53
networks. Insufficient standardization of definitions related to epidemiology, collected samples and data, included settings, testing methods (susceptibility testing),
and policies related to sharing of data are obstacles in the path of informative and
reliable surveillance done in a collaborative manner (Tacconelli et al. 2018).
Laboratory based systems too have limitations. Foremost, reported microbiological results show no association between epidemiologically or clinically relevant
data. As a result, laboratory based systems gives no information on identification of
patient populations that are at risk, sources of infection, types of infection, treatment
failure and real burden of disease related to health care acquired infections and antimicrobial resistance. Secondly, characterization and genetic typing is not commonly practised for relevant isolates and resistance mechanisms. This testing
method can establish the cause of antimicrobial resistance that can be either due to
spread of resistant strains or by transfer of determinants for resistance among different strains or species. Thirdly, biasness in sample collection might reduce external
validity, create hindrance in measurement of factor based associated infections
within institute or community and might prevent prediction of future trends.
Differences in sampling methodology among institutions and countries and inclusion of screening isolates rather than clinical isolates undermine representation of
data. Certain settings consider sample collection as best practice for more severe
infections or those that do not respond to first line treatment. These cases might have
inflated rate of antimicrobial resistance and usage of such data could lead to unsuitable choice of therapy, increased resistance along with costs of health care.
Contrarily, occasional collection rather than routine collection of samples can lead
to under reporting of antimicrobial resistance and health care associated infections.
In addition to this, dependence on laboratory based surveillance can depreciate
actual incidence of clinically significant health care associated infections. As samples are collected from subset of affected individuals so laboratory based surveillance of clinical samples as sole criteria is not much effective to provide strategic
warning for emerging pathogens and resistance mechanisms. These must be initially colonized from urine or sputum samples.
2.13 Conclusion
This era of escalating antimicrobial resistance presents urgent need for improvements in surveillance system to optimize empirical therapy, drive antimicrobial
stewardship and infection control measures, and development of novel drugs and
vaccines. Without such developments, it will be difficult to substantially reduce the
economic and medical burdens imposed by antimicrobial resistance. New initiatives
(including ESVAC, CAESAR, European Survey on Carbapenemase producing
Enterobacteriaceae project, the Center for Disease Dynamics, Economics & Policy’s
Resistance Map, Global Antimicrobial Resistance Surveillance System and EPINet) may improve the fragmentation, time lag, heterogeneity, and other inadequacies of existing surveillance strategies, but cannot achieve the obligatory advances
2 Global Surveillance Programs on Antimicrobial Resistance
networks. Insufficient standardization of definitions related to epidemiology, collected samples and data, included settings, testing methods (susceptibility testing),
and policies related to sharing of data are obstacles in the path of informative and
reliable surveillance done in a collaborative manner (Tacconelli et al. 2018).
Laboratory based systems too have limitations. Foremost, reported microbiological results show no association between epidemiologically or clinically relevant
data. As a result, laboratory based systems gives no information on identification of
patient populations that are at risk, sources of infection, types of infection, treatment
failure and real burden of disease related to health care acquired infections and antimicrobial resistance. Secondly, characterization and genetic typing is not commonly practised for relevant isolates and resistance mechanisms. This testing
method can establish the cause of antimicrobial resistance that can be either due to
spread of resistant strains or by transfer of determinants for resistance among different strains or species. Thirdly, biasness in sample collection might reduce external
validity, create hindrance in measurement of factor based associated infections
within institute or community and might prevent prediction of future trends.
Differences in sampling methodology among institutions and countries and inclusion of screening isolates rather than clinical isolates undermine representation of
data. Certain settings consider sample collection as best practice for more severe
infections or those that do not respond to first line treatment. These cases might have
inflated rate of antimicrobial resistance and usage of such data could lead to unsuitable choice of therapy, increased resistance along with costs of health care.
Contrarily, occasional collection rather than routine collection of samples can lead
to under reporting of antimicrobial resistance and health care associated infections.
In addition to this, dependence on laboratory based surveillance can depreciate
actual incidence of clinically significant health care associated infections. As samples are collected from subset of affected individuals so laboratory based surveillance of clinical samples as sole criteria is not much effective to provide strategic
warning for emerging pathogens and resistance mechanisms. These must be initially colonized from urine or sputum samples.
2.13 Conclusion
This era of escalating antimicrobial resistance presents urgent need for improvements in surveillance system to optimize empirical therapy, drive antimicrobial
stewardship and infection control measures, and development of novel drugs and
vaccines. Without such developments, it will be difficult to substantially reduce the
economic and medical burdens imposed by antimicrobial resistance. New initiatives
(including ESVAC, CAESAR, European Survey on Carbapenemase producing
Enterobacteriaceae project, the Center for Disease Dynamics, Economics & Policy’s
Resistance Map, Global Antimicrobial Resistance Surveillance System and EPINet) may improve the fragmentation, time lag, heterogeneity, and other inadequacies of existing surveillance strategies, but cannot achieve the obligatory advances
2 Global Surveillance Programs on Antimicrobial Resistance