Objectives: Documenting routine practice is significant for better diagnosis, treatment, continuity of care and medicolegal issues. However, health professionals’ routine practice documentation is poorly practised. Therefore, this study aimed to assess health professionals’ routine practice documentation and associated factors in a resource-limited setting. Methods: An institution-based cross-sectional study design was used from 24 March up to 19 April 2022. Stratified random sampling and a pretested self-administered questionnaire were used among 423 samples. Epi Info V.7.1 and STATA V.15 software were used for data entry and analysis, respectively. Descriptive statistics and a logistic regression model were employed to describe the study subjects and to measure the strength of association between dependent and independent variables, respectively. A variable with a p value of <0.2 in bivariate logistic regression was considered for multivariable logistic regression. In multivariable logistic regression, ORs with 95% CIs and a p value of <0.05 were considered to determine the strength of association between dependent and independent variables. Results: Health professionals’ documentation practice was 51.1% (95% CI: 48.64 to 53.1). Lack of motivation (adjusted OR (AOR): 0.41, 95% CI: 0.22 to 0.76), good knowledge (AOR: 1.35, 95% CI: 0.72 to 2.97), taking training (AOR: 4.18, 95% CI: 2.99 to 8.28), using electronic systems (AOR: 2.19, 95% CI: 1.36 to 3.28), availability of standard documentation tools (AOR: 2.45, 95% CI: 1.35 to 4.43) were statistically associated factors. Conclusions: Health professionals’ documentation practice is good. Lack of motivation, good knowledge, taking training, using electronic systems and the availability of documentation tools were significant factors. Stakeholders should provide additional training, and encourage professionals to use an electronic system for documentation practices.

Objectives Women’s access to healthcare services is challenged by various factors. This study aimed to assess women’s health service access and identify associated factors. Methods A cross-sectional study design with a two-stage stratified sampling technique, and 12 945 women from the 2016 Ethiopia Demographic and Health Survey dataset were used. The spatial hotspot analysis and purely Bernoulli-based model scan statistics were used to highlight hot and cold spot areas, and to detect significant local clusters of women’s health service access. A multilevel logistic regression analysis was used to assess factors that affect women’s access to health services. A variable with a p<o.o5 was considered as a significant factor. Results Overall, 29.8%% of women had health services access. 70.2% of women had problems with health services access such as: not wanting to go alone (42%), distance to health facilities (51%), getting the money needed for treatment (55%) and getting permission to go for medical care (32.3%). The spatial distribution of health service access in Ethiopia was clustered, and low health service access was observed in most areas of the country. Women who lived in primary, secondary and tertiary clusters were 96%, 39% and 72% more likely to access health services. Educational status, rich wealth status, media exposure and rural residence were statistically significant factors. Conclusions In Ethiopia, women have problems with health services access. The spatial distribution of health services access was non-random, and hotspot areas of women’s health service access were visualised in parts of Benishangul Gumez, Amhara, Afar, DireDawa, Harari, and Somali regions. Creating job opportunities, public health promotion regarding maternal health service utilisation and constructing nearby health facilities are required for better healthcare service access for women.

Background Although mHealth applications are becoming more widely available and used, there is no evidence about why people are willing to use them. Therefore, this study aimed to assess the willingness of patients with diabetes to use mHealth applications and associated factors for self-care management in Ethiopia. Methods An institutional cross-sectional study was conducted among 422 patients with diabetes. Data were collected using pretested interviewer-administered questionnaire. Epi Data V.4.6 for entering the data and STATA V.14 for analysing the data were used. A multivariable logistic regression analysis was carried out to identify factors associated with patient’s willingness to use mobile health applications. Results A total of 398 study participants were included in the study. About 284 (71.4%) 95% CI (66.8% to 75.9%)). Of participants were willing to use mobile health applications. Patients below 30 years of age (adjusted OR, AOR 2.21; 95% CI (1.22 to 4.10)), urban residents (AOR 2.12; 95% CI (1.12 to 3.98)), internet access (AOR 3.91; 95% CI (1.31 to 11.5)), favourable attitude (AOR 5.20; 95% CI (2.60 to 10.40)), perceived ease of use (AOR 2.57; 95% CI (1.34 to 4.85)) and perceived usefulness (AOR 4.67; 95% CI (1.95 to 5.77)) were significantly associated with patients’ willingness to use mobile health applications. Conclusions Overall, diabetes patients’ willingness to use mobile health applications was high. Patients’ age, place of residence, internet access, attitude, perceived ease of use and perceived usefulness were significant factors concerning their willingness to use mobile health applications. Considering these factors could provide insight for developing and adopting diabetes management applications on mobile devices in Ethiopia.

Background: Billions of people have faced the problem of accessing appropriate sanitation services. This study aimed to explore the spatial distribution of households’ access to sanitation services and identify associated factors in Ethiopia.Methods: The 2019 Ethiopian Mini Demographic and Health Survey data was used with a total of 6261 weighted samples. A cross-sectional study design with a two-stage cluster sampling technique was used. Global Moran’s I statistic measure, Getis-Ord Gi*, and the ordinary Kriging Gaussian interpolation were used for spatial autocorrelation, hot spot analysis, and interpolation of unsampled areas, respectively. A purely spatial Bernoulli-based model was employed to determine the geographical locations of the most likely clusters. A multilevel logistic regression model was used, and predictors with a P value of less than 0.05 with a 95% CI were considered significant factors.Results: Overall, 19.7% of households had access to improved sanitation services in Ethiopia. Poor sanitation service access was significantly clustered, with hotspots of poor access identified in the South Nations Nationality and People’s Region (SNNPR), Oromia, Amhara, and Benishangul Gumuz regions. A total of 275 significant clusters were identified. Households in the circled area were more vulnerable to poor sanitation service access. Rural households, on-premises water access, media exposure, and rich wealth status were statistically significant factors for access to sanitation services.Conclusions: Access to sanitation services among households in Ethiopia is insufficient. The majority of the households had no access to sanitation services. Stakeholders are recommended to raise household members’ awareness of sanitation services, give priority to the hotspot areas, and encourage poor households to have access to toilet facilities. Household members recommended using the available sanitation service and keeping the sanitation service clean. Households are recommended to construct clean shared sanitation facilities.

Introduction: Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12–23 months using the best machine learning algorithm.Methods: A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12–23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 70% and 30% of the observations were used for training, and evaluating the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. All the included algorithms were evaluated using confusion matrix elements. The synthetic minority oversampling technique was used for imbalanced data management. Informational gain value was used to select important attributes to predict childhood vaccination. The If/ then logical association was used to generate rules based on relationships among attributes, and Weka version 3.8.6 software was used to perform all the prediction analyses.Results: PART was the first best machine learning algorithm to predict childhood vaccination with 95.53% accuracy. J48, multilayer perceptron, and random forest models were the consecutively best machine learning algorithms to predict childhood vaccination with 89.24%, 87.20%, and 82.37% accuracy, respectively. ANC visits, institutional delivery, health facility visits, higher education, and being rich were the top five attributes to predict childhood vaccination. A total of seven rules were generated that could jointly determine the magnitude of childhood vaccination. Of these, if wealth status = 3 (Rich), adequate ANC visits = 1 (yes), and residency = 2 (Urban), then the probability of childhood vaccination would be 86.73%.Conclusions: The PART, J48, multilayer perceptron, and random forest algorithms were important algorithms for predicting childhood vaccination. The findings would provide insight into childhood vaccination and serve as a framework for further studies. Strengthening mothers’ ANC visits, institutional delivery, improving maternal education, and creating income opportunities for mothers could be important interventions to enhance childhood vaccination.

Background: Inadequate micronutrients in the diet and vitamin A deficiency are worldwide public health problems. In developing regions, many preschool children are undernourished, become blind every year and died before the age of 23 months. This study was aimed to explore the spatial distribution of vitamin A rich foods intake among children aged 6–23 months and identify associated factors in Ethiopia.Methods: Ethiopian Mini Demographic and Health Survey 2019 dataset with a total 1407 children aged 6–23 months was used. Data management and processing were done using STATA version 15 software and Microsoft Office Excel. ArcMap version 10.7 software was used for mapping and spatial visualization of the distribution. Spatial scan statistics was performed using SaTScan version 9.5 software for Bernoulli-based model. Multilevel mixed effect logistic regression model was employed to identify associated factors.Results: Overall, 38.99% (95% CI: 36.46–41.62) of children aged 6– 23 months took vitamin A rich foods. Poor intake of vitamin A rich foods was significantly clustered Dire Dawa city, Somali and Harari regions of Ethiopia. Children aged 6–23 months lived in the primary cluster were 70% (RR = 1.70,P-value < 0.001) more likely to intake vitamin A rich foods than children lived outside the window. In the multilevel mixed effect logistic regression analysis, Primary educational status (AOR:1.42, 95% CI: 1.05, 1.93) and higher educational status (AOR:3.0, 95% CI: 1.59, 5.65) of mother, Dire Dawa (AOR:0.49, 95% CI: 0.22, 1.12) city, Afar (AOR: 0.16, 95% CI: 0.07, 0.36), Amhara (AOR: 0.37, 95% CI: 0.19, 0.71) and Somali (AOR: 0.02, 95% CI: 0.003, 0.08) regions of Ethiopia, children aged 13–23 months (AOR: 1.80, 95% CI: 1.28, 2.36), Mothers’ exposure to media (AOR: 1.41, 95% CI: 1.04, 1.92) were statistically significant factors for vitamin A rich foods intake among children aged 6–23 months.Conclusions: Only 4 out of ten children took vitamin A rich foods which is too low compared to the national target and significantly clustered in Ethiopia. Mother’s educational status, Region, Child age and Mother’s media exposure are significant factors vitamin A rich foods intake. Stakeholders should strengthen mothers’ education status, creating awareness for mothers on child feeding and using locally available natural resource to produce vitamin A rich foods.