Antimicrobial resistance is assumed to be an important health problem and an economic burden to society. However, the relationship between the emergence of in vitro microbiological resistance and its clinical and socioeconomic consequences has not yet been satisfactorily determined for either nosocomial or community-acquired infection. In the case of both nosocomial and community-acquired infection, previous exhaustive reviews of published and unpublished reports concluded that mortality, likelihood of hospitalization, and length of hospital stay were usually at least twice as great for patients infected with drug-resistant strains as for those infected with drug-susceptible strains of the same bacteria. However, evaluation of the economic impact of resistance is still problematic and the adverse economic and health effects of drug-resistant bacterial infections can only be roughly quantified. Decision analysis models, such as decision trees, can aid evaluation of the impact of resistance on health and economic outcomes from the perspective of a given decision maker. A model of cost analysis should be based on a knowledge of the incremental consumption of resources specifically dependent on the dynamics of antimicrobial resistance in a given clinical setting (e.g. home care or hospital care). In general, we can assume that the increased rate of isolation of resistant strains from community-acquired infections correlates positively with an increase in morbidity, mortality, risk of hospitalization and the need for additional days in hospital and for more expensive and powerful antibiotics. We implemented and simulated a general decision-tree model to analyse the influence of antibiotic resistance on the economic outcomes of community-acquired lower respiratory tract infections, from the perspective of both society and the health-care local organization (HCLO). This model allows simulation of the impact of different degrees of resistance on the direct costs of an antibiotic therapy as well as on the cost-effectiveness of antibiotics with different degrees of resistance.
Economic impact of resistance in the community.
EANDI, Mario;ZARA, Gian Paolo
1998-01-01
Abstract
Antimicrobial resistance is assumed to be an important health problem and an economic burden to society. However, the relationship between the emergence of in vitro microbiological resistance and its clinical and socioeconomic consequences has not yet been satisfactorily determined for either nosocomial or community-acquired infection. In the case of both nosocomial and community-acquired infection, previous exhaustive reviews of published and unpublished reports concluded that mortality, likelihood of hospitalization, and length of hospital stay were usually at least twice as great for patients infected with drug-resistant strains as for those infected with drug-susceptible strains of the same bacteria. However, evaluation of the economic impact of resistance is still problematic and the adverse economic and health effects of drug-resistant bacterial infections can only be roughly quantified. Decision analysis models, such as decision trees, can aid evaluation of the impact of resistance on health and economic outcomes from the perspective of a given decision maker. A model of cost analysis should be based on a knowledge of the incremental consumption of resources specifically dependent on the dynamics of antimicrobial resistance in a given clinical setting (e.g. home care or hospital care). In general, we can assume that the increased rate of isolation of resistant strains from community-acquired infections correlates positively with an increase in morbidity, mortality, risk of hospitalization and the need for additional days in hospital and for more expensive and powerful antibiotics. We implemented and simulated a general decision-tree model to analyse the influence of antibiotic resistance on the economic outcomes of community-acquired lower respiratory tract infections, from the perspective of both society and the health-care local organization (HCLO). This model allows simulation of the impact of different degrees of resistance on the direct costs of an antibiotic therapy as well as on the cost-effectiveness of antibiotics with different degrees of resistance.
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