Bed occupancy rates and hospital-acquired Clostridium difficile infection: a cohort study

Bed occupancy and nosocomial infections in the intensive care unit: A retrospective observational study in a tertiary hospital

Background

Healthcare-associated infections (HAI) are a major problem globally, contributing to prolonged hospital admissions and poor outcomes.

Objectives

To examine HAI incidence and risk factors in an intensive care unit (ICU) during high v. low occupancy periods.

Methods

This retrospective, descriptive analysis investigated HAI incidence among adult patients admitted to the ICU at Chris Hani Baragwanath (CHBH) during a high (H2019) and low (L2020) occupancy. Data were extracted from the clinical records of 440 eligible patients.

Results

We found an increased risk of HAI during H2019 compared with L2020 (relative risk (RR) 1.42, 95% confidence interval (CI) 1.03 - 1.94). The overall frequency density of HAI was 25/1 000 ICU days. There was no difference in the distribution of the site of infection (blood v. other) (p=0.27) or bacterial category (Gram stain) (p=0.62). Five organisms accounted for 89% of pathogens: Klebsiella (26%), Staphylococcus (21%), Acinetobacter (16%), Candida (16%) and Enterobacter (10%). The incidence of multidrug-resistant/extensively drug-resistant (MDR/XDR) organisms was 4.2-fold higher (95% CI 1.3 - 13.4) during H2019 compared with L2020. Logistic regression analysis revealed two independent predictors of nosocomial infection: ICU length of stay (odds ratio (OR) 1.12, 95% CI 1.02 - 1.22) and intercostal drain duration in days (OR 1.27, 95% CI 1.09 - 1.47).

Conclusion

High occupancy in the ICU was associated with an increased risk of HAI and a greater incidence of MDR and XDR pathogens. Increasing ICU length of stay and invasive device duration were independent predictors of HAI.

Contribution of the study

Hospital-acquired infections are a common problem and cause of morbidity and mortality in intensive care units and general wards globally. However, there is very little literature on the topic from low- and middle-income countries. This study aims to provide insite into the unique factors that contribute to these infections in the South African context.

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

Incidence of Clostridioides difficile infection (CDI) related to antibiotic prescribing by GP surgeries in Wales

BACKGROUND

Clostridioides difficile infection (CDI) is a healthcare-acquired infection (HAI) causing significant morbidity and mortality. Welsh CDI rates are high in comparison with those in England and Scotland.

OBJECTIVES

This retrospective ecological study used aggregated disease surveillance data to understand the impact of total and high-risk Welsh GP antibiotic prescribing on total and stratified inpatient/non-inpatient CDI incidence.

METHODS

All cases of confirmed CDI, during the financial years 2014-15 to 2017-18, were linked to aggregated rates of antibiotic prescribing in their GP surgery and classified as 'inpatient', 'non-inpatient' or 'unknown' by Public Health Wales. Multivariable negative-binomial regression models, comparing CDI incidence with antibiotic prescribing rates, were adjusted for potential confounders: location; age; social deprivation; comorbidities (estimated from prevalence of key health indicators) and proton pump inhibitor (PPI) prescription rates.

RESULTS

There were 4613 confirmed CDI cases, with an incidence (95% CI) of 1.44 (1.40-1.48) per 1000 registered patients. Unadjusted analysis showed that an increased risk of total CDI incidence was associated with higher total antibiotic prescribing [relative risk (RR) (95% CI) = 1.338 (1.170-1.529) per 1000 items per 1000 specific therapeutic group age-sex related GP prescribing units (STAR-PU)] and that high-risk antibiotic classes were positively associated with total CDI incidence. Location, age ≥65 years and diabetes were associated with increased risk of CDI. After adjusting for confounders, prescribing of clindamycin showed a positive association with total CDI incidence [RR (95% CI) = 1.079 (1.001-1.162) log items per 1000 registered patients].

CONCLUSIONS

An increased risk of CDI is demonstrated at a primary care practice population level, reflecting their antibiotic prescribing rates, particularly clindamycin, and population demographics.

Infection prevention and control research priorities: what do we need to combat healthcare-associated infections and antimicrobial resistance? Results of a narrative literature review and survey analysis

BACKGROUND

Infection prevention and control (IPC) is one of the most cost-effective interventions against antimicrobial resistance (AMR). Yet, IPC knowledge gaps often receive little prominence in AMR research agendas. In this article, we construct IPC research priorities, in order to draw attention to these critical research needs.

METHODS

We developed a 4-step framework to identify IPC knowledge gaps from literature (narrative review). These gaps were then translated into research priorities and sent to two groups of European IPC experts for validation and critique through an online survey.

RESULTS

Seventy-nine publications were retrieved from the literature review, identifying fifteen IPC research gaps. Forty-four IPC experts, clustered in two groups, vetted them. The experts classified all research gaps as medium or high priority. Overall agreement between both groups was average (Kendall's τ = 0.43), with strong alignment on the highest priorities: (i) the assessment of organizational, socio-economic, and behavioural barriers/facilitators for the implementation of IPC programmes, (ii) the impact of overcrowding on the spread of infections and (iii) the impact of infrastructural changes, at facility level, on the reduction of infections. Feedback from experts also identified an additional research gap on the interaction between the human and hospital microbiomes.

CONCLUSIONS

We formulated a list of sixteen research priorities and identified three urgent needs. Now, we encourage researchers, funding agencies, policymakers and relevant stakeholders to start addressing the identified gaps.

The Association Between Hospital Capacity Strain and Inpatient Outcomes in Highly Developed Countries: A Systematic Review

BACKGROUND

Increases in patient needs can strain hospital resources, which may worsen care quality and outcomes. This systematic literature review sought to understand whether hospital capacity strain is associated with worse health outcomes for hospitalized patients and to evaluate benefits and harms of health system interventions to improve care quality during times of hospital capacity strain.

METHODS

Parallel searches were conducted in MEDLINE, CINAHL, the Cochrane Library, and reference lists from 1999-2015. Two reviewers assessed study eligibility. We included English-language studies describing the association between capacity strain (high census, acuity, turnover, or an indirect measure of strain such as delayed admission) and health outcomes or intermediate outcomes for children and adults hospitalized in highly developed countries. We also included studies of health system interventions to improve care during times of capacity strain. Two reviewers extracted data and assessed risk of bias using the Newcastle-Ottawa Score for observational studies and the Cochrane Collaboration Risk of Bias Assessment Tool for experimental studies.

RESULTS

Of 5,702 potentially relevant studies, we included 44 observational and 8 experimental studies. There was marked heterogeneity in the metrics used to define capacity strain, hospital settings, and overall study quality. Mortality increased during times of capacity strain in 18 of 30 studies and in 9 of 12 studies in intensive care unit settings. No experimental studies were randomized, and none demonstrated an improvement in health outcomes after implementing the intervention. The pediatric literature is very limited; only six observational studies included children. There was insufficient study homogeneity to perform meta-analyses.

DISCUSSION

In highly developed countries, hospital capacity strain is associated with increased mortality and worsened health outcomes. Evidence-based solutions to improve outcomes during times of capacity strain are needed.

Association Between High-Risk Medication Usage and Healthcare Facility-Onset C. difficile Infection

OBJECTIVE

National hospital performance measures for C. difficile infection (CD) are available; comparing antibacterial use among performance levels can aid in identifying effective antimicrobial stewardship strategies to reduce CDI rates.

DESIGN

Hospital-level, cross-sectional analysis.

METHODS

Hospital characteristics (ie, demographics, medications, patient mix) were obtained for 77 hospitals for 2013. Hospitals were assigned 1 of 3 levels of a CDI standardized infection ratio (SIR): ‘Worse than,’ ‘Better than,’ or ‘No different than’ a national benchmark. Analyses compared medication use (total and broad-spectrum antibacterials) for 3 metrics: days of therapy per 1,000 patient days; length of therapy; and proportion of patients receiving a medication across SIR levels. A multivariate, ordered-probit regression identified characteristics associated with SIR categories.

RESULTS

Regarding total average antimicrobial use per patient, there was a significant difference detected in mean length of therapy: ‘No different’ hospitals having the longest (4.93 days) versus ‘Worse’ (4.78 days) and ‘Better’ (4.43 days) (P<.01). ‘Better’ hospitals used fewer total antibacterials (693 days of therapy per 1,000 patient days) versus ‘No different’ (776 days) versus ‘Worse’ (777 days) (P<.05). The ‘Better’ hospitals used broad-spectrum antibacterials for a shorter average length of therapy (4.03 days) versus ‘No different’ (4.51 days) versus ‘Worse’ (4.38 days) (P<.05). ‘Better’ hospitals used fewer broad-spectrum antibacterials (310 days of therapy per 1,000 patient days) versus ‘No different’ (364 days) versus ‘Worse’ (349 days) (P<.05). Multivariate analysis revealed that the proportion of elderly patients and chemotherapy days of therapy per 1,000 patient days was significantly negatively associated with the SIR.

CONCLUSIONS

These findings have potential implications regarding the need to fully account for hospital patient mix when carrying out inter-hospital comparisons of CDI rates.

Infect Control Hosp Epidemiol 2016;37:909–915

High Variability in Nosocomial Clostridium difficile Infection Rates Across Hospitals After Colorectal Resection

BACKGROUND

Hospital-acquired Clostridium difficile infection is associated with adverse patient outcomes and high medical costs. The incidence and severity of C. difficile has been rising in both medical and surgical patients.

OBJECTIVE

Our aim was to assess risk factors and variation associated with the development of nosocomial C. difficile colitis among patients undergoing colorectal resection.

DESIGN

This was a retrospective cohort study.

SETTINGS

The study included segmental colectomy and proctectomy cases in New York State from 2005 to 2013.

PATIENTS

The study cohort included 150,878 colorectal resections. Patients with a documented previous history of C. difficile infection or residence outside of New York State were excluded.

MAIN OUTCOME MEASURES

A diagnosis of C. difficile colitis either during the index hospital stay or on readmission within 30 days was the main measure.

RESULTS

C. difficile colitis occurred in 3323 patients (2.2%). Unadjusted C. difficile colitis rates ranged from 0% to 11.3% among surgeons and 0% to 6.8% among hospitals. After controlling for patient, surgeon, and hospital characteristics using mixed-effects multivariable analysis, significant unexplained variation in C. difficile rates remained present across hospitals but not surgeons. Patient factors explained only 24% of the total hospital-level variation, and known surgeon and hospital-level characteristics explained an additional 8% of the total hospital-level variation. Therefore, ≈70% of the hospital variation in C. difficile infection rates remained unexplained by captured patient, surgeon, and hospital factors. Furthermore, there was an ≈5-fold difference in adjusted C. difficile rates across hospitals.

LIMITATIONS

A limited set of hospital and surgeon characteristics was available.

CONCLUSIONS

Colorectal surgery patients appear to be at high risk for C. difficile infection, and alarming variation in nosocomial C. difficile infection rates currently exists among hospitals after colorectal resection. Given the high morbidity and cost associated with C. difficile colitis, adopting institutional quality improvement programs and maintaining strict prevention strategies are of the utmost importance.

Lowering levels of bed occupancy is associated with decreased inhospital mortality and improved performance on the 4-hour target in a UK District General Hospital

OBJECTIVE

To evaluate whether there is an association between an intervention to reduce medical bed occupancy and performance on the 4-hour target and hospital mortality.

METHODS

This before-and-after study was undertaken in a large UK District General Hospital over a 32 month period. A range of interventions were undertaken to reduce medical bed occupancy within the Trust. Performance on the 4-hour target and hospital mortality (hospital standardised mortality ratio (HSMR), summary hospital-level mortality indicator (SHMI) and crude mortality) were compared before, and after, intervention. Daily data on medical bed occupancy and percentage of patients meeting the 4-hour target was collected from hospital records. Segmented regression analysis of interrupted time-series method was used to estimate the changes in levels and trends in average medical bed occupancy, monthly performance on the target and monthly mortality measures (HSMR, SHMI and crude mortality) that followed the intervention.

RESULTS

Mean medical bed occupancy decreased significantly from 93.7% to 90.2% (p=0.02). The trend change in target performance, when comparing preintervention and postintervention, revealed a significant improvement (p=0.019). The intervention was associated with a mean reduction in all markers of mortality (range 4.5-4.8%). SHMI (p=0.02) and crude mortality (p=0.018) showed significant trend changes after intervention.

CONCLUSIONS

Lowering medical bed occupancy is associated with reduced patient mortality and improved ability of the acute Trust to achieve the 95% 4-hour target. Whole system transformation is required to create lower average medical bed occupancy.