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
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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.
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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.
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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).
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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
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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
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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.
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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
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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
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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
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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.
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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.
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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
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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,
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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