A Multidimensional Index to Quantify Food Insecurity in Brazil (MUFII): An Approach Based on Sustainable Development Indicators

Abstract

Food insecurity is a complex and multidimensional phenomenon embedded in the 2030 Agenda for Sustainable Development. Addressing it requires robust tools and indicators that reflect the diverse and interconnected drivers of hunger, particularly in countries of the Global South. This study proposes the Multidimensional Food Insecurity Index (MUFII) and illustrates its construction and application through a case study in Brazil. MUFII’s development included four main steps: (1) collecting indicators from official open data sources, (2) selecting indicators, (3) normalizing these indicators, (4) calculating the index using the average of the standardized indicators, and (5) conducting validation and sensitivity analyses. The index ranges from 0 to 1, with higher values indicating worse levels. MUFII was calculated for all 27 Federative Units of Brazil and for the years 2018 and 2022. The index score ranged from 0.09 to 0.67 in 2018 and from 0.10 to 0.72 in 2022. Higher index scores were observed for Federative Units located in the North and Northeast of the country in both years, with the situation worsening in 2022, even among those with low scores in 2018. Validation and sensitivity analyses confirmed the robustness of the index. By expanding the diagnosis of hunger beyond single-dimension metrics, MUFII provides a systemic framework for monitoring food insecurity and identifying policy priorities. Its methodological structure is scalable and replicable, offering a practical tool for tracking progress towards the SDGs in diverse national contexts.

1. Introduction

Food insecurity is widely recognized as a systemic, complex, and multidimensional phenomenon, shaped by factors ranging from income and education to infrastructure, governance, and environmental vulnerability. According to the Food and Agriculture Organization (FAO), food security is achieved “when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life”, and food insecurity occurs when this human right is suppressed [1]. This definition, though centered on access and availability, has increasingly been interpreted in light of broader systemic concerns, such as sustainability, equity, and resilience [2,3,4].

Globally, food systems researchers and policy makers have called for improved tools to assess food insecurity not only as an individual or household experience but as a complex and structural condition that intersects with poverty, inequality, environmental degradation, and institutional capacity [5,6,7]. Also in the international scenario, despite the availability of more than 450 different indicators, there is a scarcity of measurement tools allowing a quantitative, systems-based perspective analysis of food security [8]. Undoubtedly, household experience-based measurement tools, like the FAO’s Food Insecurity Experience Scale (FIES), have proven valuable for understanding food insecurity at the household level and guiding public policies. However, these tools primarily offer a subjective, indirect assessment of food access and often rely on resource-intensive, primary data collection [9]. This is particularly evident and challenging in developing countries, especially in the Global South, which not only suffer from higher levels of food insecurity but also face various barriers to research and data production [10,11]. Furthermore, among the more than a thousand indicators and frameworks identified in the literature for assessing the sustainability of food systems, most focus primarily on impacts rather than on the factors and processes that shape these systems [12], emphasizing the need for tools that address this limitation, especially from a systemic perspective.

Specifically in Brazil, the official nationally recommended food insecurity measurement (Brazilian Food Insecurity Measurement Scale—EBIA) has been used for the past twenty years to monitor food security levels across different spatial and temporal scales [13,14]. Through this experience-based indicator, many studies have revealed a transition in the food insecurity situation in the country, evidencing a decrease between 2004 and 2013 [15], an increase mainly from 2015, and an even greater increase from 2020, with the COVID-19 pandemic—influenced by an opposite political panorama that adopted a necropolitical agenda and suppression of essential policies, programs and initiatives to combat hunger [16,17,18,19,20,21,22]. These trends captured by EBIA have consistently been confirmed by other studies employing the FIES [9,23,24]. Even so, most of the national efforts on measuring, describing, and monitoring the population’s food security have relied on a few locally adapted indicators, failing to capture long-term variabilities and rarely assessing beyond one single aspect of its multiple dimensions [13,14].

In this regard, some Brazilian experiences have already been reported on simultaneously assessing multiple indicators of food security at a municipal level [25,26], as well as on proposing multidimensional indexes for poverty [27], human development rankings [28], and sustainability of food systems measurements [29,30]. These initiatives represent important initial steps in signaling objective indicators of food security that could capture its broader concept in a sustainable perspective, accounting for the complex dynamic cycle of poverty, hunger, and poor access to the human right to adequate nutrition [5,7,31].

Indeed, such a holistic understanding of intersections and trade-offs between food insecurity, income, human development, and sustainability indicators has been evidenced in the 2030 Agenda proposed by the United Nations, which posits the end of poverty and hunger as priorities among its 17 objectives and 169 targets for global addressing from 2015 to 2030 [6]. Nevertheless, secondary data availability in a consistent short-term periodicity and at granulated subnational spatial levels is an important limitation to be overcome in such an enterprise, especially when considering vulnerable regions in countries such as Brazil, where the food systems have a substantial impact in terms of the indicators that measure the sustainability dimension of food security [29,30,32].

This study addresses these methodological gaps by proposing the Multidimensional Food Insecurity Index (MUFII), a quantitative, adaptive, and scalable tool designed to integrate twelve indicators aligned with the Sustainable Development Goals (SDGs). The main hypothesis of this study is that food insecurity is a complex phenomenon resulting from factors and processes that occur and interact simultaneously over time and therefore requires equally complex measures that address the systemic nature of this problem. Thus, the primary aim of this research is to develop and validate the MUFII as a comprehensive instrument for assessing food insecurity from a multidimensional and systemic perspective. To demonstrate its operational feasibility and analytical potential, the MUFII is applied to the Brazilian context as a case study, integrating secondary data from open sources to analyze and compare food insecurity patterns across all 27 Federative Units in the years 2018 and 2022. In this application, the temporal and regional analyses are not the central research question but serve to illustrate how the index can capture variations and inequalities within and across territories.

Brazil offers a particularly relevant context for applying the MUFII. The country made remarkable progress in reducing hunger and poverty between 2004 and 2013, supported by strong social protection systems and intersectoral policies, which led to its removal from the FAO Hunger Map in 2014. However, from 2015 onwards, a combination of economic recession, political instability, and austerity measures reversed many of these gains, resulting in the re-emergence of food insecurity [18]. The situation worsened after 2020 due to the socioeconomic effects of the COVID-19 pandemic and the dismantling of key food and nutrition security policies [19]. By 2022, Brazil had returned to the FAO Hunger Map, with an estimated 33 million people living under severe food insecurity [21]. This historical trajectory, marked by both significant progress and recent regression, provides an ideal empirical ground for testing the MUFII, enabling an assessment of structural and systemic determinants of food insecurity within a context of deep regional inequalities and dynamic policy shifts.

Therefore, this study contributes by presenting and validating a new methodological framework for the multidimensional assessment of food insecurity, which can be adapted to other national contexts and time frames, thereby enhancing the global capacity to monitor food insecurity in alignment with the 2030 Agenda.

2. Methodology

The structure of MUFII was intentionally developed to allow adaptation across different national and subnational contexts, particularly in countries with systematic open data reporting.

To demonstrate the operational feasibility of the index and to explore its analytical potential in a context marked by deep regional inequalities and recent shifts in food policy, we applied MUFII to the case of Brazil. The Brazilian case provides a relevant empirical setting due to its strong national data infrastructure—with some limitations—and the severe impact of political and economic changes in food systems in recent years. The MUFII was calculated for all 27 Brazilian Federative Units for the years 2018 and 2022, allowing for comparison before and after the COVID-19 pandemic.

To construct the Multidimensional Food Insecurity Index (MUFII), we reviewed available indicators proposed to assess the country’s progress in relation to the goals set for the 2030 Agenda. Also, we used approaches extensively applied in the construction of similar tools and complementary statistical and spatial approaches in order to guarantee the robustness and validity of our results.

2.1. Data Collection

To collect the indicators, we accessed open-access official databases of the Brazilian Institute of Geography and Statistics (IBGE) [33] and the Brazilian Federal Government [34], aggregated at the state level and available for the years 2018 and 2022. Indicators were collected for 2018 and 2022 for Brazil’s 27 federal units, covering the social, economic, and environmental dimensions of sustainable development in the country.

2.2. Selection of Indicators

We selected indicators based on the literature on food insecurity, including topics such as health, education, economy, public security, and environment. Initially, 28 indicators were identified as available in these databases at both the specified spatial and temporal levels (Table S1). After correlation analyses, for the indicators that were highly associated with the prevalence of household food insecurity—measured by the scale traditionally used in Brazil (EBIA) [35]—only the one most reasonably associated with food insecurity in Brazil was selected, based on the literature (Tables S2 and S3). For 2018, the Pearson correlation coefficients between sustainable development indicators and food insecurity prevalence ranged from −0.36 (school infrastructure) to 0.81 (proportion of the population living below the international poverty line). And for 2022, it ranged from −0.41 (education completion rate) to 0.68 (youth (ages 15–24) not in employment, education or training).

The 12 indicators included in the study, their description, the dimension and objective of sustainable development to which they are related, as well as the expected influence on the food insecurity situation, are presented in Table 1. MUFII adds to the existing literature on food security measurement indices in several ways: first, by proposing a measure based on more recent data available for all regions of a country; second, by analyzing the problem through the lens of food insecurity rather than food security, as other existing indices do [8]; and third, by including indicators that are not traditionally considered in measures for this purpose despite associated with food security, especially in countries in the global South, such as an indicator of violence—which adds the dimension of public security as a barrier to adequate access to food [36,37,38]—and an indicator of women in the informal sector—which includes the emerging aspect of gender intersectionality [39,40,41,42].

Table 1. Description of the indicators included in MUFII, their classification in relation to the dimensions of sustainable development, adherence to the Sustainable Development Goals (SDGs), and influence on food insecurity (FI).

Indicator	Description	Unit	Dimension	SDG	Influence on FI	Pillar	Relationship
Proportion of the population living below the international poverty line	Percentage of the population living on less than $2.15 per day, at 2017 international prices. Based on 2017 purchasing power parity (PPP), as defined by the World Bank’s International Comparison Program (ICP).	%	Economic/ Social	1	+	Access	Poverty limits economic access to adequate food and is one of the most direct determinants of food insecurity [43,44].
Mortality due to unsafe water, sanitation, and hygiene (ages 0–4)	Mortality rate from diseases related to exposure to unsafe Water, Sanitation, and Hygiene (WASH) services. Included diseases are diarrhea (ICD-10 codes A00, A01, A03, A04, A06–09), intestinal nematode infections (ICD-10 codes B76–B77, B79), and protein-energy malnutrition (ICD-10 codes E40–E46).	Deaths per 100,000 inhabitants	Health/ Environmental	3	+	Utilization	Reflects environmental conditions that directly affect children’s health, especially the most vulnerable, and therefore the proper use of food [45,46,47].
Female informal employment rate	Percentage of employed women engaged in informal work during the reference week, relative to the total number of employed women in the same period.	%	Economic/ Social	8	+	Access	It reflects economic precariousness and gender intersectionalities, reducing the purchasing power and access of female-headed households to adequate food [48,49].
Unemployment rate	Percentage of individuals unemployed during the reference week, relative to the labor force in the same period.	%	Economic	8	+	Access	Unemployment reduces income and access to food, especially in urban contexts [50,51,52].
Youth (ages 15–24) not in employment, education, or training (NEET)	Proportion of individuals aged 15 to 24 who are not employed, not enrolled in education, and not participating in any training programs.	%	Economic	8	+	Access	It expresses the social and economic exclusion of young people, limiting access to sustainable food resources and opportunities [53,54].
Number of victims of intentional homicide	Number of deaths due to assault, including homicide and injuries inflicted by another person with the intent to harm or kill, for any reason, per 100,000 inhabitants in the resident population.	Deaths per 100,000 inhabitants.	Public security/Social	16	+	Stability	Violence and social insecurity affect the stability of food systems, causing displacement, loss of income, community breakdown, and barriers to access [36,37,38].
Neonatal mortality rate	Number of deaths among residents within the first 27 completed days of life (per 1000) relative to the number of live births to resident mothers.	Deaths per 1000 live births	Health/ Social	3	+	Utilization	Expresses health conditions, maternal nutrition, and sanitation, factors directly linked to the biological use of food [55,56].
Live births to teenage mothers	Average number of live births among females aged 10 to 19, within the total resident population.	Live births per 1000 women	Health/ Social	3	+	Utilization	Indicates social and health vulnerabilities that compromise the nutritional status and food utilization of mothers and children [57,58].
Access to safely managed drinking water services	Proportion of the population using drinking water sources that meet the following criteria simultaneously: sourced from improved water facilities (including piped water, boreholes, protected dug wells, protected springs, or rainwater), located on premises, available when needed, and free from fecal and chemical contamination.	%	Environmental	6	−	Utilization	It is directly related to the body’s ability to properly utilize food; safe water is essential for nutrient absorption and disease prevention [46,50,59].
Average hourly earnings	Average real hourly earnings of individuals aged 15 or older who were employed during the reference week and received labor income in all jobs.	BRL/h	Economic	8	−	Access	Measures the direct economic capacity to purchase food, which is a central component of the access pillar of food security [50,51,52].
Education completion rate	Proportion of individuals 3 to 5 years older than the expected age of completion for a given education level who have completed that level. For early primary education, this refers to persons aged 13–15; for full primary education, those aged 17–19; and for secondary education, those aged 20–22.	%	Social	4	−	Access	Education increases income opportunities and the ability to access quality food, as well as promoting healthier food choices [60].
School infrastructure	Proportion of schools with access to the following: (a) electricity; (b) internet for pedagogical purposes; (c) computers for pedagogical purposes; (d) infrastructure and materials adapted for students with disabilities; (e) drinking water; (f) gender-segregated sanitation facilities; and (g) basic hand washing facilities (according to WASH indicator definitions).	%	Social	4	−	Availability	Schools are often the only places where adequate food is available for children, young people and adults [61,62,63].

2.3. Obtaining the Index

To obtain the MUFII, the selected indicators were normalized to a scale of 0 to 1, which made it possible to compare variables with different units of measurement and prevented these differences from influencing the final result. This approach was chosen because it is an intuitive technique that is widely used in composite indices, as well as being simple, transparent, and reproducible, and widely used in studies dealing with multiple dimensions of social phenomena, such as food insecurity, because it allows each indicator to contribute equally to the index. It reflects a balanced view of food insecurity, without arbitrarily assigning weights, since there is no consensus on this in the literature. Considering its influence on food insecurity (Table 1), the positive indicators were normalized using Formula (1), and the negative indicators were normalized using Equation (2):

$$I_{siy}= {\displaystyle \frac{x_{si}-min{(x}_i) }{max{(x}_i) - min{(x}_i)}}$$

(1)

$$I_{siy}={\displaystyle \frac{max{(x}_i)-x_{si} }{max{(x}_i)-min{(x}_i)}}$$

(2)

where

i: indicator;

s: Federative Unit;

y: year;

I_siy: standardized value of indicator i for the Federative Unit s in the year y;

x_si: original value of indicator i for the Federative Unit s;

min(x_i): minimum value of indicator i;

max(x_i): maximum value of indicator i.

The index was subsequently calculated using the arithmetic average of the standardized indicators, assuming equal weights for the components. The average was calculated by the Brazilian Federative Unit for the years 2018 and 2022, generating a final score that represents the degree of multidimensional food insecurity represented by MUFII, according to the following formula.

$${MUFII}_{sy}={\displaystyle \frac{1}{12}} (I_{si1}+{ I}_{si2 }+ \dots +I_{si12})$$

(3)

where

MUFII_sy: Multidimensional Food Insecurity Index for the Federative Unit s in the year y;

I_si: standardized value of indicator i for the Federative Unit s from indicator 1 to 12.

MUFII is calculated in terms that the higher the score, the higher the level of multidimensional food insecurity in the Federative Unit analyzed.

2.4. Variability

To assess the variability of our index between the years 2018 and 2022, we calculated the coefficient of variation for each of the Brazilian Federative Units, based on the standard deviation and the average of the index for these years.

$${CV}_s={\displaystyle \frac{\sigma }{\overline{x}}}\times 100$$

(4)

where

CV_s: coefficient of variation of MUFII for the Federative Unit s;

σ: standard deviation;

$\overline{x}$: arithmetic average.

2.5. Validation

To assess the empirical coherence and convergent validity of the MUFII, we examined its statistical and spatial association with the prevalence of household food insecurity in each state, measured by the Brazilian Food Insecurity Scale (EBIA). The EBIA is an experience-based instrument officially validated for the Brazilian population and derived from the U.S. Household Food Security Survey Module, following international methodologies adopted in several countries. Although it primarily measures the experiential dimension of food insecurity, the EBIA’s conceptual foundation acknowledges that food insecurity is a multidimensional phenomenon encompassing access, availability, utilization, and stability dimensions.

Given this conceptual alignment, we used the EBIA as a national benchmark to test whether the MUFII captures similar territorial patterns of food insecurity. For linear correlation analysis, we applied Spearman’s rank correlation test after assessing data normality with the Shapiro–Wilk test. Spatial coherence was examined using the global Moran’s Index, assuming state centroids and k-nearest neighbor matrices (k = 4), supported by official shapefiles of Brazilian states. A positive spatial autocorrelation indicates that neighboring states tend to exhibit similar levels of multidimensional food insecurity measured by the MUFII. Additionally, we estimated linear regression models between the EBIA-based prevalence of food insecurity and the MUFII to evaluate the extent to which the index explains state-level variations. The linear model assumption was verified according to the normality of residuals using the Shapiro–Wilk test.

2.6. Sensitivity

To check the influence of each indicator on the ranking and performance of the MUFII, we estimated the agreement between the MUFII and a modified version of the index, removing one indicator at a time. In this way, we were able to compare the MUFII for all 27 Federative Units with all the indicators included and the resulting index after removing each of the indicators. The MUFII scores and versions of the index without each indicator were categorized into quartiles. Cohen’s Kappa coefficient and the agreement rate between these quartiles were checked in both the years 2018 and 2022 in order to assess the influence of individual indicators on the overall index performance.

For all our analyses, we used R software, version 4.4.2, and results with a p-value ≤ 0.05 were considered statistically significant.

3. Results

3.1. Construction of the Multidimensional Food Insecurity Index (MUFII)

The Multidimensional Food Insecurity Index is composed of 12 indicators that are part of the social, economic, and environmental dimensions of sustainable development, adapted for the Brazilian context. Table 2 shows the descriptive statistics of these indicators in their original unit of measurement for all 27 Federative Units of Brazil in 2018 and 2022.

Table 2. Descriptive statistics of the indicators that make up the Multidimensional Food Insecurity Index (MUFII) for all 27 Federative Units of Brazil, 2018 and 2022.

Indicator	2018				2022
	Mean	SD	Min	Max	Mean	SD	Min	Max
Access to safely managed drinking water services	94.9	4.8	85.8	100	96.0	3.7	88.0	99.6
Education completion rate	80.8	5.0	69.9	89.7	84.8	3.9	76.4	93.1
Unemployment rate	12.2	3.4	6.3	20.7	9.6	3.5	3.8	15.8
Number of victims of intentional homicide	34.3	15.2	7.92	71.63	26.0	9.3	6.7	40.9
Female informal employment rate	42.3	8.4	26	58	40.9	9.1	26.3	58.4
School infrastructure	64.3	7.3	42.8	75.9	81.9	9.9	50.2	92.8
Neonatal mortality rate	9.9	2.2	6.9	15.6	9.1	1.6	6.8	12.6
Mortality due to unsafe water, sanitation, and hygiene (ages 0–4)	8.2	10.4	1.3	49.8	7.2	8.1	0.0	42.0
Live births to teenage mothers	33.7	9.1	21.01	58.77	25.4	8.2	13.5	49.5
Youth (ages 15–24) not in employment, education, or training (NEET)	23.5	4.9	14	34.6	20.3	5.4	10.2	28.4
Proportion of the population living below the international poverty line	9.4	6.1	1.6	21.8	7.2	4.2	1.8	15.0
Average hourly earnings	16.2	3.9	11.2	30	15.5	3.6	11.0	27.8

3.2. Multidimensional Patterns of Food Insecurity in Brazil

With the standardized indicators, MUFII was calculated for each of the 27 Federative Units and for the years 2018 and 2022 (Figure 1). For 2018, the MUFII score ranged from 0.09 in Santa Catarina, a Brazilian federative unit in the South region of Brazil, to 0.67 in Acre, in the North region. And for 2022, the score ranged from 0.10, also in Santa Catarina, to 0.72 in Amazonas, which is also located in the North of the country. When viewing the distribution of the MUFII among the Federative Units of Brazil (Figure 1), it can be seen that the pattern of distribution of the index illustrates the worsening of the level of multidimensional food insecurity from 2018 to 2022, especially in those Federative Units located in the North and Northeast regions of the country for which higher scores had already been verified in 2018, with worsening also among those states with lower previous scores.

Figure 1. Evolution of multidimensional food insecurity from 2018 to 2022 based on the Multidimensional Food Insecurity Index (MUFII).

When analyzing the average of the two years (Table 3), the Federative Units with the highest average scores were Acre (0.68) and Amazonas (0.67)—both located in the North region of Brazil—and Maranhão (0.65)—located in the Northeast region. On the other hand, when the coefficients of variation are analyzed, it can be seen that, between 2018 and 2022, changes occurred in the score for Federative Units located in different regions of the country, with the highest values emerging for Paraiba (Northeast) (average = 0.56; coefficient of variation = 16.07), Mato Grosso (Midwest) (mean = 0.30; coefficient of variation = 13.33), Amazonas (North) (mean = 0.67; coefficient of variation = 10.45), and Rio de Janeiro (Southeast) (mean = 0.30; coefficient of variation = 10.00).

Table 3. Mean, standard deviation, and coefficient of variation of the Multidimensional Food Insecurity Index (MUFII) in the 27 Federative Units of Brazil, 2018 and 2022.

Region/Federative Unit	2018 and 2022 Mean	SD	Coeff Var (%)
North
Rondônia	0.34	0.01	2.94
Acre	0.68	0.01	1.47
Amazonas	0.67	0.07	10.45
Roraima	0.58	0.03	5.17
Pará	0.62	0.01	1.61
Amapá	0.59	0.02	3.39
Tocantins	0.36	0.001	0.28
Northeast
Maranhão	0.65	0.02	3.08
Piauí	0.53	0.02	3.77
Ceará	0.50	0.004	0.80
Rio Grande do Norte	0.48	0.01	2.08
Paraíba	0.56	0.09	16.07
Pernambuco	0.58	0.05	8.62
Alagoas	0.61	0.01	1.64
Sergipe	0.61	0.01	1.64
Bahia	0.61	0.06	9.84
Southeast
Minas Gerais	0.24	0.01	4.17
Espírito Santo	0.28	0.01	3.57
Rio de Janeiro	0.30	0.03	10.00
São Paulo	0.16	0.01	6.25
South
Paraná	0.19	0.001	0.53
Santa Catarina	0.09	0.004	4.44
Rio Grande do Sul	0.19	0.01	5.26
Midwest
Mato Grosso do Sul	0.24	0.01	4.17
Mato Grosso	0.30	0.04	13.33
Goiás	0.26	0.003	1.15
Distrito Federal	0.10	0.003	3.00

The spatial distribution of MUFII among Brazilian states in 2018 and 2022 (Figure 2) illustrates the results of our index for both years, showing that, despite the post-COVID-19 pandemic context in 2022, Brazil was already facing a challenging situation in terms of multidimensional food insecurity in 2018. This worsening situation is particularly noticeable among the Federative Units in the North and Northeast of the country, which have historically been the most affected.

Figure 2. Spatial distribution of the Multidimensional Food Insecurity Index (MUFII), 2018 and 2022, by Federative Units of Brazil.

3.3. Validation of the MUFII

Using the correlation and association tests (Table 4), we found that our index performed well against three different approaches: Pearson’s linear correlation, spatial correlation using Moran’s Index, and linear regression models. We found a strong linear correlation between MUFII and the prevalence of household food insecurity in the Brazilian Federative Units for 2018 (r = 0.81, p-value < 0.001) and 2022 (r = 0.66, p-value < 0.001). Similarly, there was a strong spatial correlation for both 2018 (Moran Index = 0.73, p-value < 0.001) and 2022 (Moran Index = 0.74, p-value < 0.001). And when assessing the association between the prevalence of household food insecurity and MUFII in the Brazilian Federative Units, we found a positive association for both years, showing that for 2018 (β = 1.13, p-value < 0.001) and 2022 (β = 0.93, p-value < 0.001), the higher the MUFII score, the higher the prevalence of household food insecurity in the Federative Units analyzed.

Table 4. Linear and spatial correlation and association coefficients between the prevalence of household food insecurity (EBIA) and MUFII for the years 2018 and 2022.

Year	Spearman’s Correlation		Moran I Test		Linear Regression
	Coefficient	p-Value	Coefficient	p-Value	Coefficient	p-Value	R2
2018	0.81	<0.001	0.73	<0.001	1.13	<0.001	0.63
2022	0.66	<0.001	0.74	<0.001	0.93	<0.001	0.44

3.4. Sensitivity Analysis

We also analyzed the influence of each indicator on the MUFII and the agreement between our index and a modified version of it (removing one indicator at a time) using Cohen’s Kappa coefficient (Table 5). These analyses suggested that all the indicators had a major influence on the index scores across the Federative Units in 2018 and 2022 (Kappa greater than 0.91). When analyzing the levels of agreement, we found that the 50% most sensitive indicators, i.e., those that had the greatest impact on the composition of the index in 2018, were in the following order: adequate access to water (agreement rate = 77.80%), number of victims of intentional homicide, informality employment rate of women aged 15 and over, neonatal mortality rate (both with agreement rate = 85.20%), children born to teenage mothers, and the proportion of schools with appropriate infrastructure (both with agreement rate = 92.60%). For 2022, they were in the following order: informality employment rate of women aged 15 and over, neonatal mortality rate (both with agreement rate = 77.80%), adequate access to water, children born to teenage mothers, proportion of schools with appropriate infrastructure (both with an agreement rate = 85.20%), and the proportion of the population living below the international poverty line (agreement rate = 92.60%).

Table 5. Cohen’s Kappa coefficient and agreement rate between the global Multidimensional Food Insecurity Index and its modified version, 2018 and 2022.

Indicator	2018			2022
	Kappa	p-Value	Agreement	Kappa	p-Value	Agreement
Access to safely managed drinking water services	0.91	<0.001	77.80	0.94	<0.001	85.2
Number of victims of intentional homicide	0.94	<0.001	85.20	1.00	<0.001	100
Female informal employment rate	0.94	<0.001	85.20	0.91	<0.001	77.80
Neonatal mortality rate	0.94	<0.001	85.20	0.91	<0.001	77.80
Live births to teenage mothers	0.97	<0.001	92.60	0.94	<0.001	85.20
School infrastructure	0.97	<0.001	92.60	0.94	<0.001	85.20
Proportion of the population living below the international poverty line	0.97	<0.001	92.60	0.97	<0.001	92.60
Mortality due to unsafe water, sanitation, and hygiene (ages 0–4)	0.97	<0.001	92.60	0.97	<0.001	92.60
Unemployment rate	0.97	<0.001	92.60	0.97	<0.001	92.60
Youth (ages 15–24) not in employment, education, or training (NEET)	0.97	<0.001	92.60	0.97	<0.001	92.60
Education completion rate	0.97	<0.001	92.60	0.97	<0.001	92.60
Average hourly earnings	1.00	<0.001	100	0.97	<0.001	92.60

4. Discussion

Our study presents the Multidimensional Food Insecurity Index (MUFII), a tool composed of 12 indicators aligned with the SDGs, designed to assess food insecurity from a multidimensional perspective. Although this study applies the MUFII to the case of Brazil—using data from its 27 Federative Units across two time points (2018 and 2022)—the methodological framework was proposed as a guide to be adaptable to other national or regional contexts, depending on data availability and contextual relevance. The MUFII integrates dimensions such as poverty, health, education, security, and environment, offering a broader and more integrative view of food insecurity.

The application of MUFII to the Brazilian context revealed marked regional inequalities in the distribution of food insecurity. The North and Northeast regions were the most affected, and the situation deteriorated in all regions from 2018 to 2022, even in Federative Units historically marked by lower levels of food insecurity. Moreover, the MUFII performed well in validity analyses when compared to official household food insecurity data from previous national surveys, and its indicators showed strong influence in sensitivity analyses, reinforcing the tool’s internal consistency and relevance.

Brazil’s food insecurity trajectory is historically influenced by political and economic instability, and these factors were intensified in the last decade, particularly in vulnerable regions such as the North—including the Amazon region—and the Northeast [19,64,65,66]. These areas not only experience high levels of food insecurity but also concentrate some of the country’s worst indicators related to public health, children’s health, the environment, public security, the economy, education, infrastructure, and employment, placing them as the most vulnerable territories to instability panoramas as seen in the COVID-19 pandemic, for example [19,64]. Accordingly, our findings show that the states located in these regions had the highest MUFII scores while also exhibiting significant variation between 2018 and 2022. This includes variations of 16.1% in Paraíba (Northeast), 10.0% in Rio de Janeiro (Southeast), 13.3% in Mato Grosso (Midwest), 10.5% in Amazonas (North), and 4.4% in Santa Catarina (South). Such results illustrate MUFII’s sensitivity to localized dynamics and highlight its potential to guide targeted interventions within countries, especially those marked by internal heterogeneities.

Based on data on the prevalence of household food insecurity from national surveys that assessed the 27 Federative Units of Brazil, it can be seen that between 2018 and 2022, this situation increased sharply in most of the country [16,22]. These findings corroborate MUFII’s trends, particularly in historically more affluent Brazilian regions such as the South and Southeast, which experienced record increases in food insecurity—e.g., 422.4% in Mato Grosso do Sul, 242.1% in Mato Grosso, 186% in Rio Grande do Sul, and 167.1% in São Paulo [16,22]. It is important to mention that these estimates capture household-level food access disruptions, whereas MUFII also incorporates latent dimensions such as health, education, work, public security, and environmental structure. The observed high levels of spatial correlation between MUFII and national survey data support MUFII as a complementary and more holistic measure, aligned with the multidimensional nature of food security.

The correlations between MUFII and EBIA were higher in 2018 than in 2022. This difference may be attributed to the greater structural alignment between multidimensional determinants of food insecurity and household experience-based measures before the COVID-19 pandemic. In 2022, Brazil underwent profound socioeconomic disruptions, including loss of income, increased inequality, and reduced coverage of food and social protection programs [18]. These rapid changes likely produced short-term variations in household food insecurity that are less detectable through the structural indicators integrated in the MUFII, which tend to capture more stable, systemic conditions. Despite the proposal of many tools around the world dedicated to measuring the population’s food insecurity situation using different data, methods, and models [67,68,69,70,71,72,73,74], the MUFII contributes as a flexible, replicable, and transparent alternative. In Brazil, where validated multidimensional indexes are scarce, MUFII served as a prototype to test the model’s feasibility. Yet, its structure, based on indicators available from public and official databases and built around internationally recognized SDG targets, provides the basis for adaptation to other national or subnational contexts, especially in countries facing regional disparities and data constraints.

While some international tools have evaluated Brazil’s performance from a multidimensional perspective [73,75], they lack the ability to assess internal variation at subnational levels. Additionally, although another Brazilian initiative has proposed a multidimensional index [76], it relied on a wider range of indicators from databases of varied quality, excluded sustainability indicators, and aggregated data from different years, limiting its use for time-sensitive and spatially explicit evaluations. MUFII addresses these gaps, using periodically collected government data, delimiting time points (2018 and 2022), and fully covering all of the 27 Federative Units of Brazil.

MUFII also incorporates less conventional but globally relevant dimensions. For instance, we included in the MUFII framework an indicator of poor water access, a historically significant determinant of food insecurity worldwide; combined with effects on food production and difficulties in sanitizing and cooking, these factors increase infectious disease dissemination and compromise the subsistence of traditional communities [59,77]. In Brazil, poor water access particularly affects the North and Northeast states [78,79,80,81]. Especially in the North region—where the Amazon forest is located—water scarcity is exacerbated by environmental degradation from agribusiness and mining, which directly threaten food sovereignty in Indigenous communities, bringing hunger, disease, and death to these people [82,83,84,85,86]. Though these examples are from the Brazilian case study, they underscore the global importance of integrating environmental indicators into food insecurity measurement frameworks.

Another innovative dimension captured by MUFII is violence. Despite the limitations related to the availability of broader data on various types of violence, which thus require the use of proxies, these indicators are particularly relevant in vulnerable or conflict-affected areas globally. Violence can be both a determinant and a consequence of food insecurity, and its manifestation is mainly verified with indicators of living in vulnerable urban areas, structural violence, intimate partner violence, suicidality, bullying, youth dating violence, child maltreatment, and violence against women and girls [87,88,89,90]. In a current context of territorial armed conflicts, agrarian conflicts, and imminent war, food insecurity is often intensified by the different types of violence and their territorial impacts on the dimensions of food security, especially due to city destruction and mass killings of families, which bring out significant numbers of families headed by solo parent carers and orphaned children [91,92,93]. In our context, the number of victims of intentional homicide was selected for inclusion in the MUFII, portraying other indicators from the same dimension that similarly correlated with household food insecurity prevalence, such as deaths by disasters, suicides, and traffic accidents. The inclusion of a violence indicator demonstrates MUFII’s conceptual flexibility and relevance for broader geopolitical application.

In line with the experiences of the process of building consolidated indexes, such as the United Nations Multidimensional Poverty Index (MPI) [94], the MUFII proposal adds value by (1) enabling comparative subnational assessment; (2) providing transparent building criteria; (3) offering potential for international replication; (4) being adaptable to visualization tools such as dashboards; (5) complementing one-dimensional measures; (6) signaling priorities among vulnerable regions; (7) generating information to shape public policies; and (8) being robust and rigorous in its intended purposes. Its development also aligns with the goal of improving human well-being within planetary boundaries, especially when considering that even in those countries where food insecurity is at satisfactory levels, continued economic growth may not be environmentally sustainable and socially beneficial [95]. Such a scenario requires that Sustainable Development Goals be achieved through transition policies that enable food security for future generations.

Among the strengths of this study is the comparative analysis of the pre- and the post-COVID-19 periods, providing additional insight into the intersectoral deteriorating impacts of this crisis on food insecurity. The use of population-representative data from complex sampling reinforces the robustness of the indicators. Additionally, despite the subjective element in indicator selection, we aimed to describe a methodologically sound framework that other countries could use as a blueprint to develop context-specific tools aligned with the 2030 Agenda.

Nonetheless, some limitations need to be addressed. As an ecological study, the results are subject to aggregation bias, and finer-grained data at municipal or urban/rural levels were not available. Furthermore, although we excluded collinear indicators, roughly one-third of MUFII’s composition was based on SDG’s economic dimension, potentially underrepresenting other critical aspects. However, we opted not to apply weighting due to the lack of consensus on the relative importance of each dimension and because food insecurity manifests differently depending on regions and contexts, with examples of special impacts of poverty in underdeveloped regions, of income inequality in developed countries, and of security and safety concerns in conflict zones [5,70]. Finally, the limited availability of SDG data across Brazilian Federative Units and selected time points restricted long-term analyses. Nevertheless, the 12 indicators selected for the MUFII were derived from public registry data, including natality, mortality, education, and employment rates, and from household and school-based surveys, which are periodically produced and likely to be available in other countries with active SDG monitoring systems.

5. Conclusions

Given its multidimensional and systemic approach, based on globally recognized sustainable development indicators, the MUFII is a versatile tool for broader international application in diagnosing food insecurity that also takes into account the complex nature of this phenomenon. Especially in contexts where multidimensional data are available or can be generated, the MUFII offers a promising way to better understand and address food insecurity in all its ecological, socioeconomic, and territorial complexity.

In our study, Brazil proved to be a robust case for multidimensional analysis of food insecurity diagnosed by the MUFII. The index results were consistent with other estimates made for the country, which, although one-dimensional, consider the multiplicity of factors involved in food insecurity. MUFII achieves this by simultaneously revealing that the regions with the most limited access to adequate food are also those with the worst performance in socioeconomic and environmental indicators.

The applicability of MUFII is therefore broad and can be useful for analyzing different territories, monitoring purposes, and guiding public policies on sustainable food systems. Future applications and updates of MUFII may improve its ability to measure hunger, especially when considering data at a more disaggregated temporal and spatial level.