Magnitude and Determinates of Anemia among adolescent Girls in Africa: A Multilevel, Multicounty Analysis of 24 Countries

Magnitude and Determinates of Anemia among adolescent Girls in Africa: A Multilevel, Multicounty Analysis of 24 Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Magnitude and Determinates of Anemia among adolescent Girls in Africa: A Multilevel, Multicounty Analysis of 24 Countries Fentanesh Nibret Tiruneh, Bedilu Alamirie Ejigu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5004469/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : Anemia in Africa is a pressing public health issue with far-reaching consequences. Adolescents, especially girls, are more vulnerable to developing anemia due to rapid physical growth and development, menstruation and poor diets. Identifying the determinants of anemia among adolescent girls in Africa is crucial for developing appropriate interventions, yet evidence remains scarce. Objective : This study aims to investigate the magnitude and determinants of anemia among adolescent girls in Africa. Methods : Data from the recent Demographic and Health Surveys (DHSs) of 24 African countries was used, focusing on 38,966 adolescent girls aged 15–19 years. The chi-squared test was utilized for bivariate analysis, and the relationship between predictor variables and anemia was evaluated using bivariate and multivariable binary logistic regression models. Results : The overall magnitude of anemia among adolescent girls was 43.6% (95% CI: 42.97, 44.21). The research has identified several determinants associated with anemia in adolescent girls, including education status, wealth status, contraceptive use, nutritional status, pregnancy status, and net use. These factors provide valuable insight into potential avenues for intervention and prevention efforts. The multivariable analysis indicated that education status (primary education AOR = 0.72; 95% CI = 0.50–0.80 and secondary education AOR = 0.76; 95% CI = 0.66–0.86), wealth status (being rich AOR = 0.86; 95% CI = 0.76–0.98), contraceptive use (AOR = 0.46; 95% CI = 0.40–0.53), and nutritional status were inversely associated with adolescent anemia. In contrast, pregnancy status (AOR = 1.13; 95% CI = 1.01–1.27) and treated net use (AOR = 1.26; 95% CI = 1.15–1.39) were positively associated with anemia among adolescent girls. Conclusion: This study serves as a catalyst for informed action and underscores the urgent need for comprehensive interventions aimed at addressing the multifaceted determinants of anemia among adolescent girls in Africa. By targeting these key factors, public health initiatives can make significant strides towards improving the health and well-being of young women across the continent. To prevent adolescent anemia, it is recommended to encourage girls to pursue education, prevent adolescent marriage and pregnancy promote contraceptive use among married or sexually active girls, and educate on the correct use of treated nets. Anemia Adolescent girls Africa Health and Nutrition Figures Figure 1 Introduction Anemia is a condition in which the number of red blood cells or hemoglobin is lower than normal, reducing the blood's capacity to carry oxygen. It can result from several factors: nutrient deficiencies or poor nutrient absorption, infections (e.g., malaria, parasitic infections, tuberculosis, HIV), inflammation, chronic diseases, gynecological and obstetric conditions, and inherited red blood cell disorders. The most common cause of anemia is iron deficiency, which can occur due to inadequate dietary intake, poor iron absorption, or increased needs during growth spurts and menstruation[ 1 ]. Approximately one-quarter of the global population is estimated to be anemic, with the prevalence notably higher among women, expectant mothers, adolescent girls, and children under the age of five. Anemia is the third-leading cause of years lived with disability (YLDs) globally[ 2 ]. Between 1990 and 2021, the evidence indicates a global trend towards less severe anemia. The most significant reductions were observed in adult males; however progress has been slower among women of reproductive age including adolescent girls aged 15–19 and children under the age of five. In 2021, sub-Saharan Africa and South Asia are facing the greatest burden [ 3 ]. Adolescence is a period of transition from childhood to adulthood, characterized by rapid physical growth and increased nutritional needs. Among adolescents aged 10–19, especially in low- and middle-income countries, anemia is a significant issue. During this stage, the body undergoes rapid growth and development, increasing nutritional demands, including for iron. Iron deficiency anemia adversely affects the educational and economic well-being of adolescents. It has been linked to stunting, wasting, being underweight, poor cognitive function, reduced physical activity, and attention deficit hyperactivity disorder in adolescents [ 4 , 5 ] The onset of menstruation further raises the risk of anemia due to additional iron loss among adolescent girls. Adolescent girls are vulnerable especially to iron deficiency due to accelerated increase in requirements for iron, poor dietary intake of iron, menstrual losses, infection, norm of early marriage, and adolescent pregnancy. Adolescents with anemia are more likely to experience anemia during pregnancy, which can lead to various adverse effects on both pregnancy and childbirth [ 6 , 7 ]. Adolescent girls in Africa are particularly vulnerable to anemia due to a combination of factors. Inadequate diets and poor absorption of iron, compounded by high rates of infectious diseases like malaria and parasitic infections, poor hygiene management, contribute significantly. Limited access to healthcare and education, along with poverty and gender inequality, exacerbate the issue. Rapid growth during adolescence increases nutritional needs, which are difficult to meet in regions with limited resources [ 8 – 12 ]. Studies have identified factors significantly associated with anemia among adolescent girls including the respondent's age, marital status, educational level, nutritional status and wealth status [ 7 , 13 , 14 ]. Additionally, early marriage and pregnancy, which are prevalent in many African countries, are linked to a range of social, physical, and health problems, including anemia among adolescents. Early marriage often leads to early pregnancy, which increases the risk of anemia due to higher iron requirements and inadequate nutritional intake. Moreover, adolescents who marry early may face limited access to healthcare, education, and economic opportunities, further exacerbating their vulnerability to anemia. These factors contribute to a cycle of poor health and socio-economic disadvantage that can have long-lasting effects on their overall well-being and future prospects [ 15 , 16 ]. Despite global efforts to mitigate anemia, progress has been slower among adolescent girls, especially in low- and middle-income countries. The current trends in anemia make it challenging to achieve the Sustainable Development Goal (SDG) of reducing anemia among women of reproductive age by 50% by 2030 [ 17 , 18 ]. Addressing all forms of malnutrition is a key target of the SDGs. By effectively reducing malnutrition, the prevalence of anemia can also be lowered, thereby contributing to the successful achievement of the SDGs by 2030. To meet this ambitious goal, comprehensive strategies must be implemented including assessment the magnitude and identify the determinants associated with anemia among adolescent girls [ 19 ]. While studies have estimating the magnitude and determinants of anemia among women of reproductive age, less attention has been given to adolescent girls and multicounty analyses are rare. This study aims to provide a comprehensive understanding of the magnitude and determinants of anemia among adolescent girls in Africa by analysing data from 24 countries. These insights are crucial for developing targeted and effective policies and interventions to improve health outcomes and overall well-being for adolescent girls across the continent. This study will help identify which regions or countries need the most urgent attention and which determinants are most critical to address. A multicounty analysis will fill existing data gaps and highlight regional differences, allowing for tailored interventions that meet local needs and challenges. This comprehensive view will ensure that resources are allocated where they are needed most. Method Data source and study participants Data from the most recent demographic and health surveys (DHSs) from 24 African countries between 2016 and 2022 was used for this study. The DHS implements a stratified two-stage cluster sampling design which gives nationally representative samples. The sampling design involved randomly selected communities (clusters) at the first stage and households at the second stage. Further details on the implementation of DHS survey were available in other sources [ 20 ]. In total, 182,683 women age 15–49 years were agreed and their hemoglobin level measured. Anemia testing was performed after getting consent from the participants. Among reproductive-age women, only 38,966 were adolescents (15–19 years). Thus, in this study our analytical sample was limited to 38,966 female adolescents (15–19 years). Measurements Outcome variable The outcome variable was the anemia status among adolescent girls which was dichotomized into two categories: non-anemic, coded as “0,” and anemic, coded as “1” (this category includes mild, moderate, and severe anemia). The anemia status was determined using the women’s hemoglobin levels, measured according to the cut-off points defined by the World Health Organization (WHO) [ 21 ]. Predictor variables The variables included in this analysis was marital status, age at first marriage, educational level, employment status, place of residence, nutritional status, pregnancy status, history of childbearing/parity, contraceptive use, use of mosquito nets, and health insurance coverage. The wealth index was constructed based on household ownership of selected assets, such as televisions and materials used for constructing the house, Source of drinking water, type of toilet facility etc. Household asset factor scores were generated using principal component analysis, then standardized and categorized into quintiles (poorer, poor, middle, rich, and richer) [ 20 ] which later grouped in two three categories (poor, middle, and rich) in this analysis. Statistical Analysis The complexity of analyzing DHS survey datasets can be attributed to two main factors: i) the use of stratified multistage cluster sampling and, ii) the unequal probabilities of selection from target populations for sampled elements, often due to oversampling of key subgroups. To address this complexity, sampling weights were used during the estimation of proportions and their respective confidence intervals [ 22 ]. Descriptive analysis involved computing the socio-demographic characteristics of adolescent girls, presented in frequencies and percentages (see Table 1 ). Additionally, weighted percentages of anemia by different factors were computed (see Table 3 ). Given that adolescent girls living in the same cluster may have similar anemia status, a modeling approach that accounts for the correlated nature of the data within the cluster is necessary. This involves introducing sampling clusters as random effects to accommodate cluster heterogeneity and unobserved covariates within clusters. A multilevel logistic regression model was employed to investigate the association between adolescent girls’ anemia status and demographic factors, with survey clusters treated as a random effect whilst adjusting for various covariates. Let \(\:{y}_{ij}\) denote the binary outcome for adolescent girls i in cluster j, and assume \(\:{y}_{ij}\) follows a Bernoulli distribution with probability of success, \(\:{p}_{ij}\) . Then, using the usual logit link function, a binary outcome can be associated with a linear predictor as follows: $$\:\text{log}\left(\frac{{p}_{ij}}{1-{p}_{ij}}\right)=logit\left({p}_{ij}\right)={x}_{ij}^{{\prime\:}}\beta\:+{b}_{j},\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[23\right]$$ Where, \(\:X\) is a vector different covariate presented in Table 1 with associated regression coefficients \(\:\beta\:\) . The random-effect component of the model, \(\:{b}_{j},\) are mutually independent Gaussian random effects used to capture within-cluster correlation [ 24 ]. The model utilized a Bernoulli probability distribution to represent adolescent woman’s anemia level with the linear predictor assuming a specific form (Eq. 1). The random-effect component of the model captured within-cluster correlation using mutually independent Gaussian random effects with mean 0 and variance \(\:{\sigma\:}_{b}^{2}\) . The fixed effects parameter in the model had a conditional interpretation, reflecting the consequence of changing the value of an explanatory variable for a given value of the random effect and the other fixed effects [A]. Both descriptive and inferential data analyses were done using Stata 18.0 software [ 25 ]. In this study, for statistical significance, the considered type-I error alpha value is 0.05. Ethical consideration This study exclusively involved secondary analysis of existing DHS data. The data collection procedures for the DHS adhered to all relevant guidelines and regulations. Ethical approval for the surveys was obtained from the Institutional Review Boards of each respective country. For adolescent girls under the age of 18, parental or guardian permission was required prior to participation in the study. The authors obtained authorization from the DHS program to use the data: https://dhsprogram.com/data/available-datasets.cfm Results Table 1 presents the socio demographic, economic and health characteristics of 38,966 adolescent girls sampled from various regions in Africa. The majority of the adolescents (60.1%) reside in rural areas. Regarding marital status, about (79.9%) adolescents were single/never married and 21.6% were been married. Among those who have been married, a larger proportion (76.8%) of adolescents married between the ages of 15–19, while 23.2% married before the age of 15. Nearly half of the adolescents (48.9%) had attained secondary education, while 35.7% had completed primary education. Around 14.4% has no education, and a smaller fraction had higher education (1.0%). In terms of wealth distribution, about 45.6% were classified as rich, 34.1% as poor, and 20.3% as middle-income. About 67.9% of the adolescents were not working, while 32.1% were engaged in some form of employment (Table 1 ). About 6.1% adolescents were pregnant during the data collection period. A large proportion of the adolescents (89.6%) do not use contraceptives. About 15.3% adolescents had one child, and 2.7% have two or more children. Most adolescents (88.5%) do not have health insurance. The majority of adolescents had a normal weight (72.0%), while 17.6% were underweight, 8.4% were overweight, and 2.1% were obese. Most adolescents (65.7%) do not use mosquito nets, while 34.0% used treated nets, and 1.8% use untreated nets. Table 1 Sampled adolescents characteristics in Africa (N = 38,966) Predictors/factors Frequency Proportion Residence Urban 15544 39.9 Rural 23422 60.1 Marital status Never married/Single 30668 79.9 Ever Married 8298 21.62 Age at first Marriage (n = 8,174) < 15 1927 23.2 15–19 6371 76.8 Education status No education 5610 14.4 Primary 13924 35.7 Secondary 19047 48.9 Higher 385 1.0 Wealth status Poor 13300 34.1 Middle 7910 20.3 Rich 17756 45.6 Currently pregnant No 36602 93.9 Yes 2364 6.1 Use of contraceptive No 34922 89.6 Yes 4044 10.4 Health insurance No 34497 88.5 Yes 4468 11.5 Currently working No 26448 67.9 Yes 12500 32.1 Parity No 31939 82.0 1 5977 15.3 2+ 1050 2.7 Body mass index Underweight 6315 17.6 Normal weight 25845 72.0 Overweight 3009 8.4 Obese 746 2.1 Type of mosquito net No net 25222 65.7 treated net 13058 34.03 Untreated net 686 1.78 Anemia status Not anemic 21980 56.4 Anemic 16986 43.6 Table 2 presents the percentage of anemia among adolescent girls in various African countries. Several countries show alarmingly high rates of anemia among adolescent girls. For instance, Gabon has the highest prevalence at 72.0%, followed by Mali with 65.4%, Ivory Coast with 62.2%, Nigeria with 60.5%, Burkina Faso with 57.6%, Liberia with 55.1%, Mozambique with 54.9%, and Togo with 54.7%. The total data aggregated from all countries show that out of 38,966 adolescent girls 16, 986 (43.6% 95% CI:42.97_, 44.21) adolescent girls were anemic. The prevalence of anemia ranged from 14.7% in Rwanda to 72% in Gabon (Table 2 ). Table 2 Magnitude of Anemia among Adolescent Girls in Africa Country Not Anemic n(%) Anemic n(%) Burkina Faso 835 (42.4) 1136 (57.6) Burundi 1235 (64.1) 692 (35.9) Democratic Republic of the Congo 1239 (59.9) 830 (40.1) Ivory Coast 586 (37.8) 966 (62.2) Cameroon 966 (58.3) 692 (41.7) Ethiopia 2574 (80.1) 641 (19.9) Gabon 287 (28.0) 738 (72.0) Ghana 778 (56.2) 608 (43.8) Gambia 739 (56.5) 568 (43.5) Equatorial Guinea 683 (52.9) 609 (47.1) Liberia 398 (44.9) 488 (55.1) Lesotho 564 (75.9) 179 (24.1) Mali 341 (34.6) 645 (65.4) Malawi 1126 (64.7) 616 (35.3) Mozambique 1386 (45.1) 1688 (54.9) Nigeria 1088 (39.5) 1665 (60.5) Niger 461 (54.0) 393 (46.0) Rwanda 1403 (85.3) 242 (14.7) Sierra Leone 805 (50.6) 787 (49.4) Togo 400 (45.3) 484 (54.7) Tanzania 877 (55.0) 718 (45.0) Uganda 932 (67.1) 457 (32.9) South Africa 312 (66.1) 160 (33.9) Zambia 1963 (66.6) 986 (33.4) Total 21980 (56.4) 16986 (43.6) Figure 1 illustrates the prevalence of anemia among adolescent girls across various African countries. Countries with a prevalence higher than the overall average (43.6%) are marked in red, while those below the average are marked in green. The highest prevalence was observed in Central and West African countries, including Gabon, Mali, Ivory Coast, and Nigeria. In contrast, lower prevalence was found in East and East-Central African countries, as well as in Southern African countries, including Rwanda, Ethiopia, Lesotho, and South Africa (Fig. 1 ). Table 3 presents a chi-square analysis of the distribution of anemia among African adolescent girls by various predictor variables. The prevalence of anemia is slightly higher among urban residents, with 44.4% being anemic compared to 43.2% of rural residents. Adolescents who had ever been married show a higher prevalence of anemia (48.7%) compared to their never married/single counterparts (42.3%). The prevalence of anemia is higher among girls who married before the age of 15 (50.7%) compared to those who married between 15–19 years (48.2%). Educational attainment shows a strong inverse relationship with anemia prevalence. Adolescents with no education had the highest prevalence of anemia (54.2%), followed by those with primary education (40.2%), secondary education (43.2%), and higher education (37.3%). This indicates that higher education levels are protective against anemia. Wealthier adolescents had a lower prevalence of anemia. The prevalence was highest among poor girls (46.4%), followed by those from middle-income households (44.7%), and was lowest among rich girls (41.0%). Employment status does not significantly affect anemia prevalence, with both working (43.7%) and non-working (43.7%) girls showing the same prevalence rates (Table 3 ). Pregnant adolescents had a significantly higher prevalence of anemia (54.9%) compared to non-pregnant girls (43.0%). Adolescents who used contraceptives have a lower prevalence of anemia (38.3%) compared to those who do not use contraceptives (44.3%). The number of children a girl has given birth to is associated with increased prevalence of anemia. Adolescent girls with no children have a prevalence of 42.8%, those with one child have a prevalence of 47.3%, and those with two or more children have the highest prevalence at 49.0%. The prevalence of anemia was lower among girls with health insurance (39.1%) compared to those without it (44.3%). The prevalence of anemia was highest among adolescents using untreated mosquito nets (49.0%), followed by those using treated nets (45.1%), and those not using any net (42.8%). Higher BMI was associated with a lower prevalence of anemia. Underweight girls have an anemia prevalence of 45.2%, normal weight girls have 45.0%, overweight girls have 41.5%, and obese girls have the lowest prevalence at 40.9%. Table 3 A chi-square analysis of magnitude of anemia by predictor variables among African adolescent girls Factors Not anemic frequency (%) Anemic frequency (%) Residence Urban 8581(55.6) 6852(44.4) Rural 13359(56.8) 10174(43.2) Marital status Never married/Single 17687 (57.7) 12981(42.3) Ever Married 4253 (51.3) 4045 (48.7) Age at first Marriage (n = 8,298) < 15 959 (49.3) 985 (50.7) 15–19 3294 (51.8) 3060 (48.2) Education status No education 2587(45.8) 3059(54.2) Primary 8252(59.8) 5556(40.2) Secondary 10891(56.8) 8286(43.2 ) Higher 210(62.7) 125(37.3) Wealth status Poor 7632 (53.6) 6601(46.4) Middle 4336 (55.3) 3498 (44.7) Rich 9972 (59.0) 6927 (41.0) Currently pregnant No 20864 (57.0) 15714 (43.0) Yes 1076 (45.1 ) 1312 (54.9) Use of contraceptive No 19427 (55.7) 15467 (44.3) Yes 2513 (61.7) 1559 (38.3) Health insurance No 19165 (55.7) 15240 (44.3) Yes 2775 (60.9) 1785 (39.1) Currently working/Occupation No 14938 (56.3) 11591 (43.7) Yes 6991 (56.3) 5428 (43.7) Parity No 18199 (57.2) 13637 (42.8) 1 3180 (57.2) 2851 (47.3) 2+ 561 (51.0) 538 (49.0) Body Mass Index (BMI) Underweight 3499 (54.8) 2889 (45.2) Normal weight 14190 (55.0) 11592 (45.0) Overweight 1780 (58.5)) 1263 (41.5) Obese 414 (59.1) 287 (40.9) Type of mosquito net No net 14418 (57.2) 10804 (42.8) Treated net 7172 (54.9) 5886 (45.1) Not treated net 350 (51.0) 336 (49.0) Note: all predictor variables except employment status were statistically significant with anemia . Table 4 presents the binary logistic regression analysis of various factors associated with anemia among adolescent girls in Africa. The results are shown in terms of Crude Odds Ratios (COR) and Adjusted Odds Ratios (AOR), along with their 95% Confidence Intervals (CI). Adolescent girls living in rural areas had a slightly lower odds of anemia compared to those in urban areas (COR: 0.950, 95% CI: 0.91–0.99). However, this association is not significant after adjusting for other factors (AOR: 0.88, 95% CI: 0.78–1.01). Adolescent girls who had ever married showed higher odds of anemia compared to their never-married/single counterparts (COR: 1.298, 95% CI: 1.34–1.36). However, this variable was not included in the multivariable analysis due to convergence issues. Adolescents who married at ages 15–19 show slightly lower odds of anemia compared to those married before age 15 (COR: 0.90, 95% CI: 0.81-1.00), but this difference is not significant after adjustment of other factors (AOR: 0.99, 95% CI: 0.88–1.11). Education appears protective against anemia. Compared to adolescents with no education, those with primary (AOR: 0.72, 95% CI: 0.5–0.80), secondary (AOR: 0.76, 95% CI: 0.66–0.86), and higher education (AOR: 0.61, 95% CI: 0.21–1.75) had lower odds of anemia. Adolescents from rich households have significantly lower odds of anemia compared to those from poor households (AOR: 0.86, 95% CI: 0.76–0.98). Pregnancy was associated with higher odds of anemia (AOR: 1.13, 95% CI: 1.01–1.27) compared to those who were not pregnant. Using contraceptives linked to significantly lower odds of anemia (AOR: 0.46, 95% CI: 0.40–0.53). Adolescents who had given birth to one child have higher odds of anemia compared to those with no children (COR: 1.20, 95% CI: 1.13–1.26). Similarly, adolescents with two or more children also have higher odds of anemia (COR: 1.28, 95% CI: 1.13–1.44). However, when adjusted for other factors, these associations are no longer statistically significant. Having health insurance shows no significant association with anemia (AOR: 1.03, 95% CI: 0.81–1.30). Regarding to nutritional status, adolescents who were obese had significantly lower odds of anemia compared to those who were underweight (AOR: 0.57, 95% CI: 0.38–0.86). Using a treated mosquito net was associated with higher odds of anemia (AOR: 1.26, 95% CI: 1.15–1.39). The multivariable analysis revealed that 1.14% of the variation in anemia among adolescent girls was due to differences at the cluster/community level (ICC = 0.014, p < 0.05). This suggests that about 1.14% of anemia cases may be attributable to other unobserved community-level determinants. Table 4 Bivariate and multivariable analysis to determine anemia among adolescent girls in Africa. Factors COR (95% CI) AOR (95% CI) Residence Urban 1 Rural 0.950 (0.91–0.99)** 0.88(0.78–1.01) Marital status Never married/Single 1 Ever Married 1.298(1.34–1.36)** Age at first Marriage (n = 8,298) < 15 1 1 15–19 0.90(0.81–0.99)* 0.99(0.88–1.11) Education status No education 1 1 Primary 0.57(0.53–0.60)** 0.72(0.5–0.80)* Secondary 0.64(0.60–0.68)** 0.76(0.66–0.86)* Higher 0.50(0.40–0.64)** 0.61(0.21–1.75) Wealth status Poor 1 1 Middle 0.93(0.88–0.99)** 0.95(0.85–1.08) Rich 0.80(0.77–0.84)** 0.86(0.76–0.98) Currently pregnant No 1 1 Yes 1.617(1.49–1.76)** 1.13(1.00-1.27)* Use of contraceptive No 1 1 Yes 0.78(0.72–0.83)** 0.46(0.40–0.53)* Health insurance No 1 1 Yes 0.81(0.76–0.86)** 1.03(0.81–1.30) Currently working/Occupation No 1 1 Yes 0.99(0.96–1.04) Parity No 1 1 1 1.20(1.13–1.26)** 1.01(0.91–1.12) 2+ 1.28(1.13–1.44)** 1.05(0.89–1.21) Body mass index Underweight 1 1 Normal weight 0.99(0.93–1.04) 1.04(0.91–1.21) Overweight 0.85(0.78–0.94)** 0.91(0.75–1.12) Obese 0.84(0.71–0.98)** 0.57(0.38–0.86)* Type of mosquito net No net 1 1 Treated net 1.09(1.05–1.14)** 1.26(1.15–1.39)* Not treated net 1.28(1.10–1.50)** 1.31(0.9–1.76) ICC - 0.011 Note: **: P < 0.05. *: P < 0.2, boldface with *: P < 0.05 Discussion This study aimed to investigate the magnitude and determinants of anemia among adolescent girls in Africa. The analysis utilized both bivariate and multivariable logistic regression to identify relationship between various socio-demographic, economic and health-related factors with the likelihood of anemia. The study investigated that the overall prevalence of anemia among adolescent girls in Africa was 43.6% (95% CI: 42.97, 44.21_). According to the World Health Organization (WHO), anemia is categorized based on prevalence cut-off values that indicate its public health significance. If the prevalence of anemia is below 5%, it is considered not to be a public health problem. A prevalence of 5–19% is classified as a mild public health problem, while a prevalence of 20–39% is considered a moderate public health problem. When the prevalence reaches 40% or higher, anemia is considered a severe public health problem [ 26 ]. Given that the prevalence of anemia among adolescent girls in Africa exceeds this threshold at 43.6%, it is classified as a severe public health problem in the region. The pooled magnitude of anemia in this study was higher than the studies conducted in Indonesia (14.3%) [ 27 ], India (20%)[ 12 ], Nepal (31%)[ 28 ], Ethiopia (23%) [ 8 ], Kenya (28.9%)[ 29 ], but lower than in Pakistan (47.9%) [ 30 ], Tanzania (53.3%) [ 9 ] and Ghana (50.3%) [ 31 ]. Even within the same study, the magnitude of anemia among adolescent girls varied significantly across different African countries. The prevalence ranged from 14.7% in Rwanda to 72% in Gabon. This wide range in anemia prevalence reflects differences in factors such as religion, cultural and dietary practices, access to iron-rich foods, socioeconomic conditions, health system and the effectiveness of public health interventions in these countries [ 32 – 34 ]. Furthermore, out of the 24 African countries included in this study, 16 of them (66.7%) had a prevalence of anemia above 40% among adolescent girls is alarming. This indicates that in the majority of African countries, the magnitude of anemia among this population is a substantial public health problem that requires urgent attention. Addressing this public health challenge will require a comprehensive, multi-faceted approach tailored to the specific needs and contexts of each country. Interventions may include improving dietary diversity and nutrient intake, implementing supplementation programs, provide health education and enhancing access to healthcare services to adolescent girls [ 35 , 36 ]. Our multivariable analysis found that adolescents’ educational level, wealth status, being pregnant, contraceptive use, nutritional status and use of mosquito net were statistically significant with anemia. Education emerged as a crucial protective factor against anemia. Adolescent girls with primary and secondary education levels showed significantly lower odds of anemia compared to those with no education which was supported by other studies done in sub-Sahara Africa [ 13 ], in west and central Africa [ 37 ] and Ethiopia [ 15 ]. This finding indicates that education is crucial in enhancing health literacy, nutritional awareness, and overall health practices, which can considerably lower the risk of anemia. Education likely equips adolescents with the knowledge and resources needed to make health decisions, leading to improved nutritional choices and proactive health-seeking behaviors [ 38 , 39 ]. Unsurprisingly and similar with other study findings [ 40 , 41 ], our study found that, adolescents from rich households had also lower odds of anemia compared to those from poor households. This suggests that economic resources improve access to nutrition and healthcare services, both of which are essential for preventing anemia. Families with higher incomes can afford more diverse, nutritious diets, healthcare services, and supplements, all of which help to prevent anemia. Expectedly, and align with previous studies [ 42 , 43 ], this study found that, Pregnancy was significantly associated with higher odds of anemia. This association underscores the heightened nutritional demands placed on pregnant adolescents, without effective intervention, can increase the risk of anemia [ 44 ]. The physiological changes during pregnancy, such as a surge in blood volume and a substantial increase in iron requirements, make adolescent girls particularly susceptible to anemia. Given their growing bodies and developmental needs, the nutritional demands are even more acute. Anemia during adolescent pregnancy can lead to several adverse outcomes, including increased risks of preterm delivery, low birth weight, preeclampsia, unsafe abortion, mental-health related effects and maternal and child morbidity and mortality [ 45 ]. Therefore, it is crucial to implement targeted interventions to prevent adolescent pregnancy and, if it occurs, to provide adequate nutritional support and prenatal care for pregnant adolescents. This study revealed that the use of contraceptives was significantly associated with lower odds of anemia which was supported by other studies [ 46 , 47 ]. This could be justified by the fact that adolescents who use contraceptives can prevent complications related to early pregnancy and childbirth, which may reduce the prevalence of anemia caused by recurrent blood loss. Another plausible justification is that the use of contraceptives can minimize menstrual bleeding and decrease their susceptibility to anemia [ 48 ]. Adolescent girls who were obese showed significantly lower odds of anemia compared to those who were underweight. Our result was consistent with the study conducted in Pakistan [ 49 ], in Bangladesh [ 50 ] and in Ethiopia [ 15 ]. This suggests that excessive body weight, potentially linked to better micronutrient status, may confer some protection against anemia. On the other hand, underweighted adolescents have more chances to be deficient in micronutrients which may result in an increased risk of anemia. However, it's important to note that while obesity might reduce the risk of anemia, it is not an indicator of overall health and poses other health risks. Nevertheless, studies done in Korea [ 51 ] and in Morocco[ 52 ] reported a higher probability of anemia among obese adolescents. The possible explanation might be obesity is often associated with chronic inflammation, which can affect iron metabolism. Inflammatory cytokines can inhibit iron absorption and utilization, leading to anemia, despite adequate or elevated iron stores. Obese individuals may have diets that are high in calories but low in essential nutrients, including iron [ 53 ]. Furthermore, obese individuals have a higher blood volume, which may dilute the concentration of hemoglobin and ferritin, potentially masking the presence of anemia when only hemoglobin levels are measured. The relationship between obesity and anemia among adolescents is multifaceted. It underscores the importance of considering various factors, including micronutrient status, dietary quality, and inflammation, when evaluating the risk of anemia. Further research is needed to clarify these associations and inform public health strategies to improve the nutritional status of adolescent girls. Contrary to other studies suggesting a link between the use of treated mosquito nets and reduced anemia in children under five years old [ 54 , 55 ], this findings show that the use of treated mosquito nets among adolescents (15–19 years old) was associated with higher odds of anemia. This unexpected result indicates that while mosquito nets are effective in preventing malaria, their usage alone may not directly reduce the risk of anemia among adolescents. The observed association could be influenced by other unmeasured factors, such as net usage patterns among adolescents. For instance, adolescent girls experience menstruation, which leads to blood loss and makes them more susceptible to iron-deficiency anemia. Additionally, adolescent girls have increased nutritional needs to support their growth and reproductive health. Furthermore, adolescents might not use mosquito nets as consistently or correctly as needed, possibly due to changes in lifestyle, increased independence, or a lack of perceived risk, thereby reducing the nets' effectiveness in preventing malaria and, consequently, anemia. This study suggests developing targeted education programs to ensure adolescents use mosquito nets correctly. This includes training on proper hanging techniques, consistent usage, and care and maintenance of the nets to maximize their effectiveness. This study has some limitations to consider. First, due to the limited variables available in this secondary data, we did not include all predictors associated with adolescent anemia, such as dietary patterns and food security. Second, the use of data from a cross-sectional study limits our ability to infer causality between the predictors and the outcome variable (anemia). Despite these limitations, our study included important proxy variables for food security, such as wealth status, as well as other sociodemographic and economic factors. Using a continentally representative large sample, the results from this analysis may be generalized to African adolescent girls aged 15–19 years. Conclusion The study has shown that anemia among adolescent girls in Africa poses a significant public health concern, with a prevalence of 43.6%. Factors such as higher levels of education and wealth, as well as the use of contraceptives and obesity, were found to decrease the likelihood of being anemic. Conversely, being pregnant and using treated mosquito nets were associated with increased odds of anemia among adolescent girls. It is recommended that efforts be made to promote school attendance for girls, encourage contraceptive use among married or sexually active adolescents, prevent adolescent marriage and pregnancy, educate on the proper use of mosquito nets, and provide nutritional support in order to effectively combat adolescent anemia. Declarations Competing Interests The authors have declared that no competing interests exist. Human Ethics and Consent to Participate declarations not applicable. Author Contribution Conceptualization: Fentanesh Nibret TirunehData curation: Bedilu Alamirie EjiguFormal analysis: Bedilu Alamirie Ejigu Methodology: Bedilu Alamirie Ejigu, Fentanesh Nibret TirunehValidation: Bedilu Alamirie Ejigu, Fentanesh Nibret TirunehWriting – original draft: Fentanesh Nibret Tiruneh Writing – review & editing: Fentanesh Nibret Tiruneh, Bedilu Alamirie EjiguBoth authors read and approved the final manuscript. Data Availability The data we used in this study were obtained from the DHS program (www.dhsprogram.com), but the `Dataset Terms of Use' do not permit us to distribute this data as per data access instructions (http://dhsprogram.com/data/Access-Instructions.cfm). Interested scholars will have access to the relevant data used in this study in the same manner as it was accessed by the authors of this study from the aforementioned website. Funding Declaration This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. References WHO. Anaemia. 2024. Lancet T. The Lancet: New study reveals global anemia cases remain persistently high among women and children. Anemia rates decline for men. 2023. Collaborators GA. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(9):e713–34. Bahrami A, et al. Anemia is associated with cognitive impairment in adolescent girls: A cross-sectional survey. Appl Neuropsychology: Child. 2020;9(2):165–71. Soman SK, et al. Adolescent anaemia its prevalence and determinants: a cross-sectional study from south Kerala, India. Int J Community Med public health. 2017;4(8):2750–6. Sari P, et al. Iron Deficiency Anemia and Associated Factors Among Adolescent Girls and Women in a Rural Area of Jatinangor, Indonesia. 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Ekasanti I, et al. Determinants of anemia among early adolescent girls in Kendari City. Amerta Nutr. 2020;4(4):271. Habib N, Abbasi S-U-RS, Aziz W. An analysis of societal determinant of anemia among adolescent girls in Azad Jammu and Kashmir, Pakistan. Anemia. 2020;2020(1):1628357. Tandoh MA, Appiah AO, Edusei AK. Prevalence of anemia and undernutrition of adolescent females in selected schools in Ghana. J Nutr Metabolism. 2021;2021(1):6684839. Alonso EB. The impact of culture, religion and traditional knowledge on food and nutrition security in developing countries. 2015. Chouraqui J-P, et al. Religious dietary rules and their potential nutritional and health consequences. Int J Epidemiol. 2021;50(1):12–26. Mildon A, et al. Integrating and coordinating programs for the management of anemia across the life course. Volume 1525. Annals of the New York Academy of Sciences; 2023. pp. 160–72. 1. Rohmatika D et al. Education and Reminder Software for Strength-ening Anemia Prevention Program in Adolescent Girls . in ISPHE 2020: Proceedings of the 5th International Seminar of Public Health and Education . 2020. Aisah S, Ismail S, Margawati A. Animated educational video using health belief model on the knowledge of anemia prevention among female adolescents: An intervention study. Malaysian Family Physician: Official J Acad Family Physicians Malaysia. 2022;17(3):97. Sagalova V et al. Socio-economic predictors of undernutrition and anaemia in adolescent mothers in West and Central Africa. J global health, 2021. 11. Krause BL. Networks and knowledge: Women's empowerment, networks, and health information-seeking behavior in rural Guatemala. J Rural Stud. 2024;105:103169. Riddle AY, et al. Associations between dimensions of empowerment and nutritional status among married adolescent girls in East Africa: a structural equation modelling study. BMC Public Health. 2023;23(1):225. Zhu Z, et al. Anemia and associated factors among adolescent girls and boys at 10–14 years in rural western China. BMC Public Health. 2021;21:1–14. Sales CH, et al. Prevalence and factors associated with iron deficiency and anemia among residents of urban areas of São Paulo, Brazil. Nutrients. 2021;13(6):1888. Gueye M, et al. Neonatal complications of teenage pregnancies: prospective study about 209 Cases in Senegal. Am Jf Pediatr. 2020;6(4):504–7. Pons-Duran C, et al. Adolescent, pregnant, and HIV-infected: risk of adverse pregnancy and perinatal outcomes in young women from Southern Mozambique. J Clin Med. 2021;10(8):1564. Adam I, Ali AA. Anemia Dur pregnancy Nutritional Defic. 2016;978:953–51. Lambonmung A, Acheampong CA, Langkulsen U. The effects of pregnancy: a systematic review of adolescent pregnancy in Ghana, Liberia, and Nigeria. Int J Environ Res Public Health. 2022;20(1):605. Misunas C, et al. The Association Between Hormonal Contraceptive Use and Anemia Among Adolescent Girls and Young Women: An Analysis of Data From 51 Low-and Middle-Income Countries. J Adolesc Health. 2024;74(3):563–72. Worku MG, Tesema GA, Teshale AB. Prevalence and determinants of anemia among young (15–24 years) women in Ethiopia: A multilevel analysis of the 2016 Ethiopian demographic and health survey data. PLoS ONE. 2020;15(10):e0241342. Haile ZT, Teweldeberhan AK, Chertok IR. Association between oral contraceptive use and markers of iron deficiency in a cross-sectional study of Tanzanian women. Int J Gynecol Obstet. 2016;132(1):50–4. Khan UH, et al. Prevalence of Nutritional Anaemia with Association of Body Mass Index among Karachi University students, Pakistan. Volume 71. JOURNAL OF THE PAKISTAN MEDICAL ASSOCIATION; 2021. pp. 55–8. 1. Kamruzzaman M. Is BMI associated with anemia and hemoglobin level of women and children in Bangladesh: A study with multiple statistical approaches. PLoS ONE. 2021;16(10):e0259116. Jeong J, et al. Association between obesity and anemia in a nationally representative sample of South Korean adolescents: a cross-sectional study. Healthcare. MDPI; 2022. Mehdad S et al. Association between overweight and anemia in Moroccan adolescents: a cross-sectional study. Pan Afr Med J, 2022. 41(1). Harding KL, Aguayo VM, Webb P. Hidden hunger in South Asia: a review of recent trends and persistent challenges. Public Health Nutr. 2018;21(4):785–95. Mwaiswelo RO, et al. Malaria infection and anemia status in under-five children from Southern Tanzania where seasonal malaria chemoprevention is being implemented. PLoS ONE. 2021;16(12):e0260785. Adugna DG, et al. Prevalence and determinants of anemia among children aged from 6 to 59 months in Liberia: a multilevel analysis of the 2019/20 Liberia demographic and health survey data. Front Pead. 2023;11:1152083. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5004469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352025641,"identity":"482c1509-4e0d-4e04-b11f-92fc158e0d0c","order_by":0,"name":"Fentanesh Nibret Tiruneh","email":"","orcid":"","institution":"Bahir Dar Institute of Technology, Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Fentanesh","middleName":"Nibret","lastName":"Tiruneh","suffix":""},{"id":352025642,"identity":"8d07cd91-3847-4253-870a-f5c68cafd048","order_by":1,"name":"Bedilu Alamirie Ejigu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACAyA68ADI4APiA0CuHEScjYCWBKgakBZjhBYc2kBaGBKQ5BMbCGkxZ2/eeCCxjUGejf104uGKApv0/vYzBgwfyg4z8Ms3YNVi2XOsAKTFsI0nd8PBMwZpuTPO5Bgwzjh3mEGyDYfDbuQYgLQwtjEAtTQYHM5tOJBjwMzbdpjB4BgOLfffgLXYt/G/BWn5ny5//o0B81+gFntcWm7wgLUktkmAbTmQALKXmRFkCw7vW/akFRxIOCeR3CYBtiXZcOONZwUHe86l80gcS8ARYoc3f/hQZmPbz5+7+WPDHzt5ufPJGx/8KLOW428+gN0aCJBA5YLU8uBTPwpGwSgYBaMAPwAA8DJj0o21i9IAAAAASUVORK5CYII=","orcid":"","institution":"Department of Statistics, Addis Ababa University, Addis Ababa","correspondingAuthor":true,"prefix":"","firstName":"Bedilu","middleName":"Alamirie","lastName":"Ejigu","suffix":""}],"badges":[],"createdAt":"2024-08-30 14:10:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5004469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5004469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66061277,"identity":"8cc09577-54db-4b56-b007-d316e7e32e81","added_by":"auto","created_at":"2024-10-07 10:13:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485351,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of anemia among adolescent girls in Africa.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5004469/v1/0d0482f4259217596e25542c.png"},{"id":91500028,"identity":"e17233ce-ddb4-4326-a31f-c105dd825ce6","added_by":"auto","created_at":"2025-09-17 07:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5004469/v1/7982198d-f932-487f-bda2-fd929bb99cc1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnitude and Determinates of Anemia among adolescent Girls in Africa: A Multilevel, Multicounty Analysis of 24 Countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnemia is a condition in which the number of red blood cells or hemoglobin is lower than normal, reducing the blood's capacity to carry oxygen. It can result from several factors: nutrient deficiencies or poor nutrient absorption, infections (e.g., malaria, parasitic infections, tuberculosis, HIV), inflammation, chronic diseases, gynecological and obstetric conditions, and inherited red blood cell disorders. The most common cause of anemia is iron deficiency, which can occur due to inadequate dietary intake, poor iron absorption, or increased needs during growth spurts and menstruation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApproximately one-quarter of the global population is estimated to be anemic, with the prevalence notably higher among women, expectant mothers, adolescent girls, and children under the age of five. Anemia is the third-leading cause of years lived with disability (YLDs) globally[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Between 1990 and 2021, the evidence indicates a global trend towards less severe anemia. The most significant reductions were observed in adult males; however progress has been slower among women of reproductive age including adolescent girls aged 15\u0026ndash;19 and children under the age of five. In 2021, sub-Saharan Africa and South Asia are facing the greatest burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescence is a period of transition from childhood to adulthood, characterized by rapid physical growth and increased nutritional needs. Among adolescents aged 10\u0026ndash;19, especially in low- and middle-income countries, anemia is a significant issue. During this stage, the body undergoes rapid growth and development, increasing nutritional demands, including for iron. Iron deficiency anemia adversely affects the educational and economic well-being of adolescents. It has been linked to stunting, wasting, being underweight, poor cognitive function, reduced physical activity, and attention deficit hyperactivity disorder in adolescents [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe onset of menstruation further raises the risk of anemia due to additional iron loss among adolescent girls. Adolescent girls are vulnerable especially to iron deficiency due to accelerated increase in requirements for iron, poor dietary intake of iron, menstrual losses, infection, norm of early marriage, and adolescent pregnancy. Adolescents with anemia are more likely to experience anemia during pregnancy, which can lead to various adverse effects on both pregnancy and childbirth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescent girls in Africa are particularly vulnerable to anemia due to a combination of factors. Inadequate diets and poor absorption of iron, compounded by high rates of infectious diseases like malaria and parasitic infections, poor hygiene management, contribute significantly. Limited access to healthcare and education, along with poverty and gender inequality, exacerbate the issue. Rapid growth during adolescence increases nutritional needs, which are difficult to meet in regions with limited resources [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have identified factors significantly associated with anemia among adolescent girls including the respondent's age, marital status, educational level, nutritional status and wealth status [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, early marriage and pregnancy, which are prevalent in many African countries, are linked to a range of social, physical, and health problems, including anemia among adolescents. Early marriage often leads to early pregnancy, which increases the risk of anemia due to higher iron requirements and inadequate nutritional intake. Moreover, adolescents who marry early may face limited access to healthcare, education, and economic opportunities, further exacerbating their vulnerability to anemia. These factors contribute to a cycle of poor health and socio-economic disadvantage that can have long-lasting effects on their overall well-being and future prospects [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite global efforts to mitigate anemia, progress has been slower among adolescent girls, especially in low- and middle-income countries. The current trends in anemia make it challenging to achieve the Sustainable Development Goal (SDG) of reducing anemia among women of reproductive age by 50% by 2030 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Addressing all forms of malnutrition is a key target of the SDGs. By effectively reducing malnutrition, the prevalence of anemia can also be lowered, thereby contributing to the successful achievement of the SDGs by 2030. To meet this ambitious goal, comprehensive strategies must be implemented including assessment the magnitude and identify the determinants associated with anemia among adolescent girls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile studies have estimating the magnitude and determinants of anemia among women of reproductive age, less attention has been given to adolescent girls and multicounty analyses are rare. This study aims to provide a comprehensive understanding of the magnitude and determinants of anemia among adolescent girls in Africa by analysing data from 24 countries. These insights are crucial for developing targeted and effective policies and interventions to improve health outcomes and overall well-being for adolescent girls across the continent. This study will help identify which regions or countries need the most urgent attention and which determinants are most critical to address. A multicounty analysis will fill existing data gaps and highlight regional differences, allowing for tailored interventions that meet local needs and challenges. This comprehensive view will ensure that resources are allocated where they are needed most.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study participants\u003c/h2\u003e \u003cp\u003eData from the most recent demographic and health surveys (DHSs) from 24 African countries between 2016 and 2022 was used for this study. The DHS implements a stratified two-stage cluster sampling design which gives nationally representative samples. The sampling design involved randomly selected communities (clusters) at the first stage and households at the second stage. Further details on the implementation of DHS survey were available in other sources [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In total, 182,683 women age 15\u0026ndash;49 years were agreed and their hemoglobin level measured. Anemia testing was performed after getting consent from the participants. Among reproductive-age women, only 38,966 were adolescents (15\u0026ndash;19 years). Thus, in this study our analytical sample was limited to 38,966 female adolescents (15\u0026ndash;19 years).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eThe outcome variable was the anemia status among adolescent girls which was dichotomized into two categories: non-anemic, coded as \u0026ldquo;0,\u0026rdquo; and anemic, coded as \u0026ldquo;1\u0026rdquo; (this category includes mild, moderate, and severe anemia). The anemia status was determined using the women\u0026rsquo;s hemoglobin levels, measured according to the cut-off points defined by the World Health Organization (WHO) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePredictor variables\u003c/h2\u003e \u003cp\u003eThe variables included in this analysis was marital status, age at first marriage, educational level, employment status, place of residence, nutritional status, pregnancy status, history of childbearing/parity, contraceptive use, use of mosquito nets, and health insurance coverage. The wealth index was constructed based on household ownership of selected assets, such as televisions and materials used for constructing the house, Source of drinking water, type of toilet facility etc. Household asset factor scores were generated using principal component analysis, then standardized and categorized into quintiles (poorer, poor, middle, rich, and richer) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] which later grouped in two three categories (poor, middle, and rich) in this analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe complexity of analyzing DHS survey datasets can be attributed to two main factors: i) the use of stratified multistage cluster sampling and, ii) the unequal probabilities of selection from target populations for sampled elements, often due to oversampling of key subgroups. To address this complexity, sampling weights were used during the estimation of proportions and their respective confidence intervals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Descriptive analysis involved computing the socio-demographic characteristics of adolescent girls, presented in frequencies and percentages (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, weighted percentages of anemia by different factors were computed (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven that adolescent girls living in the same cluster may have similar anemia status, a modeling approach that accounts for the correlated nature of the data within the cluster is necessary. This involves introducing sampling clusters as random effects to accommodate cluster heterogeneity and unobserved covariates within clusters. A multilevel logistic regression model was employed to investigate the association between adolescent girls\u0026rsquo; anemia status and demographic factors, with survey clusters treated as a random effect whilst adjusting for various covariates. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e denote the binary outcome for adolescent girls i in cluster j, and assume \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e follows a Bernoulli distribution with probability of success, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{ij}\\)\u003c/span\u003e\u003c/span\u003e. Then, using the usual logit link function, a binary outcome can be associated with a linear predictor as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(\\frac{{p}_{ij}}{1-{p}_{ij}}\\right)=logit\\left({p}_{ij}\\right)={x}_{ij}^{{\\prime\\:}}\\beta\\:+{b}_{j},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[23\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e is a vector different covariate presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e with associated regression coefficients\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e. The random-effect component of the model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{j},\\)\u003c/span\u003e\u003c/span\u003e are mutually independent Gaussian random effects used to capture within-cluster correlation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe model utilized a Bernoulli probability distribution to represent adolescent woman\u0026rsquo;s anemia level with the linear predictor assuming a specific form (Eq.\u0026nbsp;1). The random-effect component of the model captured within-cluster correlation using mutually independent Gaussian random effects with mean 0 and variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{b}^{2}\\)\u003c/span\u003e\u003c/span\u003e. The fixed effects parameter in the model had a conditional interpretation, reflecting the consequence of changing the value of an explanatory variable for a given value of the random effect and the other fixed effects [A]. Both descriptive and inferential data analyses were done using Stata 18.0 software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, for statistical significance, the considered type-I error alpha value is 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003eThis study exclusively involved secondary analysis of existing DHS data. The data collection procedures for the DHS adhered to all relevant guidelines and regulations. Ethical approval for the surveys was obtained from the Institutional Review Boards of each respective country. For adolescent girls under the age of 18, parental or guardian permission was required prior to participation in the study. The authors obtained authorization from the DHS program to use the data: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/data/available-datasets.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/data/available-datasets.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socio demographic, economic and health characteristics of 38,966 adolescent girls sampled from various regions in Africa. The majority of the adolescents (60.1%) reside in rural areas. Regarding marital status, about (79.9%) adolescents were single/never married and 21.6% were been married. Among those who have been married, a larger proportion (76.8%) of adolescents married between the ages of 15\u0026ndash;19, while 23.2% married before the age of 15.\u003c/p\u003e \u003cp\u003eNearly half of the adolescents (48.9%) had attained secondary education, while 35.7% had completed primary education. Around 14.4% has no education, and a smaller fraction had higher education (1.0%). In terms of wealth distribution, about 45.6% were classified as rich, 34.1% as poor, and 20.3% as middle-income. About 67.9% of the adolescents were not working, while 32.1% were engaged in some form of employment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbout 6.1% adolescents were pregnant during the data collection period. A large proportion of the adolescents (89.6%) do not use contraceptives. About 15.3% adolescents had one child, and 2.7% have two or more children.\u003c/p\u003e \u003cp\u003eMost adolescents (88.5%) do not have health insurance. The majority of adolescents had a normal weight (72.0%), while 17.6% were underweight, 8.4% were overweight, and 2.1% were obese. Most adolescents (65.7%) do not use mosquito nets, while 34.0% used treated nets, and 1.8% use untreated nets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampled adolescents characteristics in Africa (N\u0026thinsp;=\u0026thinsp;38,966)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePredictors/factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNever married/Single\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEver Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge at first Marriage (n\u0026thinsp;=\u0026thinsp;8,174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUse of contraceptive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eType of mosquito net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003etreated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUntreated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAnemia status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNot anemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAnemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the percentage of anemia among adolescent girls in various African countries. Several countries show alarmingly high rates of anemia among adolescent girls. For instance, Gabon has the highest prevalence at 72.0%, followed by Mali with 65.4%, Ivory Coast with 62.2%, Nigeria with 60.5%, Burkina Faso with 57.6%, Liberia with 55.1%, Mozambique with 54.9%, and Togo with 54.7%.\u003c/p\u003e \u003cp\u003eThe total data aggregated from all countries show that out of 38,966 adolescent girls 16, 986 (43.6% 95% CI:42.97_, 44.21) adolescent girls were anemic. The prevalence of anemia ranged from 14.7% in Rwanda to 72% in Gabon (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMagnitude of Anemia among Adolescent Girls in Africa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Anemic n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnemic n(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1136 (57.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurundi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1235 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemocratic Republic of the Congo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1239 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e830 (40.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIvory Coast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e586 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e966 (62.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCameroon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e966 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2574 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e641 (19.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGabon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e738 (72.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e778 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e608 (43.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e739 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e568 (43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquatorial Guinea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e683 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e609 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiberia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesotho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e645 (65.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalawi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1126 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e616 (35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMozambique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1386 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1688 (54.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1088 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1665 (60.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393 (46.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1403 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242 (14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSierra Leone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e787 (49.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTogo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484 (54.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e877 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e718 (45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e932 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (33.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1963 (66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e986 (33.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21980 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16986 (43.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the prevalence of anemia among adolescent girls across various African countries. Countries with a prevalence higher than the overall average (43.6%) are marked in red, while those below the average are marked in green. The highest prevalence was observed in Central and West African countries, including Gabon, Mali, Ivory Coast, and Nigeria. In contrast, lower prevalence was found in East and East-Central African countries, as well as in Southern African countries, including Rwanda, Ethiopia, Lesotho, and South Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a chi-square analysis of the distribution of anemia among African adolescent girls by various predictor variables. The prevalence of anemia is slightly higher among urban residents, with 44.4% being anemic compared to 43.2% of rural residents. Adolescents who had ever been married show a higher prevalence of anemia (48.7%) compared to their never married/single counterparts (42.3%). The prevalence of anemia is higher among girls who married before the age of 15 (50.7%) compared to those who married between 15\u0026ndash;19 years (48.2%).\u003c/p\u003e \u003cp\u003eEducational attainment shows a strong inverse relationship with anemia prevalence. Adolescents with no education had the highest prevalence of anemia (54.2%), followed by those with primary education (40.2%), secondary education (43.2%), and higher education (37.3%). This indicates that higher education levels are protective against anemia. Wealthier adolescents had a lower prevalence of anemia. The prevalence was highest among poor girls (46.4%), followed by those from middle-income households (44.7%), and was lowest among rich girls (41.0%). Employment status does not significantly affect anemia prevalence, with both working (43.7%) and non-working (43.7%) girls showing the same prevalence rates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePregnant adolescents had a significantly higher prevalence of anemia (54.9%) compared to non-pregnant girls (43.0%). Adolescents who used contraceptives have a lower prevalence of anemia (38.3%) compared to those who do not use contraceptives (44.3%). The number of children a girl has given birth to is associated with increased prevalence of anemia. Adolescent girls with no children have a prevalence of 42.8%, those with one child have a prevalence of 47.3%, and those with two or more children have the highest prevalence at 49.0%.\u003c/p\u003e \u003cp\u003eThe prevalence of anemia was lower among girls with health insurance (39.1%) compared to those without it (44.3%). The prevalence of anemia was highest among adolescents using untreated mosquito nets (49.0%), followed by those using treated nets (45.1%), and those not using any net (42.8%). Higher BMI was associated with a lower prevalence of anemia. Underweight girls have an anemia prevalence of 45.2%, normal weight girls have 45.0%, overweight girls have 41.5%, and obese girls have the lowest prevalence at 40.9%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA chi-square analysis of magnitude of anemia by predictor variables among African adolescent girls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot anemic frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnemic frequency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8581(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6852(44.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13359(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10174(43.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNever married/Single\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17687 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12981(42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEver Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4253 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4045 (48.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge at first Marriage (n\u0026thinsp;=\u0026thinsp;8,298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e959 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e985 (50.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3294 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3060 (48.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2587(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3059(54.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8252(59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5556(40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10891(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8286(43.2 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210(62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125(37.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7632 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6601(46.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4336 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3498 (44.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9972 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6927 (41.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20864 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15714 (43.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1076 (45.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1312 (54.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUse of contraceptive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19427 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15467 (44.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2513 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1559 (38.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19165 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15240 (44.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2775 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1785 (39.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently working/Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14938 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11591 (43.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6991 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5428 (43.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18199 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13637 (42.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3180 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2851 (47.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e538 (49.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBody Mass Index (BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3499 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2889 (45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14190 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11592 (45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1780 (58.5))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1263 (41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287 (40.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eType of mosquito net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14418 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10804 (42.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTreated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7172 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5886 (45.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNot treated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336 (49.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote: all predictor variables except employment status were statistically significant with anemia\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the binary logistic regression analysis of various factors associated with anemia among adolescent girls in Africa. The results are shown in terms of Crude Odds Ratios (COR) and Adjusted Odds Ratios (AOR), along with their 95% Confidence Intervals (CI).\u003c/p\u003e \u003cp\u003eAdolescent girls living in rural areas had a slightly lower odds of anemia compared to those in urban areas (COR: 0.950, 95% CI: 0.91\u0026ndash;0.99). However, this association is not significant after adjusting for other factors (AOR: 0.88, 95% CI: 0.78\u0026ndash;1.01). Adolescent girls who had ever married showed higher odds of anemia compared to their never-married/single counterparts (COR: 1.298, 95% CI: 1.34\u0026ndash;1.36). However, this variable was not included in the multivariable analysis due to convergence issues. Adolescents who married at ages 15\u0026ndash;19 show slightly lower odds of anemia compared to those married before age 15 (COR: 0.90, 95% CI: 0.81-1.00), but this difference is not significant after adjustment of other factors (AOR: 0.99, 95% CI: 0.88\u0026ndash;1.11).\u003c/p\u003e \u003cp\u003eEducation appears protective against anemia. Compared to adolescents with no education, those with primary (AOR: 0.72, 95% CI: 0.5\u0026ndash;0.80), secondary (AOR: 0.76, 95% CI: 0.66\u0026ndash;0.86), and higher education (AOR: 0.61, 95% CI: 0.21\u0026ndash;1.75) had lower odds of anemia. Adolescents from rich households have significantly lower odds of anemia compared to those from poor households (AOR: 0.86, 95% CI: 0.76\u0026ndash;0.98).\u003c/p\u003e \u003cp\u003ePregnancy was associated with higher odds of anemia (AOR: 1.13, 95% CI: 1.01\u0026ndash;1.27) compared to those who were not pregnant. Using contraceptives linked to significantly lower odds of anemia (AOR: 0.46, 95% CI: 0.40\u0026ndash;0.53). Adolescents who had given birth to one child have higher odds of anemia compared to those with no children (COR: 1.20, 95% CI: 1.13\u0026ndash;1.26). Similarly, adolescents with two or more children also have higher odds of anemia (COR: 1.28, 95% CI: 1.13\u0026ndash;1.44). However, when adjusted for other factors, these associations are no longer statistically significant.\u003c/p\u003e \u003cp\u003eHaving health insurance shows no significant association with anemia (AOR: 1.03, 95% CI: 0.81\u0026ndash;1.30). Regarding to nutritional status, adolescents who were obese had significantly lower odds of anemia compared to those who were underweight (AOR: 0.57, 95% CI: 0.38\u0026ndash;0.86). Using a treated mosquito net was associated with higher odds of anemia (AOR: 1.26, 95% CI: 1.15\u0026ndash;1.39).\u003c/p\u003e \u003cp\u003eThe multivariable analysis revealed that 1.14% of the variation in anemia among adolescent girls was due to differences at the cluster/community level (ICC\u0026thinsp;=\u0026thinsp;0.014, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that about 1.14% of anemia cases may be attributable to other unobserved community-level determinants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariable analysis to determine anemia among adolescent girls in Africa.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950 (0.91\u0026ndash;0.99)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88(0.78\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNever married/Single\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEver Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.298(1.34\u0026ndash;1.36)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge at first Marriage (n\u0026thinsp;=\u0026thinsp;8,298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.81\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.88\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57(0.53\u0026ndash;0.60)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.72(0.5\u0026ndash;0.80)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64(0.60\u0026ndash;0.68)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.76(0.66\u0026ndash;0.86)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50(0.40\u0026ndash;0.64)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61(0.21\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.88\u0026ndash;0.99)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.85\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.77\u0026ndash;0.84)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86(0.76\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.617(1.49\u0026ndash;1.76)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.13(1.00-1.27)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUse of contraceptive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78(0.72\u0026ndash;0.83)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.46(0.40\u0026ndash;0.53)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81(0.76\u0026ndash;0.86)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03(0.81\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCurrently working/Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.96\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20(1.13\u0026ndash;1.26)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01(0.91\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28(1.13\u0026ndash;1.44)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05(0.89\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.93\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04(0.91\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85(0.78\u0026ndash;0.94)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91(0.75\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.71\u0026ndash;0.98)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.57(0.38\u0026ndash;0.86)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eType of mosquito net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTreated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09(1.05\u0026ndash;1.14)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.26(1.15\u0026ndash;1.39)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNot treated net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28(1.10\u0026ndash;1.50)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31(0.9\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003cp\u003eNote: **: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. *: P\u0026thinsp;\u0026lt;\u0026thinsp;0.2, boldface with *: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate the magnitude and determinants of anemia among adolescent girls in Africa. The analysis utilized both bivariate and multivariable logistic regression to identify relationship between various socio-demographic, economic and health-related factors with the likelihood of anemia. The study investigated that the overall prevalence of anemia among adolescent girls in Africa was 43.6% (95% CI: 42.97, 44.21_). According to the World Health Organization (WHO), anemia is categorized based on prevalence cut-off values that indicate its public health significance. If the prevalence of anemia is below 5%, it is considered not to be a public health problem. A prevalence of 5\u0026ndash;19% is classified as a mild public health problem, while a prevalence of 20\u0026ndash;39% is considered a moderate public health problem. When the prevalence reaches 40% or higher, anemia is considered a severe public health problem [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given that the prevalence of anemia among adolescent girls in Africa exceeds this threshold at 43.6%, it is classified as a severe public health problem in the region.\u003c/p\u003e \u003cp\u003eThe pooled magnitude of anemia in this study was higher than the studies conducted in Indonesia\u003c/p\u003e \u003cp\u003e(14.3%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], India (20%)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Nepal (31%)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Ethiopia (23%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Kenya (28.9%)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], but lower than in Pakistan (47.9%) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Tanzania (53.3%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Ghana (50.3%) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Even within the same study, the magnitude of anemia among adolescent girls varied significantly across different African countries. The prevalence ranged from 14.7% in Rwanda to 72% in Gabon. This wide range in anemia prevalence reflects differences in factors such as religion, cultural and dietary practices, access to iron-rich foods, socioeconomic conditions, health system and the effectiveness of public health interventions in these countries [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, out of the 24 African countries included in this study, 16 of them (66.7%) had a prevalence of anemia above 40% among adolescent girls is alarming. This indicates that in the majority of African countries, the magnitude of anemia among this population is a substantial public health problem that requires urgent attention. Addressing this public health challenge will require a comprehensive, multi-faceted approach tailored to the specific needs and contexts of each country. Interventions may include improving dietary diversity and nutrient intake, implementing supplementation programs, provide health education and enhancing access to healthcare services to adolescent girls [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur multivariable analysis found that adolescents\u0026rsquo; educational level, wealth status, being pregnant, contraceptive use, nutritional status and use of mosquito net were statistically significant with anemia. Education emerged as a crucial protective factor against anemia. Adolescent girls with primary and secondary education levels showed significantly lower odds of anemia compared to those with no education which was supported by other studies done in sub-Sahara Africa [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], in west and central Africa [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and Ethiopia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This finding indicates that education is crucial in enhancing health literacy, nutritional awareness, and overall health practices, which can considerably lower the risk of anemia. Education likely equips adolescents with the knowledge and resources needed to make health decisions, leading to improved nutritional choices and proactive health-seeking behaviors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Unsurprisingly and similar with other study findings [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], our study found that, adolescents from rich households had also lower odds of anemia compared to those from poor households. This suggests that economic resources improve access to nutrition and healthcare services, both of which are essential for preventing anemia. Families with higher incomes can afford more diverse, nutritious diets, healthcare services, and supplements, all of which help to prevent anemia.\u003c/p\u003e \u003cp\u003eExpectedly, and align with previous studies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], this study found that, Pregnancy was significantly associated with higher odds of anemia. This association underscores the heightened nutritional demands placed on pregnant adolescents, without effective intervention, can increase the risk of anemia [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The physiological changes during pregnancy, such as a surge in blood volume and a substantial increase in iron requirements, make adolescent girls particularly susceptible to anemia. Given their growing bodies and developmental needs, the nutritional demands are even more acute. Anemia during adolescent pregnancy can lead to several adverse outcomes, including increased risks of preterm delivery, low birth weight, preeclampsia, unsafe abortion, mental-health related effects and maternal and child morbidity and mortality [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, it is crucial to implement targeted interventions to prevent adolescent pregnancy and, if it occurs, to provide adequate nutritional support and prenatal care for pregnant adolescents.\u003c/p\u003e \u003cp\u003eThis study revealed that the use of contraceptives was significantly associated with lower odds of anemia which was supported by other studies [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This could be justified by the fact that adolescents who use contraceptives can prevent complications related to early pregnancy and childbirth, which may reduce the prevalence of anemia caused by recurrent blood loss. Another plausible justification is that the use of contraceptives can minimize menstrual bleeding and decrease their susceptibility to anemia [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescent girls who were obese showed significantly lower odds of anemia compared to those who were underweight. Our result was consistent with the study conducted in Pakistan [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], in Bangladesh [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and in Ethiopia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This suggests that excessive body weight, potentially linked to better micronutrient status, may confer some protection against anemia. On the other hand, underweighted adolescents have more chances to be deficient in micronutrients which may result in an increased risk of anemia. However, it's important to note that while obesity might reduce the risk of anemia, it is not an indicator of overall health and poses other health risks. Nevertheless, studies done in Korea [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and in Morocco[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] reported a higher probability of anemia among obese adolescents. The possible explanation might be obesity is often associated with chronic inflammation, which can affect iron metabolism. Inflammatory cytokines can inhibit iron absorption and utilization, leading to anemia, despite adequate or elevated iron stores. Obese individuals may have diets that are high in calories but low in essential nutrients, including iron [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Furthermore, obese individuals have a higher blood volume, which may dilute the concentration of hemoglobin and ferritin, potentially masking the presence of anemia when only hemoglobin levels are measured.\u003c/p\u003e \u003cp\u003eThe relationship between obesity and anemia among adolescents is multifaceted. It underscores the importance of considering various factors, including micronutrient status, dietary quality, and inflammation, when evaluating the risk of anemia. Further research is needed to clarify these associations and inform public health strategies to improve the nutritional status of adolescent girls.\u003c/p\u003e \u003cp\u003eContrary to other studies suggesting a link between the use of treated mosquito nets and reduced anemia in children under five years old [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], this findings show that the use of treated mosquito nets among adolescents (15\u0026ndash;19 years old) was associated with higher odds of anemia. This unexpected result indicates that while mosquito nets are effective in preventing malaria, their usage alone may not directly reduce the risk of anemia among adolescents. The observed association could be influenced by other unmeasured factors, such as net usage patterns among adolescents. For instance, adolescent girls experience menstruation, which leads to blood loss and makes them more susceptible to iron-deficiency anemia. Additionally, adolescent girls have increased nutritional needs to support their growth and reproductive health. Furthermore, adolescents might not use mosquito nets as consistently or correctly as needed, possibly due to changes in lifestyle, increased independence, or a lack of perceived risk, thereby reducing the nets' effectiveness in preventing malaria and, consequently, anemia. This study suggests developing targeted education programs to ensure adolescents use mosquito nets correctly. This includes training on proper hanging techniques, consistent usage, and care and maintenance of the nets to maximize their effectiveness.\u003c/p\u003e \u003cp\u003eThis study has some limitations to consider. First, due to the limited variables available in this secondary data, we did not include all predictors associated with adolescent anemia, such as dietary patterns and food security. Second, the use of data from a cross-sectional study limits our ability to infer causality between the predictors and the outcome variable (anemia). Despite these limitations, our study included important proxy variables for food security, such as wealth status, as well as other sociodemographic and economic factors. Using a continentally representative large sample, the results from this analysis may be generalized to African adolescent girls aged 15\u0026ndash;19 years.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study has shown that anemia among adolescent girls in Africa poses a significant public health concern, with a prevalence of 43.6%. Factors such as higher levels of education and wealth, as well as the use of contraceptives and obesity, were found to decrease the likelihood of being anemic. Conversely, being pregnant and using treated mosquito nets were associated with increased odds of anemia among adolescent girls. It is recommended that efforts be made to promote school attendance for girls, encourage contraceptive use among married or sexually active adolescents, prevent adolescent marriage and pregnancy, educate on the proper use of mosquito nets, and provide nutritional support in order to effectively combat adolescent anemia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e \u003ch2\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Fentanesh Nibret TirunehData curation: Bedilu Alamirie EjiguFormal analysis: Bedilu Alamirie Ejigu Methodology: Bedilu Alamirie Ejigu, Fentanesh Nibret TirunehValidation: Bedilu Alamirie Ejigu, Fentanesh Nibret TirunehWriting \u0026ndash; original draft: Fentanesh Nibret Tiruneh Writing \u0026ndash; review \u0026amp; editing: Fentanesh Nibret Tiruneh, Bedilu Alamirie EjiguBoth authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data we used in this study were obtained from the DHS program (www.dhsprogram.com), but the `Dataset Terms of Use' do not permit us to distribute this data as per data access instructions (http://dhsprogram.com/data/Access-Instructions.cfm). Interested scholars will have access to the relevant data used in this study in the same manner as it was accessed by the authors of this study from the aforementioned website.\u003c/p\u003e \u003ch2\u003eFunding Declaration\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Anaemia. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLancet T. \u003cem\u003eThe Lancet: New study reveals global anemia cases remain persistently high among women and children. Anemia rates decline for men.\u003c/em\u003e 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GA. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990\u0026ndash;2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(9):e713\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami A, et al. Anemia is associated with cognitive impairment in adolescent girls: A cross-sectional survey. Appl Neuropsychology: Child. 2020;9(2):165\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoman SK, et al. Adolescent anaemia its prevalence and determinants: a cross-sectional study from south Kerala, India. Int J Community Med public health. 2017;4(8):2750\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSari P, et al. Iron Deficiency Anemia and Associated Factors Among Adolescent Girls and Women in a Rural Area of Jatinangor, Indonesia. International Journal of Women's Health; 2022. pp. 1137\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTura MR et al. Prevalence of anemia and its associated factors among female adolescents in Ambo Town, West Shewa, Ethiopia. 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Front Pead. 2023;11:1152083.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anemia, Adolescent girls, Africa, Health and Nutrition","lastPublishedDoi":"10.21203/rs.3.rs-5004469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5004469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eIntroduction\u003c/b\u003e: Anemia in Africa is a pressing public health issue with far-reaching consequences. Adolescents, especially girls, are more vulnerable to developing anemia due to rapid physical growth and development, menstruation and poor diets. Identifying the determinants of anemia among adolescent girls in Africa is crucial for developing appropriate interventions, yet evidence remains scarce.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e: This study aims to investigate the magnitude and determinants of anemia among adolescent girls in Africa.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: Data from the recent Demographic and Health Surveys (DHSs) of 24 African countries was used, focusing on 38,966 adolescent girls aged 15\u0026ndash;19 years. The chi-squared test was utilized for bivariate analysis, and the relationship between predictor variables and anemia was evaluated using bivariate and multivariable binary logistic regression models.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: The overall magnitude of anemia among adolescent girls was 43.6% (95% CI: 42.97, 44.21). The research has identified several determinants associated with anemia in adolescent girls, including education status, wealth status, contraceptive use, nutritional status, pregnancy status, and net use. These factors provide valuable insight into potential avenues for intervention and prevention efforts. The multivariable analysis indicated that education status (primary education AOR\u0026thinsp;=\u0026thinsp;0.72; 95% CI\u0026thinsp;=\u0026thinsp;0.50\u0026ndash;0.80 and secondary education AOR\u0026thinsp;=\u0026thinsp;0.76; 95% CI\u0026thinsp;=\u0026thinsp;0.66\u0026ndash;0.86), wealth status (being rich AOR\u0026thinsp;=\u0026thinsp;0.86; 95% CI\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.98), contraceptive use (AOR\u0026thinsp;=\u0026thinsp;0.46; 95% CI\u0026thinsp;=\u0026thinsp;0.40\u0026ndash;0.53), and nutritional status were inversely associated with adolescent anemia. In contrast, pregnancy status (AOR\u0026thinsp;=\u0026thinsp;1.13; 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.27) and treated net use (AOR\u0026thinsp;=\u0026thinsp;1.26; 95% CI\u0026thinsp;=\u0026thinsp;1.15\u0026ndash;1.39) were positively associated with anemia among adolescent girls.\u003c/p\u003e \u003cp\u003eConclusion:\u003c/p\u003e \u003cp\u003eThis study serves as a catalyst for informed action and underscores the urgent need for comprehensive interventions aimed at addressing the multifaceted determinants of anemia among adolescent girls in Africa. By targeting these key factors, public health initiatives can make significant strides towards improving the health and well-being of young women across the continent.\u003c/p\u003e \u003cp\u003eTo prevent adolescent anemia, it is recommended to encourage girls to pursue education, prevent adolescent marriage and pregnancy promote contraceptive use among married or sexually active girls, and educate on the correct use of treated nets.\u003c/p\u003e","manuscriptTitle":"Magnitude and Determinates of Anemia among adolescent Girls in Africa: A Multilevel, Multicounty Analysis of 24 Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 10:13:41","doi":"10.21203/rs.3.rs-5004469/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"662b1c9c-bd3a-45fa-b90c-5843a36ede5e","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T07:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-07 10:13:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5004469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5004469","identity":"rs-5004469","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}