Modeling basal body temperature data using horseshoe process regression.

Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR; include additional covariates via a partial linear model framework; and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.

Development and validation of a life expectancy calculator for US patients with prostate cancer.

OBJECTIVE: To develop and validate an accurate, usable prediction model for other-cause mortality (OCM) in patients with prostate cancer diagnosed in the United States. MATERIALS AND METHODS: Model training was performed using the National Health and Nutrition Examination Survey 1999-2010 including men aged >40 years with follow-up to the year 2014. The model was validated in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial prostate cancer cohort, which enrolled patients between 1993 and 2001 with follow-up to the year 2015. Time-dependent area under the curve (AUC) and calibration were assessed in the validation cohort. Analyses were performed to assess algorithmic bias. RESULTS: The 2420 patient training cohort had 459 deaths over a median follow-up of 8.8 years among survivors. The final model included eight predictors: age; education; marital status; diabetes; hypertension; stroke; body mass index; and smoking. It had an AUC of 0.75 at 10 years for predicting OCM in the validation cohort of 8220 patients. The final model significantly outperformed the Social Security Administration life tables and showed adequate predictive performance across race, educational attainment, and marital status subgroups. There is evidence of major variability in life expectancy that is not captured by age, with life expectancy predictions differing by 10 or more years among patients of the same age. CONCLUSION: Using two national cohorts, we have developed and validated a simple and useful prediction model for OCM for patients with prostate cancer treated in the United States, which will allow for more personalized treatment in accordance with guidelines.

Forming a Critical Race Theory of Environmental Disaster: Understanding social meanings and health threat perception in the Flint Water Crisis.

A Critical Race Theory of Environmental Disaster can aid researchers in better contextualizing racially disproportionate environmental disasters and their intricate social meanings to survivors. Such a theory, as proposed and operationalized here, incorporates interpretations of the causes and consequences of environmental disaster. In so doing, this theory weighs the racial and economic stratification often preceding environmental disaster and that which reflexively becomes more embedded in the aftermath. Focusing on the water crisis in the racially diverse, socioeconomically diminished city of Flint, Michigan, this article examines survey data from research conducted with city residents. The analysis considers residents' attitudes and beliefs around the crisis' scope and its intentionality and residents' health outcomes. Results suggest that various institutional and community-level mechanisms contribute to processes of meaning-making during crisis, or "crisis-making," finding consistent variation in residents' understanding of the nature and scope of the water crisis that is associated with specific cultural and health-related experiences. This construction substantiates that a Critical Race Theory of Environmental Disaster must consider not only race, but class in the context of race, as instrumental in developing social understandings of, and experiences with, environmental disaster.

A Population-Based Assessment of Physical Symptoms and Mental Health Outcomes Among Adults Following the Flint Water Crisis.

Little is known about the physical and mental health outcomes of adults in the low-income, predominantly Black city of Flint, Michigan, following the city's water crisis which began in April 2014 after austerity policies led to the city switching its water source. We investigate these dynamics using data from a longitudinal community-based cohort in Flint. Between June and November 2019, surveys were administered at nine public sites across Flint. Nested models were employed to assess relationships between respondent demographics, including race/ethnicity, and self-report of clinician-diagnosed blood lead levels (BLLs) and various physical symptoms and mental health outcomes, including depression/anxiety (PHQ-4) and psychological trauma (PC-PTSD-5). Of the 331 respondents (mean age: 47.9 + 16.5), most were women (58.6%) and Black (57.7%). In total, 10.0% self-reported elevated BLLs, with borderline significantly higher reports among Blacks (p = 0.07). Skin rashes (58.1% vs. 33.9%, p < 0.01), hair loss (45.5% vs. 30.3%, p = 0.01), and nausea (35.6% vs. 20.2%, p = 0.1) were significantly higher among Blacks versus Whites. Additionally, 29.0% and 26.3% of respondents met trauma and depression/anxiety criteria, respectively. Increasing physical symptoms was associated with psychological trauma (OR 2.1, p < 0.01) and depression/anxiety (OR 1.9, p < 0.01). In closing, Flint adults, particularly Blacks, experienced deleterious physical and mental health outcomes following the city's water crisis that appear to represent a substantial burden of excess cases. Further research is needed on how austerity impacts community health in economically distressed urban cities and ways to generate capacity to identify and curb adverse consequences.

A modular framework for early-phase seamless oncology trials.

BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

Accounting for established predictors with the multistep elastic net.

Multivariable models for prediction or estimating associations with an outcome are rarely built in isolation. Instead, they are based upon a mixture of covariates that have been evaluated in earlier studies (eg, age, sex, or common biomarkers) and covariates that were collected specifically for the current study (eg, a panel of novel biomarkers or other hypothesized risk factors). For that context, we present the multistep elastic net (MSN), which considers penalized regression with variables that can be qualitatively grouped based upon their degree of prior research support: established predictors vs unestablished predictors. The MSN chooses between uniform penalization of all predictors (the standard elastic net) and weaker penalization of the established predictors in a cross-validated framework and includes the option to impose zero penalty on the established predictors. In simulation studies that reflect the motivating context, we show the comparability or superiority of the MSN over the standard elastic net, the Integrative LASSO with Penalty Factors, the sparse group lasso, and the group lasso, and we investigate the importance of not penalizing the established predictors at all. We demonstrate the MSN to update a prediction model for pediatric ECMO patient mortality.

Child Lead Screening Behaviors and Health Outcomes Following the Flint Water Crisis.

BACKGROUND: There is little research on lead (Pb) screening behaviors and outcomes and possible health sequelae of children in Flint, Michigan in the years following the city's 2014 water crisis, which included widespread tap water contamination with elevated levels of heavy metals and other environmental contaminants. METHODS: Between June and November 2019, we collected and analyzed cross-sectional data on Flint children's demographics and self-report of screenings of blood lead levels (BLLs) and results and various potential water contamination-related health symptoms and outcomes. We calculated descriptive statistics to summarize the prevalence of health outcomes and screenings in children, and fit multivariable models using generalized estimating equations to characterize the association between baseline traits and health symptoms and outcomes in children. RESULTS: A total of 244 children (mean age 8.6 ± 4.8) were included in the analysis. Overall, 76.6% of the children were reported to have been screened for elevated BLLs after the water source switch. In total, after the water source switch, 25.0% of children were reported as having clinician-diagnosed elevated BLLs. Overall, 43.9% of children experienced hyperactivity, 39.3% had emotional agitation, 29.1% had comprehension issues/learning delays, while 38.9% of children had skin rashes and 10.7% experienced hair loss. A child having elevated BLLs also significantly increased the odds of experiencing adverse cognitive/behavioral outcomes (comprehension issues/learning delays OR = 4.0, hyperactivity OR = 6.6, emotional agitation OR = 3.5). CONCLUSION: Child BLL screening following the crisis initiation was moderate, and BLLs and potential water contamination-related morbidity outcomes appeared heightened. Further research is needed to contextualize epidemiologic factors contributing to BLL screening patterns and results and the potential water contamination-associated sequelae observed here.

Theorizing on neo public assistance: How do race and class impact resource uptake and behavior following disaster?

The consequences of environmental disasters and other ecologic and communal crises are frequently worst in racially/ethnically minoritized and low-income populations relative to other groups. This disproportionality may create or deepen patterns of governmental distrust and stoke health promotion disengagement in these groups. To date, there has been limited contextualization of how historically disenfranchised populations utilize government-administered or facilitated resources following such disasters. Focusing on the water crisis in Flint, Michigan, we examine and theorize on the usage of neo public assistance, free risk reduction resources that are provided to disaster survivors as a liminal means of redressing ills created and/or insufficiently mitigated by the state. We surveyed 331 Flint residents, evaluating their usage of four neo public assistance resources following the FWC, finding low to moderate uptake: 131 residents (39.6%) indicated that they obtained blood lead level (BLL) screenings, 216 (65.3%) had their tap water tested for lead (Pb) and other contaminants, 137 (41.4%) had their home water infrastructure replaced, and 293 (88.5%) had acquired bottled water at community distribution sites. Unemployment, receiving public benefits, and lacking reliable transportation and stable housing were associated with lower uptake of some resources. Compared to White and "Other" race individuals, Black residents were generally more likely to acquire/utilize these resources, suggesting heightened concerns and health promotion proclivities even in the face of observed macro and individual-level challenges. Potential reasons and implications are discussed.

The blueprint of disaster: COVID-19, the Flint water crisis, and unequal ecological impacts.

COVID-19 is unique in the scope of its effects on morbidity and mortality. However, the factors contributing to its disparate racial, ethnic, and socioeconomic effects are part of an expansive and continuous history of oppressive social policy and marginalising geopolitics. This history is characterised by institutionally generated spatial inequalities forged through processes of residential segregation and neglectful urban planning. In the USA, aspects of COVID-19's manifestation closely mirror elements of the build-up and response to the Flint crisis, Michigan's racially and class-contoured water crisis that began in 2014, and to other prominent environmental injustice cases, such as the 1995 Chicago (IL, USA) heatwave that severely affected the city's south and west sides, predominantly inhabited by Black people. Each case shares common macrosocial and spatial characteristics and is instructive in showing how civic trust suffers in the aftermath of public health disasters, becoming especially degenerative among historically and spatially marginalised populations. Offering a commentary on the sociogeographical dynamics that gave rise to these crises and this institutional distrust, we discuss how COVID-19 has both inherited and augmented patterns of spatial inequality. We conclude by outlining particular steps that can be taken to prevent and reduce spatial inequalities generated by COVID-19, and by discussing the preliminary steps to restore trust between historically disenfranchised communities and the public officials and institutions tasked with responding to COVID-19.

Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic.

Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.