Reliable event rates for disease mapping.

When analyzing spatially referenced event data, the criteria for declaring rates as "reliable" is still a matter of dispute. What these varying criteria have in common, however, is that they are rarely satisfied for crude estimates in small area analysis settings, prompting the use of spatial models to improve reliability. While reasonable, recent work has quantified the extent to which popular models from the spatial statistics literature can overwhelm the information contained in the data, leading to oversmoothing.

Applying a Bayesian Spatiotemporal Model to Examine Excess County-Level Cardiovascular Disease Death Rates During the COVID-19 Pandemic.

Amid the COVID-19 pandemic, national cardiovascular disease (CVD) death rates increased, especially among younger adults. County-level variation has not been documented. Using county-level CVD deaths (ICD-10 codes: I00-I99) from the US National Vital Statistics System, we developed a Bayesian multivariate spatiotemporal model to estimate excess CVD death rates in 2020 based on trends from 2010-2019 for adults aged 35-64 and ≥65 years.

A two-stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties.

Racialized economic segregation, a key metric that simultaneously accounts for spatial, social and income polarization in communities, has been linked to adverse health outcomes, including morbidity and mortality. Due to the spatial nature of this metric, the association between health outcomes and racialized economic segregation could also change with space. Most studies assessing the relationship between racialized economic segregation and health outcomes have always treated racialized economic segregation as a fixed effect and ignored the spatial nature of it.

Restricted spatial models for the analysis of geographic and racial disparities in the incidence of low birthweight in Pennsylvania.

The incidence of low birthweight is a common measure of public health due to the increased risk of complications associated with infants having low and very low birthweights. Moreover, many factors that increase the risk of an infant having a low birthweight can be linked to the mother's socioeconomic status, leading to large racial/ethnic disparities in its incidence. Our objective is thus to analyze the incidence of low and very low birthweight in Pennsylvania counties by race/ethnicity.

Development and validation of statewide survey-based measures of livability in Connecticut.

Livability, or how a place and its systems (e.g., housing, transportation) supports the ability to lead a livable life, is a determinant of health.

Spatial Inequities in Life Expectancy in Small Areas of Buenos Aires, Argentina 2015-2017.

Studies of life expectancy (LE) in small areas of cities are relatively common in high-income countries but rare in Latin American countries. Small-area estimation methods can help to describe and quantify inequities in LE between neighborhoods and their predictors. Our objective was to analyze the distribution and spatial patterning of LE across small areas of Ciudad Autónoma de Buenos Aires (CABA), Argentina, and its association with socioeconomic characteristics.

Intraurban socioeconomic inequalities in life expectancy: a population-based cross-sectional analysis in the city of Córdoba, Argentina (2015-2018).

Objectives: To evaluate variability in life expectancy at birth in small areas, describe the spatial pattern of life expectancy, and examine associations between small-area socioeconomic characteristics and life expectancy in a mid-sized city of a middle-income country.

Design: Cross-sectional, using data from death registries (2015-2018) and socioeconomic characteristics data from the 2010 national population census.

Participants/setting: 40 898 death records in 99 small areas of the city of Córdoba, Argentina.

Is race-specific neighborhood social cohesion key to reducing racial disparities in late HIV diagnosis: A multiyear ecological study.

We examined whether race/ethnic-specific social cohesion is associated with race/ethnic-specific HIV diagnosis rates using Bayesian space-time zero-inflated Poisson multivariable models, across 376 Census tracts. Social cohesion data were from the Southeastern Pennsylvania Household Health Survey, 2008-2015 and late HIV diagnosis data from eHARS system, 2009-2016. Areas where trust in neighbors reported by Black/African Americans was medium (compared to low) had lower rates of late HIV diagnosis among Black/African Americans (Relative Risk (RR)=0.

Linear Relationships Between Total Hydrocarbons and Benzene, Toluene, Ethylbenzene, Xylene, and n-Hexane during the Deepwater Horizon Response and Clean-up.

Objectives: Our objectives were to (i) determine correlations between measurements of THC and of BTEX-H, (ii) apply these linear relationships to predict BTEX-H from measured THC, (iii) use these correlations as informative priors in Bayesian analyses to estimate exposures.

Methods: We used a Bayesian left-censored bivariate framework for all 3 objectives. First, we modeled the relationships (i.

Effect of Adjustment for Case Misclassification and Infection Date Uncertainty on Estimates of COVID-19 Effective Reproduction Number.

Background: Surveillance data captured during the COVID-19 pandemic may not be optimal to inform a public health response, because it is biased by imperfect test accuracy, differential access to testing, and uncertainty in date of infection.

Methods: We downloaded COVID-19 time-series surveillance data from the Colorado Department of Public Health & Environment by report and illness onset dates for 9 March 2020 to 30 September 2020. We used existing Bayesian methods to first adjust for misclassification in testing and surveillance, followed by deconvolution of date of infection.

Evaluating the informativeness of the Besag-York-Mollié CAR model.

The use of the conditional autoregressive framework proposed by Besag, York, and Mollié (1991; BYM) is ubiquitous in Bayesian disease mapping and spatial epidemiology. While it is understood that Bayesian inference is based on a combination of the information contained in the data and the information contributed by the model, quantifying the contribution of the model relative to the information in the data is often non-trivial. Here, we provide a measure of the contribution of the BYM framework by first considering the simple Poisson-gamma setting in which quantifying the prior's contribution is quite clear.

Life expectancy and mortality in 363 cities of Latin America.

The concept of a so-called urban advantage in health ignores the possibility of heterogeneity in health outcomes across cities. Using a harmonized dataset from the SALURBAL project, we describe variability and predictors of life expectancy and proportionate mortality in 363 cities across nine Latin American countries. Life expectancy differed substantially across cities within the same country.

Estimates of Occupational Inhalation Exposures to Six Oil-Related Compounds on the Four Rig Vessels Responding to the Deepwater Horizon Oil Spill.

Background: The 2010 Deepwater Horizon (DWH) oil spill involved thousands of workers and volunteers to mitigate the oil release and clean-up after the spill. Health concerns for these participants led to the initiation of a prospective epidemiological study (GuLF STUDY) to investigate potential adverse health outcomes associated with the oil spill response and clean-up (OSRC). Characterizing the chemical exposures of the OSRC workers was an essential component of the study.

Trends in Tract-Level Prevalence of Obesity in Philadelphia by Race-Ethnicity, Space, and Time.

The growing recognition of often substantial neighborhood variation in health within cities has motivated greater demand for reliable data on small-scale variations in health outcomes. The goal of this article is to explore temporal changes in geographic disparities in obesity prevalence in the City of Philadelphia by race and sex over the period 2000-2015. Our data consist of self-reported survey responses of non-Hispanic whites, non-Hispanic blacks, and Hispanics from the Southeastern Pennsylvania Household Health Survey.

Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER.

Introduction: CDC WONDER is a system developed to promote information-driven decision making and provide access to detailed public health information to the general public. Although CDC WONDER contains a wealth of data, any counts fewer than 10 are suppressed for confidentiality reasons, resulting in left-censored data. The objective of this analysis was to describe methods for the analysis of highly censored data.

Greenspace and Infant Mortality in Philadelphia, PA.

Despite mounting evidence that urban greenspace protects against mortality in adults, few studies have explored the relationship between greenspace and death among infants. Here, we describe results from an analysis of associations between greenness and infant mortality in Philadelphia, PA. We used images of the normalized difference vegetation index (NDVI), derived from processed satellite data, to estimate greenness density in each census tract.

The Rate Stabilizing Tool: Generating Stable Local-Level Measures of Chronic Disease.

Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. In response, we developed the Rate Stabilizing Tool (RST), an ArcGIS-based tool that enables users to input their own record-level data to generate more reliable age-standardized measures of chronic disease (eg, prevalence rates, mortality rates) or other population health outcomes at the county or census tract levels. The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units.

Changing rate orders of race-gender heart disease death rates: An exploration of county-level race-gender disparities.

A holistic view of racial and gender disparities that simultaneously compares multiple groups can suggest associated underlying contextual factors. Therefore, to more comprehensively understand temporal changes in combined racial and gender disparities, we examine variations in the orders of county-level race-gender specific heart disease death rates by age group from 1973-2015. We estimated county-level heart disease death rates by race, gender, and age group (35-44, 45-54, 55-64, 65-74, 75-84, ≥ 85, and ≥ 35) from the National Vital Statistics System of the National Center for Health Statistics from 1973-2015.

Using spatiotemporal models to generate synthetic data for public use.

When agencies release public-use data, they must be cognizant of the potential risk of disclosure associated with making their data publicly available. This issue is particularly pertinent in disease mapping, where small counts pose both inferential challenges and potential disclosure risks. While the small area estimation, disease mapping, and statistical disclosure limitation literatures are individually robust, there have been few intersections between them.

Ambient Fine Aerosol Concentrations in Multiple Metrics in Taconite Mining Operations.

Studies in environmental epidemiology and of occupational cohorts have implicated the effects of fine particulates with increased risk of cardiovascular diseases. Motivated by this evidence, we conducted an ambient air monitoring campaign to characterize fine aerosol concentrations around various taconite ore processes in six taconite mines in northeastern Minnesota. The ore processes were first categorized into 16 broad work areas/buildings.

Before the here and now: What we can learn from variation in spatiotemporal patterns of changing heart disease mortality by age group, time period, and birth cohort.

One hypothesized explanation for the recent slowing of declines in heart disease death rates is the generational shift in the timing and accumulation of risk factors. However, directly testing this hypothesis requires historical age-group-specific risk factor data that do not exist. Using national death records, we compared spatiotemporal patterns of heart disease death rates by age group, time period, and birth cohort to provide insight into possible drivers of trends.

A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data.

In many occupational hygiene settings, the demand for more accurate, more precise results is at odds with limited resources. To combat this, practitioners have begun using Bayesian methods to incorporate prior information into their statistical models in order to obtain more refined inference from their data. This is not without risk, however, as incorporating prior information that disagrees with the information contained in data can lead to spurious conclusions, particularly if the prior is too informative.