TYPE
Original Research
09 May 2023
10.3389/fnut.2023.1149579
PUBLISHED
DOI
OPEN ACCESS
EDITED BY
Barbara Troesch,
Self-employed, Zurich, Switzerland
REVIEWED BY
Carlo Pedrolli,
Azienda Provinciale per i Servizi Sanitari
(APSS), Italy
Alison Steiber,
Academy of Nutrition and Dietetics Foundation,
United States
Analysis of predictors of
malnutrition in adult hospitalized
patients: social determinants and
food security
Krystel Ouaijan1,2 , Nahla Hwalla3 , Ngianga-Bakwin Kandala4,5 ,
Joelle Abi Kharma6 and Emmanuel Kabengele Mpinga2*
*CORRESPONDENCE
1
Emmanuel Kabengele Mpinga
emmanuel.kabengele@unige.ch
2
22 January 2023
07 April 2023
PUBLISHED 09 May 2023
RECEIVED
ACCEPTED
Department of Clinical Nutrition, Saint George Hospital University Medical Center, Beirut, Lebanon,
Institute of Global Health, University of Geneva, Geneva, Switzerland, 3 Department of Nutrition and
Food Sciences, American University of Beirut, Beirut, Lebanon, 4 Department of Epidemiology and
Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada,
5
Division of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand,
Johannesburg, South Africa, 6 Faculty of Arts and Sciences, Lebanese American University, Beirut,
Lebanon
CITATION
Ouaijan K, Hwalla N, Kandala N-B, Abi Kharma J
and Kabengele Mpinga E (2023) Analysis of
predictors of malnutrition in adult hospitalized
patients: social determinants and food security.
Front. Nutr. 10:1149579.
doi: 10.3389/fnut.2023.1149579
COPYRIGHT
© 2023 Ouaijan, Hwalla, Kandala, Abi Kharma
and Kabengele Mpinga. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in this
journal is cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Background: Malnutrition in hospitalized patients is becoming a priority during the
patient care process due to its implications for worsening health outcomes. It can
be the result of numerous social factors beyond clinical ones. This study aimed to
evaluate the link between these various risk factors considered social determinants
of health, food security levels, and malnutrition and to identify potential predictors.
Methods: A cross-sectional observational study was conducted on a random
sample of adult patients in five different hospitals in Lebanon. Malnutrition was
assessed using the Global Leadership Initiative on Malnutrition (GLIM) criteria.
Patients were interviewed to collect social and economic characteristics and were
categorized into four criteria: (1) area of residence (urbanization level), (2) level of
education, (3) employment status, and (4) source of health coverage. The food
security level was screened by a validated two-question tool, adapted from the
US Department of Agriculture Household Food Security Survey, targeting both
quantity and quality.
Results: In a random sample of 343 patients, the prevalence of malnutrition
according to the GLIM criteria was 35.6%. Patients with low levels of food
security, mainly low quality of food, had higher odds of being malnourished (OR
= 2.93). Unemployed or retired patients and those who have only completed
only elementary school had higher odds of being diagnosed with malnutrition as
compared to those who were employed or had university degrees, respectively
(OR = 4.11 and OR = 2.33, respectively). Employment status, education level,
and type of health coverage were identified as predictors of malnutrition in the
multiple regression model. Household location (urban vs. rural) was not associated
with malnutrition.
Conclusion: The social determinants of health identified in our study, mainly the
level of education and income level, in addition to food security, were identified
as predictors of malnutrition in hospitalized patients. These findings should guide
healthcare professionals and national policies to adopt a broader perspective in
targeting malnutrition by including social determinants in their nutrition care.
KEYWORDS
social determinants, food security, hospital malnutrition, Global Leadership Initiative on
Malnutrition GLIM, prevalence, health coverage, Right to Health
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1. Introduction
Lebanon is a small country in the Middle East Region that
is divided into five main districts with an estimated population
of 6,847,712 and 144 hospitals comprising 11,742 beds (20, 21).
Studies on the prevalence of malnutrition in hospitalized patients
have been modest with a small study reporting a rate of 37.4%
in one hospital (22). The country has recently witnessed a severe
financial crisis. According to the World Bank, a drop of 36.5%
in gross domestic product per capita has reclassified the country
as a lower-middle-income country instead of an upper-middleincome country (21). These drastic changes have directly affected
employment status impacting household incomes and therefore
food security and the extent of healthcare coverage. The aim of
this study was to assess the association between indicators of social
determinants of health and food security with malnutrition in adult
hospitalized patients. We also aimed to determine whether any
of these factors are potential predictors of nutritional status. The
results of this study would suggest taking a social perspective when
identifying malnutrition in hospitalized patients and providing
guidance for national policies on including malnutrition in
hospitalized patients under the Right to Health framework.
Malnutrition in hospitalized patients has been associated with
an increasing rate of complications and worsening outcomes (1).
Malnutrition impairs many physiologic functions of the body,
impairing the immune system, delaying wound healing, and
leading to loss of muscle mass and strength (2). Major consequences
resulting from these implications include increased morbidity,
increased length of stay, nosocomial infections, and hospital
readmission (1, 3). Patients diagnosed with malnutrition have in
addition 5-fold higher mortality rate than patients with normal
nutrition status (4). Malnutrition among hospitalized patients
is typically categorized as disease-related malnutrition, as it is
assumed to be mainly caused by the patient’s clinical condition and
the inflammatory process associated with their current illness (5–7).
However, malnutrition in hospitals may also arise from a
combination of factors that extend beyond clinical factors, as
observed in community settings (8, 9). An analysis of data
from the Healthcare Cost and Utilization Project (HCUP) in the
United States revealed a correlation between patients’ income
levels and their nutritional status upon admission to the hospital,
with a higher incidence of malnutrition diagnosed in patients
below the 50th percentile of income (10). These results highlight
that a person’s socioeconomic status can significantly affect their
health, including their nutritional wellbeing (11). The World
Health Organization (WHO) has long established that various
factors, such as education level, employment, and urbanization,
in addition to income, play a role in shaping population health
via different mechanisms and have been categorized as social
determinants of health (12, 13). However, studies on the impact
of these determinants on nutritional status have been scarce and
focused only on the growth of children (14, 15). More specifically,
the influence of these determinants on the nutritional status
of adult hospitalized patients has not been accounted for in
previous studies.
Food security, another significant social determinant, has also
an impact on both the quantity and quality of food intake affecting,
as a result, the nutritional status of the hospitalized patient
(16). Decreased food intake caused by insufficient food quantity
is a primary contributor to weight loss, while inadequate food
quality leads to reduced intake of essential nutrients and impacts
nutritional status in patients (2). Although studies on food security
have mainly examined the association between poor nutrientdense foods and obesity, there is still limited evidence linking
food security with malnutrition in healthcare settings, particularly
among adults (17). Data mainly focus on growth decline in
children, and research on adults in healthcare settings is scarce (16).
The social determinants of health along with food security are
taken into consideration as part of the Right to Health, which
dictates their availability and equitable accessibility (18). The Right
to Health is recognized as a fundamental part of Human Rights in
all international treaties (11). The essential elements of the Right
to Health under the Human Rights approach ensure that all people
have equal access to the underlying determinants of good health
(18). Understanding the relationship between social and economic
factors with the risk of malnutrition in hospitalized patients adds
an important perspective of strategies targeting the patient’s Right
to Health (19).
Frontiers in Nutrition
2. Materials and methods
2.1. Study population
Patients were enrolled as a part of a cross-sectional,
observational, and multicenter study intended to assess the national
prevalence of malnutrition from May to October 2021. They signed
an informed consent form after being introduced to the aim and
process of the study. A total of five hospitals, one hospital from
each of the five districts of Lebanon, were selected by convenience
sampling. All adult patients, men and women aged 18 years and
above, admitted to the different wards of the hospitals during the
period of data collection were recruited within 48 h of admission.
Patients with dementia or other cognitive impairment were also
included, and the caregivers were approached to sign the consent
form and fill the part of the questionnaire. Exclusion criteria
included the following wards: gynecology, intensive care unit,
psychiatry, and short stay of <48 h because of the inability of
conducting questionnaires.
2.2. Data collection and social determinants
The patient’s basic characteristics, including age, gender,
marital status, and admission diagnosis, were recorded. The
World Health Organization and Office of Disease Prevention
and Health Promotion identify various indicators as integral to
social determinants of health impacting directly health outcomes
in their Healthy Report 2030 (13, 23). Four of these indicators
were considered, and patients were interviewed accordingly: (1)
area of residence (urbanization level), (2) level of education,
(3) employment status, and (4) source of health coverage
(11, 12). The source of health coverage that applies to the
country context includes National Social Security Fund (NSSF),
private insurance, and financial aid from governmental and nongovernmental organizations.
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TABLE 1 Global Leadership Initiative on Malnutrition GLIM criteria for the
diagnosis of malnutrition (43).
2.3. Food security
The level of food insecurity was screened using a simplified
tool based on two questions adapted from the 2000 United States
Department of Agriculture Report on Food Security Measurement
Project (24). It was demonstrated that a two-item screening tool
has high sensitivity and specificity and is a practical tool for
use in surveys conducted in healthcare settings (25–27). The two
questions (Q1 and Q4) from the Food Security Scale were selected
to focus on the patient’s perception of food availability in the
household. The first question was “Which of these statements best
describes the food eaten in the household in the last 12 months?”
The response categories were as follows:
(1)
(2)
(3)
(4)
Phenotypic criteria
enough of the kinds of food we want to eat
enough but not always the kinds of food we want
sometimes not enough to eat
often not enough to eat.
The second question was “Which of these statements best
describes the quality of food eaten in the household in the last 12
months?” The response categories were determined based on the
patient’s description of the number of food groups they consume as
follows (24):
(1)
(2)
(3)
(4)
Severity Moderate
level
Severe
Weight
loss
>5–10%
within past 6
months or
10-20%
beyond 6
months
>10%
within
past 6
months,
or >20%
beyond 6
months
Low BMI
<20 if <70
years, >22 if
>70 years
<18.5 if
<70
years,
<20 if
<70 years
Reduced
muscle
mass
MUACa < 23
MUAC <
20
a Mid-Upper Arm Muscle
Etiologic criteria
Reduced food
intake
<50% of
Estimated
Needs in > 1
week or any
reduction for
>2 weeks
any chronic GI
condition that
adversely
impacts food
assimilation or
absorption
Inflammation
Elevated
C-Reactive
Protein (CRP)
levels
Circumference.
and to check for out-of-range values. Continuous variables were
described using mean and standard deviations, while frequencies
and percentages were used to represent categorical variables.
Shapiro–Wilk was used to assess data normality. The median and
interquartile range (IQR) were used to describe the non-parametric
variables. A series of simple logistic regressions were conducted at
the bivariate level to identify potential predictors of malnutrition.
A multiple logistic regression model was run thereafter to assess
the independent associations between malnutrition status and
patients’ social determinants and food security level. Variables
were selected for inclusion in the model based on a p-value of <
0.2 at the bivariate level. All reported p-values were evaluated at a
significance level of 5%.
very good
good
average
poor.
2.4. Nutritional status
The Global Leadership Initiative on Malnutrition (GLIM) was
used to diagnose malnutrition and its severity in hospitalized
patients (5). It is a two-step process by first identifying at least
one phenotypic criterion and one etiologic criterion and second
assessing the severity of malnutrition as “moderate” and “severe”
based on the phenotypic criterion. Anthropometrics, including
height, weight, body mass index (BMI), and mid-upper arm muscle
circumference (MUAC), were used to evaluate the phenotypic
criteria. Patients were interviewed for the history of weight loss,
appetite, and record of food intake. Food intake was assessed using
the dietary recall of meals consumed before hospital admission
and categorized as <50% of estimated needs in >1 week or any
reduction for >2 weeks. C-reactive protein levels (CRPs) were
retrieved from the available blood tests from the patient’s records.
Reduced food intake retrieved from the patient’s interviews and
inflammatory condition assessed by their CRP levels retrieved from
the patient’s files was the etiologic criteria. Cutoff points of the
different etiologic and phenotypic criteria are described in Table 1.
3. Results
3.1. Sociodemographic characteristics
A total of 343 participants were enrolled in this study from
May to October 2021. Demographics and social characteristics are
presented in Table 2. The mean age was 60 years (SD: 17 years),
and the majority of the patients were <70 years old (65.89%).
Almost half of the patients were male (54.81%), and the majority
were married (70.55%). The majority of households (62.10%)
were located in urban areas. In total, 27.99% of participants had
university degrees, but more than half were not working (58.6%).
3.2. Nutritional status of patients
2.5. Statistical analysis
Using the GLIM diagnostic criteria, a total of 35.57% of
patients (n = 122) were identified as malnourished, 21.28%
(n = 73) had a moderate level of malnutrition, and 14.29%
(n = 49) were classified as being severely malnourished. An equal
Statistical analysis was performed using STATA v17.1.
Descriptive analysis was used to summarize the study variables
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TABLE 3 Distribution of level of food security among patients (N = 343).
TABLE 2 Sociodemographic characteristics of patients (N = 343).
N (%)
N (%)
Food security (quantity)
Age
<70 years old
226 (65.89%)
Enough of the kinds of food we want to eat
87 (25.36%)
≥70 years old
117 (34.11%)
Enough but not always the kinds of food
200 (58.31%)
Sometimes not enough to eat
50 (14.58%)
Gender
Male
Female
188 (54.81%)
Often not enough to eat
155 (45.19%)
Food security (quality)
Marital status
6 (1.75%)
Very good
80 (23.32%)
Single
45 (13.12%)
Good
111 (32.36%)
Married
242 (70.55%)
Average
131 (38.19%)
Divorced
17 (4.96%)
Widowed
39 (11.37%)
Poor
21 (6.12%)
Level of education
No schooling
32 (9.33%)
Primary school
59 (17.20%)
Intermediate school
66 (19.24%)
High school
64 (18.66%)
Technical diploma
26 (7.58%)
University degree
96 (27.99%)
3.3. Level of food security
Referring to the quantity of food consumed in the first question,
the majority of the patients (58.31%) described their household
food to be “enough but not always the kinds of food we want”
as shown in Table 3. Only six patients (1.75%) responded as not
having enough food to eat. When referring to the quality of
food in the household in the second question, responses were
mainly distributed between two categories: good (32.36%) and
average (38.19%).
Work status
Not working
157 (45.77%)
Employee full time
91 (26.53%)
Employee part time
11 (3.21%)
Self-employed
29 (8.45%)
Retired
55 (16.03%)
3.4. Association of malnutrition with social
determinants
Table 4 describes the bivariate associations between
malnutrition and different sociodemographic characteristics.
The odds of being malnourished according to the GLIM criteria
were higher among patients of older age (≥70 years old, p <
0.001) compared to those of younger age. Gender and marital
status were not significantly associated. As for the four indicators
identified as social determinants, unemployed or retired patients
(p <.001) and those who had completed basic schooling (p =
0.004) or no schooling at all (p = 0.047) had higher odds of being
malnourished as compared to those employed or had university
degrees, respectively. Household location (urban vs. rural) and
type of health coverage were not significantly associated with being
malnourished.
Household location
Urban
213 (62.10%)
Countryside
130 (37.90%)
Health coverage
None
30 (8.75%)
NSSFa
86 (25.07%)
Private insurance
84 (24.49%)
Combination of NSSF1 and insurance
40 (11.66%)
Army or other governmental institution
82 (23.91%)
Non-governmental organization
21 (6.12%)
a National social security fund.
3.5. Association of malnutrition with the
level of food security
proportion (50%) of malnourished patients were distributed in
male and female populations. Among the 122 patients identified
as malnourished, the most dominant phenotypic criterion was
“weight loss” accounting for 76.7% followed by low muscle mass
(57.5%) and low BMI (31.2%). Decreased food intake was the
most common etiologic criterion identified (88%) followed by
inflammatory status (60.7%).
Frontiers in Nutrition
Patients who described in the first question the quality of the
food eaten to be “poor” compared to “very good” have higher
odds of being malnourished (p = 0.032). There was no association
between malnutrition and the reported description of food quantity
in the second question (p = 0.4234) as shown in Table 4.
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TABLE 4 Bivariate associations between diagnosis of malnutrition and
social determinants and level of food security.
3.6. Multiple logistic regression and
potential predictors of malnutrition
Age, work status, district, and type of health coverage were
found to be independent predictors of malnutrition diagnosis as
shown in Table 5. Specifically, patients of older age (≥70 years old,
p < 0.001) and unemployed/retired (p < 0.001) had higher odds of
being diagnosed with malnutrition compared to their counterparts.
As for food security, patients who described the quality of the food
eaten to be “poor” compared to “very good” in the first question had
higher odds of being malnourished (p = 0.066), but the results were
borderline significant. However, patients who had private insurance
as medical coverage means had lower odds of being diagnosed with
malnutrition (p = 0.033). The Hosmer and Lemeshow goodnessof-fit test indicates that our model fits the data well with p-values
of 0.7247.
CI
P-value
4.16
2.58; 6.70
<0.001∗∗
1.35
0.86; 2.10
0.184
4.11
2.43; 6.95
< 0.001∗∗
No schooling
2.33
1.01; 5.39
0.047∗
Primary
school/intermediate
school
2.36
1.32; 4.21
0.004∗
High school/technical
diploma
1.43
0.75; 2.70
0.276
1.19
0.73; 1.96
0.470
0.88
0.56; 1.39
0.603
NSSF
1.03
0.44; 2.40
0.947
Private insurance
0.5
0.21; 1.20
0.123
Combination of NSSF
and insurance
0.57
0.21; 1.55
0.273
Army or other
governmental institution
1.17
0.50; 2.74
0.712
Non-governmental
organization
0.75
0.23; 2.40
0.628
Enough of the kinds of
food we want to eat
0.88
0.52; 1.49
0.650
Enough but not always
the kinds of food
sometimes/often not
enough to eat
1.11
0.56; 2.21
0.763
Good
1.24
0.67; 2.28
0.491
Average
1.15
0.64; 2.08
0.643
Poor
2.93
1.09; 7.85
0.032∗
Agea
≥70 years old
Genderb
Female
Employment statusc
Not working
Level of educationd
4. Discussion
Marital statuse
The nutritional status of hospitalized patients in this study
was assessed and diagnosed using the GLIM criteria. It is a newly
proposed diagnostic tool based on a global set of criteria that
take into consideration different characteristics of malnutrition,
including weight loss, muscle mass, and food intake (28). It is
considered an evolving concept that was designed to provide a
more specific diagnosis of malnutrition and has been validated
in numerous studies (28–31). The prevalence rate of malnutrition
among hospitalized patients in this study was found to be 36.7%
using the GLIM criteria. In an international multicenter study
that included two hospitals in Lebanon and was conducted in
2008, nutrition screening was done using Nutrition Risk Screening
(NRS) and reported a lower rate of 22% of patients being at risk
of malnutrition (32). Although both studies were done on adult
hospitalized patients without excluding any medical conditions,
they differ in two major criteria. First, the study used a screening
tool as compared to the use of a diagnostic tool in our study.
In addition, it was carried out in only one district of Lebanon
including 273 patients as compared to our study that was carried
out in all five districts including 343 patients. However, a notable
increase in the prevalence from 22% of patients at risk to 36.7%
of patients diagnosed with malnutrition is observed. A possible
explanation for this increase is the drop in GDP that the country
has experienced leading to a drastic financial crisis (21).
This proposed explanation further supports our hypothesis
that malnutrition in hospitalized patients is influenced not only
by well-known medical and clinical conditions but also by social
and economic factors. As a matter of fact, a financial crisis will
affect the ability to purchase enough food of good quality affecting
in return the nutritional status of the patients (33). In our study,
the risk of food insecurity was screened using a valid adapted tool
focusing on both the quantity and quality of food (34). Nearly 60%
of patients reported that their food intake was sufficient in quantity
but inadequate in variety, as they lacked access to different types
of food groups. This lack of adequacy described by the patients
in our study was significantly associated with malnutrition despite
the food quantity. Other numerous studies have always focused on
exploring food insecurity either starvation in the community as a
Frontiers in Nutrition
Odds
ratio (OR)
Married
Household locationf
Countryside
Health coverageg
Food Security (Quantity)h
Food security (quality)i
a reference
group “<70 years old”, b reference group “males”, c reference group “working”,
group “university degree”, e reference group “not married”, f reference group
“urban”, g reference group “none”, h reference group “Enough of the kinds of food we want
to eat”, i reference group “very good” ∗ p < 0.05.
d reference
consequence of unavailability of food or obesity as a consequence of
unhealthy food choices (16, 35). In our study, we used a regression
model and identified the level of food security as a predictor of
malnutrition in hospitalized adults. Patients who had a poor level of
food security identified by the adapted tool we used had higher odds
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TABLE 5 Adjusted multiple logistics regression model of diagnosis of
malnutrition and social determinants and level of food security.
Odds
ratio (OR)
95% CI for
OR
P-value
3.03
1.55; 5.90
0.001∗∗
0.88
0.49; 1.57
0.684
2.50
1.23; 5.09
0.011∗∗
No schooling
1.53
0.46; 5.09
0.481
Primary
school/intermediate
school
1.10
0.51; 2.42
0.799
High school/technical
diploma
0.75
0.35; 1.64
0.473
NSSF
0.85
0.31; 2.36
0.759
Private insurance
0.30
0.10; 0.90
0.033∗
Combination of NSSF
and insurance
0.32
0.09; 1.13
0.079
Army or other
governmental institution
0.84
0.29; 2.42
0.750
Non-governmental
organization
0.69
0.15; 3.19
0.645
Good
1.75
0.82; 3.73
0.151
Average
1.48
0.66; 3.35
0.343
3.54
0.92; 13.61
0.066
low educational levels in a recent systematic review (39). On the
other hand, marital status was not associated with the level of
malnutrition in our population and cannot be determined as a
risk factor.
Another predictor of malnutrition in our study was the type of
health coverage. Patients who had been insured in private insurance
had significantly lower odds of being malnourished as compared
with patients with no health coverage or relying on social security
funds and non-governmental aid. Private insurance in Lebanon is
prohibitively expensive and typically only obtained by individuals
from higher socioeconomic groups reflecting a correlation between
income level and risk of malnutrition. This correlation has also
been demonstrated when studying the nutritional status of children
and older adults in the community (33, 39). The type of residence
area, being urban or rural, was not associated with malnutrition
in our model. The small surface area of Lebanon (10,452 km2 )
has decreased the differences in the level of urbanization between
the cities and rural areas, and therefore, a discrepancy could not
be identified.
The association that we have demonstrated between social
determinants and food security should alarm healthcare
professionals to broaden their perspective when identifying
malnutrition in hospitalized patients. When conducting nutritional
assessments, including the GLIM criteria or any validated tool, it is
advisable to incorporate a social dimension and identify any factors
that increase the risk of malnutrition, such as food insecurity,
low income, or low literacy levels (29, 40). When developing a
management plan for malnutrition in hospitalized patients, it is
crucial to address social determinants and food security as essential
components. Healthcare professionals are used to focusing
primarily on biomedical and clinical care that has been recently
described as a downstream approach aiming to treat symptoms
of malnutrition without targeting root causes (41, 42). Healthy
People 2030 initiative has recently proposed a more proactive
approach that targets the causes of diseases at a macro-level.
This initiative acknowledges the economic and social factors
that are typically beyond the patient’s control (36). In order to
provide effective nutritional care, healthcare professionals should
review the patient’s living and working conditions and address the
social determinants of health directly (33). Through this tailored
approach, healthcare professionals can prioritize enhancing food
security, education, and income levels, even for hospitalized
patients, as a means of achieving the Right to Health at a broader
national level (18).
This study has several strengths. First, it has a heterogeneous
population because it included patients from five hospitals across
different areas and admitted to various wards. Second, the
identification of malnutrition was not done by a screening process
but was determined through a systematic nutritional assessment
using the new GLIM criteria. Third, it was the first study to our
knowledge to investigate the association of social determinants
with malnutrition measured in hospitalized patients and to identify
potential predictors. This study also has some limitations. First,
social and economic indicators were collected from the patients
and their caregivers through a questionnaire and they had some
reservations while answering the questions. Second, a direct
question on income level could not be collected due to the severe
devaluation of the national currency and the inadequacy of any
Agea
≥70 years old
Genderb
Female
Employment statusc
Not working
Level of educationd
Health coveragee
Food security (quality)f
Poor
a reference
d reference
b reference
c reference
group “<70 years old”,
group “males”,
group “working”,
group “university degree”, e reference group “none”, f reference group “very good”
∗ p < 0.05.
of 3.56 to being malnourished as compared to patients categorized
with a good level.
Food security is recognized as a component of the social
determinants of health that include education, economic
stability, and access to healthcare (36). These fundamental
determinants have been linked to adverse health outcomes and are
considered key drivers of health equity. Research has primarily
concentrated on the pediatric population in the community
and has established a correlation between low income and
education levels with child stunting as an indicator of poor
nutritional status (37, 38). In our study population in the hospital
setting, employment status and education level were highly
associated with malnutrition. Patients who were not working
or had completed only elementary school had higher odds of
being diagnosed with malnutrition. In addition, employment
status was considered a predictor of malnutrition in hospitalized
patients in our regression model (OR = 4.1). Malnutrition in
older people living in the community was also associated with
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10.3389/fnut.2023.1149579
relevant categorization. Third, food security was only addressed
at a screening level using a two-item questionnaire as hospitalized
patients were less responsive to surveys of longer duration.
KO and JA: formal analysis. EK, NH, and N-BK: writing—
review and editing. EK: supervision. KO and NH: funding
acquisition. All authors contributed to the article and approved the
submitted version.
5. Conclusion
Funding
To conclude, our study found a malnutrition prevalence
rate of 35.57% in hospitalized patients in Lebanon. We also
identified social determinants of health, including education level,
income level, employment status, and health coverage, as factors
associated with malnutrition, along with food security. These
determinants were also recognized as predictors of malnutrition
in hospitalized patients. Our findings suggest that healthcare
professionals should consider adopting a broader perspective in
targeting malnutrition in their patients. Their approach should
aim to address the underlying causes of malnutrition beyond
clinical factors by incorporating social determinants into their
nutritional care assessments. National authorities should also
prioritize addressing the social determinants of health in their
policy agenda to improve malnutrition at the clinical level.
This research study was partially funded by Dietitians in
Nutrition Support DNS—Academy of Nutrition and Dietetics in
the United States, grant number 104037. Open access funding by
American University of Beirut.
Acknowledgments
The authors would like to thank all the hospitals that
participated in the study: Saint George Hospital University Medical
Center (Beirut), Sacre Coeur (Mount Lebanon), Monla Hospital
(North), Raee Hospital (South), and Hospital Libano-Francais
(Bekaa). The authors expressed tremendous gratitude to all clinical
dietitians who facilitated the process of data collection.
Data availability statement
Conflict of interest
The original contributions presented in the study are included
in the article/supplementary material, further inquiries can be
directed to the corresponding author.
The authors declare that the research was conducted
in the absence of any commercial or financial relationships
that could be construed as a potential conflict
of interest.
Ethics statement
The studies involving human participants were reviewed
and approved by Institutional Review Board of the American
University of Beirut. The patients/participants provided their
written informed consent to participate in this study.
Publisher’s note
All claims expressed in this article are solely those
of the authors and do not necessarily represent those of
their affiliated organizations, or those of the publisher,
the editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by
its manufacturer, is not guaranteed or endorsed by the
publisher.
Author contributions
EK, KO, NH, and N-BK: conceptualization and methodology.
KO: data collection and writing—original draft preparation.
References
1. Kirkland LL, Kashiwagi DT, Brantley S, Scheurer D, Varkey P. Nutrition in the
hospitalized patient. J Hosp Med. (2013) 8:52–8. doi: 10.1002/jhm.1969
consensus report from the global clinical nutrition community. Clin Nutr. (2019)
38:1–9. doi: 10.1016/j.clnu.2018.08.002
2. Barker LA, Gout BS, Crowe TC. Hospital malnutrition: prevalence, identification
and impact on patients and the healthcare system. Int J Environ Res Public Health.
(2011) 8:514–27. doi: 10.3390/ijerph8020514
6. Cederholm T, Rothenberg E, Barazzoni R. Editorial: a clinically relevant diagnosis
code for “malnutrition in adults” is needed in ICD-11. J Nutr Health Aging. (2022)
26:314–315. doi: 10.1007/s12603-022-1774-z
3. Schneider SM, Veyres P, Pivot X, Soummer AM, Jambou P, Filippi J, et al.
Malnutrition is an independent factor associated with nosocomial infections. Br J Nutr.
(2004) 92:105–11. doi: 10.1079/BJN20041152
7. Cederholm T, Barazzoni RO, Austin P, Ballmer P, Biolo GI, Bischoff SC, et al.
ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr.
(2017) 36:49-64. doi: 10.1016/j.clnu.2016.09.004
4. Corkins MR, Guenter P, DiMaria-Ghalili RA, Jensen GL, Malone A, Miller S,
et al. ASPEN data brief 2014: use of enteral and parenteral nutrition in hospitalized
patients with a diagnosis of malnutrition: United States. Nutr Clin Pract. (2014)
29:698–700. doi: 10.1177/0884533614543834
8. O’Keeffe M, Kelly M, O’Herlihy E, O’Toole PW, Kearney PM, Timmons S,
et al. Potentially modifiable determinants of malnutrition in older adults: a systematic
review. Clin Nutr. (2019) 38:2477-2498. doi: 10.1016/j.clnu.2018.12.007
9. Reinhardt K, Fanzo J. Addressing chronic malnutrition through multi-sectoral,
sustainable approaches: a review of the causes and consequences. Front Nutr. (2014)
1:13. doi: 10.3389/fnut.2014.00013
5. Cederholm T, Jensen GL, Correia MI, Gonzalez MC, Fukushima R,
Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition—A
Frontiers in Nutrition
07
frontiersin.org
Ouaijan et al.
10.3389/fnut.2023.1149579
10. Corkins MR, Guenter P, DiMaria-Ghalili RA, Jensen GL, Malone A, Miller S,
et al. Malnutrition diagnoses in hospitalized patients: United States. JPEN J Parenter
Enteral Nutr. (2014) 38:186-95. doi: 10.1177/0148607113512154
28. Correia MI, Tappenden KA, Malone A, Prado CM, Evans DC, Sauer AC, et al.
Utilization and validation of the global leadership initiative on malnutrition (GLIM): a
scoping review. Clin Nutr. (2022) 41:687–97. doi: 10.1016/j.clnu.2022.01.018
11. Costa-Font J, Hernandez-Quevedo C. Measuring inequalities in health: what
do we know? What do we need to know? Health Policy. (2012) 106:195206. doi: 10.1016/j.healthpol.2012.04.007
29. Schuetz P, Seres D, Lobo DN, Gomes F, Kaegi-Braun N, Stanga Z. Management
of disease-related malnutrition for patients being treated in hospital. Lancet. (2021)
398:1927–38. doi: 10.1016/S0140-6736(21)01451-3
12. Kosaka S, Umezaki M. A systematic review of the prevalence and predictors
of the double burden of malnutrition within households. Br J Nutr. (2017) 117:11181127. doi: 10.1017/S0007114517000812
30. Balci C, Bolayir B, Eşme M, Arik G, Kuyumcu ME, Yeşil Y, et al. Comparison
of the efficacy of the global leadership initiative on malnutrition criteria, subjective
global assessment, and nutrition risk screening 2002 in diagnosing malnutrition and
predicting 5-year mortality in patients hospitalized for acute illnesses. JPEN J Parenter
Enteral Nutr. (2021) 45:1172–80. doi: 10.1002/jpen.2016
13. Commission on Social Determinants of Health Final Report. Closing the Gap in
a Generation: Health Equity Through Action on Social Determinants of Health. Geneva:
WHO (2008).
31. da Silva Couto A, Gonzalez MC, Martucci RB, Feijó PM, Rodrigues VD, de
Pinho NB, et al. Predictive validity of GLIM malnutrition diagnosis in patients
with colorectal cancer. JPEN J Parenter Enteral Nutr. (2023) 47:420–8. doi: 10.1002/
jpen.2475
14. Fakir AM, Khan MD. Determinants of malnutrition among urban slum children
in Bangladesh. Health Econ Rev. (2015) 5:59. doi: 10.1186/s13561-015-0059-1
15. Zhang N, Becares L, Chandola T. Patterns and determinants of double-burden
of malnutrition among rural children: evidence from China. PLoS ONE. (2016)
11:e0158119. doi: 10.1371/journal.pone.0158119
32. Sorensen J, Kondrup J, Prokopowicz J, Schiesser M, Krähenbühl L,
Meier R, et al. EuroOOPS: an international, multicentre study to implement
nutritional risk screening and evaluate clinical outcome. Clin Nutr. (2008)
27:340–9. doi: 10.1016/j.clnu.2008.03.012
16. Maitra C. A Review of Studies Examining the Link Between Food Insecurity and
Malnutrition, in Techanical Paper Food. Rome: Agricuture Organization FAO (2018).
33. Peregrin T. Social determinants of health: enhancing health equity. J Acad Nutr
Diet. (2021) 121:1175-−8. doi: 10.1016/j.jand.2021.02.030
17. Carvajal-Aldaz D, Cucalon G, Ordonez C. Food insecurity as a risk factor for
obesity: a review. Front Nutr. (2022) 9:1012734. doi: 10.3389/fnut.2022.1012734
34. Guide to Measuring Household Food Security. Virginia: U.S. Department of
Agriculture. (2000).
18. Willen SS, Knipper M, Abadía-Barrero CE, Davidovitch N.
Syndemic vulnerability and the right to health. Lancet. (2017) 389:964977. doi: 10.1016/S0140-6736(17)30261-1
35. Tydeman-Edwards R, Van Rooyen FC, Walsh CM. Obesity, undernutrition and
the double burden of malnutrition in the urban and rural southern Free State, South
Africa. Heliyon. (2018) 4:e00983. doi: 10.1016/j.heliyon.2018.e00983
19. Steiber A, Hegazi R, Herrera M, Zamor ML, Chimanya K, Pekcan AG, et al.
Spotlight on global malnutrition: a continuing challenge in the 21st century. J Acad
Nutr Diet. (2015) 115:1335-41. doi: 10.1016/j.jand.2015.05.015
36. Healthy People 2020. Social Determinants of Health. Maryland: U.S. Department
of Health and Human Services. (2021).
20. Long MW, Gortmaker SL, Ward ZJ, Resch SC, Moodie ML, Sacks G, et al. Cost
effectiveness of a sugar-sweetened beverage excise tax in the U.S. Am J Prev Med. (2015)
49:112–23. doi: 10.1016/j.amepre.2015.03.004
37. Harris J, Nisbett N. The basic determinants of malnutrition:
resources, structures, ideas and power. Int J Health Policy Manag. (2021)
10:817-827. doi: 10.34172/ijhpm.2020.259
21. Lebanon Econmic Monitor, in World Bank. (2022). Available online at: https://
www.worldbank.org/en/country/lebanon/publication/lebanon-economic-monitor
(accessed January 23, 2023).
38. Tette E, Sifah EK, Nartey ET, Nuro-Ameyaw P, Tete-Donkor P, Biritwum RB.
Maternal profiles and social determinants of malnutrition and the MDGs: what have
we learnt? BMC Public Health. (2016) 16:214. doi: 10.1186/s12889-016-2853-z
22. Choueiry G, Fattouh N, Hallit R, Kazour F, Hallit S, Salameh P. Nutritional status
of lebanese hospitalized patients with chronic disease: a cross-sectional study. Hosp
Pharm. (2021) 56:102–8. doi: 10.1177/0018578719867664
39. Besora-Moreno M, Llauradó E, Tarro L, Solà R. Social and economic factors
and malnutrition or the risk of malnutrition in the elderly: a systematic review and
meta-analysis of observational studies. Nutrients. (2020) 12:737. doi: 10.3390/nu12
030737
23. Healthy People 2030, U.S. Department of Health and Human Services, Office of
Disease Prevention and Health Promotion. Maryland. (2022).
40. Soriano-Moreno DR, Dolores-Maldonado G, Benites-Bullón A, Ccami-Bernal
F, Fernandez-Guzman D, Esparza-Varas AL, et al. Recommendations for nutritional
assessment across clinical practice guidelines: a scoping review. Clin Nutr ESPEN.
(2022) 49:201–7. doi: 10.1016/j.clnesp.2022.04.023
24. Guide to Measuring Household Food Security. Virginia: United States
Department of Agriculture. (2000).
25. Hager ER. Development and validity of a 2-item screen to identify families at risk
for food insecurity. Pediatrics. (2010) 126:e26–32. doi: 10.1542/peds.2009-3146
41. Booske BC, Kinding DA. Different Perspectives for Assigning Weights to
Determinants of Health, in County Health Rankings. Working Paper. Wisconsin:
Population of Health Institute. (2010).
26. Hager ER, Quigg AM, Black MM, Coleman SM, Heeren T, RoseJacobs R. Validity of a single item food security questionnaire in
Arctic Canada. Pediatrics. (2014) 133:e1616–23. doi: 10.1542/peds.20
13-3663
42. Lantz PM. The medicalization of population health: Who will stay upstream?
Milbank Q. (2019). 97:36–9. doi: 10.1111/1468-0009.12363
27. Poblacion A, Segall-Corrêa AM, Cook J, Taddei JA. Validity of a
2-item screening tool to identify families at risk for food insecurity in
Brazil. Cad Saude Publica. (2021) 37:e00132320. doi: 10.1590/0102-311x00
132320
Frontiers in Nutrition
43. Cederholm T, Jensen GL, Correia MI, Gonzalez MC, Fukushima R,
Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition—A
consensus report from the global clinical nutrition community. Clin Nutr. (2018)
3:33. doi: 10.1016/j.clnu.2019.02.033
08
frontiersin.org