Browsing by Subject "Public health surveillance"
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Item Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department(Springer Nature, 2017-06-23) Dixon, Brian E.; Zhang, Zuoyi; Lai, Patrick T. S.; Kirbiyik, Uzay; Williams, Jennifer; Hills, Rebecca; Revere, Debra; Gibson, P. Joseph; Grannis, Shaun J.; BioHealth Informatics, School of Informatics and ComputingBACKGROUND: Most public health agencies expect reporting of diseases to be initiated by hospital, laboratory or clinic staff even though so-called passive approaches are known to be burdensome for reporters and produce incomplete as well as delayed reports, which can hinder assessment of disease and delay recognition of outbreaks. In this study, we analyze patterns of reporting as well as data completeness and timeliness for traditional, passive reporting of notifiable disease by two distinct sources of information: hospital and clinic staff versus clinical laboratory staff. Reports were submitted via fax machine as well as electronic health information exchange interfaces. METHODS: Data were extracted from all submitted notifiable disease reports for seven representative diseases. Reporting rates are the proportion of known cases having a corresponding case report from a provider, a faxed laboratory report or an electronic laboratory report. Reporting rates were stratified by disease and compared using McNemar's test. For key data fields on the reports, completeness was calculated as the proportion of non-blank fields. Timeliness was measured as the difference between date of laboratory confirmed diagnosis and the date the report was received by the health department. Differences in completeness and timeliness by data source were evaluated using a generalized linear model with Pearson's goodness of fit statistic. RESULTS: We assessed 13,269 reports representing 9034 unique cases. Reporting rates varied by disease with overall rates of 19.1% for providers and 84.4% for laboratories (p < 0.001). All but three of 15 data fields in provider reports were more often complete than those fields within laboratory reports (p <0.001). Laboratory reports, whether faxed or electronically sent, were received, on average, 2.2 days after diagnosis versus a week for provider reports (p <0.001). CONCLUSIONS: Despite growth in the use of electronic methods to enhance notifiable disease reporting, there still exists much room for improvement.Item Equivalence of electronic health record data for measuring hypertension prevalence: a retrospective comparison to BRFSS with data from two Indiana health systems, 2021(Springer Nature, 2025-04-04) Allen, Katie S.; Stiles, Justin; Daye, Veronica M.; Wiensch, Ashley; Valvi, Nimish; Dixon, Brian E.; Health Policy and Management, Richard M. Fairbanks School of Public HealthBackground: Public health surveillance requires timely access to actionable data at every level. Current approaches for accessing chronic disease surveillance data are not sufficient, and health departments are increasingly looking to augment surveillance efforts using electronic health records (EHRs). While proven effective for acute syndromic surveillance, the utilization of EHR systems and health data networks for monitoring chronic conditions remains sparse. This study tested the generalizability of a previously validated hypertension computable phenotype. Methods: A previously developed phenotype was used to estimate prevalence of hypertension in a geographically and clinically distinct region from its development. To test validity, the results were compared to available, statewide Behavioral Risk Factor Surveillance System (BRFSS) data using the two one-sided t-test (TOST) of equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race/ethnicity. Results: Compared to statewide hypertension prevalence of 34.5% in the BRFSS, an EHR-based phenotype estimated an overall prevalence of 24.1%. Estimates were not equivalent overall or across most subpopulations. Like BRFSS, we observed higher prevalence among Black men and women as well as increasing prevalence with age. Conclusion: With caveats, this study demonstrates that EHR-derived prevalence estimates may serve as a complement for population-based survey estimates. Utilizing available EHR data should increase timeliness of surveillance as well as enhance the ability of states and local health agencies to more readily address the burden of chronic disease in their respective jurisdictions.Item Improving Notifiable Disease Case Reporting Through Electronic Information Exchange–Facilitated Decision Support: A Controlled Before-and-After Trial(Sage, 2020) Dixon, Brian E.; Zhang, Zuoyi; Arno, Janet N.; Revere, Debra; Gibson, P. Joseph; Grannis, Shaun J.; Epidemiology, School of Public HealthObjective: Outbreak detection and disease control may be improved by simplified, semi-automated reporting of notifiable diseases to public health authorities. The objective of this study was to determine the effect of an electronic, prepopulated notifiable disease report form on case reporting rates by ambulatory care clinics to public health authorities. Methods: We conducted a 2-year (2012-2014) controlled before-and-after trial of a health information exchange (HIE) intervention in Indiana designed to prepopulate notifiable disease reporting forms to providers. We analyzed data collected from electronic prepopulated reports and "usual care" (paper, fax) reports submitted to a local health department for 7 conditions by using a difference-in-differences model. Primary outcomes were changes in reporting rates, completeness, and timeliness between intervention and control clinics. Results: Provider reporting rates for chlamydia and gonorrhea in intervention clinics increased significantly from 56.9% and 55.6%, respectively, during the baseline period (2012) to 66.4% and 58.3%, respectively, during the intervention period (2013-2014); they decreased from 28.8% and 27.5%, respectively, to 21.7% and 20.6%, respectively, in control clinics (P < .001). Completeness improved from baseline to intervention for 4 of 15 fields in reports from intervention clinics (P < .001), although mean completeness improved for 11 fields in both intervention and control clinics. Timeliness improved for both intervention and control clinics; however, reports from control clinics were timelier (mean, 7.9 days) than reports from intervention clinics (mean, 9.7 days). Conclusions: Electronic, prepopulated case reporting forms integrated into providers' workflow, enabled by an HIE network, can be effective in increasing notifiable disease reporting rates and completeness of information. However, it was difficult to assess the effect of using the forms for diseases with low prevalence (eg, salmonellosis, histoplasmosis).Item THE PERCEIVED AND REAL VALUE OF HEALTH INFORMATION EXCHANGE IN PUBLIC HEALTH SURVEILLANCE(2011-08-22) Dixon, Brian Edward; Jones, Josette F.; McGowan, Julie J.; Grannis, Shaun J.; Gamache, Roland E.Public health agencies protect the health and safety of populations. A key function of public health agencies is surveillance or the ongoing, systematic collection, analysis, interpretation, and dissemination of data about health-related events. Recent public health events, such as the H1N1 outbreak, have triggered increased funding for and attention towards the improvement and sustainability of public health agencies’ capacity for surveillance activities. For example, provisions in the final U.S. Centers for Medicare and Medicaid Services (CMS) “meaningful use” criteria ask that physicians and hospitals report surveillance data to public health agencies using electronic laboratory reporting (ELR) and syndromic surveillance functionalities within electronic health record (EHR) systems. Health information exchange (HIE), organized exchange of clinical and financial health data among a network of trusted entities, may be a path towards achieving meaningful use and enhancing the nation’s public health surveillance infrastructure. Yet the evidence on the value of HIE, especially in the context of public health surveillance, is sparse. In this research, the value of HIE to the process of public health surveillance is explored. Specifically, the study describes the real and perceived completeness and usefulness of HIE in public health surveillance activities. To explore the real value of HIE, the study examined ELR data from two states, comparing raw, unedited data sent from hospitals and laboratories to data enhanced by an HIE. To explore the perceived value of HIE, the study examined public health, infection control, and HIE professionals’ perceptions of public health surveillance data and information flows, comparing traditional flows to HIE-enabled ones. Together these methods, along with the existing literature, triangulate the value that HIE does and can provide public health surveillance processes. The study further describes remaining gaps that future research and development projects should explore. The data collected in the study show that public health surveillance activities vary dramatically, encompassing a wide range of paper and electronic methods for receiving and analyzing population health trends. Few public health agencies currently utilize HIE-enabled processes for performing surveillance activities, relying instead on direct reporting of information from hospitals, physicians, and laboratories. Generally HIE is perceived well among public health and infection control professionals, and many of these professionals feel that HIE can improve surveillance methods and population health. Human and financial resource constraints prevent additional public health agencies from participating in burgeoning HIE initiatives. For those agencies that do participate, real value is being added by HIEs. Specifically, HIEs are improving the completeness and semantic interoperability of ELR messages sent from clinical information systems. New investments, policies, and approaches will be necessary to increase public health utilization of HIEs while improving HIEs’ capacity to deliver greater value to public health surveillance processes.Item Towards Interoperability for Public Health Surveillance: Experiences from Two States(JMIR, 2013-04-04) Dixon, Brian E.; Siegel, Jason A.; Oemig, Tanya V.; Grannis, Shaun J.; Health Policy and Management, Richard M. Fairbanks School of Public HealthObjective: To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance. Introduction: The use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary. Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test. Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice. Methods: To characterize the use of standardized vocabularies in electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, we analyzed ELR messages from two states: Indiana and Wisconsin. We examined the data in the ELR messages where tests and results are reported (3). For each field, the proportion of field values that used either LOINC or SNOMED CT codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields. Results: Results are summarized in Table-1. In Indiana, less than 17% of incoming ELR messages contained a standardized code for identifying the test performed by the laboratory, and none of the test result fields contained a standardized vocabulary concept. For Wisconsin, none of the incoming ELR messages contained a standardized code for identifying the test performed, and less than 13% of the test result fields contained a SNOMED CT concept. Conclusions: Although Wisconsin and Indiana both have high adoption of advanced health information systems with many hospitals and laboratories using commercial systems which claim to support interoperability, very few ELR messages emanate from real-world systems with interoperable codes to identify tests and clinical results. To effectively use the arriving ELR messages, Indiana and Wisconsin health departments employ software and people workarounds to translate the incoming data into standardized concepts that can be utilized by the states’ surveillance systems. These workarounds present challenges for budget constrained public health departments seeking to leverage Meaningful Use Certified technologies to improve notifiable disease surveillance.Item Traumatic Brain Injury Surveillance and Research with Electronic Health Records: Building New Capacities(2023-03) McFarlane, Timothy D.; Dixon, Brian E.; Malec, James; Vest, Joshua; Wessel, JenniferBetween 3.2 and 5.3 million U.S. civilians live with traumatic brain injury (TBI)-related disabilities. Although the post-acute phase of TBI has been recognized as both a discrete disease process and risk factor for chronic conditions, TBI is not recognized as a chronic disease. TBI epidemiology draws upon untimely, incomplete, cross-sectional, administrative datasets. The adoption of electronic health records (EHR) may supplement traditional datasets for public health surveillance and research. Methods Indiana constructed a state-wide clinical TBI registry from longitudinal (2004-2018) EHRs. This dissertation includes three distinct studies to enhance, evaluate, and apply the registry: 1) development and evaluation of a natural language processing algorithm for identification of TBI severity within free-text notes; 2) evaluation and comparison of the performance of the ICD-9-CM and ICD-10-CM surveillance definitions; and 3) estimating the effect of mild TBI (mTBI) on the risk of post-acute chronic conditions compared to individuals without mTBI. Results Automated extraction of Glasgow Coma Scale from clinical notes was feasible and demonstrated balanced recall and precision (F-scores) for classification of mild (99.8%), moderate (100%), and severe (99.9%) TBI. We observed poor sensitivity for ICD-10-CM TBI surveillance compared to ICD-9-CM (0.212 and 0.601, respectively), resulting in potentially 5-fold underreporting. ICD-10-CM was not statistically equivalent to ICD-9-CM for sensitivity (𝑑𝑑𝑑𝑑̂=0.389, 95% CI [0.388,0.405]) or positive predictive value (𝑑𝑑𝑑𝑑̂=-0.353, 95% CI [-0.362,-0.344]). Compared to a matched cohort, individuals with mTBI were more likely to be diagnosed with mental health, substance use, neurological, cardiovascular, and endocrine conditions. Conclusion ICD-9-CM and ICD-10-CM surveillance definitions were not equivalent, and the transition resulted in a underreporting incidence for mTBI. This has direct implications on existing and future TBI registries and the Report to Congress on Traumatic Brain Injury in the United States. The supplementation of state-based trauma registries with structured and unstructured EHR data is effective for studying TBI outcomes. Our findings support the classification of TBI as a chronic disease by funding bodies, which may improve public funding to replace legacy systems to improve standardization, timeliness, and completeness of the epidemiology and post-acute outcomes of TBI.