Monitoring of nosocomial invasive aspergillosis and early evidence of an outbreak using cumulative sum tests (CUSUM)

Resurgence of Respiratory Syncytial Virus in Children: An Out-of-Season Epidemic in Portugal

INTRODUCTION

An out-of-season increase in respiratory syncytial virus (RSV) incidence was observed in Portugal from June 2021 onwards, revealing a continuing surge in cases throughout 2021/2022 autumn/winter. We aimed to describe this out-of-season epidemic and define its epidemic period, by analysing RSV incidence from week 40 of 2020 (2020-W40) to week 18 of 2022 (2022-W18).

MATERIAL AND METHODS

Surveillance data on weekly RSV laboratory confirmed cases, in Portugal, was used to monitor RSV incidence using CUSUM test methodology for count data.

RESULTS

In 2021-W23, the CUSUM score identified a significant increase in the risk of RSV. By that time, the percentage of RSV positive tests rose from 1% in 2021-W22 (3/265) to 6% in 2021-W23 (18/298). Despite a sharp decrease in RSV incidence on 2021-W33 and on 2022-W02, the CUSUM score stayed over the limit up to 2022-W07, indicating that the RSV activity remained at an epidemic level. Distinct peaks of RSV cases were observed between 2021-W30 and 2021-W32 (average of 77 RSV cases per week) and between 2021-W39 and 2021-W41 (average of 79 RSV cases per week) with positivity rates around 60%.

CONCLUSION

An out-of-season RSV epidemic was identified, with a longer epidemic period compared with previous seasons. Possible reasons include relaxation of COVID-19 physical distancing measures and a greater proportion of population susceptible to disease. As several factors may change the pattern of RSV activity, countries should implement year-round surveillance RSV surveillance systems. These findings might have an impact on public health planning regarding future RSV surges, namely, on the palivizumab prophylaxis period for high-risk infants.

Future Directions for Clinical Respiratory Fungal Research

There has been a growing appreciation of the importance of respiratory fungal diseases in recent years, with better understanding of their prevalence as well as their global distribution. In step with the greater awareness of these complex infections, we are currently poised to make major advances in the characterization and treatment of these fungal diseases, which in itself is largely a consequence of post-genomic technologies which have enabled rational drug development and a path towards personalized medicines. These advances are set against a backdrop of globalization and anthropogenic change, which have impacted the world-wide distribution of fungi and antifungal resistance, as well as our built environment. The current revolution in immunomodulatory therapies has led to a rapidly evolving population at-risk for respiratory fungal disease. Whilst challenges are considerable, perhaps the tools we now have to manage these infections are up to this challenge. There has been a welcome acceleration of the antifungal pipeline in recent years, with a number of new drug classes in clinical or pre-clinical development, as well as new focus on inhaled antifungal drug delivery. The "post-genomic" revolution has opened up metagenomic diagnostic approaches spanning host immunogenetics to the fungal mycobiome that have allowed better characterization of respiratory fungal disease endotypes. When these advances are considered together the key challenge is clear: to develop a personalized medicine framework to enable a rational therapeutic approach.

Use of the CUSUM and Shewhart control chart methods to identify changes of public health significance using childhood blood lead surveillance data

Supplemental Digital Content is available in the text.

Local, state, and national childhood blood lead surveillance is based on healthcare providers and clinical laboratories reporting test results to public health departments. Increased interest in detecting blood lead level (BLL) patterns and changes of potential public health significance in a timely manner has highlighted the need for surveillance systems to rapidly detect and investigate these events.

Automated detection of hospital outbreaks: A systematic review of methods

OBJECTIVES

Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results.

METHODS

We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies.

RESULTS

Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%.

CONCLUSION

Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.

Calibrated breast density methods for full field digital mammography: a system for serial quality control and inter-system generalization

PURPOSE

The authors are developing a system for calibrated breast density measurements using full field digital mammography (FFDM). Breast tissue equivalent (BTE) phantom images are used to establish baseline (BL) calibration curves at time zero. For a given FFDM unit, the full BL dataset is comprised of approximately 160 phantom images, acquired prior to calibrating prospective patient mammograms. BL curves are monitored serially to ensure they produce accurate calibration and require updating when calibration accuracy degrades beyond an acceptable tolerance, rather than acquiring full BL datasets repeatedly. BL updating is a special case of generalizing calibration datasets across FFDM units, referred to as cross-calibration. Serial monitoring, BL updating, and cross-calibration techniques were developed and evaluated.

METHODS

BL curves were established for three Hologic Selenia FFDM units at time zero. In addition, one set of serial phantom images, comprised of equal proportions of adipose and fibroglandular BTE materials (50/50 compositions) of a fixed height, was acquired biweekly and monitored with the cumulative sum (Cusum) technique. These 50/50 composition images were used to update the BL curves when the calibration accuracy degraded beyond a preset tolerance of ±4 standardized units. A second set of serial images, comprised of a wide-range of BTE compositions, was acquired biweekly to evaluate serial monitoring, BL updating, and cross-calibration techniques.

RESULTS

Calibration accuracy can degrade serially and is a function of acquisition technique and phantom height. The authors demonstrated that all heights could be monitored simultaneously while acquiring images of a 50/50 phantom with a fixed height for each acquisition technique biweekly, translating into approximately 16 image acquisitions biweekly per FFDM unit. The same serial images are sufficient for serial monitoring, BL updating, and cross-calibration. Serial calibration accuracy was maintained within ±4 standardized unit variation from the ideal when applying BL updating. BL updating is a special case of cross-calibration; the BL dataset of unit 1 can be converted to the BL dataset for another similar unit (i.e., unit 2) at any given time point using the 16 serial monitoring 50/50 phantom images of unit 2 (or vice versa) acquired near this time point while maintaining the ±4 standardized unit tolerance.

CONCLUSIONS

A methodology for monitoring and maintaining serial calibration accuracy for breast density measurements was evaluated. Calibration datasets for a given unit can be translated forward in time with minimal phantom imaging effort. Similarly, cross-calibration is a method for generalizing calibration datasets across similar units without additional phantom imaging. This methodology will require further evaluation with mammograms for complete validation.

Sound Levels, Staff Perceptions, and Patient Outcomes During Renovation Near the Neonatal Intensive Care Unit

Objective:

Sound levels, staff perceptions, and patient outcomes were evaluated during a year-long hospital renovation project on the floor above a neonatal intensive care unit (NICU).

Background:

Construction noise may be detrimental to NICU patients and healthcare professionals. There are no comprehensive studies evaluating the impact of hospital construction on sound levels, staff, and patients.

Methods:

Prospective observational study comparing sound measures and patient outcomes before, during, and after construction. Staff were surveyed about the construction noise, and hospital employee satisfaction scores are reported.

Results:

Equivalent sound levels were not significantly higher during construction. Most staff members (89%) perceived the renovation period as louder, and 83% reported interruptions of their work. Patient outcomes were the same or more positive during construction. Very low birth weight (VLBW infants were less likely to require 24+ hours' mechanical ventilation during construction: 54% vs. 59% before ( OR = 1.6, p = 0.018) and 62% after ( OR = 1.48, p = 0.065); and they required a shorter total period of mechanical ventilation: 3.6 days vs. 8.0 before ( p = 0.011) and 9.5 after ( p = 0.001). VLBW newborns' differences in ventilation days were mostly in the upper extremes; medians were similar in all periods: 0.6 days vs. 1 day preconstruction and 2 days postconstruction.

Conclusions:

Construction above the NICU did not cause substantially louder sound levels, but staff perceived important changes in noise and work routines. No evidence suggested that patients were negatively affected by the renovation period. Meticulous construction planning remains necessary to avoid interference with patient care and caregiver work environments.