Introduction
This study sought to determine the association of individual-level food insecurity (FI) with mental health status across all global regions.
Methods
Cross-sectional data were analyzed in 2016 from the 2014 Gallup World Poll, a series of globally implemented, nationally representative surveys. FI was assessed using the Food Insecurity Experience Scale Survey Module for Individuals, an eight-question psychometric scale reporting individuals’ experiences of FI. Individual-level composite indices of mental health, the Negative Experience Index and Positive Experience Index (0–100 scale), were calculated based on responses to five questions of respondents’ recent negative and positive experiences, respectively, associated with depression and mental distress.
Results
The prevalence of any FI ranged from 18.3% in East Asia to 76.1% in Sub-Saharan Africa. In global analyses (149 countries) using adjusted multiple regression analyses, FI was associated in a dose–response fashion with poorer scores on the mental health indices (coefficient [95% CI]: Negative Experience Index: mild FI, 10.4 [9.5, 11.2]; moderate FI, 17.7 [16.4, 19.0]; severe FI, 24.5 [22.7, 26.3]; Positive Experience Index: mild FI, –8.3 [–9.3, –7.4]; moderate FI, –12.6 [–13.8, –11.3]; severe FI, –16.2 [–17.9, –14.5]). Within-region analyses (11 regions) consistently demonstrated the same trends.
Conclusions
FI is associated with poorer mental health and specific psychosocial stressors across global regions independent of SES. The numerous pathways via which FI may contribute to common mental disorders, and the broad social implications of FI linked to cultural norms and self-efficacy, may contribute to the cross-cultural consistency of the findings.
Introduction
Despite considerable progress over the past quarter century in increasing global food production, nearly 795 million people worldwide remain food insecure.
1
Food insecurity (FI) is a complex phenomenon that encompasses food availability, affordability, the cultural norms that dictate acceptable means of acquiring food, and individual food utilization.2
Given the diverse set of conditions that contribute to FI, it is perhaps not surprising that FI is associated with a diversity of nutrition-related health outcomes including dietary inadequacies,3
early child growth faltering,4
obesity,5
poor physical health,6
low educational achievement,7
and developmental deficits in children.8
However, not all health consequences of FI have direct nutritional etiologies. In fact, adverse mental health outcomes among food-insecure individuals, not necessarily linked to nutritional deficiencies or excesses, are of increasing concern.9
, 10
, 11
, 12
Mental and substance use disorders are the leading cause of years lived with disability worldwide and account for 7.4% of all disability-adjusted life years.
13
Nearly one in three individuals (29.2%) globally experience a common mental disorder during their lifetime such as depression, anxiety, and somatic symptom disorders.14
FI may be a key contributor to common mental disorders through several different mechanisms. First, by generating uncertainty over the ability to maintain food supplies, or to acquire sufficient food in the future, FI can provoke a stress response that may contribute to anxiety and depression.15
, 16
Furthermore, acquiring foods in socially unacceptable ways can induce feelings of alienation, powerlessness, shame, and guilt that are associated with depression., 18
, 19
FI may also magnify socioeconomic disparities within households and communities that could increase cultural sensitivities and influence overall mental well-being.20
Existing research examining the relation between FI and mental health has disproportionately targeted areas with high levels of FI and emphasized the experiences of women rather than men.
20
Furthermore, there is substantial variation in the design of existing studies as well as in the measurement approaches used to assess FI and mental health status.21
, 22
, 23
, 24
Therefore, it is unclear if observed associations between FI and mental health vary across diverse contexts and populations, or the extent to which differences in design and measurement may be responsible for heterogeneous findings. If FI is indeed an important contributing factor to poor mental health outcomes, interventions to reduce FI may have unintended positive impacts. Therefore, confirming the strength and consistency of the relation between FI and mental health status across contexts could be important for attracting resources both to address FI and confront burgeoning mental health challenges in novel ways through food security interventions.The specific objectives of this study were to (1) determine the association of individual-level FI with mental health status across all global regions, and (2) assess heterogeneity in this association across global regions, and by age and sex of respondents. It was hypothesized that higher levels of individual-level FI would be associated with poorer mental health status across and within global regions, independent of SES. Given that women in high- and low-income countries have higher prevalence rates of mood and anxiety disorders than men,
14
and that FI may have differential health consequences for women than men,25
, 26
it was also hypothesized that sex would modify the association between FI and mental health status. Furthermore, given that the cognitive, emotional, and physical consequences of FI may be different for individuals across the life cycle,27
, 28
, 29
it was further hypothesized that age would modify the association between FI and mental health status.Methods
Study Sample
The data used for this 2016 analysis were from the 2014 Gallup World Poll (GWP), a series of nationally representative surveys of individuals aged ≥15 years that uses probability sampling covering both urban and rural areas.
30
In a first-stage sampling, 100–135 sampling units (i.e., clusters of households) were selected based on probabilities proportional to population size or random sampling (depending on availability of population information) and stratified by population size and geographic location. Households within sampling units and index respondents within households were then randomly selected in separate stages. The survey included a core questionnaire applied in nearly all countries as well as region-specific modules. Face-to-face interviews were carried out in all countries with <80% telephone coverage. The sample size of the GWP in each country varied depending on population size (Appendix Table 1, available online).Measures
The 2014 GWP assessed FI using the Food Insecurity Experience Scale Survey Module for Individuals (FIES SM-I). The FIES SM-I is a psychometric scale composed of eight questions with dichotomous responses that ask respondents to report experiences of FI of varying degrees of severity that are common across cultural contexts (e.g., anxiety, altering food quality, and limiting food intake) (Appendix Table 2, available online).
31
A single-parameter logistic Item Response Theory model was used to estimate severity parameters from each question response using conditional maximum likelihood methods, testing for model assumptions of equal discrimination and overall model fit via Rasch reliability statistics.32
The mean Rasch reliability for FIES SM-I was 0.740, and fell between 0.70 and 0.80 for 79% of countries in the GWP.32
Respondents’ FI status was calculated by summing the scores from all eight questions and categorizing them into one of four levels of FI (raw score range): food secure (0), mild FI (1–3), moderate FI (4–6), and severe FI (7–8).33
Because the GWP is administered at different times in different countries, a recall period of 12 months was used to exclude potential confounding by seasonality and to ensure cross-country comparability. A standard protocol based on formative research was developed to guide the translation of the questionnaire in each country.34
Aggregate indices of mental health calculated by Gallup Worldwide Research were used in analyses. The Negative Experience Index (NEI) is an individual-level composite index of responses to five questions that report data on recent negative experiences of the respondent that may impact overall well-being (Appendix Table 3, available online). The index was calculated by taking the mean of coded responses to all five questions and multiplying the result by 100. Values for the final index ranged from 0 to 100, with higher values indicating more negative recent experiences. Questions with missing data were excluded from the index, and the index was only calculated for an individual if at least four questions were answered. The Positive Experience Index (PEI) was constructed in the same manner as the NEI, but included data on self-reported assessment of recent positive experiences (Appendix Table 3, available online). Higher values on the PEI indicate more positive recent experiences. Gallup Worldwide Research tested the scale reliability of the indices. The NEI and PEI demonstrate Cronbach’s alphas of 0.80 and 0.91, respectively, when aggregated at the country level.
30
Statistical Analysis
Statistical analyses were carried out using Stata, version 13.1. Means and variance by global region were calculated for the FIES SM-I, NEI, and PEI. The proportion of FI among individuals within each region was also calculated. Post-stratification weights provided by Gallup that weight data by sex, age, education, and SES were used to ensure that estimates within each country were nationally representative. Taylor-linearized SEs that adjusted for the multistage sampling frame of the 2014 GWP were applied to all estimates.
Multiple linear regression and multiple logistic regression models, respectively, were used to examine the association of FI with the NEI and PEI, and the psychosocial conditions and experiences that constitute the indices (e.g., being treated with respect, experiencing worry, sadness, stress, anger, and enjoyment). Adjusted models controlled for urbanicity; annual household income (calculated from annualized household income data converted to international dollars using World Bank purchasing power parity figures for private consumption
35
); the number of children aged <15 years in the household; the sex, age, education, and employment status of the respondent; as well as country fixed effects. Statistical interactions were tested between FI and respondent age and sex. In select countries where telephone interviews were administered, questions for the FIES SM-I, NEI, and PEI were administered to half of respondents (i.e., in one of two nationally representative survey waves) (Appendix Table 4, available online). In sensitivity analyses, missing values for these variables were imputed using an iterative Markov-chain Monte Carlo multiple imputation method (Appendix Table 5, available online). For all models, SEs and variance–covariance matrices of the estimators were adjusted for within-country correlations. Associations were considered statistically significant at p<0.05.The informed consent of all survey participants was obtained and survey protocols were approved by the required governing bodies of each country.
Results
Data were available for 190,348 individuals in the 2014 GWP; FI data were available for 147,826 individuals. Mean respondent age was 42 (SD=17) years. In total, 11.7%, 52.4%, 25.5%, and 10.5% of the sample, respectively, was aged 15–19, 20–45, 46–65, and >65 years. The proportion of male (49.5%) and female (50.5%) respondents within each age grouping were nearly identical. Across the 11 assessed world regions, encompassing 149 countries, the prevalence of any FI among individuals ranged from 18.3% in East Asia to 76.1% in Sub-Saharan Africa (Table 1).
Table 1Food Insecurity Status and Mental Health Index Scores of Survey Respondents, by Region
| Variable | Australia and New Zealand | Central Asia | East Asia | Europe | Latin America and the Caribbean | Middle East and North Africa | North America | Russia and the Caucasus | South Asia | Southeast Asia | Sub-Saharan Africa |
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | 1,985 | 3,636 | 9,370 | 38,194 | 19,505 | 15,819 | 2,001 | 5,782 | 8,766 | 7,900 | 34,868 |
| FIES SM-I Score | 0.70 (0.59, 0.81) | 1.2 (1.1, 1.3) | 0.44 (0.41, 0.48) | 0.86 (0.84, 0.89) | 2.3 (2.3, 2.4) | 1.8 (1.7, 1.8) | 0.87 (0.75, 0.99) | 1.2 (1.1, 1.3) | 2.0 (2.0, 2.1) | 1.6 (1.5, 1.7) | 4.1 (4.1, 4.2) |
| Food insecurity level (%) | |||||||||||
| Food secure | 80.4 | 54.0 | 81.7 | 74.3 | 49.0 | 57.8 | 78.2 | 56.5 | 51.8 | 58.7 | 23.9 |
| Mild food insecurity | 11.3 | 34.2 | 14.6 | 16.0 | 21.1 | 19.2 | 10.6 | 32.8 | 22.1 | 20.3 | 19.4 |
| Moderate food insecurity | 5.0 | 8.1 | 2.9 | 6.3 | 13.5 | 13.3 | 6.3 | 8.2 | 13.8 | 12.9 | 22.9 |
| Severe food insecurity | 3.3 | 3.7 | 0.90 | 3.5 | 16.5 | 9.7 | 4.9 | 2.6 | 12.3 | 8.1 | 33.8 |
| n | 2,001 | 5,000 | 9,195 | 39,089 | 20,068 | 16,091 | 2,048 | 6,000 | 9,132 | 8,028 | 36,044 |
| Negative Experience Index | 27.0 (25.4, 28.6) | 17.4 (16.6, 18.2) | 18.7 (18.1, 19.3) | 28.1 (27.8, 28.5) | 31.2 (30.7, 31.7) | 34.9 (34.3, 35.5) | 32.1 (30.4, 33.8) | 21.0 (20.2, 21.8) | 28.2 (27.4, 29.0) | 26.2 (25.5, 27.0) | 30.0 (29.6, 30.3) |
| Positive Experience Index | 77.0 (75.6, 78.4) | 67.9 (67.0, 68.8) | 70.7 (70.0, 71.4) | 67.7 (67.4, 68.1) | 79.4 (79.0, 79.8) | 63.7 (63.2, 64.3) | 78.8 (77.4, 80.2) | 59.2 (58.3, 60.2) | 65.1 (64.3, 65.9) | 74.0 (73.3, 74.7) | 66.4 (66.0, 66.7) |
Note: Values are means (95% CIs) or proportions. The number of countries included in each region is as follows (Appendix Table 1, available online): North America: 2; Europe: 39; Australia and New Zealand: 2; Latin America and the Caribbean: 22; Middle East and North Africa: 17; Russia and the Caucasus: 5; Central Asia: 5; Sub-Saharan Africa: 36; South Asia: 7; East Asia: 6; Southeast Asia: 8. F statistics from ANOVA were used for comparisons of continuous variables, and Pearson’s χ2 tests were used for comparisons of proportions. All differences across regions were significant at p<0.001. Post-stratification weights and Taylor-linearized SEs that adjusted for the multistage sampling frame of the Gallup World Poll were applied to all estimates.
FIES SM-I, Food Insecurity Experience Scale Survey Module for Individuals.
Data for the mental health indices were available for 152,696 individuals. The PEI was highest in Latin America and the Caribbean region (79.4, 95% CI=79.0, 79.8) and lowest in Russia and the Caucasus (59.2, 95% CI=58.3, 60.2); the NEI was lowest in Central Asia (17.4, 95% CI=16.6, 18.2) and highest in the Middle East and North Africa region (34.9, 95% CI=34.3, 35.5) (Table 1).
Using the aggregated global data set, individual-level FI was associated with poorer mental health status in a dose–response fashion (coefficient [95% CI]: NEI: mild FI, 10.4 [9.5, 11.2]; moderate FI, 17.7 [16.4, 19.0]; severe FI, 24.5 [22.7, 26.3]; PEI: mild FI, –8.3 [–9.3, –7.4]; moderate FI, –12.6 [–13.8, –11.3]; severe FI, –16.2 [–17.9, –14.5]) (Table 2). The magnitude of the association between FI and the mental health indices was greater than that of the assessed socioeconomic variables (i.e., education and employment status of respondent, and annual household income) (Table 2). In interaction models, no consistent interaction was observed between FI and sex on the association with the NEI or PEI (p>0.05). However, age interacted with FI such that, among older individuals, the association between FI was more positive for the NEI (p<0.001) and more negative for the PEI (p<0.01) (Table 2, Appendix Figures 1 and 2, Appendix Table 6, available online). In regional analyses, this interaction for individuals aged >65 years was observed only in middle- and high-income regions (p<0.05) (i.e., Australia and New Zealand, Europe, Latin America and the Caribbean, Middle East, and North Africa) (Appendix Table 7, available online).
Table 2Multiple Regression Analyses of the Association of Food Insecurity Status With Mental Health Indices
| Variable | Negative Experience Index, coefficient (95% CI) | Positive Experience Index, coefficient (95% CI) |
|---|---|---|
| Unadjusted analyses | ||
| n | 147,356 | 147,356 |
| Food insecurity | ||
| Food secure (ref) | — | — |
| Mild | 11.2*** (10.3, 12.1) | –9.4*** (–10.4, –8.4) |
| Moderate | 19.1*** (17.8, 20.4) | –14.2*** (–15.6, –12.9) |
| Severe | 26.7*** (24.9, 28.5) | –18.7*** (–20.5, –16.9) |
| Adjusted analyses | ||
| n | 140,351 | 140,351 |
| Food insecurity | ||
| Food secure (ref) | — | — |
| Mild | 10.4*** (9.5, 11.2) | –8.3*** (–9.3, –7.4) |
| Moderate | 17.7*** (16.4, 19.0) | –12.6*** (–13.8, –11.3) |
| Severe | 24.5*** (22.7, 26.3) | –16.2*** (–17.9, –14.5) |
| Urbanicity | ||
| Town | 0.85* (0.19, 1.5) | –0.19 (–1.0, 0.66) |
| Suburb | 1.3** (0.40, 2.2) | 0.05 (–1.1, 1.2) |
| City | 1.9*** (1.1, 2.7) | –0.36 (–1.2, 0.51) |
| Age of respondent | 0.16*** (0.13, 0.19) | –0.19*** (–0.22, –0.15) |
| Sex of respondent | ||
| Female | 3.3*** (2.7, 3.9) | –0.24 (–0.69, 0.21) |
| Education level of respondent | ||
| Secondary education | –1.1** (–1.8, –0.43) | 2.1*** (1.3, 2.8) |
| Post-secondary education | –1.2* (–2.1, –0.20) | 4.5*** (3.6, 5.5) |
| Employment status of respondent | ||
| Employed full-time by employer | –4.0*** (–5.1, –2.9) | 1.4** (0.43, 2.4) |
| Self-employed full-time | –3.4*** (–4.7, –2.1) | 2.2*** (1.0, 3.4) |
| Employed part-time (not seeking full time) | –4.5*** (–5.9, –3.2) | 2.3** (0.99, 3.5) |
| Employed part-time (seeking full time) | –2.5*** (–3.8, –1.3) | 1.9** (0.73, 3.0) |
| Out of workforce | –5.2*** (–6.4, –4.1) | 1.3** (0.35, 2.3) |
| Number of children in household | –0.02 (–0.04, 0.01) | 0.02 (–0.00, 0.05) |
| Quintiles of annual household income | ||
| Very low | 2.0*** (1.3, 2.8) | –2.0*** (–2.7, –1.3) |
| Low | 0.80** (0.32, 1.3) | –0.83** (–1.3, –0.35) |
| High | –0.77** (–1.3, –0.22) | 1.1*** (0.53, 1.6) |
| Very high | –1.5*** (–2.1, –0.92) | 2.2*** (1.5, 2.8) |
| Interaction models | ||
| n | 140,351 | 140,351 |
| Food insecurity × age of respondent | ||
| Food secure (ref) | — | — |
| Mild | 0.16*** (0.12, 0.19) | –0.08*** (–0.11, –0.05) |
| Moderate | 0.19*** (0.15, 0.24) | –0.07** (–0.11, –0.03) |
| Severe | 0.19*** (0.14, 0.25) | –0.05 (–0.10, 0.01) |
Note: Boldface indicates statistical significance of the partial regression coefficients (*p<0.05; **p<0.01; ***p<0.001). Values are partial regression coefficients and 95% CIs from separate multiple regression equations. Unadjusted analyses control for country fixed effects. Adjusted models further control for the covariates shown. Interaction models control for all of the covariates shown in the adjusted models (main effects not shown; Appendix Table 6, available online). SEs and variance–covariance matrices of the estimators were adjusted for within-country correlations. Urbanicity distinctions are described by Gallup Worldwide Research as follows: City: “large city”; Suburb: “suburb of a large city”; Town: “small town or village”; Rural: “rural area or on a farm.” Income quintiles were calculated based on country-specific distributions. The reference categories for the variables in the model are as follows: urbanicity: rural; sex of respondent: male; education level of respondent: elementary education or less; employment status of respondent: unemployed; quintiles of annual household income: medium.
Similar to the globally aggregated analysis, within-region analyses showed that individual-level FI was associated with poorer mental health status, and that the extent of poor mental health was greater with increasing severity of FI (Table 3). This dose–response relationship held for both mental health indices across all regions, with the exception of the PEI for the Australia and New Zealand and East Asia regions.
Table 3Multiple Regression Analyses of the Association of Food Insecurity With Mental Health Indices, by Region
| Variable | Australia and New Zealand, coefficient (95% CI) | Central Asia, coefficient (95% CI) | East Asia, coefficient (95% CI) | Europe, coefficient (95% CI) | LAC, coefficient (95% CI) | MENA, coefficient (95% CI) | North America, coefficient (95% CI) | Russia and the Caucasus, coefficient (95% CI) | South Asia, coefficient (95% CI) | Southeast Asia, coefficient (95% CI) | Sub-Saharan Africa, coefficient (95% CI) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Negative Experience Index | |||||||||||
| n | 1,887 | 3,626 | 8,837 | 36,832 | 19,184 | 12,764 | 1,942 | 5,780 | 8,753 | 6,891 | 33,855 |
| Food insecurity | |||||||||||
| Food secure | — | — | — | — | — | — | — | — | — | — | — |
| Mild | 14.0 (–13.8, 41.9) | 6.9* (1.8, 12.0) | 12.0** (7.7, 16.3) | 13.3** (11.8, 14.9) | 9.0*** (7.3, 10.7) | 8.9*** (6.0, 11.7) | 17.7* (12.0, 23.4) | 8.2** (4.8, 11.6) | 11.1** (5.9, 16.3) | 7.9*** (5.1, 10.8) | 6.5*** (5.0, 8.0) |
| Moderate | 16.8* (11.3, 22.3) | 13.8* (5.7, 22.0) | 27.6*** (21.9, 33.2) | 22.4*** (20.0, 24.8) | 16.5*** (14.3, 18.8) | 18.3*** (15.0, 21.7) | 24.9* (1.4, 48.3) | 17.3*** (13.7, 21.0) | 20.1** (10.8, 29.5) | 16.1*** (13.7, 18.4) | 12.1*** (10.4, 13.9) |
| Severe | 35.7* (15.6, 55.7) | 26.6** (14.7, 38.4) | 21.1*** (17.3, 24.8) | 33.6*** (29.9, 37.3) | 22.6*** (19.6, 25.6) | 26.4*** (23.4, 29.5) | 30.2** (24.6, 35.8) | 26.4*** (21.3, 31.6) | 31.1** (18.1, 44.1) | 25.2*** (20.8, 29.5) | 18.7*** (16.7, 20.7) |
| Positive Experience Index | |||||||||||
| n | 1,887 | 3,626 | 8,837 | 36,832 | 19,184 | 12,764 | 1,942 | 5,780 | 8,753 | 6,891 | 3,855 |
| Food insecurity | |||||||||||
| Food secure (ref) | — | — | — | — | — | — | — | — | — | — | — |
| Mild | –9.9 (–36.9, 17.1) | –7.2* (–13.5, –0.88) | –9.1*** (–11.3, –6.9) | –11.1*** (–12.8, –9.3) | –5.1*** (–6.8, –3.3) | –7.6*** (–9.7, –5.4) | –7.5* (–11.0, –4.1) | –9.0** (–12.9, –5.1) | –10.2* (–17.3, –3.1) | –6.4** (–8.7, –4.1) | –5.6*** (–7.0, –4.1) |
| Moderate | –5.8 (–29.1, 17.5) | –13.6* (–23.2, –4.0) | –23.9** (–33.0, –14.8) | –16.5*** (–18.5, –14.4) | –9.1*** (–11.8, –6.4) | –11.8*** (–15.2, –8.4) | –14.8 (–59.0, 29.4) | –16.5** (–21.5, –11.6) | –16.1** (–23.8, –8.3) | –9.3** (–14.8, –3.8) | –9.4*** (–10.9, –7.8) |
| Severe | –26.9* (–45.9, –8.0) | –22.7* (–45.0, –0.29) | –16.8** (–25.6, –8.0) | –22.8*** (–26.3, –19.2) | –10.4*** (–12.7, –8.1) | –14.4*** (–19.3, –9.5) | –19.2 (–75.2, 36.8) | –19.3** (–25.5, –13.0) | –21.4** (–32.7, –10.2) | –11.3** (–16.2, –6.4) | –14.3*** (–16.6, –11.9) |
Note: Boldface indicates statistical significance of the partial regression coefficients (*p<0.05; **p<0.01; ***p<0.001). Values are partial regression coefficients and 95% CIs from separate multiple regression equations. All models control for urbanicity, age, sex, education level, and employment status of respondent, number of children in household, quintiles of annual household income, and country fixed effects. SEs and variance–covariance matrices of the estimators were adjusted for within-country correlations.
LAC, Latin America and the Caribbean; MENA, Middle East and North Africa.
Individual-level FI was consistently associated, in a dose–response fashion, with higher and lower odds, respectively, of recently experiencing negative psychosocial conditions (e.g., sadness, worry, stress, anger) and positive psychosocial conditions (e.g., enjoyment, feeling well rested, being treated with respect) (p<0.001) (Table 4). FI at any level was associated with higher odds of recent experience of worry and sadness than other negative experiences and conditions.
Table 4Multiple Logistic Regression Analyses of the Association of Food Insecurity With Psychosocial Conditions and Experiences
| Variable | Physical and mental health conditions and experiences | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Experience physical pain, OR (95% CI) | Experience worry, OR (95% CI) | Experience sadness, OR (95% CI) | Experience stress, OR (95% CI) | Experience anger, OR (95% CI) | Feel well-rested, OR (95% CI) | Treated with respect, OR (95% CI) | Smile or laugh a lot, OR (95% CI) | Learn or do something interesting, OR (95% CI) | Experience enjoyment, OR (95% CI) | |
| n | 140,351 | 140,351 | 140,351 | 140,351 | 140,351 | 140,351 | 139,339 | 139,391 | 140,351 | 140,351 |
| Food insecurity | ||||||||||
| Food secure (ref) | — | — | — | — | — | — | — | — | — | — |
| Mild | 1.6*** (1.5, 1.6) | 2.1*** (2.0, 2.2) | 1.9*** (1.8, 2.0) | 1.8*** (1.7, 1.9) | 1.6*** (1.5, 1.7) | 0.64*** (0.60, 0.68) | 0.61*** (0.56, 0.65) | 0.60*** (0.57, 0.64) | 0.72*** (0.68, 0.75) | 0.60*** (0.56, 0.64) |
| Moderate | 2.1*** (2.0, 2.2) | 3.1*** (2.9, 3.3) | 2.9*** (2.7, 3.2) | 2.6*** (2.4, 2.8) | 2.3*** (2.1, 2.4) | 0.49*** (0.45, 0.52) | 0.48*** (0.44, 0.52) | 0.50*** (0.47, 0.54) | 0.62*** (0.57, 0.67) | 0.46*** (0.43, 0.50) |
| Severe | 2.5*** (2.4, 2.7) | 4.2*** (3.9, 4.6) | 4.3*** (3.9, 4.8) | 3.5*** (3.1, 4.0) | 3.1*** (2.8, 3.4) | 0.41*** (0.38, 0.45) | 0.37*** (0.33, 0.42) | 0.43*** (0.39, 0.47) | 0.56*** (0.51, 0.60) | 0.38*** (0.34, 0.42) |
Note: Boldface indicates statistical significance of the partial regression coefficients (*p<0.05; **p<0.01; ***p<0.001). Values are ORs and 95% CIs from separate multiple logistic regression equations. All models control for urbanicity, age, sex, education level, and employment status of respondent, number of children in household, quintiles of annual household income, and country fixed effects. SEs and variance–covariance matrices of the estimators were adjusted for within-country correlations. All of the psychosocial conditions and experiences shown are constituent questions from the Gallup Negative Experience and Positive Experience Indices (Appendix Table 3, available online).
Discussion
Independent of socioeconomic and demographic characteristics of respondents, individual-level FI was associated with poorer mental health status in a dose–response fashion in both global and region-specific analyses using two different aggregate indices of mental health and well-being. The consistency of this association across all global regions suggests that this relation applies across cultural contexts. Experiences of FI, including worrying about food, acquiring food in socially unacceptable ways, disruptions of meal patterns, family rituals, and intra-familial transfer of knowledge and practices, as well as alterations in food quality and quantity, are common across different cultures.
31
Many of these experiences of FI are themselves reflective of the psychosocial conditions that underlie poor mental health status (e.g., worry, anxiety), or are associated with similar social cues across cultures (e.g., feelings of shame, guilt, exclusion, and powerlessness associated with food insufficiency or acquiring food in socially unacceptable ways).36
, 37
, 38
, 39
Therefore, the cross-cultural consistency of the findings from this study is aligned both with prior research and evidence-based theory regarding people’s lived experiences of FI.The consistent dose–response trend observed in the association between FI and mental health status strengthens the plausible causal nature of this association.
40
This trend suggests that the psychosocial stressors that underlie the mental health indices examined may be amplified with increasing FI. For example, anxiety related to one’s ability to acquire sufficient food in the future may be provoked even under conditions of mild FI, and is likely to increase with moderate and severe FI.41
Alternatively, multiple pathways from FI to poorer mental health may be invoked with increasing severity of FI. Under conditions of more severe FI, for example, individuals may resort to acquiring food in socially unacceptable ways as a coping strategy.31
The feelings of shame and guilt associated with this behavior could compound pre-existing anxiety precipitated by mild FI to yield even poorer mental health conditions.It was hypothesized that sex would modify the association between FI and mental health status. However, this effect modification was not observed. Previous studies that have examined the association of FI with mental health have been largely female-dominated samples, and therefore have not assessed sex-based differences in this relation.
20
Though women in this study scored more poorly on the mental health indices than men, the findings suggest that the potential influence of FI on mental health and well-being is similar for men and women. Yet, effect modification by age was observed. The association between FI was more positive and negative for the NEI and the PEI, respectively, particularly among individuals aged >65 years. The prevalence of mental and neurologic disorders is high among older adults.42
Indeed, increasing age was associated with poorer mental health status (Table 2). Older individuals may also be especially vulnerable to FI because of limited mobility, low incomes, and ill health.43
Previous research has shown FI to be associated with depressive symptoms in the elderly.44
, 45
Variation in access to social safety net programs and familial and social support systems for elderly individuals may explain why the interaction with age was only observed among middle- and high-income regions.46
Limitations
To the author’s knowledge, this study is the first to carry out a global analysis of the association between FI and mental health status. The GWP data allow the unique opportunity to universally assess this association using standardized measures across a diverse geographic sample of both men and women. However, this study has several limitations. It was hypothesized that FI influences mental health status, yet the direction of this relation cannot be determined from this cross-sectional analysis, nor can confounding by unobserved factors (or imperfect adjustment) be ruled out despite rigorous adjustment in regression models. It is plausible, for example, that poor mental health status could influence individual-level FI or that the two conditions are mutually reinforcing. Indeed, psychosocial determinants may underlie material deprivation associated with FI,
47
, 48
and with intragenerational and intergenerational feedback loops that strengthen and perpetuate health inequities. The distinct recall periods of the FIES-SM-I, NEI, and PEI may also limit the ability to infer temporal congruity in the observed associations. Additionally, though the FIES SM-I was explicitly designed for cross-cultural comparability and assesses domains of FI that are common across contexts,31
, 32
it is possible that not all estimates of FI are directly comparable across countries.49
However, testing of the equal discrimination assumption of Rasch models for the FIES SM-I indicated satisfactory standardized item infit values to justify inclusion of all FIES SM-I items in all countries.32
Therefore, it is unlikely that incomparability across countries strongly influenced the observed results. Missing data from countries where some data were collected in only one survey wave were also a potential limitation. However, analyses using imputed data for missing values of FI, the NEI, and PEI indicated that the findings were robust to missing data (Appendix Table 5, available online). Finally, though the NEI and PEI demonstrated high internal consistency, the external validity or clinical relevance of these indices has not been assessed. Such validation is commonly done by examining scale performance in relation to a psychiatric assessment to establish cut points for diagnosing common mental disorders among specific populations.50
No cut points for the NEI or PEI were used in this analysis, and these indices cannot be interpreted as clinical diagnoses of psychosis, mood, or somatoform disorders. Rather, used as continuous scores, they indicate the extent to which individuals experienced psychosocial conditions that are common to widely used and validated instruments that assess mental health status.51
, 52
For globally aggregated analyses, it is likely not possible to develop a uniform instrument that reflects a globally valid clinical assessment of mental health status. A substantial research literature has demonstrated differences in conceptualizations of mental health distress across cultures, which raises concerns about cross-cultural meaning equivalence of validated mental health assessments.14
Therefore, assessing experiences of psychosocial conditions that have been shown to be associated with depression and mental distress in related scales across contexts, with the aim of examining an empirical relation (and not establishing diagnostic cut points), is likely an appropriate approach.Conclusions
Individual-level FI is associated with poorer mental health outcomes across all global regions, independent of socioeconomic factors. The consistency of this association suggests that scaling up successful FI interventions may yield benefits to mental health outcomes across a diversity of settings. Evaluations of interventions, however, require thoughtful measurement of FI, explicit assessment of mental health, and an examination of the impact pathways via which FI components influence psychosocial conditions and experiences. Components of FI that may have direct implications for mental health status (e.g., socially unacceptable food acquisition behaviors) are absent from common FI assessment tools.
53
, 54
, 55
Yet, culturally appropriate and valid tools are needed to assess these behaviors. Improved monitoring and evaluation of mental health outcomes is also needed. This is a particular challenge in low- and middle-income countries because nearly a quarter of these countries have no system for reporting mental health information, and many are hindered by lack of accountability.56
Developing robust monitoring systems and strengthening the measurement of both FI and mental health to more comprehensively understand their relation across contexts may help to inform interventions that can effectively address the mental health consequences of FI.Acknowledgments
ADJ conceived the research questions and study design, carried out the statistical analyses, and wrote and approved the final manuscript. This study did not receive financial support from any particular agency or institute. The data set used for this analysis was acquired through a non-funded agreement as the result of a competitive call for proposals by the Food and Agriculture Organization of the UN (FAO) “Voices of the Hungry” project. FAO had no role in the design of this study, the analysis or interpretation of data used in this study, the writing of this manuscript, or the decision to submit this manuscript for publication.
No financial disclosures are reported by the author of this paper.
SUPPLEMENTAL MATERIAL
Supplementary material
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