MyLibrary as a Collection Analysis Tool

This paper suggests that relational database-driven systems in the library or information center should be valued not only for the improvements to customer service they can provide, but also for the rich store of data held in these systems which can be queried and used in collection analysis. MyLibrary@NCState is an open source, relational database-driven system that allows users to customize their access to a library's electronic resources and current awareness services. Its backend MySQL database can be queried to show, for example, which electronic journals appear on the most user pages, which bibliographic databases or reference shelf items have been selected the most within a particular range of dates, and which resources are underused. Libraries and information centers can then use these data as a starting point to locate resources for cancellation or those resources needing additional marketing efforts. Results from queries of MyLibrary@NCState's MySQL database as of March 28, 2001 are presented and discussed

LEVERAGING SYNDROMIC SURVEILLANCE EMERGENCY DEPARTMENT VISIT DATA FOR LOCAL CHRONIC DISEASE AND MENTAL HEALTH SURVEILLANCE

Local health departments (LHDs) need timely, county level and sub-county level data to monitor health-related trends, identify health disparities, and inform areas of highest need for interventions as part of their ongoing assessment responsibilities, yet many health departments rely on secondary data that are not timely and cannot provide subcounty insights. In this research, we conducted a content analysis of the 100 most recent North Carolina local health department community health assessments to quantify the secondary data sources used to document local chronic disease and mental health burden, compared the data sources identified to syndromic surveillance emergency department (ED) visit data from NC DETECT, and developed and evaluated mental health and asthma and COPD dashboards featuring NC DETECT ED visit data. We found that death certificate data, hospital inpatient data, data on disease prevalence among Medicare recipients, County Health Rankings (CHR) data, and data from the Behavioral Risk Factor Surveillance System (BRFSS) were the most frequently used secondary data sources to measure chronic diseases (excluding cancer) and mental health. Correlations are low when comparing county level NC DETECT ED visit data to death certificate data, Medicare data and CHR data for select mental health conditions, asthma, and COPD, but stronger when comparing overall county level ED visit rates to CHR health outcomes rankings. The Web-based public-facing dashboards we built for select mental health conditions, asthma, and COPD to provide LHDs with easier access to annual ED visit trends scored well on usability surveys. More research is needed to identify best practices in disseminating multi-year syndromic surveillance ED visit data on mental health and chronic diseases to LHDs.Doctor of Philosoph

Peat Bog Wildfire Smoke Exposure in Rural North Carolina Is Associated with Cardiopulmonary Emergency Department Visits Assessed through Syndromic Surveillance

Background: In June 2008, burning peat deposits produced haze and air pollution far in excess of National Ambient Air Quality Standards, encroaching on rural communities of eastern North Carolina. Although the association of mortality and morbidity with exposure to urban air pollution is well established, the health effects associated with exposure to wildfire emissions are less well understood. Objective: We investigated the effects of exposure on cardiorespiratory outcomes in the population affected by the fire. Methods: We performed a population-based study using emergency department (ED) visits reported through the syndromic surveillance program NC DETECT (North Carolina Disease Event Tracking and Epidemiologic Collection Tool). We used aerosol optical depth measured by a satellite to determine a high-exposure window and distinguish counties most impacted by the dense smoke plume from surrounding referent counties. Poisson log-linear regression with a 5-day distributed lag was used to estimate changes in the cumulative relative risk (RR). Results: In the exposed counties, significant increases in cumulative RR for asthma [1.65 (95% confidence interval, 1.25–2.1)], chronic obstructive pulmonary disease [1.73 (1.06–2.83)], and pneumonia and acute bronchitis [1.59 (1.07–2.34)] were observed. ED visits associated with cardiopulmonary symptoms [1.23 (1.06–1.43)] and heart failure [1.37 (1.01–1.85)] were also significantly increased. Conclusions: Satellite data and syndromic surveillance were combined to assess the health impacts of wildfire smoke in rural counties with sparse air-quality monitoring. This is the first study to demonstrate both respiratory and cardiac effects after brief exposure to peat wildfire smoke

EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina.

BackgroundThe timeliness, accuracy, and completeness of data for firearm injury surveillance is crucial for public health surveillance efforts and informing injury prevention measures. While emergency department (ED) visit data can provide near real-time information on firearms injuries, there are concerns surrounding the accuracy of intent coding in these data. We examined whether emergency medical service (EMS) data provide more accurate firearm injury intent coding in comparison to ED data.MethodsWe applied a firearm injury definition to EMS encounter data in NC's statewide syndromic surveillance system (NC DETECT), from January 1, 2021, through December 31, 2022. We manually reviewed each record to determine intent, and the corresponding manual classifications were compared to the injury cause codes entered in the EMS data and to ED visit records where EMS-ED record linkage was possible. We then calculated the sensitivity, specificity, positive and negative predictive values for each intent classification in SAS 9.4 using the manually reviewed intent classifications as the gold standard.ResultsWe identified 9557 EMS encounters from January 1, 2021, through December 31, 2022 meeting our firearm injury definition. After removing false positives and duplicates, 8584 records were available for manual injury classification. Overall, our analysis demonstrated that manual and EMS injury cause code classifications were comparable. However, for the 3401 EMS encounters that could be linked to an ED visit record, sensitivity of the ED ICD-10-CM codes was low for assault and intentional self-harm encounters at 18.2% (CI 16.5-19.9%) and 22.2% (CI 16-28.5%), respectively. This demonstrates a marked difference in the reliability of the intent coding in the two data sources.ConclusionsThis study illustrates both the value of examining EMS encounters for firearm injury intent, and the challenges of accurate intent coding in the ED setting. EMS coding has the potential for more accurate intent coding than ED coding within the context of existing hospital-based coding guidance. This may have implications for future firearm injury research, especially for nonfatal firearm injuries

EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina

Background The timeliness, accuracy, and completeness of data for firearm injury surveillance is crucial for public health surveillance efforts and informing injury prevention measures. While emergency department (ED) visit data can provide near real-time information on firearms injuries, there are concerns surrounding the accuracy of intent coding in these data. We examined whether emergency medical service (EMS) data provide more accurate firearm injury intent coding in comparison to ED data. Methods We applied a firearm injury definition to EMS encounter data in NC’s statewide syndromic surveillance system (NC DETECT), from January 1, 2021, through December 31, 2022. We manually reviewed each record to determine intent, and the corresponding manual classifications were compared to the injury cause codes entered in the EMS data and to ED visit records where EMS-ED record linkage was possible. We then calculated the sensitivity, specificity, positive and negative predictive values for each intent classification in SAS 9.4 using the manually reviewed intent classifications as the gold standard. Results We identified 9557 EMS encounters from January 1, 2021, through December 31, 2022 meeting our firearm injury definition. After removing false positives and duplicates, 8584 records were available for manual injury classification. Overall, our analysis demonstrated that manual and EMS injury cause code classifications were comparable. However, for the 3401 EMS encounters that could be linked to an ED visit record, sensitivity of the ED ICD-10-CM codes was low for assault and intentional self-harm encounters at 18.2% (CI 16.5–19.9%) and 22.2% (CI 16–28.5%), respectively. This demonstrates a marked difference in the reliability of the intent coding in the two data sources. Conclusions This study illustrates both the value of examining EMS encounters for firearm injury intent, and the challenges of accurate intent coding in the ED setting. EMS coding has the potential for more accurate intent coding than ED coding within the context of existing hospital-based coding guidance. This may have implications for future firearm injury research, especially for nonfatal firearm injuries

Detecting Disease Outbreaks Using Local Spatiotemporal Methods

A real-time surveillance method is developed with emphasis on rapid and accurate detection of emerging outbreaks. We develop a model with relatively weak assumptions regarding the latent processes generating the observed data, ensuring a robust prediction of the spatiotemporal incidence surface. Estimation occurs via a local linear fitting combined with day-of-week effects, where spatial smoothing is handled by a novel distance metric that adjusts for population density. Detection of emerging outbreaks is carried out via residual analysis. Both daily residuals and AR model-based de-trended residuals are used for detecting abnormalities in the data given that either a large daily residual or an increasing temporal trend in the residuals signals a potential outbreak, with the threshold for statistical significance determined using a resampling approach

Linking Emergency Medical Services and Emergency Department Data to Improve Overdose Surveillance in North Carolina

Introduction Linking emergency medical services (EMS) data to emergency department (ED) data enables assessing the continuum of care and evaluating patient outcomes. We developed novel methods to enhance linkage performance and analysis of EMS and ED data for opioid overdose surveillance in North Carolina. Methods We identified data on all EMS encounters in North Carolina during January 1–November 30, 2017, with documented naloxone administration and transportation to the ED. We linked these data with ED visit data in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. We manually reviewed a subset of data from 12 counties to create a gold standard that informed developing iterative linkage methods using demographic, time, and destination variables. We calculated the proportion of suspected opioid overdose EMS cases that received International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for opioid overdose in the ED. Results We identified 12 088 EMS encounters of patients treated with naloxone and transported to the ED. The 12-county subset included 1781 linkage-eligible EMS encounters, with historical linkage of 65.4% (1165 of 1781) and 1.6% false linkages. Through iterative linkage methods, performance improved to 91.0% (1620 of 1781) with 0.1% false linkages. Among statewide EMS encounters with naloxone administration, the linkage improved from 47.1% to 91.1%. We found diagnosis codes for opioid overdose in the ED among 27.2% of statewide linked records. Practice Implications Through an iterative linkage approach, EMS–ED data linkage performance improved greatly while reducing the number of false linkages. Improved EMS–ED data linkage quality can enhance surveillance activities, inform emergency response practices, and improve quality of care through evaluating initial patient presentations, field interventions, and ultimate diagnoses

Genomewide Association Scan of Suicidal Thoughts and Behaviour in Major Depression

© the authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Suicidal behaviour can be conceptualised as a continuum from suicidal ideation, to suicidal attempts to completed suicide. In this study we identify genes contributing to suicidal behaviour in the depression study RADIANT. Methodology/Principal Findings: A quantitative suicidality score was composed of two items from the SCAN interview. In addition, the 251 depression cases with a history of serious suicide attempts were classified to form a discrete trait. The quantitative trait was correlated with younger onset of depression and number of episodes of depression, but not with gender. A genome-wide association study of 2,023 depression cases was performed to identify genes that may contribute to suicidal behaviour. Two Munich depression studies were used as replication cohorts to test the most strongly associated SNPs. No SNP was associated at genome-wide significance level. For the quantitative trait, evidence of association was detected at GFRA1, a receptor for the neurotrophin GDRA (p = 2e-06). For the discrete trait of suicide attempt, SNPs in KIAA1244 and RGS18 attained p-values of ,5e-6. None of these SNPs showed evidence for replication in the additional cohorts tested. Candidate gene analysis provided some support for a polymorphism in NTRK2, which was previously associated with suicidality. Conclusions/Significance: This study provides a genome-wide assessment of possible genetic contribution to suicidal behaviour in depression but indicates a genetic architecture of multiple genes with small effects. Large cohorts will be required to dissect this further

Emergency Department Visits by Patients with Mental Health Disorders — North Carolina, 2008–2010

Patients with mental health disorders (MHDs) use the emergency department (ED) for acute psychiatric emergencies, for injuries and illnesses complicated by or related to their MHD, or when psychiatric or primary-care options are inaccessible or unavailable. An estimated 5% of ambulatory-care visits in the United States during 2007-2008 were made by patients with primary mental health diagnoses. To measure the incidence of ED visits in North Carolina with MHD diagnostic codes (MHD-DCs), the Carolina Center for Health Informatics (University of North Carolina at Chapel Hill) analyzed ED visits occurring during the period 2008-2010 captured by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). This report describes the results of that analysis, which indicated that nearly 10% of ED visits had one or more MHD-DCs assigned to the visit and the rate of MHD-DC-related ED visits increased seven times as much as the overall rate of ED visits in North Carolina during the study period. Those with an MHD-DC were admitted to the hospital from the ED more than twice as often as those without MHD-DCs. Stress, anxiety, and depression were diagnosed in 61% of MHD-DC-related ED visits. The annual rate of MHD-DC-related ED visits for those aged ≥65 years was nearly twice the rate of those aged 25-64 years; half of those aged ≥65 years with MHD-DCs were admitted to the hospital from the ED. Mental health is an important component of public health (4). Surveillance is needed to describe trends in ED use for MHDs to develop strategies to prevent hospitalization, improve access to ambulatory care, and develop new ways to provide ED care for the elderly with MHDs

Integration of Syndromic Surveillance Data into Public Health Practice at State and Local Levels in North Carolina

We sought to describe the integration of syndromic surveillance data into daily surveillance practice at local health departments (LHDs) and make recommendations for the effective integration of syndromic and reportable disease data for public health use