A REVIEW OF THE LITERATURE ON FOOD INSECURITY IN THE ELDERLY By Hailey Koch A Senior Project submitted In partial fulfillment of the requirements for the degree of Bachelor of Science in Nutrition Food Science and Nutrition Department California Polytechnic State University San Luis Obispo, CA March 2013 1 Abstract For the year 2011, it was reported that 8.9% of households with seniors were classified as food insecure; this is equivalent to 2.5 million households and has been a consistent statistic since 2008. Though this percentage may not appear sizeable to the entire U.S. population, the fact that the senior population is predicted to grow to 72.1 million by 2030, puts this statistic into perspective. The purpose of this literature review was to understand the predictors and outcomes of food insecurity in the elderly and evaluate the assessment tools used to measure these factors. The key factors examined in this review included poor nutritional status, depression, functional disabilities, and healthcare expenditures. An exploration of the relationship between health related quality of life and food insecurity in older adults is important but has yet to be substantiated by research. At best, the available literature acknowledges the existence of the risk factors and predictors of food insecurity in the elderly and the potential health and nutrition related outcomes of this food insecurity. Perhaps the multi-dimensional nature of food insecurity requires research to look at it from both angles: food insecurity as predictor and food insecurity as an outcome. A variety of assessment tools have been applied separately and collaboratively in observational studies to measure associations between food insecurity and poor nutritional status, depression, functional abilities, and health expenditures. Such studies have revealed the strengths and weaknesses of these assessment tools, such as their predictive power, sensitivity, and specificity. Ultimately, the majority of studies have come to a consensus that a “gold standard” approach would involve a comprehensive assessment tool that is tailored to experiences specific to the older adult including nutritional, psychological, physical, and social aspects. Research is needed to develop and test such an assessment tool. As the U.S. anticipates the exponential growth of an aging population along with the prevalence of food insecurity among older adults, it is pertinent that research efforts aim to inform prevention and intervention strategies. 2 Introduction In the United States, the concept of food insecurity is often associated with images of homeless or impoverished persons holding cardboard signs that say, “Anything will help.” These images reflect the reality that food insecurity strikes the most vulnerable populations including women, infants, and seniors. It is more challenging to make a case for the elderly because research on food insecurity and its health implications have been limited. From what is known, as of 2011, 8.9% of households with seniors were classified as food insecure; this is equivalent to 2.5 million households and has been a consistent statistic since 2008 (Jensen, Nord, Andrews & Carlson, 2012). Though this percentage may not appear sizeable to the entire U.S. population, the fact that the senior population is predicted to grow to 72.1 million by 2030 may put this statistic into perspective. The predicted exponential growth of this population will entail increased dependence on medical and social services to support its most basic and complex needs (Anderson, 2012). Healthy People 2020 has introduced a separate category for “older adults” with goals to address access to healthcare, functional limitations, and quality of life (U.S. Department of Health & Human Services [HHS], 2013). There has been progressive research in these goal areas because they are primary contributors to senior health outcomes. Utilization of healthcare services, functional limitations, and quality of life, especially as it relates to health, have also been studied as predictors or outcomes in older adults identified as food insecure. Other predictors and outcomes of food insecurity have been evaluated but studies are too few and underpowered to substantiate their associations with this socioeconomic disparity. 3 In order to conduct studies that assess potential predictors, outcomes, and status of food insecurity there must be valid assessment tools that are appropriate for older adults. Tools that measure nutritional status, functional ability, mental health, and so on are constantly under evaluation for producing consistent and reliable data. Due to the limitations of these tools, it has been difficult to clearly identify the predictors and outcomes of food insecurity; the relationships are still unclear. Hence there is a profound need for research to learn about the aging population and how to assess the factors that most impact their health and well being. The purpose of this review is to investigate food insecurity in the elderly according to the existing literature, and in this effort, extract the key predictors and health related outcomes of this disparity and evaluate the efficacy of measurement methods used to gather such data. Overview of Food Insecurity For the purpose of this review, food insecurity will be viewed at a national level. For many years, researchers and policy makers debated the definition of hunger until they introduced the terminology of food security and food insecurity (Campbell, 1991). These terms are used mainly to describe a socioeconomic disparity although they still carry other connotations, especially when used in an international context. In the U.S., food security and food insecurity are understood in terms of cultural norms and how Americans view typical meal patterns and modes of access such as “three square meals” and making weekly trips to the grocery store (Campbell, 1991). This section defines food insecurity in contrast to food security and introduces the definitions, risk factors, and current trends in the U.S. population. A diagram of the pathway from risk factors to consequences of food insecurity is presented in Appendix B (pg. 40). 4 Definitions & Risk Factors Food insecurity can only be understood in relation to food security. The U.S. Department of Agriculture defines food security as “access to enough food by all people at all times to maintain a healthy and active lifestyle” (Coleman-Jensen, Nord, Andrews & Carlson 2012). In contrast, food insecurity is defined as the uncertain or limited acquisition of adequate, safe foods that are personally acceptable and acquired in socially acceptable ways (Campbell, 1991). Food insecurity can be described as a construct with aspects of quantity, quality, psychological acceptability, and social acceptability. Quantity refers to having enough food, quality refers to nutritional adequacy and food safety, psychological acceptability pertains to personal preferences and feelings of deprivation due to limited food choices, and social acceptability pertains to having regular meal patterns and acquiring food in normal settings (Campbell, 1991). All of these aspects are compromised by food insecurity in the context of an individual or a household. Two other aspects of food insecurity must also be considered: involuntariness of food insecurity status and the duration of a food insecure experience (Campbell, 1991). These aspects can then be measured, in turn, to identify on an individual or community level the prevalence and severity of food insecurity. Such measurements have been gathered through methods such as nutritional assessment or self-reported consumption. It has been debatable if food insecurity itself is a predictor variable or an outcome. As food insecurity becomes a greater problem, it is in the best interest of research to know the risk factors for food insecurity for purposes of prevention and intervention. Campbell (1991) states that risk factors are considered anything that limits a household’s resources or limits the proportion of these resources used for acquiring food. This may take the form of factors that 5 limit employment, wage and benefit opportunities, social assistance, or nonfood expenditures such as healthcare costs, housing and utilities, and taxes. When limited resources cause food insecurity—whether in the form of health, education, money or information—there are inherent consequences. These may prevail as mental, physical, or nutritional health outcomes or altered quality of life (Campbell, 1991). Trends in Occurrence in the U.S. Population According to 2011 reports produced by the Economic Research Service, 85.1% of American households surveyed were considered food secure while the remaining 14.9% were deemed food insecure (Coleman-Jensen et al., 2012). Study participation included 43,770 households that also participated in the Current Population Survey (CPS). Figure 1 illustrates the distribution of food secure and food insecure households in 2011. Figure 1. U.S. households with children by food security status of adults and children, 2011. From Coleman-Jensen et al., 2012. Methods of data calculations ensured that results were nationally representative. For this research study, food insecurity was stratified into two classifications: households with low food security and households with very low food security. Although the percentage of food insecure seems relatively low compared to the large proportion classified as food secure, when 6 viewing these statistics as representative of the entire U.S. population, food insecurity becomes a significant problem on a national level. The ERS report (Coleman-Jensen et al., 2012) concluded that rates of food insecurity had remained fairly constant since 2010 but when observing the trend of food security over the past decade, rates appeared to increase during the past two years, especially within the very low food security category. The survey used to collect data on food security status of the households provides 18 questions concerning food affordability, availability, and accessibility; these are presented in Appendix A (p. 39). Food insecure households are stratified into two categories: low food security and very low food security. Low food security households show repeated food access problems but lower occurrence of reduced food intake. Very low food security households consistently experience food access problems and reduced food intake as well as disrupted patterns of eating due to a lack of income and other resources to provide food (ColemanJensen et al., 2012). Very low food security could be considered chronic food insecurity. A household is considered to have very low food security when it gives an affirmative response to six or more of the questionnaire items. In households containing children, very low food security applies when eight or more items receive an affirmative response. From the most recent survey report from 2011, the Economic Research Service gathered that 5.7% of households had very low food security (Coleman-Jensen et al., 2012). According to the ERS report, approximately 3 months was the average amount of time to determine a very low food security status. Figure 2 illustrates the conditions most commonly reported by households with very low food security. 7 Figure 2. Percentage of households reporting each indicator of food Insecurity, by food security status, 2011. From Coleman-Jensen et al., 2012. The trend of low food security has fluctuated within the past decade. In 2000, 10.5% were considered food insecure and increased to 12% in the next four years. Though a couple of the annual reports claimed a decrease in food insecurity, the rates increased toward the end of the decade leading to the current statistics from 2011. Another factor that the ERS report takes into account is the actual food purchases by household. This information provides insight into how a household distributes its financial resources toward food expenditures. Food resources include items purchased at grocery stores or supermarkets, restaurants, vending machines, or cafeterias for consumption inside or 8 outside the home. Total food expenditures were determined for each household by measures of food spending per person in each household and food spending relative to the USDA Thrifty Food Plan costs. To briefly describe the Thrifty Food Plan, the USDA developed a set of standards based on age and gender to meet the dietary requirements of an individual on a low budget. After compiling reports from households that participated in a sub-survey inquiring about the usefulness of the Thrifty Food Plan, analysts found that food spending was lower than that recommended by the Thrifty Food Plan for Black, non-Hispanic and Hispanic households as well as households run by single parents (Coleman-Jensen, 2012). Households with higher income spent more money on food than those with low income. The household category that had the greatest participation in this sub-survey were households with no children <18 years old. Households containing older adults showed the lowest participation and “elderly living alone” were minimally represented. This suggests that participation in food assistance programs may be an important consideration in determining food insecurity in older adults and implementing appropriate interventions for this group. Food Insecurity in the Elderly Since the recession, prevalence of food insecurity in the elderly has become a compelling issue. This phenomenon disproportionally affects older adults classified as lowincome, less educated, of racial or ethnic minority, and residents of Southern states. A status of food insecurity has loaded health implications for the elderly. These include but are not limited to poor nutrient intake (Lee & Frongillo, 2001b), poor disease management (Bhargava, Lee, Jain, Johnson, & Brown, 2012), functional disabilities (Lee & Frongillo, 2001a), sum of healthcare expenditures and use of healthcare services (Bhargava et al., 2012; Lee, 2013), and 9 medication non-adherence (Burnett et al., 2012). Novel research efforts have sought to understand the main predictors and outcomes that are associated with food insecurity in the elderly. Because this topic concerns policy, program development, and clinical applications, it has been in the interest of research to better understand the scope of this problem. Trends in Occurrence in the Elderly Food insecurity in U.S. older adults has been measured using the Core Food Security Module (CFSM), an 18-question survey that is commonly used by researchers to collect data on the food security status of American households. A household can be classified as either marginally food insecure, food insecure or very low food secure. Marginal food insecurity is considered a “threat,” food insecurity a “risk,” and very low food security a “reality” (Ziliak & Gundersen, 2011). These classifications are determined by how many questions receive an affirmative response from either an individual response or household response. One affirmative answer, three affirmative answers, or 5 affirmative answers (assuming no children in the household) determine each classification respectively. The CFSM questions are provided in Appendix A. The American Association of Retired Persons, also known as the AARP, produces annual reports about current trends of food insecurity among older adults according to the most recent national data gathered from the Current Population Survey (CPS) and National Health and Nutrition Examination Survey (NHANES). The CFSM continues to be the survey of choice for measuring food security and food insecurity in population studies because of its proven validity and reliability (Bickel, Nord, Price, Hamilton & Cook, 2000). The AARP report considers food insecurity in adults ages 40-49, 50-59, and 60+ years. Adults from 50-59 to 60+ years of age contain the senior population and will best represent the 10 elderly for this review. Ziliak & Gundersen (2011) produced the current AARP report that outlines the prevalence of food insecurity among the elderly. Their findings are summarized here to put the definition of food insecurity in the context of older adults. Due to the recession, adults 50-59 have been significantly impacted compared to older adults 60 years and older (Ziliak & Gundersen, 2011). However, from 2007 to 2009, both age groups showed a significant increase in prevalence of marginal food insecurity, food insecurity, and very low food security. For older adults ages 60 and older, marginal food insecurity increased by 20% and for adults ages 50-59 a 38% increase was observed. A similar increase was observed in both age groups for food insecurity. Very low food security for both age groups increased by 69% and 17% respectively. Though not comparable percentages, it can be concluded that an increase occurred in both age groups. These statistics convey that for 50-59 year olds 8.1 million were marginally food insecure, 4.9 million were food insecure, and 2.1 million were very low food secure. For 60 years and older, 7.5 million were marginally food insecure, 3.9 million were food insecure, and 1.4 million were very low food secure. The prevalence of food insecurity among older adults ages 50+ is stratified according to the three categories and is illustrated by Figure 3. 11 Figure 3. Food Insecurity Rates for Persons Age 50 and Older. From Ziliak & Gundersen, 20ll. Since 2007, there has not been a substantial increase in food insecurity among elderly Hispanics, African-Americans and of other ethnicities. The trend is that ethnic minorities compared to white persons have experienced almost double rates of food insecurity as illustrated by Figure 4. African-Americans show the most enduring and highest rate of food insecurity of all races/ethnicities belonging to the 50 years and older group. Figure 4. Food Insecurity Rates for Persons Age 50 and Older, by Race/Ethnicity. From Ziliak & Gundersen, 2011. 12 A brief look at the U.S. population reveals that Southern states exhibit the greatest prevalence of food insecurity among older adults (50+ years) as illustrated by the dark blue shaded states in Figure 5. Eight of the ten states with the highest food insecurity rates among 50+ older adults are in the South. Figure 5. State Food Insecurity Rates for Adults Age 50 and Older. From Ziliak & Gundersen, 2011. Ziliak and Gundersen (2011) claim that the dramatic increase in food insecurity is mainly attributed to the recession specifically for adults 50-59 year olds belonging to higher income households. As for those 60 years and older, they are commonly marked by poor and near poor status and were not as impacted by the recession but instead, other factors have contributed to their food insecurity and hence have resulted in health consequences. These factors will be discussed in the following section as predictors and outcomes of food insecurity in the elderly. Predictors & Outcomes Research efforts through main survey vehicles such as NHANES and CPS seek to inform about the health and socioeconomic status of older adults. Food insecurity in the elderly has 13 been shown to be associated with various factors that may, in turn, predict a status of food insecurity. Also, food insecurity precedes several health related outcomes. In this section, a collection of recent literature will be evaluated to create a clear understanding of the key predictors and outcomes of food insecurity in older adults. According to the AARP report, the health outcomes that are significantly affected by food insecurity in older adults included functional disability, depression and overall health (Ziliak & Gunderesen, 2011). In addition, other research has shown food insecurity to be associated with healthcare expenditures and nutritional intake. A review of the available literature brings about questions and confirmation of these claimed relationships. Performance of ADLs and IADLs. In order to measure the functional status in older adults, two categories called “activities of daily living” (ADLs) and “instrumental activities of daily living” (IADLs) are typically assessed. ADLs include skills such as ability to toilet, feed, dress, groom, ambulate, and bathe while IADLs include skills that require independence such as telephone use, shopping, meal preparation, housekeeping chores, laundry, travelling, administration of medications, and paying the bills (Bernstein & Schmidt Luggen, 2010). The inability to perform some or all of these activities is an indication that an older adult is at risk for poor nutritional status (Bernstein & Schmidt Luggen, 2010). Here, it is in the best interest to look at the research that shows functional status as a predictor of food insecurity. Lee and Frongillo (2001a) looked at factors associated with food insecurity in the elderly and produced a separate investigation on physical impairment and its relationship to food insecurity. Using data from two survey vehicles, NHANES and Nutrition Survey of the Elderly in New York State (NSENY), they examined economic, sociodemographic, 14 and functional status in relation to participants identified as either food insufficient (NHANES) or food insecure (NSENY). Aside from the economic and sociodemographic findings, they found that physical impairments contributed to an individual’s food insecure status. Forty-eight percent of NHANES III food insufficient participants had ADL problems while twenty-four percent of NSENY food insecure participants had ADL problems. IADL problems were more apparent in the NSENY sample population. Lee and Frongillo’s (2001a, b) work has been widely cited in literature covering this topic but it may leave the reader with some questions. The data analysis, as defined in the methods section, is sound but the data that is presented in the article fails to provide p-values to confirm statistical significance of these data values. Sharkey et al. (2003) evaluated data compiled from the Nutrition and Function Study (NAFS) conducted by the University of North Carolina School of Public Health in collaboration with the Older Americans Act Nutrition Programs (OAANP) meal delivery program service providers in four North Carolina counties. The study population was composed of 279 elderly women who participated in the home-delivery meal program and were > 60 years old. Sample subjects were classified as food sufficient (FS), at risk for food insufficiency (RFI), or food insufficient (FI). Surveys were facilitated via phone interviews to collect information pertaining to sociodemographic background, mental health (i.e. depression) and ability to perform IADLs. For this study, IADLs referred directly to food related skills: the abilities to grocery shop and prepare meals without assistance. An in-home assessment was used to record medication use, anthropometrics, and dietary intake via a 24-hour recall. Finally, food insufficiency status was assessed through four separate self-reports gathered 6 months prior to the in-home assessment. 15 One key finding was that participants representing all three categories (FS, RFI, and FI) did not differ significantly in their ability to perform IADLs. This finding, though significant, may be unsubstantial due to the fact that only two IADL items were included in the overall assessment. Perhaps more than two food-related IADL problems exist among the food insufficient and could either explain the cause or result of their food insecurity. This would be a valuable consideration for purposes of improving and advancing food-related functional status assessment tools. Additional research by Sharkey (2008), found that impaired functional status was affected in part by poor nutritional status in men and women participants of home-delivered meal programs. As will be discussed next, nutrient intake has been shown to indicate food insecurity (Lee & Frongillo, 2001b). From this association between nutritional status and food insecurity, it may be possible that poor nutritional status results in functional decline that may in turn, result in food insecurity. In this case, food insecurity would be seen as the inability to acquire or access adequate food resources due to functional limitations. Nutritional & health status. In an observational study performed by Lee and Frongillo (2001b), the proposed research question hypothesized an association between nutritional and health consequences and food insecurity among older adults. This article introduced the concept that food insecurity can affect quality of life either directly or indirectly through nutritional status. However, studies revealing this concept have mainly been focused on women and children. Nutritional and health consequences observed in studies of women and children included decreased dietary intake, decreased household food supply, psychosocial dysfunction, increased body weight, health problems, decreased quality of life, and familial 16 distrubances. Lee and Frongillo performed the following study in order to identify the consequences specific to the elderly while using previous research (such as that for women and children) as a reference guide for identifying consequences of food insecurity in older adults. Data consisted of two datasets, one from the NHANES III study of 6586 older adults (1988-1994) and NSENY study of 553 older adults (1994). NHANES III contains nationally representative data while NSENY data was obtained from metro and non-metro regions of New York. The purpose of the NSENY data collection was to gather information for use of improving services for the elderly in the area; i.e. Elderly Nutrition Program. These two data sets were complementary because they contained similar baseline data (Lee & Frongillo, 2001b). The mean age of participants for NHANES III was 70.8 years and for NSENY was 67.7 years. Both surveys for these datasets assessed ADLs and IADLs. Unique to the NHANES III dataset that was nutrient intake through 24-hour dietary recall reports, skin fold thickness through standard anthropometric measures, and self-reported health status were included. The NSENY contained a portion that assessed nutritional risk through a questionnaire that was adopted from the original Nutritional Screening Initiative checklist. NHANES III and NSENY both measured food insecurity according to distinctive definitions. For NHANES III the term “food-insufficiency” was used to describe an inadequate amount of food intake due to lack of resources (Lee and Frongillo, 2001b). In contrast, NSENY measured food insecurity status defined as the period of time an individual was food insecure; specifically beyond a 6-month period. NHANES III contained one question and NSENY contained three questions to measure food insecurity within each respective study population. It was 17 noted that due to a lack of a gold standard for measuring food insecurity in the elderly, the prevalence of food insecurity within each study population could have been underestimated. To summarize the scope of the overall study, the exposure of interest was food insecurity and the outcomes measured were the nutritional and health-related consequences in the elderly. After accounting for key confounders including physical functioning (ability to perform ADLs and IADLs), chronic disease, and sociodemographic and economic variables, linear regression was applied to the combined dataset. Of the NHANES III participants, 1.7% were food insufficient. When compared to food sufficient older adults in this study, those who were food insufficient had a greater likelihood of being poor, a minority, and a food assistance program participant. Over half of the food insufficient were functionally impaired and this may indicate that undernutrition due to food insufficiency may result in an inability to perform ADLs and IADLs or vice versa (Amarantos, Martinez, & Dwyer 2001). The findings of NSENY reported one-third of food insecure subjects to be poor, a minority, living alone, and a food assistance program participant. Nutrient intakes were also analyzed by taking 19 nutrients into consideration such as energy, protein, total fat, saturated fat, carbohydrate, and essential vitamins and minerals. The combined dataset of NHANES III and NSENY revealed that those who were food insufficient in comparison to those who were food sufficient had the lowest intakes. The lowest intakes relative to recommendations were seen for energy, protein, iron, zinc, vitamins B6 and B12, riboflavin, and niacin. These findings are consistent with the common nutrient inadequacies seen in the elderly; these are protein, B12, and folate (Anderson et al, 2011). For the whole 18 study population there were 8 nutrient intakes that fell below the RDA. A limitation for using the RDA as a standard for measuring intake in a population is that RDAs apply to individual intake versus population intake. For a population study, percent below the Estimated Average Requirements (EAR) would be a more appropriate measure of nutritional inadequacy (Tarasuk, 2006). Another key finding was skinfold thickness was significantly lower for food-insufficient elderly persons. This shows that food insecurity may be indicated by physical measures. There was a strong, significant relationship between those who were food-insufficient and self-reports of fair/poor health status. What distinguishes this parameter from those of nutrient intake, anthropometrics, and nutritional risk is that self-reported health status is purely subjective. This is because self-reports are unique to an individual’s beliefs, attitudes, and values that shape his or her perception of their own health and well being (Lee and Frongillo, 2011b). Subjective data may be of great worth for developing a standard method of measurement for food insecurity and health/nutritional consequences in the elderly. At the same time, subjective data may greatly differ from person to person and would require vast control over confounding variables (ethnicity, race, socioeconomic, etc.). This study was limited because the survey that gathered nutrient intake data was based on a 24-hour dietary recall. Though this nutrition assessment tool can provide a glimpse at an individual’s typical eating day, it cannot elicit information beyond that day. Therefore, the authors concluded that the NHANES III anthropometric data was a better indicator of persistent undernutrition—beyond a day’s worth of intake—and consequently, food insufficiency. Comparatively, Morley (2011) suggests that undernutrition in older adults is attributable to 19 underlying disease. Further, it was suggested that a gold standard of measurement is still needed to ensure accuracy in measuring food insecurity and its predictors among older adults. Because such a tool does not exist, there have been scattered efforts to gauge the prevalence of food insecurity in the aging population (Amarantos, Martinez, & Dwyer, 2001; Kaiser et al., 2010; Dent et al. 2012). Lee and Frongillo’s (2001b) study contains two examples for evaluating food insecurity in order to understand its potential health and nutritional consequences and has set a framework for future research efforts on this topic. Depression. Johnson, Sharkey, and Dean (2011) studied the health outcomes of food insecurity as it was considered one of three dimensions of material hardship. The health outcome of interest was depressive symptoms. Their study population was derived from the North Carolina Nutrition and Function Study (NAFS) and contained 345 subjects. An association between a dimension of material hardship (i.e. food insecurity) and the outcome of depressive symptoms was measured according to the degree of depressive symptoms reported by study participants on the Geriatric Depression Scale. One key finding showed a statistically significant association between individuals who were food insecure and reports of depressive symptoms compared to those who were food secure; in fact, a status of food insecurity was five times more likely to result in reports of depressive symptoms. In a broader sense, the study confirmed previous research conclusions that had suggested adverse health outcomes were associated with food insecurity in older adults. One limitation of this study was the difference between reports of depressive symptom levels between the age groups studied. The younger spectrum of the older adult subjects reported higher levels of depressive symptoms than their older counterparts. However, the overall evidence showed that food-related material hardship 20 was the only dimension that perpetrated a statistically significant association with reports of depressive symptoms. This study presents a future research need to explore this association between food insecurity and depression in older adults, especially in the variety of settings in which they reside. Healthcare costs. One study looked at food insecurity and its association with Medicarerelated healthcare expenditures of older adults. The data was gathered from the Georgia Advanced Performance Outcomes Measures Project 6 (GA Advanced POMP6) and Medicare claims from 2008. This study sought to understand healthcare spending tendencies of food insecure older adults and if these individuals chose to forego food costs to cover healthcare costs or vice versa. Bhargava et al. (2012) hypothesized that those who spent less on healthcare in order to purchase food may be at greater risk for developing negative health outcomes. The results of this study indicated that food insecure individuals were less likely to make any Medicare expenditure in comparison with their food secure counterparts. Further, food insecure individuals who required congregate meals and reported poorer health status were less likely to make Medicare expenditures. An important consideration of this study is that the study population was 6 times more representative of food insecure older adults compared to the national level. Also, because only Medicare-related expenditures were accounted for, the results do not provide a clear picture of overall health expenditures made by food insecure or food secure older adults. Despite these limitations it can be concluded that there is a relationship between healthcare expenditures and food insecurity; the complexities of this relationship warrant further investigations concerning healthcare access and expenditure patterns, health outcomes, and disease management of food insecure individuals. From this 21 study it is apparent that healthcare expenditures may indicate a state of food insecurity or that food insecurity may result in a specific healthcare expenditure pattern. According to Lee (2013), clarifying this relationship is essential to improving the research, program, and policy efforts aimed toward reducing food insecurity among older adults, especially because this population is highly impacted by chronic disease and chronic disease requires increased medical attention and medical costs. Sparse literature exists that examines the relationship between health expenditures and food insecurity in the elderly. In fact, Lee (2013) claims that only three studies have evaluated this concept. The methods and tools that have been used to measure health expenditures according to this population may not be accurate in defining this relationship and hence the development of a reliable tool is still needed. There are two parts to measuring healthcare costs among the food insecure: estimating the number of food insecure and then the healthcare costs incurred by health outcomes associated with food insecurity. Identifying adverse health outcomes due to food insecurity may be a difficult feat especially considering the small amount of data available to substantiate known associations. Also, it is a challenge to account for all healthcare expenditures because of the inability to measure all sources of indirect costs. One of the most dependable ways to measure direct healthcare costs is through cost-of-illness evaluations; these measure the financial impact of specific diseases. In the case of food insecurity, the specific diseases or health outcomes would be those associated with food insecurity. Concrete examples of health outcomes resulting from food insecurity are still a matter of investigation. However, Shephard, Setren and Cooper (2011) and Brown, Shepart, Martin and Orwat (2007) used data from CPS, recent public health literature, and available 22 estimate of direct and indirect costs to calculate the healthcare costs incurred by food insecurity related health outcomes. Both were able to identify some sources of food insecurityrelated direct (i.e. hospitalizations, depression, poor health) and indirect costs (i.e. charity costs). This study is limited in it application to seniors as most of the data were gathered on younger subjects. Nielsen, Garasky, and Chatterjee (2010) evaluated the relationship between food insecurity and healthcare expenditures by using the data available through the Survey of Income and Program Participation and 5-item Household Food Security Survey Module (HFFSM) reports. Finally, as described earlier, Bhargava et al. (2012) had a statewide approach to estimate costs of food insecure elderly. This has been the only study that exclusively measured older adults and their healthcare costs. Evaluation of Assessment Tools Dent, Visvanathan, Piantadosi and Chapman (2012) conducted a systematic review of nutritional screening tools (NSTs) and their ability to predict outcomes of mortality, functional decline, and advancement to higher level care in older adults. Because the study was strictly a review, there was only an opportunity to evaluate the NSTs in predicting outcomes but not to claim an association between the NSTs and the outcomes that they predicted. This means that the NSTs were solely analyzed for how and to what degree they predicted one of the three aforementioned outcomes in the 37 studies that were included in the review. The researchers hypothesized that NSTs would strongly predict outcomes of functional decline. They supported this position by defining functional decline as an increased dependency on others for performing Activities of Daily Living (ADLs) and/or in Instrumental Activities of Daily Living (IADLs). As was discussed earlier, functional decline has been shown to be a 23 predictor and/or an outcome of food insecurity. It is important to look at methods that have been tested for validity and reliability in measuring functional ability. Recall that ADLs and IADLs are defined as basic functions such as showering and walking and secondary functions such as meal preparation, managing finances, and shopping respectively (Morley, 2011). Dent et al. (2012) suggested that ADLs and IADLs require physical strength to be performed effectively; hence functional decline might be attributed to undernutrition due to its affects on an individual’s physical health. Also discussed earlier was the relationship between poor nutritional status and food insecurity (Lee & Frongillo, 2001b). Though Dent et al. (2012) hypothesized a relationship between functional decline and undernutrition, they admitted that sparse amount of research exists supporting this relationship. The purpose of this systematic review (Dent et al., 2012) was to evaluate the quality, predictive ability, specificity, and sensitivity of a variety of NSTs to predict the aforementioned outcomes. As mentioned previously, thirty-seven studies met the inclusion criteria. These criteria were that each study utilized an NST that incorporates >1 nutritional component and identifies malnourishment or risk thereof; outcomes studied included >1 of the three specified and must have been of prospective study design (not retrospective for reasons of collecting current data); from an original study source; contained subjects > 65 years of age; published in a peerreviewed journal; and functional status was to be included in the baseline data if this data was available upon a participant’s admission to the study. 24 The review was diverse by including studies that were conducted in four types of residences: acute hospital, subacute care, residential care, and community settings. This approach provided a representative sample of living arrangments for the review. Of the thirtyseven studies, malnourishment appeared most dominant in settings with increased dependency: acute care and subacute care. Nutritional Screening Tools were evaluated by the following criteria: study quality, positive and negative predictive ability, sensitivity, and specificity. The majority of NSTs used in the studies were issued at two points within the study time frames; these varied in duration from 1 month to 5 years. Those studies that issued their NSTs within a 5-year time frame significantly predicted outcomes compared to the short-term studies. Dent et al. attributed this to the length of time given for an individual’s status to change. The most common tools used were the Mini Nutritional Assessment (MNA), MNA-Short Form (MNA-SF), Malnutrition Universal Screening Tool (MUST), Determine Your Nutritional Health (Nutritional Screening Initiative, NSI) checklist, and Geriatric Nutritional Risk Index (GNRI) (Dent et al., 2012). The most common tool used to determine functional decline was the Bathel Index; this tool measures extent of ADLs. Due to the heterogeneity of each study’s approach to measuring the three outcomes (via NSTs), the review was limited in evaluating the effectiveness of each tool. For example, in the studies focused on the health outcome of functional decline, the review committee concluded that the diversity of study methods used made it difficult to extract an association between NSTs and the prediction of this outcome (Dent et al., 2012). On the other hand, those studies utilizing MNA-SF showed associations in predicting functional decline while only one 25 study that assessed IADLs showed an association when using two versions of the MNA tool (original and short-form). Another weakness of the NSTs was that negative predictive ability (NPA) was more apparent than positive predictive ability (PPA) in the studies observed (Dent et al., 2012). All NSTs proved to have strong negative predictive ability and specificity but failed to significantly identify individuals that had positive predictive ability and/or sensitivity. As a result, the review suggested that these tools be enhanced or that a new tool be formulated to strongly assess individuals with PPA especially as it pertains to the nutritional status of an individual; PPA in this study identifies an individual as “malnourished” while NPA as “well nourished.” In the majority of studies reviewed by Dent et al. (2012), MNA and GNRI were the most frequently used to predict all three outcomes. Of all NSTs, the MNA was found to have the greatest predictive power for all outcomes. Perhaps this conclusion was made because it was the most widely used NST across all evaluated studies. In conclusion, there was evidence that NSTs can predict mortality, functional decline, and to a lesser extent, moving to a higher level of care among the older adult population. A key take-away from this review was that these tools best identified those individuals who were at lower risk and hence further research is warranted to observe the association between NSTs and outcomes in those at higher risk for these health outcomes as well as others not proposed by the authors. Specifically, the review encouraged further exploration of NSTs predictive power of functional decline, especially how it may impact nutritional status through decline of ADLs and IADLs. 26 Kaiser et al. (2010) conducted a retrospective pooled analysis on the use of the Mini Nutritional Assessment (MNA) tool for showing malnutrition in older adults. The objective of the study was to report the prevalence of malnutrition in the elderly with appropriate and available data sets from studies in all five continents. Data was collected according to a systematic research strategy and then combined in a single database to be evaluated as a whole and as stratified units for each setting in which malnutrition was assessed. As seen in previous studies, the four main settings that were targeted for assessment included hospital (acute care), rehabilitation facilities (subacute care), nursing homes, and community residences. The article suggested that one of its limitations in presenting representative data was the uneven distribution of sample size among the four settings. For example, the rehabilitation sample size was the smallest of the settings. This was important to consider because rehabilitation and hospital settings showed the greatest proportion of subjects that were malnourished or at risk for malnourishment. Consequently, the authors suggested that the findings not be generalized due to limited representation of older adults for each of the four settings. However, it can be concluded from this study that those in acute and subacute settings (as opposed to permanent residential settings) were at greater risk or had greater prevalence of malnourishment. This is consistent with the role of these settings: to intentionally treat and rehabilitate elderly patients. This finding is also consistent with the fact that an elderly individual may reach poor nutritional status before disease appears (Sharkey, 2008). 27 One key finding of this study was that over two-thirds of the study subjects in the combined database were categorized as “at risk for malnutrition” (Kaiser et al. 2010). Those in the well-nourished category were most prominent in the community setting. Ninety percent of all study subjects in the hospital and rehabilitation settings combined were malnourished or at risk for malnourishment and few were well nourished. To a lesser degree, 61.7% of subjects in nursing homes were malnourished or at risk. Unlike the systematic review carried out by Dent et al. (2012), this study by Kaiser et al. (2010) required a clear definition of the study setting and exclusive use of the MNA to assess malnourishment at an international level. Chronic disease or other debilitations associated with aging (i.e. dementia) were not considered in this study. Females were the dominant gender within the sample sizes of each setting. This may be a limitation due to the discrepancies between female and male physique and relative susceptibility to malnourishment based on body composition. Kaiser et al. (2010) made a profound statement for the efficacy of the MNA; the MNA significantly conveys the prevalence of malnourishment among elderly subjects. This tool is highly accepted and utilized on a global level and although it has yet to be acknowledged as the gold standard for elderly assessment, it is the most validated NST because it addresses items that are relevant to the target population in regards to physical, mental, and nutritional health. Quality of Life in the Elderly Quality of life and its relation to nutritional status has not received much attention especially as it concerns the elderly. Nevertheless, nutritional status has been shown to relate to some of the dimensions that determine the level of quality of life (QOL). An overall 28 measurement of QOL is based on subjective and objective dimensions as it corresponds to an individual’s assessment of life satisfaction. This section shifts from the concept of food insecurity in the elderly to another nutritionally related concept that is relevant to the aging population. Definitions Healthy People 2010 define quality of life as “an overall sense of well-being, when applied to an individual [denoting] a pleasant and supportive environment when it is applied to a community” (U.S. Department of Health & Human Services, 2013). This means that QOL is specific to its subject or sample and is dependent upon the complex dynamic of that subject or sample. Quality of life is a multi-dimensional concept that involves: behavioral competence, quality of life perceptions, psychological and physical well being, and environmental aspects (Amarantos et al., 2001). As it concerns the elderly, QOL is an important measure for understanding the impact of their current health status on their well being in terms of nutritional, functional, and psychological health. Health-related quality of life (HRQOL) specifically looks at the changes in physical and mental health dimensions that are caused by or compounded by disease, aging, or altered functional status (Amarantos et al., 2001). This measurement is pertinent to older adults because of the increased burden of chronic disease that they experience. Perceived health of a patient may be more informative for medical practitioners in tailoring treatment to him or her than making a standard decision for treatment. At this point in the life span, the primary concern may shift from efforts to reduce morbidity to assessing and monitoring life satisfaction. 29 Amarantos et al. (2001) summarized age-associated nutritional changes that may affect quality of life. These include but are not limited to changes in body composition, changes in functional status, increased incidence of disease, increased use of medications, altered social environment, and decreased income. One of the main changes that is reflective of an individual’s nutritional status is functional status. Functional ability, as discussed earlier, is determined by performance of ADLs and IADLs. Research has shown possible associations between selected abilities and nutritional problems. For example, the inability to feed oneself, classified as an ADL, has been observed in those experiencing nutritional problems such as dehydration, chronic alcoholism, vitamin B12 deficiency, and osteoporosis. (Amarantos et al., 2001). These conditions are commonly noted in the elderly. Comparatively, some of the nutritional changes observed in the elderly that may affect quality of life are also considered predictors of food insecurity. It is well known that food insecurity can cause poor nutritional status. In a four year randomized clinical trial, Corle et al. (2001) studied a cohort composed of a control group and an intervention group of patients who received continuing nutrition counseling to support the adoption of a healthy diet high in fruits, vegetables, and fiber and low in fat. The cohort, a subpopulation of 394 participants, was followed and assessed for measures of QOL starting at baseline and then annually for four years using the Quality of Life (QF) Factors Questionnaire. The average age of participants was approximately 60 years old and baseline characteristics did not significantly differ between control and intervention groups besides the overall response for the taste domain in the QF questionnaire; the reason for this discrepancy was unknown to authors. The questionnaire was unique to typical QOL surveys because it was meant to gauge the different component of QOL 30 related to diet and health. Amarantos et al. (2001) suggest that this type of QOL tool would provide nutritional insight from the patient’s perspective and assist practitioners in their treatment approach. The domains (or dimensions) included in this questionnaire were taste, convenience, cost, self-care, social, health assessment, health belief, health action, and life satisfaction. These domains were used to evaluate QOL during the four-year phase of both intervention (healthy diet) or control (typical diet) groups. Most of the questions had been tested for validity and reliability in medical contexts but others were still pending. The questionnaire was a collection of questions pulled from a variety of questionnaires. Corle et al. (2001) found that the intervention group had higher ratings for self-care, health belief, and health action compared to the control group. Intervention participants marked their positive experience by increased confidence in their ability care for their health, greater belief that their food choices would impact their health, and increased awareness of nutrition messages (Corle et al., 2001) Ratings for the domains of cost and convenience did not significantly change from baseline data or prove to have a negative impact on intervention group participants. Authors suggested that the support and education provided by the nutrition consultants may have played an incremental part in sustaining the intervention group’s adherence to the diet. This speculation may be useful for developing interventions targeted at food insecure elderly to alleviate the perceived and actual barriers to meal preparation, grocery shopping, and increasing the nutritional quality of food choices through nutrition counseling. Crole et al. (2001) also highlights the importance of a comprehensive approach in measuring QOL relative to diet and health. Demographic and lifestyle characteristics must be considered when assessing the perceived QOL among individuals. All in all, this study is a helpful guide to 31 developing a QOL questionnaire that thoroughly addresses the impact of diet and health on personal perceptions of well-being and life satisfaction in older adults. Measurements of QOL in Elderly The previous section has mentioned a few tools used to measure QOL in senior populations. In lieu of a gold standard, research has depended on generic QOL tools to gather information from the elderly. The nutritional, physical, and psychological issues unique to this age group require tailored assessments in order to effectively implement intervention and prevention strategies. Some of the tools utilized to measure HRQOL assess categories ranging from functional abilities (i.e. ADLs, IADLs, independence, self-care), psychological health, social environment, physiological discomfort or limitations, and general perceptions of health (Amarantos et al., 2001). If included, questions regarding nutritional status capture only a glimpse of what nutrition-related factors are affecting their QOL. One prospective study went as far as developing an NRQOL (nutrition related quality of life) tool through a rigorous iterative process of consulting assorted dietitian and patient focus groups (Barr & Schumacher, 2003). The purpose of this study was to develop a tool that could specifically show the impact of various forms of nutrition intervention on QOL. Their compilation of professional and public insights resulted in a 50-item questionnaire that could be efficiently administered to patients in different clinical and community settings (i.e. home care, outpatient). Within the 50-item questionnaire the following categories were created: food impact, self-image, psychological factors, social/interpersonal, physical, and self-efficacy. Though inspired by a diverse group (age, ethnicity, gender, etc.), this questionnaire overlaps categories presented in HRQOL and QOL surveys but with the intent to gather predictors of 32 nutrition related quality of life. This study can be considered a pioneer approach for developing this kind of survey and thus has limitations. The main weaknesses of this study are the small sample size and the possibility that not all participants were representative of the general population. Authors acknowledge that this questionnaire needs to be quantified and tested for reliability and validity. Findings of this study show that QOL can be measured, in addition to its general definition, to reflect a specific dimension such as nutrition (Barr & Schumacher, 2003). A cohort derived from the Manitoba Follow-up Study was evaluated for reported selfrated health and life satisfaction using the Successful Aging Questionnaire (SAQ) (Lengyel, Tate & Obirek-Blatz, 2009). The SAQ is a comprehensive tool that asks for information about living arrangement, self-rated health, life satisfaction, limitations in ADLs, as well as physical, mental, and social function. Distinct to this questionnaire in comparison to other QOL surveys is the component that asks about perspectives on successful aging. A nutrition component was added in 2000 to look at self-rated health as it pertained to quality of nutrition intake. The average age of participants was 80 years old, and the majority reported having good self-rated health and good life satisfaction. One key finding of the study was that self-rated health and life satisfaction were greatest among those who reported the highest intakes of fruit, vegetables, and grain products everyday compared to those who consumed these products “most days” and “rarely.” Overall, vegetable intake, meat and meat alternative intake significantly predicted degree of reported life satisfaction. The study concludes that quality of life measured in terms of self-rated health and life satisfaction (synonymous words) was strongly associated with the nutritional quality of participants’ diets. This means that adequate fruit and vegetable, meat and meat alternative, and grain consumption can directly impact one’s perception of quality of 33 life. Strengths of this study include the large sample size of 1211 subjects, use of a comprehensive questionnaire, and paralleled findings to comparative studies. Authors suggested that the nutrition component of the SAQ be further tested to provide a clearer measure of food intake and nutritional quality of intake. This applies to other QOL assessment tools and future research needs will be addressed later. Amarantos et al. (2001) has extensively evaluated other tools that are used to measure health related quality of life. Some examples of generic tools that were noted include the Sickness Impact Profile, Karnovsky Performance, SF-36, Campbell’s Index of Well-Being, Notingham Health Profile, and ADLs and IADLs examinations. Of the above listed the single most comprehensive and effective tool in measuring HRQOL has been the SF-36. The SF-36 has been widely used and validated in several studies but is considered too extensive for efficient healthcare applications (Centers for Disease Control, 2000). The CDC and other organizations made a collaborative effort to develop a survey that was more practical for community and clinical settings. This survey came to be known as the “Healthy Days Measures” and is frequently used by the Behavioral Risk Factor Surveillance System to collect monthly data on a national level via telephone interview. Table 1 includes the questions that comprise this survey. Though it is a widely accepted tool for measuring HRQOL, SF-36 does not make direct inquiries about nutrition related quality of life neither does it cater to the senior population. Hence the research initiated by Barr and Schumaker (2003) is valuable for advancing surveillance systems to measure NRQOL in addition to HRQOL. Furthermore, lessons learned from Corle et al. (2001) and Lengyel et al. (2009) would be useful references for customizing a survey such as the Healthy Days Measures to assess HRQOL in older adults. 34 Table 1. Health Days Measures Core Questions. Would you say that in general your health is excellent, very good, good, fair or poor? Now thinking about your physical health, which includes physical illness and injury, how many days during the past 30 days was your physical health not good? Now thinking about your mental health, which includes stress, depression, and problems with emotions, how many days during the past 30 days was your mental health not good? During the past 30 days, approximately how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation? From Centers for Disease Control, 2000. Summary Food insecurity is an emerging problem in the U.S. and is affecting the health and nutritional well being of older adults. Specifically, food insecurity has been shown to relate to adverse outcomes in the elderly in the form of physical, psychological, and nutritional consequences. Research studies that have focused on certain predictors or outcomes of food insecurity have implied the need for repeated research efforts to validate all such associations. It has also been highly recommended that assessment tools for detecting food insecurity in the elderly be refined to better capture the experiences specific to this age group. Further, assessment tools for nutritional status, healthcare cost patterns, and health related quality of life must also be considerate of the unique physiological, psychological, social, and economic challenges of the older population. At best, the available literature acknowledges the existence 35 of the risk factors and predictors of food insecurity in the elderly and the potential health and nutrition related outcomes of this socioeconomic status. Perhaps the multi-dimensional nature of food insecurity requires research to look at it from both angles: food insecurity as a predictor and food insecurity as an outcome; as Campbell et al. (1991) suggested nearly two decades ago. Future research is warranted, according to the reviewed literature summarized in Table 2, to make these discussed predictors and outcomes absolute in nature and grounded in evidencebased practice to inform assessment, prevention, and intervention strategies. 36 Table 2. Summary of Literature on Predictor and Outcomes of Food Insecurity in the Elderly Authors Research Question Predictor Outcome Population & Type of Study Future Research Needs/Intervention Needs Lee & Frongillo, 2001a Are functional impairments associated with food insecurity in the elderly? Functional impairments Food Insecurity Elderly participants in NHANES III; observational study Nutrition assistant services should meet the needs of elderly who have difficulty accessing food assistance programs and/or acquiring adequate foods due to functional limitations Johnson, Sharkey & Dean, 2011 Do unmet needs related to material hardship (i.e. food, housing, and health) influence depressive symptoms in the elderly? Material hardship; food insecurity Depressive symptoms Elderly participants in Nutrition and Function Study; observational study Future research needed to explore how unmet health needs and food insecurity are indicators of adverse health outcomes Lee & Frongillo, 2001b What are the health and nutrition related consequences of food insecurity? Food insecurity Health & Nutrition consequences Elderly participants in NHANES III and NSENY; observational study -Future development of food insecurity assessment tool specific to older adult characteristics and experiences. -Prevention of poor nutritional status in older adults/ensuring adequate nutrient intakes Bhargava et al., 2012 Is there a relationship between food insecure elderly and healthcare costs/expenditures? Food insecurity Health expenditures Data was derived from datasets: Georgia Advanced Performance Outcomes Measures Project 6 & Centers for Medicare and Medicaid Services; secondary analysis/observational study 37 -Future research needed to explore patterns of healthcare expenditures related to food insecurity and how these expenditures may affect the food insecurity status of older individuals References Amarantos, E., Martinez, A., & Dwyer. J. (2001). Nutrition and quality of life in older adults. Journals of Gerontology, 56, 54-64. Anderson, A.L., Harris, T.B., Tylavsky, F.A., Perry, S.E., Houston, D.K., Hue, T.F., Strotmeyer, E.S., & Sahyoun, N.R. (2010). Dietary patterns and survival of older adults. Journal of the American Dietetic Association, 111, 84-91. Barr, J. & Schumacher, G. (2003). Using focus groups to determine what constitutes quality of life in clients receiving medical nutrition therapy: first steps in development of a nutrition quality-of-life survey. Journal of the American Dietetic Association, 103: 844851. doi: 10.1053/jada.2003.50170. Bernstein, M. & Schmidt Luggen, A. (2010). Nutritional assessment for the older adult. In S. Goldbergy (Eds.), Nutrition for the older adult (298). Sudbury, MA: Jones and Bartlett Publishers Bhargava, V., Lee, J.S., Jain, R., Johnson, M.A., Brown, A. (2012). Food insecurity is negatively associated with home health and out-of-pocket expenditures in older adults. The Journal of Nutrition, 142: 1888-1895. doi: 10.3945/jn.112.163220. Bickel, G., Nord, M., Price, C., Hamilton, W., Cook, J. (2000). Guide to measuring household food security. Alexandria, VA: U.S. Department of Agriculture, Food and Nutrition Service. Burnett, J., Achenbaum, W.A., Hayes, L., Flores, D.V., Hochschild, A.E., Kao, D., Halphen, J.M., Dyer, C.B. (2012). Increasing surveillance and prevention efforts for elder self-neglect in clinical settings. Aging Health, 8, 647-655. doi: 10.2217/ahe.12.67. Brown, J.L., Shepard D., Martin, T., & Orwat, J. (2007). The Economic Cost of Domestic Hunger. Retrieved from http://www.sodexofoundation.org/hunger_us/Images/Cost%20of%20Domestic%20Hun ger%20Report%20_tcm150-155150.pdf. Campbell, C. (1991). Food insecurity: a nutritional outcome or a predictor variable?. The Journal of Nutrition, 121, 408-415. 38 Census Bureau for the Bureau of Labor Statistics. “Current Population Survey Food Security Supplement.” Survey. Dec 2011. Centers for Disease Control and Prevention. (2000). 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Indicators of material hardship and depressive symptoms among homebound older adults living in North Carolina. Journal of Nutrition in Gerontology and Geriatrics, 30(2), 154-168. Kaiser, M.J., Bauer, J.M., Ramsch, C., Uter, W., Guigoz, Y., Cederholm, T., Thomas, D.R., Anthony, P.S., Charlton, K.E., Maggio, M., Tsai, A.C., Vellas, B., & Sieber, C.C. (2010). Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. The American Geriatrics Society, 58, 1734-1738. Lee, J.S. (2013). Food insecurity and healthcare costs: research strategies using local, state, and national data sources for older adults. Advanced Nutrition., 4, 42-50. Lee, J.S. & Frongillo, E.A. (2001a). Factors associated with food insecurity among U.S. elderly persons: importance of functional impairments. Journal of Gerontology: Social Sciences, 56, 94-99. doi:10.1093/geronb/56.2.S94. Lee, J.S., & Frongillo, E.A. (2001b). Nutritional and health consequences are associated with food insecurity among U.S. elderly persons. The Journal of Nutrition, 131, 1503-1509. 39 Lengyel, C.O., Tate, R.B., Obirek Blatz, A.K. (2009). The relationships between food group consumption, self-related health, and life satisfaction of community-dwelling Canadian older men: the Manitoba follow-up study. Journal of Nutrition for the Elderly, 28, 158173. Morley, J.E. (2011). Undernutrition in older adults. Family Practice, 29, i89-i93. Nielsen, S.B., Garasky, S., & Chatterjee S. (2010). Food insecurity and out-of-pocket medical expenditures: competing basic needs? Journal of Family and Consumer Sciences, 39, 137-51. Sharkey, J.R., Giuliani, C., Haines, P.S., Branch, L.G., Busby-Whitehead, J., & Zohoori, N. (2003). Summary measure of dietary musculoskeletal nutrient (calcium, vitamin D, magnesium, and phosphorus) intakes is associated with lower-extremity physical performance in homebound elderly men and women. The American Journal of Clinical Nutrition, 77, 847-856. Sharkey, J.R. (2008). Diet and health outcomes in vulnerable populations. New York Academy of Sciences, 1136, 210-217. Shepard, D.S., Setren, E., & Cooper, D. (2011). Hunger in America: suffering we all pay for. Retrieved from http://www.americanprogress.org/wpcontent/uploads/issues/2011/10/pdf/hunger_pa per.pdf. Tarasuk, V. (2006). Use of population-weighted estimated average requirements as a basis for daily values on food labels. American Journal of Clinical Nutrition, 83(5), 1217S. U.S. Department of Health and Human Services. (2013). Healthy People 2020 summary of objectives: older adults. Retrieved from http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/OlderAdults.pdf. Ziliak, J.P. & Gundersen, C. AARP Foundation (2011, August). Food insecurity among older adults. Retrieved from http://www.aarp.org/content/dam/aarp/aarp_foundation/pdf_2011/AARPFoundation_ HungerReport_2011.pdf 40 Appendices 41 Appendix A 42 From Coleman-Jensen et al., 2012. Appendix B Logical Status of Nutrition Related Predictors & Outcomes, from Campbell, 1991. 43