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Item Worlds Further Apart: The Widening Gap in Life Expectancy among Communities of the Indianapolis Metropolitan Area(IU Richard M. Fairbanks School of Public Health; The Polis Center at IUPUI, 2021-08) Weathers, T; Kiehl, NT; Colbert, JT; Nowlin, M; Comer, KF; Staten, LKIndianapolis metro area residents are a diverse group of people. What we have in common is that many of our best and worst days have been lived within this larger community. We may recall warm summer hours in our favorite park, a day spent at “the track,” or taking the kids to the Children’s Museum. We may also remember days spent at the bedside of a sick family member in an area hospital or places of tragic loss. Year after year, we build our lives within the Indianapolis metro area. In this way, our lives are linked by a shared community. However, in the neighborhoods we each call home, our daily lives are often vastly different. For some, getting groceries means lugging plastic sacks onto the IndyGo bus after waiting on a patch of worn grass. For others, grocery shopping is a quick drive to one of three favorite options, and the farmer’s market is a weekend routine for fresh produce. Some kids go to school with laptops and fresh smelling pages of new textbooks, while others have worn books and no internet access. Playing outside with friends in one neighborhood builds fitness and friendships, while in another playing outside triggers an asthma attack because of all the car exhaust along the busy roadway. Place differences add up over the days of our lives to affect our health and length of life. The children of one neighborhood have the same biological capacity for a long and healthy life as do the children of any other neighborhood, but where they live and grow and learn often unfairly cuts short their opportunities and their life. In our updated analysis of 104 ZIP Codes in the metro area (2014-2018), we identified the northern suburb of Fishers as our longest living community and just 17 miles away, within the Indianapolis city limits, is the shortest living community within the metro area. Though only 17 miles of distance separate them, their life expectancy is worlds apart. As the White River winds its way through the metro area, flowing northeast to southwest, it connects us as a larger community across time and space. The history of central Indiana is rooted in access to this shared life-supporting resource, where tribes, then towns and cities grew along its banks. Following the winding path of the White River, we see a pattern in life expectancy that also plays out throughout the metro area (See Life Expectancy Mapped Along the White River, 2014-2018, on next page). Life expectancy is lowest in places within the urban core of Indianapolis and also on the outer periphery of the metro area (red), while highest life expectancy is found in the suburban transitions from the city (green). Similar to our earlier findings residents of the longest-living community are living years longer than the U.S. average with a life expectancy comparable to the top high-income countries of the world.1 Residents of the shortest living community are living only as long as U.S. residents lived on average more than six decades ago, and the gap has widened. There is no genetic reason for this inequity. These data compel us to put equity at the forefront in addressing the economic and social policies and structures driving this unfairness. Inequity, in life and health, “saps the strength of the whole society.”2Item NIH Biosketch(2023-01-01) Comer, Karen FredericksonItem SAVI Public Health Needs Assessment: Final Report and Recommendations(The Polis Center, IUPUI, 2007-07) Comer, Karen F; Kandris, Sharon; Colbert, Jay; Devadasan, Neil; Bodenhamer, David JThis report summaries the 2007 assessment of current and projected health sector uses of the SAVI Community Information System (SAVI) and recommends SAVI enhancements to meet the information needs of decision makers, practitioners, and researchers. Based on focus groups and key informant interviews, it was discovered that local decisionmakers and practitioners in Central Indiana currently used SAVI, or would like to use SAVI, to assess the relative spatial demand and supply of health and human services, select sites for new health and human service facilities, assess patient access to health and human service facilities, select locations for services and programs, and track characteristics of facility catchment areas. Health practitioners and public health professionals were interested in using geospatially-enabled indicators for more effective planning and interventions, including to track public health outcomes, understand the socio-economic and physical environment of individual patients and communities, locate target populations for existing and potential health programs and services, support grant applications, and inform the public about environmental risk factors and disease prevalence in their communities. Clinical translational and public health researchers are using, or would like to use, geospatially-enabled measures for the study of social and environmental determinants of health, health disparities, environmental exposure and health risk, predictors of health knowledge, ecological models of health behavior, health service access, quality, and cost, and efficacy of health interventions. Detailed recommendations are provided for both short- and long-term enhancements to SAVI based on the existing and potential SAVI users and uses identified via this study and toward assisting the local health sector improve health knowledge and ultimately the health and wellbeing of Central Indiana communities.Item SAVI Data Catalog(The Polis Center at IUPUI, 2011-04) Derr, Michelle; Colbert, Jay; Nalla, Goutami; Comer, Karen F.The SAVI Community Information System (SAVI) is the nation’s largest spatially-enabled system of its type, providing local organizations, researchers, and involved citizens with the detailed, geographically precise information needed to make well-informed decisions. This data catalog describes the wealth of free data provided by SAVI, including data about the social, physical, and economic conditions of Central Indiana communities from counties to neighborhoods and census tracts, as well as information on thousands of non-profit and community-based organizations and programs. SAVI exists as a Web-based, interactive system that allows users to create custom maps, graphs, charts, and data profiles of over 2,000 Central Indiana communities.Item Community Health Information Resource Guide: Volume 1 - Data(The Polis Center at IUPUI, 2011-06) Comer, Karen F; Derr, Michelle; Seyffarth, Chris; Thomaskutty, Champ; Kandris, Sharon; Ritchey, MatthewThis resource guide contains useful information for those who would like to use data to assess the health status of an Indiana community. Targeted users include local organizations such as county health departments and community health coalitions. Being able to access and use relevant data and information resources is a common hurdle for those interested in assessing and advancing community health. As a result of this need and at the request of the Community Advisory Council of the Community Health Engagement Program, we developed this resource guide to assist individuals, organizations, and coalitions in Indiana in identifying appropriate resources that guide their community health research and evaluation activities. The term “data” is used in this volume in reference to both data and information sources. While data consist of raw facts and figures, information is formed by analyzing the data and applying knowledge to it so that the findings are more meaningful and valuable to the community. The benefit of using data is that you can often manipulate it for your specific purposes. The benefit of using information sources is that the work of generating meaning from the data might already have been done, while a potential downside is that the available sources might not answer your specific questions. There are diverse sources of data that can be used as a basis for community health evaluation and decision making. Those looking to use data must consider multiple factors before determining the appropriate data to seek and use.Item 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 CenterOlder 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.Item 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, MattRetail 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.Item 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 MedicineBACKGROUND: 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.Item 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 HealthBACKGROUND: 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.Item 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 HealthBackground: 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