Abstract
Objective
Increased out-of-pocket health-care expenditures may exert budget pressure on low-income households that leads to food insecurity. The objective of the present study was to examine whether older adults with higher chronic disease burden are at increased risk of food insecurity.
Design
Secondary analysis of the 2013 Health and Retirement Study (HRS) Health Care and Nutrition Study (HCNS) linked to the 2012 nationally representative HRS.
Setting
USA.
Subjects
Respondents of the 2013 HRS HCNS with household incomes <300 % of the federal poverty line (n 3552). Chronic disease burden was categorized by number of concurrent chronic conditions (0–1, 2–4, ≥5 conditions), with multiple chronic conditions (MCC) defined as ≥2 conditions.
Results
The prevalence of food insecurity was 27·8 %. Compared with those having 0–1 conditions, respondents with MCC were significantly more likely to report food insecurity, with the adjusted odds ratio for those with 2–4 conditions being 2·12 (95 % CI 1·45, 3·09) and for those with ≥5 conditions being 3·64 (95 % CI 2·47, 5·37).
Conclusions
A heavy chronic disease burden likely exerts substantial pressure on the household budgets of older adults, creating an increased risk for food insecurity. Given the high prevalence of food insecurity among older adults, screening those with MCC for food insecurity in the clinical setting may be warranted in order to refer to community food resources.
Keywords: Multiple chronic conditions, Food insecurity, Geriatrics, Vulnerable populations
About 75 % of older adults have ≥2 concurrent chronic health conditions, referred to as ‘multiple chronic conditions’ (MCC)( 1 – 3 ). Because the prevalence and burden of MCC are anticipated to escalate with an increasing ageing population, MCC is growing as a significant public health and clinical care problem( 1 – 3 ). Older adults with MCC experience increased demands on their household budgets, including increased health-care expenditures and increased food expenditures necessary to meet the relatively expensive nutritional recommendations for conditions such as diabetes and hypertension( 3 , 4 ). These households may therefore be at greater risk of food insecurity, defined as the uncertain or limited access to nutritionally adequate and safe foods( 5 – 7 ).
From 2001 to 2015, the number of older adults who were food insecure more than doubled to 5·4 million individuals( 8 ). Individuals living in food-insecure households must sometimes reduce food intake, miss meals or alter dietary intake by shifting to less expensive foods, often including those high in added sugar, salt and fat( 5 , 7 , 9 – 12 ). Thus, not only may MCC increase risk of food insecurity, but the resulting food insecurity could then contribute to the development or exacerbation of chronic diseases such as diabetes( 5 , 6 ). Multiple studies have demonstrated that the added economic vulnerability of food insecurity can lead to trade-offs with chronic disease management, including cost-related medication non-adherence (e.g. skipping or reducing medication doses) and suboptimal chronic disease management( 2 , 13 – 16 ).
Among older adults, little is known about the relationship between food insecurity and MCC. In an analysis of the Canadian Community Health Survey focused on adults up to age 64 years, Tarasuk et al. reported an increased odds of food insecurity for respondents with 1, 2 or ≥3 chronic conditions compared with respondents with no chronic conditions( 13 ). Whether MCC increases the risk for food insecurity in vulnerable geriatric populations or in the USA remains unclear. Using the 2013 Health Care and Nutrition Study (HCNS), a supplemental survey of the national and population-based Health and Retirement Survey (HRS), we investigated the relationship between food insecurity and MCC among older adults living in the USA.
Methods
Study design and sample
We conducted a secondary cross-sectional analysis of the HRS, a national, population-based, longitudinal study of community-dwelling adults aged 50 years or older( 17 ) linked to the HCNS, which includes questions on food insecurity. The HCNS was mailed in 2013 to a sub-sample of HRS respondents (n 12 418) and had a response rate of 65 % for a final study population of 8073 HRS participants( 18 ). We excluded 165 (2·2 %) respondents from our analytic sample who did not complete the core 2012 HRS in which health and sociodemographic factors were assessed and another 474 (6·2 %) respondents who did not have valid food insecurity responses in the 2013 HCNS. In an effort to minimize unmeasured socio-economic confounding factors, we also restricted our analysis to those participants whose reported 2012 household income was under 300 % of the federal poverty line. Publicly available de-identified data were used for all analyses.
Food insecurity
The outcome of interest was food insecurity, assessed using the six-item Short Form of the US Household Food Security Survey Module( 19 ). The raw score of food security status was calculated as a sum of affirmative responses with a range of 0–6. Food insecurity status was assigned based on two or more affirmative answers (no food insecurity=score of 0–1; food insecurity=score of 2–6), based on official scoring recommendations from the US Department of Agriculture( 19 ).
Multiple chronic conditions
The main independent variable was MCC, defined as the number of self-reported co-morbid conditions, a commonly used measure for MCC( 20 ). We included self-report of fifteen conditions available in HRS: hypertension; diabetes; cancer (except skin); chronic obstructive pulmonary disease; heart disease; cerebrovascular artery disease; arthritis or rheumatism; urinary incontinence; osteoporosis; depression; emotional, nervous or psychiatric problems; cognitive impairment defined as Alzheimer’s disease, dementia, senility or any other serious memory impairment; vision impairment (fair or poor self-rated vision or being legally blind); hearing impairment (fair or poor self-rated hearing); and significant pain, defined as self-report of often troubled by moderate or severe pain. We created a categorical variable based on the number of co-morbid chronic conditions present (0–1, 2–4, ≥5).
Sociodemographic and health measures
We included the basic sociodemographic variables, as well as all the variables that were significantly associated with both MCC and food insecurity and might confound the relationship between MCC and food insecurity. We included age, sex, race/ethnicity, marital status, socio-economic status, health insurance, self-rated health, tobacco use, measured BMI and cost-related medication non-adherence. Socio-economic status variables included highest education level, wealth, household size, current employment status and housing type (own, rent or other arrangement). Cost-related medication non-adherence was defined as self-report of taking less medication than prescribed within the last 2 years due to cost.
Statistical analyses
We compared characteristics of participants with and without food insecurity using χ 2 tests for categorical variables and Kruskal–Wallis tests for continuous variables. To assess collinearity of variables, we examined the pairwise correlations and variance inflation factor for all variables; no adjustments were needed. Next, we examined unadjusted and adjusted association of food insecurity and MCC using logistic regression. In adjusted analysis, we adjusted for the following confounders: age, sex, race/ethnicity, marital status, health insurance status, self-rated health, employment status, household size, education, wealth, household size, smoking status and BMI. Age and household size were included as continuous measures. We used the HRS sampling and design weights provided to account for the probability of selection and clustering in the HRS( 21 ). Statistical significance was assessed at the 0·05 level. The statistical analyses were performed using the statistical software packages STATA version 13 (2015) and SAS version 9.4 (2013).
Results
Table 1 presents the sociodemographic and health characteristics of the 3552 respondents with an annual household income under 300 % of the federal poverty level included in the analysis. The overall prevalence of food insecurity was 27·8 %. Compared with respondents without food insecurity, food-insecure respondents were younger (mean 61·9 years old v. 69·4 years old) and more likely to be Black, Latino or Other/Unknown race/ethnicity. Food-insecure respondents were also more likely to be unmarried, have completed less than high-school education, have a larger household size, rent their primary housing and be currently employed. Food-insecure respondents reported a median wealth of $US 8500 compared with a median wealth of $US 135 000 among respondents without food insecurity. Among food-insecure respondents, almost a quarter reported no health insurance and more than 50 % reported fair or poor self-rated health.
Table 1.
Sociodemographic and health characteristics of study participants with household income under 300 % of the federal poverty line by food insecurity status: respondents to the 2013 Health and Retirement Study (HRS) Health Care and Nutrition Study (HCNS), USA
| Total study population (N 3552) | No food insecurity (N 2563) | Food insecurity (N 989) | ||
|---|---|---|---|---|
| % | % | % | P value* | |
| Age (years) | ||||
| Mean | 67·2 | 69·4 | 61·9 | <0·001 |
| sd | 10·3 | 10·2 | 8·5 | <0·001 |
| IQR | 52–100 | 52–100 | 52–97 | |
| 52–60 years | 32·5 | 22·8 | 56·4 | |
| 61–70 years | 31·7 | 33·3 | 27·6 | |
| 71–80 years | 22·9 | 27·4 | 11·7 | |
| ≥81 years | 13·0 | 16·5 | 4·3 | |
| Male | 41·3 | 42·1 | 39·6 | 0·2491 |
| Race/ethnicity | ||||
| White | 68·7 | 76·5 | 49·3 | <0·001 |
| Black | 14·3 | 11·6 | 20·9 | |
| Latino | 13·0 | 9·0 | 22·9 | |
| Other/unknown | 4·0 | 2·9 | 6·9 | |
| Married/partnered | 51·4 | 54·5 | 43·8 | <0·001 |
| Less than high-school education | 21·4 | 17·0 | 32·1 | <0·001 |
| Household size (including respondent) | ||||
| Mean | 2·15 | 2·02 | 2·49 | <0·001 |
| sd | 1·32 | 1·15 | 1·64 | |
| Range | 1–13 | 1–11 | 1–13 | |
| Housing type | ||||
| Own | 62·0 | 69·5 | 43·3 | <0·001 |
| Rent | 23·2 | 16·3 | 40·4 | |
| Other† | 14·8 | 14·2 | 16·2 | |
| Currently works for pay | 26·5 | 24·3 | 32·1 | <0·001 |
| Wealth ($US) | ||||
| Median | 80 000 | 135 000 | 8500 | <0·001 |
| IQR | 7000–249 000 | 35 000–330 000 | 0–57 000 | |
| No health insurance | 12·7 | 8·4 | 23·6 | <0·001 |
| Fair or poor self-rated health | 36·1 | 29·1 | 53·4 | <0·001 |
| Self-reported chronic conditions | ||||
| Hypertension | 62·4 | 60·7 | 66·8 | 0·009 |
| Diabetes | 24·3 | 22·5 | 28·8 | 0·001 |
| Cancer (except skin) | 14·8 | 15·7 | 12·7 | 0·093 |
| Chronic obstructive pulmonary disease | 13·6 | 10·8 | 20·4 | <0·001 |
| Heart disease | 25·3 | 25·4 | 25·0 | 0·851 |
| Cerebrovascular artery disease | 7·7 | 7·3 | 8·8 | 0·292 |
| Emotional, nervous or psychiatric problems | 23·1 | 18·6 | 34·3 | <0·001 |
| Depression | 26·4 | 20·7 | 40·7 | <0·001 |
| Cognitive impairment | 2·7 | 2·6 | 3·0 | 0·622 |
| Arthritis | 62·8 | 62·4 | 63·8 | 0·602 |
| Osteoporosis | 15·8 | 15·1 | 17·6 | 0·137 |
| Urinary incontinence | 25·7 | 24·6 | 28·4 | 0·104 |
| Vision impairment | 29·1 | 23·1 | 43·8 | <0·001 |
| Hearing impairment | 26·0 | 24·8 | 28·9 | 0·02 |
| Significant pain | 30·2 | 24·6 | 44·0 | <0·001 |
| Number of chronic conditions | ||||
| 0–1 | 16·5 | 19·4 | 9·3 | <0·001 |
| 2–4 | 46·9 | 49·1 | 41·5 | |
| ≥5 | 36·6 | 31·5 | 49·2 | |
| BMI (kg/m2) | ||||
| Mean | 28·8 | 28·2 | 30·1 | <0·001 |
| sd | 6·3 | 6·1 | 6·8 | |
| Underweight, <18·5 kg/m2 | 1·3 | 1·4 | 1·0 | |
| Normal weight, 18·5–25 kg/m2 | 29·3 | 31·2 | 24·7 | |
| Overweight, 25–30 kg/m2 | 33·6 | 35·7 | 28·5 | <0·001 |
| Obese, ≥30 kg/m2 | 35·8 | 31·7 | 45·8 | |
| Smoking status | ||||
| Never | 41·1 | 43·5 | 35·1 | <0·001 |
| Former | 39·7 | 41·9 | 34·5 | |
| Current | 19·2 | 14·6 | 30·4 | |
| Cost-related medication non-adherence‡ | 14·8 | 9·2 | 28·7 | <0·001 |
IQR, interquartile range.
P value compares respondents with no food insecurity with respondents with food insecurity.
Other includes those living on ranch, mobile home, with friends or relatives, or unspecified.
Cost-related medication non-adherence is defined as taking less medications than prescribed in the last 2 years because of the cost.
In this study sample, the most prevalent chronic conditions were hypertension (62·4 %), arthritis (62·8 %), significant pain (30·2 %) and vision impairment (29·1 %). About a quarter reported diabetes, heart disease, depression, urinary incontinence and hearing impairment. Hypertension, diabetes, depression, vision impairment and significant pain were more prevalent among those reporting food insecurity compared with those without food insecurity. Food-insecure respondents were more likely to report ≥2 chronic conditions (90·7 % v. 80·6 %, P<0·001), be obese (mean BMI 30·1 kg/m2 v. 28·2 kg/m2, P<0·001), be current smokers (30·4 % v. 14·6 %, P<0·001) and report cost-related medication non-adherence (28·7 % v. 9·2 %, P<0·001) compared with those reporting food security.
The multivariate logistic model examined the association between MCC and food insecurity (Table 2). Compared with those having 0–1 chronic conditions, respondents with ≥2 chronic conditions were more likely to have food insecurity (adjusted OR (AOR)=2·12; 95 % CI 1·45, 3·09 for 2–4 chronic conditions; AOR=3·64; 95 % CI 2·47, 5·37 for ≥5 chronic conditions). Respondents reporting cost-related medication non-adherence were about 1·9 times as likely as those without to be food insecure.
Table 2.
Adjusted odds for food insecurity by multiple chronic condition categories and sociodemographic and health measures among respondents to the 2013 Health and Retirement Study (HRS) Health Care and Nutrition Study (HCNS), USA
| Adjusted OR for food insecurity | ||
|---|---|---|
| AOR | 95 % CI | |
| Number of chronic conditions (ref.: 0–1) | ||
| 2–4 | 2·12 | 1·45, 3·09 |
| ≥5 | 3·64 | 2·47, 5·37 |
| Age | 0·93 | 0·92, 0·95 |
| Female (ref.: male) | 0·99 | 0·80, 1·24 |
| Race/ethnicity (ref.: White) | ||
| Black | 1·15 | 0·81, 1·65 |
| Latino | 1·89 | 1·34, 2·67 |
| Other/unknown | 1·93 | 1·13, 3·32 |
| Married or partnered (ref.: unmarried or single) | 0·61 | 0·45, 0·81 |
| Less than high school (ref.: high-school graduate or higher) | 1·64 | 1·28, 2·09 |
| Household size | 1·17 | 1·09, 1·26 |
| Housing type (ref.: own) | ||
| Rent | 1·10 | 0·78, 1·56 |
| Other* | 0·77 | 0·52, 1·15 |
| Currently works for pay (ref.: not working for pay) | 0·99 | 0·72, 1·36 |
| Wealth quartiles (ref.: quartile 4, wealthiest) | ||
| Quartile 3 | 1·80 | 0·97, 3·36 |
| Quartile 2 | 4·09 | 2·32, 7·23 |
| Quartile 1 | 6·43 | 3·50, 11·83 |
| No health insurance (ref.: insured) | 1·24 | 0·88, 1·73 |
| Fair or poor self-rated health (ref.: excellent to good self-rated health) | 1·19 | 0·93, 1·51 |
| BMI (ref.: normal weight, 18·5–25 kg/m2) | ||
| Underweight, <18·5 kg/m2 | 0·61 | 0·22, 1·71 |
| Overweight, 25–30 kg/m2 | 0·98 | 0·75, 1·29 |
| Obese, ≥30 kg/m2 | 1·13 | 0·88, 1·45 |
| Smoking status (ref.: never) | ||
| Former smoker | 0·88 | 0·71, 1·09 |
| Current smoker | 1·17 | 0·83, 1·66 |
| Cost-related medication non-adherence† (ref.: no cost-related medication non-adherence) | 1·90 | 1·46, 2·48 |
Ref., referent group.
Other includes those living on ranch, mobile home, with friends or relatives, or unspecified.
Cost-related medication non-adherence is defined as taking less medications than prescribed in the last 2 years because of the cost.
Discussion
To our knowledge, the present study is the first to examine the association between MCC and food insecurity in a representative population of older adults over 50 years of age in the USA. We found that among low-income older adults, older adults with two or more chronic conditions are at increased risk of food insecurity. We also found that food insecurity is associated with increased cost-related medication non-adherence.
In the current study of a diverse, nationally representative sample of older adults in the USA, the prevalence of food insecurity was 27·8 %, higher than in other population-based studies( 8 , 11 ). Consistent with national data, our study found that older adults aged <70 years with less than high-school education and lower income are at higher risk of food insecurity( 10 – 12 ). Our finding of increased risk for food insecurity with two or more chronic co-morbid conditions is consistent with analyses conducted in Canada showing similar relationships among younger adults( 13 ). In addition, a small study in the USA (North Carolina) has shown that food-insufficient homebound older women are more likely to report at least three chronic conditions( 22 ).
The mechanism by which MCC is associated with food insecurity is likely bidirectional( 13 ). A high chronic disease burden could exert added financial strain on the household budgets of low-income older adults, contributing to an increased risk for food insecurity and subsequent cost-related challenges to medication adherence( 23 , 24 ). Alternatively, sustained economic hardship including food insecurity can contribute to the development of chronic conditions and poor health status( 5 , 6 , 25 ).
Screening for food insecurity in clinical settings among low-income older adults with high chronic disease burden may be warranted to reduce the effects of food insecurity on chronic disease management. Food insecurity can be reduced by participating in food assistance programmes, such as the governmental Supplemental Nutrition Assistance Program (SNAP), which can improve access to food( 26 – 28 ). However, SNAP is underutilized among older adults, with only 40 % of eligible older adults enrolled to receive benefits( 29 ). In the primary care clinical setting, food-insecure older adults can be referred to social workers or case managers to facilitate enrolment into SNAP and connections to community-based food assistance programmes including food banks and pantries, hot meal sites and at-home delivery of meals( 30 , 31 ). In addition, clinicians can strategize to reduce out-of-pocket medication costs in order to support food security, such as minimizing medication co-payments and discontinuing non-essential medications( 2 , 14 ).
The present study has several limitations. First, the findings are based on self-report of chronic conditions and the available chronic conditions in HRS for analysis are not comprehensive of all possible chronic conditions. Second, food insecurity defined through the US Household Food Security Survey Module does not account for other ageing-related risk factors for malnutrition among older adults such as disability, functional limitations, poor dentition and change in taste that affect food choices and access to food. Future research should examine how these ageing-specific factors impact food insecurity among older adults with MCC.
Conclusion
In conclusion, our study demonstrates that low-income older adults with MCC in HRS have an increased risk for food insecurity, with more chronic conditions being associated with a higher risk. Screening for food insecurity in the primary care of older adults, especially those with substantial chronic disease burden, could identify at-risk older adults who could benefit from existing community and governmental food resources and programmes that could improve access to food and reduce the health effects of food insecurity. Food insecurity is a significant social determinant of health that has clinical implications in the chronic disease management and quality of life of this vulnerable population.
Acknowledgements
Financial support: This work was supported by the National Institute on Aging at the National Institutes of Health (W.J.B. and C.S.R., grant number P30 AG044281), (J.J., grant number R03 AG050880). The National Institute on Aging had no role in the design, analysis or writing of this article. Conflict of interest: H.K.S. receives funding from Feeding America, a 501c3 non-profit dedicated to ending hunger in the USA, to serve as its Lead Scientist and Senior Medical Advisor. All other authors declare no conflicts of interest. Authorship: J.J. and I.S.-C. led in writing the manuscript. I.S.-C. completed the data analysis. All authors contributed to the design of the study, interpretation of data, and participated in revising the content and approving the final version of the manuscript. Ethics of human subject participation: It was determined that the study did not meet the definition of human subjects research based on guidelines provided by the University of California San Francisco Institutional Review Board.
References
- 1. Wolff JL, Starfield B & Anderson G (2002) Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 162, 2269–2276. [DOI] [PubMed] [Google Scholar]
- 2. Schoenberg NE, Kim H, Edwards W et al. (2007) Burden of common multiple-morbidity constellations on out-of-pocket medical expenditures among older adults. Gerontologist 47, 423–437. [DOI] [PubMed] [Google Scholar]
- 3. Vogeli C, Shields AE, Lee TA et al. (2007) Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med 22, Suppl. 3, 391–395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Fried VM, Bernstein AB & Bush MA (2012) Multiple chronic conditions among adults aged 45 and over: trends over the past 10 years. NCHS Data Brief issue 100, 1–8. [PubMed] [Google Scholar]
- 5. Seligman HK, Laraia BA & Kushel MB (2010) Food insecurity is associated with chronic disease among low-income NHANES participants. J Nutr 140, 304–310. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Seligman HK, Jacobs EA, Lopez A et al. (2012) Food insecurity and glycemic control among low-income patients with type 2 diabetes. Diabetes Care 35, 233–238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Olson CM (1999) Nutrition and health outcomes associated with food insecurity and hunger. J Nutr 129, 2S Suppl, 521S–524S. [DOI] [PubMed] [Google Scholar]
- 8. Ziliak JP & Gundersen C (2017) The State of Senior Hunger in America 2015: An Annual Report – Supplement. http://www.feedingamerica.org/research/senior-hunger-research/state-of-senior-hunger-supplement-2015.pdf (accessed November 2017).
- 9. Kendall A, Olson C & Frongillo E (1996) Relationship of hunger and food insecurity to food availability and consumption. J Am Diet Assoc 96, 1019–1024. [DOI] [PubMed] [Google Scholar]
- 10. Lee JS & Frongillo EA (2001) Nutritional and health consequences are associated with food insecurity among US elderly persons. J Nutr 131, 1503–1509. [DOI] [PubMed] [Google Scholar]
- 11. Lee JS, Fischer JG & Johnson MA (2010) Food insecurity, food and nutrition programs, and aging: experiences from Georgia. J Nutr Elder 29, 116–149. [DOI] [PubMed] [Google Scholar]
- 12. Lee JS & Frongillo EA Jr (2001) Factors associated with food insecurity among US elderly persons: importance of functional impairments. J Gerontol B Psychol Sci Soc Sci 56, issue 2, S94–S99. [DOI] [PubMed] [Google Scholar]
- 13. Tarasuk V, Mitchell A, McLaren L et al. (2013) Chronic physical and mental health conditions among adults may increase vulnerability to household food insecurity. J Nutr 143, 1785–1793. [DOI] [PubMed] [Google Scholar]
- 14. Berkowitz SA, Seligman HK & Choudhry NK (2014) Treat or eat: food insecurity, cost-related medication underuse, and unmet needs. Am J Med 127, 303–310.e3. [DOI] [PubMed] [Google Scholar]
- 15. Bengle R, Sinnett S, Johnson T et al. (2010) Food insecurity is associated with cost-related medication non-adherence in community-dwelling, low-income older adults in Georgia. J Nutr Elder 29, 170–191. [DOI] [PubMed] [Google Scholar]
- 16. Sattler EL & Lee JS (2013) Persistent food insecurity is associated with higher levels of cost-related medication nonadherence in low-income older adults. J Nutr Gerontol Geriatr 32, 41–58. [DOI] [PubMed] [Google Scholar]
- 17. Health and Retirement Study (2017) HRS data handbook. https://hrs.isr.umich.edu/about/data-book (accessed January 2018).
- 18. Health Care and Nutrition Study (2013) HCNS 2013 data description. http://www.hrsonline.isr.umich.edu/modules/meta/2013/hcns/desc/2013HCNS_data_description.pdf? (accessed January 2016).
- 19. Blumberg S, Bialostosky K, Hamilton W et al. (1999) The effectiveness of a short form of the household food security scale. Am J Public Health 89, 1231–1234. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Huntley AL, Johnson R, Purdy S et al. (2012) Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med 10, 134–141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Health and Retirement Study (2016) HRS study design. http://www.hrsonline.isr.umich.edu/sitedocs/surveydesign.pdf? (accessed January 2016).
- 22. Sharkey JR (2003) Risk and presence of food insufficiency are associated with low nutrient intakes and multimorbidity among homebound older women who receive home-delivered meals. J Nutr 133, 3485–3491. [DOI] [PubMed] [Google Scholar]
- 23. Jeon Y-H, Essue B, Jan S et al. (2009) Economic hardship associated with managing chronic illness: a qualitative inquiry. BMC Health Serv Res 9, 182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Valtorta NK & Hanratty B (2013) Socioeconomic variation in the financial consequences of ill health for older people with chronic diseases: a systematic review. Maturitas 74, 313–333. [DOI] [PubMed] [Google Scholar]
- 25. Lynch JW, Kaplan GA & Shema SJ (1997) Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. N Engl J Med 337, 1889–1895. [DOI] [PubMed] [Google Scholar]
- 26. Gundersen C (2013) Food insecurity is an ongoing national concern. Adv Nutr 4, 36–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Ratcliffe C & McKernan S-M (2010) How Much Does SNAP Reduce Food Insecurity? Washington, DC: The Urban Institute. [Google Scholar]
- 28. Mabli J, Ohls J, Dragoset L et al. (2013) Measuring the Effect of Supplemental Nutrition Assistance Program (SNAP) Participation on Food Security. Cambridge, MA: Mathematica Policy Research for US Department of Agriculture, Food and Nutrition Service. [Google Scholar]
- 29. Food Research and Action Center (2017) Closing the senior SNAP gaps. http://www.frac.org/blog/closing-senior-snap-gaps (accessed November 2017).
- 30. Cannon M (2016) Screening and Interventions for Food Insecurity in Health Care Settings: State Strategies to Increase an Underutilized Practice in California. Oakland/Los Angeles, CA: California Food Policy Advocates. [Google Scholar]
- 31. Torres J, De Marchis E, Fichtenberg C et al. (2017) Identifying Food Insecurity in Health Care Settings: A Review of the Evidence. San Francisco, CA: Social Interventions Research & Evaluation Network. [Google Scholar]