The Interplay of Household, Maternal, and Child Characteristics in Predicting Anaemia Among Children (6-59 months) in Tanzania: Insights from 2022 Demographic and Health survey

The Interplay of Household, Maternal, and Child Characteristics in Predicting Anaemia Among Children (6-59 months) in Tanzania: Insights from 2022 Demographic and Health survey | 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 The Interplay of Household, Maternal, and Child Characteristics in Predicting Anaemia Among Children (6-59 months) in Tanzania: Insights from 2022 Demographic and Health survey Erick Donard Oguma, Elihuruma Eliufoo Stephano, Victoria Godfrey Majengo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7077412/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 Background Anaemia is a major global public health issue, severely affecting children aged 6–59 months. Anaemia in children is associated with impaired physical and cognitive development. The burden of anemia is especially high in low- and middle-income countries such as Tanzania. Therefore, the study aimed to identify predictors of anemia among 6–59 months aged children in Tanzania. Methods Analytical cross-sectional analysis using data from the 2022 Tanzania Demographic and Health Survey, a national survey conducted from February to July 2022. The survey employed a two-stage, stratified sampling design, with strata defined by geographic region and urban/rural areas. Primary sampling units were selected from census enumeration areas, followed by systematic household selection. A weighted binary logistic regression model was used to identify predictors of anemia, with results presented as odds ratio (OR) and 95% confidence intervals (CI). Statistical significance was set at p < 0.05. Results The prevalence of anemia among children aged 6–59 months in Tanzania was found to be 58.8% (95% CI: 56.7–60.9%). The study identified several factors significantly associated with childhood anemia. Children were more likely to be anemic if their mothers were also anemic (AOR = 1.87; 95% CI: 1.57–2.22), if they were younger particularly those aged 6–23 months (AOR = 2.12; 95% CI: 2.51–3.87) and 24–42 months (AOR = 1.74; 95% CI: 1.43–2.11), or if they had experienced recent episodes of diarrhea (AOR = 1.62; 95% CI: 1.19–2.27). In contrast, children were less likely to be anemic if their mothers had medical health insurance (AOR = 0.56; 95% CI: 0.38–0.83), if they were exposed to mass media such as radio or television (AOR = 0.83; 95% CI: 0.70–0.99), or if their household owned mosquito bed nets (AOR = 0.77; 95% CI: 0.62–0.95). Conclusion This study identifies several important determinants of anemia among Tanzanian children under five. The likelihood of anemia was notably higher among children whose mothers were anemic, those under the age of 42 months, particularly those between 6 and 23 months, and those who had recently suffered from diarrhea. In contrast, children whose mothers had health insurance, who lived in households with mosquito bed nets, or who were exposed to mass media were found to have a lower risk of developing anemia. These findings underscore the need for integrated, multisectoral interventions that not only address maternal health and nutrition but also expand access to health insurance, improve early childhood illness prevention, and enhance health education through mass media. Promoting appropriate feeding practices and ensuring household-level malaria prevention are also critical. A holistic approach targeting both medical and social determinants is essential to reduce the burden of anemia and improve child survival and development outcomes in Tanzania. Anaemia Children Household Maternal Tanzania Background In developing countries, anemia is a common public health concern, especially for children under five. According to World Health Organization (WHO) estimates, 39.8% of children between the ages of 6 and 59 months are anemic worldwide, with sub-Saharan Africa bearing the brunt of this burden [ 1 ]. Serious health effects, such as stunted growth, decreased immunity, poor cognitive development, and an elevated risk of morbidity and mortality, can result from anemia in early infancy [ 2 ]. In Tanzania, anemia remains a serious public health problem. The WHO has classed anemia as a significant public health problem (prevalence ≥ 40%), and according to the Tanzania Demographic and Health Survey (TDHS) 2022, 58% of children under five were anemic [ 3 ]. With frequency exceeding 70% in some areas, the highest rates are seen in children between the ages of 6 and 23 months [ 3 ]. The persistence of high anemia rates suggests ongoing challenges related to child nutrition, disease burden, and health service access. Despite decades of health and nutrition interventions, anemia continues to impact over half (58%) of children under five in Tanzania, posing a major impediment to reaching national and global child health targets, particularly those set by the Sustainable Development targets [ 4 ]. High anemia prevalence persists even in areas with established health programs, suggesting that the most important or context-specific risk factors may not be adequately addressed by current interventions. Emerging evidence from previous TDHS analyses revealed that childhood anemia is related with a variety of biological, environmental, and socioeconomic variables [ 4 , 5 ]. Without a good understanding of the unique drivers of childhood anemia at the national level, interventions may fail to reach the most vulnerable populations. As a result, this study recommends analyzing data from the Tanzania Demographic and Health Survey to look at the important determinants of anemia in children under the age of five, with a focus on household, maternal, and child characteristics. By identifying the most relevant and modifiable risk factors, this study seeks to create knowledge that may be used to support better-focused, efficient, and equitable public health policies and initiatives aimed at reducing anemia in Tanzanian underfive children. Methods Data source, setting, design, population and sampling This study was an analytical cross-sectional survey that utilized secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The Tanzania National Bureau of Statistics conducted the survey with the Ministries of Tanzania Mainland and Zanzibar, and in collaboration with other stakeholders. Tanzania is amongst the countries in East Africa with a total area of 945, 087km 2 , including 61,000km 2 of inland water. According to the 2022 census, the country has a population of approximately 62 million people. Of these, 9,484,170 were children under the age of five, with around 69% living in rural areas [ 6 ]. We used data from the recent DHS conducted between 24 February and 21 July 2022 across 32 regions in Tanzania. The TDHS aims in collecting population demographics, health indicators and other determinants of health. The target population for the 2022 TDHS included women of reproductive age (15–49 years), men, children and household. However, this study focused on children aged 6–59 months. The detailed TDHS methodology is explained elsewhere [ 3 ]. In summary, the country is stratified by urban/rural areas within each region, followed by a two-stage sampling to select a household. The first approach involves selection of primary sampling unit (PSU) and then select a household. PSUs are survey clusters usually based on census enumeration areas (EAs). For each selected PSU, a complete household listing is done. This is followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. We used the kids record (KR). After removing duplicates and focusing on children aged 6–59 months, our final analytical sample comprised of 4,420 (weighted) participants. Variable measurements Outcome variable For children aged 6–59 months, WHO defines any form of anemia as hemoglobin concentration < 11 g/dL [ 7 ]. Hemoglobin level was assessed using capillary blood and the HemoCue rapid testing technique in the TDHS. Originally, anemia was categorized into; severe, moderate, mild and not anemic. For the purpose of analysis, the categories of anemia were further dichotomized as ‘anemic’ and ‘not anemic’. Hemoglobin levels of the children and mothers were measured using HemoCue, which is the standard test used in the DHS program. The HemoCue device is recommended for point-of-care hemoglobin testing in resource-limited settings and uses capillary blood obtained from a finger prick [ 8 ]. Explanatory variables Based on the available TDHS data and previous literature, we categorized variables into maternal, child and household characteristics. Maternal characteristics Woman’s age in years (15–24, 25–34 and 35–49), education level (no formal education, primary education and secondary/higher), marital status (never married, married/cohabiting and separated/divorced), currently pregnant (yes and no), currently breastfeeding (yes and no). The BMI was categorized as per WHO guidelines into [underweight (< 18.5kg/m 2 ), normal(18.5-24.9kg/m 2 ), overweight (25-29.9kg/m 2 ) and obese (≥ 30kg/m 2 )] [ 9 ]. Maternal anemia (yes and no), working status (yes and no), covered by health insurance (yes or no). Media exposure was calculated by aggregating the exposure to reading newspaper, listening to radio or watching television, and was later categorized into (yes and no). Child characteristics Child’s age in months (6–23, 24–42 and 43–59), child’s sex (male and female), birth order (1st, 2nd & 3rd and ≥ 4th), size of the baby (large, average and small), stunted (yes and no), wasted (yes and no), underweight (yes and no), had diarrhoea recently (yes and no), received vitamin A (yes and no) and had fever recently (yes and no). Household characteristics : The wealth index was recategorized into three groups: poor, middle, and rich. It was derived using principal component analysis (PCA) based on household asset ownership [ 10 ]. Number of under-five children (none, 1–2 and ≥ 3), toilet facility (improved and not improved), source of drinking water ( improved and not improved), availability of mosquito bed net (yes and no), place of residence (urban and rural) and geographical zones (western, northern, central, southern highlands, southern, southwest highlands, lake, eastern and Zanzibar). Statistical analysis All analyses were conducted using STATA version 18 (Stata Corp, College Station, TX, USA). To account for the complex survey design of the TDHS, we used the ‘svyset’ command in Stata with the primary sampling unit (v021), stratification variable (v023), and individual sampling weights (v005/1,000,000). This adjustment ensured accurate variance estimation reflecting the stratified cluster sampling design. Descriptive statistics were used to summarize participants’ characteristics, with means and standard deviations reported for continuous variables, and frequencies and percentages for categorical variables. The results were presented using tables and narrations. Cross-tabulations using Pearson chi-squared tests were performed to examine the prevalence of anemia across participant characteristics. To identify predictors of anemia, we used a generalized logistic regression model with robust variance estimator. This generalized linear approach provides consistent and reliable estimates of odds ratio (OR) making it suitable for complex survey data. We compared the modified Poisson and logistic regression models using Akaike Information Criterion (AIC), considering that logistic regression may overestimate association when the outcome is not rare (> 10%). Nevertheless, the logistic regression was chosen due to its lower AIC value, indicating a better fit. A variance inflation factor (VIF) was used to check for multicollinearity before fitting a multivariable regression model. Variables were selected into multivariable analyses using a backward stepwise selection, and statistical significance was set at p < 0.05. Results Characteristics of study population Four thousand four hundred and twenty (4,420) children aged 6–59 months were included in this analysis. Children aged 6–23 months were the most represented (36.5%). Less than half, 31.2% were stunted, only 3.4% were wasted, and 12.4% were underweight. One in ten (11.9%) had fever recently, 55.3% received Vitamin A and just 8.7% had diarrhoea recently. Additionally, 42.8% were from poor household and 67.3% from household with improved toilet facility. More than half (66.2%) had improved source of drinking water and 82.1% lived in a household with mosquito bed net. Regarding mother characteristics, 58.0% had attained primary education, 84.5% were either married or living with a partner, 46.1% aged 25–34 years and 60.4% were working (Table 1 ). Prevalence of anemia The overall prevalence of anemia was 58.8% [56.7–60.9] among children aged 6–59 months. Of these, 1.9% had severe anemia, 35.4% had moderate anemia, and 25.6% had mild anemia. Table 1 presents the characteristics of children, their mother, their household, and the prevalenc7 of anemia across child related factors, maternal factors and household factors. A higher proportion of anemia was observed among children younger than 24 months (72.4% [95%CI; 69.4–75.2]; p < 0.001), stunted (63.3% [95%CI; 59.2–67.2]; p = 0.025), wasted (73.9% [95%CI:64.1–81.7]: p = 0.005), underweight (65.3% [95%CI: 59.4–70.7]; p = 0.033) and those who had diarrhoea recently (73.1% [95%CI; 67.5–78.1]; p < 0.001). We also observed higher proportion of anemia in children whose mothers suffered from anemia (67.8% [95% CI: 64.8–70.7]; p < 0.001), were underweight (67.1% [95%CI: 60.2–73.2]; p = 0.001) or were aged 15–24 years (65.5% [95%CI:61.0-68.9]: p < 0.001). Additionally, this proportion was also significantly higher for children who lived in eastern zone of mainland Tanzania (69.9% [95%CI: 65.2–74.3) (Table 1 ). Table 1 Maternal, Children, Household characteristics and prevalence of anemia among children aged 6–49 months in Tanzania (N = 4,420) Characteristics n(%) Anaemia (n) Prevalence of anaemia, % [95%CI] p-value Total 4,420 (100.0) 2,598 58.8 [56.7–60.9] Maternal characteristics Woman’s age (years) < 0.001 15–24 1,201 (27.2) 786 65.5 [61.0-68.9] 25–34 2,038 (46.1) 1,152 56.5 [53.4–59.6] 35–49 1,181 (26.7) 660 55.8 [52.3–59.4] Mean (± SD) 29.9 (7.1) Education level 0.163 No formal education 975 (22.1) 605 62.0 [58.0-65.9] Primary 2,566 (58.0) 1,479 57.7 [54.9–60.4] Secondary/Higher 879 (19.9) 514 58.5 [54.6–62.3] Marital status 0.745 Never married 265 (6.0) 161 60.5 [52.2–68.3] Married/Cohabiting 3,736 (84.5) 2,199 58.9 [56.6–61.1] Separated/Divorced 418 (9.5) 238 56.9 [51.4–62.3] Currently pregnant 0.958 No 4,000 (90.5) 2,352 58.8 [56.6–60.9] Yes 420 (9.5) 246 58.6 [52.5–64.5] Currently breastfeeding < 0.001 No 2,334 (52.8) 1,262 54.1 [51.2–56.9] Yes 2,086 (47.2) 1,336 64.0 [61.4–66.7] BMI category 0.001 Underweight 332 (7.5) 223 67.1 [60.2–73.2] Normal 2,894 (65.5) 1,742 60.2 [57.6–62.7] Overweight 758 (17.2) 397 52.4 [47.6–57.1] Obese 435 (9.8) 236 54.3 [49.0-59.5] Maternal anaemia < 0.001 No 2,714 (61.4) 1,442 53.1 [50.7–55.5] Yes 1,705 (38.6) 1,156 67.8 [64.8–70.7] Working status 0.025 No 1,741 (39.4) 1,073 61.6 [58.3–64.9] Yes 2,678 (60.6) 1,525 56.9 [54.4–59.5] Covered by health Insurance < 0.001 No 4,232 (95.7) 2,518 59.5 [57.4–61.6] Yes 188 (4.3) 80 42.3 [33.9–51.1] Media exposure 0.023 No 1,664 (37.7) 1,024 61.5 [58.3–64.7] Yes 2,755 (62.3) 1,574 57.1 [54.6–59.6] Children characteristics Child’s age (months) < 0.001 6–23 1,611 (36.5) 1,167 72.4 [69.4–75.2] 24–42 1,514 (34.3) 864 57.1 [53.8–60.3] 43–59 1,295 (39.2) 567 43.8 [40.4–47.3] Median (± SD) 31.4 (15.5) Children sex 0.137 Male 2,237 (50.6) 1,348 60.3 [57.1–63.4] Female 2,183 (49.4) 1,250 57.2 [54.6–59.9] Birth Order 0.879 1st 981 (22.2) 579 59.0 [54.9–62.9] 2nd & 3rd 1,691 (38.3) 984 58.2 [54.8–61.5] ≥ 4th 1,748 (39.6) 1,035 59.2 [56.4–62.0] Baby size* 0.255 Large 777 (32.0) 505 65.0 [60.3–69.4] Average 1,483 (61.2) 1,034 69.7 [66.5–72.8] Small 165 (6.8) 107 65.0 [52.6–75.7] Stunted* 0.025 No 2,970 (68.8) 1,727 58.1 [55.8–60.5] Yes 1,348 (31.2) 854 63.3 [59.2–67.2] Wasted* 0.005 No 4,193 (96.6) 2,485 59.3 [57.1–61.4] Yes 146 (3.4) 108 73.9 [64.1–81.7] Underweight* 0.033 No 3,797 (87.6) 2,236 58.9 [56.7–61.1] Yes 539 (12.4) 352 65.3 [59.4–70.7] Had diarrhoea recently < 0.001 No 4,033 (91.3) 2,315 57.4 [55.3–59.5] Yes 386 (8.7) 283 73.1 [67.4–78.1] Received Vitamin A 0.663 No 1,974 (44.7) 1,170 59.3 [56.3–62.1] Yes 2,446 (55.3) 1,428 58.4 [55.7–61.1] Had fever recently 0.324 No 3,895 (88.1) 2,273 58.4 [56.3–60.4] Yes 525 (11.9) 325 61.9 [54.7–68.6] Household Characteristics Wealth index 0.574 Poor 1986 (42.9) 1,136 59.9 [56.9–62.8] Middle 865 (19.6) 493 57.1 [52.8–61.3] Rich 1659 (37.5) 969 58.4 [54.7–62.0] Number under-five children 0.008 None 40 (0.9) 29 73.7 [56.6–85.8] 1–2 3,556 (80.5) 2,046 57.5 [55.1–59.9] ≥ 3 824 (18.7) 523 63.5 [59.5–67.2] Toilet facility 0.082 Not improved 1,443 (32.7) 881 61.1 [57.9–64.2] Improved 2,977 (67.3) 1,717 57.7 [55.1–60.2] Source of drinking water 0.549 Not improved 1,496 (33.8) 892 60.1 [56.0-63.1] Improved 2,924 (66.2) 1,706 58.3 [55.9–60.8] Household has mosquito bed net 0.062 No 791 (17.0) 496 62.7 [58.3–67.0] Yes 3,629 (82.1) 2,102 57.9 [55.6–60.3] Place of residence 0.631 Urban 1,188 (26.9) 709 59.6 [55.6–63.6] Rural 3,232 (73.1) 1,889 58.5 [55.9–60.9] Geographical zones < 0.001 Western 432 (9.8) 241 55.8 [50.8–60.7] Northern 459 (10.4) 243 52.9 [45.2–60.6] Central 473 (10.7) 218 46.0 [39.5–52.6] Southern highlands 223 (5.0) 101 45.1 [38.7–51.7] Southern 161 (3.6) 109 68.1 [59.3–75.8] Southwest highlands 426 (9.6) 256 60.1 [56.8–65.4] Lake 1,553 (35.1) 950 61.2 [56.8–65.4] Eastern 562 (12.7) 393 69.9 [65.2–74.3] Zanzibar 131 (3.0) 87 66.2 [61.9–70.2] *Frequency do not tally to total N due to missing values, BMI; Body Mass Index, SD; Standard deviation Predictors of anaemia among children aged 6–59 months Table 2 presents logistic regression findings. Children were more likely to be anemic if their mothers were also anemic (AOR = 1.87; 95% CI: 1.57–2.22), if they were younger particularly those aged 6–23 months (AOR = 2.12; 95% CI: 2.51–3.87) and 24–42 months (AOR = 1.74; 95% CI: 1.43–2.11), or if they had experienced recent episodes of diarrhea (AOR = 1.62; 95% CI: 1.19–2.27). In contrast, children were less likely to be anemic if their mothers had medical health insurance (AOR = 0.56; 95% CI: 0.38–0.83), if they were exposed to mass media such as radio or television (AOR = 0.83; 95% CI: 0.70–0.99), or if their household owned mosquito bed nets (AOR = 0.77; 95% CI: 0.62–0.95). (Table 2 ) Table 2 Weighted logistic regression model for predictors of anemia among children aged 6–59 months in Tanzania (N = 4,420) Characteristics Crude p-value Adjusted p-value OR [95%CI] OR [95%CI] Maternal characteristics Maternal age [years] 15–24 1.00 1.00 25–34 0.69 [0.57–0.83] < 0.001 0.83 [0.68–1.01] 0.057 35–49 0.67 [0.54–0.82] < 0.001 0.87 [0.70–1.09] 0.235 Education No formal education 1.16 [0.93–1.46] 0.198 Primary 0.97 [0.80–1.17] 0.738 Secondary/Higher 1.00 - Currently breastfeeding No 1.00 1.00 Yes 1.51 [1.30–1.76] < 0.001 1.19 [0.99–1.43] 0.054 BMI category Underweight 1.35 [0.99–1.82] 0.053 1.27 [0.93–1.75] 0.131 Normal 1.00 1.00 Overweight 0.73 [0.60–0.89] 0.002 0.79 [0.63–0.98] 0.030 Obese 0.79 [0.61–1.01] 0.061 0.92 [0.69–1.22] 0.557 Maternal anaemia No 1.00 1.00 Yes 1.86 [1.59–2.18] < 0.001 1.87 [1.57–2.22] < 0.001 Working status No 1.00 1.00 Yes 0.82 [0.70–0.96] 0.016 0.99 [0.84–1.18] 0.953 Covered by health Insurance No 1.00 1.00 Yes 0.50 [0.35–0.71] < 0.001 0.56 [0.38–0.83] 0.003 Media exposure No 1.00 1.00 Yes 0.83 [0.71–0.97] 0.020 0.83 [0.70–0.99] 0.035 Children characteristics Child’s age [months] 6–23 3.37 [2.77–4.09] < 0.001 2.12 [2.51–3.87] < 0.001 24–42 1.70 [1.41–2.05] < 0.001 1.74 [1.43–2.11] < 0.001 43–59 1.00 1.00 Had diarrhoea recently No 1.00 1.00 Yes 2.02 [1.52–2.67] < 0.001 1.62 [1.19–2.27] 0.002 Received Vitamin A No 1.00 - Yes 0.97 [0.83–1.12] 0.648 Had fever recently No 1.00 - Yes 1.16 [0.89–1.50] 0.261 Household Characteristics Wealth index - Poor 1.00 Middle 0.89 [0.73–1.08] 0.249 Rich 0.94 [0.79–1.12] 0.480 Number of five-children None 1.00 - 1–2 0.48 [0.22–1.07] 0.074 3+ 0.62 [0.28–1.39] 0.246 Toilet facility Not improved 1.00 - Improved 0.87 [0.74–1.02] 0.081 Source of drinking water Not improved 1.00 Improved 0.95 [0.81–1.11] 0.503 Household has mosquito bed net No 1.00 1.00 Yes 0.82 [0.67–0.99] 0.037 0.77 [0.62–0.95] 0.016 Place of residence Urban 1.00 - Rural 0.95 [0.79–1.14] 0.601 Geographical zones Western 1.00 1.00 Northern 0.89 [0.65–1.22] 0.471 0.86 [0.61–1.19] 0.364 Central 0.68 [0.50–0.91] 0.010 0.69 [0.50–0.94] 0.018 Southern highlands 0.65 [0.47–0.90] 0.009 0.80 [0.57–1.13] 0.211 Southern 1.69 [1.11–2.57] 0.014 1.87 [1.21–2.88] 0.005 Southwest highlands 1.19 [0.90–1.58] 0.218 1.32 [0.97–1.78] 0.075 Lake 1.25 [0.96–1.62] 0.092 1.35 [1.03–1.78] 0.032 Eastern 1.85 [1.34–2.54] < 0.001 2.01 [1.41–2.87] < 0.001 Zanzibar 1.55 [1.16–2.08] 0.003 1.64 [1.19–2.24] 0.002 OR; Odds Ratio, CI: Confidence Interval Discussion The study revealed that anemia affects a striking 58.8% of children aged 6–59 months in Tanzania, highlighting a severe public health concern. The figure surpasses the World Health Organization’s threshold for a "severe public health problem" (≥ 40%) [ 7 ], pointing to widespread dietary inadequacies, infections like malaria, and possibly limited access to iron supplementation and healthcare. High prevalence of anaemia in this study was also consistent with the previous study conducted in Uganda by Kuziga et al., 2017 (58.8%), Ghana by Klu et al., 2023 (69.1%), Bangladesh by Mollah et al., 2021 (61.8%), and Nigeria by Obasohan et al., 2022 (68.1%) [ 11 – 14 ]. Addressing this crisis requires multisectoral interventions, including improved infant and young child feeding (IYCF) practices, maternal nutrition, deworming programs, malaria control, and fortified complementary foods, particularly in rural and underserved areas. The finding revealed that children whose mothers were anemic were nearly 2 times more likely to suffer from anemia. This finding was consistency with the previous studies done by Mutonhodza et al., (2022) in rural Zimbabwe, Tesema et al., (2021) in Sub Saharan Africa SSA, Ntenda et al.,(2018) in (Malawi, mozambique, Namibia, and Zimbabwe), Elmardi et al., (2020) in Sudan, Tekeba et al., (2025) in Mozambique, Klu et al., (2023) in Ghana, Obasohan et al., (2022) in Nigeria, and Msaki et al., (2022) in Tanzania [ 12 , 14 – 20 ]. The possible explanation for the similarities in these studies is that maternal anemia during pregnancy and lactation can affect fetal iron stores, breastmilk quality, and early infant nutrition [ 21 – 23 ]. This strong association highlights the intergenerational transmission of poor health and nutritional status. In Tanzania, where dietary diversity and maternal healthcare access remain suboptimal in rural and peri-urban areas, t his correlation is particularly concerning [ 24 – 26 ]. This particular finding emphasizes the need for cost-effective interventions that must target both maternal and child health simultaneously. Moreover, children aged 6–23 months were more than twice as likely to be anemic, and those aged 24–42 months also had increased odds, compared to older children. These findings align with the previous studies done by Gebreegziabiher et al., (2015) in Northern Ethiopia, Kuziga et al., (2017) in Uganda, Elmardi et al., (2020) in Sudan, Mboya et al., (2023) in Kilimanjaro-Tanzania, Mollar et al., (2021) in Bangladesh [ 11 , 13 , 18 , 20 , 27 , 28 ]. The strong association in these studies might be attributed to the critical period of rapid growth and increased nutritional demand in this age group. This age group corresponds to a critical period of rapid growth and increased nutritional demand. At 6–23 months, children transition from exclusive breastfeeding to complementary feeding, a period when many are inadequately fed [ 29 – 31 ]. In Tanzania, complementary feeding practices often lack diversity and iron-rich foods, and many caregivers are unaware of optimal infant and young child feeding (IYCF) practices [ 32 – 34 ]. The persistence of anemia up to 42 months signals prolonged exposure to poor dietary intake, repeated infections, or both. Similarly, children who had recent episodes of diarrhea were 62% more likely to be anemic. A similar finding has been reported in the study conducted in SSA Countries by Tesema et al., 2021, which reported an increased odds of anaemia among children who reported diarrhea in the last two weeks [ 16 ]. The possible reason may be attributed to the strong correlation between diarrhea and anemia. Diarrhea may contribute to anemia in multiple ways, including nutrient malabsorption, blood loss, and loss of appetite [ 35 – 38 ]. In Tanzania, frequent diarrheal episodes are a major public health concern [ 3 ], especially among vulnerable populations like the rural and urban poor, due to inadequate water, sanitation, and hygiene (WASH) conditions [ 39 ]. These issues lead to increased susceptibility to diarrheal diseases, which are a leading cause of mortality in children under five. The revealed strong association between anaemia and diarrhea in this study underscores the importance of addressing the burden of diarrhea among underfive children. Furthermore, children of women with medical health insurance were significantly less likely to be anemic (AOR = 0.56, 95% CI: 0.38–0.83). This finding points to the protective effect of access to healthcare services. Health insurance likely facilitates regular antenatal visits, which subsequently increases access to iron and folic acid supplementation, deworming, and nutrition counseling. All of these interventions contribute to preventing anemia in both mothers and children [ 40 , 41 ]. In Tanzania, the National Health Insurance Fund (NHIF) and the improved Community Health Fund (iCHF) aim to expand health coverage, yet many low-income families remain uninsured. However, the government is looking forward to implementing Tanzania’s Universal Health Insurance. This protective factor underscores the importance of promoting insurance uptake among women, especially in underserved regions. Similarly, children living in households that owned and presumably used mosquito bed nets were less likely to be anemic. This finding is consistent with existing literature, as malaria is a well-established contributor to anemia in young children in malaria-endemic countries like Tanzania [ 42 , 43 ]. The Plasmodium parasite causes hemolysis (destruction of red blood cells), leading to reduced hemoglobin levels [ 42 ]. Children are particularly vulnerable due to their still-developing immune systems and higher nutritional needs. In Tanzania, despite national malaria control efforts including the distribution of insecticide-treated bed nets (ITNs), utilization remains inconsistent in some areas due to factors such as household crowding, seasonal perceptions of malaria risk, or net wear and tear [ 44 – 47 ]. The protective effect of bed net ownership observed in this study reinforces the importance of maintaining high coverage and promoting consistent usage of ITNs, especially among children under five. Additionally, children whose caregivers were exposed to mass media, such as radio and television, were found to be less likely to suffer from anemia. This relationship likely reflects improved health awareness and knowledge among parents or caregivers who receive information through media platforms. Mass media serve as a critical channel for disseminating public health messages, including those related to maternal nutrition, infant feeding practices, hygiene, and disease prevention. In Tanzania, radio remains a widely accessible form of communication, especially in rural areas. Health promotion programs that leverage mass media have proven effective in increasing awareness of nutritional needs, iron supplementation, deworming, malaria prevention, and early healthcare seeking [ 48 – 50 ]. The association observed here suggests that mass media can be a powerful tool to influence behavior change that directly impacts childhood anemia. Implications for practice and policy recommendations The findings highlight the need for integrated maternal and child health initiatives in Tanzania, which address both the medical and social determinants of childhood anemia. The substantial link between maternal and child anemia emphasizes the significance of improving prenatal and postnatal care services, such as routine screening, iron supplementation, and timely treatment for anaemia. Health practitioners should increase nutrition counseling for caregivers, particularly for children aged 6–42 months, and include diarrhea prevention and management as essential components of child health services. From a policy standpoint, increasing access to affordable health insurance for women and low-income families is crucial, as insured moms are considerably less likely to have anemic children. National health policy should encourage increased membership in programs like as the NHIF and iCHF, and coverage should be linked to greater access to nutrition, preventive care, and child health management. The protective role of mosquito net ownership and exposure to mass media emphasizes the importance of coordinated health promotion and malaria prevention activities. Integrating mass communication tactics into child nutrition initiatives can increase their impact. A multi-sectoral, equity-driven approach is required to successfully reduce the burden of anemia and enhance early childhood health outcomes in Tanzania. Conclusion This study identifies several important determinants of anemia among Tanzanian children under five. The likelihood of anemia was notably higher among children whose mothers were anemic, those under the age of 42 months, particularly those between 6 and 23 months, and those who had recently suffered from diarrhea. In contrast, children whose mothers had health insurance, who lived in households with mosquito bed nets, or who were exposed to mass media were found to have a lower risk of developing anemia. These findings underscore the need for integrated, multisectoral interventions that not only address maternal health and nutrition but also expand access to health insurance, improve early childhood illness prevention, and enhance health education through mass media. Promoting appropriate feeding practices and ensuring household-level malaria prevention are also critical. A holistic approach targeting both medical and social determinants is essential to reduce the burden of anemia and improve child survival and development outcomes in Tanzania. Abbreviations AIC Akaike Information Criterion BMI Body Mass Index CI Confidence Intervals DHS Demographic and Health Survey EA Enumeration Areas HB Haemoglobin iCHF improved Community Health Fund ITNs Insecticide-Treated Bed Nets IYCF Infant and Young Child Feeding NHIF National Health Insurance Fund OR Odds Ratio PSU Primary Sampling Units SD Standard Deviation SSA Sub Saharan Africa TDHS Tanzania Demographic and Health Survey VIF Variance Inflation Factor WASH Water, Sanitation, and Hygiene WHO World Health Organization Declarations Acknowledgements We thank the DHS program for making the data available for this study and TILAM International for statistical consultation. Authors’ Contribution EDO and MJM conceptualized the idea and conducted formal analysis. EDO, EES, VGM, AAN, and MJM interpreted the results, drafted the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding None Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com. Ethics approval and consent to participate This study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Com peting interests None declared. Clinical trial number: Not applicable References Anaemia in women. and children [Internet]. [cited 2025 Jul 8]. Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children?utm_source=chatgpt.com Anaemia [Internet]. [cited 2025 Jul 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/anaemia?utm_source=chatgpt.com TDHS-MIS. Tanzania Demographic Health survey-Malaria Indicators Health survey. 2022. Sunguya BF, Zhu S, Paulo LS, Ntoga B, Abdallah F, Assey V et al. 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Available from: http://dx.doi.org/10.1371/journal.pgph.0002529 Heri R, Malqvist M, Yahya-Malima KI, Mselle LT. Dietary diversity and associated factors among women attending antenatal clinics in the coast region of Tanzania. BMC Nutr. 2024. Ndifwa N, Katunzi T, Kengela B. Decomposing Rural-Urban Variations in Maternal Healthcare Utilisation Among Women of Reproductive Age in Tanzania. Evidence from the 2022 Tanzania Demographic Health Survey. Rural Plan J. 2025;26(2):49–67. Gebreegziabiher G, Etana B, Niggusie D. Determinants of Anemia among Children Aged 6–59 Months Living in Kilte Awulaelo Woreda, Northern Ethiopia. Anemia. 2015. Mboya IB, Mamseri R, Leyaro BJ, George J, Msuya SE, Mgongo M. Prevalence and factors associated with anemia among children under five years of age in Rombo district, Kilimanjaro region, Northern Tanzania. F1000Research. 2023. Seifu BL, Fente BM, Asmare ZA, Asnake AA, Bezie MM, Asebe HA et al. Factors associated with zero vegetable and fruit consumption among Tanzanian children. BMC Public Health. 2024;24(1). Pallangyo EE, Kimaro OJ, Mwalupani NR, George GS, Katana D, Msengwa AS. Cross-sectional analysis of risk factors associated with the coexistence of three undernutrition indicators among children aged 0–23 months in Tanzania. BMC Nutr [Internet]. 2025;11(1). Available from: https://doi.org/10.1186/s40795-024-00980-5 Mandara F, Festo C, Killel E, Lwambura S, Mrema J, Katunzi F et al. The relationship between feeding practices and stunting among children under two years in Tanzania mainland: a mixed-method approach. Bull Natl Res Cent [Internet]. 2024;48(1). Available from: https://doi.org/10.1186/s42269-024-01266-3 Sichalwe MM, Behera MR, Behera D, Dehury RK, Degge H. Knowledge and practice of complementary feeding among mothers in Dar-es-Salaam, Tanzania: Community-based cross-sectional study. Clin Epidemiol Glob Heal. 2023. Victor R, Baines SK, Agho KE, Dibley MJ. 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Kassile T, Thomas J, Niklaus H, Dale Y. A review of sanitation and hygiene in Tanzania. DFID, London. Tanzan J Health Res. 2015. World Health Organization, Williams a L, van Drongelen W, Lasky RE, Sanderson M, Lai D et al. Guideline: Daily iron and folic acid supplementation in pregnant women. World Heal Organ. 2012. Traore SS, Bo Y, Kou G, Lyu Q. Iron supplementation and deworming during pregnancy reduces the risk of anemia and stunting in infants less than 2 years of age: a study from Sub-Saharan Africa. BMC Pregnancy Childbirth. 2023. White NJ. Anaemia and malaria. Malaria Journal. 2018. McCuskee S, Brickley EB, Wood A, Mossialos E. Malaria and macronutrient deficiency as correlates of anemia in young children: A systematic review of observational studies. Annals Global Health. 2014. Worges M, Kamala B, Yukich J, Chacky F, Lazaro S, Dismas C et al. Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches. Malar J. 2022. Beer N, Ali AS, Eskilsson H, Jansson A, Abdul-Kadir FM, Rotllant-Estelrich G et al. A qualitative study on caretakers’ perceived need of bed-nets after reduced malaria transmission in Zanzibar, Tanzania. BMC Public Health. 2012. Odufuwa OG, Ross A, Mlacha YP, Juma O, Mmbaga S, Msellemu D et al. Household factors associated with access to insecticide-treated nets and house modification in Bagamoyo and Ulanga districts, Tanzania. Malar J. 2020. Ezezika O, El-Bakri Y, Nadarajah A, Barrett K. Implementation of insecticide-treated malaria bed nets in Tanzania: A systematic review. J Glob Heal Rep. 2022. Moffat R, Sayer A, DeCook K, Cornia A, Linehan M, Torres S et al. A National Communications Campaign to decrease childhood stunting in Tanzania: an analysis of the factors associated with exposure. BMC Public Health. 2022. Vey T, Kinnicutt E, Day AG, West N, Sleeth J, Nchimbi KB, et al. Targeted Behavior Change Communication Using a Mobile Health Platform to Increase Uptake of Long-Lasting Insecticidal Nets Among Pregnant Women in Tanzania: Hati Salama Secure Voucher Study Cluster Randomized Controlled Trial. J Med Internet Res. 2025;27:1–15. Mwebesa E, Awor S, Natuhamya C, Dricile R, Legason ID, Okimait D et al. Impact of mass media campaigns on knowledge of malaria prevention measures among pregnant mothers in Uganda: a propensity score-matched analysis. Malar J [Internet]. 2024;23(1). Available from: https://doi.org/10.1186/s12936-024-05083-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7077412","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498753471,"identity":"56960af9-54ef-478a-8e11-db477ee4d9f6","order_by":0,"name":"Erick Donard Oguma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACxgbGBijrAQMbiVqYDeBaDhDUBKHZJIjSwi99uPHjDAYbuw3XDj+r5imziWZgP/yA+cMf3Fok+xKbJTcwpCVvuJ1mdpvnXFpuA0+aAcMBHtxaDM4wtgG9fTjZ4HaC2W3etsO5DQw5QIdJ4NZij9CS/q0YrIX/DVCLAR5beIBaNjActjO4nWPGDNYiAbIlAbcWiTOMzZIzDNISJG/nFEvOAfqlTeKZwYEzB3Br4e9hf/ixp8LGnu92+sYPb8pscvv5kx8+qMATYlDnMSQ2wNigqMFjBwLYE6NoFIyCUTAKRigAAGR9UX/sRTTJAAAAAElFTkSuQmCC","orcid":"","institution":"The University of Dodoma, School of Nursing and Public Health","correspondingAuthor":true,"prefix":"","firstName":"Erick","middleName":"Donard","lastName":"Oguma","suffix":""},{"id":498753472,"identity":"d79aeb63-14de-47d9-a4fb-83d0cd9a6b89","order_by":1,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"The University of Dodoma, School of Nursing and Public Health","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""},{"id":498753473,"identity":"bf181cc9-7a2b-43ee-9731-66a484fb2050","order_by":2,"name":"Victoria Godfrey Majengo","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":498753474,"identity":"8e046a71-55a4-46ef-9d86-d49241fd8a48","order_by":3,"name":"Azan Abubakary Nyundo","email":"","orcid":"","institution":"The University of Dodoma, School of Medicine and Dentistry","correspondingAuthor":false,"prefix":"","firstName":"Azan","middleName":"Abubakary","lastName":"Nyundo","suffix":""},{"id":498753475,"identity":"08f1b7a1-8273-4843-a388-96f00d0184e6","order_by":4,"name":"Mtoro J. Mtoro","email":"","orcid":"","institution":"TILAM International","correspondingAuthor":false,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""}],"badges":[],"createdAt":"2025-07-08 18:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7077412/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7077412/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90799131,"identity":"fb3f3690-961d-4350-9953-0031a83f0a52","added_by":"auto","created_at":"2025-09-08 09:47:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930349,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7077412/v1/a6ba4bad-3171-4dd6-b122-57cd7a56d32f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Interplay of Household, Maternal, and Child Characteristics in Predicting Anaemia Among Children (6-59 months) in Tanzania: Insights from 2022 Demographic and Health survey","fulltext":[{"header":"Background","content":"\u003cp\u003eIn developing countries, anemia is a common public health concern, especially for children under five. According to World Health Organization (WHO) estimates, 39.8% of children between the ages of 6 and 59 months are anemic worldwide, with sub-Saharan Africa bearing the brunt of this burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Serious health effects, such as stunted growth, decreased immunity, poor cognitive development, and an elevated risk of morbidity and mortality, can result from anemia in early infancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Tanzania, anemia remains a serious public health problem. The WHO has classed anemia as a significant public health problem (prevalence\u0026thinsp;\u0026ge;\u0026thinsp;40%), and according to the Tanzania Demographic and Health Survey (TDHS) 2022, 58% of children under five were anemic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With frequency exceeding 70% in some areas, the highest rates are seen in children between the ages of 6 and 23 months [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The persistence of high anemia rates suggests ongoing challenges related to child nutrition, disease burden, and health service access.\u003c/p\u003e\u003cp\u003eDespite decades of health and nutrition interventions, anemia continues to impact over half (58%) of children under five in Tanzania, posing a major impediment to reaching national and global child health targets, particularly those set by the Sustainable Development targets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. High anemia prevalence persists even in areas with established health programs, suggesting that the most important or context-specific risk factors may not be adequately addressed by current interventions.\u003c/p\u003e\u003cp\u003eEmerging evidence from previous TDHS analyses revealed that childhood anemia is related with a variety of biological, environmental, and socioeconomic variables [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Without a good understanding of the unique drivers of childhood anemia at the national level, interventions may fail to reach the most vulnerable populations. As a result, this study recommends analyzing data from the Tanzania Demographic and Health Survey to look at the important determinants of anemia in children under the age of five, with a focus on household, maternal, and child characteristics.\u003c/p\u003e\u003cp\u003eBy identifying the most relevant and modifiable risk factors, this study seeks to create knowledge that may be used to support better-focused, efficient, and equitable public health policies and initiatives aimed at reducing anemia in Tanzanian underfive children.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData source, setting, design, population and sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was an analytical cross-sectional survey that utilized secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The Tanzania National Bureau of Statistics conducted the survey with the Ministries of Tanzania Mainland and Zanzibar, and in collaboration with other stakeholders. Tanzania is amongst the countries in East Africa with a total area of 945, 087km\u003csup\u003e2\u003c/sup\u003e, including 61,000km\u003csup\u003e2\u003c/sup\u003e of inland water. According to the 2022 census, the country has a population of approximately 62\u0026nbsp;million people. Of these, 9,484,170 were children under the age of five, with around 69% living in rural areas [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe used data from the recent DHS conducted between 24 February and 21 July 2022 across 32 regions in Tanzania. The TDHS aims in collecting population demographics, health indicators and other determinants of health. The target population for the 2022 TDHS included women of reproductive age (15\u0026ndash;49 years), men, children and household. However, this study focused on children aged 6\u0026ndash;59 months. The detailed TDHS methodology is explained elsewhere [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In summary, the country is stratified by urban/rural areas within each region, followed by a two-stage sampling to select a household. The first approach involves selection of primary sampling unit (PSU) and then select a household. PSUs are survey clusters usually based on census enumeration areas (EAs). For each selected PSU, a complete household listing is done. This is followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. We used the kids record (KR). After removing duplicates and focusing on children aged 6\u0026ndash;59 months, our final analytical sample comprised of 4,420 (weighted) participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariable measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome variable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor children aged 6\u0026ndash;59 months, WHO defines any form of anemia as hemoglobin concentration\u0026thinsp;\u0026lt;\u0026thinsp;11 g/dL [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hemoglobin level was assessed using capillary blood and the HemoCue rapid testing technique in the TDHS. Originally, anemia was categorized into; severe, moderate, mild and not anemic. For the purpose of analysis, the categories of anemia were further dichotomized as \u0026lsquo;anemic\u0026rsquo; and \u0026lsquo;not anemic\u0026rsquo;. Hemoglobin levels of the children and mothers were measured using HemoCue, which is the standard test used in the DHS program. The HemoCue device is recommended for point-of-care hemoglobin testing in resource-limited settings and uses capillary blood obtained from a finger prick [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eExplanatory variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the available TDHS data and previous literature, we categorized variables into maternal, child and household characteristics.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMaternal characteristics\u003c/strong\u003e\u003cp\u003eWoman\u0026rsquo;s age in years (15\u0026ndash;24, 25\u0026ndash;34 and 35\u0026ndash;49), education level (no formal education, primary education and secondary/higher), marital status (never married, married/cohabiting and separated/divorced), currently pregnant (yes and no), currently breastfeeding (yes and no). The BMI was categorized as per WHO guidelines into [underweight (\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e), normal(18.5-24.9kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-29.9kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e)] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Maternal anemia (yes and no), working status (yes and no), covered by health insurance (yes or no). Media exposure was calculated by aggregating the exposure to reading newspaper, listening to radio or watching television, and was later categorized into (yes and no).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eChild characteristics\u003c/strong\u003e\u003cp\u003eChild\u0026rsquo;s age in months (6\u0026ndash;23, 24\u0026ndash;42 and 43\u0026ndash;59), child\u0026rsquo;s sex (male and female), birth order (1st, 2nd \u0026amp; 3rd and \u0026ge;\u0026thinsp;4th), size of the baby (large, average and small), stunted (yes and no), wasted (yes and no), underweight (yes and no), had diarrhoea recently (yes and no), received vitamin A (yes and no) and had fever recently (yes and no).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHousehold characteristics\u003c/b\u003e: The wealth index was recategorized into three groups: poor, middle, and rich. It was derived using principal component analysis (PCA) based on household asset ownership [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Number of under-five children (none, 1\u0026ndash;2 and \u0026ge;\u0026thinsp;3), toilet facility (improved and not improved), source of drinking water ( improved and not improved), availability of mosquito bed net (yes and no), place of residence (urban and rural) and geographical zones (western, northern, central, southern highlands, southern, southwest highlands, lake, eastern and Zanzibar).\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using STATA version 18 (Stata Corp, College Station, TX, USA). To account for the complex survey design of the TDHS, we used the \u0026lsquo;svyset\u0026rsquo; command in Stata with the primary sampling unit (v021), stratification variable (v023), and individual sampling weights (v005/1,000,000). This adjustment ensured accurate variance estimation reflecting the stratified cluster sampling design. Descriptive statistics were used to summarize participants\u0026rsquo; characteristics, with means and standard deviations reported for continuous variables, and frequencies and percentages for categorical variables. The results were presented using tables and narrations. Cross-tabulations using Pearson chi-squared tests were performed to examine the prevalence of anemia across participant characteristics. To identify predictors of anemia, we used a generalized logistic regression model with robust variance estimator. This generalized linear approach provides consistent and reliable estimates of odds ratio (OR) making it suitable for complex survey data. We compared the modified Poisson and logistic regression models using Akaike Information Criterion (AIC), considering that logistic regression may overestimate association when the outcome is not rare (\u0026gt;\u0026thinsp;10%). Nevertheless, the logistic regression was chosen due to its lower AIC value, indicating a better fit. A variance inflation factor (VIF) was used to check for multicollinearity before fitting a multivariable regression model. Variables were selected into multivariable analyses using a backward stepwise selection, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eCharacteristics of study population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFour thousand four hundred and twenty (4,420) children aged 6\u0026ndash;59 months were included in this analysis. Children aged 6\u0026ndash;23 months were the most represented (36.5%). Less than half, 31.2% were stunted, only 3.4% were wasted, and 12.4% were underweight. One in ten (11.9%) had fever recently, 55.3% received Vitamin A and just 8.7% had diarrhoea recently. Additionally, 42.8% were from poor household and 67.3% from household with improved toilet facility. More than half (66.2%) had improved source of drinking water and 82.1% lived in a household with mosquito bed net. Regarding mother characteristics, 58.0% had attained primary education, 84.5% were either married or living with a partner, 46.1% aged 25\u0026ndash;34 years and 60.4% were working (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence of anemia\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall prevalence of anemia was 58.8% [56.7\u0026ndash;60.9] among children aged 6\u0026ndash;59 months. Of these, 1.9% had severe anemia, 35.4% had moderate anemia, and 25.6% had mild anemia.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of children, their mother, their household, and the prevalenc7 of anemia across child related factors, maternal factors and household factors. A higher proportion of anemia was observed among children younger than 24 months (72.4% [95%CI; 69.4\u0026ndash;75.2]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stunted (63.3% [95%CI; 59.2\u0026ndash;67.2]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), wasted (73.9% [95%CI:64.1\u0026ndash;81.7]: p\u0026thinsp;=\u0026thinsp;0.005), underweight (65.3% [95%CI: 59.4\u0026ndash;70.7]; p\u0026thinsp;=\u0026thinsp;0.033) and those who had diarrhoea recently (73.1% [95%CI; 67.5\u0026ndash;78.1]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). We also observed higher proportion of anemia in children whose mothers suffered from anemia (67.8% [95% CI: 64.8\u0026ndash;70.7]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were underweight (67.1% [95%CI: 60.2\u0026ndash;73.2]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) or were aged 15\u0026ndash;24 years (65.5% [95%CI:61.0-68.9]: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, this proportion was also significantly higher for children who lived in eastern zone of mainland Tanzania (69.9% [95%CI: 65.2\u0026ndash;74.3) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eMaternal, Children, Household characteristics and prevalence of anemia among children aged 6\u0026ndash;49 months in Tanzania (N\u0026thinsp;=\u0026thinsp;4,420)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnaemia (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence of anaemia, % [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,420 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.8 [56.7\u0026ndash;60.9]\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\u003cp\u003e\u003cb\u003eMaternal characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eWoman\u0026rsquo;s age (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,201 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.5 [61.0-68.9]\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\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,038 (46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.5 [53.4\u0026ndash;59.6]\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\u003cp\u003e35\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,181 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.8 [52.3\u0026ndash;59.4]\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\u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.9 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e975 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.0 [58.0-65.9]\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\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,566 (58.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.7 [54.9\u0026ndash;60.4]\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\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e879 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.5 [54.6\u0026ndash;62.3]\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\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e265 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.5 [52.2\u0026ndash;68.3]\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\u003cp\u003eMarried/Cohabiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,736 (84.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.9 [56.6\u0026ndash;61.1]\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\u003cp\u003eSeparated/Divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e418 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.9 [51.4\u0026ndash;62.3]\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\u003cp\u003e\u003cb\u003eCurrently pregnant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,000 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.8 [56.6\u0026ndash;60.9]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e420 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.6 [52.5\u0026ndash;64.5]\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\u003cp\u003e\u003cb\u003eCurrently breastfeeding\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,334 (52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.1 [51.2\u0026ndash;56.9]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,086 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.0 [61.4\u0026ndash;66.7]\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\u003cp\u003e\u003cb\u003eBMI category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e332 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.1 [60.2\u0026ndash;73.2]\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\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,894 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.2 [57.6\u0026ndash;62.7]\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\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e758 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.4 [47.6\u0026ndash;57.1]\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\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e435 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.3 [49.0-59.5]\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\u003cp\u003e\u003cb\u003eMaternal anaemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,714 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.1 [50.7\u0026ndash;55.5]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,705 (38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.8 [64.8\u0026ndash;70.7]\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\u003cp\u003e\u003cb\u003eWorking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,741 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.6 [58.3\u0026ndash;64.9]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,678 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.9 [54.4\u0026ndash;59.5]\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\u003cp\u003e\u003cb\u003eCovered by health Insurance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,232 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.5 [57.4\u0026ndash;61.6]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e188 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.3 [33.9\u0026ndash;51.1]\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\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,664 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.5 [58.3\u0026ndash;64.7]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,755 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.1 [54.6\u0026ndash;59.6]\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\u003cp\u003e\u003cb\u003eChildren characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eChild\u0026rsquo;s age (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,611 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.4 [69.4\u0026ndash;75.2]\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\u003cp\u003e24\u0026ndash;42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,514 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.1 [53.8\u0026ndash;60.3]\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\u003cp\u003e43\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,295 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.8 [40.4\u0026ndash;47.3]\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\u003cp\u003eMedian (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.4 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eChildren sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,237 (50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.3 [57.1\u0026ndash;63.4]\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\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,183 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.2 [54.6\u0026ndash;59.9]\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\u003cp\u003e\u003cb\u003eBirth Order\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e981 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.0 [54.9\u0026ndash;62.9]\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\u003cp\u003e2nd \u0026amp; 3rd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,691 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.2 [54.8\u0026ndash;61.5]\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\u003cp\u003e\u0026ge;\u0026thinsp;4th\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,748 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.2 [56.4\u0026ndash;62.0]\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\u003cp\u003e\u003cb\u003eBaby size*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e777 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.0 [60.3\u0026ndash;69.4]\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\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,483 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.7 [66.5\u0026ndash;72.8]\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\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.0 [52.6\u0026ndash;75.7]\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\u003cp\u003e\u003cb\u003eStunted*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,970 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.1 [55.8\u0026ndash;60.5]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,348 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.3 [59.2\u0026ndash;67.2]\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\u003cp\u003e\u003cb\u003eWasted*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,193 (96.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.3 [57.1\u0026ndash;61.4]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.9 [64.1\u0026ndash;81.7]\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\u003cp\u003e\u003cb\u003eUnderweight*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,797 (87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.9 [56.7\u0026ndash;61.1]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e539 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.3 [59.4\u0026ndash;70.7]\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\u003cp\u003e\u003cb\u003eHad diarrhoea recently\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,033 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.4 [55.3\u0026ndash;59.5]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e386 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.1 [67.4\u0026ndash;78.1]\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\u003cp\u003e\u003cb\u003eReceived Vitamin A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,974 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.3 [56.3\u0026ndash;62.1]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,446 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.4 [55.7\u0026ndash;61.1]\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\u003cp\u003e\u003cb\u003eHad fever recently\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,895 (88.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.4 [56.3\u0026ndash;60.4]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e525 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.9 [54.7\u0026ndash;68.6]\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\u003cp\u003e\u003cb\u003eHousehold Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1986 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.9 [56.9\u0026ndash;62.8]\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\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e865 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.1 [52.8\u0026ndash;61.3]\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\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1659 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.4 [54.7\u0026ndash;62.0]\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\u003cp\u003e\u003cb\u003eNumber under-five children\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.7 [56.6\u0026ndash;85.8]\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\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,556 (80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.5 [55.1\u0026ndash;59.9]\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\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e824 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.5 [59.5\u0026ndash;67.2]\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\u003cp\u003e\u003cb\u003eToilet facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,443 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.1 [57.9\u0026ndash;64.2]\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\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,977 (67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.7 [55.1\u0026ndash;60.2]\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\u003cp\u003e\u003cb\u003eSource of drinking water\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,496 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.1 [56.0-63.1]\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\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,924 (66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.3 [55.9\u0026ndash;60.8]\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\u003cp\u003e\u003cb\u003eHousehold has mosquito bed net\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e791 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.7 [58.3\u0026ndash;67.0]\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,629 (82.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.9 [55.6\u0026ndash;60.3]\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\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,188 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.6 [55.6\u0026ndash;63.6]\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\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,232 (73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.5 [55.9\u0026ndash;60.9]\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\u003cp\u003e\u003cb\u003eGeographical zones\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e432 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.8 [50.8\u0026ndash;60.7]\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\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e459 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.9 [45.2\u0026ndash;60.6]\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\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e473 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.0 [39.5\u0026ndash;52.6]\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\u003cp\u003eSouthern highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e223 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.1 [38.7\u0026ndash;51.7]\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\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e161 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.1 [59.3\u0026ndash;75.8]\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\u003cp\u003eSouthwest highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e426 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.1 [56.8\u0026ndash;65.4]\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\u003cp\u003eLake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,553 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.2 [56.8\u0026ndash;65.4]\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\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e562 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.9 [65.2\u0026ndash;74.3]\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\u003cp\u003eZanzibar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.2 [61.9\u0026ndash;70.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Frequency do not tally to total N due to missing values, BMI; Body Mass Index, SD; Standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictors of anaemia among children aged 6\u0026ndash;59 months\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents logistic regression findings. Children were more likely to be anemic if their mothers were also anemic (AOR\u0026thinsp;=\u0026thinsp;1.87; 95% CI: 1.57\u0026ndash;2.22), if they were younger particularly those aged 6\u0026ndash;23 months (AOR\u0026thinsp;=\u0026thinsp;2.12; 95% CI: 2.51\u0026ndash;3.87) and 24\u0026ndash;42 months (AOR\u0026thinsp;=\u0026thinsp;1.74; 95% CI: 1.43\u0026ndash;2.11), or if they had experienced recent episodes of diarrhea (AOR\u0026thinsp;=\u0026thinsp;1.62; 95% CI: 1.19\u0026ndash;2.27). In contrast, children were less likely to be anemic if their mothers had medical health insurance (AOR\u0026thinsp;=\u0026thinsp;0.56; 95% CI: 0.38\u0026ndash;0.83), if they were exposed to mass media such as radio or television (AOR\u0026thinsp;=\u0026thinsp;0.83; 95% CI: 0.70\u0026ndash;0.99), or if their household owned mosquito bed nets (AOR\u0026thinsp;=\u0026thinsp;0.77; 95% CI: 0.62\u0026ndash;0.95). (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\u003eWeighted logistic regression model for predictors of anemia among children aged 6\u0026ndash;59 months in Tanzania (N\u0026thinsp;=\u0026thinsp;4,420)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal age [years]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69 [0.57\u0026ndash;0.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 [0.68\u0026ndash;1.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67 [0.54\u0026ndash;0.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 [0.70\u0026ndash;1.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.16 [0.93\u0026ndash;1.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.198\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\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97 [0.80\u0026ndash;1.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.738\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\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003e\u003cb\u003eCurrently breastfeeding\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51 [1.30\u0026ndash;1.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19 [0.99\u0026ndash;1.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35 [0.99\u0026ndash;1.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27 [0.93\u0026ndash;1.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 [0.60\u0026ndash;0.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 [0.63\u0026ndash;0.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79 [0.61\u0026ndash;1.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92 [0.69\u0026ndash;1.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal anaemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.86 [1.59\u0026ndash;2.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.87 [1.57\u0026ndash;2.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 [0.70\u0026ndash;0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 [0.84\u0026ndash;1.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCovered by health Insurance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.50 [0.35\u0026ndash;0.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56 [0.38\u0026ndash;0.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83 [0.71\u0026ndash;0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 [0.70\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChildren characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eChild\u0026rsquo;s age [months]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e6\u0026ndash;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.37 [2.77\u0026ndash;4.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.12 [2.51\u0026ndash;3.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u0026ndash;42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70 [1.41\u0026ndash;2.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74 [1.43\u0026ndash;2.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e43\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003e\u003cb\u003eHad diarrhoea recently\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.02 [1.52\u0026ndash;2.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62 [1.19\u0026ndash;2.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReceived Vitamin A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97 [0.83\u0026ndash;1.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.648\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\u003cp\u003e\u003cb\u003eHad fever recently\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.16 [0.89\u0026ndash;1.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.261\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\u003cp\u003e\u003cb\u003eHousehold Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89 [0.73\u0026ndash;1.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.249\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\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94 [0.79\u0026ndash;1.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.480\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\u003cp\u003e\u003cb\u003eNumber of five-children\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48 [0.22\u0026ndash;1.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.074\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\u003cp\u003e3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62 [0.28\u0026ndash;1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.246\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\u003cp\u003e\u003cb\u003eToilet facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNot improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87 [0.74\u0026ndash;1.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081\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\u003cp\u003e\u003cb\u003eSource of drinking water\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNot improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95 [0.81\u0026ndash;1.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.503\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\u003cp\u003e\u003cb\u003eHousehold has mosquito bed net\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 [0.67\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 [0.62\u0026ndash;0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95 [0.79\u0026ndash;1.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.601\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\u003cp\u003e\u003cb\u003eGeographical zones\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89 [0.65\u0026ndash;1.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 [0.61\u0026ndash;1.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68 [0.50\u0026ndash;0.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 [0.50\u0026ndash;0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 [0.47\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80 [0.57\u0026ndash;1.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.69 [1.11\u0026ndash;2.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.87 [1.21\u0026ndash;2.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19 [0.90\u0026ndash;1.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32 [0.97\u0026ndash;1.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25 [0.96\u0026ndash;1.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35 [1.03\u0026ndash;1.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85 [1.34\u0026ndash;2.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01 [1.41\u0026ndash;2.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZanzibar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.55 [1.16\u0026ndash;2.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64 [1.19\u0026ndash;2.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR; Odds Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study revealed that anemia affects a striking 58.8% of children aged 6\u0026ndash;59 months in Tanzania, highlighting a severe public health concern. The figure surpasses the World Health Organization\u0026rsquo;s threshold for a \"severe public health problem\" (\u0026ge;\u0026thinsp;40%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], pointing to widespread dietary inadequacies, infections like malaria, and possibly limited access to iron supplementation and healthcare.\u003c/p\u003e\u003cp\u003eHigh prevalence of anaemia in this study was also consistent with the previous study conducted in Uganda by Kuziga et al., 2017 (58.8%), Ghana by Klu et al., 2023 (69.1%), Bangladesh by Mollah et al., 2021 (61.8%), and Nigeria by Obasohan et al., 2022 (68.1%) [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAddressing this crisis requires multisectoral interventions, including improved infant and young child feeding (IYCF) practices, maternal nutrition, deworming programs, malaria control, and fortified complementary foods, particularly in rural and underserved areas.\u003c/p\u003e\u003cp\u003eThe finding revealed that children whose mothers were anemic were nearly 2 times more likely to suffer from anemia. This finding was consistency with the previous studies done by Mutonhodza et al., (2022) in rural Zimbabwe, Tesema et al., (2021) in Sub Saharan Africa SSA, Ntenda et al.,(2018) in (Malawi, mozambique, Namibia, and Zimbabwe), Elmardi et al., (2020) in Sudan, Tekeba et al., (2025) in Mozambique, Klu et al., (2023) in Ghana, Obasohan et al., (2022) in Nigeria, and Msaki et al., (2022) in Tanzania [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe possible explanation for the similarities in these studies is that maternal anemia during pregnancy and lactation can affect fetal iron stores, breastmilk quality, and early infant nutrition [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This strong association highlights the intergenerational transmission of poor health and nutritional status. In Tanzania, where dietary diversity and maternal healthcare access remain suboptimal in rural and peri-urban areas, \u003cb\u003et\u003c/b\u003ehis correlation is particularly concerning [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This particular finding emphasizes the need for cost-effective interventions that must target both maternal and child health simultaneously.\u003c/p\u003e\u003cp\u003eMoreover, children aged 6\u0026ndash;23 months were more than twice as likely to be anemic, and those aged 24\u0026ndash;42 months also had increased odds, compared to older children. These findings align with the previous studies done by Gebreegziabiher et al., (2015) in Northern Ethiopia, Kuziga et al., (2017) in Uganda, Elmardi et al., (2020) in Sudan, Mboya et al., (2023) in Kilimanjaro-Tanzania, Mollar et al., (2021) in Bangladesh [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strong association in these studies might be attributed to the critical period of rapid growth and increased nutritional demand in this age group. This age group corresponds to a critical period of rapid growth and increased nutritional demand. At 6\u0026ndash;23 months, children transition from exclusive breastfeeding to complementary feeding, a period when many are inadequately fed [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Tanzania, complementary feeding practices often lack diversity and iron-rich foods, and many caregivers are unaware of optimal infant and young child feeding (IYCF) practices [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The persistence of anemia up to 42 months signals prolonged exposure to poor dietary intake, repeated infections, or both.\u003c/p\u003e\u003cp\u003eSimilarly, children who had recent episodes of diarrhea were 62% more likely to be anemic. A similar finding has been reported in the study conducted in SSA Countries by Tesema et al., 2021, which reported an increased odds of anaemia among children who reported diarrhea in the last two weeks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The possible reason may be attributed to the strong correlation between diarrhea and anemia. Diarrhea may contribute to anemia in multiple ways, including nutrient malabsorption, blood loss, and loss of appetite [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Tanzania, frequent diarrheal episodes are a major public health concern [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], especially among vulnerable populations like the rural and urban poor, due to inadequate water, sanitation, and hygiene (WASH) conditions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These issues lead to increased susceptibility to diarrheal diseases, which are a leading cause of mortality in children under five. The revealed strong association between anaemia and diarrhea in this study underscores the importance of addressing the burden of diarrhea among underfive children.\u003c/p\u003e\u003cp\u003eFurthermore, children of women with medical health insurance were significantly less likely to be anemic (AOR\u0026thinsp;=\u0026thinsp;0.56, 95% CI: 0.38\u0026ndash;0.83). This finding points to the protective effect of access to healthcare services. Health insurance likely facilitates regular antenatal visits, which subsequently increases access to iron and folic acid supplementation, deworming, and nutrition counseling. All of these interventions contribute to preventing anemia in both mothers and children [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Tanzania, the National Health Insurance Fund (NHIF) and the improved Community Health Fund (iCHF) aim to expand health coverage, yet many low-income families remain uninsured. However, the government is looking forward to implementing Tanzania\u0026rsquo;s Universal Health Insurance. This protective factor underscores the importance of promoting insurance uptake among women, especially in underserved regions.\u003c/p\u003e\u003cp\u003eSimilarly, children living in households that owned and presumably used mosquito bed nets were less likely to be anemic. This finding is consistent with existing literature, as malaria is a well-established contributor to anemia in young children in malaria-endemic countries like Tanzania [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The Plasmodium parasite causes hemolysis (destruction of red blood cells), leading to reduced hemoglobin levels [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Children are particularly vulnerable due to their still-developing immune systems and higher nutritional needs.\u003c/p\u003e\u003cp\u003eIn Tanzania, despite national malaria control efforts including the distribution of insecticide-treated bed nets (ITNs), utilization remains inconsistent in some areas due to factors such as household crowding, seasonal perceptions of malaria risk, or net wear and tear [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The protective effect of bed net ownership observed in this study reinforces the importance of maintaining high coverage and promoting consistent usage of ITNs, especially among children under five.\u003c/p\u003e\u003cp\u003eAdditionally, children whose caregivers were exposed to mass media, such as radio and television, were found to be less likely to suffer from anemia. This relationship likely reflects improved health awareness and knowledge among parents or caregivers who receive information through media platforms. Mass media serve as a critical channel for disseminating public health messages, including those related to maternal nutrition, infant feeding practices, hygiene, and disease prevention.\u003c/p\u003e\u003cp\u003eIn Tanzania, radio remains a widely accessible form of communication, especially in rural areas. Health promotion programs that leverage mass media have proven effective in increasing awareness of nutritional needs, iron supplementation, deworming, malaria prevention, and early healthcare seeking [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The association observed here suggests that mass media can be a powerful tool to influence behavior change that directly impacts childhood anemia.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for practice and policy recommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings highlight the need for integrated maternal and child health initiatives in Tanzania, which address both the medical and social determinants of childhood anemia. The substantial link between maternal and child anemia emphasizes the significance of improving prenatal and postnatal care services, such as routine screening, iron supplementation, and timely treatment for anaemia. Health practitioners should increase nutrition counseling for caregivers, particularly for children aged 6\u0026ndash;42 months, and include diarrhea prevention and management as essential components of child health services.\u003c/p\u003e\u003cp\u003eFrom a policy standpoint, increasing access to affordable health insurance for women and low-income families is crucial, as insured moms are considerably less likely to have anemic children. National health policy should encourage increased membership in programs like as the NHIF and iCHF, and coverage should be linked to greater access to nutrition, preventive care, and child health management. The protective role of mosquito net ownership and exposure to mass media emphasizes the importance of coordinated health promotion and malaria prevention activities. Integrating mass communication tactics into child nutrition initiatives can increase their impact. A multi-sectoral, equity-driven approach is required to successfully reduce the burden of anemia and enhance early childhood health outcomes in Tanzania.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies several important determinants of anemia among Tanzanian children under five. The likelihood of anemia was notably higher among children whose mothers were anemic, those under the age of 42 months, particularly those between 6 and 23 months, and those who had recently suffered from diarrhea. In contrast, children whose mothers had health insurance, who lived in households with mosquito bed nets, or who were exposed to mass media were found to have a lower risk of developing anemia. These findings underscore the need for integrated, multisectoral interventions that not only address maternal health and nutrition but also expand access to health insurance, improve early childhood illness prevention, and enhance health education through mass media. Promoting appropriate feeding practices and ensuring household-level malaria prevention are also critical. A holistic approach targeting both medical and social determinants is essential to reduce the burden of anemia and improve child survival and development outcomes in Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAIC\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAkaike Information Criterion\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eBMI\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBody Mass Index\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eCI\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eConfidence Intervals\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eDHS\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eDemographic and Health Survey\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eEA\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eEnumeration Areas\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eHB\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eHaemoglobin\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eiCHF\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eimproved Community Health Fund\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eITNs\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eInsecticide-Treated Bed Nets\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eIYCF\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eInfant and Young Child Feeding\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eNHIF\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eNational Health Insurance Fund\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eOR\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eOdds Ratio\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003ePSU\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePrimary Sampling Units\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSD\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eStandard Deviation\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSSA\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eSub Saharan Africa\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eTDHS\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eTanzania Demographic and Health Survey\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eVIF\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eVariance Inflation Factor\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eWASH\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eWater, Sanitation, and Hygiene\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eWHO\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eWorld Health Organization\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study and TILAM International for statistical consultation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEDO and MJM conceptualized the idea and conducted formal analysis. EDO, EES, VGM, AAN, and MJM interpreted the results, drafted the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCom\u003c/strong\u003e\u003cstrong\u003epeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnaemia in women. and children [Internet]. [cited 2025 Jul 8]. 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J Nutr Educ Behav. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasche C, Lomer MCE, Cavill I, Weiss G. Iron, anaemia, and inflammatory bowel diseases. Gut. 2004.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoward CT, de Pee S, Sari M, Bloem MW, Semba RD. Association of diarrhea with anemia among children under age five living in rural areas of Indonesia. J Trop Pediatr. 2007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuah HO, Amankwa CE, Adomako I, Owusu B, Agbadi P. Comorbid patterns of anaemia and diarrhoea among children aged under 5 years in Ghana: A multivariate complex sample logistic regression analysis and spatial mapping visualisation. Int Health. 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemba RD, de Pee S, Ricks MO, Sari M, Bloem MW. Diarrhea and fever as risk factors for anemia among children under age five living in urban slum areas of Indonesia. Int J Infect Dis. 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKassile T, Thomas J, Niklaus H, Dale Y. A review of sanitation and hygiene in Tanzania. DFID, London. Tanzan J Health Res. 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization, Williams a L, van Drongelen W, Lasky RE, Sanderson M, Lai D et al. Guideline: Daily iron and folic acid supplementation in pregnant women. World Heal Organ. 2012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTraore SS, Bo Y, Kou G, Lyu Q. Iron supplementation and deworming during pregnancy reduces the risk of anemia and stunting in infants less than 2 years of age: a study from Sub-Saharan Africa. BMC Pregnancy Childbirth. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite NJ. Anaemia and malaria. Malaria Journal. 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCuskee S, Brickley EB, Wood A, Mossialos E. Malaria and macronutrient deficiency as correlates of anemia in young children: A systematic review of observational studies. Annals Global Health. 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorges M, Kamala B, Yukich J, Chacky F, Lazaro S, Dismas C et al. Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches. Malar J. 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeer N, Ali AS, Eskilsson H, Jansson A, Abdul-Kadir FM, Rotllant-Estelrich G et al. A qualitative study on caretakers\u0026rsquo; perceived need of bed-nets after reduced malaria transmission in Zanzibar, Tanzania. BMC Public Health. 2012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOdufuwa OG, Ross A, Mlacha YP, Juma O, Mmbaga S, Msellemu D et al. Household factors associated with access to insecticide-treated nets and house modification in Bagamoyo and Ulanga districts, Tanzania. 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J Med Internet Res. 2025;27:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMwebesa E, Awor S, Natuhamya C, Dricile R, Legason ID, Okimait D et al. Impact of mass media campaigns on knowledge of malaria prevention measures among pregnant mothers in Uganda: a propensity score-matched analysis. Malar J [Internet]. 2024;23(1). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-024-05083-x\u003c/span\u003e\u003cspan address=\"10.1186/s12936-024-05083-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Anaemia, Children, Household, Maternal, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-7077412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7077412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAnaemia is a major global public health issue, severely affecting children aged 6\u0026ndash;59 months. Anaemia in children is associated with impaired physical and cognitive development. The burden of anemia is especially high in low- and middle-income countries such as Tanzania. Therefore, the study aimed to identify predictors of anemia among 6\u0026ndash;59 months aged children in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAnalytical cross-sectional analysis using data from the 2022 Tanzania Demographic and Health Survey, a national survey conducted from February to July 2022. The survey employed a two-stage, stratified sampling design, with strata defined by geographic region and urban/rural areas. Primary sampling units were selected from census enumeration areas, followed by systematic household selection. A weighted binary logistic regression model was used to identify predictors of anemia, with results presented as odds ratio (OR) and 95% confidence intervals (CI). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe prevalence of anemia among children aged 6\u0026ndash;59 months in Tanzania was found to be 58.8% (95% CI: 56.7\u0026ndash;60.9%). The study identified several factors significantly associated with childhood anemia. Children were more likely to be anemic if their mothers were also anemic (AOR\u0026thinsp;=\u0026thinsp;1.87; 95% CI: 1.57\u0026ndash;2.22), if they were younger particularly those aged 6\u0026ndash;23 months (AOR\u0026thinsp;=\u0026thinsp;2.12; 95% CI: 2.51\u0026ndash;3.87) and 24\u0026ndash;42 months (AOR\u0026thinsp;=\u0026thinsp;1.74; 95% CI: 1.43\u0026ndash;2.11), or if they had experienced recent episodes of diarrhea (AOR\u0026thinsp;=\u0026thinsp;1.62; 95% CI: 1.19\u0026ndash;2.27). In contrast, children were less likely to be anemic if their mothers had medical health insurance (AOR\u0026thinsp;=\u0026thinsp;0.56; 95% CI: 0.38\u0026ndash;0.83), if they were exposed to mass media such as radio or television (AOR\u0026thinsp;=\u0026thinsp;0.83; 95% CI: 0.70\u0026ndash;0.99), or if their household owned mosquito bed nets (AOR\u0026thinsp;=\u0026thinsp;0.77; 95% CI: 0.62\u0026ndash;0.95).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study identifies several important determinants of anemia among Tanzanian children under five. The likelihood of anemia was notably higher among children whose mothers were anemic, those under the age of 42 months, particularly those between 6 and 23 months, and those who had recently suffered from diarrhea. In contrast, children whose mothers had health insurance, who lived in households with mosquito bed nets, or who were exposed to mass media were found to have a lower risk of developing anemia. These findings underscore the need for integrated, multisectoral interventions that not only address maternal health and nutrition but also expand access to health insurance, improve early childhood illness prevention, and enhance health education through mass media. Promoting appropriate feeding practices and ensuring household-level malaria prevention are also critical. A holistic approach targeting both medical and social determinants is essential to reduce the burden of anemia and improve child survival and development outcomes in Tanzania.\u003c/p\u003e","manuscriptTitle":"The Interplay of Household, Maternal, and Child Characteristics in Predicting Anaemia Among Children (6-59 months) in Tanzania: Insights from 2022 Demographic and Health survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 14:21:35","doi":"10.21203/rs.3.rs-7077412/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":"5b677730-9ecd-4cc4-a38e-317390804044","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T09:39:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 14:21:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7077412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7077412","identity":"rs-7077412","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}