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. Due to the small number of births in many Pennsylvania counties when stratified by race/ethnicity, our methods must walk a fine line: While we wish to leverage spatial structure to improve the precision of our estimates, we also wish to avoid oversmoothing the data, which can yield spurious conclusions. As such, we develop a framework by which we can measure (and control) the informativeness of our spatial model. To analyze the data, we first model the Pennsylvania birth data using the conditional autoregressive model to demonstrate the extent to which it can lead to oversmoothing. We then reanalyze the data using our proposed framework and highlight its ability to detect (or not detect) evidence of racial/ethnic disparities in the incidence of low birthweight.

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. This paper proposes a two-stage Bayesian statistical framework that provides a broad, flexible approach to studying the spatially varying association between premature mortality and racialized economic segregation while accounting for neighborhood-level latent health factors across US counties. The two-stage framework reduces the dimensionality of spatially correlated data and highlights the importance of accounting for spatial autocorrelation in racialized economic segregation measures, in health equity focused settings.

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. There is a lack of standard, validated measures to assess livability in the US. This study employed factor analytic methods to create measures of livability in Connecticut using data from the DataHaven Community Wellbeing Survey (DCWS) (n = 32,262). Results identified a 3-factor model (safety, opportunity, and infrastructure) as the best fit, explaining 69% of the variance in survey items. Newly created livability measures had high internal consistency, in addition to high convergent validity with other area-level measures.

Spatial Patterning of Spontaneous and Medically Indicated Preterm Birth in Philadelphia.

Preterm birth (PTB) remains a key public health issue that disproportionately affects Black individuals. Since spontaneous PTB (sPTB) and medically indicated PTB (mPTB) may have different causes and interventions, we quantified racial disparities for sPTB and mPTB, and we characterized the geographic patterning of these phenotypes, overall and according to race/ethnicity. We examined a pregnancy cohort of 83,952 singleton births at 2 Philadelphia hospitals from 2008-2020, and classified each PTB as sPTB or mPTB. We used binomial regression to quantify the magnitude of racial disparities between non-Hispanic Black and non-Hispanic White individuals, then generated small area estimates by applying a Bayesian model that accounts for small numbers and smooths estimates of PTB risk by borrowing information from neighboring areas. Racial disparities in both sPTB and mPTB were significant (relative risk of sPTB = 1.83, 95% confidence interval: 1.70, 1.98; relative risk of mPTB = 2.20, 95% confidence interval: 2.00, 2.42). The disparity was 20% greater in mPTB than sPTB. There was substantial geographic variation in PTB, sPTB, and mPTB risks and racial disparity. Our findings underscore the importance of distinguishing PTB phenotypes within the context of public health and preventive medicine. Future work should consider social and environmental exposures that may explain geographic differences in PTB risk and disparities.

Social inequalities and COVID-19 mortality between neighborhoods of Bariloche city, Argentina.

BACKGROUND: The COVID-19 pandemic has shown how intraurban inequalities are likely to reinforce health and social inequalities. Studies at small area level help to visualize social inequialities hidden in large areas as cities or regions. AIM: To describe the spatial patterning of COVID-19 death rates in neighborhoods of the medium-sized city of Bariloche, Argentina, and to explore its relationship with the socioeconomic characteristics of neighborhoods. METHODS: We conducted an ecological study in Bariloche, Argentina. The outcome was counts of COVID-19 deaths between June 2020 and May 2022 obtained from the surveillance system and georeferenced to neighborhoods. We estimated crude- and age-adjusted death rates by neighborhood using a Bayesian approach through a Poisson regression that accounts for spatial-autocorrelation via Conditional Autoregressive (CAR) structure. We also analyzed associations of age-adjusted death rates with area-level socioeconomic indicators. RESULTS: Median COVID-19 death rate across neighborhoods was 17.9 (10th/90th percentile of 6.3/35.2) per 10,000 inhabitants. We found lower age-adjusted rates in the city core and western part of the city. The age-adjusted death rate in the most deprived areas was almost double than in the least deprived areas, with an education-related relative index of inequality (RII) of 2.14 (95% CI 1.55 to 2.96). CONCLUSION: We found spatial heterogeneity and intraurban variability in age-adjusted COVID-19 death rates, with a clear social gradient, and a higher burden in already deprived areas. This highlights the importance of studying inequalities in health outcomes across small areas to inform placed-based interventions.

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. As part of the SALURBAL project, we used georeferenced death certificates in 2015-2017 for CABA, Argentina. We used a spatial Bayesian Poisson model using the TOPALS method to estimate age- and sex-specific mortality rates. We used life tables to estimate LE at birth. We obtained data on neighborhood socioeconomic characteristics from the 2010 census and analyzed their associations. LE at birth was higher for women (median of across neighborhoods = 81.1 years) compared to men (76.7 years). We found a gap in LE of 9.3 (women) and 14.9 years (men) between areas with the highest and the lowest LE. Better socioeconomic characteristics were associated with higher LE. For example, mean differences in LE at birth in areas with highest versus lowest values of composite SES index were 2.79 years (95% CI: 2.30 to 3.28) in women and 5.61 years (95% CI: 4.98 to 6.24) in men. We found large spatial inequities in LE across neighborhoods of a large city in Latin America, highlighting the importance of place-based policies to address this gap.

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)=0.52, 95% credible interval (CrI)= 0.34, 0.80). In contrast, areas where trust in neighbors reported by Black/African Americans were highest had lower late HIV diagnosis rates among Whites (RR=0.35, 95% CrI= 0.16, 0.76). Race/ethnic-specific differences in social cohesion may have implications for designing interventions aimed at modifying area-level social factors to reduce racial disparities in late HIV diagnosis.

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. Workers on the four oil rig vessels mitigating the spill and located within a 1852 m (1 nautical mile) radius of the damaged wellhead [the Discoverer Enterprise (Enterprise), the Development Driller II (DDII), the Development Driller III (DDIII), and the HelixQ4000] had some of the greatest potential for chemical exposures. OBJECTIVES: The aim of this paper is to characterize potential personal chemical exposures via the inhalation route for workers on those four rig vessels. Specifically, we presented our methodology and descriptive statistics of exposure estimates for total hydrocarbons (THCs), benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H) for various job groups to develop exposure groups for the GuLF STUDY cohort. METHODS: Using descriptive information associated with the measurements taken on various jobs on these rig vessels and with job titles from study participant responses to the study questionnaire, job groups [unique job/rig/time period (TP) combinations] were developed to describe groups of workers with the same or closely related job titles. A total of 500 job groups were considered for estimation using the available 8139 personal measurements. We used a univariate Bayesian model to analyze the THC measurements and a bivariate Bayesian regression framework to jointly model the measurements of THC and each of the BTEX-H chemicals separately, both models taking into account the many measurements that were below the analytic limit of detection. RESULTS: Highest THC exposures occurred in TP1a and TP1b, which was before the well was mechanically capped. The posterior medians of the arithmetic mean (AM) ranged from 0.11 ppm ('Inside/Other', TP1b, DDII; and 'Driller', TP3, DDII) to 14.67 ppm ('Methanol Operations', TP1b, Enterprise). There were statistical differences between the THC AMs by broad job groups, rigs, and time periods. The AMs for BTEX-H were generally about two to three orders of magnitude lower than the THC AMs, with benzene and ethylbenzene measurements being highly censored. CONCLUSIONS: Our results add new insights to the limited literature on exposures associated with oil spill responses and support the current epidemiologic investigation of potential adverse health effects of the oil spill.

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.e. correlations) between THC and each BTEX-H chemical for various overarching groups of measurements using linear regression to determine if correlations derived from linear relationships differed by various exposure determinants. We then used the same linear regression relationships to predict (or impute) BTEX-H measurements from THC when only THC measurements were available. Finally, we used the same linear relationships as priors for the final exposure models that used real and predicted data to develop exposure estimate statistics for each individual exposure group. RESULTS: Correlations between measurements of THC and each of the BTEX-H chemicals (n = 120 for each of BTEX, 36 for n-hexane) differed substantially by area of the Gulf of Mexico and by time period that reflected different oil-spill related exposure opportunities. The correlations generally exceeded 0.5. Use of regression relationships to impute missing data resulted in the addition of >23 000 n-hexane and 541 observations for each of BTEX. The relationships were then used as priors for the calculation of exposure statistics while accounting for censored measurement data. CONCLUSIONS: Taking advantage of observed relationships between THC and BTEX-H allowed us to develop robust exposure estimates where a large amount of data were missing, strengthening our exposure estimation process for the epidemiologic study.

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. We summarised variability in life expectancy at birth by using the difference between the 90th and 10th percentile of the distribution of life expectancy across small areas (P90-P10 gap) and evaluated associations with small-area socioeconomic characteristics by calculating a Slope Index of Inequality in linear regression. PRIMARY OUTCOME: Life expectancy at birth. RESULTS: The median life expectancy at birth was 80.3 years in women (P90-P10 gap=3.2 years) and 75.1 years in men (P90-P10 gap=4.6 years). We found higher life expectancies in the core and northwest parts of the city, especially among women. We found positive associations between life expectancy and better small-area socioeconomic characteristics, especially among men. Mean differences in life expectancy between the highest versus the lowest decile of area characteristics in men (women) were 3.03 (2.58), 3.52 (2.56) and 2.97 (2.31) years for % adults with high school education or above, % persons aged 15-17 attending school, and % households with water inside the dwelling, respectively. Lower values of % overcrowded households and unemployment rate were associated with longer life expectancy: mean differences comparing the lowest versus the highest decile were 3.03 and 2.73 in men and 2.57 and 2.34 years in women, respectively. CONCLUSION: Life expectancy is substantially heterogeneous and patterned by socioeconomic characteristics in a mid-sized city of a middle-income country, suggesting that small-area inequities in life expectancy are not limited to large cities or high-income countries.

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. We propagated forward uncertainty from each step corresponding to 10,000 posterior time-series of doubly adjusted epidemic curves. The effective reproduction number (Rt), a parameter of principal interest in tracking the pandemic, gauged the impact of the adjustment on inference. RESULTS: Observed period prevalence was 1.3%; median of the posterior of true (adjusted) prevalence was 1.7% (95% credible interval [CrI]: 1.4%, 1.8%). Sensitivity of surveillance declined over the course of the epidemic from a median of 88.8% (95% CrI: 86.3%, 89.8%) to a median of 60.8% (95% CrI: 60.1%, 62.6%). The mean (minimum, maximum) values of Rt were higher and more variable by report date, 1.12 (0.77, 4.13), compared to those following adjustment, 1.05 (0.89, 1.73). The epidemic curve by report date tended to overestimate Rt early on and be more susceptible to fluctuations in data. CONCLUSION: Adjusting for epidemic curves based on surveillance data is necessary if estimates of missed cases and the effective reproduction number play a role in management of the COVID-19 pandemic.

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. We then propose a relationship between gamma and lognormal priors that we then extend to cover the framework proposed by BYM. Following a brief simulation study in which we illustrate the accuracy of our lognormal approximation of the gamma prior, we analyze a dataset comprised of county-level heart disease-related death data across the United States. In addition to demonstrating the potential for the BYM framework to correspond to a highly informative prior specification, we also illustrate the sensitivity of death rate estimates to changes in the informativeness of the BYM framework.

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. Cause-specific mortality also varied across cities, with some causes of death (unintentional and violent injuries and deaths) showing large variation within countries, whereas other causes of death (communicable, maternal, neonatal and nutritional, cancer, cardiovascular disease and other noncommunicable diseases) varied substantially between countries. In multivariable mixed models, higher levels of education, water access and sanitation and less overcrowding were associated with longer life expectancy, a relatively lower proportion of communicable, maternal, neonatal and nutritional deaths and a higher proportion of deaths from cancer, cardiovascular disease and other noncommunicable diseases. These results highlight considerable heterogeneity in life expectancy and causes of death across cities of Latin America, revealing modifiable factors that could be amenable to urban policies aimed toward improving urban health in Latin America and more generally in other urban environments.

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. To analyze these data-and to obtain more reliable estimates of the prevalence of obesity-we apply a Bayesian model that simultaneously accounts for spatial-, temporal-, and between-race/ethnicity dependence structures. This approach yields estimates of the obesity prevalence by age, race/ethnicity, sex, and poverty status for each census tract at all time-points in our study period. While the data suggest that the prevalence of obesity has increased at the city-level for men and women of all three race/ethnicities, the magnitude and geographic distribution of these increases differ substantially by race/ethnicity and sex. The method can be flexibly used to describe and visualize spatial heterogeneities in levels, trends, and in disparities. This is useful for targeting, surveillance, and etiologic research.

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. METHODS: A substitution approach was compared with 1) a simple, nonspatial Bayesian model that smooths rates toward their statewide averages and 2) a more complex Bayesian model that accounts for spatial and between-age sources of dependence. Age group-specific county-level data on heart disease mortality were used for the comparisons. RESULTS: Although the substitution and nonspatial approach provided age-standardized rate estimates that were more highly correlated with the true rate estimates, the estimates from the spatial Bayesian model provided a superior compromise between goodness-of-fit and model complexity, as measured by the deviance information criterion. In addition, the spatial Bayesian model provided rate estimates with greater precision than the nonspatial approach; in contrast, the substitution approach did not provide estimates of uncertainty. CONCLUSION: Because of the ability to account for multiple sources of dependence and the flexibility to include covariate information, the use of spatial Bayesian models should be considered when analyzing highly censored data from CDC WONDER.

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. We linked these data with census tract level counts of total infant mortality cases (n = 963) and births (n = 113,610) in years 2010-2014, and used Bayesian spatial areal unit, conditional autoregressive models to estimate associations between greenness and infant mortality. The models included a set of random effects to account for spatial autocorrelation between neighboring census tracts. Infant mortality counts were modeled using a Poisson distribution, and the logarithm of total births in each census tract was specified as the offset term. The following variables were included as potential confounders and effect modifiers: percentage non-Hispanic black, percentage living below the poverty line, an indicator of housing quality, and population density. In adjusted models, the rate of infant mortality was 27% higher in less green compared to more green tracts (95% CI 1.02-1.59). These results contribute further evidence that greenspace may be a health promoting environmental asset.

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. The RST also provides indicators of the reliability of point estimates. In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. We demonstrate the dramatic reduction in root mean-squared error (rMSE), indicating a better compromise between accuracy and stability for both smoothing approaches relative to the unsmoothed estimates. Finally, we provide an example of the RST's use. This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test.

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. We then took air samples at 91 fixed locations using an array of direct-reading instruments to obtain measurements of mass (PM2.5 or particles with aerodynamic diameter <2.5 µm, and respirable particulate matter or RPM), alveolar-deposited surface area (ADSA), and particle number (PN) concentrations. At each location, a respirable gravimetric pump (which was used for calibration purposes) and the instruments measured the ambient dust level for 4 h producing ~240 1-min averaging real-time measurements. To analyze these data, we fit a Bayesian hierarchical model with an autoregressive order 1 correlation structure to estimate pooled concentrations for the 16 work areas/buildings while accounting for temporal correlation. PM2.5 and RPM average ambient concentrations were highly correlated to each other (Pearson's correlation = 0.98), followed by ADSA and PN correlation (R = 0.77). Office and control room areas were found to have the lowest concentrations in all four metrics when compared to other groups. Distinguishing between concentration levels among the remaining groups was more difficult due to the high uncertainty associated with the geometric mean estimates. The geometric standard deviation within location (GSDWL) generally ranged from 1 to 3 for all exposure metrics, except for a few locations that may have had changes in the work activities that generated the observed peaks and variability during the sampling duration. The geometric standard deviation between locations estimates were generally higher than GSDWL, which may indicate larger variability in the processes/activities between locations within each broad work area/building. Future work may look into whether it is feasible to use area measurements for epidemiological investigation and use personal measurements (if available) to validate such approach.

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. We then ordered these rates from lowest to highest for each county and year. The predominant national rate order (i.e., white women (WW) < black women (BW) < white men (WM) < black men (BM)) was most common in younger age groups. Inverted rates for black women and white men (WW

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. Here, we formally propose the use of spatiotemporal data analysis methods to generate synthetic data for public use. Specifically, we analyze ten years of county-level heart disease death counts for multiple age-groups using a Bayesian model that accounts for dependence spatially, temporally, and between age-groups; generating synthetic data from the resulting posterior predictive distribution will preserve these dependencies. After demonstrating the synthetic data's privacy-preserving features, we illustrate their utility by comparing estimates of urban/rural disparities from the synthetic data to those from data with small counts suppressed.