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Original Research Articles: Original Clinical Research Report

A Prospective, Cohort Study of the Effect of Acute and Chronic Malnutrition on Length of Stay in Children Having Surgery in Rwanda

Seneza, Celestin MD, MMed*; McIsaac, Daniel I. MD; Twagirumugabe, Theogene MD, PhD; Bould, M. Dylan MB, ChB, MEd§

Author Information
doi: 10.1213/ANE.0000000000005956

Abstract

KEY POINTS

  • Question: What is the effect of malnutrition on length of stay (LOS) among pediatric surgical patients in Rwanda?
  • Findings: Malnutrition is independently associated with increased LOS after surgery.
  • Meaning: Some malnutrition may be due to the surgical condition; however, it may also be a modifiable social determinant of health.

Studies in high-income countries have identified malnutrition as a potentially modifiable risk factor for poor perioperative outcomes in children.1 However, recent literature reviews found no strong association between preoperative malnutrition and poor surgical outcomes.2,3 Existing data are heterogeneous in terms of exposure and outcome definitions, and a recent US study found that low height for age (stunting, a marker of chronic malnutrition) was associated with increased risk of postoperative complications, whereas low weight for height (wasting, a marker of acute malnutrition) was not.4

There is a paucity of literature on preoperative malnutrition in children in sub-Saharan Africa, including Rwanda, a low-income country in East Africa. While preoperative malnutrition may be due to the surgical pathology, malnutrition is also common in the general population of Rwandan children due to a high prevalence of food insecurity. In Rwanda, there is no routine assessment of preoperative nutritional status or the need for nutritional support in children undergoing surgery. Even at a tertiary center, a child’s perioperative nutrition is generally the responsibility of the parents. Moreover, surgical outcomes tend to be worse in low- and middle-income countries,5 with children at particular risk. For these reasons, one might expect that pediatric preoperative malnutrition would be more common in Rwanda than in high-income settings, and that it may be more strongly associated with negative outcomes. Data from Zambia, a lower middle-income country in southern Africa, found an 8% incidence of severe acute malnutrition in a pediatric surgical population.6 A recent study from Zimbabwe, another lower middle-income southern African country, found a 7-fold increase in postoperative complications in malnourished children.7

We aimed to determine the prevalence of both chronic malnutrition (or stunting, as defined by low height for age) and acute malnutrition (or wasting, as defined by low weight for height) in children presenting for surgery in a Rwandan tertiary referral center. We hypothesized that both chronic and acute malnutrition would be associated with increased length of stay (LOS) in-hospital after surgery.

METHODS

This study was approved by the Institutional Research Ethics Committees of the University of Rwanda (No. 055/CMHS IRB/2018) and the Kigali University Teaching Hospital (CHUK) ethics committee (EC/CHUK/568/2018). Written informed patient consent was obtained from all parents, and written assent was obtained from patients 7 years of age or older. This manuscript adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.8

We undertook a prospective observational cohort study in children undergoing surgery in a Rwandan tertiary referral hospital. Data collection occurred between May 15, 2018, and November 15, 2018. Children were recruited after they were scheduled for elective or emergency surgery at the CHUK. Data on patient characteristics, as well as measurement of height and weight, were collected during preoperative assessment for anesthesia. Children were followed daily until discharge from hospital or death (in cases of in-hospital mortality), using ward registers and the patient case notes. Data were collected by trained research assistants.

We aimed to recruit every consecutive child presenting for surgery at CHUK between 1 month and 15 years of age, the latter being the accepted cutoff age for pediatric patients at that institution. No patients or families declined to participate in the study.

The primary outcome measure was LOS in the hospital after surgery, measured in days. The secondary outcome measure was in-hospital mortality. Exposures were chronic and acute malnutrition. Chronic malnutrition or stunting (low height for age) and acute malnutrition or wasting (low weight for height) were defined according to the World Health Organization growth charts.9,10 Moderate malnutrition was defined as being between 2 and 3 Z-scores below the median (wasted or stunted) and severe malnutrition was defined as being >3 Z-scores below the median (severely wasted/severely stunted). Weight was measured with a digital weight scale (HN 289, Omron). Height measurement depended on the patient’s age and ability to stand, but generally, the height was measured in recumbent position for patient younger than 2 years with a tape measure and in a standing position for patients older than 2 years using a height board. Data were also collected on potential confounders including age, sex, American Society of Anesthesiologists (ASA) Physical Status Classification System,11 whether surgery was elective or emergency, whether the surgery was major or minor (according to previously published criteria),12 the Ubudehe socioeconomic classification, and whether the child had a medical condition considered to cause malnutrition as per the pediatric nutrition risk score (PNRS).13 The 2015 Rwandan Ministry of Local Government Ubudehe classification is a classification of household income and determines coverage by the government health insurance policy.14 Ubudehe category 1 refers to very poor and vulnerable citizens who are homeless and unable to feed themselves; category 2 refers to citizens who have rented accommodation but are not employed and can only afford to feed themselves once or twice a day; category 3 refers to citizens who are employed and may themselves employ laborers, including owners of small- or medium-size enterprises; and category 4 refers to the wealthiest citizens including chief executive officers (CEOs) of large businesses and government employees. Minor surgery was defined as generally being associated with minimal blood loss, minimal fluid shifts and done on an ambulatory basis, including most cutaneous and superficial procedures. Major surgery was defined as surgery with an expected blood loss of >20 mL/kg and significant fluid shifts.15

Sample Size

Estimating adequate sample size to achieve 90% power to identify a clinically significant difference in LOS in this population is difficult as there is no established minimally important difference for LOS, LOS values are not well reported for Rwandan pediatric surgery patients, and such data often follow a skewed distribution. Using the mean value reported by Bergkvist et al7 from Zimbabwe (3, no standard deviation reported), postulating a minimally important difference of 1 day, and assuming a standard deviation of 4, we retrospectively estimate that, based on an equal variance t test, we would require 674 participants if they were equally distributed between 2 exposure groups respecting an alpha = 0.05.

As we were working in a resource-limited environment, recognized the need to use multivariable-adjusted generalized linear models to account for an expected nonnormal distribution, and which require simulation to estimate power using parameter estimates that were not available to us, we were limited to using a pragmatic approach to sample size estimation. Therefore, we collected data on all available patients within a 6-month period when we had sufficient human resources available to allow data collection. On the basis of previous case logs, we estimated recruiting around 600 children (100/mo), which would make the sample similar or higher than most other published papers investigating outcomes in pediatric patients with malnutrition internationally.16 However, we recognized that our study could be underpowered, especially if certain exposure categories were sparsely populated.

Statistical Analysis

All data management and analyses were performed using SAS v9.4 (SAS Institute). Descriptive statistics were used to compare measured characteristics between malnutrition categories using analysis of variance (normal, continuous variables), Kruskal-Wallis tests (skewed, continuous variables), or χ2 tests (categorical variables). Skew was determined based on visual inspection. Our primary outcome was prespecified as LOS, and a 5% level of significance was applied to all analyses without adjustment for multiple comparisons. However, a conservative approach accounting for our 2 analyses (ie, acute and chronic malnutrition) would suggest a 2.5% level of significance. Sensitivity analyses were considered exploratory.

Table 1. - Patient Characteristics
Characteristic No/mild stunting Moderate stunting Severe stunting P value No/mild wasting Moderate wasting Severe wasting P value All patients
Number 433 (73.0%) 79 (13.3%) 81 (13.7%) 469 (79.0%) 85 (14.5%) 39 (6.6%) 593 (100%)
Sex (male) 274 (63.3%) 59 (74.7%) 62 (75.3%) .03 303 (65.3%) 60 (70.6%) 29 (74.4%) .37 394 (66.4%)
Age (mo) 60 (25–96) 44 (24–144) 24 (6–66) <.001 50 (25–96) 60 (12–156) 15 (1–84) .001 48 (18–96)
Ubudehe
 Missing 19 (4.4%) 2 (2.5%) 2 (2.5%) .02 18 (3.9%) 5 (5.9%) 0 (0.0%) .002 23 (3.9%)
 1 48 (11.1%) 18 (22.8%) 20 (24.7%) 55 (11.9%) 16 (18.8%) 14 (35.9%) 86 (14.5%)
 2 138 (31.9%) 25 (31.6%) 27 (33.3%) 145 (31.3%) 32 (37.6%) 12 (30.8%) 190 (32.0%)
 3 225 (52.0%) 34 (43.0%) 32 (39.5%) 243 (52.4%) 32 (37.6%) 13 (33.3%) 291(49.1%)
 4 3 (0.7%) 0 (0.0%) 0 (0.0%) 3 (0.6%) 0 (0.0%) 0 (0.0%) 3 (0.5%)
ASA classification
 I 335 (77.5%) 50 (63.3%) 48 (60.0%) <.001 361 (78.1%) 53 (62.4%) 18 (46.2%) <.001 433 (73.0%)
 II 72 (16.7%) 15 (19.0%) 23 (28.7%) 74 (16.0%) 21 (24.7%) 13 (33.3%) 110 (18.5%)
 III 23 (5.3%) 13 (16.5%) 9 (11.3%) 25 (5.4%) 11 (12.7%) 7 (17.9%) 45 (7.6%)
 IV 2 (0.5%) 1 (1.3%) 0 (0.0%) 2 (0.4%) 0 (0.0%) 1 (2.6%) 3 (0.5%)
 V 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
Type of anesthesia
 GA 424 (97.9%) 75 (94.9%) 80 (98.9%) .21 454 (97.8%) 82 (96.5%) 38 (97.4%) .75 579 (97.6%)
 Spinal 9 (2.1%) 4 (5.1%) 1 (1.2%) 10 (2.2%) 3 (3.5%) 1 (2.6%) 14 (2.4%)
Case urgency
 Elective 362 (83.6%) 62 (78.5%) 63 (77.8%) .07 385 (83.0%) 65 (76.5%) 32 (82.1%) .68 487 (82.1%)
 Urgent/emergent 71 (16.4%) 17 (21.5%) 18 (22.2%) 79 (17.0%) 20 (23.5%) 7 (17.9%) 106 (17.9%)
Major surgical case 193 (77.8%) 47 (85.5%) 34 (82.9%) .38 196 (75.7%) 54 (94.7%) 22 (84.6%) .004 274 (79.4%)
Surgery on GI tract 43 (17.3%) 12 (21.8%) 21 (51.2%) <.001 50 (19.3%) 17 (29.8%) 7 (26.9%) .17 76 (22.0%)
Numbers are number (percentage) or median (interquartile range). Wasting (acute malnutrition) is defined as low weight for height, either between 2 and 3 Z-scores below the median (moderate wasting) or >3 Z-scores below the median (severe wasting). Stunting (chronic malnutrition) is defined as low weight for height, either between 2 and 3 Z-scores below the median (moderate stunting) or >3 Z-scores below the median (severe stunting). The Ubudehe classification system categorizes all Rwandan households into 4 socioeconomic classes, ranging from the poorest households (category 1) to the richest households (category 4).
Abbreviations: ASA indicates American Society of Anesthesiologists; GA, general anesthesia; GI, gastrointestinal.

We then estimated the unadjusted and adjusted associations of chronic and acute malnutrition with LOS using generalized linear models with a log-link and gamma response distribution (based on recommendations for LOS modeling in surgical patients, where a long right-sided tail is common in the distribution).17 Exponentiation of the regression coefficient of a log-gamma model results in a ratio of means (RoM), which represents the relative increase in LOS in the exposed, compared to reference, group. Separate models were derived for acute and chronic malnutrition with a sensitivity analysis for patients with both acute and chronic malnutrition. Each exposure was parameterized as a categorical 3-level variable, and the reference category was the group of patients with either no malnutrition or mild malnutrition in each case. Adjusted models also contained terms for age (in months, parameterized based on the best fitting fractional polynomial [the inverse square root]), sex (binary), Ubudehe socioeconomic indicator (categorical), ASA score (3-level categorical [1, 2, and ≥3]), elective versus emergency surgery (binary), gastrointestinal (GI) versus non-GI surgery (binary), major versus minor surgery (binary), and a 3-way multiplicative interaction term between surgical urgency, whether the surgery was on the GI tract and the classification of the surgical case as major or minor. There were few patients in the highest (wealthiest) level of the Ubudehe in our study (n = 3, 0.5%) and so we decided, post hoc, to collapse Ubudehe levels 3 and 4 together. We also used the predicted LOS values from the adjusted models to estimate the absolute number of days’ difference between exposure categories (along with percentile-based confidence intervals) across 5000 bootstrap samples generated with 1:1 sampling with replacement.

Missing Data

Five participants were lost to follow-up for the primary outcome. Data on the ASA Physical Status Classification were missing for 2 patients; otherwise, there were complete data for all included children. To assess the impact of missing data, we reran our adjusted analyses using multiple imputations. Five imputation data sets were created using all available outcome and covariate data, with LOS imputation based on predictive mean matching (as the underlying distribution was skewed).

RESULTS

A total of 593 consecutive pediatric surgical patients aged between 1 month and 15 years of age were recruited in this study, with no family declining to participate. Five cases were lost to follow up, leaving 588 available for our primary, complete case analysis. Patient characteristics are shown in Table 1. A total of 160 (26.9%) children had chronic malnutrition (stunting), of which 81 (13.7%) were severely stunted. A total of 124 children (21.2%) had acute malnutrition (wasting) of which 39 were (6.6%) severely wasted. A total of 52 children (8.8%) had both acute and chronic malnutrition.

Outcomes

Adjusted and unadjusted estimates of the effects of wasting on LOS are detailed in Table 2. Adjusted and unadjusted estimates of the effects of stunting on LOS are detailed in Table 3. Adjusted and unadjusted estimates of the effects of combined wasting and stunting on LOS are detailed in Table 4. The full models are included in Supplemental Digital Content 1, Online Appendices 1 and 2, https://links.lww.com/AA/D884. Bootstrap analysis estimated an additional 3.7 days in-hospital (95% confidence interval [CI], 1.9–4.6) postoperatively if patients had severe chronic malnutrition, but the effect of moderate chronic malnutrition was not statistically significant in the adjusted analyses. Bootstrap analysis estimated an additional 4.3-day in-hospital (95% CI, 3.1–5.1) postoperatively if patients have moderate chronic malnutrition, but the effect of severe chronic malnutrition was not statistically significant in the adjusted analyses. Bootstrap analysis estimates an additional 5.6-day in-hospital (95% CI, 3.8–9.4) if patients had both chronic and acute malnutrition.

Table 2. - Analysis of the Effects of Wasting (Acute Malnutrition) on Length of Stay
Number of patients Length of stay (median [IQR]) Unadjusted RoM(95% CI) Adjusted RoM(95% CI) Adjusted difference(95% CI)
No or mild wasting 464 2 (1–5) Reference category
Moderate wasting 85 6 (3–12) 2.0 (1.6–2.5)a 1.6 (1.3–1.9)a 4.3 (3.1–5.1)
Severe wasting 39 6 (2–15) 2.4 (1.7–3.2)a 1.3 (0.9–1.7)b 6.0 (−3 to 8.5)
Adjusted difference refers to the median extra days of attributable length of stay for each category of the exposure over 5000 bootstrap samples.
Abbreviations: CI, confidence interval; IQR, interquartile range; RoM, ratio of means.
aP < .001.
bP = .13.

Table 3. - Analysis of the Effects of Stunting (Chronic Malnutrition) on Length of Stay
Number of patients Length of stay (median [IQR]) Unadjusted RoM(95% CI) Adjusted RoM(95% CI) Adjusted difference(95% CI)
No or mild stunting 431 2 (1–6) Reference category
Moderate stunting 79 3 (1–10) 1.4 (1.2–1.8)a 1.1 (0.9–1.3)b 1.2 (−0.3 to 2.2)
Severe stunting 78 5 (2.5–11.5) 1.9 (1.5–2.4)c 1.4 (1.1–1.7)d 3.7 (1.9–4.6)
Adjusted difference refers to the median extra days of attributable length of stay for each category of the exposure over 5000 bootstrap samples.
Abbreviations: CI, confidence interval; IQR, interquartile range; RoM, ratio of means.
aP < .001.
bP = .56.
cP = .12.
dP = .003.

Table 4. - Analysis of the Effects of Combined Stunting (Chronic Malnutrition) and Wasting (Acute Malnutrition) on Length of Stay
Number of patients Length of stay (median [IQR]) Unadjusted RoM (95% CI) Adjusted RoM (95% CI) Adjusted difference (95% CI)
Neither stunting nor wastinga 359 2.0 (1.0–5.0) Reference category
Moderate or severe stunting only 72 4.0 (2.0–11.0) 1.5 (1.3–1.9)b 1.1 (0.9–1.4)c 1.3 (−0.4 to 2.2)
Moderate or severe wasting only 105 6.0 (2.0–13.0) 2.0 (1.6–2.6)d 1.4 (1.2–1.8)e 3.7 (2.7–5.0)
Moderate or severe stunting and moderate or severe wasting 52 6.0 (3.0–19.25) 2.8 (2.2–3.7)d 1.7 (1.4–2.2)d 5.6 (3.8–9.4)
Abbreviations: CI, confidence interval; IQR, interquartile range; RoM, ratio of means.
aThis line refers to patients having neither moderate/severe stunting nor moderate/severe wasting. Adjusted difference refers to the median extra days of attributable length of stay for each category of the exposure over 5000 bootstrap samples.
bP = .001.
cP = .20.
dP = .001.
e P = .0008.

Multiply imputed analyses were identical to complete case analyses (Supplemental Digital Content 1, Online Appendix 3, https://links.lww.com/AA/D884).

Nine patients died (1.5%) in-hospital postoperatively, of these, 6 deaths were in children with no/mild stunting (1.4%), no deaths were in children with moderate stunting, and 3 deaths (3.8%) were in children with severe stunting. Of the 9 children who died, 4 deaths (0.9%) were in children with no wasting, 2 deaths (2.4%) were in children with moderate wasting, and 3 deaths (7.7%) were in children with severe wasting. Median (interquartile range [IQR] [range]) LOS was 3 (1–6 [0–75]) days.

DISCUSSION

In a single-center cohort study from a Rwandan tertiary care center we found a high prevalence of both acute and chronic malnutrition (21.1% and 26.9%, respectively), although the rate of chronic malnutrition was lower than that reported in the Rwandan population, which is 33% in children under 5.18 In contrast, the prevalence of acute malnutrition was much higher than the national prevalence, which is only around 1%. The higher prevalence of acute malnutrition in our study compared to the national average may be related to the underlying disease and clinical conditions affecting nutritional status. The lower rate of chronic malnutrition may reflect progress in nutrition in Rwanda since the last national statistics.

Both acute and severe chronic malnutrition were associated with increased LOS after surgery, including after adjustment for confounders. These data suggest that malnutrition may be an important and potentially modifiable risk factor in pediatric surgical patients.

There are several important implications of these findings. Although we did not investigate postoperative morbidity, and our study is underpowered to investigate the effect of malnutrition on perioperative mortality, it seems likely that the increased LOS could be related to poor postoperative outcomes. Malnutrition, and especially chronic malnutrition, represents a modifiable social determinant of health, and our data support further investment by key stakeholders in Rwandan nutrition programs. Malnutrition is not routinely screened for pediatric surgical patients at CHUK, and preoperative assessment and identification of the patient with malnutrition or at risk from malnutrition may allow optimization of nutritional status before elective surgery. Future work is required to identify whether screening for malnutrition and appropriate management would improve surgical outcomes. Although we included major versus minor surgery in the analysis, we were not able to adjust for individual procedures, which were too numerous to be included in the model as a categorical variable.

We did not collect data on the cost attributed to the increased LOS in our study, but it seems highly likely that increased LOS may be associated with increased costs. Potential increased costs are likely to be largely met by the Rwandan health care system, as 90% of inpatient costs for patients in Ubudehe categories 2 to 4 (81% of our sample) and 100% of costs for patients in Ubudehe category 1 are covered by government health insurance. However, even 10% of health care costs often prove unmanageable for patients in Ubudehe category 2, and we note that >80 million people are globally driven to catastrophic expenditure from the costs of surgery alone.19

To the best of our knowledge, there are no other studies looking at the effect of malnutrition on pediatric surgical outcomes in low-income settings. Bergkvist et al7 investigated malnutrition in 136 children in a tertiary center in Zimbabwe, a middle-income country in southern Africa. They found less stunting (21%) and wasting (12%) than our sample despite a similar population in terms of surgical urgency, ASA score, and age, and that undernutrition was associated with an increase in any postoperative complication (mortality, surgical site infection, reoperation, and readmission) with an odds ratio of 7.2 (2.3–22.8). These data, with our own sample, support the hypothesis that malnutrition in children increases risk for poor postoperative outcomes.

This study has important limitations. Data are from a single tertiary referral center, in fact the only institution with a pediatric surgeon in Rwanda, and may not reflect the situation in other institutions in Rwanda, particularly district hospitals in rural areas. The effect of malnutrition on LOS was not consistent across severity of malnutrition, and in particular, there is no clear explanation why moderate acute malnutrition would be associated with increased LOS but not severe acute malnutrition. These inconsistencies may be due to the study being underpowered. We acknowledge the possibility of residual confounding due to unadjusted covariates/factors that might be associated with the LOS outcome. LOS may be affected by other issues than the patients’ medical status and recovery from surgery; however, our study did not collect data on the cause of delays in discharge. While missing data were present only for 1% of cases, the mechanism underlying these missing data and the extent to which data were randomly missing are unknown. Our primary approach (complete case analysis) and our sensitivity analysis using multiple imputations both rely on substantial assumptions. Reassuringly, results of both analyses were consistent, suggesting that our findings were robust to the impacts of these missing values.

In conclusion, malnutrition is prevalent and associated with increased LOS after surgery, even after adjusting for individual and family-level confounders. Although some of this malnutrition may be related to the surgical condition, severe malnutrition may represent a modifiable social risk factor that could be targeted to improve postoperative outcomes and resource use. Future research should include larger scale data sets powered to look at mortality as well as interventional studies to manage nutrition in children identified at risk of perioperative malnutrition.

ACKNOWLEDGMENTS

We thank the head of the Department of Anesthesia of the University of Rwanda, Dr Paulin Banguti Ruhato, for his support with this project. We also thank Vital Muvunyi, research assistant, for data collection.

DISCLOSURES

Name: Celestin Seneza, MD, MMed.

Contribution: This author helped with development of the research question, protocol development, ethics application, data collection, drafting the first version of the manuscript, and critical review of the manuscript.

Name: Daniel I. McIsaac, MD.

Contribution: This author helped with the design, analysis, and critical review of the manuscript.

Name: Theogene Twagirumugabe, MD, PhD.

Contribution: This author helped with critical review of the manuscript.

Name: M. Dylan Bould, MB, ChB, MEd.

Contribution: This author helped with development of the research question, protocol development, drafting the first version of the manuscript, and critical review of the manuscript.

This manuscript was handled by: Angela Enright, MB, FRCPC.

    REFERENCES

    1. Roberson ML, Egberg MD, Strassle PD, Phillips MR. Measuring malnutrition and its impact on pediatric surgery outcomes: a NSQIP-P analysis. J Pediatr Surg. 2021;56:439–445.
    2. Wessner S, Burjonrappa S. Review of nutritional assessment and clinical outcomes in pediatric surgical patients: does preoperative nutritional assessment impact clinical outcomes? J Pediatr Surg. 2014;49:823–830.
    3. Hill R, Paulus S, Dey P, Hurley MA, Carter B. Is undernutrition prognostic of infection complications in children undergoing surgery? A systematic review. J Hosp Infect. 2016;93:12–21.
    4. Alshehri A, Afshar K, Bedford J, Hintz G, Skarsgard ED. The relationship between preoperative nutritional state and adverse outcome following abdominal and thoracic surgery in children: results from the NSQIP database. J Pediatr Surg. 2018;53:1046–1051.
    5. Torborg A, Cronje L, Thomas J, et al.; South African Paediatric Surgical Outcomes Study Investigators. South African paediatric surgical outcomes study: a 14-day prospective, observational cohort study of paediatric surgical patients. Br J Anaesth. 2019;122:224–232.
    6. Bowen L, Zyambo M, Snell D, Kinnear J, Bould MD. Evaluation of the accuracy of common weight estimation formulae in a Zambian paediatric surgical population. Anaesthesia. 2017;72:470–478.
    7. Bergkvist E, Zimunhu T, Mbanje C, Hagander L, Muguti GI. Nutritional status and outcome of surgery: a prospective observational cohort study of children at a tertiary surgical hospital in Harare, Zimbabwe. J Pediatr Surg. 2020; 56:368-373.
    8. Equator Network. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Accessed November 19, 2020. https://www.equator-network.org/reporting-guidelines/strobe/.
    9. WHO Child Growth Standards. Accessed November 19, 2020. https://www.who.int/toolkits/child-growth-standards/standards.
    10. UNICEF 2019 Edition of the Joint Child Malnutrition Estimates. Accessed November 19, 2020. https://www.unicef.org/reports/joint-child-malnutrition-estimates-levels-and-trends-child-malnutrition-2019.
    11. American Society of Anesthesiologists' Physical Status Classification System. Accessed November 19, 2020. https://www.asahq.org/standards-and-guidelines/asa-physical-status-classification-system.
    12. Winnipeg Regional Health Authority. Routine Preoperative Lab Test Guidelines. Accessed May 11. https://professionals.wrha.mb.ca/old/extranet/eipt/files/EIPT-003-002.pdf.
    13. Sermet-Gaudelus I, Poisson-Salomon AS, Colomb V, et al. Simple pediatric nutritional risk score to identify children at risk of malnutrition. Am J Clin Nutr. 2000;72:64–70.
    14. Ezeanya C. The Rise of Homegrown Ideas and Grassroots Voices: New Directions in Social Policy in Rwanda. United National Research Institute for Social Development. 2017. Accessed March 24, 2022. https://www.unrisd.org/unrisd/website/document.nsf/(httpPublications)/3AC45BEF8587AD6AC1258122003E9475?OpenDocument.
    15. Accessed November 19, 2020. https://professionals.wrha.mb.ca/old/extranet/eipt/files/EIPT-003-001.pdf.
    16. Daskalou E, Galli-Tsinopoulou A, Karagiozoglou-Lampoudi T, Augoustides-Savvopoulou P. Malnutrition in hospitalized pediatric patients: assessment, prevalence, and association to adverse outcomes. J Am Coll Nutr. 2016;35:372–380.
    17. Austin PC, Rothwell DM, Tu JV. A comparison of statistical modeling strategies for analyzing length of stay after CABG surgery. Health Serv Outcomes Res Methodol. 2002;3;107–133.
    18. Accessed November 19, 2020. https://www.who.int/nutgrowthdb/estimates/.
    19. Shrime MG, Dare AJ, Alkire BC, O’Neill K, Meara JG. Catastrophic expenditure to pay for surgery worldwide: a modelling study. Lancet Glob Health. 2015;3(suppl 2):S38–S44.

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