Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients.

BACKGROUND: Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients. METHODS: We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt). We used Bayesian inference to adjust SIRS criteria for transplant recipients. RESULTS: We analyzed 38.7 million hourly measurements from 41 725 patients at UVA, including 1897 transplant recipients with 193 episodes of BSI and 53 608 patients at Pitt, including 1614 transplant recipients with 768 episodes of BSI. The univariate responses to BSI were comparable in transplant and non-transplant recipients. The area under the receiver operating characteristic curve (AUC) was 0.82 (95% confidence interval [CI], .80-.83) for the model using all UVA patient data and 0.80 (95% CI, .76-.83) when using only transplant recipient data. The UVA all-patient model had an AUC of 0.77 (95% CI, .76-.79) in non-transplant recipients and 0.75 (95% CI, .71-.79) in transplant recipients at Pitt. The relative importance of the 27 predictors was similar in transplant and non-transplant models. An upper temperature of 37.5°C in SIRS criteria improved reclassification performance in transplant recipients. CONCLUSIONS: Critically ill transplant and non-transplant recipients had similar responses to BSI. An upper temperature of 37.5°C in SIRS criteria improved BSI screening in transplant recipients.

Solar ultraviolet radiation exposure, and incidence of childhood acute lymphocytic leukaemia and non-Hodgkin lymphoma in a US population-based dataset.

BACKGROUND: Acute lymphocytic leukaemia (ALL) and non-Hodgkin lymphoma (NHL) are among the commonest types of childhood cancer. Some previous studies suggested that elevated ultraviolet radiation (UVR) exposures increase ALL risk; many more indicate NHL risk is reduced. METHODS: We assessed age<20 ALL/NHL incidence in Surveillance, Epidemiology and End Results data using AVGLO-derived UVR irradiance/cumulative radiant exposure measures, using quasi-likelihood models accounting for underdispersion, adjusted for age, sex, racial/ethnic group and other county-level socioeconomic variables. RESULTS: There were 30,349 cases of ALL and 8062 of NHL, with significant increasing trends of ALL with UVR irradiance (relative risk (RR) = 1.200/mW/cm2 (95% CI 1.060, 1.359, p = 0.0040)), but significant decreasing trends for NHL (RR = 0.646/mW/cm2 (95% CI 0.512, 0.816, p = 0.0002)). There was a borderline-significant increasing trend of ALL with UVR cumulative radiant exposure (RR = 1.444/MJ/cm2 (95% CI 0.949, 2.197, p = 0.0865)), and significant decreasing trends for NHL (RR = 0.284/MJ/cm2 (95% CI 0.166, 0.485, p < 0.0001)). ALL and NHL trend RR is substantially increased among those aged 0-3. All-age trend RRs are most extreme (increasing for ALL, decreasing for NHL) for Hispanics for both UVR measures. CONCLUSIONS: Our more novel finding, of excess UVR-related ALL risk, is consistent with some previous studies, but is not clear-cut, and in need of replication.

Accuracy of a Risk Alert Threshold for ICU Hypoglycemia: Retrospective Analysis of Alert Performance and Association With Clinical Deterioration Events.

OBJECTIVES: To quantify the accuracy of and clinical events associated with a risk alert threshold for impending hypoglycemia during ICU admissions. DESIGN: Retrospective electronic health record review of clinical events occurring greater than or equal to 1 and less than or equal to 12 hours after the hypoglycemia risk alert threshold was met. SETTING: Adult ICU admissions from June 2020 through April 2021 at the University of Virginia Medical Center. PATIENTS: Three hundred forty-two critically ill adults that were 63.5% male with median age 60.8 years, median weight 79.1 kg, and median body mass index of 27.5 kg/m2. INTERVENTIONS: Real-world testing of our validated predictive model as a clinical decision support tool for ICU hypoglycemia. MEASUREMENTS AND MAIN RESULTS: We retrospectively reviewed 350 hypothetical alerts that met inclusion criteria for analysis. The alerts correctly predicted 48 cases of level 1 hypoglycemia that occurred greater than or equal to 1 and less than or equal to 12 hours after the alert threshold was met (positive predictive value = 13.7%). Twenty-one of these 48 cases (43.8%) involved level 2 hypoglycemia. Notably, three myocardial infarctions, one medical emergency team call, 19 deaths, and 20 arrhythmias occurred greater than or equal to 1 and less than or equal to 12 hours after an alert threshold was met. CONCLUSIONS: Alerts generated by a validated ICU hypoglycemia prediction model had a positive predictive value of 13.7% for real-world hypoglycemia events. This proof-of-concept result suggests that the predictive model offers clinical value, but further prospective testing is needed to confirm this.

Race, Class, and Place Modify Mortality Rates for the Leading Causes of Death in the United States, 1999-2021.

BACKGROUND: Race and ethnicity, socioeconomic class, and geographic location are well-known social determinants of health in the US. Studies of population mortality often consider two, but not all three of these risk factors. OBJECTIVES: To disarticulate the associations of race (whiteness), class (socioeconomic status), and place (county) with risk of cause-specific death in the US. DESIGN: We conducted a retrospective analysis of death certificate data. Bayesian regression models, adjusted for age and race/ethnicity from the American Community Survey and the county Area Deprivation Index, were used for inference. MAIN MEASURES: County-level mortality for 11 leading causes of death (1999-2019) and COVID-19 (2020-2021). KEY RESULTS: County "whiteness" and socioeconomic status modified death rates; geospatial effects differed by cause of death. Other factors equal, a 20% increase in county whiteness was associated with 5-8% increase in death from three causes and 4-15% reduction in death from others, including COVID-19. Other factors equal, advantaged counties had significantly lower death rates, even when juxtaposed with disadvantaged ones. Patterns of residual risk, measured by spatial county effects, varied by cause of death; for example: cancer and heart disease death rates were better explained by age, socioeconomic status, and county whiteness than were COVID-19 and suicide deaths. CONCLUSIONS: There are important independent contributions from race, class, and geography to risk of death in the US.

Impact of race on heart rate characteristics monitoring in very low birth weight infants.

BACKGROUND: A multicenter RCT showed that displaying a heart rate characteristics index (HRCi) predicting late-onset sepsis reduced mortality for VLBW infants. We aimed to assess whether HRCi display had a differential impact for Black versus White infants. METHODS: We performed secondary data analysis of Black and White infants enrolled in the HeRO RCT. We evaluated the predictive performance of the HRCi for infants with Black or White maternal race. Using models adjusted for birth weight, we assessed outcomes and interventions for a race × randomization interaction. RESULTS: Among 2607 infants, Black infants had lower birth weight, gestational age, length of stay, and ventilator days, while sepsis and mortality were similar. The HRCi performed equally for sepsis prediction in Black and White infants. We found no differential effect of randomization by race on sepsis, mortality, antibiotic days, length of stay, or ventilator days. However, there was a differential randomization effect by race for blood cultures per patient: White RR 1.11 (95% CrI 1.04-1.18), Black RR 1.00 (0.93-1.07). CONCLUSIONS: The HRCi performed similarly for sepsis prediction in Black and White infants. Randomization to HRCi display increased blood cultures in White but not in Black infants, while the impact on other outcomes or interventions was similar. IMPACT: Predictive analytics, such as heart rate characteristics (HRC) monitoring for late-onset neonatal sepsis, should have equal impact among patients of different race. Infants with Black or White maternal race randomized to HRC display had similar outcomes, but randomization to the study arm increased a related clinical intervention, blood cultures, in White but not in Black infants. This study provides evidence of a differential effect of predictive models on clinical care by race. The work will promote consideration and analysis of equity in the implementation of predictive analytics.

Partitioning macroscale and microscale ecological processes using covariate-driven non-stationary spatial models.

Ecological inference requires integrating information across scales. This integration creates a complex spatial dependence structure that is most accurately represented by fully non-stationary models. However, ecologists rarely use these models because they are difficult to estimate and interpret. Here, we facilitate the use of fully non-stationary models in ecology by improving the interpretability of a recently developed non-stationary model and applying it to improve our understanding of the spatial processes driving lake eutrophication. We reformulated a model that incorporates non-stationary correlation by adding environmental predictors to the covariance function, thereby building on the intuition of mean regression. We created ellipses to visualize how data at a given site correlate with their surroundings (i.e., the range and directionality of underlying spatial processes). We applied this model to describe the spatial dependence structure of variables related to lake eutrophication across two different regions: a Midwestern United States region with highly agricultural landscapes, and a Northeastern United States region with heterogeneous land use. For the Midwest, increases in forest cover increased the homogeneity of the residual spatial structure of total phosphorus, indicating that macroscale processes dominated this nutrient's spatial structure. Conversely, high forest cover and baseflow reduced the spatial homogeneity of chlorophyll a residuals, indicating that microscale processes dominated for chlorophyll a in the Midwest. In the Northeast, increases in urban land use and baseflow decreased the homogeneity of phosphorus concentrations indicating the dominance of microscale processes, but none of our covariates were strongly associated with the residual spatial structure of chlorophyll a. Our model showed that the spatial dependence structure of environmental response variables shifts across space. It also helped to explain this structure using ecologically relevant covariates from different scales whose effects can be interpreted intuitively. This provided novel insight into the processes that lead to eutrophication, a complex and pervasive environmental issue.

Spatially varying age-period-cohort analysis with application to US mortality, 2002-2016.

Many public health databases index disease counts by age groups and calendar periods within geographic regions (e.g., states, districts, or counties). Issues around relative risk estimation in small areas are well-studied; however, estimating trend parameters that vary across geographic regions has received less attention. Additionally, small counts (e.g., $<10$) in most publicly accessible databases are censored, further complicating age-period-cohort (APC) analysis in small areas. Here, we present a novel APC model with left-censoring and spatially varying intercept and trends, estimated with correlations among contiguous geographic regions. Like traditional models, our model captures population-scale trends, but it can also be used to characterize geographic disparities in relative risk and age-adjusted trends over time. To specify the joint distribution of our three spatially varying parameters, we adapt the generalized multivariate conditional autoregressive prior, previously used for multivariate disease mapping. Specified in this manner, region-specific parameters are correlated spatially, and also to one another. Estimation is performed using the No-U-Turn Hamiltonian Monte Carlo sampler in Stan. We conduct a simulation study to assess the performance of the proposed model relative to the standard model, and conclude with an application to US state-level opioid overdose mortality in men and women aged 15-64 years.

Heterogeneity of colon and rectum cancer incidence across 612 SEER counties, 2000-2014.

Recent analyses have suggested decreases over time in colorectal cancer incidence at older ages (≥55 years) but increases at younger ages (20-54 years). Understanding the geographic heterogeneity of incidence facilitates resource allocation for potential interventions and advances our knowledge of differential etiologies for these cancers. We performed age-period-cohort analysis using 2000-2014 county-level incidence from the Surveillance, Epidemiology, and End Results (SEER) database, estimating relative risk (RR) and age-adjusted annual percent change (Net Drifts) simultaneously for 612 counties via a hierarchical model, separately for colon and rectum cancer, stratified by age group (20-54 vs. 55-84). We also studied correlates of RR and Net Drift with various county-level characteristics. In all SEER counties, colon and rectum cancer incidence rates increased at ages 20-54, whereas rates decreased at ages 55-84. There was marked heterogeneity in both RR and Net Drift among states and counties for both cancer types. Maps of county RR and Net Drift revealed localized clusters in several states. For both cancer types, counties with high RR and unfavorable Net Drift tended to have higher prevalence of obesity and diabetes and to be of a lower socioeconomic status. Counties with higher overall screening rates tended to have lower Net Drifts for both cancer types. Increasing colorectal cancer incidence in the younger age group is geographically widespread, although there is significant heterogeneity in temporal trends and risk both within and between states. These geographic patterns correlate with different county-level characteristics depending on cancer type and age group.

Increasing risk of uterine cervical cancer among young Japanese women: Comparison of incidence trends in Japan, South Korea and Japanese-Americans between 1985 and 2012.

In Japan, cervical cancer incidence has increased since the late 1990s especially among young women, despite a decreasing trend in most developed countries. Here, we examined age, period and birth cohort trends in cervical cancer incidence rates from 1985 to 2012. Incidence rates were ascertained using three population-based cancer registries and analyzed using Joinpoint regression and age-period-cohort models. We compared the findings in Japan to trends among Japanese-Americans in the Surveillance, Epidemiology, and End Results Registries and among women in South Korea using the Korea Central Registry. Age-standardized incidence rates in Japan decreased by 1.7% per year (95% confidence interval - 3.3%, 0.0%) until 1997 and thereafter increased by 2.6% per year (1.1%, 4.2%). Incidence rates increased among women under age 50, were stable among women aged 50-54, and decreased or remained stable among women aged 55 and over. The age-standardized incidence rate ratio by birth cohort showed a U-shaped pattern with the lowest rates in women born in the late 1930s and 1940s. In comparison, women born before 1920 and after 1970 had about double the incidence. Increasing risk in recent birth cohorts was not evident in Japanese-American or South Korean women. The trends in Japan may be attributable to increasing prevalence of human papillomavirus (HPV) infection among young women. Screening and vaccination have been shown to be highly effective and would help reverse these trends.

Trends in premature mortality in the USA by sex, race, and ethnicity from 1999 to 2014: an analysis of death certificate data.

BACKGROUND: Reduction of premature mortality is a UN Sustainable Development Goal. Unlike other high-income countries, age-adjusted mortality in the USA plateaued in 2010 and increased slightly in 2015, possibly because of rising premature mortality. We aimed to analyse trends in mortality in the USA between 1999 and 2014 in people aged 25-64 years by age group, sex, and race and ethnicity, and to identify specific causes of death underlying the temporal trends. METHODS: For this analysis, we used cause-of-death and demographic data from death certificates from the US National Center for Health Statistics, and population estimates from the US Census Bureau. We estimated annual percentage changes in mortality using age-period-cohort models. Age-standardised excess deaths were estimated for 2000 to 2014 as observed deaths minus expected deaths (estimated from 1999 mortality rates). FINDINGS: Between 1999 and 2014, premature mortality increased in white individuals and in American Indians and Alaska Natives. Increases were highest in women and those aged 25-30 years. Among 30-year-olds, annual mortality increases were 2·3% (95% CI 2·1-2·4) for white women, 0·6% (0·5-0·7) for white men, and 4·3% (3·5-5·0) and 1·9% (1·3-2·5), respectively, for American Indian and Alaska Native women and men. These increases were mainly attributable to accidental deaths (primarily drug poisonings), chronic liver disease and cirrhosis, and suicide. Among individuals aged 25-49 years, an estimated 111 000 excess premature deaths occurred in white individuals and 6600 in American Indians and Alaska Natives during 2000-14. By contrast, premature mortality decreased substantially across all age groups in Hispanic individuals (up to 3·2% per year), black individuals (up to 3·9% per year), and Asians and Pacific Islanders (up to 2·6% per year), mainly because of declines in HIV, cancer, and heart disease deaths, resulting in an estimated 112 000 fewer deaths in Hispanic individuals, 311 000 fewer deaths in black individuals, and 34 000 fewer deaths in Asians and Pacific Islanders aged 25-64 years. During 2011-14, American Indians and Alaska Natives had the highest premature mortality, followed by black individuals. INTERPRETATION: Important public health successes, including HIV treatment and smoking cessation, have contributed to declining premature mortality in Hispanic individuals, black individuals, and Asians and Pacific Islanders. However, this progress has largely been negated in young and middle-aged (25-49 years) white individuals, and American Indians and Alaska Natives, primarily because of potentially avoidable causes such as drug poisonings, suicide, and chronic liver disease and cirrhosis. The magnitude of annual mortality increases in the USA is extremely unusual in high-income countries, and a rapid public health response is needed to avert further premature deaths. FUNDING: US National Cancer Institute Intramural Research Program.

Correlated Poisson models for age-period-cohort analysis.

Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cancer incidence and mortality. These models are used for descriptive epidemiology, comparative risk analysis, and extrapolating future disease burden. Traditional APC models have 2 major limitations: (1) they lack parsimony because they require estimation of deviations from linear trends for each level of age, period, and cohort; and (2) rates observed at similar ages, periods, and cohorts are treated as independent, ignoring any correlations between them that may lead to biased parameter estimates and inefficient standard errors. We propose a novel approach to estimation of APC models using a spatially correlated Poisson model that accounts for over-dispersion and correlations in age, period, and cohort, simultaneously. We treat the outcome of interest as event rates occurring over a grid defined by values of age, period, and cohort. Rates defined in this manner lend themselves to well-established approaches from spatial statistics in which correlation among proximate observations may be modeled using a spatial random effect. Through simulations, we show that in the presence of spatial dependence and over-dispersion: (1) the correlated Poisson model attains lower AIC; (2) the traditional APC model produces biased trend parameter estimates; and (3) the correlated Poisson model corrects most of this bias. We illustrate our approach using brain and breast cancer incidence rates from the Surveillance Epidemiology and End Results Program of the United States. Our approach can be easily extended to accommodate comparative risk analyses and interpolation of cells in the Lexis with sparse data.

High-energy particle beam and gamma radiation exposure, familial relatedness and cancer in mice.

BACKGROUND: Some highly penetrant familial cancer syndromes exhibit elevated leukaemia risk, and there is evidence for familial clustering of lung cancer and other common cancers. Lung cancer and leukaemia are strongly radiogenic, but there are few indications that high-energy beam irradiation is markedly more effective than lower-energy radiation. METHODS: We used a Cox model with familially structured random effects to assess 16 mortality end points in a group of 1850 mice in 47 families maintained in a circular-breeding scheme, exposed to accelerated Si or Fe ions (0.4 Gy) or 137Cs gamma rays (3 Gy). RESULTS: There is periodicity in the effect of familial relatedness, which is most pronounced for pulmonary adenoma, Harderian-gland adenoma, Harderian-gland tumour, ectodermal tumour, pulmonary adenocarcinoma and hepatocellular carcinoma (P=0.0001/0.0003/0.0017/0.0035/0.0257/0.0340, respectively) with families that are 3-4 generations apart most strongly correlated; myeloid leukaemia also exhibited a striking periodic correlation structure. The relative risks of high-energy Si or Fe ions are not significantly different and are less than for 137Cs gamma-rays for most end points at the doses used. CONCLUSIONS: There is periodicity in the effect of familial relatedness for various cancer sites. The effects per unit dose of high-energy charged particle beams are no higher than ninefold those of lower-energy gamma radiation.

Computerized dynamic posturography detects balance deficits in individuals with a history of chronic severe traumatic brain injury.

BACKGROUND: Mild balance deficits can be challenging to detect in individuals with long-standing traumatic brain injuries. This study compared Computerized Dynamic Posturography (CDP) scores from individuals with traumatic brain injuries (TBI) to controls to determine if CDP could differentiate between the two groups and determine if there was a learning effect associated with testing that could be used to guide evaluation of baseline balance. METHODS: Ten ambulatory individuals with a history of severe TBI and 10 individuals without participated in three CDP sessions (24-72 hours apart). During each session, participants performed the Berg Balance Test, Dynamic Gait Index and three trials of a standardized balance assessment and Dynamic Movement Analysis (DMA) scores were recorded for each test. RESULTS: Individuals with TBI scored 93% higher (i.e. reflecting poorer balance) than the control group. The group with TBI exhibited 6.6-times more variability compared to the control group, with estimated variances of 0.3407 and 0.0517, respectively. A learning effect was detected in the group with TBI on the first day of testing (ßTBI F = -0.1241, p-value < 0.01). DISCUSSION: The CDP system detected balance differences between individuals with TBI and controls. Given the documented learning effect, the best of three trials should be used to accurately assess baseline scores.

Association of the Area Deprivation Index With Dementia Basic Workup and Diagnosis in Central and Western Virginia: A Cross-Sectional Study.

Background and Objectives: The Area Deprivation Index (ADI) provides a validated and multidimensional metric of areal disadvantage. Our goals were to determine if the ADI influences the likelihood of receiving workup based on published guidelines and an etiologic diagnosis of dementia in Central and Western Virginia. Methods: We collected deidentified data from the electronic health record of individuals aged 50-105 years diagnosed with dementia at the University of Virginia (UVA) Medical Center (2016-2021) and at Carillion Clinic (2018-2021). Visit-specific ICD-10 codes were used to classify each dementia diagnosis as "disease-specific" (e.g., Alzheimer disease) or "general" (e.g., unspecified dementia). Following the American Academy of Neurology guidelines, we considered the evaluation performed as "adequate" if patients had vitamin B12, thyroid-stimulating hormone, and brain CT or magnetic resonance imaging within 6 months of the initial diagnosis. Census tract ADI was linked to study participants using the unique census tract identifier derived from the participants' home addresses at the time of diagnosis. Statistical modeling occurred under a Bayesian paradigm implemented using a standard code in R. Results: The study included 13,431 individuals diagnosed with dementia at UVA (n = 7,152) and Carillion Clinic (n = 6,279). Of those, 32.5% and 20.4% received "disease-specific" diagnoses at UVA and Carillion Clinic and 8.2% and 20.4% underwent "adequate" workup, respectively. The adjusted relationship between census tract ADI and the likelihood of a disease-specific diagnosis was U-shaped: Residence in moderately disadvantaged areas was associated with the lowest likelihood of disease-specific diagnosis. Discussion: Most patients diagnosed with dementia did not receive an adequate evaluation or an etiologic diagnosis. Those living in locations just above the national median ADI levels had the lowest likelihood of receiving an etiologic diagnosis, lower than those in the least and most deprived areas. Renewed awareness efforts among providers are needed to increase compliance with diagnostic guidelines.

Comparison of Deep Learning Approaches for Conversion of International Classification of Diseases Codes to the Abbreviated Injury Scale.

The injury severity classifications generated from the Abbreviated Injury Scale (AIS) provide information that allows for standardized comparisons in the field of trauma injury research. However, the majority of injuries are coded in International Classification of Diseases (ICD) and lack this severity information. A system to predict injury severity classifications from ICD codes would be beneficial as manually coding in AIS can be time-intensive or even impossible for some retrospective cases. It has been previously shown that the encoder-decoder-based neural machine translation (NMT) model is more accurate than a one-to-one mapping of ICD codes to AIS. The objective of this study is to compare the accuracy of two architectures, feedforward neural networks (FFNN) and NMT, in predicting Injury Severity Score (ISS) and ISS ≥16 classification. Both architectures were tested in direct conversion from ICD codes to ISS score and indirect conversion through AIS for a total of four models. Trauma cases from the U.S. National Trauma Data Bank were used to develop and test the four models as the injuries were coded in both ICD and AIS. 2,031,793 trauma cases from 2017-2018 were used to train and validate the models while 1,091,792 cases from 2019 were used to test and compare them. The results showed that indirect conversion through AIS using an NMT was the most accurate in predicting the exact ISS score, followed by direct conversion with FFNN, direct conversion with NMT, and lastly indirect conversion with FFNN, with statistically significant differences in performance on all pairwise comparisons. The rankings were similar when comparing the accuracy of predicting ISS ≥16 classification, however the differences were smaller. The NMT architecture continues to demonstrate notable accuracy in predicting exact ISS scores, but a simpler FFNN approach may be preferred in specific situations, such as if only ISS ≥16 classification is needed or large-scale computational resources are unavailable.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs.

Background: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. Methods: The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. Discussion: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. Study registration: PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs.

BACKGROUND: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. METHODS: The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. DISCUSSION: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. STUDY REGISTRATION: PROSPERO protocol #CRD42023384709.

Evaluation of Neural Machine translation for conversion of International Classification of disease codes to the Abbreviated injury Scale.

The Abbreviated Injury Scale (AIS) is an essential tool for injury research since it allows for comparisons of injury severity among patients, however, the International Classification of Diseases (ICD) is more widely used to capture medical information. The problem of conversion between these two medical coding systems has similarities to the challenges encountered in language translation. We therefore hypothesize that neural machine translation (NMT), a deep learning technique which is commonly used for human language translation, could be used to convert ICD codes to AIS. The objective of this study was to compare the accuracy of a NMT model for determining injury severity compared to two established methods of conversion. The injury severity classifications used for this study were Injury Severity Score (ISS) ≥ 16, Maximum AIS severity (MAIS) ≥ 3, and MAIS ≥ 2. Data from a US national trauma registry, which has patient injuries coded in both AIS and ICD, was used to train a NMT model. Testing data from a separate year was used to determine the accuracy of the NMT model predictions against the actual ISS recorded in the registry. The prediction accuracy of the NMT model was compared to that of the official Association for the Advancement of Automotive Medicine (AAAM) ICD-AIS map and the R package 'ICD Program for Injury Categorization in R' (ICDPIC-R). The results show that the NMT model was the most accurate across all injury severity classifications, followed by the ICD-AIS map and then ICDPIC-R package. The NMT model also showed the highest correlation between the predicted and observe ISS scores. Overall, NMT appears to be a promising method for predicting injury severity from ICD codes, however, validation in external databases is needed.

Smoothing Lexis diagrams using kernel functions: A contemporary approach.

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point-the number of leading singular vectors to retain-based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer.

Development of an Injury Risk Function for the Anterior Pelvis Under Frontal Lap Belt Loading Conditions.

Iliac wing fractures due to lap belt loading have been identified in laboratory tests for almost 50 years and an analysis of recent data suggests these injuries are also occurring in the field. With the introduction of highly autonomous vehicles on the horizon, vehicle manufacturers are exploring open cabin concepts that permit reclined postures and separation of the occupant from the knee bolster and instrument panel. This will result in greater reliance on the lap belt and lap belt/pelvis loading to restrain occupants. No injury criteria exist for iliac wing fractures resulting from lap belt loading like that seen in frontal crash conditions. This study tested the tolerance of isolated iliac wings in a controlled lap belt-like loading environment while incorporating the effect of loading angle after analyzing lap belt loading experiments from a previous study. Twenty-two iliac wings were tested; nineteen of them sustained fracture (exact), but the loading input was insufficient to cause fracture in the other three (right censored). The fracture tolerance of the tested specimens ranged widely (1463-8895 N) and averaged 4091 N (SD 2381 N). Injury risk functions were created by fitting Weibull survival models to data that integrated censored and exact failure observations.