Scott D. Siegel, PhD, MHCDS

Director of Population Health Research

Publications

Mapping the Burden of Chronic Diseases in Delaware for Public Health Decision Making
Mapping the Burden of Chronic Diseases in Delaware for Public Health Decision Making
McIntire RK, Brooks M, Siegel SD, , Papas M

Dela J Public Health 2019 Feb;5(1):26-28

PMID: 34467011

Abstract

Public health decision-makers need to consider geographic differences in rates of chronic disease risk factors and outcomes in order to focus intervention efforts on populations exhibiting the greatest burden of disease. Increasingly, public health agencies are using geographic information systems (GIS) to analyze area-based variations and identify geographic priority areas for health promoting interventions. The articles in this issue are descriptive studies presenting the geographic distribution of select chronic disease risk factors and outcomes among Delaware communities. These studies emerged from a collaboration between the Christiana Care Value Institute and the Jefferson College of Population Health. These studies show that the burden of chronic diseases is not distributed evenly among communities in Delaware. The results of these studies add to the evidence base about public health in Delaware, and should inform public health practitioners working to improve the health of Delaware communities.
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Using Geospatial Analyses of Linked Electronic Health Records and Tobacco Outlet Data to Address the Social Determinants of Smoking
Using Geospatial Analyses of Linked Electronic Health Records and Tobacco Outlet Data to Address the Social Determinants of Smoking
Siegel SD, Brooks MM, Gbadebo BM, Laughery JT

Prev Chronic Dis 2019 11;16():E152

PMID: 31726020

Abstract

No abstract available.
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Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute's Geospatial Analytics Core
Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute's Geospatial Analytics Core
Brooks M, Brown C, , Liu W, Siegel SD,

Dela J Public Health 2020 Jul;6(2):66-70

PMID: 34467114

Abstract

INTRODUCTION: COVID-19 exemplifies the spatial nature of infectious disease in both its mechanism of transmission and the community-level conditions that facilitate its spread. With a long history of use for infectious disease applications, maps and geographic information systems (GIS) have been widely used in recent months for surveillance and risk prediction mapping. The Value Institute's Geospatial Analytics Core applied spatial methodologies to inform ChristianaCare's pandemic response around telehealth, testing disparities, and test site prioritization.

METHODS: Descriptive data related to disparities in telehealth utilization were mapped to identify areas in which intervention is needed to increase telehealth access. Cluster detection methodology was used to identify "hot" and "cold" spots for COVID-19 testing by place and race across New Castle County, DE. A composite risk score was created to prioritize communities for testing sites. All analyses took place in Delaware from March-June 2020, with particular emphasis on New Castle County.

RESULTS: Parts of northeastern New Castle County and western Sussex County were highlighted for intervention to increase broadband internet access for telehealth utilization. "Cold" spots for COVID-19 testing were found in New Castle County, indicating neighborhoods in which testing levels were significantly lower than expected. Data for testing levels, disease positivity, and socioeconomic risk factors were used to identify communities in northeastern New Castle County that warranted new test sites to mitigate disease spread.

PUBLIC HEALTH IMPLICATIONS: Geospatial methodologies can be used to combine electronic health record data and population-level spatial data for pandemic response efforts. This allows health systems to confidently identify areas of need while mitigating disparities in resource allocation.

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Operationalizing the Population Health Framework: Clinical Characteristics, Social Context, and the Built Environment
Operationalizing the Population Health Framework: Clinical Characteristics, Social Context, and the Built Environment
Siegel SD, Brooks M, Curriero FC

Popul Health Manag 2021 08;24(4):454-462

PMID: 34406088

Abstract

As a framework, population health emphasizes health outcomes for entire populations, the broad range of determinants of these outcomes, and the comparative effectiveness of medical and public health interventions. In practice, however, many contemporary population health programs instead focus on small subsets of patients who account for a disproportionate share of health care utilization, often with disappointing results. The authors proposed a new approach to operationalize population health in clinical settings, with the example of tobacco use. Electronic health record (EHR) data from a mid-Atlantic health system were used to: (1) define and describe a hospital-based population of current smokers, (2) analyze the demographic characteristics of the population to consider how the social context may impact treatment, and (3) join EHR data with public licensing data on tobacco retail locations to assess the relationship between the built environment and smoking status. Out of a total of 20,310 unique adult admissions to the health system, 3749 (18.5%) were current smokers. Compared to never smokers, current smokers were significantly younger, more likely to be male, more likely to be Black/African American, less likely to be Hispanic/Latino/a, and more likely to be on Medicaid or be self-pay. Current vs. former smokers had significantly higher exposure to tobacco retail locations, even after adjusting for demographic and other covariates. By defining populations around leading modifiable medical determinants of health, and accounting for the larger context of sociodemographic factors and the built environment, health systems can invest in comprehensive programs designed to produce the greatest population health returns.
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Characterizing the spatial relationship between smoking status and tobacco retail exposure: Implications for policy development and evaluation
Characterizing the spatial relationship between smoking status and tobacco retail exposure: Implications for policy development and evaluation
Brooks MM, Siegel SD, Curriero FC

Health Place 2021 03;68():102530

PMID: 33609995

Abstract

Tobacco retail density and smoking prevalence remain elevated in marginalized communities, underscoring the need for strategies to address these place-based disparities. The spatial variation of smokers and tobacco retailers is often measured by aggregating them to area-level units (e.g., census tracts), but spatial statistical methods that use point-level data, such as spatial intensity and K-functions, can better describe their geographic patterns. We applied these methods to a case study in New Castle County, DE to characterize the cross-sectional spatial relationship between tobacco retailers and smokers, finding that current smokers experience greater tobacco retail exposure and clustering relative to former smokers. We discuss how analysis at different geographic scales can provide complementary insights for tobacco control policy.
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The co-occurrence of smoking and alcohol use disorder in a hospital-based population: Applying a multimorbidity framework using geographic information system methods
The co-occurrence of smoking and alcohol use disorder in a hospital-based population: Applying a multimorbidity framework using geographic information system methods
Siegel SD, Brooks M, Ragozine-Bush HE, Schnoll RA, Curriero FC

Addict Behav 2021 07;118():106883

PMID: 33714034

Abstract

Tobacco and alcohol use are leading causes of premature mortality in the US and concurrent use is associated with even greater health risks. A cross-sectional study of 20,310 patients admitted to a Mid-Atlantic acute health care system between July 1, 2018 and June 30, 2019 were categorized according to smoking and alcohol use disorder (AUD) status. Of the total admissions, 1464 (7.2%) were current smokers with an AUD. These patients were younger (52.4 vs. 63.9), more likely to be male (64.1% vs. 38.0%) and covered by Medicaid (46.9% vs. 11.6%), and resided in proximity to higher counts of tobacco (10.3 vs. 4.72) and alcohol (2.24 vs. 1.14) retailers than never smokers without an AUD. Clinically, these patients had higher rates of other substance use disorders (60.4% vs. 6.1%), depression (64.6% vs. 34.8%), HIV/AIDS (3.3% vs. 0.6%), and liver disease (40.7% vs. 13.2%) than never smokers without an AUD. Patients who concurrently smoke and have an AUD face unique and serious health risks. A multimorbidity framework can guide clinical and community-based interventions for individuals with concurrent psychiatric and chronic medical conditions, complex social needs, and adverse environmental exposures.
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A Population Health Assessment in a Community Cancer Center Catchment Area: Triple-Negative Breast Cancer, Alcohol Use, and Obesity in New Castle County, Delaware
A Population Health Assessment in a Community Cancer Center Catchment Area: Triple-Negative Breast Cancer, Alcohol Use, and Obesity in New Castle County, Delaware
Siegel SD, Brooks MM, Sims-Mourtada J, Schug ZT, Leonard DJ, Petrelli N, Curriero FC

Cancer Epidemiol Biomarkers Prev 2022 01;31(1):108-116

PMID: 34737210

Abstract

BACKGROUND">The NCI requires designated cancer centers to conduct catchment area assessments to guide cancer control and prevention efforts designed to reduce the local cancer burden. We extended and adapted this approach to a community cancer center catchment area with elevated rates of triple-negative breast cancer (TNBC).</Abstr: The NCI requires designated cancer centers to conduct catchment area assessments to guide cancer control and prevention efforts designed to reduce the local cancer burden. We extended and adapted this approach to a community cancer center catchment area with elevated rates of triple-negative breast cancer (TNBC).

METHODS">Cancer registry data for 462 TNBC and 2,987 "Not-TNBC" cases diagnosed between 2012 and 2020 at the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), located in New Castle County, Delaware, were geocoded to detect areas of elevated risk (hot spots) and decreased risk (cold spots). Next, electronic health record (EHR) data on obesity and alcohol use disorder (AUD) and catchment area measures of fast-food and alcohol retailers were used to assess for spatial relationships between TNBC hot spots and potentially modifiable risk factors.</Abstr: Cancer registry data for 462 TNBC and 2,987 "Not-TNBC" cases diagnosed between 2012 and 2020 at the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), located in New Castle County, Delaware, were geocoded to detect areas of elevated risk (hot spots) and decreased risk (cold spots). Next, electronic health record (EHR) data on obesity and alcohol use disorder (AUD) and catchment area measures of fast-food and alcohol retailers were used to assess for spatial relationships between TNBC hot spots and potentially modifiable risk factors.

RESULTS">Two hot and two cold spots were identified for TNBC within the catchment area. The hot spots accounted for 11% of the catchment area but nearly a third of all TNBC cases. Higher rates of unhealthy alcohol use and obesity were observed within the hot spots.</Abstr: Two hot and two cold spots were identified for TNBC within the catchment area. The hot spots accounted for 11% of the catchment area but nearly a third of all TNBC cases. Higher rates of unhealthy alcohol use and obesity were observed within the hot spots.

CONCLUSIONS">The use of spatial methods to analyze cancer registry and other secondary data sources can inform cancer control and prevention efforts within community cancer center catchment areas, where limited resources can preclude the collection of new primary data.</Abstr: The use of spatial methods to analyze cancer registry and other secondary data sources can inform cancer control and prevention efforts within community cancer center catchment areas, where limited resources can preclude the collection of new primary data.

IMPACT">Targeting community outreach and engagement activities to TNBC hot spots offers the potential to reduce the population-level burden of cancer efficiently and equitably.©2021 American Association for Cancer Research.</CopyrightInfo: Targeting community outreach and engagement activities to TNBC hot spots offers the potential to reduce the population-level burden of cancer efficiently and equitably.

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Racial disparities in triple negative breast cancer: toward a causal architecture approach
Racial disparities in triple negative breast cancer: toward a causal architecture approach
Siegel SD, Brooks MM, Lynch SM, Sims-Mourtada J, Schug ZT, Curriero FC

Breast Cancer Res 2022 Jun;24(1):37

PMID: 35650633

Abstract

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that disproportionately affects Black women and contributes to racial disparities in breast cancer mortality. Prior research has suggested that neighborhood effects may contribute to this disparity beyond individual risk factors.

METHODS: The sample included a cohort of 3316 breast cancer cases diagnosed between 2012 and 2020 in New Castle County, Delaware, a geographic region of the US with elevated rates of TNBC. Multilevel methods and geospatial mapping evaluated whether the race, income, and race/income versions of the neighborhood Index of Concentration at the Extremes (ICE) metric could efficiently identify census tracts (CT) with higher odds of TNBC relative to other forms of invasive breast cancer. Odds ratios (OR) and 95% confidence intervals (CI) were reported; p-values < 0.05 were significant. Additional analyses examined area-level differences in exposure to metabolic risk factors, including unhealthy alcohol use and obesity.

RESULTS: The ICE-Race, -Income-, and Race/Income metrics were each associated with greater census tract odds of TNBC on a bivariate basis. However, only ICE-Race was significantly associated with higher odds of TNBC after adjustment for patient-level age and race (most disadvantaged CT: OR = 2.09; 95% CI 1.40-3.13), providing support for neighborhood effects. Higher counts of alcohol and fast-food retailers, and correspondingly higher rates of unhealthy alcohol use and obesity, were observed in CTs that were classified into the most disadvantaged ICE-Race quintile and had the highest odds of TNBC.

CONCLUSION: The use of ICE can facilitate the monitoring of cancer inequities and advance the study of racial disparities in breast cancer.

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A Population Health Assessment in a Community Cancer Center Catchment Area: Triple-Negative Breast Cancer, Alcohol Use, and Obesity in New Castle County, Delaware
A Population Health Assessment in a Community Cancer Center Catchment Area: Triple-Negative Breast Cancer, Alcohol Use, and Obesity in New Castle County, Delaware
Siegel SD, Brooks MM, Sims-Mourtada J, Schug ZT, Leonard DJ, Petrelli N, Curriero FC

Cancer Epidemiol Biomarkers Prev 2021 Nov;():

PMID: 34737210

Abstract

BACKGROUND: The NCI requires designated cancer centers to conduct catchment area assessments to guide cancer control and prevention efforts designed to reduce the local cancer burden. We extended and adapted this approach to a community cancer center catchment area with elevated rates of triple-negative breast cancer (TNBC).

METHODS: Cancer registry data for 462 TNBC and 2,987 "Not-TNBC" cases diagnosed between 2012 and 2020 at the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), located in New Castle County, Delaware, were geocoded to detect areas of elevated risk (hot spots) and decreased risk (cold spots). Next, electronic health record (EHR) data on obesity and alcohol use disorder (AUD) and catchment area measures of fast-food and alcohol retailers were used to assess for spatial relationships between TNBC hot spots and potentially modifiable risk factors.

RESULTS: Two hot and two cold spots were identified for TNBC within the catchment area. The hot spots accounted for 11% of the catchment area but nearly a third of all TNBC cases. Higher rates of unhealthy alcohol use and obesity were observed within the hot spots.

CONCLUSIONS: The use of spatial methods to analyze cancer registry and other secondary data sources can inform cancer control and prevention efforts within community cancer center catchment areas, where limited resources can preclude the collection of new primary data.

IMPACT: Targeting community outreach and engagement activities to TNBC hot spots offers the potential to reduce the population-level burden of cancer efficiently and equitably.

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The Use of the Nicotine Metabolite Ratio as a Biomarker to Personalize Smoking Cessation Treatment: Current Evidence and Future Directions
The Use of the Nicotine Metabolite Ratio as a Biomarker to Personalize Smoking Cessation Treatment: Current Evidence and Future Directions
Siegel SD, Lerman C, Flitter A, Schnoll RA

Cancer Prev Res (Phila) 2020 03;13(3):261-272

PMID: 32132120

Abstract

The nicotine metabolite ratio (NMR), a genetically informed biomarker of rate of nicotine metabolism, has been validated as a tool to select the optimal treatment for individual smokers, thereby improving treatment outcomes. This review summarizes the evidence supporting the development of the NMR as a biomarker of individual differences in nicotine metabolism, the relationship between the NMR and smoking behavior, the clinical utility of using the NMR to personalize treatments for smoking cessation, and the potential mechanisms that underlie the relationship between NMR and smoking cessation. We conclude with a call for additional research necessary to determine the ultimate benefits of using the NMR to personalize treatments for smoking cessation. These future directions include measurement and other methodologic considerations, disseminating this approach to at-risk subpopulations, expanding the NMR to evaluate its efficacy in predicting treatment responses to e-cigarettes and other noncigarette forms of nicotine, and implementation science including cost-effectiveness analyses..
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