Browsing by Author "Rosenman, Marc"

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    Area-level incarceration and STI risk among a cohort of justice-involved adolescents and adults
    (2014-11) Wiehe, Sarah E.; Rosenman, Marc; Scanlon, Michael L.; Fortenberry, J. Dennis; Aalsma, Matthew C.
    Background: Living in areas of high incarceration is associated with increased risk of STI; however, STI risk with respect to both this area-level exposure and an individual’s involvement with the justice system is not known. Objective: Among individuals before and after arrest or incarceration, assess the association between area-level incarceration rates and risk of chlamydia, gonorrhea, or syphilis. Methods: Retrospective cohort study of individuals living in Marion County (Indianapolis), Indiana who were arrested or in jail, prison, juvenile detention, or juvenile prison between 2005-2008 (N=97,765). Area-level incarceration exposure was defined by the proportion of person-days incarcerated among the total population*365 within a census block group. A 1-year period was assessed before and after a randomly-selected arrest/incarceration per person. Multivariable logistic regression, controlling for age, race, STI history, and year, was performed to assess chlamydia, gonorrhea, or syphilis risk by quartile area-level incarceration exposure, adjusting for individual clustering and stratifying by gender. Results: Area-level incarceration was associated with increased odds of each STI, with a dose response relationship particularly among those with an arrest or jail stay. Women with a history of arrest or jail/prison stay and living in high incarceration areas had higher odds of STI, compared to men with comparable incarceration history and living in similar areas.
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    Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory
    (Oxford University Press, 2014-03-01) Rosenman, Marc; He, Jinghua; Martin, Joel; Nutakki, Kavitha; Eckert, George; Lane, Kathleen; Gradus-Pizlo, Irmina; Hui, Siu L.; Department of Pediatrics, IU School of Medicine
    Background and objective Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical ‘phenotypes’ accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one ‘gold standard’ chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across different definitions. Materials and methods We used retrospective queries of hospitalizations (2002–2011) in the Indiana Network for Patient Care with any CHF ICD-9 diagnoses, a primary diagnosis, an echocardiogram performed, a B-natriuretic peptide (BNP) drawn, or BNP >500 pg/mL. We used a hybrid between proportional sampling by Venn zone and over-sampling non-overlapping zones. The acute CHF (presence/absence) outcome was based on expert chart review using a priori criteria. Results Among 79 091 hospitalizations, we reviewed 908. A query for any ICD-9 code for CHF had PPV 42.8% (SE 1.5%) for acute CHF and sensitivity 94.3% (1.3%). Primary diagnosis of 428 and BNP >500 pg/mL had PPV 90.4% (SE 2.4%) and sensitivity 28.8% (1.1%). PPV was <10% when there was no echocardiogram, no BNP, and no primary diagnosis. ‘False positive’ hospitalizations were for other heart disease, lung disease, or other reasons. Conclusions This novel method successfully allowed flexible application and validation of queries for patients hospitalized with acute CHF.
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    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.
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    Evolving availability and standardization of patient attributes for matching
    (Oxford University Press, 2023-10-12) Deng, Yu; Gleason, Lacey P.; Culbertson, Adam; Chen, Xiaotian; Bernstam, Elmer V.; Cullen, Theresa; Gouripeddi, Ramkiran; Harle, Christopher; Hesse, David F.; Kean, Jacob; Lee, John; Magoc, Tanja; Meeker, Daniella; Ong, Toan; Pathak, Jyotishman; Rosenman, Marc; Rusie, Laura K.; Shah, Akash J.; Shi, Lizheng; Thomas, Aaron; Trick, William E.; Grannis, Shaun; Kho, Abel; Health Policy and Management, School of Public Health
    Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.
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    Identifying Nonfatal Firearm Assault Incidents through Linking Police Data and Clinical Records: Cohort Study in Indianapolis, Indiana, 2007 – 2016
    (Elsevier, 2021) Magee, Lauren A.; Ranney, Megan L.; Fortenberry, J. Dennis; Rosenman, Marc; Gharbi, Sami; Wiehe, Sarah E.; School of Public and Environmental Affairs
    Nonfatal firearm assault incidents are more prevalent than gun homicides, however, little is understood about nonfatal firearm assault incidents due to a lack of accurate data in the United States. This is a descriptive study of all nonfatal firearm assault incidents identified through police and clinical records from 2007 to 2016 in Indianapolis, Indiana. Records were linked at the incident level to demonstrate the overlap and non-overlap of nonfatal firearm assault incidents in police and clinical records and describe differences in demographic characteristics of the victims. Incidents were matched within a 24-h time window of the recorded date of the police incident. Data were analyzed in fall 2020. There were 3797 nonfatal firearm assault incidents identified in police reports and 3131 clinical encounters with an ICD 9/10 diagnosis-based nonfatal firearm-related injury. 62% (n = 2366) of nonfatal firearm assault incidents matched within 24 h to a clinical encounter, 81% (n = 1905) had a firearm related ICD code: 40% (n = 947) were coded as a firearm-related assault, 32% (n = 754) were coded as a firearm-related accident; and 8.6% (n = 198) were coded as undetermined, self-inflicted or law enforcement firearm-related. The other 20% (n = 461) did not have an ICD firearm related diagnosis code. Results indicate most nonfatal firearm assault incidents overlap between police and clinical records systems, however, discrepancies between the systems exist. These findings also demonstrate an undercounting of nonfatal firearm assault incidents when relying on clinical data systems alone and more efforts are needed to link administrative police and clinical data in the study of nonfatal firearm assaults.
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    Implementing a pragmatic clinical trial to tailor opioids for acute pain on behalf of the IGNITE ADOPT PGx investigators.
    (Wiley, 2022-07-28) Cavallari, Larisa H.; Cicali, Emily; Wiisanen, Kristin; Fillingim, Roger B.; Chakraborty, Hrishikesh; Myers, Rachel A.; Blake, Kathryn V.; Asiyanbola, Bolanle; Baye, Jordan F.; Bronson, Wesley H.; Cook, Kelsey J.; Elwood, Erica N.; Gray, Chancellor F.; Gong, Yan; Hines, Lindsay; Kannry, Joseph; Kucher, Natalie; Lynch, Sheryl; Nguyen, Khoa A.; Obeng, Aniwaa Owusu; Pratt, Victoria M.; Prieto, Hernan A.; Ramos, Michelle; Sadeghpour, Azita; Singh, Rajbir; Rosenman, Marc; Starostik, Petr; Thomas, Cameron D.; Tillman, Emma; Dexter, Paul R.; Horowitz, Carol R.; Orlando, Lori A.; Peterson, Josh F.; Skaar, Todd C.; Van Driest, Sara L.; Volpi, Simona; Voora, Deepak; Parvataneni, Hari K.; Johnson, Julie A.
    Opioid prescribing for postoperative pain management is challenging because of inter-patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers [IMs] or poor metabolizers [PMs], respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6-guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype-guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient-reported pain-related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype-guided opioid selection for improving postoperative pain control and reducing opioid-related risks.
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    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.
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    Multihospital Infection Prevention Collaborative: Informatics Challenges and Strategies to Prevent MRSA
    (2013-11) Doebbeling, Bradley N.; Flanagan, Mindy E.; Nall, Glenna; Hoke, Shawn; Rosenman, Marc; Kho, Abel
    We formed a collaborative to spread effective MRSA prevention strategies. We conducted a two-phase, multisite, quasi-experimental study of seven hospital systems (11 hospitals) in IN, MT, ME and Ontario, Canada over six years. Patients with prior MRSA were identified at admission using regional health information exchange data. We developed a system to return an alert message indicating a prior history of MRSA, directed to infection preventionists and admissions. Alerts indicated the prior anatomic site, and the originating institution. The combined approach of training and coaching, implementation of MRSA registries, notifying hospitals on admission of previously infected or colonized patients, and change strategies was effective in reducing MRSA infections over 80%. Further research and development of electronic surveillance tools is needed to better integrate the varied data source and support preventing MRSA infections. Our study supports the importance of hospitals collaborating to share data and implement effective strategies to prevent MRSA.
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    Two-year prevalence rates of mental health and substance use disorder diagnoses among repeat arrestees
    (BMC, 2021) Magee, Lauren A.; Fortenberry, J. Dennis; Rosenman, Marc; Aalsma, Matthew C.; Gharbi, Sami; Wiehe, Sarah E.; School of Public and Environmental Affairs
    Background Individuals with mental illness and co-occurring substance use disorders often rapidly cycle through the justice system with multiple arrests. Therefore, is it imperative to examine the prevalence of mental health and substance use diagnoses among arrestees and repeat arrestees to identify opportunities for intervention. Methods We linked police arrest and clinical care data at the individual level to conduct a retrospective cohort study of all individuals arrested in 2016 in Indianapolis, Indiana. We classified arrestees into three levels: 1 arrest, 2 arrests, or 3 or more arrests. We included data on clinical diagnoses between January 1, 2014 and December 31, 2015 and classified mental health diagnoses and substance use disorder (SUD) based on DSM categories using ICD9/10 diagnoses codes. Results Of those arrested in 2016, 18,236 (79.5%) were arrested once, 3167 (13.8%) were arrested twice, and 1536 (6.7%) were arrested three or more times. In the 2 years before the arrest, nearly one-third (31.3%) of arrestees had a mental health diagnosis, and over a quarter (27.7%) of arrestees had an SUD diagnosis. Most of those with a mental health or SUD diagnosis had both (22.5% of all arrestees). Arrestees with multiple mental health (OR 2.68, 95% CI 2.23, 3.23), SUD diagnoses (OR 1.59, 95% CI 1.38, 1,82), or co-occurring conditions (1.72, 95% CI 1.48, 2.01) in the preceding 2 years had higher odds of repeat arrest. Conclusions Our findings show that linked clinical and criminal justice data systems identify individuals at risk of repeat arrest and inform opportunities for interventions aimed at low-level offenders with behavioral health needs.
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    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