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The emergence and spread of antimicrobial resistance (AMR) is still an unresolved problem worldwide. The most visible effect of AMR, healthcare-associated infections (HAIs), imposes a substantial financial burden on the healthcare system through exacerbation or prolongation of illness and subsequent in-hospital treatment. The impact of the emergence and spread of AMR, however, is not limited to an increase in the number of HAIs. AMR also impacts patients who do not become infected. 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. 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.
The effects of such resistance-induced antibiotic substitution effects are highly relevant in intensive care units (ICUs) where treatment failure often has severe consequences. In ICU settings, first line antibiotic therapy is highly standardized and widely empiric. On the other hand, there is a limited number of reserve antibiotics, whose prices and/or side effects are substantially higher than first-line therapy. We hypothesize that a rise in resistance to first line agents increases demand for the respective reserve agents. In order to provide first estimates of these resistance-induced substitution effects, we conducted a panel data regression analysis on monthly antibiotic use and resistance data from 66 ICUs between 2001 and 2012.
We chose an estimation using unit-fixed effects and selected combinations of first line agents and their substituting reserve agent in accordance with an expert opinion-guided and pre-defined set of variables. The investigated reserve antibiotics (Carbapenems, Glycopeptides and Linezolid) represent a large part of all reserve antibiotics and on average 15% of all prescribed antibiotics.
The findings of the three core regressions support the hypothesis that demand for antibiotics significantly increases when lower level resistance rises. For some regressions the lagged effect of resistance is also significant, supporting the conjecture that part of the substitution effect is caused by physicians changing antibiotic choices in empiric treatment by adapting their resistance expectation to new information on resistance prevalence. In contrast, there is no lagged effect related to the occurrence of methicillin-resistant Staphylococcus aureus (MRSA). This may be explained by the availability of rapid testing, which is increasingly used to immediately screen patients for MRSA, commonly resulting in definitive therapy being promptly available.
The results are robust to different specifications of the empirical model, and the findings imply that expectations play an important role in the demand for antibiotics. For policy makers, an important finding may be that the availability and accuracy of information about prevalence of pathogens and resistance rates can increase treatment optimality by allowing physicians to efficiently balance the trade-off of resistance and treatment success.
Using a large international data set we analyze whether business cycle forecasters tend to herd or anti-herd. Applying different measures of economic crises, we distinguish between normal economic circumstances and times of crises. We fnd evidence for anti-herding behavior for most industrial economies, i.e. forecasters eliberately stick out their neck with extreme forecasts for strategic reasons. For a set of emerging market economies, by contrast, we find evidence for herding behavior. We relate this finding to the high incidence of economic and financial crises in these countries. A test for herding behavior during economic crises confirms that forecasters tend to herd in times of high forecast uncertainty.
Die vorliegende Arbeit befasst sich auf Basis von Routinedaten der AOK-Plus mit Pflegeverläufen von über 60-Jährigen in Deutschland. Unsere Analysen zeigen eine rechtsschiefe Verteilung der Pflegeverläufe für beide Ge-schlechter. Demnach sind 50 Prozent der weiblichen (männlichen) Pflegefälle nach 45 (26) Monaten verstorben, wohingegen die mittlere Verweildauer in der Pflege bei 51,4 (36,6) Monaten liegt. Die mit diesen Pflegverläufen verbundenen Gesamtkosten der Pflege summieren sich nach unseren Berechnungen im Mittel auf einen Barwert von 68.871 (45.233) Euro. Die Analyse der Pflegestufen mittels eines multinominalen logistischen Modells zeigt für die vorliegenden Daten zudem, dass im Beobachtungszeitraum eine Kompression der Pflegeverläufe stattge-funden hat. So wird eine höhere Pflegestufe und somit ein schwerwiegenderer Pflegeverlauf mit zunehmendem Abstand vom Tod sowie zunehmendem Alter für beide Geschlechter unwahrscheinlicher.