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Aim: To characterize the pediatric population that presented necrotizing soft tissue infection at the Fundación Hospital Pediátrico la Misericordia (HOMI) Methods: This was a retrospective study of pediatric patients with diagnosis of necrotizing soft tissue infections treated by the pediatric surgery service at HOMI, a referral hospital for oncological pediatric patients over a 5-year period (2017 to 2021). A descriptive analysis was performed. Results: The study included 52 patients, 65.4% were female, the average age was 8.1 years (sd: 5.53). The infection was most frequently located in the perineum (59.6%). Early symptoms were present in 88.5% (pain being the most frequent) and late symptoms in 73.1%. All but one patient had identifiable risk factors for infection, immunocompromised was the most frequent (73.1%), followed by oncological diagnosis (51.9%) and surgical procedures (abdominoperineal, colostomy and bone marrow biopsy, with 13.3% each one) All patients underwent surgical debridement (mean 3 procedures per patient, SD: 2.0) and 32.7% underwent colostomy. The most isolated microorganism in the tissues in monomicrobial and polymicrobial infections was Escherichia coli (11.5% in each group). 57.7% required management in the Intensive Care Unit. The average mortality was 23.1%, being higher in patients with acute lymphoblastic leukemia and neutropenia (35.0%). Conclusion: In the present study, the highest frequency of soft tissue infections was in the immunosuppressed and oncological population. The characteristics of our study and mortality were different from those described in the literature due to the predominance of this population.

Abstract Background Evidence on the relative costs and effects of interventions that do not consider ‘real-world’ constraints on implementation may be misleading. However, in many low- and middle-income countries, time and data scarcity mean that incorporating health system constraints in priority setting can be challenging. Methods We developed a ‘proof of concept’ method to empirically estimate health system constraints for inclusion in model-based economic evaluations, using intensified case-finding strategies (ICF) for tuberculosis (TB) in South Africa as an example. As part of a strategic planning process, we quantified the resources (fiscal and human) needed to scale up different ICF strategies (cough triage and WHO symptom screening). We identified and characterised three constraints through discussions with local stakeholders: (1) financial constraint: potential maximum increase in public TB financing available for new TB interventions; (2) human resource constraint: maximum current and future capacity among public sector nurses that could be dedicated to TB services; and (3) diagnostic supplies constraint: maximum ratio of Xpert MTB/RIF tests to TB notifications. We assessed the impact of these constraints on the costs of different ICF strategies. Results It would not be possible to reach the target coverage of ICF (as defined by policy makers) without addressing financial, human resource and diagnostic supplies constraints. The costs of addressing human resource constraints is substantial, increasing total TB programme costs during the period 2016–2035 by between 7% and 37% compared to assuming the expansion of ICF is unconstrained, depending on the ICF strategy chosen. Conclusions Failure to include the costs of relaxing constraints may provide misleading estimates of costs, and therefore cost-effectiveness. In turn, these could impact the local relevance and credibility of analyses, thereby increasing the risk of sub-optimal investments.

South Africa has the highest tuberculosis (TB) disease incidence rate in the world, and TB is the leading infectious cause of death. Decisions on, and funding for, TB prevention and care policies are decentralised to the provincial governments and therefore, tools to inform policy need to operate at this level. We describe the use of a mathematical model planning tool at provincial level in a high HIV and TB burden country, to estimate the impact on TB burden of achieving the 90-(90)-90 targets of the Stop TB Partnership Global Plan to End TB. "TIME Impact" is a freely available, user-friendly TB modelling tool. In collaboration with provincial TB programme staff, and the South African National TB Programme, models for three (of nine) provinces were calibrated to TB notifications, incidence, and screening data. Reported levels of TB programme activities were used as baseline inputs into the models, which were used to estimate the impact of scale-up of interventions focusing on screening, linkage to care and treatment success. All baseline models predicted a trend of decreasing TB incidence and mortality, consistent with recent data from South Africa. The projected impacts of the interventions differed by province and were greatly influenced by assumed current coverage levels. The absence of provincial TB burden estimates and uncertainty in current activity coverage levels were key data gaps. A user-friendly modelling tool allows TB burden and intervention impact projection at the sub-national level. Key sub-national data gaps should be addressed to improve the quality of sub-national model predictions.