We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.

We synthesize 10 topics within climate research where there have been significant advances or emerging scientific consensus since January 2021. The selection of these insights was based on input from an international open call with broad disciplinary scope. Findings concern: (1) new aspects of soft and hard limits to adaptation; (2) the emergence of regional vulnerability hotspots from climate impacts and human vulnerability; (3) new threats on the climate–health horizon – some involving plants and animals; (4) climate (im)mobility and the need for anticipatory action; (5) security and climate; (6) sustainable land management as a prerequisite to land-based solutions; (7) sustainable finance practices in the private sector and the need for political guidance; (8) the urgent planetary imperative for addressing losses and damages; (9) inclusive societal choices for climate-resilient development and (10) how to overcome barriers to accelerate mitigation and limit global warming to below 2°C.

Science has evidence on barriers to mitigation and how to overcome them to avoid limits to adaptation across multiple fields.

To study the impact of duration of mechanical ventilation, hospitalization and multiple ventilation episodes on the development of pneumonia while accounting for extubation as a competing event.

A multicenter data base from a Spanish surveillance network was used to conduct a retrospective analysis of prospectively collected intensive care patients followed from admission to discharge.

Spanish intensive care units (ICUs).

Mechanically ventilated adult patients from 158 ICUs with 45,486 admissions, 48,705 ventilation episodes, and 314,196 ventilator days.

Competing-risk models were applied to account for extubation plus 48 hours as a competing event for acquiring ventilator-associated pneumonia (VAP).

Time in the ICU before mechanical ventilation was associated with an increased VAP hazard rate and with longer intubation time. This indirect prolongation of intubation increased the cumulative risk to eventually acquire VAP. For instance, comparing 3–4 versus 0 days, the adjusted VAP hazard ratio was 1.40 (95% confidence interval [CI], 1.19–1.64) and the adjusted extubation hazard ratio was 0.64 (95% CI, 0.61–0.68), which leads to an adjusted VAP subdistribution hazard ratio (sHR) of 2.13 (95% CI, 1.83–2.50). Similarly, due to prolonged intubation, multiple ventilation episodes increase the risk for VAP; the adjusted sHR is 1.52 (95% CI, 1.35–1.72) for the second episode compared to the first episode, and the adjusted sHR is 1.54 (95% CI, 1.03–2.30) for the third episode compared to the first episode. The Kaplan-Meier method produced an upward biased estimated cumulative risk for VAP.

A competing-risk analysis is necessary to receive unbiased risk estimates and to quantify the indirect effect of intubation time on the cumulative VAP risk. Our findings may guide physicians to improve medical decisions related to the harms and benefits of the duration of ventilation.

Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.

Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.

The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.

Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

Multistate and competing risks models have become an established and adequate tool with which to quantify determinants and consequences of nosocomial infections. In this tutorial article, we explain and demonstrate the basics of these models to a broader audience of professionals in health care, infection control, and hospital epidemiology.

Using a publicly available data set from a cohort study of intensive care unit patients, we show how hospital infection data can be displayed and explored graphically and how simple formulas are derived under some simplified assumptions for illustrating the basic ideas behind multistate models. Only a few simply accessible values (event counts and patient days) and a pocket calculator are needed to reveal basic insights into cumulative risk and clinical outcomes of nosocomial infection in terms of mortality and length of stay.

We show how to use these values to perform basic multistate analyses in own data or to correct biased estimates in published data, as these values are often reported. We also show relationships between multistate-based hazard ratios and odds ratios, which are derived from the popular logistic regression model.

No sophisticated statistical software is required to apply a basic multistate model and to avoid typical pitfalls such as time-dependent or competing-risks bias.

Infect Control Hosp Epidemiol 2017;38:953–959

To assess the representativeness of the data in the Krankenhaus Infektions Surveillance System (KISS), which is a nosocomial infections surveillance system for intensive care units (ICUs) in Germany.

Prospective and retrospective surveillance study.

Medical-surgical ICUs in Germany.

A sample of medical-surgical ICUs from all over Germany, stratified according to hospital size, was randomly selected. Surveillance personnel from the hospitals were trained in surveillance of nosocomial infections, and they subsequently conducted a 2-month surveillance in their ICUs. Data were compared with KISS data for medical-surgical ICUs.

During the period from 2004 through 2005, a total of 50 medical-surgical ICUs agreed to participate in our study: 21,832 patient-days were surveyed, and 262 cases of nosocomial infection were registered, 176 of which were cases of device-associated nosocomial infection (100 cases of lower respiratory tract infection, 47 cases of urinary tract infection, and 29 cases of bloodstream infection). The overall incidence density of all types of nosocomial infections was estimated to be 10.65 cases per 1,000 patient-days. Device utilization rates in the study ICUs and in the KISS medical-surgical ICUs were similar. The pooled mean device-associated infection rates were higher in the study ICUs than in the KISS medical-surgical ICUs (10.2 vs 5.1 cases of pneumonia; 2.0 vs 1.2 cases of bloodstream infection; and 2.7 vs 1.2 cases of urinary tract infection), but the pooled mean device-associated infection rates in the study ICUs were comparable to those of the KISS ICUs during their first year of participation in KISS. The incidence density for nosocomial infections in the study ICUs varied according hospital size, with ICUs in larger hospitals having a higher incidence density than those in smaller hospitals.

KISS ICUs started with nosocomial infection rates comparable to those found in our study ICUs. Over the years of participation, however, a decrease in nosocomial infections is seen. Thus, rates of nosocomial infection from KISS should be used as benchmarks, but estimations for Germany that are based on KISS data may underestimate the real burden of nosocomial infections.

To investigate whether the correlation between patients' antibiotic treatment (yes/no) and patients' infections (yes/no) in each hospital department, described by Pearson's correlation coefficient (ρ) for binary data as a measure for adequate use of antibiotics, is an appropriate quality indicator.

Comparison of the results of repeated prevalence studies in different hospitals with the data of a national prevalence study, comparing the hospital (ρ) and reference (ρNIDHP[Nosokomiale Infektionen in Deutschland: Erfassung und Prävention]) correlation coefficients for “use of antibiotics/ presence of infections.”

The data of 5,377 surgical patients were separated from the total data of a national prevalence study in 72 representative hospitals to create a reference correlation coefficient (ρNIDEP) with a reference range. Nine additional prevalence studies, involving a total of 4,984 patients, were repeatedly performed in the surgical departments of 8 other hospitals during a 12-month period, whereby the correlation coefficients ρn for every prevalence investigation were determined.

In the national prevalence study, 15.3% of the surgical patients received antibiotics on the study day. Surgical patients had a 3.8% prevalence of nosocomial infections and a 7.0% prevalence of community-acquired infections. Pearson's correlation coefficient ρNIDEP for correlation between patients' binary data use of antibiotics and presence of infection was 0.62. To compare the correlation coefficient of each department with the appropriate reference range, the coefficients of the single departments were plotted against the number of patients; in these plots, three lines indicated the value ρNIDEP and the upper and lower reference ranges, depending on the number of patients. Seven of eight surgical departments investigated during the repeated prevalence studies were found to be within the reference range, near the reference value, in the majority of prevalence studies; only one of the departments was identified as an outlier as regards antibiotic use.

The correlation between patients' antibiotic treatment (yes/no) and patients' infections (yes/no) in hospitals or departments, as described by Pearson's correlation coefficient ρ for binary data with a definitive reference range depending on the number of patients, is useful for quality management in identifying the overall necessity for evaluating the indications for antibiotic use in one's own hospital.

To determine whether an investigator effect remained on the first German study on the prevalence of nosocomial infections Nosokomiale Infektionen in Deutschland Erfassung und Prävention (NIDEP), despite extensive validation efforts.

Two validation methods were applied: bedside validation and validation by case studies. In both cases, the results of the four investigators were compared with the diagnosis of gold standard observers.

Validation measures were applied before, intermittently, during, and at the end of the surveillance period in 72 acute-care hospitals with 14,966 patients.

The overall sensitivity in the bedside-validation periods was 89.0%; the overall specificity was 99.5%. For validation by case studies, overall sensitivity was 95.6%, and overall specificity was 92.8%. At the end of the surveillance, a remarkable investigator effect was found.

Despite validation results that were assessed as satisfactory, based on available literature, an investigator effect was observed. This underlines the need for data validation and the formulation of recommendations for data validation. Clarification of the Centers for Disease Control and Prevention criteria for pneumonia and primary bloodstream infection and the inclusion of some diagnostic test results may reduce or prevent an investigator effect in future studies.

To demonstrate the limits of comparison of national prevalence rates of nosocomial infections.

Critical analysis of prevalence rates and methods of the Nosocomial Infections in Germany (NIDEP) study and other prevalence surveys with particular attention to the selection of patients, the qualification and training of the investigators, and the methods of identifying nosocomial infections.

The lowest prevalence rate was found in Germany (3.5 %), the highest in Belgium (9.3 %). These differences may not be accurate, because variations in methods allow for differing explanations.

Because of numerous methodological factors, comparison of infection rates between countries should be avoided. In contrast to other prevalence studies, the methodology of the German-NIDEP study permits registration of only certain infections, which is the main reason for the low rate.