The burden of health expenditure on household impoverishment in Ethiopia: a systematic review and meta-analysis.

BACKGROUND: Ethiopia, like many low-income countries, faces significant challenges in providing accessible and affordable healthcare to its population. Health expenditure is a critical factor in determining the quality and accessibility of healthcare. However, high health expenditure can also have detrimental effects on households, potentially leading to impoverishment. To the best knowledge of investigators, no similar study has been conducted in Ethiopia. Therefore, this systematic review and meta-analysis aimed to determine the pooled burden of health expenditure on household impoverishment in Ethiopia. METHODS: This systematic review and meta-analysis used the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Cochrane Library, HINARI, Google Scholar and Epistemonikos electronic databases were searched systematically. Moreover, direct manual searching through google was conducted. The analysis was performed using STATA version 17 software. Heterogeneity and publication bias were assessed using I2 statistics and Egger's test, respectively. The trim and fill method was also performed to adjust the pooled estimate. Forest plots were used to present the pooled incidence with a 95% confidence interval of meta-analysis using the random effect model. RESULTS: This systematic review and meta-analysis included a total of 12 studies with a sample size of 66344 participants. The pooled incidence of impoverishment, among households, attributed to health expenditure in Ethiopia was 5.20% (95% CI: 4.30%, 6.20%). Moreover, there was significant heterogeneity between the studies (I2 = 98.25%, P = 0.000). As a result, a random effect model was employed. CONCLUSION: The pooled incidence of impoverishment of households attributed to their health expenditure in Ethiopia was higher than the incidence of impoverishment reported by the world health organization in 2023.

Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016.

BACKGROUND: An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the most important issues the public health system is currently facing, and it comes at a significant cost to society both economically and socially. The burden of an undesired pregnancy still weighs heavily on Ethiopia. The purpose of this study was to assess the effectiveness of machine learning algorithms in predicting unintended pregnancy in Ethiopia and to identify the key predictors. METHOD: Machine learning techniques were used in the study to analyze secondary data from the 2016 Ethiopian Demographic and Health Survey. To predict and identify significant determinants of unintended pregnancy using Python software, six machine-learning algorithms were applied to a total sample of 7193 women. The top unplanned pregnancy predictors were chosen using the feature importance technique. The effectiveness of such models was evaluated using sensitivity, specificity, accuracy, and area under the curve. RESULT: The ExtraTrees classifier was chosen as the top machine learning model after various performance evaluations. The region, the ideal number of children, religion, wealth index, age at first sex, husband education, refusal sex, total births, age at first birth, and mother's educational status are identified as contributing factors in that predict unintended pregnancy. CONCLUSION: The ExtraTrees machine learning model has a better predictive performance for identifying predictors of unintended pregnancies among the chosen algorithms and could improve with better policy decision-making in this area. Using these important features to help direct appropriate policy can significantly increase the chances of mother survival.

Spatial distribution and determinants of Early sexual initiation in Ethiopia.

INTRODUCTION: Early sexual initiation has negative health, social, and economic consequences for both women and future generations. The trend of early sexual initiation is increasing globally, leading to higher rates of sexually transmitted diseases and unplanned pregnancies. Ethiopia has been challenged various disasters that makes women vulnerable and position them at heightened risk of early sexual initiation in the last four years. The spatial patterns and factors of early sexual initiation in the post-conflict-post pandemic settings is not well understood. Hence this research aimed at mapping Spatial Patterns and identifying determinant factors in the Post-COVID-Post-Conflict Settings. METHODS: The study was conducted on secondary data from the PMA 2021 cross-sectional survey which conducted nationally from November 2021 to January 2022 which is in the post pandemic and post-war period. Total weighted sample of 6,036 reproductive age women were included in the analysis. ArcGIS Pro and SaTScan software were used to handle spatial analysis. Multilevel logistic regression model was used to estimate the effects of independent variables on early sexual initiation at individual and community level factors. Adjusted odds ratio with the 95% confidence interval was reported to declare the strength and statistical significance of the association. RESULT: The spatial distribution of early sexual initiation was clustered in Ethiopia with a global Moran's I index value of 0.09 and Z-score 6.01 (p-value < 0.001).Significant hotspots were detected in East Gojjam zone of Amhara region, Bale, Arsi, West Hararge, East Wellega and Horo Gudru Wellega zones of Oromia region. The odds of having early sexual initiation was higher in women with primary education (AOR = 1.23, 95%CI: 1.03, 1.47), secondary or above education (AOR = 4.36, 95%CI: 3.49, 5.44), Women aged 26 to 25 (AOR = 1.91, 95%CI: 1.61, 2.26), women aged 36 to 49(AOR = 1.51, 95%CI: 1.24, 1.84). However, there was a significant lower likelihood of early sexual initiation in rural resident women (AOR = 0.53, 95%CI: 0.35, 0.81) and women living in 5 to 7 family size (AOR = 0.79, 95%CI: 0.68, 0.92), and more than 7 members (AOR = 0.63, 95%CI: 0.49, 0.81). CONCLUSIONS: The spatial distribution of early sexual initiation was clustered in Ethiopia. Interventions should be taken to eliminate the observed variation by mobilizing resources to high-risk areas. Policies and interventions targeted to this problem may also take the identified associated factors into account for better results.

Magnitude of sugar-sweetened beverage consumption and associated factors among women aged 15-49 years old in two Sub-Saharan African countries.

BACKGROUND: The consumption of Sugar-Sweetened Beverages (SSBs) has been linked to the global epidemic of obesity and chronic disease. Following the economic growth, urbanization, and attractive market for beverage companies, the consumption of SSBs is a rising public health challenge in low and middle-income countries. Hence, this study aimed to assess the magnitude of SSBs consumption and associated factors among women of reproductive age group in two SSA countries. METHODS: This cross-sectional study used data from Integrated Public Use Micro Data Series-Performance Monitoring for Action (IPUMS-PMA) with a total sample of 3759 women aged 15-49 years old in Burkina Faso and Kenya. The data was collected on June - August 2018 in Burkina Faso, and May -August 2018 in Kenya. SSBs consumption was measured by asking a woman if she drank SSBs yesterday during the day or night, whether at home or anywhere else. A mixed-effect logistic regression model was employed to identify associated factors. RESULT: Half (50.38%) [95%CI; 46.04, 54.71] of women consumed SSBs. Sociodemographic characteristics like primary education (AOR = 1.35; 95%CI: 1.05-1.74), secondary education (AOR = 1.46; 95%CI: 1.13-1.90), being employed (AOR = 1.28; 95%CI: 1.05-1.56),and dietary characteristics like consumption of savory and fried snack (AOR = 1.61; 95%CI = 1.24-2.09), achieved minimum dietary diversity (AOR = 1.67; 95%CI: 1.38-2.01), moderate household food insecurity (AOR = 0.74, 95% CI: 0.58, 0.95), and sever household food insecurity (AOR = 0.71, 95% CI: 0.56, 0.89) had significant statistical association with SSBs consumption. CONCLUSION: Consumption of SSBs among women in two Sub-Saharan African countries (Burkina Faso and Kenya) is high. Having higher educational status, being employed, achieved minimum dietary diversity, and having low/no household food in-security were found to be significantly associated with SSBs compared with their counterparts. We recommend for further study in other African countries using objective measurements of SSBs consumption.

Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach.

BACKGROUND: Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia. METHODOLOGY: Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software. RESULT: Random forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women's education level, family size, husband's desire for children, husband's education level, and women's fertility preference as predictors most frequently associated with contraceptive discontinuation. CONCLUSION: Results have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.

Geographical variation and predictors of zero utilization for a standard maternal continuum of care among women in Ethiopia: a spatial and geographically weighted regression analysis.

BACKGROUND: Maintaining and effectively utilizing maternal continuum of care could save an estimated 860,000 additional mothers and newborn lives each year. In Ethiopia, the number of maternal and neonatal deaths occurred during pregnancy, childbirth, and the postpartum period was very high. It is indisputable that area-based heterogeneity of zero utilization for a standard maternal continuum of care is critical to improve maternal and child health interventions. However, none of the previous studies explored the spatial distribution of zero utilization for maternal continuum of care. Hence, this study was aimed to explore geographical variation and predictors of zero utilization for a standard maternal continuum of care among women in Ethiopia. METHODS: A total of 4178 women who gave birth five years preceding the 2016 Ethiopian demographic and health survey were included. ArcGIS version 10.7, SaT Scan version 9.6, and GWR version 4.0 Software was used to handle mapping, hotspot, ordinary least square, Bernoulli model analysis, and to model spatial relationships. Finally, a statistical decision was made at a p-value< 0.05 and at 95% confidence interval. MAIN FINDINGS: The proportion of mothers who had zero utilization of a standard maternal continuum of care was 48.8% (95% CI: 47.3-50.4). Hot spot (high risk) regions for zero utilization of maternal continuum of care was detected in Afder, Warder, Korahe and Gode Zones of Somali region and West Arsi Zone of Oromia region. Respondents who had poor wealth index, uneducated mothers, and mothers who declared distance as a big problem could increase zero utilization of maternal continuum of care by 0.24, 0.27, and 0.1 times. CONCLUSION: Five women out of ten could not utilize any components of a standard maternal continuum of care. Hot spot (high risk) areas was detected in Afder, Warder, Korahe and Gode Zones of Somali region and West Arsi Zone of Oromia region. Poor wealth index, uneducated mothers, and mothers who declare distance as a big problem were factors significantly associated with zero utilization of maternal continuum of care. Thus, geographical based intervention could be held to curve the high prevalence of zero utilization of maternal continuum of care.

Digital health literacy and associated factors among health professionals during the outbreak of corona virus pandemic in Ethiopia: A systematic review and meta-analysis.

Introduction: The COVID-19 pandemic had a significant impact on healthcare delivery worldwide. Digital tools emerged as a preferred solution for maintaining healthcare services during this crisis. This study aimed to assess the magnitude of digital health literacy among healthcare professionals in Ethiopia in 2020-2023. Methods: A systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Articles published from 2020 to 2023 were reviewed using various electronic databases such as Medline, PubMed, and Cochrane Library, CINAHL, HINARI, Science Direct, Google Scholar, and Global Health. Meta-analysis was performed using STATA 17, and publication bias and heterogeneity were assessed. Results: Six studies involving a total of 2739 participants were included in the analysis. The pooled level of high digital health literacy among health professionals in Ethiopia during the pandemic was found to be 56.0% (95% CI: 55, 58). Several factors were identified as significant contributors to high digital health literacy, including internet use (AOR = 2.72, 95% CI: 1.86, 3.98), perceived ease of use (AOR = 2.79, 95% CI: 1.83, 4.25), favorable attitude (AOR = 2.49, 95% CI: 1.61, 3.85), perceived usefulness (AOR = 2.29, 95% CI: 1.65, 3.18), information-communication-technology training (AOR = 6.09, 95% CI: 1.83, 24.27), and educational level (AOR = 3.60, 95% CI: 2.96, 4.37). Conclusion and recommendation: The study findings revealed a moderate level of high digital health literacy among Ethiopian health professionals. Factors such as internet use, favorable attitude, and information-communication-technology training were associated with high-level digital health literacy. To enhance digital health literacy, it is crucial to provide timely training and improve internet access for healthcare professionals. Additionally, promoting the perception of digital tools as useful and supporting evidence-based decision-making can further improve digital health literacy. Comprehensive information-communication-technology training programs should be implemented to equip healthcare professionals with necessary skills to effectively combat outbreaks like the COVID-19 pandemic.

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP).

Background: Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques. Methods: Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction. Results: The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia. Conclusion: The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

Geospatial patterns, and individual and community levels factors of cesarean section deliveries in Ethiopia: A spatial and multilevel analysis.

BACKGROUND: Cesarean Section (CS) is the most popular surgery worldwide in obstetric care to save a mother's or the fetus's life. The prevalence of CS delivery in Ethiopia was 0.7% and 1.9% in 2000 and 2016 respectively and its spatial distribution and variation in Ethiopia are limited. This study provides evidence for healthcare providers and pregnant women on the national CS geospatial distribution and variation to promote evidence-based decision-making and improve maternal and neonatal outcomes. Therefore, this study aimed to determine geospatial patterns and individual and community-level factors of CS deliveries in Ethiopia. METHOD: A secondary data analysis of 5,527 weighted samples of mothers using the 2019 Ethiopian mini demographic and health survey was conducted. The spatial hotspot analysis using Getis-Ord Gi* hot spot analysis of ArcGIS version 10.7.1 was used to show the spatial cluster of CS and multilevel mixed effect logistic regression analyses were employed. Statistical significance was declared at p-value <0.05 and adjusted odds ratio (AOR) with 95% confidence interval (CI) was reported. RESULT: The prevalence of CS delivery in Ethiopia was 5.4% with 95%CI (4.6%, 6.4%). The spatial autocorrelation shows CS was clustered in Ethiopia (global Moran's index = 1.009, and p-value<0.001). Spatial hotspot indicates CS was prevalent in Addis Ababa, Diredewa, Oromo, and Somali regions. The odds of CS delivery were higher among mothers aged 24-35: AOR = 1.98, 95% CI (1.3, 3.1), and 35-49: AOR = 3.7, 95% CI(2.2, 6.1), live with female household heads: AOR = 1.9, 95% CI (1.2, 3.1), mothers with primary AOR = 1.6, 95% CI (1.07, 2.7), secondary: AOR = 2.4, 95% CI (1.3, 4.25) and higher education: AOR = 2.06, 95% CI (1.03, 4.2), multiple births: AOR = 8.1, 95% CI (3.4, 19.74), mothers in Addis Ababa: AOR = 3.4, 95% CI (1.1, 11.9) and Diredewa: AOR = 7, 95% CI (1.23, 40.7) as compared to their counterparts. CONCLUSION: In Ethiopia, CS deliveries remain below the World Health Organization estimate with distinct geospatial variation. Geographically, CS delivery is concentrated in urban areas like Addis Ababa and Diredewa, as well as in parts of the Somali and Oromia regions. Maternal age, female household head, Muslim religion, higher educational status, and multiple births at the individual level, and region at the community level were factors of CS delivery in Ethiopia. Therefore, efforts should be geared towards strategies and interventions to ensure fair access to CS delivery in line with WHO recommendations, especially in the regions where the CS delivery rate is below WHO estimates in Tigray, Amhara, Afar, and Benishangul Gumze regions.

Patient knowledge of surgical informed consent and shared decision-making process among surgical patients in Ethiopia: a systematic review and meta-analysis.

BACKGROUND: Informed consent is one of the safeguarding of the patient in medical practice at different standards such as ethical, legal, and administrative purposes. Patient knowledge and perception of informed consent are one of the priority concerns in surgical procedures. Patient knowledge and perception towards informed consent increased patient satisfaction, feeling high power on their determination, and accountability for the management, and facilitated positive treatment outcomes. Despite this, in Ethiopia, there are small-scale primary studies with inconsistent and inconclusive findings. Therefore, this systematic review and meta-analysis study estimated the pooled prevalence of patient knowledge and perception of informed consent and its determinants in Ethiopia. METHODS: We searched major databases such as PubMed, Hinary, MEDLINE, Cochrane Library, EMBASE, Scopus, African Journal Online (AJO), Semantic Scholar, Google Scholar, google, and reference lists. Besides this, University databases in the country were also searched from August 20, 2023, until September 30, 2023,. All published and unpublished studies that report the prevalence of patient knowledge and perception toward informed consent and its associated factors were included. All studies reported in English were included. Studies conducted between January 01, 2015 to September 30, 2023 were included. There are three outcome measurements pooled level of patient knowledge towards informed consent, pooled level of patient perception towards informed consent, and pooled effect that affects patient knowledge of informed consent. Three reviewers (MMM, NK, and YT) independently screened the articles that fulfilled the inclusion criteria to avoid the risk of bias. The studies' quality was appraised using a modified Newcastle-Ottawa Scale (NOS) version. RESULTS: The pooled prevalence of appropriate patient knowledge and perception towards informed consent was 32% (95% CI: 21, 43) and 40% (95% CI: 16, 65) respectively. Having formal education 2.69 (95% CI: 1.18, 6.15) and having a history of signed informed consent before 3.65 (95% CI:1.02,13.11) had a statistically significant association with good patient knowledge towards informed consent. CONCLUSION: The appropriate patient knowledge and perception of informed consent in Ethiopia is low. Formal education and history of signed informed consent were positive factors for appropriate patient knowledge of informed consent in Ethiopia. Physicians, policymakers, and health facility managers should focus on patients without prior experience with signed informed consent and not have formal education to improve patient knowledge towards informed consent. The protocol was registered at Prospero with number CRD42023445409 and is available from: https://www.crd.york.ac.uk/PROSPERO/#myprospero .

Urban-rural disparities in minimum acceptable diet intake among children aged 6-23 months in Ethiopia: A multivariable Decomposition analysis of Ethiopian demographic and health survey 2019.

Introduction: The achievement of the minimum acceptable diet intake (MAD) stands at 14% among urban and 10% among rural under-five children in Ethiopia. Consequently, identifying the determinants of the urban-rural gap is vital for advancing Sustainable Development Goals (SDGs), fostering healthier communities, and developing evidence-driven approaches to enhance health outcomes and address disparities. Objective: The objective of the study was to decompose the urban-rural disparities in minimum acceptable diet intake in Ethiopia using the Ethiopian Mini-Demographic and Health Survey 2019 data. Method: The study was conducted using the Ethiopian Demographic and Health Survey 2019 report. A total of 1,496 weighted children aged 6-23 months were included using stratified sampling techniques. The main outcome variable minimum acceptable diet was calculated as a combined proportion of minimum dietary diversity and minimum meal frequency. A decomposition analysis was used to analyze the factors associated with the urban-rural discrepancy of minimum acceptable diet intake, and the results were presented using tables and figures. Result: The magnitude of minimum acceptable diet among children aged 6-23 months in Ethiopia was 11.0%. There has been a significant disparity in the intake of minimum acceptable diet between urban and rural under-five children with 14 and 10%, respectively. Endowment factors were responsible for 70.2% of the discrepancy, followed by 45.1% with behavioral coefficients. Educational status of college and above was responsible for narrowing the gap between urban and rural residents by 23.9% (ß = 0.1313, 95% CI: 0.0332-0.245). The number of children in the household and the age of the child between 18 and 23 months were responsible for widening the gap in minimum acceptable diet intake discrepancy between urban and rural residents by 30.7% and 3.36%, respectively (ß = -0.002, 95% CI: -0.003 to -0.0011 and ß = -30.7, 95% CI: -0.025 - -0.0085). From the effect coefficients, the effect of institutional delivery was responsible for 1.99% of the widening of the gap between urban and rural residents in minimum acceptable diet intake (ß = -0.0862, 95% CI: -0.1711 - -0.0012). Conclusion: There is a significant variation between urban and rural residents in minimum acceptable diet. The larger portion of the discrepancy was explained by the endowment effect. Educational status of mothers with college and above, parity, age of child, and place of delivery were the significant factors contributing to the discrepancy of minimum acceptable diet intake between urban and rural residents.

Exposure to indoor air pollution and adverse pregnancy outcomes in low and middle-income countries: a systematic review and meta-analysis.

Introduction: Exposure to indoor air pollution such as biomass fuel and particulate matter is a significant cause of adverse pregnancy outcomes. However, there is limited information about the association between indoor air pollution exposure and adverse pregnancy outcomes in low and middle-income countries. Therefore, this meta-analysis aimed to determine the association between indoor air pollution exposure and adverse pregnancy outcomes in low and middle-income countries. Methods: International electronic databases such as PubMed, Science Direct, Global Health, African Journals Online, HINARI, Semantic Scholar, and Google and Google Scholar were used to search for relevant articles. The study was conducted according to the updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A random effect model at a 95% confidence interval was used to determine the association between indoor air pollution exposure and adverse pregnancy outcomes using STATA version 14. Funnel plot and Higgs I2 statistics were used to determine the publication bias and heterogeneity of the included studies, respectively. Results: A total of 30 articles with 2,120,228 study participants were included in this meta-analysis. The pooled association between indoor air pollution exposure and at least one adverse pregnancy outcome was 15.5% (95%CI: 12.6-18.5), with significant heterogeneity (I2 = 100%; p < 0.001). Exposure to indoor air pollution increased the risk of small for gestational age by 23.7% (95%CI: 8.2-39.3) followed by low birth weight (17.7%; 95%CI: 12.9-22.5). Exposure to biomass fuel (OR = 1.16; 95%CI: 1.12-1.2), particulate matter (OR = 1.28; 95%CI: 1.25-1.31), and kerosene (OR = 1.38; 95%CI: 1.09-1.66) were factors associated with developing at least one adverse pregnancy outcomes. Conclusions: We found that more than one in seven pregnant women exposed to indoor air pollution had at least one adverse pregnancy outcome. Specifically, exposure to particulate matter, biomass fuel, and kerosene were determinant factors for developing at least one adverse pregnancy outcome. Therefore, urgent comprehensive health intervention should be implemented in the area to reduce adverse pregnancy outcomes.

The association between children's exposure to pesticides and asthma, wheezing, and lower respiratory tract infections. A systematic review and meta-analysis.

Background: Exposure to pesticides is a global public health problem, especially for children. Its association with chronic respiratory disease among children has attracted considerable attention, but the existing evidence remains inconclusive and cannot be certain. Therefore, this systematic review and meta-analysis aim to determine the global pooled effect size of association with pesticide exposure and asthma, wheezing, and respiratory tract infections among children. Methods: A comprehensive search was conducted for relevant literature from electronic databases, including PubMed, Google Scholar, Hinari, Semantic Scholar, and Science Direct. Studies that provided effect size on the association between pesticide exposure and childhood asthma, wheezing, and respiratory tract infections in children were included. The articles were screened, data was extracted, and the quality of each study was assessed with four independent reviewers. Random effects models for significant heterogeneity and fixed effect models for homogeneous studies were conducted to estimate pooled effect sizes with 95% confidence intervals using Comprehensive Meta-Analysis version 3.3.070 and MetaXL version 2. Funnel plot and Higgins I 2 statistics were used to determine the heterogeneity of the included studies. Subgroup analyses were computed based on the types of pesticide exposure, study design, sample size category, and outcome assessment technique. Result: A total of 38 articles with 118,303 children less than 18 years of age were included in this meta-analysis. Pesticide exposure among children increased the risk of asthma by 24%; (OR = 1.24, 95% CI: 1.14-1.35) with extreme heterogeneity (I 2 = 81%, p < 0.001). Exposure to pesticides increased the odds of developing wheezing among children by 34% (OR = 1.34, 95% CI: 1.14-1.57), with high heterogeneity (I 2 = 79%, p < 0.001) and also increased the risk of developing lower respiratory tract infection by 79% (OR = 1.79, 95% CI: 1.45-2.21) with nonsignificant low heterogeneity (I 2 = 30%, p-value = 0.18). Conclusion: This meta-analysis provided valuable evidence supporting the association between childhood asthma, wheezing, and lower respiratory tract infection with pesticide exposure. The findings would contribute to a better understanding of the estimate of the effect of pesticide exposure on respiratory health in children and inform evidence-based preventive strategies and public health interventions.

Proportional Hazards Model on Attrition and its Predictors in Community Antiretroviral Refill Groups among ART Users in Eastern Ethiopia.

BACKGROUND: The HIV epidemic continues to be a major public health challenge worldwide, particularly in sub-Saharan African countries such as Ethiopia. Community-based antiretroviral refill groups are emerging as a patient-centered approach, but there is limited evidence. Therefore, this study aimed to assess attrition and predictors in community antiretroviral refill groups among ART users in Eastern Ethiopia. METHODS: Institutional-based retrospective cohort study was conducted. Systematic random sampling techniques were used. Data were collected via Kobo Collect and exported to Stata. Statistically significant effects were assumed for a P-value < 0.05 at a confidence interval of 95%. RESULTS: The incidence of attrition in community-based ART refill groups was 6.63 (95% CI: 5.78, 7.48) per 100 person-years. The median duration of months in CAGs from the start till the end of the follow-up period was 9 months (IQR = 24). Thus, recruitment level from health facilities, history of LTFU, and stage IV were statistically significant variables. CONCLUSION: The findings of this study highlight the importance of improving the use of community antiretroviral groups in care. Healthcare programs can ultimately improve health outcomes for individuals living with HIV.

Machine learning modeling for identifying predictors of unmet need for family planning among married/in-union women in Ethiopia: Evidence from performance monitoring and accountability (PMA) survey 2019 dataset.

Unmet need for contraceptives is a public health issue globally that affects maternal and child health. Reducing unmet need reduces the risk of abortion or childbearing by preventing unintended pregnancy. The unmet need for family planning is a frequently used indicator for monitoring family planning programs. This study aimed to identify predictors of unmet need for family planning using advanced machine learning modeling on recent PMA 2019 survey data. The study was conducted using secondary data from PMA Ethiopia 2019 cross-sectional household and female survey which was carried out from September 2019 to December 2019. Eight machine learning classifiers were employed on a total weighted sample of 5819 women and evaluated using performance metrics to predict and identify important predictors of unmet need of family planning with Python 3.10 version software. Data preparation techniques such as removing outliers, handling missing values, handling unbalanced categories, feature engineering, and data splitting were applied to smooth the data for further analysis. Finally, Shapley Additive exPlanations (SHAP) analysis was used to identify the top predictors of unmet need and explain the contribution of the predictors on the model's output. Random Forest was the best predictive model with a performance of 85% accuracy and 0.93 area under the curve on balanced training data through tenfold cross-validation. The SHAP analysis based on random forest model revealed that husband/partner disapproval to use family planning, number of household members, women education being primary, being from Amhara region, and previously delivered in health facility were the top important predictors of unmet need for family planning in Ethiopia. Findings from this study suggest various sociocultural and economic factors might be considered while implementing health policies intended to decrease unmet needs for family planning in Ethiopia. In particular, the husband's/partner's involvement in family planning sessions should be emphasized as it has a significant impact on women's demand for contraceptives.

Awareness and readiness of mental healthcare providers to implement telemental health services and associated factors at public referral hospitals in Addis Ababa City, Ethiopia.

OBJECTIVE: To assess the awareness and readiness of mental healthcare providers to implement telemental health services and the associated factors at public referral hospitals in Addis Ababa City, Ethiopia. STUDY DESIGN: A cross-sectional study was conducted among mental health professionals working at public referral hospitals in Addis Ababa City from 4 May to 10 June 2022. PARTICIPANTS: A total of 413 (55.7% male and 44.3% female) health professionals participated in the study. The study participants were selected using a simple random sampling technique. All mental healthcare providers working at public referral hospitals in Addis Ababa City were considered as the source population. Mental healthcare providers who meet the inclusion criteria were considered the study population. Having a minimum diploma qualification and 6 months or more of work experience and giving written consent were considered the inclusion criteria. OUTCOME MEASURE: The main outcome measure was awareness and readiness to implement telemental health services. RESULT: A total of 413 participants were enrolled, with a response rate of 90.6%. The prevalence of good awareness towards telemental health services was 41% (95% CI 37%, 47%). Being female (adjusted OR (AOR)=0.34; 95% CI 0.15, 0.76), having poor information technology support (AOR=0.32; 95% CI 0.11, 0.99) and having electronic health technology experience (AOR=1.21; 95% CI 1.1, 2.44) were significantly associated with awareness of telemental health services, whereas computer access at the workplace (AOR=0.04; 95% CI 0.003, 0.55) and awareness (AOR=4.34; 95% CI 1.02, 18.48) and attitude (AOR=1.01; 95% CI 1.03, 2.19) towards telemental health services were the variables that showed statistically significant association with readiness of mental healthcare providers for telemental health services. CONCLUSION: Although majority of healthcare providers in this survey were prepared to implement telemental health services, there was typically little awareness among mental healthcare providers.

Predicting healthcare professionals' acceptance towards electronic personal health record systems in a resource-limited setting: using modified technology acceptance model.

OBJECTIVES: Personal health record systems allow users to manage their health information in a confidential manner. However, there is little evidence about healthcare providers' intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess predicting healthcare providers' acceptance of electronic personal health record systems. METHODS: An institutional-based cross-sectional study was conducted from 19 July to 23 August 2022 at teaching hospitals in the Amhara regional state of Ethiopia. A total of 638 health professionals participated in the study. Simple random sampling techniques were used to select the study participants. Structural equation modelling analysis was employed using AMOS V.26 software. RESULT: Perceived ease of use had a significant effect on the intention to use electronic personal health records (ß=0. 377, p<0.01), perceived usefulness (ß=0.104, p<0.05) and attitude (ß=0.204, p<0.01); perceived ease of use and information technology experience had a significant effect on perceived usefulness (ß=0.077, p<0.05); and digital literacy (ß=0.087, p<0.05) and attitude had also a strong effect on intention to use electronic personal health records (ß=0.361, p<0.01). The relationship between perceived ease of use and the intention to use was mediated by attitude (ß=0.076, p<0.01). CONCLUSION: Perceived ease of use, attitude and digital literacy had a significant effect on the intention to use electronic personal health records. The perceived ease of use had a greater influence on the intention to use electronic personal health record systems. Thus, capacity building and technical support could enhance health providers' acceptance of using electronic personal health records in Ethiopia.

Evidence-Based Practice and Its Associated Factors among Health Professionals Working at Public Hospitals in Southwest Ethiopia.

Introduction: "Evidence-based practice" (EBP) is the process of incorporating clinical expertise and taking patient values and preferences into consideration when making clinical decisions. It is used to describe the provision of high-quality patient care. Objective: This study is aimed at assessing evidence-based practice and associated factors among health professionals working at public hospitals in Illu Aba Bora and Buno Bedele Zones, Oromia Region, Southwest Ethiopia, in 2022. Methods: An institution-based cross-sectional study design was conducted from May 8 to June 20 at public hospitals in Illu Aba Bora and Buno Bedele Zones, Oromia Region, Southwest Ethiopia. A total of 423 health professionals were included, using proportional allocation and simple random sampling. The data were collected using a self-administered questionnaire. Data was entered using EpiData version 4.6, and the collected data was cleared, arranged, coded, and then analyzed using Statistical Package for the Social Sciences version 26. Descriptive statistics and bivariable and multivariable analyses of logistic regression with AOR (95% CI) were performed at p < 0.05. Result: The study revealed that 36.2% of health professionals had good evidence-based practice. The factors found to be significantly associated with good EBP include having training in EBP (AOR = 5.43; 95% CI: 4.323, 9.532), good knowledge (AOR = 1.91; 95% CI: 1.065, 3.541), a favorable attitude (AOR = 1.91; 95% CI: 1.884, 2.342), and work experience greater than 5 years (AOR = 1.58; 95% CI: 1.482, 2.437). Conclusion: The evidence-based practice of health professionals was poor. Evidence-based practice should included in the curriculum, and also planned trainings need to be delivered to all health professionals, inorder to enhancing their knowledge as well as their attitudes by motivating them to increase evidence-based practice.

Exploring facilitators and barriers of the sustainable acceptance of e-health system solutions in Ethiopia: A systematic review.

BACKGROUND: eHealth is the use of information and communications technologies in support of health and health-related fields, including healthcare services, health surveillance, health literature, and health education knowledge and research, has the potential to improve the delivery and support of healthcare services by promoting information sharing and evidence-based health practice. Acceptance of e-health in Ethiopia using systematic review is uncertain. As a result, this study aimed to assess barriers and facilitators of the sustainable acceptance of e-health system adoption in Ethiopia through a systematic review of the literature. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist was used to conduct this study. Relevant articles have been searched in Google Scholar, Medline, PubMed, Embrace, Web of Science, Scopus, Cochrane Library, and empirical research done in Ethiopia is the main emphasis of the search strategy. The total number of studies that satisfied the criteria for inclusion was ten. In this research, empirical data related to e-health acceptance factors were retrieved, examined, and summarized by the authors. RESULTS: This systematic review identified a total of 25 predictors that have been found in the ten studies. The identified facilitators were effort expectancy, performance expectancy, facilitating conditions, social influences, attitude, computer literacy, participant age, perceived enjoyment, and educational status, duration of mobile device use, organizational culture, and habit. Moreover, technology anxiety was the most barrier to sustainable acceptance of e-health systems in Ethiopia. CONCLUSIONS: The most common facilitator identified from the predictors was effort expectancy, which played a major role in the adoption of the e-health system in Ethiopia. Therefore, eHealth implementers and managers in those settings should give users of the system priority in improving the technical infrastructure by regularly providing them with basic facilitating conditions. They should also pay attention to the system they want to implement because doing so will improve the users' perception of the system's value and attitude.