Keywords
STRONGKids - malnutrition - stunting - wasting - under-five - anthropometry
Introduction
Malnutrition is a major public health problem worldwide especially in developing countries
with a great impact on children under five.[1]
World Health Organization (WHO) define malnutrition as an imbalance between requirements
and intake of energy and/or nutrients that affect the growth—physical and cognitive
functions. Malnutrition includes under-nutrition, either stunting (low height for
age [HFA]) or wasting (low weight for height [WFH]), micronutrient deficiencies and
overweight or obesity.[1]
[2] Globally, pediatric under-nutrition contributes to 45% of all children's deaths
[3] with an estimated 159 million of under-five children stunted and 50 million wasted
worldwide.[1]
In hospitalized children under-nutrition is linked to a prolonged hospitalization,
delayed recovery, unfavorable clinical outcome and in general it is associated with
increase in morbidity and mortality, thus affecting patients with a great financial
burden on the health care system.[4] Despite that, malnutrition is common in hospitalized children. It is frequently
unrecognized by pediatric hospitals' staff who are focusing on the primary disease
and paying less attention to the nutritional status. Most of the patients were admitted
and discharged from the hospital without any assessment of nutritional status. Therefore
it is important to identify and detect children with nutritional risk early to allow
pediatricians to intervene early and to prevent further deterioration of nutritional
condition.
In the current practice Anthropometric measurements and interpretation enable the
hospital staff to identify patients with malnutrition at admission. Recently several
screening and nutritional assessment tools had been designed to detect nutritional
risk of hospitalized children at an early stage. Currently, for hospitalized children
and adolescents there are six tools, however, there is no consensus on which is the
best tool to be used.[5]
[6]
[7] In clinical practice, the most frequently used screening tools are: Screening of
Risk for Nutritional Status and Growth (STRONGkids) and the Subjective Global Assessment
of Nutritional Status (SGA).[5]
[7]
We have therefore conducted this study to assess the nutritional status of hospitalized
children at the time of admission and to evaluate the usefulness of STRONGkids in
the setting of Benghazi, Libya.
Patients and Methods
A cross sectional study was conducted in Benghazi Medical Center (BMC), which is a
referral and tertiary teaching hospital in Benghazi, Libya. The period of study was
from July 2020 to November 2020. All children admitted to the general pediatric ward
were enrolled in the study.
All children admitted older than 1 month and younger than 60 months (5 years) of age
were included in the study. Children younger than 1 month and older than 60 months
and patients admitted to intensive care unit were excluded from the study. Syndromic
children and children with neurological disability whose WHO software is not applicable
and precise height measure couldn't be obtained, were also excluded.
Demographic data such as sex, age (date of birth), and diagnosis at admission were
collected from the hospital records and screening tool STRONGkids was applied for
all enrolled patients. The anthropometric data including body weight, body length/
height, weight for length/height, and mid-arm circumference were taken of all children
upon admission.
STRONGKids Tool
The STRONGkids is considered to be a fast, practical, and easy to apply for nutritional
risk screening tool of hospitalized children, developed according to the European
Society for Parenteral and Enteral Nutrition (ESPEN) guidelines.
It consists of the scores of four items: 1—presence of a disease with high risk of
malnutrition; 2—subjective clinical evaluation; 3—reduced food intake and presence
of vomiting or diarrhea during the last 1 to 3 days and 4—recent weight loss. Depending
on the total score obtained, the nutritional risk of malnutrition is classified to:
i—low nutritional risk, (the score is zero), ii—moderate nutritional risk (the score
is 1 to 3), iii—high nutritional risk (the score is 4 to 5).[8]
[9]
[10]
Anthropometric Measurements
Anthropometric Measurements
All anthropometric measurements were taken by main investigators. Height was measured
to nearest 0.1 cm with a stadiometer (Seca700 mechanical column scales with eye-level
beam) with a fixed vertical backboard and an adjustable headboard with children standing
bare foot while, recumbent length was measured for all children aged less than 24
months by a portable foldable infantometer (Seca 417) with a fixed head piece, horizontal
backboard, and an adjustable foot piece.
Patients who weighed less than 10 kg, were measured by digital scale (Digital baby
scale Seca 334) in a supine position, while, patients who weighed more than 10 kg
were measured by scales (Seca 700 mechanical scale) in a standing position with the
patients wearing clean diaper or light cloths, respectively.[11]
Nutritional Status Assessment
Nutritional Status Assessment
According to WHO, Global Database on Child Growth and Malnutrition which uses a Z-score cutoff point for all anthropometric measurements [weight for age [WFA], height/length
for age, and WFH/length for age]. Children were classified into normal nutritional
status when Z-score was between (−2 and 2) standard deviation (SD), moderate malnourished with
Z-score less than −2 SD, or severe with Z-score less than −3 SD.[12]
Z-score of WFH, HFA, WFA. According to WHO classification of malnutrition; children
were classified as;
-
Wasting or acute malnutrition if they had low weight-for-height/length.
-
Stunting or chronic malnutrition if they had low height/length-for-age.
-
Underweight or wasting and stunting combined if they had a low weight-for-age.[12]
[13]
[14]
Statistical Analysis
Statistical package for social science was used for data entry and analysis. Continuous
variables were transformed to categorical variables. Descriptive statistics in the
form of percentage was applied and analytical statistics was used for comparison between
age, gender, and weight, WFH and STRONGkids categories. Chi-square was applied and
result was considered statistically significant if p-value <0.05.
Results
[Fig. 1A], demonstrates that children aged between 13 and 48 months of age were mostly male
(31.90%). Male children aged more than 48 months represent 7.76% and female represent
10.34%.
Fig. 1 (A) Relationship between age and sex of studied children. A comparison of STRONGkids and anthropometry. (B) Relationship between weight and sex of studied children.
Male children with normal weight represent the highest proportion (41.38%) and females
with 35.34%. Regarding children who are underweight, females have higher proportion
compared with males (10.34 vs. 8.62%) as shown in [Fig. 1B].
[Table 1] illustrates that 83.6% of cases were normal stature, 8.6% with severe stunting,
and 6.9% were with moderate stunting. Regarding WFH, 78.5% of children were well nourished,
10.3% had moderate wasting, and 6.9% had severe wasting. Concerning STRONGkids categories,
half of the cases have medium risk of malnutrition, 7.8% of cases have high risk of
malnutrition, and 42.2% of cases have low risk of malnutrition.
Table 1
Distribution of children according to height, weight for height, and STRONGkids categories
|
Categories of height
No = 116
|
Frequency (%)
|
|
Normal stature
|
978 (3.6)
|
|
Sever stunting
|
10 (8.6)
|
|
Moderate stunting
|
8 (6.9)
|
|
Tall stature
|
1(0.9)
|
|
Categories of weight for height
|
|
Well nourished
|
90 (77.6)
|
|
Moderate wasting
|
12 (10.3
|
|
Sever wasting
|
8 (6.9)
|
|
Over weight
|
6 (5.2)
|
|
Categories of STRONGkids
|
|
Low risk
|
49 (42.2)
|
|
Medium risk
|
58 (50.0)
|
|
High risk
|
9 (7.8)
|
[Table 2], shows that there is a relationship between scores of STRONGkids and WFA. All children
diagnosed as high risk according to STRONGkids and similarly are classified as underweight.
Therefore, there is an agreement between result of STRONGkids and WFA, p = 0.000.
Table 2
Relationship between weight for age and STRONGkids scores
|
Weight for age categories
|
Categories of STRONGkids
|
|
Low risk
|
Medium risk
|
High risk
|
|
Frequency
|
Percent
|
Frequency
|
Percent
|
Frequency
|
Percent
|
|
Normal weight
|
46
|
93.9
|
43
|
74.1
|
0
|
0%
|
|
Under weight
|
1
|
2.0
|
12
|
20.7
|
9
|
100.0
|
|
Over weight
|
2
|
4.1
|
3
|
5.2
|
0
|
0
|
|
Total
|
49
|
100.0
|
58
|
100.0
|
9
|
100.0
|
Note: p = 0.000.
Regarding the relationship between STRONGkids and height for age, children with moderate
and severe stunting were diagnosed as high risk of malnutrition (33.3 and 44.4%, respectively).
This difference was statistically significant at p = 0.000 ([Table 3]).
Table 3
Relationship between height for age and STRONGkids scores
|
Height for age categories
|
Categories of STRONGkids
|
|
Low risk
|
Medium risk
|
High risk
|
|
Frequency
|
Percent
|
Frequency
|
Percent
|
Frequency
|
Percent
|
|
Normal height
|
48
|
98.0
|
47
|
81
|
2
|
22.3
|
|
Moderated stunted
|
1
|
2
|
4
|
6.9
|
3
|
33.3
|
|
Sever stunted
|
0
|
0
|
6
|
10.3
|
4
|
44.4
|
|
Tall stature
|
0
|
0
|
1
|
1.7
|
0
|
0
|
|
Total
|
49
|
100.0
|
58
|
100.0
|
9
|
100.0
|
Note: p = 0.000.
[Table 4] illustrates that 33.3 and 55.6% of children were diagnosed as moderate and sever
wasting, respectively and have high risk of malnutrition according to STRONGkids .This
result was statistically significant at p = 0.000.
Table 4
Relationship between weight for height and STRONGkids scores
|
Weight for height category
|
Categories of STRONGkids
|
|
Low risk
|
Medium risk
|
High risk
|
|
Frequency
|
Percent
|
Frequency
|
Percent
|
Frequency
|
Percent
|
|
Well nourished
|
43
|
87.8
|
46
|
79.3
|
1
|
11.1
|
|
Moderate wasting
|
3
|
6.1
|
6
|
10.3
|
3
|
33.3
|
|
Severe wasting
|
0
|
0
|
3
|
5.2
|
5
|
55.6
|
|
Over weight
|
3
|
6.1
|
3
|
5.2
|
0
|
0
|
|
Total
|
49
|
100.0
|
58
|
100.0
|
9
|
100.0
|
Note: p = 0.000.
Discussion
In our study, the STONGkids score showed significant association with the anthropometric
parameter analyzed (WFA, height/length for age, and WFH).
The present study showed that by using STRONGkids score, it was observer that 42.2%
of the patients had low risk of malnutrition, 50.0% had medium risk, and 7.8% had
high risk of malnutrition. Similarly Ling et al[10] studied a group 43 children admitted to the Children's Hospital, Oxford to screen
for malnutrition. Screening Tool for the Assessment of Malnutrition in Pediatrics
(STAMP) and STRONGkids scores were applied for all patients. The authors reported
that 49% of the studied patients had medium risk of malnutrition and there was a correlation
between STRONGkids and all anthropometric measures.
In agreement with our results, Spagnuolo et al[15] performed a prospective observational multicenter study in 12 hospitals in Italy,
2012 including 144 Italian children aged from 1 to 18 years of age. The study showed
that 53% of the studied children had moderate risk of malnutrition. Additionally there
was a significant but weak correlation existed between STRONGkids and anthropometric
measurments.[15]
Huysentruyt et al studied 368 children (105 hospitalized in a tertiary and 263 in
three secondary hospitals). Their age ranged between 0.08 and 16.95 years. The SRONGkids
tool was applied and it was revealed that 47.3% were scored at low risk, 45.1% at
the moderate risk, and 7.6% of children were at high nutritional risk. There was a
good correlation with weight-for-height Z-score, but not with the height- for-age Z-score.[16]
Matak et al performed a study among 124 children admitted to a University Hospital
in Zagreb to estimate the nutritional status and risk of malnutrition by using STRONGkids
score and anthropometric measurements. They found out that children with low risk
score represented 24.2%, 64.5% at moderate risk, and 11.3% children were at high nutritional
risk. They concluded that there was no correlation between HFA Z-scores and STRONGkids risk categories were present.[17]
Although de Oliveira et al reported in their study which included 71 children aged
1 month to 17 years that 5.6% of the studied children had high nutritional risk, 63.4%
had moderate nutritional risk, and 31.0% had low nutritional risk, there was no significant
association between this tool and anthropometric data.[18]
The limitation of the study is the small sample size and according to our aim we included
children less than 5 years as malnutrition is more prevalent in this age group. AS
other studies included elder age group, we planned to extend our study to include
older age group as well.
Conclusions
The present study illustrated that half of the studied children had moderate risk
of malnutrition, 42.2% had low risk, and 7.8% had severe risk of malnutrition. Thus
the STRONGkids screening tool could be used as an initial screening tool for children
on admission to the hospital to find out children at risk of malnutrition and to provide
them the right intervention at the right time.
