have to write bibliography Write an Outline for your paper. From the articles you’ve looked at, what are some ideas/arguments you can make about the topic? What about a main thesis? Introduction — es
Swamped with your writing assignments? We'll take the academic weight off your shoulders. We complete all our papers from scratch. You can get a plagiarism report upon request just to confirm.
Order a Similar Paper Order a Different Paper
have to write bibliography
Write an Outline for your paper.
From the articles you’ve looked at, what are some ideas/arguments you can make about the topic? What about a main thesis?
Introduction — establishing the problem & thesis
Body — arguments & academic support
Conclusion — summary & future directions for research
Write your Annotated Bibliography (see resources in Module 3 for instructions)
Use APA citation style for your sources.
Include full reference for each source (ex: Name, N. (Year). Title. Location.)
Organize sources in alphabetical order
Write a 4-5 sentence annotation under each source
have to write bibliography Write an Outline for your paper. From the articles you’ve looked at, what are some ideas/arguments you can make about the topic? What about a main thesis? Introduction — es
European Journal of Public Health,Vol. 32, No. 4, 593–599 The Author(s) 2022. Published by Oxford University Press on behalf of the European Public Health Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. https://doi.org/10.1093/eurpub/ckac034 Advance Access published on 13 May 2022 …………………………………………………………………………………………… Food insecurity among disabled adults Mia Hadfield-Spoor 1, Mauricio Avendano 2, Rachel Loopstra 1 1 Department of Nutritional Sciences, King’s College London, London, UK 2 Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisant e), University of Lausanne, Lausanne, Switzerland Correspondence:Mia Hadfield-Spoor, Department of Nutritional Sciences, Franklin Wilkins Building, King’s College London, 150 Stamford Street, London SE1 9NH, UK, Tel:þ44 7760 200 457, e-mail: [email protected] Background:The relationship between disability and food insecurity is under-researched. Risk of food insecurity may vary by type and number of disabilities. We examine the hypotheses that (i) a higher number of disabilities increases risk of food insecurity and (ii) associations of physical disabilities, mental/cognitive disabilities or a combination of both types with food insecurity may differ in strength.Methods:Data came from the fifth wave of the UK’s Food Standards Agency’s Food and You survey (2018), which contains detailed information on disability and household food insecurity. We used logistic and multinomial logistic regression to model the number and type of disabilities as predictors for food insecurity outcomes, controlling for socio-demographic factors.Results:Both type and number of disabilities predicted food insecurity. Every additional disability was associated with higher odds of food insecurity [odds ratio (OR): 1.60, 95% confidence interval (CI): 1.40–1.83]. Among people with a disability, every additional disability was associated with 19% higher odds of food insecurity (OR: 1.19, 95% CI: 1.05–1.34). People with both physical and mental/cognitive disabilities had increased odds of severe food insecurity (OR: 8.97, 95% CI: 3.54–22.7).Conclusion:Number and type of disabilities are associated with higher risk of food insecurity. A combination of physical and mental/cognitive disabilities, as well as having multiple disabilities are each independently associated with higher risk of food insecurity. Policy-makers may thus consider using targeted and tailored policies to reduce barriers to social and financial inclusion of disabled people to reduce food insecurity. …………………………………………………………………………………………… Introduction A n emerging literature on food insecurity and disability has shown that disability is associated with higher risk of food in- security. 1–3 According to the biopsychosocial model of disability, 4 disability is understood as an interaction between a person and so- cial context. Thus, the relationship between disability and food in- security may reflect the fact that disabled people are at higher risk of socio-economic disadvantage and exclusion 5,6 due to facing signifi- cant barriers to education, work, adequate income and financial security. 7,8,9 Disabled people also endure higher costs of living and are more likely to experience ill-health. 10,11 Lower socio-economic status and ill-health have both been shown to increase the risk of food insecurity. 12,13 While studies in high-income countries have found that food insecurity is strongly associated with mental, 2,14,15 physical and chronic illness, 16,17,18 research looking at the relationship between disability and food insecurity is limited and of mixed quality. 3This relationship is likely to be bidirectional, as food insecurity may in- crease the risk of physical disability, while at the same time, poor health among disabled people may lead to food insecurity. 19,20 A limited number of studies in high-income countries suggest that the type and intensity of disability, as well as chronicity of food inse- curity, may be important to understand the relationship between disability and food insecurity. Previous studies have focused primar- ily on the USA or have faced important limitations regarding the measurement and modelling of food insecurity. Studies in the USA and Canada have found that work-limiting disabilities are associated with food insecurity, as well as being disabled and of working age. 2,17,21 Functional disabilities such as mobility limitations,barriers particularly faced by physically disabled people, as well as barriers as a result of cognitive impairments, have been associated with a higher risk of food insecurity. 22,23 Studies also suggest that some groups of disabled people, such as people with lower or more insecure incomes, may be at higher risk of facing food insecur- ity. 12,24 However, few studies have examined how different types of disability as well as the number of disabilities relate to the risk of food insecurity, particularly in the UK context. A better under- standing of how different experiences of disability relate to food insecurity and to what extent food insecurity risk varies among disabled people is critical for developing targeted and tailored pol- icies and programmes for reducing food insecurity among disabled people. In this paper, we explore the hypothesis that categories and num- ber of disabilities are independently associated with food insecurity. This hypothesis is motivated by literature suggesting that different types of disability pose different barriers and facilitators to inclusion and equality of access to adequate food. For example, mentally/cog- nitively disabled people may face particular knowledge, information and income and work barriers, such as difficulty building or main- taining social networks and facing discrimination and stigma. 25On the other hand, physically disabled people may be more likely to face particular food access barriers associated with the physical and built environment. 26–28 Based on this literature, we focus on two hypoth- eses. Firstly, we hypothesize that having multiple disabilities puts people at higher risk of food insecurity due to more and/or a higher intensity of barriers to access and participation that increase social disadvantage, 29e.g. public transport and supermarket access, exclu- sion from secure and sufficient income, higher likelihood of poorer health. 30Second, we hypothesize that mentally/cognitively disabled Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 people have higher risk of food insecurity than people who are physically disabled due to lack of parity in terms of social support and support services which could lead to higher unmet need 31and that people with a combination of categories will experience higher risk of food insecurity than people who have only physical or only mental disabilities. Methods Data source and sample Data came from the UK’s Food Standards Agency’s Food & You survey (F&Y), a repeated cross-sectional, representative survey of adults aged 16 and over in England, Wales and Northern Ireland. 32 The survey uses random probability sampling and face-to-face computer-assisted personal interviewing. At the time of analysis, it was the only nationally representative dataset in the UK containing an internationally agreed measure of household food insecurity: the United States Department of Agriculture (USDA)’s Adult Food Security Survey Module. 33Data from Wave 5 of F&Y, conducted in 2018, were used, as this wave collected detailed information about disability. 32 Of the 6346 eligible addresses approached, the response rate was 48.2%, resulting in a sample size of 3059 adults. 32 Number and type of disability In line with the definition of disability in the UK Equality Act, 34 respondents were asked if they had any physical or mental health conditions or illnesses lasting or expected to last for 12 months or more. Respondents who answered affirmatively were then asked whether any conditions or illnesses affected them in any of nine specific areas: mental health; social or behavioural problems; mem- ory; learning, understanding or concentration problems; vision; hearing; mobility; dexterity; or stamina or breathing or fatigue. Respondents could indicate if their condition affected them in other areas or ‘none of the above’. Based on this information, we con- structed a continuous variable that indicated the number of areas of disability, which ranged from 0 to 8. To provide visualization of the age-adjusted relationship between number of disability areas and food insecurity (figure 1), we also created a four-level categorical variable (0: no disabilities; 1–2, 3–4 and 5 or more). We created a separate categorical variable that captured the broad type of disability individuals experienced. Subcategories were as follows: no disability; physical (vision, hearing, mobility, dexterity, stamina/breathing/fatigue) disability only; mental or cognitive (social/behavioural, memory, learning, understanding/concentration) disability only; or both physical and cognitive/ mental disability. We combined cognitive and mental disabilities into a single category based on prior literature (i.e. disabling mental ill-health can be considered a cognitive limitation), and we expected barriers and impacts associated with these types of disability to share common mechanisms. 2,23 We grouped a range of physical disabilities into one category, each of which may have different associations with food insecurity. 22 Unfortunately, low sample sizes for each individual physical condition precluded a more refined analysis for specific disabilities. People who indicated having physical or mental health conditions or illnesses but who did not provide information on domains affected by them (i.e. selected ‘other’) were excluded (n¼124), as it was not possible to accurately establish the number of areas in which disability was experienced. Food insecurity Food insecurity was measured by the USDA’s 10-item Adult Food Security module, a validated scale that aims to capture the preva- lence of food insecurity in the general population. According to standard USDA practice, food insecurity is identified by three or more affirmative responses to questions on the module, and severe food insecurity is identified by six or more affirmative responses. At this level, respondents have indicated experiences of going without food. In addition to examining food insecurity and severe food in- security outcomes, a measure of chronicity of food insecurity was derived from the first three module questions which ask respondents how often they worried about running out of food; how often food actually ran out; and how often they could not afford to eat balanced meals. Respondents who indicated ‘Never true’ for all three ques- tions were coded as not experiencing food insecurity; respondents who indicated ‘sometimes true’ to at least one question, but did not indicate ‘often’ in any of the questions, were coded as ‘sometimes experiencing food insecurity’. Finally, respondents who indicated ‘often true’ to at least one question were coded as ‘often experiencing food insecurity’. Ten respondents were excluded as they provided no information on these questions. Control variables Reflecting a biopsychosocial model of disability, we control for socio-economic factors that may influence experiences of disability. The following control variables were used: age group (16–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75þ), gender, ethnicity (white vs ‘other’), household composition (single and no children, single with children, married with no children and married with children); Figure 1Relationship of food insecurity with number and category of disabilities. Probability of food insecurity by disability number and category.Note:Predicted probabilities adjusted for age 594 European Journal of Public Health Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 work status (in work, retired, unemployed, ‘other’), income bracket (<£10 399, £10 400–£25 999, £26 000–51 999,>£52 000, missing) and level of education (no qualification, ‘other’, university degree); ‘other’ referred to another kind of educational, professional, voca- tional or work-related qualification. Household income was not reported by 754 respondents (24.6%) so a derived variable was created with a new level for ‘missing’. The numbers of respondents missing data for other covariates were as follows: age (n¼9, 0.29%), sex (n¼0, 0.00%), highest qualification (n¼16, 0.52%), work sta- tus (n¼1, 0.03%), household composition (n¼11, 0.36%) and ethnicity (n¼13, 0.42%). Across these covariates combined, 16 respondents were dropped from the analysis due to missing data. Statistical analysis Logistic regression was used to model the probability of ‘any’ and ‘severe’ food insecurity as a function of (i) the number of disabilities and (ii) the type of disability, controlling for socio-demographic variables. In a third model, we examine if, conditional on having at least one disability, the number of disabilities is associated with increased odds of food insecurity. This model captures the risk associated with each additional disability among those who have multiple disabilities. We use the same modelling strategy but a multinomial regression model to examine the chronicity of food insecurity as outcome variable. Sensitivity analyses Relationships between disability and food insecurity may be stronger among younger people. Disability becomes more prevalent in older ages, affecting a wider range of socio-demographic groups. In add- ition, older people may become eligible for pensions or other welfare programmes, which reduces their risk of food insecurity. These factors may mean that disability is less strongly associated with food insecurity at older ages. To examine this, we present results stratified by age, using two alternative cut-offs: age 55 (the age at which claims for disability benefits start to increase rapidly) and age 65 (a common age cut-off used to define older age in the ageing literature). All analyses use survey weights provided in the F&Y data to ac- count for sampling design and stratification. Results Descriptive statistics Table 1summarizes key descriptive statistics and shows that 22% of respondents reported a disability. Thirteen percent of the sample had a physical (but not mental/cognitive) disability; 4% had a men- tally/cognitive (but not physical) disability, while 5% had both a physical and mental/cognitive disability. Higher proportions of respondents who had a mental/cognitive disability and both physical and mental/cognitive disabilities reported any, severe and more chronic food insecurity than respondents who had physical or no disabilities. There were higher proportions of people in older age groups among physically disabled people, while mental/cognitive disabilities were more concentrated at ages 18–64. Disability was associated with several forms of social and econom- ic disadvantage. Disabled people were less likely to be in work and more likely to be retired (if physically disabled only), unemployed or not working for other reasons (if mentally/cognitive disabled only). For people who reported combined disabilities, larger proportions were either retired or not working. Disabled people were less likely to have achieved a degree qualification and were more likely to have an annual income below £25 999. Food insecurity and the type and number of disabilities Figure 1shows predicted probabilities of food insecurity derived from a logistic regression model that controlled for age. The probability of food insecurity increased linearly with the number of disabilities. To illustrate, 51% [95% confidence interval (CI): 37–66%] of people who had five or more disabilities reported food insecurity, compared with only 7% (95% CI: 6–9%) of non-disabled people and 18% (95% CI: 13–23%) for people with 1–2 disabilities. The age-adjusted prevalence of food insecurity was 16% (95% CI: 11–22%) for people physically disabled; 23% (95% CI: 13–33%) for people mentally/cognitively dis- abled; and 32% (95% CI: 22–42%) for people both physically and mentally/cognitively disabled. Similar associations were observed for severe food insecurity (Supplementary figure SA1) and chronic food insecurity (Supplementary figure SA2). Table 2shows the results of logistic regression models that adjusted for socio-economic and demographic controls. Model 1 shows that every additional disability was associated with higher odds of food insecurity [odds ratio (OR): 1.60, 95% CI: 1.40–1.83]. Model 2 shows there was an increased risk of food insecurity for people who had a physical (OR: 2.58, 95% CI: 1.45–4.60), mental/cognitive (OR: 3.17, 95% CI: 1.85–7.47) or both physical and mental/cognitive disability (OR: 6.21, 95% CI: 3.22–12.0). Among disabled people (model 3), each additional disability conferred a 19% increased odds (OR: 1.19, 95% CI: 1.05–1.34) of food insecurity. In models that used severe food insecurity as the outcome (seeSupplementary table SA1), number of disabilities and a combination of physical and mental/cognitive disability predicted severe food insecurity, but not physical or mental/cognitive disabilities on their own. Chronicity of food insecurity Results from multinomial regression analyses examining chronicity food insecurity as the outcome are presented inSupplementary table SA2. Both number and each type of disability were associated with less frequent food insecurity as well as chronic food insecurity. Among disabled people (model 3), however, an increasing number of disabilities was significantly associated with chronic food insecur- ity (OR: 1.27, 95% CI: 1.09–1.49) but not less frequent food insecurity. Sensitivity analysis Table 3reports results from sensitivity analyses examining whether associations between disability and food insecurity differ between older and younger adults. We observed stronger associations be- tween number of disabilities, physical disabilities and a combination of physical and mental/cognitive disabilities with food insecurity for younger age groups (defined as<55 and<65), with these relation- ships becoming non-significant for adults 65þ. However, among older adults (55þor 65þ), the odds of food insecurity were par- ticularly high for people with mental/cognitive disabilities, though confidence intervals were large (e.g. OR for mental/cognitive dis- ability 3.36 [95% CI: 1.65–6.87] among<65; OR: 12.6 [1.22–130] for 65þ). Discussion This study adds to the current literature by examining how number and type of disabilities are associated with food insecurity. Our results suggest that physical and mental/cognitive disabilities are differentially associated with food insecurity, with a combination of mental and physical disabilities conferring particularly high risk. We also observed that each additional disability conferred higher risk of food insecurity, even conditional on having any dis- ability. These associations were generally stronger for working-age people. Food insecurity among disabled adults595 Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 Our findings shed new light on the relationship between disability and food insecurity. We expected different types of disability to have different associations with food insecurity, reflecting the fact that underlying mechanisms might differ due to the heterogeneity of disability experience. Though both physical and mental disabilities were associated with food insecurity, a combination of both was more strongly associated with food insecurity, including severe food insecurity and chronic food insecurity. These results suggest different mechanisms may underlie associations between physical disability and mental-cognitive disability with food insecurity and that when combined, food access is particularly compromised. Similarly, our findings that an increasing number of disabilities was associated with higher risk of food insecurity could reflectincreasing barriers to resources important to achieve food security, 9 including economic stability. 35Research has shown that people on low incomes develop coping strategies to try to avoid food insecurity and shopping around for cheaper food and discounts is often a coping mechanism to secure an adequate diet. 36 However, for some disabled people, this may be more difficult to implement or may not be an option at all. 3Such ‘coping mechanisms’ may become more complex, more costly in terms of finance, impact on other areas of life, and less possible to pursue with an increasing number of disabilities. If increasing numbers of disabilities reflect increased barriers, disabled people may experience an intersection of both multiplied and new disadvantages. 29Additionally, barriers may be- come even harder to navigate if someone has a mental/cognitive Table 1Descriptive statistics for non-disabled people and by disability category (n¼3609) Not disabled Physical only Mental/cognitive only Physical and mental/cognitive n% (95% CI)n% (95% CI)n% (95% CI)n% (95% CI) Total Whole sample 2094 78.0 (75.9–80.0) 513 13.3 (11.8–15.0) 110 3.65 (2.78–4.78) 215 4.99 (4.16–5.97) No. of disabilities None 2094 1 – – – – – – 1–2 0 0 408 84.1 (79.9–87.6) 102 88.4 (75.7–94.9) 40 20.1 (13.2–29.4) 3–4 0 0 99 15.0 (11.6–19.1) 8 11.6 (5.06–24.3) 111 49.5 (39.9–59.1) 5þ0 0 6 0.94 (0.35–2.51) 0 0 64 30.4 (22.2–40.1) Age 16–24 160 14.8 (12.6–17.4) 10 5.58 (2.38–12.5) 10 18.3 (9.31–26.1) 4 10.7 (3.89–26.1) 25–34 344 19.0 (16.5–21.7) 15 4.35 (2.45–7.62) 24 24.3 (16.0–35.2) 19 9.12 (4.20–18.7) 35–44 373 17.3 (15.4–19.5) 30 6.48 (4.17–9.94) 27 20.2 (12.5–30.9) 24 8.10 (4.68–13.7) 45–54 380 19.1 (16.8–21.6) 49 11.8 (8.40–16.3) 22 14.2 (8.39–22.9) 41 18.9 (12.8–27.1) 55–64 323 12.8 (11.1–14.8) 107 19.9 (15.8–24.9) 17 15.5 (9.36–24.4) 50 22.2 (15.4–30.9) 65–74 305 10.8 (9.33–12.5) 146 27.0 (22.0–32.6) 8 6.95 (3.11–14.8) 31 12.3 (7.78–19.0) 75þ203 6.13 (5.10–7.36) 155 24.9 (20.3–30.1) 2 0.60 (0.11–3.39) 46 18.7 (12.5–27.0) Sex Female 1214 50 (47.1–52.9) 311 54.6 (49.0–60.0) 60 49.1 (35.7–62.5) 135 54.2 (43.6–64.4) Male 880 50 (47.1–52.9) 202 45.4 (40.0–51.0) 50 51.0 (37.5–64.3) 80 45.8 (35.7–56.4) Work status In work 1262 68.5 (65.3–71.5) 123 32.6 (26.9–38.8) 59 59.0 (47.8–69.4) 50 32.5 (23.4–43.0) Retired 517 16.5 (14.4–18.8) 307 51.5 (45.4–57.5) 10 7.56 (3.56–15.3) 81 31.9 (24.0–41.0) Unemployed 75 2.83 (2.17–3.69) 12 3.25 (1.59–6.54) 17 12.9 (6.81–23.2) 16 10.0 (5.62–17.3) Other 239 12.2 (10.3–14.3) 71 12.7 (10.3–14.3) 24 20.5 (12.7–31.3) 68 25.6 (17.7–35.6) Qualification University degree 673 34.5 (31.6–37.5) 108 23.5 (19.0–28.8) 26 24.3 (12.8–41.2) 34 20.2 (14.0–28.3) Other 1054 50.9 (47.7–54.1) 252 51.9 (45.6–58.2) 63 62.8 (46.6–76.6) 116 56.3 (46.8–65.3) None 360 14.6 (12.4–17.2) 153 24.5 (19.7–30.1) 21 12.9 (7.04–22.4) 65 23.5 (17.3–31.1) HH income <£10 399 159 4.34 (3.46–5.43) 59 8.08 (5.85–11.1) 18 7.51 (4.12–13.3) 33 9.76 (6.52–14.4) £10 400–£25 999 459 16.1 (14.2–18.2) 151 26.8 (22.3–31.7) 41 33.0 (23.3–44.3) 69 25.7 (19.4–33.2) £26 000–£51 999 541 25.6 (23.0–28.4) 115 24.2 (19.9–29.2) 12 13.6 (7.46–23.4) 32 16.5 (10.9–24.4) >£52 000 437 25.4 (22.8–28.1) 54 14.2 (10.0–19.8) 17 20.1 (10.8–34.4) 22 11.0 (6.38–18.4) Missing 498 28.6 (25.1–32.4) 134 26.7 (21.9–32.2) 22 25.9 (16.2–38.6) 59 37.0 (27.6–47.4) HH composition Married with kids 462 25.6 (23.0–28.4) 29 9.66 (6.21–14.7) 18 17.0 (10.4–26.5) 27 16.3 (10.0–25.4) Single with kids 157 6.27 (4.99–7.85) 25 7.22 (3.79–13.3) 7 4.67 (1.52–13.5) 17 8.51 (3.39–19.8) Married no kids 780 39.6 (36.6–42.7) 236 54.2 (48.1–60.3) 30 26.2 (17.9–36.6) 68 37.4 (29.2–46.4) Single no kids 689 28.5 (25.7–31.6) 220 28.9 (24.5–33.7) 55 52.1 (41.4–62.6) 102 37.8 (28.7–47.8) Ethnicity White 1867 83.3 (79.6–86.5) 490 94.0 (91.0–96.0) 104 96.3 (91.3–98.5) 204 88.3 (77.2–94.4) Not white 222 16.7 (13.6–20.5) 23 6.00 (3.97–8.97) 6 3.70 (1.52–8.71) 11 11.7 (5.60–22.8) Food security status High FS 1726 82.3 (79.8–84.6) 422 80.5 (75.1–85.0) 64 60.7 (49.0–71.3) 134 59.8 (49.5–69.3) Marginal FS 203 10.1 (8.40–12.2) 39 9.10 (6.11–13.4) 13 11.8 (6.61–20.2) 30 13.4 (8.49–20.4) Food insecurity 99 4.83 (3.74–6.21) 33 7.58 (4.73–11.9) 16 16.4 (9.24–27.4) 14 9.69 (5.23–17.3) Severe food insecurity 66 2.71 (1.97–3.73) 19 2.81 (1.61–4.87) 17 11.1 (5.96–19.8) 37 17.2 (10.7–26.5) Chronicity of FI Never 1724 82.5 (79.9–84.7) 421 80.5 (75.0–84.9) 63 60.5 (48.6–71.2) 134 59.9 (49.6–69.4) Sometimes 266 13.3 (11.4–15.6) 60 12.6 (9.05–17.2) 27 24.9 (16.4–36.0) 52 23.1 (16.6–31.3) Often 100 4.24 (3.16–5.65) 31 6.99 (4.43–10.9) 18 14.6 (8.25–24.6) 28 17.0 (10.1–27.1) Note::P-values for all covariates 0.0001 except for sex (P¼0.5331). 596 European Journal of Public Health Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 disability as well as a physical disability, as suggested by the observed association of a combination of categories with severe food insecurity. Multiple disabilities may also reflect an increased likelihood of experiencing chronic disadvantage, poverty and marginalization.This may also be a particular concern for people who have life- long and work-limiting disability who may experience more discrimination and be less likely to build up long-term social or financial assets. 9,11 We observed that relationships between disability and food insecurity were generally weaker at older ages. This may be Table 2Odds of food insecurity for number, category and number if disabled Model 1 Model 2 Model 3 a Number Category Number if disabled OR (95% CI) OR (95% CI) OR (95% CI) Number Per disability1.60 (1.40–1.83)–1.19 (1.05–1.34) Category (reference¼None) Physical only –2.58 (1.45–4.60) – Mental/cognitive only –3.71 (1.85–7.47) – Physical and mental/cognitive –6.21 (3.22–12.0) – Age (reference¼45–54) 16–24 1.67 (0.82–3.41) 1.59 (0.76–3.31) 1.13 (0.41–3.15) 25–341.94 (1.10–3.42) 1.89 (1.05–3.41)1.44 (0.68–3.06) 35–442.55 (1.40–4.65) 2.45 (1.31–4.56)1.52 (0.64–3.00) 55–64 0.76 (0.38–1.52) 0.79 (0.40–1.56) 0.66 (0.36–1.22) 65–740.32 (0.14–0.75) 0.34 (0.15–0.76) 0.32 (0.11–0.88) 75þ0.13 (0.03–0.53) 0.16 (0.04–0.61) 0.56 (0.01–0.21) Sex (reference¼male) Female 1.41 (0.92–2.15) 1.36 (0.88–2.10) 1.14 (0.74–1.76) Ethnicity (reference¼White) Other ethnicity 1.48 (0.90–2.45) 1.58 (0.94–2.68)3.64 (1.72–7.72) Qualification (reference¼university degree) Other 1.53 (0.90–2.60) 1.55 (0.90–2.66) 1.52 (0.82–2.81) None4.13 (2.06–8.29) 4.18 (2.04–8.55) 2.55 (1.28–2.11) Work status (reference¼in work) Retired 0.68 (0.32–1.42) 0.66 (0.33–1.34) 1.09 (0.43–2.73) Unemployed2.18 (1.11–4.27)2.01 (0.98–4.12)2.46 (1.14–4.89) Other0.59 (0.37–0.95)0.64 (0.40–1.01) 1.22 (0.71–2.11) HH income (reference¼ £26 000–51 999) <£10 3992.46 (1.40–4.34) 2.49 (1.42–4.35) 3.13 (1.45–6.74) £10 400–£25 9992.28 (1.39–3.73) 2.18 (1.34–3.56)1.67 (0.84–3.29) >£52 0000.33 (0.17–0.64) 0.33 (0.17–0.61)0.60 (0.22–1.59) Missing 1.02 (0.61–1.73) 1.03 (0.60–1.74) 1.23 (0.57–2.63) HH composition (reference¼single, no children) Married, with children 0.97 (0.58–1.63) 0.95 (0.56–1.62) 1.00 (0.51–1.95) Single, with children 1.03 (0.57–1.85) 1.00 (0.56–1.76) 1.33 (0.67–2.63) Married, no children 0.65 (0.39–1.07) 0.64 (0.39–1.05)0.42 (0.25–0.72) Notes:n¼2906. Data in bold are statistically significant. Model adjusted for: age, sex, ethnicity, highest level of qualification, work status, household income and household composition. a: Model 3 was run only for disabled people (n¼955) and was unweighted due to small cell counts. Table 3Odds of food insecurity by number and category for adults<55 years of age and 55þyears of age and for adults<65 years of age and 65þyears of age Under 55s n51522Over 55s n51348Under 65s n52012Over 65s n5892 Model 1 a Model 2 a Model 1 a Model 2 a Model 1 b Model 2 b Model 1 b Model 2 b OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Number Continuous1.74 (1.44–2.10)–1.45 (1.23–1.71) – 1.70 (1.46–1.97) –1.15 (0.92–1.43)– Category (reference¼none) – – – Physical only –2.85 (1.35–6.02) – 2.38 (1.08–5.24) – 2.34 (1.23–4.47) –2.97 (0.86–10.2) Mental/cognitive only –3.10 (1.49–6.46) – 6.59 (1.36–32.1) – 3.36 (1.65–6.87) – 12.6 (1.22–130) Physical and mental/cognitive –7.32 (2.81–19.1) – 5.23 (2.42–11.3) – 7.23 (3.50–15.0) –1.98 (0.52–7.47) Note: Data in bold are statistically significant. a: Less than 55 and>55 adjusted: age, sex, ethnicity, highest level of qualification, work status, household income and household composition. b: Less than 65 adjusted for age, sex, ethnicity, highest level of qualification, work status, household income and household composition. Greater than 65 adjusted for sex, ethnicity, highest level of qualification, household income and household composition. Food insecurity among disabled adults597 Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 due to more effective support services designed for pensioners as well as more generous social security programmes. Disability also becomes more prevalent at older age so may be less closely tied to socio-economic disadvantage. We observed a strong relationship between mental/cognitive disability and food insecurity among older people, however. This group may face unique barriers to informa- tion and knowledge related to food access and may face more sub- stantial barriers than physically disabled people to accessing support services. 14,37 Mentally/cognitively disabled people may experience more long-term disability and therefore reduced opportunities to building and maintaining resources that prevent against risk factors for food insecurity such as a secure and sufficient income, and asset accumulation for older ages. 2Similarly, mentally/cognitively dis- abled people may be at higher risk of lacking strong informal social networks. 38–40 This may be a particular risk at older ages when social isolation can be more of a concern. Strengths and limitations Our study uses an internationally standardized measure of food se- curity assessed in a nationally representative sample and incorpo- rated measures of the number and type of disabilities. However, several important limitations should be considered. Our study is based on a relatively small sample size, and our results are based on a cross-sectional analysis that only examined associations, rather than causal relationships. Food insecurity is correlated with other forms of social and economic disadvantage, which may confound the relationship with disability. Socio-demographic variables were limited in the dataset. In particular, age was only provided in age brackets. The crude measure of household income available in the dataset meant that it was not possible to equivalize income by household size. However, we note that including controls for the size of the household did not alter our results. In addition, the limited measures of financial hardship also meant that it was not possible to explore whether insufficiency of income explains the relationship between disability and food insecurity, especially as it does not account for the additional costs of living associated with disabilities. Though the biopsychosocial model of disability informed our conceptualization of how disability relates to food insecurity, variables reflecting the social contexts of disabled people were limited in the dataset. Future research would benefit from further exploring the role of social contexts in conceptualizing dis- ability and food insecurity. Our measurement of food insecurity measures food insecurity as a result of economic affordability and may not account for non-financial barriers that also reduce disabled people’s food access. However, suffi- cient financial resources can help to overcome other access barriers, e.g. transport, meal preparation, carers, help with shopping. Importantly, it is an internationally agreed, robust, standardized measure. The internal reliability of the food insecurity scale has been examined in other countries but not in this sample. The measure of disability available in the dataset did not assess impacts of impairments on activities of daily living nor the severity of disability. Nor did our data allow us to test whether more specific disabilities beyond the broad categorizations of physical disabilities and mental/cognitive disabilities relate differently to food insecurity. Some research in the USA has found that functional and sensory disabilities may not relate to food insecurity in the same ways among older adults. 22There was also only a general range of cognitive and mental conditions captured; in particular, mental conditions did not distin- guish between common mental disorders and severe psychiatric disor- ders. However, a strength of this measure is that it is in line with the standard ONS harmonization question for impairments. Given that our findings point to a significant role for the number of disabilities, future research would benefit from understanding more about how disability severity and types relate to food insecur- ity outcomes. Conclusion and implications Results from this study suggest that number and type of disabilities are associated with higher risk of food insecurity and chronic food insecurity. They also indicate that a combination of mental/cogni- tive and physical disability is associated with higher risk of severe food insecurity. Policy-makers may thus consider using targeted and tailored policies to reduce barriers to social and financial inclusion of disabled people to reduce food insecurity. For example, improv- ing access to education, adequate and secure incomes, social care, welfare support and health services as well as supporting reduction of stigma and discrimination, may offer possible targets of public policy to address barriers to food security. Supplementary data Supplementary dataare available atEURPUBonline. Funding The study was funded as part of an Economic and Social Research Council (ESRC) Doctoral Studentship (ES/P000703/1). It also rep- resents independent research partly supported by the ESRC Centre for Society and Mental Health at King’s College London (ESRC Reference: ES/S012567/1). Conflicts of interest: None declared. References 1Loopstra R, Reeves A, Tarasuk V. The rise of hunger among low-income house- holds: an analysis of the risks of food insecurity between 2004 and 2016 in a population-based study of UK adults.J Epidemiol Community Health2019;73: 668–73. 2Coleman-Jensen A, Nord M. Food insecurity among households with working- age adults with disabilities. In: Penderson C, editor.Food Insecurity among Disabled Adult Households. New York: Nova Science Publishers, Inc., 2013: 1–60. 3Schwartz N, Buliung R, Wilson K. Disability and food access and insecurity: a scoping review of the literature.Health and Place2019; 57:107–21. 4WHO, World Bank.World Report on Disability. Geneva: World Health Organization, 2011: 325. 5Brownlee K, Cureton AS, editors.Disability and Disadvantage. Oxford: Oxford University Press, 2009. 6Leslie Rubin I, Geller RJ, Nodvin J, et al. Break the cycle of disadvantage and disability: environmental factors, education, AIDS, and food insecurity.Int J Child Health Hum Dev2014;7:207–13. 7She P, Livermore GA. Material hardship, poverty, and disability among working- age adults.Social Science Q2007;88:970–89. Key points •Physical and mental/cognitive disabilities are associated with food insecurity, but in particular, a combination of both is strongly associated with food insecurity and severe food insecurity. •An increasing number of disabling conditions is associated with an increased risk of food insecurity as well as chronic food insecurity. •The increased risk of food insecurity among disabled people with multiple disabilities highlights the need for interventions that reduce multiple disadvantages faced by disabled people. 598 European Journal of Public Health Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 8de Jong PR. Sickness, disability and work: breaking the barriers—a synthesis of findings across OECD countries—OECD.Internationale Revue Fu¨ r Soziale Sicherheit2011;64:115–7. 9Huang J, Guo B, Kim Y. Food insecurity and disability: do economic resources matter?Soc Sci Res2010;39:111–24. 10 Mitra S, Palmer M, Kim H, et al. Extra costs of living with a disability: a review and agenda for research.Disabil Health J2017;10:475–84. 11 Frier A, Barnett F, Devine S, Barker R. Understanding disability and the ‘social determinants of health’: how does disability affect peoples’ social determinants of health?Disabil Rehabil2018;40:538–47. 12 Loopstra R, Lalor D. Financial insecurity, food insecurity, and disability. Salisbury: The Trussell Trust 2017. 13 Berkowitz SA, Basu S, Meigs JB, Seligman HK. Food insecurity and health care expenditures in the United States, 2011–2013.Health Serv Res2018;53:1600–20. 14 Afulani PA, Coleman-Jensen A, Herman D. Food insecurity, mental health, and use of mental health services among nonelderly adults in the United States.J Hunger Environ Nutr2020;15:29–50. 15 Martin MS, Maddocks E, Chen Y, et al. Food insecurity and mental illness: dis- proportionate impacts in the context of perceived stress and social isolation.Public Health2016;132:86–91. 16 Dominick SR, Widmar NJO, Ruple A, et al. The intersection of food insecure populations in the Midwest U.S. and rates of chronic health conditions.Agric Food Secur2018;3:7. 17 Tarasuk V, Mitchell A, McLaren L, McIntyre L. Chronic physical and mental health conditions among adults may increase vulnerability to household food insecurity.J Nut2013;143:1785–93. 18 Bishop NJ, Wang K. Food insecurity, comorbidity, and mobility limitations among older U.S. adults: findings from the Health and Retirement Study and Health Care and Nutrition Study.Preventive Medicine2018;114:180–7. 19 Garthwaite KA, Collins PJ, Bambra C. Food for thought: an ethnographic study of negotiating ill health and food insecurity in a UK foodbank.Soc Sci Med2015;132: 38–44. 20 Coleman-Jensen A. U.S. food insecurity and population trends with a focus on adults with disabilities.Physiol Behav2020;220:112865. 21 Brucker DL, Coleman-Jensen A. Food insecurity across the adult life span for persons with disabilities.J Disabil Policy Stud2017;28:109–18. 22 Heflin CM, Altman CE, Rodriguez LL. Food insecurity and disability in the United States.Disabil Health J2019;12:220–6. 23 Brucker DL, Nord D. Food insecurity among young adults with intellectual and developmental disabilities in the United States: evidence from the National Health Interview Survey.Am J Intellect Dev Disabil2016;121:520–32.24 Sosenko F, Littlewood M, Bramley G, et al. A study of poverty and food insecurity in the UK. Salisbury: The Trussell Trust 2019. 25 Hatzenbuehler ML, Phelan JC, Link BG. Stigma as a fundamental cause of popu- lation health inequalities.Am J Public Health2013;103:813–21. 26 Huang DL, Rosenberg DE, Simonovich SD, Belza B. Food access patterns and barriers among midlife and older adults with mobility disabilities.J Aging Res2012; 2012:231489. 27 Webber CB, Sobal J, Dollahite JS. Physical disabilities and food access among limited resource households.DSQ2007;27. https://doi.org/10.18061/dsq.v27i3.20 28 Schwartz N, Tarasuk V, Buliung R, Wilson K. Mobility impairments and geo- graphic variation in vulnerability to household food insecurity.Soc Sci Med2019; 243. 29 Emmett T, Alant E. Women and disability: exploring the interface of multiple disadvantage.Dev South Afr2006;23:445–60. 30 Bengle R, Sinnett S, Johnson T, et al. Food insecurity is associated with cost-related medication non adherence in community-dwelling, low-income older adults in Georgia.J Nutr Elder2010;29:170–91. 31 O’Reilly D, Rosato M, Wright DM, et al. Social variations in uptake of disability benefits: a census-based record linkage study. Soc Sci Med2021;276:113821. 32 NatCen. Food and You Survey, Wave 5 : User Guide. 2018. Available at: www. natcen.ac.uk. Last accessed March 2022. 33 Bickel G, Nord M, Price C, et al. Measuring Food Security in the United States Guide to Measuring Household Food Security Revised . 2000. Available at: http:// www.fns.usda.gov/oane. Last Accessed February 2021 34 Acts of Parliament (UK).Equality Act 2010. UK: Her Majesty’s Government 2010. 35 Richards J, Sang K. The intersection of disability and in-work poverty in an advanced industrial nation: The lived experience of multiple disadvantage in a post- financial crisis UK.Econ Ind Democr2018;40:636–59. 36 Perry J, Williams M, Sefton T, Haddad M. Emergency Use Only: Understanding and Reducing the Use of Food Banks in the UK. 2014. Available at: https://cpag.org. uk/sites/default/files/Foodbank%20Report_web.pdf. Last accessed July 2020 37 Si ska J, Beadle-Brown J, Ka´ nova´ S, Sumn kova´ P. Social inclusion through com- munity living: current situation, advances and gaps in policy, practice and research. Soc Incl2018;6:94–109. 38 Koyanagi A, Veronese N, Stubbs B, et al. Food insecurity is associated with mild cognitive impairment among middle-aged and older adults in South Africa: find- ings from a nationally representative survey.Nutrients2019;11:749. 39 Milner P, Kelly B. Community participation and inclusion: people with disabilities defining their place.Disabil Soc2009;24:47–62. 40 Kamstra A, van der Putten AAJ, Vlaskamp C. The structure of informal social networks of persons with profound intellectual and multiple disabilities.J Appl Res Intellect Disabil2015;28:249–56. Food insecurity among disabled adults599 Downloaded from https://academic.oup.com/eurpub/article/32/4/593/6585567 by Jnls Cust Serv on 02 August 2022 © 2022 European PublicHealth Association. CopyrightofEuropean JournalofPublic Health is the property ofOxford University Press/USA anditscontent maynotbecopied or emailed tomultiple sitesorposted toalistserv without thecopyright holder’sexpresswritten permission. However,usersmayprint, download, oremail articles forindividual use.
have to write bibliography Write an Outline for your paper. From the articles you’ve looked at, what are some ideas/arguments you can make about the topic? What about a main thesis? Introduction — es
Food Insecurity, Hunger, Stress, and Homelessness Among Young Adults Eldin Dzubur 1, Sara Semborski 1, Brian Redline 1, Donald Hedeker 2, Genevieve F. Dunton 3, and Benjamin F. Henwood 1 1Suzanne Dworak-Peck School of Social Work, University of Southern California 2Department of Public Health Sciences, The University of Chicago 3Department of Preventative Medicine, Keck School of Medicine, University of Southern California Background:Compared to the effects of stress on hunger, the temporal effect of hunger on stress levels is less understood, especially in the context of everyday lives of vulnerable populations with unstable access to food.Objective:Our objective was to examine the effects of food insecurity and momentary hunger on momentary stress and stress variability in a sample of currently and formerly homeless young adults.Method:We used a 7-day ecological momentary assessment study querying affect, hunger, and risky behaviors. A mixed-effects location scale model was used to examine the effects of hunger on mean levels and within- and between-subjects variability of stress with 100 currently homeless and 69 formerly homeless young adults ages 18–29 in Los Angeles County, California.Results:When individuals experienced greater-than-average hunger, they then experi- enced greater stress variability at the next prompt, showing the impact of hunger on stress at the momentary level. Those with higher average levels of stress, regardless of hunger, became substan- tially more stressed when becoming hungry compared to their generally less stressed counterparts. Conclusions:The study shows the extent to which food insecurity results in erratic stress among vul- nerable populations and how high levels of hunger may lead to a more inconsistent stress response. Findings reinforce the need for more mental health services and food programs for young adults who have experienced homelessness. Keywords:hunger, stress, ecological momentary assessment, homeless persons, young adults A limited but growing body of research has examined food inse- curity among young adults who experience homelessness (Crawford et al., 2014;Haskettetal.,2021;Johnson et al., 2019;Tucker et al., 2022). For example, a series of studies have found food insecurity prevalence estimates in this population to range between 28% and 85% (Bowen & Irish, 2018;Crawford et al., 2015;Goldman-Hasbun et al., 2019;Tarasuk et al., 2009;Whitbeck et al., 2006)andthat food insecurity persists even after a young adult has exited home- lessness through programs such as permanent supportive housing (Johnson et al., 2019). Studies have also identiﬁed increased risk behaviors among young adults experiencing homelessness thatresult from food insecurity including exchange sex, stealing, and scavenging in dumpsters for food (Bowen & Irish, 2018;Crawford et al., 2015;Goldman-Hasbun et al., 2019;Whitbeck et al., 2006). Even when food programs such as food stamps and soup kitchens are available, young adults may not access them due to feelings of stigma (Johnson et al., 2019). Resulting hunger from experiencing homelessness and food insecurity could lead to increased levels of stress that add to cumu- lative health risk (Hernandez et al., 2019). Biologically, stress results in an increase in cortisol levels, which is thought to pro- mote hunger through an increase in the amount of insulin, leptin, Eldin Dzubur https://orcid.org/0000-0002-3248-5327 Sara Semborski https://orcid.org/0000-0003-3295-0759 Brian Redline https://orcid.org/0000-0002-0694-1106 Donald Hedeker https://orcid.org/0000-0001-8134-6094 Genevieve F. Dunton https://orcid.org/0000-0002-4129-3829 Benjamin F. Henwood https://orcid.org/0000-0001-8346-3569 Eldin Dzubur served as lead for conceptualization, formal analysis, and writing—original draft. Sara Semborski contributed equally to project administration, validation, writing—original draft, and writing—review and editing and served in a supporting role for formal analysis. Brian Redline served as lead for project administration and contributed equally to data curation, writing—original draft, and writing—review and editing. Donald Hedeker served as lead for validation, contributed equally to formal analysis, and served in a supporting role for writing—review and editing.Genevieve F. Dunton contributed equally to formal analysis and served in a supporting role for validation and writing—review and editing. Benjamin F. Henwood served as lead for funding acquisition, resources, and supervision and contributed equally to writing—original draft and writing—review and editing. The study was funded by grants from the National Institute of Mental Health (1R01 MH110206 and F31MH126641). We thank the study participants, the homelessness services agencies that helped with recruitment, and Danielle Madden for her contributions to the team. We have no conﬂicts of interest to disclose. Correspondence concerning this article should be addressed to Sara Semborski, Suzanne Dworak-Peck School of Social Work, University of Southern California, 669 West 34th Street, Montgomery Ross Fisher (MRF) Building, Suite 203, Los Angeles, CA 90089, United States. Email: [email protected] 559 Health Psychology ©2022 American Psychological Association2022, Vol. 41, No. 8, 559–565 ISSN: 0278-6133https://doi.org/10.1037/hea0001214 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. and neuropeptide (Brownell & Walsh, 2017). Furthermore, evi- dence from human and animal models suggests that eating reduces the physiological stress response, acting as a negative feedback loop in the presence of stress (Finch & Tomiyama, 2014), and may thus be worsened under conditions of chronic stress, leading to excess calorie consumption. In addition to biological models that have identiﬁed a temporally plausible causal mechanism to hunger as a result of stress, studies using real-time sampling have substantiated theseﬁndings in situ. For example, a 10-day ecologi- cal momentary assessment (EMA) protocol using a sample of chil- dren and adults found that reasons for eating were primarily hunger-driven under conditions of higher stress (Reichenberger et al., 2018). Still, the relationship between hunger and stress is thought to vary in the general population due to individual factors (e.g., body mass index), environmental factors, and time of day (Block et al., 2009;Brownell & Walsh, 2017). For instance, time- varying effects models using EMA data from young adult college students showed a nonlinear relationship between hunger and stress, suggesting that the relationship between hunger and stress was stronger in the late afternoons and evenings (Huh et al., 2015). Further, there is an established relationship between the number of stressful events and deterioration of mental health (Kar- atekin, 2018), making those who experience homelessness particu- larly vulnerable. In fact, data have shown up to 98% of young adults experiencing homelessness meet criteria for a mental health disorder (Hodgson et al., 2013). Yet compared to the effects of stress on hunger, the temporal effect of hunger on stress levels is less understood, especially in the context of everyday lives of vulnerable populations with unsta- ble access to food. Qualitative research has revealed how hunger has impacted mental health and contributed to chronic stress of parents, often also impacting their children’s well-being (Knowles et al., 2016). Negative developmental effects related to hunger have also been documented in younger populations, particularly regarding self-control, attentiveness, and task persistence, all of which contributed to poorer social skills (Howard, 2011). Simi- larly, population-level studies, especially in children, have found signiﬁcant associations between hunger and psychiatric morbid- ities, including suicidal ideation (McIntyre et al., 2013;Muldoon et al., 2013). However, such qualitative and population-level stud- ies are limited by recall biases, the inability to understand temporal processes, and poor ecological validity. The current study used EMA to better understand the relation- ship between hunger and stress among young adults who have experienced homelessness and food insecurity. In particular, this study investigated the less examined effects of hunger on stress, rather than the commonly explored impact stress has on hunger. These methods provide an ability to delineate the microtemporal order of the association between hunger and stress. Further, research examining the experience of homelessness among young adults often focuses on those actively experiencing homelessness while neglecting the increasing population of formerly homeless young adults being placed in supportive housing, a primary inter- vention being applied to homelessness (National Academies of Sciences, Engineering, and Medicine, 2018). Using a sample of young adults who are either currently homeless or formerly home- less and now in stable housing, the speciﬁc study questions we sought to answer included the following: Are there differences in food insecurity, hunger, and stress based on current housingstatus? Does food insecurity and/or hunger lead to increased stress? And how does food insecurity and/or hunger affect vari- ability in stress? It is expected that those in supportive housing will feel more food secure than those actively experiencing home- lessness, but this may not translate into greater momentary hunger. Additionally, hunger was expected to increase momentary stress such that those in housing were expected to be less stressed, on av- erage, than those who were unhoused. Method The study used an intensive longitudinal repeated-measures design to examine how a time-lagged primary predictor, prior hun- ger, is associated with mean and variability of stress. This study used a baseline questionnaire and EMA data from a larger obser- vational, mixed-methods geographically explicit EMA study investigating health risk behaviors in a sample of young adults who have experienced homelessness in Los Angeles County, Cali- fornia. Of the 230 participants in the study, 109 were currently homeless (i.e., street-based or living in a dwelling not meant for human habitation or couch surﬁng in temporary locations), en- rolled via drop-in centers and shelters, and 121 were formerly homeless and residing in supportive housing at the time of the study. The complete procedures for the Log My Life study are available elsewhere (Henwood et al., 2019). Participants Those who were recruited and enrolled in the study were asked to complete a baseline questionnaire consisting of person-level measures such as demographics and food insecurity, followed by a 7-day EMA protocol querying hunger, stress, and other variables. Participants were allowed to use their own smartphones or a study phone with a data plan for the duration of the study. A custom application was installed on the devices, and EMA prompts were delivered at 2-hour intervals throughout the day, excluding times that participants identiﬁed as sleep. Participants receivedﬁve prompts per day, on average, and took approximately 60 s to com- plete each prompt. Participants were compensated up to $130 for the study. All protocols were approved by the institutional review board at the University of Southern California. Data are not cur- rently publicly available but may be available upon request. Measures Demographics for the study were collected at the onset of the study, including age and self-identiﬁed gender, race, and ethnicity. Participants completed the Household Food Insecurity Access Scale (HFIAS), a nine-item (a= .91) questionnaire indicating both the occurrence and frequency of food scarcity events in the past 4 weeks, such as worry about access to food, limiting eating, or going to bed hungry (Coates et al., 2007). Previous research sug- gests the HFIAS to be most preferred measure of food insecurity among individuals experiencing homelessness and more appropri- ate for use with this population than other measures (Holland et al., 2011). The HFIAS was scored using published guidelines, and participants were identiﬁed as either food secure, mildly food inse- cure, moderately food insecure, or severely food insecure (Coates et al., 2007). Stress and hunger were queried using single-item 560 DZUBUR ET AL. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. EMA questions (“Just before the phone went off, how STRESSED were you feeling?”and“Just before the phone went off, how HUNGRY were you feeling?”respectively), with a 5-point Likert- style scaling as follows:slightly/not at all,a little,moderately, quite a bit, andextremely. The stress measure has been validated and used in other EMA studies with child, adolescent, and adult populations as part of a circumplex model of affect (Dunton et al., 2015;O’Reilly et al., 2015). Data Analysis Each day was divided into 12 two-hour panels (i.e., windows), based on the EMA prompting scheme. The primary predictor, hun- ger, was time-lagged at the prior 2-hour interval (t-1) to establish temporal precedence. For the purpose of this analysis, gender (male vs. female or other gender), race (Black or African Ameri- can vs. any other race), and food insecurity (food secure or mildly food insecure vs. moderately or severely food insecure) were dichotomized based on the distribution of responses and to aid in interpretation of the model. Age was grand mean centered, and hunger (lagged;t-1) was disaggregated into between- and within- subject components using person-centered and grand-centered means. MixWILD, a statistical software program designed for lon- gitudinal data (Dzubur et al., 2020), was used to run all analyses. A mixed-effects multiple location (i.e., intercept and slope) and scale (i.e., within-subject variance) model (Nordgren et al., 2020) was used to examine the effects of hunger and food insecurity on mean levels of stress and within- and between-subjects variability of stress. Within-subject variability of stress captures the extent to which a participant’s stress differed from their own mean during the study period and may represent instability of life or repeated occurrences of stressful events; both high mean levels of stress and high variability of stress are associated with poor health out- comes (Shields & Slavich, 2017). A subsequent mixed-effectsmultiple location and scale model tested for the interaction of hun- ger and food insecurity on both mean levels of stress and stress variability. MixWILD also allows for second-stage models (e.g., logistic regressions) using subject speciﬁc random effects found at theﬁrst stage; hence, a second-stage model was used to examine the association of subjective-speciﬁc mean stress and stress vari- ability with housing status. The following terms are used inter- changeably to describe models produced by MixWILD: random location: between-subjects variance of stress; random location effects: variance-covariance matrix of the random intercept and slope; random scale: within-subject variance of stress; and random slope: relationship between current stress and prior hunger. Results Data Availability and Descriptive Statistics A total of 187 participants completed the enrollment (i.e., base- line) questionnaire with food insecurity measures, of which 176 completed at least some portion of the EMA protocol. Participants completed 28 prompts on average, ranging from one to 54 prompts for the duration of the study period (Mdn= 30 prompts). Partici- pants were excluded from the analysis if they had no variance in the outcome across all EMA prompts (N= 4) or if they had miss- ing values for covariates in the model (N= 3). Descriptive statis- tics of the analytic sample (Level-2N= 169, Level-1n= 5,737) are presented inTable 1, with bivariate analyses testing for differ- ences based on housing status. Participants were approximately 22 years of age, with 59% of the sample currently homeless and 41% residing in supportive housing. There was a signiﬁcant difference in gender distribution between unhoused and housed participants, with a greater proportion of men in the unhoused sample and a smaller proportion of other identiﬁed genders compared to the housed sample (p,.05). Race was associated with housing status Table 1 Descriptive Statistics of a Sample of Currently and Formerly Homeless Young Adults VariablesUnhoused (N= 100)Housed (N= 69)Total (N= 169)pvalue Age (in years),M(SD) 21.90 (2.00) 22.07 (2.16) 21.97 (2.06) 0.594 Gender0.016 Male only 60 (60.0%) 27 (39.1%) 87 (51.5%) Female only 30 (30.0%) 27 (39.1%) 57 (33.7%) Other 10 (10.0%) 15 (21.7%) 25 (14.8%) Race0.010 Another race 8 (8.0%) 9 (13.0%) 17 (10.1%) Multiracial 9 (9.0%) 8 (11.6%) 17 (10.1%) Black or African American 54 (54.0%) 19 (27.5%) 73 (43.2%) Unknown or no race 17 (17.0%) 24 (34.8%) 41 (24.3%) White 12 (12.0%) 9 (13.0%) 21 (12.4%) Ethnicity0.005 Not Hispanic 73 (73.0%) 36 (52.2%) 109 (64.5%) Hispanic 27 (27.0%) 33 (47.8%) 60 (35.5%) Food insecurity0.003 Food secure 27 (27.0%) 24 (34.8%) 51 (30.2%) Mildly food insecure 4 (4.0%) 9 (13.0%) 13 (7.7%) Moderately food insecure 15 (15.0%) 17 (24.6%) 32 (18.9%) Severely food insecure 54 (54.0%) 19 (27.5%) 73 (43.2%) Stress (momentary),M(SD) 2.03 (0.83) 1.94 (0.78) 2.00 (0.81) 0.460 Hunger (momentary),M(SD) 2.20 (0.86) 2.02 (0.65) 2.13 (0.78) 0.131FOOD INSECURITY, HUNGER, STRESS, AND HOMELESSNESS 561 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. such that participants who were currently homeless were predomi- nantly Black or African American, whereas housed participants largely did not identify as any speciﬁc race (p,.05). Similarly, participants in the housed group were more likely to be of His- panic ethnicity as compared to the unhoused group (p,.01). Last, housing status was associated with food insecurity such that participants who were experiencing homeless were more likely to report being severely food insecure compared to housed partici- pants (p,.01). Mean levels for momentary stress and momentary hunger did not differ based on housing status. Effects of Hunger and Food Insecurity on Momentary Stress Table 2presents results from the mixed-effects multiple loca- tion scale model predicting stress as a function of food insecurity. Participants with greater-than-average mean levels of hunger (i.e., between-subjects effect) reported higher levels of momentary stress (p,.001). Similarly, participants reported higher momen- tary stress at the current prompt when hunger at the prior prompt was greater than one’s own average (i.e., within-subject effect; p,.001). After adjusting for the effects of hunger and all other covariates on the mean of stress, moderate or severe food insecur- ity at baseline was associated with an increase in mean levels of momentary stress (p,.01). Furthermore, those participants iden- tifying as Black or African American reported lower mean levels of momentary stress compared to participants identifying as another race after adjusting for all covariates (p,.05). Random location effect estimates (i.e., variances and covariances of the random location effects—intercept and slope) indicated that sub- jects differed signiﬁcantly from each other in their levels of stress(z= 8.32,p,.001). As indicated by the random slope estimate of hunger, the relationship between prior hunger and current stress varied signiﬁcantly across subjects (z= 2.58,p,.05). The rela- tionship between random slope and random location was statisti- cally signiﬁcant (z= 2.69,p,.05), indicating that participants with higher mean levels of stress had greater slopes (i.e., relation- ship between current stress and prior hunger). Effects of Hunger and Food Insecurity on Stress Variability Random scale effects indicated that subjects differed from each other signiﬁcantly in their variability of stress (SD= .92,SE= .05, z= 16.35,p,.001). Additionally, participants with greater-than- average levels of hunger had greater variability in stress (z= 2.45, p,.05). Likewise, participants reporting higher than usual levels of hunger at the prior prompt had increased variability in stress at the subsequent prompt compared to their mean variability (z= 3.01,p,.01). Compared to participants who were food secure or mildly food insecure, those who were moderately or severely food insecure had greater variability in stress (z= 2.21,p,.05). The interaction of random location and random scale revealed that par- ticipants with greater mean levels of stress also had greater vari- ability in reported stress (z= 4.94,p,.001). However, there was no statistically signiﬁcant effect of random slope (i.e., relationship between current stress and prior hunger) and random scale (i.e., the variability of stress;z= .55,p= .58). An additional model revealed a statistically signiﬁcant interaction between moderate or severe food insecurity (as compared to low food insecurity) and hunger at the prior prompt (i.e., within-subject effect) on variabili- ty of stress (z= 2.17,p= .03) but not on mean levels of stress Table 2 Results of a Mixed-Effects Multiple Location Scale Model Predicting Stress in a Sample of Young Adults Experiencing Homelessness Predictors EstimateSE z p Mean model effects Intercept 1.87 0.13 14.07 0.00 Age 0.01 0.02 0.39 0.69 Food insecure (ref = food secure) 0.32 0.10 3.05 0.00 Housed (ref = unhoused) 0.02 0.10 0.19 0.85 Black (ref = not Black) 0.28 0.11 2.57 0.01 Hispanic (ref = not Hispanic) 0.01 0.11 0.09 0.93 Male (ref = not male) 0.01 0.10 0.10 0.92 Hunger (within subject) 0.08 0.01 5.46 0.00 Hunger (between subject) 0.46 0.06 7.37 0.00 Random location effects a Intercept 0.38 0.05 8.32 0.00 Hunger (within subject) 0.01 0.01 2.58 0.01 Covariance 0.02 0.01 2.69 0.01 Within-subject variance effects Intercept 0.68 0.13 5.18 0.00 Hunger (within subject) 0.06 0.02 3.01 0.00 Hunger (between subject) 0.37 0.17 2.45 0.01 Food insecure (ref = food secure) 0.26 0.10 2.21 0.03 Random Location3Scale Effects b Intercept 0.40 0.08 4.94 0.00 Hunger (within subject) 0.07 0.13 0.55 0.58 Note. Analytic sample size isn= 5,737 at Level 1 andN= 169 at Level 2. Ref = reference. aRandom location effects are the variance-covariance matrix of the random intercept and slope. bThis interac- tion is the effects of the random intercept and slope on within-subject variance. 562 DZUBUR ET AL. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. (z= .71,p= .41). As indicated inFigure 1, participants who were food secure or mildly food insecure had more stress variabili- ty at lower levels of hunger compared to those who were moder- ately or severely food insecure. Conversely, participants who were food secure or mildly food insecure had less stress variability at higher levels of hunger compared to those who were moderately or severely food insecure. Association Between Housing Status and Mean Stress and Stress Variability Second-stage logistic regressions using subject-speciﬁc random effects found that, after adjusting for all covariates in previous models and average stress variability (i.e., random scale), mean levels of stress were not associated with housing status (z= .28, p= .78). Similarly, after adjusting for all covariates and average mean stress (i.e., random location), stress variability was not asso- ciated with housing status (z= .13,p= .90). However, the logistic regression found a signiﬁcant interaction between stress variability and mean stress, as depicted inFigure 2(z= 2.77,p,.01). Mean levels of stress were found to be a moderator of the relation- ship between stress variability and housing status. The odds of being housed were lower for individuals with greater-than-average stress variability when mean stress levels were also high. How- ever, the odds of being housed were higher for individuals with greater-than-average stress variability when mean stress levels were low. Discussion Results show that those who were more food insecure experi- enced greater stress variability across the EMA week. This indi- cates that food secure individuals were less affected by hunger compared to those who felt they were more food insecure. Simi- larly, when individuals experienced greater-than-average hunger (i.e., hunger greater than their average rate of hunger), they then experienced greater stress variability at the next prompt, showing the impact of hunger on stress at the momentary level.Other mainﬁndings centered on the signiﬁcance of stress. Those with higher average levels of stress, regardless of hunger, became substantially more stressed when becoming hungry com- pared to their generally less stressed counterparts. High mean lev- els of stress with no variability, often referred to as allostatic load, has been proven highly problematic as it is linked to increased dis- ease over time (McEwen, 1998). However, individuals with greater mean levels of stress in this sample were also found to have greater variability in reported stress, and these individuals were also less likely to be housed. Young adults currently experiencing homelessness were more food insecure than those who now reside in housing programs. Speciﬁcally, results indicated that over half of the unhoused young adults were severely food insecure, compared to only a quarter of young adults in housing programs. This is largely consistent with existing studies that found that between 28% and 85% of unhoused young adults experience food insecurity (Bowen & Irish, 2018;Crawford et al., 2015;Goldman-Hasbun et al., 2019;Tara- suk et al., 2009;Whitbeck et al., 2006), but this is theﬁrst known study that has compared those who are currently and formerly homeless (Johnson et al., 2019). It should be noted, however, that there were no differences in mean levels of reported stress or hun- ger between young adults who were experiencing homelessness or those who now reside in supportive housing. In addition, the rela- tionship between hunger, stress, and food insecurity did not depend on one’s housing status. This suggests that food insecurity and heightened levels of momentary hunger may be a better indi- cator of irregular stress than the housing environment (Businelle et al., 2013). Findings from this study provide evidence for the inﬂuence of both food insecurity and hunger on the variability of stress, with greater levels of momentary hunger or food insecurity resulting in variability of perceived stress. Momentary hunger predicted subse- quent stress in young adults who have experienced homelessness, even after adjusting for variables like food insecurity and demo- graphics. As self-reported hunger increased, the association between prior hunger and subsequent perceived stress increased, indicating a potentially curvilinear relationship. Stress variability was higher among food-secure young adults at lower-than-average Figure 1 Interaction Plot of Food Insecurity and Momentary Hunger on Stress Variability Figure 2 Interaction Plot of Mean Stress and Stress Variability on Housing Status Note. Low = 1 standard deviation, high =þ1 standard deviation, mod- erate = mean. FOOD INSECURITY, HUNGER, STRESS, AND HOMELESSNESS 563 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. hunger levels compared to food-insecure young adults. Inversely, stress variability was higher among food-insecure young adults at greater-than-average hunger levels. Environments related to home- lessness are already stressful such that homelessness can disrupt the normative development associated with young adulthood and increase adverse outcomes (Masten et al., 1993). Results suggest the simultaneous experience of hunger in already stressful contexts exacerbates stress levels, and those who reported higher average stress levels also experienced greater variability in stress. Ulti- mately, individuals with higher mean stress and greater stress vari- ability were less likely to be housed. Theseﬁndings are novel but also merit caution as food-secure young adults may simply not have sufﬁcient variance of higher-than-average hunger scores to il- licit stress variability, and similarly, food-insecure young adults may not have sufﬁcient variance at lower-than-average levels of hunger. Limitations Theﬁndings from this study were limited in generalizability as a result of design. The study took place in a dense, urban city, which may not adequately generalize to rural areas where food access and the cost of food may vary. Additionally, there was no item examining food access (e.g.,“Could you eat right now, if you wanted to?”) that could establish a more ecologically valid rela- tionship between food insecurity and hunger at the momentary level. Last, the study used a novel single-item hunger measure that has not been used in previous studies. However, the measure shows preliminary convergent validity with food insecurity as measured in the context of this study (r= .15). Conclusion The study sought to address gaps in the context of food insecur- ity by examining the effects of momentary hunger on mean and variability of momentary stress in a sample of current and for- merly homeless young adults. The study showed, in situ, the extent to which food insecurity results in variability of stress among vul- nerable populations and how high levels of hunger may lead to a stronger and more consistent stress response. Findings can be interpreted as reinforcing the need for mental health services and food programs for young adults even after they access housing programs. That is, feelings of hunger and stress may continue even after young adults are placed in supportive housing, but additional longitudinal research is warranted on young adults transitioning from homelessness into housing to establish causal effects. In addition, future research may seek to examine what happens to young adults with extended periods of food insecurity to determine how long-term exposure to hunger-related stress impacts health outcomes, mental health, other health behavior (e.g., drug and alcohol use) and whether the relationship between hunger and stress changes as a result of allostatic load or similar processes (Kim et al., 2017). Of course, givenﬁndings that unhoused youth are highly food insecure, this study also provides more evidence to the literature that has consistently found food insecurity to be neg- atively associated with health and supports calls for the eradication of hunger and food insecurity entirely as a detrimental and danger- ous national health problem (Gundersen & Ziliak, 2015;Thomas et al., 2019). References Block, J. P., He, Y., Zaslavsky, A. M., Ding, L., & Ayanian, J. Z. (2009). Psychosocial stress and change in weight among U.S. adults.American Journal of Epidemiology,170(2), 181–192.https://doi.org/10.1093/aje/ kwp104 Bowen,E.A.,&Irish,A.(2018).‘Hello, you’re not supposed to be here’: Homeless emerging adults’experiences negotiating food access.Public Health Nutrition,21(10), 1943–1951.https://doi.org/ 10.1017/S1368980018000356 Brownell, K. D., & Walsh, B. T. (2017).Eating disorders and obesity, third edition: A comprehensive handbook. Guilford Press. Businelle, M. S., Cuate, E. L., Kesh, A., Poonawalla, I. B., & Kendzor, D. E. (2013). Comparing homeless smokers to economically disadvan- taged domiciled smokers.American Journal of Public Health,103(S2), S218–S220.https://doi.org/10.2105/AJPH.2013.301336 Coates, J., Swindale, A., & Bilinsky, P. (2007).Household Food Insecurity Access Scale (HFIAS) for measurement of food access: Indicator guide: Version 3 (576842013-001)[Data set]. American Psychological Associ- ation.https://doi.org/10.1037/e576842013-001 Crawford, B., Yamazaki, R., Franke, E., Amanatidis, S., Ravulo, J., Steinbeck, K., Ritchie, J., & Torvaldsen, S. (2014). Sustaining dignity? food insecurity in homeless young people in urban Australia.Health Promotion Journal of Australia,25(2), 71–78.https://doi.org/10.1071/ HE13090 Crawford, B., Yamazaki, R., Franke, E., Amanatidis, S., Ravulo, J., & Torvaldsen, S. (2015). Is something better than nothing? Food insecurity and eating patterns of young people experiencing homelessness.Austra- lian and New Zealand Journal of Public Health,39(4), 350–354.https:// doi.org/10.1111/1753-6405.12371 Dunton, G. F., Liao, Y., Dzubur, E., Leventhal, A. M., Huh, J., Gruenewald, T., Margolin, G., Koprowski, C., Tate, E., & Intille, S. (2015). Investigating within-day and longitudinal effects of maternal stress on children’s physical activity, dietary intake, and body composi- tion: Protocol for the MATCH study.Contemporary Clinical Trials,43, 142–154.https://doi.org/10.1016/j.cct.2015.05.007 Dzubur, E., Ponnada, A., Nordgren, R., Yang, C.-H., Intille, S., Dunton, G., & Hedeker, D. (2020). MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive lon- gitudinal data.Behavior Research Methods,52(4), 1403–1427. https:// doi.org/10.3758/s13428-019-01322-1 Finch, L. E., & Tomiyama, A. J. (2014). Stress-induced eating dampens physiological and behavioral stress responses. In R. S. Watson (Ed.), Nutrition in the prevention & treatment of abdominal obesity(pp. 189– 195). Elsevier.https://doi.org/10.1016/B978-0-12-407869-7.00018-0 Goldman-Hasbun, J., Nosova, E., DeBeck, K., Dahlby, L., & Kerr, T. (2019). Food insufficiency is associated with depression among street- involved youth in a Canadian setting.Public Health Nutrition,22(1), 115–121.https://doi.org/10.1017/S1368980018002574 Gundersen, C., & Ziliak, J. P. (2015). Food insecurity and health out- comes.Health Affairs (Project Hope),34(11), 1830–1839.https://doi .org/10.1377/hlthaff.2015.0645 Haskett, M. E., Majumder, S., Kotter-Grühn, D., & Gutierrez, I. (2021). The role of university students’wellness in links between homelessness, food insecurity, and academic success.Journal of Social Distress and the Homeless,30(1), 59–65.https://doi.org/10.1080/10530789.2020 .1733815 Henwood, B. F., Redline, B., Dzubur, E., Madden, D. R., Rhoades, H., Dunton, G. F., Rice, E., Semborski, S., Tang, Q., & Intille, S. S. (2019). Investigating health risk environments in housing programs for young adults: Protocol for a geographically explicit ecological momentary assessment study.JMIR Research Protocols,8(1), Article e12112. https://doi.org/10.2196/12112 Hernandez, D. C., Daundasekara, S. S., Arlinghaus, K. R., Tobar, N., Reitzel, L. R., Kendzor, D. E., & Businelle, M. S. (2019). Cumulative 564 DZUBUR ET AL. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. risk factors associated with food insecurity among adults who experi- ence homelessness.Health Behavior Research,2(1), Article 7.https:// doi.org/10.4148/2572-1836.1033 Hodgson, K. J., Shelton, K. H., van den Bree, M. B. M., & Los, F. J. (2013). Psychopathology in young people experiencing homelessness: A systematic review.American Journal of Public Health,103(6), e24–e37. https://doi.org/10.2105/AJPH.2013.301318 Holland, A. C., Kennedy, M. C., & Hwang, S. W. (2011). The assessment of food security in homeless individuals: A comparison of the Food Se- curity Survey Module and the Household Food Insecurity Access Scale. Public Health Nutrition,14(12), 2254–2259.https://doi.org/10.1017/ S1368980011001327 Howard, L. L. (2011). Does food insecurity at home affect non-cognitive performance at school? A longitudinal analysis of elementary student classroom behavior.Economics of Education Review,30(1), 157–176. https://doi.org/10.1016/j.econedurev.2010.08.003 Huh, J., Shiyko, M., Keller, S., Dunton, G., & Schembre, S. M. (2015). The time-varying association between perceived stress and hunger within and between days.Appetite,89, 145–151.https://doi.org/10 .1016/j.appet.2015.02.001 Johnson, K., Drew, C., & Auerswald, C. (2019). Structural violence and food insecurity in the lives of formerly homeless young adults living in permanent supportive housing.Journal of Youth Studies,23(10), 1249– 1272.https://doi.org/10.1080/13676261.2019.1667492 Karatekin, C. (2018). Adverse childhood experiences (ACEs), stress and mental health in college students.Stress and Health,34(1), 36–45. Kim, J. E., Flentje, A., Tsoh, J. Y., & Riley, E. D. (2017). Cigarette smok- ing among women who are homeless or unstably housed: Examining the role of food insecurity.Journal of Urban Health,94(4), 514–524. https://doi.org/10.1007/s11524-017-0166-x Knowles, M., Rabinowich, J., Ettinger de Cuba, S., Cutts, D. B., & Chilton, M. (2016).“Do you wanna breathe or eat?”: Parent perspectives on child health consequences of food insecurity, trade-offs, and toxic stress.Maternal and Child Health Journal,20(1), 25–32.https://doi.org/ 10.1007/s10995-015-1797-8 Masten, A. S., Miliotis, D., Graham-Bermann, S. A., Ramirez, M., & Neemann, J. (1993). Children in homeless families: Risks to mental health and development.Journal of Consulting and Clinical Psychology, 61(2), 335 –343.https://doi.org/10.1037/0022-006X.61.2.335 McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allo- static load.Annals of the New York Academy of Sciences,840(1), 33–44. https://doi.org/10.1111/j.1749-6632.1998.tb09546.x McIntyre, L., Williams, J. V. A., Lavorato, D. H., & Patten, S. (2013). Depression and suicide ideation in late adolescence and early adulthood are an outcome of child hunger.Journal of Affective Disorders,150(1), 123–129.https://doi.org/10.1016/j.jad.2012.11.029Muldoon, K. A., Duff, P. K., Fielden, S., & Anema, A. (2013). Food insuf- ficiency is associated with psychiatric morbidity in a nationally repre- sentative study of mental illness among food insecure Canadians.Social Psychiatry and Psychiatric Epidemiology,48(5), 795–803.https://doi .org/10.1007/s00127-012-0597-3 Nordgren, R., Hedeker, D., Dunton, G., & Yang, C.-H. (2020). Extending the mixed-effects model to consider within-subject variance for ecologi- cal momentary assessment data.Statistics in Medicine,39(5), 577–590. https://doi.org/10.1002/sim.8429 National Academies of Sciences, Engineering, and Medicine. (2018). Permanent supportive housing: Evaluating the evidence for improving health outcomes among people experiencing chronic homelessness. The National Academies Press.https://doi.org/10.17226/25133 O’Reilly, G. A., Huh, J., Schembre, S. M., Tate, E. B., Pentz, M. A., & Dunton, G. (2015). Association of usual self-reported dietary intake with ecological momentary measures of affective and physical feeling states in children.Appetite,92, 314–321.https://doi.org/10.1016/j.appet .2015.05.032 Reichenberger, J., Kuppens, P., Liedlgruber, M., Wilhelm, F. H., Tiefengrabner, M., Ginzinger, S., & Blechert, J. (2018). No haste, more taste: An EMA study of the effects of stress, negative and positive emo- tions on eating behavior.Biological Psychology,131,54–62.https://doi .org/10.1016/j.biopsycho.2016.09.002 Shields, G. S., & Slavich, G. M. (2017). Lifetime stress exposure and health: A review of contemporary assessment methods and biological mechanisms.Social and Personality Psychology Compass,11(8), e12335. https://doi.org/10.1111/spc3.12335 Tarasuk, V., Dachner, N., Poland, B., & Gaetz, S. (2009). Food deprivation is integral to the‘hand to mouth’existence of homeless youths in To- ronto.Public Health Nutrition,12(9), 1437–1442.https://doi.org/10 .1017/S1368980008004291 Thomas, M. M. C., Miller, D. P., & Morrissey, T. W. (2019). Food inse- curity and child health.Pediatrics ,144(4), Article e20190397.https:// doi.org/10.1542/peds.2019-0397 Tucker, J. S., Davis, J. P., Perez, L. G., Klein, D. J., & D’Amico, E. J. (2022). Late adolescent predictors of homelessness and food insecurity during emerging adulthood.Journal of Adolescent Health,70(5), 736– 742.https://doi.org/10.1016/j.jadohealth.2021.10.035 Whitbeck, L. B., Chen, X., & Johnson, K. D. (2006). Food insecurity among homeless and runaway adolescents.Public Health Nutrition, 9(1), 47–52.https://doi.org/10.1079/PHN2005764 Received August 28, 2021 Revision received May 4, 2022 Accepted May 9, 2022 n FOOD INSECURITY, HUNGER, STRESS, AND HOMELESSNESS 565 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
have to write bibliography Write an Outline for your paper. From the articles you’ve looked at, what are some ideas/arguments you can make about the topic? What about a main thesis? Introduction — es
The Relationship Among Social Support, Food Insecurity and Mental Health for Adults With Severe Mental Illness and Type 2 Diabetes: A Survey Study Cameron Michels 1, Kevin A. Hallgren 1, Allison Cole 2, Lydia Chwastiak 1, and Sunny Chieh Cheng 3 1Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine 2Department of Family Medicine, University of Washington School of Medicine3School of Nursing and Healthcare Leadership, University of Washington Tacoma Objective: People living with severe mental illness are at increased risk of medical comorbidity as well as poverty, food insecurity, and inadequate social support in managing their mental and physical health conditions. Lack of access to suf ﬁcient food negatively affects a person ’s ability to manage health conditions, in particular diabetes, which is twice as common among people with severe mental illness as the general population. This study aimed to explore associations among food insecurity, social support, and psychiatric symptoms among adults with severe mental illness and diabetes. Method: A cross-sectional survey was conducted between January and May 2021 among adults ( N=156) with severe mental illness and type 2 diabetes who received primary care through a large academic health-care system (26% response rate). Valid and reliable questionnaires were implemented to measure food insecurity, social support, and mental health. Regression analysis was applied to examine the associations between food security status, social support, and mental health. Results: Food insecurity and social support are both correlated with psychiatric symptom severity. Speci ﬁcally, support from family members has the largest protective role against food insecurity. Conclusions and Implications for Practice: This study found food insecurity is likely a critical issue to address whenever it is present in adults with severe mental illness (SMI) and type 2 diabetes. The presence offamily support mitigates the need for addressing food insecurity. Practices and policies aimed at both addressing health inequities such as food insecurity and strengthening family support among people living with SMI and comorbid medical conditions are important adjuncts to self-management interventions. Impact and Implications Medical care for diabetes for people who have SMI must address the impact of social determinants of health, including food insecurity and social support. For adults with SMI and type 2 diabetes, family support may have important effects on the link between food insecurity and adverse mental health outcomes. Keywords: food insecurity, social support, psychotic disorders, psychiatric rehabilitation Background Severe mental illnesses (SMI), such as schizophrenia and bipolar disorder, are associated with substantial premature cardiovascular mortality ( Olfson et al., 2015 ). Schizophrenia is the third most disabling health condition worldwide and has an annual economic burden of $300 billion in the U.S., due in large part to high rates of disability ( Wander, 2020 ). People living with severe mental ill- nesses are twice as likely to develop type 2 diabetes compared with the general population ( Stubbs et al., 2015 ). There are complex interrelated factors that increase risk for diabetes in people with psychosis. First, antipsychotic medications can increase the risk of diabetes through causing weight gain or increasing insulin resistance ( Gucciardi et al., 2019 ;Vancampfort et al., 2016 ). Second, people with SMI are disproportionally impacted by poverty and food insecurity ( Coleman-Jensen, 2010 ). Food insecurity is de ﬁned as the disruption of food intake or eating patterns because of lack of money and other resources ( Koh et al., 2014 ). Food insecurity is correlated with poorer mental health, poorer physical health, multiple chronic conditions including diabetes, and heightened nutritional vulnerability ( Elgar et al., 2021 ;Seligman et al., 2012 ;Tarasuk et al., 2013 ). For instance, people with food insecurity are more likely to develop mental disorders ( Fang et al., 2021 ). Approximately 71% of adults with severe mental illness e xperience food insecurity ( Mangurian et al., 2013 ). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. This article was published Online First May 5, 2022. Cameron Michels https://orcid.org/0000-0001-7877-4370 Kevin A. Hallgren https://orcid.org/0000-0001-8386-3984 Allison Cole https://orcid.org/0000-0003-4393-464X Lydia Chwastiak https://orcid.org/0000-0003-4159-7934 Sunny Chieh Cheng https://orcid.org/0000-0001-8073-5733 We thank the participants of this study. The study was approved by the University of Washington research ethics committees. Informed consent was obtained from participants and con ﬁdentiality was maintained. All authors have nothing to disclose.Correspondence concerning this article should be addressed to Sunny Chieh Cheng, School of Nursing and Healthcare Leadership, University of Wahington Tacoma, 1900 Commerce Street, Box 358421, Tacoma, WA 98402, United S tates. Email: [email protected] Psychiatric Rehabilitation Journal © 2022 American Psychological Association 2022, Vol. 45, No. 3, 212 –218 ISSN: 1095-158X https://doi.org/10.1037/prj0000525 212 Improving household food security status has the potential to reduce the impact of mental disorders. Limited access to adequate nutrition and high-quality diet is especially problematic to diabetes management efforts ( Krishnan et al., 2010 ) as food-insecurity is associated with poorer glycemic control. Therefore, several diabetes self-management interventions emphasize the importance of dietary interventions —such as strategies for purchasing healthy food on a budget —to mitigate the impact of poverty and social disadvantages on mental health symptoms among people with severe mental illnesses ( Druss et al., 2010 ). Low social support from natural supporters (e.g., family, schoolmates, coworkers, and community) is also strongly associated with poor mental health and food security ( Hammami et al., 2020 ; Russell & Fish, 2016 ;Stowkowy et al., 2012 ). A meta-analysis of 64 studies af ﬁrmed the association between social support and better mental health functioning ( Harandi et al., 2017 ). Substantial research and theoretical models indicate that family support plays an especially important role in self-management of severe mental illness ( Frongillo et al., 2017 ). The Family and Self-management framework by Gray et al. (2015) linked self-management of chronic conditions with family-level risk and protective factors as self-management occur in the context of family management over the lifecycle. Chronic disease management practices need to be incorporated into daily family routines in order to ensure the best possible outcomes. An Institute of Medicine (IOM) and National Research Council report ( 2011 ) also indicated that family-level involvement is an essential component of managing chronic conditions, which can impact the health and well-being of the entire family. However, research to date has been inconsistent about the nature of social support ’s moderating effect in the relationship between food security status and mental well-being among people with SMI and comorbid diabetes ( Na et al., 2019 ). This study aimed to test whether perceived social support is a moderating factor in the relationship between food insecurity and mental illness self-management among adults with SMI and comor- bid diabetes. The primary aim of the study was to characterize the association of food security, social support, and psychiatric symp- toms among adults with SMI and comorbid diabetes who receive primary care services through a large academically af ﬁliated health- care system in Western Washington State. Speci ﬁcally, we hypoth- esized that food insecurity would be associated with more frequent psychiatric symptoms and lower social support, as shown in previous studies. Further, we hypothesized that the association between food insecurity and psychiatric symptoms would be stronger among individuals with lower social support from family members. Method Participants and Data Collection The study involved a cross-sectional survey of adults who had diagnoses of both type 2 diabetes and a severe mental illness (described below). Surveys were administered online or by tele- phone. Potential participants were identi ﬁed through administrative data from a large academically af ﬁliated health-care system in Western Washington State. Adults over 21 years old who received primary care services at one of twelve primary care clinics in the healthcare system between 2017 and 2020 were included in the sample if they 1) had at least one inpatient or two outpatient diagnoses of type 2 diabetes (ICD-10 codes E08-E13.9) and at least one inpatient or two outpatient diagnoses of a severe mental illness, which included schizophrenia or schizoaffective disorder (F20-F29), bipolar disorder (F31), or major depressive disorder with psychotic symp- toms (F32.3; F33.3). Those with a diagnosis of dementia were excluded, as were individuals who could not speak or read English. Figure 1 outlines the ﬂow of participant recruitment. Potential participants were initially introduced to the study by a mailed letter, which explained the purpose of the study and invited them to either complete the survey, learn more about the study, or decline future contact from study staff. Two weeks after the letter was mailed, an email was sent to those who had email addresses listed in the administrative data. Two email reminders were automatically sent one and 2 weeks after the email introduction to those who had neither completed the survey nor declined further contact. Indi- viduals who did not respond also received a phone call and were offered the opportunity to complete the survey over the phone. All questionnaires were administered using research electronic data capture (REDCap), a secure web-based tracking and online data acquisition system ( Harris et al., 2009 ). Participants received a $30 gift card for completing the survey. The study was approved by the University of Washington Inst itutional Review Board (IRB). Measures Demographic and Clinical Variables The survey included questions related to participants ’age, gen- der, race, ethnicity, marital status, highest level of education, and type of insurance. The survey also included questions characterizing This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Figure 1 Recruitment Flowchart * 14 potential participants ’caregivers or family members declined on their behalf when contacted by phone. ** One potential participant was deemed cognitively impaired and two potential participants had a language barrier and could not complete the survey over the phone with the research coordinator. IMPACT OF SOCIAL SUPPORT AND FOOD INSECURITY 213 participant ’s diabetes and primary psychiatric diagnosis, including duration of illnesses. Food Insecurity Food insecurity was measured by participant self-report using the U.S. Department of Agriculture ’s Food Security Survey Module. The six items in this module address inadequate food in the household, reduced or skipped meals, or hunger because of inability to afford food. Five of the items have response options of yes or no; one item includes response options of almost every month, some months, but not every month, or only 1 or 2 months. By established convention, participants were food-insecure if two or more items were answered af ﬁrmatively ( Bickel et al., 2000 ). This survey has been widely used in community surveys and has shown associations with reduced dietary variety, increased consumption of calorically dense foods, and reduced intake of fruits and vegetables ( Hanson & Connor, 2014 ). Cronbach ’sαcoef ﬁcient in this sample was 0.68. Multidimensional Scale of Perceived Social Support The multidimensional scale of perceived social support (MSPSS) is a brief 12-item self-reported measurement tool that measures perceived adequacy of social support from three domains: family, friends, and signi ﬁcant others ( Wilcox, 2010 ). Participants are asked to indicate their agreement with items on a 7-point Likert scale, ranging from 1 =very strongly disagree to 7 =very strongly agree , yielding a total score between 12 and 84. Scores of 12 –48 indicate low social support, scores of 49 –68 indicate moderate social sup- port, and scores of 69 –84 indicate high social support. Several studies have demonstrated its robust psychometric properties in adults with schizophrenia ( Rabinovitch et al., 2013 ;Teh et al., 2019 ). Cronbach ’sαcoef ﬁcient in this sample was 0.93. Mental Health (Modi ﬁed Colorado Symptom Index) The modi ﬁed Colorado Symptom Index (CSI) is a 14-item self- reported measurement tool that assesses frequency of mental illness symptoms ( Conrad et al., 2001 ). Survey respondents are asked to indicate how often in the last 30 days they have experienced a range of mental illness symptoms on a 0 –4 scale where 0 =not at all and 4 =at least every day . The scale includes symptoms of depression, mania, and psychosis. As this was a cross-sectional survey that could be completed online and had no clinical follow-up, the ﬁnal two items that relate to suicidal and homicidal ideation were removed from this measure and the range of possible scores for this modi ﬁed scale was 0 –48, with higher scores indicating more frequent psychiatric symptoms. The CSI has been shown to have excellent internal consistency (.92) and test-retest reliability (.71); research suggests that 30 is an appropriate “clinical ”cutoff score, that is, discriminates individuals with psychiatric disabilities ( Boothroyd & Chen, 2008 ). Cronbach ’sαcoef ﬁcient in this sample was 0.90. Data Analysis Data were reviewed for completeness, potential errors, and consistency issues. Variable distributions were examined to identify potential outliers and the appropriateness of outcome distributions for analysis. Descriptive analyses were performed to identify the percentage of participants reporting food insecurity and to charac- terize the mean ( SD ) responses to the measures of social support and psychiatric symptoms. Differences in mean levels of social support (full scale and family, friends, and signi ﬁcant other subscales) and psychiatric symptoms were compared between food secure and food insecure participants using independent sample ttests. Effect sizes and 95% CI ’s of these differences were estimated using the Cohen ’s d statistics, which re ﬂects the difference in subgroup means in pooled standard deviation units. Linear regression analysis was used to test the hypothesis that social support would moderate the association between food inse- curity and psychiatric symptoms. Psychiatric symptoms were entered as the dependent variable, and independent variables included mean-centered MSPSS scores (main effect of social sup- port), a dummy-coded food insecurity variable (main effect of food insecurity; 0 =food secure, 1 =food insecure), and the interaction of these two measures (moderation effect). All analyses were con- ducted using SPSS statistical software ( IBM Corp, 2020 ). Results Hospital administrative data identi ﬁed ( n=624) individuals with severe mental illness (SMI) and type 2 diabetes who received primary care between 2017 and 2020 in one of the health-care system ’s primary care clinics. Six individuals had mailing addresses that were out of state and were presumed to no longer be receiving primary care in the system. Twenty of these individuals had no valid mailing addresses, phone numbers, nor email addresses in the administrative data and could not be contacted. Of the 598 individuals who we attempted to reach by letter, email, or phone, 156 completed the survey for a response rate of 26%. Three individuals were ineligible (two did not speak or read English and one could not demonstrate capacity to consent to the study) and 146 (22%) were reached out to but declined to participate. Fifty-eight percent of respondents ﬁlled out the survey online and 42% completed the survey over the phone with the study coordinator. Of the 156 respondents ( Table 1 ), majority were White ( N=107; 69%), male ( N=73; 46.8%); reported income below the federal poverty line ( N=67; 44%) and had a primary psychiatric diagnosis of bipolar disorder ( N = 93; 59.6%). Most participants were Medicaid/Medicare bene ﬁciaries while 24.6% of the patricians were privately insured. The average age was 51.89 years old ( SD =12.59) and duration of diabetes ranged from 0 to 41 years ( M=12.8, SD =9.3). The study results are representative of the population of adults with diagnosis and diabetes because the sex and ethnicity distributions of the (57% White, 56% male) in this partici- pated health-care system in Western Washington State are aligned with the demographics distributions in our study. Thirty-nine participants (25% of the sample) met the criteria for food insecurity (i.e., af ﬁrmative response to ≥2 of the 6 questions on the Food Security Survey Module). These respondents cut the size of their meals or skipped meals because there was not enough money for food. Of people who skipped meals in the past year, 50% did so almost every month. Twenty-three participants (16% of the sample) reported having low social support, 61 (43% of sample) reported moderate social support, and 58 (41% of sample) reported high social support. The mean score on the CSI was 19.36 ( SD =12.00), suggesting that participants, on average, experienced psychiatric symptoms like racing thoughts and paranoia several times per month This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 214 MICHELS, HALLGREN, COLE, CHWASTIAK, AND CHENG (not daily or weekly). Table 2 presents the differences between food- insecure and food-secure individuals on social support and psychi- atric symptom severity. Compared with participants with food security, those with food insecurity had signi ﬁcantly lower social support ( Mdifference =0.69, d=0.47, p=.008), with a signi ﬁcant difference between food insecure and food secure patients being present for the subscale re ﬂecting social support from family ( Mdifference = 1.17, d= 0.26, p< .001), but not the subscale re ﬂecting social support from friends ( Mdifference = 0.54, d= 0.29, p= .061) or signi ﬁcant others ( Mdifference = 0.30, d= − 0.20, p = .19). People with food insecurity also had greater psychiatric symptom severity than people with food security ( Mdifference =6.57, d=−0.95, p<.001). Table 3 presents results from the regression model testing for the potential moderating effect of social support on the association between food insecurity and psychiatric symptom severity. The results indicate that both food security and perception of social support were independently and additively associated with fre- quency of mental health symptoms ( p< .01), such that food insecurity and/or lower perception of social support were each associated with more frequent psychiatric symptoms. However, social support did not signi ﬁcantly moderate the relationship between food security and frequency of psychiatric symptoms, as indicated by a nonsigni ﬁcant food insecurity ×Perception of social support interaction (see Table 3 ). Discussion This study ﬁlls an important knowledge gap related to the relationships among food security, social support, and psychiatry symptoms ( Gray et al., 2020 ) for individuals with severe mental illness (SMI) and type 2 diabetes. Our study found that food insecurity is signi ﬁcantly associated with both perceived low sup- port from family and elevated severity of psychiatric symptoms. In this sample of adults with SMI and type 2 diabetes receiving primary care through a large academically af ﬁliated health-care organization, 25% were living with food insecurity. Forty-four percent of parti- cipants reported annual household income less than $15,000, which could be a main source of food insecurity ( Sareen et al., 2011 ). 59.6% of our sample reported that they or someone else in their household was receiving some form of food assistance like food stamps. This could contribute to the lower rate of food insecurity when compared with the rate of individuals reporting income levels below the federal poverty line. As suggested by both the evidence from this and previous studies ( Silverman et al., 2015 ), future research should explore the feasibility and ef ﬁcacy of screening low-income individuals with both SMI and diabetes for food insecurity in both mental health and primary care settings. Our study reaf ﬁrms the signi ﬁcant association between food insecurity and elevated severity of psychiatric symptoms suggested by previous research ( Martin et al., 2016 ). In one study for example, two-third of the people with food insecurity had a bipolar diagnosis and 39% had a major depression diagnosis. The impact of the mood episodes of bipolar disorder on diabetes may be quite different than the impact of schizophrenia symptoms and functional impairment on diabetes. Because previous studies have revealed that, among people with diabetes, food insecurity is signi ﬁcantly associated with depressive symptoms, experts have recommended addressing eco- nomic issues in conjunction with addressing psychosocial issues for comprehensive diabetes care ( Silverman et al., 2015 ). Given this, physicians should connect adults who experience food insecurity in food assistance programs ( Patil et al., 2018 ;Sareen et al., 2011 ) such as Supplemental Nutrition Assistance Program, Meals on Wheels and local food pantries ( Holben & Myles, 2004 ). Strategies to strengthen the accessibility and reach of nutritional assistance This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Table 1 Characteristics of Survey Respondents (N =156) Characteristics N % Age (years) 21 –35 18 11.5 36 –44 29 18.6 45 –59 62 39.7 60 –74 42 26.9 75 + 5 3.2 GenderMale 73 47.0 Female 81 52.0 Nonbinary 2 1.0Race a White 107 68.6 Black 32 20.5 Asian 9 5.8Native American/Native Alaskan 11 7.1 Native Hawaiian or Paci ﬁc Islander 2 1.3 Other 11 7.1 Latinx (any race) 7 4.5Marital statusSingle 62 39.7 Divorced/widowed 45 28.9 Married/living with partner 49 31.4Education levelBelow high school 13 8.3 High school or equivalent 79 50.6 2-year or 4-year degree 53 33.9Higher than 4-year degree 11 7.2 Household income (in USD) b Below $15,000 67 44.1 $15,000 –$24,999 19 12.5 $25,000 –$49,000 25 16.4 $50,000 –$74,999 12 7.9 $75,000 –$99,999 10 6.6 $100,000 and above 19 12.5Health insurance a Medicaid 78 50.0 Medicare 96 61.5 Private insurance 38 24.4VA or military 4 2.6 None 2 1.3 Primary psychiatric diagnosis c Schizophrenia 28 17.9 Schizoaffective disorder 15 9.6 Bipolar disorder 93 59.6 Major depressive disorder 11 7.1 Other psychotic disorder 6 3.8Duration of diabetes (years since diagnosis)0 –1 years 22 14.1 2 –5 years 24 15.4 6 –10 years 33 21.2 11 –15 years 28 17.9 16 –20 years 20 12.8 More than 20 years 29 18.6 Note . VA = Veterans Affairs. aParticipants were allowed to select more than one response. bFour participants did not disclose income. cThree participants did not disclose primary psychiatric diagnosis IMPACT OF SOCIAL SUPPORT AND FOOD INSECURITY 215 programs ( Melchior et al., 2009 ) are worthy public policy goals to ensure that resources are prioritized for this vulnerable population with mental health conditions. We additionally con ﬁrmed the association between food insecurity and lower social support. Our study further examined the roles of social support and found support from families was signi ﬁcantly associated with food security status. This ﬁnding is consistent with a recent study ( Mokari-Yamchi et al., 2020 ) that among support from family, friends, and signi ﬁcant others as measured by the MSPSS, family support has the largest protective role against food insecurity. This result provides a better understanding of the relationship between food insecurity status and perceived support from families and may identify new intervention targets for individuals with SMI and diabetes. In our study, food insecurity is signi ﬁcantly higher among individuals reporting elevated psychiatric symptom severity and lower perceived social support, but the moderating effect of social support on the relationship between food insecurity and psychiatry symptoms, was not statistically signi ﬁcant. This ﬁnding is consistent with a prior study ( Martin et al., 2016 ). There are two potential explanations for this lack of moderating effect. First, previous research suggests that there is a gender difference in self-reported level of food insecurity. Men tend to underreport food insecurity ( Carter et al., 2011 ) while women have a higher probability of being food insecure relative to men ( Broussard, 2019 ). Inconsistent with previous ﬁndings, there was no signi ﬁcant difference between the food security status of men and that of other genders in this sample. Second, both social support levels and the food security status were self-reported, and there is a risk of self-report bias. The ﬁnding that the presence of social support did not mitigate the need for addressing food insecurity suggests a critical need for government and community-based programs who care for people with SMI and comorbid diabetes to speci ﬁcally address food insecurity and additional social and contextual factors ( Muldoon et al., 2013 ). The ﬁndings from this study should be interpreted in light of several limitations. First, causality cannot be determined from this cross-sectional study. This is especially important as the frequency of mental illness symptoms only represent respondent ’s experiences in the most recent month. Second, self-reported data may result in recall bias. Third, only 26% of the 614 people identi ﬁed by hospital administrative data completed the survey. The sample restriction limits the generalizability of our ﬁndings to the U.S. adults with severe mental illness and diabetes but provides important new information about the population generally at risk for food insecurity. In particular, it is possible that those whom we were unable to reach or who did not complete the survey were more likely to be experiencing food insecurity, which would result in our ﬁndings under-estimating the true prevalence of food insecurity in this population. The individuals who completed this survey were man- aging their mental illnesses effectively as they were only experienc- ing symptoms several times a month as opposed to daily or weekly. A sample with more frequent psychiatric symptoms might have a higher prevalence of food insecurity. It is important to note that 26% is a high response rate for mail surveys, and the only individuals we can be certain that we reached are those who either completed the survey, agreed to complete the survey but were ineligible, or who actively declined. Finally, our study did not explore how speci ﬁc mechanisms of social support might impact psychiatric symptom frequency or food insecurity, but we did investigate which speci ﬁc group of natural supports (family, friends, and signi ﬁcant others) might have the greatest effect. Our ﬁnding that support from family may have the greatest impact on food insecurity is consistent with previous studies which have shown that strengthening supports from family may alleviate mental distress ( Cheng et al., 2014 ). In conclusion, food insecurity is a critical issue to address whenever it is present and may have particularly signi ﬁcant clinical implications for people with mental health conditions who also manage chronic conditions such as diabetes. References Bickel, G., Nord, M., Price, C., Hamilton, W., & Cook, J. (2000). Measuring food security in the United States: Guide to measuring household food security . US department of agriculture. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Table 2 Differences Between Food-Insecure and Food-Secure Individuals on Social Support and Psychiatric Symptoms Measure Food insecure ( n=39) Food secure ( n=117) Cohen ’sd p M(SD) M(SD) Estimate [95% CI] Social support (full scale) 3.97 (1.28) 4.66 (1.54) 0.47 [0.09, 0.85] .008 Family subscale 3.54 (1.69) 4.71 (1.87) 0.26 [0.26, 1.02] <.001 Friend subscale 3.85 (1.98) 4.38 (1.76) 0.29 [ −0.08, 0.66] .061 Signi ﬁcant other subscale 4.62 (1.89) 4.92 (1.83) −0.20 [ −0.20, −0.53] .192 Psychiatric symptoms mean 27.34 (11.91) 16.77 (10.89) −0.95 [ −1.34, −0.55] < .001 Note . The social support full scale and the family, friends, and signi ﬁcant other subscales have a possible range of 1 –5, with higher scores re ﬂecting greater perceived social support. The measure for psychiatric symptoms has a possible range of 0 –48, with higher scores re ﬂecting more frequent and severe mental illness symptoms. Table 3 Linear Regression Model Predicting Frequency and Severity of Mental Illness Symptoms by Food Insecurity and Perceived Social Support Effect Unstandardized coefficients Standardized coefficients p B (SE) β (Intercept) 16.84 (1.07) <.001 Food insecurity 10.37 (2.35) 0.37 <.001 Perceived social support − 1.90 (0.70) −0.24 .008 Food insecurity ×Perceived social support 1.274 (1.61) 0.07 .430 216 MICHELS, HALLGREN, COLE, CHWASTIAK, AND CHENG Boothroyd, R. A., & Chen, H. J. (2008). The psychometric properties of the Colorado Symptom Index. Administration and Policy in Mental Health , 35 (5), 370 –378. https://doi.org/10.1007/s10488-008-0179-6 Broussard, N. H. (2019). What explains gender differences in food insecurity? Food Policy ,83,180 –194. https://doi.org/10.1016/j.foodpol.2019.01.003 Carter, K. N., Kruse, K., Blakely, T., & Collings, S. (2011). The association of food security with psychological distress in New Zealand and any gender differences. Social Science & Medicine ,72(9), 1463 –1471. https:// doi.org/10.1016/j.socscimed.2011.03.009 Cheng, Y., Li, X., Lou, C., Sonenstein, F. L., Kalamar, A., Jejeebhoy, S., Delany-Moretlwe, S., Brahmbhatt, H., Olumide, A. O., & Ojengbede, O. (2014). The association between social support and mental health among vulnerable adolescents in ﬁve cities: Findings from the study of the well-being of adolescents in vulnerable environments. The Journal of Adolescent Health ,55(Suppl. 6), S31 –S38. https://doi.org/10.1016/j. jadohealth.2014.08.020 Coleman-Jensen, A. J. (2010). U.S. food insecurity status: Toward a re ﬁned de ﬁnition. Social Indicators Research ,95(2), 215 –230. https://doi.org/10 .1007/s11205-009-9455-4 Conrad, K. J., Yagelka, J. R., Matters, M. D., Rich, A. R., Williams, V., & Buchanan, M. (2001). Reliability and validity of a modi ﬁed Colorado Symptom Index in a national homeless sample. Mental Health Services Research ,3(3), 141 –153. https://doi.org/10.1023/A:1011571531303 Druss, B. G., Zhao, L., von Esenwein, S. A., Bona, J. R., Fricks, L., Jenkins- Tucker, S., Sterling, E., & Lorig, K. (2010). The Health and Recovery Peer (HARP) program: A peer-led intervention to improve medical self- management for persons with serious mental illness. Schizophrenia Research , 118 (1–3), 264 –270. https://doi.org/10.1016/j.schres.2010.01.026 Elgar, F. J., Pickett, W., Pförtner, T. K., Gariépy, G., Gordon, D., Georgiades, K., Davison, C., Hammami, N., MacNe il, A. H., Azevedo Da Silva, M., & Melgar-Qui ˜nonez, H. R. (2021). Relative food insecurity, mental health and wellbeing in 160 countries. Social Science & Medicine ,268 ,Article 113556. https://doi.org/10.1016/j.socscimed.2020.113556 Fang, D., Thomsen, M. R., & Nayga, R. M. (2021). The association between food insecurity and mental health during the COVID-19 pandemic. BMC Public Health ,21(1), Article 607. https://doi.org/10.1186/s12889-021- 10631-0 Frongillo, E. A., Nguyen, H. T., Smith, M. D., & Coleman-Jensen, A. (2017). Food insecurity is associated with subjective well-being among individuals from 138 countries in the 2014 Gallup World Poll. The Journal of Nutrition , 147 (4), 680 –687. https://doi.org/10.3945/jn.116.243642 Grey, I., Arora, T., Thomas, J., Saneh, A., Tohme, P., & Abi-Habib, R. (2020). The role of perceived social support on depression and sleep during the COVID-19 pandemic. Psychiatry Research ,293 , Article 113452. https:// doi.org/10.1016/j.psychres.2020.113452 Grey, M., Schulman-Green, D., Kna ﬂ, K., & Reynolds, N. R. (2015). A revised Self- and family management framework. Nursing Outlook ,63(2), 162 –170. https://doi.org/10.1016/j.outlook.2014.10.003 Gucciardi, E., Yang, A., Cohen-Olivenstein, K., Parmentier, B., Wegener, J., & Pais, V. (2019). Emerging practices supporting diabetes self-management among food insecure adults and families: A scoping review. PLOS ONE , 14 (11), Article e0223998. https://doi.org/10.1371/journal.pone.0223998 Hammami, N., Leatherdale, S. T., & Elgar, F. J. (2020). Does social support moderate the association between hunger and mental health in youth? A gender-speci ﬁc investigation from the Canadian Health Behaviour in School-aged Children study. Nutrition Journal ,19(1), Article 134. https:// doi.org/10.1186/s12937-020-00648-3 Hanson, K. L., & Connor, L. M. (2014). Food insecurity and dietary quality in US adults and children: A systematic review. The American Journal of Clinical Nutrition ,100 (2), 684 –692. https://doi.org/10.3945/ajcn.114 .084525 Harandi, T. F., Taghinasab, M. M., & Nayeri, T. D. (2017). The correlation of social support with mental health: A meta-analysis. Electronic Physician , 9 (9), 5212 –5222. https://doi.org/10.19082/5212 Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap) —A metadata-driven methodology and work ﬂow process for providing translational research informatics support. Journal of Biomedical Informatics ,42(2), 377 –381. https://doi.org/10.1016/j.jbi.2008.08.010 Holben, D. H., & Myles, W. (2004). Food insecurity in the United States: Its effect on our patients. American Family Physician ,69(5), 1058 –1063. https://www.aafp.org/afp/2004/0301/p1058.html IBM Corp. (2020). IBM SPSS statistics for windows (Version 27.0). Koh, H. K., Blakey, C. R., & Roper, A. Y. (2014). Healthy People 2020: A report card on the health of the nation. JAMA ,311 (24), 2475 –2476. https:// doi.org/10.1001/jama.2014.6446 Krishnan, S., Cozier, Y. C., Rosenberg, L., & Palmer, J. R. (2010). Socioeconomic status and incidence of type 2 diabetes: Results from the Black Women ’s Health Study. American Journal of Epidemiology , 171 (5), 564 –570. https://doi.org/10.1093/aje/kwp443 Mangurian, C., Sreshta, N., & Seligman, H. (2013). Food insecurity among adults with severe mental illness. Psychiatric Services ,64(9), 931 –932. https://doi.org/10.1176/appi.ps.201300022 Martin, M. S., Maddocks, E., Chen, Y., Gilman, S. E., & Colman, I. (2016). Food insecurity and mental illness: Disproportionate impacts in the context of perceived stress and social isolation. Public Health ,132 ,86 –91. https:// doi.org/10.1016/j.puhe.2015.11.014 Melchior, M., Caspi, A., Howard, L. M., Ambler, A. P., Bolton, H., Mountain, N., & Mof ﬁtt, T. E. (2009). Mental health context of food insecurity: A representative cohort of families with young children. Pediatrics ,124 (4), e564 –e572. https://doi.org/10.1542/peds.2009-0583 Mokari-Yamchi, A., Faramarzi, A., Salehi-Sahlabadi, A., Barati, M., Ghodsi, D., Jabbari, M., & Hekmatdoost, A. (2020). Food security and its association with social support in the rural households: A cross-sectional study. Preventive Nutrition and Food Science ,25(2), 146 –152. https:// doi.org/10.3746/pnf.2020.25.2.146 Muldoon, K. A., Duff, P. K., Fielden, S., & Anema, A. (2013). Food insuf ﬁciency is associated with psychiatric morbidity in a nationally representative study of mental illness among food insecure Canadians. Social Psychiatry and Psychiatric Epidemiology ,48(5), 795 –803. https://doi.org/10.1007/s00127-012-0597-3 Na, M., Miller, M., Ballard, T., Mitchell, D. C., Hung, Y. W., & Melgar- Qui ˜nonez, H. (2019). Does social support modify the relationship between food insecurity and poor mental health? Evidence from thirty-nine sub-Saharan African countries. Public Health Nutrition ,22(5), 874 –881. https://doi.org/10.1017/S136898001800277X National Research Council. (2011). Toward an integrated science of research on families: Workshop report . Olfson, M., Gerhard, T., Huang, C., Crystal, S., & Stroup, T. S. (2015). Premature mortality among adults with schizophrenia in the United States. JAMA Psychiatry ,72(12), 1172 –1181. https://doi.org/10.1001/jamapsychia try.2015.1737 Patil, S. P., Craven, K., & Kolasa, K. (2018). Food insecurity: How you can help your patients. American Family Physician ,98(3), 143 –145. Rabinovitch, M., Cassidy, C., Schmitz, N., Joober, R., & Malla, A. (2013). The in ﬂuence of perceived social support on medication adherence in ﬁ rst-episode psychosis. Canadian Journal of Psychiatry ,58(1), 59 –65. https://doi.org/10.1177/070674371305800111 Russell, S. T., & Fish, J. N. (2016). Mental health in lesbian, gay, bisexual, and transgender (LGBT) youth. Annual Review of Clinical Psychology , 12 , 465 –487. https://doi.org/10.1146/annurev-clinpsy-021815-093153 Sareen, J., A ﬁﬁ, T. O., McMillan, K. A., & Asmundson, G. J. G. (2011). Relationship between household income and mental disorders: Findings from a population-based longitudinal study. Archives of General Psychiatry , 68 (4), 419 –427. https://doi.org/10.1001/archgenpsychiatry.2011.15 Seligman,H.K.,Jacobs,E.A.,Lo ´pez, A., Tschann, J., & Fernandez, A. (2012). Food insecurity and glycemic control among low-income patients with type 2 diabetes. Diabetes Care ,35(2), 233 –238. https://doi.org/10.2337/dc11-1627 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. IMPACT OF SOCIAL SUPPORT AND FOOD INSECURITY 217 Silverman, J., Krieger, J., Kiefer, M., Hebert, P., Robinson, J., & Nelson, K. (2015). The relationship between food insecurity and depression, diabetes distress and medication adherence among low-income patients with poorly-controlled diabetes. Journal of General Internal Medicine ,30(10), 1476 –1480. https://doi.org/10.1007/s11606-015-3351-1 Stowkowy, J., Addington, D., Liu, L., Hollowell, B., & Addington, J. (2012). Predictors of disengagement from treatment in an early psychosis program. Schizophrenia Research ,136 (1–3), 7 –12. https://doi.org/10.1016/j.schres .2012.01.027 Stubbs, B., Vancampfort, D., De Hert, M., & Mitchell, A. J. (2015). The prevalence and predictors of type two diabetes mellitus in people withschizophrenia: A systematic review and comparative meta-analysis. Acta Psychiatrica Scandinavica ,132 (2), 144 –157. https://doi.org/10.1111/ acps.12439Tarasuk, V., Mitchell, A., McLaren, L., & McIntyre, L. (2013). Chronic physical and mental health conditions among adults may increase vulner- ability to household food insecurity. The Journal of Nutrition ,143 (11), 1785 –1793. https://doi.org/10.3945/jn.113.178483 Teh, W. L., Shahwan, S., Abdin, E., Zhang, Y., Sambasivam, R., Devi, F.,Verma, S., Chong, A. S., & Subramaniam, M. (2019). Con ﬁrmatory factor analysis and measurement invariance of the multidimensional scale of perceived social support in young psychiatric and Non-Psychiatric Asians. Annals of the Academy of Medicine, Singapore ,48, 314 –320. Vancampfort, D., Correll, C. U., Galling, B., Probst, M., De Hert, M., Ward, P. B., Rosenbaum, S., Gaughran, F., Lally, J., & Stubbs, B. (2016). Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: A systematic review and large scale meta- analysis. World Psychiatry ,15(2), 166 –174. https://doi.org/10.1002/ wps.20309 Wander, C. (2020). Schizophrenia: Opportunities to improve outcomes and reduce economic burden through managed care. The American Journal of Managed Care ,26(Suppl. 3), S62 –S68. https://doi.org/10.37765/ajmc .2020.43013 Wilcox, S. (2010). Multidimensional scale of perceived social support. Psychological Trauma: Theory, Research, Practice, and Policy ,2(3), 175 –182. https://doi.org/10.1037/a0019062 Received August 31, 2021 Revision received December 17, 2021 Accepted March 10, 2022 ▪ This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 218 MICHELS, HALLGREN, COLE, CHWASTIAK, AND CHENG
We offer CUSTOM-WRITTEN, CONFIDENTIAL, ORIGINAL, and PRIVATE writing services. Kindly click on the ORDER NOW button to receive an A++ paper from our masters- and PhD writers.
Get a 10% discount on your order using the following coupon code SAVE10
Order a Similar Paper Order a Different Paper