Food insecurity (FI) is defined by the United States Department of Agriculture (USDA) as a ‘limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire acceptable foods in socially acceptable ways’(Reference Bickel, Nord and Price1). Estimates from a study in 2017 showed that 11·8 % of US households are food insecure with higher prevalence among adults living alone, non-White and low-income households(Reference Coleman-Jensen, Rabbit and Gregory2). The National Health and Nutrition Examination Survey (NHANES) data have shown that the prevalence of FI doubled from approximately 9 % in 2005–2006 to 18 % in 2011–2012(Reference Berkowitz, Berkowitz and Meigs3). This has been partially attributed to the Economic Recession (2007–2009), which has subsequently improved although not to pre-recession proportions(Reference Gundersen and Ziliak4,Reference Seligman and Berkowitz5) . Prevalence estimates of FI are much higher in low-income, underserved, and substance-using individuals and communities, with estimates ranging from 42 to 71 %(Reference Kushel, Gupta and Gee6–Reference Weiser, Frongillo and Ragland10).
In addition to income and poverty, other factors appear to exacerbate risk of FI such as minority status or comorbid health problems. Sexual minority communities have higher proportions of FI compared to the general population(Reference Patterson, Russomanno and Tree11), as do people reporting chronic health conditions (e.g. back problems and arthritis)(Reference Tarasuk, Mitchell and McLaren12). In turn, FI may increase risk of adverse health outcomes by hindering access and adherence to medical care(Reference Gundersen and Ziliak4). Among people living with HIV, FI is observed at nearly fivefold higher than in the general population(Reference Normén, Chan and Braitstein13) and has also been associated with prevalent drug use(Reference Palar, Laraia and Tsai14). Global HIV research has examined the bidirectionality of this association, suggesting that FI may increase HIV risk transmission behaviours (e.g. risky sexual practices), susceptibility to HIV once exposed (e.g. nutrient deficiencies leading to immunologic decline), and decreased access and adherence to treatment and care(Reference Rollins15–Reference Weiser, Young and Cohen20). Among low-income urban men who have sex with men (MSM), FI was reported to be a strong contributor of risky sexual practices (transactional and condom-less sex)(Reference Whittle, Palar and Napoles21).
The link between FI (and food insufficiency, defined by measures other than the USDA tool) and depressive symptoms has been well documented. A recent systematic review of 57 US studies has shown that FI is associated with a 2·74 (95 % CI 2·52, 2·97) increase in the odds of testing positive for depression (n 135 500)(Reference Arenas, Thomas and Wang22). Cohort data among those with HIV and hepatitis C virus (HCV) co-infection have shown that FI precedes depressive symptoms, and that even when FI remits, depressive symptoms can persist(Reference Canadian Co-infection Cohort, Aibibula and Cox23). Other studies have investigated depressive symptoms as an intermediary rather than as an outcome. Among those with HIV–HCV co-infection, depressive symptoms partially mediate the effect of severe FI on HIV viral load suppression(Reference Aibibula, Cox and Hamelin24). In this analysis, we conceptualise FI as a driver of depressive symptoms.
Biological pathways linking FI to depressive symptoms are likely to exist. However, this may require the assumption that FI is linked to poor dietary intake. To illustrate, within the burgeoning field of nutritional psychiatry, there is emerging evidence suggesting that the gut–brain axis may play a role in depression(Reference Evrensel and Ceylan25–Reference Adan, van der Beek and Buitelaar27). For example, chronic low-fibre diets may lead to microbiome depletion, resulting in intestinal permeability, inflammation, neuroinflammation and depressive symptoms(Reference Deehan and Walter28–Reference Swann, Kilpatrick and Breslin30). A recent systematic review and meta-analysis concluded that diet may have a direct effect on biological processes (e.g. chronic inflammation and neurotransmitter production) involved in depression(Reference Wu, Lin and Tsai31). Interestingly, data from the US suggest that FI is associated with obesity(Reference Pan, Sherry and Njai32–Reference Dhurandhar35) which may be linked to overall poor diet quality(Reference Hanson and Connor36). Given that obesity has been strongly correlated with depressive symptoms(Reference Milano, Ambrosio and Carizzone37–Reference Gowey, Khodneva and Tison39), we adjust for BMI in all analyses.
Individuals using substances (which includes drugs and alcohol) are more likely to be food insecure. In an urban youth population in Boston (n 400, ages 15–25), substance use was associated with a 2·5 (95 % CI 1·5, 4·3) increase in the odds of FI(Reference Baer, Scherer and Fleegler40). Among people who use drugs, FI may have biological implications that link exacerbation of mental health problems to ongoing substance use. Relationships between FI and substance use may be reciprocal, related to: (1) reduced intake and absorption of food; (2) reduced access to food due to competing demands on resources; (3) stigma and lack of social support; (4) negative coping behaviour related to depression/anxiety; and (5) help-seeking and treatment adherence(Reference Anema, Mehra, Weiser and Watson41). Many investigators believe that drug use contributes to FI due to prioritisation of substances over food purchases(Reference Whittle, Sheira and Frongillo42). These authors have also posited that FI can be a structural driver of drug use (e.g. individuals with FI find the appetite-suppressing effect of stimulants appealing). The current study investigates the former hypothesis that drug use drives FI. This relationship may be particularly pronounced among people who inject drugs(Reference McLinden, Moodie and Harper43). Given that individuals using drugs are frequently malnourished and report preferences for nutrient-poor foods(Reference Baptiste and Hamelin44–Reference Saeland, Haugen and Eriksen47), we hypothesise that FI will partially mediate the established association between drug use and depressive symptoms(Reference Javanbakht, Shoptaw and Ragsdale48,Reference Davis, Uezato and Newell49) .
Taken together, FI strongly correlates with poverty, BMI, depressive symptoms, drug use and HIV status. This study examines FI as a mediator between drug use and mental health outcomes. The goals of the current study are to test if: (1) drug use is an independent predictor of FI, controlling for HIV status and other known risk factors; (2) FI is an independent predictor of depressive symptoms, controlling for drug use, HIV status and other known risk factors; and (3) the relationship between drug use and depressive symptoms is partially mediated by FI.
Materials and methods
Study population
Data for the present study come from the mSTUDY(Reference Javanbakht, Shoptaw and Ragsdale48,Reference Javanbakht, Ragsdale and Shoptaw50) , an ongoing National Institute on Drug Abuse (NIDA)-sponsored longitudinal study of HIV-positive and HIV-negative MSM with varied substance use behaviours (NIDA project U01DA036267). The mSTUDY was approved by the University of California, Los Angeles (UCLA) Institutional Review Board, and all individuals provided written informed consent at study entry. Study enrolment began in August 2014 and is ongoing. Eligible participants reported being assigned male sex at birth, aged 18–45 years; if HIV-negative, reported having sex with men in the past 12 months, and were recruited from two community clinics in Los Angeles, CA. Participants complete assessments every 6 months, including a comprehensive physical exam and medical history, urine drug panel, clinical laboratory tests and computer-assisted detailed behavioural questionnaire. By design, half of the sample is HIV-positive and the other half HIV-negative. Participants were remunerated for study participation ($75 per study visit). The current analysis uses data collected from May 2017 when the FI questions were added to the battery of behavioural data collected as part of the computer-assisted self-interviews. Data for this analysis include all visits from May 2017 (prior visits were excluded because FI measure was not yet introduced) through June 2019, totalling 1192 person-visits from 431 participants.
Social, behavioural and biological covariates
FI is operationalised using the US Household Food Security Module(51) from the Economic Research Service of the USDA. It is a three-stage design with screeners, where an affirmative answer on a question from one stage advances participants to the next. The final stage asks about not eating for an entire day because there was not enough money to buy food. The raw score (0–10) is then categorised as: high food security, marginal food security, low food security and very low food security, which can then be dichotomised into food secure (high or marginal) or food insecure (low or very low). Depressive symptoms is measured using the Center for Epidemiologic Studies Depression (CESD) scale, a 20-item validated measure for capturing depressive symptomatology in the general population(Reference Radloff52). Scores range from 0 to 60. The standard cut-point for ‘likely depressed’ is 16; however, the cut-point of <23/23+ is used to classify clinically meaningful symptoms linked with likely diagnosis of depressive disorder(Reference Henry, Grant and Cropsey53).
Education is categorised as: didn’t finish high school (0–11 years), high school (12 years), some college (13–15 years) and college grad+ (16 years) (reference group). Ethnicity is categorised as Black, White (reference group), Other Race and Hispanic/Latino. Other Race includes categories: American Indian or Alaskan Native, Asian, Asian Indian, and Native Hawaiian Pacific Islander which were all collapsed due to small sample size. Income is categorised as: $0–19 999, $20 000–39 999 and $40 000+ (reference group). An indicator variable was created if the participant endorsed current use of cigarette or e-cig/vape (combined). Urine drug screen (Fastect® II Drug Screen Dipstick Test D, Brenan Medical Corporation, Irvine, CA) documented recent use of any of the following: methamphetamine, opiates, cocaine, ecstasy, marijuana, amphetamines and fentanyl (all drugs were tested using separate drug screen tests). A positive test for any of these drugs of misuse defined the participant as positive for current drug use. HIV status is determined positive or negative based on serology (positive antibody + viral load and negative antibody test). Age is classified as: 18–29, 30–39 and 40–49 years (reference group). Height and weight are measured at each visit to calculate BMI and was categorised using the standard US definition: underweight (below 18·5), normal weight (18·5–25) (reference group), overweight (25–29·99) and obese (30 and above).
Statistical analysis
All data analyses were performed using Stata version 16.0(54). Sample characteristics were ascertained at the first visit where FI data were collected (i.e. index visit for this analysis). Bivariate analysis of all covariates in relation to FI were conducted to examine trends and significance (set at P < 0·05). All CI were set to 95 %. Logistic regression using a random intercept with unstructured correlation was conducted using the dichotomised FI outcome. The second logistic model used the dichotomised depression score (at or above 23) as the outcome. General structural equation modelling tested whether FI mediates the relationship between drug use and depressive symptoms. Mediation analysis was conducted using linear regression (continuous variables for FI and depressive symptoms), adjusting for the same set of covariates. Because mediation analysis is less robust when using binary mediators and outcomes(Reference VanderWeele55), continuous versions of the FI and CESD were used. Mediation percent was ascertained by dividing indirect effects by direct effects.
Results
Sample characteristics
The mean age of study participants at the index visit, defined as the first study visit for this analysis based on availability of FI data (n 431), was 32·9 years (sd = 6·93). In our sample, 40·4 % identified as African American/Black, 37·6 % as Hispanic/Latino and more than half had some college or were college graduates. At the index visit, FI was detected in 50·8 % of participants and depressive symptoms in 36·7 %. Table 1 summarises characteristics of the sample stratified by food security status, as well as the overall total. At this index visit, those who were food insecure were more likely to use drugs (as verified by urine drug test), endorse current use of cigarette/vape, meet criteria for likely depressed and be in the lowest income category (P < 0·001 for all comparisons).
Sample characteristics of MSM from two clinics in Los Angeles, California, at baseline visit (n 431), stratified by food security status
MSM, men who have sex with men; CESD, Center for Epidemiologic Studies Depression.
* Based on CESD cut-point 22 (likely depressed is 23 or greater).
† Underweight <18·5; normal 18·5–24·9; overweight 25–29·9; obese ≥ 30.
Association between drug use and food insecurity
Table 2 summarises the logistic model with FI as the outcome, with n 426 across 1147 person-visits. As hypothesised, drug use is associated with 96 % increase in the odds of being food insecure (95 % CI 1·26, 3·07), after adjustment for covariates.
Adjusted mixed logistic random intercept model of drug use in food insecurity among MSM from two clinics in Los Angeles, California (n 426; 1147 person-visits)
MSM, men who have sex with men; aOR: adjusted OR.
* Underweight <18·5; normal 18·5–24·9; overweight 25–29·9; obese ≥ 30.
Association between food insecurity and depressive symptoms
Figure 1 displays the relationship between FI and depressive symptoms, demonstrating a graded relationship between FI and CESD score, with a line indicating our cut-point for likely depressed. The gradient remained after adjusting for HIV status, drug use, current cigarette/vape use, age, ethnicity, education, income and BMI in our final model. Table 3 shows the signal increases between FI and likelihood of depression (i.e. CESD score ≥ 23) in a significant and stepwise manner, after adjusting for HIV status, drug use, current cigarette/vape use, age, ethnicity, education, income and BMI. The final adjusted model showed that drug use associated with 2·22 times increase in the odds of depressive symptoms (95 % CI 1·36, 3·63), compared to those who did not screen positive for drugs.
Relationship between food security and mean depressive symptoms (CESD) score in an MSM cohort at two clinics in Los Angeles (n 430; 1170 person-visits). Red Line (Y = 23) represents cut-point for likely depressed. CESD, Center for Epidemiologic Studies Depression; MSM, men who have sex with men
Adjusted mixed logistic random intercept model of food security on depressive symptoms among MSM from two clinics in Los Angeles, California (n 425; 1126 person-visits)
MSM, men who have sex with men; aOR: adjusted OR.
* Underweight <18·5; normal 18·5–24·9; overweight 25–29·9; obese ≥ 30.
Mediation of drug use on depressive symptoms by food insecurity
Figure 2 shows that after adjusting for covariates, indirect effects equal 0·432 (product of the mediating pathways) and direct effects equal 2·905 (sum of main effect and indirect effect) using linear regression models. Our model suggests that 14·9 % of the relationship between drug use and depressive symptoms can be explained by FI, after adjusting for covariates. By using continuous variables for FI and CESD, we have generated the most robust and conservative mediation estimate relative to other approaches.
Mediation analysis using general structural equation modelling with linear regression, examining food insecurity as an intermediate between drug use and depressive symptoms among MSM in Los Angeles, California. Mediation analysis using general structural equation modeling with linear regression, examining food insecurity (continuous) as an intermediate between drug use (binary) and depressive symptoms (continuous), adjusting for covariates in a longitudinal sample of MSM at two clinics in Los Angeles, California (n=431; 1,190 visits). Dark lines represent indirect and direct effects, lighter lines represent model adjustment. MSM, men who have sex with men
Discussion
The current study showed that our community-based sample of diverse MSM in Los Angeles have approximately four times higher prevalence of FI than the general US population from 2017(Reference Coleman-Jensen, Rabbit and Gregory2). Our observed prevalence of FI (51 %) was similar to other studies of at-risk groups such as Hispanic adults (n 10 966) living in poverty in California (43 %)(Reference Becerra, Sis-Medina and Reyes9), HIV-positive participants in San Francisco, CA, (49 %)(Reference Weiser, Frongillo and Ragland10) and HIV-positive persons using intravenous drugs (71 %)(Reference Shannon, Kerr and Milloy7). When stratifying by individuals who screened positive for drug use, 59 % were FI at the index visit and 54 % reported FI over the entire study period. While FI decreased over time, depressive symptoms were relatively stable (36·7 % at index visit and 36 % overall).
Consistent with our hypothesis, testing positive for recent drug use almost doubled the odds of being food insecure. This compares with a study of people who inject drugs that showed being food insecure was associated with sharing of needles, increasing the likelihood of HIV transmission(Reference Strike, Rudzinski and Patterson56). In conjunction with the findings of the current study, addressing food-related needs among persons who use drugs is timely and warranted. A simple, actionable effort to provide assistance with food access is one avenue to reducing suffering and towards improving mood in marginalised groups with substance use disorders. Meanwhile, provision of food assistance does not necessarily address the upstream determinants of FI; therefore, greater attention is needed to address the occurrence of FI in the first place(Reference Tarasuk57). Notwithstanding, findings provide evidence for including FI measures in programmes and efforts to address health conditions faced by those with comorbidities of HIV, HIV-risk, mental health, substance use and poverty.
The signal strength that increased in a linear stepwise fashion between increasing levels of FI and increasing scores on the CESD suggest a strong relationship between these variables, even after adjustment for social, behavioural and biological factors. A similar finding of increasing signal strength between FI and likelihood of depression is reported in studies of nationally representative samples of Supplemental Nutrition Assistance Program (SNAP) participants(Reference Leung, Epel and Willett58). Future research might aim to determine if improving FI decreases depressive symptoms, in both drug-using and non-using populations.
Our hypothesis for mediation analysis of drug use on depressive symptoms by FI was generated to explore the questions: (1) what happens when individuals spend their money on drugs instead of food? and (2) does chronic malnourishment lead to depression? Importantly, FI is not the same thing as malnourishment, or even hunger(Reference Jones, Ngure and Pelto59). Although these concepts overlap, they are not synonymous. However, for the sake of this discussion, we are assuming that individuals with FI have worse nutritional status than those who are food secure, particularly in the context of drug use. This assumption is used to triangulate findings from the field of nutritional psychiatry which describes links between nutrition and depressive symptoms(Reference Jacka26–Reference Wu, Lin and Tsai31). Given the emerging evidence on the relationship between nutrition and mental health (i.e. the gut–brain axis), it is biologically plausible that limited or compromised eating patterns can exacerbate mood disorders, particularly in those who are using drugs.
Using general equation structural modelling, our data suggest that nearly 15 % of the relationship between drug use and depressive symptoms can be explained by FI, adjusting for covariates. While it has been established that FI is associated with poor diet quality (e.g. processed foods) known to increase chronic disease risk(Reference Leung, Epel and Ritchie60), less is known about the connection between FI and mental health in people who use drugs. However, one recent study examined effect modification between drug use and mood disorders with FI as the outcome(Reference Davison, Holloway and Gondara61). Novel findings linking nutrition to mental health have led to new conceptual models which identify ecological (e.g. food availability), economic (e.g. income, education) and social (e.g. gender, stigma) factors in the FI–mood–substance intersection. Our work provides additional support for the explanatory pathways suggested in their model, but there are many questions in the domain of nutritional psychiatry that remain unanswered. For example, there is a need to understand how drugs impact gut health in humans, and whether gut-based nutrition interventions can improve mental health during abstinence(Reference Wiss62,Reference Wiss63) or if/when the individual continues to use drugs. Future research should investigate if efforts to address FI lead to improved nutritional status, and whether or not improved nutritional status can improve mood. It has been established that anti-inflammatory diets (high in dietary fibre and n-3 fats) may reduce risk for depressive symptoms(Reference Taylor and Holscher64–Reference Spencer, Korosi and Layé66), but this remains less clear among disadvantaged groups.
There are multiple potential modes by which FI might exacerbate risk of depressive symptoms. In addition to the potential for nutrient deficiencies/imbalances and altered gastrointestinal function leading to inflammatory conditions, there is a potential pathway through the psychological impact of experiencing FI. Deprivation and socio-economic disadvantage will be stressful independent of nutritional factors. Meanwhile, we are interested in highlighting potential mechanisms explaining the association between FI and mental health, because it may inform novel intervention targets. For example, there is substantial literature suggesting that probiotics significantly reduce depressive symptoms(Reference Huang, Wang and Hu67–Reference Smith, Greene and Babu69), such as through the production of butyrate (a short chain fatty acid) in the large intestine(Reference Knudsen, Lærke and Hedemann70). Other authors have proposed that the microbial production of and delivery of neuroactive substances such as serotonin and gamma-aminobutyric acid might explain links to depression(Reference Evrensel and Ceylan25). Emerging evidence suggests that substances (e.g. methamphetamine) alter the microbiome, promote inflammation and may even mediate behavioural responses to drugs(Reference Cook, Fulcher and Tobin71–Reference Lee, Vuong and Nusbaum73). Taken together, FI may partially explain the impact of drug use on depression, with a cumulative impact over the life course. Intervening on FI through targeted nutrition interventions including life skills such as grocery shopping and cooking(Reference Moore, Gray and Wiss74,Reference Wiss, Schellenberger and Prelip75) might have potential to alleviate depressive symptoms among those dependent on drugs, which should be investigated in prospective cohorts.
The findings from this study should be interpreted in light of its limitations. While the data come from a longitudinal cohort and were analysed with repeated measures analysis, there remains challenges with ascertaining temporal precedence between all study variables. The current analysis was underpowered for lagged analysis across multiple time points. Therefore, it is possible that FI can increase risk of drug use, creating a reverse causation effect. Since many drugs decrease appetite, this is not implausible and has been proposed(Reference Whittle, Sheira and Frongillo42). The role of alcohol consumption could also contribute to FI as well as depressive symptoms and was not included in our analysis. Furthermore, the current study did not directly address dietary intake, which would be required to make any conclusions about the link between nutrition and mental health. It is also possible that FI is merely a proxy for other qualities of low socioeconomic status which can exacerbate risk for drug use and comorbid mental health problems. Finally, medication status was not included in our analysis and will be important to consider when making future inferences linking nutrition to mental health, particularly for those on HIV medications (antiretroviral therapy), anti-depressants or medication-assisted treatment.
Conclusion
In this cohort of HIV-positive and high-risk HIV-negative MSM, drug use almost doubled the odds of being food insecure. Increasing levels of FI was associated with a graded increase in the odds of depressive symptoms. Our models found that drug use more than doubled the odds of meeting criteria for being likely depressed. Mediation analysis adjusting for covariates found that FI partially explained the association between drug use and depressive symptoms. Public health efforts to enhance access to food and nutrition may be one way to improve depressive symptoms among MSM who use drugs and are at risk for HIV infection. Efforts to address the structural drivers of FI including social factors that predispose individuals to use drugs may prove beneficial for long-term mental health outcomes among MSM.
Acknowledgements
Acknowledgements: None. Financial support: Data for the present study come from the mSTUDY, an ongoing NIDA-sponsored longitudinal study of HIV-positive and HIV-negative MSM with varied substance use behaviours (NIDA project U01DA036267). Conflict of interest: There are no conflicts of interest. Authorship: All authors participated in the conception of the study and contributed to the final manuscript. D.A.W. generated hypotheses, conducted statistical analysis and wrote the first draft. M.J. and M.J.L. assisted with conceptualisation, statistical analysis and contributed to each draft revision. R.B. was involved in data collection and draft revision. M.P., S.S. and P.M.G. were involved along the entire process including multiple revisions of the manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the University of California, Los Angeles (UCLA) Institutional Review Board (IRB). Written informed consent was obtained from all subjects/patients.
