An Infant with High-Output Heart Failure and Pulmonary Hypertension Resulting from a Giant Cutaneous Hemangioma.

We present the clinical course of an 8-month-old infant with a giant cutaneous hemangioma resulting in high-output heart failure and pulmonary hypertension. The lesion was successfully embolized and excised, with rapid resolution of heart failure and improvement in pulmonary hypertension.

The Estimated Fick Method Systematically Over-Estimates Cardiac Index Compared to Thermodilution in Children.

Cardiac index (CI) may be derived from the Fick method, using measured or estimated oxygen consumption (VO2), or from thermodilution. In children, LaFarge VO2 estimates correlate poorly with measured VO2 values. In a large adult cohort, there was only modest correlation between estimated Fick CI (eFick CI) and thermodilution CI (TDCI). We evaluated the extent of agreement between eFick CI using LaFarge estimates of VO2 and TDCI in a pediatric cohort. A retrospective, single-center chart review of patients 3-18 years of age who underwent cardiac catheterization with documented eFick CI and TDCI from 2004 to 2020 included 201 catheterizations from 161 unique patients. The mean patient age at catheterization was 12.2y (SD 4.4y). The most frequent diagnosis was cardiomyopathy, followed by congenital heart disease and pulmonary hypertension. TDCI and eFick CI differed by > 20% in 49% of catheterizations. eFick CI systematically exceeded TDCI by a mean percentage difference of 24% (SD 31%). Higher mean CI ((eFick CI + TDCI)/2) and older age were predictive of greater percent difference between eFick CI and TDCI. For each increase in mean CI by 1.0 L/min/m2, the expected percent difference in CI increased by 9.9% (p < 0.001). In pediatric patients undergoing cardiac catheterization, eFick CI with LaFarge VO2 systematically exceeds TDCI. The difference between methods is frequently > 20%, which may have clinically significant implications. Discrepancies between eFick CI and TDCI increase at higher mean CI.

Establishing Geriatric Opiate Use Patterns in Trauma.

Introduction: There is evidence that providers often overprescribe opiates in the postoperative period. Despite an ever-growing geriatric population, there is little research detailing current opiate usage in older patients after trauma. This population presents a unique set of challenges for pain management and prescription drug dependence due to sensitivity, a narrow therapeutic window, and high rates of pre-existing polypharmacy.Objective: Assess the use of narcotics in geriatric trauma patients with various injury patterns to establish a reference point for future intervention for reduction in narcotic dependence.Methods: We created a database of trauma patients' age ≥65 years admitted to a single level 1 trauma center in the Southeastern United States during the 2019 calendar year. Information gathered included patient factors, injury patterns, operative intervention, pain medications prescribed during hospitalization and at discharge, total and average daily morphine milligram equivalents (MME) inpatient and outpatient, and requests/prescriptions for narcotics at follow-up.Results: In 2019, there were 475 patients aged ≥65 admitted to our level 1 trauma center for acute traumatic injuries. 219 of those patients required operative intervention. Average total inpatient MME for this cohort was 169.0 with average daily MME of 22.89. The average total prescribed MME upon discharge was 79.27. There were 29 patients documented to request narcotic prescription refill at time of clinic follow-up, 27 of whom were prescribed a narcotic medication at follow-up.Conclusion: This dataset establishes a reference point for opiate use in geriatric trauma patients to facilitate further research for mitigation of risk in this population.

Tuberculous Spondylodiscitis with Psoas Abscess Descending into the Anterior Femoral Compartment Identified Using 2-deoxy-2-[18F]fluoroglucose Positron Emission Tomography Computed Tomography.

A 24-year-old immunocompetent woman underwent whole-body 18F-FDG PET/CT for the evaluation of MRI-suspicious tuberculous spinal lesions. The PET/CT results showed no pathological uptake in either lung, and there were no pathological changes on CT. There was increased uptake in the right psoas muscle, extending continuously down anterior to the right hip joint, posterior to and around the trochanteric region of the right femur, and into the right thigh, with an SUVmaxbw of 17.0. Subsequently, the patient underwent CT-guided biopsy as per protocol, which revealed drug-sensitive Mycobacterium tuberculosis, and the patient was started on standard tuberculosis treatment for 12 months.

Bayesian modeling of spatially differentiated multivariate enamel defects of the children's primary maxillary central incisor teeth.

BACKGROUND: The analysis of dental caries has been a major focus of recent work on modeling dental defect data. While a dental caries focus is of major importance in dental research, the examination of developmental defects which could also contribute at an early stage of dental caries formation, is also of potential interest. This paper proposes a set of methods which address the appearance of different combinations of defects across different tooth regions. In our modeling we assess the linkages between tooth region development and both the type of defect and associations with etiological predictors of the defects which could be influential at different times during the tooth crown development. METHODS: We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application. RESULTS: The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results. CONCLUSION: We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model.

Virtual Access to STEM Careers: In the Field Experiments.

The Virtual Access to STEM Careers (VASC) project is an intertwined classroom and virtual reality (VR) curricular program for third through fourth graders. Elementary school students learn about and take on the roles and responsibilities of STEM occupations through authentic, problem-based tasks with physical kits and immersive VR environments. This article reports on a round of curriculum and virtual environment development and in-classroom experimentation that was guided by preliminary results gathered from our initial VASC prototyping and testing. This specific iteration focuses on curriculum for learning about sea turtles and tasks regularly completed by park rangers and marine biologists who work with these creatures and a new backend data collection component to analyze participant behavior. Our results showed that educators were able to setup and integrate VASC into their classrooms with relative ease. Elementary school students were able to learn how to interface with our system quickly and enjoyed being in the environment, making a positive link to STEM education.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.

BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Haplotype-specific assembly of shattered chromosomes in esophageal adenocarcinomas.

The epigenetic landscape of cancer is regulated by many factors, but primarily it derives from the underlying genome sequence. Chromothripsis is a catastrophic localized genome shattering event that drives, and often initiates, cancer evolution. We characterized five esophageal adenocarcinoma organoids with chromothripsis using long-read sequencing and transcriptome and epigenome profiling. Complex structural variation and subclonal variants meant that haplotype-aware de novo methods were required to generate contiguous cancer genome assemblies. Chromosomes were assembled separately and scaffolded using haplotype-resolved Hi-C reads, producing accurate assemblies even with up to 900 structural rearrangements. There were widespread differences between the chromothriptic and wild-type copies of chromosomes in topologically associated domains, chromatin accessibility, histone modifications, and gene expression. Differential epigenome peaks were most enriched within 10 kb of chromothriptic structural variants. Alterations in transcriptome and higher-order chromosome organization frequently occurred near differential epigenetic marks. Overall, chromothripsis reshapes gene regulation, causing coordinated changes in epigenetic landscape, transcription, and chromosome conformation.

Predictors of Developmental Defects of Enamel in Primary Maxillary Central Incisors Using Bayesian Model Selection.

INTRODUCTION: Localized non-inheritable developmental defects of tooth enamel (DDE) are classified as enamel hypoplasia (EH), opacity (OP), and post-eruptive breakdown (PEB) using the enamel defects index. To better understand the etiology of DDE, we assessed the linkages amongst exposome variables for these defects during the specific time duration for enamel mineralization of the human primary maxillary central incisor enamel crowns. In general, these two teeth develop between 13 and 14 weeks in utero and 3-4 weeks' postpartum of a full-term delivery, followed by tooth eruption at about 1 year of age. METHODS: We utilized existing datasets for mother-child dyads that encompassed 12 weeks' gestation through birth and early infancy, and child DDE outcomes from digital images of the erupted primary maxillary central incisor teeth. We applied a Bayesian modeling paradigm to assess the important predictors of EH, OP, and PEB. RESULTS: The results of Gibbs variable selection showed a key set of predictors: mother's prepregnancy body mass index (BMI); maternal serum concentrations of calcium and phosphorus at gestational week 28; child's gestational age; and both mother's and child's functional vitamin D deficiency (FVDD). In this sample of healthy mothers and children, significant predictors for OP included the child having a gestational period >36 weeks and FVDD at birth, and for PEB included a mother's prepregnancy BMI <21.5 and higher serum phosphorus concentration at week 28. CONCLUSION: In conclusion, our methodology and results provide a roadmap for assessing timely biomarker measures of exposures during specific tooth development to better understand the etiology of DDE for future prevention.

Using Non-immersive VR Simulations in Conjunction with Priming to Enhance Conceptualizing Radiation and Risk.

Radiation is present daily and used throughout many industries for beneficial purposes. Safety professionals and workers need a general understanding of radiation identification techniques and associated risks to manage the work environment with radiation protection practices. This is especially important in workplaces where the radiation source is ancillary to the primary activities of the industry. The elusive nature of radiation makes learners understanding in an educational setting a challenge. Furthermore, delivering a hands-on experience to support learning and training of radiation, its assessment, and protection measures is quite difficult, given its hazardous nature. Virtual reality, even using non-immersive environments, offers significant benefits through simulating sources of radiation. A non-immersive simulator titled AssessRadVR, facilitated the radiation experience and presented dynamically the effects of the trainees' interactions with shielding materials, detection equipment, and the working environment. Yet, interaction with simulation alone may not trigger effective encoding of these critical aspects in long-term memory. This paper discusses using the AssessRadVR simulator in conjunction with a procedure for subliminally priming the long-term memory encoding to enhance radiation's activated representation. Students in an introductory industrial hygiene class were divided into two groups and primed with either alarming or non-alarming words following the Bargh, Chen, & Borrows procedure (1996). Results demonstrated that students primed with alarming words were more open to objectively evaluating the radiation sources due to their overall perception that the risk was manageable. Why is this of interest to the community: The results provided that even with limited prior knowledge in radiation, after engaging with the simulator, most students were able to recognize and apply radiation principles, which indicates they could apply key principles in a real-world radiation workplace. Further, the impact of activating with alarming words led to a statistically significantly higher perception that working in the radiation environment was manageable.

Late-gadolinium enhancement is common in older pediatric heart transplant recipients and is associated with lower ejection fraction.

BACKGROUND: Chronic graft failure and cumulative rejection history in pediatric heart transplant recipients (PHTR) are associated with myocardial fibrosis on endomyocardial biopsy (EMB). Cardiovascular magnetic resonance imaging (CMR) is a validated, non-invasive method to detect myocardial fibrosis via the presence of late gadolinium enhancement (LGE). In adult heart transplant recipients, LGE is associated with increased risk of future adverse clinical events including hospitalization and death. We describe the prevalence, pattern, and extent of LGE on CMR in a cohort of PHTR and its associations with recipient and graft characteristics. METHODS: This was a retrospective study of consecutive PHTR who underwent CMR over a 6-year period at a single center. Two independent reviewers assessed the presence and distribution of left ventricular (LV) LGE using the American Heart Association (AHA) 17-segment model. LGE quantification was performed on studies with visible fibrosis (LGE+). Patient demographics, clinical history, and CMR-derived volumetry and ejection fractions were obtained. RESULTS: Eighty-one CMR studies were performed on 59 unique PHTR. Mean age at CMR was 14.8 ± 6.2 years; mean time since transplant was 7.3 ± 5.0 years. The CMR indication was routine surveillance (without a clinical concern based on laboratory parameters, echocardiography, or cardiac catheterization) in 63% (51/81) of studies. LGE was present in 36% (29/81) of PHTR. In these LGE + studies, patterns included inferoseptal in 76% of LGE + studies (22/29), lateral wall in 41% (12/29), and diffuse, involving > 4 AHA segments, in 21% (6/29). The mean LV LGE burden as a percentage of myocardial mass was 18.0 ± 9.0%. When reviewing only the initial CMR per PHTR (n = 59), LGE + patients were older (16.7 ± 2.9 vs. 12.8 ± 4.6 years, p = 0.001), with greater time since transplant (8.3 ± 5.4 vs. 5.7 ± 3.9 years, p = 0.041). These patients demonstrated higher LV end-systolic volume index (LVESVI) (34.7 ± 11.7 vs. 28.7 ± 6.1 ml/m2, p = 0.011) and decreased LV ejection fraction (LVEF) (56.2 ± 8.1 vs. 60.6 ± 5.3%, p = 0.015). There were no significant differences in history of moderate/severe rejection (p = 0.196) or cardiac allograft vasculopathy (CAV) (p = 0.709). CONCLUSIONS: LV LGE was present in approximately one third of PHTR, more commonly in older patients with longer time since transplantation. Grafts with LGE have lower LVEF. CMR-derived LGE may aid in surveillance of chronic graft failure in PHTR.

Effects of psoriasis and psoralen exposure on the somatic mutation landscape of the skin.

Somatic mutations are hypothesized to play a role in many non-neoplastic diseases. We performed whole-exome sequencing of 1,182 microbiopsies dissected from lesional and nonlesional epidermis from 111 patients with psoriasis to search for evidence that somatic mutations in keratinocytes may influence the disease process. Lesional skin remained highly polyclonal, showing no evidence of large-scale spread of clones carrying potentially pathogenic mutations. The mutation rate of keratinocytes was similarly only modestly affected by the disease. We found evidence of positive selection in previously reported driver genes NOTCH1, NOTCH2, TP53, FAT1 and PPM1D and also identified mutations in four genes (GXYLT1, CHEK2, ZFP36L2 and EEF1A1) that we hypothesize are selected for in squamous epithelium irrespective of disease status. Finally, we describe a mutational signature of psoralens-a class of chemicals previously found in some sunscreens and which are used as part of PUVA (psoralens and ultraviolet-A) photochemotherapy treatment for psoriasis.

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis.

BACKGROUND: Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes. METHODS: The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period. DISCUSSION: Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

The Association between Mediated Deprivation and Ovarian Cancer Survival among African American Women.

BACKGROUND: Deprivation indices are often used to adjust for socio-economic disparities in health studies. Their role has been partially evaluated for certain population-level cancer outcomes, but examination of their role in ovarian cancer is limited. In this study, we evaluated a range of well-recognized deprivation indices in relation to cancer survival in a cohort of self-identified Black women diagnosed with ovarian cancer. This study aimed to determine if clinical or diagnostic characteristics lie on a mediating pathway between socioeconomic status (SES) and deprivation and ovarian cancer survival in a minority population that experiences worse survival from ovarian cancer. METHODS: We used mediation analysis to look at the direct and indirect causal effects of deprivation indices with main mediators of the SEER stage at diagnosis and residual disease. The analysis employed Bayesian structural equation models with variable selection. We applied a joint Bayesian structural model for the mediator, including a Weibull mixed model for the vital outcome with deprivation as exposure. We selected modifiers via a Monte Carlo model selection procedure. RESULTS: The results suggest that high SES-related indices, such as Yost, Kolak urbanicity (URB), mobility (MOB) and SES dimensions, and concentrated disadvantage index (CDI), all have a significant impact on improved survival. In contrast, area deprivation index (ADI)/Singh, and area level poverty (POV) did not have a major impact. In some cases, the indirect effects have very wide credible intervals, so the total effect is not well estimated despite the estimation of the direct effect. CONCLUSIONS: First, it is clear that commonly used indices such as Yost, or CDI both significantly impact the survival experience of Black women diagnosed with epithelial ovarian cancer. In addition, the Kolak dimension indices (URB, MOB, mixed immigrant: MICA and SES) also demonstrate a significant association, depending on the mediator. Mediation effects differ according to the mediator chosen.

Association of inflammation-related exposures and ovarian cancer survival in a multi-site cohort study of Black women.

BACKGROUND: An association was observed between an inflammation-related risk score (IRRS) and worse overall survival (OS) among a cohort of mostly White women with invasive epithelial ovarian cancer (EOC). Herein, we evaluated the association between the IRRS and OS among Black women with EOC, a population with higher frequencies of pro-inflammatory exposures and worse survival. METHODS: The analysis included 592 Black women diagnosed with EOC from the African American Cancer Epidemiology Study (AACES). Cox proportional hazards models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of the IRRS and OS, adjusting for relevant covariates. Additional inflammation-related exposures, including the energy-adjusted Dietary Inflammatory Index (E-DIITM), were evaluated. RESULTS: A dose-response trend was observed showing higher IRRS was associated with worse OS (per quartile HR: 1.11, 95% CI: 1.01-1.22). Adding the E-DII to the model attenuated the association of IRRS with OS, and increasing E-DII, indicating a more pro-inflammatory diet, was associated with shorter OS (per quartile HR: 1.12, 95% CI: 1.02-1.24). Scoring high on both indices was associated with shorter OS (HR: 1.54, 95% CI: 1.16-2.06). CONCLUSION: Higher levels of inflammation-related exposures were associated with decreased EOC OS among Black women.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service.

BACKGROUND: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread.

BACKGROUND: Bayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important. METHODS: In this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons. RESULTS: A particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall. DISCUSSION: The models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall 'best' model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality. CONCLUSIONS: From a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance. In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.

BACKGROUND: COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. METHODS: We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. RESULTS: The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. CONCLUSION: We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Deprivation and segregation in ovarian cancer survival among African American women: a mediation analysis.

PURPOSE: Deprivation and segregation indices are often examined as possible explanations for observed health disparities in population-based studies. In this study, we assessed the role of recognized deprivation and segregation indices specifically as they affect survival in a cohort of self-identified Black women diagnosed with ovarian cancer who enrolled in the African American Cancer Epidemiology Study. METHODS: Mediation analysis was used to examine the direct and indirect effects between deprivation or segregation and overall survival via a Bayesian structural equation model with Gibbs variable selection. RESULTS: The results suggest that high socioeconomic status-related indices have an association with increased survival, ranging from 25% to 56%. In contrast, index of concentration at the extremes-race does not have a significant impact on overall survival. In many cases, the indirect effects have very wide credible intervals; consequently, the total effect is not well estimated despite the estimation of the direct effect. CONCLUSIONS: Our results show that Black women living in higher socioeconomic status neighborhoods are associated with increased survival with ovarian cancer using area-level economic indices such as Yost or index of concentration at the extremes-income. In addition, the Kolak urbanization index has a similar impact and highlights the importance of area-level deprivation and segregation as potentially modifiable social factors in ovarian cancer survival.