A Multilevel Analysis of Factors Associated with Minimum Acceptable Diets Among Children Aged 6-23 Months in Lesotho: A Study of The Lesotho Multiple Cluster Indicator Study of 2018

A Multilevel Analysis of Factors Associated with Minimum Acceptable Diets Among Children Aged 6-23 Months in Lesotho: A Study of The Lesotho Multiple Cluster Indicator Study of 2018 | 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 A Multilevel Analysis of Factors Associated with Minimum Acceptable Diets Among Children Aged 6-23 Months in Lesotho: A Study of The Lesotho Multiple Cluster Indicator Study of 2018 Nthatisi Leseba, Kerry Vermaak, Tiisetso Makatjane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4657862/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2025 Read the published version in BMC Nutrition → Version 1 posted 11 You are reading this latest preprint version Abstract Background The World Health Organization’s Infant and Young Children Feeding Guidelines (IYCF) has been adopted as an international acceptable complementary feeding guideline known as the Minimum Acceptable Diet (MAD). MAD is a combination of Minimum Meal Frequency (MMF) and Minimum Dietary Diversity (MDD). MAD is not met in many countries in the world. This study aimed to determine the prevalence and multilevel determinants of a minimum acceptable diet among children aged 6–23 months in Lesotho. Methods We conducted a multilevel logistic regression using data from the Lesotho Multiple Cluster Indicator Study of 2018. Results In Lesotho only 22.7% [CI: 19.6 26.2] of children aged 6–23 months received MAD. At individual level, higher odds of receiving MAD were observed among females (WAR = 1.43; CI: 1.1 1.3) and children aged 9–23 months (WAR = 1.67; CI: 1.3 2.2). At household level, only maternal age of 20–25 and 35–39 were statistically significant to MAD; on the other hand, the odds of receiving MAD were higher for children with maternal age of 30–34 (WAR = 1.15; CI: 0.8 1.7) and 40+ (WAR = 1.13; CI: 0.6 2.0). Moreover, at community level, children in communities with high proportions of poor households had lower odds of receiving MAD (WAR = 0.64; CI: 0.5 0.8) and children in communities with high proportions of maternal media exposure had higher odds of receiving MAD (WAR = 1.53: CI:1.1 2.2). Conclusion At individual level, child sex and age were determinants of MAD. At household level maternal age was a determinant of MAD, maternal age in this case indicates that knowledge and experience in childcare contributed to better dietary intake for children aged 6–23. At community level, lack of care resources, food availability and knowledge acquisition were determinants of MAD. Therefore, strategies and programs to improve MDD nationwide should be done at community level. Multilevel Minimum Acceptable Diet Childhood Introduction Adequate nutrition the first 1000 days after birth are a critical window for the behavioral development, health, and optimal growth of the child [1, 2, 3, 4, 5, 6]. The World Health Organization recommends that, children should be exclusively breastfed for the first six months of life and at six months, the child must be introduced to semi-solid or solid foods also known as complementary feeding [7, 8]. Complementary feeding should be given in appropriate amounts, frequencies, consistencies, and using a variety of foods to meet the nutritional demands of the developing and growing child, taking into consideration that dietary demands vary from each age group [9, 10].The World Health Organization’s Infant and Young Children Feeding Guidelines (IYCF) has been adopted as an international acceptable complementary feeding guideline known as the Minimum Acceptable Diet (MAD) [11]. MAD is a combination of Minimum Meal Frequency (MMF) and Minimum Dietary Diversity (MDD) [12, 13, 11]. Interventions have been implemented globally, continentally and in Lesotho to address child health and nutrition. However, the impact of poor child nutrition is still prominent in low-income and middle-income countries, particularly in South Asia and Sub-Saharan Africa (SSA) [14, 15, 16, 17]. Nine out of ten African children do not meet the criteria for MAD outlined by the WHO; and two out of five do not eat meals regularly [18]. In Lesotho achieving MAD is still an obstacle. Complementary feeding in Lesotho is introduced either too early or too late [19, 20]. In 2009, only 54% of children were exclusively breastfed, 74% were introduced to complementary feeding at the time of the survey, 66% met MMF and only 26% met MDD. In 2014, the number of exclusively breastfed children increased to 76%, complementary feeding increased to 76%, MMF decreased to 61% and MDD decreased to 23%. These led to very low prevalence of MAD. In 2009 only 13% of children aged 6-23 months received MAD and that number decreased to 11% in 2014 [19, 20]. Adversely, children that do not meet MAD are more likely to be malnourished, have a lower intellectual quotient, delayed cognitive development, greater behavioral problems, and deficient social skills, and susceptibility to contracting diseases [21, 22]. Food consumption patterns depend on several factors related to the child and the child’s household and community such as child’s age, sex and disease, household food security, household wealth and size, and maternal education, place of residence, home garden and media exposure, antenatal care visits, usage of health facilities, safe drinking water and maternal [11, 12]. Despite the importance of MAD and WHO recommendations, there is a paucity of studies on multilevel determinants of MAD. Therefore, this study aimed to determine the prevalence and multilevel determinants of a minimum acceptable diet among children aged 6–23 months in Lesotho. Methodology Data Source and Study Design The analysis is based on the nationally representative study of the Multiple Indicator Cluster Survey (MICS-2018). The study was conducted by the Lesotho Ministry of Development Planning through the Department of the Bureau of Statistics in collaboration with the United Nations Children's Fund (UNICEF) as part of the Global MICS program. MICS covered several modules and indicators across households, children (0–17), women (15–49 and males (15–49) across all 10 districts of Lesotho and all ecological zones (highlands, lowlands, foothills and the Senqu Valley). The data was collected between April 2018 to September 2018. Lesotho MICS is a multistage stratified sampling survey. The sample was designed to meet the surveys objectives by providing an estimate for a larger number of indicators at national/ area / sub-population level, for urban and rural areas together with four ecological zones: lowlands, foothills, mountains, and Senqu River Valley [ 23 ]. In stage one, urban, peri-urban areas within each district were identified as the main sampling strata (primary sampling units (PSUs); and in stage two, a sample of households were selected [ 23 ]. Within each stratum, a specific number of the Lesotho Census of Population and Housing of 2016 enumeration areas (EAs) were selected systematically with the probability proportional to size [ 23 ]. After the household listing was carried out within the selected EAs, a sample of 26 households was drawn in each sample EA [ 23 ]. However, in the data peri-urban strata were treated as rural to allow for comparability with previous surveys [ 23 ]. It was calculated that a minimum of 36 sample clusters were selected in each district [ 23 ]. There was unequal allocation of the sample size within all ten districts- where some districts 42 clusters were collected in each district with the sample size of 10 400 households (400 clusters and 26 sample households per cluster) [ 23 ]. In total 10 413 households were sampled and 8847 were interviewed (85.9% response rate). Among women aged 15–49, 7197 women were sampled and 6453 were interviewed (89.7% response rate), men (aged 15–49) 3417 were sampled but 2873 were interviewed with 84.1% response rate. Moreover, children under-five had a response rate of 91.2% and children aged 5–17 had a response rate of 94.0% [ 23 ]. For this study, the sample was 903 children aged 6–23 months. Data Analysis Dependent Variable Minimum acceptable diet (MAD) is a merged indicator formulated from minimum dietary diversity (MDD) and minimum meal frequency (MMF), intended to measure dietary intake for breastfeeding children and MDD and MMF along with minimum milk feeding frequency (MMF) for non-breastfeeding children [ 24 ]. MMF is defined as when breastfed children receive solid, semisolid, or soft foods at least twice a day for infants of age 6–8 months and at least three times a day for children of age 9–23 months: while non-breastfed children aged 6–23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least four times a day [ 17 ]. On the other hand, Solomon et al., (2017 pg, 1) and Khamis et al., (2019) defined MDD as the consumption of four or more food groups from the seven food groups for higher dietary quality and to meet daily energy and nutrient requirements of the seven recommended food groups [ 12 , 13 ]. Independent Variable This study follows the UNICEF framework for analyzing factors associated with child health and nutrition. There are three levels predictors namely: immediate, underlying, and basic variables. Immediate variables are child sex, weight at birth and diarrhea as well as respiratory infection. At household level, underlying variables are households’ size, place of residence, female headed households, household wealth, maternal age, and education. Moreover, at community level, basic variables are community health seeking behavior, child immunization and antenatal care. Community food security, poverty, proportion of mothers and males in the households with at least secondary, as well as community with high proportions of female headed households, and community media exposure, safe drinking water and inadequate toilet facilities. The variables were constructed as follows, for child level factors, child sex is binary (male and female), child weight at birth is categorized in three categories (Low birth weight (Less than 2.5kg), average weight (between 2.6kg and 3.8kg) and above average weight (greater than 3.8kg). For diarrhea and respiratory infections two week before the survey was categorized into three categories (yes, no and I don’t know). Among household variables, household size is categorized into 2–5, and 5+, place of residence (rural and urban), maternal residential status (yes and no), female headed households is also binary (yes and no). Household wealth was categorized in to poorest, second, middle, fourth and richest wealth quintiles, maternal age (15–24, 25–34, 35 + maternal education (primary/and or no education, secondary and highest education). In relation to basic variables, firstly the variables were categorized as follows: community health seeking behavior (sought medical care), community immunization (fully immunized), community antenatal care (attended antenatal care), community food security (households that own land and livestock), community poverty (households in the poorest and second wealth quintile), community male and mother education (males and mothers with at least secondary education), community female headed household (households headed by females), community media exposure (mothers with exposure to media at least once a week. All the aforementioned variables were categorised as low and high proportions, low proportions were clusters with proportions less than 40% and high proportions were clusters with proportions more than 40%. Data Analyses Data analyses was conducted in three steps, descriptive analyses, bivariate and multilevel logistic regression. Descriptive analysis of all the independent variables involves, frequencies, percentages, confidence intervals to determine statistical differences and p-values. Secondly, a chi-square analysis (bivariate analysis) was conducted to determine which variables are statistically significant to be included into the main model at 75% confidence internal ( - values < 0.25). A multilevel logistic regression analysis was carried out because the data has evidence of clustering and hierarchy. In multi-level research, the structure of data in the population is hierarchical, and a sample for such a population can be viewed as a multistage sample [ 25 ]. With this explanation, the data analysis model best suits the MICS dataset because it was nested in two stages. The units at lower-level (level-1) are individual and clusters are again nested within units at the next higher level- which is level 2. This kind of clustering can introduce multi-level dependency or correlation among the observations that can have implication for model parameter estimates [ 25 ]. To measure the dependency in the data a three-stage multilevel analysis was conducted by running an empty model and calculating the intra-class correlation coefficient (ICC). The aim of the empty model is to find log-odds of the dependent variable while including no predictors [ 26 ]. Secondly, by running a model that measured the effects of lower-level variables because these intermediate variables are allowed to vary from one cluster to another. Thirdly, running a model with level-2 predictors and level-3 predictors. Finally, by running the final model that include individual, household, and community level variables. The statistical analyses are carried out in this study with the help of STATA15 software. Ethical Statement The Lesotho Multiple Indicators Survey (LMICS) 2018 data is publicly available on the MICS data official website (http://mics.unicef.org/surveys). Informed consent was taken from all the respondents in LMICS, and the use of this MICS data for the purpose of this secondary analysis was obtained from Human and Social Sciences Research Ethics Committee (HSSREC/00002395/2021). Results Data Description Profile of the Study Population There are a total of 903 children aged 6-23, and 200 (22.7%) of them received MAD. Data description in Table 1 shows that, at immediate level, majority of children in the sample were 9-23 months (66%), in terms of child age there was no significant difference between males and females (52% vs 48%). Moreover, majority of them did not have respiratory infections (58%) and diarrhea (88%) two weeks before the survey. At household level, majority of them were from households’ size of 5+ (63%), rural areas (61%), resident mothers (87%), female headed households (62%), second wealth index (23%) as well as maternal age of 20-25(26%) and maternal education of secondary education (53%). At community level, majority of them were from communities with low proportions of health seeking behavior (57%), immunization (61%), antenatal care (87%), as well as from communities with low proportions of males with at least secondary education (54%). They were also from communities with low proportions of female headed households (87%), poor households (61%) and maternal media exposure (82%). Moreover, majority of them were from communities with high proportions of food secure households (74%) and mothers with at least secondary education (68%). Table 1: Characteristics of the study participants Immediate Variables N Percentages (%) Child Age 6-8 309 34 9-23 594 66 Child Sex Male 467 52 Female 436 48 Child Weight at Birth Less than 2.8kg 104 11 2.6kg to 3.8kg 553 61 Greater than 3.9kg 246 28 Diarrhea Yes 106 12 No 797 88 Respiratory Infection Yes 383 42 No 520 58 Underlying Variables Household Size 2-5 335 37 5+ 568 63 Place of Residence Urban 350 39 Rural 553 61 Maternal Residential Status Yes 782 87 No 103 13 Female Headed Households Yes 558 62 No 350 38 Household Wealth Poorest 198 22 Second Wealth Quintile 211 23 Middle Wealth Quintile 193 21 Fourth Wealth Quintile 166 18 Richest 135 16 Maternal Age 15-19 108 12 20-25 237 26 25-29 171 19 30-35 137 15 35-39 106 12 40+ 71 8 Maternal Education No/Primary Education 258 29 Secondary Education 479 53 Tertiary Education 68 7 Missing 98 11 Basic Variables Community Health Seeking Behavior (proportions) Low 518 57 High 385 43 Community Immunization (proportions) Low 545 61 High 358 39 Community Antenatal Care (proportions) Low 119 87 High 784 13 Community Food Security (proportions) Low 234 26 High 669 74 Community Maternal Education (proportions) Low 310 32 High 593 68 Community Male Education (proportions) Low 487 54 High 416 46 Community Female Headed Households (proportions) Low 786 87 High 117 13 Community Poverty (proportions) Low 551 61 High 352 39 Community Media Exposure (proportions) Low 738 82 High 165 18 Bivariate Analysis Table 2 presents all the three level factors associated with stunting at bivariate analysis, together with the frequencies, percentages, confidence intervals and p-values. In Lesotho only 22.7% of children aged 6-23 received MAD. All variables with a p-value less than 0.25 from Chi-Square were considered significantly associated with stunting. Variables that were statistically significant to MAD were child sex, age and respiratory infections. At household level only maternal age was statistically significant. At community level, community poverty, and maternal media exposure. Furthermore, all variables that were statistically significant in Table 2 were further tested using an Adjusted Wald Statistics where all variables were included the main model. Table 2: Prevalence of Minimum Acceptable Diet in Lesotho Variables Not Stunted Stunted P-Value % N CI % N CI Immediate Variables Sex of child Male 79.9 363 [75.01,84.06] 20.1 91 [15.94,24.99] 0.108 Female 74.5 317 [69.34,79.04] 25.5 109 [20.96,30.66] Child Age 6-8 Months 81.9 253 [75.55,86.96] 18.1 56 [13.04,24.45] 0.057 9-23 Months 74.8 427 [70.43,78.66] 25.2 144 [21.34,29.57] Child Weight at Birth < 2.5kg 75.1 77 [64.20,83.51] 24.9 26 [16.49,35.80] 0.754 2.6 kg - 3.8kg 78.2 420 [73.88,82.04] 21.8 117 [17.96,26.12] ≥3.9kg 76.1 183 [68.98,82.03] 23.9 57 [17.97,31.02] Diarrhea Yes 78.9 83 [67.60,86.96] 21.1 22 [13.04,32.40] 0.734 No 77.1 597 [73.43,80.36] 22.9 178 [19.64,26.57] Respiratory Infection Yes 74.4 278 [66.85,79.29] 25.6 97 [20.71,31.15] 0.124 No 79.4 402 [75.07,83.16] 20.6 104 [16.84,24.93] Underlying Variables Household Size 2-5 78.0 256 [71.9,83.1] 22.0 72 [16.9,28.1] 0.644 5+ 76.8 424 [72.7,80.6] 23.2 128 [19.4,27.4] Place of Residence Urban 76.7 258 [70.3 82.1] 23.3 78 [17.9 29.8] 0.731 Rural 77.6 422 [73.5 81.3] 22.4 121 [18.7 26.5] Sex of Household Head Male 78.5 422 [73.9 82.4] 21.5 116 [17.6 26.1] 0.405 Female 75.4 257 [69.4 80.7] 24.6 84 [19.4 30.7] Household Wealth Poorest 79.9 154 [73.9 84.7] 20.1 39 [15.3 26.1] 0.619 Second 77.4 160 [70.4 83.1] 22.6 47 [16.9 29.6] Middle 78.6 145 [68.9 85.8] 21.4 39 [14.2 31.1] Fourth 77.6 127 [69.3 84.3] 22.4 37 [15.7 30.8] Richest 71.1 93 [60.0 80.2] 28.9 38 [19.8 40.0] Maternal Age 15-19 73.5 76 [62.5 82.1] 26.5 27 [17.9 37.5] 0.102 20-25 81.5 192 [75.6 86.2] 18.5 44 [13.8 24.4] 25-29 81.0 136 [73.8 86.6] 19.0 32 [13.4 26.3] 30-34 66.8 86 [55.2 76.6] 33.2 43 [23.4 44.8] 35-39 79.1 84 [66.8 87.7] 20.9 22 [12.3 33.2] 40+ 79.5 23 [61.7 90.3] 20.5 6 [9.7 38.2] Maternal Residential Status Yes 77.0 586 [73.0 80.6] 23.0 175 [19.4 27.0] 0.520 No 79.1 93 [73.5 83.8] 20.9 25 [16.2 26.5] Maternal Education Primary or None 79.7 200 [73.9 84.5] 20.3 51 [15.5 26.1] 0.266 Secondary 77.3 361 [72.2 81.8] 22.7 106 [18.2 27.8] Beyond secondary 68.5 47 [52.9 80.8] 31.5 22 [19.2 47.1] Basic Variables Health Seeking Behavior Low 76.2 387 [0.71 0.80] 23.8 121 [0.20 0.29] 0.437 High 78.8 293 [0.74 0.83] 21.2 79 [0.17 0.27] Level of immunization Low 77.1 412 [0.72,0.81] 22.9 122 [0.19,0.28] 0.880 High 77.6 268 [0.72,0.82] 22.4 77 [0.18,0.28] Level of antenatal Care Low 73.1 83 [0.59,0.84] 26.9 30 [0.16,0.41] 0.452 High 77.9 597 [0.74,0.81] 22.1 169 [0.19,0.26] Community Food Security Low 75.7 184 [67.1,82.6] 24.3 59 [17.4,32.9] 0.605 High 77.9 496 [74.1,81.3] 22.1 141 [18.7,25.9] Community Maternal Education Low 77.1 234 [71.9,81.6] 22.9 69 [18.4,28.1] 0.946 High 77.4 446 [72.5,81.6] 22.6 130 [18.4,27.5] Community Male Education Low 78.5 375 [74.1,82.3] 21.5 103 [17.7,25.9] 0.434 High 75.9 305 [70.2,80.8] 24.1 97 [19.2,29.8] Female Headed Communities Low 77.4 592 [73.6,80.9] 22.6 173 [19.2,26.4] 0.829 High 76.4 88 [66.6,84.0] 23.6 27 [16.0,33.4] Community Poverty Low 75.2 402 [70.1,79.8] 24.8 132 [20.2,29.9] 0.102 High 80.5 278 [76.1,84.2] 19.5 67 [15.8,23.9] Community Media Exposure Low 78.5 565 [74.8,81.7] 21.5 155 [18.3,25.2] 0.138 High 72.0 114 [62.5,79.8] 28.0 45 [20.2,37.5] Multilevel Model The empty model (null model) was run to determine clustering, the second model included immediate and underlying variables (level 1 and level 2) and the third model included basic variables (level 3). In this study, there was evidence of variability of clustering. The null model had a Chi-Square of 19.91 and a p-value of 0.0000 making it statistically significant thus indicating clustering in the data. The Interclass correlation (ICC) of this model was right at the cut-off point at 0.054. Heck et al., 2014 discussed that 0.05 is often regarded as a conventional threshold to indicate more substantial evidence of clustering [27]. Moreover, the probability of being stunted in each community was (odd of being stunted/ (1+ odds of being stunted) 0.302. In general, the unconditional probability of a child being stunted is 30.2%. There was also variability of clustering between households and communities with a chi square of 36.33 and p-value of 0.000 and ICC of 0.2574 (above the threshold). Factors associated with MAD Table 3 presents the main model of level one (immediate variables), level two (underlying variables) and level three (basic variables). Seventy five percent confidence interval was used hence p-value less than 0.25 was considered statistically significant. At individual level, higher odds of receiving MAD were observed among females (WAR=1.43; CI: 1.1,1.3) and children aged 9-23 months (WAR=1.67; CI: 1.3,2.2). At household level, only maternal age was associated with MAD. The odds of receiving MAD were lower for children whose mothers were aged 20-24 (WAR=0.70; CI: 0.5,1.0) and 35-39 (WAR=0.62; CI: 0.4,1.0). At community level, community poverty, and community media exposure were associated with MAD. Children residing in communities with high community poverty had lower odds of receiving MAD (WAR=0.64; CI: 0.5,0.8) while children residing in communities with high maternal media exposure had higher odds of receiving MAD (WAR=1.53: CI: 1.1,2.2). Table 3: Factors associated with MAD: Lesotho 2018 Variables UNOR (75% CI) WAOR (75% CI) WA (P-value) Immediate Variables Child Sex Male 1.00 1.00 1.00 Female 1.31 [1.1,1.6] 1.43 [1.1,1.8] 0.110 Child Age 6-8 Months 1.00 1.00 1.00 9-23 Months 1.57 [1.3,2.0] 1.67 [1.3,2.2] 0.037 Respiratory Infections Yes 1.00 1.00 1.00 No 0.77 [0.6,1.0] 0.69 [0.5,0.9] 0.086 Underlying Variables Maternal Age 15-19 1.00 1.00 1.00 20-25 0.68 [0.5,1.0] 0.70 [0.5,1.0] 0.231 25-29 0.66 [0.5,1.0] 0.70 [0.5,1.0] 0.259 30-34 1.10 [0.7,1.6] 1.15 [0.8,1.7] 0.659 35-39 0.57 [0.4,0.9] 0.62 [0.4,1.0] 0.200 40+ 0.99 [0.6,1.8] 1.13 [0.6,2.0] 0.808 Basic Variables Community Poverty Low 1.00 1.00 1.00 High 0.88 [0.7,0.1] 0.64 [0.5,0.8] 0.060 Community Media Exposure Low 1.00 1.00 1.00 High 1.24 [0.9,1.7] 1.53 [1.1,2.2] 0.171 Notes: UNOR denotes unadjusted odds ratio, WAOR, Wald adjusted odds ratio. WA Wald adjusted Discussion The prevalence of minimum acceptable diet child feeding practice in Lesotho was 22.71% [95%CI=0.1955 0.2622]. The prevalence was lower than that in Central Ethiopia (31.6%) and Nepal (30.1%), [28, 24]. It was higher than West Africa (11.56%), West Ethiopia (12.6%) and Northwest Ethiopia (8.6%) [29, 5]. Child age was a associated with MAD in this study. Children aged 9-23 months were more likely to meet the MAD compared to those aged 6-8 months. In many countries, less than one fourth of infants aged 6-23 months meet the criteria for dietary and feeding frequency [12]. Worku et al., (2022) in support indicated that, the reason why children are not meeting MAD requirements is because in most cases complementary feeding happens late, and the start of complementary feeding is commonly done with only limited items such as milk or cereal [29]. Late introduction of complementary feeding could also be attributed to the simultaneous occurrence of the eruption of teeth and the introduction of solid and semi-solid food [12]. The teething process typically leads to loss of appetite and negatively affecting the frequencies and diversity of meals [2]. Moreover, younger children are mainly breastfed, and their mothers might assume complementary feeding their younger children is not important as for older children, since older children usually have family meals [1, 34, 38]. Younger children are also likely to be introduced to complementary feeding late thus affecting the meal diversity and frequency [30, 31]. In Lesotho, complementary feeding is normally introduced too early or too late with insufficient diversity with more children eating cereal than other food types [32]. Sex of the child was also associated with MAD with girls more likely to receive MAD compared to boys. This was also reported in Southeast Ethiopia [33]. In the contrary, most studies found that boys had better dietary intake compared to girls [33, 34]. Hien et al., (2023) highlighted that, in some communities in Burkina Faso, there were gender preferences and children feeding by gender is common [34]. In Lesotho, being a boy from poor communities and rural areas is a great disadvantage than girls in the same environment when it comes to diets and access to education [35]. In Lesotho boys are often herding livestock from as young as three years [39]. This sociocultural phenomenon of herdboyship can lead to less care given to boy children from birth despite Lesotho being a patriarchal country. These herdboys are expected to live in isolation for months at a time despite their ages herding sheep and cattle and make their way home only twice a year in winter, so the animals could be checked, counted, and kept warm for a brief period [36]. At household level, maternal age was associated with MAD where children mothers were aged 20-25 and 35-39 were less likely to receive MAD compared to children whose mothers were aged 15-19 years. Children from the rest of the age groups (25-29, 30-34 and 40+) were equally likely to receive MAD compared to children whose mothers were aged 15-19 years. This means children to teenage mothers were no different from being fed MAD compared to most age groups. Teenage pregnancy in Lesotho remains a challenge [37]. The teenage pregnancy birth rates are high, estimated at 94 per 1000 girls aged 15-19 [37]. Teenage pregnancy is associated with several social and health risks and one of them is parental involvement [35, 37, 39]. Lack of parental involvement leaves grandparents with the problem of finding other ways to support their grandchildren [40]. The number of grandparents raising their grandchildren has increased significantly globally [41]. In 2010, 7 million grandmothers lived with their grandchildren globally, with 2.7 million grandparents responsible for the basic needs of one or more grandchildren [41]. Moreover, grandparents providing childcare are likely to be females, with a partner, with higher educational attainment and wealthier, thus increasing chances of children receiving MDD and MAD [42]. In China, grandparents were also more likely to support young mothers with children with appropriate mother and child feeding practices [43]. Breastfeeding support by grandmothers has been explored in Nepal, showing that grandmother’s involvement can promote and endorse positive health feeding practices [43]. In the United States, grandparents with knowledge about the importance of healthy diets can assist their grandchildren [43]. In support, children raised by grandparents and children of older mothers had higher odds of receiving MAD. In East Africa and Northwest Ethiopia, older aged women had high chances of providing MAD to their children compared to younger mothers [29]. Older mothers have experience on feeding practices and this might play a significant role in appropriate feeding practices [29, 42]. At community level, community poverty was a determinant of MAD, with children in communities with high community poverty less likely to meet the minimal dietary diversity and acceptable diet. Poverty, food insecurity and lower socio-economic status are considered as major challenges affecting feeding practices [44]. In India, Argentina, Indonesia, and Rwanda households with lower wealth quintiles were associated with unmet minimum dietary diversity [44, 45, 46, 47]. This is because poorest socioeconomic status families are unable to buy diverse food items [45]. In rural Rwanda, mothers highlighted that, poverty leads to reduced number of meals received by children, and they breastfeed children longer than they should to maximize food for the rest of the family [45]. Poverty also leads to food insecurity. Lindsay et al., (2012) highlighted that, household food insecurity is a critical variable for understanding the nutritional status in low-income populations [44]. Food insecurity is defined as the limited or uncertain availability of nutritionally adequate and safe foods which leads to poor diets [44]. Low food security also affects children through the reduced quality and variety of recommended diets [47]. Moreover, food security also occurs when nutritious food is not available to households because of areas they reside in [47]. Communities have different economic conditions that affect food availability, access and cultural preference which can influence food diversity in child feeding practices [46, 48]. On the other hand, Community Media Exposure was a determinant of MAD with children residing in communities with high community maternal media exposure more likely to receive MAD. In Lesotho, majority of households are exposure to mass media. The greater part of the population owns radios, with radio signal covering about 87% of the country, with most areas that are not covered are able to tune into radio stations in South Africa [49]. Mass media directly influences people’s behaviour because it is used to deliver health messages to promote social and behavioural change [50]. Zebodia and Atmaka (2021) highlighted that, mass media has a crucial role in educating mothers and caregivers on appropriate complementary feeding practices especially in the diversification of food [51]. Moreover, mass media is also associated with utilization of maternal health services like antenatal care, delivery in health facilities and appropriate feeding practices [27, 24]. In Indonesia, India, Nepal, Southern-Asia, East-Africa, Ethiopia, Bangladesh access to information from mass media was significantly associated with meeting recommended child feeding practices [52, 51, 27, 24, 31, 29] Conclusion This study aimed to find determinants of MAD in Lesotho at three levels- individual, household, and community level. At individual level, child sex and age were determinants of MAD. At household level maternal age was a determinant of MAD, maternal age in this case indicates that knowledge and experience in childcare contributed to better dietary intake for children aged 6-23. At community level, lack of care resources, food availability and knowledge acquisition were determinants of MAD. Therefore, strategies should be in place to educate people in communities through mass media, gatherings about child nutrition. Limitations The study shares a common limitation of cross-sectional study-the study supports the association between child diet, stunting, and independent variables, but not proving the causal relationship. Moreover, this study had limitations in that, the majority of the data is self-reported by mothers/caregivers, making it subject to recall bias and it can be subject to social-desirability bias. On the other hand, the study used clustering by aggregating individual and household variables, these may in misclassification or overestimation. Moreover, communities were created using clusters derived from Enumeration areas which might also be subject to coverage error. Declarations Acknowledgements The study is part of the author’s thesis for a doctoral dissertation with the School of Built Environment and Development Studies at the University of Kwa-Zulu Natal, Durban, South Africa. We are grateful to the UNICEF, MICS team for providing the 2018 MICS dataset for the analysis. Funding No grant was received for the study from any agency, university or public. Availability of data and materials The study used the LMICS 2018 dataset which is publicly available on the MICS data official website https://mics.unicef.org/surveys with all respondents identifier information removed. Authors’ contributions The study was designed by NL 1 and KV 2 . TT 3 was involved in the revision of the paper as well as the editing of the final manuscript. Competing interests The author declares that they have no competing interests. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles stated in the Helsinki Declaration. Before the launch of MICS data collection, the Bureau of Statistics Lesotho was approved by the Ethical Review Committee in February 2018 to conduct the study. The author communicated with the UNICEF MICS team in 3 UN Plaza, New York, USA and was granted permission to download and use the LMICS dataset. Furthermore, the author received ethical approval from the University of Kwa-Zulu Natal, Durban, South Africa to use MICS datasets for the purpose of this secondary analysis was obtained from Human and Social Sciences Research Ethics Committee (HSSREC/00002395/2021). Authors details 1 Department of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru. 2 Population Studies, School of Built Environment and Development Studies, University of KwaZulu-Natal, Durban, South Africa. 3 Department of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru. 4 Department of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru. References Moghames, P., Hammami, N., Hwalla, N., Yazbeck, N., Shoaib, H., Nasreddine, L. And Naja, F., 2015. Validity and reliability of a food frequency questionnaire to estimate dietary intake among Lebanese children. Nutrition Journal, 15(1). Tegegne, M. et al. (2017) ‘Factors associated with minimal meal frequency and dietary diversity practices among infants and young children in the predominantly Agrarian Society of Bale Zone, Southeast Ethiopia: A Community Based Cross Sectional Study’, Archives of Public Health, 75(1). doi:10.1186/s13690-017-0216-6 Custodio, E., Herrador, Z., Nkunzimana, T., Węziak-Białowolska, D., Perez-Hoyos, A. And Kayitakire, F., 2019. Children’s dietary diversity and related factors in Rwanda and Burundi: A multilevel analysis using 2010 Demographic and Health Surveys. PLOS ONE, 14(10), p.e0223237. Wagris, M. et al. (2019) ‘Minimum meal frequency practice and its associated factors among children aged 6–23 months in Amibara District, North East Ethiopia’, Journal of Environmental and Public Health, 2019, pp. 1–7. doi:10.1155/2019/8240864. Birie, Kassa and Kebede, M. (2020) Minimum acceptable diet practice and its associated factors among children’s aged 6–23 months in rural communities of Goncha District, North West Ethiopia, 2020 [Preprint]. doi:10.21203/rs.3.rs-126420/v1. Asif, A.M. and Akbar, M. (2022) ‘A non-linear decomposition analysis of children’s dietary diversity scores: Explaining rural-urban inequality’, Quality & Quantity, 57(4), pp. 3273–3284. doi:10.1007/s11135-022-01501-x. UNICEF (2019) Breastfeeding from the first hour of birth: What works and what hurts. Available at: https://www.unicef.org/stories/breastfeeding-first-hour-birth-what-works-and-what-hurts (Accessed: 24 March 2024). WHO. 2020. Infant and young child feeding. [online] Available at: https://www.who.int/news-room/fact-sheets/detail/infant-and-young-child-feeding [Accessed 7 March 2022]. Kaur M, Kaur J, Sirswal S, Kumari S, Thakur N. (2017). Assessment of feeding practices of young children less than 2 years of age. EC Paediatr 6: 474-487. Terati, Yuniarti, H. And Susanto, E., 2018. Effects of Diet and Breastfeeding Duration on the Stunting Status of Children under 5 Years of Age at Maternal and Child Health Centers of the Palembang Regional Office of Health. Pakistan Journal of Nutrition, 17(2), pp.51-56. Heeman, M. et al. (2022) ‘Food Group consumption patterns among children meeting and not meeting who’s recommended dietary diversity: Evidence from 197,514 children in 59 countries’, Food Policy, 112, p. 102368. doi:10.1016/j.foodpol.2022.102368. Solomon, D., Aderaw, Z. And Tegegne, T., 2017. Minimum dietary diversity and associated factors among children aged 6–23 months in Addis Ababa, Ethiopia. International Journal for Equity in Health, 16(1). Khamis, A., Mwanri, A., Ntwenya, J. And Kreppel, K., 2019. The influence of dietary diversity on the nutritional status of children between 6 and 23 months of age in Tanzania. BMC Pediatrics, 19(1). UNICEF, WHO and World Bank Group, (2016). LEVELS AND TRENDS IN CHILD MALNUTRITION. [online] Available at: https://data.unicef.org/wp-content/uploads/2017/05/JME-2017-brochure-1.pdf [Accessed 27 May 2020]. United Nations (2021) How children in Lesotho experience poverty... in Lesotho, United Nations. Available at: https://lesotho.un.org/en/164949-how-children-lesotho-experience-poverty (Accessed: 26 June 2023). Anjorin, S., Uthman, O., Ameyaw, E., Ahinkorah, B., Chattu, V., Bishwajit, G., Seidu, A., Darteh, E. And Yaya, S., 2020. Undernutrition, polygynous context and family structure: a multilevel analysis of cross-sectional surveys of 350 000 mother–child pairs from 32 countries. BMJ Global Health, 5(10), p.e002637. WHO. 2020. Infant and young child feeding. [online] Available at: https://www.who.int/news-room/fact-sheets/detail/infant-and-young-child-feeding [Accessed 7 March 2022]. FAO. 2021. The State of Food Security and Nutrition in the World 2021. [online] Available at: https://www.fao.org/state-of-food-security-nutrition [Accessed 28 February 2022]. Lesotho Demographic and Health Survey Report., (2009) Ministry of Health Lesotho. Lesotho Demographic and Health Survey Report., (2014) Ministry of Health Lesotho. Akombi, B., Agho, K., Merom, D., Hall, J. And Renzaho, A., 2017. Multilevel Analysis of Factors Associated with Wasting and Underweight among Children Under-Five Years in Nigeria. Nutrients, 9(1), p.44. Kandala, N., Madungu, T., Emina, J., Nzita, K. And Cappuccio, F., 2019. Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter?. BMC Public Health, 11(1). Lesotho Multiple Indicator Study Report. (2018) Available at: https://mics-surveys-prod.s3.amazonaws.com/MICS6/Eastern%20and%20Southern%20Africa/Lesotho/2018/Survey%20findi ngs/Lesotho%202018%20MICS%20Survey%20Findings%20Report_English.pdf [Accessed 27 May 2019]. Sapkota, S. et al. (2022) ‘Predictors of minimum acceptable diet among children aged 6–23 months in Nepal: A multilevel analysis of Nepal Multiple Indicator Cluster Survey 2019’, Nutrients, 14(17), p. 3669. doi:10.3390/nu14173669. Khan, M. N., & Islam, M. M. (2017). Effect of Exclusive Breastfeeding on Selected Adverse Health and Nutritional Outcomes: A Nationally Representative Study. BMC Public Health, 17, ArticleNo.889. https://doi.org/10.1186/s12889-017-4913-4. Sommet, N. and Morselli, D., 2017. Keep calm and learn multilevel logistic modeling: a simplified three-step procedure using Stata, R, mplus, and SPSS. International Review of Social Psychology, 30, pp.203-218 Lencha Moga, F. et al. (2022a) ‘Minimum dietary diversity and associated factors among children under the age of five attending public health facilities in Wolaita Soddo Town, southern Ethiopia, 2021: A cross-sectional study’, BMC Public Health, 22(1). doi:10.1186/s12889-022-14861-8. Molla, W. et al. (2021) ‘Dietary diversity and associated factors among children (6–23 months) in Gedeo Zone, Ethiopia: Cross - Sectional Study’, Italian Journal of Pediatrics, 47(1). doi:10.1186/s13052-021-01181-7. Worku, M.G. et al. (2022) ‘Minimum acceptable diet feeding practice and associated factors among children aged 6–23 months in East Africa: A multilevel binary logistic regression analysis of 2008–2018 demographic health survey data’, Archives of Public Health, 80(1). doi:10.1186/s13690-022-00882-7. Birhanu, H. et al. (2022) ‘Minimum acceptable diet and associated factors among children aged 6–23 months during fasting days of Orthodox Christian mothers in Gondar City, North West Ethiopia’, BMC Nutrition, 8(1). doi:10.1186/s40795-022-00558-z. Sisay, B.G. et al. (2022) ‘Dietary diversity and its determinants among children aged 6–23 months in Ethiopia: Evidence from the 2016 Demographic and Health Survey’, Journal of Nutritional Science, 11. doi:10.1017/jns.2022.87. Lesotho Zero Hunger Strategic Review (2018) . Available at: https://www.wfp.org/publications/lesotho-zero-hunger-strategic-review-2018#:~:text=To%20respond%20to%20the%20Zero,eliminating%20food%20insecurity%20and%20malnutrition . (Accessed: 25 March 2020). Mekonnen, T.C. et al. (2017) ‘Meal frequency and dietary diversity feeding practices among children 6–23 months of age in Wolaita Sodo Town, southern Ethiopia’, Journal of Health, Population and Nutrition, 36(1). doi:10.1186/s41043-017-0097-x. Hien, A. et al. (2023) ‘Factors associated with minimum dietary diversity, minimum meal frequency and minimum acceptable diet practices among children 6-23 months of age in Bobo-Dioulasso, Burkina Faso’, African Journal of Food, Agriculture, Nutrition and Development, 23(03), pp. 22831–22852. doi:10.18697/ajfand.118.22580. UNESCO (2022) Leave no child behind: boys’ disengagement from education: Lesotho case study. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000381155 (Accessed: 26 March 2023). HelpLesotho., (2016). Herd Boy Program.. https://www.helplesotho.org/wp-content/uploads/2016/04/Computer-and-Life-Skills-Training-March-2016.pdf (accessed April 9, 2024). UNFPA (2021) Early and unintended pregnancies rife in Lesotho: 13-year-olds among those bearing children. Available at: https://esaro.unfpa.org/en/news/early-and-unintended-pregnancies-rife-lesotho-13-year-olds-among-those-bearing-children (Accessed: 26 March 2024). Wilson, D. et al. (2017) ‘Association of Teen Mothers’ and grandmothers’ parenting capacities with child development: A study protocol’, Research in Nursing & Health, 40(6), pp. 512–518. doi:10.1002/nur.21839. HelpLesotho (2023) Help Lesotho: Young Mother Program Overview. Available at: https://helplesotho.org/young-mother-program/ (Accessed: 09 April 2024). Sampson, D., Hertlein, K. (2015). The experience of grandparents raising grandchildren. GrandFamilies: The Contemporary Journal of Research, Practice and Policy, 2 (1). Available at: http://scholarworks.wmich.edu/grandfamilies/vol2/iss1/4 Choi, M., Sprang, G. and Eslinger, J.G. (2016a) ‘Grandparents raising grandchildren’, Family & Community Health, 39(2), pp. 120–128. doi:10.1097/fch.0000000000000097. Damanik, S.M., Wanda, D. and Hayati, H. (2020) ‘Feeding practices for toddlers with stunting in Jakarta: A case study’, Pediatric Reports, 12(11), p. 8695. doi:10.4081/pr.2020.8695. Liu, Y., Zhao, J. and Zhong, H. (2022) ‘Grandparental care and childhood obesity in China’, SSM - Population Health, 17, p. 101003. doi:10.1016/j.ssmph.2021.101003. Lindsay, A.C., Ferarro, M., Franchello, A., La Barrera, R.D., Machado, M.M.T., Pfeiffer, M.E. and Peterson, K.E., 2012. Child feeding practices and household food insecurity among low-income mothers in Buenos Aires, Argentina. Ciencia & saude coletiva, 17, pp.661-669. Dusingizimana, T. et al. (2020) ‘A qualitative analysis of infant and young child feeding practices in rural Rwanda’, Public Health Nutrition, 24(12), pp. 3592–3601. doi:10.1017/s1368980020001081. Simbolon, D., Ludji, I.D. and Rahyani, Y. (2022) ‘Nutrition assistance of breastfeeding and complementary feeding associated with the Linier growth of stunted children aged 6-24 months’, Public Health and Preventive Medicine Archive , 10(2), pp. 120–129. doi:10.53638/phpma.2022.v10.i2.p03. Raru, T.B. et al. (2023) ‘Minimum dietary diversity among children aged 6–59 months in East Africa countries: A Multilevel Analysis’, International Journal of Public Health, 68. doi:10.3389/ijph.2023.1605807. Francis-Devine, B., Malik, X. and Danechi, S. (2023) Food poverty: Households, Food Banks and Free School Meals, House of Commons Library. Available at: https://researchbriefings.files.parliament.uk/documents/CBP-9209/CBP-9209.pdf (Accessed: 26 October 2023). Friedrich-Ebert-Stiftung (2018) Friedrich-Ebert-Stiftung Mass Media. Available at: https://www.fes.de/ (Accessed: 26 March 2023). Kim, S.S. et al. (2018) ‘Factors influencing the uptake of a mass media intervention to improve child feeding in Bangladesh’, Maternal & Child Nutrition, 14(3). doi:10.1111/mcn.12603. Zebadia, E. and Atmaka, D.R. (2021) ‘Factors associated with minimum dietary diversity among 6-11-month-old children in Indonesia: analysis of the 2017 Indonesian demographic and Health Survey’, Public Health and Preventive Medicine Archive, 9(2), pp. 132–138. doi:10.15562/phpma.v9i2.340. Eshete, H., Abebe, Y., Loha, E., Gebru, T. And Tesheme, T., 2017. Nutritional status and effect of maternal employment among children aged 6–59 months in Wolayta Sodo Town, Southern Ethiopia: a cross-sectional study. Ethiopian Journal of Health Sciences, 27(2), p.155. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2025 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviews received at journal 04 Sep, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers invited by journal 19 Aug, 2024 Editor invited by journal 03 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 29 Jun, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4657862","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323359479,"identity":"94380b58-ffc8-423a-b1c5-d269a82fbd62","order_by":0,"name":"Nthatisi Leseba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACdiBmbGCQA7EPPCBKCzNEizFYSwIpWhIbQByitJgzMx/+8HGHXfr8sMMPgbbYyek2ENBi2cyWJjnzTHLuxttpBkAtycZmBwhoMTjMY8bM28acu3F2AkjLgcRthLXwf/78t60+3XB2+gditfAwSDO2HU6Ql84h2hY2M8neM8cNN0jnFBxIMCDGL8ebH3/4uaNaXn52+uYPHyrs5AhqQegFqzQgVjkIyDeQonoUjIJRMApGFAAA4WZGYjxjcAYAAAAASUVORK5CYII=","orcid":"","institution":"National University of Lesotho","correspondingAuthor":true,"prefix":"","firstName":"Nthatisi","middleName":"","lastName":"Leseba","suffix":""},{"id":323359480,"identity":"e8c9d7e6-5fcf-4f0b-9a57-cf536991c156","order_by":1,"name":"Kerry Vermaak","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Kerry","middleName":"","lastName":"Vermaak","suffix":""},{"id":323359481,"identity":"81a1fe13-2adf-4e26-be4b-dc235c6e6d6b","order_by":2,"name":"Tiisetso Makatjane","email":"","orcid":"","institution":"National University of Lesotho","correspondingAuthor":false,"prefix":"","firstName":"Tiisetso","middleName":"","lastName":"Makatjane","suffix":""}],"badges":[],"createdAt":"2024-06-29 06:36:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4657862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4657862/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-025-01030-4","type":"published","date":"2025-02-26T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77622483,"identity":"8631213c-4702-4b1e-881d-d11b7257d4c3","added_by":"auto","created_at":"2025-03-03 16:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1357562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4657862/v1/30432ec8-8f22-4795-a65a-5723f4d97a03.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multilevel Analysis of Factors Associated with Minimum Acceptable Diets Among Children Aged 6-23 Months in Lesotho: A Study of The Lesotho Multiple Cluster Indicator Study of 2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdequate nutrition the first 1000 days after birth are a critical window for the behavioral development, health, and optimal growth of the child [1, 2, 3, 4, 5, 6]. The World Health Organization recommends that, children should be exclusively breastfed for the first six months of life and at six months, the child must be introduced to semi-solid or solid foods also known as complementary feeding [7, 8]. Complementary feeding should be given in appropriate amounts, frequencies, consistencies, and using a variety of foods to meet the nutritional demands of the developing and growing child, taking into consideration that dietary demands vary from each age group [9, 10].The World Health Organization\u0026rsquo;s Infant and Young Children Feeding Guidelines (IYCF) has been adopted as an international acceptable complementary feeding guideline known as the Minimum Acceptable Diet (MAD) [11]. MAD is a combination of Minimum Meal Frequency (MMF) and Minimum Dietary Diversity (MDD) [12, 13, 11]. Interventions have been implemented globally, continentally and in Lesotho to address child health and nutrition. However, the impact of poor child nutrition is still prominent in low-income and middle-income countries, particularly in South Asia and Sub-Saharan Africa (SSA) [14, 15, 16, 17]. Nine out of ten African children do not meet the criteria for MAD outlined by the WHO; and two out of five do not eat meals regularly [18]. In Lesotho achieving MAD is still an obstacle. Complementary feeding in Lesotho is introduced either too early or too late [19, 20]. In 2009, only 54% of children were exclusively breastfed, 74% were introduced to complementary feeding at the time of the survey, 66% met MMF and only 26% met MDD. In 2014, the number of exclusively breastfed children increased to 76%, complementary feeding increased to 76%, MMF decreased to 61% and MDD decreased to 23%. These led to very low prevalence of MAD. In 2009 only 13% of children aged 6-23 months received MAD and that number decreased to 11% in 2014 [19, 20].\u003c/p\u003e\n\u003cp\u003eAdversely, children that do not meet MAD are more likely to be malnourished, have a lower intellectual quotient, delayed cognitive development, greater behavioral problems, and deficient social skills, and susceptibility to contracting diseases [21, 22]. Food consumption patterns depend on several factors related to the child and the child\u0026rsquo;s household and community such as child\u0026rsquo;s age, sex and disease, household food security, household wealth and size, and maternal education, place of residence, home garden and media exposure, antenatal care visits, usage of health facilities, safe drinking water and maternal [11, 12].\u003c/p\u003e\n\u003cp\u003eDespite the importance of MAD and WHO recommendations, there is a paucity of studies on multilevel determinants of MAD. Therefore, this study aimed to determine the prevalence and multilevel determinants of a minimum acceptable diet among children aged 6\u0026ndash;23 months in Lesotho.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Study Design\u003c/h2\u003e \u003cp\u003eThe analysis is based on the nationally representative study of the Multiple Indicator Cluster Survey (MICS-2018). The study was conducted by the Lesotho Ministry of Development Planning through the Department of the Bureau of Statistics in collaboration with the United Nations Children's Fund (UNICEF) as part of the Global MICS program. MICS covered several modules and indicators across households, children (0\u0026ndash;17), women (15\u0026ndash;49 and males (15\u0026ndash;49) across all 10 districts of Lesotho and all ecological zones (highlands, lowlands, foothills and the Senqu Valley). The data was collected between April 2018 to September 2018.\u003c/p\u003e \u003cp\u003eLesotho MICS is a multistage stratified sampling survey. The sample was designed to meet the surveys objectives by providing an estimate for a larger number of indicators at national/ area / sub-population level, for urban and rural areas together with four ecological zones: lowlands, foothills, mountains, and Senqu River Valley [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In stage one, urban, peri-urban areas within each district were identified as the main sampling strata (primary sampling units (PSUs); and in stage two, a sample of households were selected [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Within each stratum, a specific number of the Lesotho Census of Population and Housing of 2016 enumeration areas (EAs) were selected systematically with the probability proportional to size [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After the household listing was carried out within the selected EAs, a sample of 26 households was drawn in each sample EA [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, in the data peri-urban strata were treated as rural to allow for comparability with previous surveys [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It was calculated that a minimum of 36 sample clusters were selected in each district [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There was unequal allocation of the sample size within all ten districts- where some districts 42 clusters were collected in each district with the sample size of 10 400 households (400 clusters and 26 sample households per cluster) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In total 10 413 households were sampled and 8847 were interviewed (85.9% response rate). Among women aged 15\u0026ndash;49, 7197 women were sampled and 6453 were interviewed (89.7% response rate), men (aged 15\u0026ndash;49) 3417 were sampled but 2873 were interviewed with 84.1% response rate. Moreover, children under-five had a response rate of 91.2% and children aged 5\u0026ndash;17 had a response rate of 94.0% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For this study, the sample was 903 children aged 6\u0026ndash;23 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eDependent Variable\u003c/h2\u003e \u003cp\u003eMinimum acceptable diet (MAD) is a merged indicator formulated from minimum dietary diversity (MDD) and minimum meal frequency (MMF), intended to measure dietary intake for breastfeeding children and MDD and MMF along with minimum milk feeding frequency (MMF) for non-breastfeeding children [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. MMF is defined as when breastfed children receive solid, semisolid, or soft foods at least twice a day for infants of age 6\u0026ndash;8 months and at least three times a day for children of age 9\u0026ndash;23 months: while non-breastfed children aged 6\u0026ndash;23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least four times a day [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. On the other hand, Solomon et al., (2017 pg, 1) and Khamis et al., (2019) defined MDD as the consumption of four or more food groups from the seven food groups for higher dietary quality and to meet daily energy and nutrient requirements of the seven recommended food groups [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eIndependent Variable\u003c/h2\u003e \u003cp\u003eThis study follows the UNICEF framework for analyzing factors associated with child health and nutrition. There are three levels predictors namely: immediate, underlying, and basic variables. Immediate variables are child sex, weight at birth and diarrhea as well as respiratory infection. At household level, underlying variables are households\u0026rsquo; size, place of residence, female headed households, household wealth, maternal age, and education. Moreover, at community level, basic variables are community health seeking behavior, child immunization and antenatal care. Community food security, poverty, proportion of mothers and males in the households with at least secondary, as well as community with high proportions of female headed households, and community media exposure, safe drinking water and inadequate toilet facilities.\u003c/p\u003e \u003cp\u003eThe variables were constructed as follows, for child level factors, child sex is binary (male and female), child weight at birth is categorized in three categories (Low birth weight (Less than 2.5kg), average weight (between 2.6kg and 3.8kg) and above average weight (greater than 3.8kg). For diarrhea and respiratory infections two week before the survey was categorized into three categories (yes, no and I don\u0026rsquo;t know). Among household variables, household size is categorized into 2\u0026ndash;5, and 5+, place of residence (rural and urban), maternal residential status (yes and no), female headed households is also binary (yes and no). Household wealth was categorized in to poorest, second, middle, fourth and richest wealth quintiles, maternal age (15\u0026ndash;24, 25\u0026ndash;34, 35\u0026thinsp;+\u0026thinsp;maternal education (primary/and or no education, secondary and highest education).\u003c/p\u003e \u003cp\u003eIn relation to basic variables, firstly the variables were categorized as follows: community health seeking behavior (sought medical care), community immunization (fully immunized), community antenatal care (attended antenatal care), community food security (households that own land and livestock), community poverty (households in the poorest and second wealth quintile), community male and mother education (males and mothers with at least secondary education), community female headed household (households headed by females), community media exposure (mothers with exposure to media at least once a week. All the aforementioned variables were categorised as low and high proportions, low proportions were clusters with proportions less than 40% and high proportions were clusters with proportions more than 40%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003eData analyses was conducted in three steps, descriptive analyses, bivariate and multilevel logistic regression. Descriptive analysis of all the independent variables involves, frequencies, percentages, confidence intervals to determine statistical differences and p-values. Secondly, a chi-square analysis (bivariate analysis) was conducted to determine which variables are statistically significant to be included into the main model at 75% confidence internal (\u003cem\u003e-\u003c/em\u003evalues\u0026thinsp;\u0026lt;\u0026thinsp;0.25). A multilevel logistic regression analysis was carried out because the data has evidence of clustering and hierarchy. In multi-level research, the structure of data in the population is hierarchical, and a sample for such a population can be viewed as a multistage sample [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. With this explanation, the data analysis model best suits the MICS dataset because it was nested in two stages. The units at lower-level (level-1) are individual and clusters are again nested within units at the next higher level- which is level 2. This kind of clustering can introduce multi-level dependency or correlation among the observations that can have implication for model parameter estimates [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo measure the dependency in the data a three-stage multilevel analysis was conducted by running an empty model and calculating the intra-class correlation coefficient (ICC). The aim of the empty model is to find log-odds of the dependent variable while including no predictors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Secondly, by running a model that measured the effects of lower-level variables because these intermediate variables are allowed to vary from one cluster to another. Thirdly, running a model with level-2 predictors and level-3 predictors. Finally, by running the final model that include individual, household, and community level variables. The statistical analyses are carried out in this study with the help of STATA15 software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Lesotho Multiple Indicators Survey (LMICS) 2018 data is publicly available on the MICS data official website (http://mics.unicef.org/surveys). Informed consent was taken from all the respondents in LMICS, and the use of this MICS data for the purpose of this secondary analysis was obtained from Human and Social Sciences Research Ethics Committee (HSSREC/00002395/2021).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eData Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are a total of 903 children aged 6-23, and 200 (22.7%) of them received MAD. Data description in Table 1 shows that, at immediate level, majority of children in the sample were 9-23 months (66%), in terms of child age there was no significant difference between males and females (52% vs 48%). Moreover, majority of them did not have respiratory infections (58%) and diarrhea (88%) two weeks before the survey. At household level, majority of them were from households\u0026rsquo; size of 5+ (63%), rural areas (61%), resident mothers (87%), female headed households (62%), second wealth index (23%) as well as maternal age of 20-25(26%) and maternal education of secondary education (53%). At community level, majority of them were from communities with low proportions of health seeking behavior (57%), immunization (61%), antenatal care (87%), as well as from communities with low proportions of males with at least secondary education (54%). They were also from communities with low proportions of female headed households (87%), poor households (61%) and maternal media exposure (82%). Moreover, majority of them were from communities with high proportions of food secure households (74%) and mothers with at least secondary education (68%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Characteristics of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmediate Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentages (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e6-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e9-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Weight at Birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 2.8kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e2.6kg to 3.8kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eGreater than 3.9kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiarrhea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory Infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderlying Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e2-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of Residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Residential Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Headed Households\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Wealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eSecond Wealth Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle Wealth Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eFourth Wealth Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e30-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e40+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eNo/Primary Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eTertiary Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Health Seeking Behavior (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Immunization (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Antenatal Care (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Food Security (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Maternal Education (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Male Education (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Female Headed Households (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Poverty (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Media Exposure (proportions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.54409005628519%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.129455909943715%\" valign=\"top\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.326454033771107%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eBivariate Analysis\u003c/h3\u003e\n\u003cp\u003eTable 2 presents all the three level factors associated with stunting at bivariate analysis, together with the frequencies, percentages, confidence intervals and p-values. In Lesotho only 22.7% of children aged 6-23 received MAD. All variables with a p-value less than 0.25 from Chi-Square were considered significantly associated with stunting. Variables that were statistically significant to MAD were child sex, age and respiratory infections. At household level only maternal age was statistically significant. At community level, community poverty, and maternal media exposure. Furthermore, all variables that were statistically significant in Table 2 were further tested using an Adjusted Wald Statistics where all variables were included the main model.\u003c/p\u003e\n\u003cp\u003eTable 2: Prevalence of Minimum Acceptable Diet in Lesotho\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Stunted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStunted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0625%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmediate Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eSex of child\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\" valign=\"top\"\u003e\n \u003cp\u003e79.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[75.01,84.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[15.94,24.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.089887640449437%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98876404494382%\" valign=\"top\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[69.34,79.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[20.96,30.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eChild Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6-8 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\" valign=\"top\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[75.55,86.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[13.04,24.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.089887640449437%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9-23 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98876404494382%\" valign=\"top\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[70.43,78.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[21.34,29.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eChild Weight at Birth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 2.5kg \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e75.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[64.20,83.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[16.49,35.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e2.6 kg - 3.8kg \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[73.88,82.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[17.96,26.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;3.9kg \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[68.98,82.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[17.97,31.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eDiarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[67.60,86.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[13.04,32.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[73.43,80.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.64,26.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory Infection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[66.85,79.29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[20.71,31.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[75.07,83.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[16.84,24.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderlying Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e2-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[71.9,83.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[16.9,28.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[72.7,80.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.4,27.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003ePlace of Residence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[70.3 82.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[17.9 29.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[73.5 81.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[18.7 26.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eSex of Household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[73.9 82.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[17.6 26.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[69.4 80.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.4 30.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eHousehold Wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[73.9 84.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[15.3 26.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"5\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eSecond \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[70.4 83.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[16.9 29.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[68.9 85.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[14.2 31.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eFourth \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[69.3 84.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[15.7 30.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[60.0 80.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.8 40.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eMaternal Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[62.5 82.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[17.9 37.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"6\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[75.6 86.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[13.8 24.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[73.8 86.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[13.4 26.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[55.2 76.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[23.4 44.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[66.8 87.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[12.3 33.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003e40+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[61.7 90.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[9.7 38.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eMaternal Residential Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[73.0 80.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[19.4 27.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[73.5 83.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[16.2 26.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eMaternal Education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary or None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[73.9 84.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[15.5 26.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[72.2 81.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[18.2 27.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eBeyond secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[52.9 80.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.2 47.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eHealth Seeking Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[0.71 0.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[0.20 0.29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[0.74 0.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[0.17 0.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eLevel of immunization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[0.72,0.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[0.19,0.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[0.72,0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[0.18,0.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eLevel of antenatal Care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[0.59,0.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[0.16,0.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[0.74,0.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[0.19,0.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eCommunity Food Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[67.1,82.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[17.4,32.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[74.1,81.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[18.7,25.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eCommunity Maternal Education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[71.9,81.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[18.4,28.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[72.5,81.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[18.4,27.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eCommunity Male Education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[74.1,82.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[17.7,25.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[70.2,80.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[19.2,29.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eFemale Headed Communities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[73.6,80.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[19.2,26.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[66.6,84.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[16.0,33.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eCommunity Poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e75.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[70.1,79.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[20.2,29.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[76.1,84.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[15.8,23.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eCommunity Media Exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.166666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e[74.8,81.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9%\" valign=\"top\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7%\" valign=\"top\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" valign=\"top\"\u003e\n \u003cp\u003e[18.3,25.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.655430711610485%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423220973782772%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97752808988764%\" valign=\"top\"\u003e\n \u003cp\u003e[62.5,79.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.112359550561798%\" valign=\"top\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.865168539325842%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10112359550562%\" valign=\"top\"\u003e\n \u003cp\u003e[20.2,37.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMultilevel Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe empty model (null model) was run to determine clustering, the second model included immediate and underlying variables (level 1 and level 2) and the third model included basic variables (level 3). In this study, there was evidence of variability of clustering. The null model had a Chi-Square of 19.91 and a p-value of 0.0000 making it statistically significant thus indicating clustering in the data. The Interclass correlation (ICC) of this model was right at the cut-off point at 0.054. Heck et al., 2014 discussed that 0.05 is often regarded as a conventional threshold to indicate more substantial evidence of clustering [27]. Moreover, the probability of being stunted in each community was (odd of being stunted/ (1+ odds of being stunted) 0.302. In general, the unconditional probability of a child being stunted is 30.2%. There was also variability of clustering between households and communities with a chi square of 36.33 and p-value of 0.000 and ICC of 0.2574 (above the threshold).\u003c/p\u003e\n\u003ch2\u003eFactors associated with MAD\u003c/h2\u003e\n\u003cp\u003eTable 3 presents the main model of level one (immediate variables), level two (underlying variables) and level three (basic variables). Seventy five percent confidence interval was used hence p-value less than 0.25 was considered statistically significant. At individual level, higher odds of receiving MAD were observed among females (WAR=1.43; CI: 1.1,1.3) and children aged 9-23 months (WAR=1.67; CI: 1.3,2.2). At household level, only maternal age was associated with MAD. The odds of receiving MAD were lower for children whose mothers were aged 20-24 (WAR=0.70; CI: 0.5,1.0) and 35-39 (WAR=0.62; CI: 0.4,1.0). At community level, community poverty, and community media exposure were associated with MAD. Children residing in communities with high community poverty had lower odds of receiving MAD (WAR=0.64; CI: 0.5,0.8) while children residing in communities with high maternal media exposure had higher odds of receiving MAD (WAR=1.53: CI: 1.1,2.2).\u003c/p\u003e\n\u003cp\u003eTable 3: Factors associated with MAD: Lesotho 2018\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNOR (75% CI) \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWAOR (75% CI) \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWA (P-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmediate Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.31 [1.1,1.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.43 [1.1,1.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e6-8 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e9-23 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.57 [1.3,2.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.67 [1.3,2.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory Infections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 [0.6,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.69 [0.5,0.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderlying Variables \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.68 [0.5,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.70 [0.5,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.66 [0.5,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.70 [0.5,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.10 [0.7,1.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 [0.8,1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 [0.4,0.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.62 [0.4,1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e40+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 [0.6,1.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 [0.6,2.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Poverty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 [0.7,0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.64 [0.5,0.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Media Exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLow \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eHigh \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.291666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 [0.9,1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.53 [1.1,2.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e UNOR denotes unadjusted odds ratio, WAOR, Wald adjusted odds ratio. WA Wald adjusted\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe prevalence of minimum acceptable diet child feeding practice in Lesotho was 22.71% [95%CI=0.1955 0.2622]. The prevalence was lower than that in Central Ethiopia (31.6%) and Nepal (30.1%), [28, 24]. It was higher than West Africa (11.56%), West Ethiopia (12.6%) and Northwest Ethiopia (8.6%) [29, 5].\u003c/p\u003e\n\u003cp\u003eChild age was a associated with MAD in this study. Children aged 9-23 months were more likely to meet the MAD compared to those aged 6-8 months. In many countries, less than one fourth of infants aged 6-23 months meet the criteria for dietary and feeding frequency [12]. Worku et al., (2022) in support indicated that, the reason why children are not meeting MAD requirements is because in most cases complementary feeding happens late, and the start of complementary feeding is commonly done with only limited items such as milk or cereal [29]. Late introduction of complementary feeding could also be attributed to the simultaneous occurrence of the eruption of teeth and the introduction of solid and semi-solid food [12]. The teething process typically leads to loss of appetite and negatively affecting the frequencies and diversity of meals [2]. Moreover, younger children are mainly breastfed, and their mothers might assume complementary feeding their younger children is not important as for older children, since older children usually have family meals [1, 34, 38]. Younger children are also likely to be introduced to complementary feeding late thus affecting the meal diversity and frequency [30, 31]. In Lesotho, complementary feeding is normally introduced too early or too late with insufficient diversity with more children eating cereal than other food types [32].\u003c/p\u003e\n\u003cp\u003eSex of the child was also associated with MAD with girls more likely to receive MAD compared to boys. This was also reported in Southeast Ethiopia [33]. In the contrary, most studies found that boys had better dietary intake compared to girls [33, 34]. Hien et al., (2023) highlighted that, in some communities in Burkina Faso, there were gender preferences and children feeding by gender is common [34]. In Lesotho, being a boy from poor communities and rural areas is a great disadvantage than girls in the same environment when it comes to diets and access to education [35]. In Lesotho boys are often herding livestock from as young as three years [39]. This sociocultural phenomenon of herdboyship can lead to less care given to boy children from birth despite Lesotho being a patriarchal country. These herdboys are expected to live in isolation for months at a time despite their ages herding sheep and cattle and make their way home only twice a year in winter, so the animals could be checked, counted, and kept warm for a brief period [36].\u003c/p\u003e\n\u003cp\u003eAt household level, maternal age was associated with MAD where children mothers were aged 20-25 and 35-39 were less likely to receive MAD compared to children whose mothers were aged 15-19 years. Children from the rest of the age groups (25-29, 30-34 and 40+) were equally likely to receive MAD compared to children whose mothers were aged 15-19 years. This means children to teenage mothers were no different from being fed MAD compared to most age groups. Teenage pregnancy in Lesotho remains a challenge [37]. The teenage pregnancy birth rates are high, estimated at 94 per 1000 girls aged 15-19 [37]. Teenage pregnancy is associated with several social and health risks and one of them is parental involvement [35, 37, 39]. Lack of parental involvement leaves grandparents with the problem of finding other ways to support their grandchildren [40]. The number of grandparents raising their grandchildren has increased significantly globally [41]. In 2010, 7 million grandmothers lived with their grandchildren globally, with 2.7 million grandparents responsible for the basic needs of one or more grandchildren [41]. Moreover, grandparents providing childcare are likely to be females, with a partner, with higher educational attainment and wealthier, thus increasing chances of children receiving MDD and MAD [42]. In China, grandparents were also more likely to support young mothers with children with appropriate mother and child feeding practices [43]. Breastfeeding support by grandmothers has been explored in Nepal, showing that grandmother\u0026rsquo;s involvement can promote and endorse positive health feeding practices [43]. In the United States, grandparents with knowledge about the importance of healthy diets can assist their grandchildren [43]. In support, children raised by grandparents and children of older mothers had higher odds of receiving MAD. In East Africa and Northwest Ethiopia, older aged women had high chances of providing MAD to their children compared to younger mothers [29]. Older mothers have experience on feeding practices and this might play a significant role in appropriate feeding practices [29, 42].\u003c/p\u003e\n\u003cp\u003eAt community level, community poverty was a determinant of MAD, with children in communities with high community poverty less likely to meet the minimal dietary diversity and acceptable diet. Poverty, food insecurity and lower socio-economic status are considered as major challenges affecting feeding practices [44]. In India, Argentina, Indonesia, and Rwanda households with lower wealth quintiles were associated with unmet minimum dietary diversity [44, 45, 46, 47]. This is because poorest socioeconomic status families are unable to buy diverse food items [45]. In rural Rwanda, mothers highlighted that, poverty leads to reduced number of meals received by children, and they breastfeed children longer than they should to maximize food for the rest of the family [45]. Poverty also leads to food insecurity. Lindsay et al., (2012) highlighted that, household food insecurity is a critical variable for understanding the nutritional status in low-income populations [44]. Food insecurity is defined as the limited or uncertain availability of nutritionally adequate and safe foods which leads to poor diets [44]. Low food security also affects children through the reduced quality and variety of recommended diets [47]. Moreover, food security also occurs when nutritious food is not available to households because of areas they reside in [47]. Communities have different economic conditions that affect food availability, access and cultural preference which can influence food diversity in child feeding practices [46, 48]. On the other hand,\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eCommunity Media Exposure was a determinant of MAD with children residing in communities with high community maternal media exposure more likely to receive MAD. In Lesotho, majority of households are exposure to mass media. The greater part of the population owns radios, with radio signal covering about 87% of the country, with most areas that are not covered are able to tune into radio stations in South Africa [49]. Mass media directly influences people\u0026rsquo;s behaviour because it is used to deliver health messages to promote social and behavioural change [50]. Zebodia and Atmaka (2021) highlighted that, mass media has a crucial role in educating mothers and caregivers on appropriate complementary feeding practices especially in the diversification of food [51]. Moreover, mass media is also associated with utilization of maternal health services like antenatal care, delivery in health facilities and appropriate feeding practices [27, 24]. In Indonesia, India, Nepal, Southern-Asia, East-Africa, Ethiopia, Bangladesh access to information from mass media was significantly associated with meeting recommended child feeding practices [52, 51, 27, 24, 31, 29]\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to find determinants of MAD in Lesotho at three levels- individual, household, and community level. At individual level, child sex and age were determinants of MAD. At household level maternal age was a determinant of MAD, maternal age in this case indicates that knowledge and experience in childcare contributed to better dietary intake for children aged 6-23. At community level, lack of care resources, food availability and knowledge acquisition were determinants of MAD. Therefore, strategies should be in place to educate people in communities through mass media, gatherings about child nutrition.\u003c/p\u003e\n"},{"header":"Limitations","content":"\u003cp\u003eThe study shares a common limitation of cross-sectional study-the study supports the association between child diet, stunting, and independent variables, but not proving the causal relationship. Moreover, this study had limitations in that, the majority of the data is self-reported by mothers/caregivers, making it subject to recall bias and it can be subject to social-desirability bias. On the other hand, the study used clustering by aggregating individual and household variables, these may in misclassification or overestimation. Moreover, communities were created using clusters derived from Enumeration areas which might also be subject to coverage error.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is part of the author\u0026rsquo;s thesis for a doctoral dissertation with the School of Built Environment and Development Studies at the University of Kwa-Zulu Natal, Durban, South Africa. We are grateful to the UNICEF, MICS team for providing the 2018 MICS dataset for the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo grant was received for the study from any agency, university or public.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used the LMICS 2018 dataset which is publicly available on the MICS data official website https://mics.unicef.org/surveys with all respondents identifier information removed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was designed by NL\u003csup\u003e1\u003c/sup\u003e and KV\u003csup\u003e2\u003c/sup\u003e. TT\u003csup\u003e3\u003c/sup\u003e was involved in the revision of the paper as well as the editing of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that they have no competing interests. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles stated in the Helsinki Declaration. Before the launch of MICS data collection, the Bureau of Statistics Lesotho was approved by the Ethical Review Committee in February 2018 to conduct the study. The author communicated with the UNICEF MICS team in 3 UN Plaza, New York, USA and was granted permission to download and use the LMICS dataset. Furthermore, the author received ethical approval from the University of Kwa-Zulu Natal, Durban, South Africa to use MICS datasets for the purpose of this secondary analysis was obtained from Human and Social Sciences Research Ethics Committee (HSSREC/00002395/2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru. \u003csup\u003e2 \u003c/sup\u003ePopulation Studies, School of Built Environment and Development Studies, University of KwaZulu-Natal, Durban, South Africa. \u003csup\u003e3 \u003c/sup\u003eDepartment of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru. \u003csup\u003e4 \u003c/sup\u003eDepartment of Statistics and Demography, Faculty of Social Sciences. National University of Lesotho. Maseru.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoghames, P., Hammami, N., Hwalla, N., Yazbeck, N., Shoaib, H., Nasreddine, L. And Naja, F., 2015. Validity and reliability of a food frequency questionnaire to estimate dietary intake among Lebanese children. Nutrition Journal, 15(1).\u003c/li\u003e\n\u003cli\u003eTegegne, M. et al. (2017) \u0026lsquo;Factors associated with minimal meal frequency and dietary diversity practices among infants and young children in the predominantly Agrarian Society of Bale Zone, Southeast Ethiopia: A Community Based Cross Sectional Study\u0026rsquo;, Archives of Public Health, 75(1). doi:10.1186/s13690-017-0216-6\u003c/li\u003e\n\u003cli\u003eCustodio, E., Herrador, Z., Nkunzimana, T., Węziak-Białowolska, D., Perez-Hoyos, A. And Kayitakire, F., 2019. Children\u0026rsquo;s dietary diversity and related factors in Rwanda and Burundi: A multilevel analysis using 2010 Demographic and Health Surveys. PLOS ONE, 14(10), p.e0223237.\u003c/li\u003e\n\u003cli\u003eWagris, M. et al. (2019) \u0026lsquo;Minimum meal frequency practice and its associated factors among children aged 6\u0026ndash;23 months in Amibara District, North East Ethiopia\u0026rsquo;, Journal of Environmental and Public Health, 2019, pp. 1\u0026ndash;7. doi:10.1155/2019/8240864. \u003c/li\u003e\n\u003cli\u003eBirie, Kassa and Kebede, M. (2020) Minimum acceptable diet practice and its associated factors among children\u0026rsquo;s aged 6\u0026ndash;23 months in rural communities of Goncha District, North West Ethiopia, 2020 [Preprint]. doi:10.21203/rs.3.rs-126420/v1.\u003c/li\u003e\n\u003cli\u003eAsif, A.M. and Akbar, M. (2022) \u0026lsquo;A non-linear decomposition analysis of children\u0026rsquo;s dietary diversity scores: Explaining rural-urban inequality\u0026rsquo;, Quality \u0026amp;amp; Quantity, 57(4), pp. 3273\u0026ndash;3284. doi:10.1007/s11135-022-01501-x.\u003c/li\u003e\n\u003cli\u003eUNICEF (2019) Breastfeeding from the first hour of birth: What works and what hurts. Available at: https://www.unicef.org/stories/breastfeeding-first-hour-birth-what-works-and-what-hurts (Accessed: 24 March 2024). \u003c/li\u003e\n\u003cli\u003eWHO. 2020. Infant and young child feeding. [online] Available at: https://www.who.int/news-room/fact-sheets/detail/infant-and-young-child-feeding [Accessed 7 March 2022].\u003c/li\u003e\n\u003cli\u003eKaur M, Kaur J, Sirswal S, Kumari S, Thakur N. (2017). Assessment of feeding practices of young children less than 2 years of age. EC Paediatr 6: 474-487.\u003c/li\u003e\n\u003cli\u003eTerati, Yuniarti, H. And Susanto, E., 2018. Effects of Diet and Breastfeeding Duration on the Stunting Status of Children under 5 Years of Age at Maternal and Child Health Centers of the Palembang Regional Office of Health. Pakistan Journal of Nutrition, 17(2), pp.51-56.\u003c/li\u003e\n\u003cli\u003eHeeman, M. et al. (2022) \u0026lsquo;Food Group consumption patterns among children meeting and not meeting who\u0026rsquo;s recommended dietary diversity: Evidence from 197,514 children in 59 countries\u0026rsquo;, Food Policy, 112, p. 102368. doi:10.1016/j.foodpol.2022.102368.\u003c/li\u003e\n\u003cli\u003eSolomon, D., Aderaw, Z. And Tegegne, T., 2017. Minimum dietary diversity and associated factors among children aged 6\u0026ndash;23 months in Addis Ababa, Ethiopia. International Journal for Equity in Health, 16(1).\u003c/li\u003e\n\u003cli\u003eKhamis, A., Mwanri, A., Ntwenya, J. And Kreppel, K., 2019. The influence of dietary diversity on the nutritional status of children between 6 and 23\u0026thinsp;months of age in Tanzania. BMC Pediatrics, 19(1).\u003c/li\u003e\n\u003cli\u003eUNICEF, WHO and World Bank Group, (2016). LEVELS AND TRENDS IN CHILD MALNUTRITION. [online] Available at: https://data.unicef.org/wp-content/uploads/2017/05/JME-2017-brochure-1.pdf [Accessed 27 May 2020].\u003c/li\u003e\n\u003cli\u003eUnited Nations (2021) How children in Lesotho experience poverty... in Lesotho, United Nations. Available at: https://lesotho.un.org/en/164949-how-children-lesotho-experience-poverty (Accessed: 26 June 2023).\u003c/li\u003e\n\u003cli\u003eAnjorin, S., Uthman, O., Ameyaw, E., Ahinkorah, B., Chattu, V., Bishwajit, G., Seidu, A., Darteh, E. And Yaya, S., 2020. Undernutrition, polygynous context and family structure: a multilevel analysis of cross-sectional surveys of 350 000 mother\u0026ndash;child pairs from 32 countries. BMJ Global Health, 5(10), p.e002637.\u003c/li\u003e\n\u003cli\u003eWHO. 2020. Infant and young child feeding. [online] Available at: https://www.who.int/news-room/fact-sheets/detail/infant-and-young-child-feeding [Accessed 7 March 2022].\u003c/li\u003e\n\u003cli\u003eFAO. 2021. The State of Food Security and Nutrition in the World 2021. [online] Available at: https://www.fao.org/state-of-food-security-nutrition [Accessed 28 February 2022].\u003c/li\u003e\n\u003cli\u003eLesotho Demographic and Health Survey Report., (2009) Ministry of Health Lesotho.\u003c/li\u003e\n\u003cli\u003eLesotho Demographic and Health Survey Report., (2014) Ministry of Health Lesotho.\u003c/li\u003e\n\u003cli\u003eAkombi, B., Agho, K., Merom, D., Hall, J. And Renzaho, A., 2017. Multilevel Analysis of Factors Associated with Wasting and Underweight among Children Under-Five Years in Nigeria. Nutrients, 9(1), p.44.\u003c/li\u003e\n\u003cli\u003eKandala, N., Madungu, T., Emina, J., Nzita, K. And Cappuccio, F., 2019. Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter?. BMC Public Health, 11(1).\u003c/li\u003e\n\u003cli\u003eLesotho Multiple Indicator Study Report. (2018) Available at: https://mics-surveys-prod.s3.amazonaws.com/MICS6/Eastern%20and%20Southern%20Africa/Lesotho/2018/Survey%20findi\nngs/Lesotho%202018%20MICS%20Survey%20Findings%20Report_English.pdf [Accessed 27 May 2019].\u003c/li\u003e\n\u003cli\u003eSapkota, S. et al. (2022) \u0026lsquo;Predictors of minimum acceptable diet among children aged 6\u0026ndash;23 months in Nepal: A multilevel analysis of Nepal Multiple Indicator Cluster Survey 2019\u0026rsquo;, Nutrients, 14(17), p. 3669. doi:10.3390/nu14173669.\u003c/li\u003e\n\u003cli\u003eKhan, M. N., \u0026amp; Islam, M. M. (2017). Effect of Exclusive Breastfeeding on Selected Adverse Health and Nutritional Outcomes: A Nationally Representative Study. BMC Public Health, 17, ArticleNo.889. https://doi.org/10.1186/s12889-017-4913-4.\u003c/li\u003e\n\u003cli\u003eSommet, N. and Morselli, D., 2017. Keep calm and learn multilevel logistic modeling: a simplified three-step procedure using Stata, R, mplus, and SPSS. International Review of Social Psychology, 30, pp.203-218 \u003c/li\u003e\n\u003cli\u003eLencha Moga, F. et al. (2022a) \u0026lsquo;Minimum dietary diversity and associated factors among children under the age of five attending public health facilities in Wolaita Soddo Town, southern Ethiopia, 2021: A cross-sectional study\u0026rsquo;, BMC Public Health, 22(1). doi:10.1186/s12889-022-14861-8.\u003c/li\u003e\n\u003cli\u003eMolla, W. et al. (2021) \u0026lsquo;Dietary diversity and associated factors among children (6\u0026ndash;23 months) in Gedeo Zone, Ethiopia: Cross - Sectional Study\u0026rsquo;, Italian Journal of Pediatrics, 47(1). doi:10.1186/s13052-021-01181-7.\u003c/li\u003e\n\u003cli\u003eWorku, M.G. et al. (2022) \u0026lsquo;Minimum acceptable diet feeding practice and associated factors among children aged 6\u0026ndash;23 months in East Africa: A multilevel binary logistic regression analysis of 2008\u0026ndash;2018 demographic health survey data\u0026rsquo;, Archives of Public Health, 80(1). doi:10.1186/s13690-022-00882-7.\u003c/li\u003e\n\u003cli\u003eBirhanu, H. et al. (2022) \u0026lsquo;Minimum acceptable diet and associated factors among children aged 6\u0026ndash;23 months during fasting days of Orthodox Christian mothers in Gondar City, North West Ethiopia\u0026rsquo;, BMC Nutrition, 8(1). doi:10.1186/s40795-022-00558-z.\u003c/li\u003e\n\u003cli\u003eSisay, B.G. et al. (2022) \u0026lsquo;Dietary diversity and its determinants among children aged 6\u0026ndash;23 months in Ethiopia: Evidence from the 2016 Demographic and Health Survey\u0026rsquo;, Journal of Nutritional Science, 11. doi:10.1017/jns.2022.87.\u003c/li\u003e\n\u003cli\u003eLesotho Zero Hunger Strategic Review (2018) . Available at: https://www.wfp.org/publications/lesotho-zero-hunger-strategic-review-2018#:~:text=To%20respond%20to%20the%20Zero,eliminating%20food%20insecurity%20and%20malnutrition . (Accessed: 25 March 2020). \u003c/li\u003e\n\u003cli\u003eMekonnen, T.C. et al. (2017) \u0026lsquo;Meal frequency and dietary diversity feeding practices among children 6\u0026ndash;23 months of age in Wolaita Sodo Town, southern Ethiopia\u0026rsquo;, Journal of Health, Population and Nutrition, 36(1). doi:10.1186/s41043-017-0097-x.\u003c/li\u003e\n\u003cli\u003eHien, A. et al. (2023) \u0026lsquo;Factors associated with minimum dietary diversity, minimum meal frequency and minimum acceptable diet practices among children 6-23 months of age in Bobo-Dioulasso, Burkina Faso\u0026rsquo;, African Journal of Food, Agriculture, Nutrition and Development, 23(03), pp. 22831\u0026ndash;22852. doi:10.18697/ajfand.118.22580.\u003c/li\u003e\n\u003cli\u003eUNESCO (2022) Leave no child behind: boys\u0026rsquo; disengagement from education: Lesotho case study. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000381155 (Accessed: 26 March 2023). \u003c/li\u003e\n\u003cli\u003eHelpLesotho., (2016). Herd Boy Program.. https://www.helplesotho.org/wp-content/uploads/2016/04/Computer-and-Life-Skills-Training-March-2016.pdf (accessed April 9, 2024). \u003c/li\u003e\n\u003cli\u003eUNFPA (2021) Early and unintended pregnancies rife in Lesotho: 13-year-olds among those bearing children. Available at: https://esaro.unfpa.org/en/news/early-and-unintended-pregnancies-rife-lesotho-13-year-olds-among-those-bearing-children (Accessed: 26 March 2024). \u003c/li\u003e\n\u003cli\u003eWilson, D. et al. (2017) \u0026lsquo;Association of Teen Mothers\u0026rsquo; and grandmothers\u0026rsquo; parenting capacities with child development: A study protocol\u0026rsquo;, Research in Nursing \u0026amp;amp; Health, 40(6), pp. 512\u0026ndash;518. doi:10.1002/nur.21839.\u003c/li\u003e\n\u003cli\u003eHelpLesotho (2023) Help Lesotho: Young Mother Program Overview. Available at: https://helplesotho.org/young-mother-program/ (Accessed: 09 April 2024).\u003c/li\u003e\n\u003cli\u003eSampson, D., Hertlein, K. (2015). The experience of grandparents raising grandchildren. GrandFamilies: The Contemporary Journal of Research, Practice and Policy, 2 (1). Available at: http://scholarworks.wmich.edu/grandfamilies/vol2/iss1/4\u003c/li\u003e\n\u003cli\u003eChoi, M., Sprang, G. and Eslinger, J.G. (2016a) \u0026lsquo;Grandparents raising grandchildren\u0026rsquo;, Family \u0026amp;amp; Community Health, 39(2), pp. 120\u0026ndash;128. doi:10.1097/fch.0000000000000097.\u003c/li\u003e\n\u003cli\u003eDamanik, S.M., Wanda, D. and Hayati, H. (2020) \u0026lsquo;Feeding practices for toddlers with stunting in Jakarta: A case study\u0026rsquo;, Pediatric Reports, 12(11), p. 8695. doi:10.4081/pr.2020.8695.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Zhao, J. and Zhong, H. (2022) \u0026lsquo;Grandparental care and childhood obesity in China\u0026rsquo;, SSM - Population Health, 17, p. 101003. doi:10.1016/j.ssmph.2021.101003. \u003c/li\u003e\n\u003cli\u003eLindsay, A.C., Ferarro, M., Franchello, A., La Barrera, R.D., Machado, M.M.T., Pfeiffer, M.E. and Peterson, K.E., 2012. Child feeding practices and household food insecurity among low-income mothers in Buenos Aires, Argentina. Ciencia \u0026amp; saude coletiva, 17, pp.661-669.\u003c/li\u003e\n\u003cli\u003eDusingizimana, T. et al. (2020) \u0026lsquo;A qualitative analysis of infant and young child feeding practices in rural Rwanda\u0026rsquo;, Public Health Nutrition, 24(12), pp. 3592\u0026ndash;3601. doi:10.1017/s1368980020001081. \u003c/li\u003e\n\u003cli\u003eSimbolon, D., Ludji, I.D. and Rahyani, Y. (2022) \u0026lsquo;Nutrition assistance of breastfeeding and complementary feeding associated with the Linier growth of stunted children aged 6-24 months\u0026rsquo;, \u003cem\u003ePublic Health and Preventive Medicine Archive\u003c/em\u003e, 10(2), pp. 120\u0026ndash;129. doi:10.53638/phpma.2022.v10.i2.p03. \u003c/li\u003e\n\u003cli\u003eRaru, T.B. et al. (2023) \u0026lsquo;Minimum dietary diversity among children aged 6\u0026ndash;59 months in East Africa countries: A Multilevel Analysis\u0026rsquo;, International Journal of Public Health, 68. doi:10.3389/ijph.2023.1605807. \u003c/li\u003e\n\u003cli\u003eFrancis-Devine, B., Malik, X. and Danechi, S. (2023) Food poverty: Households, Food Banks and Free School Meals, House of Commons Library. Available at: https://researchbriefings.files.parliament.uk/documents/CBP-9209/CBP-9209.pdf (Accessed: 26 October 2023). \u003c/li\u003e\n\u003cli\u003eFriedrich-Ebert-Stiftung (2018) Friedrich-Ebert-Stiftung Mass Media. Available at: https://www.fes.de/ (Accessed: 26 March 2023). \u003c/li\u003e\n\u003cli\u003eKim, S.S. et al. (2018) \u0026lsquo;Factors influencing the uptake of a mass media intervention to improve child feeding in Bangladesh\u0026rsquo;, Maternal \u0026amp;amp; Child Nutrition, 14(3). doi:10.1111/mcn.12603.\u003c/li\u003e\n\u003cli\u003eZebadia, E. and Atmaka, D.R. (2021) \u0026lsquo;Factors associated with minimum dietary diversity among 6-11-month-old children in Indonesia: analysis of the 2017 Indonesian demographic and Health Survey\u0026rsquo;, Public Health and Preventive Medicine Archive, 9(2), pp. 132\u0026ndash;138. doi:10.15562/phpma.v9i2.340.\u003c/li\u003e\n\u003cli\u003eEshete, H., Abebe, Y., Loha, E., Gebru, T. And Tesheme, T., 2017. Nutritional status and effect of maternal employment among children aged 6\u0026ndash;59 months in Wolayta Sodo Town, Southern Ethiopia: a cross-sectional study. Ethiopian Journal of Health Sciences, 27(2), p.155.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multilevel, Minimum Acceptable Diet, Childhood","lastPublishedDoi":"10.21203/rs.3.rs-4657862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4657862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003e The World Health Organization\u0026rsquo;s Infant and Young Children Feeding Guidelines (IYCF) has been adopted as an international acceptable complementary feeding guideline known as the Minimum Acceptable Diet (MAD). MAD is a combination of Minimum Meal Frequency (MMF) and Minimum Dietary Diversity (MDD). MAD is not met in many countries in the world. This study aimed to determine the prevalence and multilevel determinants of a minimum acceptable diet among children aged 6\u0026ndash;23 months in Lesotho.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a multilevel logistic regression using data from the Lesotho Multiple Cluster Indicator Study of 2018.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn Lesotho only 22.7% [CI: 19.6 26.2] of children aged 6\u0026ndash;23 months received MAD. At individual level, higher odds of receiving MAD were observed among females (WAR\u0026thinsp;=\u0026thinsp;1.43; CI: 1.1 1.3) and children aged 9\u0026ndash;23 months (WAR\u0026thinsp;=\u0026thinsp;1.67; CI: 1.3 2.2). At household level, only maternal age of 20\u0026ndash;25 and 35\u0026ndash;39 were statistically significant to MAD; on the other hand, the odds of receiving MAD were higher for children with maternal age of 30\u0026ndash;34 (WAR\u0026thinsp;=\u0026thinsp;1.15; CI: 0.8 1.7) and 40+ (WAR\u0026thinsp;=\u0026thinsp;1.13; CI: 0.6 2.0). Moreover, at community level, children in communities with high proportions of poor households had lower odds of receiving MAD (WAR\u0026thinsp;=\u0026thinsp;0.64; CI: 0.5 0.8) and children in communities with high proportions of maternal media exposure had higher odds of receiving MAD (WAR\u0026thinsp;=\u0026thinsp;1.53: CI:1.1 2.2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt individual level, child sex and age were determinants of MAD. At household level maternal age was a determinant of MAD, maternal age in this case indicates that knowledge and experience in childcare contributed to better dietary intake for children aged 6\u0026ndash;23. At community level, lack of care resources, food availability and knowledge acquisition were determinants of MAD. Therefore, strategies and programs to improve MDD nationwide should be done at community level.\u003c/p\u003e","manuscriptTitle":"A Multilevel Analysis of Factors Associated with Minimum Acceptable Diets Among Children Aged 6-23 Months in Lesotho: A Study of The Lesotho Multiple Cluster Indicator Study of 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 08:25:33","doi":"10.21203/rs.3.rs-4657862/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T08:43:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-07T20:09:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T12:27:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34235740073803434368942619532938948323","date":"2024-08-28T06:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25566248608473103895640804727063325566","date":"2024-08-21T07:06:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309158190018298439015269389902014938881","date":"2024-08-21T06:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-19T11:43:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-03T11:58:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-01T06:19:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T06:16:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-06-29T06:33:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1ee6282-bbba-4284-b7ec-ace74a113bc5","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:01:36+00:00","versionOfRecord":{"articleIdentity":"rs-4657862","link":"https://doi.org/10.1186/s40795-025-01030-4","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2025-02-26 15:57:32","publishedOnDateReadable":"February 26th, 2025"},"versionCreatedAt":"2024-07-23 08:25:33","video":"","vorDoi":"10.1186/s40795-025-01030-4","vorDoiUrl":"https://doi.org/10.1186/s40795-025-01030-4","workflowStages":[]},"version":"v1","identity":"rs-4657862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4657862","identity":"rs-4657862","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}