Development of DDD-prescriptions

Prescription trends for various drugs exhibit recognizable patterns. Certain exceptions exist, often due to strong distortions, like those seen in clindamycin. All prescription curves for the TOP15 are illustrated in Fig. 1 and S1–S15. A decreasing trend in recent years is shown for amoxicillin, cefuroxime axetil, clindamycin, cefaclor, phenoxymethylpenicillin, sulfamethoxazole-trimethoprim, and doxycycline (see Figs. S1, S2, S5, S7, S8, S10, and S12). Amoxicillin and cefaclor became popular in the mid-1990s and showed a strong increase in prescriptions. Around 2010, both showed a plateau until a decline set in, with cefaclor around 2014 and amoxicillin around 2017. Cefuroxime axetil and clindamycin (see Figs. S2 and S5) showed slow growth at a very low level at the beginning, followed by a strong increase, for cefuroxime axetil from 2007 and for clindamycin a jump in prescriptions in 2012. In recent years, after a plateau, a decline set in for both. In clindamycin and phenoxymethylpenicillin (see Figs. S5 and S7), a strong distortion occurred due to the inclusion of dental prescriptions in 2012 (Schwabe and Paffrath 2013). Doxycycline, phenoxymethylpenicillin, and sulfamethoxazole-trimethoprim (see Figs. S3, S7, and S8) became popular in the 1990s. At this time, all three of them showed a quick increase, followed by an ongoing decrease. A similar case exists with ciprofloxacin and cefaclor (see Figs. S10 and S12). Both of them increased in 2002 and started decreasing in 2014.

Antibacterial drugs showing an increasing trend are amoxicillin clavulanic acid, cefpodoxime, and pivmecillinam (see Figs. S4, S13, and S14). Amoxicillin clavulanic acid has shown an increase in prescriptions that is gathering pace since the beginning. Cefpodoxime shows an increasing trend too, with a more rapid increase since 2019. Pivmecillinam is a relatively new drug with a market launch in 2016. Since then, it has shown steadily growing prescription rates. All three drugs show a stringent increasing trend without strong declines.

Development of DDD-costs

Like the trends observed in prescriptions, the correlations in costs also reveal distinct patterns. In most cases, decreasing costs are evident. However, rising DDD-costs are occurring in nitrofurantoin and pivmecillinam. All curves of DDD-costs for the TOP15 are illustrated in Fig. 2 and S1–S15.

Several characteristic patterns emerge in the cost curves. A sudden, sharp drop in prices is typically seen when the initial price reduction is remarkable. A continuous, long-term decline suggests sustained cost reductions over time. Drugs like cefuroxime axetil, azithromycin, and cefpodoxime show a sudden drop followed by a plateau (see Figs. S2, S6, and S13). On the other hand, doxycycline, clindamycin, phenoxymethylpenicillin, sulfamethoxazole-trimethoprim, clarithromycin, and cefaclor exhibit a more gradual, continuous price reduction without a sudden jump at the beginning (see Figs. S3, S5, S7, S8, S10, and S12). A combination of these characteristics is observed in amoxicillin, amoxicillin clavulanic acid, ciprofloxacin, and roxithromycin (see Figs. S1, S4, S10, and S15).

In some cases, cost increases are observed before sharp decreases, notably in ciprofloxacin and cefaclor (see Figs. S10 and S12). Smaller increases occur in cefuroxime axetil, amoxicillin clavulanic acid, clindamycin, and azithromycin (see Figs. S2, S4, S5, and S6). A sudden spike in costs around 2004 is noted across many drugs, likely due to the pharmaceutical law GMG 2004 (Schwabe and Paffrath 2007). Doxycycline, phenoxymethylpenicillin, sulfamethoxazole-trimethoprim, and roxithromycin show particularly high increases, suggesting distortion of the cost curve (see Figs. S3, S7, S8, and S15).

A plateau formed in most cases around 2011, suggesting that strong cost reductions may have ceased due to several factors. In addition to the AMNOG law in 2011 (AOK 2023), supply shortages, delivery bottlenecks, or a profitability limit reached by the pharmaceutical industry could explain this stabilization.

Overall, most drugs show a decrease in costs, with some notable fluctuations. Rising DDD-costs are observed in sulfamethoxazole-trimethoprim and pivmecillinam (see Figs. S7 and S14). While pivmecillinam is still patent-protected, sulfamethoxazole-trimethoprim’s costs increased so much in 2004 that they remain higher today than at the start of the recordings. Though many drugs show a consistent downward trend, others exhibit more pronounced fluctuations.

Specific events, such as changes in pharmaceutical laws or market dynamics, can lead to significant shifts in cost trends. The notable changes in 2004 and 2011 are likely due to GMG 2004 and AMNOG 2011, respectively. Although the COVID-19 pandemic had profound effects on prescriptions, it does not appear to have impacted DDD-costs in the TOP15 drugs (see Fig. 2).

Correlation of prescriptions vs. costs during the entire available period

During the entire period from 1985 to 2022, a significant and strong negative correlation exists between prescriptions and costs for most analyzed antibacterial drugs (see Table 1 and Fig. 3). Exceptions include doxycycline and pivmecillinam. The non-significance in doxycycline can be attributed to a sudden increase in costs in 2004 due to the measures of the drug law GMG, which introduced a standardized pharmacy dispensing fee (Schwabe and Paffrath 2007). Pivmecillinam, being a relatively new drug, has not yet experienced patent expiry, and thus, no significant cost reduction has been realized due to the lack of generics (see Fig. S14).

The negative correlation indicates that decreasing costs are associated with rising prescriptions for almost all drugs, except roxithromycin and pivmecillinam (see Table 1, S14 and S15). The positive correlation for roxithromycin can be explained by its long-term downward trend in prescriptions, where falling costs did not reverse this trend (see Fig. S15).

Drugs with a correlation coefficient exceeding ( ±) 0.8, indicating a strong correlation, include amoxicillin (− 0.941), cefuroxime axetil (− 0.900), clindamycin (− 0.800), nitrofurantoin (− 0.895), and cefaclor (− 0.819). These drugs typically exhibit a sharp increase in prescriptions following significant price reductions (see Figs. 1 and 2; Figs. S1, S2, S5, S9, and S12). This pattern underscores the importance of DDD-costs as a key factor influencing prescriptions, particularly for the most frequently used antibacterial drugs.

Correlation of prescriptions vs. costs until 2011

In the analysis of the early period of data recording, only negative correlations are depicted. Except the non-significance of roxithromycin and the lack of data for pivmecillinam, all other antibacterial drugs show a significant value (see Table 1). The non-significance of roxithromycin might be explainable through a sudden increase in costs in 2004, as shown in Fig. S15. This strong uplift is caused by the measures of the drug law GMG, due to a standardized pharmacy dispensing fee (Schwabe and Paffrath 2007). Beside this, DDD-prescriptions were rising before a decline in DDD-costs took place. This might indicate that other factors were more important than DDD-costs for roxithromycin in the time period of 1991–2011.

There are no positive correlations depicted, supporting the suggestion of declining costs. Since a quite long time span of 27 years is considered, most of the depicted antibacterial drugs experienced the introduction of generics, followed by declining costs. Since the most popular drugs from today’s perspective are analyzed, it can be assumed that they increased in popularity at least during a certain period of time in order to reach their current level.

Beside the fact that only negative correlations are available, many of them are considered strong as they have a correlation coefficient of over ( ±) 0.8. The TOP5 have a very high proportion of strong on significant correlations again. In the TOP5, four of five drugs show a strong negative correlations, while among the TOP6-15, three of nine drugs have a correlation considered strong.

Correlation of prescriptions vs. costs from 2012 to 2022

In the analysis of the late period of data recording, both positive and negative correlations are depicted. Strong correlations can be both positive and negative; noticeable is the high number of non-significant correlations (see Table 1).

Non-significant correlations are depicted for eight drugs: cefuroxime axetil (− 0.272), clindamycin (0.448), phenoxymethylpenicillin (0.029), sulfamethoxazole-trimethoprim (0.193), cefaclor (0.415), cefpodoxime (0.537), pivmecillinam (0.761), and roxithromycin (0.009). This large number can be explained by the comparable short time period of 11 years under consideration combined with a strong effect of the COVID pandemic in 2020 and 2021 on prescriptions (see Fig. 1). Due to the small number of data analyzed, outliers are particularly influential.

Regarding the significant correlations, four of them are positive and three of them are negative. The share of strong correlations is balanced too, with two negative and three strong positive correlations. A summary of aspect characterizing the analyses can be found in Table 2.

Positive correlations are depicted for doxycycline (0.722), azithromycin (0.747), ciprofloxacin (0.950), and clarithromycin (0.962). These drugs show a decreasing trend in DDD-prescriptions and slightly decreasing DDD-costs (see Figs. S3, S6, S10, and S11). Negative correlations are shown for amoxicillin (− 0.915), amoxicillin clavulanic acid (− 0.912), and nitrofurantoin (− 0.879). While decreasing DDD-prescriptions and increasing DDD-costs are occurring in amoxicillin and nitrofurantoin (see Figs. S1 and S9), amoxicillin clavulanic acid shows increasing DDD-prescriptions and decreasing DDD-costs (see Fig. S4).

A suggestion for the comparable large number of positive correlations might be that other factors are becoming more important than DDD-costs on prescription figures. This is supported by the DDD-costs reaching a plateau in most drugs in recent years (see Fig. 2).

The TOP5 are outstanding by their comparatively high number of significant as well as strong correlations. Regarding the number of significant correlations, the TOP5 have three of five correlations which are significant vs. the TOP6-15 with four of ten. While two of the five strong correlations are presented in the TOP5, only three belong to the TOP6-15 (see Table 1).

Comparison of the entire period and time segments

The correlations between DDD-prescriptions and DDD-costs were analyzed for three distinct time spans: the entire period from 1985 to 2022, the early period from 1985 to 2011, and the recent period from 2012 to 2022. This comparison aims to identify similarities and differences in these correlations across the different intervals. A summary of aspects regarding the three analyses can be found in Table 2, while all correlation parameters are depicted in Table 1 and S1–S43.

The changes in correlation coefficients of individual antibacterial drugs can be categorized into three main patterns. Some drugs, such as amoxicillin, amoxicillin clavulanic acid, and nitrofurantoin, exhibit consistent negative correlations across all periods. The strength of these correlations remains similar and significant, indicating a stable relationship where decreasing costs are associated with increasing prescriptions. In contrast, drugs like cefuroxime axetil, clindamycin, phenoxymethylpenicillin, sulfamethoxazole-trimethoprim, cefaclor, and cefpodoxime show significant negative correlations in the early period and the entire period. However, in the recent period, the correlation becomes non-significant or changes direction from negative to positive. This suggests a weakening influence of DDD-costs on DDD-prescriptions and the potential emergence of other influencing factors. Lastly, it is important to note that the data for pivmecillinam is insufficient for robust analysis as it has only been available since 2017.

The consistency of the relationship between DDD-prescriptions and DDD-costs for drugs like amoxicillin, amoxicillin clavulanic acid, and nitrofurantoin suggests that their cost dynamics have remained relatively stable over time. Conversely, the changes observed for drugs like cefuroxime axetil and clindamycin indicate that factors other than cost might be influencing prescription trends in the recent period. The observed shift from significant to non-significant correlations, or from negative to positive correlations, in the recent period may be attributed to a variety of factors. These include the smaller number of data points available for analysis, which affects the significance of the results, and the potential impact of external influences such as the COVID pandemic on prescription patterns.

Overall, the analysis of the entire period reveals a higher proportion of significant correlations compared to the recent period. This could be due to the larger number of data points available for the longer period, which enhances the robustness of the findings. The early period from 1985 to 2011 shows the highest proportion of significant correlations, followed by the entire period, with the recent period showing a comparatively low share of significant values. This trend highlights the importance of data quantity and the potential impact of short-term fluctuations in more recent years.

Regarding the direction of the correlations, significant negative correlations dominate the entire period and the early period. In contrast, the recent period shows a more balanced ratio of positive and negative correlations. This shift may be explained by the plateauing of DDD-costs for many drugs since 2011, suggesting that other factors might become increasingly important in determining prescription trends.

In terms of the strength of the correlations, all three analyses display similar behavior, with a slightly higher number and ratio of strong correlations observed in the early period. The recent period, however, includes both positive and negative strong correlations, further supporting the hypothesis that the influence of DDD-costs was particularly high at the beginning of the recording period but remains significant in recent years.