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The incidence of nosocomial bloodstream infection and urinary tract infection in Australian hospitals before and during the COVID-19 pandemic: an interrupted time series study

The incidence of nosocomial bloodstream infection and urinary tract infection in Australian... Background The COVID-19 pandemic has had a significant impact on healthcare including increased awareness of infection prevention and control (IPC). The aim of this study was to explore if the heightened awareness of IPC measures implemented in response to the pandemic influenced the rates of healthcare associated infections (HAI) using positive bloodstream and urine cultures as a proxy measure. Methods A 3 year retrospective review of laboratory data from 5 hospitals (4 acute public, 1 private) from two states in Australia was undertaken. Monthly positive bloodstream culture data and urinary culture data were collected from January 2017 to March 2021. Occupied bed days (OBDs) were used to generate monthly HAI incidence per 10,000 OBDs. An interrupted time series analysis was undertaken to compare incidence pre and post February 2020 (the pre COVID-19 cohort and the COVID-19 cohort respectively). A HAI was assumed if positive cultures were obtained 48 h after admission and met other criteria. Results A total of 1,988 bloodstream and 7,697 urine positive cultures were identified. The unadjusted incident rate was 25.5 /10,000 OBDs in the pre-COVID-19 cohort, and 25.1/10,000 OBDs in the COVID-19 cohort. The overall rate of HAI aggregated for all sites did not differ significantly between the two periods. The two hospitals in one state which experienced an earlier and larger outbreak demonstrated a significant downward trend in the COVID-19 cohort (p = 0.011). Conclusion These mixed findings reflect the uncertainty of the effect the pandemic has had on HAI’s. Factors to consider in this analysis include local epidemiology, differences between public and private sector facilities, changes in patient populations and profiles between hospitals, and timing of enhanced IPC interventions. Future studies which factor in these differences may provide further insight on the effect of COVID-19 on HAIs. *Correspondence: Philip L Russo Philip.russo@monash.edu Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 2 of 9 Keywords Infection prevention, COVID-19, Surveillance, Healthcare associated infection, Blood cultures, Urine cultures Background of COVID-19, these activities will also prevent many The COVID-19 pandemic has seen an unprecedented other types of infection. On the other hand, as emerging increase in awareness and focus on infection prevention research is indicating, the increased stress on healthcare precautions, including hand-hygiene, cleaning, air qual- workers and organisations may increase HAIs, [13] par- ity, ventilation and correct use of personal protective ticularly given evidence that increased glove use often equipment (PPE) [1]. Recent research has documented leads to poor hand hygiene compliance [14]. There are that although standard precautions were adopted glob- also several reports of increases in carbapenemase-pro- ally prior to the pandemic, deficits in implementation ducing Enterobacteriaceae in intensive care units in the and compliance persist [2–4]. With an increased focus on context of COVID-19 and increased infection preven- infection prevention and control practices and processes tion activity related to non compliance with PPE, misuse in healthcare settings as a result of the pandemic, it could of gloves, high antibiotic use and overwork [15–18]. The be hypothesised that this in turn may have a positive overall aim of this study is to explore if there has been effect on reducing the overall risk of infection transmis - any effect on HAI rates as a result of the increased infec - sion in these settings. Conversely, hospitals and health- tion prevention awareness brought about by COVID-19. care workers have been under enormous strain from COVID-19 and this may result in a reduced focus on pre- Methods venting infections other than COVID-19. Study design Emerging research contains mixed results about the The study was a three-year retrospective review of inpa - effect the COVID-19 pandemic has had on the rates of tient laboratory data. healthcare associated infections (HAIs). Substantial increases in central-line associated bloodstream infec- Setting and population tions and catheter associated urinary tract infections Data were sourced from five Australian hospitals from have been observed, along with an increase in contami- two different Australian jurisdictions (New South Wales nated specimens and potential reduction in local HAI [NSW] and Victoria). These five hospitals consisted of reporting [5–7]. Researchers have suggested that this four acute public hospitals (two Principal Referral Hos- may be due to: resources shortages; influx of patients; pitals [Hospitals A and B]), and two Acute Group A changing recommendations; and general stress [5–7]. hospitals [Hospitals C and D]) and one acute private hos- However, reductions in Clostridioides difficile have also pital (Private Acute Group A [Hospital E]). Differences been reported, [1, 8, 9] and generally a lower rate of between these hospital types are detailed in Supplemen- multidrug resistant organisms - although this was in an tary Table S1. Combined, these hospitals have over 2400 area which was at the time not significantly affected by overnight beds and over 290,000 hospital admissions per COVID-19 infections [10] These have been attributed to year. the increased awareness and practice of standard precau- We constructed two cohorts; first, the pre-COVID-19 tions [1, 10, 11]. cohort, defined as inpatients who had specimens col - The infection prevention challenges presented by lected between January 2017 to February 2020, and sec- COVID-19 are significant. To prepare for the admission ond, the COVID-19 cohort, defined as inpatients who and treatment of COVID-19 positive patients, a number had specimens collected between March 2020 to March of new and modified infection prevention initiatives have 2021, inclusive. This time point was chosen following the been implemented across healthcare sites. These include, first identification of a COVID-19 case in Australia on but are not limited to: an overall heightened awareness 25th January 2020. of infection prevention; increase in education regarding PPE; increase in the use of PPE; increase in promotion Data sources of hand hygiene; changes to cleaning regimes; restriction Microbiology data were obtained from the laboratories in visiting hours; improvement in ventilation and limited of participating hospitals for the period of January 2017 patient movement [12]. Whilst the correct and appro- through to March 2021 (inclusive) for positive blood- priate use of PPE, adequate air quality, hand hygiene stream and urine cultures. For each positive culture, and cleaning are fundamental in every infection preven- patient level data were collected, including age, gender, tion program, the heightened awareness COVID-19 has date of admission, date of specimen collection and name introduced, may mean there is increased compliance and of organism. Positive cultures that were collected within diligence. At the same time, whilst preventing the spread 48  h of admission, and repeat bloodstream cultures Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 3 of 9 within 14 days, or urine cultures within 30 days, were (numerator) in a given month to the corresponding num- excluded. To generate the incidence rate, monthly occu- ber of OBD (denominator) and expressed as a rate per pied bed day (OBD) [19] data were collected from each 10,000 admissions. We assessed changes in HAI rates hospital for the same time period. To allow for uniform overall, aggregated across hospitals, as well as changes in reporting of organisms, each organism reported from the HAI rates for each hospital. We also assessed changes in source was categorised into a pathogen group (Supple- the rates of BSI and UTI pooled across all sites and sepa- mentary Table S2). rately for each site. To assess the influence of individual sites on the overall HAI rates, jack-knife sensitivity analy- Definitions ses were undertaken by removing one hospital at a time For the purposes of this study, we applied the following and estimating ITS model for the rates pooled across the definitions for HAIs: remaining hospitals. • Bloodstream infection (BSI): positive culture collected > 48 h post admission. Results • Urinary tract infection (UTI): positive culture Positive culture data from all hospitals were collected on collected > 48 h post admission. specimens taken between 1 and 2017 to 31 March 2021. All hospitals reported data on BSI and UTI. Statistical analyses A total of 9,685 positive cultures (1,988 bloodstream Interrupted time series (ITS) regression analyses with and 7,697 urine) from 8,194 patients were included Newey-West autocorrelated errors [20] were carried out in the final analysis. The median age of the pre- to assess differences in the log-transformed level and COVID-19 cohort was 71 (quartile range 58–82) and trend of HAI between the pre-COVID-19 and COVID- 59% (3843/6481) were female. In the COVID-19 cohort 19 periods. The regression is performed on log-trans - the median age was 71 (quartile range 59–82) and 58% formed data as the outcome variable (cases per 100,000 (992/1713) were female. The mean monthly number OBD) cannot be less than zero. The transformation of occupied bed days combined in the pre COVID-19 ensures that the predicted outcome variable will remain cohort was 75,317 compared to 73,157 for the COVID- non-negative. The ITS models assessed the baseline rate 19 cohort. All sites reported a notable drop in occupied of HAI (intercept), trend during the pre-COVID-19 bed days in April 2020, but by June 2020 numbers had interval (slope), and the change in slope between the two returned to similar pre COVID-19 numbers (Supplemen- time periods. Values with a response variable of zero had tary Figure S1). a small pseudo-count added to ensure the transforma- Hospital A contributed the most culture positive epi- tion was valid. Prior to analyses, model assumptions were sodes with 4,792, followed by Hospital B 2,943 episodes, evaluated through the inspection of autocorrelations and Hospital E 1,614 episodes, Hospital C 230 episodes and model residuals. Infection rates were also examined for Hospital D 106 episodes. The unadjusted incidence rates potential seasonal trends, with no discernible seasonal for all HAIs in the pre-COVID-19 cohort was 25.5 per trends detected. To ensure that the models accounted for 10,000 OBDs (95%CI:24.9–26.1) and in the COVID-19 the correct autocorrelation structure, Baum and Schaffer cohort was 25.1 per 10,000 OBDs (95%CI:24.1–26.1). autocorrelation test for autocorrelation was used to test (Table 1) Sensitivity analysis on the influence of each site for up to 12 lags. Lags that had significant autocorrela - on combined BSI and UTI infections demonstrated that tions were incorporated into the model [21]. In all sta- hospital A had a significant downward influence in the tistical analyses, nominal alpha level of 0.05 was used to pre-COVID-19 cohort (p = 0.008), and Hospitals B and interpret the results of significance tests. E had a significant upward influence on the COVID-19 To create the time series, the number of infections and cohort (p = 0.009 and p < 0.001 respectively. (Supplemen- number of OBD were aggregated by month. HAI rates tary Figure S2). were calculated as a ratio of the number of infections Table 1 Unadjusted incidence rates per 10,000 occupied bed days (OBDs) Pre-COVID-19 cohort (Jan 2017 – Feb 2020) COVID-19 cohort (Mar 2020 – Mar 2021) Number Incidence per 10,000 OBDs (95%CI) Number Incidence per 10,000 OBDs (95%CI) Bloodstream cultures 1,518 5.3 (5.0-5.6) 470 4.9 (4.5–5.4) Urinary tract cultures 5,781 20.2 (19.7–20.7) 1,916 20.1 (19.2–21.1) Total 7,299 25.5 (24.9–26.1) 2,386 25.1 (24.1–26.1) Occupied bed days 2,864,089 -- 951,042 -- OBDs – Occupied bed days 95%CI – 95% Confidence intervals Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 4 of 9 Differences in laboratory reporting nomenclature, and Urinary tract infections small numbers of certain species, resulted in the group- There is a downward trend in the COVID-19 cohort when ing of several species for analysis, such as Escherichia combining all hospitals UTI data, however it was not sig- species, Staphylococcus species and Candida species nificant (Fig. 3). Hospital A had a significant increase in (Supplementary Table S2). Escherichia species were the the pre-COVID-19 cohort (p < 0.001) and a significant most frequently identified organism in both cohorts decrease in the COVID-19 cohort (p = 0.005). Hospitals (Table 2 and Supplementary Tables 3 and 4). B, C and D all demonstrated significant decreases in UTI in the pre-COVID-19 cohort (p = 0.026, p = 0.043 and Time series analysis of pre COVID-19 cohort and COVID-19 p = 0.041 respectively), whilst hospital B and C showed an cohort increase in the COVID-19 cohort, and Hospitals D and E Combined bloodstream and urinary tract infections had a downward trend, none were significant. There was no significant difference in the two cohorts when all hospital data was pooled (Fig. 1). Across all Combined infections by state services, the incidence rate of infection was increasing Combining BSI and UTI data and grouping by state in the pre-COVID-19 cohort (p = 0.077), with a drop of demonstrated that Victoria had a significant increase in approximately 1 case per month (back-transformed) the pre-COVID-19 cohort (p = 0.005) and a significant in the COVID-19 cohort (p = 0.064). Hospital A dem- decrease in the COVID-19 cohort (p = 0.011). No signifi - onstrated a significant increase in the pre-COVID-19 cant trends were identified in combined NSW data (Fig. cohort (p < 0.001), and a significant decrease in the 4). COVID-19 cohort (p = 0.004) when combining both BSI and UTI data. Hospital D had a significant decrease in Discussion the COVID-19 cohort (p = 0.002). There were no other This is the first multicentred study exploring the impact significant trends identified, however Hospitals C and of COVID-19 on healthcare associated infections in Aus- D had a slight decrease in the COVID-19 cohort, whilst tralian hospitals using positive blood and urine cultures Hospital B demonstrated an increase in the COVID-19 post 48 h admission as a proxy marker, resulting in mixed cohort. findings that may have several explanations. Australia’s first case of COVID-19 was identified on Bloodstream infections 25th January 2020 in Victoria. By mid-March, Australia When combing all BSI data, although a downward trend had closed its international borders, and towards the end is noted in the COVID-19 cohort, it was not significant of March 2020, States and Territories had implemented (Fig. 2). Hospital A had significant increase in BSI in stay at home orders. By the end of 2020, there were the pre COVID-19 cohort (p = 0.028) and a significant approximately 28,500 cases Australia wide highlighted decrease in the COVID-19 cohort (p = 0.042). No other by two distinct peaks; nationally in March and April, and significant trends were identified, however Hospitals C, D in Victoria in June to September [22]. There were also and E all had downward trends in the COVID-19 cohort. differences in the epidemiology between states. In this Table 2 Frequency of most common organisms by cohort* Pre-COVID-19 cohort (Jan 2017 – Feb 2020) (n = 6566) COVID-19 cohort (Mar 2020 – Mar 2021) (n = 3119) Organism Number Proportion Organism Number Proportion Escherichia species 1746 26.6% Escherichia species 728 23.4% Enterococcus species 1069 16.3% Enterococcus species 427 13.7% Candida species 809 12.3% Candida species 388 12.4% Klebsiella species 523 8.0% Klebsiella species 242 7.8% Pseudomonas species 510 7.8% Pseudomonas species 205 6.6% Staphylococcus species 287 4.4% Staphylococcus species 183 5.9% Proteus species 257 3.9% Enterobacter species 141 4.5% Enterobacter species 244 3.7% VRE 140 4.5% VRE 188 2.9% MSSA 100 3.2% Citrobacter species 115 1.7% Proteus species 71 2.3% *Not all organisms reported (only most common 10 species) Includes cultures where more than one organism was reported Staphylococcus other than aureus VRE - Vancomycin resistant enterococci MSSA – methicillin sensitive Staphylococcus aureus Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 5 of 9 Fig. 1 Time series analysis – Combined BSI and UTI by hospital study, we reviewed data from Victorian and NSW hospi- population also changed during 2020. A decrease in elec- tals only. tive surgery facilitated the establishment of COVID-19 Victoria experienced Australia’s largest COVID-19 wards, capacity for intensive care beds increased, and wave in 2020 (Supplementary Figure S3) and imple- the use of telehealth possibly enabled some patients to mented enhanced infection prevention measures prior to remain out of hospital. Staff were redeployed to areas of NSW. This may influence the decrease in HAIs in Victo - greatest need, and many staff were furloughed for peri - rian hospitals in this data (Hospitals A and E). Accord- ods of up to two weeks if they had COVID-19 or were ing to the local epidemiology, hospitals implemented a close contact. Whilst enhanced infection prevention enhanced infection prevention and controls, limitations activity may be expected to reduce HAIs, the changes in on visitors and a decrease in elective surgery at various patient populations and staff profile may in fact increase times during the year, largely directed by the local author- the risk of HAI. ity. Furthermore, public sector hospitals had a higher Our mixed findings reflect the uncertainty of the effect burden of COVID-19 patients than the private sector COVID-19 has had on HAIs in other settings. Although which may have also influenced our data. The inpatient there are numerous reports of increases in HAI, [6, Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 6 of 9 Fig. 2 Time series analysis - Bloodstream infections combined and by hospital 23–30] and decreases, [31–35] variations in settings and hospital would have remained relatively stable during the methodology prevent comparisons between those find - study period. Although we had data from five hospitals, ings and with our study. the number of positive cultures were relatively small. The There are a number of limitations with this study. period of data collection for the COVID-19 cohort was Without a national HAI surveillance program in Austra- 13 months, which resulted in lower levels of statistical lia, the effect of COVID-19 on HAIs nationally is unable power to detect trends in the second study period com- to be estimated. As such, we have used proxy measures pared with the first period. Finally, differences in report - of HAI being positive cultures from blood and urine that ing between the hospital laboratories meant that we had were sampled greater than 48 h post admission from five to report some groups at a genus level only, and data hospitals. We did not explore the triggers for taking cul- were not reviewed for potential contaminants. tures within each hospitals, therefore our results could have been influenced by differences in the practices of taking cultures between hospitals. However, we expect that practices for taking cultures within each individual Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 7 of 9 Fig. 3 Time series analysis – Urinary tract infections combined and by hospital Fig. 4 Time series analysis – Bloodstream infections and Urinary tract infections combined by state Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 8 of 9 Consent for publication Conclusion Not applicable. Although the findings of this study are uncertain, such Competing interests large and widespread increase in the awareness and The authors have no conflicts of interest to declare. implementation of infection prevention in hospitals Author details nationally warrant further research. The COVID-19 pan - School of Nursing, Avondale University, Cooranbong, NSW demic has and will continue to have significant impact 2265, Australia on healthcare in Australia, whilst much of the response Nursing and Midwifery, Monash University, Frankston, VIC 3199, Australia Gosford Hospital, Central Coast Local Health District, NSW 2250, Australia is reactive, we must also continue to explore effectiveness Department of Infectious Diseases, The Alfred and Central Clinical of infection prevention and control measures and adapt School, Monash University, Melbourne, VIC 3004, Australia as knowledge increases. Further larger studies that aggre- Department of Nursing Research, Cabrini Institute, Malvern, VIC 3144, Australia gate hospitals by state, and by hospital category, with School of Nursing and Midwifery, Deakin University, Burwood, Australia time series analyses performed which consider the local Division of Medicine, John Hunter Hospital, Newcastle Regional Mail epidemiology of COVID, may provide further insight on Centre, 2310 NSW, Australia University of Newcastle, Callaghan, NSW 2308, Australia the effect of COVID on HAIs. Infection Prevention Service, Hunter New England Health, John Hunter Hospital, NSW 2310, Australia Abbreviations Department of Epidemiology and Preventive Medicine, School of Public BSI Bloodstream infection Health and Preventive Medicine, Monash University, Melbourne, VIC HAI Healthcare associated infection 3004, Australia IPC Infection prevention and control Department of Econometrics and Business Statistics, Monash University, ITS I nterrupted time series Melbourne 3800, Australia NSW New South Wales Infection Prevention and Control, Central Coast Local Health District, OBD Occupied bed days Gosford, NSW 2250, Australia PPE Personal protective equipment UTI Urinary tract infection Received: 15 March 2023 / Accepted: 20 June 2023 Supplementary Information The online version contains supplementary material available at https://doi. org/10.1186/s13756-023-01268-2. Supplementary Material 1 References 1. Ponce-Alonso M, Sáez de la Fuente J, Rincón-Carlavilla A, et al. 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The incidence of nosocomial bloodstream infection and urinary tract infection in Australian hospitals before and during the COVID-19 pandemic: an interrupted time series study

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Abstract

Background The COVID-19 pandemic has had a significant impact on healthcare including increased awareness of infection prevention and control (IPC). The aim of this study was to explore if the heightened awareness of IPC measures implemented in response to the pandemic influenced the rates of healthcare associated infections (HAI) using positive bloodstream and urine cultures as a proxy measure. Methods A 3 year retrospective review of laboratory data from 5 hospitals (4 acute public, 1 private) from two states in Australia was undertaken. Monthly positive bloodstream culture data and urinary culture data were collected from January 2017 to March 2021. Occupied bed days (OBDs) were used to generate monthly HAI incidence per 10,000 OBDs. An interrupted time series analysis was undertaken to compare incidence pre and post February 2020 (the pre COVID-19 cohort and the COVID-19 cohort respectively). A HAI was assumed if positive cultures were obtained 48 h after admission and met other criteria. Results A total of 1,988 bloodstream and 7,697 urine positive cultures were identified. The unadjusted incident rate was 25.5 /10,000 OBDs in the pre-COVID-19 cohort, and 25.1/10,000 OBDs in the COVID-19 cohort. The overall rate of HAI aggregated for all sites did not differ significantly between the two periods. The two hospitals in one state which experienced an earlier and larger outbreak demonstrated a significant downward trend in the COVID-19 cohort (p = 0.011). Conclusion These mixed findings reflect the uncertainty of the effect the pandemic has had on HAI’s. Factors to consider in this analysis include local epidemiology, differences between public and private sector facilities, changes in patient populations and profiles between hospitals, and timing of enhanced IPC interventions. Future studies which factor in these differences may provide further insight on the effect of COVID-19 on HAIs. *Correspondence: Philip L Russo Philip.russo@monash.edu Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 2 of 9 Keywords Infection prevention, COVID-19, Surveillance, Healthcare associated infection, Blood cultures, Urine cultures Background of COVID-19, these activities will also prevent many The COVID-19 pandemic has seen an unprecedented other types of infection. On the other hand, as emerging increase in awareness and focus on infection prevention research is indicating, the increased stress on healthcare precautions, including hand-hygiene, cleaning, air qual- workers and organisations may increase HAIs, [13] par- ity, ventilation and correct use of personal protective ticularly given evidence that increased glove use often equipment (PPE) [1]. Recent research has documented leads to poor hand hygiene compliance [14]. There are that although standard precautions were adopted glob- also several reports of increases in carbapenemase-pro- ally prior to the pandemic, deficits in implementation ducing Enterobacteriaceae in intensive care units in the and compliance persist [2–4]. With an increased focus on context of COVID-19 and increased infection preven- infection prevention and control practices and processes tion activity related to non compliance with PPE, misuse in healthcare settings as a result of the pandemic, it could of gloves, high antibiotic use and overwork [15–18]. The be hypothesised that this in turn may have a positive overall aim of this study is to explore if there has been effect on reducing the overall risk of infection transmis - any effect on HAI rates as a result of the increased infec - sion in these settings. Conversely, hospitals and health- tion prevention awareness brought about by COVID-19. care workers have been under enormous strain from COVID-19 and this may result in a reduced focus on pre- Methods venting infections other than COVID-19. Study design Emerging research contains mixed results about the The study was a three-year retrospective review of inpa - effect the COVID-19 pandemic has had on the rates of tient laboratory data. healthcare associated infections (HAIs). Substantial increases in central-line associated bloodstream infec- Setting and population tions and catheter associated urinary tract infections Data were sourced from five Australian hospitals from have been observed, along with an increase in contami- two different Australian jurisdictions (New South Wales nated specimens and potential reduction in local HAI [NSW] and Victoria). These five hospitals consisted of reporting [5–7]. Researchers have suggested that this four acute public hospitals (two Principal Referral Hos- may be due to: resources shortages; influx of patients; pitals [Hospitals A and B]), and two Acute Group A changing recommendations; and general stress [5–7]. hospitals [Hospitals C and D]) and one acute private hos- However, reductions in Clostridioides difficile have also pital (Private Acute Group A [Hospital E]). Differences been reported, [1, 8, 9] and generally a lower rate of between these hospital types are detailed in Supplemen- multidrug resistant organisms - although this was in an tary Table S1. Combined, these hospitals have over 2400 area which was at the time not significantly affected by overnight beds and over 290,000 hospital admissions per COVID-19 infections [10] These have been attributed to year. the increased awareness and practice of standard precau- We constructed two cohorts; first, the pre-COVID-19 tions [1, 10, 11]. cohort, defined as inpatients who had specimens col - The infection prevention challenges presented by lected between January 2017 to February 2020, and sec- COVID-19 are significant. To prepare for the admission ond, the COVID-19 cohort, defined as inpatients who and treatment of COVID-19 positive patients, a number had specimens collected between March 2020 to March of new and modified infection prevention initiatives have 2021, inclusive. This time point was chosen following the been implemented across healthcare sites. These include, first identification of a COVID-19 case in Australia on but are not limited to: an overall heightened awareness 25th January 2020. of infection prevention; increase in education regarding PPE; increase in the use of PPE; increase in promotion Data sources of hand hygiene; changes to cleaning regimes; restriction Microbiology data were obtained from the laboratories in visiting hours; improvement in ventilation and limited of participating hospitals for the period of January 2017 patient movement [12]. Whilst the correct and appro- through to March 2021 (inclusive) for positive blood- priate use of PPE, adequate air quality, hand hygiene stream and urine cultures. For each positive culture, and cleaning are fundamental in every infection preven- patient level data were collected, including age, gender, tion program, the heightened awareness COVID-19 has date of admission, date of specimen collection and name introduced, may mean there is increased compliance and of organism. Positive cultures that were collected within diligence. At the same time, whilst preventing the spread 48  h of admission, and repeat bloodstream cultures Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 3 of 9 within 14 days, or urine cultures within 30 days, were (numerator) in a given month to the corresponding num- excluded. To generate the incidence rate, monthly occu- ber of OBD (denominator) and expressed as a rate per pied bed day (OBD) [19] data were collected from each 10,000 admissions. We assessed changes in HAI rates hospital for the same time period. To allow for uniform overall, aggregated across hospitals, as well as changes in reporting of organisms, each organism reported from the HAI rates for each hospital. We also assessed changes in source was categorised into a pathogen group (Supple- the rates of BSI and UTI pooled across all sites and sepa- mentary Table S2). rately for each site. To assess the influence of individual sites on the overall HAI rates, jack-knife sensitivity analy- Definitions ses were undertaken by removing one hospital at a time For the purposes of this study, we applied the following and estimating ITS model for the rates pooled across the definitions for HAIs: remaining hospitals. • Bloodstream infection (BSI): positive culture collected > 48 h post admission. Results • Urinary tract infection (UTI): positive culture Positive culture data from all hospitals were collected on collected > 48 h post admission. specimens taken between 1 and 2017 to 31 March 2021. All hospitals reported data on BSI and UTI. Statistical analyses A total of 9,685 positive cultures (1,988 bloodstream Interrupted time series (ITS) regression analyses with and 7,697 urine) from 8,194 patients were included Newey-West autocorrelated errors [20] were carried out in the final analysis. The median age of the pre- to assess differences in the log-transformed level and COVID-19 cohort was 71 (quartile range 58–82) and trend of HAI between the pre-COVID-19 and COVID- 59% (3843/6481) were female. In the COVID-19 cohort 19 periods. The regression is performed on log-trans - the median age was 71 (quartile range 59–82) and 58% formed data as the outcome variable (cases per 100,000 (992/1713) were female. The mean monthly number OBD) cannot be less than zero. The transformation of occupied bed days combined in the pre COVID-19 ensures that the predicted outcome variable will remain cohort was 75,317 compared to 73,157 for the COVID- non-negative. The ITS models assessed the baseline rate 19 cohort. All sites reported a notable drop in occupied of HAI (intercept), trend during the pre-COVID-19 bed days in April 2020, but by June 2020 numbers had interval (slope), and the change in slope between the two returned to similar pre COVID-19 numbers (Supplemen- time periods. Values with a response variable of zero had tary Figure S1). a small pseudo-count added to ensure the transforma- Hospital A contributed the most culture positive epi- tion was valid. Prior to analyses, model assumptions were sodes with 4,792, followed by Hospital B 2,943 episodes, evaluated through the inspection of autocorrelations and Hospital E 1,614 episodes, Hospital C 230 episodes and model residuals. Infection rates were also examined for Hospital D 106 episodes. The unadjusted incidence rates potential seasonal trends, with no discernible seasonal for all HAIs in the pre-COVID-19 cohort was 25.5 per trends detected. To ensure that the models accounted for 10,000 OBDs (95%CI:24.9–26.1) and in the COVID-19 the correct autocorrelation structure, Baum and Schaffer cohort was 25.1 per 10,000 OBDs (95%CI:24.1–26.1). autocorrelation test for autocorrelation was used to test (Table 1) Sensitivity analysis on the influence of each site for up to 12 lags. Lags that had significant autocorrela - on combined BSI and UTI infections demonstrated that tions were incorporated into the model [21]. In all sta- hospital A had a significant downward influence in the tistical analyses, nominal alpha level of 0.05 was used to pre-COVID-19 cohort (p = 0.008), and Hospitals B and interpret the results of significance tests. E had a significant upward influence on the COVID-19 To create the time series, the number of infections and cohort (p = 0.009 and p < 0.001 respectively. (Supplemen- number of OBD were aggregated by month. HAI rates tary Figure S2). were calculated as a ratio of the number of infections Table 1 Unadjusted incidence rates per 10,000 occupied bed days (OBDs) Pre-COVID-19 cohort (Jan 2017 – Feb 2020) COVID-19 cohort (Mar 2020 – Mar 2021) Number Incidence per 10,000 OBDs (95%CI) Number Incidence per 10,000 OBDs (95%CI) Bloodstream cultures 1,518 5.3 (5.0-5.6) 470 4.9 (4.5–5.4) Urinary tract cultures 5,781 20.2 (19.7–20.7) 1,916 20.1 (19.2–21.1) Total 7,299 25.5 (24.9–26.1) 2,386 25.1 (24.1–26.1) Occupied bed days 2,864,089 -- 951,042 -- OBDs – Occupied bed days 95%CI – 95% Confidence intervals Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 4 of 9 Differences in laboratory reporting nomenclature, and Urinary tract infections small numbers of certain species, resulted in the group- There is a downward trend in the COVID-19 cohort when ing of several species for analysis, such as Escherichia combining all hospitals UTI data, however it was not sig- species, Staphylococcus species and Candida species nificant (Fig. 3). Hospital A had a significant increase in (Supplementary Table S2). Escherichia species were the the pre-COVID-19 cohort (p < 0.001) and a significant most frequently identified organism in both cohorts decrease in the COVID-19 cohort (p = 0.005). Hospitals (Table 2 and Supplementary Tables 3 and 4). B, C and D all demonstrated significant decreases in UTI in the pre-COVID-19 cohort (p = 0.026, p = 0.043 and Time series analysis of pre COVID-19 cohort and COVID-19 p = 0.041 respectively), whilst hospital B and C showed an cohort increase in the COVID-19 cohort, and Hospitals D and E Combined bloodstream and urinary tract infections had a downward trend, none were significant. There was no significant difference in the two cohorts when all hospital data was pooled (Fig. 1). Across all Combined infections by state services, the incidence rate of infection was increasing Combining BSI and UTI data and grouping by state in the pre-COVID-19 cohort (p = 0.077), with a drop of demonstrated that Victoria had a significant increase in approximately 1 case per month (back-transformed) the pre-COVID-19 cohort (p = 0.005) and a significant in the COVID-19 cohort (p = 0.064). Hospital A dem- decrease in the COVID-19 cohort (p = 0.011). No signifi - onstrated a significant increase in the pre-COVID-19 cant trends were identified in combined NSW data (Fig. cohort (p < 0.001), and a significant decrease in the 4). COVID-19 cohort (p = 0.004) when combining both BSI and UTI data. Hospital D had a significant decrease in Discussion the COVID-19 cohort (p = 0.002). There were no other This is the first multicentred study exploring the impact significant trends identified, however Hospitals C and of COVID-19 on healthcare associated infections in Aus- D had a slight decrease in the COVID-19 cohort, whilst tralian hospitals using positive blood and urine cultures Hospital B demonstrated an increase in the COVID-19 post 48 h admission as a proxy marker, resulting in mixed cohort. findings that may have several explanations. Australia’s first case of COVID-19 was identified on Bloodstream infections 25th January 2020 in Victoria. By mid-March, Australia When combing all BSI data, although a downward trend had closed its international borders, and towards the end is noted in the COVID-19 cohort, it was not significant of March 2020, States and Territories had implemented (Fig. 2). Hospital A had significant increase in BSI in stay at home orders. By the end of 2020, there were the pre COVID-19 cohort (p = 0.028) and a significant approximately 28,500 cases Australia wide highlighted decrease in the COVID-19 cohort (p = 0.042). No other by two distinct peaks; nationally in March and April, and significant trends were identified, however Hospitals C, D in Victoria in June to September [22]. There were also and E all had downward trends in the COVID-19 cohort. differences in the epidemiology between states. In this Table 2 Frequency of most common organisms by cohort* Pre-COVID-19 cohort (Jan 2017 – Feb 2020) (n = 6566) COVID-19 cohort (Mar 2020 – Mar 2021) (n = 3119) Organism Number Proportion Organism Number Proportion Escherichia species 1746 26.6% Escherichia species 728 23.4% Enterococcus species 1069 16.3% Enterococcus species 427 13.7% Candida species 809 12.3% Candida species 388 12.4% Klebsiella species 523 8.0% Klebsiella species 242 7.8% Pseudomonas species 510 7.8% Pseudomonas species 205 6.6% Staphylococcus species 287 4.4% Staphylococcus species 183 5.9% Proteus species 257 3.9% Enterobacter species 141 4.5% Enterobacter species 244 3.7% VRE 140 4.5% VRE 188 2.9% MSSA 100 3.2% Citrobacter species 115 1.7% Proteus species 71 2.3% *Not all organisms reported (only most common 10 species) Includes cultures where more than one organism was reported Staphylococcus other than aureus VRE - Vancomycin resistant enterococci MSSA – methicillin sensitive Staphylococcus aureus Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 5 of 9 Fig. 1 Time series analysis – Combined BSI and UTI by hospital study, we reviewed data from Victorian and NSW hospi- population also changed during 2020. A decrease in elec- tals only. tive surgery facilitated the establishment of COVID-19 Victoria experienced Australia’s largest COVID-19 wards, capacity for intensive care beds increased, and wave in 2020 (Supplementary Figure S3) and imple- the use of telehealth possibly enabled some patients to mented enhanced infection prevention measures prior to remain out of hospital. Staff were redeployed to areas of NSW. This may influence the decrease in HAIs in Victo - greatest need, and many staff were furloughed for peri - rian hospitals in this data (Hospitals A and E). Accord- ods of up to two weeks if they had COVID-19 or were ing to the local epidemiology, hospitals implemented a close contact. Whilst enhanced infection prevention enhanced infection prevention and controls, limitations activity may be expected to reduce HAIs, the changes in on visitors and a decrease in elective surgery at various patient populations and staff profile may in fact increase times during the year, largely directed by the local author- the risk of HAI. ity. Furthermore, public sector hospitals had a higher Our mixed findings reflect the uncertainty of the effect burden of COVID-19 patients than the private sector COVID-19 has had on HAIs in other settings. Although which may have also influenced our data. The inpatient there are numerous reports of increases in HAI, [6, Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 6 of 9 Fig. 2 Time series analysis - Bloodstream infections combined and by hospital 23–30] and decreases, [31–35] variations in settings and hospital would have remained relatively stable during the methodology prevent comparisons between those find - study period. Although we had data from five hospitals, ings and with our study. the number of positive cultures were relatively small. The There are a number of limitations with this study. period of data collection for the COVID-19 cohort was Without a national HAI surveillance program in Austra- 13 months, which resulted in lower levels of statistical lia, the effect of COVID-19 on HAIs nationally is unable power to detect trends in the second study period com- to be estimated. As such, we have used proxy measures pared with the first period. Finally, differences in report - of HAI being positive cultures from blood and urine that ing between the hospital laboratories meant that we had were sampled greater than 48 h post admission from five to report some groups at a genus level only, and data hospitals. We did not explore the triggers for taking cul- were not reviewed for potential contaminants. tures within each hospitals, therefore our results could have been influenced by differences in the practices of taking cultures between hospitals. However, we expect that practices for taking cultures within each individual Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 7 of 9 Fig. 3 Time series analysis – Urinary tract infections combined and by hospital Fig. 4 Time series analysis – Bloodstream infections and Urinary tract infections combined by state Mitchell et al. Antimicrobial Resistance & Infection Control (2023) 12:61 Page 8 of 9 Consent for publication Conclusion Not applicable. Although the findings of this study are uncertain, such Competing interests large and widespread increase in the awareness and The authors have no conflicts of interest to declare. implementation of infection prevention in hospitals Author details nationally warrant further research. The COVID-19 pan - School of Nursing, Avondale University, Cooranbong, NSW demic has and will continue to have significant impact 2265, Australia on healthcare in Australia, whilst much of the response Nursing and Midwifery, Monash University, Frankston, VIC 3199, Australia Gosford Hospital, Central Coast Local Health District, NSW 2250, Australia is reactive, we must also continue to explore effectiveness Department of Infectious Diseases, The Alfred and Central Clinical of infection prevention and control measures and adapt School, Monash University, Melbourne, VIC 3004, Australia as knowledge increases. Further larger studies that aggre- Department of Nursing Research, Cabrini Institute, Malvern, VIC 3144, Australia gate hospitals by state, and by hospital category, with School of Nursing and Midwifery, Deakin University, Burwood, Australia time series analyses performed which consider the local Division of Medicine, John Hunter Hospital, Newcastle Regional Mail epidemiology of COVID, may provide further insight on Centre, 2310 NSW, Australia University of Newcastle, Callaghan, NSW 2308, Australia the effect of COVID on HAIs. Infection Prevention Service, Hunter New England Health, John Hunter Hospital, NSW 2310, Australia Abbreviations Department of Epidemiology and Preventive Medicine, School of Public BSI Bloodstream infection Health and Preventive Medicine, Monash University, Melbourne, VIC HAI Healthcare associated infection 3004, Australia IPC Infection prevention and control Department of Econometrics and Business Statistics, Monash University, ITS I nterrupted time series Melbourne 3800, Australia NSW New South Wales Infection Prevention and Control, Central Coast Local Health District, OBD Occupied bed days Gosford, NSW 2250, Australia PPE Personal protective equipment UTI Urinary tract infection Received: 15 March 2023 / Accepted: 20 June 2023 Supplementary Information The online version contains supplementary material available at https://doi. org/10.1186/s13756-023-01268-2. Supplementary Material 1 References 1. Ponce-Alonso M, Sáez de la Fuente J, Rincón-Carlavilla A, et al. Impact of the coronavirus disease 2019 (COVID-19) pandemic on nosocomial Clostridioides Acknowledgements difficile infection. Infect Control Hosp Epidemiol. 2021;42(4):406–10. https:// The researchers acknowledge the work and co-operation of the infection doi.org/10.1017/ice.2020.454. prevention and control teams and laboratories at participating healthcare 2. Wong EL-Y, Ho K-F, Dong D, et al. Compliance with Standard Precautions and facilities. its relationship with views on infection control and Prevention Policy among Healthcare Workers during COVID-19 pandemic. Int J Environ Res Public Author contributions Health. 2021;18(7):3420. https://doi.org/10.3390/ijerph18073420. PLR and BGM conceptualised and designed the study, undertook data 3. Maroldi MAC, Felix AMdS, Dias AAL, et al. Adherence to precautions for validation and primary analysis. AJS was involved in the design, validation preventing the transmission of microorganisms in primary health care: and primary analysis. JF undertook data curation and validation and primary a qualitative study. BMC Nurs. 2017;16(1):49–9. https://doi.org/10.1186/ analysis. SC undertook data curation and validation. LB and ML undertook all s12912-017-0245-z. statistical analysis and generated ITS plots. KG and LK undertook data curation 4. Powers DDNPRN, Armellino DDNPRN, Dolansky MDNPRN, Fitzpatrick JMDP. and validation. PLR and LK undertook the original draft of the manuscript. All Factors influencing nurse compliance with standard precautions. Am J Infect authors reviewed, edited and approved the manuscript. Control. 2016;44(1):4–7. https://doi.org/10.1016/j.ajic.2015.10.001. 5. Fakih MG, Bufalino A, Sturm L, et al. Coronavirus disease 2019 (COVID-19) Funding pandemic, central-line–associated bloodstream infection (CLABSI), and This study was funded by the Allan Jackson Research Grant through Cabrini catheter-associated urinary tract infection (CAUTI): the urgent need to Research. 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Journal

Antimicrobial Resistance and Infection Control – Springer Journals

Published: Jul 3, 2023

Keywords: Infection prevention; COVID-19; Surveillance; Healthcare associated infection; Blood cultures; Urine cultures

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