A Comprehensive Examination and Burden Assessment of Firearm-Related Injury and Death in North Carolina from 2010-2017

Firearms are the 12th largest cause of death in the United States; in North Carolina (NC), more than 60% of violent deaths are caused by firearms. The objective of this dissertation was to examine firearm-related fatal and nonfatal injury by intent, age, sex, and race, and demonstrate a novel method of burden assessment, the Z-Score Burden Metric (ZSBM), to identify communities suffering from a high burden of firearm-related death.Data from the 2010-2017 North Carolina Violent Death Reporting System and North Carolina Disease Event Tracking and Epidemiologic Collection Tool were used to identify fatal firearm-related injuries and nonfatal firearm-related injury emergency department (ED) visits, respectively. Aim 1 described fatal and nonfatal firearm-related injury by intent and demographic characteristics. Aim 2 compared the ZSBM to unadjusted and age-adjusted death rates for each county.From 2010-2017 there were 9,783 fatal firearm-related injuries and 30,545 nonfatal firearm-related injury ED visits in NC. The most common nonfatal injuries were unintentional (N=13,667), while most fatal injuries were self-harm (N=6,173). Males accounted for 83.7% of fatal and 87.8% of nonfatal injuries. Black males had the highest rates of fatal and nonfatal assault and unintentional injuries, while White males had the highest rates of fatal self-harm injuries. Fatal self-harm injury rates increased with age, but the majority of both fatal (58.9%) and nonfatal (83.0%) injuries happened in those younger than 45 years. In Aim 2, the ZSBM produced different ordering of counties than ranking by unadjusted and age-adjusted rates; eight counties (11.0%) differed in burden rank by at least 10%.One quarter of firearm-related injuries in NC are fatal; males, especially Black males, and younger ages are disproportionately affected. Use of the ZSBM provides an alternative way of measuring community burden of injury, while still facilitating comparisons between communities with different age distributions. This method can be used for any injury or disease outcome and may help to prioritize allocation of resources to communities suffering high burdens of injury and disease. Results from this work will allow the development of firearm-related injury prevention programs targeted to the populations and geographic areas most affected by firearm violence.Doctor of Philosoph

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in network, rather than spatial, data, motivated by applications in social network data. We demonstrate that existing tests for autocorrelation in spatial data for continuous variables and our new test for categorical variables can both be used in the network setting

Congenital Transmission of Chagas Disease in Latin American Immigrants in Switzerland

International migration has changed the epidemiologic patterns of Chagas disease. Recently, 2 cases of Chagas disease transmitted from Latin American women to their newborns were diagnosed in Geneva, Switzerland. A retrospective study to detect Chagas disease showed a prevalence of 9.7% among 72 Latin American women tested during pregnancy in Switzerland

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale

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

Sex Disparities in Access to Acute Stroke Care: Can Telemedicine Mitigate this Effect?

Background: Women have more frequent and severe ischemic strokes than men, and are less likely to receive treatment for acute stroke. Primary stroke centers (PSCs) have been shown to utilize treatment more frequently. Further, as telemedicine (TM) has expanded access to acute stroke care we sought to investigate the association between PSC, TM and access to acute stroke care in the state of Texas. Methods: Texas hospitals and resources were identified from the 2009 American Hospital Association Annual Survey. Hospitals were categorized as: (1) stand-alone PSCs not using telemedicine for acute stroke care, (2) PSCs using telemedicine for acute stroke care (PSC-TM), (3) non-PSC hospitals using telemedicine for acute stroke care, or (4) non-PSC hospitals not using telemedicine for acute stroke care. The proportion of the population who could reach a PSC within 60 minutes was determined for stand-alone PSCs, PSC-TM, and non-PSCs using TM for stroke care. Results: Overall, women were as likely to have 60-minute access to a PSC or PSC-TM as their male counterparts (POR 1.02, 95% CI 1.02-1.03). Women were also just as likely to have access to acute stroke care via PSC or PSC-TM or TM as men (POR 1.03, 95% CI 1.02-1.04). Discussion: Our study found no sex disparities in access to stand alone PSCs or to hospitals using TM in the state of Texas. The results of this study suggest that telemedicine can be used as part of an inclusive strategy to improve access to care equally for men and women

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities:Malaria as an Example

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements

Appetite and gut hormone responses to moderate-intensity continuous exercise versus high-intensity interval exercise, in normoxic and hypoxic conditions.

This study investigated the effects of continuous moderate-intensity exercise (MIE) and high-intensity interval exercise (HIIE) in combination with short exposure to hypoxia on appetite and plasma concentrations of acylated ghrelin, peptide YY (PYY), and glucagon-like peptide-1 (GLP-1). Twelve healthy males completed four, 2.6 h trials in a random order: 1) MIE-normoxia, 2) MIE-hypoxia, 3) HIIE-normoxia, and 4) HIIE-hypoxia. Exercise took place in an environmental chamber. During MIE, participants ran for 50 min at 70% of altitude-specific maximal oxygen uptake ( 2max) and during HIIE performed 6 x 3 min running at 90% 2max interspersed with 6 x 3 min active recovery at 50% 2max with a 7 min warm-up and cool-down at 70% 2max (50 min total). In hypoxic trials, exercise was performed at a simulated altitude of 2,980 m (14.5% O2). Exercise was completed after a standardised breakfast. A second meal standardised to 30% of participants’ daily energy requirements was provided 45 min after exercise. Appetite was suppressed more in hypoxia than normoxia during exercise, post-exercise, and for the full 2.6 h trial period (linear mixed modelling, p 0.05). These findings demonstrate that short exposure to hypoxia causes suppressions in appetite and plasma acylated ghrelin concentrations. Furthermore, appetite responses to exercise do not appear to be influenced by exercise modality