Background The emergence of antimalarial drug resistance poses a major threat to effective malaria treatment and control in sub-Saharan Africa. The RTS, S/AS01 vaccine has the potential to reduce both resistant infections and antimalarial use. Modeling studies projecting aggregate health burden averted under different scenarios can support further vaccine development and implementation. Methods A mathematical model projecting cases, drug-resistant cases, and deaths averted from 2021 to 2030 with a vaccine against clinical malaria caused by Plasmodium falciparum administered yearly to one-year-olds in the WHO Africa Region. Findings Under a scenario in which vaccine efficacy (VE) was constant at 40% for four years and dropped to 0% in year five, approximately 92.5 million cases, 700,000 resistant cases, and 253,000 deaths were averted by 2030. In a scenario in which VE began at 80% and dropped 20 percentage points each year, approximately 123 million cases, one million resistant cases, and 336,000 deaths were averted. The highest burden averted occurred when VE remained 40% for 10 years with approximately 151 million cases, 1.1 million resistant cases, and 411,000 deaths averted. In a scenario of rapidly increasing drug resistance and an effective vaccine, over 4.5 million resistant cases were averted. Interpretation Swift and widespread deployment of an effective malaria vaccine in Africa, alongside other prevention and control interventions, could substantially reduce health and economic burden caused by drug-resistant malaria. Funding This work was funded by a grant from the Bill & Melinda Gates Foundation (OPP1190803) to the Center for Disease Dynamics, Economics & Policy under the ARVac Consortium.

Objective: To explore an approach to identify the risk of local prevalence of extended-spectrum beta-lactamase producing Enterobacterales (ESBL-E) on ESBL-E colonization or infection, and reassess known risk factors. Design: Case-control study. Setting: Johns Hopkins Health System emergency departments (EDs) in the Baltimore-Washington, D.C. region. Patients: Patients aged ≥18 years with a culture growing Enterobacterales between 4/2019-12/2021. Cases had a culture growing an ESBL-E. Methods: Addresses were linked to Census Block Groups and placed into communities using a clustering algorithm. Prevalence in each community was estimated by the proportion of ESBL-E among Enterobacterales isolates. Logistic regression was used to determine risk factors for ESBL-E colonization or infection. Results: ESBL-E were detected in 1167 of 11,229 patients (10.4%). Risk factors included a history of ESBL-E in the prior year (OR, 15.62; 95% CI, 11.25-21.68), and exposure to a skilled nursing or long term care facility (OR, 1.66; 95% CI, 1.39-1.99), 3rd generation cephalosporin (OR, 1.83; 95% CI, 1.50-2.23), carbapenem (OR, 2.01; 95% CI, 1.46-2.78) or trimethoprim-sulfamethoxazole (OR, 1.53; 95% CI, 1.05-2.22) within the prior 6 months. Patients were at lower risk if their community had a prevalence <25th percentile in the prior 3 months (OR, 0.84; 95% CI, 0.71-0.98), 6 months (OR, 0.84; 95% CI, 0.71-0.99) or 12 months (OR, 0.81; 95% CI, 0.69-0.96). There was no association between being in a community >75th percentile and the outcome. Conclusions: This method of defining the recent local prevalence of ESBL-E may partially capture differences in the likelihood of a patient having an ESBL-E.

Objective: To explore an approach to identify the risk of local prevalence of extended-spectrum β-lactamase–producing Enterobacterales (ESBL-E) on ESBL-E colonization or infection and to reassess known risk factors. Design: Case–control study. Setting: Johns Hopkins Health System emergency departments (EDs) in the Baltimore–Washington, DC, region. Patients: Patients aged ≥18 years with a culture growing Enterobacterales between April 2019 and December 2021. Cases had a culture growing an ESBL-E. Methods: Addresses were linked to Census Block Groups and placed into communities using a clustering algorithm. Prevalence in each community was estimated using the proportion of ESBL-E among Enterobacterales isolates. Logistic regression was used to determine risk factors for ESBL-E colonization or infection. Results: ESBL-E were detected in 1,167 of 11,224 patients (10.4%). Risk factors included a history of ESBL-E in the prior 6 months (aOR, 20.67; 95% CI, 13.71–31.18), exposure to a skilled nursing or long-term care facility (aOR, 1.64; 95% CI, 1.37–1.96), exposure to a third-generation cephalosporin (aOR, 1.79; 95% CI, 1.46–2.19), exposure to a carbapenem (aOR, 2.31; 95% CI, 1.68–3.18), or exposure to a trimethoprim-sulfamethoxazole (aOR, 1.54; 95% CI, 1.06–2.25) within the prior 6 months. Patients were at lower risk if their community had a prevalence <25th percentile in the prior 3 months (aOR, 0.83; 95% CI, 0.71–0.98), 6 months (aOR, 0.83; 95% CI, 0.71–0.98), or 12 months (aOR, 0.81; 95% CI, 0.68–0.95). There was no association between being in a community in the >75th percentile and the outcome. Conclusions: This method of defining the local prevalence of ESBL-E may partially capture differences in the likelihood of a patient having an ESBL-E.

No abstract