Determinants of anemia level among reproductive-age women in 29 Sub-Saharan African countries: A multilevel mixed-effects modelling with ordered logistic regression analysis.

BACKGROUND: Despite the implementation of different nutritional and non-nutritional interventions, 43% of reproductive-age women in Africa suffer from anemia. Recent evidence also shows that none of the Sub-Saharan African (SSA) countries are on the track to achieve the nutrition target of 50% anemia reduction by 2030. To date, information on the level of anemia and its determinants among reproductive-age women at the SSA level is limited. Thus, this study aimed to estimate the pooled prevalence of anemia level and its determinants in SSA countries. METHODS: We used a pooled data of 205,627 reproductive-age women from the recent demographic and health surveys of 29 SSA countries that were conducted between 2010-2021. A multilevel mixed-effects analysis with an ordered logistic regression model was fitted to identify determinants of anemia level and the deviance value was used to select the best-fitted model. First, bivariable ordinal logistic regression analysis was done and the proportional odds assumption was checked for each explanatory variable using a Brant test. Finally, in a multivariable multilevel ordinal logistic regression model, a p-value<0.05 and AOR with the corresponding 95% CI were used to identify determinants of anemia level. All analyses were done using Stata version 17 software. RESULTS: The pooled prevalence of anemia among women of reproductive age in SSA was 40.5% [95% CI = 40.2%-40.7%], where 24.8% [95% CI: 24.6%-25.0%], 11.1% [95% CI = 10.9%-11.2%], and 0.8% [95% CI = 0.7%-0.8%] had mild, moderate, and severe anemia, respectively. The prevalence significantly varied from the lowest of 13% in Rwanda to the highest of 62% in Mali, and anemia was found as a severe public health problem (prevalence of ≥ 40%) in 18 countries. The regression result revealed that polygamous marriage, women and husband illiteracy, poor household wealth, shorter birth interval, non-attendance of antenatal care, underweight, unimproved toilet and water facilities, and low community-level women literacy were positively linked with high anemia level. Additionally, the likelihood of anemia was lower in women who were overweight and used modern contraception. CONCLUSIONS: Overall results showed that anemia among women of reproductive age is a severe public health problem in SSA countries, affecting more than four in ten women. Thus, enhancing access to maternal health services (antenatal care and contraception) and improved sanitation facilities would supplement the existing interventions targeted to reduce anemia. Moreover, strengthening women's education and policies regulating the prohibition of polygamous marriage are important to address the operational constraints.

Suboptimal birth spacing practice and its predictors among reproductive-age women in Sub-Saharan African countries: a multilevel mixed-effects modeling with robust Poisson regression.

BACKGROUND: Despite the availability of exempted family planning services, a significant proportion of women in African countries continue to experience inadequately spaced pregnancies. To the authors' knowledge, evidence of suboptimal birth intervals at the SSA level is lacking and previous studies have been limited to specific geographic area. Therefore, this analysis was aimed to estimate the pooled prevalence of suboptimal birth spacing and its predictors among childbearing women in SSA. METHODS: Pooled DHS data from 35 SSA countries were used and a weighted sample of 221,098 reproductive-age women was considered in the analysis. The survey across all countries employed a cross-sectional study design and collected data on basic sociodemographic characteristics and different health indicators. Forest plot was used to present the overall and country-level prevalence of suboptimal birth spacing. Multilevel mixed-effects models with robust Poisson regression were fitted to identify the predictors of suboptimal birth spacing. Akaike's and Bayesian information criteria and deviance were used to compare the models. In a multivariable regression model, a p-value less than 0.05 and an adjusted prevalence ratio with the corresponding 95% CI were used to assess the statistical significance of the explanatory variables. RESULTS: The pooled prevalence of suboptimal birth spacing among women in SSA was 43.91% (43.71%-44.11%), with South Africa having the lowest prevalence (23.25%) and Chad having the highest (59.28%). It was also found that 14 of the 35 countries had a prevalence above the average for SSA. Rural residence [APR (95% CI) = 1.10 (1.12-1.15)], non-exposure to media [APR (95% CI) = 1.08 (1.07-1.11)], younger maternal age [APR (95% CI) = 2.05 (2.01-2.09)], non-use of contraception [APR (95% CI) = 1.18 (1.16-1.20)], unmet need for family planning [APR (95% CI) = 1.04 (1.03-1.06)], higher birth order [APR (95% CI) = 1.31 (1.28-1.34)], and desire to have at least six children [APR (95% CI) = 1.14 (1.13-1.16)] were the predictors of suboptimal birth spacing practice. CONCLUSION: More than four out of ten reproductive-age women in SSA countries gave birth to a subsequent child earlier than the recommended birth spacing, with considerable variations across the countries. Thus, interventions designed at enhancing optimal birth spacing should pay particular attention to young and socioeconomically disadvantaged women and those residing in rural regions. Strengthening community health programs and improving accessibility and availabilities of fertility control methods that ultimately impacts optimal reproductive behaviors is crucial to address contraceptive utilization and unmet need.

Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.

Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.

Willingness to pay for community-based health insurance and associated factors in Ethiopia: A systematic review and meta-analysis.

In Ethiopia, there is low enrollment and a wide discrepancy in willingness to pay for community-based health insurance schemes, and there is a lack of nationally representative data on willingness to pay for community-based health insurance. Thus, this systematic review and meta-analysis aimed to estimate the pooled prevalence of willingness to pay for community-based health insurance and associated factors in Ethiopia. This was developed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Databases such as PubMed/Medline, CINAHL, African Journals Online, and Google Scholar searches were performed to retrieve available published and unpublished studies from December 15 to May 17, 2022. Two independent reviewers screened the retrieved articles. Critical quality appraisal was performed using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. To investigate the sources of heterogeneity, subgroup analysis and meta-regression were performed based on region, study setting (rural/urban), and sample size. RevMan software and STATA 14 software were used for the statistical analysis. A random-effect model was used to estimate the effect size at a 95% confidence interval. A total of 190 studies were retrieved, and six studies were included in the final meta-analysis. The pooled prevalence of willingness to pay for community-based health insurance was 78 (95% confidence interval: 74, 81). A subgroup analysis by region indicated the lowest proportion of willingness to pay community-based health insurance in the Oromia region, 76% (95% confidence interval: 68, 84), and the highest in the Amhara region, 79% (95% confidence interval: 77, 81). Nearly three in four households were willing to pay for community-based health insurance in Ethiopia. Thus, awareness of willingness to pay community-based health insurance is mandatory to improve the implementation of community-based health insurance.

Factors affecting utilization of long-acting reversible contraceptives among sexually active reproductive-age women in the pastoral community of Northeast Ethiopia: A community-based cross-sectional study.

INTRODUCTION: In Ethiopia, only one in ten reproductive-age women use long-acting reversible contraceptives. Evidence on the utilization of these methods and associated factors among sexually active reproductive-age women in the pastoral area of Northeast Ethiopia is limited. Thus, this study aimed to assess the utilization of long-acting reversible contraceptives and associated factors among sexually active reproductive-age women in the pastoral community of Northeast Ethiopia. METHODS: A community-based cross-sectional study was conducted from 1 to 30 April 2021 among 572 reproductive-age women selected by a systematic random sampling method. Data were collected using a structured interviewer-administered questionnaire and entered into Epi-info version 7 and then finally exported to Stata version 16 for further analysis. Bivariable and multivariable binary logistic regression analyses were done to identify factors affecting the utilization of long-acting reversible contraceptives. Odds ratio with the corresponding 95% confidence interval were computed and the statistical significance of the explanatory variables was declared at p-value < 0.05. RESULTS: Overall, the utilization of long-acting reversible contraceptives was (24.3%; 95% confidence interval = 20.9%-28.0%). It was also revealed that being Orthodox (adjusted odds ratio = 4.10; 95% confidence interval = 2.20-7.65) and Protestant (adjusted odds ratio = 7.86; 95% confidence interval = 1.26-18.97) religion followers, attending higher education (adjusted odds ratio = 3.31; 95% confidence interval = 1.37-7.98), and having a husband who attended higher education (adjusted odds ratio = 4.37; 95% confidence interval = 1.98-9.67) were associated with an increased odds of using long-acting reversible contraceptive methods. Besides, having a good (adjusted odds ratio = 6.69; 95% confidence interval = 2.64-16.95) and moderate (adjusted odds ratio = 3.03; 95% confidence interval = 1.06-8.56) knowledge, and positive attitude (adjusted odds ratio = 3.65; 95% confidence interval = 1.90-7.01) toward long-acting reversible contraceptives were also associated with the utilization of these methods. CONCLUSION: Less than one-fourth of sexually active reproductive-age women in the study area were using long-acting reversible contraceptives. Thus, improving women's and husbands' education and women's knowledge and attitude toward long-acting reversible contraceptives is important to scale up the uptake of these contraceptive methods.

Level of non-adherence and its associated factors among adults on first-line antiretroviral therapy in Amhara Regional State, Ethiopia.

BACKGROUND: In Ethiopia, nearly one-third of people living with human immunodeficiency viruses do not adhere to antiretroviral therapy. Moreover, information regarding non-adherence and its associated factors among adults on first-line antiretroviral therapy in Northeast Ethiopia is limited. Therefore, this study aimed to assess the level of non-adherence and its associated factors among adults on first-line antiretroviral therapy in North Shewa Zone, Amhara Regional State, Ethiopia. METHODS: A facility-based cross-sectional study was conducted on 326 participants selected by systematic random sampling technique from the five randomly selected public health facilities. Data were collected using the questionnaire adapted from the studies conducted previously and the collected data were entered into Epi data version 3.1 and exported to Stata version 14 for further analysis. Multivariable logistic regression analysis was done and an adjusted odds ratio with its corresponding 95% confidence interval was used to declare a statistical significance. RESULTS: The overall prevalence of non-adherence was 17.4% [95% CI: (12.8%, 21.2%)]. Patients with no formal education [AOR (95% CI) = 5.57 (1.97, 15.88)], those who did not use memory aids to take their medications [AOR (95% CI) = 3.01 (1.27, 7.11)], travel more than 10 kilometers to visit the nearby antiretroviral therapy clinics [AOR (95% CI) = 2.42 (1.22, 25.86)], those who used substance [AOR (95% CI) = 3.57 (1.86, 28.69)], and patients whose medication time interfered with their daily routine activities [AOR (95% CI) = 15.46 (4.41, 54.28) had higher odds of having non-adherence to first-line antiretroviral therapy compared to their counter groups. CONCLUSION: The level of non-adherence to first-line antiretroviral therapy was 17.4%, higher compared to WHO's recommendation. Hence, patients counseling focused on avoiding substance use, use memory aids, and adjusting working time with medication schedule are very crucial. Furthermore, the ministry of health and the regional health bureau with other stakeholders should expand antiretroviral therapy service delivery at health facilities that are close to the community to address distance barriers.

Urban-rural disparities in institutional delivery among women in East Africa: A decomposition analysis.

BACKGROUND: Though institutional delivery plays a significant role in maternal and child health, there is substantial evidence that the majority of rural women have lower health facility delivery than urban women. So, identifying the drivers of these disparities will help policy-makers and programmers with the reduction of maternal and child death. METHODS: The study used the data on a nationwide representative sample from the most recent rounds of the Demographic and Health Survey (DHS) of four East African countries. A Blinder-Oaxaca decomposition analysis and its extensions was conducted to see the urban-rural differences in institutional delivery into two components: one that is explained by residence difference in the level of the determinants (covariate effects), and the other components was explained by differences in the effect of the covariates on the outcome (coefficient effects). RESULTS: The findings showed that institutional delivery rates were 21.00% in Ethiopia, 62.61% in Kenya, 65.29% in Tanzania and 74.64% in Uganda. The urban-rural difference in institutional delivery was higher in the case of Ethiopia (61%), Kenya (32%) and Tanzania (30.3%), while the gap was relatively lower in the case of Uganda (19.2%). Findings of the Blinder-Oaxaca decomposition and its extension showed that the covariate effect was dominant in all study countries. The results were robust to the different decomposition weighting schemes. The frequency of antenatal care, wealth and parity inequality between urban and rural households explains most of the institutional delivery gap. CONCLUSIONS: The urban-rural institutional delivery disparities were high in study countries. By identifying the underlying factors behind the urban-rural institutional birth disparities, the findings of this study help in designing effective intervention measures targeted at reducing residential inequalities and improving population health outcomes. Future interventions to encourage institutional deliveries to rural women of these countries should therefore emphasize increasing rural women's income, access to health care facilities to increase the frequency of antenatal care utilization.

Geographic variation and associated factors of long-acting contraceptive use among reproductive-age women in Ethiopia: a multi-level and spatial analysis of Ethiopian Demographic and Health Survey 2016 data.

BACKGROUND: High fertility rates and unintended pregnancies are public health concerns of lower and middle income countries such as Ethiopia. Long acting contraceptives (LACs) take the lion's share in reducing unintended pregnancies and high fertility rates. Despite their numerous advantages, the utilization of LACs remains low in Ethiopia. This study is aimed to explore the geographic variation and associated factors of long acting contraceptive use among reproductive-age women in Ethiopia. METHODS: This is a secondary data analysis of 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of weighted sample sizes of 10,439 reproductive-age women were included in the final analysis. To clean and analyze the none-spatial data Stata 14 was used while ArcGIS 10.6 and SaTScanTM version 9.6 software were used for spatial analysis. Multilevel Mixed-effect Logistic regression model was used to identify associated factors of LACs utilization. An Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was reported to identify significant variables. RESULTS: Long acting contraceptive utilization was non-random (Moran's I: 0.30, p-value < 0.01). Statistically, clusters with significant low utilization of LACs were found in Somali, Afar, Gambela, northern Amhara, eastern Oromia and western part of Southern Nations Nationalities and Peoples (SNNP) regions. Adjusting for other factors such as being married (AOR = 2.51, 95% CI: 1.29-4.87), having one to two (AOR = 2.14, 95% CI: 1.43-3.22), and three to four children (AOR = 1.68, 95% CI: 1.02-2.76), urban (AOR = 1.59, 95% CI: 1.16-2.19), want no more children (AOR = 1.40, 95% CI: 1.08-1.83), working status of women (AOR = 1.33, 95% CI: 1.07-1.65) increased the odds of LACs utilization. While previous history of abortion (AOR = 0.56, 95% CI: 0.39-0.80), and living in the pastoralist community (AOR = 0.22, 95% CI: 0.14-0.35) reduced the odds of LACs utilization in Ethiopia CONCLUSIONS: Significant geographic variation of LACs utilization was observed in Ethiopia. Spots with Low LACs utilization were found in the eastern, north eastern and western part of the country. Socio-demographic and pregnancy related factors were significant determinants of LACs utilization. Designing intervention programs targeting the identified hot spot clusters, and variables that can hinder the utilization of LACs is very important to increase the utilization.

Lower and middle-income countries such as Ethiopia face a challenge of an ever increasing population with high maternal mortality. In Ethiopia, the population is estimated to be more than 110 million. High fertility rates, unintended pregnancy, maternal and child mortalities are the main concerns of the country. Accordingly, it is required of the country to make a robust intervention to limit these public concerns. In relation to this, LACs utilization happens to be one of the solutions to solving the concerns. Despite their efficacy, availability, and acceptability the utilization of LACs remains low and varies among different geographic areas. However, the reason is still undefined and geographic variation was not assessed before.In our study, we analyze the Ethiopian demographic and health survey 2016 data to assess the presence of significant geographic variation and associated factors of long-acting contraceptive utilization. Hence, a spatial and multilevel analysis were employed to assess the geographic variation and associated factors of LACs utilization in Ethiopia.A statistically significant geographic variation was observed among different clusters. Clusters with significantly low utilization of LACs were found in the pastoralist (Afar, Gambela, and Somalia) regions of the country. Thus, more organized efforts need to be made to increase the utilization of LACs. Controlling for others: Marital statuses, occupation, future pregnancy interest, urban residence, previous history of abortion, living in the pastoralist community were statistically significant determinant factors of LACs utilization.In conclusion, significant geographic variation of LACs utilization was observed among different clusters. Besides, different socio-demographic, pregnancy, and child health-related variables were significant determinants of LACs utilization.