research paper on food insecurity

• Research article
• Open access
• Published: 15 February 2023

The impact of food insecurity on health outcomes: empirical evidence from sub-Saharan African countries

• Sisay Demissew Beyene   ORCID: orcid.org/0000-0001-7347-4168 1  

BMC Public Health volume  23 , Article number:  338 ( 2023 ) Cite this article

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Food insecurity adversely affects human health, which means food security and nutrition are crucial to improving people’s health outcomes. Both food insecurity and health outcomes are the policy and agenda of the 2030 Sustainable Development Goals (SDGs). However, there is a lack of macro-level empirical studies (Macro-level study means studies at the broadest level using variables that represent a given country or the whole population of a country or economy as a whole. For example, if the urban population (% of the total population) of XYZ country is 30%, it is used as a proxy variable to represent represent country's urbanization level. Empirical study implies studies that employ the econometrics method, which is the application of math and statistics.) concerning the relationship between food insecurity and health outcomes in sub-Saharan African (SSA) countries though the region is highly affected by food insecurity and its related health problems. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries.

The study was conducted for the whole population of 31 sampled SSA countries selected based on data availability. The study uses secondary data collected online from the databases of the United Nations Development Programme (UNDP), the Food and Agricultural Organization (FAO), and the World Bank (WB). The study uses yearly balanced data from 2001 to 2018. This study employs a multicountry panel data analysis and several estimation techniques; it employs Driscoll-Kraay standard errors (DKSE), a generalized method of momentum (GMM), fixed effects (FE), and the Granger causality test.

A 1% increment in people’s prevalence for undernourishment reduces their life expectancy by 0.00348 percentage points (PPs). However, life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. A 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs. However, a 1% increment in average dietary energy supply reduces infant mortality by 0.0139 PPs.

Conclusions

Food insecurity harms the health status of SSA countries, but food security impacts in the reverse direction. This implies that to meet SDG 3.2, SSA should ensure food security.

Peer Review reports

Food security is essential to people’s health and well-being [ 1 ]. Further, the World Health Organization (WHO) argues that health is wealth and poor health is an integral part of poverty; governments should actively seek to preserve their people’s lives and reduce the incidence of unnecessary mortality and avoidable illnesses [ 2 ]. However, lack of food is one of the factors which affect health outcomes. Concerning this, the Food Research and Action Center noted that the social determinants of health, such as poverty and food insecurity, are associated with some of the most severe and costly health problems in a nation [ 3 ].

According to the FAO, the International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP), food insecurity is defined as "A situation that exists when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life" ([ 4 ]; p50). It is generally believed that food security and nutrition are crucial to improving human health and development. Studies show that millions of people live in food insecurity, which is one of the main risks to human health. Around one in four people globally (1.9 billion people) were moderately or severely food insecure in 2017 and the greatest numbers were in SSA and South Asia. Around 9.2% of the world's population was severely food insecure in 2018. Food insecurity is highest in SSA countries, where nearly one-third are defined as severely insecure [ 5 ]. Similarly, 11% (820 million) of the world's population was undernourished in 2018, and SSA countries still share a substantial amount [ 5 ]. Even though globally the number of people affected by hunger has been decreasing since 1990, in recent years (especially since 2015) the number of people living in food insecurity has increased. It will be a huge challenge to achieve the SDGs of zero hunger by 2030 [ 6 ]. FAO et al. [ 7 ] projected that one in four individuals in SSA were undernourished in 2017. Moreover, FAO et al. [ 8 ] found that, between 2014 and 2018, the prevalence of undernourishment worsened. Twenty percent of the continent's population, or 256 million people, are undernourished today, of which 239 million are in SSA. Hidden hunger is also one of the most severe types of malnutrition (micronutrient deficiencies). One in three persons suffers from inadequacies related to hidden hunger, which impacts two billion people worldwide [ 9 ]. Similarly, SSA has a high prevalence of hidden hunger [ 10 , 11 ].

An important consequence of food insecurity is that around 9 million people die yearly worldwide due to hunger and hunger-related diseases. This is more than from Acquired Immunodeficiency Syndrome (AIDS), malaria, and tuberculosis combined [ 6 ]. Even though the hunger crisis affects many people of all genders and ages, children are particularly affected in Africa. There are too many malnourished children in Africa, and malnutrition is a major factor in the high infant mortality rates and causes physical and mental development delays and disorders in SSA [ 12 ]. According to UN statistics, chronic malnutrition globally accounts for 165 million stunted or underweight children. Around 75% of these kids are from SSA and South Asia. Forty percent of children in SSA are impacted. In SSA, about 3.2 million children under the age of five dies yearly, which is about half of all deaths in this age group worldwide. Malnutrition is responsible for almost one child under the age of five dying every two minutes worldwide. The child mortality rate in the SSA is among the highest in the world, about one in nine children pass away before the age of five [ 12 ].

In addition to the direct impact of food insecurity on health outcomes, it also indirectly contributes to disordered eating patterns, higher or lower blood cholesterol levels, lower serum albumin, lower hemoglobin, vitamin A levels, and poor physical and mental health [ 13 , 14 , 15 ]. Iodine, iron, and zinc deficiency are the most often identified micronutrient deficiencies across all age groups. A deficiency in vitamin A affects an estimated 190 million pre-schoolers and 19 million pregnant women [ 16 ]. Even though it is frequently noted that hidden hunger mostly affects pregnant women, children, and teenagers, it further affects people’s health at all stages of life [ 17 ].

With the above information, researchers and policymakers should focus on the issue of food insecurity and health status. The SDGs that were developed in 2015 intend to end hunger in 2030 as one of its primary targets. However, a growing number of people live with hunger and food insecurity, leading to millions of deaths. Hence, this study questioned what is the impact of food insecurity on people's health outcomes in SSA countries. In addition, despite the evidence implicating food insecurity and poor health status, there is a lack of macro-level empirical studies concerning the impact of food insecurity on people’s health status in SSA countries, which leads to a knowledge (literature) gap. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries for the period ranging from 2001–2018 using panel mean regression approaches.

Theoretical and conceptual framework

Structural factors, such as climate, socio-economic, social, and local food availability, affect people’s food security. People’s health condition is impacted by food insecurity through nutritional, mental health, and behavioral channels [ 18 ]. Under the nutritional channel, food insecurity has an impact on total caloric intake, diet quality, and nutritional status [ 19 , 20 , 21 ]. Hunger and undernutrition may develop when food supplies are scarce, and these conditions may potentially lead to wasting, stunting, and immunological deficiencies [ 22 ]. However, food insecurity also negatively influences health due to its effects on obesity, women's disordered eating patterns [ 23 ], and poor diet quality [ 24 ].

Under the mental health channel, Whitaker et al. [ 25 ] noted that food insecurity is related to poor mental health conditions (stress, sadness, and anxiety), which have also been linked to obesity and cardiovascular risk [ 26 ]. The effects of food insecurity on mental health can worsen the health of people who are already sick as well as lead to disease acquisition [ 18 ]. Similarly, the behavioral channel argues that there is a connection between food insecurity and health practices that impact disease management, prevention, and treatment. For example, lack of access to household food might force people to make bad decisions that may raise their risk of sickness, such as relying too heavily on cheap, calorically dense, nutrient-poor meals or participating in risky sexual conduct. In addition, food insecurity and other competing demands for survival are linked to poorer access and adherence to general medical treatment in low-income individuals once they become sick [ 27 , 28 , 29 , 30 ]

Food insecurity increases the likelihood of exposure to HIV and worsens the health of HIV-positive individuals [ 18 ]. Weiser et al. [ 31 ] found that food insecurity increases the likelihood of unsafe sexual activities, aggravating the spread of HIV. It can also raise the possibility of transmission through unsafe newborn feeding practices and worsening maternal health [ 32 ]. In addition, food insecurity has been linked to decreased antiretroviral adherence, declines in physical health status, worse immunologic status [ 33 ], decreased viral suppression [ 34 , 35 ], increased incidence of serious illness [ 36 ], and increased mortality [ 37 ] among people living with HIV.

With the above theoretical relationship between target variables and since this study focuses on the impact of food insecurity on health outcomes, and not on the causes, it adopted the conceptual framework of Weiser et al. [ 18 ] and constructed Fig.  1 .

A conceptual framework of food insecurity and health. Source: Modified and constructed by the author using Weiser et al. [ 18 ] conceptual framework. Permission was granted by Taylor & Francis to use their original Figs. (2.2, 2.3, and 2.4); to develop the above figure. Permission number: 1072954

Several findings associate food insecurity with poorer health, worse disease management, and a higher risk of premature mortality even though they used microdata. For instance, Stuff et al. [ 38 ] found that food insecurity is related to poor self-reported health status, obesity [ 39 ], abnormal blood lipids [ 40 ], a rise in diabetes [ 24 , 40 ], increased gestational diabetes[ 41 ], increased perceived stress, depression and anxiety among women [ 25 , 42 ], Human Immunodeficiency Virus (HIV) acquisition risk [ 43 , 44 , 45 ], childhood stunting [ 46 ], poor health [ 47 ], mental health and behavioral problem [ 25 , 48 , 49 ].

The above highlight micro-level empirical studies, and since the scope of this study is macro-level, Table 1 provides only the existing macro-level empirical findings related to the current study.

Empirical findings in Table 1 are a few, implying a limited number of macro-level level empirical findings. Even the existing macro-level studies have several limitations. For instance, most studies either employed conventional estimation techniques or overlooked basic econometric tests; thus, their results and policy implications may mislead policy implementers. Except for Hameed et al. [ 53 ], most studies’ data are either outdated or unbalanced; hence, their results and policy implications may not be valuable in the dynamic world and may not be accurate like balanced data. Besides, some studies used limited (one) sampled countries; however, few sampled countries and observations do not get the asymptotic properties of an estimator [ 56 ]. Therefore, this study tries to fill the existing gaps by employing robust estimation techniques with initial diagnostic and post-estimation tests, basic panel econometric tests and robustness checks, updated data, a large number of samples.

Study setting and participants

According to Smith and Meade [ 57 ], the highest rates of both food insecurity and severe food insecurity were found in Sub-Saharan Africa in 2017 (55 and 28%, respectively), followed by Latin America and the Caribbean (32 and 12%, respectively) and South Asia (30 and 13%). Similarly, SSA countries have worst health outcomes compared to other regions. For instance, in 2020, the region had the lowest life expectancy [ 58 ] and highest infant mortality [ 59 ]. Having the above information, this study's target population are SSA countries chosen purposively. However, even though SSA comprises 49 of Africa's 55 countries that are entirely or partially south of the Sahara Desert. This study is conducted for a sample of 31 SSA countries (Angola, Benin, Botswana, Burkina Faso, Cameroon, Cabo Verde, Chad, Congo Rep., Côte d'Ivoire, Ethiopia, Gabon, The Gambia, Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, and Togo). The sampled countries are selected based on data accessibility for each variable included in the empirical models from 2001 to 2018. Since SSA countries suffer from food insecurity and related health problems, this study believes the sampled countries are appropriate and represent the region. Moreover, since this study included a large sample size, it improves the estimator’s precision.

Data type, sources, and scope

This study uses secondary data collected in December 2020 online from the databases of the Food and Agricultural Organization (FAO), the United Nations Development Programme (UNDP), and the World Bank (WB) (see Table 2 ). In addition, the study uses yearly balanced data from 2001 to 2018, which is appropriate because it captures the Millennium Development Goals, SDGs, and other economic conditions, such as the rise of SSA countries’ economies and the global financial crisis of the 2000s. Therefore, this study considers various global development programs and events. Generally, the scope of this study (sampled countries and time) is sufficient to represent SSA countries. In other words, the study has n*T = 558 observations, which fulfills the large sample size criteria recommended by Kennedy [ 56 ].

The empirical model

Model specification is vital to conduct basic panel data econometric tests and estimate the relationship of target variables. Besides social factors, the study includes economic factors determining people's health status. Moreover, it uses two proxies indicators to measure both food insecurity and health status; hence, it specifies the general model as follows:

The study uses four models to analyze the impact of food insecurity on health outcomes.

where LNLEXP and LNINFMOR (dependent variables) refer to the natural logarithm of life expectancy at birth and infant mortality used as proxy variables for health outcomes. Similarly, PRUND and AVRDES are the prevalence of undernourishment and average dietary energy supply adequacy – proxy and predictor variables for food insecurity.

Moreover, to regulate countries’ socio-economic conditions and to account for time-varying bias that can contribute to changes in the dependent variable, the study included control variables, such as GDPPC, GOVEXP, MNSCHOOL, and URBAN. GDPPC is GDP per capita, GOVEXP refers to domestic general government health expenditure, MNSCHOOL is mean years of schooling and URBAN refers to urbanization. Further, n it , v it , ε it , and μ it are the stochastic error terms at period t. The parameters \({\alpha }_{0}, { \beta }_{0}, { \theta }_{0},{ \delta }_{0}\) refer to intercept terms and \({\alpha }_{1}-{\alpha }_{5}, {\beta }_{1}-{\beta }_{5}, { \theta }_{1}-{\theta }_{5}, and {\delta }_{1}-{\delta }_{5}\) are the long-run estimation coefficients. Since health outcomes and food insecurity have two indicators used as proxy variables, this study estimates different alternative models and robustness checks of the main results. Furthermore, the above models did not address heterogeneity problems; hence, this study considers unobserved heterogeneity by introducing cross-section and time heterogeneity in the models. This is accomplished by assuming a two-way error component for the disturbances with:

From Eq.  2 , the unobservable individual (cross-section) and unobservable time heterogeneities are described by \({\delta }_{i} and {\tau }_{t}\) (within components), respectively. Nonetheless, the remaining random error term is \({\gamma }_{it}\) (panel or between components). Therefore, the error terms in model 1A-1D will be substituted by the right-hand side elements of Eq.  2 .

Depending on the presumptions of whether the error elements are fixed or random, the FE and RE models are the two kinds of models that will be evaluated. Equation ( 2 ) yields a two-way FE error component model, or just a FE model if the assumptions are that \({\delta }_{i} and {\tau }_{t}\) are fixed parameters to be estimated and that the random error component, \({\gamma }_{it}\) , is uniformly and independently distributed with zero mean and constant variance (homoscedasticity).

Equation ( 2 ), on the other hand, provides a two-way RE error component model or a RE model if we suppose \({\delta }_{i} and {\tau }_{t}\) are random, just like the random error term, or \({\delta }_{i},{\tau }_{t}, and {\gamma }_{it}\) are all uniformly and independently distributed with zero mean and constant variance, or they are all independent of each other and independent variables [ 60 ].

Rather than considering both error components, \({\delta }_{i}, and {\tau }_{t}\) , we can examine only one of them at a time (fixed or random), yielding a one-way error component model, FE or RE. The stochastic error term \({\varpi }_{it}\) in Eq.  2 will then be:

Statistical analysis

This study conducted descriptive statistics, correlation analysis, and initial diagnosis tests (cross-sectional and time-specific fixed effect, outliers and influential observations, multicollinearity, normality, heteroscedasticity, and serial correlation test). Moreover, it provides basic panel econometric tests and panel data estimation techniques. For consistency, statistical software (STATA) version 15 was used for all analyses.

Descriptive statistics and correlation analysis

Descriptive statistics is essential to know the behavior of the variables in the model. Therefore, it captures information, such as the mean, standard deviation, minimum, maximum, skewness, and kurtosis. Similarly, the study conducted Pearson correlation analysis to assess the degree of relationship between the variables.

Initial diagnosis

Cross-sectional and time-specific fixed effect.

One can anticipate differences arising over time or within the cross-sectional units, given that the panel data set comprises repeated observations over the same units gathered over many periods. Therefore, before estimation, this study considered unexplained heterogeneity in the models. One fundamental limitation of cross-section, panel, and time series data regression is that they do not account for country and time heterogeneity [ 60 ]. These unobserved differences across nations and over time are crucial in how the error term is represented and the model is evaluated. These unobserved heterogeneities, however, may be represented by including both country and time dummies in the regression. However, if the parameters exceed the number of observations, the estimate will fail [ 60 ]. However, in this study, the models can be estimated. If we include both country and time dummies, we may assume that the slope coefficients are constant, but the intercept varies across countries and time, yielding the two-way error components model. As a result, this study examines the null hypothesis that intercepts differ across nations and time in general.

Detecting outliers and influential observations

In regression analysis, outliers and influential observations may provide biased findings. Therefore, the Cooks D outlier and influential observation test was used in the study to handle outliers and influencing observations. To evaluate whether these outliers have a stronger impact on the model to be estimated, each observation in this test was reviewed and compared with Cook’s D statistic [ 61 ]. Cook distance evaluates the extent to which observation impacts the entire model or the projected values. Hence, this study tested the existence of outliers.

Normality, heteroscedasticity, multicollinearity, and serial correlation test

Before the final regression result, the data used for the variables were tested for normality, heteroscedasticity, multicollinearity, and serial correlation to examine the characteristics of the sample.

Regression models should be checked for nonnormal error terms because a lack of Gaussianity (normal distribution) can occasionally compromise the accuracy of estimation and testing techniques. Additionally, the validity of inference techniques, specification tests, and forecasting critically depends on the normalcy assumption [ 62 ]. Similarly, multicollinearity in error terms leads to a dataset being highly sensitive to a minor change, instability in the regression model, and skewed and unreliable results. Therefore, this study conducted the normality using Alejo et al. [ 62 ] proposed command and multicollinearity (using VIF) tests.

Most conventional panel data estimation methods rely on homoscedastic individual error variance and constant serial correlation. Since the error component is typically connected to the variance that is not constant during the observation and is serially linked across periods, these theoretical presumptions have lately reduced the applicability of various panel data models. Serial correlation and heteroskedasticity are two estimate issues frequently connected to cross-sectional and time series data, respectively. Similarly, panel data is not free from these issues because it includes cross-sections and time series, making the estimated parameters ineffective, and rendering conclusions drawn from the estimation incorrect [ 63 ]. Therefore, this study used the Wooldridge [ 63 ] test for serial correlation in linear panel models as well as the modified Wald test for heteroskedasticity.

Basic panel econometric tests

The basic panel data econometric tests are prerequisites for estimating the panel data. The three main basic panel data tests are cross-sectional dependence, unit root, and cointegration.

Cross-sectional dependence (CD)

A growing body of the panel data literature concludes that panel data models are likely to exhibit substantial CD in the errors resulting from frequent shocks, unobserved components, spatial dependence, and idiosyncratic pairwise dependence. Even though the impact of CD in estimation depends on several factors, relative to the static model, the effect of CD in dynamic panel estimators is more severe [ 64 ]. Moreover, Pesaran [ 65 ] notes that recessions and economic or financial crises potentially affect all countries, even though they might start from just one or two countries. These occurrences inevitably introduce cross-sectional interdependencies across the cross-sectional unit, their regressors, and the error terms. Hence, overlooking the CD in panel data leads to biased estimates and spurious results [ 64 , 66 ]. Further, the CD test determines the type of panel unit root and cointegration tests we should apply. Therefore, examining the CD is vital in panel data econometrics.

In the literature, there are several tests for CD, such as the Breusch and Pagan [ 67 ] Lagrange multiplier (LM) test, Pesaran [ 68 ] scaled LM test, Pesaran [ 68 ] CD test, and Baltagi et al. [ 69 ] bias-corrected scaled LM test (for more detail, see Tugcu and Tiwari [ 70 ]). Besides, Friedman [ 71 ] and Frees [ 72 , 73 ] also have other types of CD tests (for more detail, see De Hoyos and Sarafidis [ 64 ]). This study employs Frees [ 72 ] and Pesaran [ 68 ] among the existing CD tests. This is because, unlike the Breusch and Pagan [ 67 ] test, these tests do not require infinite T and fixed N, and are rather applicable for both a large N and T. Additionally, Free’s CD test can overcome the irregular signs associated with correlation. However, it also employs Friedman [ 71 ] CD for mixed results of the above tests.

Unit root test

The panel unit root and cointegration tests are common steps following the CD test. Generally, there are two types of panel unit root tests: (1) the first-generation panel unit root tests, such as Im et al. [ 74 ], Maddala and Wu [ 75 ], Choi [ 76 ], Levin et al. [ 77 ], Breitung [ 78 ] and Hadri [ 79 ], and (2) the second-generation panel unit root tests, such as [ 66 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ].

The first-generation panel unit root tests have been criticized because they assume cross-sectional independence [ 90 , 91 , 92 , 93 ]. This hypothesis is somewhat restrictive and unrealistic, as macroeconomic time series exhibit significant cross-sectional correlation among countries in a panel [ 92 ], and co-movements of economies are often observed in the majority of macroeconomic applications of unit root tests [ 91 ]. The cross-sectional correlation of errors in panel data applications in economics is likely to be the rule rather than the exception [ 93 ]. Moreover, applying first-generation unit root tests under CD models can generate substantial size distortions [ 90 ], resulting in the null hypothesis of nonstationary being quickly rejected [ 66 , 94 ]. As a result, second-generation panel unit root tests have been proposed to take CD into account. Therefore, among the existing second-generation tests, this study employs Pesaran’s [ 66 ] cross-sectionally augmented panel unit root test (CIPS) for models 1A–1C . The rationale for this is that, unlike other unit root tests that allow CD, such as Bai and Ng [ 80 ], Moon and Perron [ 87 ], and Phillips and Sul [ 84 ], Pesaran’s [ 66 ] test is simple and clear. Besides, Pesaran [ 66 ] is robust when time-series’ heteroscedasticity is observed in the unobserved common factor [ 95 ]. Even though theoretically, Moon and Perron [ 87 ], Choi [ 96 ] and Pesaran [ 66 ] require large N and T, Pesaran [ 66 ] is uniquely robust in small sample sizes [ 97 ]. Therefore, this study employs the CIPS test to take into account CD, and heteroskedasticity in the unobserved common factor and both large and small sample countries. However, since there is no CD in model 1D , this study employs the first-generation unit root tests called Levin, Lin, and Chu (LLC), Im, Pesaran, Shin (IPS) and Fisher augmented Dickey–Fuller (ADF) for model 1D .

Cointegration test

The most common panel cointegration tests when there is CD are Westerlund [ 98 ], Westerlund and Edgerton [ 99 ], Westerlund and Edgerton [ 100 ], Groen and Kleibergen [ 101 ], Westerlund’s [ 102 ] Durbin-Hausman test, Gengenbach et al. [ 103 ] and Banerjee and Carrion-i-Silvestre [ 104 ]. However, except for a few, most tests are not coded in Statistical Software (STATA) and are affected by insufficient observations. The current study primarily uses Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] for models 1A–1C . However, to decide uncertain results, it also uses McCoskey and Kao [ 105 ] cointegration tests for model 1C . The rationale for using Westerlund’s [ 98 ] cointegration test is that most panel cointegration has failed to reject the null hypothesis of no cointegration due to the failure of common-factor restriction [ 106 ]. However, Westerlund [ 98 ] does not require any common factor restriction [ 107 ] and allows for a large degree of heterogeneity (e.g., individual-specific short-run dynamics, intercepts, linear trends, and slope parameters) [ 92 , 107 , 108 ]. Besides, its command is coded and readily available in STATA. However, it suffers from insufficient observations, especially when the number of independent variables increases. The present study employs the Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ] cointegration tests to overcome this limitation. The two Engle-Granger-based cointegration tests applicable when there is no CD and are widely used and available in STATA are Pedroni [ 109 , 110 ] and Kao [ 111 ]. However, the Pedroni test has two benefits over Kao: it assumes cross-sectional dependency and considers heterogeneity by employing specific parameters [ 112 ]. Hence, this study uses the Pedroni cointegration test for model 1D .

Panel data estimation techniques

The panel data analysis can be conducted using different estimation techniques and is mainly determined by the results of basic panel econometric tests. Thus, this study mainly employs the Driscoll-Kraay [ 113 ] standard error (DKSE) (for models 1A and 1B ), FE (for model 1C ), and two-step GMM (for model 1D ) estimation techniques to examine the impact of food insecurity on health outcomes. It also employs the Granger causality test. However, for robustness checks, it uses fully modified ordinary least squares (FMOLS), panel-corrected standard error (PCSE), and feasible generalized least squares (FGLS) methods (for models 1A and 1B ). Moreover, it uses a random effect (RE) for model 1C and panel dynamic fixed effect (DFE) techniques for model 1D .

Even though several panel estimation techniques allow CD, most of them – such as cross-section augmented autoregressive distributed lag (CS-ARDL), cross-section augmented distributed lag (CS-DL), common correlated effects pooled (CCEP), and common correlated effects mean group (CCEMG) estimators – require a large number of observations over groups and periods. Similarly, the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators are not coded in STATA. Others, like the PCSE, FGLS, and seemingly unrelated regression (SUR), are feasible for T (the number of time series) > N (the number of cross-sectional units) [ 114 , 115 ]. However, a DKSE estimate is feasible for N > T [ 114 ]. Therefore, depending on the CD, cointegration test, availability in STATA, and comparing N against T, this study mainly employs the DKSE regression for models 1A and 1B , FE model for model 1C , and GMM for model 1C .

Finally, to check the robustness of the main result, this study employs FMOLS, FGLS, and PCSE estimation techniques for models 1A and 1B . Furthermore, even though the Hausman test confirms that the FE is more efficient, the study employs the RE for model 1C . This is because Firebaugh et al. [ 116 ] note that the RE and FE models perform best in panel data. Besides, unlike FE, RE assumes that individual differences are random. In addition, this study uses panel DFE for model 1D (selected based on the Hausman test). Finally, the robustness check is also conducted using an alternative model (i.e., when a dependent variable is without a natural log and Granger causality test).

Table 3 shows the overall mean of LNLEXP of the region is 4.063 years which indicates that the region can achieve only 57.43 (using ln(x) = 4.063 = loge (x)  = e 4.063 , where e = 2.718) years of life expectancy. This is very low compared to other regions. Besides, the ranges in the value of LNLEXP are between 3.698 and 4.345 or (40–76 years), implying high variation. Similarly, the mean value of LNINFMOR is 3.969; implying SSA countries recorded 52 infants death per 1000. Moreover, the range of LNINFMOR is between 2.525 and 4.919 or (12 – 135 infant death per 1000), implying high variation within the region. The mean value of people’s prevalence for undernourishment is 21.26; indicating 21% of the population is undernourished. However, the mean value of AVRDES is 107.826, which is greater than 100, implying that the calorie supply is adequate for all consumers if the food is distributed according to the requirements of individuals. When we observe the skewness and kurtosis of the variables of the models, except for LNLEXP and LNINFMOR, all variables are positively skewed. In addition, all variables have positive kurtosis with values between 2.202 and 6.092.

Table 3 also shows the degree of relationship between variables, such that most values are below the threshold or rule of thumb (0.7) for a greater association [ 117 ]. However, the association between LNINFMOR and LNLEXP, as well as between PRUNP and AVRDES, is over the threshold and seems to have a multicollinearity issue. Nevertheless, these variables did not exist together in the models, indicating the absence of a multicollinearity problem.

Table 4 shows whether the cross-sectional specific and time-specific FE in extended models ( model 1A-1D plus Eq.  2 ) are valid. The result reveals that the null hypothesis of the captured unobserved heterogeneity is homogenous across the countries, and time is rejected at 1%, implying the extended models are correctly specified. Besides, to check the robustness of the two-way error component model relative to the pooled OLS estimator, this study conducted an additional poolability test. The result shows the null hypothesis that intercepts homogeneity (pooling) is rejected at a 1% level; thus, the FE model is most applicable, but the pooled OLS is biased.

Cooks D is an indicator of high leverage and residuals. The impact is high when D exceeds 4/N, (N = number of observations). A D > 1 implies a significant outlier problem. The Cooks D result of this study confirms the absence of outliers' problem (see supplementary file 1 ).

Normality, heteroscedasticity, serial correlation, and multicollinearity tests

The results in Table 5 indicate that the probability value of the joint test for normality on e and u are above 0.01, implying that the residuals are normally distributed. The heteroscedasticity results show that the probability value of the chi-square statistic is less than 0.01 in all models. Therefore, the null hypothesis of constant variance can be rejected at a 1% level of significance. In other words, the modified Wald test result for Groupwise heteroskedasticity presented in Table 5 , rejects the null hypothesis of Groupwise homoskedasticity observed by the probability value of 0.0000, which implies the presence of heteroscedasticity in the residuals. Similarly, all models suffer from serial correlation since the probability value of 0.0000 rejects the null hypothesis of no first-order serial correlation, indicating the presence of autocorrelation in all panel models. Finally, the multicollinearity test reveals that the models have no multicollinearity problem since the Variance inflation Factors (VIF) values are below 5.

Cross-sectional dependence test

Results in Table 6 strongly reject the null hypothesis of cross-sectional independence for models 1A – 1C . However, for model 1D , the study found mixed results (i.e., Pesaran [ 68 ] fails to reject the null hypothesis of no CD while Frees [ 72 ] strongly rejects it). Thus, to decide, this study employs the Friedman [ 71 ] CD test. The result fails to reject the null hypothesis of cross-sectional independence, implying that two out of three tests fail to reject the null hypothesis of cross-sectional independence in model 1D . Therefore, unlike others, there is no CD in model 1D (see Table 6 ).

Unit root tests

Table 7 shows that all variables are highly (at 1% level) significant either at level (I(0)) or first difference (I(1)), which implies all variables are stationary. In other words, the result fails to reject the null hypothesis of unit root (non-stationary) for all variables at a 1%-significance level, either at levels or the first differences. Thus, we might expect a long-run connection between these variables collectively.

Cointegration tests

The results in Table 8 show that both the Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] cointegration tests strongly reject the null hypothesis of no-cointegration in models 1A and 1B . However, model 1C provides a mixed result, i.e. the Banerjee and Carrion-i-Silvestre [ 104 ] test rejects the null hypothesis of no cointegration, yet the reverse is true for the Westerlund [ 98 ] test. Therefore, this study conducted further cointegration tests for model 1C . Even though Westerlund and Edgerton [ 99 ] suffer from insufficient observation, it is based on the McCoskey and Kao [ 105 ] LM test [ 118 ]. Thus, we can use a residual-based cointegration test in the heterogeneous panel framework proposed by McCoskey and Kao [ 105 ]. However, an efficient estimation technique of cointegrated variables is required, and hence the FMOLS and DOLS estimators are recommended. The residuals derived from the FMOLS and DOLS will be tested for stationarity with the null hypothesis of no cointegration amongst the regressors. Since the McCoskey and Kao [ 105 ] test involves averaging the individual LM statistics across the cross-sections, for testing the residuals FMOLS and DOLS stationarity, McCoskey, and Kao [ 105 ] test is in the spirit of IPS (Im et al. [ 74 ]) [ 119 ].

Though FMOLS and DOLS are recommended for the residuals cointegration test, DOLS is better than FMOLS (for more detail, see Kao and Chiang [ 120 ]); therefore, this study uses a residual test derived from DOLS. The result fails to reject the null hypothesis of no cointegration. Two (Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ]) out of three tests fail to reject the null hypothesis of no cointegration; hence, we can conclude that there is no long-run relationship among the variables in model 1C .

Unlike other models, since there is CD in model 1D , this study employs the Pedroni [ 109 ] and Kao [ 111 ] cointegration tests for model 1D . The result strongly rejects the null hypothesis of no cointegration, which is similar to models 1A and 1B , that a long-run relationship exists among the variables in model 1D (see Table 5 ).

Panel data estimation results

Table 9 provides long-run regression results of all models employing appropriate estimation techniques such as DKSE, FE, and two-step GMM, along with the Granger causality test. However, the DKSE regression can be estimated in three ways: FE with DKSE, RE with DKSE, and pooled Ordinary Least Squares/Weighted Least Squares (pooled OLS/WLS) regression with DKSE. Hence, we must choose the most efficient model using Hausman and Breusch-Pagan LM for RE tests (see supplementary file 2 ). As a result, this study employed FE with DKSE for models 1A and 1B . Further, due to Hausman's result, absence of cointegration and to deal with heterogeneity and spatial dependence in the dynamic panel, this study employs FE for the model1C (see the supplementary file 2). However, due to the absence of CD, the presence of cointegration, and N > T, this study uses GMM for model 1D . Moreover, according to Roodman [ 121 ], the GMM approach can solve heteroskedasticity and autocorrelation problems. Furthermore, even though two-step GMM produces only short-run results, it is possible to generate long-run coefficients from short-run results [ 122 , 123 ].

The DKSE result of model 1A shows that a 1% increment in people's prevalence for undernourishment reduces their life expectancy by 0.00348 PPs (1 year or 366 days). However, in model 1C, a 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs (1 year or 369 days). The DKSE estimations in model 1B reveal that people’s life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. However, the GMM result for model 1D confirms that a 1% incrementin average dietary energy supply reduces infant mortality by 0.0139 PPs. Moreover, this study conducted a panel Granger causality test to confirm whether or not food insecurity has a potential causality to health outcomes. The result demonstrates that the null hypothesis of change in people’s prevalence for undernourishment and average dietary energy supply does not homogeneously cause health outcomes is rejected at 1% significance, implying a change in food insecurity does Granger-cause health outcomes of SSA countries (see Table 9 ).

In addition to the main results, Table 9 also reports some post-estimation statistics to ascertain the consistency of the estimated results. Hence, in the case of DKSE and FE models, the validity of the models is determined by the values of R 2 and the F statistics. For instance, R 2 quantifies the proportion of the variance in the dependent variable explained by the independent variables, representing the model’s quality. The results in Table 9 demonstrate that the explanatory variables explain more than 62% of the variance on the dependent variable. Cohen [ 125 ] classifies the R 2 value of 2% as a moderate influence in social and behavioral sciences, while 13 and 26% are considered medium and large effects, respectively. Therefore, the explanatory variables substantially impact this study's models. Similarly, the F statistics explain all independent variables jointly explain the dependent one. For the two-step system GMM, the result fails to reject the null hypothesis of no first (AR(1)) and second-order (AR(2)) serial correlation, indicating that there is no first and second-order serial correlation. In addition, the Hansen [ 126 ] and Sargan [ 127 ] tests fail to reject the null hypothesis of the overall validity of the instruments used, which implies too many instruments do not weaken the model.

Robustness checks

The author believes the above findings may not be enough for policy recommendations unless robustness checks are undertaken. Hence, the study estimated all models without the natural logarithm of the dependent variables (see Table 10 ). The model 1A result reveals, similar to the above results, individuals’ prevalence for undernourishment significantly reduces their life expectancy in SSA countries. That means a 1% increase in the people's prevalence of undernourishment reduces their life expectancy by 0.1924 PPs. Moreover, in model 1B , life expectancy rises by 0.1763 PPs with every 1% increase in average dietary energy supply. In model 1C , the rise in infants’ prevalence for undernourishment has a positive and significant effect on their mortality rate in SSA countries. The FE result implies that a 1% rise in infants’ prevalence for undernourishment increases their mortality rate by 0.9785 PPs. The GMM result in model 1D indicates that improvement in average dietary energy supply significantly reduces infant mortality. Further, the Granger causality result confirms that the null hypothesis of change in the prevalence of undernourishment and average dietary energy supply does not homogeneously cause health outcomes and is rejected at a 1% level of significance. This implies a change in food insecurity does Granger-cause health outcomes in SSA countries (see Table 10 ).

The study also conducted further robustness checks using the same dependent variables (as Table 9 ) but different estimation techniques. The results confirm that people’s prevalence of undernourishment has a negative and significant effect on their life expectancy, but improvement in average dietary energy supply significantly increases life expectancy in SSA countries. However, the incidence of undernourishment in infants contributes to their mortality; however, progress in average dietary energy supply for infants significantly reduces their mortality (see Table 11 ).

The main objective of this study is to examine the impact of food insecurity on the health outcomes of SSA countries. Accordingly, the DKSE result of model 1A confirms that the rise in people’s prevalence for undernourishment significantly reduces their life expectancy in SSA countries. However, the FE result shows that an increment in the prevalence of undernourishment has a positive and significant impact on infant mortality in model 1C . This indicates that the percentage of the population whose food intake is insufficient to meet dietary energy requirements is high, which leads to reduce life expectancy but increases infant mortality in SSA countries. The reason for this result is linked to the insufficient food supply in SSA due to low production and yields, primitive tools, lack of supporting smallholder farms and investment in infrastructure, and government policies. Besides, even though the food is available, it is not distributed fairly according to the requirements of individuals. Moreover, inadequate access to food, poor nutrition, and chronic illnesses are caused by a lack of well-balanced diets. In addition, many of these countries are impacted by poverty, making it difficult for citizens to afford nutritious food. All these issues combine to create an environment where individuals are more likely to suffer malnutrition-related illnesses, resulting in a lower life expectancy rate. The DKSE estimation result in model 1B reveals that improvement in average dietary energy supply positively impacts people's life expectancy in SSA countries. However, the improvement in average dietary energy supply reduces infant mortality.

Based on the above results, we can conclude that food insecurity harms SSA nations' health outcomes. This is because the prevalence of undernourishment leads to increased infant mortality by reducing the vulnerability, severity, and duration of infectious diseases such as diarrhea, pneumonia, malaria, and measles. Similarly, the prevalence of undernourishment can reduce life expectancy by increasing the vulnerability, severity, and duration of infectious diseases. However, food security improves health outcomes – the rise in average dietary energy supply reduces infant mortality and increases the life expectancy of individuals.

Several facts and theories support the above findings. For instance, similar to the theoretical and conceptual framework section, food insecurity in SSA countries can affect health outcomes in nutritional, mental health, and behavioral channels. According to FAO et al. [ 128 ], the prevalence of undernourishment increased in Africa from 17.6% of the population in 2014 to 19.1% in 2019. This figure is more than twice the global average and the highest of all regions of the world. Similarly, SSA is the world region most at risk of food insecurity [ 129 ]. According to Global Nutrition [ 130 ] report, anemia affects an estimated 39.325% of women of reproductive age. Some 13.825% of infants have a low weight at birth in the SSA region. Excluding middle African countries (due to lack of data), the estimated average prevalence of infants aged 0 to 5 months who are exclusively breastfed is 35.73%, which is lower than the global average of 44.0%. Moreover, SSA Africa still experiences a malnutrition burden among children aged under five years. The average prevalence of overweight is 8.15%, which is higher than the global average of 5.7%. The prevalence of stunting is 30.825%—higher than the worldwide average of 22%. Conversely, the SSA countries’ prevalence of wasting is 5.375%, which is higher than most regions such as Central Asia, Eastern Asia, Western Asia, Latin America and the Caribbean, and North America. The SSA region's adult population also faces a malnutrition burden: an average of 9.375% of adult (aged 18 and over) women live with diabetes, compared to 8.25% of men. Meanwhile, 20.675% of women and 7.85% of men live with obesity.

According to Saltzman et al. [ 17 ], micronutrient deficiencies can affect people’s health throughout their life cycle. For instance, at the baby age, it causes (low birth weight, higher mortality rate, and impaired mental development), child (stunting, reduced mental capacity, frequent infections, reduced learning capacity, higher mortality rate), adolescent (stunting, reduced mental capacity, fatigue, and increased vulnerability to infection), pregnant women (increased mortality and perinatal complications), adult (reduced productivity, poor socio-economic status, malnutrition, and increased risk of chronic disease), elderly (increased morbidity (including osteoporosis and mental impairment), and higher mortality rate).

Though this study attempts to fill the existing gaps, it also has limitations. It examined the impact of food insecurity on infant mortality; however, their association is reflected indirectly through other health outcomes. Hence, future studies can extend this study by examining the indirect effect of food insecurity on infant mortality, which helps to look at in-depth relationships between the variables. Moreover, this study employed infant mortality whose age is below one year; hence, future studies can broaden the scope by decomposing infant mortality into (neonatal and postnatal) and under-five mortality.

Millions of people are dying every year due to hunger and hunger-related diseases worldwide, especially in SSA countries. Currently, the link between food insecurity and health status is on researchers' and policymakers' agendas. However, macro-level findings in this area for most concerned countries like SSA have been given only limited attention. Therefore, this study examined the impact of food insecurity on life expectancy and infant mortality rates. The study mainly employs DKSE, FE, two-step GMM, and Granger causality approaches, along with other estimation techniques for robustness checks for the years between 2001 and 2018. The result confirms that food insecurity harms health outcomes, while food security improves the health status of SSA nations'. That means that a rise in undernourishment increases the infant mortality rate and reduces life expectancy. However, an improvement in the average dietary energy supply reduces infant mortality and increases life expectancy. Therefore, SSA countries need to guarantee their food accessibility both in quality and quantity, which improves health status. Both development experts and political leaders agree that Africa has the potential for agricultural outputs, can feed the continent, and improve socio-economic growth. Besides, more than half of the world's unused arable land is found in Africa. Therefore, effective utilization of natural resources is essential to achieve food security. Moreover, since the majority of the food in SSA is produced by smallholder farmers [ 131 ] while they are the most vulnerable to food insecurity and poverty [ 132 , 133 ]; hence, special focus and support should be given to smallholder farmers that enhance food self-sufficiency. Further, improvement in investment in agricultural research; improvement in markets, infrastructures, and institutions; good macroeconomic policies and political stability; and developing sub-regional strategies based on their agroecological zone are crucial to overcoming food insecurity and improving health status. Finally, filling a stomach is not sufficient; hence, a person's diet needs to be comprehensive and secure, balanced (including all necessary nutrients), and available and accessible. Therefore, SSA countries should ensure availability, accessibility, usability, and sustainability to achieve food and nutrition security.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in supplementary materials.

Abbreviations

Augmented Dickey–Fuller

Acquired Immunodeficiency Syndrome

Average Dietary Energy Supply

Common Correlated Effects Mean Group

Common Correlated Effects Pooled

Cross-Sectional Dependence

Cross-Sectionally Augmented Panel Unit Root Test

Cross-Section Augmented Autoregressive Distributed Lag

Cross-Section Augmented Distributed Lag

Continuously Updated Bias-Corrected

Continuously Updated Full Modified

Dynamic Fixed Effect

Driscoll-Kraay Standard Errors

Dynamic Ordinary Least Square

Error Correction Model

Food and Agricultural Organization

Fixed Effect

Feasible Generalised Least Squares

Fully Modified Ordinary Least Square

Gross Domestic Product (GDP) per capita

Generalised Method of Momentum

Domestic General Government Health Expenditure

Human Immunodeficiency Virus

Integration at First Difference

International Fund for Agricultural Development

Infant Mortality Rate

Im, Pesaran, Shin

Lag of Infant Mortality Rate

Lag of Natural Logarithm of Infant Mortality Rate

Life Expectancy at Birth

Levin, Lin, and Chu

Lagrange Multiplier

Natural Logarithm of Infant Mortality Rate

Natural Logarithm of Life Expectancy at Birth

Mean Years of Schooling

Ordinary Least Squares

Panel-Corrected Standard Error

Pooled Mean Group

Prevalence of Undernourishment

Random Effect

Sustainable Development Goals

Sub-Saharan African

Statistical Software

Seemingly Unrelated Regression

Urbanisation

World Food Programme

World Health Organization

Weighted Least Squares

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Climate change is a confounding factor that can affect agriculture and food security in many different ways. Climate-resilient food systems are needed to ensure food security and to support mitigation efforts.

World Food Day — 16 October, with the theme this year of ‘leave no one behind’ — is an appropriate time to reflect on global progress toward Sustainable Development Goal 2: Zero Hunger. The 2022 Global Hunger Index, released October 2022 ( https://www.globalhungerindex.org/ ), highlights that progress has stagnated, with the war in Ukraine, climate change and related extreme events, and the related increased price of food, fuel and fertilizer all contributing. The 2022 Global Hunger Index report 1 highlights that 44 countries are currently suffering serious or alarming levels of hunger, although there is large within-country variability. The report estimates that 828 million people are currently undernourished, with parts of Africa south of the Sahara and South Asia having the highest hunger levels, and being the most vulnerable to future shocks.

Climate change can affect crops in different ways and the impacts of climate change due to higher levels of atmospheric CO 2 are often deleterious. While higher levels of atmospheric CO 2 may enhance photosynthesis and growth in some crops 2 , there isn’t a clear picture on the overall effects on crops. Further, it has been reported that plants grown under higher CO 2 levels have changed nutritional value 3 .

Warming temperatures due to climate change also impact crop productivity, with an example discussed in this issue of Nature Climate Change . In an Article , Peng Zhu and colleagues consider how warming temperatures affect cropping frequency and yields. They find that warmer temperatures are increasing productivity and the possibility of multiple cropping seasons in cold regions, but increased temperatures in warm regions are causing decreases that outweigh the cold-region increases for an overall loss in crop productivity. The authors note that irrigation can offset the losses in warm regions, but water availability and the infrastructure needed suggest that the required 5% expansion of irrigation areas would be difficult to achieve.

Research has highlighted the risks of concurrent regional droughts; for example, work looking at 26 main crop-producing countries that found the probability under a high-emissions scenario to be at least 5% compared with 0% in the historical period 4 , as well as work showing increases by 40–60% for 10 global regions, with disproportionate risk increase across North America and the Amazon region 5 . With many regions relying on rain-fed agriculture, drought is a major risk to crop failure.

The shifting of seasons, in particular wet periods, can also affect planting. The northern USA had heavy spring rains this year that limited corn planting. This reduced planting led the US Department of Agriculture to lower the predicted yield per acre by 4 bushels, which equals more than 9 million tonnes less corn crop across the country. This lower yield, alongside lower-than-expected grain harvests in China, India, South America and part of Europe, reduced the available produce, not only for consumption but also for stock feed.

As well as their impact on production, extreme events can be a major disruption of supply chains. Yet, international trade has been highlighted as a possible way to mitigate climate change impacts on food security. It has been shown that high-emissions climate scenarios lead to increased hunger risk of 33–47% when trade is restricted, but decreases to 11–64% when trade is open 6 . However, production for export does need to be carefully considered to minimize negative effects in the producing region 7 .

The need to transform food systems to ensure resilience to climate change and other external pressures is well recognized, yet in climate change discussions it has not always been at the fore; at COP27, there will be a Food Systems Pavilion for the first time. How to achieve food systems transformation needs careful consideration and discussion, but work needs to begin now to push past the current stagnation and to ensure that no one is left behind.

von Grebmer, K. et al. 2022 Global Hunger Index: Food Systems Transformation and Local Governance (Welthungerhilfe, Concern Worldwide, 2022).

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Food insecurity affects all communities worldwide as many find it difficult to consistently access nutritious, affordable food. This problem is exacerbated by conflict, political insecurity, climate change, and global shocks such as the COVID-19 pandemic.

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Increasing Access to Healthy Foods through Improving Food Environment: A Review of Mixed Methods Intervention Studies with Residents of Low-Income Communities

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Food insecurity is a broad and serious public health issue in the United States, where many people are reporting lack of access to healthy foods. The reduced availability of healthy, affordable foods has led to increased consumption of energy-dense and nutrient-poor foods, resulting in increasing the risk for many chronic diseases such as obesity, cardiovascular diseases, and type 2 diabetes mellitus. Thus, identifying promising approaches to increase access to healthy foods through improving the food environment is of importance. The purpose of this review article is to highlight how the food environment affects directly a person’s food choices, and how to increase access to healthy foods through improving environmental approaches. The literature search was focused on finding different approaches to improve food security, primarily those with an impact on food environment. Overall, potential solutions were gathered through multilevel environmental approaches, including nutrition education and peer education, community-based participatory research, and policy changes in supplemental nutrition programs. A recommendation to reduce food insecurity is learning to create meals with a variety of seasonal fruits and vegetables purchased from affordable farmers’ markets.

1. Introduction

Food insecurity comprises limited or uncertain access to nutritious and adequate food intake and is widespread in the United States [ 1 ]. The United States Department of Agriculture (USDA) reported that 10.5% of Americans were food insecure at least some of the time during 2019 [ 2 ]. Many individuals who are food insecure utilize food banks and food pantries to procure food, but studies have shown that pantry users consume limited amounts of fruits, vegetables, and fiber [ 3 ]. The majority of the calories for this population are taken from energy-dense, nutrient-poor foods, including refined grains and foods high in added sugars and saturated fats, in contrast with the food-secure population, who have access to more nutrient-dense and healthier options [ 4 ]. This can result in the consumption of empty calories, rather than insufficient calories [ 4 ]. In general, food-secure individuals are more likely to meet the recommended dietary allowance for nutrients than food-insecure individuals [ 5 ]. This is displayed in a study by Champagne et al. [ 5 ], in which researchers determined the food security status through self-reported food intake, and found that food insecurity is associated with lower-quality diets assessed by the healthy eating index (HEI) scores.

A few factors that affect poor diet quality are related to lack of access to healthy foods in the surrounding neighborhood and limited household income [ 3 , 6 ]. The macro-environmental sector refers to broad infrastructure (including food advertising and health systems), whereas micro-environments indicate local settings (such as workplaces and homes) [ 7 ]. Through improving these food environments, access to healthy food options can be increased, resulting in better diet quality and reduced chronic disease risk in these populations. Therefore, developing effective strategies to improve the diet quality and nutritional status of high-risk populations is important for the prevention of diet-related chronic disease. This review paper will evaluate specific risks for chronic disease associated with food insecurity and strategies to improve the food environment and individuals’ choices towards a healthier diet and lifestyle. The objectives will be reached through assessing the literature on mixed-method intervention studies with residents of low-income communities.

2. Materials and Methods

To learn more about food insecurity and the risk of chronic disease, as well as approaches to improve food access, a literature search was conducted in June 2021 using the databases PubMed, Scopus, and Google Scholar, with a combination of keywords “food insecurity”, “chronic disease”, “food access”, “multilevel approaches”, “low-income”, “healthy eating”, “community”, and “policy changes”. Reading through each study, the titles and abstracts were observed to identify which articles provided informative data that helped towards reducing food insecurity. As summarized in Table 1 , full-length articles and book chapters in English with the US population in focus for all age ranges were selected. There were no limits to the state or region in the US in which the studies were conducted. The study designs conducted were randomized controlled trials, clinical trials, comparative studies, multicenter studies, and cohort studies. Notes, comments, editorials, and review articles were excluded from the articles with an exception of two review articles that were complementary to the data from other major studies. Studies that had other populations in focus, including low-income communities outside of the US, were excluded. Additionally, there were studies selected from the reference lists of relevant articles. Studies that compared the lifestyle and diet of food-secure populations vs. food-insecure populations were included, with a specific focus on the impact of the food environment. As different approaches to improve food security were searched for this narrative review, there was a focus on nutrition and peer education, community-based research, policy changes, and multilevel approaches.

Criteria for studies included.

3. Food Insecurity and Risk for Diet-Related Chronic Disease

3.1. obesity.

At the individual level, rates of obesity are higher among groups with low education and low incomes [ 8 , 9 ]. Current meta-analysis studies show that food insecurity increases the risk for obesity for adults (odds ratio 1.15) in food-insecure households, especially women odds ratio of 1.26) [ 10 ]. Paeratakul et al. [ 9 ] assessed data from the USDA Agricultural Research Service Continuing Survey of Food Intakes by Individuals, and reported that, among other factors, socioeconomic condition is one of the main factors leading to greater obesity severity. The study further linked obesity rates with an increased incidence of diabetes, hypertension, and high serum cholesterol, which further supports the role of socioeconomic factors in increasing these disease risks through an increase in obesity [ 9 ]. Individuals with food insecurity commonly use resources, such as food pantries, for access to a variety of products. An important issue that has been shown to lead to obesity among low-income populations is the poor nutritional quality of food and lack of knowledge of how to prepare certain fresh produce provided by the food pantries and other food assistance programs [ 5 , 11 ]. A great focus has been towards children in low-income populations and their greater risk of obesity due to their diet patterns and food choices [ 12 ]. Kaur et al. [ 12 ] analyzed the National Health and Nutrition Examination Survey (NHANES) data assessing personal food insecurity through USDA’s Food Security Survey Module to determine its relationship with obesity risk. The researchers determined that obesity was significantly associated with levels of food insecurity among children of ages 6 to 11 years, with an odds ratio of 1.81; 95% confidence interval (CI) 1.33 to 2.48 [ 12 ].

Food pantries are good resources from which to analyze the needs of food-insecure populations and determine obesity rates. Studies of food pantry participants concluded that the mean body mass index (BMI) of the pantry users was 29.5 kg/m 2 , and 78.0% of the population of obese pantry users were women [ 3 , 5 , 11 , 13 ]. Many people from the population have shown an interest in regularly consuming nutritious food and fresh produce, but they reported that these products were unaffordable [ 14 , 15 ].

3.2. Cardiovascular Diseases

According to the American Heart Association, the seven cardiovascular health metrics are based on smoking, diet, physical activity, BMI, blood pressure, total cholesterol, and fasting glucose [ 16 ]. Individuals with food insecurity face barriers meeting the ideal cardiovascular health metrics, including lower odds of meeting at least three metrics ( p < 0.01) [ 17 ]. Data further show a greater presence of hypertension and heart disease in individuals with lower education and income compared to others [ 9 ]. Furthermore, hypertension is associated with greater intake of added sugar and sugar-sweetened beverages ( p < 0.05) [ 18 ]. Adults from food-insecure households had a 21% higher risk of clinical hypertension than adults from food-secure households [ 19 ]. A systemic review and meta-analysis of 36 studies indicated an association between food-insecure adults and self-reported hypertension with an odds ratio of 1.13 [ 20 ]. Additionally, very low food security was associated with increased risk for cardiovascular disease, and a 58% higher risk for cardiovascular disease mortality [ 21 , 22 , 23 ]. Sun et al. focused on associations of adult food insecurity with all-cause and cardiovascular disease mortality in US adults [ 23 ]. This study concluded that participants with very low food security had a higher risk of cardiovascular disease compared with those with high food security, with an odds ratio of 1.54 (95% CI 1.04–2.26) [ 23 ].

Bazzano et al. [ 24 ] conducted the first NHANES Follow-up Study to observe fruit and vegetable intake through a food-frequency questionnaire, and the incidence of mortality from cardiovascular disease from medical records and death certificates. A significant association was identified between frequency of fruit and vegetable intake and incidence of and mortality from stroke, ischemic heart disease, and cardiovascular disease [ 24 ]. Bazzano et al. [ 24 ] further concluded that consuming three or more fruits and vegetables per day, compared to less than one per day, was associated with a 27% lower stroke incidence, a 42% lower stroke mortality, a 24% lower ischemic heart disease mortality, a 27% lower cardiovascular disease mortality, and a 15% lower all-cause mortality. Due to the high cost of fruits and vegetables, lack of transportation, low quality products in low-income areas, and other factors, individuals in these neighborhoods consume fewer servings of fruits and vegetables than the Dietary Guidelines recommendations, leading to an increased risk for cardiovascular disease [ 25 ]. Among nonelderly adults with household income <200% of the federal poverty level, analysis of NHANES data by Seligman et al. [ 19 ] (a nationally representative survey of the US population) found an association between food insecurity and clinical evidence of hypertension and diabetes.

3.3. Diabetes Mellitus

As previously mentioned, poor diet quality includes low nutrient consumption and high intake of energy dense foods. This lifestyle factor is one of the main factors leading to diabetes mellitus. Walker et al. [ 13 ] conducted a cross-sectional analysis with data from the NHANES survey 2005–2014 to evaluate levels of food security and its association to diabetes. The results indicated higher risk for food insecurity, with an odds ratio for prediabetes of 1.39 (95% CI 1.21–1.59), for undiagnosed diabetes of 1.81 (95% CI 1.37–2.38), and for diagnosed diabetes of 1.58 (95% CI 1.29–1.93) [ 13 ]. The results are supported by a cross-sectional study; when compared to individuals with food security, participants with low food security were 1.35 times more likely to have prediabetes (95% CI: 1.17–1.55) [ 26 ]. Very-low-food-secure participants, compared with both low-food-secure and food-secure participants, have been reported to have greater diabetes distress and more frequent and severe hypoglycemic episodes [ 27 ]. Individuals with food insecurity live paycheck by paycheck or wait for monthly assistance, which can lead to a cycle of restraining dietary intake during hard times and overeating during food restock [ 28 ]. This process can lead to insulin resistance and diabetes [ 13 , 28 , 29 ]. In individuals with food security the self-management to improve diabetes is easier, but a lack of quality food makes diabetes self-management more difficult, worsening the food-insecure individual’s health condition [ 27 ]. A meta-analysis study by Abdurahman et al. further strengthens the hypothesis for an association between household food insecurity and increased risk of type 2 diabetes, with an odds ratio value of 1.27 (95% CI 1.11–1.42) [ 30 ]. Based on the studies mentioned previously, it appears that food insecurity is associated with different stages of diabetes mellitus; however, there are some recent studies that contrast the findings and do not suggest an association between food insecurity and clinically determined type 2 diabetes or significant differences in fasting blood glucose and HbA1c [ 31 ]. Beltran et al. additionally stated that food insecurity is a complex issue and it might look different depending on factors such as social, economic, and geographic consideration [ 31 ]. The study explained that the differences in the findings might be a result of the difference in food insecurity concepts, where in some cases hunger and chronic starvation are the primary drives of food insecurity [ 31 ]. In another study, starvation due to food insecurity was related to worsening signs of insulin sensitivity in type 2 diabetes [ 29 ]. Meanwhile, food-insecure areas in the United States are usually not associated with hunger, but mainly with intake of lower-quality, high-calorie foods, which increase a person’s risk for type 2 diabetes [ 31 ].

4. The Impact of Food Environment on Access to Healthy Food Choices

The nutritional environment is affected not only by the number of stores in an area but also the availability and cost of healthy food items. Reduced access to fresh produce greatly impacts food choices for low-income populations [ 32 ]. Typically, individuals with reliable transportation make frequent trips to supermarkets [ 32 , 33 ]. However, many people in low-income neighborhoods rely greatly on food pantries due to the lack of transportation and availability of fresh produce in their areas [ 33 ]. A research study, conducted in a low-income neighborhood in Pomona, CA, determined that 41% of the food pantry clients did not live within walking distance of a store with a variety of fresh produce and 13% did not have access to any type of food store with fresh produce [ 33 ]. This problem is widespread in many areas in the US and highlights rural adults not being able to meet recommended nutrition guidelines due to environmental barriers and lack of community resources [ 33 ]. In areas with food insecurity, convenience stores are more common than supermarkets or grocery stores [ 32 ]. These stores have a very limited range of food items and are more likely to stock less healthy versions of products (for example: low-fiber bread vs. high-fiber bread, whole milk vs. reduced-fat milk vs. low-fat or nonfat milk) [ 32 ]. A multicenter study in Hartford, CT, combined customer shopping behavior with store food inventory data, and concluded that when there is a greater variety of fresh products, such as fruits and vegetables, there is an increased likelihood of these products being purchased [ 34 ]. This research study conducted face-to-face interviews on different days and at different times of the day to measure typical food shopping behaviors and determine whether shoppers had access to and used the Supplemental Nutrition Assistance Program (SNAP) and Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) benefits in the store [ 34 ]. They also maintained inventories, using a modified version of the Nutrition Environment Measures Survey in Stores (which included the availability of fresh/canned fruits and vegetables, and whole grain and reduced-fat dairy products) to measure availability and quality of healthy food in stores [ 34 ]. Due to an increased variety of fruits and vegetables offered, those receiving SNAP were significantly more likely to purchase fruit ( p < 0.05) and vegetables ( p < 0.01), compared with those who were not receiving SNAP [ 34 ].

When it comes to the cost of fresh products, more healthful versions of food items are typically more expensive than the corresponding less-healthful versions, with the exception of milk [ 35 ]. At the same time, foods with high energy density provide the most calories at a lower cost, which contributes to people in low-income populations consuming them over fresh, nutrient-dense products [ 35 ]. Alteration of this environment, as with the study discussed above by Martin et al. [ 34 ], may significantly change these consumption habits.

5. Environmental Approaches to Improve Food Security and Nutritional Status: Multilevel Intervention Studies with Residents of Low-Income Communities

Figure 1 uses the framework of the socioecological model (SEM) concept, as outlined by the Centers for Disease Control and Prevention [ 36 ], to summarize the key multilevel approaches discussed in this review for improving food security and nutritional status. Environmental approaches and nutrition programs can improve food security more efficiently if they work in a multilevel interaction approach [ 36 ]. This interaction starts from working personally with the individual, to creating a support system of family, friends, and social networks [ 36 ]. Other more significant factors that can assist this population are resources provided through schools or workplaces, communities (considering cultural values and norms), and policy changes from local laws to national changes [ 36 ]. Multilevel approaches have been used to increase the fruit and vegetable intake in low-income housing communities. Table 2 summarizes the key approaches that have led to improvements in food security. A 12-month cluster, randomized controlled trial (RCT) was conducted to demonstrate the benefit of year-round fruit and vegetable markets at improving fruit and vegetable (F&V) intake for low-income adults [ 37 ]. This research study featured discount fresh fruit and vegetable markets called ‘Fresh to You’ (FTY), as well as a multicomponent, educational intervention, which was a combination of individual, community, and policy change approaches [ 37 ]. The prices of the fruits and vegetables were kept at or below the retail prices at local supermarkets [ 37 ]. This study also included chef-run cooking demonstrations, taste-testing events, shared recipes, and educational boxes (including newsletters, DVDs, reusable shopping bags, and kitchenware) and two six-week educational/motivational campaigns, which focused on increasing intake and variety of fruits and vegetables [ 37 ]. The results indicated that more than half of the participants attended a few of the FTY markets [ 37 ]. From baseline to 12 months, there was an increase in total fruit and vegetable intake by 0.44 cup/day (as assessed by the National Cancer Institute’s ‘Eating at America’s Table All Day Screener’) in the intervention group with the control group decreasing intake by 0.08 cup/day [ 37 ]. There was also an increase in the frequency of fruits and vegetables by 0.24 servings per day, which was assessed through the Fruit and Vegetable Habits Questionnaire Score [ 37 ]. Reading newsletters or attending campaign events was not associated with any change in fruit and vegetable consumption, but watching DVDs was associated with an increase in fruit and vegetable intake by 0.69 cup/day [ 37 ].

Summary of key studies to increase access to healthy foods.

↑ indicates increase, ↓ indicates reduction.

Multilevel approaches to increase healthy food consumption in low-income populations, based on the SEM [ 36 ].

Adding culturally competent aspects to a community approach, with resources focused on multiethnic communities, materials provided in the population’s native language, and incorporation of recipes and food demonstrations from their country of origin, may result in improvements in diet quality [ 38 ]. Hammons et al. [ 38 ] added these cultural aspects and reported greater interest for the individuals to try recipes provided with fruits and vegetables and an overall increase in daily servings of fruits and vegetables consumed [ 38 ]. The results are supported by another study that reported an increase in variety of fruits and vegetables ( p < 0.01 for both), after a six-week cooking program at the Community Food Bank that teaches food pantry clients plant-based recipes [ 39 ]. These data further indicated a decrease in purchases of carbonated beverages and desserts with a value of p < 0.01 [ 39 ].

B’more Healthy Communities for Kids was a multilevel obesity prevention study that sought to improve healthier food purchasing and help reduce sweet-snack consumption among low-income African-American youths [ 40 ]. This group worked to achieve the objectives for two-year time period, through youth-led nutrition education in recreation centers, in-store promotion, and social media programs [ 40 ]. In this multilevel approach, many areas for education were considered, including promoting healthier alternatives to beverages and snacks, and sharing information for healthier cooking methods through promoting healthier cooking ingredients, such as low-fat milk, 100% whole wheat bread, and fresh/canned/frozen vegetables [ 40 ]. The multiple components involved wholesales, which were encouraged to stock B’more Healthy Communities for Kids-promoted healthier food alternatives [ 40 ]. They also worked on improving supply for healthier options of foods and beverages in corner stores and carryout restaurants, as well as improving demand through taste tests for healthier alternatives [ 40 ]. Posters, handouts, and educational sessions were provided. Through these sessions there were peer-led, hands-on activities where participants learned different sugar and fat contents in drinks and snacks and were introduced to the traffic light labeling method for beverages and snacks [ 40 ]. For additional resources, recipes, news, and educational activities related to healthier eating behaviors were sent through social media and texting [ 40 ]. The text message platform was focused towards caregivers with goal setting strategies and educational activities, where they received messages three to five times a week related to healthier eating behavior [ 40 ].

B’more Healthy Communities for Kids also worked with key city stakeholders to support policies for a healthier food environment and provided evidence-based information to support the development of policies at the city level using a Geographic Information System/System Science simulation model to help stakeholder decision-making in regard to sugar-sweetened beverage warning labels, urban farm tax credits, mobile meals, etc. [ 40 ]. The results showed that the overall intervention group purchased 1.4 more healthier foods and beverages per week in relation to the comparison youth. Additionally, there was a decrease in calorie intake from sweet snacks and desserts among older intervention youths [ 40 ]. This finding was supported by other results in improving the availability of healthier foods and beverages in small food stores in intervention zones, indicating that food availability affects an individual’s choice [ 40 ]. Overweight and obesity are major issues, especially in low-income communities, but community-enhanced school programs can be effective in reducing childhood obesity in these populations [ 41 ]. Schools with health program training and community partnerships decreased the percentage of students classified as overweight/obese by 8.3%, compared to a 1.3% decrease in schools that were provided only with health program training, without the community aspect [ 41 ].

In the case of emergency food aid, there are also food banks open and available around the US. Wetherill et al. [ 42 ] conducted a study to look a strategies and innovative programs that are focused on advancing nutrition-focused food banking in the United States. This study included in-person or phone interviews to obtain further information regarding personal experiences, perceptions, and practices related to nutrition-focused food banking [ 42 ]. Overall, the study findings indicated that food banks are implementing a variety of multi-level approaches to improve healthy food access among users [ 42 ]. This is done through four major themes: building a healthier food inventory at the food bank; enhancing partner agency healthy food access, storage, and distribution capacity; nutrition education outreach; and expanding community partnerships and intervention settings for healthy food distribution, including healthcare and schools [ 42 ].

5.1. Individual Nutrition Education and Peer Education

The main factors that have been shown to increase fruit and vegetable consumption in multilevel approaches are taste tests and nutrition education, including providing healthy recipes for participants [ 37 ]. Along with healthy recipes, some studies have focused on helping improve participants’ cooking skills to make low-cost meals, which further decreased food insecurity, by increasing cooking at home and reducing the amount of time people ate out at restaurants, leading to increased total variety of vegetables and fruits in the diet [ 34 , 37 , 39 , 40 ]. Even short-term nutrition education has made people more aware concerning sodium in processed foods and nutrient and calorie-dense foods, the importance of physical activity, and interpreting nutrition levels [ 43 ].

WIC focuses on improving the health and nutritional status of pregnant and postpartum low-income women and children up to the age of 5 years. The participants of these programs are faced with many barriers when shopping for the correct foods on their WIC food list, therefore nutrition education is a great tool to better assist this population [ 44 ]. Through mobile phone apps, there are educational tools to help the participants with shopping management features, clinic appointments, and informational resources [ 44 ]. Shopping management features assisted the WIC participants with real-time shopping for WIC foods in the grocery store, including food benefit balance checking, barcode scanning, and checking if the item was WIC eligible [ 44 ]. The participants shared that this app was very easy to use and convenient in helping pick the right products, while saving time [ 44 ]. Some applications also allowed participants to manage their WIC clinic visits, and view their future appointments and the documents needed to be provided in the appointments [ 44 ]. Required nutrition education modules were also features, which could be completed from home instead of going to the clinic [ 44 ]. Throughout a 7 month period, Weber et al. [ 44 ] reviewed the main features of a publicly available mobile phone app for WIC participants and concluded that, even though all the features were beneficial and useful, the most frequently used were the shopping management features. Some users shared their feedback, giving the app 4–5 stars out of 5 stars for being time saving, convenient, and an overall great app [ 44 ]. Other users would have liked in addition a nutrition/healthy section with recipes and ideas to help people learn to consume healthier choices, not just the WIC-approved foods [ 44 ].

Another approach to increase diet quality for low-income populations is individual tailoring of nutrition education [ 18 ]. A recent pilot 8 week study assessed the effectiveness and acceptability of personalized nutrition intervention for mobile food pantry users [ 45 ]. When comparing the treatment group with the control group, a personalized nutrition education intervention was effective in improving the diet for food-insecure participants (4.54% vs. 0.18% improvement in healthy eating index scores) [ 45 ]. Culturally tailored nutrition education involving family time and physical activity has also been a way to incorporate healthier food choices [ 46 , 47 ]. These approaches also include educational information in the participant’s native language, including all the handouts, recipes, and visual guides [ 47 ]. Focusing on diet based on culture, studies have also worked with tiendas, small Latino stores, to promote greater intake of fresh produce among consumers [ 46 ].

Education programs culturally tailored to a specific group have also been shown to be effective. Flores-Luevano et al. [ 48 ] conducted a bilingual culturally tailored, hands-on diabetes education program among Mexican American adults with diabetes. The sessions were interactive with demonstrations, activities to promote problem-solving, and facilitated group dynamics through sharing personal experiences [ 48 ]. This, intertwined with peer-education, resulted in improvements in glycated hemoglobin by −1.1% and total cholesterol with −17.2 mg/dL at 6 months post-intervention [ 48 ]. There were also behavioral changes, such as glucose self-monitoring improvement by 1.3 times increase a week, increased exercise levels, and increased positive nutritional behavior by 2.23, and the benefits were observed with attendance rates as low as 50% [ 48 ].

Marshall et al. [ 49 ] conducted a two-year follow-up study using a one-group pre-post evaluation design that focused on school-based nutrition education and food co-op intervention and how it can increase children’s intake of fruits and vegetables. In this study, 407 families completed baseline data, of which 262 parent-dyads agreed to participate in the two-year follow-up study, where the parents were provided with education along with their children [ 49 ]. This nutrition education included changes in home setting, such as increased frequency of cooking behaviors, increased usage of nutrition facts labels in making grocery purchasing decisions, and increased food availability of fruits and vegetables [ 49 ]. The results of the study showed an increase in child intake of fruits by 0.18 cup/day, vegetables by 0.14 cup/day, and fiber by 1.06 g/1000 kcal, and a significant decrease in total fat intake by 1.55 g/1000 kcal and percent daily calories from sugary beverages by 0.52% [ 49 ]. Parents also reported an increase in daily intake of vegetables by 0.6 cups/d and combined fruits and vegetables ( p < 0.05) [ 49 ].

5.2. Community-Based Participatory Research

Community-based participatory research is a kind of study where community members, organizational representatives, and academic researchers are all equally involved in the process [ 50 ]. This research method is important to collect the data on what kind of lessons and resources will be beneficial for the population, but also to make the participants more comfortable and more open when conducting interviews with educators of the community. This approach was used in the selected studies to modify elements in the environment, which would result in an increase in nutrient-dense food consumption. A number of studies have focused on improving diet quality and reducing metabolic risk through gardening and cooking [ 47 , 48 , 51 ]. These studies organized classes tailored towards low-income youths and consisted of lessons about gardening, where they used “hands-on” approaches to facilitate participation in planting, growing, and harvesting organic fruits and vegetables [ 47 , 48 , 51 ]. Additional interactive cooking and nutrition lessons were included with the fruits and vegetables raised from the garden [ 47 , 51 ].

These elements worked to increase fruit and vegetable consumption and preparation of healthy snacks and meals [ 47 , 51 ]. Creative food preparation with blending of new vegetables into juices or other dishes has also been a way to introduce unfamiliar foods to children and other family members [ 47 , 51 , 52 ]. Participants shared that their children would be curious of the new items introduced each week and they were excited to include them in their smoothies [ 47 , 51 , 52 ].

As previously mentioned, accessibility and affordability are two of the main factors that lead to food security. Since supermarkets and convenience stores are known to have expensive products, a way to provide access to food in low-income families is community-supported agriculture products. Community-supported agriculture products are more affordable and flexible in their accessibility [ 52 ]. Farm Fresh Foods for Healthy Kids examined the perception of food access among low-income families in nine communities participating in community-supported agriculture [ 52 ]. Participants reported improved access to food products and benefited from flexible pick-up times and locations; however, despite the cost being relatively low, payment remained a barrier for some [ 52 ]. A multistate randomized intervention trial targeted obesity prevention in low-income families through improving access to affordable, local, seasonal produce through community-supported agriculture and support of obesity-related behavior changes through tailored education to increase knowledge and skills, and provide increased revenue and business to support community-supported agriculture farmers [ 53 ]. Even though the community took steps to help low-income populations, there are still barriers that need to be faced for more effective results of current and future studies [ 53 ]. When shopping through community-supported agriculture, participants believed that they were saving money for produce of high quality, compared to the grocery stores [ 53 ].

McGuirt et al. [ 54 ] examined the influence of farmers’ market pricing and accessibility on willingness to shop at farmers’ markets, among low-income women. Percentage price savings were presented visually as discounts at the standard amount, or there were pictures of the amount of produce a consumer could buy at the farmers’ market compared to the supermarket, reflecting the savings [ 54 ]. The different quantity bought with the same price was determined by a member of the research group who went to local supermarkets to establish the price per pound and calculate the amount to compare with the products from the farmers’ market [ 54 ]. The results of this study showed that there was an increased interest to shop at farmers’ markets when there was at least a 20% price saving [ 54 , 55 ]. Additionally, participants were more influenced by the visual representation of a greater quantity of produce with the price savings, rather than the money saved by the reduced price [ 54 ].

5.3. Policy Changes in Supplemental Nutrition Programs

To reduce energy-dense, low-nutrient food consumption, there need to be environmental and policy changes that promote healthy eating. The main categories that need to be in focus, based on research, are pricing, nutrition labeling, and access to healthier ready-to-go foods [ 55 , 56 , 57 ]. Additionally, to increase food security and diet quality, policy changes are needed in school programs that work on strengthening links to the traditional, nutrient-dense food system in schools [ 58 ]. A significant increase in fruit and vegetable intake has been shown with greater access to healthier ready-to-go foods [ 58 ]. Additionally, reducing the cost of healthier snacks increases the consumption of these products [ 57 , 59 ]. When prices of fruits and vegetables were reduced by 50% for high school students, their consumption increased by twofold to fourfold [ 55 ]. Low-fat snack sales increased by 93% with a 50% reduction in their prices [ 59 ]. Studies also worked with key city stakeholders to support policies for a healthier food environment and provided evidence-based information to support the development of these policies [ 40 ].

6. Discussion and Conclusions

Food insecurity is a widespread problem in the US that greatly affects quality of life, leading to greater risk for obesity, diabetes, and cardiovascular diseases. There are a number of barriers causing food insecurity in certain areas, such as lack of transportation, food deserts or food swamps, and lack of nutrition education. This review paper discussed certain approaches to reduce food insecurity in low-income communities and increase access to healthful foods, especially consumption of fruits and vegetables. Multilevel approaches are shown to have the most distinguishable results and also take into consideration a wide spectrum of reasons and factors. Multilevel approaches have included nutrition educational material, taste-testing events, price reduction of healthy products, increased access to healthy options, and overall policy changes. The major limitation to multilevel approaches is that when a change is observed, there is not a way to specifically identify which components of the intervention led to changes in food behaviors [ 35 ]. Other potential limitations of studies thus far have included small sample sizes and sample populations that were predominantly women, self-reporting of data, potentially leading to bias, and the possibility that participants may not have fully represented the low-income population of interest [ 38 , 39 , 53 , 55 ]. Beneficial approaches comprise community-based research, which obtains an input from the community, where the main focus is on factors that lead to food insecurity and how to reduce these barriers. Farmers’ markets that provide local, seasonal, affordable produce are shown to be a way to support behavior changes and increase access to fresh produce [ 12 ]. Nutrition education helps decrease food insecurity from a different approach, through hands-on activities and peer-education to increase cooking skills and help incorporate a variety of fruits and vegetables in the diet [ 17 , 37 , 49 ].

Additionally, current studies have shown a need for more research, but it is important to point out that perception of an individual’s food environment may impact the foods they purchase and consume [ 60 ]. For example, if they perceive the environment to be poor, they may be less likely to buy fruits and vegetables and other healthy options [ 60 ]. Furthermore, future research can further focus on how to effectively improve diet quality and reduce diet-related chronic disease risk by developing and validating multi-dimensional intervention studies tailored for target populations with special needs and barriers, and studying the impact of the perceived food environment and social support on improving the diet quality of a population with poor access to healthy foods.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, D.Z., O.K.C. and M.J.P.; writing—original draft preparation, D.Z.; writing—review and editing, D.Z., O.K.C. and M.J.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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