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Current formulations of ready-to-use therapeutic foods (RUTFs) to treat severe acute malnutrition (SAM) in children focus on nutrient density and quantity. Less attention is given to foods targeting gut microbiota metabolism and mucosal barrier functions. Heat-stabilised rice bran contains essential nutrients, prebiotics, vitamins and unique phytochemicals that have demonstrated favourable bioactivity to modulate gut microbiota composition and mucosal immunity. This study seeks to examine the impact of RUTF with rice bran on the microbiota during SAM treatment, recovery and post-treatment growth outcomes in Jember, Indonesia. Findings are expected to provide insights into rice bran as a novel food ingredient to improve SAM treatment outcomes.
A total of 200 children aged 6–59 months with uncomplicated SAM (weight-for-height z-scores (WHZ)
The findings of this trial will be submitted to peer-reviewed journals and will be presented at relevant conferences. Ethics approval obtained from the Medical and Health Research Ethical Committee at the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Madain Yogyakarta Ref. No.: KE/FK/0546/EC/2022 and KE/FK/0703/EC/2023 and from Colorado State University IRB#1823, OHRP FWA00000647.
Background
A realist approach has gained popularity in evaluation research, particularly in understanding causal explanations of how a program works (or not), the circumstances, and the observed outcomes. In qualitative inquiry, the approach has contributed to better theoretically based explanations regarding causal interactions.
Objective
The aim of this study was to discuss how we conducted a realist-informed data analysis to explore the causal interactions within qualitative data.
Methods
We demonstrated a four-step realist approach of retroductive theorizing in qualitative data analysis using a concrete example from our empirical research rooted in the critical realism philosophical stance. These steps include (a) category identification, (b) elaboration of context-mechanism-outcome configuration, (c) demi-regularities identification, and (d) generative mechanism refinement.
Results
The four-step qualitative realist data analysis underpins the causal interactions of important factors and reveals the underlying mechanisms. The steps produce comprehensive causal explanations that can be used by related parties—especially when making complex decisions that may affect wide communities.
Discussion
The core process of realist data analysis is retroductive theorizing. The four-step qualitative realist data analysis facilitates this theorizing by allowing the researcher to identify (a) patterns, (b) fluctuation of patterns, (c) mechanisms from collected data, and (d) to confirm proposed mechanisms. by Idham Jaya Ganda, Try Kartika Eka Putri, Syarifuddin Rauf, Amiruddin Laompo, Ninny Meutia Pelupessy, Sitti Aizah Lawang, Nadirah Rasyid Ridha, Bahrul Fikri, Muhammad Nasrum Massi
IntroductionDysregulated immune responses are developed in Coronavirus disease-2019 (COVID-19) and Interleukin-6 (IL-6) levels are reflecting the severity of the clinical presentation. This study aimed to analyze IL-6 serum level, Acute Respiratory Distress Syndrome (ARDS), and Acute Kidney Injury (AKI) as risk factors for mortality in children with COVID-19.
MethodsThis prospective cohort study was conducted on children with COVID-19 infection confirmed by Real Time Polymerase Chain Reaction (RT-PCR) who were admitted to infection center at Dr. Wahidin Sudirohusodo Hospital from September 2021 to September 2022. Subjects were selected using the consecutive sampling method.
ResultsA total of 2,060 COVID-19 RT-PCR tests were performed, and 1,065 children were confirmed positive. There were 291 cases that met the inclusion criteria, with 28.52 percent non-survives and 71.48% survives. The risk factors for mortality were IL-6, ARDS, AKI, Prothrombin Time / Activated Partial Thromboplastin Time (PT/aPTT), oxygen saturation, Absolut lymphocyte count (ALC), leukocytes, Length of Stay (LOS), and nutritional status (p80.97 pg/ml with 93% sensitivity and 90% specificity. Area Under Curve was 0.981 (95% CI), 0.960–1.000). A multivariate analysis showed IL-6 levels with OR 18.570 (95% CI 5.320–64.803), ARDS with Odds Ratio (OR) 10.177, (95% Confidence Interval (CI) 1.310–9.040), and AKI with OR 3.220 (95% CI 1.070–10.362). A combination of increased IL-6, ARDS, and AKI can predict a mortality probability as high as 98.3%.
ConclusionIL-6, ARDS, and AKI are risk factors for mortality in children with COVID-19. IL-6 level was the highest mortality risk factor.
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