistic regression analysis where we used p-value <0.05 and adjusted odds ratios (AOR) with 95% confidence intervals (CI). Result: The abortion among the youth population in this study was 2.5%. Factors associated with pregnancy were age group 20–24 2.5(1.6–3.8), youth with one birth 0.65(0.44–0.96), youth with 2–5 births 0.31(0.18–0.55), age ≥18 0.50(0.33–0.76), married 38(17–84), divorced 20(7–55), birth in the last five years 0.65(0.44–0.96), middle wealth youth 1.7(1.0.4–2.8), being in Amhara0.31(0.11–0.85), and 0.30(0.12–0.77). Conclusion: Less abortion occurred in economically poor youths. It is a noble finding; however, the access problem might lead to the result. We observed more abortions in age <18years; those have not given birth until the data collection date. It portrays forth clear policy direction for politicians and all other stakeholders to intervene in the problem. The analysis also showed abortion increased with age. It shows that as age increased, youths disclose abortion which is rare at an early age, and again given an essential clue for the next interventions. The fact in this study is both age and marriage affected abortion similarly. It might be because of various culture-related perceptions where it is not appropriate for an unmarried woman to appear with any pregnancy outcome as the reason behind the decreased number of abortions at a younger age. Thus, more attention is required during implementation for unmarried and lower age youth regardless of the magnitude of the abortion.

Background: Modern contraceptive methods have immense influences on the health of mothers and their children. Using contraceptive methods is seen to control family size and unnecessary pregnancies. Considering different factors like resources and various cultural aspects, assessing the intention to use contraception might bring areas with these problems into the light for intervention. Methods: We analyzed the cross-sectional survey data from EDHS 2016, which comprised 5651 reproductive-age women. Spatial autocorrelation was checked with global Moran’s statistics, at ±1 for dispersion and clustering. Aselin Local moran’s statistics also indicated types of clusters. Hot spot(Getis-Ord Gi) statistics further used to measure autocorrelation over different spatial locations. The significance level was checked by calculating Z-score and hot and cold spots indicated the variation in intention to use contraceptives per catchments. Interpolation was also applied to see the number of intents to use contraceptive areas other than the sampled using ordinary Kriging spatial interpolation. We used Kulldorff’s SatScan for specific local clustering and the Bernoulli model test was applied to check significance. Individual and community-level factors were examined using multilevel logistic regression. Due to the clustering nature of data where p-value< 0.05 signaled associations. The disproportional nature of data was adjusted using sampling weights. Result: From the total sample of women, the intention to use contraceptive methods was 2366.08(44.11%) and was highly clustered in North and Western Ethiopia. The mean number of children was (4.5 ± 2.90); age at first cohabitation was (16.9 ± 3.99); the ideal number of children was (4.77 ± 2.00). Age and the ideal number of children were negatively associated with the use of contraception. Primary education, number of children, counseling at health facilities, and age at first cohabitation were negatively associated. Conclusion: We observed various distributions among regions. Educational status and various socio-cultural including working with the religious organization might need serious considerations to increase the intention to use contraceptive methods. Besides the efforts done, policy decisions might need to consider this finding and uphill the intervention against the negatively associated socio-cultural and demographic variables in outplayed areas.