Institut für Medizinische Biometrie
und Statistik (IMBI)

Funding: DFG - German Research Council (DFG KA 4199/1-1)
Project duration: 36 months

The emergence and spread of antimicrobial resistance (AMR) is still an unresolved problem worldwide. The major direct effect of AMR, healthcare-associated infections (HAIs) are considered to be the most frequent adverse event in health care delivery. HAIs caused by multidrug-resistant bacteria impose a substantial financial burden on the healthcare system through exacerbation or prolongation of illness and subsequent in-hospital treatment. This direct medical and financial burden of AMR was described in a huge basket of empirical studies. Basically, nearly any of these studies is focussing on the medical and/or economic burden of patients facing infections. The emergence and spread of AMR, however, does not lead to an increase in the number of HAIs only. AMR also impacts patients who do not become infected. Generally spoken, infectious diseases are communicable and the fear of transmission or infection leads patients and physicians to alter their behaviour. As in-hospital resistance patterns change over time, rationale weighting induces decision-makers to adjust their behaviour in order to provide the best possible treatment in a changing environment of resistance. Such adjustment reactions may be seen as prophylactic measures to avoid the adverse events of HAIs. In settings where resistant organisms are prevalent, for instance, physicians routinely change empirical antibiotic therapy in accordance to the relevant resistance indicator leading to differences in costs, dosing schedules and/or side-effect profiles. With respect to the available literature, resistance-induced adjustment reactions were often postulated as having a major influence on modern health care delivery, but rarely addressed in empirical works.

The general aim of the project is to identify and apply methods for quantifying the hidden cost of resistance in the hospital setting. Appropriate data sources and statistical approaches will be identified enabling to show the impact of resistance on specific clinical adjustment reactions and allow quantifying this relationship in order to determine the hidden cost of resistance. These methods will be applicable to three specific scenarios of resistance-induced adjustment reactions in the clinical setting. Moreover, a maximum level of transferability of results for further research synthesis will be warranted.

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Publications

• Kaier, K., T. Heister, E. Motschall, P. Hehn, T. Bluhmki und M. Wolkewitz (2019): Impact of mechanical ventilation on the daily costs of ICU care: a systematic review and meta regression. Epidemiology and Infection 147:e314.
• Kaier, K., T. Heister, T. Götting, M. Wolkewitz und N.T. Mutters (2019): Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital. BMC Infectious Diseases 19:1028.
• Heister, T., M. Wolkewitz, P. Hehn, J. Wolff, M. Dettenkofer, H. Grundmann und K. Kaier (2019): Costs of hospital-acquired Clostridium difficile infections: an analysis on the effect of time-dependent exposures using routine and surveillance data. Cost Effectiveness and Resource Allocation 17:16.
• Kaier, K., M. Wolkewitz, P. Hehn, N.T. Mutters and T. Heister (2019): The impact of hospital-acquired infectionson the patient-level reimbursement-cost relationshipin a DRG-based hospital payment system. International Journal of Health Economics and Management [Epub ahead of print].
• Kaier, K., N.T. Mutters and M. Wolkewitz (2019): Measuring the financial burden of resistance: What should be compared? Clinical Infectious Diseases [Epub ahead of print].
• Engler-Hüsch, S., T. Heister, N.T. Mutters, J. Wolff and K. Kaier (2018): In-hospital costs of community-acquired colonization with multidrug-resistant organisms at a German teaching hospital, BMC Health Services Research 18:737.
• Kaier, K., M. Wolkewitz and T. Heister (2018): Estimating the attributable costs of hospital-acquired infections requires a distinct categorization of cases based on time of infection, American Journal of Infection Control 46(6):729.
• Heister, T., M. Wolkewitz and K. Kaier (2018): Determining the Attributable Costs of Clostridium difficile Infections When Exposure Time Is Lacking: Be Wary of "Conditioning on the Future", Infection Control & Hospital Epidemiology 39(6):759-760.
• Heister, T., M. Wolkewitz and K. Kaier (2018): Estimating the additional costs of surgical site infections: length bias, time-dependent bias, and conditioning on the future, Journal of Hospital Infection 99(1):103-104.
• Wolff, J., T. Heister, C. Normann and K. Kaier (2018): Hospital costs associated with psychiatric comorbidities: a retrospective study, BMC Health Services Research 18(1):67.
• Heister, T., C. Hagist and K. Kaier (2017): Resistance Elasticity of Antibiotic Demand in Intensive Care, Health Economics 26(7):892-909.
• Heister, K. Kaier and M. Wolkewitz (2017): Estimating the burden of nosocomial infections: Time dependency and cost clustering should be taken into account, American Journal of Infection Control 45(1): 94-95.