City's infectious disease treatment with antimicrobial agentsa longitudinal one year study of antimicrobials in two cities via wastewater-based epidemiology
Antimicrobial resistance (AMR) is one of the most significant global health threats. Inappropriate and over-usage of antimicrobial agents (AAs) is a major driver for AMR. Wastewater-based epidemiology (WBE) is a promising tool for monitoring AA usage in communities. Two urban catchment areas have been investigated in this study, one city and one small town in the Southwest of the UK over a 13month period in 2018-2019. Population normalised loads (PNDLs) of 17 AAs and metabolites have been calculated and compared to catchment specific prescription data. Correction factors (CFs) have been applied to several AAs and metabolites in order to back-calculate consumption at the community level and compare to the corresponding catchment prescription data. Results have demonstrated positive correlations for all quantifiable parent AAs and metabolites in wastewater, and spatial variability in AA usage was observed even in neighbouring urban areas. The application of correction factors demonstrated good agreement for several of the studied AAs, including clarithromycin, oxytetracycline, trimethoprim, hydroxy metronidazole, and acetyl sulfamethoxazole. It was noted that in general, the larger population city gave closer agreement to estimated prescription intake than the smaller town. Differences in wastewater and between prescription data could be due to variable prescribing patterns and potential lack of compliance of AAs at the community level. This study has demonstrated that data triangulation, of both prescription data and wastewater data, provides the most comprehensive approach to understanding AA usage in communities.
Introduction<!>Target Analytes<!>Sampling sites and sample collection<!>Sample Preparation<!>Liquid Chromatography<!>Mass Spectrometry<!>Correction factors<!>AA Intake calculations and population normalisation<!>Prescription tool<!>Population equivalent estimation<!>Statistical analysis<!>Abundance of AAs in a 13-months intercity longitudinal study<!>Correlations of AA/Metabolites to assess routes of AAs disposal including direct disposal<!>Population normalised AAs, temporal and spatial differencesdata triangulation with catchment demographics and prescribing patterns<!>Comparison of WBE to prescription datapatient compliance<!>AA Intake calculations of AAs<!>Conclusions
<p>Antimicrobial resistance (AMR) is an ongoing and growing global health crisis, causing increases in mortality, higher risks of routine medical procedures, and increased economic burden (World Health Organisation, 2015). AMR surveillance is key to understand the extent of resistance, developing and monitoring effective intervention strategies as well as identifying new and emerging resistance threats. In response to growing resistance, the World Health Organisation (WHO) launched the Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2015, with the aims of establishing the first global collaborative effort to standardise AMR surveillance (WHO, 2015). Participation in the programme has grown from 729 surveillance sites in 22 countries in 2017, to 24 803 surveillance sites in 70 countries reported in 2019 (World Health Organisation, 2021). Whilst this shows positive trends, the high rates of resistance observed stress the importance of ongoing and harmonised global AMR surveillance. Furthermore, concerns and predictions have highlighted that the COVID-19 outbreak could further exacerbate AMR (Rezasoltani et al., 2020), and decreases in surveillance capacity could reduce the ability to provide AMR data during the pandemic (Tomczyk et al., 2021).</p><p>The ability to monitor antimicrobial agents (AAs) consumption is critical for AMR surveillance. Whilst prescription data can provide valuable insight, several challenges exist to estimating true consumption: i) prescription data may not be easily accessible for both primary and secondary care; ii) prescription data does not account for AAs that can be purchased over the counter or online; iii) just because an AA is prescribed, does not necessarily mean it has been consumed; and iv) consumption/excretion of AAs may happen in a different catchment to prescription. Compliance to AA is a well-regarded problem, with one study reporting over 1/3 of patients not completing the AA course (Kardas et al., 2005), or individuals reporting stopping treatment before course has finished due to feeling better or having sideeffects (Pechere, 2000). Studies have shown that individuals not only admit to not finishing courses of treatment but they also admit stockpiling for self-prescribing at a later date (Kardas et al., 2005;Pechère, 2001).</p><p>Wastewater-based epidemiology (WBE) provides a multifaceted approach to community-wide public health monitoring and is a powerful tool for estimating near-real time monitoring of pharmaceutical consumption (Sims and Kasprzyk-Hordern, 2020). The concept is based on an assumption that pharmaceuticals consumed within a community will be excreted either unchanged (parent) and/or as a mixture of metabolites into the sewage system. By measuring these pharmaceutical residues, otherwise known as biomarkers, in influent (untreated) wastewater, back-calculation can be applied to estimate consumption at the community level. WBE has been utilized to inform on a wide range of diverse public health, including illicit drugs (Castiglioni et al., 2006;Zuccato et al., 2005) (Thomas, Bijlsma et al. 2012, Ort, van Nuijs et al. 2014, Gonzalez-Marino, Baz-Lomba et al. 2020), pharmaceuticals (Kasprzyk-Hordern et al., 2008) (Baz-Lomba, Salvatore et al. 2016, Xiao, Shao et al. 2019, Ahmed, Tscharke et al. 2020, Ahmed, Tscharke et al. 2021), tobacco (Castiglioni et al., 2015;Lai et al., 2017;Rodríguez-Álvarez et al., 2014) and alcohol (Boogaerts et al., 2016;Reid et al., 2011); as well as exposure to chemicals such as pesticides (Rousis et al., 2017), industrial chemicals (Been et al., 2017;Lopardo et al., 2019) (Gonzalez-Marino, Ares et al. 2021) or personal care products (Lopardo et al., 2018).</p><p>Due to the dynamic nature of AMR and the growing requirement for up-to-date and harmonised surveillance data, WBE could be an appropriate community-wide estimation tool for providing complimentary information to GLASS. Applying WBE for AA usage could not only allow monitoring of local to international trends of AAs, allowing spatial and temporals to be established; but could provide baselines for community AA usage and assess effectiveness of intervention strategies. Thereby, offering potential evidence and support for AMR policy decisions. The combined analysis of parent compound and metabolite in wastewater could also give information about community compliance to AAs as well as incidents of pharmaceuticals being directly disposed of into the sewage system (Kasprzyk-Hordern et al., 2021;Petrie et al., 2016).</p><p>A handful of studies have investigated back-calculation of biomarkers to compare with prescription rates at the community level (Baz-Lomba et al., 2016;van Nuijs et al., 2015). One study focusing on four pharmaceuticals and respective metabolites of non-communicable diseases, investigated WBE to assess compliance to pharmaceuticals within in a community (Riva et al., 2020). Estimating backcalculation of pharmaceuticals from wastewater and exploring elements of community compliance or incorrect disposal is not without its challenges. Several critical aspects have to be considered: i) establishing relationships between parent AAs and metabolites, ii) understanding human metabolism and establishment and use of metabolic transformation correction factors (CFs), iii) understanding stability of both AAs and their metabolites in wastewater, iv) appreciating all possible sources of AAs, e.g., veterinary usage. Prior to this study, a systematic review from our group has published a series of CFs for AAs (Holton et al., 2022) as well as a new PrAna tool was developed to monitor community prescriptions in selected catchment (Jagadeesan et al., 2022). In this study, validated CFs and catchment prescription data have been applied in a longitudinal one-year study of AA usage in two urban areas from the Southwest England: 120K city of Bath and 21K town Keynsham). The primary aims of this study was to:</p><p>1. Explore relationships between daily loads of AAs and their metabolites across a longitudinal study in two contrasting urban areas in Southwest England. 2. Understand spatiotemporal changes to AA/metabolite ratios to inform usage patterns of AAs (e.g. oral vs topical) as well as identify any direct disposal events. 3. Estimate AA intake in the studied catchments using WBE and compare with prescription data to understand prescription compliance and spatiotemporal prescription-AA consumption trends.</p><!><p>For the study, 58 AAs and 26 metabolites were investigated using a method that has previously been developed within the group (Holton and Kasprzyk-Hordern, 2021) covering a broad range of AA classes (table 1), further chemical information on targets may be found in the supplementary information (table S1). Analytical standards and internal standards (deuterated labelled standards) were purchased from the following companies: Sigma-Aldrich (Gillingham, UK), TRC (Toronto, Canada), LGC (Middlesex, UK), or MCE (Cambridge, UK). All methanol (MeOH) used was HPLC-grade and formic acid (>95 % purity), were both obtained from Sigma-Aldrich. All glassware used in this project was deactivated using 5 % dimethylchlorosilane in toluene to avoid any loss of target analytes via adsorption. Regarding solid phase extraction (SPE), Oasis HLB (60 mg, 3 mL) cartridges were used for the extraction of target analytes, purchased from Waters (Manchester, UK). Polypropylene LC vials, along with Whatman GF/F 0.7 μm filters, were also obtained from Waters.</p><!><p>Two wastewater treatment plants (WWTPs), corresponding to two catchment areas, were selected for this project. This study has previously been reported in Sims et al. (Sims et al., 2022). Both sites are within the Southwest of the UK and are a close distance from each other (figure 1). First, is the city of Bath with a population of 120,113 and second is a small town called Keynsham, population estimation of 21,247. Both sites have limited contribution from industry waste (<1 %). Bath, a UNESCO heritage world site, is popular with tourism, and has two Universities residing in its catchment area. Keynsham resides between Bath and the city of Bristol and so observes less tourism in general but will likely have a commuter input. Influent (untreated) wastewater was collected using 24-hour composite flow proportional samplers for this project. Samples were collected every 15 minutes over the 24-hour period with composite samples combined, to create one 24-hour sample, before being transported on ice to the laboratory for sample processing (<1 hour). Wastewater samples were collected over a 13-month period between 2018-2019.</p><!><p>Influent wastewater samples that arrived at the lab were portioned into 50 mL samples and spiked with 50 ng of internal standard spiking mix (50 µL of a 1 µg mL −1 internal standard mix in MeOH). Samples were then frozen at this point, prior to the next steps. Samples, when fully defrosted, were filtered via GF/F filters before loading onto preconditioned Oasis HLB cartridges (2 mL of MeOH followed by 2 mL of miliq H2O). Preconditioning of cartridges occurred at a rate of 1 mL min -1 and samples were loaded at a rate of 5 mL min -1 . Once loaded, samples were dried under vacuum for at least 30 minutes. Elution from SPE cartridges was achieved using 4 mL of MeOH at a rate of 1 mL min -1 . The extracts were then dried under N2 using a TurboVap evaporator (Caliper, UK) at 40 °C. Reconstitution into polypropylene vials occurred with 500 μL of 80:20 H2O: MeOH. Samples were then stored at -18 °C until analysis.</p><!><p>Full method development and analytical procedures have been previously described (Holton and Kasprzyk-Hordern, 2021). Internal standard assignment and instrument and method performance can be found in the supplementary information (table S2). Briefly, liquid chromatography-mass spectrometry was achieved using a Waters, Acquity UPLC system coupled to a XEVO triple quadrupole mass spectrometer. A reverse phase BEH C18 column (50 x 2.1 mm x 1.7 μm) was used. Mobile phase A consisted of 95:5 H2O:MeOH with 0.1 % formic acid and mobile phase B was 100 % MeOH. The total method is 19 minutes long, with an injection volume was 20 µL and flow rate was at 0.2 mL min -1 . Starting conditions were 0 % B (1 min), followed by 8.5 min gradient to 40 % B, 3.5 min gradient to 100 % B, 3 min hold, before dropping back to 0 % B.</p><!><p>As previously mentioned, a XEVO triple quadrupole mass spectrometer was used. This method was in ESI positive mode. The source desolvation temperature was at 400 °C. Regarding gas flows, the cone gas was at 100 L h -1 and the desolvation gas was at 1000 L h -1 . Argon was used as the collision gas and nitrogen for the nebulising and desolvation gas. Full breakdown on specific compounds transitions and collision cell energies may be found in Holton et al. (Holton and Kasprzyk-Hordern, 2021).</p><!><p>Correction factors (CFs) (table 3) have been previously calculated in a systematic pharmacokinetic review by Holton. et al. (Holton et al., 2022). To achieve representative results, the number of observations performed in each study was used for weighting calculated CF values. These CFs are utilised to calculate drug intake in a community, PNDI, by using the amount of AA and/or metabolites detected in wastewater and correcting for human excretion percentage. SPY has been included in this study but it is of note that this AA is no longer prescribed for use in humans in the UK. It is however, a major vetrinary pharmaceutical. SPY and aSPY are both major metabolites for SLZ, which is prescribed for humans in the UK as an anti-inflammatory drug.</p><!><p>Daily mass loads (DLs, g day -1 ) of AAs were calculated to account for variable wastewater flows. This was done by multiplying the total concentrations of AAs (mg L -1 ) in wastewater over the 24-hour period by daily wastewater flow rates (L day -1 )</p><p>𝐷𝐿𝑠 𝐴𝑛𝑎𝑙𝑦𝑡𝑒 (𝑔 𝑑𝑎𝑦 −1 ) = 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝐴𝑛𝑎𝑙𝑦𝑡𝑒 (𝑔 𝐿 −1 ) * 𝑑𝑎𝑖𝑙𝑦 𝑤𝑎𝑠𝑡𝑒𝑤𝑎𝑡𝑒𝑟 𝑓𝑙𝑜𝑤 (𝐿 𝑑𝑎𝑦 −1 )</p><p>DLs (g day -1 ) of AAs could then be further normalised to population size of the two catchment areas studied in this project to give population normalised daily loads, PNDLs (mg 1000inh -1 day -1 ). This allows for the fair comparison between different sized communities.</p><p>To calculate population normalised daily intakes, PNDI (mg 1000inh -1 day -1 ), correction factors (CFs) were taken into account using:</p><p>Where CF were calculated using:</p><p>Where MWAA and MWAnalyte are the molecular weights of the parent AA and the molecular weights of the analyte (either parent compound of respective metabolite). CFs were previously calculated in Holton. et al. (Holton et al., 2022). The % ExcretionAnalyte is the percentage of the target analyte (parent or metabolite) excreted in urine or faeces.</p><!><p>An R package, PrAna (Jagadeesan et al., 2022), was utilised to extract total prescription quantities of the pharmaceuticals for the WWTP catchments for the study period 2018 and 2019. This R package generates list of general practices (GPs) and the total prescription quantity of pharmaceuticals inside each WWTP catchment, specific to a study period. This R package uses England-level monthly prescription datasets and GP information (such as GP name, address, and postcode), provided by NHS Digital.</p><!><p>A population equivalent (PE-NHS) for the each WWTP catchment was estimated, by the number of people registered in the general practices (GPs) located inside each WWTP catchment zone. Individual GP information, such as postcode and number of people registered, were obtained from NHS Digital (https://digital.nhs.uk/). WWTPs catchment maps were provided by Wessex Water. Briefly, an R (Ihaka and Gentleman, 1996) package was used, PrAna (Jagadeesan et al., 2022) and WWTPs catchment maps to identify the GPs located inside each WWTPs catchments. The number of patients registered for these GPs were then calculated using R, an open-source software for statistical computing and graphics.</p><p>Water utility estimates (table 2) were used to calculate population equivalents in wastewater (PE-WW). The resident population estimate was calculated by multiplying the occupancy rate with the number of properties residing in the designated catchment area. Multi-occupancy buildings were also taken into consideration, including care-homes and university halls. Other additional inputs into the wastewater stream were also considered (e.g. commercial or industrial waste) by using certain water quality indicators (WQIs). For example, industrial PE was calculated via the supply flow to commercial properties and using estimates of 60 g biological oxygen demand (BOD) per capita per day. Chemical oxygen demand (COD) was used to calculate input from tankard waste, by observing the amount of COD present in the known volume of waste (assumption that there was 120 g of COD per capita per day).</p><!><p>Pearson correlation coefficients were used to explore the potential relationships between parent compounds/metabolites and catchment prescription data. P values were calculated, via paired sample T tests, to investigate statistical significance in longitudinal trends in AAs and metabolites. Statistical significance in all tests was defined as p ≤ 0.05.</p><!><p>A total number of 58 parent AAs and 26 metabolites were tested in two contrasting catchments (>100K inhabitants in the city of Bath and <20K inhabitants in the town Keynsham) over 13 months. A diverse range of AAs (17 parent AAs and 8 metabolites) were quantified in the wastewater of both communities. AA average loads were, as expected, higher in Bath than in Keynsham (on average 83 ± 24% higher) as AA loads are population size driven. Full breakdowns of loads of all studied AAs can be found in Sims et al. (Sims et al., 2022). Seasonal variability of a handful of AAs across different classes was also observed, including MTZ, SLZ, LEX, CIP, and CLR reporting higher loads in winter versus summer. Other AAs reported low temporal variability during the studied period, including the sulphonamides SMX and SPY. A common observation was that the total prescribed AA mass in winter was higher than in summer -particularly for the macrolide, penicillins and cephalosporin classes (Curtis et al., 2019).</p><p>A hospital within the catchment area of Bath was also investigated to compare differences in composition between community influent and hospital effluent. Concentrations of certain AAs were found to be elevated in hospital effluent in comparison with community derived wastewater, including for SMX, TMP, and MTZ. It was also noted that there was a higher percentage of macrolides in hospital effluent than in community influent (42% versus 11%). Compositions in wastewater were compared to hospital prescribing, with notable differences in what was prescribed with what was reflected in wastewater. For example, penicillins were 33% of all AAs prescribed in hospitals during the sampling moth but only 6% was observed in hospital effluent. These differences in composition of prescription data and for wastewater results, are mainly attributed to human metabolism and possible transformation in wastewater environments. Beta-lactam AAs, like penicillins, have high susceptibility to hydrolysis due to the constrained four-member ring and usually rapidly degrade in the environment (Li et al., 2008).</p><!><p>The relationship between parent compounds and metabolites has been investigated further (figure 2), including calculating ratios for individual sampling points for the two catchment areas. Applying correlation statistics between AAs and associated metabolites showed mostly strong correlations in mass loads for both Bath and Keynsham (table 4). CLR and its major metabolite dmCLR demonstrated the strongest correlations (r=0.9, p≤0.05 and r=0.8, p≤0.05 for Bath and Keynsham, respectively). Furthermore, correlations between AAs that are prescribed as a combination were also observed. For example, SMX and TMP are commonly prescribed together in the UK as a combination known as cotrimoxazole (National Institute for Health and Care Excellence, n.d.). This is due to the different mechanisms of action and combined ability to tackle infections to reduce emerging AA resistance. Positive correlations were observed between SMX and TMP (r value 0.5 and 0.6 for Bath and Keynsham respectively, both p values ≤0.05), aSMX and TMP (r value 0.6 for both Bath and Keynsham, p values ≤0.05). Furthermore, co-trimoxazole is one-part TMP to five parts SMX. On average, whilst loads of SMX in wastewater were higher in general than TMP, the ratio between loads of SMX:TMP were 1.41 ± 0.50 and 2.83 ± 2.41 for Bath and Keynsham, respectively. Reasoning why the ratio between the two is lower than expected could be due to differing metabolism patterns of SMX and TMP, and that TMP can be prescribed by itself.  The comparison of ratios between AA/metabolites in the two sites was investigated (figure 3). Due to the presence of outliers, medians were also calculated. In general, median values were close to that of the average, demonstrating normal distribution (table 5). The ratio of CLR/dmCLR in Bath and Keynsham had concordant results, of 2.74 ± 0.63 and 2.81 ± 0.90 respectively. This is potentially attributed to both CLR and dmCLR having demonstrated good stability in wastewater, with minimal degradation over a 24-hour period at room temperature (Holton et al., 2022). The SMX/aSMX ratio, again, was consistent across both catchment areas at 2.40 ± 1.23 and 2.52 ± 1.96 for Bath and Keynsham. During June and July 2019, in Keynsham, several days measured ratios of ~10. These days often had lower levels of aSMX in wastewater, whereas no noticeable increase of SMX. SMX is taken orally in the UK, via tablet or liquid suspension and no topical usages have been identified (National Institute for Health and Care Excellence, n.d.). The lower metabolite levels in Keynsham could potentially be due to direct disposal of the parent pharmaceutical into the sewage system.</p><p>Previous work done in the catchment area has demonstrated several potential disposal events of pharmaceuticals, including fluoxetine (Petrie et al., 2016); and carbamazepine, propranolol and diltiazem (Kasprzyk-Hordern et al., 2021). It has previously been reported that incorrect disposal practices of pharmaceuticals is a global issue, where unused pharmaceuticals are disposed of into sinks, toilets, or rubbish bins (Tong et al., 2011). This study also found that leftover tablets and capsules were more likely to be thrown into the bin, whereas liquid medication was likely to be disposed of down the sewage system. As many AAs, including those monitored in this study, can be prescribed in liquid formulations or oral suspensions (table S3), education and awareness of the environmental risks of incorrect disposal is essential to ensure unused AAs do not unnecessarily end up in the sewar system.</p><p>Regarding MTZ/hMTZ, the ratios for Keynsham were slightly higher than for Bath, and whilst comparable, the difference was statistically significant (p≤0.05). The averaged ratio for MTZ/hMTZ in Bath was 1.70 ± 0.68, and for Keynsham it was 2.07 ± 1.27. Comparing these ratios to the urinary ratioexcretion (parent/metabolite), MTZ/hMTZ had the closest match, with a ratio of 1.03 (table 4). Both parent and metabolite exhibited similar levels of degradation by ~40% in wastewater over 24 hours (Holton et al., 2022). Interestingly, on the 28/02/2019 in Keynsham, MTZ/hMTZ had a ratio of 9.37 ± 0.14. The parent MTZ levels observed were 82.6 ± 2.2 mg 1000inh -1 day -1 with hMTZ levels recorded only at 8.82 ± 0.36 mg 1000inh -1 day -1 . Whilst this was not the highest MTZ level recorded over the sampling period, it was still above average (average PNDL, MTZ: 28.4 ± 23.4 mg 1000inh -1 day -1 ). Several days measured ~100 mg 1000inh -1 day -1 for MTZ, but these days observed corresponding high levels of hMTZ (e.g. 09/02/2019, MTZ: 113.51 ± 0.21 mg 1000inh -1 day -1 , hMTZ: 74.5 ± 1.45 mg 1000inh -1 day -1 , ratio 1.52 ± 0.03). This, therefore, could be another potential disposal event. Of note, however, is that MTZ prescriptions can be prescribed as a gel rather than tablets, gels applied topically will result in significantly different metabolism in the body. Hence, the proportion of MTZ prescriptions in gel or tablet form could impact observed ratios in wastewater. However, MTZ in both Keynsham and Bath was predominantly prescribed in oral form in comparison to topical form during the sampling period (figure S1). With Bath reporting on average 0.45 kg/month of tablets and capsules prescribed, versus 0.016 kg/month of creams and gels; and Keynsham reporting on average 0.39 kg/month of tablets capsules, versus 0.013 kg/month of creams and gel. Topical treatments of AAs tend to contain a lower dose than tablet or capsule form and are applied more frequently, so potential run off into the sewage system (e.g. from showering) may result in a low and consistent contribution to the overall AA level observed in wastewater.  Ratios between SLZ/SPY were consistent in both Bath and Keynsham, at 0.15 ± 0.11 and 0.16 ± 0.11 respectively. SPY and aSPY are both active, major metabolites of SLZ consumption (a prescribed antiinflammatory). Ratios of SLZ/aSPY and SPY/aSPY did observe statistical differences between the two catchment sites, with Keynsham having higher ratios in both (figure 3). This corresponds to lower levels of aSPY in Keynsham (PNDLs Bath:268 ± 109 mg -1 1000inh day -1 vs Keynsham: 65 ± 53 mg -1 1000inh day -1 ). This could be attributed to stability. Whilst SPY was reportedly stable in wastewater at room temperature (24 hour: 93.0 ± 7.6%), aSPY demonstrated a drop to 75.4 ± 6.0% after 24 hours (Holton et al., 2022). SLZ observed similar stability to aSPY, reporting a drop to 73.4 ± 5.4% after 24 hours.</p><p>These dissimilarities observed in ratio, between the two catchment sites, could be attributed to the differences in sewage systems. Resident times for Bath vary between <0.5-9 hours, and for Keynsham between <0.5-2 hours. Bath also has a larger sewage system with an anoxic transport pipe that carries most of the community wastewater to the WWTP. This is likely an environment that can facilitate degradation, and potential transformation from SLZ to aSPY could be occurring in Bath's pipes, increasing the loads of aSPY. Further studies are needed to better understand potential biodegradation within the sewage system. SLZ had similar PNDLs (Bath: 50.0 ± 44.3 Keynsham: 38.8 ± 32.4 g/day). It is worth mentioning that SLZ is prescribed purely in tablet form and is not utilised topically in the UK (National Institute for Health and Care Excellence, n.d.). Complexities will arise for these compounds; as, whilst SPY is no longer prescribed for human use, it is still used in veterinary practices. The Southwest of England, where both catchments reside, has a strong agricultural presence. Hence, SPY and aSPY could be present in influent wastewater because of run-off, rather than from consumption of SLZ at the community level.</p><p>The ratio between CLI and its metabolite dmCLI was higher than the other AAs/metabolites observed in this study at 7.10 ± 2.51 and 7.20 ± 3.67 for Bath and Keynsham, respectively. A previous study has demonstrated a lack of stability for these compounds, with degradation of over 80% reported for both CLI and dmCLI in wastewater after 24 hours (Holton et al., 2022). As degradation was similar for both CLI and dmCLI, it might be theorised that the ratios should have been lower. Stability could be an important factor here, as the standard deviations are large. However, as with MTZ, CLI creams can be applied topically, as a first-line acne treatment and for the treatment of bacterial vaginosis. Whilst MTZ was shown to be predominantly prescribed as oral treatment, compared to topical; this was not the case for CLI, which observed higher amounts of topical prescriptions (figure S1). Bath reported an average of 0.11 kg/month of oral prescriptions versus 0.052 kg/month of topical prescriptions. Keynsham reported 0.096 kg/month of oral versus 0.044 kg/month of topical prescriptions. Whilst, in general, the monthly oral prescriptions outweighed topical, November and December 2019 in both catchment sites observed similar prescribing for both (~0.059 kg/month for both Bath and Keynsham). This could therefore also cause higher levels of parent compound in comparison to the metabolite, hence could also push this ratio up. Whilst contribution from topical AAs could be low, understanding the breakdown of prescription data (topical versus oral) is still important when back calculating consumption at the community level. For other AA/metabolites that have previously demonstrated good stability in wastewater, e.g. CLR and dmCLR, concordance in the ratios between the two catchment sites is observed. The importance of biomarker stability is therefore highlighted again, especially for comparing between different communities. Different catchment areas will likely have unique conditions in the sewage pipes which could be facilitating faster degradation or transformation of either parent or metabolite. Again, more studies on fate of AAs and metabolites from source to WWTP could provide invaluable insight to these potential processes. However, by comparing the ratios of stable biomarkers between different catchment areas, WBE has promising potential to provide highly accurate data and comparisons. Statistically significant values are in bold.</p><!><p>To understand spatial and temporal variability of AA usage in the two catchment areas, AA loads in wastewater were normalised to population size (PNDLs) to allow a representative comparison (appendix 1). When comparing overall distribution of AA and metabolite loads per capita between Bath and Keynsham, similar usage of AAs in the two communities can be observed (figure S2). When investigating annual trends with monthly averages (figure S3), higher population normalised loads of AAs could be observed for Bath in general all year round when compared to Keynsham. For example, monthly averages of SMX were found consistently higher in Bath in comparison to Keynsham throughout the sampling period by 60 ± 17% (with overall year averages 121 ± 43 vs 45 ± 36 mg/1000inh/day respectively). Unsurprisingly, aSMX followed a similar trend with population normalised loads as its parent compound, with ~60 % higher each month in Bath when compared to Keynsham. Similar results of consistent higher loads per capita in Bath were observed for MTZ and the two antiviral drugs 3TC and FTC (51 ± 22, 54 ± 20, and 53 ± 9 % higher each month in Bath vs Keynsham, respectively). This confirms the hypothesis that daily loads of AAs in wastewater are population size driven.</p><p>A less consistent inter-city trend was observed for CLR. Interestingly whilst over the winter months, monthly averages of PNDls of CLR were significantly higher in Bath when compared to Keynsham (p≤0.05). Between December 2018-February 2019, CLR averaged 273 ± 72 mg 1000inh -1 day -1 in Bath vs 176 ±51 mg 1000inh -1 day -1 in Keynsham (77% difference). In contrast, in summer, PNDLs were much lower, and comparable between the two sites; with CLR averaging 121 ± 35 and 102 mg 1000inh -1 day -1 in Bath and Keynsham, respectively (19% difference). Whilst it is known that macrolide prescriptions often peak in winter, more macrolide AAs were prescribed per person in winter in Bath than Keynsham. Respiratory infections do tend to follow predictable fluctuations, with colder months leading to rapid spreading of infections, due to individuals spending increased time in enclosed spaces indoors (Price et al., 2019). Furthermore, the spread of infections occurs much quicker in urbanised areas, such as cities, due to higher population densities and an increased amount of shared air space (Alirol et al., 2011). Potentially, the reason why Bath had more CLR prescribed per person in winter could be due to more rapid spreading of viral infections in a city versus a town.</p><p>It has also been highlighted that spatial differences in AA prescribing do occur. Higher AA prescribing rates have been associated with a higher proportion of patients over 65 and under 18, larger population sizes, ruralness, and deprivation (Curtis et al., 2019;Devine et al., 2021;Thomson et al., 2020).</p><p>Comparing age demographics between the two sites (figure S4), Keynsham and Bath had a similar percentage of younger children (0-9 years old) at 10%. Regarding the older population, Keynsham had a slightly higher proportion in the 65-95+ age bracket, at 24%, with Bath reporting 16%. Perhaps unsurprisingly, Bath had a higher proportion of younger adults (20-34) around 29%, with Keynsham at 18%. This is in line with the strong university presence in Bath, with two universities residing in the catchment area.</p><p>Regarding spatial trends, whilst seasonality of certain diseases will influence AA usage, fluctuating populations will also play a role here. Bath will have population fluctuations due to tourism and an active student population (equating to an estimated 7,250 and 5,800, respectively, additional to the calculated resident PE (table 2)). Whilst university students will typically be away over summer months (June-September), this is considered peak season for tourism. It is estimated that populations fluctuations in Bath will be around ±20%. Considering the high uncertainties associated with wastewater sampling, it is unlikely the effects of a variable population at this scale will have a visible and notable impact upon AA levels.</p><p>Whilst Keynsham has a significantly reduced non-residential input in comparison to Bath, there will be fluctuations in the population from those commuting. Keynsham is in close proximity to two cities (Bristol and Bath), resulting in additional contribution to respective WWTPs. Population fluctuations due to commuting however, will be occurring all year round so Keynsham is likely considered a more stable population in comparison to Bath.</p><!><p>Catchment prescription data for both Bath and Keynsham was also compared with PNDLs in wastewater (figure 4). Interestingly for Keynsham, TET, SMX, and OFX all had months where these were not prescribed by GPs in the catchment area, but the parent AAs, along with SMX metabolite, aSMX, were still quantifiable in wastewater. Looking at average prescribing lengths of these AAs, TET can be prescribed long-term for the treatment of acne and rosacea, both requiring longer treatment times of minimum 12 weeks for acne and 6-12 weeks for rosacea. OFX is used in the treatment of different infections including taken orally for the treatment of urinary tract infections (UTIs), and lower respiratory infections but also can be found in eye drops for the treatment of certain infections. SMX, as previously mentioned, is often prescribed with TMP due to rising sulphonamide resistance rates and the combined synergistic activity. Again, used as a treatment option for a range of infections including respiratory and ear infections. Its use for urinary tract infections is only prescribed alongside bacteriological evidence of sensitivity. OFX and SMX tend to be prescribed in much shorter lengths in comparison to TET (days rather than months). Their detection in wastewater during those periods could be attributed to many reasons, including individuals registered to GPs outside the catchment area, delayed prescriptions (in the case of UTIs) or potential self-prescribing. This does highlight the complexity of estimating community usage from wastewater, but also how using prescription data at face value does not always necessarily give the reflection of what is being actually used in the community. To further investigate relationships between AAs in wastewater and prescribing patterns, correlations between average monthly loads in wastewater with the abundance of AAs prescribed in each catchment per month were investigated. CLR in Bath had strong positive correlation (r value 0.88, p≤0.05) between wastewater loads and prescription data. As previously mentioned, CLR has demonstrated strong seasonal changes, with prescription rates being higher in winter months when compared to summer (figure 4). However, whilst catchment prescribing rates for CLR peaked in Keynsham during winter, correlations between prescription and loads were low with no statistical significance (r value 0.19, p value ≥0.05). Keynsham is a commuter town, so reasoning for this could be partially explained due to fluctuating populations with individuals being prescribed AAs in the catchment area but working outside the catchment area in the cities in close proximity. Weaker correlations between wastewater and prescription data could be partially due to the nature of AA prescribing, with shorter courses for some AAs, some lasting days with others lasting months. Whilst prescribing data is an invaluable source of information for identifying prescribing patterns and trends, it does not necessarily give a true representation of how AAs are being used in the community. Items prescribed by a GP could be consumed, stock-piled or disposed of. Furthermore, AA compliance could be a further issue, as previously mentioned one survey identified 1/3 of patients not fully complying to AA treatment with 1/4 admitting to stockpiling of AAs to use at a later date (Kardas et al., 2005). For AAs that are not stable, and as a result are rarely observed in wastewater (e.g. penicillins), prescription data can give valuable insight. Notably, just because an AA is in low concentration or not observed, it does not mean there is no potential concern with regards to resistance, as low levels could be as a result of resistance mechanisms (e.g. microbial metabolic transformation) to the AA in the bacterial community. Here, metagenomic or targeted gene analysis in wastewater could provide community resistance patterns. WBE is therefore an excellent complimentary technique for healthcare data such as prescriptions, offering a much broader picture.</p><p>Notably, two antivirals FTC and 3TC were monitored in influent wastewater of both sites, however prescriptions of these were not found in the catchment region. It could be that these antivirals were prescribed outside the catchment region or could be from an unknown source. A similar observation was observed for several tuberculosis (TB) drugs, including metabolites hPZA and INa. The usual course of treatment is two antibiotics (isoniazid and rifampicin) for six months, along with additional two antibiotics (pyrazinamide and ethambutol) for the first two months of the six-month period (NHS, n.d.). These are often prescribed in combination due to challenges of multi-drug resistance with many TB infections. Of note is that there are no drugs licensed in the UK for treating animals, and so presence in community wastewater will originate from human sources (Animal and Plant Health Agency, 2017).</p><!><p>Population normalised daily prescription (PNDPs mg/day/1000inh) were calculated from the total monthly mass of the drug prescribed; these were compared against the population normalised daily intake (PNDIs). As previously mentioned these were calculated in a prior comprehensive literature review investigating the pharmacokinetic data of selected AAs to generate CFs (Holton et al., 2022). The calculated CF values were used to back calculate the mass of drug consumed at the community level, using the amount of AA or respective metabolite quantified in wastewater and the proportion that target analyte is excreted via urine or faeces (figure 5). The macrolide CLR gave good agreement between the PNDI and PNDP, with CLR in Bath (PNDI: 577 ± 189 mg/day/1000inh, PNDP: 524 ± 88 mg/day/1000inh), and in Keynsham (PNDI: 427 ± 183, PNDP:480 ± 125 mg/day/1000inh). The metabolite dmCLR did not give this same closeness being much higher than its parent compound after correction (PNDI for Bath: 1347 ± 402 mg/day/1000inh, PNDI for Keynsham: 922 ± 323 mg/day/1000inh). . Notably, PNDIs for several AAs were in good agreement between the two sites but the PNDP was different. Agreement between PNDIs between different locations could indicate that WBE provides a better estimate of community AA usage than prescriptions alone. This is because WBE can account for the potential problems with prescriptions. As mentioned earlier, drugs prescribed may not have been used, delayed prescription, stored or disposed of, and some treatments may last many months (e.g. AAs prescribed for acne treatment). Patients of GP surgeries may also live outside the studied wastewater catchment area, so use/disposal of those pharmaceuticals not captured in the studied wastewater data. Vice versa some individuals may be consuming pharmaceuticals that were prescribed elsewhere within the studied catchment area so would show in wastewater data and not the catchment prescription data. Also, whilst some laboratory stability studies in wastewater have been investigated (Holton et al., 2022), it is recognised that wastewater from different sites could provide very different matrices and the environment within the sewage system could be very different.</p><!><p>This study investigated AA usage in two different catchment areas, and results have demonstrated similarities in trends of AA usage in both Bath and Keynsham across the sampling period. They have also indicated, even with their close proximity, that more AA is consumed per person in Bath than in Keynsham. A similar observation has been identified by previous work in the group done within the catchment area, identifying human population size as a key driver of AAs in the environment (Elder et al., 2021). This study aimed to further build upon knowledge in the catchment area and results have demonstrated some key findings:</p><p>• Parent AAs and metabolites all observed positive correlations in community wastewater • Spatial variability in AA usage was observed, even in neighbouring urban areas • WBE and prescription data showed similar seasonal trends but with low correlation in intake.</p><p>The reasons might be variable prescribing patterns and/or lack of patient compliance Application of CFs to several AAs to back-calculate intake observed good agreement with estimated intake from the prescription data, closer matches were observed in the catchment with the larger population size (Bath) • WBE proved useful in differentiating between consumption vs topical usage and/or direct disposal of unused AA • WBE is superior to prescription data as it provides information on AAs prescribed outside of the monitoring catchment (e.g. antiviral and TB drugs) • Data triangulation (WBE, prescription, and demographics data) provides the most comprehensive approach to AA usage in any given catchment</p>