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| ORIGINAL ARTICLE |
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| Year : 2015 | Volume
: 52
| Issue : 2 | Page : 210-215 |
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Nutritional status at presentation, comparison of assessment tools, and importance of arm anthropometry in children with cancer in India
P Shah1, U Jhaveri2, TB Idhate3, S Dhingra4, P Arolkar5, B Arora3
1 Department of Pediatrics, SAACHI Children Hospital, Surat, Gujarat, India
2 Cuddles Foundation, Tata Memorial Hospital, Mumbai, Maharashtra, India
3 Department of Pediatric Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
4 Sun Pharma Laboratories Ltd., Mumbai, Maharashtra, India
5 Department of Medical Gastroenterology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| Date of Web Publication | 5-Feb-2016 |
Correspondence Address:
B Arora
Department of Pediatric Oncology, Tata Memorial Hospital, Mumbai, Maharashtra
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0019-509X.175838
Background: In India, approximately 40,000 new cases of cancer in children are diagnosed each year. However, there are no good studies analyzing their nutritional status. Also, since accurate and sensitive nutritional assessment is critical for optimal clinical outcomes through timely remediation of malnutrition, it is important to assess the relative sensitivity and feasibility of commonly used nutritional screening tools. Methods: This observational study analyzed height/length (cm), weight (kg), mid-upper arm circumference (MUAC), triceps skinfold thickness (TSFT) as well as their Z-scores or percentiles, albumin levels and history of weight loss at diagnosis in children aged 2–15 years being treated for cancer between November 2008 to December 2013. Body mass index (BMI) and arm muscle circumference (AMC) were calculated respectively from height and weight, and MUAC and TSFT. Results: A total of 1693 new patients were enrolled; 1187 had all anthropometric measurements performed. The prevalence of malnutrition was 38%, 57%, 76%, 69% and 81% on the basis of BMI, TSFT, MUAC, AMC, and arm TSFT + MUAC respectively with the highest prevalence in solid abdominal tumours. Addition of BMI and serum albumin to arm anthropometry increased the proportion classified as severely nutritionally depleted by a mere 2% & 1.5% respectively. Positive history of significant weight loss additionally identified 16.5% at nutritional risk over arm anthropometry. Conclusions: The prevalence of malnutrition in Indian children with cancer at presentation is very high ranging from 40% and 80% depending on the method used for assessment, being higher with MUAC and lowest with BMI. Either MUAC alone or TSFT + MUAC (wherever feasible) should be used for screening for malnutrition in children with cancer at diagnosis to plan timely nutritional interventions, reduce the treatment-related morbidity and optimise their chance of long-term cure.
Keywords: Arm anthropometry, children with cancer, India, nutrition assessment tools, nutritional status
How to cite this article:
Shah P, Jhaveri U, Idhate T B, Dhingra S, Arolkar P, Arora B. Nutritional status at presentation, comparison of assessment tools, and importance of arm anthropometry in children with cancer in India. Indian J Cancer 2015;52:210-5 |
How to cite this URL:
Shah P, Jhaveri U, Idhate T B, Dhingra S, Arolkar P, Arora B. Nutritional status at presentation, comparison of assessment tools, and importance of arm anthropometry in children with cancer in India. Indian J Cancer [serial online] 2015 [cited 2020 Nov 29];52:210-5. Available from: https://www.indianjcancer.com/text.asp?2015/52/2/210/175838 |
| » Introduction | |  |
The World Bank estimates that India is one of the highest-ranking countries in the world, for the number of children suffering from malnutrition.[1] Recent malnutrition figures from the Rapid Survey on Children data found that 29.4% children (aged <3 years) were underweighted, 15% were wasted, and 38.7% were stunted.[2] On the face of it, this compares well with the previous data from 2006, in which the corresponding figures were 40.4% (underweight), 22.9% (wasted), and 44.9% (stunted).[3] Despite such high prevalence of malnutrition, there are hardly any good quality data on its prevalence in children with cancer in India. This is a critical deficit since children who are either under- or over-weight have increased infection rate, decreased tolerance of chemotherapy, higher relapse rate, increased mortality, and poorer prognosis overall compared to well-nourished children.[4] Further, the prevalence of malnutrition varies significantly from 10% to 60% depending on the assessment tool used, tumor type and stage, timing of evaluation, and socioeconomic background of the population.[4] In addition, it has been established recently that timely remediation of malnutrition can normalize the outcome of such children.[5],[6] Hence, it is important that pediatric oncology units assess the nutritional status of all children to allow early intervention. To ensure this, the nutritional assessment methods not only should be sensitive and accurate but also need to be simple, cost-effective, and easily doable in low- and middle-income countries (LMICs) due to limited infrastructure and time constraints. Methods that fit these criteria are weight, height, albumin, triceps skinfold thickness (TSFT), and mid-upper arm circumference (MUAC), and calculation of weight loss over 1 month and serum albumin.[4],[7],[8],[9] However, the level of nutritional status has been shown to vary with the method used, and each method has limitations. Therefore, the aim of the present study is to evaluate two things:
- To determine the prevalence and severity of malnutrition at diagnosis in children with cancer in India
- To compare the relative sensitivity and feasibility of commonly used nutritional screening tools.
| » Patients and Methods | | |
Subjects
This was a retrospective, observational study of newly diagnosed patients, aged 2–15 years, with cancers diagnosed between November 2008 and December 2013, who attended the outpatient clinics or were admitted to the inpatient and/or Intensive Care Units at the Pediatric Oncology Department of the Tata Memorial Hospital, Mumbai, India. Patients were excluded from the study if assessment of nutritional status was performed more than 72 h after the beginning of chemotherapy.
Measurements
The following data were collected routinely at diagnosis for all newly diagnosed patients as per the standard guidelines and recorded retrospectively on a study datasheet.
- Body weight and height: Weight was measured to the nearest 0·05 kg using calibrated digital scales and height was measured to the nearest 0·1 cm using a wall-mounted stadiometer. Body mass index (BMI) was calculated as weight divided by height squared. Height, weight, and BMI Z-scores were calculated using World Health Organization (WHO) Anthroplus software (version 1.0.2; Department of Nutrition, WHO).[10],[11] Weight loss was evaluated qualitatively by questioning the patient or caregiver if they noticed some weight loss in the last 1 month and if possible, assessed quantitatively by the absolute difference between the usual weight (reported by the patient/caregiver) and current weight (on admission). A 5% weight loss in the previous month was considered significant
- Serum albumin concentration (g/L) was recorded from clinical blood report
- Arm anthropometry: MUAC and TSFT were measured at the halfway point between the acromion and olecranon process of the dependent right upper arm with the forearm held at a right angle. MUAC was measured with a fiberglass tape to the nearest 1 mm and TSFT was measured using a Harpenden Caliper (John Bull British Indicators Ltd., UK) to the nearest 0.2 mm at the same level of the site used for the MUAC. The measurements were repeated once, and the mean of the two measurements was used for analysis. Arm muscle circumference (AMC) was calculated from these two measurements as follows: AMC = MUAC − (TSFT × 0.314)
- Definition of malnutrition: Nutritional status was categorized into three groups according to the WHO criteria,[10],[11] arm anthropometry (TSFT, MUAC, and AMC) according to the classification of Frisancho,[12] and St. Jude Children's Research Hospital Algorithm [8],[13] as in [Table 1].
Statistical analysis
Statistical evaluation included descriptive statistics on children enrolled in the study. The Chi-square test was used to assess the association between diagnosis and prevalence of nutritional deficit as evaluated by TSFT, MUAC, and AMC. The nonparametric Kruskal–Wallis test was used to assess the association between diagnosis and nutritional deficit as measured by the BMI. Significance threshold (α) was set at 5%. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corporation was used for all analyses.
| » Results | | |
A total of 1693 children were audited in this study; 1187 had all anthropometric measurements performed, and 700 of this group (59%) also had measurements of serum albumin at presentation. The median age at diagnosis was 7.1 years (range, 2–15 years) and 69.5% were male.
- Prevalence of malnutrition (defined by WHO criteria): [Table 2] shows the data on weight, height, and BMI for all disease categories in 1693 patients. Overall, 38.4% of the patients were underweight, and this did not vary significantly by category of disease (overall Chi-square test: P > 0.05). Less than 18.5% were severely malnourished. The overall proportion of subjects who were severely malnourished as per BMI ranged from 14% in neuroblastoma to 26% in bone tumors. As measured conventionally by BMI Z-scores greater than the 2 standard deviation (SD) (85th percentile) and 3 SD (95th percentile), 8 (0.5%) and 14 (0.8%) children were overweight and obese, respectively, among the whole group and 0.8% and 1.3% in the adequately nourished group defined by BMI (n = 1045)
- Prevalence of malnutrition by various tools in different disease categories: [Table 3] shows the data on malnutrition using BMI, MUAC, TSFT, and AMC in the 1187 patients who had all the parameters available. MUAC was the most sensitive indicator and using only MUAC, TSFT, or BMI to categorize nutritional status, 75.7%, 56.9%, and 39.9% of patients were undernourished, respectively. The incremental value of MUAC over BMI alone to detect undernutrition was maximal in solid tumors including Wilms' tumor, neuroblastoma, and retinoblastoma
- Assessment of malnutrition by arm anthropometry: On this basis, 7.5% and 75% children were diagnosed as moderately undernourished and severely undernourished, respectively compared to 22% and 18% by BMI alone [Table 4]. Based on the St. Jude algorithm, 8.5% of patients were moderately depleted and 75.5% of patients were severely depleted. However, on further analysis, it was apparent that addition of TSFT to MUAC has only limited incremental value over MUAC alone as it detected only 1% more moderately undernourished children and 4% more severely undernourished children [Table 5]. Among the 700 children in whom nutritional status was evaluated according to arm anthropometry and serum albumin, the BMI was <3 SD in 1%, 0%, and 24% of those classified as adequately nourished (n = 121), moderately depleted (n = 52), and severely depleted (n = 527), respectively. Similarly, of 1187 children who had both BMI Z-scores and MUAC values, addition of BMI to MUAC alone detected only 8 (0.7%) more children as moderately malnourished which shows that MUAC alone may be a good single screening tool for detection of undernutrition
- Value of addition of BMI and albumin over arm anthropometry: [Table 6] and [Table 7] show the distribution of children by category of nutritional status using arm anthropometry alone and with addition of BMI or albumin, respectively. It is clearly apparent that there was minimal added value to the inclusion of BMI Z-scores and albumin with only 2% and 1.5% additional detection of undernourishment, respectively
- History of significant weight loss (1 month): The history of significant recent weight loss was available in 197 patients of whom 115 (58.3%) reported weight loss either qualitatively or quantitatively (>5%). There was added value to the inclusion of a history of recent weight loss to screen children at high nutritional risk [Table 8] as it detected an additional 16.6%, 41%, and 24% at-risk children over MUAC, TSFT, and BMI, respectively.
 | Table 2: Nutritional status according to disease groups based on weight, height, and body mass index
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 | Table 3: Prevalence of undernutrition across disease groups according to different anthropometric parameters
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 | Table 4: Distribution of children based on nutritional status: Comparison of arm anthropometry against body mass index alone
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 | Table 5: Distribution of children based on nutritional status: Comparison of arm anthropometry against mid-upper arm circumference alone
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 | Table 6: Distribution of children by category of nutritional status using 2 (triceps skinfold thickness, mid-upper arm circumference) or 3 (triceps skinfold thickness, mid-upper arm circumference, albumin) indicators
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 | Table 7: Distribution of children by category of nutritional status using 2 (triceps skinfold thickness, mid-upper arm circumference) or 3 (triceps skinfold thickness, mid-upper arm circumference, body mass index) indicators
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 | Table 8: Correlation of history of recent weight loss (1 month) with other anthropometric indicators
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 | Table 9: Comparisons of rates (%) of malnutrition in children with cancer in high-income countries versus low- - and middle-income countries and by weight-for-height or body mass index versus arm anthropometry (triceps skinfold thickness and mid-upper arm circumference)
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| » Discussion | | |
There is a paucity of adequately sized good quality studies on the prevalence of malnutrition in children with cancer in India despite the country having among the highest rates of malnutrition in childhood in the world.[14],[15],[16] In this, the largest study so far from India, the prevalence of malnutrition at the time of diagnosis of cancer was very high with 84% children having undernourishment, of whom 90% (75% overall) were severely depleted. Brinksma et al., in a review of studies from high-income countries, found that patients with leukemia had a low prevalence of malnutrition, about 5–10% at diagnosis and 0–5% during treatment, whereas the prevalence was 0–30% in children with solid tumors.[4] The prevalence rates generally depend on the type of methods and criteria used to assess nutritional status, timing of assessment, socioeconomic status, and the composition of the study population (types of malignancies).[5] In general, rates of undernutrition have been shown to be much higher in low- or low-middle-income countries (40–90%) compared to high- or upper-middle-income countries (0–30%)[4],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28] [Table 9]. Our rates are high in part because of high background rates of malnutrition in Indian children (~40%) and the poor socioeconomic status of children presenting to our center as 80% belong to low/low-middle socioeconomic strata.[1],[2],[3] We did not find any significant difference in the rates of undernutrition between different disease groups, consistent with similar large studies published recently.[25] On the other extreme, only 14 (0.8%) of children in our study were obese among the whole group, much lower than the 14% rate of obesity in a recent large study from the USA, reflecting the socioeconomic influence on nutritional status.[6] Although obesity is currently not a big problem in LMICs, with rising incomes, reduced activity and increasing “junk food” consumption, this is likely to become an important focus in future.
There is a limited research on the relative merits of various simple measures used to assess the nutritional status. Weight indices are the most commonly employed assessment tools in clinical practice. The weight for height/length or BMI Z-score has been shown to be the preferred indices in children with cancer. The present study showed that the BMI Z-score is an insensitive method for screening for undernutrition as it detected only half of the cases compared to arm anthropometry. Furthermore, only 18.5% of children were detected to have severe undernutrition with BMI compared to 75% by arm anthropometry. This is consistent with many recent studies which have supported the use of arm anthropometry, including MUAC and TSFT, as a preferred indicator of undernutrition in oncology patients.[8],[14],[18],[26],[28] BMI is often used incorrectly as a measure of body composition as it does not distinguish between fat mass and lean mass.[30] In children with adequate or excessive body weight, loss of lean body mass may be concealed as fat decreases or remains unchanged while skeletal muscle is wasting.[9],[29],[30] Conversely, MUAC and TSFT can be considered surrogate measures of lean and fat mass respectively.[9],[29] Furthermore, weight and BMI may be affected by hydration during chemotherapy, medication use, organ enlargement, or tumor mass and do not identify any long-term changes in body cell mass.[9],[30] In our study also, MUAC was significantly more sensitive in children with abdominal tumors, such asWilms' tumor and neuroblastoma, due to overestimation of body weight in these children because of large tumors. Finally, unlike BMI/weight measures, MUAC and TSFT are independent of ethnicity although having local normative data is definitely useful for comparison.[12] In our study, addition of BMI to arm anthropometry for detecting undernutrition had no benefit, suggesting that arm anthropometry can be used alone to screen for undernutrition.
One of the biggest challenges with complete arm anthropometry is the need for trained manpower as well as expensive calipers for doing TSFT, both of which may not be easily accessible in LMICs. In fact, despite the recommendation to use TSFT, a Children's Oncology Group survey of nutritional practices found that only 5% of institutions in the USA measured TSFT, reflecting the practical challenges in its routine measurement.[31] The present study showed that there was no advantage of using TSFT with MUAC over MUAC alone. Hence, MUAC appears to be the most sensitive, easy, inexpensive, and quick screening tool in children with cancer that can be done in all LMICs, even by volunteers and nurses, using simple colored plastic strips as recommended by WHO and UNICEF.[32] The only caveat is the need for reference charts for children more than 5 years of age. In the absence of charts, SIOP-PODC adaptive guidelines can be followed to rapidly screen for undernutrition in the LMIC setting.[33]
Serum albumin is a commonly utilized biochemical index of nutritional assessment despite being affected by infection/inflammation, fever, fluid shifts, liver or renal dysfunction and the use of drugs such as asparaginase as well as steroids.[9],[21] Recently, Sala et al., in a large Central American study, found that there was considerable value to the addition of serum albumin to arm anthropometry, especially in the proportion classified as severely depleted, raising this from 46% to 59%.[8] Other smaller studies found that serum albumin did not accurately reflect a depletion in body mass in pediatric oncology patients.[34] Our study found no added usefulness to serum albumin over MUAC alone. The difference from the finding of Sala et al.[8] remains unexplained.
An important yet simple tool recommended for use in oncology patients is recent weight loss over 1 or 3 months.[4],[9],[21] According to the recommended cut-off of more than 5% weight loss in the previous 1 month, 58% of 197 children were at high nutritional risk in our study. Furthermore, it identified 16.5% of children at nutritional risk who were labeled normal by MUAC. Although the number of children with documentation of weight loss was small, this suggests that the nutritional risk in a significant number may be missed in this population if weight loss is not taken into account. Unlike adult cancer patients, there are limited data on additional benefit of assessing weight loss over standard anthropometric tools in pediatric oncology, and it needs more investigation.
The main limitation of our study is its cross-sectional design, which did not allow the assessment of the impact of systematic and sequential nutritional intervention. Another important limitation is the estimated weight loss, which was made retrospectively, based on the caregiver's report of the patient's usual weight. Further, despite a large sample, its heterogeneity did not allow detailed analysis of disease-related factors. Further, the demographic and socioeconomic predictors of malnutrition were not studied. Despite those limitations, our results are of importance because there are, to date, very few adequately sized studies on the prevalence of malnutrition in children with cancer in India and the value of various nutritional assessment tools.
There is currently no clinical “gold standard” method for assessment of nutritional status in children since data comparing the different methods with outcomes such as morbidity or survival are limited. Hence, the choice of a method may depend upon the clinical situation, local logistics, and the aim of assessment. A combination of measures, as in the Central American study [8] and ours, may increase significantly the proportion of malnourished children, especially the proportion who are severely depleted. This possibly over-sensitive screening strategy, as used in previous community studies,[35] allows early detection and timely remediation of malnutrition which can improve survival in malnourished children with cancer.
Finally, it is clear from our study that malnutrition is highly prevalent among children with cancer in India, as elsewhere in the developing world. Body weight/BMI and serum albumin are not sufficiently sensitive tools for assessment of nutritional status in children with cancer, and incorporation of arm anthropometric measurement, especially MUAC, is urged. Given the limited infrastructure and human resource in LMICs, MUAC as a single screening tool for malnutrition in children with cancer is sensitive, quick, cheap, and easily doable by even minimally trained providers and at any level of care. It can prompt timely and effective nutritional interventions to reduce the malnutrition-related morbidity and mortality in LMIC.
Acknowledgments
The authors wish to thank Amey Paradkar MD and Roshni Sonone RD for their support for data entry and Nirav Thacker MD, Elena Ladas RN Ph.D., and Ronald Barr MD for critical review of the manuscript.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]
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