Illinois Department of Public Health

Abstract. Quickflow (QF) is the fraction of rainfall that rapidly runs off to channels, a key element for understanding floods and designing control measures. It reflects rapid runoff pathways that drive peak flows, sediment pulses, water-quality downstream impacts, and hazards. QF is influenced by the precipitation regime (seasonality and event concentration) and by surface properties represented by land use/land cover (LULC) and soil types, which modulate infiltration and storage. We applied the InVEST–Seasonal Water Yield (SWY) model at 30 m resolution in the San Fernando–Soto la Marina basin. We used monthly climate, 2020 LULC, and soil type data, and analyzed (i) the basin-wide distribution of QF, (ii) the spatial influence of LULC- and soil-based on QF, and (iii) the precipitation–QF relationship. Results show a multimodal distribution of QF ratios; urban areas, bare soils, and low-cover croplands yield higher QF than woody covers; and the precipitation–QF relationship is positive but dispersed, modulated by the sequence and intensity of events and by surface conditions. We conclude that integrated land and water management should (a) focus on targeted interventions in high-QF zones that maintain or improve vegetation cover and promote infiltration to reduce flooding and erosion and promote increased dry season flows and (b) integrate improved temporal and spatial representation of precipitation events with realistic LULC and soil parameterizations for more accurate model outputs and reliable planning.

In order to learn distributed port-Hamiltonian systems (dpHs) using Gaussian processes (GPs), the partitioned finite element method (PFEM) is combined with the Gp-dpHs method. By following a late lumping approach, the discretization of the functional hyperparameters of the GP prior over the Hamiltonian functional is chosen independently from the discretization of the dpHs, thus reducing the numerical complexity of our method. We next model the mean of the GP prior of the Hamiltonian as a quadratic form, enabling the GP kernel to focus on the nonlinear part of a given dpHs. We illustrate our method on a nonlinear one dimensional wave equation with unknown physical parameters (tension and linear mass).

Point-of-care, immunochromatographic tests have not reliably detected rabies virus and have not utilized a consistent diagnostic protocol. International diagnostic standards established by the World Organization for Animal Health require strategic and large-scale validation studies prior to widescale use. The United States (US) Centers for Disease Control and Prevention, National Rabies Reference Laboratory undertook a large-scale validation of the BioNote Anigen Rapid Rabies Antigen Test in collaboration with eight US public health laboratories and three international laboratories. Modifications were made to manufacturer instructions to maximize antigen concentration in the test diluent. A total of 1,399 samples underwent paired testing with one of three US gold standard tests, consisting of 31 types of animals and 9 rabies virus variants. Test sensitivity and specificity was 97.1% (CI: 95.2% - 98.3%) and 99.9% (CI: 99.4% - 100.0%). Fourteen samples resulted in false-negative results, primarily impacting dogs that were euthanized early or shown to have a low viral load. Limit of detection studies found that false-negative results often occurred when the sample had a PCR Ct value >23. The BioNote Anigen Test performed well across diverse RVVs found in North America. Sensitivity of the test was slightly lower than, but not statistically inferior to, the minimum 98% value established for the current gold-standard tests. This is the largest systematic evaluation of a rabies point-of-care test that includes diverse RVV and animal type and results suggest that the BioNote Anigen Test with the procedural changes would have broad benefits for rapid diagnosis in animals.

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM10, PM2.5, CO and O3) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R2) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records.

Background:Metal exposures impact children’s intellectual functioning from pregnancy through early childhood and beyond, being historically evaluated with single-pollutant models which might create errors estimating individual metal impacts beyond other correlated metals which arise from the same shared sources.Aim:We evaluated the effect of exposure to non-essential and essential metals on the cognitive function of Mexican children at 48 months of age.Methodology:We included persons from the Programming Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) longitudinal birth cohort in Mexico City with biomarker data on 13 non-essential (lead, cadmium, mercury, arsenic, strontium, barium, and cesium) and essential (manganese, copper, selenium, molybdenum, magnesium, and zinc) metals during pregnancy and early childhood. We assessed the child’s intellectual functioning with McCarthy Scales of Children’s Abilities. We created a multi-media biomarker (MMB) index utilizing the Weighted Quantile Sum Regression (WQS) method. We utilized generalized linear models to estimate the change in the General Cognitive Index (GCI) per interquartile increase in the MMB index(β) and corresponding 95 % confidence intervals(CIs) in single and multipollutant models.Results:After adjusting for confounders in multipollutant models, MMB-cadmium (β: −1.52, 95 %CI: −3.03 −0.01) and MMB-arsenic (β: −1.53; 95 %CI: −3.02, −0.03) showed significant negative effects on intellectual functioning. In contrast, MMB-manganese (β: 2.28; 95 %CI: 0.77, 3.77), MMB-selenium (β:2.07, 95 %CI: 0.69, 3.43), and MMB-molybdenum (β: 1.76; 95 %CI: 0.21, 3.31) were the metals with the highest positive effects. We did find variations comparing results from single and multi-pollutant models, indicating the presence of multipollutant confounding.Conclusion:Our study adds to the current body of literature about the co-pollutant confounding problem of evaluating metals and children’s intellectual functioning, as well as the effect of understudied toxic metals of public health interest, such as barium and cesium as candidates that warrant further investigation for a possible role in children’s intellectual functioning.

INTRODUCTIONAge‐related cognitive decline occurs, in part, due to diminishing white matter integrity. Higher cardiorespiratory fitness (CRF) is associated with better cognitive performance, but the neurobiological mechanisms underlying this association remain uncertain. Previous magnetic resonance imaging (MRI) studies have suggested that CRF‐related changes in white matter microstructure might prevent or slow age‐related cognitive decline, but have been limited by small sample sizes and methodological limitations. Specifically, most prior studies used tensor‐based diffusion‐weighted MRI metrics, which are insensitive to complex white matter architectures, including crossing fibers.METHODSHere, we leveraged a novel analysis framework capable of resolving individual fiber populations at the within‐voxel level—fixel‐based analysis (FBA)—to analyze three metrics of white matter organization from diffusion‐weighted MRI scans: fiber density (FD), fiber cross‐section (FC), and their combined measure (FDC). Using a cross‐sectional sample of 636 cognitively unimpaired older adults aged 65 to 80 years (mean age = 69.8 years; 71% female), we hypothesized that FBA metrics would be associated with CRF and that this variation in FBA metrics would mediate associations between CRF and cognitive performance.RESULTSIn whole‐brain analyses, higher CRF was associated with greater FD, FC, and FDC. Furthermore, these FBA‐derived metrics statistically mediated the relationship between CRF and cognitive performance in the domains of visuospatial abilities, processing speed, working memory, and executive function/attentional control, but not episodic memory.DISCUSSIONThese findings highlight the potential for CRF in the preservation of multiple aspects of cognition as a function of white matter micro‐ and macro‐structural properties. Our results provide novel insights into the neurobiological mechanisms of fitness‐related cognitive resilience.HighlightsHigher cardiorespiratory fitness (CRF) is linked to better white matter integrity in older adults.Fixel‐based analysis reveals CRF associations with fiber density and cross‐section.White matter properties mediate CRF effects on cognition, excluding episodic memory.Findings suggest CRF supports cognitive resilience via white matter organization.

Background:Although medication for opioid use disorder (MOUD) is the most effective form of treatment for reducing opioid misuse and related fatalities, programs continue to struggle to reach and engage those with relatively severe disorders and most in need of treatment.Objectives:This study assessed whether providing a rapid medical appointment conducted via telehealth could improve MOUD linkage versus the standard model of an in-person referral.Methods and Design:We recruited participants from a syringe service prevention program (SSP) and the surrounding communities, randomly assigning them to have an initial medical examination via telemedicine (n = 136) or an in-person (n = 135) referral. We administered structured interviews and collected urine samples for drug testing at baseline and 90 days. The primary outcome was treatment linkage within 14 days of study enrollment, measured by attendance at their first in-person appointment. Secondary outcomes included MOUD engagement (34 days), 3-month retention, and self-reported and urine-detected non-MOUD opioid use at 3-month follow-up.Findings:Contrary to expectations, telemedicine participants had lower odds of a successful linkage (aOR=0.42, CI=0.36, 0.49) than control condition participants. A significant interaction between the treatment condition and the timing of the initial treatment appointment indicated that having a treatment appointment scheduled within 48 hours of the medical evaluation had a more pronounced and positive impact than having a telehealth evaluation (aOR=2.9, CI=2.04, 4.12). There were no other significant differences for any secondary outcomes.Conclusions:Telemedicine participants were less successfully linked to MOUD than standard referrals, likely owing to experiencing longer delays between their medical evaluation and first in-person treatment appointment compared with those having an in-person medical evaluation. More effective alternatives for coordinating MOUD and SSP services include co-locating MOUD within an SSP or full delivery of MOUD via telemedicine at an SSP, both of which would further obviate delays in treatment initiation.Trial Registration:ClinicalTrials.gov (NCT04575324)

BackgroundIn Illinois, an average of 88 pregnant and postpartum individuals died annually from 2018 to 2020. Of these, 66% had at least one emergency department (ED) visit. To improve maternal health outcomes, the Illinois Maternal Mortality Review Committees recommended ED education on obstetric care, mental health conditions, and substance use disorders (SUD).MethodsFrom January 1, 2023, to June 30, 2024, the Maternal Health Emergency Department Toolkit (Toolkit) training was piloted in six diverse Illinois hospitals. The Toolkit's effectiveness was evaluated using electronic medical record (EMR) data to assess changes in practice within the ED.ResultsA total of 281 (63%) ED providers and staff completed the training. From pre‐ to post‐pilot, EMR documentation of pregnancy or postpartum status in female patients of reproductive age increased from 56.4% to 83.4%, and screening pregnant and postpartum patients for mental health or SUD rose from 52.2% to 69.6%.ConclusionsThe Toolkit is an evidence‐based training of educational modules that led to positive changes in ED provider and staff's practice when caring for pregnant and postpartum patients. By fostering education, engagement, and collaboration, the Toolkit has the potential to play a key role in helping reduce maternal mortality and morbidity in Illinois. The Toolkit is now available statewide for all Illinois EDs.