Prevalence and determinants of double and triple burden of malnutrition among mother– child pairs in Lesotho: pooled analysis of DHS 2009, 2014 and 2023

Prevalence and determinants of double and triple burden of malnutrition among mother– child pairs in Lesotho: pooled analysis of DHS 2009, 2014 and 2023 | 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 Prevalence and determinants of double and triple burden of malnutrition among mother– child pairs in Lesotho: pooled analysis of DHS 2009, 2014 and 2023 Hilary Takunda Takawira, Esther Tatenda Chawanda, Praise Rudaviro Zimunya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8165828/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The simultaneous occurrence of under- and over-nutrition among households is an emerging public health problem in Sub-Saharan Africa. In Lesotho, the increasing burden of overweight in mothers alongside persistent child undernutrition is indicative of this double burden crisis. However, research on the magnitude and drivers of household double and triple burden malnutrition among mother-child pairs is limited. The objective of this study is to report on household double and triple malnutrition burden prevalence and the correlates among mother-child (MC) pairs in Lesotho. Methods The research pooled cross-sectional data from Lesotho Demographic and Health Surveys in 2009, 2014, and 2023 (representing a total of 4,150 mother-child pairs). Women’s and children’s record data files were combined, and the sample weights were used to adjust for sampling design. Survey-weighted univariable and multivariable logistic regression models were estimated to determine factors associated with the household's double burden and triple burden of malnutrition, adjusting for socioeconomic, demographic, and biological covariates. Result The HDBM and HTBM prevalence was estimated at 14.9% and 3.6%, respectively in the pooled analysis. HDBM trended in a U-shape up to 18.3% in 2023, and HTBM more than doubled to 6.0%in the same year Analysis found women from middle class households were more likely to engage in HDBM (aOR = 1.63, 95% CI: 1.17–2.28) and those with four or more children comprising an over two-fold increase in cumulative odds (aOR = 2.23, 95% CI: 1.45–3.42). Breastfeeding was highly protective with reduced odds of HDBM (aOR = 0.69, 95% CI: 0.53–0.91) and HTBM (aOR = 0.61, 95% CI: 0.40–0.94). In 2023, women were at a significantly greater risk of HTBM than in 2009 (aOR = 1.89, 95% CI: 1.21–2.97). Conclusion This study confirms that Lesotho experiences a significant double and triple burden of malnutrition at the household level. The dual burden of maternal overnutrition and child undernutrition, and their overlap with micronutrient inadequacy, adds a layer of complexity to the public health problem. This burden is significantly affected by key social and biological determinants, including household economic status, parity and maternal height. The increasing trend emphasises the urgent requirement for synergy among public health policies that would tackle simultaneously all forms of malnutrition through a combination approach addressing common underlying determinants. Double burden of malnutrition triple burden stunting maternal obesity Lesotho DHS sub-Saharan Africa Figures Figure 1 Figure 2 Introduction Malnutrition in all its forms remains a major global public health problem. In Low- and Middle-Income Countries (LMICs), undernutrition continues to be a focus of attention because its burden persists, particularly in the form of stunting, which affects over 25% of children aged less than five years in sub-Saharan Africa (SSA), with rates exceeding 33.6% in countries such as Lesotho [ 1 , 2 ]. Rapid nutrition transitions, due to urbanisation, economic change and diets high in energy-dense low-nutrient food, have driven increases in overweight and obesity that are now co-existing with ongoing undernutrition at national, community and household levels [ 3 , 4 ]. This phenomenon, known as double burden of malnutrition (DBM), is illustrated by households comprised of overweight mothers and stunted children, the prevalence rate of which ranged from 1.8 to 25% in LMICs [ 5 , 6 ]. Nutrition transitions are the expected and systematic changes in diet, nutrient intake and health status that come with development, urbanisation and demographic change. Furthermore, the challenge is compounded by the triple burden of malnutrition (TBM) that occurs when malnutrition due to undernourishment, over-nourishment, and micronutrient deficiencies , such as anaemia, happens simultaneously. This affects seven out of ten households in sub-Saharan Africa [ 1 , 7 ]. DBM is manifested through the confluence of multiple life-course pathways. Early-life undernutrition may even favour metabolic disorders and abdominal adiposity later in life, especially when subjects are exposed after weaning to obesity-promoting environments [ 8 , 9 ]. The double burden of malnutrition (DBM) is multi-dimensional in that it appears at population, household and individual levels and represents various but related adaptations to the current nutrition transition. At the individual level, DBM is manifested as the dual burden of overweight and underweight in the mother–child pair, an apparent contradiction which has evolved into a major public health challenge among low- and middle-income countries [ 10 , 11 ]. This results from common sociodemographic and dietary factors. With better household wealth, consumption of cheap, energy-dense but nutrient-poor foods increases, along with excessive caloric intake and maternal overweight or obesity. Concurrently, childhood undernutrition continues to be driven by sub-optimal complementary feeding practices, risk for repeated infections and low dietary diversity [ 12 , 13 ]. Complicating this dual burden is now the emerging triple burden of malnutrition (TBN) that includes effects from maternal overnutrition, child undernutrition and micronutrient deficiencies like anaemia, shown to be increasingly prevalent in sub-Saharan Africa, Asia as well [ 1 , 14 , 15 ]. It will take integrated, multisectoral nutrition strategies that approach undernutrition and overnutrition simultaneously to tackle these related burdens, both of which can be addressed by parallel policy, community, and household actions [ 16 ]. Globally, the prevalence of the double burden of malnutrition is rising, although with considerable variance through sub-Saharan Africa, depending on regions and the socio-economic background. A multi-body study in SSA reported DBM and TBM, with their anaemia problem, to be a public threat whose occurrence was linked to household wealth status and the particular residence having access to improved sanitation. Country-specific studies in Ethiopia, Tanzania, and Malawi in SSA and India underscore the occurrence of DBM among the mother-child pair, with the possible determinants being maternal age and education, household wealth, dietary diversity, and the child's birth order. Moreover, there is an indication of wealthier households in transition economies having a high DBM prevalence. A similar nutrition trend is observed in Lesotho as it is in some other SSA countries, characterised by a double burden of persisting undernutrition trends and rising overnutrition rates. Despite making progress on some health indicators, the country still carries a substantial burden of the child undernutrition problem, with stunting, an important indicator of chronic malnutrition, being prevalent and the main threat to child growth and survival. Additionally, the country has a rising overnutrition trend, particularly overweight and obesity among women of the productive ages, a situation that is similar to other sad countries associated with urbanisation, reduced physical activity and the consumption of low-cost, high-energy, low-nutrient diets. This state calls for a comprehensive and public health strategy to alleviate malnutrition in Lesotho. While DBM/TBM is a reported public health emergency in sub-Saharan Africa, with multi-country studies that confirm its prevalence and household-level correlates [ 1 , 7 , 12 ], the extent of DBM/TBM in Lesotho remains hidden within broader accounts. Recent data (2015) suggest a high prevalence of childhood stunting (33.6%) in Lesotho [ 2 ]), pointing to an ongoing undernutrition problem. Simultaneously, evidence from neighbouring countries in Southern Africa shows this undernutrition often constitutes a ‘double burden’ with maternal overnutrition within the same households [ 11 , 20 , 25 ] – and it is likely that Lesotho too faces this dual crisis. However, there is a gap in research that is nationally representative with peer-reviewed published data that specifically examines whether DBM and TBM exist among mother–child pairs of this age cohort in Lesotho. This knowledge gap does not enable a well understanding of the national prevalence, geographic distribution and Lesotho-specific drivers of this phenomenon, which would limit the capacity to develop country-targeted “double-duty” interventions and make optimal allocation on resources for action against all forms of malnutrition in the country. Despite this burden convergence, research on the domestic double burden of malnutrition in Lesotho is lacking. Studies and surveys have generally reported maternal nutrition and child nutrition in separate columns. A systematic comparison of their aggregation within the same individual sample is still lacking, and it remains unclear whether the comorbidity pattern evolves with time. Estimation of the burden and trend of DBM was critical for improved public health management. Integration of the most recent dataset of 2023 with that for 2009, and 2014, the LDHS offers an opportunity to do a robust temporal analysis. Hence, this study seeks to assess the prevalence and factors (both socioeconomic, demographic and environmental) associated with household double burden of malnutrition among mother-child pairs in Lesotho. Thus, by utilising 15 years of nationally-representative population data, this study will generate vital evidence for the design of integrated and nutrition-sensitive interventions that work to effectively tackle both undernutrition and overnutrition in context-specific Basotho settings, thereby contributing largely to the attainment of national and global nutritional goals. Methods Study design and data source We conducted a secondary analysis of nationally representative data from the Lesotho Demographic and Health Surveys (DHS) for the years 2009, 2014, and 2023–2024. The DHS employs a two-stage stratified cluster sampling design to collect comparable, population-based data on health and nutrition indicators. For this analysis, we used the Women's Recode (IR) and the Children's Recode (KR) files, which contain information from women of reproductive age (15–49 years) and data on the health and nutritional status of their children under five, respectively. Study population and sample selection The study focused on mother–child pairs as the unit of analysis. The initial sample included all children under five years of age and their biological mothers from the three survey waves. A series of sequential inclusion criteria was applied to obtain the final analytic sample. Only pairs where the child could be successfully matched to their biological mother in the dataset were retained. Only living children were included in the analysis. Pairs were required to have complete anthropometric information, with children having a valid height-for-age z-score and mothers having a valid body mass index (BMI) measurement. Mothers were restricted to those aged 15–49 years at the time of the survey to align with the standard reproductive age range used in Demographic and Health Surveys (DHS). The final pooled analytic sample across all three survey waves comprised 4,150 mother–child pairs. Variables Outcome We defined stunting, underweight, and wasting using height-for-age, weight-for-age, and weight-for-height z -scores, respectively. Each indicator was classified according to the World Health Organization (WHO, 2006). Child Growth Standards, with values below − 2 standard deviations (SD) denoting stunting, underweight, or wasting. Children with biologically implausible measurements ( + 5 SD) were excluded in accordance with WHO guidelines (2011). The primary outcome was the household-level double burden of malnutrition. The double burden of malnutrition (DBM) was defined as the coexistence of undernutrition in children manifested as underweight (weight-for-age < − 2 SD), stunting (height-for-age < − 2 SD), or wasting (weight-for-height < − 2 SD) an overweight or obese mother (body mass index ≥ 25 kg/m²) within the same household that is mother-child pair [ 21 ]. Child stunting: Stunting was defined as a height-for-age z-score (HAZ) more than two standard deviations below the WHO growth standard median (HAZ < -2). HAZ scores were derived from the DHS variable hw70 after converting it to a continuous z-score and excluding biologically implausible values. Maternal overweight/obesity: Maternal overweight or obesity was defined as a body mass index (BMI) ≥ 25 kg/m², calculated from the DHS variable v445 . The HDBM variable was coded as a binary indicator, with a value of 1 assigned if both child stunting and maternal overweight/obesity were present in the same household, and 0 if either or both conditions were absent. Triple burden of malnutrition (TBM) refers to the coexistence of child undernutrition manifested as underweight (weight-for-age < − 2 SD), stunting (height-for-age < − 2 SD), or wasting (weight-for-height < − 2 SD), maternal overnutrition, and child micronutrient deficiency, specifically anaemia defined as a haemoglobin concentration below 11 g/dL [ 14 ]. Independent variables Independent variables were selected based on their conceptual relevance to child and maternal nutrition. Socioeconomic factors included household wealth and maternal education, reflecting differences in access to resources and health knowledge. Demographic factors captured maternal age, parity, and child characteristics, with age and parity categorised to reflect meaningful reproductive and family-size groups while maintaining sufficient sample sizes (Table 1 ). Child age was grouped to correspond to critical developmental stages. Geographic factors included urban-rural residence and administrative region to account for differences in access to services and environmental influences. Maternal biological factors included height, age and BMI, categorised based on standard thresholds associated with child growth outcomes and maternal nutritional status. Child dietary diversity was summarised as the number of food groups consumed and categorised to identify children at risk of insufficient nutrient intake. Overall, collapsing variables into meaningful categories enhanced interpretability, facilitated comparisons across risk groups, and supported robust statistical analysis, while the accompanying table provides the detailed coding and classification of each variable. Table 1 Description of study variables, coding, and measurement. Variable Name Type Categories / Coding Child sex Categorical 1 = Male, 2 = Female Child age broad Categorical 1 = 0–11 months, 2 = 12–23 months, 3 = 24–35 months, 4 = 36–59 months Breastfeeding Binary 0 = No, 1 = Yes Poor dietary diversity Binary 0 = ≥ 4 food groups, 1 = < 4 food groups Maternal age group Categorical 1 = 15–24 years, 2 = 25–34 years, 3 = 35–49 years Parity cat Categorical 1 = 1 child, 2 = 2–3 children, 3 = 4 + children Maternal education Categorical 0 = No Education, 1 = Primary, 2 = Secondary, 3 = Higher Maternal height Categorical 1 = Short (< 150 cm), 2 = Average (150–159.9 cm), 3 = Tall (≥ 160 cm) Wealth quintile Categorical 1 = Poorest, 2 = Poorer, 3 = Middle, 4 = Richer, 5 = Richest Residence Categorical 1 = Urban, 2 = Rural Survey year Categorical 1 = 2009, 2 = 2014, 3 = 2023 HDBM Binary 0 = No double burden, 1 = Double burden HTBM Binary 0 = No double burden, 1 = Double burden Statistical analysis All analyses accounted for the complex survey design of the Demographic and Health Surveys, including clustering, stratification, and sampling weights, using the svyset command in Stata 18.0. Data preparation involved merging the women’s and children’s datasets for each survey year using cluster, household, and line identifiers, followed by appending the 2009, 2014, and 2023 datasets into a pooled file with a survey year indicator. Univariable analyses described sample characteristics, presenting categorical variables as weighted frequencies and percentages, and continuous variables as means and standard deviations (or medians and interquartile ranges where appropriate). Weighted prevalence estimates were calculated for the household double burden of malnutrition (HDBM) and its components. Bivariate associations between HDBM and explanatory variables were assessed using design-based chi-square tests for categorical variables and survey-adjusted mean comparisons for continuous variables. Survey-adjusted univariable logistic regression models provided crude odds ratios (cORs) and 95% confidence intervals (CIs). Variables with p < 0.20 in univariable analyses, along with those of theoretical importance, were included in multivariable logistic regression models. Results were reported as adjusted odds ratios (aOR) with 95% CIs. Analyses were two-sided, with statistical significance set at p < 0.05. Ethical considerations This study utilised existing, de-identified, publicly available data from the DHS program. The ICF International Institutional Review Board and the relevant ethics committee in Lesotho obtained ethical approval for the original surveys. Permission to use the data was granted by the DHS program Results Characteristics of study participants A total of 4150 mother–child pairs were in the study. Mothers had a mean age of 27.6 (SD = 6.8), ranging from 15 to 49 years. The mean age of the children was 27.0 months (SD = 17.4), and the ages ranged from 0 to 59 months. Sampling took place in ten regions, with the largest proportions of participants coming from Thaba-Tseka (12.5%), Maseru (11.0%) and Mokhotlong (10.9%) and the lowest from Qacha’s-Nek (8.2%) and Mafeteng (8.5%). The rest of the districts were Butha-Buthe (9.4%), Leribe (10.6%), Berea (10.4%), Mohale’s Hoek and Quthing at 9.0% and 9.4%, respectively. The demographic profile of the study population is illustrated in Table 2 . Table 2 Descriptive characteristics of the study sample (N = 4,150 mother-child pairs) Characteristic Category Unweighted Weighted N (%) % (95% CI) Child characteristics Child Sex Male 2,018 (48.6) 48.7 (46.7–50.6) Female 2,132 (51.4) 51.3 (49.4–53.3) Child Age 0–11 months 910 (21.9) 22.0 (20.6–23.5) 12–23 months 848 (20.4) 20.0 (18.5–21.6) 24–35 months 1,271 (30.6) 30.7 (29.1–32.4) 36–59 months 1,121 (27.0) 27.3 (25.9–28.7) Breastfeeding No 2,168 (52.2) 54.8 (52.5–57.2) Yes 1,982 (47.8) 45.2 (42.8–47.6) Poor Dietary Diversity* No 357 (8.6) 10.0 (8.6–11.6) Yes 3,793 (91.4) 90.0 (88.4–91.4) Mother characteristics Maternal age group 15–24 years 1,637 (39.4) 39.0 (37.0–41.1) 25–34 years 1,789 (43.1) 44.0 (41.9–46.1) 35–49 years 724 (17.4) 17.0 (15.5–18.4) Parity 1 1,346 (32.4) 32.8 (30.9–34.6) 2–3 1,962 (47.3) 49.2 (47.0–51.3) 4+ 842 (20.3) 18.1 (16.4–19.9) Maternal education No Education 75 (1.8) 1.4 (1.0–1.9) Primary 2,029 (48.9) 44.6 (42.1–47.2) Secondary 1,811 (43.6) 46.9 (44.3–49.4) Higher 235 (5.7) 7.1 (5.8–8.7) Maternal Height Short (< 150 cm) 449 (10.8) 10.6 (9.3–12.0) Average (150–159.9 cm) 2,440 (58.8) 59.5 (57.5–61.5) Tall (≥ 160 cm) 1,261 (30.4) 29.9 (28.0–31.8) Household Characteristics Wealth Quintile Poorest 1,221 (29.4) 22.4 (20.1–24.8) Poorer 942 (22.7) 20.8 (19.0–22.7) Middle 834 (20.1) 20.4 (18.6–22.4) Richer 656 (15.8) 20.4 (18.3–22.6) Richest 497 (12.0) 16.0 (14.0–18.2) Residence Urban 862 (20.8) 26.3 (23.2–29.6) Rural 3,288 (79.2) 73.7 (70.4–76.8) Survey Year 2009 1,667 (40.2) 38.9 (35.7–42.2) 2014 1,357 (32.7) 34.6 (31.3–38.0) 2023 1,126 (27.1) 26.5 (23.4–29.9) Note: Poor dietary diversity defined as consumption of < 4 food groups. CI = Confidence Interval. The total number of mother–child pairs was 4150, among which there were slightly more female (51.3%) than male children (48.7%), and most of the children were aged between 24 and 35 months (30.7%). Approximately one-half (54.8%) of the mothers were not breastfeeding, and 90.0% reported poor dietary diversity. Most of the mothers were between 25 and 34 years old (44.0%), had attended primary education (44.6%) or the first year of secondary school (46.9%); most women had an average height (59.5%). Household wealth was skewed toward the lower and middle quintiles; 73.7% of households were in rural areas. Prevalence of malnutrition indicators and combined outcomes A total of 4150 mother–child pairs were eligible for the analysis. Approximately half the mothers were overweight or obese, with a weighted prevalence of 46.2% (95% CI: 43.9–48.5). The most common form of undernutrition in children was stunting (Table 3 ), affecting 34.5% (95% CI: 32.8–36.2), followed by anaemia at 27.8% (95% CI: 25.4–30 − 3%) and underweight at 11.7% (95% CI:10.5–13%). Household double burden of malnutrition (HDBM) occurred in 14.9% (95% CI: 13.4–16.3) of households. The HTBM (defined as the simultaneous existence of maternal overnutrition, any form of child undernutrition [underweight: weight-for-age < − 2 SD, stunting: height-for-age < − 2 SD or wasting: weight-for-height < − 2 SD]) and child anaemia occurred in 3.6% (95% CI: 3.0–4.3) of households. When analysed by pairs, the most commonly encountered pair was: obese mother with stunted child (13.4%, 95% CI: 12.1–14.8), followed by obese mother with anaemic child (12.1%, 95%CI: 10.7–13.6). The less common combinations were obese mother with underweight child (4.0%, 95% CI: 3.4–4.8), and obese mother with wasted child (4.8%, 95% CI: 4.0–5.8). Table 3 Frequency analysis for malnutrition indicators and combination outcomes. Unweighted Weighted Variable Frequency, n (%) % (95% CI) Individual indicators Maternal overweight/obesity 1,832 44.3 46.2 (43.9–48.5) Child stunting 1,494 37.2 34.5 (32.8–36.2) Child underweight 496 12.3 11.7 (10.5–13.0) Child anaemia 1,127 27.5 27.8 (25.4–30.3) Combination outcomes Overall HDBM 610 15.1 14.9 (13.4–16.3) Overall HTBM 163 4.0 3.6 (3.0-4.3) Obese mother + stunted child 565 13.7 13.4 (12.1–14.8) Obese mother + underweight child 165 4.1 4.0 (3.4–4.8) Obese mother + wasted child 137 4.7 4.8 (4.0-5.8) Obese mother + anemic child 490 12.0 12.1 (10.7–13.6) Note: HDBM = Household Double Burden of Malnutrition. Sample sizes vary per indicator due to missing data. All estimates are based on the survey-weighted analysis. Temporal trends in malnutrition indicators Analysis of data from three survey waves revealed significant shifts in Lesotho's malnutrition profile between 2009 and 2023 (Fig. 1 ). The household double burden of malnutrition (HDBM) demonstrated a concerning U-shaped trajectory, decreasing from 14.7% (95% CI: 12.6–17.0) in 2009 to 12.5% (95% CI: 10.5–14.9) in 2014, then rising sharply to 18.3% (95% CI: 15.3–21.7) in 2023 (p = 0.015). This represents a 46% increase in HDBM prevalence over the most recent nine-year period (Fig. 1 ). The household triple burden of malnutrition (HTBM) displayed an even steeper rise, tripling from 2.1% (95% CI: 1.3–3.2) in 2014 to 6.0% (95% CI: p < 0.001). Mother overweight and obesity prevalence slowly but steadily increased between 2009 (42.8% (95% CI:39.2–46.4)) and 2023 (53.5% (95% CI:48.4–58.6)), resulting in a prevalence of over half of reproductive-aged women now falling victim to overnutrition. There was modest improvement in child stunting presence, from a decrease of 36.1% (95% CI: 33.5–38.8) in 2009 to 31.3% (95% CI: 28.1–34.8) in 2023 (p = 0·008). But this progress has largely since plateaued, with little deviation between 2014 (31.4%, 95% CI: 28.5–34.6) and 2023. All indicators indicated that there were significant trends (p < 0.05) and therefore variations in malnutrition over time. Determinants of the double burden of malnutrition In the survey-weighted multivariable model, several independent predictors of double burden of malnutrition remained after adjusting for covariates (results are shown in Table 4 ) which accounted for the complex survey design. Middle wealth households were 63% more likely to report HDBM than their poorest counterparts (aOR = 1.63, 95% CI:1.17–2.28), which suggests that middle income families may also be vulnerable. Parity showed a strong dose-response relation with odds of HDBM: compared to women reporting one childbirth, 2–3 children increased the odds by 45% (aOR = 1.45, 95% CI 1.07–1.95), while four or more children had more than two-fold greater chances (aOR = 2.23, 95% CI: 1.45, 3.42) of experiencing HDBM events in lifetime. Older child age was an additional factor that contributed significantly, 24–35 month-old children had approximately 75% greater likelihood of living in a household with HDBM (aOR = 1.75, CI2 : 1.23–2.50), and those aged between 36 and 59 months had around half the odds of having HDBM in their households(aOR = 1.56, CI3: 1.08–2.25) compared to infants (0-11months). Table 4 Survey-weighted univariable and multivariable analysis of determinants of household double burden of malnutrition Univariable Multivariable Variable Category % with HDBM OR (95% CI) P value aOR (95% CI) P-value Wealth Quintile Poorest 19.9 1.00 (ref) – 1.00 (ref) – Poorer 23.2 1.28 (0.95–1.73) 0.109 1.32 (0.97–1.80) 0.074 Middle 25.8 1.49 (1.08–2.04) 0.015 1.63 (1.17–2.28) 0.004 Richer 19.8 1.11 (0.79–1.55) 0.550 1.21 (0.85–1.72) 0.290 Richest 11.4 0.78 (0.49–1.24) 0.297 0.82 (0.51–1.32) 0.412 Maternal Age 15–24 29.7 1.00 (ref) – 1.00 (ref) – 25–34 45.6 1.42 (1.10–1.85) 0.008 1.13 (0.82–1.55) 0.454 35–49 24.7 2.15 (1.60–2.89) < 0.001 1.30 (0.85–2.01) 0.230 Parity 1 22.5 1.00 (ref) – 1.00 (ref) – 2–3 50.6 1.59 (1.24–2.04) < 0.001 1.45 (1.07–1.95) 0.015 4+ 27.0 2.50 (1.86–3.35) < 0.001 2.23 (1.45–3.42) < 0.001 Child sex Male 51.7 1.00 (ref) – 1.00 (ref) – Female 48.3 0.86 (0.69–1.08) 0.197 0.88 (0.71–1.10) 0.271 Child Age(mon) 0–11 12.7 1.00 (ref) – 1.00 (ref) – 12–23 18.0 1.59 (1.09–2.30) 0.015 1.49 (1.03–2.16) 0.032 24–35 37.7 2.29 (1.65–3.18) < 0.001 1.75 (1.23–2.50) 0.002 36–59 months 31.6 2.17 (1.57–2.99) < 0.001 1.56 (1.08–2.25) 0.019 Breastfeeding No 65.7 1.00 (ref) – 1.00 (ref) – Yes 34.4 0.59 (0.47–0.75) < 0.001 0.69 (0.53–0.91) 0.008 Maternal Height Short 14.8 1.00 (ref) – 1.00 (ref) – Average 65.9 0.75 (0.53–1.07) 0.114 0.72 (0.50–1.03) 0.071 Tall 19.3 0.41 (0.27–0.62) < 0.001 0.37 (0.24–0.56) < 0.001 Survey Year 2009 38.4 1.00 (ref) – 1.00 (ref) – 2014 29.3 0.83 (0.63–1.09) 0.188 0.84 (0.64–1.10) 0.208 2023 32.3 1.30 (0.98–1.72) 0.065 1.32 (0.99–1.76) 0.058 *Note: Model accounts for complex survey design. Bolded p-values indicate statistical significance at p < 0.05.* Protective factors identified were breastfeeding, with a reduced odds of HDBM in the household by 31% (aOR = 0.69, 95% CI0.53–0.91); and taller maternal height, resulting in a 63% decreased odds of HDBM in the household (aOR = 0.37, 95%CI = 0.24–0.56). These findings demonstrate the role of household structure, child age and maternal characteristics in the risk of HDBM and emphasise the necessity for targeted strategies to reduce exposure to this dual burden. Determinants of HTBM As a result of multivariable analyses (Table 5 ), household wealth, breastfeeding and survey year were independently associated factors with the household triple burden of malnutrition (HTBM). The final multivariable model showed several important predictors of HTBM. Table 5 Determinants of HTBM in Lesotho: Findings from univariable and multivariable analyses. Univariable Multivariable Variable Category % with HTBM* Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value Wealth Quintile Poorest 19.1 1.00 (Reference) 1.00 (Reference) Poorer 28.4 1.61 (0.94–2.77) 0.084 1.59 (0.94–2.68) 0.085 Middle 28.1 1.62 (0.93–2.82) 0.086 1.61 (0.91–2.85) 0.104 Richer 8.0 0.45 (0.22–0.96) 0.038 0.44 (0.21–0.94) 0.033 Richest 16.4 1.22 (0.59–2.55) 0.589 1.15 (0.56–2.35) 0.711 Parity 1 child 22.8 1.00 (Reference) 1.00 (Reference) 2–3 children 56.6 1.69 (1.03–2.75) 0.037 1.61 (0.99–2.62) 0.055 4 + children 20.6 1.66 (0.94–2.93) 0.082 1.79 (0.99–3.23) 0.055 Breastfeeding No 66.1 1.00 (Reference) 1.00 (Reference) Yes 33.9 0.62 (0.40–0.95) 0.028 0.61 (0.40–0.94) 0.024 Survey Year 2009 35.9 1.00 (Reference) 1.00 (Reference) 2014 19.9 0.61 (0.35–1.06) 0.081 0.61 (0.35–1.07) 0.085 2023 44.2 1.88 (1.20–2.93) 0.006 1.89 (1.21–2.97) 0.005 *Note: HTBM = Household Triple Burden of Malnutrition (coexistence of maternal overweight/obesity, child undernutrition, and child anaemia). Data from Lesotho Demographic and Health Surveys 2009, 2014, and 2023. All analyses account for complex survey design. Bolded values indicate statistical significance (p < 0.05).* The richer wealth quintile had a lower likelihood of HTBM (aOR = 0.44, 95% CI: 0.21–0.94), with the least quintile serving as the reference category for comparison to richer quintiles in the analysis. Breastfeeding was inversely associated with HTBM rates (aOR = 0.61; 95% CI: 0.40, 0.94). Particularly, the year 2023 of the survey was found to be associated with 89% increased odds of HTBM(aOR = 1.89, 95% CI: 1.21–2.97) compared to the year 2009, suggesting a considerable time trend hike. Increased parity was marginally associated with a higher risk of HTBM. The overall model was highly significant (F = 4.71, p < 0.001), indicating that the group-level effects of these socioeconomic, behavioural, and time-related factors are important in understanding the triple burden of malnutrition in Lesotho. Discussion This is a particularly timely and thorough analysis that rigorously scrutinises the changing profile of malnutrition in Lesotho, where we currently have an enigma where historical widespread undernutrition meets the new, simultaneous phenomenon of rapid emergence of overnutrition. The findings add to the growing evidence that double and triple burden of malnutrition (DBM, TBM) has shifted from an emerging to a major public health problem in sub-Saharan Africa (SSA) and other low- and middle-income countries (LMICs) [ 7 , 1 ]. The results have established that Lesotho is affected by a serious double and triple burden of malnutrition. The HDBM was 14.9% which is very high compared to that of the pooled prevalence of 8% reported in 23 SSA countries[ 1 ] and the one in India reported as 7.7% [ 15 ]. It is similarly greater than the 11.3% reported in Tanzania [ 19 ] and 12.3% predicted in East Ethiopia [ 18 ], indicating that Lesotho may be characterised by a very serious form of DBM. Further, the prevalence of HTBM, namely a double-burden household in which there is an overnourished mother and under- or overweight/anaemic child/children, was 3.6%, comparable with reported estimates of 3–5% from other SSA contexts [ 7 , 1 ]. These findings demonstrate that micronutrient malnutrition-including childhood anemia-aggravates already existing macronutrient imbalances and that the nutrition situation becomes complicated. At the individual level, these studies singled out suboptimal breastfeeding (34.5% of children) and overweight or obese mothers (46.2% of the mothers ) as leading factors for co-morbidities among under-nutrition and over-nutrition. The dyad with the highest (13.4%) co-occurrence of an obese mother and a stunted child epitomises this paradox of double burden, where household-level factors, including poor dietary quality low in nutrients but high in energy, seem to dominate. It concurs with previous findings from studies in SSA, which found that the nutrition transition has an impact on dietary patterns even in low-resource sociodemographic environments [ 1 , 7 ]. Our multivariate analysis demonstrates several relevant and modifiable determinants ofHDBM, consistent with the literature pool. Middle-wealth families were the most common to engage in HDBM when analyzed by wealth quintile. This non-linear association is in contrast to Indian studies that report a monotonic increase with wealth [ 4 ] but consistent with our “the economics of the food gap” hypothesis [ 1 , 12 ]. Reasons as to why these two groups of processes or food types are pushing and pulling the middle-income family in different directions may be because the middle-income family has the consumption power for processed, energy-dense foods in a shifting economy but they also have limited means for acquiring a diverse and higher-quality diet. This puts them in a unique scenario, and they are at a high risk of having an over-nutritioned mother and an under-nutritioned child. Parity remained a strong predictor, with a dose-response pattern: women with higher numbers of children (especially parity ≥ 4) had significantly increased odds of HDBM. Comparable associations have been reported in Tanzania and Ethiopia [ 19 , 18 ]. This may reflect household resource depletion with inadequate food quantity and/or quality for all members, as well as cumulative pregnancy-related maternal nutritional depletion and associated postnatal metabolic changes predisposing women to weight retention. Child age was also significantly associated with undernutrition, and older children (24–59 months) were more likely to be undernourished compared to young infants (0–11 months). This is consistent with the evidence that children are increasingly exposed to suboptimal complementary feeding practices and higher levels of infection as they get older, particularly following breastfeeding cessation, a practice which our analysis found to be protective. Indeed, breastfeeding was found to be associated with a reduction in odds of HDBM by 31%, highlighting its position as a “double duty” intervention, protecting maternal and child malnutrition through promotion via normal metabolism and healthy growth [ 12 ]. Similarly to Table 3 , maternal height, a second strong protective factor, reduced HDBModds by 63%. This result is consistent with the "intergenerational cycle of malnutrition" hypothesis, according to which taller women (who have a history of good nutrition in early life) would exhibit healthier pregnancies with better growth potential for their offspring [ 23 , 22 ]. Conversely, short maternal height (a proxy for childhood malnutrition) is associated with a high risk of LBW and stunting in the offspring, continuing the vicious cycle of intergenerational malnutrition. Our indication that a large proportion of our sample was rural (73.7%) is important for contextualising findings. It was expected that DBM would be predominantly an urban problem, as exemplified in Malawi [ 20 ]; however, the high rural prevalence of this condition in Lesotho indicates that the nutrition transition is moving into rural villages as food systems change and physical activity reduces due to urbanisation. The low child dietary diversity (90%) is of specific interest here: it shows a missing factor for DBM; there is not enough food and all from few food types that lack the most important vitamins. This is consistent with the study in Peru, where low dietary diversity was significantly associated with DBM [ 26 ]. Importantly, results from this study challenge Dieffenbach and Stein's [ 10 ] assertion that the DBM dyad is no more than a statistical artefact due to co-variation of maternal and child malnutrition. Our multivariate analysis demonstrated that independent household-level factors that were associated with the comorbidity of these conditions included parity and wealth. This provides empirical evidence that DBM is a distinct entity from M. incognita, which needs specific and integrated management strategies. Taken together, the research offers a troubling yet informative snapshot of the malnutrition landscape in Lesotho. Child undernutrition and maternal overnutrition (albeit for overweight and obesity at least) frequently co-exist in the same households, reflecting concurrent nutritional problems. HDBM levels in Lesotho are some of the highest in SSA, and a concern that needs immediate policy focus. The squeeze on middle-income levels and their greater vulnerability suggest that public health must target the squeezed middle, who are facing a disproportionate share of dietary transition costs. The breastfeeding and maternal height protective effects also underscore the importance of interventions to promote maximum maternal nutrition and child feeding throughout life course. The nearly complete low dietary diversity also implies policies beyond caloric adequacy in favor of nutrient-rich diets. This would need “doubleduty” combined action programs to tackle both dimensions of malnutrition simultaneously, consistent with what the WHO and more recent analyses have advocated [ 13 , 1 ]. This will have to include interventions towards promotion and protection of breastfeeding, regulation of marketing on unhealthy food, improving nutritional quality of food in social programs and fortification exercises and strengthening nutrition education that enhance diversity in the family diet. The shift over of the direction of the association in univariate and multivariable analysis may be because of confounding or due to collinearity amongst the explanatory variables. For example, the effect of social group (maternal age group) was significant when analysed alone (univariable analysis), but not after the adjustment for other risk factors clustered by maternal age, including parity and household wealth. Older children should have higher parity and more stable socioeconomic condition which we know is related to in-house nutritional outcomes. After adjustment for these associated variables in the samemultivariable model, maternal age remained no longer as independent effect. Wealth quintile and child age, however remained significant, suggesting a more direct relationship to the HHDBM less influenced by other demographic or biological factors. It is concerning, however, that patterns we identified in this analysis particularly the U-shaped pattern for HDBM and steep increase for HTBM between 2014 and 2023 may be symptomatic of broader socioeconomic and environmental trends occurring elsewhere in Lesotho. The decline in HDBM up to 2014 can be attributed, partly, to the eroding of coverage for child health and nutrition interventions initiated during that time (inclusive of universal immunisation and maternal–child health programmes). But the recovery that ensued, from 2014 on, came at a time of economic stagnation and frequent droughts and food insecurity in many areas of southern Africa. Lesotho’s heavy dependence on imported food and declining agricultural productivity could have increased households’ vulnerability to poor diet quality, which is associated with both child undernutrition and maternal overnutrition. In addition, rapid urbanisation and lifestyle changes such as sedentary habits and rising consumption of inexpensive, processed foods may have fueled the nutrition transition that contributed to both the positive overweight trends in women and high levels of stunting and aemia in children. The data of the present study reveal a 3.6% HTBM burden on Lesotho households studied, comparable to that obtained in previously published reports elsewhere from sub-Saharan Africa [ 1 ]. The HTBM prevalence in Lesotho (4%) was consistent with that for adolescents from Malawi (3.1% [ 20 ]) and Ethiopia (1.2% [ 30 ]), but lower than the range reported among Indian adolescents 12.7 to 14.4% [ 15 , 27 ]. The inconsistency may result from different phases of nutrition transition and methodological differences in the terms of reference (TOR) selected for defining malnutrition burdens across studies. Some of the predictors for HTBM in Lesotho were also found with multivariable analysis. The strong protective effect of breastfeeding (aOR = 0.61) aligns with the global evidence where it is established that breastfeeding can provide a ‘double-duty’ intervention for under- and over-nutrition [ 16 ]. Such a negative relationship between wealth and HTBM, with 56% less likely to be observed in higher wealth, was contrary to Nepal and Malawi studies, which indicated increased TBM/DBM rates [ 15 , 30 ]. This suggests that Lesotho may be in an early phase of nutrition transition where poverty lies at the root cause of all forms of malnutrition. Most importantly, however, is the 89% higher risk of HTBM in 2023than in 2009 which indicates a sharper decrease (~ 50% worse) than what we would expect based on variables depicting individual malnutrition indicators. This pattern of timing highlights the timeliness for integrated interventions addressing concurrent burdens of maternal overnutrition, child undernutrition, and mucronutrient deficiencies in Basotho households. The distinct predictors of HDBM and HTBM found in the present study show that though the two burdens are subject to similar socio-economic and behavioural determinants, the added pressure from micronutrient deficiency (anaemia) introduces independent risks pathways. HDBM was particularly high in middle-income group, illustrating the common nature of the nutrition transition sequence, the intermediate wealthy stage having access to energy-dense, low-micronutrient foods with little dietary diversity. Meanwhile, for HTBM, protective association was evident only in the “richest” households, indicating that higher socioeconomic status might buffer/protect against micronutrient deficiencies, possibly by ensuring adequate intake through variety in diet and health care utilisation. Furthermore, exclusive breastfeeding showed a continued protective effect against both outcomes, with the strongest association observed for HTBM, suggesting the importance of EBF in preventing anaemia while providing required micronutrients to young children. Moreover, these data illustrate that although both are nutrition statuses which is related to household resource insufficiency and poverty, HTBM measures a more severe level of nutritional deficiency that requires an intervention focusing not only on the quality and diversity of the diet but also unconventional methods of nutrient supplementation and food fortification. The current study has several strengths. In addition, it employs nationally representative data, which helps in generalising the results to Lesotho, and strong multivariable analysis for determining independent predictors of household double and triple burden of malnutrition. Inclusion of macro- and micronutrient indicators affords complete evidence on malnutrition at a household level along with child and maternal nutritional status, which provides an understanding over intergenerational patterns. But the study has its limitations. Its cross-sectional nature precludes any claim of causality and self-reports for some variables as child feeding practices may be biased by recall error or social desirability bias. Conclusion This study provides the first nationally representative analysis of the double and triple burden of malnutrition among mother-child pairs in Lesotho, revealing a complex nutrition crisis. The findings show that middle-income households are particularly vulnerable, reflecting the paradox of the nutrition transition, and that higher parity increases the risk of HDBM. Breastfeeding acted as protective, while taller maternal stature highlights the importance of adolescent and maternal nutrition in breaking intergenerational cycles of malnutrition. These results underscore the urgent need for integrated interventions rather than isolated approaches. Public health strategies should strengthen breastfeeding promotion and support, improve access to diverse, nutrient-rich foods, especially for middle-income households, incorporate nutrition education into maternal and child health services, and prioritize adolescent and maternal nutrition to prevent future malnutrition. By addressing shared underlying determinants, Lesotho can implement efficient, multi-sectoral strategies to reduce malnutrition and achieve national and global nutrition targets. Declarations Acknowledgements The authors gratefully acknowledge the Demographic and Health Surveys (DHS) Program and the Lesotho Ministry of Health for implementing the surveys and providing the data used in this analysis. We also thank the study participants and field staff who contributed to the data collection for the Lesotho Demographic and Health Surveys. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The analysis was conducted using publicly available secondary data. Authors and affiliations Hilary Takunda Takawira (Corresponding author: [email protected] ) Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe Contributions H.T.T. conceptualized the study; conducted the data processing, formal analysis, and investigation; and drafted the original manuscript. PRZ and ETC contributed to the methodology, validated the analysis, assisted with the interpretation of the results, and reviewed and edited the manuscript. All the authors reviewed and approved the final manuscript. Corresponding author Please send correspondence to Hilary Takunda Takawira ( [email protected] ). Ethical approval The Lesotho Ministry of Health Research and Ethics Committee and the ICF International Institutional Review Board approved Lesotho Demographic and Health Survey protocols. Permission to use the data for this analysis was obtained from the Demographic and Health Surveys (DHS) Program. All DHS datasets are available to the public and fully anonymized prior to release; thus, no further ethical approval was needed for this secondary analysis. All procedures were performed following appropriate guidelines and regulations. Data availability This research was based on secondary data from the Lesotho Demographic and Health Surveys (2009, 2014, and 2023). These datasets are publicly available through the Demographic and Health Surveys (DHS) Program. Data can be accessed upon reasonable request following registration and through data requests at https://dhsprogram.com/data/. All personal information has been processed to be anonymous, and this study does not include any individually identifiable data. Consent for publication Not applicable. (This study uses anonymized, publicly available survey data from the Lesotho Demographic and Health Surveys.) Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate The original Lesotho Demographic and Health Surveys (2009, 2014, 2023) received ethical approval from the Lesotho Ministry of Health Research and Ethics Committee and the ICF International Institutional Review Board. All survey participants provided written informed consent prior to data collection. For children, consent was provided by a parent or guardian. Permission to use the de-identified datasets for this secondary analysis was granted by The DHS Program (www.dhsprogram.com). As the data are publicly available and anonymized, no further ethical approval was required for this study. Clinical trial number: Not applicable. Human ethics and consent to participate declarations: The study used secondary, anonymized data from the DHS Program. Ethical approval and participant consent were obtained in the original surveys by the implementing agencies. References Christian AK, Dake FAA. Profiling household double and triple burden of malnutrition in sub-Saharan Africa: Prevalence and influencing household factors. Public Health Nutr. 2022;25(6):1563–76. Leseba N, Vermaak K, Makatjane T, Lebuso M. A multilevel analysis of factors associated with stunting among children under five years in Lesotho: A study of the Lesotho multiple cluster indicator survey 2018. J Health Popul Nutr. 2025;44(1):8. Pomati M, Mendoza-Quispe D, Anza-Ramirez C, Hernández-Vásquez A, Carrillo Larco RM, Fernandez G, et al. Trends and patterns of the double burden of malnutrition (DBM) in Peru: A pooled analysis of 129,159 mother–child dyads. Int J Obes. 2021;45(3):609–18. Singh S, Shri N, Singh A. Inequalities in the prevalence of double burden of malnutrition among mother–child dyads in India. Sci Rep. 2023;13(1):16939. Tarekegn BT, Taye BT, Tadele AT. Household-level double burden of malnutrition in low- and middle-income countries: a systematic review. Curr Dev Nutr. 2022;6(5):nzac091. Sagastume G, Mank I, Smit GSA, Phori PM, Bärnighausen K, Bärnighausen T, et al. The double burden of malnutrition at the household level in rural South Africa. Front Nutr. 2024;11:1340155. Ahinkorah BO, Amadu I, Seidu AA, Okyere J, Duku E, Hagan JE, et al. Prevalence and factors associated with the triple burden of malnutrition among mother-child pairs in sub-Saharan Africa. Nutrients. 2021;13(6):2050. Sahiledengle B, Mwanri L, Kumie A, Beressa G, Atlaw D, Tekalegn Y, et al. The coexistence of stunting and overweight or obesity in Ethiopian children: Prevalence, trends and associated factors. BMC Pediatr. 2023;23(1):194. Gebremichael B, Tsegaye D, Belachew T. The double burden of malnutrition and its associated factors among school-aged children and adolescents in sub-Saharan Africa: a systematic review and meta-analysis. J Health Popul Nutr. 2025;44(1):15. Dieffenbach S, Stein AD. Stunted child/overweight mother pairs represent a statistical artifact, not a distinct entity. J Nutr. 2012;142(4):771–3. Modjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health. 2019;19(1):1087. Okyere J, Donkoh IE, Seidu AA, Ahinkorah BO, Aboagye RG, Yaya S. Mother–child dyads of overnutrition and undernutrition in sub-Saharan Africa. J Health Popul Nutr. 2024;43(1):14. Jeyakumar A, Godbharle S, Kesa H, Shambharkar P, Bhalekar P, Chalwadi S, et al. Comparison of household double burden of malnutrition among mother-child dyads in different settings in Maharashtra. Cities Health. 2024;8(6):1031–9. Kumar P, Chauhan S, Patel R, Srivastava S, Bansod DW. Prevalence and factors associated with triple burden of malnutrition among mother–child pairs in India: A study based on National Family Health Survey 2015–16. BMC Public Health. 2021;21:391. Ramasubramani P, Krishnamoorthy Y, Rajaa S. Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: Findings from a nationally representative survey (NFHS-5). Heliyon. 2024;10(18):e37794. World Health Organization. Double-duty actions for nutrition: Policy brief. Geneva: WHO; 2017. Eshete T, Kumera G, Bazezew Y, Marie T, Alemu S, Shiferaw K. The coexistence of maternal overweight or obesity and child stunting in low-income country: Further data analysis of the 2016 Ethiopia demographic health survey (EDHS). Sci Afr. 2020;9:e00524. Yigezu M, Oumer A, Damtew B, Birhanu D, Workie SG, Hamza A, et al. The dual burden of malnutrition and its associated factors among mother-child pairs at the household level in Ethiopia: An urgent public health issue demanding sector-wide collaboration. PLoS ONE. 2024;19(11):e0307175. Faustini FT, Mniachi AR, Msengwa AS. Coexistence and correlates of forms of malnutrition among mothers and under-five child pairs in Tanzania. J Nutr Sci. 2022;11:e103. Khaki JJ, Macharia PM, Benová L, Giorgi E, Semaan A. Prevalence and determinants of double and triple burden of malnutrition among mother-child pairs in Malawi: A mapping and multilevel modelling study. Public Health Nutr. 2024;27(1):e87. Patel R, Srivastava S, Kumar P, Chauhan S. Factors associated with double burden of malnutrition among mother-child pairs in India: a study based on National Family Health Survey 2015–16. Child Youth Serv Rev. 2020;116:105256. Félix-Beltrán L, Macinko J, Kuhn R. Maternal height and double-burden of malnutrition households in Mexico: Stunted children with overweight or obese mothers. Public Health Nutr. 2021;24(1):106–16. Sahiledengle B, Mwanri L, Agho KE. Association between maternal stature and household-level double burden of malnutrition: Findings from a comprehensive analysis of Ethiopian Demographic and Health Survey. J Health Popul Nutr. 2023;42(1):14. Anik AI, Rahman MM, Rahman MM, Tareque MI, Khan MN, Alam MM. Double burden of malnutrition at household level: A comparative study among Bangladesh, Nepal, Pakistan, and Myanmar. PLoS ONE. 2019;14(8):e0221274. Lukwa AT, Chiwire P, Akinsolu FT, Okova D, Hongoro C. Double burden of malnutrition among women and children in Zimbabwe: a pooled logistic regression and Oaxaca-Blinder decomposition analysis. Front Public Health. 2024;12:1451898. Pradeilles R, Landais E, Pareja R, Eymard-Duvernay S, Markey O, Holdsworth M, et al. Exploring the magnitude and drivers of the double burden of malnutrition at maternal and dyad levels in peri-urban Peru: A cross-sectional study of low-income mothers, infants and young children. Matern Child Nutr. 2023;19(4):e13549. Ji N, Kumar A, Joe W, Kuriyan R, Sethi V, Finkelstein JL, et al. Prevalence and Correlates of Double and Triple Burden of Malnutrition Among Children and Adolescents in India: The Comprehensive National Nutrition Survey. J Nutr. 2024;154(10):2932–47. Ramasubramani P, Krishnamoorthy Y, Rajaa S. Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: Findings from a nationally representative survey (NFHS-5). Heliyon. 2024;10(18):e37794. Sunuwar DR, Singh DR, Pradhan PMS. Prevalence and factors associated with double and triple burden of malnutrition among mothers and children in Nepal: evidence from 2016 Nepal demographic and health survey. BMC Public Health. 2020;20(1):405. Tarekegn BT, Assimamaw NT, Atalell KA, Kassa SF, Muhye AB, Techane MA, et al. Prevalence and associated factors of double and triple burden of malnutrition among child-mother pairs in Ethiopia: Spatial and survey regression analysis. BMC Nutr. 2022;8(1):34. Additional Declarations No competing interests reported. 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08:42:02","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152464,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8165828/v1/78f5f387b2da439a64555231.html"},{"id":97141658,"identity":"4512d096-d6ce-4d9e-95c1-431f827f731f","added_by":"auto","created_at":"2025-12-01 10:06:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134967,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical framework for assessing double and triple burden of malnutrition among mother-child pairs in Lesotho using pooled DHS data (2009-2023).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8165828/v1/69983178d7abea929fc9546e.png"},{"id":97131182,"identity":"71e0d8b1-8c1c-46b8-bcb3-7ea7ca9e1aaf","added_by":"auto","created_at":"2025-12-01 08:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. Trends in malnutrition prevalence among mother-child pairs in Lesotho, 2009-2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8165828/v1/f9a9cf90cdfcd9bade2326c2.png"},{"id":97145107,"identity":"04c6a04d-7a9b-4e00-a1cc-a37c8befbf6a","added_by":"auto","created_at":"2025-12-01 10:13:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8165828/v1/d1c0c283-e8e0-49c4-ae8a-be535ad3bbcd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and determinants of double and triple burden of malnutrition among mother– child pairs in Lesotho: pooled analysis of DHS 2009, 2014 and 2023","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition in all its forms remains a major global public\u0026ensp;health problem. In Low- and Middle-Income Countries (LMICs), undernutrition continues to be a focus of attention because its burden persists, particularly in the form of stunting, which affects over 25% of children aged less than five years in sub-Saharan Africa (SSA), with rates exceeding 33.6% in countries such as Lesotho [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rapid nutrition transitions, due to urbanisation, economic change\u0026ensp;and diets high in energy-dense low-nutrient food, have driven increases in overweight and obesity that are now co-existing with ongoing undernutrition at national, community and household levels [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This phenomenon, known as double burden of malnutrition (DBM), is\u0026ensp;illustrated by households comprised of overweight mothers and stunted children, the prevalence rate of which ranged from 1.8 to 25% in LMICs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nutrition transitions are the expected and\u0026ensp;systematic changes in diet, nutrient intake and health status that come with development, urbanisation and demographic change. Furthermore, the challenge is compounded by the triple burden of malnutrition (TBM) that occurs when malnutrition due to undernourishment, over-nourishment, and micronutrient deficiencies\u0026ensp;, such as anaemia, happens simultaneously. This affects seven out of ten households in sub-Saharan Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. DBM is manifested through the confluence\u0026ensp;of multiple life-course pathways. Early-life undernutrition may even favour metabolic disorders and abdominal adiposity later in life, especially when subjects are exposed after weaning to obesity-promoting environments\u0026ensp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe double burden of malnutrition (DBM) is multi-dimensional in that it appears at population, household and\u0026ensp;individual levels and represents various but related adaptations to the current nutrition transition. At the individual level, DBM is\u0026ensp;manifested as the dual burden of overweight and underweight in the mother\u0026ndash;child pair, an apparent contradiction which has evolved into a major public health challenge among low- and middle-income countries [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This results from common sociodemographic and dietary\u0026ensp;factors. With better household wealth, consumption of cheap, energy-dense but nutrient-poor foods increases, along\u0026ensp;with excessive caloric intake and maternal overweight or obesity. Concurrently, childhood undernutrition continues to be\u0026ensp;driven by sub-optimal complementary feeding practices, risk for repeated infections and low dietary diversity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Complicating this dual burden is now the emerging triple burden of malnutrition (TBN) that\u0026ensp;includes effects from maternal overnutrition, child undernutrition and micronutrient deficiencies like anaemia, shown to be increasingly prevalent in sub-Saharan Africa, Asia as well [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It will take integrated, multisectoral nutrition strategies that approach undernutrition and overnutrition simultaneously to tackle these related burdens, both of which can be addressed by parallel policy, community, and household\u0026ensp;actions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, the prevalence of the double burden of malnutrition is rising, although with considerable variance through sub-Saharan Africa, depending on regions and the socio-economic background. A multi-body study in SSA reported DBM and TBM, with their anaemia problem, to be a public threat whose occurrence was linked to household wealth status and the particular residence having access to improved sanitation. Country-specific studies in Ethiopia, Tanzania, and Malawi in SSA and India underscore the occurrence of DBM among the mother-child pair, with the possible determinants being maternal age and education, household wealth, dietary diversity, and the child's birth order. Moreover, there is an indication of wealthier households in transition economies having a high DBM prevalence. A similar nutrition trend is observed in Lesotho as it is in some other SSA countries, characterised by a double burden of persisting undernutrition trends and rising overnutrition rates. Despite making progress on some health indicators, the country still carries a substantial burden of the child undernutrition problem, with stunting, an important indicator of chronic malnutrition, being prevalent and the main threat to child growth and survival. Additionally, the country has a rising overnutrition trend, particularly overweight and obesity among women of the productive ages, a situation that is similar to other sad countries associated with urbanisation, reduced physical activity and the consumption of low-cost, high-energy, low-nutrient diets. This state calls for a comprehensive and public health strategy to alleviate malnutrition in Lesotho.\u003c/p\u003e\u003cp\u003eWhile DBM/TBM is a reported public health emergency in sub-Saharan Africa, with multi-country studies that confirm its prevalence and household-level correlates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the extent\u0026ensp;of DBM/TBM in Lesotho remains hidden within broader accounts. Recent data (2015) suggest a high prevalence of childhood stunting (33.6%) in Lesotho [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]), pointing to an ongoing\u0026ensp;undernutrition problem. Simultaneously, evidence from neighbouring countries in Southern Africa shows this undernutrition\u0026ensp;often constitutes a \u0026lsquo;double burden\u0026rsquo; with maternal overnutrition within the same households [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] \u0026ndash; and it is likely that Lesotho too faces this dual crisis.\u003c/p\u003e\u003cp\u003eHowever,\u0026ensp;there is a gap in research that is nationally representative with peer-reviewed published data that specifically examines whether DBM and TBM exist among mother\u0026ndash;child pairs of this age cohort in Lesotho. This knowledge gap does not enable a well understanding of the national prevalence, geographic distribution and Lesotho-specific drivers of this phenomenon, which would limit the capacity to develop country-targeted \u0026ldquo;double-duty\u0026rdquo; interventions and make optimal allocation on resources for action against\u0026ensp;all forms of malnutrition in the country.\u003c/p\u003e\u003cp\u003eDespite this burden convergence, research\u0026ensp;on the domestic double burden of malnutrition in Lesotho is lacking. Studies and surveys have generally reported maternal nutrition and child nutrition in separate\u0026ensp;columns. A systematic comparison of their aggregation within the same individual sample is still\u0026ensp;lacking, and it remains unclear whether the comorbidity pattern evolves with time. Estimation of the burden and trend of DBM was critical\u0026ensp;for improved public health management. Integration of the most recent dataset of 2023 with that for 2009, and 2014, the LDHS offers an opportunity\u0026ensp;to do a robust temporal analysis.\u003c/p\u003e\u003cp\u003eHence, this study seeks to\u0026ensp;assess the prevalence and factors (both socioeconomic, demographic and environmental) associated with household double burden of malnutrition among mother-child pairs in Lesotho. Thus, by utilising 15 years of nationally-representative population data, this study will generate vital evidence for the design of integrated and nutrition-sensitive interventions\u0026ensp;that work to effectively tackle both undernutrition and overnutrition in context-specific Basotho settings, thereby contributing largely to the attainment of national and global nutritional goals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and data source\u003c/h2\u003e\u003cp\u003eWe conducted a secondary analysis of nationally representative data from the Lesotho Demographic and Health Surveys (DHS) for the years 2009, 2014, and 2023\u0026ndash;2024. The DHS employs a two-stage stratified cluster sampling design to collect comparable, population-based data on health and nutrition indicators. For this analysis, we used the Women's Recode (IR) and the Children's Recode (KR) files, which contain information from women of reproductive age (15\u0026ndash;49 years) and data on the health and nutritional status of their children under five, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population and sample selection\u003c/h3\u003e\n\u003cp\u003eThe study focused on mother\u0026ndash;child pairs as the unit of analysis. The initial sample included all children under five years of age and their biological mothers from the three survey waves. A series of sequential inclusion criteria was applied to obtain the final analytic sample. Only pairs where the child could be successfully matched to their biological mother in the dataset were retained. Only living children were included in the analysis. Pairs were required to have complete anthropometric information, with children having a valid height-for-age z-score and mothers having a valid body mass index (BMI) measurement. Mothers were restricted to those aged 15\u0026ndash;49 years at the time of the survey to align with the standard reproductive age range used in Demographic and Health Surveys (DHS). The final pooled analytic sample across all three survey waves comprised 4,150 mother\u0026ndash;child pairs.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eOutcome\u003c/h2\u003e\u003cp\u003eWe defined stunting, underweight, and wasting using height-for-age, weight-for-age, and weight-for-height \u003cem\u003ez\u003c/em\u003e-scores, respectively. Each indicator was classified according to the World Health Organization (WHO, 2006). Child Growth Standards, with values below \u0026minus;\u0026thinsp;2 standard deviations (SD) denoting stunting, underweight, or wasting. Children with biologically implausible measurements (\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;5 SD or \u0026gt;\u0026thinsp;+\u0026thinsp;5 SD) were excluded in accordance with WHO guidelines (2011).\u003c/p\u003e\u003cp\u003eThe primary outcome was the household-level double burden of malnutrition. The double burden of malnutrition (DBM) was defined as the coexistence of undernutrition in children manifested as underweight (weight-for-age \u0026lt; \u0026minus;\u0026thinsp;2 SD), stunting (height-for-age \u0026lt; \u0026minus;\u0026thinsp;2 SD), or wasting (weight-for-height \u0026lt; \u0026minus;\u0026thinsp;2 SD) an overweight or obese mother (body mass index\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;) within the same household that is mother-child pair [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChild stunting: Stunting was defined as a height-for-age z-score (HAZ) more than two standard deviations below the WHO growth standard median (HAZ \u0026lt; -2). HAZ scores were derived from the DHS variable \u003cem\u003ehw70\u003c/em\u003e after converting it to a continuous z-score and excluding biologically implausible values. Maternal overweight/obesity: Maternal overweight or obesity was defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;, calculated from the DHS variable \u003cem\u003ev445\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe HDBM variable was coded as a binary indicator, with a value of 1 assigned if both child stunting and maternal overweight/obesity were present in the same household, and 0 if either or both conditions were absent. Triple burden of malnutrition (TBM) refers to the coexistence of child undernutrition manifested as underweight (weight-for-age \u0026lt; \u0026minus;\u0026thinsp;2 SD), stunting (height-for-age \u0026lt; \u0026minus;\u0026thinsp;2 SD), or wasting (weight-for-height \u0026lt; \u0026minus;\u0026thinsp;2 SD), maternal overnutrition, and child micronutrient deficiency, specifically anaemia defined as a haemoglobin concentration below 11 g/dL [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eIndependent variables were selected based on their conceptual relevance to child and maternal nutrition. Socioeconomic factors included household wealth and maternal education, reflecting differences in access to resources and health knowledge. Demographic factors captured maternal age, parity, and child characteristics, with age and parity categorised to reflect meaningful reproductive and family-size groups while maintaining sufficient sample sizes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Child age was grouped to correspond to critical developmental stages. Geographic factors included urban-rural residence and administrative region to account for differences in access to services and environmental influences.\u003c/p\u003e\u003cp\u003eMaternal biological factors included height, age and BMI, categorised based on standard thresholds associated with child growth outcomes and maternal nutritional status. Child dietary diversity was summarised as the number of food groups consumed and categorised to identify children at risk of insufficient nutrient intake. Overall, collapsing variables into meaningful categories enhanced interpretability, facilitated comparisons across risk groups, and supported robust statistical analysis, while the accompanying table provides the detailed coding and classification of each variable.\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\u003eDescription of study variables, coding, and measurement.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategories / Coding\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male, 2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild age broad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;0\u0026ndash;11 months, 2\u0026thinsp;=\u0026thinsp;12\u0026ndash;23 months, 3\u0026thinsp;=\u0026thinsp;24\u0026ndash;35 months, 4\u0026thinsp;=\u0026thinsp;36\u0026ndash;59 months\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No, 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor dietary diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;4 food groups, 1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;4 food groups\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal age group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;15\u0026ndash;24 years, 2\u0026thinsp;=\u0026thinsp;25\u0026ndash;34 years, 3\u0026thinsp;=\u0026thinsp;35\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParity cat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;1 child, 2\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 children, 3\u0026thinsp;=\u0026thinsp;4\u0026thinsp;+\u0026thinsp;children\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No Education, 1\u0026thinsp;=\u0026thinsp;Primary, 2\u0026thinsp;=\u0026thinsp;Secondary, 3\u0026thinsp;=\u0026thinsp;Higher\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Short (\u0026lt;\u0026thinsp;150 cm), 2\u0026thinsp;=\u0026thinsp;Average (150\u0026ndash;159.9 cm), 3\u0026thinsp;=\u0026thinsp;Tall (\u0026ge;\u0026thinsp;160 cm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth quintile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Poorest, 2\u0026thinsp;=\u0026thinsp;Poorer, 3\u0026thinsp;=\u0026thinsp;Middle, 4\u0026thinsp;=\u0026thinsp;Richer, 5\u0026thinsp;=\u0026thinsp;Richest\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Urban, 2\u0026thinsp;=\u0026thinsp;Rural\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;2009, 2\u0026thinsp;=\u0026thinsp;2014, 3\u0026thinsp;=\u0026thinsp;2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No double burden, 1\u0026thinsp;=\u0026thinsp;Double burden\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No double burden, 1\u0026thinsp;=\u0026thinsp;Double burden\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\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses accounted for the complex survey design of the Demographic and Health Surveys, including clustering, stratification, and sampling weights, using the svyset command in \u003cem\u003eStata\u003c/em\u003e 18.0. Data preparation involved merging the women\u0026rsquo;s and children\u0026rsquo;s datasets for each survey year using cluster, household, and line identifiers, followed by appending the 2009, 2014, and 2023 datasets into a pooled file with a survey year indicator.\u003c/p\u003e\u003cp\u003eUnivariable analyses described sample characteristics, presenting categorical variables as weighted frequencies and percentages, and continuous variables as means and standard deviations (or medians and interquartile ranges where appropriate). Weighted prevalence estimates were calculated for the household double burden of malnutrition (HDBM) and its components.\u003c/p\u003e\u003cp\u003eBivariate associations between HDBM and explanatory variables were assessed using design-based chi-square tests for categorical variables and survey-adjusted mean comparisons for continuous variables. Survey-adjusted univariable logistic regression models provided crude odds ratios (cORs) and 95% confidence intervals (CIs). Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariable analyses, along with those of theoretical importance, were included in multivariable logistic regression models. Results were reported as adjusted odds ratios (aOR) with 95% CIs. Analyses were two-sided, with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThis study utilised existing, de-identified, publicly available data from the DHS program. The ICF International Institutional Review Board and the relevant ethics committee in Lesotho obtained ethical approval for the original surveys. Permission to use the data was granted by the DHS program\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e\u003cp\u003eA total of 4150 mother\u0026ndash;child pairs\u0026ensp;were in the study. Mothers had a\u0026ensp;mean age of 27.6 (SD\u0026thinsp;=\u0026thinsp;6.8), ranging from 15 to 49 years. The mean age of the children was 27.0 months (SD\u0026thinsp;=\u0026thinsp;17.4), and the ages ranged from 0 to\u0026ensp;59 months. Sampling took place in ten regions, with the largest proportions of participants coming from Thaba-Tseka (12.5%), Maseru (11.0%) and Mokhotlong (10.9%) and the lowest from Qacha\u0026rsquo;s-Nek (8.2%) and\u0026ensp;Mafeteng (8.5%). The rest of the districts were Butha-Buthe (9.4%), Leribe (10.6%), Berea (10.4%), Mohale\u0026rsquo;s Hoek and Quthing at 9.0% and 9.4%,\u0026ensp;respectively. The demographic profile of the study population is illustrated in Table\u0026ensp;\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\u003eDescriptive characteristics of the study sample (N\u0026thinsp;=\u0026thinsp;4,150 mother-child pairs)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnweighted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e% (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChild characteristics\u003c/em\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\u003eChild Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,018 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.7 (46.7\u0026ndash;50.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,132 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.3 (49.4\u0026ndash;53.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;11 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e910 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.0 (20.6\u0026ndash;23.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;23 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e848 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0 (18.5\u0026ndash;21.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u0026ndash;35 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,271 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.7 (29.1\u0026ndash;32.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u0026ndash;59 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,121 (27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.3 (25.9\u0026ndash;28.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,168 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.8 (52.5\u0026ndash;57.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,982 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.2 (42.8\u0026ndash;47.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor Dietary Diversity*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e357 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0 (8.6\u0026ndash;11.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,793 (91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.0 (88.4\u0026ndash;91.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMother characteristics\u003c/em\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\u003eMaternal age group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,637 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.0 (37.0\u0026ndash;41.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,789 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.0 (41.9\u0026ndash;46.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e724 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.0 (15.5\u0026ndash;18.4)\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,346 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.8 (30.9\u0026ndash;34.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,962 (47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.2 (47.0\u0026ndash;51.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e842 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.1 (16.4\u0026ndash;19.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4 (1.0\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,029 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.6 (42.1\u0026ndash;47.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,811 (43.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.9 (44.3\u0026ndash;49.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1 (5.8\u0026ndash;8.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShort (\u0026lt;\u0026thinsp;150 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e449 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6 (9.3\u0026ndash;12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage (150\u0026ndash;159.9 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,440 (58.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.5 (57.5\u0026ndash;61.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTall (\u0026ge;\u0026thinsp;160 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,261 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.9 (28.0\u0026ndash;31.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHousehold Characteristics\u003c/em\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\u003eWealth Quintile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,221 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.4 (20.1\u0026ndash;24.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e942 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.8 (19.0\u0026ndash;22.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e834 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4 (18.6\u0026ndash;22.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4 (18.3\u0026ndash;22.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e497 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.0 (14.0\u0026ndash;18.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e862 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.3 (23.2\u0026ndash;29.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,288 (79.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.7 (70.4\u0026ndash;76.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,667 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.9 (35.7\u0026ndash;42.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,357 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.6 (31.3\u0026ndash;38.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,126 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.5 (23.4\u0026ndash;29.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: Poor dietary diversity defined as consumption of \u0026lt;\u0026thinsp;4 food groups. CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe total number of mother\u0026ndash;child pairs\u0026ensp;was 4150, among which there were slightly more female (51.3%) than male children (48.7%), and most of the children were aged between 24 and 35 months (30.7%). Approximately one-half (54.8%) of the\u0026ensp;mothers were not breastfeeding, and 90.0% reported poor dietary diversity. Most of the mothers were between 25 and 34 years old (44.0%), had attended primary education (44.6%) or\u0026ensp;the first year of secondary school (46.9%); most women had an average height (59.5%). Household wealth was skewed toward the lower and middle quintiles; 73.7% of households\u0026ensp;were in rural areas.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of malnutrition indicators and combined outcomes\u003c/h2\u003e\u003cp\u003eA total of 4150 mother\u0026ndash;child pairs were eligible for the\u0026ensp;analysis. Approximately half the mothers were overweight or obese, with\u0026ensp;a weighted prevalence of 46.2% (95% CI: 43.9\u0026ndash;48.5). The most common form of undernutrition in children was stunting (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e),\u0026ensp;affecting 34.5% (95% CI: 32.8\u0026ndash;36.2), followed by anaemia at 27.8% (95% CI: 25.4\u0026ndash;30\u0026thinsp;\u0026minus;\u0026thinsp;3%) and underweight at 11.7% (95% CI:10.5\u0026ndash;13%). Household double burden of malnutrition (HDBM)\u0026ensp;occurred in 14.9% (95% CI: 13.4\u0026ndash;16.3) of households. The HTBM (defined as the simultaneous existence of maternal overnutrition, any form of child undernutrition [underweight: weight-for-age \u0026lt; \u0026minus;\u0026thinsp;2 SD, stunting: height-for-age\u0026ensp;\u0026lt; \u0026minus;\u0026thinsp;2 SD or wasting: weight-for-height \u0026lt; \u0026minus;\u0026thinsp;2 SD]) and child anaemia occurred in 3.6% (95% CI: 3.0\u0026ndash;4.3) of households.\u003c/p\u003e\u003cp\u003eWhen analysed by pairs, the most commonly encountered pair\u0026ensp;was: obese mother with stunted child (13.4%, 95% CI: 12.1\u0026ndash;14.8), followed by obese mother with anaemic child (12.1%, 95%CI: 10.7\u0026ndash;13.6). The less common combinations were obese mother with underweight child (4.0%,\u0026ensp;95% CI: 3.4\u0026ndash;4.8), and obese mother with wasted child (4.8%, 95% CI: 4.0\u0026ndash;5.8).\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\u003eFrequency analysis for malnutrition indicators and combination outcomes.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnweighted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFrequency, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% (95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual indicators\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\u003eMaternal overweight/obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.2 (43.9\u0026ndash;48.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild stunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.5 (32.8\u0026ndash;36.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.7 (10.5\u0026ndash;13.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild anaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.8 (25.4\u0026ndash;30.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombination outcomes\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\u003eOverall HDBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.9 (13.4\u0026ndash;16.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall HTBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.6 (3.0-4.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese mother\u0026thinsp;+\u0026thinsp;stunted child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.4 (12.1\u0026ndash;14.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese mother\u0026thinsp;+\u0026thinsp;underweight child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0 (3.4\u0026ndash;4.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese mother\u0026thinsp;+\u0026thinsp;wasted child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8 (4.0-5.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese mother\u0026thinsp;+\u0026thinsp;anemic child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.1 (10.7\u0026ndash;13.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: HDBM\u0026thinsp;=\u0026thinsp;Household Double Burden of Malnutrition. Sample sizes vary per indicator due to missing data. All estimates are based on the survey-weighted analysis.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTemporal trends in malnutrition indicators\u003c/h2\u003e\u003cp\u003eAnalysis of data from three survey waves revealed significant shifts in Lesotho's malnutrition profile between 2009 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The household double burden of malnutrition (HDBM) demonstrated a concerning U-shaped trajectory, decreasing from 14.7% (95% CI: 12.6\u0026ndash;17.0) in 2009 to 12.5% (95% CI: 10.5\u0026ndash;14.9) in 2014, then rising sharply to 18.3% (95% CI: 15.3\u0026ndash;21.7) in 2023 (p\u0026thinsp;=\u0026thinsp;0.015). This represents a 46% increase in HDBM prevalence over the most recent nine-year period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe household triple burden of malnutrition (HTBM) displayed an even steeper rise, tripling from 2.1% (95% CI: 1.3\u0026ndash;3.2) in 2014 to 6.0% (95% CI: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mother overweight and obesity prevalence slowly but steadily increased between 2009 (42.8% (95% CI:39.2\u0026ndash;46.4)) and 2023 (53.5% (95% CI:48.4\u0026ndash;58.6)), resulting in a prevalence of over half of reproductive-aged women now falling victim to overnutrition. There\u0026ensp;was modest improvement in child stunting presence, from a decrease of 36.1% (95% CI: 33.5\u0026ndash;38.8) in 2009 to 31.3% (95% CI: 28.1\u0026ndash;34.8) in 2023 (p\u0026thinsp;=\u0026thinsp;0\u0026middot;008). But this\u0026ensp;progress has largely since plateaued, with little deviation between 2014 (31.4%, 95% CI: 28.5\u0026ndash;34.6) and 2023. All indicators indicated that there were significant trends (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u0026ensp;and therefore variations in malnutrition over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDeterminants of the double burden of malnutrition\u003c/h2\u003e\u003cp\u003eIn the survey-weighted multivariable model, several independent predictors of double burden of\u0026ensp;malnutrition remained after adjusting for covariates (results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) which accounted for the complex survey design. Middle wealth households were 63% more likely to report\u0026ensp;HDBM than their poorest counterparts (aOR\u0026thinsp;=\u0026thinsp;1.63, 95% CI:1.17\u0026ndash;2.28), which suggests that middle income families may also be vulnerable. Parity showed a strong dose-response relation with odds of HDBM: compared to women reporting one childbirth, 2\u0026ndash;3 children increased the odds by 45%\u0026ensp;(aOR\u0026thinsp;=\u0026thinsp;1.45, 95% CI 1.07\u0026ndash;1.95), while four or more children had more than two-fold greater chances (aOR\u0026thinsp;=\u0026thinsp;2.23, 95% CI: 1.45, 3.42) of experiencing HDBM events in lifetime. Older child age was an additional factor that contributed significantly, 24\u0026ndash;35 month-old children had approximately 75% greater likelihood of living in a household with HDBM (aOR\u0026thinsp;=\u0026thinsp;1.75, CI2 : 1.23\u0026ndash;2.50), and those\u0026ensp;aged between 36 and 59 months had around half the odds of having HDBM in their households(aOR\u0026thinsp;=\u0026thinsp;1.56, CI3: 1.08\u0026ndash;2.25) compared to infants (0-11months).\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\u003eSurvey-weighted univariable and multivariable analysis of determinants of household double burden of malnutrition\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e% with HDBM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eaOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth Quintile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.95\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (0.97\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49 (1.08\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.63 (1.17\u0026ndash;2.28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.79\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21 (0.85\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78 (0.49\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82 (0.51\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42 (1.10\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13 (0.82\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15 (1.60\u0026ndash;2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30 (0.85\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.230\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.24\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.45 (1.07\u0026ndash;1.95)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50 (1.86\u0026ndash;3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.23 (1.45\u0026ndash;3.42)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.69\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88 (0.71\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild Age(mon)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.09\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.49 (1.03\u0026ndash;2.16)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29 (1.65\u0026ndash;3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.75 (1.23\u0026ndash;2.50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u0026ndash;59 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17 (1.57\u0026ndash;2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.56 (1.08\u0026ndash;2.25)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59 (0.47\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.69 (0.53\u0026ndash;0.91)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75 (0.53\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72 (0.50\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41 (0.27\u0026ndash;0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.37 (0.24\u0026ndash;0.56)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 (0.63\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84 (0.64\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30 (0.98\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (0.99\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Note: Model accounts for complex survey design. Bolded p-values indicate statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eProtective factors identified were breastfeeding, with a reduced\u0026ensp;odds of HDBM in the household by 31% (aOR\u0026thinsp;=\u0026thinsp;0.69, 95% CI0.53\u0026ndash;0.91); and taller maternal height, resulting in a 63% decreased odds of HDBM in the household (aOR\u0026thinsp;=\u0026thinsp;0.37, 95%CI\u0026thinsp;=\u0026thinsp;0.24\u0026ndash;0.56). These findings demonstrate the role of household structure, child age and maternal characteristics in the risk of HDBM and emphasise the necessity for\u0026ensp;targeted strategies to reduce exposure to this dual burden.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDeterminants of HTBM\u003c/h2\u003e\u003cp\u003eAs a result of multivariable analyses (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), household wealth, breastfeeding and survey year were independently associated factors with\u0026ensp;the household triple burden of malnutrition (HTBM). The final multivariable model showed several important predictors\u0026ensp;of HTBM.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDeterminants of HTBM in Lesotho: Findings from univariable and multivariable analyses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% with HTBM*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCrude OR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdjusted\u003c/p\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth Quintile\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61 (0.94\u0026ndash;2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.59 (0.94\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62 (0.93\u0026ndash;2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61 (0.91\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.45 (0.22\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.44 (0.21\u0026ndash;0.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22 (0.59\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15 (0.56\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.711\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\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;3 children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.69 (1.03\u0026ndash;2.75)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61 (0.99\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;+\u0026thinsp;children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66 (0.94\u0026ndash;2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79 (0.99\u0026ndash;3.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreastfeeding\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.62 (0.40\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.61 (0.40\u0026ndash;0.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Year\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61 (0.35\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61 (0.35\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.88 (1.20\u0026ndash;2.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.89 (1.21\u0026ndash;2.97)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*Note: HTBM\u0026thinsp;=\u0026thinsp;Household Triple Burden of Malnutrition (coexistence of maternal overweight/obesity, child undernutrition, and child anaemia). Data from Lesotho Demographic and Health Surveys 2009, 2014, and 2023. All analyses account for complex survey design. Bolded values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).*\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe richer wealth quintile had a lower likelihood of HTBM (aOR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.21\u0026ndash;0.94), with the least quintile serving as the reference category for comparison to richer quintiles in\u0026ensp;the analysis. Breastfeeding was inversely associated with\u0026ensp;HTBM rates (aOR\u0026thinsp;=\u0026thinsp;0.61; 95% CI: 0.40, 0.94). Particularly, the year 2023 of the survey was found to be associated with 89% increased\u0026ensp;odds of HTBM(aOR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.21\u0026ndash;2.97) compared to the year 2009, suggesting a considerable time trend hike. Increased parity was marginally\u0026ensp;associated with a higher risk of HTBM. The overall model was highly significant (F\u0026thinsp;=\u0026thinsp;4.71, p \u0026lt;\u0026ensp;0.001), indicating that the group-level effects of these socioeconomic, behavioural, and time-related factors are important in understanding the triple burden of malnutrition in Lesotho.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is a particularly timely and thorough analysis that rigorously scrutinises the changing profile of malnutrition in Lesotho, where we currently have an enigma where historical widespread undernutrition meets the new, simultaneous phenomenon\u0026ensp;of rapid emergence of overnutrition. The findings add to the growing evidence that double and triple burden of malnutrition (DBM, TBM) has\u0026ensp;shifted from an emerging to a major public health problem in sub-Saharan Africa (SSA) and other low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results have established that Lesotho is\u0026ensp;affected by a serious double and triple burden of malnutrition. The HDBM was 14.9% which is very\u0026ensp;high compared to that of the pooled prevalence of 8% reported in 23 SSA countries[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and the one in India reported as 7.7% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is similarly greater than the 11.3% reported in Tanzania [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and 12.3% predicted in East Ethiopia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], indicating that Lesotho may be characterised by a very serious\u0026ensp;form of DBM. Further, the prevalence of HTBM, namely a double-burden\u0026ensp;household in which there is an overnourished mother and under- or overweight/anaemic child/children, was 3.6%, comparable with reported estimates of 3\u0026ndash;5% from other SSA contexts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These findings demonstrate that micronutrient malnutrition-including childhood anemia-aggravates already existing macronutrient imbalances and that the nutrition situation becomes\u0026ensp;complicated.\u003c/p\u003e\u003cp\u003eAt the individual level, these studies singled out suboptimal breastfeeding (34.5% of children) and overweight or obese mothers (46.2% of the mothers ) as leading factors for\u0026ensp;co-morbidities among under-nutrition and over-nutrition. The dyad with the highest (13.4%) co-occurrence of an obese mother and a\u0026ensp;stunted child epitomises this paradox of double burden, where household-level factors, including poor dietary quality low in nutrients but high in energy, seem to dominate. It concurs\u0026ensp;with previous findings from studies in SSA, which found that the nutrition transition has an impact on dietary patterns even in low-resource sociodemographic environments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur multivariate analysis demonstrates several relevant\u0026ensp;and modifiable determinants ofHDBM, consistent with the literature pool. Middle-wealth families were the most common to engage in HDBM when analyzed\u0026ensp;by wealth quintile. This non-linear association is in contrast to Indian studies that\u0026ensp;report a monotonic increase with wealth [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] but consistent with our \u0026ldquo;the economics of the food gap\u0026rdquo; hypothesis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Reasons as to why these two groups of processes or food types are pushing and pulling the middle-income family in different directions may be because the middle-income family has the consumption power for processed, energy-dense foods in a shifting economy but they\u0026ensp;also have limited means for acquiring a diverse and higher-quality diet. This puts them in a unique scenario, and they are at a high risk of having an over-nutritioned mother and\u0026ensp;an under-nutritioned child.\u003c/p\u003e\u003cp\u003eParity remained a strong predictor, with a dose-response pattern: women with higher numbers of children (especially parity\u0026thinsp;\u0026ge;\u0026thinsp;4) had\u0026ensp;significantly increased odds of HDBM. Comparable associations have been reported\u0026ensp;in Tanzania and Ethiopia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This may reflect household resource depletion with inadequate food quantity and/or quality for all members, as well as cumulative pregnancy-related maternal nutritional depletion and associated postnatal\u0026ensp;metabolic changes predisposing women to weight retention.\u003c/p\u003e\u003cp\u003eChild age was also significantly associated with undernutrition, and older children (24\u0026ndash;59 months)\u0026ensp;were more likely to be undernourished compared to young infants (0\u0026ndash;11 months). This is consistent with the evidence that children are increasingly exposed to suboptimal complementary feeding practices\u0026ensp;and higher levels of infection as they get older, particularly following breastfeeding cessation, a practice which our analysis found to be protective. Indeed, breastfeeding was found to be associated with a reduction in odds of HDBM by 31%, highlighting its position as a \u0026ldquo;double duty\u0026rdquo; intervention, protecting maternal\u0026ensp;and child malnutrition through promotion via normal metabolism and healthy growth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilarly to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, maternal height, a second strong protective factor, reduced HDBModds by\u0026ensp;63%. This result is consistent with the \"intergenerational cycle of malnutrition\" hypothesis, according to which taller women (who have a history of good\u0026ensp;nutrition in early life) would exhibit healthier pregnancies with better growth potential for their offspring [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Conversely, short maternal height (a proxy for childhood malnutrition) is associated\u0026ensp;with a high risk of LBW and stunting in the offspring, continuing the vicious cycle of intergenerational malnutrition.\u003c/p\u003e\u003cp\u003eOur indication that a\u0026ensp;large proportion of our sample was rural (73.7%) is important for contextualising findings. It was expected that DBM would be predominantly an urban problem, as exemplified in Malawi [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; however, the high rural prevalence of this condition in Lesotho indicates that the nutrition transition is moving into rural villages\u0026ensp;as food systems change and physical activity reduces due to urbanisation. The low\u0026ensp;child dietary diversity (90%) is of specific interest here: it shows a missing factor for DBM; there is not enough food and all from few food types that lack the most important vitamins. This is consistent with the\u0026ensp;study in Peru, where low dietary diversity was significantly associated with DBM [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, results from this study challenge Dieffenbach and Stein's\u0026ensp;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] assertion that the DBM dyad is no more than a statistical artefact due to co-variation of maternal and child malnutrition. Our multivariate analysis demonstrated that independent household-level factors that were associated with the comorbidity of these conditions included parity\u0026ensp;and wealth. This\u0026ensp;provides empirical evidence that DBM is a distinct entity from M. incognita, which needs specific and integrated management strategies.\u003c/p\u003e\u003cp\u003eTaken together, the research\u0026ensp;offers a troubling yet informative snapshot of the malnutrition landscape in Lesotho. Child undernutrition and maternal overnutrition (albeit for overweight and obesity at least) frequently co-exist in the same households, reflecting concurrent\u0026ensp;nutritional problems. HDBM levels in Lesotho\u0026ensp;are some of the highest in SSA, and a concern that needs immediate policy focus. The squeeze on middle-income levels and their greater vulnerability\u0026ensp;suggest that public health must target the squeezed middle, who are facing a disproportionate share of dietary transition costs. The breastfeeding and maternal\u0026ensp;height protective effects also underscore the importance of interventions to promote maximum maternal nutrition and child feeding throughout life course.\u003c/p\u003e\u003cp\u003eThe nearly complete low dietary diversity also implies policies beyond caloric adequacy in\u0026ensp;favor of nutrient-rich diets. This would need \u0026ldquo;doubleduty\u0026rdquo; combined action programs to\u0026ensp;tackle both dimensions of malnutrition simultaneously, consistent with what the WHO and more recent analyses have advocated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This will have to\u0026ensp;include interventions towards promotion and protection of breastfeeding, regulation of marketing on unhealthy food, improving nutritional quality of food in social programs and fortification exercises and strengthening nutrition education that enhance diversity in the family diet.\u003c/p\u003e\u003cp\u003eThe shift over of the direction of the association\u0026ensp;in univariate and multivariable analysis may be because of confounding or due to collinearity amongst the explanatory variables. For example, the effect of social group (maternal age group)\u0026ensp;was significant when analysed alone (univariable analysis), but not after the adjustment for other risk factors clustered by maternal age, including parity and household wealth. Older children should have higher parity and\u0026ensp;more stable socioeconomic condition which we know is related to in-house nutritional outcomes. After adjustment for these\u0026ensp;associated variables in the samemultivariable model, maternal age remained no longer as independent effect. Wealth quintile and child age, however remained significant, suggesting a more direct relationship to the HHDBM less influenced by\u0026ensp;other demographic or biological factors.\u003c/p\u003e\u003cp\u003eIt is concerning, however,\u0026ensp;that patterns we identified in this analysis particularly the U-shaped pattern for HDBM and steep increase for HTBM between 2014 and 2023 may be symptomatic of broader socioeconomic and environmental trends occurring elsewhere in Lesotho. The decline in HDBM up to 2014 can be attributed, partly, to the eroding of coverage for child health and nutrition interventions initiated\u0026ensp;during that time (inclusive of universal immunisation and maternal\u0026ndash;child health programmes). But the recovery that ensued, from 2014 on, came at a time of economic stagnation and\u0026ensp;frequent droughts and food insecurity in many areas of southern Africa. Lesotho\u0026rsquo;s heavy dependence on imported food and declining agricultural productivity could have increased\u0026ensp;households\u0026rsquo; vulnerability to poor diet quality, which is associated with both child undernutrition and maternal overnutrition. In addition, rapid urbanisation and lifestyle changes such as\u0026ensp;sedentary habits and rising consumption of inexpensive, processed foods may have fueled the nutrition transition that contributed to both the positive overweight trends in women and high levels of stunting and aemia in children.\u003c/p\u003e\u003cp\u003eThe data of the present study reveal a 3.6% HTBM burden on Lesotho households studied, comparable to that obtained in previously\u0026ensp;published reports elsewhere from sub-Saharan Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The HTBM prevalence in Lesotho (4%) was consistent with that for\u0026ensp;adolescents from Malawi (3.1% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]) and Ethiopia (1.2% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]), but lower than the range reported among Indian adolescents 12.7 to 14.4% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The\u0026ensp;inconsistency may result from different phases of nutrition transition and methodological differences in the terms of reference (TOR) selected for defining malnutrition burdens across studies.\u003c/p\u003e\u003cp\u003eSome of the predictors for HTBM in Lesotho were also found with\u0026ensp;multivariable analysis. The strong protective effect of breastfeeding (aOR\u0026thinsp;=\u0026thinsp;0.61) aligns with the global evidence where it is established that\u0026ensp;breastfeeding can provide a \u0026lsquo;double-duty\u0026rsquo; intervention for under- and over-nutrition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Such a negative relationship between wealth and HTBM, with 56% less likely to be observed in higher wealth, was contrary to Nepal and Malawi studies, which indicated increased TBM/DBM rates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This suggests that\u0026ensp;Lesotho may be in an early phase of nutrition transition where poverty lies at the root cause of all forms of malnutrition. Most importantly, however, is the 89% higher risk of HTBM in 2023than in 2009 which indicates a sharper decrease (~\u0026thinsp;50%\u0026ensp;worse) than what we would expect based on variables depicting individual malnutrition indicators. This pattern of timing highlights the timeliness for integrated interventions addressing\u0026ensp;concurrent burdens of maternal overnutrition, child undernutrition, and mucronutrient deficiencies in Basotho households.\u003c/p\u003e\u003cp\u003eThe distinct predictors of HDBM and HTBM found in the\u0026ensp;present study show that though the two burdens are subject to similar socio-economic and behavioural determinants, the added pressure from micronutrient deficiency (anaemia) introduces independent risks pathways. HDBM was particularly high in\u0026ensp;middle-income group, illustrating the common nature of the nutrition transition sequence, the intermediate wealthy stage having access to energy-dense, low-micronutrient foods with little dietary diversity. Meanwhile, for HTBM, protective association was evident only in the \u0026ldquo;richest\u0026rdquo; households, indicating that higher socioeconomic status might buffer/protect against micronutrient deficiencies, possibly by ensuring adequate\u0026ensp;intake through variety in diet and health care utilisation. Furthermore, exclusive breastfeeding showed a continued protective effect against both outcomes, with the strongest association observed for HTBM, suggesting the\u0026ensp;importance of EBF in preventing anaemia while providing required micronutrients to young children. Moreover, these data illustrate that although both are nutrition statuses which is related to household resource insufficiency and poverty, HTBM measures a more severe level of nutritional deficiency that requires an intervention focusing not only on the quality and diversity of the diet but also unconventional methods of nutrient supplementation and\u0026ensp;food fortification.\u003c/p\u003e\u003cp\u003eThe current study has several\u0026ensp;strengths. In addition, it employs nationally representative data, which helps in generalising the results to Lesotho, and strong multivariable analysis for determining independent predictors of household double and triple burden of\u0026ensp;malnutrition. Inclusion of\u0026ensp;macro- and micronutrient indicators affords complete evidence on malnutrition at a household level along with child and maternal nutritional status, which provides an understanding over intergenerational patterns. But the study has\u0026ensp;its limitations. Its cross-sectional nature precludes any\u0026ensp;claim of causality and self-reports for some variables as child feeding practices may be biased by recall error or social desirability bias.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first nationally representative analysis of the double and triple burden of malnutrition among mother-child pairs in Lesotho, revealing a complex nutrition crisis. The findings show that middle-income households are particularly vulnerable, reflecting the paradox of the nutrition transition, and that higher parity increases the risk of HDBM. Breastfeeding acted as protective, while taller maternal stature highlights the importance of adolescent and maternal nutrition in breaking intergenerational cycles of malnutrition.\u003c/p\u003e\u003cp\u003eThese results underscore the urgent need for integrated interventions rather than isolated approaches. Public health strategies should strengthen breastfeeding promotion and support, improve access to diverse, nutrient-rich foods, especially for middle-income households, incorporate nutrition education into maternal and child health services, and prioritize adolescent and maternal nutrition to prevent future malnutrition. By addressing shared underlying determinants, Lesotho can implement efficient, multi-sectoral strategies to reduce malnutrition and achieve national and global nutrition targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Demographic and Health Surveys (DHS) Program and the Lesotho Ministry of Health for implementing the surveys and providing the data used in this analysis. We also thank the study participants and field staff who contributed to the data collection for the Lesotho Demographic and Health Surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The analysis was conducted using publicly available secondary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHilary Takunda Takawira (Corresponding author: [email protected])\u003c/p\u003e\n\u003cp\u003eZvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.T.T. conceptualized the study; conducted the data processing, formal analysis, and investigation; and drafted the original manuscript. PRZ and ETC contributed to the methodology, validated the analysis, assisted with the interpretation of the results, and reviewed and edited the manuscript. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlease send correspondence to Hilary Takunda Takawira ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Lesotho Ministry of Health Research and Ethics Committee and the ICF International Institutional Review Board approved Lesotho Demographic and Health Survey protocols. Permission to use the data for this analysis was obtained from the Demographic and Health Surveys (DHS) Program. All DHS datasets are available to the public and fully anonymized prior to release; thus, no further ethical approval was needed for this secondary analysis. All procedures were performed following appropriate guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was based on secondary data from the Lesotho Demographic and Health Surveys (2009, 2014, and 2023). These datasets are publicly available through the Demographic and Health Surveys (DHS) Program. Data can be accessed upon reasonable request following registration and through data requests at https://dhsprogram.com/data/. All personal information has been processed to be anonymous, and this study does not include any individually identifiable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. (This study uses anonymized, publicly available survey data from the Lesotho Demographic and Health Surveys.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original Lesotho Demographic and Health Surveys (2009, 2014, 2023) received ethical approval from the Lesotho Ministry of Health Research and Ethics Committee and the ICF International Institutional Review Board. All survey participants provided written informed consent prior to data collection. For children, consent was provided by a parent or guardian. Permission to use the de-identified datasets for this secondary analysis was granted by The DHS Program (www.dhsprogram.com). As the data are publicly available and anonymized, no further ethical approval was required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate declarations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study used secondary, anonymized data from the DHS Program. Ethical approval and participant consent were obtained in the original surveys by the implementing agencies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChristian AK, Dake FAA. Profiling household double and triple burden of malnutrition in sub-Saharan Africa: Prevalence and influencing household factors. Public Health Nutr. 2022;25(6):1563\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeseba N, Vermaak K, Makatjane T, Lebuso M. A multilevel analysis of factors associated with stunting among children under five years in Lesotho: A study of the Lesotho multiple cluster indicator survey 2018. J Health Popul Nutr. 2025;44(1):8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePomati M, Mendoza-Quispe D, Anza-Ramirez C, Hern\u0026aacute;ndez-V\u0026aacute;squez A, Carrillo Larco RM, Fernandez G, et al. Trends and patterns of the double burden of malnutrition (DBM) in Peru: A pooled analysis of 129,159 mother\u0026ndash;child dyads. Int J Obes. 2021;45(3):609\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh S, Shri N, Singh A. Inequalities in the prevalence of double burden of malnutrition among mother\u0026ndash;child dyads in India. Sci Rep. 2023;13(1):16939.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarekegn BT, Taye BT, Tadele AT. Household-level double burden of malnutrition in low- and middle-income countries: a systematic review. Curr Dev Nutr. 2022;6(5):nzac091.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSagastume G, Mank I, Smit GSA, Phori PM, B\u0026auml;rnighausen K, B\u0026auml;rnighausen T, et al. The double burden of malnutrition at the household level in rural South Africa. Front Nutr. 2024;11:1340155.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhinkorah BO, Amadu I, Seidu AA, Okyere J, Duku E, Hagan JE, et al. Prevalence and factors associated with the triple burden of malnutrition among mother-child pairs in sub-Saharan Africa. Nutrients. 2021;13(6):2050.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahiledengle B, Mwanri L, Kumie A, Beressa G, Atlaw D, Tekalegn Y, et al. The coexistence of stunting and overweight or obesity in Ethiopian children: Prevalence, trends and associated factors. BMC Pediatr. 2023;23(1):194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGebremichael B, Tsegaye D, Belachew T. The double burden of malnutrition and its associated factors among school-aged children and adolescents in sub-Saharan Africa: a systematic review and meta-analysis. J Health Popul Nutr. 2025;44(1):15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDieffenbach S, Stein AD. Stunted child/overweight mother pairs represent a statistical artifact, not a distinct entity. J Nutr. 2012;142(4):771\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eModjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health. 2019;19(1):1087.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkyere J, Donkoh IE, Seidu AA, Ahinkorah BO, Aboagye RG, Yaya S. Mother\u0026ndash;child dyads of overnutrition and undernutrition in sub-Saharan Africa. J Health Popul Nutr. 2024;43(1):14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeyakumar A, Godbharle S, Kesa H, Shambharkar P, Bhalekar P, Chalwadi S, et al. Comparison of household double burden of malnutrition among mother-child dyads in different settings in Maharashtra. Cities Health. 2024;8(6):1031\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar P, Chauhan S, Patel R, Srivastava S, Bansod DW. Prevalence and factors associated with triple burden of malnutrition among mother\u0026ndash;child pairs in India: A study based on National Family Health Survey 2015\u0026ndash;16. BMC Public Health. 2021;21:391.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamasubramani P, Krishnamoorthy Y, Rajaa S. Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: Findings from a nationally representative survey (NFHS-5). Heliyon. 2024;10(18):e37794.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Double-duty actions for nutrition: Policy brief. Geneva: WHO; 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEshete T, Kumera G, Bazezew Y, Marie T, Alemu S, Shiferaw K. The coexistence of maternal overweight or obesity and child stunting in low-income country: Further data analysis of the 2016 Ethiopia demographic health survey (EDHS). Sci Afr. 2020;9:e00524.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYigezu M, Oumer A, Damtew B, Birhanu D, Workie SG, Hamza A, et al. The dual burden of malnutrition and its associated factors among mother-child pairs at the household level in Ethiopia: An urgent public health issue demanding sector-wide collaboration. PLoS ONE. 2024;19(11):e0307175.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaustini FT, Mniachi AR, Msengwa AS. Coexistence and correlates of forms of malnutrition among mothers and under-five child pairs in Tanzania. J Nutr Sci. 2022;11:e103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhaki JJ, Macharia PM, Benov\u0026aacute; L, Giorgi E, Semaan A. Prevalence and determinants of double and triple burden of malnutrition among mother-child pairs in Malawi: A mapping and multilevel modelling study. Public Health Nutr. 2024;27(1):e87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel R, Srivastava S, Kumar P, Chauhan S. Factors associated with double burden of malnutrition among mother-child pairs in India: a study based on National Family Health Survey 2015\u0026ndash;16. Child Youth Serv Rev. 2020;116:105256.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eF\u0026eacute;lix-Beltr\u0026aacute;n L, Macinko J, Kuhn R. Maternal height and double-burden of malnutrition households in Mexico: Stunted children with overweight or obese mothers. Public Health Nutr. 2021;24(1):106\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahiledengle B, Mwanri L, Agho KE. Association between maternal stature and household-level double burden of malnutrition: Findings from a comprehensive analysis of Ethiopian Demographic and Health Survey. J Health Popul Nutr. 2023;42(1):14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnik AI, Rahman MM, Rahman MM, Tareque MI, Khan MN, Alam MM. Double burden of malnutrition at household level: A comparative study among Bangladesh, Nepal, Pakistan, and Myanmar. PLoS ONE. 2019;14(8):e0221274.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLukwa AT, Chiwire P, Akinsolu FT, Okova D, Hongoro C. Double burden of malnutrition among women and children in Zimbabwe: a pooled logistic regression and Oaxaca-Blinder decomposition analysis. Front Public Health. 2024;12:1451898.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePradeilles R, Landais E, Pareja R, Eymard-Duvernay S, Markey O, Holdsworth M, et al. Exploring the magnitude and drivers of the double burden of malnutrition at maternal and dyad levels in peri-urban Peru: A cross-sectional study of low-income mothers, infants and young children. Matern Child Nutr. 2023;19(4):e13549.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJi N, Kumar A, Joe W, Kuriyan R, Sethi V, Finkelstein JL, et al. Prevalence and Correlates of Double and Triple Burden of Malnutrition Among Children and Adolescents in India: The Comprehensive National Nutrition Survey. J Nutr. 2024;154(10):2932\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamasubramani P, Krishnamoorthy Y, Rajaa S. Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: Findings from a nationally representative survey (NFHS-5). Heliyon. 2024;10(18):e37794.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSunuwar DR, Singh DR, Pradhan PMS. Prevalence and factors associated with double and triple burden of malnutrition among mothers and children in Nepal: evidence from 2016 Nepal demographic and health survey. BMC Public Health. 2020;20(1):405.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarekegn BT, Assimamaw NT, Atalell KA, Kassa SF, Muhye AB, Techane MA, et al. Prevalence and associated factors of double and triple burden of malnutrition among child-mother pairs in Ethiopia: Spatial and survey regression analysis. BMC Nutr. 2022;8(1):34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Double burden of malnutrition, triple burden, stunting, maternal obesity, Lesotho, DHS, sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-8165828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8165828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe simultaneous occurrence of under- and over-nutrition among households is an emerging public health problem\u0026ensp;in Sub-Saharan Africa. In Lesotho, the increasing burden of overweight in mothers alongside persistent child undernutrition is indicative of this double burden crisis. However, research on the magnitude and drivers of household\u0026ensp;double and triple burden malnutrition among mother-child pairs is limited. The objective of this study is to report\u0026ensp;on household double and triple malnutrition burden prevalence and the correlates among mother-child (MC) pairs in Lesotho.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe research pooled cross-sectional data from Lesotho Demographic and Health Surveys in 2009, 2014, and 2023 (representing a total of\u0026ensp;4,150 mother-child pairs). Women\u0026rsquo;s and children\u0026rsquo;s record data\u0026ensp;files were combined, and the sample weights were used to adjust for sampling design. Survey-weighted univariable and multivariable logistic regression models were estimated to determine factors associated with the household's double burden and\u0026ensp;triple burden of malnutrition, adjusting for socioeconomic, demographic, and biological covariates.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eThe HDBM and HTBM prevalence was estimated at\u0026ensp;14.9% and 3.6%, respectively in the pooled analysis. HDBM trended in a U-shape up to 18.3% in 2023,\u0026ensp;and HTBM more than doubled to 6.0%in the same year Analysis found women from middle class households were more likely to engage in HDBM (aOR\u0026thinsp;=\u0026thinsp;1.63,\u0026ensp;95% CI: 1.17\u0026ndash;2.28) and those with four or more children comprising an over two-fold increase in cumulative odds (aOR\u0026thinsp;=\u0026thinsp;2.23, 95% CI: 1.45\u0026ndash;3.42). Breastfeeding was highly protective with reduced odds of\u0026ensp;HDBM (aOR\u0026thinsp;=\u0026thinsp;0.69, 95% CI: 0.53\u0026ndash;0.91) and HTBM (aOR\u0026thinsp;=\u0026thinsp;0.61, 95% CI: 0.40\u0026ndash;0.94). In 2023, women were at\u0026ensp;a significantly greater risk of HTBM than in 2009 (aOR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.21\u0026ndash;2.97).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study confirms that Lesotho experiences a significant double and\u0026ensp;triple burden of malnutrition at the household level. The dual burden of maternal overnutrition and child undernutrition, and their overlap with micronutrient inadequacy, adds a layer of complexity to the public health\u0026ensp;problem. This burden is significantly affected by\u0026ensp;key social and biological determinants, including household economic status, parity and maternal height. The increasing trend emphasises the urgent requirement for synergy among public health policies that would\u0026ensp;tackle simultaneously all forms of malnutrition through a combination approach addressing common underlying determinants.\u003c/p\u003e","manuscriptTitle":"Prevalence and determinants of double and triple burden of malnutrition among mother– child pairs in Lesotho: pooled analysis of DHS 2009, 2014 and 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:41:57","doi":"10.21203/rs.3.rs-8165828/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-26T08:02:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-27T06:58:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T16:20:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T16:18:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-11-20T14:49:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e8690af-8b35-4560-8330-a0a1e02f2d46","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-26T08:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:41:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8165828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8165828","identity":"rs-8165828","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}