Polis Center Works

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Promotion and tenure for community-engaged research: An examination of promotion and tenure support for community-engaged research at three universities collaborating through a Clinical and Translational Science Award
(http://dx.doi.org/10.1111/cts.12061, 2013-06-06) Marrero, David G.; Hardwick, Emily J.; Staten, Lisa K.; Savaiano, Dennis A.; Odell, Jere D.; Frederickson, Karen; Saha, Chandan
Introduction. Community engaged health research, an approach to research which includes the participation of communities, promotes the translation of research to address and improve social determinants of health. As a way to encourage community engaged research, the National Institutes of Health required applicants to the Clinical and Translational Science Award (CTSA) to include a community engagement component. Although grant-funding may support an increase in community engaged research, faculty also respond to the rewards and demands of university promotion and tenure standards. This paper measures faculty perception of how three institutions funded by a CTSA support community engaged research in the promotion and tenure process. Methods: At three institutions funded by a CTSA, tenure track and non-tenure track faculty responded to a survey regarding perceptions of how promotion and tenure committees value community engaged research. Results: Faculty view support for community engaged research with some reserve. Only 36% agree that community engaged research is valued in the promotion and tenure process. Discussion: Encouraging community engaged scholarship requires changing the culture and values behind promotion and tenure decisions. Institutions will increase community engaged research and more faculty will adopt its principles, when it is rewarded by promotion and tenure committees.
Digital Atlas of American Religion
(2013-04-05) Bodenhamer, David; Kandris, Sharon; Devadasan, Neil; Colbert, Jay; Dowling, Jim; Danielson, Laura
Our poster presentation will introduce DAAR, the Digital Atlas of American Religion (http://www.religionatlas.org). DAAR is a web-based research platform with innovative data exploration and visualization tools to support research in the humanities. Time and location are essential components of humanities exploratory research; however, GIS technology, especially in its web form, does not support the easy exploration and visualization of the complex spatio-temporal data manipulated by humanists. DAAR presents researchers with an integrated solution stemming from several fields including GIS, visualization, and classification theory. Researchers using DAAR are provided with the following exploration/visualization techniques: maps, cartograms, tree maps, pie charts, and motion charts. Using these tools and methods, researchers can explore patterns, trends, and relationships in the data that otherwise are not apparent with traditional GIS or statistical software. DAAR allows researchers to understand the multiple dimensions and diversity of religion across geographies, or within geographies. Paired with historic census data, it allows them to explore relationships to give better context and meaning to the patterns and trends. Maps provide the spatial patterns and relationships, tree maps show relative strength and relationships, charts show trends, cartograms reveal relative numbers of adherence, and motion charts animate trends over time.
Improved Analysis and Visualization of Community Indicators and Indices
(Office of the Vice Chancellor for Research, 2013-04-05) Farah, Christopher; Kandris, Sharon; Frederickson, Karen Comer
The analysis and interpretation of community indicators has been widely conducted to understand community trends, and subsequently, to support program planning, public policy initiatives, and target geographic regions for research. Given the importance of the outcomes, selecting good indicators is key, and usually a balance of stakeholder input and analytical evaluation. The most common analysis method used to evaluate a set of indicators is principal component analysis (PCA), a linear multivariate analysis method. However, the assumptions of PCA may be too restrictive and consequently, the analysis may fail to provide a sound evaluation of the set of indicators. In response to this shortcoming, we paired PCA with an unsupervised, non-linear multivariate method, known as self-organizing maps (SOMs), to analyze a set of indicators focused on population trends in education, income, employment, among others, at both the county level and the census tract level. The joint results were used to: exclude or include indicators from the indicator set, determine the latent primary dimensions of the dataset, identify peer counties and census tracts (relative to Indiana counties / census tracts), identify associations among different indicators at different geographic scales, identify temporal changes in the value of indicators, and develop one or more indices to describe socio-economic conditions of communities. Outcomes are presented geographically, topologically, tabularly, and graphically, offering different mechanisms of understanding and interpreting the analysis results. A goal of this project is to provide a web-based interface for researchers and community stakeholders to identify and evaluate candidate sets of community indicators, potentially accelerating sound public policy decisions and public health research.
Measuring Population Health Using Electronic Health Records: Exploring Biases and Representativeness in a Community Health Information Exchange
(IOS, 2015) Dixon, Brian E.; Gibson, P. Joseph; Comer, Karen Frederickson; Rosenman, Marc; Department of Epidemiology, Richard M. Fairbanks School of Public Health
Assessment is a core function of public health. Comprehensive clinical data may enhance community health assessment by providing up-to-date, representative data for use in public health programs and policies, especially when combined with community-level data relevant to social determinants. In this study we examine routinely collected and geospatially-enhanced EHR data to assess population health at various levels of geographic granularity available from a regional health information exchange. We present preliminary findings and discuss important biases in EHR data. Future work is needed to develop methods for correcting for those biases to support routine epidemiology work of public health.
Spatial Integration of Community Data with Clinical Data in Support of Community Health Research and Practice
(Office of the Vice Chancellor for Research, 2011-04-08) Frederickson Comer, Karen; Wiehe, Sarah E.; Wilson, Jeffrey S.; Dixon, Brian E.; Grannis, Shaun
This poster will describe the recent integration of one of the nation’s largest health information exchanges, the Indiana Network for Patient Care developed by the Regenstrief Institute, with one of the nation’s most comprehensive community information system, the SAVI CIS developed by The Polis Center at IUPUI. Integrating community data that quantifies the social and physical environment with clinical data has great potential for supporting and advancing community health research and practice. Multi-sector collaboration on the development and evaluation of associated uses cases informed system integration is allowing spatially-aware research and practice to be more quickly realized.
Using Electronic Health Record Data to Improve Community Health Assessment
(UKY, 2016) Dixon, Brian E.; Zou, Jian "Frank"; Comer, Karen F.; Rosenman, Marc; Craig, Jennifer L.; Gibson, P.; Epidemiology, School of Public Health
Background: Community health assessments assist health departments in identifying health needs as well as disparities, and they enable linking of needs with available interventions. Electronic health record (EHR) systems possess growing volumes of clinical and administrative data, making them a valuable source of data for ongoing community health assessment. Purpose: To produce population health indicators using data from EHR systems that could be combined and visually displayed alongside social determinants data, and to provide data sets at geographic levels smaller than a county. Methods: Data from multiple EHR systems used by major health systems covering >90% of the population in a metropolitan urban area were extracted and linked using a health information exchange (HIE) network for individuals who had at least two clinical encounters within the HIE network over a 3-year period. Population health indicators of highest interest to public health stakeholders were calculated and visualized at varying levels of geographic granularity. Results: Ten population health indicators were calculated, visualized, and shared with public health partners. Indicators ranged from the prevalence of a disease to the proportion of individuals with poor maintenance of their chronic condition. Calculating rates at the census-tract level or larger (e.g., average population size > 4000 people) is preferable to smaller geographic units of analysis. Implications: Extraction and linking of EHR system data are feasible for public health via an HIE network. While indicators can be derived, biases exist in the data that require more study. Further, HIE networks do not yet possess data for all conditions and measures desired by local public health stakeholders. The data that can be extracted, however, can be combined with public datasets on social determinants
Electronic Health Record (EHR)-Based Community Health Measures: An Exploratory Assessment of Perceived Usefulness by Local Health Departments
(BMC, 2018-05-22) Comer, Karen F.; Gibson, P. Joseph; Zou, Jian; Rosenman, Marc; Dixon, Brian E.; Health Policy and Management, School of Public Health
BACKGROUND: Given the widespread adoption of electronic health record (EHR) systems in health care organizations, public health agencies are interested in accessing EHR data to improve health assessment and surveillance. Yet there exist few examples in the U.S. of governmental health agencies using EHR data routinely to examine disease prevalence and other measures of community health. The objective of this study was to explore local health department (LHD) professionals' perceptions of the usefulness of EHR-based community health measures, and to examine these perceptions in the context of LHDs' current access and use of sub-county data, data aggregated at geographic levels smaller than county. METHODS: To explore perceived usefulness, we conducted an online survey of LHD professionals in Indiana. One hundred and thirty-three (133) individuals from thirty-one (31) LHDs participated. The survey asked about usefulness of specific community health measures as well as current access to and uses of sub-county population health data. Descriptive statistics were calculated to examine respondents' perceptions, access, and use. A one-way ANOVA (with pairwise comparisons) test was used to compare average scores by LHD size. RESULTS: Respondents overall indicated moderate agreement on which community health measures might be useful. Perceived usefulness of specific EHR-based community health measures varied by size of respondent's LHD [F(3, 88) = 3.56, p = 0.017]. Over 70% of survey respondents reported using community health data, but of those < 30% indicated they had access to sub-county level data. CONCLUSION: Respondents generally preferred familiar community health measures versus novel, EHR-based measures that are not in widespread use within health departments. Access to sub-county data is limited but strongly desired. Future research and development is needed as LHD staff gain access to EHR data and apply these data to support the core function of health assessment.
Learning in the zone: toward workforce development of evidence-based public policy communication
(BMC, 2018-06-05) Meyerson, Beth E.; Haderxhanaj, Laura T.; Comer, Karen; Zimet, Gregory D.; Medicine, School of Medicine
BACKGROUND: Evidence-based policy communication (EBPC) is an important, emerging focus in public health research. However, we have yet to understand public health workforce ability to develop and/or use it. The study objective was to characterize capacity to develop and use EBPC and identify cooperative learning and development opportunities using the case of Human papillomavirus (HPV). METHODS: Vygotsky's Zone of Proximal Development (ZPD) informed guided interviews with 27 advocates in Indiana from government, industry, research, state associations and individuals. Participants focused on HPV, cancer, women's health, school health and minority health. RESULTS: Capacity to develop and use EBPC was reported to develop through cooperative learning opportunities on the job or in advocacy focused coalitions. Coalition learning appeared to translate across health topics. Notably, policy experience did not assure understanding or use of EBPC. CONCLUSIONS: The ZPD framework can inform workforce EBPC interventions by focusing on actual development, potential development and factors for learning and development in the ZPD. Future studies should further clarify and evaluate emerging indicators in additional public health policy areas with a larger sample.
Unequal access: Tobacco Retail in the Indianapolis Metro Area
(The Polis Center, Indiana University at Indianapolis, 2017-07) Comer, Karen; Davila, Kelly; Hollon, Deb; Nowlin, Matt
Retail access to various smoking products is an important consideration when discussing community action to improve a community’s health. Studies show that tobacco outlet density and proximity are linked to tobacco use–particularly in poor areas. We used socioeconomic data culled from the SAVI community information system to examine the density and proximity of tobacco outlets relative to vulnerable communities in Marion County. The report serves as a companion piece to the IU Richard M. Fairbanks School of Public Health’s September 2016 Report on the Tobacco Epidemic in Marion County and Indiana.
Central Indiana Senior Fund State of Aging in Central Indiana Report (SoAR) Newsletter No. 1
(The Polis Center, Indiana University at Indianapolis, 2022-11) The Polis Center
Older adults are the fastest growing demographic in Central Indiana. Approximately 20,000 individuals in Central Indiana reach the age of 60 every year. By the year 2030, one in every five residents will be over the age of 65. To enhance the ability of older adults to live and thrive in Central Indiana, it is important to understand the population trends and basic needs of the growing older adult demographic. The Central Indiana Senior Fund is partnering with The Polis Center at IUPUI to develop the State of Aging in Central Indiana Report, a trusted source of information about Central Indiana’s older adult population.

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