A demographic-sensitive model for estimating micronutrient inadequacy risk among nutritionally vulnerable households using household food consumption data

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Ferguson, Lucia Segovia de la Revilla, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7538597/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Household Consumption and Expenditure Surveys often estimate nutrient adequacy using a single reference individual – typically a non-pregnant, non-lactating (NPNL) woman of reproductive age, known as the Adult Female Equivalent (AFE). However, due to the diversity in nutrient requirements across demographic groups, the AFE approach may underestimate the proportion of households where at least one member has increased risk of inadequate intake. We developed a novel modeling approach – the Vulnerable Household Equivalent (VHE), which identifies the household member with the highest nutrient density requirement as the reference individual. Using data from Malawi’s 2019/20 Fifth Integrated Household Survey and local food composition tables, we estimated micronutrient inadequacy for vitamin A and zinc using AFE and VHE metrics. Prevalence of apparent micronutrient inadequacy was consistently higher using the VHE approach compared to the AFE across national, rural/urban, and socioeconomic strata. The most common reference groups for VHE were NPNL women aged 18–29 years (25.0%) and lactating women (20.5%) for vitamin A, and adult men aged 30–59 years (25.7%) and 18–29 years (21.5%) for zinc. The VHE metric offers a more inclusive and equitable approach for estimating household-level micronutrient inadequacy, though its complexity may require computational support for broader applications. Nutrition & Dietetics Epidemiology Health Policy Adult Female Equivalent Household Consumption and Expenditure Survey Malawi Micronutrients Vulnerable Household Equivalent Figures Figure 1 Introduction Dietary data play a critical role in informing policies that address nutrient deficiencies due to inadequate dietary intake [ 1 ]. However, collecting nationally representative individual-level dietary data such as 24-hour recalls (24HR) is costly and often misses certain demographic groups, such as adult men [ 2 , 3 ]. Because individual-level dietary data are rarely collected in low- and middle-income countries (LMICs) [ 4 , 5 ], there is growing interest in using Household Consumption and Expenditure surveys (HCESs) for nutrition research purposes. These surveys, typically conducted at the national level, can help identify sub-populations at risk of inadequate dietary intake in LMICs [ 5 , 6 ]. The advantages of household-level food consumption data collected via HCESs are that they are collected regularly in many LMICs, are nationally representative, and capture food consumption data over one to two weeks – often across seasons – allowing for both cross-sectional and seasonal trend analyses [ 6 , 7 ]. However, a key limitation is that HCES food consumption or acquisition data are collected at the household level and typically carry large recall error, and methods for individualizing consumption are based on several potentially incorrect assumptions, including equitable intra-household food distribution [ 8 , 9 ]. Three main metrics for individualizing HCES food consumption data have been developed and used, including the Per Capita (PC), the Adult Male Equivalent (AME), and the Adult Female Equivalent (AFE) metrics [ 3 , 8 , 10 , 11 , 12 ]. The PC approach assumes that household food is distributed equally among household members. Such estimates, which are calculated by dividing the household consumption by the number of household members, overlook variations in age-sex energy requirements. Both AME and AFE approaches address this limitation by assuming that household food distribution is proportional to an individual’s energy requirements. The two metrics only differ in terms of the household member selected to represent the household unit (i.e., an adult male for AME or an adult female of reproductive age for AFE). Both metrics can be used to calculate intakes and dietary adequacy, but their food consumption would be calculated using a proportion of the base AME (i.e. adult male, usually 18–29 years) or AFE (i.e. non-pregnant non-lactating (NPNL) woman of reproductive age (WRA), usually 18–29 years). Food consumption data generated through HCESs can be used to estimate the proportion of households in which members are at risk of deficiency due to inadequate dietary intake, rendering the data particularly suitable for programs and policies targeting the entire household rather than a single demographic group. However, normalizing apparent intakes based on a stringent definition of household members such as NPNL WRA (i.e., the AFE metric) may underestimate the proportion of households with members at risk of inadequate nutrient intakes if the selected demographic group is not the most nutritionally vulnerable household member for the nutrient of interest. Advancing from these established metrics, a new approach was developed to provide a more demographic-sensitive model for assessing household nutrition using quantitative household food consumption data. We developed a novel analytical model for estimating individualized apparent dietary intakes from household-level data called the ‘vulnerable household equivalent’ (VHE) metric, which is sensitive to demographic structures of households in populations. In this study, we aimed to compare the prevalence of inadequate intakes of vitamin A and zinc (Zn) estimated using the VHE and AFE approaches and examine the extent to which the estimates differ when analyzed nationally and by residence (rural and urban) and socio-economic positions in Malawi. Malawi was selected as a case study due to the availability of HCES data and its classification as a low-income setting where HCESs are frequently conducted. By 2025, six such surveys had been conducted nearly every 5 years. We compared vitamin A and Zn because both are nutrients of public health concern in Malawi. The country has long grappled with vitamin A deficiency, which declined significantly from 59% in 2001 to 4% in 2015/16 among children under five [ 13 ]. In contrast, Zn deficiency was assessed at the national level only once, during the 2015/16 survey, which revealed a high prevalence ranging from 60–69%—with the highest rates among adolescent girls aged 10–14 years and the lowest among preschool and school-aged children [ 13 ]. Given the diverse dietary sources of these two nutrients in the Malawian context, their inadequate intake is likely to yield distinct insights when comparing VHE and AFE approaches. Methods Food consumption data Food consumption data were derived from 2019/20 Malawi’s Fifth Integrated Household Survey (IHS5) – a comprehensive nationally representative survey conducted as part of the World Bank’s Living Standards Measurement Study (LSMS) program. We obtained the data from the World Bank’s open-data repository as entirely secondary, de-identified data [ 14 ]. Food consumption data for IHS5 were collected as part of the questionnaire which recorded household food consumption at the food item level. The study utilized the module ‘household_module_g1 ( hh_mod_g1 )’ which recorded household apparent food intake based on a predefined list of 135 items, recalled over a 7-day period. Specifically, the module asked “Over the past 7-days, did you or others in your household consume any (food item)? How much in total did your household consume in the past 7-days?” . The person most knowledgeable about food consumed in that household answered these questions on behalf of the entire household. The National Statistics Office (NSO) in collaboration with the World Bank implemented the survey nationwide between April 2019 and April 2020. A stratified two-stage sampling design was used based on the cartography and data from the 2018 Malawi Census of Population. A total of 12,288 households were selected from 768 Enumeration Areas (EAs). However, due to the COVID-19 pandemic, 51 EAs (854 households) could not be visited at the end of the 12-month fieldwork period. The aggregation resulted in a final sample size of 11,434 households, which was statistically representative at the national, district, and urban/rural residence levels. Preprocessing of food consumption data The food consumption data were preprocessed and transformed into usable metrics by converting all quantities recorded in standard (e.g. milliliter) and non-standard (e.g. pail, basin, heap) units to the metric unit grams using country-specific conversion factors provided with the IHS5 dataset (i.e. caloric conversion factor file). Where relevant, the foods were adjusted for edible portions by subtracting the non-edible portions of foods from the total quantity apparently consumed. Then for each food item, the total quantity of food was divided by the number of days of the recall period (i.e. 7 days) to estimate daily apparent household consumption. To identify implausible values (outliers), we normalized the quantities using the logarithmic transformation, and quantities greater than five standard deviations above the mean of the logarithmically transformed consumption quantities were deemed outliers and were replaced with the median consumption quantity among consumers for each food item, as described in Tang et al.[ 10 ]. The food consumption data were matched to relevant food composition data to estimate each food item’s vitamin A, Zn, and energy content. The main food composition table (FCT) used was the 2019 Malawian FCT [ 15 ], contributing about 71% of the values. Other FCTs that contributed nutrient values were the Kenyan FCT [ 16 ] – 16%, the Western Africa FCT [ 17 ] – 12%, and the USDA Food Data Central tool [ 18 ] – 1%. The Government of Malawi issued a mandatory fortification policy for sugar and oil with vitamin A, and wheat flour with nine micronutrients (vitamins A, B1, B2, B3, B6, B9 and B12, and the trace elements iron (Fe) and Zn) [ 19 ]. Therefore, we assumed status quo fortification levels of vitamin A and Zn in these food vehicles, i.e., all oil, sugar, and wheat flour consumed by households was fortified, with adjustments for compliance and degradation to estimate the fortificant concentration in food vehicles at the household level [ 10 ]. Household member energy requirements Daily energy requirements were calculated according to the Human Energy Requirements recommendations provided by the Joint Food and Agriculture Organization of the United Nations (FAO)/World Health Organization (WHO)/United Nations University (UNU) Expert Consultation [ 20 ], as detailed in Supplementary Table S1 online. For individuals under 18 years, age- and sex-specific body weights and Physical Activity Levels (PAL) were obtained from FAO/WHO/UNU guidelines, assuming a moderately active lifestyle [ 20 ]. For women aged 18–29.9 years, an average body weight of 55 kg was used, based on data from the 2015–16 Malawi Demographic and Health Survey (MDHS) [ 19 ]. For all other age categories of women and for men, average body weights were derived from a cross-sectional, population-based Non-Communicable Disease study conducted in both rural and urban Malawi, which enrolled approximately 28,891 adults between 2013 and 2016 [ 21 ]. Based on this study, the following average body weights were applied: 65 kg for women aged 30–59.9 years, and 60 kg for women aged 60 years and above. For men, average body weights of 60 kg, 65 kg, and 60 kg were used for the 18–29.9, 30–59.9, and 60 + age groups, respectively. These body weights were harmonized with standard reference weights used in FAO energy requirement tables to ensure consistency in applying the PAL-based energy estimates. For all adults, we assumed moderate active PAL corresponding to 1.75xBMR (basal metabolic rate). This PAL value was chosen to reflect the average activity level of the Malawian population [ 22 ]. Data on women’s lactation status and whether a child was breastfeeding were not collected in IHS5. Therefore, rather than assuming that all children under 2 years of age were breastfeeding, as recommended by the WHO – which advises exclusive breastfeeding for the first 6 months and continued breastfeeding alongside complementary foods up to 2 years of age or beyond, we applied age-specific breastfeeding prevalence rates based on the 2015–16 MDHS [ 19 ]. While the MDHS does not provide a precise estimate for children aged 6–11 months, it indicates that nearly all children (98%) are breastfed at some point, making it reasonable to infer that breastfeeding prevalence among children aged 0–11 months was very high, particularly for any breastfeeding. For older age groups, we used reported prevalence rates: 90.5% for children aged 12–17 months and 76.6% for those aged 18–23 months [ 19 ]. These more realistic estimates were used to randomly assign breastfeeding status and adjust energy requirements accordingly. Importantly, the analysis maintained actual mother–child relationships by linking each child to their biological mother within the household. This approach allowed for the identification of multiple lactating women in households with more than one young child, ensuring that energy requirements were assigned accurately and realistically to the correct individuals. For women, this assumption meant an additional 330 kcal per day was included in her energy requirement estimation if her child was under 6 months of age, and an additional 400 kcal per day if her child was between 6–24 months of age [ 23 ]. These additions were applied only when the child was assumed to be breastfeeding, as determined by the random assignment method described above. For breastfed children, we subtracted the estimated energy contributions from breastmilk from their daily energy requirements to calculate their AFE and VHE ratios, which represented their total energy requirements from complementary foods [ 24 ]. Pregnancy status was reported for few women (29 records – 0.3% of the sample) in IHS5. These women were assigned an additional 300 kcal per day to their base energy requirements [ 25 ]. Adult female equivalent (AFE) approach The AFE approach is standardized to the nonpregnant, non-lactating 18 to 29-year-old female who serves as a reference household member or one AFE [ 10 ]. The number of AFEs per household was calculated as the sum of AFE ratios, where the AFE ratio for each household member was their estimated energy requirement, which for children under 2-years of age was their energy requirement from complementary foods, divided by the energy requirement of a nonpregnant, non-lactating 18 to 29-year-old female. The number of AFEs was calculated for all households, including those that did not include a NPNL household member, to standardize all intakes to an AFE as done previously [ 10 ]. Vulnerable household equivalent (VHE) approach The VHE approach, like the AFE, assumes that a household’s reported food consumption is distributed in proportion to each household member's energy requirements. However, for the VHE, each household member’s estimated energy requirement was divided by the estimated energy requirement of the most nutritionally vulnerable household member. The most nutritionally vulnerable household member was defined, for each nutrient of interest, as the household member with the highest critical nutrient density (CND) for that nutrient (i.e., either vitamin A or Zn in this study). The CND values for each specific age, sex, and maternal status (pregnant/lactating women) groups were calculated as the ratio of their harmonized average requirements (H-AR) for vitamin A or Zn to their daily energy requirement expressed per 1,000 kcal [ 26 , 27 ] represented by the following equation: $$\:CND\:values\:=\left(\frac{\:{H-AR}_{age,\:\:\:sex,\:\:\:specific\:condition,\:and\:micronutrient}\:}{\:{Daily\:energy\:requirement\left(kcal\right)}_{\:age,\:\:\:sex\:and\:specific\:condition}}\right)*\text{1,000}\:kcal$$ 1 The H-AR is the average daily micronutrient intake estimated to meet the requirements of half of healthy individuals in a particular life stage and gender group [ 27 ]. The Zn H-AR assumed a low bioavailability because Malawian diets are characterized by high intakes of unrefined grains and legumes [ 28 , 29 ] with high phytate content, which reduces Zn bioavailability. Unlike the AFE, the VHE differed between households depending on the family member structure in each household and the nutrient modelled (i.e., the most vulnerable family member was not the same in all households). The gender-age/life-stage requirements and CNDs for vitamin A and Zn for the VHE calculation are presented in Supplementary Table S2 online. For children under 2 years of age, we calculated the CND for vitamin A and Zn from complementary foods by subtracting the H-AR from the assumed amount of the nutrient contributed by breastmilk in the diets of these children. We assumed the nutrient contribution from breastmilk was 61 µg RAE of vitamin A and 0.17 mg of Zn based on Kenya Food Composition Table [ 16 ], and an estimated breastmilk intake of 474 g per day [ 24 ]. The person with the highest CND for vitamin A or Zn was considered the most nutritionally vulnerable household member, and their nutrient requirements were used as a benchmark for calculating the VHE. For example, in the case of vitamin A, lactating women exhibited the highest CND. Consequently, if a household included a lactating woman, the energy requirements of a lactating woman and nutrient requirements would be utilized to calculate the VHE values and assess nutrient apparent intake and inadequacy, respectively. Subsequently, this person's H-AR would be employed to estimate micronutrient inadequacy. Figure 1 shows the steps taken in the R-script to calculate the prevalence of apparent inadequacy using VHE and AFE approaches. Data analysis Estimated apparent vitamin A and Zn intakes were compared against H-AR values to categorize households with low intakes per AFE and VHE, represented by the following equations: $$\:\left(\frac{Daily\:household\:apparent\:micronutrient\:intake\:\left(units\right)\:\:}{{\sum\:}_{Household\:AFEs}}\right)<{H-AR}_{adult\:females}$$ 2 $$\:\left(\frac{Daily\:household\:apparent\:micronutrient\:intake\:\left(units\right)\:\:}{{\sum\:}_{Household\:VHEs}}\right)<{H-AR}_{vulnerable\:household\:members}$$ 3 To calculate the prevalence of inadequate vitamin A or Zn intakes, the total number of households that were categorized with inadequate intakes per AFE and VHE for these nutrients were divided by the total number of households in each specific subpopulation group. The estimated prevalence of apparent inadequacy of vitamin A and Zn using were compared at the national level, stratified by residence (urban and rural), and by socioeconomic quintiles. These quintiles were based on the total inflation-adjusted annual household expenditures per capita and were supplied with the IHS5 data. All data cleaning, transformation, and analysis were performed in R (version 4.1.3, R Foundation for Statistical Computing), and the results were weighted according to IHS5 survey weights using functions available on the srvyr package [ 30 , 31 ]. Results Demographic groups selected as the most nutritionally vulnerable household members for vitamin A and Zn using the VHE approach Table 1 presents the proportion of demographic groups identified as the most vulnerable members for assessing the risk of vitamin A and Zn inadequacy using the VHE approach. For vitamin A, the most common demographic group selected to calculate the VHE was NPNL WRA, 18–29 years, accounting for 25.0% of households. This group also serves as the default reference in the AFE approach. The next most common groups were lactating women (20.5%) and NPNL WRA aged 30–49 years (15.7%). For Zn, the most common demographic groups were adult men aged 30–59 years (25.7%) and 18–29 years (21.5%). Additionally, older men and women were selected in a notable proportion of households for both nutrients, highlighting their nutritional vulnerability. Although there were differences in the proportions of demographic groups selected to characterize households at risk of inadequacy, the H-AR used for some groups was the same across both the VHE and AFE approaches. For instance, the same H-AR for vitamin A used in the AFE approach was also applied when NPNL WRA aged 30–49 years or older women (50 + years) were identified as the most vulnerable members in the VHE approach. Similarly, for Zn, the same H-AR used in the AFE approach was applied when these same groups were selected under the VHE approach (Table 1 ). This overlap reflects the shared nutrient requirements among these groups despite differences in household composition and their energy requirements. Table 1 Proportion of demographic groups identified as the most nutritionally vulnerable member to assess risk of vitamin A and Zn inadequacy using the VHE approach Demographic group Vitamin A (%) Zinc (%) H-AR for vitamin A (µg RAE) H-AR for zinc (mg) Women NPNL WRA, 18–29 years 25.0 4.8 490 10.2 NPNL WRA, 30–49 years 15.7 5.3 490 10.2 Older women 50 + years 11.7 10.1 490 10.2 Lactating women 20.5 15.0 1020 13.7 Men 18–29 years 2.8 21.5 570 12.7 30–59 years 2.2 25.7 570 12.7 Older men 60 + years 10.0 10.7 570 12.7 Adolescent Girls/boys 11–14 years 9.9 – 480 8.9 Children 1–3 years – 6.8 205 3.6 7–10 years 1.9 – 320 6.2 Note : Values represent the percentage of households in which each demographic group was identified as the most nutritionally vulnerable for vitamin A or zinc intake, based on the VHE approach. Harmonized Average Requirement (H-AR) values are sourced from Allen et al. [ 27 ] and represent daily intake recommendations for each group. Abbreviations : NPNL WRA – non-pregnant and non-lactating women of reproductive age; µg RAE – micrograms of retinol activity equivalents. As shown in Supplementary Table S3 online, the demographic group identified as the most nutritionally vulnerable using the VHE approach varied by residence and socioeconomic position (SEP). In rural areas, lactating women were generally the most frequently selected group for calculating the VHE for vitamin A, while adult men aged 30–59 years were most selected for Zn. In urban areas, NPNL women aged 18–29 years were most frequently selected for vitamin A, whereas men aged 30–59 years consistently dominated Zn vulnerability. Across both nutrients, older adults and young children were also identified as the most vulnerable in a notable proportion of households. Comparison of the estimated prevalence of households at risk of apparent inadequacy for vitamin A and Zn between VHE and AFE Table 2 presents a comparison of the prevalence of apparent inadequacy for vitamin A and Zn using two estimation approaches – VHE and AFE, stratified by rural and urban residence and across SEPs. Nationally, the prevalence of apparent inadequacy for both vitamin A and Zn was higher when estimated using the VHE approach compared to the AFE approach – 51.7% vs. 46.2% for vitamin A and 72.6% vs. 68.4% for Zn. When stratified by residence (rural/urban) and SEP, the VHE approach consistently yielded higher inadequacy estimates across all subgroups than AFE approach. In rural areas, the percentage-point differences (PPDs) between VHE and AFE ranged from 3.7 to 6.9 for vitamin A and 1.4 to 8.1 for Zn. In urban areas, PPDs ranged from 0.7 to 12.9 for vitamin A and 0.4 to 4.8 for Zn. The largest observed shift in vitamin A inadequacy occurred among urban households in the lower-middle SEP, with a 12.9 percentage point increase under the VHE approach. For Zn, the greatest shift was observed among rural households in the higher-middle SEP, with an 8.1 percentage point increase. Table 2 Comparison of prevalence of apparent inadequacy for vitamin A and Zn between VHE and AFE in rural and urban residences and across socioeconomic positions Population Households (n) Vitamin A Zinc VHE AFE Percentage point ∆ (VHE – AFE) VHE AFE Percentage point ∆ (VHE – AFE) % % National (total) 11,432 51.7 46.2 5.5 72.6 68.4 4.2 Residence and socioeconomic position (SEP) by quintile of total annual household expenditure per capita Rural 9342 56.1 50.7 5.4 73.2 68.9 4.3 Lowest SEP 1869 86.6 82.4 4.2 97.4 96.0 1.4 Lower middle SEP 1869 72.2 66.4 5.8 87.2 84.8 2.4 Middle SEP 1868 58.6 52.0 6.6 76.3 71.4 4.9 Higher middle SEP 1868 40.7 33.8 6.9 62.7 54.6 8.1 Highest SEP 1868 14.3 10.6 3.7 35.7 30.0 5.7 Urban 2090 29.1 22.9 6.3 69.3 66.0 3.3 Lowest SEP 418 70.3 64.4 5.9 94.3 93.9 0.4 Lower middle SEP 419 38.2 25.3 12.9 83.4 79.3 4.1 Middle SEP 418 18.9 11.5 7.4 72.4 68.9 3.5 Higher middle SEP 417 7.1 4.4 2.7 57.1 52.3 4.8 Highest SEP 418 3.6 1.9 0.7 32.5 28.3 4.2 Note : Values represent the percentage of households with apparent inadequacy of vitamin A and zinc intake, estimated using the Vulnerable Household Member (VHE) and Adult Female Equivalent (AFE) approaches. The “Percentage point ∆ (VHE – AFE)” column shows the difference in prevalence between the two methods. Discussion In this study, we introduced the vulnerable household equivalent (VHE) metric, a novel approach for estimating the risk of inadequate micronutrient intake from household-level food consumption data. Unlike the AFE metric, which assumes a fixed reference individual (usually a NPNL woman aged 18–29), the VHE approach accounts for the demographic structure and nutritional needs of the most vulnerable household member. Using HCES data from Malawi, we applied the VHE and AFE metrics to estimate households at risk of inadequate apparent vitamin A and Zn intake. Our analysis has shown that estimates of apparent inadequate intake of vitamin A and Zn were consistently higher using the VHE than AFE approach at national, rural/urban residences, and across all SEPs in both rural and urban areas. Notably, the PPD in the prevalence of inadequacy between the two approaches was generally below 10%, except for vitamin A in the urban lower-middle SEP, where it reached 12.9%. This suggests that while both metrics produce broadly similar results, the VHE offers a more sensitive lens for identifying nutritional vulnerability, particularly in subpopulations with unique demographic profiles. For vitamin A, the CND of NPNL women 18–29 years was higher than or equal to all demographic groups except for lactating women, elderly men and women, and 11-year-old girls. Similarly for Zn, their CND was higher for the elderly, lactating women and 1-year old female children (see Supplementary Table S2 online). While the VHE approach was applicable to any population group, its impact on estimating nutrient inadequacies compared with the AFE will vary depending on a country's demographic profile, the difference in CNDs across demographic groups, the nutrient of interest, dietary patterns, and the usual intake of foods rich (or not rich) in the specific nutrients of interest in the population. In terms of the differences in CNDs across demographic groups, lactating women had the highest CNDs for vitamin A, while for Zn, elderly men had the highest CNDs. These differences (AFE vs VHE) will likely be highest for vitamin A when there is a high percentage of lactating women in the population, and highest for Zn when there is a high percentage of elderly men in the population. For example, examining populations with a higher proportion of elderly men would shed more light on the value of the VHE metric for assessing Zn inadequacy, since older men require a greater amount of Zn per unit energy requirement than other demographic groups. Similarly, populations with higher fertility rates present an opportunity to investigate the value of the VHE metric in assessing vitamin A inadequacy as lactating women require a greater amount of vitamin A per unit energy requirement than other demographic groups. The study findings reveal a shortcoming with the AFE approach, when estimating the prevalence of households with at least one member at risk of inadequate intakes. In circumstances where there was no NPNL woman in the household, the NPNL woman (aged 18–29 years) was nevertheless used as a reference member. This was demonstrated by the findings of this study, which reveal that a small number of households (55 households – 0.5% for vitamin A and 34 households – 0.3% for Zn) were re-classified from inadequate to adequate when compared to the VHE, which can only happen in households without a resident NPNL woman. Therefore, using a fixed single, hypothetical, household member to establish whether apparent micronutrient intake is adequate will underestimate the percentage of households where some members are at risk of inadequate nutrient intakes if the CND, for most nutritionally vulnerable demographic household member in the household is higher than that of a NPNL woman. In contrast, the VHE approach addresses this limitation by adopting a more realistic and flexible approach that considers the CND of each household member to select the most nutritionally vulnerable member. Moreover, in settings where household age/sex structures differ across socio-economic groups or regions the VHE approach is more appropriate than the AFE approach to individualize comparisons of household nutritional vulnerability. The VHE approach aligns closely with the objectives of Sustainable Development Goals (SDGs), particularly Goal 2, which aims at ensuring access to safe, nutritious, and sufficient food for all people, with a particular emphasis on the poor and those in vulnerable situations [ 32 ]. By considering a range of vulnerabilities within households, such as lactating women, young children, and the elderly, the VHE approach has the potential to more realistically monitor progress towards equitable access to nutritious food for meeting the nutrient requirements of all individuals in the household – leaving no one behind. One of the key strengths of the VHE metric is its ability to capture the diverse nutritional needs of various demographic groups, and unlike the AFE approach, it will reflect the demographic composition of households. This more informed approach recognizes that different members of a household may have different nutrient requirements in relation to their energy requirements, which a one-size-fits-all approach, such as AFE, fails to capture. Strengths and limitations of the study There are several strengths inherent in the study's methodology and approach. Firstly, the study capitalized on a nationally representative sample, ensuring that the findings are generalizable to the broader population. Additionally, the analysis was conducted at multiple levels, including national, residence (rural/urban), and SEP, providing a comprehensive understanding of how the VHE compared with the AFE would perform across different demographic strata in Malawi. Furthermore, the study's consistency in applying both metrics, VHE and AFE, in the same populations and using the same parameters such as body weight, PALs, and assumptions regarding additional energy requirements for lactation and pregnancy, enhances the comparability and reliability of the estimates generated. In addition, we used realistic assumptions to assign breastfeeding status and adjust energy needs accordingly. By linking each child to their biological mother, the approaches accurately identified multiple lactating women within households, ensuring energy requirements were assigned to the correct individuals. However, we acknowledge some limitations. One of the limitations is that, despite its demographic sensitivity, the VHE approach like the AFE still relies on the assumption that food is distributed within households in proportion to each member’s age- and sex-specific energy requirements. This assumption may not reflect actual intra-household food allocation, which can be influenced by cultural norms, individual preferences, or household dynamics. Another limitation is the apparent underrepresentation of pregnant women in the dataset. Only 29 pregnant women were identified, which likely reflects the way pregnancy status was captured through a health-related module rather than direct observation—leading to underreporting, particularly in early pregnancy. As a result, some pregnant women may have been misclassified as NPNL. Finally, the performance of the VHE metric was evaluated using only two micronutrients – vitamin A and Zn. While this provides a strong starting point, further research is needed to assess the applicability of the VHE approach to other nutrients with different dietary sources and requirement profiles. Conclusion Through this study, we have developed a novel metric for evaluating the percentage of households where at least one member is at risk of inadequate intakes of selected nutrients, using household-level food consumption data, which is called the ‘vulnerable household equivalent (VHE). The development of the VHE approach was driven by the recognition that even though the AFE approach, used widely in this field of research to assess the percentage of households at nutritional risk, may under-represent the risk of inadequacy for some demographic groups because it overlooks the diverse nutritional needs of different demographic groups. This study demonstrates that the VHE metric addresses some limitations in the existing AFE approach and may contribute to more equitable design of nutrition interventions when these are informed by household food consumption data. While the VHE is a more flexible approach than the AFE, it is also more complex to calculate. This complexity is reduced by using scripted approaches, as developed in this study, that can be adapted for other contexts. While the findings from Malawi are promising, further research is needed to explore the applicability of the VHE metric in populations with diverse demographic structures and for other nutrients beyond those analyzed in this study. Declarations Author contributions G.O: Conceptualization; Writing – original draft; Formal analysis; Data curation; Methodology; Writing – review & editing. E.L.F.: Conceptualization; Writing – original draft; Methodology; Formal analysis; Writing – review & editing. L.S.d.l.R.: Formal analysis, Methodology, Data curation, Writing – review & editing. T.C.: Formal analysis, Methodology, Data curation, Writing – review & editing. K.P.A.: Conceptualization; Methodology; Validation; Writing – review & editing. E.J.M.J: Conceptualization; Methodology; Validation; Writing – review & editing. TN-M.: Conceptualization; Supervision; Methodology; Writing – review & editing. L.E.A.: Conceptualization; Supervision; Methodology; Writing – original draft; Writing – review & editing. A.A.K.: Conceptualization; Supervision; Methodology; Writing – original draft; Writing – review & editing. Data availability The 2019/20 Malawi’s Fifth Integrated Household Survey (IHS5) were conducted as part of the World Bank’s Living Standards Measurement Study (LSMS) program and are available from the World Bank repository at https://microdata.worldbank.org/index.php/catalog/3818 Code availability The code book, and analytic code for calculating the AFE and VHE metrics is publicly and freely available in a GitHub repository at https://github.com/Gare94/VHE_Metric. The source code for cleaning and preprocessing the IHS5 food consumption data is publicly available in a GitHub repository at https://github.com/Gare94/ihs5_cleaning_github Competing interests The authors declare no competing interests. Funding This work was supported, in part, by the Gates Foundation [INV-002855; the Micronutrient Action Policy Support project]. The conclusions and opinions expressed in this work are those of the authors alone and shall not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Please note works submitted as a preprint have not undergone a peer review process. Ethics statement This study used the publicly available, de-identified dataset from the 2019 – 2020 Malawi Fifth Integrated Household Survey (IHS5), accessible via the World Bank Microdata Library. Additional ethical approval for secondary data analysis was obtained from the Lilongwe University of Agriculture and Natural Resources Research Ethics Committee (LUANAR-REC), under protocol number LUANAR-REC-REVIEW-2025-001 References Food and Agriculture Organization of the United Nations (FAO). FAO/WHO Global Individual Food Consumption Data Tool (FAO/WHO GIFT): Developing Capacities at Country Level to Produce Dietary Data to Support Evidence-Based Policy Making. FAO (2022) Available at: https://www.fao.org/gift-individual-food-consumption/en/ [Accessed 7 September 2024] Deitchler M, Arimond M, Carriquiry A, Hotz C, Tooze JA (2020) Planning and Design Considerations for Quantitative 24-Hour Recall Dietary Surveys in Low- and Middle-Income Countries . Intake – Center for Dietary Assessment/FHI Solutions, Washington, DC Available at: https://www.intake.org/sites/default/files/2020-01/Intake-Considerations-Brief-Jan2020_0.pdf [Accessed 15 September 2024] Coates J, Rogers BL, Blau A, Lauer J, Roba A (2017) Filling a dietary data gap? Validation of the adult male equivalent method of estimating individual nutrient intakes from household-level data in Ethiopia and Bangladesh. Food Policy 72:27–42. https://doi.org/10.1016/j.foodpol.2017.08.010 Coates JC, Colaiezzi BA, Bell W, Charrondiere UR, Leclercq C (2017) Overcoming dietary assessment challenges in low-income countries: Technological solutions proposed by the international dietary data expansion (INDDEX) project. Nutrients 9:289. https://doi.org/10.3390/nu9030289 Pisa PT, Landais E, Margetts B et al (2018) Inventory on the dietary assessment tools available and needed in Africa: a prerequisite for setting up a common methodological research infrastructure for nutritional surveillance, research, and prevention of diet-related non-communicable diseases. Crit Rev Food Sci Nutr 58:37–61. https://doi.org/10.1080/10408398.2014.981630 Fiedler JL, Lividini K, Bermudez OI, Smitz MF (2012) Household Consumption and Expenditures Surveys (HCES): a primer for food and nutrition analysts in low- and middle-income countries. Food Nutr Bull 33(3 Suppl):S170–S184. https://doi.org/10.1177/15648265120333s205 Russell J, Lechner A, Hanich Q, Delisle A, Campbell B, Charlton K (2018) Assessing food security using household consumption expenditure surveys (HCES): a scoping literature review. Public Health Nutr 21:2200–2210. https://doi.org/10.1017/S136898001800068X Tang K, Adams KP, Ferguson EL et al (2022) Systematic review of metrics used to characterise dietary nutrient supply from household consumption and expenditure surveys. Public Health Nutr 25:1153–1165. https://doi.org/10.1017/S1368980022000118 Harris-Fry HA, Paudel P, Shrestha N et al (2018) Status and determinants of intra-household food allocation in rural Nepal. Eur J Clin Nutr 72:1524–1536. https://doi.org/10.1038/s41430-017-0063-0 Tang K, Adams KP, Ferguson EL et al (2022) Modeling food fortification contributions to micronutrient requirements in Malawi using Household Consumption and Expenditure Surveys. Ann N Y Acad Sci 1508:105–122. https://doi.org/10.1111/nyas.14697 Karageorgou D, Imamura F, Zhang J, Shi P, Mozaffarian D, Micha R (2018) Assessing dietary intakes from household budget surveys: a national analysis in Bangladesh. PLoS ONE 13:e0202831. https://doi.org/10.1371/journal.pone.0202831 Weisell R, Dop MC (2012) The adult male equivalent concept and its application to Household Consumption and Expenditures Surveys (HCES). Food Nutr Bull 33(3 Suppl):157–S162. https://doi.org/10.1177/15648265120333S203 National Statistical Office (NSO) [Malawi] & ICF International. Malawi Demographic and Health Survey 2015–16: Key Indicators Report. NSO and, ICF, Zomba, Malawi, Rockville, Maryland USA (2017) Available at: https://cms.nsomalawi.mw/api/download/61/Malawi-DHS-2015-16-KIR.pdf [Accessed 18 October 2024] National Statistical Office [Malawi] & The World Bank. Fifth Integrated Household Survey 2019–2020 (IHS5). NSO and World Bank (2020) Available at: https://microdata.worldbank.org/index.php/catalog/3818 [Accessed 10 April 2023] van Graan A, Chetty J, Jumat M, Masangwi S, Mwangwela A, Phiri FP, Ausman LM, Ghosh S (2019) Malawian Food Composition Table 2019. Government of Malawi and Tufts University Food and Agriculture Organization of the United Nations & Government of Kenya. Kenya Food Composition Tables. FAO, Rome (2018) Available at: https://openknowledge.fao.org/handle/20.500.14283/i8897en [Accessed 18 February 2024] Vincent A, Grande F, Compaoré E et al (2020) FAO/INFOODS Food Composition Table for Western Africa. FAO, Rome Available at: https://openknowledge.fao.org/handle/20.500.14283/ca7779b [Accessed 20 February 2024] U.S. Department of Agriculture, Agricultural Research Service. FoodData Central [Internet]. USDA, Beltsville Human Nutrition Research Center (2021) Available at: https://fdc.nal.usda.gov [Accessed 19 February 2024] National Statistical Office [Malawi] & ICF. Malawi Demographic and Health Survey 2015–16. NSO and, Zomba ICF, Malawi, Rockville (2017) Maryland, USA Available at: https://preview.dhsprogram.com/pubs/pdf/FR319/FR319.pdf [Accessed 6 November 2023] FAO/WHO/UNU. Human Energy Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation. FAO Food and Nutrition Technical Report Series 1, Rome (2004) Available at: https://www.fao.org/4/y5686e/y5686e00.htm [Accessed 18 August 2023] Price AJ, Crampin AC, Amberbir A et al (2018) Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol 6:208–222. https://doi.org/10.1016/S2213-8587(17)30432-130432-1 Pratt M, Sallis JF, Cain KL et al (2020) Physical activity and sedentary time in a rural adult population in Malawi compared with an age-matched US urban population. BMJ Open Sport Exerc Med 6:e000812. https://bmjopensem.bmj.com/content/6/1/e000812 U.S. Department of Agriculture & U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025. 9th Edition. USDA and HHS, Washington DC (2020) Available at: https://www.dietaryguidelines.gov/resources/2020-2025-dietary-guidelines-online-materials [Accessed 5 December 2023] WHO Programme of Nutrition. Complementary Feeding of Young Children in Developing Countries: A Review of Current Scientific Knowledge. World Health Organization, Geneva (1998) Available at: https://iris.who.int/handle/10665/65932 [Accessed 6 December 2023] Kominiarek MA, Rajan P (2016) Nutrition recommendations in pregnancy and lactation. Med Clin North Am 100:1199–1215. https://doi.org/10.1016/j.mcna.2016.06.004 Vossenaar M, Solomons NW, Muslimatun S et al (2021) Nutrient density as a dimension of dietary quality: findings of the nutrient density approach in a multi-center evaluation. Nutrients 13:4016. https://doi.org/10.3390/nu13114016 Allen LH, Carriquiry AL, Murphy SP (2020) Perspective: proposed harmonized nutrient reference values for populations. Adv Nutr 11:469–483. https://doi.org/10.1093/advances/nmz096 Government of Malawi. Food System Transformative Integrated Policy: Accelerating Malawi’s Food System Transformation, Initiative FS-TIP (2021) August Available at: https://www.rockefellerfoundation.org/wp-content/uploads/2022/02/Accelerating-Malawis-Food-System-Transformation.pdf [Accessed 24 March 2025] Gilbert R, Benson T, Ecker O (2016) Are Malawian Diets Changing? An Assessment of Nutrient Consumption and Dietary Patterns Using Household-Level Evidence from 2010/11 and Washington, DC Freedman Ellis G, Lumley T, Schneider B et al (2024) srvyr: 'dplyr'-Like Syntax for Summary Statistics of Survey Data. R package version 1.3.0. The Comprehensive R Archive Network Available at: https://CRAN.R-project.org/package=srvyr [Accessed 17 January 2025] Wickham H, Averick M, Bryan J et al (2019) Welcome to the Tidyverse. J Open Source Softw 4:1686. https://joss.theoj.org/papers/ 10.21105/joss.01686 FAO, IFAD, UNICEF, WFP & WHO (2021) The State of Food Security and Nutrition in the World 2021: Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All. FAO, Rome. https://doi.org/10.4060/cb4474en Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterialVHEAFEResearchSquare.docx Supplementary Table S1-S3 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7538597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510424493,"identity":"0030484e-27ac-48ce-922e-74842792e8e2","order_by":0,"name":"Gareth Osman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYLACxgYo4wOPDYjbeICgloNQLYwzZNLAJhCvhZnH5jCYgVeLvPvZh48/7mCQk592+NljnpzzdmvbDwNtqbGJxqXF8Ey6scHBMwzGBrfTzA3nnLmdvO1MIlDLsbTcBlxaGtLYJA62MSRukE4wk3jbczvZ7ABQC2PDYdxa+p+x/wBpmT87/ZsE779zyWbnH+LXIi+RxsYA0tJwO8dMkofngJ3ZDQK2GEg8Y5Y42yYB9EtOmeQMnuQEsxtAWxLw+EW+P43xQ2WbjZz87PRtEh947OzNzqc/fPChxga3LQfAlARcIBGsMgGHcrAt6GbZ41E8CkbBKBgFIxQAAL1hZR5R+ar7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8379-9940","institution":"Department of Human Nutrition and Health, Bunda College, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi","correspondingAuthor":true,"prefix":"","firstName":"Gareth","middleName":"","lastName":"Osman","suffix":""},{"id":510424494,"identity":"c53e6d24-4bb6-4596-91f4-959ed6a28816","order_by":1,"name":"Elaine L. 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Joy","email":"","orcid":"","institution":"Faculty of Epidemiology and Population Health, London School of Hygiene \u0026 Tropical Medicine, Keppel Street, London WC1E 7HT, UK","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"J.M.","lastName":"Joy","suffix":""},{"id":510424500,"identity":"65335724-4349-4d84-a63c-9e03002512ba","order_by":7,"name":"Louise E. Ander","email":"","orcid":"","institution":"School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK","correspondingAuthor":false,"prefix":"","firstName":"Louise","middleName":"E.","lastName":"Ander","suffix":""},{"id":510424501,"identity":"6d653520-b6cd-4a1d-9cc7-ac5fe4d95c45","order_by":8,"name":"Alexander A. Kalimbira","email":"","orcid":"","institution":"Department of Human Nutrition and Health, Bunda College, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"A.","lastName":"Kalimbira","suffix":""}],"badges":[],"createdAt":"2025-09-04 18:37:55","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7538597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7538597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90817127,"identity":"8674097e-96fa-4b26-b09b-fc6066e25b80","added_by":"auto","created_at":"2025-09-08 13:24:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":230402,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart for calculating the prevalence of apparent inadequacy using Vulnerable Household Equivalent (VHE) and Adult Female Equivalent (AFE) approaches\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7538597/v1/7b255215edc7436da2af9b2d.png"},{"id":90817730,"identity":"80441092-61d7-4089-915c-5a7a65ffb26a","added_by":"auto","created_at":"2025-09-08 13:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7538597/v1/094e089a-f93c-41a7-b70b-33eeb2393fed.pdf"},{"id":90817109,"identity":"85e8a8fc-1c5d-4256-8c70-2bed2aaaa012","added_by":"auto","created_at":"2025-09-08 13:24:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42884,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1-S3\u003c/p\u003e","description":"","filename":"SupplementaryMaterialVHEAFEResearchSquare.docx","url":"https://assets-eu.researchsquare.com/files/rs-7538597/v1/e73b05df40499264adaf64a5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA demographic-sensitive model for estimating micronutrient inadequacy risk among nutritionally vulnerable households using household food consumption data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDietary data play a critical role in informing policies that address nutrient deficiencies due to inadequate dietary intake [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, collecting nationally representative individual-level dietary data such as 24-hour recalls (24HR) is costly and often misses certain demographic groups, such as adult men [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Because individual-level dietary data are rarely collected in low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], there is growing interest in using Household Consumption and Expenditure surveys (HCESs) for nutrition research purposes. These surveys, typically conducted at the national level, can help identify sub-populations at risk of inadequate dietary intake in LMICs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The advantages of household-level food consumption data collected via HCESs are that they are collected regularly in many LMICs, are nationally representative, and capture food consumption data over one to two weeks \u0026ndash; often across seasons \u0026ndash; allowing for both cross-sectional and seasonal trend analyses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, a key limitation is that HCES food consumption or acquisition data are collected at the household level and typically carry large recall error, and methods for individualizing consumption are based on several potentially incorrect assumptions, including equitable intra-household food distribution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThree main metrics for individualizing HCES food consumption data have been developed and used, including the Per Capita (PC), the Adult Male Equivalent (AME), and the Adult Female Equivalent (AFE) metrics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The PC approach assumes that household food is distributed equally among household members. Such estimates, which are calculated by dividing the household consumption by the number of household members, overlook variations in age-sex energy requirements. Both AME and AFE approaches address this limitation by assuming that household food distribution is proportional to an individual\u0026rsquo;s energy requirements. The two metrics only differ in terms of the household member selected to represent the household unit (i.e., an adult male for AME or an adult female of reproductive age for AFE). Both metrics can be used to calculate intakes and dietary adequacy, but their food consumption would be calculated using a proportion of the base AME (i.e. adult male, usually 18\u0026ndash;29 years) or AFE (i.e. non-pregnant non-lactating (NPNL) woman of reproductive age (WRA), usually 18\u0026ndash;29 years).\u003c/p\u003e\u003cp\u003eFood consumption data generated through HCESs can be used to estimate the proportion of households in which members are at risk of deficiency due to inadequate dietary intake, rendering the data particularly suitable for programs and policies targeting the entire household rather than a single demographic group. However, normalizing apparent intakes based on a stringent definition of household members such as NPNL WRA (i.e., the AFE metric) may underestimate the proportion of households with members at risk of inadequate nutrient intakes if the selected demographic group is not the most nutritionally vulnerable household member for the nutrient of interest.\u003c/p\u003e\u003cp\u003eAdvancing from these established metrics, a new approach was developed to provide a more demographic-sensitive model for assessing household nutrition using quantitative household food consumption data. We developed a novel analytical model for estimating individualized apparent dietary intakes from household-level data called the \u0026lsquo;vulnerable household equivalent\u0026rsquo; (VHE) metric, which is sensitive to demographic structures of households in populations. In this study, we aimed to compare the prevalence of inadequate intakes of vitamin A and zinc (Zn) estimated using the VHE and AFE approaches and examine the extent to which the estimates differ when analyzed nationally and by residence (rural and urban) and socio-economic positions in Malawi. Malawi was selected as a case study due to the availability of HCES data and its classification as a low-income setting where HCESs are frequently conducted. By 2025, six such surveys had been conducted nearly every 5 years.\u003c/p\u003e\u003cp\u003eWe compared vitamin A and Zn because both are nutrients of public health concern in Malawi. The country has long grappled with vitamin A deficiency, which declined significantly from 59% in 2001 to 4% in 2015/16 among children under five [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, Zn deficiency was assessed at the national level only once, during the 2015/16 survey, which revealed a high prevalence ranging from 60\u0026ndash;69%\u0026mdash;with the highest rates among adolescent girls aged 10\u0026ndash;14 years and the lowest among preschool and school-aged children [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given the diverse dietary sources of these two nutrients in the Malawian context, their inadequate intake is likely to yield distinct insights when comparing VHE and AFE approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFood consumption data\u003c/h2\u003e\u003cp\u003eFood consumption data were derived from 2019/20 Malawi\u0026rsquo;s Fifth Integrated Household Survey (IHS5) \u0026ndash; a comprehensive nationally representative survey conducted as part of the World Bank\u0026rsquo;s Living Standards Measurement Study (LSMS) program. We obtained the data from the World Bank\u0026rsquo;s open-data repository as entirely secondary, de-identified data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Food consumption data for IHS5 were collected as part of the questionnaire which recorded household food consumption at the food item level. The study utilized the module \u0026lsquo;household_module_g1 (\u003cem\u003ehh_mod_g1\u003c/em\u003e)\u0026rsquo; which recorded household apparent food intake based on a predefined list of 135 items, recalled over a 7-day period. Specifically, the module asked \u003cem\u003e\u0026ldquo;Over the past 7-days, did you or others in your household consume any (food item)? How much in total did your household consume in the past 7-days?\u0026rdquo;\u003c/em\u003e. The person most knowledgeable about food consumed in that household answered these questions on behalf of the entire household.\u003c/p\u003e\u003cp\u003eThe National Statistics Office (NSO) in collaboration with the World Bank implemented the survey nationwide between April 2019 and April 2020. A stratified two-stage sampling design was used based on the cartography and data from the 2018 Malawi Census of Population. A total of 12,288 households were selected from 768 Enumeration Areas (EAs). However, due to the COVID-19 pandemic, 51 EAs (854 households) could not be visited at the end of the 12-month fieldwork period. The aggregation resulted in a final sample size of 11,434 households, which was statistically representative at the national, district, and urban/rural residence levels.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePreprocessing of food consumption data\u003c/h3\u003e\n\u003cp\u003eThe food consumption data were preprocessed and transformed into usable metrics by converting all quantities recorded in standard (e.g. milliliter) and non-standard (e.g. pail, basin, heap) units to the metric unit grams using country-specific conversion factors provided with the IHS5 dataset (i.e. caloric conversion factor file). Where relevant, the foods were adjusted for edible portions by subtracting the non-edible portions of foods from the total quantity apparently consumed. Then for each food item, the total quantity of food was divided by the number of days of the recall period (i.e. 7 days) to estimate daily apparent household consumption. To identify implausible values (outliers), we normalized the quantities using the logarithmic transformation, and quantities greater than five standard deviations above the mean of the logarithmically transformed consumption quantities were deemed outliers and were replaced with the median consumption quantity among consumers for each food item, as described in Tang et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe food consumption data were matched to relevant food composition data to estimate each food item\u0026rsquo;s vitamin A, Zn, and energy content. The main food composition table (FCT) used was the 2019 Malawian FCT [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], contributing about 71% of the values. Other FCTs that contributed nutrient values were the Kenyan FCT [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] \u0026ndash; 16%, the Western Africa FCT [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] \u0026ndash; 12%, and the USDA Food Data Central tool [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] \u0026ndash; 1%. The Government of Malawi issued a mandatory fortification policy for sugar and oil with vitamin A, and wheat flour with nine micronutrients (vitamins A, B1, B2, B3, B6, B9 and B12, and the trace elements iron (Fe) and Zn) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, we assumed status quo fortification levels of vitamin A and Zn in these food vehicles, i.e., all oil, sugar, and wheat flour consumed by households was fortified, with adjustments for compliance and degradation to estimate the fortificant concentration in food vehicles at the household level [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eHousehold member energy requirements\u003c/h3\u003e\n\u003cp\u003eDaily energy requirements were calculated according to the Human Energy Requirements recommendations provided by the Joint Food and Agriculture Organization of the United Nations (FAO)/World Health Organization (WHO)/United Nations University (UNU) Expert Consultation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as detailed in \u003cb\u003eSupplementary Table S1\u003c/b\u003e online. For individuals under 18 years, age- and sex-specific body weights and Physical Activity Levels (PAL) were obtained from FAO/WHO/UNU guidelines, assuming a moderately active lifestyle [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For women aged 18\u0026ndash;29.9 years, an average body weight of 55 kg was used, based on data from the 2015\u0026ndash;16 Malawi Demographic and Health Survey (MDHS) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For all other age categories of women and for men, average body weights were derived from a cross-sectional, population-based Non-Communicable Disease study conducted in both rural and urban Malawi, which enrolled approximately 28,891 adults between 2013 and 2016 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on this study, the following average body weights were applied: 65 kg for women aged 30\u0026ndash;59.9 years, and 60 kg for women aged 60 years and above. For men, average body weights of 60 kg, 65 kg, and 60 kg were used for the 18\u0026ndash;29.9, 30\u0026ndash;59.9, and 60\u0026thinsp;+\u0026thinsp;age groups, respectively. These body weights were harmonized with standard reference weights used in FAO energy requirement tables to ensure consistency in applying the PAL-based energy estimates. For all adults, we assumed moderate active PAL corresponding to 1.75xBMR (basal metabolic rate). This PAL value was chosen to reflect the average activity level of the Malawian population [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData on women\u0026rsquo;s lactation status and whether a child was breastfeeding were not collected in IHS5. Therefore, rather than assuming that all children under 2 years of age were breastfeeding, as recommended by the WHO \u0026ndash; which advises exclusive breastfeeding for the first 6 months and continued breastfeeding alongside complementary foods up to 2 years of age or beyond, we applied age-specific breastfeeding prevalence rates based on the 2015\u0026ndash;16 MDHS [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While the MDHS does not provide a precise estimate for children aged 6\u0026ndash;11 months, it indicates that nearly all children (98%) are breastfed at some point, making it reasonable to infer that breastfeeding prevalence among children aged 0\u0026ndash;11 months was very high, particularly for any breastfeeding. For older age groups, we used reported prevalence rates: 90.5% for children aged 12\u0026ndash;17 months and 76.6% for those aged 18\u0026ndash;23 months [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These more realistic estimates were used to randomly assign breastfeeding status and adjust energy requirements accordingly. Importantly, the analysis maintained actual mother\u0026ndash;child relationships by linking each child to their biological mother within the household. This approach allowed for the identification of multiple lactating women in households with more than one young child, ensuring that energy requirements were assigned accurately and realistically to the correct individuals.\u003c/p\u003e\u003cp\u003eFor women, this assumption meant an additional 330 kcal per day was included in her energy requirement estimation if her child was under 6 months of age, and an additional 400 kcal per day if her child was between 6\u0026ndash;24 months of age [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These additions were applied only when the child was assumed to be breastfeeding, as determined by the random assignment method described above. For breastfed children, we subtracted the estimated energy contributions from breastmilk from their daily energy requirements to calculate their AFE and VHE ratios, which represented their total energy requirements from complementary foods [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Pregnancy status was reported for few women (29 records \u0026ndash; 0.3% of the sample) in IHS5. These women were assigned an additional 300 kcal per day to their base energy requirements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAdult female equivalent (AFE) approach\u003c/h3\u003e\n\u003cp\u003eThe AFE approach is standardized to the nonpregnant, non-lactating 18 to 29-year-old female who serves as a reference household member or one AFE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The number of AFEs per household was calculated as the sum of AFE ratios, where the AFE ratio for each household member was their estimated energy requirement, which for children under 2-years of age was their energy requirement from complementary foods, divided by the energy requirement of a nonpregnant, non-lactating 18 to 29-year-old female. The number of AFEs was calculated for all households, including those that did not include a NPNL household member, to standardize all intakes to an AFE as done previously [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eVulnerable household equivalent (VHE) approach\u003c/h3\u003e\n\u003cp\u003eThe VHE approach, like the AFE, assumes that a household\u0026rsquo;s reported food consumption is distributed in proportion to each household member's energy requirements. However, for the VHE, each household member\u0026rsquo;s estimated energy requirement was divided by the estimated energy requirement of the most nutritionally vulnerable household member. The most nutritionally vulnerable household member was defined, for each nutrient of interest, as the household member with the highest critical nutrient density (CND) for that nutrient (i.e., either vitamin A or Zn in this study). The CND values for each specific age, sex, and maternal status (pregnant/lactating women) groups were calculated as the ratio of their harmonized average requirements (H-AR) for vitamin A or Zn to their daily energy requirement expressed per 1,000 kcal [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] represented by the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:CND\\:values\\:=\\left(\\frac{\\:{H-AR}_{age,\\:\\:\\:sex,\\:\\:\\:specific\\:condition,\\:and\\:micronutrient}\\:}{\\:{Daily\\:energy\\:requirement\\left(kcal\\right)}_{\\:age,\\:\\:\\:sex\\:and\\:specific\\:condition}}\\right)*\\text{1,000}\\:kcal$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe H-AR is the average daily micronutrient intake estimated to meet the requirements of half of healthy individuals in a particular life stage and gender group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Zn H-AR assumed a low bioavailability because Malawian diets are characterized by high intakes of unrefined grains and legumes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] with high phytate content, which reduces Zn bioavailability.\u003c/p\u003e\u003cp\u003eUnlike the AFE, the VHE differed between households depending on the family member structure in each household and the nutrient modelled (i.e., the most vulnerable family member was not the same in all households). The gender-age/life-stage requirements and CNDs for vitamin A and Zn for the VHE calculation are presented in \u003cb\u003eSupplementary Table S2\u003c/b\u003e online. For children under 2 years of age, we calculated the CND for vitamin A and Zn from complementary foods by subtracting the H-AR from the assumed amount of the nutrient contributed by breastmilk in the diets of these children. We assumed the nutrient contribution from breastmilk was 61 \u0026micro;g RAE of vitamin A and 0.17 mg of Zn based on Kenya Food Composition Table [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and an estimated breastmilk intake of 474 g per day [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe person with the highest CND for vitamin A or Zn was considered the most nutritionally vulnerable household member, and their nutrient requirements were used as a benchmark for calculating the VHE. For example, in the case of vitamin A, lactating women exhibited the highest CND. Consequently, if a household included a lactating woman, the energy requirements of a lactating woman and nutrient requirements would be utilized to calculate the VHE values and assess nutrient apparent intake and inadequacy, respectively. Subsequently, this person's H-AR would be employed to estimate micronutrient inadequacy. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the steps taken in the R-script to calculate the prevalence of apparent inadequacy using VHE and AFE approaches.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eEstimated apparent vitamin A and Zn intakes were compared against H-AR values to categorize households with low intakes per AFE and VHE, represented by the following equations:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\left(\\frac{Daily\\:household\\:apparent\\:micronutrient\\:intake\\:\\left(units\\right)\\:\\:}{{\\sum\\:}_{Household\\:AFEs}}\\right)\u0026lt;{H-AR}_{adult\\:females}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\left(\\frac{Daily\\:household\\:apparent\\:micronutrient\\:intake\\:\\left(units\\right)\\:\\:}{{\\sum\\:}_{Household\\:VHEs}}\\right)\u0026lt;{H-AR}_{vulnerable\\:household\\:members}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo calculate the prevalence of inadequate vitamin A or Zn intakes, the total number of households that were categorized with inadequate intakes per AFE and VHE for these nutrients were divided by the total number of households in each specific subpopulation group. The estimated prevalence of apparent inadequacy of vitamin A and Zn using were compared at the national level, stratified by residence (urban and rural), and by socioeconomic quintiles. These quintiles were based on the total inflation-adjusted annual household expenditures per capita and were supplied with the IHS5 data. All data cleaning, transformation, and analysis were performed in R (version 4.1.3, R Foundation for Statistical Computing), and the results were weighted according to IHS5 survey weights using functions available on the \u003cem\u003esrvyr\u003c/em\u003e package [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDemographic groups selected as the most nutritionally vulnerable household members for vitamin A and Zn using the VHE approach\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the proportion of demographic groups identified as the most vulnerable members for assessing the risk of vitamin A and Zn inadequacy using the VHE approach. For vitamin A, the most common demographic group selected to calculate the VHE was NPNL WRA, 18\u0026ndash;29 years, accounting for 25.0% of households. This group also serves as the default reference in the AFE approach. The next most common groups were lactating women (20.5%) and NPNL WRA aged 30\u0026ndash;49 years (15.7%). For Zn, the most common demographic groups were adult men aged 30\u0026ndash;59 years (25.7%) and 18\u0026ndash;29 years (21.5%). Additionally, older men and women were selected in a notable proportion of households for both nutrients, highlighting their nutritional vulnerability.\u003c/p\u003e\u003cp\u003eAlthough there were differences in the proportions of demographic groups selected to characterize households at risk of inadequacy, the H-AR used for some groups was the same across both the VHE and AFE approaches. For instance, the same H-AR for vitamin A used in the AFE approach was also applied when NPNL WRA aged 30\u0026ndash;49 years or older women (50\u0026thinsp;+\u0026thinsp;years) were identified as the most vulnerable members in the VHE approach. Similarly, for Zn, the same H-AR used in the AFE approach was applied when these same groups were selected under the VHE approach (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This overlap reflects the shared nutrient requirements among these groups despite differences in household composition and their energy requirements.\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\u003eProportion of demographic groups identified as the most nutritionally vulnerable member to assess risk of vitamin A and Zn inadequacy using the VHE approach\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVitamin A (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZinc (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH-AR for vitamin A (\u0026micro;g RAE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH-AR for zinc (mg)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPNL WRA, 18\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPNL WRA, 30\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOlder women 50\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactating women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOlder men 60\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdolescent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGirls/boys 11\u0026ndash;14 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u0026ndash;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: Values represent the percentage of households in which each demographic group was identified as the most nutritionally vulnerable for vitamin A or zinc intake, based on the VHE approach. Harmonized Average Requirement (H-AR) values are sourced from Allen et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and represent daily intake recommendations for each group. \u003cem\u003eAbbreviations\u003c/em\u003e: NPNL WRA \u0026ndash; non-pregnant and non-lactating women of reproductive age; \u0026micro;g RAE \u0026ndash; micrograms of retinol activity equivalents.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in \u003cb\u003eSupplementary Table S3\u003c/b\u003e online, the demographic group identified as the most nutritionally vulnerable using the VHE approach varied by residence and socioeconomic position (SEP). In rural areas, lactating women were generally the most frequently selected group for calculating the VHE for vitamin A, while adult men aged 30\u0026ndash;59 years were most selected for Zn. In urban areas, NPNL women aged 18\u0026ndash;29 years were most frequently selected for vitamin A, whereas men aged 30\u0026ndash;59 years consistently dominated Zn vulnerability. Across both nutrients, older adults and young children were also identified as the most vulnerable in a notable proportion of households.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of the estimated prevalence of households at risk of apparent inadequacy for vitamin A and Zn between VHE and AFE\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a comparison of the prevalence of apparent inadequacy for vitamin A and Zn using two estimation approaches \u0026ndash; VHE and AFE, stratified by rural and urban residence and across SEPs. Nationally, the prevalence of apparent inadequacy for both vitamin A and Zn was higher when estimated using the VHE approach compared to the AFE approach \u0026ndash; 51.7% vs. 46.2% for vitamin A and 72.6% vs. 68.4% for Zn. When stratified by residence (rural/urban) and SEP, the VHE approach consistently yielded higher inadequacy estimates across all subgroups than AFE approach. In rural areas, the percentage-point differences (PPDs) between VHE and AFE ranged from 3.7 to 6.9 for vitamin A and 1.4 to 8.1 for Zn. In urban areas, PPDs ranged from 0.7 to 12.9 for vitamin A and 0.4 to 4.8 for Zn.\u003c/p\u003e\u003cp\u003eThe largest observed shift in vitamin A inadequacy occurred among urban households in the lower-middle SEP, with a 12.9 percentage point increase under the VHE approach. For Zn, the greatest shift was observed among rural households in the higher-middle SEP, with an 8.1 percentage point increase.\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\u003eComparison of prevalence of apparent inadequacy for vitamin A and Zn between VHE and AFE in rural and urban residences and across socioeconomic positions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHouseholds (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eVitamin A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVHE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage point ∆ (VHE \u0026ndash; AFE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVHE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePercentage point ∆ (VHE \u0026ndash; AFE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNational (total)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,432\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\u003e46.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eResidence and socioeconomic position (SEP) by quintile of total annual household expenditure per capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLowest SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower middle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher middle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLowest SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower middle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher middle SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest SEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e: Values represent the percentage of households with apparent inadequacy of vitamin A and zinc intake, estimated using the Vulnerable Household Member (VHE) and Adult Female Equivalent (AFE) approaches. The \u0026ldquo;Percentage point ∆ (VHE \u0026ndash; AFE)\u0026rdquo; column shows the difference in prevalence between the two methods.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we introduced the vulnerable household equivalent (VHE) metric, a novel approach for estimating the risk of inadequate micronutrient intake from household-level food consumption data. Unlike the AFE metric, which assumes a fixed reference individual (usually a NPNL woman aged 18\u0026ndash;29), the VHE approach accounts for the demographic structure and nutritional needs of the most vulnerable household member. Using HCES data from Malawi, we applied the VHE and AFE metrics to estimate households at risk of inadequate apparent vitamin A and Zn intake. Our analysis has shown that estimates of apparent inadequate intake of vitamin A and Zn were consistently higher using the VHE than AFE approach at national, rural/urban residences, and across all SEPs in both rural and urban areas. Notably, the PPD in the prevalence of inadequacy between the two approaches was generally below 10%, except for vitamin A in the urban lower-middle SEP, where it reached 12.9%. This suggests that while both metrics produce broadly similar results, the VHE offers a more sensitive lens for identifying nutritional vulnerability, particularly in subpopulations with unique demographic profiles.\u003c/p\u003e\u003cp\u003eFor vitamin A, the CND of NPNL women 18\u0026ndash;29 years was higher than or equal to all demographic groups except for lactating women, elderly men and women, and 11-year-old girls. Similarly for Zn, their CND was higher for the elderly, lactating women and 1-year old female children (see \u003cb\u003eSupplementary Table S2\u003c/b\u003e online). While the VHE approach was applicable to any population group, its impact on estimating nutrient inadequacies compared with the AFE will vary depending on a country's demographic profile, the difference in CNDs across demographic groups, the nutrient of interest, dietary patterns, and the usual intake of foods rich (or not rich) in the specific nutrients of interest in the population.\u003c/p\u003e\u003cp\u003eIn terms of the differences in CNDs across demographic groups, lactating women had the highest CNDs for vitamin A, while for Zn, elderly men had the highest CNDs. These differences (AFE vs VHE) will likely be highest for vitamin A when there is a high percentage of lactating women in the population, and highest for Zn when there is a high percentage of elderly men in the population. For example, examining populations with a higher proportion of elderly men would shed more light on the value of the VHE metric for assessing Zn inadequacy, since older men require a greater amount of Zn per unit energy requirement than other demographic groups. Similarly, populations with higher fertility rates present an opportunity to investigate the value of the VHE metric in assessing vitamin A inadequacy as lactating women require a greater amount of vitamin A per unit energy requirement than other demographic groups.\u003c/p\u003e\u003cp\u003eThe study findings reveal a shortcoming with the AFE approach, when estimating the prevalence of households with at least one member at risk of inadequate intakes. In circumstances where there was no NPNL woman in the household, the NPNL woman (aged 18\u0026ndash;29 years) was nevertheless used as a reference member. This was demonstrated by the findings of this study, which reveal that a small number of households (55 households \u0026ndash; 0.5% for vitamin A and 34 households \u0026ndash; 0.3% for Zn) were re-classified from inadequate to adequate when compared to the VHE, which can only happen in households without a resident NPNL woman. Therefore, using a fixed single, hypothetical, household member to establish whether apparent micronutrient intake is adequate will underestimate the percentage of households where some members are at risk of inadequate nutrient intakes if the CND, for most nutritionally vulnerable demographic household member in the household is higher than that of a NPNL woman. In contrast, the VHE approach addresses this limitation by adopting a more realistic and flexible approach that considers the CND of each household member to select the most nutritionally vulnerable member. Moreover, in settings where household age/sex structures differ across socio-economic groups or regions the VHE approach is more appropriate than the AFE approach to individualize comparisons of household nutritional vulnerability.\u003c/p\u003e\u003cp\u003eThe VHE approach aligns closely with the objectives of Sustainable Development Goals (SDGs), particularly Goal 2, which aims at ensuring access to safe, nutritious, and sufficient food for all people, with a particular emphasis on the poor and those in vulnerable situations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By considering a range of vulnerabilities within households, such as lactating women, young children, and the elderly, the VHE approach has the potential to more realistically monitor progress towards equitable access to nutritious food for meeting the nutrient requirements of all individuals in the household \u0026ndash; leaving no one behind. One of the key strengths of the VHE metric is its ability to capture the diverse nutritional needs of various demographic groups, and unlike the AFE approach, it will reflect the demographic composition of households. This more informed approach recognizes that different members of a household may have different nutrient requirements in relation to their energy requirements, which a one-size-fits-all approach, such as AFE, fails to capture.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e\u003cp\u003eThere are several strengths inherent in the study's methodology and approach. Firstly, the study capitalized on a nationally representative sample, ensuring that the findings are generalizable to the broader population. Additionally, the analysis was conducted at multiple levels, including national, residence (rural/urban), and SEP, providing a comprehensive understanding of how the VHE compared with the AFE would perform across different demographic strata in Malawi. Furthermore, the study's consistency in applying both metrics, VHE and AFE, in the same populations and using the same parameters such as body weight, PALs, and assumptions regarding additional energy requirements for lactation and pregnancy, enhances the comparability and reliability of the estimates generated. In addition, we used realistic assumptions to assign breastfeeding status and adjust energy needs accordingly. By linking each child to their biological mother, the approaches accurately identified multiple lactating women within households, ensuring energy requirements were assigned to the correct individuals.\u003c/p\u003e\u003cp\u003eHowever, we acknowledge some limitations. One of the limitations is that, despite its demographic sensitivity, the VHE approach like the AFE still relies on the assumption that food is distributed within households in proportion to each member\u0026rsquo;s age- and sex-specific energy requirements. This assumption may not reflect actual intra-household food allocation, which can be influenced by cultural norms, individual preferences, or household dynamics. Another limitation is the apparent underrepresentation of pregnant women in the dataset. Only 29 pregnant women were identified, which likely reflects the way pregnancy status was captured through a health-related module rather than direct observation\u0026mdash;leading to underreporting, particularly in early pregnancy. As a result, some pregnant women may have been misclassified as NPNL. Finally, the performance of the VHE metric was evaluated using only two micronutrients \u0026ndash; vitamin A and Zn. While this provides a strong starting point, further research is needed to assess the applicability of the VHE approach to other nutrients with different dietary sources and requirement profiles.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough this study, we have developed a novel metric for evaluating the percentage of households where at least one member is at risk of inadequate intakes of selected nutrients, using household-level food consumption data, which is called the \u0026lsquo;vulnerable household equivalent (VHE). The development of the VHE approach was driven by the recognition that even though the AFE approach, used widely in this field of research to assess the percentage of households at nutritional risk, may under-represent the risk of inadequacy for some demographic groups because it overlooks the diverse nutritional needs of different demographic groups. This study demonstrates that the VHE metric addresses some limitations in the existing AFE approach and may contribute to more equitable design of nutrition interventions when these are informed by household food consumption data. While the VHE is a more flexible approach than the AFE, it is also more complex to calculate. This complexity is reduced by using scripted approaches, as developed in this study, that can be adapted for other contexts. While the findings from Malawi are promising, further research is needed to explore the applicability of the VHE metric in populations with diverse demographic structures and for other nutrients beyond those analyzed in this study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.O: Conceptualization; Writing \u0026ndash; original draft; Formal analysis; Data curation; Methodology; Writing \u0026ndash; review \u0026amp; editing. E.L.F.: Conceptualization; Writing \u0026ndash; original draft; Methodology; Formal analysis; Writing \u0026ndash; review \u0026amp; editing. L.S.d.l.R.: Formal analysis, Methodology, Data curation, Writing \u0026ndash; review \u0026amp; editing. T.C.: Formal analysis, Methodology, Data curation, Writing \u0026ndash; review \u0026amp; editing. K.P.A.: Conceptualization; Methodology; Validation; Writing \u0026ndash; review \u0026amp; editing. E.J.M.J: Conceptualization; Methodology; Validation; Writing \u0026ndash; review \u0026amp; editing. TN-M.: Conceptualization; Supervision; Methodology; Writing \u0026ndash; review \u0026amp; editing. L.E.A.: Conceptualization; Supervision; Methodology; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing. A.A.K.: Conceptualization; Supervision; Methodology; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 2019/20 Malawi\u0026rsquo;s Fifth Integrated Household Survey (IHS5) were conducted as part of the World Bank\u0026rsquo;s Living Standards Measurement Study (LSMS) program and are available from the World Bank repository at https://microdata.worldbank.org/index.php/catalog/3818\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code book, and analytic code for calculating the AFE and VHE metrics is publicly and freely available in a GitHub repository at https://github.com/Gare94/VHE_Metric.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe source code for cleaning and preprocessing the IHS5 food consumption data is publicly available in a GitHub repository at https://github.com/Gare94/ihs5_cleaning_github\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported, in part, by the Gates Foundation [INV-002855; the Micronutrient Action Policy Support project]. The conclusions and opinions expressed in this work are those of the authors alone and shall not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Please note works submitted as a preprint have not undergone a peer review process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the publicly available, de-identified dataset from the 2019 \u0026ndash; 2020 Malawi Fifth Integrated Household Survey (IHS5), accessible via the World Bank Microdata Library. Additional ethical approval for secondary data analysis was obtained from the Lilongwe University of Agriculture and Natural Resources Research Ethics Committee (LUANAR-REC), under protocol number LUANAR-REC-REVIEW-2025-001\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFood and Agriculture Organization of the United Nations (FAO). FAO/WHO Global Individual Food Consumption Data Tool (FAO/WHO GIFT): Developing Capacities at Country Level to Produce Dietary Data to Support Evidence-Based Policy Making. 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FAO, Rome. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4060/cb4474en\u003c/span\u003e\u003cspan address=\"10.4060/cb4474en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"88eb762e-4e16-4d27-9c58-bcfc46705fc9","identifier":"10.13039/100000865","name":"Bill and Melinda Gates Foundation","awardNumber":"INV-002855","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lilongwe University of Agriculture and Natural Resources","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adult Female Equivalent, Household Consumption and Expenditure Survey, Malawi, Micronutrients, Vulnerable Household Equivalent","lastPublishedDoi":"10.21203/rs.3.rs-7538597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7538597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHousehold Consumption and Expenditure Surveys often estimate nutrient adequacy using a single reference individual \u0026ndash; typically a non-pregnant, non-lactating (NPNL) woman of reproductive age, known as the Adult Female Equivalent (AFE). However, due to the diversity in nutrient requirements across demographic groups, the AFE approach may underestimate the proportion of households where at least one member has increased risk of inadequate intake. We developed a novel modeling approach \u0026ndash; the Vulnerable Household Equivalent (VHE), which identifies the household member with the highest nutrient density requirement as the reference individual. Using data from Malawi\u0026rsquo;s 2019/20 Fifth Integrated Household Survey and local food composition tables, we estimated micronutrient inadequacy for vitamin A and zinc using AFE and VHE metrics. Prevalence of apparent micronutrient inadequacy was consistently higher using the VHE approach compared to the AFE across national, rural/urban, and socioeconomic strata. The most common reference groups for VHE were NPNL women aged 18\u0026ndash;29 years (25.0%) and lactating women (20.5%) for vitamin A, and adult men aged 30\u0026ndash;59 years (25.7%) and 18\u0026ndash;29 years (21.5%) for zinc. The VHE metric offers a more inclusive and equitable approach for estimating household-level micronutrient inadequacy, though its complexity may require computational support for broader applications.\u003c/p\u003e","manuscriptTitle":"A demographic-sensitive model for estimating micronutrient inadequacy risk among nutritionally vulnerable households using household food consumption data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 13:24:00","doi":"10.21203/rs.3.rs-7538597/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10258b2e-a057-4164-9f48-1730bd2e75d9","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54218185,"name":"Nutrition \u0026 Dietetics"},{"id":54218186,"name":"Epidemiology"},{"id":54218187,"name":"Health Policy"}],"tags":[],"updatedAt":"2025-09-08T13:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 13:24:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7538597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7538597","identity":"rs-7538597","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}