InsightTX dashboard exemplifies the Texas Spatial-Temporal Data Science, Informatics, and Modeling Core Facility (TEX-SIM) mission in providing advanced spatial-temporal data sources, visualization and analysis tools that address cancer-related population health challenges across the geographic regions in Texas.
Texas Spatial-Temporal Data Science, Informatics and Modeling Core (TEX-SIM)
TEX-SIM is an innovative initiative to transform cancer population health research, strengthen cancer prevention and control, improve health outcomes across all communities, and support the mission of CPRIT (Cancer Prevention & Research Institute of Texas) to reduce the cancer burden in Texas.
Understanding Cancer Where It Happens
Texas Spatial-Temporal Data Science, Informatics, and Modeling Core Facility (TEX-SIM) transforms how we understand and address cancer across Texas by focusing on communities.
Cancer affects everyone, but not equally. Many communities face a heavier cancer burden due to social and economic challenges, environmental exposures, limited access to healthcare, and geographic barriers. Addressing these barriers requires understanding how place and time influence cancer risk, care, and outcomes.
What Is Cancer Population Health?
Cancer population health focuses on improving prevention, treatment access, and outcomes at the community and group level. Rather than looking only at individual patients, it identifies patterns and differences in cancer risk, access and outcomes across populations revealing unmet community needs and opportunities for action where they matter most.
The TEX-SIM Core Facility
The Texas Spatial-Temporal Data Science, Informatics, and Modeling Core Facility (TEX-SIM) was created to support this work.
TEX-SIM integrates environmental exposures, georeferenced health data, and healthcare access metrics to reveal how these factors shape cancer risk and outcomes over time and across geography. By combining advanced analytics with spatial-temporal modeling, TEX-SIM helps researchers and communities see patterns that are otherwise hidden.
Unlike traditional approaches, TEX-SIM focuses on community-level trends, developing customized community profiles that highlight needs, strengths, and opportunities. Many of these efforts prioritize cancers that disproportionately affect underserved and disadvantaged populations.
The result: actionable insights that reduce barriers to cancer prevention and care, support early detection and community engagement, and strengthen Texas communities.
Figure 1. TEX-SIM workflow to advance population-focused cancer research.
How TEX-SIM Helps Turn Data Into Action
TEX-SIM provides the infrastructure, analytics, and visualization tools needed to:
- Identify high-risk populations
- Understand factors influencing cancer risk, and outcomes across communities
- Design targeted prevention and treatment strategies
- Expand access to high-quality, harmonized data
- Support evidence-based decision-making across Texas
Our Mission and Impact
Our Focus Areas
Build the Infrastructure
Develop a scalable, state-of-the-art data and computing platform for spatial-temporal cancer research, integrating diverse datasets with advanced modeling tools.
Deliver Data, Analytics, and Visualizations
Provide harmonized datasets, interactive dashboards, and sophisticated analytics to help researchers identify population-level patterns, address unmet community needs and develop targeted solutions.
Serve as a Hub for Collaboration and Training
Establish TEX-SIM as a central resource for spatial-temporal data science in cancer population health through collaboration, training, and workshops — advancing precision prevention and improving outcomes nationwide.
Available Services
TEX-SIM provides specialized population-health data science services to advance cancer research through bioinformatics and data linkage, spatial-temporal data management and modeling, population epidemiology and study design, and visualization, including dashboard development and deployment. A list of our service offerings is provided below.
Bioinformatics and Data Linkage Service
- Link individual or patient-level cancer outcome data (e.g., cohorts, EHR)
- Link patient locations (e.g., residential addresses) with TEX-SIM curated datasets on environmental exposure, social determinants of health (SDOH), or nearby healthcare facilities
- Comprehensive geocoding of locations
- All data linkage will be conducted in a professionally curated, HIPAA-compliant environment to ensure data security and integrity
Spatial-Temporal Data and Modeling Service
- Investigate how environmental, contextual, and socioeconomic factors influence cancer outcomes across regions and time periods
- Utilize advanced statistical techniques, including spatial-temporal modeling, small area estimation, multi-level models, and tailored simulation studies
- Translate data science findings into clear, accessible summaries tailored for non-technical audiences to ensure stakeholders and community partners can effectively understand and apply insights
Population Epidemiology Service
- Provide guidance on epidemiological study designs for population-level research by assessing study bias, validity, and sensitivity
- Interpret results from both observational and intervention studies to inform public health action and improve health outcomes across communities
- Measure and utilize environmental exposures across various scales and granularities
Dashboard and Visualization Service
- Create user-friendly dashboards and visualizations to better understand and communicate data and results
- Guide resource allocation and intervention efforts to empower our communities
InsightTX: Texas Cancer Screening & Environmental Risk Dashboard
Key Features of InsightTX
The Community Selection Design module operationalizes TEX-SIM’s approach for generating customized community profiles and prioritizing intervention areas. It currently pilots a data-driven method to identify census tracts for colorectal cancer screening among adults aged 45 and older using percentile-based thresholds across three dimensions: cancer screening rates, the CDC/ATSDR Social Vulnerability Index, and a composite Risk Factor Index (obesity, diabetes, binge drinking, smoking, and physical inactivity). This module demonstrates how real-time analytics can guide community-based intervention planning.
InsightTX supports TEX-SIM’s mission to improve cancer prevention and control in Texas by providing actionable, place-based insights through real-time data visualization.
As one of TEX-SIM’s public health insight tools, the dashboard will continue to expand by integrating additional data sources and advanced analytical features to strengthen statewide cancer research and improve cancer outcomes across Texas.
CPRIT 2026 Innovations Conference
TEX-SIM actively collaborates with CPRIT researchers and public health partners statewide. Connect with our team to explore our spatial-temporal datasets or submit a service request.
- Date: October 5-7, 2026
- Location: Moody Gardens Hotel, Spa and Convention Center, Galveston, Texas
TEX-SIM in the News
- CPRIT | May 21, 2025
Grants include millions for recruitment, prevention services, and product development.
Featured Publications
Journal of Clinical Medicine | May 30, 2026
Rural US populations bear a high burden of cardiovascular disease, with hypertension contributing to the rural–urban gap. We examined hypertension prevalence and control across the rural–urban continuum among patients in a large healthcare system in Northeast Texas.
JAMA Network Open Invited Commentary | February 18, 2026
A substantial body of literature is devoted to relationships between myriad environmental factors and cancer survival, yet the role of geographic operational scale has been paid little attention.
Journal of the Royal Statistic Society | November 4, 2025
Melanoma is projected to become the second most diagnosed cancer in the United States by 2040, emphasizing the importance of early detection for improving survival outcomes. Accurately estimating late-stage melanoma incidence at granular geographical levels is challenging due to its rarity and data sparsity.
Conference Abstracts & Presentations
- AMIA Annual Symposium | November, 2026 | Student Paper Presentation
- CARE-DQ: A clinical and social context–informed framework for EHR data quality assessment and readiness
- AMIA Annual Symposium | November, 2026 | Peer-Reviewed Poster Presentation
- Comparison of General-Purpose and Specialized Commercial Large Language Models for Clinical Question Answering with Respect to Accuracy and Completeness
- AMIA Annual Symposium | November, 2026 | Peer-Reviewed Poster Presentation
- Evaluation of Claude 3.7 in Structured Extraction of RCT and Target Trial Emulation Protocols
- 2025 APHA Conference | November, 2025 | Student Research Award Competition
- Bayesian heterogeneity learning for spatial-temporal count data: An application to melanoma cancer registry data in Texas
Our Expertise
TEX-SIM offers specialized services to the cancer research community, focusing on spatial-temporal data, advanced analytical methods, data visualizations, interactive dashboards, and applications tailored to individual research needs. TEX-SIM is directed and managed by Dr. Cici Bauer, Associate Professor at the School of Public Health and the Director (and founder) of the Center for Spatial-Temporal Modeling for Applications in Population Sciences (CSMAPS) at UTHealth Houston. TEX-SIM further leverages the expertise of a wide range of experienced faculty housed within one of TEX-SIM’s three major cores:
Spatial-Temporal Data Science Core
Led by Dr. Cici Bauer
Expertise in spatial-temporal analysis and modeling, small area estimation, multi-level modeling, Bayesian predictive modeling, simulation studies, advanced spatial accessibility analysis, and large-scale data analyses, and interactive visualization.
Clinical Informatics Core
Led by Dr. Elmer Bernstam
Expertise in data security, and integrity, clinical data integration, comprehensive geocoding, and data linkage.
Cancer Population Epidemiology Core
Led by Dr. Qian Xiao
Expertise in epidemiological study design and methods, sensitivity analysis, environmental health, social determinants of health and population-level variation in health outcomes.
Collaborating Co-Investigators (External)
TEX-SIM also benefits from the expertise of the Learning Health System Integration team at The University of Texas at Austin Dell Medical School. Led by Dr. Hongfang Liu, the team contributes expertise in clinical informatics, health data integration, artificial intelligence, and the translation of data-driven methods into learning health system applications.
Meet the TEX-SIM Team
Spatial-Temporal Data Science Core
Cici Bauer, PhD, MS (PI, Lead)
Associate Professor
Department of Biostatistics and Data Science
Center Director, Center for Spatial-temporal Modeling for Applications in Population Sciences
[email protected]
Expertise: Spatial-Temporal data analysis and modeling, Bayesian predictive models, social determinants of health and population-level variation in health outcomes
Cecilia Ganduglia Cazaban, MD, DrPH (Co-I)
Associate Professor
Department of Management, Policy & Community Health
Director, Center for Health Care Data
[email protected]
Expertise: Big data and data science, health services and outcome, social determinants of health, management and health policy
Hulin Wu, PhD (Co-I)
The Betty Wheless Trotter Professor
Department of Biostatistics & Data Science
Director, Center for Big Data in Health Sciences
[email protected]
Expertise: Statistical methods for big data, real-world EHR data, machine learning and dynamic models with complex time course and longitudinal data
Dejian Lai, PhD (Co-I)
Professor
Department of Biostatistics & Data Science
[email protected]
Expertise: Modeling longitudinal and correlated data, time series analysis, spatial statistics
Clinical Informatics Core
Elmer Bernstam, MD, MSE (Co-I)
Professor
Department of Clinical and Health Informatics
[email protected]
Expertise: Clinical and translational informatics, clinical information retrieval
W Jim Zheng, PhD (Co-I)
Professor
Department of Bioinformatics and Systems Medicine
[email protected]
Expertise: Big data computing, translational data science and clinical informatics, bioinformatics
Cancer Population Epidemiology Core
Qian Xiao, PhD (Co-I, Lead)
Associate Professor
Department of Epidemiology
[email protected]
Expertise: Cancer epidemiology, epidemiological methods, environmental science, social determinants of health and health outcomes across diverse populations
Xianglin Du, PhD (Co-I)
Professor
Department of Epidemiology
[email protected]
Expertise: Epidemiological methods, health services and outcomes research, population-level variation in health outcomes, clinical comparative effectiveness
Irene Tami-Maury, PhD (Co-I)
Assistant Professor
Department of Epidemiology
[email protected]
Expertise: Population health research, cancer epidemiology, cancer prevention and control in vulnerable populations
Ami Sedani, PhD (Co-I)
Assistant Professor
Department of Epidemiology
[email protected]
Expertise: Cancer prevention and control, health services and outcomes research, population health research
External Team
Learning Health System Integration
Hongfang Liu, PhD (Co-I, Lead)
Chair & Professor
Department of Quantitative and Systems Health Sciences
The University of Texas at Austin, Dell Medical School
[email protected]
Expertise: Population health informatics, health data science, learning health system, translational AI
Sunyang Fu, PhD (Co-I)
Assistant Professor
Department of Quantitative and Systems Health Sciences
The University of Texas at Austin, Dell Medical School
Expertise: Clinical research informatics, aging research, real-world EHR data, and health outcomes across diverse populations
Questions for our team?
TEX-SIM is proudly supported by the Cancer Prevention and Research Institute of Texas (Grant ID: RP250630).
Interested in working with us? Let us know what you need by filling out our quick TEX-SIM Survey.