Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.

Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods.

A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Prevalence of amyotrophic lateral sclerosis in the United States, 2018.

OBJECTIVE: To estimate prevalent ALS cases in the United States for calendar year 2018. METHODS: The National ALS Registry (Registry) compiled data from national administrative databases (from the Centers for Medicare and Medicaid Services, the Veterans Health Administration, and the Veterans Benefits Administration) and enrollment data voluntarily submitted through a web portal (www.cdc.gov/als). We used log-linear capture-recapture (CRC) model-based methodology to estimate the number of cases not ascertained by the Registry. RESULTS: The Registry identified 21,655 cases of ALS in 2018, with an age-adjusted prevalence of 6.6 per 100,000 U.S. population. When CRC methods were used, an estimated 29,824 cases were identified, for an adjusted prevalence of 9.1 per 100,000 U.S. population. The demographics of cases of ALS did not change from previous year's reports. ALS continues to impact Whites, males, and persons over 50 years of age more so than other comparison groups. The results from the present report suggest case ascertainment for the Registry has improved, with the estimate of missing prevalent cases decreasing from 44% in 2017 to 27% in in 2018. DISCUSSION: Consistent with previous estimates that used CRC, ALS prevalence in the United States is about 29,824 cases per year.

Association of Walking and Biking to School Policies and Active Commuting to School in Children.

BACKGROUND: Active commuting to school (ACS) can be an important source of physical activity for children. Schools are an important setting for policy-related ACS promotion. The purpose of this study was to examine the association between school policies and ACS, and to assess whether this relation varied by grade. METHODS: This cross-sectional study used data from schools recruited to the Safe Travel Environment Evaluation in Texas School study (n = 94). The percent of trips made by active travel modes was measured through tallies among third to fifth grade classrooms from 5 school districts in Central Texas in 2018-2019. School ACS policies and practices were measured through 8 survey items aggregated into a score. Linear mixed effects models were used to assess the association between policies and ACS. RESULTS: School health policy surveys and ACS data were collected from 69 elementary schools. An average of 14.6% of trips to/from school was made using active travel modes. Schools with higher numbers of policies had significantly higher percentages of students using active travel modes (P = .03), and for each additional policy, the predicted percentage of trips made by active travel modes was 1.46% higher. There was a significant interaction effect between school policy and grade, with stronger correlations among higher grades (P = .002). CONCLUSIONS: Results from this study demonstrate a correlation between the school policies designed to support walking and biking and ACS. Results from this study can be used to justify the use of school-based policy interventions to promote ACS.

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

Sensitivity and Uncertainty Analysis for Two-stream Capture-Recapture Methods in Disease Surveillance.

Capture-recapture methods are widely applied in estimating the number ( ) of prevalent or cumulatively incident cases in disease surveillance. Here, we focus the bulk of our attention on the common case in which there are 2 data streams. We propose a sensitivity and uncertainty analysis framework grounded in multinomial distribution-based maximum likelihood, hinging on a key dependence parameter that is typically nonidentifiable but is epidemiologically interpretable. Focusing on the epidemiologically meaningful parameter unlocks appealing data visualizations for sensitivity analysis and provides an intuitively accessible framework for uncertainty analysis designed to leverage the practicing epidemiologist's understanding of the implementation of the surveillance streams as the basis for assumptions driving estimation of . By illustrating the proposed sensitivity analysis using publicly available HIV surveillance data, we emphasize both the need to admit the lack of information in the observed data and the appeal of incorporating expert opinion about the key dependence parameter. The proposed uncertainty analysis is a simulation-based approach designed to more realistically acknowledge variability in the estimated associated with uncertainty in an expert's opinion about the nonidentifiable parameter, together with the statistical uncertainty. We demonstrate how such an approach can also facilitate an appealing general interval estimation procedure to accompany capture-recapture methods. Simulation studies illustrate the reliable performance of the proposed approach for quantifying uncertainties in estimating in various contexts. Finally, we demonstrate how the recommended paradigm has the potential to be directly extended for application to data from >2 surveillance streams.

A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems.

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

Short-term associations between warm-season ambient temperature and emergency department visits for Alzheimer's disease and related dementia in five US states.

BACKGROUND: Ambient temperatures are projected to increase in the future due to climate change. Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) affect millions of individuals and represent substantial health burdens in the US. High temperature may be a risk factor for AD/ADRD outcomes with several recent studies reporting associations between temperature and AD mortality. However, the link between heat and AD morbidity is poorly understood. METHODS: We examined short-term associations between warm-season daily ambient temperature and AD/ADRD emergency department (ED) visits for individuals aged 45 years or above during the warm season (May to October) for up to 14 years (2005-2018) in five US states: California, Missouri, North Carolina, New Jersey, and New York. Daily ZIP code-level maximum, average and minimum temperature exposures were derived from 1 km gridded Daymet products. Associations are assessed using a time-stratified case-crossover design using conditional logistic regression. RESULTS: We found consistent positive short-term effects of ambient temperature among 3.4 million AD/ADRD ED visits across five states. An increase of the 3-day cumulative temperature exposure of daily average temperature from the 50th to the 95th percentile was associated with a pooled odds ratio of 1.042 (95% CI: 1.034, 1.051) for AD/ADRD ED visits. We observed evidence of the association being stronger for patients 65-74 years of age and for ED visits that led to hospital admissions. Temperature associations were also stronger among AD/ADRD ED visits compared to ED visits for other reasons, particularly among patients aged 65-74 years. CONCLUSION: People with AD/ADRD may represent a vulnerable population affected by short-term exposure to high temperature. Our results support the development of targeted strategies to reduce heat-related AD/ADRD morbidity in the context of global warming.

A prognostic signature based on adenosine metabolism related genes for ovarian cancer.

Background: Ovarian cancer is one of the most common cause of cancer death in women due to its late diagnosis and susceptibility to drug resistance. Adenosine (ADO) signaling plays a key role in immune activity and tumor progression. In this study, we constructed a signature of ADO metabolism related genes expression in patients with ovarian cancer. Methods: A total of 372 ovarian cancer patients from TCGA was used as training set and 1,137 patients from six GEO datasets were as validation set. The gene expression and drug response inhibitory concentration values for ovarian cancer cell line from GDSC were used for drug sensitivity analysis. The non-negative matrix factorization algorithm and ssGSVA were used to construct the ADO score. Results: Patients with high ADO score had shorter overall survival (OS) than those with low ADO score in both training set (HR = 1.42, 95% CI, 1.06-1.88) and validation sets (pooled HR = 1.24, 95% CI = 1.02-1.51). In GSEA analysis, genes in ATP synthesis related pathways were enriched in the low ADO score group (adjusted P value = 0.02). Further, we observed that the high ADO score group had significantly higher levels of most cancer hallmark signatures (all adjusted P values < 0.01) and T cell dysfunction and exclusion signatures than the low ADO score group (all adjusted P values < 0.001). Patients with lower ADO score tended to be sensitive to common drugs including Olaparib and Paclitaxel (adjusted P values = 0.05 and 0.04, respectively). Conclusions: In conclusion, the established ADO signature could be used as a prognostic biomarker to stratify ovarian cancer patients and had the potential to guide the drug exploitation and personalized therapy selection.

Using Capture-Recapture Methodology to Enhance Precision of Representative Sampling-Based Case Count Estimates.

The application of serial principled sampling designs for diagnostic testing is often viewed as an ideal approach to monitoring prevalence and case counts of infectious or chronic diseases. Considering logistics and the need for timeliness and conservation of resources, surveillance efforts can generally benefit from creative designs and accompanying statistical methods to improve the precision of sampling-based estimates and reduce the size of the necessary sample. One option is to augment the analysis with available data from other surveillance streams that identify cases from the population of interest over the same timeframe, but may do so in a highly nonrepresentative manner. We consider monitoring a closed population (e.g., a long-term care facility, patient registry, or community), and encourage the use of capture-recapture methodology to produce an alternative case total estimate to the one obtained by principled sampling. With care in its implementation, even a relatively small simple or stratified random sample not only provides its own valid estimate, but provides the only fully defensible means of justifying a second estimate based on classical capture-recapture methods. We initially propose weighted averaging of the two estimators to achieve greater precision than can be obtained using either alone, and then show how a novel single capture-recapture estimator provides a unified and preferable alternative. We develop a variant on a Dirichlet-multinomial-based credible interval to accompany our hybrid design-based case count estimates, with a view toward improved coverage properties. Finally, we demonstrate the benefits of the approach through simulations designed to mimic an acute infectious disease daily monitoring program or an annual surveillance program to quantify new cases within a fixed patient registry.

Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Plasma cfDNA methylation markers for the detection and prognosis of ovarian cancer.

BACKGROUND: Plasma cell-free DNA (cfDNA) methylation has shown the potential in the detection and prognostic testing in multiple cancers. Herein, we thoroughly investigate the performance of cfDNA methylation in the detection and prognosis of ovarian cancer (OC). METHODS: The OC-specific differentially methylated regions (DMRs) were identified by sequencing ovarian tissue samples from OC (n = 61), benign ovarian disease (BOD, n = 49) and healthy controls (HC, n = 37). Based on 1,272 DMRs, a cfDNA OC detection model (OC-D model) was trained and validated in plasma samples from patients of OC (n = 104), BOD (n = 56) and HC (n = 56) and a prognostic testing model (OC-P model) was developed in plasma samples in patients with high-grade serous OC (HG-SOC) in the training cohort and then tested the rationality of this model with International Cancer Genome Consortium (ICGC) tissue methylation data. Mechanisms were investigated in the TCGA-OC cohort. FINDINGS: In the validation cohort, the cfDNA OC-D model consisting of 18 DMRs achieved a sensitivity of 94.7% (95% CI: 85.4%‒98.9%) at a specificity of 88.7% (95% CI: 78.7%‒94.9%), which outperformed CA 125 (AUC: 0.967 vs 0.905, P = 0.03). Then the cfDNA OC-P model consisting of 15 DMRs was constructed and associated with a better prognosis of HG-SOC in multivariable Cox regression analysis (HR: 0.29, 95% CI, 0.11‒0.78, P = 0.01) in the training cohort, which was also observed in the ICGC cohort using tissue methylation (HR: 0.56, 95% CI, 0.32‒0.98, P = 0.04). Investigation into mechanisms revealed that the low-risk group had higher homologous recombination deficiency and immune cell infiltration (P < 0.05). INTERPRETATION: Our study demonstrated the potential utility of cfDNA methylation in the detection and prognostic testing in OC. Future studies with a larger population are warranted. FUNDING: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Molecular Landscape of ERBB2 Alterations in 14,956 Solid Tumors.

ERBB2 abnormalities frequently occur and serve as rationale therapeutic targets in cancer. In this study, clinical and next-generation sequencing data from 14,956 patients across more than 20 tumor types were collected. A total of 406 (2.7%) patients were identified with ERBB2 amplifications, and 303 (2.0%) patients with pathogenic somatic ERBB2 mutations. ERBB2 amplifications fell most frequently in breast (15.9%) and stomach (8.3%) cancers. Somatic ERBB2 SNVs/indels occurred most common in bladder/urinary tract (7.3%) and intestine (6.1%) cancers. The top mutated ERBB2 SNVs/indels were p.Y772_A775dup (25.5%) and p.S310F/Y (19.9%). Significantly higher rates of ERBB2 SNV/indels were found in women compared to men (2.8% vs. 1.5%, p < 0.0001). CDK12 was the most common co-amplification gene with ERBB2 in cancers with a high frequency of ERBB2 amplifications. Patients with ERBB2 amplifications or mutations had higher TMB compared with patients with non-ERBB2 alterations. The study provided the landscape of ERBB2 alterations across a variety of solid tumors that may benefit from anti-HER2 agents.

Longitudinal changes in objectively-measured physical activity and sedentary time among school-age children in Central Texas, US during the COVID-19 pandemic.

BACKGROUND: Most available evidence on the effects of the COVID-19 pandemic on child movement behaviors is from cross-sectional studies using self-report measures. This study aimed to identify change trajectories and their associated factors for objectively-assessed physical activity and sedentary time among an ethnically and socioeconomically diverse sample of school-age children from Central Texas, U.S.A., during COVID-19. METHODS: Pre- (Sept. 2019 - Feb. 2020) and during- (Oct. 2020 - March 2021) COVID-19 physical activity and sedentary behavior data were collected for school-age children (8-11 years) enrolled in the Safe Travel Environment Evaluation in Texas Schools (STREETS) cohort study. Daily time spent in moderate- to vigorous-intensity physical activity (MVPA) and sedentary time were assessed using GT3X-wBT Actigraph accelerometers. Parent surveys were used to assess socio-ecological factors. Latent class linear mixed models were used to identify change trajectories of MVPA and sedentary time. Logistic regression models were used to assess the association between socio-ecological characteristics with physical activity and sedentary time change trajectory groups. RESULTS: There was a significant decrease in mean daily MVPA (- 9.4 mins, SD = 18.54) and an increase in sedentary behavior (0.83 hrs, SD = 1.18). Two trajectory groups were identified for MVPA ('decrease MPVA' and 'maintain high MVPA'), with the majority (82.1%) being in the 'decrease MVPA' group. Three trajectory groups were identified for sedentary behavior ('moderate increase sedentary, 'steep increase sedentary,' and 'decrease sedentary'), with most children (78.5%) being in the 'moderate increase' group. Girls had significantly lower odds of being in the 'maintain high MVPA' group than boys (OR = 0.27, 95% CI = 0.11, 0.61). Children living in neighborhoods with higher perceived social cohesion had significantly higher odds of being in the 'maintain high MVPA' group (OR = 1.22, 95% CI = 1.06, 1.41), while those in neighborhoods with higher social cohesion had lower odds of being in the 'decrease sedentary' group (OR = 0.86, 95% CI = 0.74, 0.99). CONCLUSIONS: Declines in physical activity and increases in sedentary time among most school-age children during COVID-19 in a socioeconomically and ethnically diverse U.S. sample, were observed in our study, especially among girls. These findings highlight the need to counteract the short-term negative changes in movement behaviors in response to COVID-19 among children.

Common DNA methylation changes in biliary tract cancers identify subtypes with different immune characteristics and clinical outcomes.

BACKGROUND: DNA methylation-associated studies on biliary tract cancer (BTC), including cholangiocarcinoma (CCA) and gallbladder cancer (GBC), may improve the BTC classification scheme. We proposed to identify the shared methylation changes of BTCs and investigate their associations with genomic aberrations, immune characteristics, and survival outcomes. METHODS: Multi-dimensional data concerning mutation, DNA methylation, immune-related features, and clinical data of 57 CCAs and 48 GBCs from Eastern Hepatobiliary Surgery Hospital (EHSH) and 36 CCAs in the TCGA-CHOL cohort were analyzed. RESULTS: In our cohort including 24 intrahepatic CCAs (iCCAs), 20 perihilar CCAs (pCCAs), 13 distal CCAs (dCCAs), and 48 GBCs, 3369 common differentially methylated regions (DMRs) were identified by comparing tumor and non-tumor samples. A lower level of methylation changes of these common DMRs was associated with fewer copy number variations, fewer mutational burden, and remarkably longer overall survival (OS, hazard ratio [HR] = 0.07, 95% confidence interval [CI] 0.01-0.65, P = 0.017). Additionally, a 12-marker model was developed and validated for prognostication after curative surgery (HR = 0.21, 95% CI 0.10-0.43, P < 0.001), which exhibited undifferentiated prognostic effects in subgroups defined by anatomic location (iCCAs, d/pCCAs, GBCs), TNM stage, and tumor purity. Its prognostic utility remained significant in multivariable analysis (HR = 0.26, 95% CI 0.11-0.59, P = 0.001). Moreover, the BTCs with minimal methylation changes exhibited higher immune-related signatures, infiltration of CD8+ lymphocytes, and programmed death-ligand 1 (PD-L1) expression, indicating an inflamed tumor immune microenvironment (TIME) with PD-L1 expression elicited by immune attack, potentially suggesting better immunotherapy efficacy. CONCLUSIONS: In BTCs, DNA methylation is a powerful tool for molecular classification, serving as a robust indicator of genomic aberrations, survival outcomes, and tumor immune microenvironment. Our integrative analysis provides insights into the prognostication after curative surgery and patient selection for immunotherapy.

Change in Depression and Its Determinants during the COVID-19 Pandemic: A Longitudinal Examination among Racially/Ethnically Diverse US Adults.

This study examined longitudinal data to identify changes in the occurrence of depressive symptoms, and to explore if such changes were associated with socio-demographic, movement behaviors, and health variables during the COVID-19 pandemic, among a diverse sample of central Texas residents. Participants who completed two online surveys in 2020 (in June and November) from an on-going longitudinal study were included. Depressive symptoms were measured by Patient Health Questionnaire-2. Change in depressive symptoms' occurrence status between the two time points was categorized into (1) stable/improved, and (2) consistent depressive symptoms/declined. Sociodemographic factors, movement behaviors and health data were self-reported. Statistical analyses utilized descriptive statistics and logistical regression. Among a total of 290 individuals (84.1% female; 71.0% racial/ethnic minorities), 13.5% were categorized as consistent depressive symptoms/declined. Multivariable logistic regression indicated that racial/ethnic minorities, older age, and increased physical activity were associated with a lower likelihood, while greater sedentary time was associated with higher likelihood of consistent depressive symptoms/declined status. Between 3 months and 8 months into the pandemic, various socio-demographic and behavioral variables were associated with changes in depressive symptoms' occurrence status. Future research should explore the longer-term impacts of COVID-19 on depression among a diverse population and identify risk factors for depression.

Risk Stratification of Prostatic Adenocarcinoma Metastatic to the Lymph Nodes.

CONTEXT.­: The pathologic nodal staging of prostatic adenocarcinoma is binary for regional lymph nodes. Stages pN0 and pN1 indicate the absence or presence of regional nodal metastasis, respectively, whereas patients with metastasis to nonregional lymph nodes are staged as pM1a. OBJECTIVE.­: To determine the risk of recurrence of pN1 prostatic adenocarcinoma patients based on the extent of nodal tumor burden. DESIGN.­: We retrospectively reviewed pN1 patients with prostatic adenocarcinoma managed with radical prostatectomy seen between 2011 and 2019. Kaplan-Meier and Cox regression analyses were performed to compare disease-free survival. RESULTS.­: Ninety-six patients were included (median [interquartile range] age, 62 years [57-67 years]; 70 of 96 [73%] White). On univariate analysis, age >65 years (P = .008), ≥2 positive regional lymph nodes (P < .001), and a maximum size of the tumor deposit ≥2 mm (P = .004) were significantly associated with an unfavorable outcome. Controlling for age, stage, metastatic deposit size, margin status, and the presence of extranodal extension, patients with ≥2 positive regional lymph nodes were 3.03 times more likely (95% confidence interval, 1.39-6.60; P = .005) to have an unfavorable outcome. Patients with pN1M1a stage showed a disease-free survival similar to that of pN1M0 patients, after controlling for the number of positive regional lymph nodes (P = .36). CONCLUSIONS.­: Overall, pN1 patients with ≥2 positive regional lymph nodes are 3 times more likely to have an unfavorable outcome. The results suggest a benefit in further stratifying patients with metastatic prostatic adenocarcinoma to the lymph nodes into prognostically significant risk categories that could help the treating clinicians tailor subsequent patient follow-up and therapy.

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged ≥ 85 years who died. First, we used a spatial-temporal binomial regression model to estimate the percent of positive SARS-CoV-2 test results. A spatial-temporal negative-binomial model was then used to estimate unrecognized COVID-19 deaths by exploiting the spatial-temporal association between SARS-CoV-2 percent positive and all-cause mortality counts using an excess mortality approach. Computationally efficient Bayesian inference was accomplished via the Polya-Gamma representation of the binomial and negative-binomial models. Among those aged ≥ 85 years, we estimated 58,200 (95% CI: 51,300, 64,900) unrecognized COVID-19 deaths, which accounts for 26% (95% CI: 24%, 29%) of total COVID-19 deaths in this age group. Our modeling results suggest that COVID-19 mortality and the proportion of unrecognized deaths among deaths attributed to COVID-19 vary by time and across states.