Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Tanzania: A multilevel analysis of the 2022 Demographic and Health Survey

Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Tanzania: A multilevel analysis of the 2022 Demographic and Health Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Tanzania: A multilevel analysis of the 2022 Demographic and Health Survey Mtoro J. Mtoro, Victoria Godfrey Majengo, Theresia Ponsiano Ngungulu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6733841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Appropriate complementary feeding (CF) is an important factor in the growth, development and nutritional status of young children. Inadequate complementary feeding is one of the major causes of undernutrition among 6-23‐month‐old children. Therefore, this study was conducted to determine the prevalence of appropriate CF practices and associated factors among mothers having children aged 6–23 months in Tanzania. Methods An Analytical cross-sectional study of the 2022 Tanzania Demographic and Health Surveys data was conducted. The sampling frame was stratified by geographic region and urban/rural areas, using a two-stage sampling method that selected primary sampling units based on census enumeration areas, followed by household selection using probability systematic sampling. Given the complex survey design, multilevel logistic regression was used to identify individual and community-level factors associated with CF practices. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were used to estimate the strength and magnitude of association, and statistical significance was set at p < 0.05. Results Appropriate CF practices were observed in only 15.4% of mothers with children aged 6–23 months (95% CI: 13.76–17.21). Factors associated with this were that women in the middle quintile had a lower likelihood of appropriate CF practices than women in the poor quintile (AOR = 0.58, 95% CI: 0.39–0.84). Women with media exposure (AOR = 1.83, 95% CI: 1.38–2.43) had a higher likelihood of appropriate CF practices than their counterparts. Children aged 12–17 months (AOR = 1.93, 95% CI: 1.47–2.52) and those aged 18–23 (AOR = 2.32, 95% CI: 1.78–3.02) were more likely to have appropriate CF practices than their counterparts. At the community level, women in high poverty communities were 58% less likely to have appropriate feeding practices than their counterparts (AOR = 0.42, 95% CI: 0.29–0.60). Conclusion Appropriate complementary feeding practices were suboptimal among children aged 6–23 months in Tanzania. Multifaceted interventions addressing poverty, leveraging health information through media platforms, and targeting specific age groups and communities would be crucial to improve child nutrition status in Tanzania. Appropriate complementary feeding Breastfeeding Children aged 6–23 months Tanzania Figures Figure 1 Background Appropriate complementary feeding (CF) is a critical determinant of young children’s growth, development, and nutritional status during the early life [ 1 ]. CF refers to the introduction of solid, semi-solid, or soft foods alongside breast milk or formula, typically starting at six months of age when milk alone can no longer meet the increasing nutritional demands of infants [ 2 ]. The period between 6 and 24 months is especially crucial as infants transition from a milk-based diet to one that fulfils nutrient needs from diverse food groups, supporting rapid physical and cognitive development [ 3 ]. Globally, exclusive breastfeeding is recommended for the first six months, followed by the introduction of nutrient-rich complementary foods while continuing breastfeeding for up to two years or beyond [ 1 , 2 ]. Optimal CF practices emphasize timely initiation, dietary diversity, meal frequency, and food safety to prevent malnutrition and support immune function [ 4 ]. Despite its recognized importance, global CF practices remain suboptimal [ 5 ]. According to recent United Nations Children's Fund (UNICEF) and World Health Organization (WHO) reports, less than a quarter of infants aged 6–23 months worldwide meet the criteria for minimum dietary diversity and feeding frequency appropriate for their age [ 1 , 6 ]. Data from low- and middle-income countries highlight alarming gaps; for instance, nearly one in four infants aged 8–9 months and one in five aged 10–11 months have not yet received any solid foods, exposing them to risks of growth faltering and malnutrition [ 2 ]. In sub-Saharan Africa (SSA), the situation is aggravated by the prevalence of suboptimal infant feeding practices, limited quality of complementary foods, micronutrient deficiencies, and high rates of infection, all of which contribute substantially to child morbidity and mortality [ 2 ]. Regional surveys indicate that approximately 13% of children aged 6–23 months receive appropriate CF in SSA, underscoring the urgency to improve feeding practices in this context [ 3 ]. The prevalence of appropriate CF practices among mothers of children aged 6–23 months varies significantly across countries, with prevalence rates reported at 7.7% in Malawi [ 7 ], 9.8% in Ethiopia [ 8 ], and between 4.2–10.0% in Nigeria [ 9 ]. In East Africa, a recent study in Kenya revealed that 37% of children aged 6–23 months received a minimally diverse diet, and 31% were fed a minimum acceptable diet as per WHO guidelines, reflecting persistent challenges in infant nutrition within the region [ 10 ]. However, studies show suboptimal CF practice rates, like 15.7–29.8% in Ghana [ 11 ] and 51.3% in India [ 12 ]. Similarly, Rwanda reports CF rates where around 79% of children aged 6–8 months [ 13 ], while in Uganda, CF practices also remain inadequate [ 14 ]. Exclusive breastfeeding for the first six months stands at 64%, while only 19% of children aged 6–23 months meet the minimum dietary diversity requirement [ 15 ]. Globally recommended meal frequencies, at least two to three meals daily for breastfed infants and four for non-breastfed, are often unmet [ 16 ], with evidence from India highlighting that children not fed the minimum number of meals had a 60% higher chance of stunting [ 17 ]. Beyond basic demographic factors such as a child's age, maternal education, wealth index, and antenatal care (ANC) visits, additional determinants specifically influencing CF practices have been identified in studies conducted in SSA. These include maternal marital status, with unmarried mothers being less likely to adopt appropriate CF, household headship, where female-headed households exhibit higher CF rates, and media exposure, which significantly enhances the likelihood of proper feeding practices by providing access to nutritional information. Factors related to health service utilization, such as the number of ANC visits, the place of delivery, and postnatal care (PNC) check-ups, have also demonstrated strong positive associations with CF adherence, underscoring the critical role of maternal healthcare engagement. Furthermore, community-level variables, including community illiteracy rates and regional disparities within the country, further influence feeding practices, indicating socio-economic and geographical disparities in CF behaviors. Other factors, such as birth order, the number of under-five children in the household, and current breastfeeding status, contribute to variability in CF practices across different SSA communities.t SSA communities [ 3 ]. In Tanzania, as study in Dar es Salaam showed that 41.9% had CF. This inadequate practice of CF leads to stunting in under-five children and underweight that may affect young children growth [ 19 ]. These statistics demonstrate the critical need to address feeding practices in Tanzania to prevent malnutrition and its long-term health consequences. The high prevalence of poor CF practices in Tanzania poses a significant public health challenge, given its strong association with growth faltering, cognitive deficits, and increased risk of non-communicable diseases later in life [ 20 ]. Despite ongoing efforts, the majority of infants and young children are not receiving adequate complementary foods in terms of diversity, frequency, or quantity, contributing to persistent undernutrition and stunting rates [ 15 ]. Exploring the prevalence and determinants of appropriate CF practices among mothers with children aged 6–23 months in Tanzania is essential. Traditional single-level analyses often overlook the hierarchical nature of Demographic Health Survey (DHS) data, where individuals are nested within communities, leading to biased estimates and an incomplete picture of the CF. Multilevel analysis addresses these limitations by simultaneously examining factors at multiple levels, thereby providing a more accurate and comprehensive assessment. Therefore, this study aimed to provide a comprehensive multilevel analysis of factors influencing these practices using the 2022 Tanzania Demographic and Health Survey (TDHS) data, informing policymaking and targeted interventions to improve national child nutrition outcomes. Methods and materials Data source and design This study was an analytical cross-sectional survey that utilized secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The Tanzania National Bureau of Statistics conducted the survey with the Ministries of Tanzania Mainland and Zanzibar. Tanzania is amongst the countries in East Africa with a total area of 945, 087 square kilometers, including 61,000 square kilometers of inland water. According to the 2022 census, the country has a population of approximately 62 million people. Among them, 9,484,170 are children under the age of five, with around 69% living in rural areas [ 21 ]. Population and sampling Data for this study were obtained from the latest DHS conducted between 24 February and 21 July 2022 across all regions in Tanzania. The target population for the 2022 TDHS include women of reproductive age (15–49 years), men, children and households across the 32 administrative regions in Tanzania. However, this study focused on women of reproductive age and their children aged 6–23 months. At the country level, a sampling frame is usually obtained. To minimize sampling errors, the country is stratified by geographic region and urban/rural areas within each region, followed by a two-stage sampling to select a household to be surveyed. The first sampling is to select a primary sampling unit (PSU) and then select a household. PSUs are survey clusters usually based on census enumeration areas (EAs). A probability proportion to size is employed in each stratum to select the PSU. For each selected PSU, a complete household listing is done. This is followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. We used the kids record (KR). After removing duplicates and focusing on the last-born child aged 6–23 months who lived with their caregiver, our final analytical sample comprised of 3,090 (weighted) participants. Study Variable Dependent variable The outcome variable was complementary feeding practices as a binary (appropriate or inappropriate). Independent variables The independent variables were selected based on data availability from the 2022 TDHS and relevant literature and were categorized into individual and community-level factors, as detailed in Table 1 . Individual-level variables included child-related factors, mother/household-related factors and obstetrics/health facility factors. Community-level variables represented contextual characteristics. Table 1 Individual and community level variables used under this study Variable description Coding categories Individual-level Child-related factors Sex 1 = Male or 2 = Female Age in months 1 = 6–11, 2 = 12–17 or 3 = 18–23 Birth order 1 = 1st, 2 = 2nd & 3rd or 3 = 4th & more Birth interval 1 = ≤ 32 or 2 = ≥ 33 Number of children under 5 in the household 1 = None, 2 = 1–2 or 3 = ≥ 3 Mother-related factors Age in years 1 = 15–24, 2 = 25–34, or 3 = 35–49 Education 1 = No formal education, 2 = Primary, or 3 = Secondary/Higher Marital status 1 = Never married, 2 = Married/Cohabiting, or 3 = Previously married Parity 1 = Primiparous, 2 = Multiparous or 3 = Grand multiparous Wealth index 1 = Poor, 2 = Middle, or 3 = Rich Own mobile phone 1 = Yes or 2 = No Media exposure: Media exposure was calculated by aggregating TV watching, radio listening, and reading newspapers. Women exposed to any media sources were categorized as having media exposure, and the rest were considered as having no media exposure. 1 = Yes or 2 = No Sex of household head 1 = Male or 2 = Female Household members 1 = Less than 6 or 2 = Six or more Working status 1 = Working or 2 = Not working Obstetrics-related characteristics Type of pregnancy 1 = Wanted or 2 = Unwanted Place of delivery 1 = Home or 2 = Health facility Mode of delivery 1 = Vaginal delivery or 2 = Caesarian section ANC attendance during pregnancy 1 = < 4 or 2 = ≥ 4 visits Postnatal care checkup after delivery and before discharge 1 = Yes or No Community-level Community-level factors Community poverty: The poverty level in the community was determined by the proportion of women in the poorer and poorest wealth quintiles, as indicated by the wealth index. It was classified as low (communities where < 50% of women were in the poorer or poorest quintiles) or high (communities where ≥ 50% of women were in the poorer or poorest quintiles). 1 = Low or 2 = High Place of residence 1 = Urban or 2 = Rural Geographical zones 1 = Western, 2 = Northern, 3 = Central, 4 = Southern, 5 = Lake, 6 = Eastern or 7 = Zanzibar Operational definitions Appropriate CF practices refer to infant and young child feeding that meets the recommended standards for minimum dietary diversity (MDD), minimum meal frequency (MMF), minimum acceptable diet (MAD), and timely introduction of solid, semi-solid, or soft foods (SSSFs) with adequate diversity and frequency. These practices are assessed using the composite indicators MDD, MMF, MAD, and SSSF, as recommended by the World Health Organization (WHO) [ 22 ] and used by previous studies in SSA [ 2 , 3 ]. Minimum dietary diversity (MDD) The percentage of children aged 6 to 23 months consumed foods and beverages from at least five of the eight specified food groups during the day or night before the survey. These food groups include breast milk; grains, roots, and tubers; legumes and nuts; dairy products (such as infant formula, milk, yogurt, and cheese); flesh foods (including meat, fish, poultry, and organ meats); eggs; vitamin A-rich fruits and vegetables; and other fruits and vegetables. Minimum meal frequency (MMF) The proportion of children aged 6 to 23 months who received solid, semi-solid, or soft foods and milk feeds for non-breastfed children at least the minimum recommended number of times during the 24 hours preceding the survey. The minimum feeding frequency is defined as: at least two solid, semi-solid, or soft meals for breastfed children aged 6–8 months; at least three for breastfed children aged 9–23 months; and at least four feeds (including at least one solid, semi-solid, or soft meal) for non-breastfed children aged 6–23 months. Minimum acceptable diet (MAD) The proportion of children aged 6 to 23 months who received a Minimum Acceptable Diet (MAD) during the day or night prior to the survey. For breastfed children, MAD is defined as fulfilling both the MDD and MMF. For non-breastfed children, it requires fulfilling the MDD (excluding dairy products), receiving the MMF, and having at least two milk feeds. Inappropriate CF practices refer to feeding practices for infants and young children that do not meet any of the aforementioned WHO criteria. Statistical analysis To address the TDHS's complex survey design, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18.5 (STATA Corp, College Station, TX). Our analysis began with descriptive statistics, using means for continuous variables and frequency and proportion for categorical variables. The results were presented in tables and narrations. Random effects and model fitness Given the hierarchical structure of the data, in which women are nested within households and households are nested within clusters, a multilevel logistic regression model (MLRM) was employed to examine the association between individual and community-level factors and CF practices in Tanzania, using the most recent DHS dataset. The analysis was conducted using the Stata command “melogit”. At level one, individual women were nested within households, and at level two, households were nested within PSU, representing communities. Four models were fitted. Model 0 was the null model, used to assess the extent of variation in CF practices attributable to clustering at the PSU level, without any explanatory variables. Model I included only individual-level variables, while Model II incorporated only community-level factors. Model III was the whole model, combining individual- and community-level variables to evaluate joint effect on the CF practices. The MLRM consisted of fixed and random effects [ 23 ]. The fixed effects (measures of association) showed results of the association between the selected explanatory variables and the outcome variable (appropriate CF practices) and were reported as adjusted odds ratios (AOR) with their corresponding 95% confidence intervals (CI). Random effects (measures of variation) such as Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion change in variance (PCV) were computed to measure the variation of CF practices across clusters. Taking clusters as a random variable, the ICC quantifies the degree of heterogeneity of CF practices between clusters (the proportion of the total observed variation in CF practices attributable to between-cluster variations) [ 24 ]. The MOR is the median value of the odds ratio which quantifies the variation or heterogeneity in CF practices between clusters in terms of the odds ratio scale and is defined as the median value of the odds ratio between the cluster at high likelihood of CF practices and cluster at lower risk when randomly picking out individuals from two clusters [ 25 ]. The PCV was calculated by PCV=(V e –V mi )/V e , where V e is the variance in the null model and V mi is variance in successive models [ 25 ]. Both Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to measure how well the different models fitted the data. Deviance = − 2 (log likelihood ratio) was used to compare the models due to the nested nature of the model; the model with the lowest deviance was selected as the best-fit model. A variance inflation factor (VIF) was used to assess multicollinearity between independent variables before fitting a multivariable regression model. Statistical significance was set at p < 0.05. Results Individual and community-level characteristics Among all women analyzed, the mean age was 28.6 years, with 44.0% aged 25–34 years. The majority (83.2%) were married or cohabited, 55.7% had attained primary education, and 73.0% were literate. In terms of socioeconomic status, 42.3% were from the poor quintile and 38.6% from the rich quintile. More than half (60.7%) were working, and 56.2% owned a mobile phone, while 63.4% had media exposure. Regarding reproductive characteristics, over half (66.0%) attended ≥ 4 ANC Visits, and 81.8% delivered at the facility. Most women (89.5%) had births by spontaneous vaginal delivery, and more than half (52.2%) were multiparous. Over half (60.6%) had more than 33 months between births, and only 16.8% received post-natal check-ups. Among children aged 6–23 months, the mean age was 14.3 months, with 33.2% aged 12–17 months. Five in ten (51.1%) were male, and only 22.7% were firstborn. At the community level, 72.3% were from rural areas, and 32.7% resided in the lake zone of mainland Tanzania (Table 2 ). Table 2 Individual and community level characteristics of mothers of children aged 6–23 months and their children in Tanzania (N = 3,090) Variables Frequency (n) Percentage (%) Maternal and household variables Age group (years) 15–24 1,078 34.9 25–34 1,360 44.0 35–49 652 21.1 Mean (± SD) 28.6 (7.0) Marital Status Never married 249 8.1 Married/Cohabiting 2,572 83.2 Previously married 269 8.7 Education Level No formal education 644 20.8 Primary 1,720 55.7 Secondary/Higher 726 23.5 Literacy Illiterate 833 27.0 Literate 2,257 73.0 Wealth Index Poor 1,306 42.3 Middle 591 19.1 Rich 1,193 38.6 Media Exposure No 1,131 36.6 Yes 1,958 63.4 Working Status Not working 1,215 39.3 Working 1,875 60.7 Owns mobile phone No 1,354 43.8 Yes 1,735 56.2 Sex of Household Head Male 2,439 78.9 Female 651 21.1 Household members < 6 1,471 47.6 ≥ 6 1,619 52.4 Obstetrics and health facility variables ANC Visits (n = 3,077) < 4 1,045 34.0 ≥ 4 2,032 66.0 Place of delivery Home 564 18.2 Health facility 2,526 81.8 Mode of delivery Vaginal delivery 2,765 89.5 Caesarean section 324 10.5 Parity Primiparous 696 22.5 Multiparous 1,613 52.2 Grand multiparous 781 25.3 PNC Check-up (n = 2,516) No 947 37.6 Yes 1569 62.4 Birth interval (n = 2,387) ≥ 33 moths 1,446 60.6 ≤ 32 months 941 39.4 Child-related variables Child sex Male 1,580 51.1 Female 1,510 48.9 Age in months 6–11 1,056 34.2 12–17 1,025 33.2 18–23 1,009 32.6 Mean (± SD) 14.3 (5.2) Birth order 1st 701 22.7 2nd & 3rd 1,190 38.5 4th & more 1,199 38.8 Number of under-five children None 56 1.8 1–2 2,448 79.2 ≥ 3 587 19.0 Community-level variables Community poverty Low 1,605 51.9 High 1,485 48.1 Residence Urban 856 27.7 Rural 2,234 72.3 Geographical Zones Western 315 10.2 Northern 334 10.8 Central 321 10.4 Southern 588 19.0 Lake 1,010 32.7 Eastern 431 14.0 Zanzibar 91 2.9 Prevalence of appropriate complementary practices Appropriate CF practices were observed in only 15.4% of mothers with children aged 6–23 months (95% CI: 13.8–17.2). Among these mothers, 13.2% reported their children met the minimum acceptable diet, while 94.8% met the minimum meal frequency. The vast majority (96.5%) of children in this age group had consumed solid, semi-solid, or soft foods (SSSF) 24 hours before the interview. Regarding specific food groups consumed by children aged 6–23 months, 94.1% had consumed grains, roots, and tubers, but only 4.9% consumed eggs, 42.1% consumed flesh foods, and 34.2% consumed other fruits and vegetables (Fig. 1 ). Individual and community-level factors associated with appropriate CF practices In the final fitted model of multivariable multilevel logistic regression, women in the middle quintile had a lower likelihood of appropriate CF practices than women in the poor quintile (AOR = 0.58, 95%CI: 0.39–0.84). Women with media exposure (AOR = 1.83, 95%CI: 1.38–2.43) had higher odds of appropriate CF practices than their counterparts. Children aged 12–17 months (AOR = 1.93, 95%CI: 1.47–2.52) and those aged 18–23 (AOR = 2.32, 95%CI: 1.78–3.02) were more likely to have appropriate CF practices than their counterparts. At the community level, women in high poverty community were 58% less likely to have appropriate feeding practices than their counterparts (AOR = 0.42, 95%CI: 0.29–0.60). (Table 3 ). Table 3 Individual and community-level factors associated with appropriate CF practices in Tanzania Variables Model I Model II Model III Individual level variables Mother’s age (years) 15–24 1.00 1.00 25–34 1.15 (0.89–1.46) 1.13 (0.88–1.44) 35–49 1.03 (0.75–1.40) 0.99 (0.73–1.36) Education Level No formal education 1.00 1.00 Primary 0.73 (0.45–1.16) 0.71 (0.44–1.12) Secondary/Higher 0.95 (0.57–1.59) 0.90 (0.54–1.51) Literacy Illiterate 1.00 1.00 Literate 1.52 (0.98–2.38) 1.50 (0.97–2.32) Wealth Index Poor 1.00 1.00 Middle 0.84 (0.60–1.18) 0.58 (0.39–0.84)* Rich 1.38 (1.01–1.88)* 0.84 (0.57–1.24) Household members < 6 1.00 1.00 ≥ 6 0.81 (0.65–1.02) 0.84 (0.67–1.05) Media Exposure No 1.00 1.00 Yes 1.87 (1.40–2.48)* 1.83 (1.38–2.43)* Working Status Not working 1.00 1.00 Working 0.81 (0.65-1.00) 0.85 (0.68–1.06) Owns mobile phone No 1.00 1.00 Yes 1.67 (1.28–2.17)* 1.60 (1.23–2.09)* PNC Check-up No 1.00 1.00 Yes 1.24 (0.94–1.64) 1.20 (0.91–1.59) Child’s age in months 6–11 1.00 1.00 12–17 1.90 (1.45–2.49)* 1.93 (1.47–2.52)* 18–23 2.27 (1.73–2.96)* 2.32 (1.78–3.02)* Community-level variables Community poverty Low 1.00 1.00 High 0.36 (0.27–0.49) 0.42 (0.29–0.60)* Residence Urban 1.00 1.00 Rural 0.83 (0.63–1.10) 1.13 (0.83–1.52) Geographical Zones Western 0.85 (0.50–1.44) 1.08 (0.63–1.86) Northern 1.56 (1.01–2.40)* 1.64 (1.05–2.57)* Central 0.48 (0.28–0.84)* 0.56 (0.32–0.98)* Southern 0.97 (0.68–1.39) 1.11 (0.76–1.62) Lake 0.93 (0.65–1.34) 1.07 (0.73–1.57) Eastern 0.80 (0.51–1.26) 0.87 (0.54–1.39) Zanzibar 1.00 1.00 Measures of variation and model fitness To assess the influence of community-level variation on CF practices, a null model was fitted. This model revealed significant variation across clusters (variance = 0.61, p < 0.001), with 15.6% of the total variance occurring between clusters and 84.4% within clusters. The Median Odds Ratio (MOR) was 1.33, indicating notable community-level effects. In Model I, which included individual-level variables, 8.4% of the variation was explained by individual differences, with an MOR of 1.27. Model II added community-level variables and yielded an MOR of 1.25 and an intraclass correlation (ICC) of 7.9%, highlighting continued cluster-level variation. The final model (Model III), combining both individual and community-level factors, showed the best fit supported by the lowest deviance. In this model, 6.9% of the variation was attributable to both individual and community-level influences, with an MOR of 1.25. (Table 4 ). Table 4 Model comparison and random effect analysis for complementary feeding practices in Tanzania Parameters Model 0 Model I Model II Model III Random effects Variance 0.61 0.30 0.28 0.24 PCV (%) Ref 50.8% 54.1% 60.7% ICC (%) 15.6% 8.4% 7.9% 6.9% MOR 1.35 1.27 1.25 1.25 Model fitness AIC 2682.01 2516.7 2593.75 2492.67 BIC 2694.07 2613.17 2654.07 2637.38 LLR -1339.00 -1242.35 -1286.87 -1222.33 Deviance 2678.01 2484.70 2573.75 2444.67 ICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, LLR; Log-likelihood ratio. Discussion This study aims to assess the prevalence and determinants of CF practices among mothers of children aged 6 to 23 months in Tanzania, using a multilevel analysis of the 2022 DHS. The observed prevalence of appropriate CF practices among mothers with children aged 6–23 months is low, reflecting a critical public health concern. This finding is consistent with findings from a study conducted in rural Bangladesh and Indonesia, which reported a similarly low level of MAD, showing an inadequate dietary diversity and meal frequency in young children despite ongoing national nutritional programs [ 26 – 28 ]. The consequences of such inadequate feeding patterns are profound, increasing risks for malnutrition, stunting, and impaired cognitive development during a critical window of child growth [ 29 ]Thus, consistent evidence across diverse contexts emphasizes the need for integrated strategies focusing on improving maternal nutritional knowledge and access to quality health services to enhance CF practices globally. This study examined several factors associated with CF. The findings showed that women in the middle wealth quintile had a significantly lower likelihood of practicing appropriate CF compared to women in the poor quintile, which is noteworthy and somewhat counterintuitive [ 30 ]. This is supported by studies showing that nutritional behaviors are multifactorial, and intermediate socioeconomic groups sometimes lag in health practices due to less targeted public health interventions or awareness compared to the poorest or wealthiest groups [ 31 , 32 ]. Therefore, addressing the specific barriers within the middle-income population is crucial for comprehensively improving infant and young child nutrition. This requires further qualitative research to understand the specific barriers. The strong positive association between median media exposure and appropriate CF practices shows the role of information dissemination through media use. Media exposure likely enhances maternal knowledge and awareness about CF's timing, diversity, and frequency, empowering mothers to make informed decisions that promote child health [ 15 , 30 ]. This finding aligns with previous research indicating that access to health information through radio, television, or digital media effectively influences maternal behaviors and feeding practices, especially in low- and middle-income countries [ 30 ]. Media campaigns can effectively reach broad audiences, overcome educational and social barriers, and improve CF. Age of the child also emerged as a significant determinant, with children aged 12–17 months and 18–23 months being more likely to be fed appropriately compared to younger children. This trend reflects common feeding practices where CF improves as children grow older and caregivers become more experienced or responsive to nutritional needs [ 33 ]. Younger infants between 6–11 months may face challenges due to feeding difficulties, lack of caregiver knowledge on age-appropriate diets, or cultural practices that delay the introduction of diverse foods, as seen in the previous study in Indonesia [ 28 ]. This suggests the crucial need for early intervention in the CF period to ensure adequate nutrition during the critical first year of life, which is a sensitive window for growth and development. Additionally, at the community level, residing in high-poverty areas was associated with a 58% reduction in the likelihood of appropriate feeding, reflecting systemic structural barriers such as limited food availability, poor access to health services, and lower community-level education or resources [ 30 ]. This underlines the importance of community-based approaches to address socioeconomic disparities impacting child nutrition. Strengths and limitations This study boasts several notable strengths. The use of nationally representative data from the 2022 TDHS, along with a large sample size, high response rates enhances the generalizability of our findings to Tanzanian women of reproductive age. Moreover, the comprehensive data collection methods, involving experienced field assistants, ensure high data quality and reliability. The application of multilevel binary logistic regression provided a robust analysis, effectively accounting for the survey's cluster design and strengthening the validity of our findings. Additionally, this study utilized the updated WHO indicators to assess the prevalence of appropriate CF practices in Tanzania. However, the study also has some limitations. The reliance on mothers' self-reports for meal frequency, dietary diversity, and the introduction of solid, semi-solid, and soft foods may have introduced recall and social desirability biases. Furthermore, cross-sectional study design limits causal relationships. Implications for practice and future research The findings of this study highlight the need for nutritional counselling and interventions that consider socioeconomic diversity, especially targeting middle-income households, which may paradoxically be underserved despite their resources. Health practitioners should leverage media platforms to disseminate clear, culturally relevant CF guidelines that reach mothers across socioeconomic spectrums. In communities with high poverty rates, integrated support programs that include food supplementation, education, and strengthened healthcare infrastructure are crucial for mitigating structural barriers. Future research should explore the underlying reasons for lower appropriate feeding in middle-income groups through qualitative and longitudinal studies, examining the pathways by which media exposure influences behaviors, and evaluating community-driven interventions that address the impact of poverty on feeding practices. Policymakers are encouraged to develop multiple strategies that combine economic, educational, and health communication efforts, prioritizing vulnerable populations to enhance the effective implementation of CF practices. Conclusion The multilevel analysis identifies complex relationships between socioeconomic status, media exposure, child age, and community poverty in shaping CF practices. While media exposure and older child age are associated with improved feeding adequacy, the unexpected lower likelihood among middle-income women and the detrimental effect of living in impoverished communities illustrate persistent inequalities and persistent challenges. To improve child nutrition outcomes, interventions must be context-specific and comprehensive, addressing both individual-level and contextual determinants. Enhancing education, health communication, and community support will be essential to close existing gaps and promote appropriate CF practices during the critical early years of child development. Abbreviations AIC Akaike Information Criterion AOR Adjusted odd ratio ANC Antenatal care BIC Bayesian Information Criterion CF Complementary feeding CI Confidence interval DHS Demographic and Health Survey ICC Intra-class Correlation Coefficient LLR Log-likelihood ratio MAD Minimum Acceptable Diet MDD Minimum Dietary Diversity MMF Minimum Meal Frequency MOR Median Odds Ratio MLRM Multilevel logistic regression model PSU Primary sampling unit PNC Postnatal care SSA Sub-Saharan Africa TDHS Tanzania Demographic and Health Survey PCV Proportional Change in Variance UNICEF United Nations Children's Fund VIF Variance inflation factor WHO World Health Organization Declarations Acknowledgements We thank the DHS program for making the data available for this study and TILAM International for statistical consultation. Authors’ Contribution MJM and EES conceptualized the idea and conducted formal analysis. MJM, VGM, TPN, PPM, AGL, JVN, and EES interpreted the results, drafted the manuscript and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding Not Applicable. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com. Ethics approval and consent to participate This study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Competing interests None declared. References WHO. Complementary feeding [Internet]. 2024 [cited 2025 May 11]. Available from: https://www.who.int/health-topics/complementary-feeding Tololu AK, Teshome B, Fessaha HZ, Kaso AW. Determinants of appropriate complementary feeding practices among mothers of children aged 6–23 months in Bokoji town, Oromia region, Ethiopia. BMC Pediatr. 2025;25. Mekonen EG, Zegeye AF, Workneh BS. Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Sub-saharan African countries: a multilevel analysis of the recent demographic and health survey. BMC Public Health. 2024;24:1–13. Olatona FA, Adenihun JO, Aderibigbe SA, Adeniyi OF. Complementary Feeding Knowledge, Practices, and Dietary Diversity among Mothers of Under-Five Children in an Urban Community in Lagos State, Nigeria. Int J MCH AIDS. 2017;6:46–59. Ogbo FA, Ogeleka P, Awosemo AO. Trends and determinants of complementary feeding practices in Tanzania, 2004–2016. Trop Med Health. 2018;46:40. Arora A. WHO/UNICEF discussion paper: The extension of the 2025 maternal, infant and young child nutrition targets to 2030 [Internet]. UNICEF DATA. 2019 [cited 2025 Mar 12]. Available from: https://data.unicef.org/resources/who-unicef-discussion-paper-nutrition-targets/ Twabi HS, Manda SO, Small DS. Evaluating the effect of appropriate complementary feeding practices on child growth in Malawi using cross-sectional data: an application of propensity score matching. Front Nutr. 2021;8:714232. Shagaro SS, Mulugeta BT, Kale TD. Complementary feeding practices and associated factors among mothers of children aged 6–23 months in Ethiopia: Secondary data analysis of Ethiopian mini demographic and health survey 2019. Arch Public Health. 2021;79:1–12. Ariyo O, Aderibigbe OR, Ojo TJ, Sturm B, Hensel O. Determinants of appropriate complementary feeding practices among women with children aged 6–23 months in Iseyin, Nigeria. Sci Afr. 2021;13:e00848. Kamudoni P, Kiige L, Ortenzi F, Beal T, Nordhagen S, Kirogo V, et al. Identifying and understanding barriers to optimal complementary feeding in Kenya. Matern Child Nutr. 2024;20:e13617. Saaka M, Awini S, Nang E. Prevalence and predictors of appropriate complementary feeding practice among mothers with children 6–23 months in Northern Ghana. World Nutr. 2022;13:14–23. Varghese A, Agarwal M, Singh VK. Complementary feeding practices in children aged 6–23 months in rural Lucknow: A cross-sectional study. Clin Epidemiol Glob Health. 2023;22:101331. Umugwaneza M, Havemann-Nel L, Vorster HH, Wentzel-Viljoen E. Factors influencing complementary feeding practices in rural and semi-urban Rwanda: a qualitative study. J Nutr Sci. 2021;10:e45. Birungi TL, Ejalu DL. Optimal complementary feeding practices among caregivers and their children aged 6–23 months in Kisoro district, Uganda. BMC Nutr. 2022;8:81. Sichalwe MM, Behera MR, Behera D, Dehury RK, Degge H. Knowledge and practice of complementary feeding among mothers in Dar-es-Salaam, Tanzania: Community-based cross-sectional study. Clin Epidemiol Glob Health [Internet]. 2023 [cited 2025 May 11];24. Available from: https://www.ceghonline.com/article/S2213-3984(23)00217-8/fulltext WHO. Minimum Meal Frequency (6–23 months) [Internet]. 2025 [cited 2025 May 11]. Available from: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/7045 Nambiar A, Arunachalam D. Child Food Deficiency in India: Socio-demographic and Regional Patterns. Child Indic Res [Internet]. 2025 [cited 2025 May 11]; Available from: https://doi.org/10.1007/s12187-025-10246-6 Ministry of Health (MoH). [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. Tanzania Demographicand Health Survey and Malaria Indicator Survey 2022 Key Indicators Report. Dodoma, Rockville: MoH, NBS, OCGS, and ICF; 2023. Pallangyo EE, Kimaro OJ, Mwalupani NR, George GS, Katana D, Msengwa AS. Cross-sectional analysis of risk factors associated with the coexistence of three undernutrition indicators among children aged 0–23 months in Tanzania. BMC Nutr. 2025;11:4. Reinhardt K, Fanzo J. Addressing Chronic Malnutrition through Multi-Sectoral, Sustainable Approaches: A Review of the Causes and Consequences. Front Nutr [Internet]. 2014 [cited 2025 May 11];1. Available from: https://www.frontiersin.orghttps://www.frontiersin.org/journals/nutrition/articles/ 10.3389/fnut.2014.00013/full The United Republic of Tanzania (URT). Ministry of Finance and Planning, Tanzania National Bureau of Statistics and President’s Office - Finance and Planning, Office of the Chief Government Statistician, Zanzibar. The 2022 Population and Housing Census: Administrative Units Population Distribution Report; Tanzania Zanzibar, December 2022. Note: [Internet]. [cited 2025 May 22]. Available from: chrome- extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.nbs.go.tz/nbs/takwimu/Census2022/Administrative_units_ Population_Distribution_Report_Tanzania_volume1a.pdf WHO. Indicators for assessing infant and young child feeding practices: definitions and measurement methods [Internet]. 2021 [cited 2025 May 4]. Available from: https://www.who.int/publications/i/item/9789240018389 Austin PC, Merlo J. Intermediate and advanced topics in multilevel logistic regression analysis. Stat Med. 2017;36:3257–77. Merlo J, Wagner P, Ghith N, Leckie G. An original stepwise multilevel logistic regression analysis of discriminatory accuracy: The case of neighbourhoods and health. PLoS ONE. 2016;11:1–31. Dong N, Reinke WM, Herman KC, Bradshaw CP, Murray DW. Meaningful Effect Sizes, Intraclass Correlations, and Proportions of Variance Explained by Covariates for Planning Two- and Three-Level Cluster Randomized Trials of Social and Behavioral Outcomes. Eval Rev. 2016;40:334–77. Jubayer A, Nowar A, Islam S, Islam MH, Nayan MM. Complementary feeding practices and their determinants among children aged 6–23 months in rural Bangladesh: evidence from Bangladesh Integrated Household Survey (BIHS) 2018–2019 evaluated against WHO/UNICEF guideline – 2021. Arch Public Health. 2023;81:114. Yunitasari E, Al Faisal AH, Efendi F, Kusumaningrum T, Yunita FC, Chong MC. Factors associated with complementary feeding practices among children aged 6–23 months in Indonesia. BMC Pediatr. 2022;22:727. Nurrizka RH, Wenny DM, Amalia R. Complementary Feeding Practices and Influencing Factors Among Children Under 2 Years of Age: A Cross-Sectional Study in Indonesia. Pediatr Gastroenterol Hepatol Nutr. 2021;24:535–45. Ahmad I, Khalique N, Khalil S, Urfi, Maroof M. Complementary feeding practices among children aged 6–23 months in Aligarh, Uttar Pradesh. J Fam Med Prim Care. 2017;6:386–91. Mekonen EG, Zegeye AF, Workneh BS. Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Sub-saharan African countries: a multilevel analysis of the recent demographic and health survey. BMC Public Health. 2024;24:115. Gatica-Domínguez G, Neves PAR, Barros AJD, Victora CG. Complementary Feeding Practices in 80 Low- and Middle-Income Countries: Prevalence of and Socioeconomic Inequalities in Dietary Diversity, Meal Frequency, and Dietary Adequacy. J Nutr. 2021;151:1956–64. Scarpa G, Berrang-Ford L, Twesigomwe S, Kakwangire P, Galazoula M, Zavaleta-Cortijo C, et al. Socio-economic and environmental factors affecting breastfeeding and complementary feeding practices among Batwa and Bakiga communities in south-western Uganda. PLOS Glob Public Health. 2022;2:e0000144. Kassa T, Meshesha B, Haji Y, Ebrahim J. Appropriate complementary feeding practices and associated factors among mothers of children age 6–23 months in Southern Ethiopia, 2015. BMC Pediatr. 2016;16:131. 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Mtoro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYNACHoYEBgbmA0CWhAxh1WxwLWwJIC08RGphAGnhMYDoJgR05zcf+/BBxi6Pf/aZz69u1FjwMLAfProBnxazY2zJM2fwJBdLnMvdZp1zDOgwnrS0G/i18Bgz8/AwJzac4d1mnMMG1CLBY0aMlvrE+Wd4nhnn/CNey+HEDWd4mB/nthGlJS2ZcQbP8WLDM2xmzLl9EjxsBP1y+PBhho891XlyZ5gff875VifHz374GF4tYMDYA6bYJMAkQeVg8ANMMn8gTvUoGAWjYBSMNAAA+BlCoA3AKzYAAAAASUVORK5CYII=","orcid":"","institution":"TILAM International","correspondingAuthor":true,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""},{"id":473512266,"identity":"6b3a7e3c-b961-406b-8c6d-7f829029feac","order_by":1,"name":"Victoria Godfrey Majengo","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":473512267,"identity":"104835c7-af38-4643-939f-00ddce8e791d","order_by":2,"name":"Theresia Ponsiano Ngungulu","email":"","orcid":"","institution":"Department of Public Health and Community Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Theresia","middleName":"Ponsiano","lastName":"Ngungulu","suffix":""},{"id":473512268,"identity":"e072daad-b9ff-4da3-9e50-f142711611dc","order_by":3,"name":"Pendo Pascal Masanja","email":"","orcid":"","institution":"Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Pendo","middleName":"Pascal","lastName":"Masanja","suffix":""},{"id":473512269,"identity":"d69e8035-28f4-43dc-bfe3-5c253dd2e609","order_by":4,"name":"Arnold Gideon Lumbe","email":"","orcid":"","institution":"Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Arnold","middleName":"Gideon","lastName":"Lumbe","suffix":""},{"id":473512270,"identity":"db836051-b090-46fa-a40d-ec8ee39994b9","order_by":5,"name":"Jestina Vincent Nyondo","email":"","orcid":"","institution":"Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Jestina","middleName":"Vincent","lastName":"Nyondo","suffix":""},{"id":473512271,"identity":"ea681184-2338-4eaa-a4f1-13f810925f2a","order_by":6,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""}],"badges":[],"createdAt":"2025-05-23 14:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6733841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6733841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85618791,"identity":"6cc56724-95d6-444d-acd8-5549459f76de","added_by":"auto","created_at":"2025-06-29 14:52:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15320,"visible":true,"origin":"","legend":"\u003cp\u003eFood groups consumed by children aged 6-23 months\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6733841/v1/28cce8afd3d86664e8dff509.png"},{"id":95563662,"identity":"7e4000fa-bc28-48e2-99ec-ac5f94641f26","added_by":"auto","created_at":"2025-11-10 15:53:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6733841/v1/f75fff6d-2e02-4da8-8f0e-ec0147b00e8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Tanzania: A multilevel analysis of the 2022 Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eAppropriate complementary feeding (CF) is a critical determinant of young children\u0026rsquo;s growth, development, and nutritional status during the early life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CF refers to the introduction of solid, semi-solid, or soft foods alongside breast milk or formula, typically starting at six months of age when milk alone can no longer meet the increasing nutritional demands of infants [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The period between 6 and 24 months is especially crucial as infants transition from a milk-based diet to one that fulfils nutrient needs from diverse food groups, supporting rapid physical and cognitive development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, exclusive breastfeeding is recommended for the first six months, followed by the introduction of nutrient-rich complementary foods while continuing breastfeeding for up to two years or beyond [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Optimal CF practices emphasize timely initiation, dietary diversity, meal frequency, and food safety to prevent malnutrition and support immune function [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its recognized importance, global CF practices remain suboptimal [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to recent United Nations Children's Fund (UNICEF) and World Health Organization (WHO) reports, less than a quarter of infants aged 6\u0026ndash;23 months worldwide meet the criteria for minimum dietary diversity and feeding frequency appropriate for their age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Data from low- and middle-income countries highlight alarming gaps; for instance, nearly one in four infants aged 8\u0026ndash;9 months and one in five aged 10\u0026ndash;11 months have not yet received any solid foods, exposing them to risks of growth faltering and malnutrition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In sub-Saharan Africa (SSA), the situation is aggravated by the prevalence of suboptimal infant feeding practices, limited quality of complementary foods, micronutrient deficiencies, and high rates of infection, all of which contribute substantially to child morbidity and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Regional surveys indicate that approximately 13% of children aged 6\u0026ndash;23 months receive appropriate CF in SSA, underscoring the urgency to improve feeding practices in this context [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of appropriate CF practices among mothers of children aged 6\u0026ndash;23 months varies significantly across countries, with prevalence rates reported at 7.7% in Malawi [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], 9.8% in Ethiopia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and between 4.2\u0026ndash;10.0% in Nigeria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In East Africa, a recent study in Kenya revealed that 37% of children aged 6\u0026ndash;23 months received a minimally diverse diet, and 31% were fed a minimum acceptable diet as per WHO guidelines, reflecting persistent challenges in infant nutrition within the region [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, studies show suboptimal CF practice rates, like 15.7\u0026ndash;29.8% in Ghana [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and 51.3% in India [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, Rwanda reports CF rates where around 79% of children aged 6\u0026ndash;8 months [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], while in Uganda, CF practices also remain inadequate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Exclusive breastfeeding for the first six months stands at 64%, while only 19% of children aged 6\u0026ndash;23 months meet the minimum dietary diversity requirement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Globally recommended meal frequencies, at least two to three meals daily for breastfed infants and four for non-breastfed, are often unmet [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with evidence from India highlighting that children not fed the minimum number of meals had a 60% higher chance of stunting [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond basic demographic factors such as a child's age, maternal education, wealth index, and antenatal care (ANC) visits, additional determinants specifically influencing CF practices have been identified in studies conducted in SSA. These include maternal marital status, with unmarried mothers being less likely to adopt appropriate CF, household headship, where female-headed households exhibit higher CF rates, and media exposure, which significantly enhances the likelihood of proper feeding practices by providing access to nutritional information. Factors related to health service utilization, such as the number of ANC visits, the place of delivery, and postnatal care (PNC) check-ups, have also demonstrated strong positive associations with CF adherence, underscoring the critical role of maternal healthcare engagement. Furthermore, community-level variables, including community illiteracy rates and regional disparities within the country, further influence feeding practices, indicating socio-economic and geographical disparities in CF behaviors. Other factors, such as birth order, the number of under-five children in the household, and current breastfeeding status, contribute to variability in CF practices across different SSA communities.t SSA communities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Tanzania, as study in Dar es Salaam showed that 41.9% had CF. This inadequate practice of CF leads to stunting in under-five children and underweight that may affect young children growth [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These statistics demonstrate the critical need to address feeding practices in Tanzania to prevent malnutrition and its long-term health consequences. The high prevalence of poor CF practices in Tanzania poses a significant public health challenge, given its strong association with growth faltering, cognitive deficits, and increased risk of non-communicable diseases later in life [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Despite ongoing efforts, the majority of infants and young children are not receiving adequate complementary foods in terms of diversity, frequency, or quantity, contributing to persistent undernutrition and stunting rates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Exploring the prevalence and determinants of appropriate CF practices among mothers with children aged 6\u0026ndash;23 months in Tanzania is essential. Traditional single-level analyses often overlook the hierarchical nature of Demographic Health Survey (DHS) data, where individuals are nested within communities, leading to biased estimates and an incomplete picture of the CF. Multilevel analysis addresses these limitations by simultaneously examining factors at multiple levels, thereby providing a more accurate and comprehensive assessment. Therefore, this study aimed to provide a comprehensive multilevel analysis of factors influencing these practices using the 2022 Tanzania Demographic and Health Survey (TDHS) data, informing policymaking and targeted interventions to improve national child nutrition outcomes.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and design\u003c/h2\u003e \u003cp\u003eThis study was an analytical cross-sectional survey that utilized secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The Tanzania National Bureau of Statistics conducted the survey with the Ministries of Tanzania Mainland and Zanzibar. Tanzania is amongst the countries in East Africa with a total area of 945, 087 square kilometers, including 61,000 square kilometers of inland water. According to the 2022 census, the country has a population of approximately 62\u0026nbsp;million people. Among them, 9,484,170 are children under the age of five, with around 69% living in rural areas [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and sampling\u003c/h3\u003e\n\u003cp\u003eData for this study were obtained from the latest DHS conducted between 24 February and 21 July 2022 across all regions in Tanzania. The target population for the 2022 TDHS include women of reproductive age (15\u0026ndash;49 years), men, children and households across the 32 administrative regions in Tanzania. However, this study focused on women of reproductive age and their children aged 6\u0026ndash;23 months. At the country level, a sampling frame is usually obtained. To minimize sampling errors, the country is stratified by geographic region and urban/rural areas within each region, followed by a two-stage sampling to select a household to be surveyed. The first sampling is to select a primary sampling unit (PSU) and then select a household. PSUs are survey clusters usually based on census enumeration areas (EAs). A probability proportion to size is employed in each stratum to select the PSU. For each selected PSU, a complete household listing is done. This is followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. We used the kids record (KR). After removing duplicates and focusing on the last-born child aged 6\u0026ndash;23 months who lived with their caregiver, our final analytical sample comprised of 3,090 (weighted) participants.\u003c/p\u003e\n\u003ch3\u003eStudy Variable\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eThe outcome variable was complementary feeding practices as a binary (appropriate or inappropriate).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eThe independent variables were selected based on data availability from the 2022 TDHS and relevant literature and were categorized into individual and community-level factors, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Individual-level variables included child-related factors, mother/household-related factors and obstetrics/health facility factors. Community-level variables represented contextual characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndividual and community level variables used under this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoding categories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual-level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-related factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male or 2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;6\u0026ndash;11, 2\u0026thinsp;=\u0026thinsp;12\u0026ndash;17 or 3\u0026thinsp;=\u0026thinsp;18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;1st, 2\u0026thinsp;=\u0026thinsp;2nd \u0026amp; 3rd or 3\u0026thinsp;=\u0026thinsp;4th \u0026amp; more\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;\u0026le;\u0026thinsp;32 or 2\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children under 5 in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;None, 2\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 or 3\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother-related factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;15\u0026ndash;24, 2\u0026thinsp;=\u0026thinsp;25\u0026ndash;34, or 3\u0026thinsp;=\u0026thinsp;35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;No formal education, 2\u0026thinsp;=\u0026thinsp;Primary, or 3\u0026thinsp;=\u0026thinsp;Secondary/Higher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never married, 2\u0026thinsp;=\u0026thinsp;Married/Cohabiting, or 3\u0026thinsp;=\u0026thinsp;Previously married\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Primiparous, 2\u0026thinsp;=\u0026thinsp;Multiparous or 3\u0026thinsp;=\u0026thinsp;Grand multiparous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Poor, 2\u0026thinsp;=\u0026thinsp;Middle, or 3\u0026thinsp;=\u0026thinsp;Rich\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn mobile phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia exposure: Media exposure was calculated by aggregating TV watching, radio listening, and reading newspapers. Women exposed to any media sources were categorized as having media exposure, and the rest were considered as having no media exposure.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male or 2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Less than 6 or 2\u0026thinsp;=\u0026thinsp;Six or more\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Working or 2\u0026thinsp;=\u0026thinsp;Not working\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstetrics-related characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Wanted or 2\u0026thinsp;=\u0026thinsp;Unwanted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Home or 2\u0026thinsp;=\u0026thinsp;Health facility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Vaginal delivery or 2\u0026thinsp;=\u0026thinsp;Caesarian section\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC attendance during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;4 or 2\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostnatal care checkup after delivery and before discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity poverty: The poverty level in the community was determined by the proportion of women in the poorer and poorest wealth quintiles, as indicated by the wealth\u003c/p\u003e \u003cp\u003eindex. It was classified as low (communities where \u0026lt;\u0026thinsp;50% of women were in the poorer or poorest quintiles) or high (communities where \u0026ge;\u0026thinsp;50% of women were in the poorer or poorest\u003c/p\u003e \u003cp\u003equintiles).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Low or 2\u0026thinsp;=\u0026thinsp;High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Urban or 2\u0026thinsp;=\u0026thinsp;Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographical zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Western, 2\u0026thinsp;=\u0026thinsp;Northern, 3\u0026thinsp;=\u0026thinsp;Central, 4\u0026thinsp;=\u0026thinsp;Southern,\u003c/p\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;Lake, 6\u0026thinsp;=\u0026thinsp;Eastern or 7\u0026thinsp;=\u0026thinsp;Zanzibar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOperational definitions\u003c/h2\u003e \u003cp\u003eAppropriate CF practices refer to infant and young child feeding that meets the recommended standards for minimum dietary diversity (MDD), minimum meal frequency (MMF), minimum acceptable diet (MAD), and timely introduction of solid, semi-solid, or soft foods (SSSFs) with adequate diversity and frequency. These practices are assessed using the composite indicators MDD, MMF, MAD, and SSSF, as recommended by the World Health Organization (WHO) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and used by previous studies in SSA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMinimum dietary diversity (MDD)\u003c/h3\u003e\n\u003cp\u003eThe percentage of children aged 6 to 23 months consumed foods and beverages from at least five of the eight specified food groups during the day or night before the survey. These food groups include breast milk; grains, roots, and tubers; legumes and nuts; dairy products (such as infant formula, milk, yogurt, and cheese); flesh foods (including meat, fish, poultry, and organ meats); eggs; vitamin A-rich fruits and vegetables; and other fruits and vegetables.\u003c/p\u003e\n\u003ch3\u003eMinimum meal frequency (MMF)\u003c/h3\u003e\n\u003cp\u003eThe proportion of children aged 6 to 23 months who received solid, semi-solid, or soft foods and milk feeds for non-breastfed children at least the minimum recommended number of times during the 24 hours preceding the survey. The minimum feeding frequency is defined as: at least two solid, semi-solid, or soft meals for breastfed children aged 6\u0026ndash;8 months; at least three for breastfed children aged 9\u0026ndash;23 months; and at least four feeds (including at least one solid, semi-solid, or soft meal) for non-breastfed children aged 6\u0026ndash;23 months.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMinimum acceptable diet (MAD)\u003c/h2\u003e \u003cp\u003eThe proportion of children aged 6 to 23 months who received a Minimum Acceptable Diet (MAD) during the day or night prior to the survey. For breastfed children, MAD is defined as fulfilling both the MDD and MMF. For non-breastfed children, it requires fulfilling the MDD (excluding dairy products), receiving the MMF, and having at least two milk feeds.\u003c/p\u003e \u003cp\u003eInappropriate CF practices refer to feeding practices for infants and young children that do not meet any of the aforementioned WHO criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo address the TDHS's complex survey design, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18.5 (STATA Corp, College Station, TX). Our analysis began with descriptive statistics, using means for continuous variables and frequency and proportion for categorical variables. The results were presented in tables and narrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRandom effects and model fitness\u003c/h2\u003e \u003cp\u003eGiven the hierarchical structure of the data, in which women are nested within households and households are nested within clusters, a multilevel logistic regression model (MLRM) was employed to examine the association between individual and community-level factors and CF practices in Tanzania, using the most recent DHS dataset. The analysis was conducted using the Stata command \u0026ldquo;melogit\u0026rdquo;. At level one, individual women were nested within households, and at level two, households were nested within PSU, representing communities. Four models were fitted. Model 0 was the null model, used to assess the extent of variation in CF practices attributable to clustering at the PSU level, without any explanatory variables. Model I included only individual-level variables, while Model II incorporated only community-level factors. Model III was the whole model, combining individual- and community-level variables to evaluate joint effect on the CF practices.\u003c/p\u003e \u003cp\u003eThe MLRM consisted of fixed and random effects [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The fixed effects (measures of association) showed results of the association between the selected explanatory variables and the outcome variable (appropriate CF practices) and were reported as adjusted odds ratios (AOR) with their corresponding 95% confidence intervals (CI). Random effects (measures of variation) such as Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion change in variance (PCV) were computed to measure the variation of CF practices across clusters. Taking clusters as a random variable, the ICC quantifies the degree of heterogeneity of CF practices between clusters (the proportion of the total observed variation in CF practices attributable to between-cluster variations) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The MOR is the median value of the odds ratio which quantifies the variation or heterogeneity in CF practices between clusters in terms of the odds ratio scale and is defined as the median value of the odds ratio between the cluster at high likelihood of CF practices and cluster at lower risk when randomly picking out individuals from two clusters [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The PCV was calculated by PCV=(V\u003csub\u003ee\u003c/sub\u003e\u0026ndash;V\u003csub\u003emi\u003c/sub\u003e)/V\u003csub\u003ee\u003c/sub\u003e, where V\u003csub\u003ee\u003c/sub\u003e is the variance in the null model and V\u003csub\u003emi\u003c/sub\u003e is variance in successive models [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Both Akaike\u0026rsquo;s Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to measure how well the different models fitted the data. Deviance\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2 (log likelihood ratio) was used to compare the models due to the nested nature of the model; the model with the lowest deviance was selected as the best-fit model. A variance inflation factor (VIF) was used to assess multicollinearity between independent variables before fitting a multivariable regression model. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIndividual and community-level characteristics\u003c/h2\u003e \u003cp\u003eAmong all women analyzed, the mean age was 28.6 years, with 44.0% aged 25\u0026ndash;34 years. The majority (83.2%) were married or cohabited, 55.7% had attained primary education, and 73.0% were literate. In terms of socioeconomic status, 42.3% were from the poor quintile and 38.6% from the rich quintile. More than half (60.7%) were working, and 56.2% owned a mobile phone, while 63.4% had media exposure. Regarding reproductive characteristics, over half (66.0%) attended\u0026thinsp;\u0026ge;\u0026thinsp;4 ANC Visits, and 81.8% delivered at the facility. Most women (89.5%) had births by spontaneous vaginal delivery, and more than half (52.2%) were multiparous. Over half (60.6%) had more than 33 months between births, and only 16.8% received post-natal check-ups. Among children aged 6\u0026ndash;23 months, the mean age was 14.3 months, with 33.2% aged 12\u0026ndash;17 months. Five in ten (51.1%) were male, and only 22.7% were firstborn. At the community level, 72.3% were from rural areas, and 32.7% resided in the lake zone of mainland Tanzania (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndividual and community level characteristics of mothers of children aged 6\u0026ndash;23 months and their children in Tanzania (N\u0026thinsp;=\u0026thinsp;3,090)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal and household variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.6 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreviously married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/Higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiteracy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwns mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of Household Head\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstetrics and health facility variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANC Visits (n\u0026thinsp;=\u0026thinsp;3,077)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMode of delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrand multiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNC Check-up (n\u0026thinsp;=\u0026thinsp;2,516)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth interval (n\u0026thinsp;=\u0026thinsp;2,387)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;33 moths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;32 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild-related variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge in months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.3 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd \u0026amp; 3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th \u0026amp; more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of under-five children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity poverty\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Zones\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of appropriate complementary practices\u003c/h2\u003e \u003cp\u003eAppropriate CF practices were observed in only 15.4% of mothers with children aged 6\u0026ndash;23 months (95% CI: 13.8\u0026ndash;17.2). Among these mothers, 13.2% reported their children met the minimum acceptable diet, while 94.8% met the minimum meal frequency. The vast majority (96.5%) of children in this age group had consumed solid, semi-solid, or soft foods (SSSF) 24 hours before the interview. Regarding specific food groups consumed by children aged 6\u0026ndash;23 months, 94.1% had consumed grains, roots, and tubers, but only 4.9% consumed eggs, 42.1% consumed flesh foods, and 34.2% consumed other fruits and vegetables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIndividual and community-level factors associated with appropriate CF practices\u003c/h2\u003e \u003cp\u003eIn the final fitted model of multivariable multilevel logistic regression, women in the middle quintile had a lower likelihood of appropriate CF practices than women in the poor quintile (AOR\u0026thinsp;=\u0026thinsp;0.58, 95%CI: 0.39\u0026ndash;0.84). Women with media exposure (AOR\u0026thinsp;=\u0026thinsp;1.83, 95%CI: 1.38\u0026ndash;2.43) had higher odds of appropriate CF practices than their counterparts. Children aged 12\u0026ndash;17 months (AOR\u0026thinsp;=\u0026thinsp;1.93, 95%CI: 1.47\u0026ndash;2.52) and those aged 18\u0026ndash;23 (AOR\u0026thinsp;=\u0026thinsp;2.32, 95%CI: 1.78\u0026ndash;3.02) were more likely to have appropriate CF practices than their counterparts. At the community level, women in high poverty community were 58% less likely to have appropriate feeding practices than their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.42, 95%CI: 0.29\u0026ndash;0.60). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndividual and community-level factors associated with appropriate CF practices in Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual level variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.89\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.88\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.75\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.73\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.45\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.44\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/Higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.57\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.54\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiteracy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52 (0.98\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50 (0.97\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.60\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.39\u0026ndash;0.84)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.01\u0026ndash;1.88)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.57\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.65\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.67\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.87 (1.40\u0026ndash;2.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83 (1.38\u0026ndash;2.43)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.65-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.68\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwns mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.28\u0026ndash;2.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60 (1.23\u0026ndash;2.09)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNC Check-up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.94\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.91\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild\u0026rsquo;s age in months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90 (1.45\u0026ndash;2.49)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93 (1.47\u0026ndash;2.52)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.27 (1.73\u0026ndash;2.96)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.32 (1.78\u0026ndash;3.02)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity-level variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity poverty\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36 (0.27\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.29\u0026ndash;0.60)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.63\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.83\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Zones\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.50\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.63\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.01\u0026ndash;2.40)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.05\u0026ndash;2.57)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.28\u0026ndash;0.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56 (0.32\u0026ndash;0.98)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.68\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.76\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.65\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.73\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.51\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.54\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMeasures of variation and model fitness\u003c/h2\u003e \u003cp\u003eTo assess the influence of community-level variation on CF practices, a null model was fitted. This model revealed significant variation across clusters (variance\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 15.6% of the total variance occurring between clusters and 84.4% within clusters. The Median Odds Ratio (MOR) was 1.33, indicating notable community-level effects. In Model I, which included individual-level variables, 8.4% of the variation was explained by individual differences, with an MOR of 1.27. Model II added community-level variables and yielded an MOR of 1.25 and an intraclass correlation (ICC) of 7.9%, highlighting continued cluster-level variation. The final model (Model III), combining both individual and community-level factors, showed the best fit supported by the lowest deviance. In this model, 6.9% of the variation was attributable to both individual and community-level influences, with an MOR of 1.25. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel comparison and random effect analysis for complementary feeding practices in Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel fitness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2682.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2516.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2593.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2492.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2694.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2613.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2654.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2637.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1339.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1242.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1286.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1222.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2678.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2484.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2573.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2444.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, LLR; Log-likelihood ratio.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aims to assess the prevalence and determinants of CF practices among mothers of children aged 6 to 23 months in Tanzania, using a multilevel analysis of the 2022 DHS. The observed prevalence of appropriate CF practices among mothers with children aged 6\u0026ndash;23 months is low, reflecting a critical public health concern. This finding is consistent with findings from a study conducted in rural Bangladesh and Indonesia, which reported a similarly low level of MAD, showing an inadequate dietary diversity and meal frequency in young children despite ongoing national nutritional programs [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The consequences of such inadequate feeding patterns are profound, increasing risks for malnutrition, stunting, and impaired cognitive development during a critical window of child growth [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]Thus, consistent evidence across diverse contexts emphasizes the need for integrated strategies focusing on improving maternal nutritional knowledge and access to quality health services to enhance CF practices globally.\u003c/p\u003e \u003cp\u003eThis study examined several factors associated with CF. The findings showed that women in the middle wealth quintile had a significantly lower likelihood of practicing appropriate CF compared to women in the poor quintile, which is noteworthy and somewhat counterintuitive [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is supported by studies showing that nutritional behaviors are multifactorial, and intermediate socioeconomic groups sometimes lag in health practices due to less targeted public health interventions or awareness compared to the poorest or wealthiest groups [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, addressing the specific barriers within the middle-income population is crucial for comprehensively improving infant and young child nutrition. This requires further qualitative research to understand the specific barriers.\u003c/p\u003e \u003cp\u003eThe strong positive association between median media exposure and appropriate CF practices shows the role of information dissemination through media use. Media exposure likely enhances maternal knowledge and awareness about CF's timing, diversity, and frequency, empowering mothers to make informed decisions that promote child health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This finding aligns with previous research indicating that access to health information through radio, television, or digital media effectively influences maternal behaviors and feeding practices, especially in low- and middle-income countries [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Media campaigns can effectively reach broad audiences, overcome educational and social barriers, and improve CF.\u003c/p\u003e \u003cp\u003eAge of the child also emerged as a significant determinant, with children aged 12\u0026ndash;17 months and 18\u0026ndash;23 months being more likely to be fed appropriately compared to younger children. This trend reflects common feeding practices where CF improves as children grow older and caregivers become more experienced or responsive to nutritional needs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Younger infants between 6\u0026ndash;11 months may face challenges due to feeding difficulties, lack of caregiver knowledge on age-appropriate diets, or cultural practices that delay the introduction of diverse foods, as seen in the previous study in Indonesia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This suggests the crucial need for early intervention in the CF period to ensure adequate nutrition during the critical first year of life, which is a sensitive window for growth and development. Additionally, at the community level, residing in high-poverty areas was associated with a 58% reduction in the likelihood of appropriate feeding, reflecting systemic structural barriers such as limited food availability, poor access to health services, and lower community-level education or resources [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This underlines the importance of community-based approaches to address socioeconomic disparities impacting child nutrition.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study boasts several notable strengths. The use of nationally representative data from the 2022 TDHS, along with a large sample size, high response rates enhances the generalizability of our findings to Tanzanian women of reproductive age. Moreover, the comprehensive data collection methods, involving experienced field assistants, ensure high data quality and reliability. The application of multilevel binary logistic regression provided a robust analysis, effectively accounting for the survey's cluster design and strengthening the validity of our findings. Additionally, this study utilized the updated WHO indicators to assess the prevalence of appropriate CF practices in Tanzania. However, the study also has some limitations. The reliance on mothers' self-reports for meal frequency, dietary diversity, and the introduction of solid, semi-solid, and soft foods may have introduced recall and social desirability biases. Furthermore, cross-sectional study design limits causal relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice and future research\u003c/h2\u003e \u003cp\u003eThe findings of this study highlight the need for nutritional counselling and interventions that consider socioeconomic diversity, especially targeting middle-income households, which may paradoxically be underserved despite their resources. Health practitioners should leverage media platforms to disseminate clear, culturally relevant CF guidelines that reach mothers across socioeconomic spectrums. In communities with high poverty rates, integrated support programs that include food supplementation, education, and strengthened healthcare infrastructure are crucial for mitigating structural barriers. Future research should explore the underlying reasons for lower appropriate feeding in middle-income groups through qualitative and longitudinal studies, examining the pathways by which media exposure influences behaviors, and evaluating community-driven interventions that address the impact of poverty on feeding practices. Policymakers are encouraged to develop multiple strategies that combine economic, educational, and health communication efforts, prioritizing vulnerable populations to enhance the effective implementation of CF practices.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe multilevel analysis identifies complex relationships between socioeconomic status, media exposure, child age, and community poverty in shaping CF practices. While media exposure and older child age are associated with improved feeding adequacy, the unexpected lower likelihood among middle-income women and the detrimental effect of living in impoverished communities illustrate persistent inequalities and persistent challenges. To improve child nutrition outcomes, interventions must be context-specific and comprehensive, addressing both individual-level and contextual determinants. Enhancing education, health communication, and community support will be essential to close existing gaps and promote appropriate CF practices during the critical early years of child development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eAdjusted odd ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eANC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eAntenatal care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eBayesian Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eComplementary feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eDemographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eIntra-class Correlation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eLog-likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eMinimum Acceptable Diet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eMinimum Dietary Diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eMinimum Meal Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eMedian Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMLRM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eMultilevel logistic regression model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003ePSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003ePrimary sampling unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003ePNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003ePostnatal care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eTDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eTanzania Demographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eProportional Change in Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eUNICEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eUnited Nations Children\u0026apos;s Fund\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eVariance inflation factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80%;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study and TILAM International for statistical consultation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM and EES conceptualized the idea and conducted formal analysis. MJM, VGM, TPN, PPM, AGL, JVN, and EES interpreted the results, drafted the manuscript and reviewed all versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Complementary feeding [Internet]. 2024 [cited 2025 May 11]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/complementary-feeding\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/complementary-feeding\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTololu AK, Teshome B, Fessaha HZ, Kaso AW. 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Meaningful Effect Sizes, Intraclass Correlations, and Proportions of Variance Explained by Covariates for Planning Two- and Three-Level Cluster Randomized Trials of Social and Behavioral Outcomes. Eval Rev. 2016;40:334\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJubayer A, Nowar A, Islam S, Islam MH, Nayan MM. Complementary feeding practices and their determinants among children aged 6\u0026ndash;23 months in rural Bangladesh: evidence from Bangladesh Integrated Household Survey (BIHS) 2018\u0026ndash;2019 evaluated against WHO/UNICEF guideline \u0026ndash;\u0026thinsp;2021. Arch Public Health. 2023;81:114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYunitasari E, Al Faisal AH, Efendi F, Kusumaningrum T, Yunita FC, Chong MC. Factors associated with complementary feeding practices among children aged 6\u0026ndash;23 months in Indonesia. BMC Pediatr. 2022;22:727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurrizka RH, Wenny DM, Amalia R. Complementary Feeding Practices and Influencing Factors Among Children Under 2 Years of Age: A Cross-Sectional Study in Indonesia. Pediatr Gastroenterol Hepatol Nutr. 2021;24:535\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad I, Khalique N, Khalil S, Urfi, Maroof M. Complementary feeding practices among children aged 6\u0026ndash;23 months in Aligarh, Uttar Pradesh. J Fam Med Prim Care. 2017;6:386\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonen EG, Zegeye AF, Workneh BS. Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Sub-saharan African countries: a multilevel analysis of the recent demographic and health survey. BMC Public Health. 2024;24:115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatica-Dom\u0026iacute;nguez G, Neves PAR, Barros AJD, Victora CG. Complementary Feeding Practices in 80 Low- and Middle-Income Countries: Prevalence of and Socioeconomic Inequalities in Dietary Diversity, Meal Frequency, and Dietary Adequacy. J Nutr. 2021;151:1956\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScarpa G, Berrang-Ford L, Twesigomwe S, Kakwangire P, Galazoula M, Zavaleta-Cortijo C, et al. Socio-economic and environmental factors affecting breastfeeding and complementary feeding practices among Batwa and Bakiga communities in south-western Uganda. PLOS Glob Public Health. 2022;2:e0000144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassa T, Meshesha B, Haji Y, Ebrahim J. Appropriate complementary feeding practices and associated factors among mothers of children age 6\u0026ndash;23 months in Southern Ethiopia, 2015. BMC Pediatr. 2016;16:131.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Appropriate complementary feeding, Breastfeeding, Children aged 6–23 months, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6733841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6733841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAppropriate complementary feeding (CF) is an\u0026ensp;important factor in the growth, development and nutritional status of young children. Inadequate complementary feeding is one of\u0026ensp;the major causes of undernutrition among 6-23‐month‐old children. Therefore, this\u0026ensp;study was conducted to determine the prevalence of appropriate CF practices and associated factors among mothers having children aged 6\u0026ndash;23 months in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn Analytical cross-sectional study of the 2022 Tanzania Demographic and Health Surveys data was conducted. The sampling frame was stratified by geographic region and urban/rural areas, using a two-stage sampling method that selected primary sampling units based on census enumeration areas, followed by household selection using probability systematic sampling. Given the complex survey design, multilevel logistic regression was used to identify individual and community-level factors associated with CF practices. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were used to estimate the strength and magnitude of association, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAppropriate CF practices were observed in only 15.4% of mothers with children aged 6\u0026ndash;23 months (95% CI: 13.76\u0026ndash;17.21). Factors associated with this were that women in the middle quintile had a lower likelihood of appropriate CF practices than women in the poor quintile (AOR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.39\u0026ndash;0.84). Women with media exposure (AOR\u0026thinsp;=\u0026thinsp;1.83, 95% CI: 1.38\u0026ndash;2.43) had a higher likelihood of appropriate CF practices than their counterparts. Children aged 12\u0026ndash;17 months (AOR\u0026thinsp;=\u0026thinsp;1.93, 95% CI: 1.47\u0026ndash;2.52) and those aged 18\u0026ndash;23 (AOR\u0026thinsp;=\u0026thinsp;2.32, 95% CI: 1.78\u0026ndash;3.02) were more likely to have appropriate CF practices than their counterparts. At the community level, women in high poverty communities were 58% less likely to have appropriate feeding practices than their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.29\u0026ndash;0.60).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAppropriate complementary feeding practices were suboptimal among children aged 6\u0026ndash;23 months in Tanzania. Multifaceted interventions addressing poverty, leveraging health information through media platforms, and targeting specific age groups and communities would be crucial to improve child nutrition status in Tanzania.\u003c/p\u003e","manuscriptTitle":"Complementary feeding practices and associated factors among mothers of children aged 6 to 23 months in Tanzania: A multilevel analysis of the 2022 Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:52:02","doi":"10.21203/rs.3.rs-6733841/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"540f6708-609f-42b3-a047-2e26a5a15190","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:52:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6733841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6733841","identity":"rs-6733841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}