Katherine Keenan , Carlos Roberto Veiga Kiffer , Érico V.S. Carmo , Juliana Silva Corrêa , André Luiz de Abreu , Adriano Massuda , Ana Christina Gales , Arnaldo Lopes Colombo , the Institute of Antimicrobial Resistance of São Paulo (ARIES) group
{"title":"自底向上估计抗菌素耐药性负担:估计巴西抗菌素耐药性影响的研究重点","authors":"Katherine Keenan , Carlos Roberto Veiga Kiffer , Érico V.S. Carmo , Juliana Silva Corrêa , André Luiz de Abreu , Adriano Massuda , Ana Christina Gales , Arnaldo Lopes Colombo , the Institute of Antimicrobial Resistance of São Paulo (ARIES) group","doi":"10.1016/j.ijregi.2024.100558","DOIUrl":null,"url":null,"abstract":"<div><div>Recent estimates of deaths attributable to bacterial antimicrobial resistance (AMR) highlight the immense public health threat of AMR to healthcare systems, economies, and communities in Latin America. Although global modelling studies generate important statistics to motivate and guide global and national agendas, their complex methodology and aggregation mean that they have a more limited impact at the local scales where AMR is experienced and tackled. At the same time, it is increasingly recognised that we need to study and design AMR policies ‘from the bottom-up’, drawing on data and perspectives that ensure local ownership of the research and policy agenda. But how do we integrate ‘bottom-up’ perspectives into AMR burden estimation? Brazil is used as a case study to illustrate the importance of this approach. Brazil's vast and decentralised healthcare system would benefit from robust regional estimates of AMR's clinical, economic, and social burdens to move political decision-making and design appropriate interventions. We report on recommendations gathered from interdisciplinary stakeholder exercises and propose strategic priorities for estimating the AMR burden in Brazil at subnational scales of governance. These include focusing on individual-level data linkages at various scales; capturing public and private healthcare systems; understanding AMR inequalities; and capturing linked clinical, economic, and social burdens.</div></div>","PeriodicalId":73335,"journal":{"name":"IJID regions","volume":"14 ","pages":"Article 100558"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Antimicrobial resistance burden estimates from the bottom-up: research priorities for estimating the impact of antimicrobial resistance in Brazil\",\"authors\":\"Katherine Keenan , Carlos Roberto Veiga Kiffer , Érico V.S. 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Antimicrobial resistance burden estimates from the bottom-up: research priorities for estimating the impact of antimicrobial resistance in Brazil
Recent estimates of deaths attributable to bacterial antimicrobial resistance (AMR) highlight the immense public health threat of AMR to healthcare systems, economies, and communities in Latin America. Although global modelling studies generate important statistics to motivate and guide global and national agendas, their complex methodology and aggregation mean that they have a more limited impact at the local scales where AMR is experienced and tackled. At the same time, it is increasingly recognised that we need to study and design AMR policies ‘from the bottom-up’, drawing on data and perspectives that ensure local ownership of the research and policy agenda. But how do we integrate ‘bottom-up’ perspectives into AMR burden estimation? Brazil is used as a case study to illustrate the importance of this approach. Brazil's vast and decentralised healthcare system would benefit from robust regional estimates of AMR's clinical, economic, and social burdens to move political decision-making and design appropriate interventions. We report on recommendations gathered from interdisciplinary stakeholder exercises and propose strategic priorities for estimating the AMR burden in Brazil at subnational scales of governance. These include focusing on individual-level data linkages at various scales; capturing public and private healthcare systems; understanding AMR inequalities; and capturing linked clinical, economic, and social burdens.