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Personalized infection prevention and control: identifying patients at risk of healthcare-associated infection

      Summary

      Background

      Few healthcare-associated infection (HAI) studies focus on risk of HAI at the point of admission. Understanding this will enable planning and management of care with infection prevention at the heart of the patient journey from the point of admission.

      Aim

      To determine intrinsic characteristics of patients at hospital admission and extrinsic events, during the two years preceding admission, that increase risk of developing HAI.

      Methods

      An incidence survey of adults within two hospitals in NHS Scotland was undertaken for one year in 2018/19 as part of the Evaluation of Cost of Nosocomial Infection (ECONI) study. The primary outcome measure was developing any HAI using recognized case definitions. The cohort was derived from routine hospital episode data and linkage to community dispensed prescribing data.

      Findings

      The risk factors present on admission observed as being the most significant for the acquisition of HAI were: being treated in a teaching hospital, increasing age, comorbidities of cancer, cardiovascular disease, chronic renal failure and diabetes; and emergency admission. Relative risk of developing HAI increased with intensive care unit, high-dependency unit, and surgical specialties, and surgery <30 days before admission and a total length of stay of >30 days in the two years to admission.

      Conclusion

      Targeting patients at risk of HAI from the point of admission maximizes the potential for prevention, especially when extrinsic risk factors are known and managed. This study proposes a new approach to infection prevention and control (IPC), identifying those patients at greatest risk of developing a particular type of HAI who might be potential candidates for personalized IPC interventions.

      Keywords

      Introduction

      Healthcare-associated infections (HAIs) are a threat to patient safety; they affect patients in all healthcare settings contributing significantly to morbidity and mortality and cost of care [
      World Health Organization
      Report on the burden of endemic health care-associated infection worldwide.
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      Effect of healthcare-acquired infection on length of hospital stay and cost.
      ]. Multiple factors contribute to risk of HAI including ageing population, complexity of therapies and emergence of antibiotic-resistant bacteria [
      • Freeman J.
      • McGowan Jr., J.E.
      Risk factors for nosocomial infection.
      ].
      HAI describes multiple infection types that can affect any organ; these infections range from urinary tract infections to bloodstream infections (BSIs) [
      European Centre for Disease Prevention and Control
      Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals – protocol version 5.3.
      ]. Risk factors for these vary, and as the incidence of HAI at the hospital population level is relatively low, specific infection prevention and control (IPC) measures should be targeted at individuals at increased risk of developing particular HAIs. Identifying and understanding potential risk factors is an essential step in reducing individual risk of developing HAI. Risk factors include intrinsic characteristics of the patient and extrinsic risk factors or exposures that happen to patients during their hospital treatment. Many risk factors have been described for specific populations, for example intensive care unit (ICU) patients' risk of pneumonia [
      • Kaye K.S.
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      Risk factors for the development of nosocomial pneumonia and mortality on intensive care units: application of competing risks models.
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      Emerging trends of nosocomial pneumonia in intensive care unit of a tertiary care public teaching hospital in Western India.
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      Prognostic risk factors in ventilator-associated pneumonia.
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      Using flexible methods to determine risk factors for ventilator-associated pneumonia in the Netherlands.
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      • Wolkewitz M.
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      • Schumacher M.
      Analyzing the impact of duration of ventilation, hospitalization, and ventilation episodes on the risk of pneumonia.
      ,
      • Xu Y.
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      • Xu G.
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      Risk factors of ventilator-associated pneumonia in elderly patients receiving mechanical ventilation.
      ]. However, these studies are by nature retrospective and include risks encountered by patients until the point that they develop HAI. Patient-centred care relies on the identification of high-risk patients on admission in order to enable effective IPC measures to be instigated from the outset. Whereas some studies have focused on extrinsic risk factors and others looked at point-of-transfer risks, some studies report whole-hospital data and a range of HAI types and risks [
      • Kaye K.S.
      • Marchaim D.
      • Chen T.Y.
      • Chopra T.
      • Anderson D.J.
      • Choi Y.
      • et al.
      Predictors of nosocomial bloodstream infections in older adults.
      ,
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      Risk factors for healthcare-associated urinary tract infection and their applications in surveillance using hospital administrative data: a systematic review.
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      • Nguyen-Van-Tam S.E.
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      • Pearson J.C.
      Risk factors for hospital-acquired urinary tract infection in a large English teaching hospital: a case–control study.
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      • Chang Y.-J.
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      • Hsu M.-S.
      • et al.
      Predicting hospital-acquired infections by scoring system with simple parameters.
      ]. Cheng et al. used a model analysing data from electronic patient records in Taiwan, recording intrinsic and extrinsic risk factors throughout the patient stay [
      • Chang Y.-J.
      • Yeh M.-L.
      • Li Y.-C.
      • Hsu C.-Y.
      • Lin C.-C.
      • Hsu M.-S.
      • et al.
      Predicting hospital-acquired infections by scoring system with simple parameters.
      ]. They proposed that this could be developed in future to identify patients at risk throughout their stay. This study reports a simpler approach to identify patients at increased risk of HAI on admission. A recent systematic review identified a wide range of intrinsic and extrinsic risk factors and concluded that there was a need to identify patients who are most at risk of HAI, in order to maximize patient safety [
      • Rodriguez-Acelas A.L.
      • de Abreu Almeida M.
      • Engelman B.
      • Canon-Montanez W.
      Risk factors for health care-associated infection in hospitalized adults: systematic review and meta-analysis.
      ].
      This study aimed to assess the relative risk of developing the most frequently occurring HAI types in hospitals. The risk factors included are based on information that would be available on admission and could potentially be used to tailor interventions to manage specific patient risk of HAI. They include a combination of intrinsic and extrinsic factors to which patients have been exposed in the two years before their current admission.

      Methods

      Study design

      The Evaluation of Cost of Nosocomial Infection (ECONI) study was an observational incidence surveillance study. The primary outcome measure was HAI defined by the internationally recognized European Centre for Disease Prevention and Control (ECDC) HAI case definitions [
      European Centre for Disease Prevention and Control
      Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals – protocol version 5.3.
      ]. Denominator data were collected via routine data sets and linked to the primary data collected on patients who developed HAI. A full list of data items obtained from routine data sets is shown in Appendix Table A2.

      Setting

      This study was undertaken within a teaching hospital and a general hospital in Scotland for one year ending June 2019. These settings were selected as being broadly representative of other acute hospitals in Scotland in terms of patient specialties, distribution of elective, emergency and transfers, mean length of stay, HAI prevalence, patient mix and rurality. For the two selected hospitals there were 107,244 inpatient admissions in 2014–15 when the study was being planned [
      Health Protection Scotland
      Scottish National Point Prevalence Survey of Healthcare Associated Infection and Antimicrobial Prescribing 2016.
      ,
      Information Services Division (ISD)
      Scotland. SMR01 – General/Acute Inpatient and Day Case.
      ]. During the study period the large teaching hospital had 981 beds including 25 intensive care beds (16 general, and nine cardiothoracic), and 33 high-dependency beds (13 general, eight cardiothoracic, and 12 transplant and renal). The large general hospital had 492 beds including five intensive care and four high-dependency beds [
      • Stewart S.
      • Robertson C.
      • Manoukian S.
      • Haahr L.
      • Mason H.
      • McFarland A.
      • et al.
      How do we evaluate the cost of nosocomial infection? The ECONI protocol: an incidence study with nested case–control evaluating cost and quality of life.
      ,
      Scottish Intensive Care Society Audit Group
      Audit of critical care in Scotland 2019 reporting on 2018.
      ].

      Participants

      All adult overnight inpatient admissions were included within the denominator. Patients admitted as a day case to a study hospital or treated in accident and emergency and discharged without overnight admission were excluded.

      Data collection

      ECDC HAI epidemiological case definitions were used to define HAI as outcome variable [
      European Centre for Disease Prevention and Control
      Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals – protocol version 5.3.
      ]. ECONI study research nurses employed within the participating hospitals reviewed all positive reports from the hospital microbiology databases each day, which highlighted patients with suspected HAI. The patient information systems were then reviewed to establish whether the suspected cases met the ECDC HAI case definitions. These data sources included: patient management systems (TrakCare); prescribing systems; IC (infection control) net; medical and nursing notes; and direct observation. Cases meeting the ECDC HAI case definitions were recorded on a bespoke REDCap database [
      • Harris P.A.
      • Taylor R.
      • Minor B.L.
      • Elliott V.
      • Fernandez M.
      • O’Neal L.
      • et al.
      The REDCap consortium: building an international community of software platform partners.
      ,
      • Harris P.A.
      • Taylor R.
      • Thielke R.
      • Payne J.
      • Gonzalez N.
      • Conde J.G.
      Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support.
      ]. This incidence data set included: hospital, HAI type, date of onset, causative organism, and antibiotic susceptibilities, along with the patients' Community Health Index number (CHI) [
      • Stewart S.
      • Robertson C.
      • Manoukian S.
      • Haahr L.
      • Mason H.
      • McFarland A.
      • et al.
      How do we evaluate the cost of nosocomial infection? The ECONI protocol: an incidence study with nested case–control evaluating cost and quality of life.
      ]. CHI is the unique identifier for all patients registered for healthcare in NHS Scotland and was used to link patient records from routine data sources to the denominator record. The CHI number recorded by the ECONI research nurses was used to identify HAI cases within the incidence cohort and all admissions were classified as HAI admissions and non-HAI admissions. HAI admissions contained one or more episodes of HAI. The selection of covariates was based on risk factors identified for developing HAI in a systematic review and meta-analysis [
      • Rodriguez-Acelas A.L.
      • de Abreu Almeida M.
      • Engelman B.
      • Canon-Montanez W.
      Risk factors for health care-associated infection in hospitalized adults: systematic review and meta-analysis.
      ].

      Data items collected through record linkage

      The denominator for the incidence cohort study was all hospital stays containing one or more episodes of care in a study hospital within the study period, and was identified from Scottish Morbidity Records ‘SMR01’. This data set includes dates and durations of all admissions and discharges to acute hospitals [
      Information Services Division (ISD)
      Scotland. SMR01 – General/Acute Inpatient and Day Case.
      ].
      Data on all eligible inpatients included: date of admission and discharge, specialty, diagnosis for admission (International Classification of Diseases, 10th Revision: ICD-10), surgery defined by Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures (OPCS)-4.8 in two years before admission and during admission, number and location of episodes of care, pre-admission outpatient attendances and surgical interventions in the year before admission [
      NHS National Services Scotland
      Scottish Clinical Coding Standards ICD-10.
      ,
      Health and Social Care Information Centre
      OPCS Classification of Interventions and Procedures Version 4.7 Volume I – Tabular List.
      ]. The Scottish Index of Multiple Deprivation (SIMD) 2016 quintiles for Scotland were mapped to the National Records of Scotland (NRS) 2019 Scottish Postcode Directory and linked using the SMR01 patient postcode []. Charlson Comorbidity Index (CCI) values were calculated using ICD-10 V4 codes from all available SMR01 diagnosis fields, and scores divided into mild (1–2), moderate (3–5), and severe (≥6) categories for analysis [
      • Charlson M.E.
      • Pompei P.
      • Ales K.L.
      • MacKenzie C.R.
      A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
      ,
      • Quan H.
      • Li B.
      • Couris C.M.
      • Fushimi K.
      • Graham P.
      • Hider P.
      • et al.
      Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.
      ]. A bespoke specialty group was derived using a combination of significant facility of treatment and specialty of consultant caring for the patients. These groups were high-dependency unit (HDU); intensive care unit (ICU); medicine (MED); obstetrics and gynaecology (OBGYN); all surgical specialties were combined (SUR). A bespoke group of comorbidities was derived based on the ICD-10 codes within SMR dataset. These included diabetes ICD-10 codes (E08–E14); chronic renal failure (CRF) (N17–N19); cardiovascular disease (I00–I99); cancers/malignant neoplasms (C00–C96); chronic pulmonary disease (COPD) (J40–J47); and nervous system diseases (G00–G99). These all referred to admissions prior to the admission episode analysed in the study.
      The number of outpatient hospital visits and any outpatient procedures within the year preceding their admission to hospital were taken from the SMR00 data set, which records outpatient attendances [
      Information Services Division (ISD) Scotland
      SMR00 – Outpatient Attendance.
      ]. Linkage to community dispensed prescribing via the ‘Prescribing Information System for Scotland’ (PIS) provided information on antibiotic exposure in the year before admission [
      Information Services Division (ISD) Scotland
      Prescribing information system for Scotland (PRISMS).
      ]. Details of all of the risk factors included in the model are outlined within the Supplementary Appendix. Antibiotic exposure refers to all community dispensed antibiotics and is expressed as the cumulative number of defined daily doses (DDDs) dispensed in the year before admission [
      World Health Organization
      Anatomical therapeutic chemical (ATC)/defined daily dose (DDD) toolkit.
      ]. Time from when the last antibiotic was taken to admission was calculated. The date corresponding to the end of the antibiotic course was estimated by adding the number of DDDs to the dispensed date, and time to admission calculated in days as the duration from the end of course to admission. Time was set to zero where admission occurred before the estimated last day of taking the antibiotic.

      Statistical methods

      Each hospital stay contributed once to the analysis. A patient who was readmitted within the study period could contribute more than one stay to the denominator. Admissions with an onset of HAI on or before the date of admission or a date of onset of HAI that did not occur within an inpatient stay were excluded. Episode records within the SMR01 data set were created when a patient was discharged. Patients who remained in hospital at the time of the analysis would not yet have generated a discharge record and were also excluded. Linkage to the routine data sets was undertaken 90 days after the end of the study data collection period to allow for the time lag in data being available for analysis. Records with no valid CHI (N = 10) could not be linked and were excluded. Where a patient's admission contained more than one HAI, the first HAI was used to group the HAI type. The data, sourced from SMR01, were largely complete as these are mandatory data fields that are recorded for all patients []. SIMD was linked using patient postcode; where this was missing (N = 340) SIMD was categorized as ‘unknown’.
      Thirty-two potential risk factors were included within the analysis, and were based on biological plausibility and previously proposed factors within the existing literature [
      • Rodriguez-Acelas A.L.
      • de Abreu Almeida M.
      • Engelman B.
      • Canon-Montanez W.
      Risk factors for health care-associated infection in hospitalized adults: systematic review and meta-analysis.
      ,
      Health Protection Scotland
      Scottish National Point Prevalence Survey of Healthcare Associated Infection and Antimicrobial Prescribing 2016.
      ]. This initial set of factors was included within a series of univariate analyses to identify predictors significantly associated with developing HAI. A test for trend was performed for continuous variables by comparing a linear regression model containing the only variable on its own against the null (intercept only) model using likelihood ratio test. No linear trends were identified. Backwards stepwise model selection was performed to identify those risk factors associated with HAI at the 5% significance level. Statistical significance of individual variables was evaluated using Wald's test, and likelihood ratio tests were used to compare nested models in the model build procedure.
      A sequential approach was developed when building the model. Risk factors were split into three conceptual themes for modelling to help focus selection: m1, demographics; m2, factors pertaining to the denominator stay; and m3, factors pertaining to the period before admission to the denominator stay. The models were run sequentially, with m2 consisting of those factors identified in the m1 best-fitting model plus additional m2 factors, and m3 consisting of those factors identified in the m2 best-fitting model plus additional m3 factors. Details of the factors included within each model are described in detail in Appendix Table A2. The version retained for multivariate modelling was identified via the lowest Akaike information criterion (AIC) score from the previous best-fitting adjusted model. The factors included within the final model were then used within an adjusted analysis to evaluate the relative risk of the six most frequently occurring HAI types. A multinomial model was run to investigate the relative risk of developing six specific HAI types. All statistical analysis was performed using R software (version 3.5.1) [
      R Core Team
      R: a language and environment for statistical computing.
      ].

      Ethics

      This study was surveillance and therefore was confirmed as ineligible for ethical review (Bailey A. Personal communication to S. Stewart, September 8th, 2016. South East Scotland Research Ethics Service). It was approved by national information governance approvals: Public Benefit of Health and Social Care: Incidence study: 1617-0037.

      Results

      During the study period there were a total of 99,018 adult overnight admissions. After the application of statistical restrictions, the analysis included 63,890 eligible admissions to the study hospitals during the study period. There were 44,399 for the teaching hospital and 19,491 for the general hospital (Figure 1). A total of 893 admissions had one or more HAI, with 822 in the teaching hospital and 71 within the general hospital. Overall incidence was 1.4% with 1.9% within the teaching hospital and 0.4% in the general hospital (reported elsewhere [
      • Stewart S.
      • Robertson C.
      • Pan J.
      • Kennedy S.
      • Dancer S.
      • Haahr L.
      • et al.
      Epidemiology of healthcare-associated infection reported from a hospital-wide incidence study: considerations for infection prevention and control planning.
      ]).
      Figure 1
      Figure 1CONSORT diagram for inpatient stays included within the cohort study risk analysis. ∗Ten patients did not have valid Community Health Index number (CHI; non-Scottish residents), seven patients had no Scottish Morbidity Records (SMR01) available (still in hospital or transcription error). LOS, length of stay; HAI, healthcare-associated infection.
      Table I shows the numbers and percentage of the risk factors included in the final multivariate model within these inpatient stays. Patients aged >60 years made up 61% of admissions. There were similar numbers of males and females. More than half of the admissions had been admitted to hospital at least once in the preceding two years (54.5%) and more than one-tenth (12.8%) were patients who had been in hospital for >30 days in the preceding two years. SIMD category two (second most deprived quintile) was the most frequently occurring (26.3%). More than three-quarters of admissions (77.9%) were emergency admissions. Descriptive frequencies and percentages of all potential risk variables included in the analysis are shown in Appendix Table A1.
      Table IDemographics, univariate and multivariate risk ratios, and potential confounding variables for HAI within the final multivariate model
      VariableGroupNo. of admissions eligible for study (variable %)No. of HAIs (group %)Univariate RR (95% CI)Multivariate best-fitting RR (95% CI)
      Hospital (first study hospital in stay)Teaching44,399 (69.5)822 (1.9)11
      General19,491 (30.5)71 (0.4)0.20 (0.15–0.25)0.16 (0.12–0.20)
      Age group (years)<409252 (14.5)53 (0.6)11
      40–496105 (9.6)65 (1.1)1.86 (1.30–2.67)1.59 (1.11–2.29)
      50–599638 (15.1)137 (1.4)2.48 (1.81–3.40)1.97 (1.44–2.74)
      60–6911,540 (18.1)157 (1.4)2.37 (1.74–3.24)1.76 (1.29–2.44)
      70–7913,451 (21.1)222 (1.7)2.88 (2.14–3.88)2.19 (1.62–3.01)
      ≥8013,904 (21.8)259 (1.9)3.25 (2.42–4.36)2.47 (1.82–3.40)
      SexMale30,145 (47.2)442 (1.5)11
      Female33,745 (52.8)451 (1.3)0.91 (0.80–1.04)1.03 (0.90–1.17)
      SIMD (1: most deprived; 5: least deprived)112,705 (19.9)177 (1.4)11
      216,824 (26.3)271 (1.6)1.16 (0.96–1.40)1.05 (0.88–1.27)
      312,088 (18.9)157 (1.3)0.93 (0.75–1.15)0.84 (0.68–1.04)
      410,338 (16.2)124 (1.2)0.86 (0.69–1.08)0.74 (0.59–0.93)
      511,595 (18.1)160 (1.4)0.99 (0.80–1.22)0.77 (0.62–0.95)
      Unknown340 (0.5)4 (1.2)0.84 (0.32–2.26)0.85 (0.26–1.97)
      Cancer (denominator stay)No59,206 (92.7)780 (1.3)11
      Yes4684 (7.3)113 (2.4)1.83 (1.51–2.23)1.72 (1.40–2.09)
      Cardiovascular disease (denominator stay)No39,566 (61.9)425 (1.1)11
      Yes24,324 (38.1)468 (1.9)1.79 (1.57–2.04)1.40 (1.22–1.62)
      Chronic renal failure (denominator stay)No56,278 (88.1)621 (1.1)11
      Yes7612 (11.9)272 (3.6)3.24 (2.81–3.73)2.19 (1.88–2.54)
      Diabetes (denominator stay)No57,385 (89.8)748 (1.3)11
      Yes6505 (10.2)145 (2.2)1.71 (1.43–2.04)1.26 (1.05–1.51)
      Admission type (denominator stay)Elective14,149 (22.1)125 (0.9)11
      Emergency49,741 (77.9)768 (1.5)1.75 (1.45–2.11)2.00 (1.64–2.46)
      Specialty (admission to first study hospital in denominator stay)MED35,158 (55.2)387 (1.1)11
      HDU1764 (2.7)71 (4.2)3.66 (2.85–4.69)2.70 (2.08–3.47)
      ICU2761 (4.2)124 (4.7)4.08 (3.35–4.98)4.11 (3.34–5.02)
      OBGYN1774 (2.8)12 (0.7)0.61 (0.35–1.09)1.21 (0.63–2.08)
      SUR22,433 (35.1)299 (1.4)1.21 (1.04–1.41)1.67 (1.43–1.96)
      Time since last surgery (days to admission)None
      Patients did not have a surgery as defined by Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures within the two years preceding the admission.
      35,656 (55.8)406 (1.1)11
      ≤304523 (7.1)135 (3.0)2.62 (2.16–3.18)1.86 (1.49–2.32)
      31–905250 (8.2)105 (2.0)1.76 (1.42–2.17)1.29 (1.01–1.62)
      91–1805026 (7.9)82 (1.6)1.43 (1.13–1.81)1.19 (0.92–1.52)
      ≥18113,435 (21.0)165 (1.2)1.08 (0.90–1.29)0.96 (0.79–1.17)
      Total length of stay in 2 years to admission (days)029,083 (45.5)307 (1.1)11
      1–26763 (10.6)82 (1.2)1.15 (0.90–1.46)1.11 (0.86–1.42)
      3–78231 (12.9)86 (1.0)0.99 (0.78–1.26)0.85 (0.66–1.09)
      8–145681 (8.9)94 (1.7)1.57 (1.25–1.97)1.19 (0.93–1.52)
      15–305978 (9.4)119 (2.0)1.89 (1.53–2.33)1.37 (1.08–1.73)
      >308154 (12.8)205 (2.5)2.38 (2.00–2.84)1.65 (1.34–2.04)
      HAI, healthcare-associated infection; RR, relative risk; CI confidence interval; IMD, Scottish Index of Multiple Deprivation. Bespoke specialties: HDU, high-dependency unit; ICU, intensive care unit; MED, medicine; OBGYN, obstetrics and gynaecology; SUR, all surgical specialties.
      The full tables including factors not included within the final multivariate model are available in the Supplementary Appendix.
      a Patients did not have a surgery as defined by Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures within the two years preceding the admission.
      Table I shows the unadjusted and adjusted relative risk (RR) and 95% confidence interval (CI). Comparing within variable groups, patients admitted to a general hospital had significantly lower risk of developing HAI compared with those in the teaching hospital. Risk of developing HAI increased with age, with those aged >80 years showing an RR of 2.47 (CI: 1.82–3.40) compared with those aged <40 years. The highest risk, adjusted for the effects of all other risk factors, was admission to an ICU specialty compared with general medicine (RR: 4.11; 95% CI: 3.34–5.02). This was followed by admission to an HDU specialty (RR: 2.70; 95% CI: 2.08–3.47) compared with a general medical specialty, those with chronic renal failure (RR: 2.19; 95% CI: 1.88–2.54) compared with those without the condition and emergency admissions compared with elective (RR: 2.0; CI: 1.64–2.46). There was an increased risk associated with surgeries within ≤30 days prior to admission (RR: 1.86; 95% CI: 1.49–2.32), and prior hospital stays of more than two weeks during the previous two years (1.37; 95% CI: 1.08–1.73 for 15–30 days; increasing to RR: 1.65; 95% CI: 1.34–2.04 for >30 days).
      Despite showing no association at this point of analysis, sex was retained throughout the analysis as it was considered a clinically important patient characteristic, and later was shown to relate to specific HAI types. Patients in the two least deprived SIMD categories were less likely to develop HAI during their hospital stay than those in the most deprived.
      Table II shows the adjusted risk by HAI type. There was a reduced risk of onset of any HAI type during a stay in the general hospital compared with the teaching hospital, although the small numbers in the general hospital studied, when analysed by infection type, may reduce the power to accurately detect associations for some infections.
      Table IIAdjusted risk ratios (95% confidence intervals) for the risk of developing one of six HAI types during an inpatient stay in hospital, based on the multivariate final model
      VariableGroupBSIGILRIPNSSIUTI
      Hospital (first study hospital in stay)Teaching111111
      General0.11 (0.06–0.21)0.02 (0.01–0.10)0.01 (0.00–0.08)0.10 (0.03–0.26)0.47 (0.30–0.75)0.19 (0.12–0.31)
      Age group (years)<40111111
      40–491.55 (0.60–4.06)2.84 (1.08–7.52)1.72 (0.76–3.86)2.23 (0.66–7.55)0.94 (0.40–2.24)1.67 (0.60–4.63)
      50–592.09 (0.91–4.78)1.92 (0.73–5.04)2.10 (1.01–4.36)2.76 (0.90–8.49)1.72 (0.86–3.44)2.71 (1.13–6.50)
      60–692.13 (0.95–4.78)1.93 (0.75–4.93)1.49 (0.71–3.14)2.37 (0.77–7.29)1.26 (0.62–2.60)2.95 (1.26–6.94)
      70–792.86 (1.31–6.25)2.58 (1.05–6.37)1.93 (0.93–3.99)2.01 (0.63–6.35)1.81 (0.91–3.61)3.56 (1.55–8.18)
      ≥802.33 (1.05–5.18)5.22 (2.18–12.47)1.85 (0.87–3.93)2.87 (0.91–9.07)0.96 (0.43–2.14)5.13 (2.26–11.65)
      SexMale111111
      Female0.87 (0.63–1.19)0.84 (0.60–1.19)0.87 (0.63–1.20)0.95 (0.60–1.51)0.88 (0.61–1.28)1.67 (1.23–2.26)
      SIMD (1: most deprived; 5: least deprived)1111111
      21.35 (0.85–2.15)1.22 (0.75–2.01)1.17 (0.73–1.88)0.91 (0.48–1.74)0.78 (0.48–1.25)0.96 (0.64–1.46)
      30.96 (0.56–1.63)0.85 (0.47–1.51)1.07 (0.64–1.80)1.19 (0.62–2.28)0.50 (0.28–0.91)0.75 (0.47–1.21)
      40.81 (0.45–1.44)0.96 (0.54–1.71)0.77 (0.43–1.39)0.65 (0.29–1.44)0.54 (0.30–0.97)0.64 (0.38–1.06)
      50.95 (0.56–1.60)0.75 (0.42–1.32)0.94 (0.55–1.59)0.48 (0.21–1.09)0.57 (0.32–1.02)0.80 (0.51–1.25)
      Unknown1.49 (0.20–11.17)1.64 (0.22–12.34)1.66 (0.38–7.25)---
      Cancer (denominator stay)No111111
      Yes3.24 (2.14–4.91)1.72 (1.03–2.89)2.19 (1.33–3.59)1.87 (0.92–3.80)0.84 (0.45–1.57)1.32 (0.79–2.21)
      Cardiovascular disease (denominator stay)No111111
      Yes1.39 (0.99–1.94)0.68 (0.47–0.98)1.21 (0.85–1.72)1.47 (0.87–2.48)2.02 (1.34–3.02)2.23 (1.63–3.07)
      Chronic renal failure (denominator stay)No111111
      Yes4.73 (3.38–6.63)2.18 (1.49–3.18)2.20 (1.53–3.17)1.51 (0.87–2.63)1.59 (0.98–2.58)1.92 (1.37–2.70)
      Diabetes (denominator stay)No111111
      Yes1.18 (0.77–1.81)1.64 (1.05–2.56)1.80 (1.19–2.70)0.76 (0.36–1.62)0.79 (0.44–1.42)1.15 (0.76–1.76)
      Admission type (denominator stay)Elective111111
      Emergency3.38 (1.97–5.79)2.36 (1.26–4.43)1.02 (0.66–1.59)1.56 (0.79–3.10)1.88 (1.19–2.96)2.91 (1.79–4.74)
      Specialty (admission to first study hospital in denominator stay)MED111111
      HDU2.90 (1.65–5.08)1.70 (0.82–3.52)3.53 (2.01–6.18)4.63 (1.90–11.30)11.18 (4.06–30.79)1.38 (0.60–3.23)
      ICU1.40 (0.69–2.86)1.44 (0.61–3.39)9.86 (6.54–14.88)19.56 (10.92–35.02)17.90 (7.59–42.21)1.23 (0.61–2.49)
      OBGYN1.21 (0.28–5.20)0.37 (0.05–2.79)1.36 (0.17–10.77)22.43 (6.96–72.31)1.75 (0.52–5.87)
      SUR1.81 (1.26–2.60)0.75 (0.49–1.16)0.63 (0.39–1.03)1.07 (0.53–2.16)19.28 (9.58–38.79)2.04 (1.48–2.80)
      Time since last inpatient surgery (days to admission)None
      No surgery in previous two years.
      111111
      ≤301.70 (0.99–2.94)1.80 (1.03–3.15)0.68 (0.34–1.38)1.66 (0.78–3.53)6.28 (3.60–10.95)1.53 (0.88–2.66)
      31–900.97 (0.52–1.80)1.26 (0.70–2.25)0.81 (0.44–1.50)1.19 (0.54–2.62)2.75 (1.45–5.21)1.42 (0.84–2.42)
      91–1801.08 (0.57–2.04)1.40 (0.78–2.51)1.15 (0.65–2.05)0.67 (0.25–1.83)1.38 (0.63–3.03)1.42 (0.82–2.43)
      ≥1811.23 (0.79–1.92)0.75 (0.45–1.24)0.83 (0.52–1.34)0.66 (0.32–1.36)0.92 (0.47–1.81)1.08 (0.72–1.63)
      Total length of stay in 2 years to admission (days)0111111
      1–21.16 (0.67–2.01)0.54 (0.19–1.54)1.27 (0.72–2.24)2.74 (1.31–5.73)1.09 (0.54–2.19)0.87 (0.51–1.50)
      3–70.66 (0.36–1.19)1.42 (0.74–2.73)0.54 (0.26–1.11)1.33 (0.56–3.16)1.14 (0.60–2.16)0.67 (0.39–1.15)
      8–140.82 (0.44–1.51)2.12 (1.13–3.99)1.49 (0.83–2.67)1.42 (0.53–3.76)1.54 (0.81–2.96)0.72 (0.40–1.29)
      15–300.99 (0.56–1.72)2.95 (1.66–5.24)1.48 (0.82–2.67)2.12 (0.89–5.07)1.17 (0.58–2.37)1.02 (0.62–1.68)
      >301.14 (0.69–1.88)3.07 (1.79–5.28)1.98 (1.17–3.35)3.56 (1.68–7.55)1.65 (0.87–3.13)1.11 (0.71–1.74)
      HAI, healthcare-associated infection; BSI, bloodstream infection; GI, gastrointestinal infection; LRI, lower respiratory tract infection; PN, pneumonia; SSI, surgical site infection; UTI, urinary tract infection; SIMD, Scottish Index of Multiple Deprivation. Bespoke specialties: HDU, high-dependency unit; ICU, intensive care unit; MED, medicine; OBGYN, obstetrics and gynaecology; SUR, all surgical specialties.
      a No surgery in previous two years.
      For BSI the strongest associations were seen in patients previously diagnosed with chronic renal failure (RR: 4.73; 95% CI: 3.38–6.63) and cancer (3.24; 2.14–4.91), in emergency admissions (3.38; 1.97–5.79) or admission to HDU specialty, and an increased risk with age. BSI showed an increased risk (2.86; 1.31–6.25) in those aged >70 years relative to those aged <40 years.
      Gastrointestinal infection (GI) also showed an increased risk in those aged >70 years, and in patients aged >80 years the risk was a factor of 5.22 (95% CI: 2.18–12.47) greater than that of those aged <40 years. Emergency admissions showed a 2.36 increase in RR (CI: 1.26–4.43). Patients with diabetes, chronic renal failure, and cancer all showed an increased risk of GI along with those with hospital stays of more than one week in the two years preceding denominator admission. Patients with total length of stay (LOS) >8 days in the two years before admission had an increased RR of GI infection; patients with LOS >30 days had three times greater risk (3.07; 1.79–5.28) than patients with no hospital stay in the previous two years.
      There was an increased risk of lower respiratory tract infections (LRI) (RR: 9.86; 95% CI: 6.54–14.88) and pneumonia (19.56; 10.92–35.02) in those with an ICU admission. HDU patients had an increased risk relative to medical patients of LRI and pneumonia 3.53 (2.01–6.18) and 4.63 (1.90–11.30) respectively. Age showed less of an effect on relative risk of developing LRI or pneumonia. Total length of stay >30 days in the preceding two years was significant for both LRI (1.98; 1.17–3.35) and pneumonia (3.56; 1.68–7.55).
      Although there was some evidence of a reduced risk in the intermediate compared with most deprived SIMD categories for those with a surgical site infection (SSI), there was no clear linear trend. There was an increased risk of SSI associated with time from last surgery to admission, with those having surgery within the 30 days before admission having a risk 6.28 times that of those not having surgery. There was no clear pattern observed in associations with time from surgery for other HAI types. Patients who had recent surgery are at increased risk of developing SSI on a subsequent admission. This is greatest during the first 30 days after surgery (RR: 6.28; 95% CI: 3.60–10.95) but an increased risk is shown in the period from 31–90 days (2.75; 1.45–5.21). It is important to note that the cohort study analysis did not include surgery within the current admission.
      Urinary tract infection (UTI) showed an increased RR with age, with those aged >80 years having five times the risk of those aged <40 years (RR: 5.13; 95% CI: 2.26–11.65). There was an increased risk in emergency compared with elective admissions for patients with UTI. There was 67% increased risk of UTI onset during a hospital stay in females compared with males. This was the only HAI type which showed an increased RR for either sex.

      Discussion

      Identifying patients who are at greater risk of developing HAI at the start of their hospital stay and managing their care to optimize infection prevention is critical. HAI in the hospital population is a relatively infrequent event, with less than 2% of admissions developing HAI during their stay within the ECONI study [
      • Stewart S.
      • Robertson C.
      • Pan J.
      • Kennedy S.
      • Dancer S.
      • Haahr L.
      • et al.
      Epidemiology of healthcare-associated infection reported from a hospital-wide incidence study: considerations for infection prevention and control planning.
      ]. This measure of disease frequency is only useful if it can be used to prevent HAI following admission to hospital. Extrinsic risk factors, such as clinical device insertion, are well established in the literature but illustrated only a small proportion of all infections in the ECONI study [
      • Stewart S.
      • Robertson C.
      • Pan J.
      • Kennedy S.
      • Dancer S.
      • Haahr L.
      • et al.
      Epidemiology of healthcare-associated infection reported from a hospital-wide incidence study: considerations for infection prevention and control planning.
      ]. Previous studies have tended to focus on specific HAIs, types, causative organisms, or risk factors [
      • Rodriguez-Acelas A.L.
      • de Abreu Almeida M.
      • Engelman B.
      • Canon-Montanez W.
      Risk factors for health care-associated infection in hospitalized adults: systematic review and meta-analysis.
      ]. However, identifying risk factors that are known at the start of a patient's admission is more valuable because this allows targeted IPC measures to be implemented early [
      • Freeman J.
      • McGowan Jr., J.E.
      Risk factors for nosocomial infection.
      ]. This study provides a novel whole-hospital analysis of the factors that increase the risk of HAI, and individual HAI types at the point of admission. The findings have important implications for hospital IPC strategy nationally and locally. This should inform targeting interventions to patients in selected specialties, with the greatly increased risk of developing HAI or a particular HAI type. The opportunity to undertake these types of analyses is rare as data collection is intensive and costly.
      The greatest risk factors for development of any HAI were: being treated in a teaching hospital, increasing age, emergency admission and comorbidities of cancer, cardiovascular disease, chronic renal failure, and diabetes. There are differences in teaching and general hospitals in terms of severity of illness of patients and complexity of treatments provided for those patients. Previous studies have shown there to be a higher risk of infection in larger teaching hospitals [
      • Magill S.S.
      • O’Leary E.
      • Janelle S.J.
      • Thompson D.L.
      • Dumyati G.
      • Nadle J.
      • et al.
      Changes in prevalence of health care-associated infections in U.S. hospitals.
      ,
      • Deptula A.
      • Trejnowska E.
      • Ozorowski T.
      • Hryniewicz W.
      Risk factors for healthcare-associated infection in light of two years of experience with the ECDC point prevalence survey of healthcare-associated infection and antimicrobial use in Poland.
      ,
      • Donker T.
      • Wallinga J.
      • Grundmann H.
      Patient referral patterns and the spread of hospital-acquired infections through national health care networks.
      ]. Comparisons tend to focus on the difference in prevalence of HAI between acute care, combining general and teaching hospitals and primary care combining community and long-stay hospitals [
      Health Protection Scotland
      Scottish National Point Prevalence Survey of Healthcare Associated Infection and Antimicrobial Prescribing 2016.
      ,
      European Centre for Disease Prevention and Control
      Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals 2011–2012.
      ]. Whole-hospital incidence studies are rare and a previous study within the UK focused on a single general hospital [
      • Plowman R.
      • Graves N.
      • Griffin M.A.
      • Roberts J.A.
      • Swan A.V.
      • Cookson B.
      • et al.
      The rate and cost of hospital-acquired infections occurring in patients admitted to selected specialties of a district general hospital in England and the national burden imposed.
      ]. Many of these factors have featured in similar studies to date. However, hospital presentation and socioeconomic status have not been identified in the latest systematic review and require further research [
      • Rodriguez-Acelas A.L.
      • de Abreu Almeida M.
      • Engelman B.
      • Canon-Montanez W.
      Risk factors for health care-associated infection in hospitalized adults: systematic review and meta-analysis.
      ]. This may be because previous studies have not considered risk for all HAIs at the point of admission, but have retrospectively considered all risks for the entire inpatient stay. Further, the focus of many papers continues to be on those risk factors that are modifiable, rather than considering how best to manage those factors, from an IPC perspective, which cannot be modified.
      Hospital type and specialty are an important consideration for prioritizing organizational IPC resources based on risk [
      • Wang R.F.
      • Shen S.H.
      • Yen A.M.
      • Wang T.L.
      • Jang T.N.
      • Lee S.H.
      • et al.
      Risk factors for incidence and case-fatality rates of healthcare-associated infections: a 20-year follow-up of a hospital-based cohort.
      ]. In addition, consideration of the historical patient pathway to the current admission is essential. Patients with more than 15 days total stay in hospital, during the two years before their current admission, made up 22.2% of all admissions and those with >30 days were 12.8% of all admissions. Patients at risk of readmissions are linked to long-term conditions and frailty, or emergency admission in younger patients [
      Information and Statistics Division
      Scottish patients at risk of readmission and admission (SPARRA).
      ]. Previous studies have found that length of stay in older medical patients was related to functional status score, illness severity, cognitive score, poor nutrition, comorbidity score, diagnosis or presenting illness, polypharmacy, age, and gender [
      • Campbell S.E.
      • Seymour D.G.
      • Primrose W.R.
      • for the ACMEplus project
      A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.
      ]. With the move from hospital care to managing patients with complex needs in the community, there is clear potential for reduction in both number and length of hospital stay to reduce risk of HAI.
      Most studies of HAI identify age as a risk factor, in line with the findings here. Older adults are vulnerable to a range of adverse events within the hospital setting compared with younger patients, including malnutrition and pressure ulcers [
      • Aranaz-Andrés J.M.
      • Limón R.
      • Mira J.J.
      • Aibar C.
      • Gea M.T.
      • Agra Y.
      • et al.
      What makes hospitalized patients more vulnerable and increases their risk of experiencing an adverse event?.
      ,
      • Nauman J.
      • Soteriades E.S.
      • Hashim M.J.
      • Govender R.
      • Al Darmaki R.S.
      • Al Falasi R.J.
      • et al.
      Global incidence and mortality trends due to adverse effects of medical treatment, 1990–2017: a systematic analysis from the Global Burden of Diseases, Injuries and Risk Factors Study.
      ,
      • O’Shea E.
      • Trawley S.
      • Manning E.
      • Barrett A.
      • Browne V.
      • Timmons S.
      Malnutrition in hospitalised older adults: a multicentre observational study of prevalence, associations and outcomes.
      ,
      • Jaul E.
      • Barron J.
      • Rosenzweig J.P.
      • Menczel J.
      An overview of co-morbidities and the development of pressure ulcers among older adults.
      ]. These patients have an increased incidence of comorbidities [
      • Kuh D.
      • Wong A.
      • Shah I.
      • Moore A.
      • Popham M.
      • Curran P.
      • et al.
      The MRC National Survey of Health and Development reaches age 70: maintaining participation at older ages in a birth cohort study.
      ], multiple drugs, and impaired motor and sensory and immune function [
      • Rawle M.J.
      • Richards M.
      • Davis D.
      • Kuh D.
      The prevalence and determinants of polypharmacy at age 69: a British birth cohort study.
      ,
      • Guthrie B.
      • Makubate B.
      • Hernandez-Santiago V.
      • Dreischulte T.
      The rising tide of polypharmacy and drug–drug interactions: population database analysis 1995–2010.
      ]. In line with previous studies, older age increased the relative risk for BSI, GI, and UTI [
      • King C.
      • Garcia Alvarez L.
      • Holmes A.
      • Moore L.
      • Galletly T.
      • Aylin P.
      Risk factors for healthcare-associated urinary tract infection and their applications in surveillance using hospital administrative data: a systematic review.
      ,
      • Poolman J.T.
      • Anderson A.S.
      Escherichia coli and Staphylococcus aureus: leading bacterial pathogens of healthcare associated infections and bacteremia in older-age populations.
      ,
      • Daneman N.
      • Guttmann A.
      • Wang X.
      • Ma X.
      • Gibson D.
      • Stukel T.A.
      The association of hospital prevention processes and patient risk factors with the risk of Clostridium difficile infection: a population-based cohort study.
      ,
      • Belliot G.
      • Lopman B.A.
      • Ambert-Balay K.
      • Pothier P.
      The burden of norovirus gastroenteritis: an important foodborne and healthcare-related infection.
      ]. The increasing risk of HAI with age, especially in those aged >70 years, points to a need for risk assessment for HAI at point of admission, particularly given the projected 34% increase in the population aged ≥75 years by 2030 in the UK and what this means for risk of HAI and need for refocused IPC in hospitals [
      • Fried L.P.
      • Ferrucci L.
      • Darer J.
      • Williamson J.D.
      • Anderson G.
      Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care.
      ]. For example, specific interventions, that may appear costly prevention measures if implemented at a whole-hospital level, could be targeted to a group of patients who are known to be vulnerable to infection and who have poorer outcomes if they develop HAI.
      Multimodal prevention of UTI – the HAI type on which the effect of age is greatest – has been a focus for some years in the form of care bundles for all catheterized patients [
      Health Protection Scotland
      Preventing catheter associated urinary tract infections – acute settings.
      ]. There has been a great deal of research into device-related UTI over the last 10 years, especially in relation to antimicrobial materials used in catheters; and although evidence exists for efficacy, there is very limited literature on the cost-effectiveness of infection prevention interventions [
      • Majeed A.
      • Sagar F.
      • Latif A.
      • Hassan H.
      • Iftikhar A.
      • Darouiche R.O.
      • et al.
      Does antimicrobial coating and impregnation of urinary catheters prevent catheter-associated urinary tract infection? A review of clinical and preclinical studies.
      ,
      • Al-Qahtani M.
      • Safan A.
      • Jassim G.
      • Abadla S.
      Efficacy of anti-microbial catheters in preventing catheter associated urinary tract infections in hospitalized patients: a review on recent updates.
      ]. Despite the increased cost of these specialized IPC measures, if overall numbers of infections are reduced significantly and their use is limited to patients who are likely to benefit the most, it is possible that they may well be cost-effective [
      • Loveday H.P.
      • Wilson J.A.
      • Pratt R.J.
      • Golsorkhi M.
      • Tingle A.
      • Bak A.
      • et al.
      epic3: national evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England.
      ].
      There is a considerable overlap in the factors predicting extended LOS and risk of HAI, many of which contribute to overall frailty of the patient. Incidence, bed-day use and cost of HAI are often compared with other diseases in the literature, showing that cancers, cardiovascular diseases, and diabetes achieve greater recognition and funding than HAI, when HAI is a similar burden [
      • Cassini A.
      • Plachouras D.
      • Eckmanns T.
      • Abu Sin M.
      • Blank H.P.
      • Ducomble T.
      • et al.
      Burden of six healthcare-associated infections on European population health: estimating incidence-based disability-adjusted life years through a population prevalence-based modelling study.
      ]. However, this study has shown that such a ‘comparative approach’ is a rather simplistic way of looking at the problem [
      • Cassini A.
      • Plachouras D.
      • Eckmanns T.
      • Abu Sin M.
      • Blank H.P.
      • Ducomble T.
      • et al.
      Burden of six healthcare-associated infections on European population health: estimating incidence-based disability-adjusted life years through a population prevalence-based modelling study.
      ,
      • Guest J.F.
      • Keating T.
      • Gould D.
      • Wigglesworth N.
      Modelling the annual NHS costs and outcomes attributable to healthcare-associated infections in England.
      ]. The patients with these conditions are the very same patients who are most at risk of HAI.
      This study has shown that, for several HAI types, the factors affecting development of HAI that could be identified on admission include some data not currently routinely available to clinical teams. A potential limitation of this risk factor analysis, and of national data sets more broadly, is the exclusion of devices present on admission as a risk factor. However, this is likely to be a small proportion of all admissions to hospital. There is a need for further development of current patient management systems in order to fully achieve this potential. In order to develop the potential for risk assessment on admission, we recommend that hospital admission systems not only include the risk factors identified within this study but in addition record devices in situ. This study demonstrates the potential of creating risk assessment tools on admission, if these data are available, for identifying those most at risk of HAI and prioritizing additional IPC measures in their care pathways to mitigate that risk.
      The ECONI surveillance study included all patient admissions to the study hospitals. The two hospitals selected to participate in the study represent about 10% of annual admissions to acute Scottish hospitals. The patient population is similar to the overall NHS Scotland population [
      Information Services Division (ISD)
      Scotland. SMR01 – General/Acute Inpatient and Day Case.
      ]. Limited identifiable data were recorded for the study; only CHI number was used to divide the cases and non-cases into two groups. This reduced recruitment bias, but resulted in exclusion of those not resident in Scotland (0.1% of all admissions). Charlson comorbidity score data showed that 35.6% of patients did not have a previous admission to derive a CCI score, and that the overall comorbidity summary was not as strong a predictor as a set of individual comorbidities.
      In conclusion, this study used a large cohort design to identify and quantify risk of HAI based on factors that are known at point of admission. Most previous studies examined retrospective risk based on cumulative factors that occur during the hospital stay. These findings may help develop a clinical decision tool to identify patients at greatest risk of developing a particular type of HAI. These patients would be potential candidates for personalized infection prevention interventions.

      Acknowledgements

      Data collection could not have been completed without the enormous effort from the ECONI research nurses N. Williamson, H. Thain, R. Boyle, A. McAlpine, and C. Beith. The study was supported by the clinical staff in the study hospitals, particularly the infection control teams, laboratory staff, and research and development teams.

      Author contributions

      S.S. led the study design, wrote study protocols and ethics and Public Benefit and Privacy Panel approvals, patient facing materials, contributed to the design of the collection tools, contributed to development of statistical analysis, and developed the manuscript. C.R. contributed to the concept of the study, study design, and statistical analysis plan. C.R., S.K., K.K. undertook the statistical analysis and contributed to the manuscript. L.H. contributed to the development of study design, protocol and data management. S.M. contributed to aspects of the study design, contributed to the manuscript. H.M. contributed to the study design and health economic aspects of the study. S.D. and B.C. are the Principal Investigators at the recruiting sites. J.R. conceived the study and is Chief Investigator for the study.

      Non-author collaborators

      The ECONI Steering Committee. M.A. represented the funder, A.L., R.D., A.M., M.S. and J.I. represented the Scottish Government HAI policy unit on the Steering Committee. E.R. and L.R. represented Infection Prevention Society (IPS). M.W., L.B., and M.R. were lay representatives on the Steering Committee and M.W. and L.B. contributed to the development of the patient-facing materials for the study. Committee: Professor J. Reilly (J.R.), Professor M. Adil (M.A.), Dr H. Mason (H.M.), Professor C. Robertson (C.R.), Professor N. Graves (N.G.), J. Ives (J.I.), M. Syme (M.S.), R. Dunk (R.D.), A. Mullings (A.M.), E. Ross (E.R.), L. Ritchie (L.R.), Professor S. Dancer (S.D.), Dr B. Cook (B.C.), Professor A. Leonard (A.L.), M. Whyte (M.W.), M. Rodgers (M.R.), L. Brown (L.B.), S. Stewart (S.S.).

      Conflict of interest statement

      None declared.

      Funding sources

      This work was supported by Health Protection Scotland Programme , NHS National Services Scotland (RIE reference 14-154) from October 2015 to January 2020. The work was also pump-primed by the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI), which has been set up with Scottish Government funding via the Scottish Infection Research Network.

      Appendix ASupplementary data

      The following is the Supplementary data to this article:

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