Initially, 483 studies were identified through an electronic database search. After duplicates were removed (n = 54), the titles and abstracts were screened. Completed the entire screening process, 391 studies were excluded. The main reasons for the exclusion of studies were: i) did not consider the research scope (n=335); ii) study contexts were exclusively hospital/nursing homes /inpatient (n=32); iii) followed systematic review and meta-analysis methodology (n = 21); iv) include articles published in languages excluded (n=1); v) opinion´s articles (n=1); vi) book chapters (n=1). Of the 38 full-text articles assessed for eligibility, 13 met the inclusion criteria. References of these studies were manually analyzed, resulting in 3 additional studies. In total, 16 studies were identified for this scoping review (Fig.1).
Characteristics of included studies
Country of origin, year of publication, and sample sizes. The last 10 years (between 2011 and 2021) evoke research interest in the topic across a broad geographical context, most notably (18.75%) in Belgium (Geurden et al., 2015; Hegendörfer et al., 2020; Vandewoude et al., 2019) and (12.5%) in the USA (Ahmed et al., 2018; Yang et al., 2011), followed by (0.06%), respectively in Saudi Arabia (Alhamadan et al., 2019), Spain (Galiot et al., 2015), Slovenia (Klemenc-Ketis et al., 2020), India (Krishnamoorthy et al., 2017), Italy (Mastronuzzi et al., 2015), Denmark (Pedersen et al., 2016), Australia (Preston et al., 2018), Mexico (Rodriguez-Tadeo et al., 2012), Netherlands (Schilp et al., 2014), Sweden (Shakersain et al., 2016) and Lithuania (Spirgienė et al., 2018). Sample sizes of the included studies ranged from a minimum of n=57 (Galiot et al., 2015) to, maximum of n=15.121.31 (Ahmed et al., 2018).
Sample characteristics. The sample characteristics of the included studies reveal that the age of the participants ranged between 60 (Klemenc-Ketis et al., 2020; Krishnamoorthy et al., 2017; Shakersain et al., 2016) and 99 years (Klemenc-Ketis et al., 2020). Most of the malnourished elderly were female (Ahmed et al., 2018; Alhamadan et al., 2019; Galiot et al., 2015; Geurden et al., 2015; Hegendörfer et al., 2020; Klemenc-Ketis et al., 2020; Krishnamoorthy et al., 2017; Pedersen et al., 2016; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Schilp et al., 2014; Shakersain et al., 2016; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011), lived with another person (Alhamadan et al., 2019; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Spirgienė et al., 2018), and had associated comorbidities (Ahmed et al., 2018; Klemenc-Ketis et al., 2020; Krishnamoorthy et al., 2017; Pedersen et al., 2016; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016; Vandewoude et al., 2019; Yang et al., 2011). It should be noted that given the other terms that we previously established in the analysis of the sample characteristics, only two studies considered the assessment of the socioeconomic level of the participants (Krishnamoorthy et al., 2017; Alhamadan et al., 2019) five studies reveal the educational level (Yang et al., 2011; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016; Krishnamoorthy et al., 2017; Alhamadan et al., 2019) and two consider the race (Ahmed et al., 2018; Yang et al., 2011).
Design. The research design was mostly (75%) observational, cross-sectional descriptive studies (Ahmed et al., 2018; Alhamadan et al., 2019; Galiot et al., 2015; Hegendörfer et al., 2020; Klemenc-Ketis et al., 2020; Krishnamoorthy et al., 2017; Mastronuzzi et al., 2015; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011), followed by (18.7%) quantitative studies (Geurden et al., 2015; Pedersen et al., 2016; Schilp et al., 2014), and 0.06% mixed (Preston et al., 2018). The dissemination of the articles was essentially (56.2%) from the health area (Ahmed et al., 2018; Geurden et al., 2015; Hegendörfer et al., 2020; Krishnamoorthy et al., 2017; Preston et al., 2018; Schilp et al., 2014; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011), with 43.8% from the nutrition scope (Alhamadan et al., 2019; Galiot et al., 2015; Klemenc-Ketis et al., 2020; Mastronuzzi et al., 2015; Pedersen et al., 2016; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016).
Procedures. Fifteen samples of malnourished elderly were obtained based on validated screening instruments (Alhamadan et al., 2019; Galiot et al., 2015; Geurden et al., 2015; Hegendörfer et al., 2020; Klemenc-Ketis et al., 2020; Krishnamoorthy et al., 2017; Mastronuzzi et al., 2015; Pedersen et al., 2016; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Schilp et al., 2014; Shakersain et al., 2016; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011) and only one study (Ahmed et al., 2018) based on the clinical diagnosis made by the physician, reported in the elderly patient's clinical file.
Measures. Twelve studies used the Mini Nutritional Assessment (MNA) for the diagnosis of malnutrition in the elderly (Alhamadan et al., 2019; Galiot et al., 2015; Hegendörfer et al., 2020; Krishnamoorthy et al., 2017; Mastronuzzi et al., 2015; Pedersen et al., 2016; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011). Of highlighting those nine studies used it exclusively for the sample selection (Galiot et al., 2015; Krishnamoorthy et al., 2017; Mastronuzzi et al., 2015; Pedersen et al., 2016; Rodriguez-Tadeo et al., 2012; Shakersain et al., 2016; Spirgienė et al., 2018; Vandewoude et al., 2019; Yang et al., 2011). Hegendörfer et al., (2020) used it combined with pre-albumin levels, and Preston et al. (2018) used it in association with three other screening instruments (namely, Australian Nutritional Screening Initiative – ANSI; Malnutrition Universal Screening Tool – MUST and Malnutrition Screening Tool - MST). Schilp et al. (2014) appealed to Short Nutritional Assessment Questionnaire 65+ (SNAQ 65+). Galiot, Torrado and Cambrodón, (2015) and Klemenc-Ketis et al. (2020) just used MUST. Ahmed et al. (2018) does not consider the screening instrument. The study selected malnourished participants based on the clinical diagnosis in the medical record (ICD-9 and 10).
Elderly´s health outcomes. Concerning health outcomes in the elderly at risk of malnutrition, analysis of the articles reveals a greater interest (37.5%) in studying the phenomenon associated with morbidity (Galiot et al., 2015; Klemenc-Ketis et al., 2020; Mastronuzzi et al., 2015; Preston et al., 2018; Rodriguez-Tadeo et al., 2012; Spirgienė et al., 2018), followed by mortality (31.25%) (Ahmed et al., 2018; Hegendörfer et al., 2020; Mastronuzzi et al., 2015; Shakersain et al., 2016; Yang et al., 2011), (25%) activities day living (ADL) (Alhamadan et al., 2019; Pedersen et al., 2016; Rodriguez-Tadeo et al., 2012; Vandewoude et al., 2019), hospitalization (25%) (Hegendörfer et al., 2020; Mastronuzzi et al., 2015; Pedersen et al., 2016; Yang et al., 2011), social risk (18.75%) (Galiot et al., 2015; Preston et al., 2018; Rodriguez-Tadeo et al., 2012), and lastly health costs and functional capacity (12.5%). In terms of the results, the study of Krishnamoorthy et al. (2017) only carried out the characterization of the sample (highlighting the prevalence of malnutrition in the elderly, without its correlation with the variables under analysis). However, we opted for its inclusion considering the relationship between the diagnosis phase and the value of PHC intervention. Only Schilp et al. (2014) have established the relationship between malnutrition and the quality of life.
Table 1 Description of main sample characteristics, study design, and procedures.
Authors (Year)
|
Sample size (n)
|
Sample characteristics (mean age, socio-economic and educational variables, clinical conditions, gender, living arrangement, and race)
|
Design
|
Procedures (recruitment)
|
Ahmed et al. (2018)
|
n=15.121.131
(801.272) malnourished
|
Malnourished elderly
M = 75.4 years
Social level= NR
Educational level=NR
Clinical conditions = 100% diabetes
Gender= 57.63% women
Living arrangement= NR
Race = 74.93% whites
Normo-nourished elderly
M = 71.3 years
Social level= NR
Educational level=NR
Clinical conditions = 100% diabetes
Gender= 52.79% women
Living arrangement= NR
Race = 79.24% whites
|
Observational retrospective study
|
Clinical diagnosis in the medical record (ICD-9 and 10)
|
Alhamadan et al. (2019)
|
n= 2045
(427.4) risk/malnourished
|
Risk/malnourished Elderly
M = 66.1 years
Social level= 83.7% Not Employed
Educational level= 27% Intermediate & Secondary
Clinical conditions = 63.2% without comorbidities
Gender= 55 % women
Living arrangement= 79.4% married
Race =NR
|
Cross‐sectional descriptive study
|
MNA & BMI
|
Galiot et al. (2015)
|
n= 57
(14.89) risk/malnourished
|
Risk/malnourished Elderly
Age = range from 75 to 80 years (59.65%)
Social level= NR
Educational level= NR
Clinical conditions = 63.2% without comorbidities
Gender= 55 % women
Living arrangement= NR
Race =NR
|
Cross‐sectional descriptive study
|
MNA
|
Geurden et al. (2015)
|
n= 100
(29) risk/malnourished
|
Risk/malnourished Elderly
M = 76.1 years
Social level= NR
Educational level= NR
Clinical conditions = NR
Gender= 86.2 % women
Living arrangement= NR
Race =NR
|
Randomized cross sectional study design
|
MUST
|
Hegendörfer et al. (2020)
|
n=567
(69) were at risk of malnutrition based on MNA and (72) based on pre-albumin
|
Malnourished elderly based on MNA
M = 85.6 years
Social level= NR
Educational level=NR
Clinical conditions = NR
Gender= 72.1% women
Living arrangement=NR
Race = NR
Malnourished elderly based on pre-albumin levels
M = 95.9 years
Social level= NR
Educational level=NR
Clinical conditions = NR
Gender= 76.9% women
Living arrangement=NR
Race = NR
|
Observational prospective cohort study
|
MNA & prealbumin levels
|
Klemenc-Ketis et al. (2020)
|
n= 1641
(216) risk malnourished
|
Risk malnourished Elderly
Age = 60.4 % (range from 20 to 99 years)
Social level= NR
Educational level=NR
Clinical conditions =43.6%
Gender= 68.1% women
Living arrangement=NR
Race =NR
|
Cross-sectional observational study
|
MUST
|
Krishnamoorthy et al. (2017)
|
n=279
(50) risk/malnourished
|
Risk/malnourished Elderly
Age = range from 60 to 70 years (62%)
Social level= 96% unemployed
Educational level= 88% no formal education
Clinical conditions = NR
Gender= 76 % women
Living arrangement= NR
Race =NR
|
Cross‐sectional descriptive study
|
MNA
|
Mastronuzzi et al. (2015)
|
n=274
(99) risk/malnourished
|
Risk/malnourished Elderly
M = 85 years
Social level= NR
Educational level= NR
Clinical conditions = 23.3% dementia
Gender= 63.63% women
Living arrangement= NR
Race =NR
|
Cross‐sectional descriptive study
|
MNA
|
Pedersen et al. (2016)
|
n=208 malnourished
|
Intervention Group
M = 86 years
Social level= NR
Educational level=NR
Clinical conditions =35% comorbidities
Gender= 59% women
Living arrangement=64.5% eat alone
Race = NR
Control Group
M = 86.3 years
Social level= NR
Educational level=NR
Clinical conditions =32% comorbidities
Gender= 55% women
Living arrangement=63% eat alone
Race = NR
|
Randomized study
|
MNA
|
Preston et al. (2018)
|
n=59
(37/59) malnourished based on ANSI
(10/59) malnourished based on MNA
(9/59) malnourished based on MUST
(12/59) malnourished based on MST
|
Malnourished elderly
Age= >=80 years (60%)
Social level= NR
Educational level=NR
Clinical conditions =60% fracture risk
Gender= 60% women
Living arrangement=70% living with another
Race = NR
|
Mixed Study
|
MNA, ANSI, MUST & MST
|
Rodriguez-Tadeo et al. (2012)
|
n=760
(384) risk/malnourished
|
Elderly
Age = range from 70 to 79 years (44.6%)
Social level= NR
Educational level= 41.8% Primary or lower
Clinical conditions = 52.7 % depression
Gender= 63.63% women
Living arrangement= 47.1% married
Race =NR
|
Observational, descriptive, and cross-sectional study
|
MNA
|
Schilp et al. (2014)
|
n=146 malnourished
|
Intervention Group
M = 80.6 years
Social level= NR
Educational level=NR
Clinical conditions =NR
Gender= 62.5% women
Living arrangement=43% living alone
Race = NR
Control Group
M = 80.5 years
Social level= NR
Educational level=NR
Clinical conditions =NR
Gender= 66.2% women
Living arrangement=53% living alone
Race = NR
|
Randomized study
|
SNAQ 65+
|
Shakersain et al. (2016)
|
n=3041
(802) risk/malnourished
|
Risk/Malnourished Elderly
Age = 60-69 years
Social level= NR
Educational level=42.6% High School
Clinical conditions =80.1% vascular disease
Gender= 70.9% women
Living arrangement=65.2% living alone
Race = NR
|
Observational prospective cohort study
|
MNA
|
Spirgienė et al. (2018)
|
n= 169
(82) malnourished
|
Malnourished Elderly
Age = range from 65 to 75 years (55%)
Social level= NR
Educational level=NR
Clinical conditions =NR
Gender= 63.9 % women
Living arrangement= 45% living with another person
Race =NR
|
Cross‐sectional descriptive study
|
MNA
|
Vandewoude et al. (2019)
|
n= 3299
(1389) risk/malnourished
|
Risk/Malnourished Elderly
M = 82.7 years
Social level= NR
Educational level=NR
Clinical conditions =20% depression
Gender= 69,5% women
Living arrangement=NR
Race = NR
|
Observational Cross-sectional study
|
MNA
|
Yang et al. (2011)
|
n= 198
(124.7) risk/malnourished
|
Risk/Malnourished Elderly
M = 78 years
Social level= NR
Educational level=48.9% High School
Clinical conditions =NR
Gender= 84.9% women
Living arrangement=22.8% living with another person
Race =35.6% African American
|
Observational prospective cohort study
|
MNA
|
Note. ANSI = Australian Nutritional Screening Initiative; BMI = Body Mass Index; ICD = International Classifications of Desiese; MNA = Mini Nutritional Assessment; MUST = Malnutrition Universal Screening Tool; MST = Malnutrition Screening Tool; NR = Not Reported; SNAQ 65+ = Short Nutritional Assessment Questionnaire 65+
Table 2
Description of elderly´s health outcomes (morbidity, mortality, functional capacity, and quality of life), and main conclusions of the eligible studies.
Authors (Year)
|
Elderly´s health outcomes
(morbidity, mortality, functional capacity, and quality of life)
|
Main conclusions
|
Ahmed et al. (2018)
|
The risk of death for those with
diabetes increased 69% in malnourished versus normal nutrients (P<0,0001). Malnutrition increased the risk of death
within reach of the common comorbid conditions, including
ischaemic heart disease, chronic
obstructive pulmonary disorder, stroke
or transient ischemic attack, heart
failure, chronic kidney disease, and acute myocardial infarction. In addition, the total annual expenditure for the
malnourished beneficiaries were significantly higher than
then for the normal-nutrient beneficiaries ($36 079 vs 20
787) (P<0.0001)
|
Malnutrition is significant comorbidity affecting the survival and health care costs of the person with diabetes. There is a need to develop and implement evidence-based clinical decision pathways for appropriate screening, assessment, diagnosis, and treatment of malnourished patients, and to prevent malnutrition in normal-nutritious patients with diabetes
|
Alhamadan et al. (2019)
|
There was a significant association between nutritional status and ADL. Among those assessed as fully functional, only 15.2% were at risk of malnutrition or malnourishment. Among those rated as moderately functional or severely unfunctional, 55.3% and 73.9% were classified as at risk of malnutrition or malnourished, respectively
|
Assessing the nutritional status of the elderly identified a high prevalence of undernutrition and obesity. Such assessments should be routine practice in PHC
|
Galiot et al. (2015)
|
The risk of malnutrition was positively related to social risk and the number of chronic diseases. 3.6% of participants who had social problems, were at risk of malnutrition and malnutrition (1.8%). People suffering from more than six pathologies,
also had a higher nutritional risk
|
The risk of undernutrition seems to be associated with a more disadvantaged social condition and comorbidities. The development of training programs in nutrition education and the use of simple tools to identify nutritional risk in primary health care could be effective to reduce the prevalence of malnutrition, avoid negative health consequences and improve the quality of care. If a situation of nutritional risk is not detected and treated early, it can lead to malnutrition, a serious pathological situation with very negative consequences for the elderly´s health, not to mention the social and health costs that this situation entails
|
Geurden et al., (2015)
|
Patients at risk of malnutrition were significantly sicker (P < 0.001), and reveals more eating problems such as difficulties with chewing or swallowing and loss of appetite (P < 0.001)
|
Belgian nurses providing care at home do not yet
fully comply with international nutritional recommendations. Our survey of nurses revealed low awareness, low knowledge capacity, and poor communication between stakeholders. Systematic screening should be further developed and evaluated in this population at risk. Additional training in nutritional nursing care and screening methods for malnutrition is needed
|
Hegendörfer et al. (2020)
|
Survival is statistically significantly lower for those with a risk of malnutrition based on either MNA (P < 0.001) or pre-albumin (P= 0.001). No significantly higher hospitalization is observed for those at risk of malnutrition based on MNA (P = 0.068) or pre-albumin (P= 0.058)
|
There is the need for further research on the assessment of nutritional status in community-dwelling, independent
very old adults who would benefit from a combination
of both anthropometric and/or dietary assessment and
biomarkers such as pre-albumin. This is important in
light of the growing global population of adults
80 years and older, the impact of malnutrition on their
quality of life and risk of adverse outcomes, and the
availability of potentially beneficial interventions
|
Klemenc-Ketis et al. (2020)
|
There is a significant association of increased risk of malnutrition with age and BMI (P=0.022); several chronic diseases (P=0.001); a misperception of their current health status (P=0.001); feeling lonely (P<0.001); and increased pain intensity (P=0.003)
|
A screening program in primary health care can help identify people at risk of malnutrition. In addition, appropriate nutritional support is suggested as it can help to reverse or stop the trajectory of malnutrition and the negative outcomes associated with poor nutritional status. It should be noted that by limiting screening for malnutrition only to hospitalized and elderly patients, we are missing a large percentage of the population living at home, especially those who do not attend PHC
|
Krishnamoorthy et al. (2017)
|
Not Reported
|
Nutritional dysfunctions are important health issues to
be considered among the elderly population. Opportunistic screening may be useful at the PHC level. Strengthening primary health care to address and prevent this health issue through
balanced dietary practices can improve their nutritional status, thereby improving their quality of life
|
Mastronuzzi et al. (2015)
|
A significantly higher number of major events, including death, were observed in the undernourished group. In (20.4%) of patients were identified as having a high risk of developing major complications in the future. The sensitivity of the MNA test in identifying these patients was 84%. Several major events were registered both in patients at risk of malnutrition and in malnourished ones (bone fractures – 20.5%; hospitalization – 30%; death – 2.8-16%)
|
The prevalence of malnutrition is high among the elderly in the context of family practice. The MNA allows for better identification of malnourished subjects than the BMI and effectively. The application of a simple, quick, and easy-to-fill screening tool such as the MNA makes it possible to identify better than BMI those older adults who are malnourished or at risk, which is also useful for quantifying the risk of future major events. Malnutrition is often underestimated as nutritional status is not routinely checked and reported in the patient's electronic file. The classification obtained by the MNA makes it possible to stratify patients and obtain information on the risk of complications, at least in older and frail subjects, and indirectly to estimate the burden of care
|
Pedersen et al. (2016)
|
Early and integrated nutritional monitoring (Hosp-PHC) of the elderly by health teams in a home setting, prevents and improves the deterioration of activities of daily living and the independence of the individual when associated with malnutrition (in 96% of cases and statically significant p<0.001). There is also a reduction in the length of hospital stay
|
The early identification of the risk of malnutrition can prevent a negative spiral of results for the elderly (functional deterioration, hospital readmissions, death)
|
Preston et al. (2018)
|
50% of participants at nutritional risk were at risk of isolation. A large proportion of participants (79.9%) had multiple illnesses with almost 50% more than six prescribed medications. 80% of the nutritional risk group are considered frail.
Frailty (P<0.004) and prescribed medications (P<0.042) reveal a statistically significant relationship with nutritional status
|
Screening practices with valid and reliable screening tools are imperative to ensure
identification and management of older people at risk
|
Rodriguez-Tadeo et al. (2012)
|
Functional impairment, cognitive impairment, and depression were 3.0, 1.5, and 2.9 times more likely in the presence of malnutrition or risk of malnutrition. Malnutrition correlates positively with depression, functional impairment to move, and living alone (P <0.001)
|
PHC plays an important role in early detection, improved quality of life, and better prognosis for malnourished individuals
|
Schilp et al. (2014)
|
No statistically significant differences were found between introduction of dietary treatment VS usual care in body weight change (mean difference 0.78 kg, 95% CI-0.26e1.82), QALYs (mean difference 0.001, 95% CI - 0.04e0.04) and total costs (mean difference V1645, 95% CI -525e3547). The incremental cost-utility ratio (ICUR) for QALYs was not interpretable. The incremental cost-effectiveness ratio (ICER) for body weight gain was 2111. The probability of dietary treatment being cost-effective was 0.78 for a cost-effectiveness ratio of V5000 for body weight and 0.06 for a threshold ratio
|
Dietary treatment in older, undernourished, community-dwelling individuals was not cost-effective
|
Shakersain et al. (2016)
|
The mean age at death of participants with malnutrition and risk of malnutrition was ~ 3 and 1.5 years shorter (CI - 95%, P<0.001) than that of participants with normal nutritional status, respectively, while malnutrition or risk of malnutrition together with abnormal biomarker levels (hemoglobin and albumin) was related to one year shorter survival
|
Malnutrition and the risk of malnutrition are significantly associated with shorter survival. Poor nutritional status in combination with abnormalities in biomarkers is associated with even shorter survival
|
Spirgienė et al. (2018)
|
The risk of/malnutrition was associated with chronic (P < 0.004) and intermittent pain (P < 0.001), chewing difficulties (P < 0.001), swallowing disorders (P < 0.001), dental problems (P < 0.001), and medication use (P < 0.001). The risk of malnutrition and undernutrition was related to depression (P = 0.001) and Alzheimer's disease or other dementia (P < 0.001), but had no statistically significant relationship with cancer (P = 0.120) or diabetes mellitus (P = 0.065)
|
Educating community elders about healthy nutrition and providing them with specific updated guidelines to follow over the long term contributed to favorable changes. The findings infer that community nurses' efforts to ensure better health outcomes for the elderly, using minimal financial and human resources, appeared to be effective in improving elderly people's nutrition knowledge and practices on nutrition
|
Vandewoude et al. (2019)
|
Of all undernourished individuals, 49% were diagnosed by PHC and 13% of the undernourished recognized themselves as such. Mobility (climbing stairs and walking) and ADL dependence (Belgian KATZ score) were impaired in older people with (risk of) malnutrition compared to individuals with normal nutritional status (P< 0.001)
|
Under-diagnosis of malnutrition is problematic, because the associated loss of mobility and independence may accelerate the transformation of frailty into disability in older people
|
Yang et al. (2011)
|
Participants who were malnourished or at risk of malnutrition were more likely to experience sequential under-hospitalization (P = 0.040), emergency room visits (P = 0.047), use of home health aides (P = 0.027), and mortality (P = 0.031)
|
Malnourished people or at risk of malnutrition, are more likely to use greater amounts of health care resources subsequently and experience mortality. Nutritional interventions aimed at addressing undernutrition in this vulnerable population can improve health outcomes and decrease health service utilization
|
Note. ADL = Activities Day Living; BMI = Body Mass Index; KATZ = Index Independence Activities Day Living; MNA = Mini Nutritional Assessment; QALYs = Quality Adjusted Life Years