Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

skater: an R package for SNP-based kinship analysis, testing, and evaluation

Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed. Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.

Major cardiorespiratory events do not increase after immunizations, eye exams, and other stressors in most very low birth weight infants

BACKGROUND

Increased cardiorespiratory events with bradycardia and oxygen desaturation have been reported in very low birthweight (VLBW) infants following stressors such as immunizations. These events are difficult to quantify and may be mild. Our group developed an automated algorithm to analyze bedside monitor data from NICU patients for events with bradycardia and prolonged oxygen desaturation (BDs) and used this to compare BDs 24 hours before and after potentially stressful interventions.

METHODS

We included VLBW infants from 2012-2017 with data available around at least one of four interventions: two-month immunizations, retinopathy of prematurity (ROP) examinations, ROP therapy, and inguinal hernia surgery. We used a validated algorithm to analyze electrocardiogram heart rate and pulse oximeter saturation data (HR, SpO2) to quantify BD events of HR <  100 beats/minute for≥4 seconds with oxygen desaturation <  80%SpO2 for≥10 seconds. BDs were analyzed 24 hours before and after interventions using Wilcoxon rank-sum tests.

RESULTS

In 354 of 493 (72%) interventions, BD frequency stayed the same or decreased in the 24 hours after the event. An increase of at least five BD's occurred in 17/146 (12%) after immunizations, 85/290 (29%) after ROP examinations, 4/33 (12%) after ROP therapy, and 3/25 (12%) after hernia surgery. Infants with an increase in BDs after interventions had similar demographics compared to those without. More infants with an increase in BDs following immunizations were on CPAP or caffeine than those without.

CONCLUSIONS

Most VLBW infants in our cohort had no increase in significant cardiorespiratory events in the 24 hours following potentially stressful interventions.

Blood pressure ranges via non-invasive and invasive monitoring techniques in premature neonates using high resolution physiologic data

BACKGROUND

There are limited evidence-based published blood pressure ranges for premature neonates. The aim of the study was to determine blood pressure ranges in a large cohort of premature neonates based on gestational and post-menstrual age.

METHODS

Retrospective observational study of premature neonates admitted to the neonatal intensive care unit at our institution between January 2009 and October 2015. We stratified data by gestational and post-menstrual age groups as well as by method of blood pressure measurement (non-invasive vs. invasive).

RESULTS

Over two billion blood pressure values in 1708 neonates were analyzed to generate heat maps and establish percentile-based reference ranges. The median gestational age of the cohort was 31 weeks (IQR 28-33 weeks). We found moderate correlation (r = 0.57) between simultaneously obtained non-invasive and invasive blood pressure measurements.

CONCLUSIONS

Our results can serve as a reference during the bedside assessment of the critically-ill neonate.

Clinical and vital sign changes associated with late-onset sepsis in very low birth weight infants at 3 NICUs

BACKGROUND

In premature infants, clinical changes frequently occur due to sepsis or non-infectious conditions, and distinguishing between these is challenging. Baseline risk factors, vital signs, and clinical signs guide decisions to culture and start antibiotics. We sought to compare heart rate (HR) and oxygenation (SpO2) patterns as well as baseline variables and clinical signs prompting sepsis work-ups ultimately determined to be late-onset sepsis (LOS) and sepsis ruled out (SRO).

METHODS

At three NICUs, we reviewed records of very low birth weight (VLBW) infants around their first sepsis work-up diagnosed as LOS or SRO. Clinical signs prompting the evaluation were determined from clinician documentation. HR-SpO2 data, when available, were analyzed for mean, standard deviation, skewness, kurtosis, and cross-correlation. We used LASSO and logistic regression to assess variable importance and associations with LOS compared to SRO.

RESULTS

We analyzed sepsis work-ups in 408 infants (173 LOS, 235 SRO). Compared to infants with SRO, those with LOS were of lower GA and BW, and more likely to have a central catheter and mechanical ventilation. Clinical signs cited more often in LOS included hypotension, acidosis, abdominal distension, lethargy, oliguria, and abnormal CBC or CRP(p <  0.05). HR-SpO2 data were available in 266 events. Cross-correlation HR-SpO2 before the event was associated with LOS after adjusting for GA, BW, and postnatal age. A model combining baseline, clinical and HR-SpO2 variables had AUC 0.821.

CONCLUSION

In VLBW infants at 3-NICUs, we describe the baseline, clinical, and HR-SpO2 variables associated with LOS versus SRO.

883. Evidence from a Multistate Cohort: Enrollment in Affordable Care Act Qualified Health Plans Results in Viral Suppression

Background

In individual states, the Patient Protection and Affordable Care Act has been associated with improved viral suppression (VS) rates for AIDS Drug Assistance Program (ADAP) clients or low-income people living with HIV (PLWH). This study aims to assess whether this association is consistent in multiple states (Nebraska, South Carolina, Virginia).

Methods

The multistate cohort included ADAP clients who were eligible for ADAP-funded Qualified Health Plans (QHPs). Data were collected from 2014 through 2015. A log-binomial model was used to estimate the association of demographics (age, race/ethnicity, sex, AIDS, rurality, HIV risk factor, previous VS) and healthcare delivery factors (income, previous ADAP plan, previous HIV care engagement) with QHP enrollment prevalence and 1-year risk of VS.

Results

For the cohort (n = 7,800; 5% NE, 36% SC, 59% VA), 52% enrolled in ADAP-funded QHPs with enrollment ranging from 35% to 63% by state. Enrollment in ADAP-funded QHPs in 2015 was higher for those who had ADAP-funded QHPs in 2014 (adjusted prevalence ratio [aPR] 3.28; 95% confidence interval [CI] 3.21–3.35) and those who were engaged in care in 2014 (aPR 1.16; 95% CI 1.05–1.27), and it was lower for those with a rural residence (aPR 0.91; 95% CI 0.81–1.00). Of those who were consistently engaged in care (n = 4,597), as defined by one viral load in 2014 and one viral load in 2015 separated by at least 180 days, those who received medications from Direct ADAP had a VS rate of 80.2% and those with ADAP-funded QHPs had a VS rate of 86.0%. The number needed to enroll in ADAP-funded QHPs for an additional PLWH to achieve VS is 18. Those who achieved VS in 2014 (adjusted risk ratio [aRR] 1.39, 95% CI 1.30–1.48) and those who enrolled in QHPs in 2015 (aRR 1.06, 95% CI 0.99–1.13) were more likely to achieve/maintain VS.

Conclusion

Additional efforts should be made to reach rural PLWH for QHP enrollment. State ADAPs, especially those in the South and those in states without Medicaid expansion, should consider investing in purchasing QHPs for PLWH because increased enrollment could improve VS rates. This evidence-based intervention could be a part of “Ending the HIV Epidemic.” Once ADAP clients are enrolled in ADAP-funded QHPs, they stay enrolled, and QHP enrollment is associated with VS across states and demographic groups.

Disclosures

All Authors: No reported Disclosures.

LOLAweb: a containerized web server for interactive genomic locus overlap enrichment analysis

The past few years have seen an explosion of interest in understanding the role of regulatory DNA. This interest has driven large-scale production of functional genomics data and analytical methods. One popular analysis is to test for enrichment of overlaps between a query set of genomic regions and a database of region sets. In this way, new genomic data can be easily connected to annotations from external data sources. Here, we present an interactive interface for enrichment analysis of genomic locus overlaps using a web server called LOLAweb. LOLAweb accepts a set of genomic ranges from the user and tests it for enrichment against a database of region sets. LOLAweb renders results in an R Shiny application to provide interactive visualization features, enabling users to filter, sort, and explore enrichment results dynamically. LOLAweb is built and deployed in a Linux container, making it scalable to many concurrent users on our servers and also enabling users to download and run LOLAweb locally.

epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease.  epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease.  epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

epicontacts: Handling, visualisation and analysis of epidemiological contacts

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

epicontacts: Handling, visualisation and analysis of epidemiological contacts

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

Abstract P3-13-03: Bilateral mastectomy and immediate reconstruction are associated with increased postoperative complications

This abstract was withdrawn by the authors.