Abstract
Background
Acute respiratory infections (ARIs) account for 30% of long-term care facility (LTCF) infections, frequently leading to antibiotics over-prescription.
Objectives
This study evaluates the impact of SARS-CoV-2 testing on LTCF antibiotic prescriptions.
Methods
A retrospective study was conducted across 45 LTCFs in Vaud Canton, Switzerland, encompassing 2427 long-stay beds, from July 2021 to June 2023. Monthly data on SARS-CoV-2 tests and antibiotic prescriptions were collected. Using linear regression adjusted for Swiss viral epidemiology, we assessed the association between (i) SARS-CoV-2 testing and (ii) positive test results on antibiotic prescriptions.
Results
SARS-CoV-2 testing rates in LTCFs varied, ranging from 0.3% to 16% of residents tested per facility, peaking in January 2022, July 2022, and November 2022. Similar trends were observed for SARS-CoV-2 test positivity, except for the last testing peak. Antibiotic prescription rates fluctuated moderately, from 3.9% to 7.4% monthly, with minor peaks in December 2021 and April 2022, and a notable peak in January 2023.
No significant association was found between SARS-CoV-2 testing and antibiotic prescriptions (coefficient = −0.03 [95%CI: −0.16; 0.10], P = 0.65). However, positive SARS-CoV-2 tests were negatively associated with prescriptions (coefficient = −0.28 [95%CI: −0.53; −0.03], P = 0.029); a 10% increase in positive tests is associated with a 2.8% reduction in antibiotic prescriptions, with an estimated 3.5 positive tests needed to prevent one prescription.
Conclusions
Overall testing rates showed no impact on antibiotic prescribing, but positive SARS-CoV-2 results correlated with reduced consumption, suggesting more informed prescribing practices and a reduction in unnecessary antibiotic use.
Introduction
A recent study in European long-term care facilities (LTCFs) reports that acute respiratory infections (ARIs) account for 30% of all infections, making them the most common infection among LTCF residents.1 Up to two-thirds of LTCF residents receive antibiotics each year,2 with as many as half of these prescriptions considered unnecessary.3 Over-prescription is particularly high for ARIs, while a recent meta-analysis highlighted pronounced antibiotic prescription rates even in the context of viral ARIs, ranging from 21% to 78%.4 Multiple factors contribute to this excessive use of antibiotics, including the difficulty in distinguishing viral from bacterial ARIs, particularly in this frail, elderly population, where identifying those who could benefit from antibiotics is complex.5
The growing threat of antibiotic resistance highlights the importance of reducing unnecessary antibiotic use.6 Over-prescription of antibiotics exposes LTCF residents to avoidable side effects, such as Clostridioides difficile infections, altered intestinal microbiota and diarrhoea.7 Additionally, residents in LTCFs with high antibiotic usage face greater risk of antibiotic-related harms, even without direct antibiotic exposure.8
Optimizing antibiotic prescribing is essential, with respiratory virus testing being part of the antimicrobial stewardship toolkit.9 Although a meta-analysis found no link between respiratory virus testing and antibiotic use, this has not been studied in the SARS-CoV-2 era.10 Considering that bacterial superinfection occurs in fewer than 10% of SARS-CoV-2 cases, identifying COVID-19 can help reduce unnecessary antibiotic consumption.11,12
This study aims to evaluate the impact of SARS-CoV-2 testing on antibiotic prescriptions in LTCFs and its potential role in antimicrobial stewardship.
Materials and methods
Ethics
As this study only included anonymized and monthly aggregated personal data, the local ethic committee (CER-VD) deemed the project outside the scope of the Swiss Human Research Act and therefore did not require ethical approval.
Study design and participants
This retrospective observational study includes routinely collected data from 45 LTCFs in the Canton of Vaud, Switzerland, covering 2427 long-stay beds over a 2-year period (July 2021 to June 2023). The analysis focused on residents aged 65 years and older.
These 45 institutions represent 32% (45/140) of all LTCFs in the region, for which laboratory and prescription data were accessible.
Data collection
LTCF characteristics data
The included LTCFs were affiliated with 11 pharmacies, which provided information on facility characteristics (location and type) and a monthly resident count based on the number receiving at least one prescription, as actual bed occupancy varies.
Antibiotic prescription data
The pharmacies extracted the number of antibiotic packs billed per LTCF each month, with each antibiotic active ingredient counted as one prescription course. Duplicate courses per resident within a month were excluded. As indications for antibiotic prescriptions were not available, antibiotics recommended for respiratory infections according to local LTCF-tailored guidelines (amoxicillin, co-amoxicillin, cephalosporins, and macrolides) were used as a proxy for antibiotic prescriptions for ARIs.13
Viral PCR data
The included LTCFs were affiliated with seven external laboratories, which extracted data on the number and types of viral PCR tests conducted monthly, along with the number of positive results for SARS-CoV-2, RSV, and influenza (A and B). Test types included single-pathogen (Influenza, SARS-CoV-2, and RSV) and multiplex PCRs covering various combination of respiratory pathogens. Repeated tests for the same pathogen within a month were counted only once per resident.
Viral epidemiological data
We used data from the Swiss sentinel surveillance network ‘Sentinella’ to track the local epidemiology of viral infections, using monthly case numbers of influenza, SARS-CoV-2, and RSV as a proxy for the ARI burden during the study period.14
Statistical analysis
Descriptive statistics were used to present LTCFs characteristics. Monthly proportions of residents receiving antibiotics for ARI, tested for SARS-CoV-2 (overall and positive) and influenza in the different LTCFs were plotted using scatter plots. Due to low RSV testing, these data are not shown. Mean monthly proportions of residents receiving antibiotics, tested for SARS-CoV-2, testing positive for SARS-CoV-2, and tested for influenza were estimated using zero-inflated mixed-effect Poisson models, with an offset for the number of residents in each LTCF, representing a fixed denominator of each proportion.
Spearman correlations and linear regressions assessed the association over time between estimated mean proportions of antibiotic prescription and of SARS-CoV-2 testing (and positive tests). Linear regressions were adjusted for community viral epidemiology (total raw number of positive influenza, SARS-CoV-2 and RSV cases per month, using Sentinella data). From these models, we estimated the number of tests needed to avoid one antibiotic prescription based on the probability of a positive test. Analyses were performed using R (version 4.4.2) and the glmmADMB package for zero-inflated mixed-effect Poisson regression.15,16
Results
LTCF characteristics
Among the 45 included LTCFs, 26 (58%) are specialized in geriatric care, 6 (13%) in psycho-geriatric care, 5 (11%) in adult psychiatry, and 8 (18%) are mixed facilities. Twenty-three (51%) are located in urban areas, 7 (16%) in rural areas, and 15 (33%) in intermediate areas. Urban, rural and intermediate areas’ were characterized by Swiss Federal Statistical Office according to multiple criteria, such as size, density and accessibility.17 The LTCFs had a median capacity of 48 beds (interquartile range [IQR] 35–76), with 21 (47%) of facilities with 50 or more beds. As presented in Table 1, the characteristics of the 45 LTCFs included in the study are similar to LTCFs of the Canton of Vaud, in terms of mission, localization and size. Although not specific to the study LTCFs, data from the canton indicate that most rooms are single-occupancy (80%), while a minority are double-occupancy (20%).18 In the LTCFs studied, the mean age of residents was 86 years (standard deviation 8). Overall, 69% of residents were female. Regarding vaccination status, a national campaign was launched in January 2021 using the mRNA vaccines Comirnaty and Spikevax. Following this campaign, 70% of residents received at least one dose, and 60% were fully vaccinated.19 Regarding influenza, the median vaccination coverage among LTCF residents in the canton was 83% during both the 2021–22 and 2022–23 winter seasons.20 Of note, no SARS-CoV-2 treatments with Paxlovid or remdesivir were prescribed in the LTCFs during the study period. Residents in LTCFs who tested positive for SARS-CoV-2 were recommended to undergo isolation with droplet precautions for five days and until 48 h after symptom resolution. For residents in double-bed rooms, this involved either transfer to a single-bed room or delineation of an isolation area under droplet precautions within the same room. When three or more symptomatic cases occurred, universal face mask use was implemented throughout the institution until 48 hours after the last case had recovered.
Table 1.
Characteristics of the long-term care facilities (LTCFs) included in the study compared to those of all LTCFs in the Canton of Vaud
| LTCFs in the study N = 45 |
All LTCFs in the Canton of Vaud N = 140 |
P value* | |
|---|---|---|---|
| LTCF mission | 0.528 | ||
| Geriatric, N (%) | 26 (58) | 69 (49) | |
| Geriatric psychiatry, N (%) | 6 (13) | 25 (18) | |
| Adult psychiatry, N (%) | 5 (11) | 20 (14) | |
| Mixed, N (%) | 8 (18) | 26 (19) | |
| LTCF localization | 0.365 | ||
| Urban, N (%) | 23 (51) | 80 (57) | |
| Rural, N (%) | 7 (16) | 24 (17) | |
| Intermediate, N (%) | 15 (33) | 36 (26) | |
| LTCF size | 0.324 | ||
| LTCF ≥ 50 residents, N (%) | 21 (47) | 57 (41) | |
| LTCF < 50 residents, N (%) | 24 (53) | 83 (59) |
*Comparisons between LTCFs included and not included in the study was done using the chi-square test
Swiss epidemiology of respiratory viruses
Community circulation of respiratory viruses, as reported in the Sentinella network, can be seen in Figure 1. High circulation of SARS-CoV-2 was observed between November 2021 and April 2022, followed by a stable period of medium-low level detection until cases began to decline in March 2023. Influenza displayed usual seasonal patterns with intense circulation from January to April 2022, and November 2022 to April 2023. RSV numbers showed a minor peak in December 2021 and a more significant increase in December 2022.
Figure 1.
The epidemiology of SARS-CoV-2, influenza, and RSV according to the Sentinella network in Switzerland from June 2021 to July 2023.
Patterns of antibiotic prescription and respiratory virus testing
Figure 2 illustrates, for each LTCF, the monthly proportions of residents who: received at least one antibiotic prescription for ARIs, underwent at least one SARS-CoV-2 test, tested positive for SARS-CoV-2 at least once, and underwent at least one influenza test. Among the selected antibiotics, the most frequently prescribed were amoxicillin and co-amoxicillin (56%), followed by macrolides (29%) and cephalosporins (15%). On average, per month and per facility, 1.6 residents received amoxicillin or co-amoxicillin (SD 1.9), 0.83 received macrolides (SD 1.2), and 0.44 received cephalosporins (SD 1.0).
Figure 2.
Observed proportions of antibiotics for acute respiratory infections (ARIs), SARS-CoV-2 tests, positive SARS-CoV-2 tests, and influenza tests in each LTCF each month. The monthly proportions of zeros over time are indicated in red.
Across all four variables, a substantial proportion of LTCFs had zero monthly occurrences (in red in figure 2). The proportions of LTCFs with zero ARI antibiotic prescriptions remained relatively stable over time, around 20%. However, the proportion of LTCFs not performing tests showed greater variability. For SARS-CoV-2 testing, this proportion was around 20% during the wave period of late 2021 and early 2022, increased to 30–40% by late 2022 and reached 90% by early 2023. Influenza testing followed a similar pattern, with even higher proportions of LTCFs not performing tests.
Figure 3 shows the monthly summaries of these proportions, estimated using the zero-inflated mixed-effect Poisson regression model. Antibiotic prescriptions presented relatively small fluctuations over time, with the monthly mean proportion ranging from 3.9% and 7.4% of residents per facility receiving antibiotic for ARI. We observed two minor peaks in the mean trajectory, the first in December 2021, 5.3% (95% CI: 3.8%–7.5%), the second in April 2022, 5.2% (95% CI: 3.7%–7.4%). A more important peak occurred in January 2023, 7.4% (95%CI: 5.3%–10%).
Figure 3.
Mean monthly proportion of antibiotic prescription, SARS-CoV-2 and influenza testing and positive SARS-CoV-2 testing in the studied LTCFs from July 2021 to June 2023 obtained via zero-inflated mixed-effect Poisson models.
The monthly mean proportions of SARS-CoV-2 tests showed greater fluctuations over time, ranging from 0.3% to 16% (Figure 3). From October 2021 onwards, SARS-CoV-2 testing gradually increased, peaking in January 2022 with a mean of 16% (95% CI: 10%–24%) of residents tested per facility. After a brief decline, testing levels rose again, reaching a second peak in July 2022, with 11% (95% CI: 6.6%–17%) of residents tested per facility. A third peak occurred in November 2022, with 16% (95% CI: 10%–25%) of residents tested per facility. Testing activity then dropped significantly, reaching its lowest level in June 2023. SARS-CoV-2 test positivity followed similar trends, except during the period from September to December 2022, when a wave of SARS-CoV-2 tests was observed, without being accompanied by a corresponding increase in positive results.
Influenza testing activity was sensibly lower than that for SARS-CoV-2 (Figure 3). It followed a seasonal pattern with the more important testing activity from November 2021 to April 2022 and November 2022 to April 2023. RSV testing remained very low during the study period, with a mean percentage of RSV testing per LTCF per month of 0.4% (data not shown).
SARS-CoV-2 testing and antibiotic prescriptions
The association between SARS-CoV-2 testing or positive SARS-CoV-2 tests and antibiotic prescriptions was evaluated using the Spearman correlation between the corresponding summaries presented in Figure 3. No significant association was observed between SARS-CoV-2 testing and antibiotic prescriptions, with a Spearman correlation coefficient of ρ = −0.22 (P = 0.40). However, a negative association was identified between positive SARS-CoV-2 tests and antibiotic prescriptions, with a Spearman correlation coefficient of ρ = −0.60 (P = 0.01). No correlation was found between negative SARS-CoV-2 tests and antibiotic prescription (ρ = −0.01, P = 0.75, data not shown). These results aligned with linear regression models adjusted for community viral epidemiological data. The analysis showed no association between the mean proportion of SARS-CoV-2 testing and antibiotics prescriptions (coefficient = −0.03 [95%CI: −0.16; 0.10], P = 0.65). In contrast, a negative association was observed between the mean proportion of positive SARS-CoV-2 tests and antibiotic prescriptions (coefficient = −0.28 [95%CI: −0.53; −0.03], P = 0.029). A 10% increase in the proportion of positive SARS-CoV-2 tests was associated with a 2.8% decrease in antibiotic prescriptions. This corresponds to 3.5 (1/0.28) positive SARS-CoV-2 tests needed to avoid one antibiotic prescription. Figure 4 shows the number needed to test (NNT) for SARS-CoV-2 to avoid one antibiotic prescription as a function of the probability of a positive test p (NNT = 3.5/p). With a positivity rate of approximately 10%, about 35 tests would be required to prevent one antibiotic prescription.
Figure 4.
Number needed to test to avoid one antibiotic prescription as a function of the positivity probability (linear regression of the mean proportion of antibiotic for acute respiratory infections on the mean proportion of positive SARS-CoV-2 tests across the time, adjusted for viral epidemiology).
Discussion
This study explores the relationship between SARS-CoV-2 testing and antibiotic prescriptions in Swiss LTCFs. Although SARS-CoV-2 testing was not significantly associated with antibiotic use, positive SARS-CoV-2 test results were linked to a reduction in antibiotic prescription rates. Testing 3.5 residents positive for SARS-CoV-2 prevents one antibiotic course. These findings suggest that SARS-CoV-2 testing during outbreaks with high prevalence of positive cases could help reduce unnecessary antibiotics prescriptions for ARIs, participating in the antimicrobial stewardship effort.
A recent meta-analysis of pre-SARS-CoV-2 studies conducted in emergency departments (EDs) found no reduction in antibiotic use associated with respiratory virus testing.10 Like our findings, fewer patients with positive viral test results were prescribed antibiotics. However, unlike our results, the meta-analysis reported higher antibiotic use among patients with negative test results. The authors concluded that routine viral testing in EDs offers limited benefits and recommended reserving such testing for patients where the results are likely to influence treatment decision. The largest randomized controlled trial assessing the impact of routine molecular point-of-care testing for respiratory viruses on various clinical outcomes in adults treated in EDs found no reduction in the proportion of patients receiving antibiotics. However, a higher proportion of patients in the respiratory virus testing group received single doses or short courses of antibiotics compared to the control group. This suggests that testing may help optimize antibiotic use, with the potential for even greater reductions if the time to obtain results is shortened.21
No studies have evaluated the impact of SARS-CoV-2 testing on antibiotic prescriptions. As shown in our study, we hypothesize that confirming SARS-CoV-2 as the cause of respiratory symptoms may prompt clinicians to withhold antibiotics, recognizing the viral origin of the illness. This aligns with the understanding that bacterial co-infections in SARS-CoV-2 cases are relatively rare, occurring in less than 10% of cases.11,12 Supporting this hypothesis, a recent observational study among LTCF residents with ARI showed an association between molecular respiratory virus testing, primarily for SARS-CoV-2, and a reduction in inappropriate antibiotic prescriptions, regardless of the test results.22 The difference between these findings and ours may be due to our analysis of overall antibiotic prescription using aggregated data, which did not allow us to assess the appropriateness of these prescriptions. Additionally, this could be attributed to the increased SARS-CoV-2 testing during the influenza peak 2022–2023, when the prevalence of SARS-CoV-2 positive tests was very low. This suggests that caregivers may have misinterpreted the influenza epidemic as another SARS-CoV-2 outbreak, potentially influencing their prescription practices when facing negative test results.
There was a large variability in prescription practices between LTCFs. Research examining the impact of LTCF features on antibiotic prescriptions for ARI found that both facility-related factors, such as being located in an urban area, and provider-related factors, such as having a male physician, were associated with higher prescription rates.22 Testing practices also varied widely, with some facilities testing the majority of their residents, while others conducted almost no testing at all, despite the existence of cantonal guidelines.13 Both variability in antibiotic prescription and virus testing highlight the importance of developing tailored interventions in the LTCF setting to promote a more prudent antibiotic use.3
Finally, we were surprised by the low rate of influenza testing, particularly during the influenza epidemic of winter 2022–2023, despite local guidelines recommending testing elderly individuals with symptoms of ARIs for influenza to guide antiviral treatment.13
Strengths and limitations of the study
This study has several strengths. Its multicentre design, including a large and representative sample of institutions with characteristics similar to other LTCFs in the region, enhances the generalizability of findings. Moreover, the variability among included LTCFs makes the results representative of Swiss LTCF practices and potentially applicable to similar settings worldwide. Additionally, by focusing on LTCFs, the study addresses an under-researched area, providing valuable insights into antibiotic prescribing patterns for a vulnerable population.
This study has also limitations. Data aggregation by facility and month prevented accounting for individual resident characteristics in the analysis, which may confound results. The lack of specific indications for antibiotic prescriptions necessitated using antibiotics commonly prescribed for ARI as a proxy for those prescribed for ARI, although some were probably prescribed for other reasons, potentially weakening the association between SARS-CoV-2 testing and antibiotic use. Additionally, while the analyses were adjusted for common viruses (SARS-CoV-2, influenza, RSV), not all circulating respiratory pathogens were accounted for. Finally, the study focused on PCR testing, which typically provides results after 24 hours, potentially influencing the duration rather than the initiation of antibiotic courses. Further research using real-time antigenic POCT SARS-CoV-2 tests is needed to better assess the impact of viral testing on antibiotic prescriptions.
Conclusion
In conclusion, while SARS-CoV-2 testing rates alone did not influence antibiotic prescribing, positive SARS-CoV-2 test results were associated with reduced antibiotic prescriptions among LTCF residents. These findings underscore the importance of clear diagnostic information in guiding clinicians towards more judicious prescribing practices, reducing unnecessary antibiotic use when bacterial infections are unlikely. By integrating SARS-CoV-2 testing into antimicrobial stewardship programmes, nursing homes can address the persistent challenge of antibiotic overuse, improving care quality for residents and contributing to global efforts to combat antibiotic resistance.
Acknowledgements
We would like to thank all participating pharmacies: pharmacie de Gimel, pharmacie Nyonnaise, pharmacie de Provence, pharmacie d'Unisanté, pharmacie Moudonnoise, pharmacie de la Tour d'Ivoire, pharmacie de Begnins, pharmacie hospitalière de l'Est lémanique (PHEL), pharmacie de l'Hôtel de Ville, pharmacie Amavita Alpha et la pharmacie de Chexbre. We also thank all participating laboratories: La source, HIB, Unilabs, Proxilab, ICHV, MCL and Synlab for their collaboration and the transmission of data concerning antibiotic prescriptions and data concerning all the respiratory tests.
We are grateful to Marie Immaculée Nahimana Tessemo, MD from the cantonal unit for Infection control and Prevention of Vaud (HPCi Vaud) who acted as a facilitator for microbiological data obtention. We also thank Catherine Plüss-Suard, pharmacist in Anresis, Institute for Infectious Diseases of the University of Bern, Switzerland who critically reviewed our analyses.
Contributor Information
Marc Jeanneret, Faculty of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Alexia Roux, Infectious Diseases Service, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland.
Isabella Locatelli, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Alessandro Cassini, Cantonal Doctor Office, Public Health Department, Canton of Vaud, Lausanne, Switzerland; Infection Prevention and Control Unit, Infectious Diseases Service, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Emmanouil Glampedakis, Cantonal Infection Prevention and Control Unit, Cantonal Doctor Office, Public Health Department, Canton of Vaud, Lausanne, Switzerland.
Anne Niquille, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Switzerland; Department of Ambulatory Care, Unisanté, Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
Noémie Boillat-Blanco, Infectious Diseases Service, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland.
Funding
This work was supported by (1) the Chuard-Schmid Foundation, Switzerland, to NBB and (2) the Faculty of Medicine, University of Lausanne, Lausanne, Switzerland (Solis Grant) to AR.
Transparency declarations
None declared.
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