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Severe acute malnutrition (SAM) affects 12.2 million children globally. Integrating a water, sanitation and hygiene (WASH) kit in outpatient SAM treatment can improve recovery rates by preventing WASH‐related diseases and complications, but its cost at scale remains unknown. This study estimates the cost of integrating a WASH kit, composed of chlorine‐based water treatment, safe water storage with a lid, soap, and a hygiene promotion component into Senegal's national protocol for treating uncomplicated SAM. This costing study was nested within the TISA randomised controlled trial, which evaluated the addition of a WASH component to standard SAM treatment for children aged 6–59 months. Cost data were collected from 660 participants enroled between December 2020 and December 2021. We took a societal perspective and used a micro‐costing approach to estimate direct medical, non‐medical and indirect costs. The WASH component led to a 2021 international$105.32 additional cost per child treated, with the WASH kit, transportation and management representing $ 33.03. Sensitisation to hygiene and water treatment cost$13.46 at health posts and $ 29.63 for two at‐home visits. No additional out‐of‐pocket expenses were incurred by households, but$1.58 in opportunity costs (income loss) was observed. Human resources were the main cost driver for the WASH component, exceeding the human resources for standard SAM treatment. The total societal cost per child treated was $ 338.77, ranging from$238.09 to $ 517.29 in sensitivity analysis, with the SAM treatment representing 69% ( $233.40) of this total cost. The main expense for this component was Ready‐to‐Use‐Therapeutic Food (RUTF) ($ 154.39). The absence of additional costs for households induced by the WASH component is encouraging, as it suggests that it would not represent an obstacle to integration into the national protocol. We produced a robust and comprehensive cost estimate for integrating a WASH kit and hygiene promotion into Senegal's SAM treatment protocol. This increased the treatment cost by 45% which was lower than estimates from a previous study. Results inform budget planning and support future cost‐effectiveness analyses of integrating WASH interventions into SAM protocols. Integrating a WASH component increased treatment costs by $105.32 per child treated, representing a 45% rise in total costs. The WASH component did not result in additional households out‐of‐pocket expenses, making it a viable option for scale‐up in similar low of middle income settings. The main expenses of the WASH component were the HR related to the training sessions and the at‐home sensitisations, as well as the purchase cost of the kit. These findings are useful for implementers, policymakers and donors aiming to improve SAM treatment outcomes while ensuring efficient resource allocation.

During the COVID-19 pandemic, risk stratification has been used to decide patient eligibility for inpatient, critical and domiciliary care. Here, we sought to validate the MSL-COVID-19 score, originally developed to predict COVID-19 mortality in Mexicans. Also, an adaptation of the formula is proposed for the prediction of COVID-19 severity in a triage setting (Nutri-CoV). We included patients evaluated from March 16th to August 17th, 2020 at the Instituto Nacional de Ciencias Médicas y Nutrición, defining severe COVID-19 as a composite of death, ICU admission or requirement for intubation (n = 3,007). We validated MSL-COVID-19 for prediction of mortality and severe disease. Using Elastic Net Cox regression, we trained (n = 1,831) and validated (n = 1,176) a model for prediction of severe COVID-19 using MSL-COVID-19 along with clinical assessments obtained at a triage setting. The variables included in MSL-COVID-19 are: pneumonia, early onset type 2 diabetes, age > 65 years, chronic kidney disease, any form of immunosuppression, COPD, obesity, diabetes, and age <40 years. MSL-COVID-19 had good performance to predict COVID-19 mortality (c-statistic = 0.722, 95%CI 0.690-0.753) and severity (c-statistic = 0.777, 95%CI 0.753-0.801). The Nutri-CoV score includes the MSL-COVID-19 plus respiratory rate, and pulse oximetry. This tool had better performance in both training (c-statistic = 0.797, 95%CI 0.765-0.826) and validation cohorts (c-statistic = 0.772, 95%CI 0.0.745-0.800) compared to other severity scores. MSL-COVID-19 predicts inpatient COVID-19 lethality. The Nutri-CoV score is an adaptation of MSL-COVID-19 to be used in a triage environment. Both scores have been deployed as web-based tools for clinical use in a triage setting.

IntroductionDiabetes and hyperglycemia are risk factors for critical COVID-19 outcomes; however, the impact of pre-diabetes and previously unidentified cases of diabetes remains undefined. Here, we profiled hospitalized patients with undiagnosed type 2 diabetes and pre-diabetes to evaluate its impact on adverse COVID-19 outcomes. We also explored the role of de novo and intrahospital hyperglycemia in mediating critical COVID-19 outcomes.Research design and methodsProspective cohort of 317 hospitalized COVID-19 cases from a Mexico City reference center. Type 2 diabetes was defined as previous diagnosis or treatment with diabetes medication, undiagnosed diabetes and pre-diabetes using glycosylated hemoglobin (HbA1c) American Diabetes Association (ADA) criteria and de novo or intrahospital hyperglycemia as fasting plasma glucose (FPG) ≥140 mg/dL. Logistic and Cox proportional regression models were used to model risk for COVID-19 outcomes.ResultsOverall, 159 cases (50.2%) had type 2 diabetes and 125 had pre-diabetes (39.4%), while 31.4% of patients with type 2 diabetes were previously undiagnosed. Among 20.0% of pre-diabetes cases and 6.1% of normal-range HbA1c had de novo hyperglycemia. FPG was the better predictor for critical COVID-19 compared with HbA1c. Undiagnosed type 2 diabetes (OR: 5.76, 95% CI 1.46 to 27.11) and pre-diabetes (OR: 4.15, 95% CI 1.29 to 16.75) conferred increased risk of severe COVID-19. De novo/intrahospital hyperglycemia predicted critical COVID-19 outcomes independent of diabetes status.ConclusionsUndiagnosed type 2 diabetes, pre-diabetes and de novo hyperglycemia are risk factors for critical COVID-19. HbA1c must be measured early to adequately assess individual risk considering the large rates of undiagnosed type 2 diabetes in Mexico.