Long-term forecast for antibacterial drug consumption in Germany using ARIMA models

Area of Science:

• Pharmacoeconomics
• Public Health
• Epidemiology

Background:

• Antibacterial drug shortages pose significant public health challenges in Germany.
• Accurate forecasting of drug consumption is crucial for effective healthcare planning and resource allocation.

Purpose of the Study:

• To predict the future consumption of the ten most prescribed antibacterial drugs in Germany until 2040.
• To evaluate the plausibility and reliability of long-term consumption forecasts for antibacterial agents.

Main Methods:

• Utilized Auto Regressive Integrated Moving Average (ARIMA) models, specifically ARIMA(0,1,0) (random walk with drift), for forecasting.
• Analyzed historical prescription data to model trends in antibacterial drug usage.
• Assessed the reliability and potential discrepancies of short-term (5-10 years) and long-term (up to 2040) forecasts.

Main Results:

• ARIMA(0,1,0) demonstrated the best fit for capturing overall antibacterial drug trends.
• Forecasted increases in consumption for amoxicillin, cefuroxime axetil, amoxicillin clavulanic acid, clindamycin, azithromycin, nitrofurantoin, and ciprofloxacin.
• Projected declines in consumption for doxycycline, phenoxymethylpenicillin, and sulfamethoxazole-trimethoprim, with varying forecast reliability.

Conclusions:

• Long-term forecasting of antibacterial drug consumption is feasible and essential for addressing potential supply shortages.
• Proactive healthcare planning is urgently needed, informed by these consumption predictions, to mitigate future drug supply disruptions.
• Further research should explore links between consumption trends, bacterial resistance, and other drug classes.