Background Malnutrition happens when there are insufficient amounts of nutrients and energy consumed improperly. Included are both undernutrition and overnutrition. This study is aimed to evaluate the relationship among undernutrition indicators of stunting, underweight, and wasting among those under 5 years given other predictors. Methods The data were obtained from the measure of DHS program. A total of 2399 under-five children were involved in this study. A multivariate binary logistic regression model is used to assess the association between stunting, wasting, and being underweight given the effect of other predictors. Results Of the 2399 under-five children considered in this study, 13.5, 18.7, and 5.9% of them suffered from stunting, underweight, and wasting, respectively. The majority of children (40.1%) were obtained from the Brikama local government area of Gambia; more than half of the children (52.9%) were male, and 63.3% of children lived in urban areas. The association between stunting and underweight, underweight and wasting, and stunting and wasting was measured by the odds ratio (OR) of 15.87, 46.34, and 1.75, respectively, given the other predictors. The estimated odds ratio for children who had an average birth size to become stunted, underweight, and wasted were 0.965, 0.885, and 0.989 times the estimated odds ratio of children who had a small birth size, respectively. Conclusion The prevalence of stunting and wasting for under-five children in Gambia was lower than the world prevalence, but the prevalence of being underweight was higher. Children who are underweight have a significant association with both stunting and wasting. The age of the child, the child’s anemia level, and the birth type of the child are the common important determinants of stunting and underweight. The small birth size of a child was highly associated with a higher risk of stunting, underweight, and wasting among under five-year-olds.

Background: In low and middle-income countries such as Rwanda, undernutrition and anemia were major causes of death and morbidity among children under the age of five. Thus, this study aims to conduct a bivariate binary logistic regression model by accounting for the possible dependency of childhood undernutrition and anemia. Methods:The data came from the DHS program's measurement. A total of 3,206 under-five children were involved in this study. A single composite index measure was calculated for stunting, wasting, and underweight using principal component analysis. A bivariate binary logistic regression model is used to assess the association between undernutrition and anemia given the effect of other predictors. Results: Among 3,206 under-five children considered in this study, 1482 (46.2%) and 658 (20.5%) children were agonized by anemia and undernutrition, respectively. Nearly half of the children (48.8%) were female and 83.0% lived in rural areas. Children from urban areas were 0.663 and 0.751 times less likely to be undernourished and anemic, respectively, as compared to children from rural areas, and multiple birth children were more likely to be undernourished and anemic as compared to single-birth children. Children from families with an improved water source were 0.681 and 0.581 times less likely to be anemic and undernourished, respectively, as compared to children from families without an improved water source. The estimated odds of children who had diarrhea were 1.134 and 1.052 times anemic and undernourished, respectively, as compared to children who had no diarrhea. Conclusion: The prevalence of both undernutrition and anemia was high in Rwanda. The following determinants are statistically associated with both childhood undernutrition and anemia: place of residence; source of drinking water; maternal anemia; being a twin; birth size of children; diarrhea; fever; and child age. Policy measures that reduce the burden of undernutrition and anemia can be applied to increase access to health care through providing vital services. Besides, it is better to strengthen the strategies of early recognition and organization of maternal anemia to decrease the prevalence of childhood undernutrition and anemia.

Background The evaluation of utilization and the factors influencing optimal ANC visits is critical to improving maternal and neonatal health outcomes. The goal of antenatal care is to reduce maternal and perinatal mortality and morbidity. The study's goal is to identify the factors that influence optimal ANC visits among reproductive-age women in low-income countries. Methods The study included a total weighted sample of 329,721 women who gave birth during the study period. For model fitness and comparison, the intra-class correlation coefficient, median odds ratio, proportional change in variance, AIC, BIC, and deviance were used. To identify the determinants of optimal ANC visits in LMICs, a multilevel multivariable logistic regression model was fitted. To declare significant determinants of optimal ANC visits, the adjusted odds ratio and its 95% confidence interval were used. Results The overall prevalence of optimal ANC visits was 60.1%, and this ranged from 16.8% in Afghanistan to 97.0% in the Dominican Republic. In the Multilevel multivariable logistic regression model; age 20 to 34 (AOR = 1.267; 95%CI: 1.233–1.303)), age above 34 (AOR = 1.342; 95%CI: 1.302–1.384)), primary educated women (AOR = 1.529; 95%CI: 1.496–1.563), secondary educated women (AOR = 2.626; 95%CI: 2.561–2.694), higher educated women (AOR = 4.563; 95%CI: 4.341–4.796), middle wealth index (AOR = 1.033; 95%CI: 1.015–1.052), rich wealth index (AOR = 1.340; 95%CI: 1.284–1.399), having media exposure (AOR = 1.273; 95%CI: 1.251–1.293)), employed women (AOR = 1.252; 95%CI: 1.212–1.293), and being Central America resident (AOR = 5.967; 95%CI: 5.655–6.297) were significantly associated with optimal ANC visits. Conclusion Maternal age, maternal education level, family size, number of children, sex of household head, wealth index, marital status, husband/partner education level, husband/partner occupation, maternal occupation, media exposure, place of delivery, and region were all significant predictors of optimal ANC visits in low- and middle-income countries. This discovery assists health care providers and policymakers in implementing appropriate policies and programs to ensure optimal ANC coverage. It is critical to develop strategies to improve antenatal care access and availability.

Background Maternal and neonatal mortality is a significant public health issue that reflects the overall status of a country’s healthcare system and socioeconomic development. ANC remains one way to reduce maternal and neonatal deaths. Thus, the goal of this study is to run a bivariate binary logistic regression model that takes into account the possible dependency of optimal ANC visits and timing of ANC initiation. Methods The data came from the DHS program's measurements. A total of 5,492 women were involved in this study. Given the effect of other predictors, a bivariate binary logistic regression model is used to assess the relationship between optimal ANC visits and timing of ANC initiation. Results The prevalence of optimal ANC visits and timing of ANC initiation were 59.7% and 19.8%, respectively. The odds of timing ANC initiation and optimal ANC visits among women from households with middle and rich wealth status were 1.391 times (AOR = 1.391; 95%CI: 1.121–1.726), 2.047 times (AOR = 2.047; 95%CI: 1.669–2.511), 1.141 times (AOR = 1.141; 95%CI: 1.007–1.321), and 1.197 times (AOR = 1.197; 95%CI: 1.017–1.409), respectively, as compared to those from households with poor wealth status. The estimated odds ratio of timing ANC initiation among women who reside in rural areas was lower by 0.790 (AOR = 0.790; 95% CI: 0.652–0.957) as compared to women who reside in urban areas. Conclusion According to the results of bivariate logistic regression, maternal age, region, maternal education, wealth index, and total number of children ever born were common determinants of both optimal ANC visits and timing of ANC initiation, whereas place of residence and family size were significantly related to timing of ANC initiation. Finally, raising awareness and improving women's living conditions may increase antenatal care utilization. As a result, maternal mortality and morbidity can be reduced, and Ethiopia can meet the SDG target.

Background Diarrhea is the opening of three and more movable or liquid stools per day, or more frequently than is common for the individual. It is usually an indicator of gastrointestinal infection which can be caused by a variety of bacterial, viral, and parasitic organisms. Objective This study aimed to analyze the trend and determinants of diarrhea prevalence among under-five children within the Gambia for the last five years (2013–2019/20). Methods A total of 6,705 in 2013, and 5,780 in 2019/20 under-five aged children were involved in this study. Multivariate decomposition and multilevel analysis based on a binary logistic regression analysis approach were performed. Results 74.57% of the change in diarrhea prevalence over time was attributable to differences in behavior. Unimproved water source (AOR = 1.4061; 95% CI: 1.0415–1.8982), Wollof ethnicity (AOR = 1.5442; 95% CI: 1.2196–1.9551), not vaccinated for rotavirus (AOR = 3.1476; 95% CI: 2.1486–4.6110) and for measles (AOR = 1.5128; 95% CI: 1.2384–1.8479), average birth size (AOR = 0.8038; 95% CI: 0.6555–0.9855), child age less than one year (AOR = 0.6160; 95% CI: 0.4710–0.8057). Conclusion The prevalence of diarrhea was significantly increased over the last five years and the decline was due to differences in behavior between the surveys. Source of drinking water, ethnicity, birth size, age of children, rotavirus, and measles vaccine were significantly associated with diarrhea among under-five children in the Gambia. Therefore, the Gambian régime should attention to the creation and topping up of behavioral change packages of the public regarding the protection of hygiene and sanitation of the community and their environment, vaccinating of their children to prevent diarrheal disease.

Background Malnutrition is the main cause of illness and death in children under the age of five. It affects millions of children worldwide, putting their health and future in jeopardy. Therefore, this study aimed to identify and estimate the effects of important determinants of anthropometric indicators by taking into account their association and cluster effects. Method The study was carried out in 10 countries in East Africa: Burundi, Ethiopia, Comoros, Uganda, Rwanda, Tanzania, Zimbabwe, Kenya, Zambia, and Malawi. A weighted total sample of 53,322 children under the age of five was included. Given the impact of other predictors such as maternal, child, and socioeconomic variables, a multilevel multivariate binary logistic regression model was employed to analyze the relationship between stunting, wasting, and underweight. Result The study included 53,322 children, and 34.7%, 14.8%, and 5.1% were stunted, underweight, and wasted, respectively. Almost half of the children (49.8%) were female, and 22.0% lived in urban areas. The estimated odds of children from secondary and higher education mothers being stunted and wasted were 0.987; 95% CI: 0.979 – 0.994 and 0.999; 95% CI: 0.995 – 0.999, respectively, times the estimated odds of children from no education mothers. Children from middle-class families were less likely to be underweight than children from poorer families. Conclusion The prevalence of stunting was higher than in the sub-Saharan Africa region, but the prevalence of wasting and underweight was lower. According to the study's findings, undernourishment among young children under the age of five continues to be a significant public health issue in the East African region. Governmental and non-governmental organizations should therefore plan public health participation focusing on paternal education and the poorest households in order to improve the undernutrition status of children under five. Additionally, improving the delivery of healthcare at health facilities, places of residence, children's health education, and drinking water sources are essential for lowering child undernutrition indicators.

Background Malnutrition is a scarcity or inappropriate consumption of energy and nutrients. It comprises both undernutrition and overnutrition. The present study is aimed to evaluate the relationship between undernutrition indicators of stunting, underweight, and wasting among those under five years given other predictors. Method The data was obtained from the measure of DHS program. A total of 2,399 in 2019/20 under-five children were involved in this study. A multivariate binary logistic regression model is used to assess the association between stunting, wasting, and being underweight given the effect of other predictors. Result Of 2,399 children under-five years considered in this study 13.5, 18.7 and 5.9% of them suffered from stunting, underweight, and wasting respectively. The majority of children (40.1%) were obtained from the Brikama local government area of Gambia, more than half of the children (52.9%) were male, and 63.3% of children were live in urban areas. The association between stunting and underweight, underweight and wasting, and stunting and wasting was measured by odds ratio (OR) 15.87, 46.34, and 0.31 respectively given the other predictors. The estimated odds ratio for children who have an average birth size to become stunted, underweight, and wasted were 0.965, 0.885, and 0.989 times the estimated odds ratio of children who have a small birth size respectively. Conclusion The prevalence of stunting and wasting for under-five children in Gambia was lower than the world prevalence. Birth size of children was the important determinant of stunting, underweight, and wasting for children under-five years. Whereas, the birth type of children, anemia level of children, and age of children were significantly correlated with stunting and underweight. But body mass index of a mother is the only predictor significantly correlated with stunting.

Background High prevalence of maternal mortality in LMICs has been attributed to the low patronage of antenatal care and health facility delivery. Childbirth at health facilities is one of the safest ways to prevent maternal morbidity and mortality. The study aims to identify the determinants of health facility of delivery among reproductive age women in LMICs. Methods A total weighted sample of 329,721 women who gave birth during the study period was included in the study. Intra-class Correlation Coefficient, Median Odds Ratio, Proportional Change in Variance, AIC, BIC, and deviance were used for model fitness and comparison. Multilevel multivariable logistic regression model was fitted to identify determinants of health facility delivery in LMICs. Adjusted Odds Ratio with its 95% Confidence Interval was used to declare significant determinants of health facility delivery. Results The overall prevalence of health facility delivery was 67.6% and this ranged from 19.6% in Chad to 99.8% in Armenia. In the Multilevel multivariable logistic regression model; age less than 20 (AOR = 0.958; 95%CI: 0.928–0.990), age 20 to 34 (AOR = 0.986; 95%CI: 0.957–1.015), rural women (AOR = 0.668; 95%ci: 0.489–0.913), primary educated women (AOR = 1.545; 95%CI: 1.511–1.583), secondary educated women (AOR = 2.145; 95%CI: 2.087–2.206), higher educated women (AOR = 3.362; 95%CI: 3.167–3.570), middle wealth index (AOR = 1.894; 95%CI: 1.859–1.930), rich wealth index (AOR = 2.012; 95%CI: 1.563–2.123), having media exposure (AOR = 1.311; 95%CI: 1.287–1.336), had 4 and more antenatal care visit (AOR = 2.402; 95%CI: 2.360–2.446), unemployed women (AOR = 0.844; 95%CI: 0.843–0.846), and being Western Africa resident (AOR = 0.103; 95%CI: 0.058–0.181) were significantly associated with health facility delivery. Conclusion Maternal age, place of residence, maternal education level, family size, number of children, wealth index, marital status, and antenatal care visits were significant determinants of health facility delivery in LMICs. These findings will be useful for the government and stakeholders in planning, designing, and implementing appropriate interventions.