THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS STEPHANIE SINNETT

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1 THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS by STEPHANIE SINNETT (Under the Direction of Jung Sun Lee) ABSTRACT This study examined the ability of the Nutrition Screening Initiative DETERMINE Checklist (NSI) to evaluate nutrition risk status in Older Americans Act Nutrition Program (OAANP) applicants in Georgia. The study sample included new OAANP participants and waitlisted people who completed all Georgia NSI (GANSI) and matching questions (GANSI-match) in the self-administered survey (n = 924, mean age 75.0 ± 9.2 years, 68.8% women, 26.1% black). Agreement among six GANSI and GANSI-match pairs (eating < two meals daily, eating few fruits and vegetables or dairy, eating most meals alone, food insecurity, using three or more medications daily) were compared. All six question pairs showed significant discordance (all p< 0.01); 94% of the sample provided at least one discordant response pair. Being black, living alone and food insecurity impacted the likelihood of discordant responses. This study indicates some NSI items may have limited ability to reliably identify older adults at nutritional risk. INDEX WORDS: Nutrition Screening Initiative DETERMINE Checklist, older adults, nutrition risk, Older Americans Act Nutrition Program

2 THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS by STEPHANIE SINNETT B.S., Florida International University, 2007 A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE ATHENS, GEORGIA 2009

3 2009 Stephanie Sinnett All Rights Reserved

4 THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS by STEPHANIE SINNETT Major Professor: Committee: Jung Sun Lee Mary Ann Johnson Anne P. Glass Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2009

5 ACKNOWLEDGEMENTS I would like to thank my advisory committee, Drs Lee, Johnson and Glass, for their support, guidance and assistance throughout my graduate career. Through the multiple metamorphoses my thesis underwent, you were each there to encourage me and lead me in the most appropriate direction. I would like to extend a very special thank you to all those in Dr. Johnson s lab. Working with our seniors during assessment periods and during all the farmers markets was an amazing blessing to me and what I learned with travel with me wherever I go. To my network of friends in Athens and around the world, I couldn t have done this without you! Endless cups of coffee and hours of conversations made the challenges of graduate school more bearable and helped me to fight through mental exhaustion and emerge victorious. To the Father and my family, thank you is not enough. From early on in my life you have all lent me the support and strength that I needed to reach for the stars and offer my best, both academically and personally. I pray that I will continue to make you all proud and follow Your path in the days ahead. iv

6 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS...iv LIST OF TABLES...vii CHAPTER 1 INRODUCTION LITERATURE REVIEW... 4 The Older Adult Population in the United States... 4 Older Americans Act Nutrition Program... 5 Importance of and Measures Used to Ascertain Nutritional Risk... 7 Validation of the Nutrition Screening Initiative Checklist as a Screening Tool... 9 Considerations in Nutrition Risk and Nutrition Screening Results Targeting of the OAANP and the Impact of Participation Rationale, Hypothesis and Specific Aims THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS Abstract Introduction Methods Results v

7 Discussion CONCLUSIONS REFERENCES APPENDICES A Georgia Advanced POMP6 survey: Home-delivered meals participants vi

8 LIST OF TABLES Page Table 3.1: GANSI Questions and GANSI-match Data Table 3.2: Baseline Characteristics of Participants Table 3.3: GANSI Question Responses, by OAANP Group Table 3.4: Participant Characteristics, by NSI Risk Level Table 3.5: Fruit and Vegetable and Dairy Intake, by GANSI Responses Table 3.6: GANSI-Match Responses, by OAANP Group Table 3.7: Characteristics of Respondents with Mismatched Data Responses, by Number of Mismatched Responses Table 3.8: Comparison of Answers to Six GANSI Questions Against Matched Survey Data, with McNemar s Test Table 3.9: Characteristics of Respondents with Mismatched Data Responses, by GANSI Question Table 3.10: Multivariate Logistic Regression of Characteristics Related to Mismatching Questions, by Number of Mismatches Table 3.11: Multivariate Logistic Regression of Characteristics Related to Mismatching Questions, by GANSI Question vii

9 CHAPTER 1 INTRODUCTION By 2030, it is estimated that there will be 71 million older adults living in the U.S. comprising almost 20% of the total population [1]. Good nutrition plays an important role in helping older adults to remain independent in their homes, avoid premature institutionalization and improve their quality of life [2]. The Older Americans Act Nutrition Programs (OAANP), including congregate (CM) and home-delivered meals (HDM), provide nutritious meals and improve the dietary intakes of participants, thereby reducing hunger and food insecurity [3, 4]. Food insecurity is defined as a householdlevel economic and social condition of limited or uncertain access to adequate food by the USDA [4]. The OAANP is intended to target those with greatest social and economic need including low-income minorities and rural older adults [3]. With the growing older adult population comes an increase in the demand for the OAANP. In response, the federal government has increased program funding for fiscal year 2009 by $44.2 million dollars over the fiscal year 2008 budget, bringing the total funding for the HDM and CM programs to approximately $214.5 million and $434 million dollars, respectively. Of this, Georgia is set to receive just over $2.4 million dollars for OAANP programs [5]. Waitlists exists for the OAANP in many parts of the country [6, 7] and, with the current economic conditions and corresponding increase in demand for the program, such lists are likely to persist despite the increased funding. 1

10 The Nutrition Screening Initiative DETERMINE Checklist (NSI checklist) is the most frequently used nutrition screening tool for community-dwelling older adults. It is intended to prevent impairment by identifying and treating nutritional problems before they become a detriment to the lives of older adults. The checklist was designed to be administered by older adults or their caregivers [8] and is also used by healthcare and home and community-based service (HCBS) providers, like those providing meals through the OAANP, to screen for nutrition risk. Studies have demonstrated that barriers exist in obtaining accurate measures of the needs of older adults, especially those participating in federal food assistance programs such as the OAANP [9, 10]. Thus, it is vital to determine the validity and reliability of tools used to evaluate this group of older adults. Controversy surrounds the use of the NSI checklist as a nutrition screening tool; its ability to identify those at nutritional risk or with poor dietary intake is in question [11, 12]. The creators of this checklist never intended it to be used as a nutrition screening tool, but only as an educational tool [13]. Further, it was never independently validated as a screening tool in diverse population of older adults. Validation study populations consisted of 94-96% white, middle-class, educated people [8, 14] which is not a population representative of OAANP participants or applicants. Answers to specific checklist questions have been associated with inadequate nutrition [8] and a poor score, indicating high nutritional risk, has been shown to be a weak predictor of mortality [14]. If a cumulative score is used and individual question scores are not addressed, it is possible that existing problems may be overlooked. 2

11 Little research has been done evaluating the use and validity of the NSI checklist in low-income, minority populations, like those targeted by the OAANP. Therefore, the purpose of this study was to characterize the applicants (new participants and waitlisted persons (WL)) to the OAANP (n= 924, mean age 75.0±9.2 years, 68.8% women, 26.1% black) and examine agreement/disagreement between responses given to NSI and additional, matched survey questions. Chapter 2 provides a review of the literature regarding the aging population in the United States, the Older Americans Act Nutrition Programs, nutritional screening and risk, the development, validation and use of the Nutrition Screening Initiative DETERMINE Checklist as a screening tool and the targeting of the OAANP. Chapter 3 provides a manuscript to be submitted to the Journal of the American Dietetic Association. Per publication guidelines, the manuscript contains a structured abstract, introduction, methods, results and discussion sections. Data tables are provided at the end of the chapter. Chapter 4 provides conclusions and implications of the present study regarding the use of the Nutrition Screening Initiative DETERMINE Checklist as a screening tool in those seeking to participate in the OAANP. All references are provided after Chapter 4. These references are followed by Appendix A which provides a copy of the Georgia Advanced POMP6 survey for homedelivered meals participants; this is the longest survey used and all questions used in some or all of the additional, group surveys are included. 3

12 CHAPTER 2 LITERATURE REVIEW The Older Adult Population in the United States The demographic face of the U.S. is changing. Longer life spans and an aging baby boomer generation are leading to an increase in the older adult population. By 2030, it is estimated that there will be 71 million older adults living in the U.S. comprising almost 20% of the total population [1]. With the increasing population of those 65 and above, healthcare spending is expected to increase by 25% during the same time frame [1]. Health status, of which nutrition is a part, plays an integral role in controlling these increasing healthcare costs. To increase life expectancy and improve the quality of life for individuals is the first goal of Healthy People 2010 [15]. It is the position of the American Dietetic Association that food and nutrition may add an important dimension to quality of life and improving or maintaining quality of the life is a viable outcome, especially for older adults [16]. Declining nutritional status is not an inevitable consequence of aging [17] and good nutrition is vital in order to help older adults remain independent in their homes, avoid premature institutionalization and improve their quality of life [2]. To achieve the first goal of Healthy People 2010 and to attenuate the increasing expenditures on healthcare for older adults, it is necessary to identify and understand the factors involved in maintaining or improving the nutritional health of the aging U.S. population. 4

13 Older Americans Act Nutrition Program The Older Americans Act Nutrition Program (OAANP) began in 1968 as a Congressionally-mandated three-year demonstration project, was added as a part of the Older Americans Act (OAA) in 1972 and then consolidated under Title III of the OAA in 1975 [3]. The goals of the OAANP are to provide nutritious meals and improve the dietary intakes of participants, thereby reducing hunger and food insecurity, while also offering opportunities for social interaction, nutrition screening and education and referrals to other needed services [3, 4]. These services are available to persons age 60 or above and their spouses and persons under 60 if disabilities exist [18], with some state to state variation in additional stipulations to qualify for home-delivered meals (HDM). The OAANP is intended to target those with greatest social and economic need including low-income minorities and rural older adults [19]. A great deal of growth has occurred since its consolidation, with the number of meals provided increasing from 48.5 million in 1975 to over million in 2006 [6, 20]. Funding for the OAANP, however, has remained relatively flat over the last several years despite the growing demand for both congregate meal (CM) and HDM services [21]. This problem is illustrated by the prevalence of waitlists for OAANP programs, especially for HDM. Between 30-41% of nutrition delivery sites report waitlists, some with a wait time of up to four months [6, 7]. Limited funding and resources are being further strained by the growing demand for HDM, which has increased from 22% of meals served through the OAANP in 1980 to 58.9% in 2006 [6, 20]. In response to increasing demand for the OAANP, the federal government is increasing program funding for fiscal year 2009 by $44.2 million over the fiscal year 5

14 2008 budget. This brings the total funding for the HDM and CM programs to approximately $214.5 million and $434 million, respectively. Of this, Georgia is set to receive just over $2.4 million for OAANP programs [5]. In contrast to the OAANP, funding for the Women, Infants and Children (WIC) program has been increasing since it s inception in 1972 [22]. The WIC program has served all eligible applicants since 2001; in 2008, it was funded at $5.4 billion to provide services to 8.3 million women, infants and children each month [23]. Recent budget constraints have decreased the ability of states to provide CM and HDM to current participants. To address this issue, the U.S. Department of Health and Human Services allocated $100 million of their Recovery Act funds to the OAANP: $65 million for CM, $32 million for HDM and $3 million for Native American nutrition programs [24]. With the current economic conditions and corresponding increase in demand for the program, waitlists for the OAANP are likely to persist despite the increased funding. The state of Georgia served 4,128,560 OAANP meals in 2006 with 63% of them being HDM [25]. OAANP meals in Georgia cost $5.82 and $7.60 in 2006 for HDM and CM, respectively, while nationally they cost $4.99 and $6.18 [20, 25]. Of these costs, Title III funding covered 47.8% of the cost of each CM and 24.4% of the cost of each HDM in Georgia; the remaining percentages were covered by other sources, such as by state governments and by meal recipients [20, 25]. This cost burden may prevent some of those seeking OAANP services from starting or continuing participation in these programs. Thus, in addition to increased overall program funding, attention must be paid to the cost-effectiveness of each of the OAANP meal programs. 6

15 In order to measure the needs characteristics of participants and to measure OAANP performance in meeting its goals and target audience, the Administration on Aging (AoA) developed the Performance Outcomes Measures Project (POMP) and the advanced POMP. These projects provide outcomes measurement tools to state and area agencies on aging for their administration in order to evaluate their program performance and report findings back to the AoA. Data from both the POMP and advanced POMP studies can be used to improve program performance in terms of participant satisfaction, targeting, efficiency and cost control and may impact program and governmental policy decisions as they relate to program funding and implementation. To date, eight POMP projects have been completed nationally and Georgia has participated in six of these [26]. Importance of and Measures Used to Ascertain Nutritional Risk Nutritional screening is used to determine an individual s need for a service or program and to identify those at risk of developing nutrition-related problems, as well as to differentiate between those at risk and those who already have nutritional problems [27]. Screening differs from nutritional assessment in that assessment is used to determine nutritional status and can be used to diagnose problems as well as to suggest possible etiologies of and solutions to these problems [28]. Use of screening to identify a problem within a population is appropriate when: (1) a small but important part of the population is at risk, (2) those at risk can be identified using a screening tool, (3) an effective intervention for the problem is available and (4) public health is not sacrificed when those at low to moderate risk are not treated [29]. The first criterion for nutrition-related screening in community-dwelling older adults is being met and 7

16 interventions (i.e., the OAANP, Food Stamps) exist to combat these nutrition-related problems. However, the availability of an appropriate screening tool to identify those at nutritional risk is uncertain. The Nutrition Screening Initiative (NSI) DETERMINE Checklist is the most frequently used screening tool for community-dwelling older adults. The NSI is a collaboration between the American Dietetic Association, the American Academy of Family Physicians and the National Council on Aging [30]. The overall goal of the NSI is to prevent impairment by identifying and treating nutritional problems before they become a detriment to the lives of older adults. Seven risk factors associated with poor nutritional and health status in older adults were identified: inappropriate food intake, poverty, social isolation, dependency/disability, acute/chronic disease or conditions, chronic medication use and advanced age [31]. Based on these factors, indicators were suggested to detect those at nutritional risk and 14 questions were developed [8]. The checklist was written at a fourth to sixth grade reading level and was designed to be self-administered by older adults or administered by their caregivers [8] and is also used by healthcare and home and community-based service (HCBS) providers; a slightly modified 11-question version of the checklist (GANSI) is used by HCBS providers in the state of Georgia. Applicants are assessed initially and then periodically thereafter for changes in nutritional risk status. In addition to the checklist, Level 1 and Level 2 screening criteria were developed for further problem delineation in those identified by the checklist as being at moderate or high nutritional risk [13]. Nutritional need is viewed as the gap between the current and the desired states of nutrition, but limited information is available characterizing the nutritional needs of this 8

17 older adult population [10]. This problem is compounded by the fact that availability, accessibility and personal as well as social acceptability of a program affects an older adult s willingness to participate [9]. Studies have demonstrated that barriers exist in obtaining accurate measures of the needs of older adults, especially those participating in federal food assistance programs such as the OAANP. For these reasons, special attention must be paid to the validity and reliability of tools used to evaluate this group of older adults. Validation of the Nutrition Screening Initiative Checklist as a Screening Tool The validity of the NSI checklist as a nutrition screening tool and in identifying those at nutritional risk or with poor dietary intake is in question [11, 12, 14]. The creators of the NSI checklist never intended it to be used as a nutrition screening tool, but only as an educational tool [13]. In fact, it was not until a study published by Posner and colleagues in 1993 that the 14-question checklist was adopted as a 10-item screening tool and weights for each question were assigned [8]. Based on weighted responses to the 10 questions, a nutritional risk score from 0-21 is determined and classified as good/low risk (score of 0-2), moderate (score of 3-5) or high (score of 6 or higher). In a sample of 749 subjects, the sensitivity of the checklist in identifying those with intakes of less than 75% of the recommended dietary allowance of key nutrients (i.e., calories, protein, vitamin A) and those with fair to poor self-reported health was 36% and 46%, respectively; the corresponding positive predictive values are 37.9% and 55.6% [8]. These results indicate that between one-third and one-half of participants who met these criteria were detected by the NSI checklist and, of those identified at high risk according to the NSI, almost one-half to two-thirds were not at risk according to 9

18 their nutrient intake or self-reported health status. No set markers for acceptable sensitivity, specificity or positive/negative predictive values exist and many factors, such as cost-benefit analysis, prevalence of the condition and alternative measures of assessment, impact what is considered acceptable [32]. One 24-hour dietary recall was the only reference from which dietary intakes were calculated, which is one of the primary criticisms of the study by Posner and colleagues [29]. Further controversy exists surrounding the fact that the checklist was not independently validated as the sensitivities and predictive values cited were for the population used to develop the scoring system, a weakness noted by the authors [8]. In addition, 96% of the sample was white, so the results and scoring system may not be generalizable to non-whites [8]. An evaluation of the NSI checklist as a screening tool was later completed [14] and, like the original validation study, the sample was comprised of predominantly white (94%), middle class elders. Additional studies must be carried out in minority and low-income elders, groups targeted by the OAANP, in order to determine the applicability of the NSI checklist in these populations [8, 14]. One study using the NSI checklist was carried out in a sample of 230 meals-onwheels (or HDM) applicants. The researchers found that, when compared to nutritional assessment criteria (anthropometric, dietary and laboratory data), the NSI checklist identified 83% of the population as being at high risk compared to 74% using the nutritional assessment criteria [33]. If it was assumed that the assessment criteria were the gold standard in measuring nutritional risk, the checklist would have a sensitivity of 84%, a specificity of 19%, a positive predictive value of 75% and a negative predictive value of 29% in this sample. Thus, a majority of people at high nutritional risk would be 10

19 detected, but many people who are at low to moderate risk would be falsely identified as high risk; the result could be a large number of people unnecessarily alarmed and burdening the healthcare system. Answers to specific NSI checklist questions have shown that a lack of money, eating fewer than two meals per day and eating few fruits and vegetables are most highly associated with inadequate nutrition [8]. One study (n = 581) demonstrated that NSI checklist questions regarding eating alone, problems biting or chewing, difficulties in cooking/shopping/feeding ones self and taking three or more medications daily were associated with mortality, but that a poor score, indicating high nutritional risk, is only a weak predictor of mortality [14]. If a cumulative score is used and individual question scores are not addressed, it is possible that existing problems may be overlooked. Also, not all of the questions on the checklist point to specific nutrition-related problems. Thus, it has been suggested that the NSI checklist may be more appropriately used in observational epidemiological studies than for population screening [29] or as an educational tool within the older adult population, the purpose for which it was originally intended [14]. In addition to the NSI checklist, several other screening tools are available for use in a population of community-dwelling older adults; the Mini Nutritional Assessment Short Form (MNA-SF), the malnutrition risk scale (SCALES) and the Senior in the Community: Risk Evaluation for Eating and Nutrition (SCREEN) are among them can be summarized as follows: MNA-SF: This is part of the original MNA, an 18-item questionnaire designed for nutrition assessment of frail older adults by clinical professionals. The MNA-SF is a six-item screening tool with a total score 11

20 of 0-14 points with a higher score being better. If the total score is eleven or less points, the older adult is considered to possibly have malnutrition and the full MNA must then be completed. According to a recent review of literature regarding the MNA, the MNA-SF was shown to have a sensitivity of 86-96% with specificities ranging from 13-98% [34] based on a variety of comparative measures (i.e., serum albumin level, protein energy malnutrition, detailed nutritional assessment). It has not been assessed for use as a self-administered assessment tool in older adults. SCALES: This tool was developed for use as an outpatient screening tool. It covers six indicators of nutritional status, represented in the pneumonic name. However, little data has been produced regarding the sensitivity, specificity, validity or reliability of this tool [35]. In addition, evaluation using this tool requires biochemical measures (blood cholesterol, serum albumin), which complicates the use of this tool in community settings. SCREEN: This tool was originally created to be used in epidemiological research in community-dwelling seniors [36]. It consists of 15 questions related to nutrition status. After being used in the community and through research [36, 37], it was determined that the tool could be improved and SCREEN II, a 17-item expanded version of the original, was created. Based on the limited time generally afforded nutrition professionals in a community setting, an eight-item abbreviated version of SCREEN II was created [36]. Scoring varies with each version of this tool, but higher scores are associated with higher nutritional risk. SCREEN II has been 12

21 shown to have 84% sensitivity and 62% specificity while the abbreviated version has 84% sensitivity and 58% specificity [36]. As it was created for use in community-dwelling seniors and requires no biochemical measures, SCREEN II or its abbreviated version present a valid option for use in the OAANP. None of these tools have been validated in an OAANP-representative population, however, and testing in this population would be required before a recommendation could be made to use these instruments. Considerations in Nutrition Risk and Nutrition Screening Results Food Insecurity An important, but often underestimated aspect of sustaining adequate nutrition in older adults is food security, the access by all people at all times to enough food for an active, healthy life, as defined by the U.S. Department of Agriculture (USDA). An older adult s level of food security is extremely important to consider when assessing their nutritional status. Those considered food insecure, or not having food security, are more likely to report fair or poor health, be at increased nutritional risk, have lower intakes of many macro- and micronutrients and have functional impairments, while also being at increased risk of institutionalization and loss of physical or psychological well-being [38-41]. Poor cognitive performance has also been associated with food insecurity [42]. Older adults with low-incomes are at greater risk for food insecurity and may experience a food insecurity cycle, wherein less money towards the end of the month causes greater anxiety over the possibility of a food shortage [9]. A survey of OAANP program 13

22 providers in upstate New York suggests that the NSI checklist question relating to ability to buy food needed may not accurately identify those with a lack of food [43]. Nutrition-Related Knowledge Knowledge of the recommended intakes of different foods and food groups is important to consider when assessing dietary intake. The NSI checklist question regarding food group intake assumes the knowledge of dietary recommendations for those being screened, as this question makes no mention of acceptable levels of intake; studies indicate that the possession of this knowledge is not a safe assumption. Approximately one-third, or less, of older adults in Georgia senior centers knew the recommended intakes of fruits, vegetables and dairy products [44, 45]. Thus, to rely on the NSI checklist to accurately reflect dietary intake, the checklist question must include specific levels of acceptable intake or assess the knowledge of participants in regards to recommended intakes. Health Literacy Health literacy, or the ability to read, understand and act on health information, plays a large role in the comprehension and utilization of health-related information [46]. The 2003 National Assessment of Adult Literacy showed that low-income, education less than a high school diploma, being over 65 years of age and being a minority increase your risk of having below basic health literacy levels, which strongly impacts their ability to read and understand documents like the NSI checklist [46]; these characteristics closely describe the target audience for the OAANP. The same 2003 study showed that 29% of adults ages 65 and over had below basic health literacy skills. The consequences of inadequate health literacy are widespread and far-reaching. 14

23 They include poorer health and self-reported statuses, poorer understanding of medical care being given and medical conditions present, lower comprehension of medical information, lower understanding and utilization of preventive services, increased health care costs, poorer compliance with medical advice and increased hospitalizations [47]. The previous studies evaluating the use of the NSI checklist as a nutrition screening tool used predominantly white (94-96%), middle-class, educated samples [8, 14] or provided limited information on demographic and economic characteristics of their study participants [33]; no assessments of the health literacy of these populations were completed. This results in the use of a semi-validated screening tool in a population in which it has never been tested, bringing into further question the ability of NSI checklist to assess nutrition risk status in the OAANP population. Several methods exist by which health literacy can be measured. These include word recognition tests, reading comprehension tests and functional health literacy tests; they vary not only by content, but also by level of difficulty and the length of time needed to complete the test. Some of these tests are appropriate for testing the understanding of health- and healthcare-related information, such as the Rapid Estimate of Adult Literacy in Medicine (REALM). The REALM is one of the more commonly used health literacy assessment tools and comes in several forms: the REALM contains 66 questions, the REALM-Short Form (REALM-SF) contains seven questions and the Spanish version, the Short Assessment of Health Literacy for Spanish Adults (SAHLSA- 50) contains 50 questions [48]. Although the SAHLSA-50 would likely have to be abbreviated for use in a community setting, the REALM-SF and, when appropriate, the SAHLSA-50 health literacy assessment tools may be added as a step in the 15

24 assessment of OAANP applicants. This assessment would enhance the ability of OAANP personnel to obtain more accurate health-related assessments and enable education providers to afford well-targeted and appropriate education materials to at risk seniors. Targeting of the OAANP and the Impact of Participation Evaluating the outcome of a program is one of the best ways to determine the program s efficacy and evaluation results can be used to improve programs when necessary [49]. Program evaluation is in fact required by the OAA [19]. The ability of currently used screening tools to detect nutritional risk and food security as well as the impact of participation in the OAANP has on these factors and nutritional health is of great importance. The higher prevalence of low-incomes and food insecurity in the state of Georgia make programs like the OAANP vital to the older adult population [50, 51]. Moreover, ensuring that the OAANP is effective and reaching its target populations is paramount in securing the nutritional status and health of older Georgians. National studies show that the OAANP is achieving some of its goals and reaching its target groups. The OAANP and the Food Stamp Program are the foremost sources of food assistance for older adults [6]. Each OAANP meal provides approximately 30-50% of the daily nutrient intake in participants and macro- and micronutrient intake is higher in participants than in non-participants [6, 40]. Using NSI checklist scores, a national evaluation of the OAANP showed that 64% of CM participants and 88% of HDM participants were at moderate or high nutritional risk [6]. Studies also indicate that HDM recipients tend to be older, at greater nutritional risk, have lower income, have greater functional impairments related to Activities of Daily 16

25 Living (ADL) and Instrumental Activities of Daily Living (IADL) and have more limited access to food than the general older adult population [6, 41, 52, 53]. In addition, the proportion of minority elders participating in the program is higher than in the general population [40]. Queries in the state of Georgia also show that the OAANP is reaching its target audience. Use of the NSI checklist in Georgia mirrored national findings as OAANP participants who were female, black or lived in rural areas were at increased nutritional risk compared to other participants [6, 54]. Over 75% of participants surveyed in Georgia were at moderate or high nutritional risk [54] and older adults in Georgia are more likely than national samples to have an income less than 200% of the poverty threshold [50]. Food insecure older adults are more likely to participate in food assistance programs such as the OAANP than those who are food secure [10] as evidenced in the state of Georgia where 11.5% of OAANP participants surveyed in Northeast Georgia, as opposed to 6% of older adults nationally, reported food insecurity over the previous year [55, 56]. None of these studies examined the effects of OAANP participation over time, however, despite the defined need for such evaluations [4, 10, 53], and similar data relating to people on the waitlist for OAANP participation are not available. The changing demographics and growing size of the older adult population are impacting the ability of the OAANP to fully achieve its goals. The burgeoning elder population is far exceeding the budget allotted for the OAANP and program waitlists are common, especially in Georgia. No studies, save one in a group of HDM applicants [33], 17

26 have assessed the ability of the NSI checklist to reflect the nutrition risk status of those seeking OAANP participation. Rationale, Hypothesis and Specific Aims Previous studies in this area have failed to characterize and compare OAANP participants and waitlisted persons. The study presented here summarizes the demographic and economic characteristics of OAANP participants and waitlisted persons that will be used in a longitudinal analysis of the impact of OAANP participation/non-participation on nutritional health, nutrition risk and food security. This analysis focuses on the validity of the NSI checklist as a nutrition screening tool in older Georgians seeking to participate in the OAANP. The hypothesis was that data collected using the GANSI would differ with statistical significance from additional, matched survey data (GANSI-match) gathered from additional survey questions and that certain participant characteristics (those 85 and above, minorities, less than high school education, fair to poor self-reported health, incomes below the federal poverty level) would increase the likelihood of providing mismatched answers. The rationale behind the selection of these characteristics was their relationships to an increased risk for below basic health literacy or to poorer health literacy in general; thus, they will be more likely to provide discordant responses as they are more likely not to comprehend the questions. The specific aims were to: 1) characterize OAANP participants and waitlisted persons, 2) determine the degree of discordance, if any, between answers given to GANSI and GANSI-match survey questions and 3) determine which, if any, participant characteristics were associated with an increased/decreased likelihood of providing discordant responses. 18

27 CHAPTER 3 THE ABILITY OF THE NUTRITION SCREENING INITIATIVE DETERMINE CHECKLIST TO EVALUATE THE NUTRITION RISK STATUS OF OLDER GEORGIANS 1 1 Sinnett S, Bengle R, Brown A, Glass AP, Johnson MA, Lee JS. To be submitted to the Journal of the American Dietetic Association. 19

28 Abstract Background: The Nutrition Screening Initiative DETERMINE Checklist (NSI checklist) is a tool used nationally to assess nutrition risk in those seeking to participate in the Older Americans Act Nutrition Programs (OAANP). However, the validity of the checklist in this population of older adults is unproven. Objective: This cross-sectional study was intended to: 1) characterize OAANP participants and waitlisted persons, 2) determine the degree of discordance, if any, between answers given to an 11-question, modified version of the NSI checklist (GANSI) and GANSI-matched survey questions and 3) determine which, if any, participant characteristics impacted the likelihood of providing discordant responses. Methods: The study sample included all new OAANP participants and waitlisted people who completed all GANSI and matching questions in the self-administered mail survey from July to mid-november, 2008 (n = 924, mean age 75.0 ± 9.2 years, 68.8% women, 26.1% black). We compared the agreements among six GANSI/GANSI-match pairs using McNemar s Test. Logistic regression was used to determine predictors of providing mismatched responses to GANSI/GANSI-match pairs. Results: Using GANSI-match data as a comparative gold standard, GANSI question sensitivities and specificities ranged from % and %, respectively. All six GANSI/GANSI-match pairs showed significant discordance (all p< 0.01). Approximately 94% of our sample provided at least one discordant response (mean 2.1 ± 1.1 discordant responses); pairs regarding food intake were most frequently discordant. Those who were black or living alone were more or less likely to provide 20

29 discordant responses, respectively. Food insecure individuals were less likely to provide discordant responses regarding food intake. Conclusions: This study shows that some NSI checklist questions may have limited ability to reliably identify older adults at nutritional risk, particularly black and food insecure individuals. Further research is warranted to improve the validity of nutritional assessment tools used to assess the nutritional risk of vulnerable older Georgians. Introduction With older adults expected to comprise almost 20% of the U.S. population by the year 2030 [1], the provision of proper nutrition is becoming more vital as it can help older adults remain independent in their homes, avoid premature institutionalization and improve their quality of life [2]. Not all older adults are able to obtain this level of nutrition without assistance. The Older Americans Act Nutrition Programs (OAANP), including congregate (CM) and home-delivered meals (HDM), provide nutritious meals and improve the dietary intakes of participants [3, 4]. These programs are intended to target those with greatest social and economic need, including low-income minorities and rural older adults [3]. With the growing older adult population comes an increase in the demand for the OAANP, but budgetary constraints prevent all those seeking program participation from receiving it, creating program waitlists in many parts of the country. In response to budget shortages and recent cutbacks, the federal government has increased program funding for fiscal year 2009 by $44.2 million bringing the total funding for the HDM and CM programs to approximately $214.5 million and $434 million, respectively. Of this, Georgia is set to receive just over $2.4 million for its OAANP [5]. 21

30 Both national and local studies show that the OAANP is well-targeted and meeting some of its intended goals. Each OAANP meal provides approximately 30-50% of the daily nutrient intake in participants and macro- and micronutrient intake is higher in participants than in non-participants [6, 40]. HDM recipients tend to be older, at greater nutritional risk, have lower incomes, have greater functional impairments related to activities of daily living and instrumental activities of daily living and have more limited access to food than the general older adult population [6, 41, 52, 53]. In addition, the proportion of minority elders participating in the program is higher than in the general population [6, 40]. Studies in Georgia mirrored national findings as OAANP participants who were female, black or lived in rural areas were at increased nutritional risk compared to other participants [6, 54]. Over 75% of participants surveyed in Georgia were at moderate or high nutritional risk [54], and older adults in Georgia are more likely than national samples to have an income less than 200% of the poverty threshold [50]. Despite the recent and anticipated increases in OAANP funding, waitlists for the program still exist. To assist in the determination of who will be served first, multiple applicant characteristics are assessed and participants are ranked in order of greatest to least need. One such screening criteria is nutrition risk as measured by the Nutrition Screening Initiative DETERMINE Checklist (NSI checklist), the most frequently and widely used nutrition screening tool for community-dwelling older adults. It is intended to prevent impairment by identifying and treating nutritional problems before they become a detriment to the lives of older adults. The checklist was written at a fourth-to-sixth grade reading level and designed to be self-administered by older adults or their caregivers [8]. It is also used by healthcare and home and community-based service 22

31 (HCBS) providers, such as those providing meals through the OAANP. Based on weighted responses to the 10 questions, a nutritional risk score from 0-21 is determined and classified as good/low risk (score of 0-2), moderate (score of 3-5) or high (score of 6 or higher); a slightly modified 11-question version of the NSI checklist (GANSI) is used by HCBS providers in the state of Georgia However, a participant s level of health literacy and knowledge of appropriate nutrition must be considered during the evaluation, as they are likely to affect the ability of the person to comprehend and answer the questions [45-47, 57, 58]; these are not taken into full consideration with the current NSI checklist. Posner and colleagues carried out the primary validation study of the NSI checklist as a screening tool in Using the suggested cutoff score for high risk of six in a sample of 749 subjects, they determined the sensitivity of the checklist in identifying those with intakes of less than 75% of the recommended dietary allowance of key nutrients (i.e., calories, protein, vitamin A) and those with fair to poor self-reported health to be 36% and 46%, respectively; the corresponding positive predictive values are 37.9% and 55.6% [8]. These results indicate that between one-third and one-half of participants who met these criteria were detected by the NSI checklist and, of those identified at high risk according to the checklist, almost one-half to two-thirds were not at risk according to their nutrient intake or self-reported health status. Although no set standards exist for acceptable levels of sensitivity and specificity, the results from Posner and colleagues evaluation of the NSI checklist can be compared to those of other nutrition risk screening tools used in older adults (i.e., the Mini Nutritional Assessment Short Form, the Seniors in the Community: Risk Evaluation for Eating and 23

32 Nutrition); the sensitivities and specificities of these tools range from 84-96% and 13-98%, respectively [34, 36]. However, like the NSI checklist, these tools have not been validated for use in an OAANP-representative population. One study (n = 230) using the NSI checklist was carried out in a sample of meals-on-wheels (also known as HDM) applicants. It found that, when compared to nutritional assessment criteria (anthropometric, dietary and laboratory data), the NSI checklist identified 83% of the sample as being at high risk compared to 74% using the nutritional assessment criteria [33]. Also, answers to specific checklist questions have been associated with inadequate nutrition [8] and a poor score, indicating high nutritional risk, has been shown to be a weak predictor of mortality in study samples [14]. If a cumulative score is used and individual question scores are not addressed, it is possible that existing problems may be overlooked. Further, it was never independently validated as a screening tool in diverse populations of older adults. Validation study populations consisted of 94-96% white, middle-class, educated people [8, 14]; such a population is not representative of OAANP applicants. The ability of the NSI checklist to identify those at nutritional risk or with poor dietary intake is in question [11, 12]. Little research has been conducted to evaluate the use and validity of the NSI checklist in low-income, minority populations, such as those targeted by the OAANP. This cross-sectional study examined the ability of the GANSI to evaluate nutrition risk status of older Georgians seeking OAANP participation by determining discordance between responses to the GANSI and matched, additional survey questions. Also, we sought to determine which, if any, measured demographic or socioeconomic characteristics are associated with an increased or decreased risk of 24

33 providing discordant responses in order to provide a basis to guide the direction of future studies. We hypothesized that data collected using the GANSI would differ with statistical significance from GANSI-match data gathered from additional survey questions and that certain participant characteristics (those 85 and above, minorities, less than high school education, fair to poor self-reported health, incomes below the federal poverty level) would increase the likelihood of providing mismatched answers. The rationale behind the selection of these characteristics was their relationships to an increased risk for below basic health literacy or to poorer health literacy in general; thus, they will be more likely to provide discordant responses as they are more likely not to comprehend the questions. Methods This study used the data from the Georgia Advanced Performance Outcomes Measures Project 6 (POMP 6) continuation study, a cooperative research project between the Georgia Department of Human Resources Division of Aging Services (DAS) and the University of Georgia. The primary objective of the parent study is to examine the effects of OAANP participation, or lack thereof, on an individual s level of food security, nutritional and general health statuses. The Georgia Advanced POMP 6 study consists of self-administered mail surveys completed by community-dwelling new OAANP participants and waitlisted persons. It involves both a cross-sectional and a longitudinal component; only data from the baseline wave of the longitudinal component are analyzed here. The Institutional Review Board of the University of Georgia approved proposed study methods requiring university staff and faculty involvement. 25

34 Sample The study sample was drawn from the state of Georgia client database systems including Aging Information Management System (AIMS) and Client Health Assessment Tool (CHAT) databases by DAS within the Georgia Department of Human Resources. All persons who were 60 years and above who began OAANP participation (CM and HDM) as well as those added to program waitlists (WL) between July and mid- November 2008 were identified (n = 4,731). Exclusion criteria included a lack of English proficiency, legal blindness and a length of OAANP participation greater than 60 days at the time of sample selection; 28 potential participants were excluded from the receipt of surveys based on these criteria. English proficiency was required as the mail survey was only available in English and a length of participation greater than 60 days at study onset may not reflect the status of applicants before program participation began. Those who are legally blind were excluded as they would be unable to read and complete the study. Survey Tool Development and Administration Expert members from both organizations were involved in the development and review of survey tools and approval of the surveys was granted by the Georgia Division of Human Resources. Survey components were adapted from the following previously validated survey tools: Arizona POMP 6-Advanced Year 2 Senior Center Participant Questionnaire [59], modified 6-item USDA-Household Food Security Survey Module (HFSSM) [60], POMP 5 Home Delivered Meals Extended Core Survey (phone version: April 19, 2004) [61] and POMP 4 Nutrition Questionnaire: Health Module (April 24, 2003) [62], Nutrition and Health of Older Adults Questionnaire 2006 [44] and medication 26

35 management questions as adapted from Briesacher et al [63]. Supplemental questions were developed by nutrition professionals at the University of Georgia and the DAS. In its entirety, the survey contains seven different sections: health-related questions, food security questions, food and nutrition risk questions, food and nutrient intake questions, food acquisition questions, health and medication management questions and demographics. A full copy of the baseline survey for HDM participants, which includes all questions found on any of the surveys, can be found in Appendix A. A pilot test of the baseline survey version was conducted and results were analyzed by staff members at the University of Georgia. Twenty HDM participants, five participants new to the HDM program and five participants new to the CM program received surveys by mail; a cover letter introducing the study and a postage paid return envelope were included in the mailing. In addition, twenty surveys were administered to CM program participants in three senior centers in northeast Georgia (Monroe, Greene and Oglethorpe Counties). Feedback regarding the survey tool was tape-recorded in addition to observations made by the staff members conducting the pilot testing. Data collected during this two-pronged pilot testing indicates that survey questions were generally well understood. Minor revisions were made and approved. Mailed surveys reflect a response rate of 33% (10 of 30 surveys) with one additional blank survey returned with a note declining participation. Surveys were mailed to eligible participants between October 2008 and January Two reminder postcards were mailed to each survey recipient to promote high response rates. Of the 4,731 people who were sent surveys, 1,594 (33.7%) returned them. All surveys were mailed from and returned to the Georgia DAS who organized the 27

36 collected data for further analysis by the University of Georgia. In addition to data from mailed surveys, demographic characteristics were retrieved from the AIMS and CHAT databases for use in data analysis. By including questions assessing nutritional health and food security in addition to the NSI checklist, this study allowed for the comparison of data collected using multiple approaches in order to evaluate the ability of the NSI checklist to accurately reflect the nutritional risk status of the study population. Survey Measures and Data from AIMS and CHAT Databases The NSI Checklist: Nutrition risk was assessed using a modified, 11-question version of the NSI checklist (Table 3.1), referred to here as the GANSI. The Georgia DAS splits one of the 10 standard questions, creating 11 questions: I eat few fruits or vegetables or milk products becomes I eat few fruits or vegetables and I eat few milk products. Scoring for this two-point question is also split with each affirmative answer adding one point to the nutritional risk score. A total score of 0-21 points (0-2 low nutrition risk, 3-5 moderate nutrition risk and 6+ high nutrition risk) was assessed based on GANSI responses. GANSI-Match Questions: Of the 11 GANSI questions, comparable survey data was available for comparison to six questions; these are referred to as GANSI # and GANSI-match #. Of these six questions, two direct correlates were available on the survey: GANSI 2 (less than two meals daily) and 4 (daily dairy servings). The other four were derived from additional survey data for comparison (see Table 3.1). Dietary intake was assessed using 20 questions from the POMP 5 Home Delivered Meals Extended Core Survey (phone version: April 19, 2004) [61]. Of these, questions regarding food group intake (fruits, potatoes, other vegetables, dairy) were 28

37 used to determine yes/no responses to GANSI-match questions 3 (daily fruit and vegetable servings) and 4 (daily dairy servings). For GANSI 3 (daily fruit and vegetable servings) and GANSI 4 (daily dairy servings), different cutoff levels were used to define few, as referenced by the NSI checklist: GANSI-match 3 (daily fruit and vegetable servings): Three cutoff levels of fruit and vegetable intake were used: less than five servings, less than four servings and less than three servings per day. The selection of five servings per day was made based on 2005 Dietary Guidelines for Americans minimum recommended intake of fruits and vegetables for a 1,200 calorie diet, the lowest likely energy needs of this study population [64]. Three servings was the cutoff used by Sahyoun and colleagues [14] as a surrogate for the same NSI checklist question. The average intake in this sample was 3.4 servings of fruits and vegetables per day, similar to the average intake seen in other studies in Georgia seniors [44, 45], so four servings was also used as a cutoff point. GANSI-match 4 (daily dairy servings): Three cutoff levels of dairy intake were used: less than three servings, less than two servings and less than one serving per day. The selection of three servings per day is based on the Key Recommendations in the 2005 Dietary Guidelines for Americans [64]. Sahyoun and colleagues [14] assigned less than one half serving of dairy per day as few servings. This survey did not allow for determination of half servings so one serving was determined to be a comparable cutoff. The average dairy intake of our sample was 2.4 servings per day, somewhere between these cutoffs and similar to previous study findings in 29

38 Georgia elders [44], thus two servings daily was also compared to GANSI 4 responses. GANSI-match 7: Food security was assessed using the modified version of the sixitem USDA HFSSM. This validated survey tool [60] includes the question: In the past month, did you ever cut the size of your meals or skip meals because there wasn t enough money for food? This question was split into two questions in order to individually assess the two behaviors addressed. The six-item HFSSM allows for differentiation between high, marginal, low and very low food security. Those who were considered high or marginally food secure were combined for analysis as they are categorized as food secure by the USDA. Similarly, those considered low or very low food security were categorized as food insecure by the USDA and combined for analysis as food insecure. Other GANSI-match data: GANSI-match 2 (less than two meals daily) responses were determined by the reported number of meals consumed daily. GANSI-match 8 (most meals eaten alone) was determined using reported number of meals consumed and meals eaten alone. GANSI-match 9 (3+ medications daily) was determined using medication management questions from Briesacher [63]. Demographics and other variables: Demographic and socioeconomic information obtained from the DAS client database systems included age, race, living arrangement, education and county of residence. Individuals were classified as either age (young-old), age (old-old) or age 85+ (oldest-old). The majority of participants surveyed were either white or black; approximately 14% of participants did not disclose race information and 1% were of another race/ethnicity. All individuals who did not 30

39 disclose and all other individuals (Asian, American Indian or Alaskan Native and Other) were classified as other/did not disclose for analysis. Additional variables assessed by the survey included income, from which income-based poverty status was determined (below federal poverty level (FPL), 100% to less than 130% FPL, 130% FPL to less than 185% FPL and 185% FPL or above), and number of people in the household, used to determine whether or not participants lived alone. These were used for respondent characterization and for comparison amongst respondents. Survey respondents counties of residence were categorized based on the Five Georgias classification scheme by Bachtel and colleagues [65]. The 159 Georgia counties are divided into five categories: urban, urbanizing, suburban, rural growth and rural declining. Under this classification system, urban counties are defined as those having a population core of 50,000 or more people with large numbers of minorities and impoverished individuals. Suburban counties surround urban centers and are characterized by a primarily white and affluent population, of which 25% or more individuals commute daily to urban areas for work. Urbanizing counties are rapidly developing rural areas with improved job opportunities, transportation options and overall quality of life that will likely develop into metropolitan areas. Counties characterized by rural growth are typically characterized by scenic or natural beauty; some are located near a military base or regional growth center which allows them to sustain economic growth. Counties in rural decline are characterized by long-term population loss, lack of employment opportunities, poor infrastructure and business development, low education levels and limited access to health care [50]. 31

40 Self-reported health status: Self-reported health status was evaluated on a fivepoint scale ranging from poor to excellent [66]. This measure has been shown to correlate well with health outcomes and mortality in older adults [67, 68]. Participants were classified according to their response as either fair to poor or good to excellent self-reported health status. Statistical Analysis Data from the AIMS and CHAT databases and from collected surveys provided by the DAS was analyzed by the University of Georgia using the Statistical Analysis System (SAS, Version 9.13, SAS Institute, Cary, NC). The analytic sample included only those who completed all 11 GANSI, all GANSI-match data, self-reported health, monthly income, number of people in household and education questions and had all demographic information (age, gender, race) (n = 924, mean age 75.0 ± 9.2 years, 68.8% women, 26.1% black). Missing information regarding race (n = 338) and GANSI 7 (n = 160) were the most likely causes for sample exclusion. Those who did not complete all required questions were not significantly different than those who did except that they were more likely to self-report fair to poor health (p = ), to have incomes below the FPL (p = ) and to be food insecure (p = ) (data not shown). Descriptive statistics for the analytic sample including frequencies, ranges, medians, means and standard deviations were calculated; differences by group (CM, CM WL, HDM, HDM WL) and by NSI risk level (low, moderate, high) were assessed using Chi-Square and ANOVA tests for categorical and continuous variables, respectively. The median intake of fruits and vegetables and dairy products was 32

41 calculated to GANSI 3 and 4, respectively, by response category (yes, no); the Mann- Whitney test was used to determine whether or not there were significant differences between median intakes by response category. Significant discordance between GANSI and GANSI-match responses was determined using McNemar s Test; sensitivities and specificities for each matched response pair were determined using GANSI-match data as a comparative gold standard. Differences between those who did and did not provide discordant responses to any of the six GANSI and GANSI-match questions were assessed by the total number of mismatched responses (0-6) and by GANSI question mismatched; Chi-Square and ANOVA tests were used for categorical and continuous variables, respectively. A logistic regression was used to determine predictors of providing any mismatched responses between GANSI and GANSI-match questions using three categories: 0, 1-2, 3 or more mismatches. An additional logistic regression determined predictors of mismatched responses to the most frequently mismatched questions (GANSI 2, GANSI 3, GANSI 4). Potential predictors were determined by those used in previous validation studies of the NSI checklist [8, 14] and included age, gender, race, living arrangement (alone/not alone), education level, self-reported health status, urban/rural classification and income-based poverty status. Results Demographic and economic characteristics for the analytic sample are shown in Table 3.2. The mean age was 75.0 ± 9.2 years with 68.8% of the sample being female and 58.8% white. A majority of people lived in urban, urbanizing or suburban neighborhoods (76.3%) and almost half lived alone (48.3%). Approximately half (50.2%) of the total sample had at least a high school diploma. A majority (74.4%) reported fair 33

42 to poor health and a large portion reported incomes below the FPL (45.0%) or were food insecure (48.0%). Those receiving HDM were more likely to be older while those on the HDM WL were more likely to be less educated, food insecure, and report fair to poor health (all p < 0.05). People on the CM WL were the youngest, most likely to not disclose their race, to live in rural areas, report incomes below the FPL and to be food secure (all p < 0.05). CM participants were predominantly female (77.4%) and most likely to be white, have attained at least a high school diploma, report good to excellent health and have incomes < 185% FPL (all p < 0.05). Table 3.3 presents GANSI question responses and assessed nutrition risk level by group. The questions with a response of yes with greater than 50% frequency were: GANSI 1, 3, 4, 8, 9 and 11 (illness or condition changing intake, few fruits or vegetables, few dairy, eat alone most of the time, 3 or more daily medications, not physically able to shop/cook/feed self, respectively). The mean GANSI score was 9.0 ± 4.7 points with those in the HDM WL groups having the highest average score (p < 0.001). Approximately 74% of the total sample was found to be at high nutrition risk with the greatest prevalence of high risk in the HDM WL group (82.5%). Around 19% and 7% of the total sample were at moderate and low risk, respectively. Those on the HDM WL were the most likely to respond yes to all GANSI questions with the exception of consuming 3 or more alcoholic beverages or not always being physically able to shop, cook and/or feed myself. Also, over 60% of those on the HDM WL reported consuming few fruits and vegetables or dairy products (GANSI 3 and 4). Table 3.4 shows the characteristics of the study sample by NSI nutrition risk level. There is strong evidence that the mean ages between the three risk level groups are not 34

43 equal (p < 0.001). The mean age of the high risk group (74.0 ± 9.1 years) was lower than the other two risk groups. A larger percentage of people in the high risk group were less educated, poor, food insecure, reported fair to poor health status and lived alone (all p < 0.01) than in the moderate and low risk groups. Approximately 60% of those in the high risk group were on the HDM WL while around 29% received HDM. Table 3.5 shows the daily fruit and vegetable or dairy intake by GANSI question. Participants responding yes to GANSI 3 had a median fruit and vegetable intake of three servings daily while those responding no had a median intake of four servings. Almost 18% of those responding no to GANSI 3 consumed less than three servings of fruits and vegetables daily. The median dairy intake for all participants was one serving daily regardless of whether they responded yes or no to GANSI 4; almost half of those responding no to GANSI 4 consumed one serving daily while 7.6% of them consumed no dairy. The percent of survey respondents consuming few fruits and vegetables varied depending on the level of intake used to define few. Approximately one third of them consumed less than three servings and over three-quarters had less than five servings daily (Table 3.6). Those in the CM WL were most likely to report consuming less than five servings per day while HDM WL group members were most likely to consume less than four or three servings daily (p < 0.001). Almost 19% of respondents reported consuming less than one serving of dairy products per day while 93.6% consumed less than three servings. As with fruit and vegetable intake, those on the HDM WL were most likely to have low intake when the lowest two levels of dairy intake were used to represent few, 73.5% and 22.7% with intake of two and one servings, respectively (p < 35

44 0.01). The HDM WL also had the highest proportion of food insecure elders (56.6%) and people reporting three or more medications used daily (92.0%). Of the total sample (n = 924), 872 provided discordant responses to at least one GANSI/GANSI-match pair; 52 did not provide any discordant responses. When Chi- Square and ANOVA tests were carried out to assess the characteristics of those who did and did not provide discordant responses (Table 3.7), only race was significantly different; black older adults were more likely than their white counterparts to provide any mismatched responses (p < 0.01). Looking at the total number of discordant responses (range: 0-6), those who provided three or four, five or six mismatched answers were more likely to be younger, black and live below the FPL than those who provided fewer or no mismatched answers (all p < 0.05). Also, providing a greater number of mismatched responses was related to not living alone (p < 0.001). Gender, urban/rural living environment, education level, self-reported health status and group (CM, CM WL, HDM, HDM WL) were not related to the provision of discordant responses. Using McNemar s Test, survey responses to GANSI and GANSI-match responses were tested for discordance; all six answer pairs were significantly different (Table 3.8). Respondents were more likely to respond yes to GANSI 2 (less than two meals daily) than to GANSI-match 2 (p < 0.001). GANSI-matches 7 (food insecure), 8 (eats most meals alone) and 9 (three+ medications daily) were more likely to elicit a yes response than GANSI 7-9 (all p < 0.01). The cutoff levels to represent few fruits and vegetables or dairy products in GANSI-match 3 and 4 yielded significant discordance in different directions, depending on the cutoff level used; the exception was less than four fruits and vegetable servings in GANSI-match 3 (p = ), which 36

45 was then excluded from further analyses. When using the GANSI-match data as a comparative gold standard, GANSI questions had sensitivities between 58.8% and 87.1% with specificities showing greater range between 48.7% and 96.0%. GANSI 2 had the highest sensitivity/specificity pair at 87.0% and 81.8%, respectively, while GANSI 3 had the lowest, 62.8% and 60.6%, respectively, using a cutoff of five servings of fruits and vegetables daily. The most frequently mismatched questions were those regarding number of meals consumed and number of fruits and vegetables or dairy servings daily (Table 3.9). Race was also a significant predictor of the likelihood to provide discordant responses to GANSI and GANSI-match questions 2, 4 and 7 with blacks being more likely to mismatch responses (all p < 0.01). Age was related to discordance in responses with younger age being related to GANSI and GANSI-match questions 2 and 3 and older age being related to GANSI and GANSI-match question 4 (all p <0.05). Poor to fair self-reported health was significantly related to discordant responses to GANSI and GANSI-match questions 2, 4 and 7 while living below FPL was predictive of discordance in GANSI and GANSI-match questions 4, 7, 8 and 9 (all p < 0.05). Food security, however, was the most common predictor of discordant responses. Those with very low food security were more likely to provide mismatched responses to GANSI 2 and its match, while being less likely to mismatch GANSI 3, 4, 7 and 9 and their respective matches (all p < 0.01). In Table 3.10, a logistic regression predicting different levels of discordant responses shows that only age, race and living alone significantly increased the odds of a greater number of mismatched answers. Those who were black were 2.7 times (95% 37

46 CI: 1.93, 3.77) more likely and those who lived alone were 41% less likely (95% CI: 0.45, 0.79) (both p < 0.05) to provide a higher level of (0-2 vs. 3 or more) discordant responses. Gender, urban/rural living environment, education level and self-reported health status were not found to increase the odds of providing discordant responses. Table 3.11 provides the results of a logistic regression of characteristics related to providing discordant responses to the most frequently mismatched responses: GANSI and GANSI-match 2, 3 (using < five and < three servings of fruits and vegetables) and 4 (using < three, < two and < one dairy serving). Very low food security and being black were related to an increased likelihood of discordance between GANSI and GANSImatch 2 (OR = 3.86, 95% CI: 1.37, 3.17, and 2.08, 95% CI: 2.38, 6.24, respectively; p < 0.05). Additional analysis showed that participants who were black and had very low food security (n = 52) were more likely than those who did not have these two characteristics to provide mismatched answers regarding number of meals consumed (OR: 6.31, 95% CI: 3.55, 11.22). This indicates a compounding negative effect of these characteristics. Very low food security also decreased the odds of discordant responses to GANSI and GANSI-matches 3 and 4 (OR = ; p < 0.05). Living alone and being in the HDM or HDM WL groups only reduced the odds of discordant responses to GANSI and GANSI-match question 4 (OR = 0.73 and 0.67, respectively) using the highest dairy intake level of less than three servings. Discussion This study characterized applicants to the OAANP in Georgia and identified any discordance between data gathered using a modified, 11-question version of the NSI checklist and additional, matched survey questions. Results presented here provide 38

47 data regarding the validity of the NSI checklist for use in older Georgians seeking to participate in the OAANP, a group targeted to include low-income minorities and those living in rural areas. The major findings of this study include the high likelihood of discordance in responses to analogous data gathered from two data sources and the identification of characteristics, primarily race and food security, that strongly impact the risk of providing mismatched responses. The study was well-targeted to a high-risk population of older adults. Participants were more likely than older adults on a national and state level, respectively, to be black (26.1% vs. 9.1%, 19.9%), live alone (48.3% vs. 38.3%, 25.7%), have incomes below the federal poverty level (45.0% vs. 9.5%, 12.1%) and have not received a high school diploma (49.8% vs. 19.2%, 33.5%) [69-72]. Further, almost 75% of participants were at high nutrition risk and almost half were food insecure. No other comprehensive statewide statistics on nutrition risk and food insecurity are available. All six GANSI and GANSI-match questions were found to have responses with significant discordance. These questions included: consuming less than two meals, few fruits and vegetables or few dairy/milk products each day, food insecurity status, eating most meals alone and taking three or more medications daily. Sensitivities and specificities corresponding to GANSI questions were highly variable, ranging from 58.8%-87.1% and 48.7%-96.0%, respectively. This indicates that, assuming the GANSImatch questions serve as a comparative gold standard, some of the GANSI questions were very accurate in detecting nutrition risk (GANSI 2, 7, 8 and 9) while others (GANSI 3 and 4) were less accurate. The sensitivities and specificities other tools used to screen for nutrition risk in community-dwelling older adults range from 84-96% and 13-39

48 98%, respectively [34, 36]. However, like the NSI checklist, these tools have not been validated for use in an OAANP-representative population. Again, no standards have been set as to what should be considered an acceptable sensitivity or specificity so it is not possible to dictate which individual questions must be revisited. The most frequently mismatched questions were those regarding number of meals consumed and number of fruits and vegetables or dairy servings daily. The average daily fruit and vegetable consumption in this study was 3.4 ± 1.8 servings, well below the recommendations, while 24.5% consumed five or more servings daily, the minimum recommended by the 2005 Dietary Guidelines for Americans and by the American Cancer Society [64, 73]. Similar intake findings have previously been reported with 29% of older Georgians consuming five or more daily fruit and vegetable servings [74] and 3.4 average daily servings in a group of Georgia OAANP participants [58]. One of the strongest predictors of increasing fruit and vegetable consumption is knowledge of the recommended levels of intake [45, 75]. A study by Hendrix and colleagues (2008) reported that only 7% of OAANP participants in Georgia knew the recommended intakes of fruits and vegetables, based on the energy requirements for most older adults (7 to 10 servings daily) [45]. There are several possible causes for the provision of mismatched answers; nonspecificity in question wording is one. As mentioned previously, knowledge of the recommended intakes is required in order to provide an accurate response to food intake questions. The GANSI and standard NSI checklist questions referencing intake are worded I eat few This phrasing does not give any indication of how many servings of each food group would be considered few. When comparing GANSI and 40

49 GANSI-match responses, the only non-discordant answer pair was GANSI 3 (few fruits or vegetables) and GANSI-match 3 with an intake of less than four servings, which may indicate the belief that an intake of three servings daily is sufficient. The same can be said for knowledge regarding GANSI 4 (few dairy products) as the number of people reporting few dairy products daily on the GANSI (n = 528) falls between GANSI-match response categories of less than two (n = 624) and less than one (n = 172) servings daily. Another possible cause of answer discordance is a low level of health literacy in those completing the survey questions. Although the NSI checklist was written at a fourth-grade to sixth-grade reading level [13] and the tools used to create the GANSImatch questions had been validated for use in older adults, those seeking participation in the OAANP may not be able to read or comprehend the questions. For instance, misunderstanding what information is requested may impact responses to the standard NSI checklist question 2: I eat few fruits or vegetables, or milk products. It may be construed to mean: I eat few fruits OR I eat few vegetables OR I eat few milk products. If someone thought they had enough fruits (e.g.: four servings), but too few vegetables (e.g.: three servings), it may yield a false positive response as they are actually consuming an adequate number of fruit and vegetable servings. Specific participant characteristics were found to be linked to an increased risk of providing discordant responses. Results from the logistic regression analysis in this study lend support to the existence of these problems in our study population as black participants were two-times more to provide discordant responses to GANSI 2 and its match (fewer than two meals daily) and 2.7-times more likely to provide a greater 41

50 number of discordant responses as white participants (p < 0.05). Also, individuals with very low food security were almost four-times more likely than food secure individuals to provide discordant responses to GANSI 2 and its match (p < 0.05). Additional analysis showed that participants who were black and had very low food security were more likely than those who did not have these two characteristics to provide mismatched answers to GANSI 2 and its match. This finding indicates a compounding negative effect of these characteristics and further illustrates the need for a survey tool validated in this population. Participants who had very low food security were significantly more likely to be age 60-74, have an income less than 130% of the federal poverty and were less likely to have received a high school diploma (p < 0.05) (data not shown). Two of these three characteristics are linked to lower literacy levels and would support the notion that participants with very low food security would provide discordant responses to questions with possible comprehension issues, such as GANSI 3 and 4 (related to fruit and vegetable or dairy intake, respectively). Conversely, participants with very low food security were 46-71% less likely to mismatch responses to GANSI 3 and 4 when the highest cutoffs representing few servings were used, five fruit and vegetable servings and three dairy servings, respectively. One possible explanation is that those who have very low food security are more aware of their dietary intake and were therefore more likely to provide consistent responses to these types of questions. These results are inconsistent with recent research indicating that very low food security is associated with lower cognitive performance in adults [42]. The potential reason for our finding is not clear. 42

51 This study had several strengths. It was a large study (n = 924) with a larger number of participants than the two validation studies completed by Posner and colleagues (n = 749) and Sahyoun and colleagues (n = 581) [8, 14] as well as the study completed in HDM applicants by Coulston and colleagues (n = 230) [33]. Further, the study population represented the targeted participants for the OAANP. Previous studies regarding the use of the NSI checklist as a screening tool were neither representative of the national population of older adults nor of the OAANP target audience as they were predominantly white, middle-class, educated older adults [8, 14]. The use of participants in the OAANP and those on the waitlist for both CM and HDM allowed for a demographic and socioeconomic comparison of these groups providing data lacking in current literature. This comprehensive descriptive data will also be used as baseline data for a longitudinal study investigating the impact of OAANP participation or nonparticipation over time. There are limitations to the present study as well. First, it was not originally created to measure the validity or reliability of the NSI checklist as a nutrition screening tool so matching data was not able to be provided for all NSI checklist questions. However, the standard checklist questions that can be directly impacted by participation in either program within the OAANP (eating fewer than two meals daily, eating few or vegetables, eating few dairy products) were assessed. Selection bias may also exist as those who were not English-literate and those with legal blindness were excluded from study participation. Those who are non-english speakers represent a growing portion of the older adult population and may be more likely than others to provide discordant responses to GANSI and GANSI-match questions. Also, several factors, such as poor 43

52 health literacy or low educational attainment, may have affected survey response rates. However, no data is available regarding the characteristics of those who did not return the survey so no correlations can be made between participant characteristics and nonresponsiveness. For a screening tool to provide valid answers it must be studied and proven in the target population; the current NSI checklist lacks such validation in a diverse, OAANPrepresentative population. All six of the GANSI questions produced responses found to be discordant with answers given to GANSI-match questions (all p < 0.01). Survey respondents with specific demographic or economic characteristics (being black, living alone, having very low food security) were more or less likely to provide discordant responses, indicating a need for more research relating to the use of the NSI checklist in these populations. The delineation of the questions to which discordant responses are most likely to be provided and the identification of the characteristics of those likely to provide discordant responses are important; they serve as a starting point from which more targeted evaluations of the validity of the NSI checklist can be developed. Also, should a lack of knowledge regarding recommended intakes be one of the primary causes for discordance among dietary intake questions, an intervention to increase the knowledge of the recommended intakes in those seeking to participate in the OAANP may improve answer agreement. Several interventions have shown that nutrition education is an effective way to improve the dietary intake of older adults, especially when the education is presented as a series on specific topics, like recommended intakes of fruits and vegetables or dairy products [45, 58, 76, 77]. Also, the health literacy of those seeking the OAANP must be determined. Screening and educational 44

53 tools used with this population of older adults may require adjustments in order to ensure that they are understood by those whom they are intended to help. The results presented here indicate that the current form of the NSI checklist may not be the best method of assessing nutrition risk in those seeking OAANP participation as some questions may not provide an accurate reflection of nutrition risk. Based on questions assessed in this study, GANSI 3 and 4 would be the primary targets for revision. Further investigation must be completed if this screening tool is to continue as the primary method of assessing the nutrition risk status of this high-risk group of older adults. 45

54 Table 3.1: GANSI a Questions and GANSI-match Data Question GANSI 1: I have an illness or condition that made me change the kind and/or amount of food I eat (2 points). GANSI 2: I eat fewer than 2 meals per day (3 points). GANSI 3: I eat few fruits or vegetables (1 point). GANSI 4: I eat few dairy/milk products (1 point). GANSI 5: I have 3 or more drinks of beer, liquor, or wine almost every day (2 points). Survey Data to Serve as a Match N/A 1 Question: How many meals do you usually eat each day (including Home Delivered/Congregate Meals)? 3 Questions: How many servings of fruit/potatoes/vegetables do you usually eat each day? Three cutoff levels used: 5 daily, 4 daily, 3 daily 1 Question: How many servings of milk, cheese, or yogurt do you usually have each day? Two cutoff levels used: 3 daily, 2 daily, 1 daily N/A GANSI 6: I have tooth or mouth problems that make it hard for me to eat (2 points). GANSI 7: I don't always have enough money to buy the food I need (4 points). GANSI 8: I eat alone most of the time (1 point). GANSI 9: I take 3 or more prescribed or over-thecounter drugs a day (1 point). GANSI 10: Without meaning to, I have lost or gained 10 pounds in the last 6 months (2 points). GANSI 11: I am not always physically able to shop, cook, and/or feed myself (2 points). N/A Compared to food insecurity level as measured by the modified 6-item Household Food Security Survey Module (range 0-6). If High/Moderate food security (0-1), match to "no" else if Low/Very Low food security (2-6), match to "yes." 2 Questions: About how many meals do you eat alone in a day? and How many meals do you usually eat each day (including Home Delivered/Congregate Meals)? If eats alone more than half of meals consumed, match to "yes" else if eats alone half or less of meals consumed, match to "no." 2 Questions: How many different medications are currently prescribed for you? and About how many different over-the-counter medications (OTC) (Examples: aspirin, Colace, ibuprofen) do you take every day? NOTE: Some survey data was modified (i.e.: converted from 3-7 answer categories to two) to match the answer categories of the NSI questions. a GANSI = Georgia Nutrition Screening Initiative Checklist N/A N/A 46

55 Table 3.2: Baseline Characteristics of Participants TOTAL SAMPLE CM a CM WL b HDM c HDM WL d (n=924, 100%) (n=155, 16.8%) (n=24, 2.6%) (n=247, 26.7%) (n=498, 53.9%) Percent (%) or Percent (%) or Percent (%) or Percent (%) or Percent (%) or p-value e Characteristic Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Age Category (in years) 75.0 ± ± ± ± ± f Gender Female Male Race < White Black Other/ Did not disclose Live Alone Yes No Rural/Urban < Urban Urbanizing Suburban Rural growth Rural declining Education Level < Less than high school diploma High school diploma or above Self-Reported Health < Good-Excellent Poor-Fair

56 TOTAL SAMPLE (n=924, 100%) CM (n=155, 16.8%) CM WL (n=24, 2.6%) HDM (n=247, 26.7%) HDM WL (n=498, 53.9%) p-value Percent (%) or Percent (%) or Percent (%) or Percent (%) or Percent (%) or Characteristic Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Poverty Rates < FPL g FPL to <130% FPL % to <185% FPL %+ FPL Food Security < High or Marginal Low Very Low a CM=Congregate Meals Participants b CM WL=Congregate Meals Waitlisted c HDM=Home Delivered Meals Participant d HDM WL=Home Delivered Meals Waitlisted e p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables. f Percentages rounded to the nearest tenth. g FPL=Federal Poverty Level 48

57 Table 3.3: GANSI a Question Responses, by OAANP b Group (n=924) Yes (%) or Mean ± SD CM c CM WL d HDM e HDM WL f p-value g Question TOTAL (n=155) (n=24) (n=247) (n=498) GANSI 1: I have an illness or condition that made me change the kind and/or amount of food I eat h GANSI 2: I eat fewer than 2 meals per day < GANSI 3: I eat few fruits or vegetables GANSI 4: I eat few dairy/milk products GANSI 5: I have 3 or more drinks of beer, liquor, or wine almost every day. GANSI 6: I have tooth or mouth problems that make it hard for me to eat. GANSI 7: I don t always have enough money to buy the food I need < GANSI 8: I eat alone most of the time GANSI 9: I take 3 or more prescribed or over the counter drugs a day. GANSI 10: Without meaning to, I have lost or gained 10 pounds in the past 6 months < GANSI 11: I am not always physically able to shop, cook, and/or feed myself < NSI Risk (score range: 0-21) 9.0 ± ± ± ± ± 4.5 Low (score 0-2) Moderate (score 3-5) High (score 6+) a GANSI=Georgia Nutrition Screening Initiative Checklist b OAANP=Older Americans Act Nutrition Program c CM=Congregate Meals Participants d CM WL=Congregate Meals Waitlisted < <

58 e HDM=Home Delivered Meals Participant f HDM WL=Home Delivered Meals Waitlisted g p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables h Percentages rounded to the nearest tenth 50

59 Table 3.4: Participant Characteristics, by NSI a Risk Level (n=924) NSI Risk Level Low (n=68) Moderate (n=175) High (n=681) p-value b Characteristic Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Age (in years) 78.8 ± ± ± 9.1 < c < Gender Female Male Race White Black Other/ Did not disclose Live Alone Yes No Rural/Urban Urban Urbanizing Suburban Rural growth Rural declining Education Level Less than high school diploma High school diploma or above Self-Reported Health < Good-Excellent Poor-Fair

60 NSI Risk Level Low (n=68) Moderate (n=175) High (n=681) p-value Characteristic Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Poverty Rates < < FPL d FPL to <130% FPL % to <185% FPL %+ FPL Food Security (0-6 points) < High or Marginal (0-1) Low (2-4) Very Low (5-6) Group < CM e CM WL f HDM g HDM WL h a NSI=Nutrition Screening Initiative Checklist b p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables c Percentages rounded to the nearest tenth d FPL=Federal Poverty Level e CM=Congregate Meals Participants f CM WL=Congregate Meals Waitlisted g HDM=Home Delivered Meals Participants h HDM WL=Home Delivered Meals Waitlisted 52

61 Table 3.5: Fruit and Vegetable and Dairy Intake, by GANSI a Responses (n=924) Question GANSI 3: few fruits and vegetables? (%) p-value b yes (n=527) no (n=397) Total Sample 57.0 c 43.0 Servings Daily (n=924) by % of n=527 by % of n= Median Daily (Range) 3.0 (0-10) 3 (0-10) 4 (0-10) < GANSI 4: few dairy/milk products? (%) yes (n=528) no (n=396) Total Sample Servings Daily (n=924) by % of n=528 by % of n= Median Daily 1 (0-3) 1 (0-3) 1 (0-3) < (Range) a GANSI=Georgia Nutrition Screening Initiative Checklist b p-values in relation to results of Mann-Whitney U Test for non-normally distributed data c percentages rounded to the nearest tenth 53

62 Table 3.6: GANSI a -match Responses, by OAANP b Group (n=924) Yes (%) CM c CM WL d HDM e HDM WL f p-value g NSI Question TOTAL (n=155) (n=24) (n=247) (n=498) GANSI-match 2 (less than two meals) 8.3 h GANSI-match 3 (few fruits and vegetables), < < GANSI-match 3 (few fruits and vegetables), < < GANSI-match 3 (few fruits and vegetables), < < GANSI-match 4 (few dairy), < GANSI-match 4 (few dairy), < GANSI-match 4 (few dairy), < GANSI-match < (enough money for food) GANSI-match (most meals alone) GANSI-match < (3+ medications daily) a GANSI=Georgia Nutrition Screening Initiative Checklist b OAANP=Older Americans Act Nutrition Program c CM=Congregate Meals Participants d CM WL=Congregate Meals Waitlisted e HDM=Home Delivered Meals Participant f HDM WL=Home Delivered Meals Waitlisted g p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables h percentages rounded to the nearest tenth 54

63 Table 3.7: Characteristics of Respondents with Mismatched Data Responses, by Number of Mismatched Responses (n=924) Total Sample with no Total Sample with mismatch 2 mismatches 3 mismatches 4-6 mismatches mismatches mismatches (n=212) (n=355) (n=226) (n=79) (n=52) (n=872) p-value a Characteristic Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Age (in years) 75.7 ± ± ± ± ± ± b Gender Female Male Race * c * < White Black Other/ Did not disclose Live Alone < Yes No Rural/Urban Urban Urbanizing Suburban Rural growth Rural declining

64 Total Sample with no mismatches (n=52) Percent (%) or Mean ± SD Total Sample with 1-6 mismatches (n=872) Percent (%) or Mean ± SD 1 mismatch (n=212) 2 mismatches (n=355) 3 mismatches (n=226) 4-6 mismatches (n=79) p-value Percent (%) or Percent (%) or Percent (%) or Percent (%) or Characteristic Mean ± SD Mean ± SD Mean ± SD Mean ± SD Education Level Less than high school diploma High school diploma or above Self-Reported Health Good-Excellent Poor-Fair Poverty Rates < FPL c FPL to <130%FPL % to <185% FPL %+ FPL Group CM d CM WL e HDM f HDM WL g a p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables b percentages rounded to the nearest tenth c FPL=Federal Poverty Level d CM=Congregate Meals Participants e CM WL=Congregate Meals Waitlisted f HDM=Home Delivered Meals Participants g HDM WL=Home Delivered Meals Waitlisted * p < 0.05 in comparison of characteristics between those with zero and those with any mismatches 56

65 Table 3.8: Comparison of Answers to Six GANSI a Questions Against Matched Survey Data, with McNemar s Test (n=924) Percent (%) Yes No p-value b sensitivity c specificity Less than two meals daily? GANSI d 76.1 GANSI-match Too few fruits and vegetables? < % 81.8% GANSI GANSI-match 3 (<5 daily) < % 60.6% GANSI-match 3 (<4 daily) % 56.4% GANSI-match 3 (<3 daily) < % 52.4% Too few dairy? GANSI GANSI-match 4 (<3 daily) < % 67.8% GANSI-match 4 (<2 daily) < % 61.7% GANSI-match 4 (<1 daily) < % 48.7% Food Insecure? GANSI GANSI-match Eat most meals alone? GANSI GANSI-match Take 3+ medications daily? % 86.9% < % 96.0% GANSI GANSI-match < % 67.3% a GANSI = Georgian Nutrition Screening Initiative Checklist b p-values in relation to results of McNemar s Test for discordance c sensitivity and specificity were calculated using GANSI-match data as the gold standard against which GANSI responses were compared d percentages rounded to the nearest tenth 57

66 Table 3.9: Characteristics of Respondents with Mismatched Data Responses, by GANSI a Question (n=924) GANSI 2 (n=164) GANSI 3 w/ cutoff 5 F&V b (n=349) GANSI 3 w/ cutoff 3 F&V (n=367) GANSI 4 w/ cutoff 3 dairy (n=375) GANSI 4 w/ cutoff 2 dairy (n=326) GANSI 4 w/ cutoff 1 dairy (n=416) GANSI 7 (n=165) GANSI 8 (n=162) GANSI 9 (n=140) Characteristic Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD ** * * * * Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Age (in years) 72.8 ± ± ± ± ± ± ± ± ± 9.2 ** ** * * c Gender Female Male Race *** ** ** ** White Black Other/Did not disclose Live Alone *** Yes No Rural/Urban Urban Urbanizing Suburban Rural growth Rural declining Education Level * * Less than high school diploma High school diploma or above

67 GANSI 2 (n=164) GANSI 3 w/ cutoff 5 F&V (n=349) GANSI 3 w/ cutoff 3 F&V (n=367) GANSI 4 w/ cutoff 3 dairy (n=375) GANSI 4 w/ cutoff 2 dairy (n=326) GANSI 4 w/ cutoff 1 dairy (n=416) GANSI 7 (n=165) GANSI 8 (n=162) GANSI 9 (n=140) Characteristic Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Percent (%) or Mean ± SD Self-Reported Health * ** ** * * Percent (%) or Mean ± SD Percent (%) or Mean ± SD Good-Excellent Poor-Fair Poverty Rates * * * ** < FPL d FPL to <130% FPL % to <185% FPL %+ FPL Food Security *** *** *** ** *** *** * High or Marginal Low Very low Group * ** CM e CM WL f HDM g HDM WL h a GANSI =Georgia Nutrition Screening Initiative Checklist b F & V=Fruits and Vegetables c percentages rounded to the nearest tenth d FPL=Federal Poverty Level e CM=Congregate Meals Participants f CM WL=Congregate Meals Waitlisted g HDM=Home Delivered Meals Participants h HDM WL=Home Delivered Meals Waitlisted * p < 0.05, ** p < 0.01, *** p < Asterisk placed above column to which it refers. p-values are for related test statistic: ANOVA was used for analysis of continuous variables, Chi-Square test was used for categorical variables 59

68 Table 3.10: Multivariate Logistic Regression of Characteristics Related to Mismatching Questions, by Number of Mismatches (n=924) Groups: 0, 1-2, 3+ Mismatches Characteristic Odds Ratio (95% Confidence Interval) Age (in years) (1.06, 2.28) a (1.05, 2.32) 85+ reference Gender Female 1.12 (0.83, 1.51) Male reference Race White reference Black 2.70 (1.93, 3.77) * Other/Did not disclose 1.84 (1.22, 2.77) Live Alone Yes 0.59 (0.45, 0.79) * No reference Rural/Urban Urban reference Urbanizing 1.38 (0.90, 2.13) Suburban 1.40 (0.96, 2.03) Rural growth 1.33 (0.82, 2.14) Rural declining 1.01 (0.61, 1.67) Education Level Less than high school diploma 1.16 (0.87, 1.55) High school diploma or above reference Self-Reported Health Good-Excellent reference Poor-Fair 1.14 (0.82, 1.59) Poverty Rates < FPL b 1.39 (0.92, 2.11) FPL to <130%FPL 1.34 (0.83, 2.15) 130% to <185% FPL 0.95 (0.59, 1.53) 185%+ FPL reference Program CM/CM WL c reference HDM/HDM WL d 0.94 (0.63, 1.40) Participation Status Participant reference Waitlisted 0.78 (0.57, 1.06) a percentages rounded to the nearest hundredth b FPL=federal poverty level c CM=Congregate Meals Participants, CM WL=Congregate Meals Waitlisted d HDM=Home Delivered Meals Participants, HDM WL=Home Delivered Meals Waitlisted * p-value <

69 Table 3.11: Multivariate Logistic Regression of Characteristics Related to Mismatching Questions, by GANSI a Question GANSI 3 w/ GANSI 3 w/ GANSI 4 w/ GANSI 4 w/ GANSI 4 w/ GANSI 2 cutoff cutoff cutoff cutoff cutoff (n=164) 5 F&V b (n=349) 3 F&V (n=367) 3 dairy (n=375) 2 dairy (n=326) 1 dairy (n=416) Characteristic OR (95% CI) c OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Age (in years) (0.96, 3.13) d 0.98 (0.67, 1.45) 1.34 (0.90, 1.98) 0.77 (0.52, 1.13) 0.73 (0.50, 1.09) 1.46 (0.99, 2.15)* (0.76, 2.58) 1.02 (0.69, 1.51) 1.30 (0.87, 1.93) 1.02 (0.69, 1.51) 0.71 (0.48, 1.05) 1.10 (0.74, 1.62) 85+ reference reference reference reference reference reference Gender Female 1.01 (0.68, 1.50) 1.11 (0.82, 1.51) 1.28 (0.95, 1.72) 0.85 (0.63, 1.14) 1.19 (0.88, 1.62) 1.33 (0.99, 1.79) Male reference reference reference reference reference reference Race White reference reference reference reference reference reference Black 2.08 (1.37, 3.17)* 1.32 (0.94, 1.86) 1.19 (0.85, 1.66) 0.73 (0.52, 1.04) 0.91 (0.64, 1.29) 1.06 (0.76, 1.47) Other/ Did not disclose 1.42 (0.83, 2.44) 1.18 (0.77, 1.81) 1.18 (0.78, 1.76) 0.99 (0.65, 1.50) 0.97 (0.63, 1.49) 0.84 (0.56, 1.27) Live Alone Yes 1.25 (0.87, 1.80) 0.87 (0.66, 1.16) 0.85 (0.64, 1.12) 0.73 (0.55, 0.97)* 0.88 (0.66, 1.17) 0.98 (0.75, 1.29) No reference reference reference reference reference reference Rural/Urban Urban reference reference reference reference reference reference Urbanizing 1.07 (0.62, 1.85) 1.09 (0.70, 1.71) 1.22 (0.79, 1.87) 0.92 (0.59, 1.43) 1.05 (0.68, 1.63) 1.02 (0.67, 1.56) Suburban 1.12 (0.79, 1.81) 1.26 (0.86, 1.85) 1.33 (0.92, 1.94) 0.80 (0.55, 1.17) 0.91 (0.62, 1.33) 0.95 (0.66, 1.37) Rural growth 0.87 (0.46, 1.66) 1.54 (0.95, 2.51) 1.09 (0.67, 1.76) 0.91 (0.56, 1.48) 0.92 (0.56, 1.51) 0.87 (0.54, 1.40) Rural declining 0.86 (0.46, 1.70) 1.32 (0.79, 2.19) 1.10 (0.67, 1.81) 1.13 (0.68, 1.89) 1.16 (0.70, 1.93) 0.69 (0.42, 1.14) Education Level Less than high school diploma 1.08 (0.74, 1.59) 0.88 (0.66, 1.19) 1.18 (0.88, 1.57) 1.21 (0.90, 1.63) 1.20 (0.89, 1.62) 1.11 (0.83, 1.48) High school diploma or above reference reference reference reference reference reference Self-Reported Health Good-Excellent reference reference reference reference reference reference Poor-Fair 1.29 (0.82, 2.05) 0.90 (0.65, 1.26) 0.81 (0.59, 1.13) 0.78 (0.56, 1.09) 0.74 (0.53, 1.03) 1.23 (0.88, 1.71) 61

70 GANSI 2 (n=164) GANSI 3 w/ cutoff 5 F&V (n=349) GANSI 3 w/ cutoff 3 F&V (n=367) GANSI 4 w/ cutoff 3 dairy (n=375) GANSI 4 w/ cutoff 2 dairy (n=326) GANSI 4 w/ cutoff 1 dairy (n=416) Characteristic OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Poverty Rates < FPL e 1.41 (0.73, 2.74) 0.98 (0.65, 1.48) 1.03 (0.68, 1.57) 0.94 (0.62, 1.43) 0.76 (0.50, 1.15) 1.38 (0.91, 2.11) FPL to <130%FPL 1.27 (0.62, 2.59) 0.92 (0.57, 1.49) 1.20 (0.75, 1.92) 1.12 (0.69, 1.80) 0.94 (0.58, 1.51) 1.37 (0.85, 1.21) 130% to <185% FPL 1.54 (0.75, 3.16) 0.81 (0.50, 1.29) 0.90 (0.56, 1.45) 1.03 (0.64, 1.65) 0.84 (0.52, 1.36) 1.16 (0.69, 1.79) 185%+ FPL reference reference reference reference reference reference Food Security High or Marginal reference reference reference reference reference reference Low 1.67 (1.08, 2.65) 0.73 (0.53, 1.01) 1.30 (0.94, 1.79) 0.60 (0.43, 0.83)* 0.95 (0.69, 1.33) 1.37 (1.00, 1.89) Very Low 3.86 (2.38, 6.24)* 0.29 (0.18, 0.46)* 0.86 (0.59, 1.33) 0.38 (0.24, 0.58)* 0.54 (0.34, 0.83)* 1.45 (0.98, 2.15) Program CM/CM WL f reference reference reference reference reference reference HDM/HDM WL g 1.02 (0.57, 1.84) 1.15 (0.77, 1.74) 1.00 (0.67, 1.49) 0.67 (0.44, 1.00)* 0.70 (0.47, 1.05) 1.16 (0.78, 1.75) Participation Status Participant reference reference reference reference reference reference Waitlisted 1.21 (0.79, 1.86) 1.09 (0.79, 1.50) 0.85 (0.62, 1.16) 0.93 (0.67, 1.28) 1.04 (0.75, 1.45) 0.88 (0.64, 1.20) a GANSI =Georgia Nutrition Screening Initiative Checklist b F&V=Fruits and Vegetables c OR=Odds Ratio, CI=Confidence Interval d percentages rounded to the nearest hundredth e FPL=Federal Poverty Level f CM=Congregate Meals Participants, CM WL=Congregate Meals Waitlisted g HDM=Home Delivered Meals Participants, HDM WL=Home Delivered Meals Waitlisted * p-value <

71 CHAPTER 4 CONCLUSIONS This cross-sectional study examined the ability of the NSI checklist to evaluate nutrition risk status in older Georgians seeking to participate in the OAANP. The hypothesis was that data collected using the GANSI checklist would differ with statistical significance from GANSI-match data gathered from additional survey questions and that certain participant characteristics (those 85 and older, minorities, less than high school education, fair to poor self-reported health) would increase the likelihood of providing mismatched answers; OAANP program type and participation status would not impact these associations. The specific aims were: 1) characterize OAANP participants and waitlisted persons, 2) determine the degree of discordance, if any, between answers given to GANSI checklist and GANSI-match survey questions and 3) determine which, if any, participant characteristics were associated with an increased/decreased likelihood of providing discordant responses. The major findings of this study include: 1) the high likelihood of discordance in responses to GANSI and GANSI-match questions regarding food intake and the identification of characteristics, primarily race and food security, that strongly impact the risk of providing mismatched responses. In addition, this study provided data regarding the characteristics of each of the OAANP programs (CM and HDM) and the waitlists for each program. Those receiving HDM were more likely to be older and report fair to poor health while those on the HDM WL were more likely to be less educated and food 63

72 insecure, and report fair to poor health (all p < 0.05). People on the CM WL were the youngest, most likely to not disclose their race, to live in rural areas, report incomes below the FPL and, paradoxically, to be food secure (all p < 0.05). CM participants most likely to be white, have attained at least a high school diploma, report good to excellent health and have incomes < 185% FPL (all p < 0.05). Approximately 60% of those in the high risk group were on the HDM WL while around 29% received HDM; CM participants and members of the CM WL were likely to be at low to moderate risk when compared to people receiving HDM or on the HDM WL (p < 0.001). All six GANSI and GANSI-match questions were found to have responses with significant discordance (all p < 0.01). These questions included: consuming less than two meals, few fruits and vegetables or few dairy/milk products each day, food insecurity status, eating most meals alone and taking three or more medications daily (all p < 0.01). The most frequently mismatched questions were those regarding number of meals consumed and number of fruits and vegetables or dairy servings daily. There are several possible causes for the provision of mismatched answers; non-specificity in question wording is one. Knowledge of the recommended intakes is required in order to provide an accurate response to food intake questions. The GANSI and standard NSI checklist questions referencing intake are worded I eat few This phrasing does not give any indication of how many servings of each food group would be considered few. Further, although the NSI checklist was written at a fourth-grade to sixth-grade reading level [13] and the tools used to create the GANSI-match questions had been validated for use in older adults (described previously), those seeking participation in the OAANP may not be able to read or comprehend the questions. The 2003 National Assessment 64

73 of Adult Literacy showed that low income, education less than a high school diploma, being over 65 years of age and being a minority increase the risk of having below basic health literacy levels, which strongly impacts the ability to read and understand documents such as the NSI checklist [46]; these characteristics closely describe the target group for the OAANP. As a result, many questions may be misinterpreted and answers given to these questions may be unreliable. Results from the logistic analysis in this study indicated that specific participant characteristics were linked to an increased risk of providing discordant responses. Black participants were two-times as likely to provide discordant responses to GANSI 2 and its match (fewer than two meals daily) and 2.7-times as likely to provide a greater number of discordant responses as white participants (p < 0.05). Also, individuals with very low food security were almost four-times as likely as food secure individuals to provide discordant responses to GANSI 2 and its match (p < 0.05). Participants who had very low food security were significantly more likely to be age 60-74, have an income less than 130% of the federal poverty and were less likely to have received a high school diploma (p < 0.05) (data not shown). Two of these three characteristics are linked to lower literacy levels and would support the notion that participants with very low food security would provide discordant responses to questions with possible comprehension issues, like GANSI 3 and 4. Our results showed the opposite: participants with very low food security were 46-71% less likely to mismatch responses to GANSI 3 and 4 when the highest cutoffs representing few servings were used, five fruit and vegetable servings and three dairy servings, respectively. One possible explanation is that those who have very low food security are more aware of 65

74 their dietary intake and were therefore more likely to provide consistent responses to these types of questions. These results are inconsistent with recent research indicating that very low food security is associated with lower cognitive performance in adults [42]. A search of the available literature regarding food security failed to provide an explanation for this phenomenon. For a screening tool to provide valid answers it must be studied and proven in the target population; the current NSI checklist lacks such validation in a diverse, OAANPrepresentative population. The delineation of the questions to which discordant responses are most likely to be provided and the identification of the characteristics of those likely to provide discordant responses are important; they serve as a starting point from which more targeted evaluations of the validity of the NSI checklist can be developed. Also, should a lack of knowledge regarding recommended intakes be one of the primary causes for discordance among dietary intake questions, an intervention to increase the knowledge of the recommended intakes in those seeking to participate in the OAANP may improve answer agreement. Also, the health literacy of those seeking the OAANP must be determined. Screening and educational tools used with this population of older adults may require adjustments in order to ensure that they are understood by those the tools are intended to help. The results presented here indicate that the NSI checklist, as it is currently written, may not be the best method of assessing nutrition risk in those seeking OAANP participation. Further investigation must be completed if this screening tool is to continue as the primary method of assessing the nutrition risk status of this high-risk group of older adults. 66

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82 Appendix A Georgia Advanced POMP6 survey: Home-delivered meals participants 74

83 H o m e - D e l i v e r e d M e a l s P a r t i c i p a n t S u r v e y L/0 MARKING INSTRUCTIONS *Please use a pencil or blue or black ink pen only and fill in answer circles completely. Use a No. 2 pencil or a blue or black ink pen only. *Erase Do not use completely pens with if ink needed. that soaks through the paper. *Write Make solid comments marks that only fill the in response the boxes completely. provided. Make no stray marks on this form. CORRECT: INCORRECT: Office ID NUMBER Use Only # I D > Health-Related Questions 1. Would you say that in general your health is... 1 Excellent 2 Very Good 3 Good 4 Fair 5 Poor 2. Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health n o t good? 3. Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health n o t good? 4. During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self care, work or recreation? During the past 6 months, about how many different times did you stay In the hospital overnight or longer? 6. In a rehabilitation or nursing facility ( E x a m p l e: for recovery after a surgery)? Never 1-2 times 3-4 times 5 or more times Page 1 of See reverse side