Fatality Counts Research Articles

Bayesian spatio-temporal statistical modeling of violent-related fatality in western and central Africa

Fatality arising from violent events is a critical public health problem in Africa. Although numerous studies on crime and violent events have been conducted, adequate attention has not been given to the distribution of fatalities arising from these events. This study unraveled the spatio-temporal pattern of fatality from violent events in Western and Central Africa. A two-component spatio-temporal zero-inflated model on a continuous spatial domain within a Bayesian framework was adopted. The stochastic partial differential equation was used to quantify the continuous pattern and make projections in unsampled regions. Fatality data from 1997 to 2021 was obtained from the Armed Conflict Location and Event Data Project (ACLED). Findings from the result revealed a spatial and temporal divide in the prevalence of fatality in the study region. Between the years 1997 and 2010, fatality from violence was most prevalent in Central Africa, whereas in more recent years, it was most prevalent in Western Africa. The posterior predictive probabilities of fatality occurrence due to violent events in Nigeria and Cameroon were highest and above 0.6, and the probability of more than one death per violent event is highest in Angola and Chad with probability 0.2. On violent event type, findings showed that suicide bombs had the highest likelihood of fatality occurrence whereas the event of violent non-state actors overtaking territory had the highest impact on the likelihood of multiple fatality counts. Among the armed actors who participated in violent events, armed religious groups were linked to the highest likelihood of fatality occurrence whereas Military forces were linked to the highest likelihood of multiple fatality counts per event. The finding also revealed that there is a higher likelihood of multiple fatalities in the Winter temperate season. These findings could be used for planning and policy design geared towards mitigating fatality and providing a guide towards resource distribution to support the affected communities.

Are Pedestrian Crashes Becoming More Severe? A Breakdown of Pedestrian Crashes in Urban Tennessee

Similar to the nationwide trend of declining pedestrian safety, pedestrian fatalities in Tennessee increased by 117 % between 2009 and 2019. During the same time, pedestrians involved in traffic crashes have only increased by 26 %, suggesting that the reason for the increased number of fatalities is that pedestrian crashes are becoming more severe. The media, and safety advocates speculate about the different scenarios that could be responsible for the decrease in pedestrian safety. Past studies have failed to integrate pedestrian involvement into their studies and confirm whether pedestrian crashes are becoming more severe. Our study collected all reported pedestrian crashes and their outcomes from crash data kept by the Tennessee police, thus providing fatality and involvement numbers. Excluding crashes on controlled access roads, we measured the pedestrian fatality rate (PFR) for essential variables for each year from 2009 to 2019. An upward PFR graph shows that pedestrian crashes are becoming more severe compared with the past. To substantiate our claims with adequate statistical significance, we employed separate Poisson regression models for the pedestrian fatality count and PFR associated with each attribute of pedestrian crashes. The results show that an increase in severity is linked with multilane urban arterials with speeds above 35 mph at midblock locations. The study results also confirm that cars are still the cause of the high fatality rate in Tennessee rather than sport utility vehicles or other large vehicles. We recommend that cities reduce design speeds to 35 mph, increase safe pedestrian crossing opportunities, and install more pedestrian-scale lighting infrastructure on urban arterials.

An in-depth analysis of head-on crash severity and fatalities in Ghana

Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low- and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random parameter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on collisions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those between 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeconomic factors, which pose obstacles to the Government's endeavor to upgrade the major highways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.

Seasonal patterns of bird and bat collision fatalities at wind turbines.

Information on when birds and bats die from collisions with wind turbines can help refine efforts to minimize fatalities via curtailment of energy productions and can offer insight into the risk factors associated with collision fatalities. Using data pooled from 114 post-construction monitoring studies conducted at wind facilities across the United States, we described seasonal patterns of fatalities among birds and bats. Bat fatalities peaked in the fall. Silver-haired bat (Lasionycteris noctivagans), a long-distance migrant, and Mexican free-tailed bat (Tadarida brasiliensis) both showed maximum fatality counts later in the year-October and November, respectively-than any other bat species. The other common species in our sample-hoary bat (Aeorestes cinereus), Eastern red bat (Lasiurus borealis), and big brown bat (Eptesicus fuscus)-showed broadly overlapping peaks of fatality counts in August. Fatalities of silver-haired bat showed a smaller spring peak in some ecoregions; no other bat species exhibited this pattern. Seasonal patterns of bird fatalities varied among guilds. Woodland birds, many of which were long-distance migrants, showed two peaks in fatalities corresponding to spring and fall migration. Grassland birds and soaring birds, most of which were resident or short-distance migrants, did not exhibit strong seasonal peaks in fatalities. Species in these guilds tend to inhabit regions with extensive wind-energy development year-round, which may explain the more consistent numbers of fatalities that we observed. Our results highlight the value of pooling data to develop science-based solutions to reduce conflicts between wind-energy development and wildlife but also emphasize the need for more extensive data and standardization of post-construction monitoring to support more robust inferences regarding wind-wildlife interactions and collision risk.

Theory as guide to the analysis of polygyny and conflict: A response to Ash (2022)

Ash (2022, "Ash" hereafter), in this journal, re-assesses our 2020 article “Polygynous Neighbors, Excess Men, and Intergroup Conflict in Africa.” The article analyzed the relationship between polygyny (one man marrying several wives) and intergroup conflict events in Africa. Ash includes assessments that support our original results, though others lead Ash to call our findings into question. While some of Ash’s data transformations are reasonable robustness tests, others seem to be insufficiently rooted in theory and ignore statistical power. Specifically, using fatality counts as an outcome requires theorization, in particular against the backdrop of different underlying theoretical mechanisms, issues of measurement accuracy, and other statistical considerations. Overall, we argue that there are good theoretical and statistical reasons why some of the operationalizations in Ash are not appropriate substitutes for our outcome variable. We appreciate the re-assessment but dismiss the claim in Ash that its findings give sufficient evidence to change the conclusion in our article.

Seismic fatality risk evaluation framework for existing buildings in Switzerland

The Swiss code SIA 269/8 for the seismic evaluation of existing structures is risk-based. It relies on the compliance factor αeff, defined as the ratio of the seismic capacity of an existing structure to the required seismic capacity of a hypothetical identical new structure, to indirectly evaluate the casualty risk posed by the existing structure. While it is convenient to have a direct relationship between the casualty risk and the degree of code compliance of the existing structure, it is debatable if the contributions of the seismic hazard, structural performance and occupancy to the casualty risk should be merged into a single quantity, conflicting with the multi-component nature of seismic risk. This paper presents a graphical framework to evaluate the seismic fatality risk for existing buildings. The framework is based on the PEER Center performance-based earthquake engineering methodology to provide a transparent view of the contributions of different sources of hazard and risk. Starting from a seismic hazard model, probabilistic seismic demand, damage and loss models are introduced, taking the uncertainty into account at each step. The final product of the proposed framework, a loss curve representing the (annual) exceedance rate of fatality counts, enables engineers and building owners to make better, risk-based decisions. The proposed framework is demonstrated for an existing building in Switzerland. Notably, in addition to the SIA 269/8 evaluation method, this demonstration compares eight hybrid approaches differentiated by the utilized structural assessment techniques (nonlinear static pushover vs. incremental dynamic analysis), uncertainty propagation in the demand model (record variability only vs. record variability and epistemic uncertainty) and solution strategies (closed-form vs. numerical) to find a practical compromise between the accuracy and the computational effort. A comparison of the obtained results shows that the SIA 269/8 evaluation method underestimates the fatality risk for the presented building and identifies a practical alternative based on the proposed graphical framework.

Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19

Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: (i) use a model-derived measure of COVID-19 severity; (ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; (iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.

Trends, Causal Analysis, and Recommendations from 14 Years of Ferry Accidents

Ferries and other passenger vessels provide a crucial mode of transportation for many in the developing world, especially in archipelagic nations like Indonesia and in river delta nations like Bangladesh. However, this dependence on passenger vessels coincides with a high rate of accidents and fatalities in many countries, linked to purchase of old, substandard, and/or inappropriate vessels in low-income nations; overcrowding; inadequate training; and sudden hazardous weather. Any serious attempt to decrease the number and fatality count of ferry accidents in the developing world must have a complete record of past incidents on which to draw. This report compiles detailed information on the 232 major accidents that occurred around the world between 2000 and 2014. It assesses the prevalence of various common factors in ferry accidents, including human error, hazardous weather, and overcrowding, and makes recommendations for future research into the prevention of ferry accidents.

Dark Side of Nudges

Among other methods, governments use highway message signs to encourage responsible driving. We find that displaying year-to-day roadside fatality counts leads to an immediate increase in traffic crashes. We argue that the statistics distract drivers at precisely the moments when they should focus on the road.

Predicting political violence using a state-space model

We provide a proof-of-concept for a novel state-space modelling approach for predicting monthly deaths due to political violence. Attention is focused on developing the method and demonstrating the utility of this approach, which provides exciting opportunities to engage with domain experts in developing new and improved state-space models for predicting violence. The prediction is made on a grid of cells with spatial resolution of 0.5 × 0.5 degrees, and each cell is modeled to have two mathematically well-defined unobserved/latent/hidden states that evolves over time and encode the “onset risk” and “potential severity”, respectively. This offers a certain level of interpretability of the model. By using the model for computing the probability distribution for a death count at a future time conditioned on all data observed up until the current time, a predictive distribution is obtained. The predictive distribution typically places a certain mass at the death count 0 (no violent outbreak) and the remaining mass indicating a likely interval of the fatality count, should a violent outbreak appear. To evaluate the model performance we—lacking a better alternative—report the mean of the predictive distribution, but the access to the predictive distribution is in itself an interesting contribution to the application. This work merely serves as a proof-of-concept for the state-space modeling approach for this type of data and several possible directions for further work that could improve the predictive performance are suggested.

Can behavioral interventions be too salient? Evidence from traffic safety messages.

Although behavioral interventions are designed to seize attention, little consideration has been given to the costs of doing so. We estimated these costs in the context of a safety campaign that, to encourage safe driving, displays traffic fatality counts on highway dynamic message signs for 1 week each month. We found that crashes increase statewide during campaign weeks, which is inconsistent with any benefits. Furthermore, these effects do not persist beyond campaign weeks. Our results show that behavioral interventions, particularly negatively framed ones, can be too salient, crowding out more important considerations and causing interventions to backfire-with costly consequences.

Investigating risk factors associated with pedestrian crash occurrence and injury severity in Texas

Objective This study investigates various risk factors associated with pedestrian crash occurrence and injury severity based on 78,497 reported pedestrian-involved crashes across Texas from 2010 through 2019. Methods Crashes are mapped to over 708,738 road segments, along with road design, land use, transit, hospital, rainfall, and other location features. Negative binomial models examine the association between pedestrian crash frequency and various contributing factors, and a heteroskedastic ordered probit model investigates the severity of injuries at the individual crash level. Results Results from this study show the practical significance of microlevel variables in predicting pedestrian crashes. Proximity to schools and hospitals and presence of transit are all associated with higher pedestrian crash frequencies yet are rarely included in other models. Total pedestrian crash and fatal crash counts rise with the number of lanes, population, and job densities, though greater median and shoulder widths provide some protection. Higher speed limits are associated with lower crash frequencies but more deaths. Pedestrian crashes are more likely to be severe and fatal at night (8 p.m. to 5 a.m.), without overhead lighting, and when involved pedestrians and/or drivers are intoxicated. Use of light-duty trucks also significantly increases risk of severe or fatal pedestrian injury. Though newer vehicle safety features may be argued to lower crash severity or protect vehicle occupants, newer crash-involved vehicles in Texas are not found to deliver less severe pedestrian injury. Pedestrian and driver characteristics—both age and gender—are practically (and statistically) significant. Injury severity rises with pedestrian age, yet younger and/or female pedestrians on straight roadways, off the state (and interstate) highway system, and in the presence of a traffic control device (stop sign or signal) are less likely to be seriously injured, on average. Conclusions Findings underscore the benefit of enhanced vehicle safety features for pedestrians, campaigns against driving and walking while intoxicated, improved roadway design, enforcement of safety countermeasures near schools and bus stops, and installment of additional traffic controls and streetlights wherever more pedestrians exist.

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

Pedestrian Deaths During the COVID-19 Pandemic.

The COVID pandemic provides a natural experiment examining how a 50-60% reduction in pedestrian activity might lead to a reduction in pedestrian deaths. We assessed whether the reduction in pedestrian deaths was proportional to a one-to-one matching presumed in statistics correlating mobility with fatality. The primary analysis examined New York (largest city in US), and the validation analysis examined Toronto (largest city in Canada). We identified pedestrian activity in each location from the Apple Mobility database, normalized to the baseline in January 2020. We calculated monthly pedestrian deaths from the Vision Zero database in each city with baseline data from 3 prior years. We found a large initial reduction in pedestrian deaths during the lockdown in New York that was transient and not statistically significant during the summer and autumn despite sustained reductions in pedestrian activity. Similarly, we found a large initial reduction in pedestrian deaths during the lockdown in Toronto that was transient and not sustained. Together, these data suggest the substantial reductions in pedestrian activity during the COVID pandemic have no simple correlation with pedestrian fatality counts in the same locations. An awareness of this finding emphasizes the role of unmeasured modifiable individual factors beyond pedestrian infrastructure or other structural contributors.

COVID-19 impact on excess deaths of various causes in the United States

ABSTRACT Media regarding COVID-19 fatality counts is crucial, affecting policy and health measures nationwide. However, misinformation regarding other causes of death has led to dubious claims about the seriousness of the coronavirus. This research aims to identify the changes in a dozen causes of death during the pandemic using CDC data from 1999 to 2020. Using the Exponential Triple Smoothing (ETS) algorithm, this project estimated the mortality of eleven causes of death for 2020 under the assumption of no COVID-19 pandemic. Using Power BI and Tableau, this data was visualized together with 2020 actual death counts to determine which causes of death were significantly impacted by the coronavirus. The dashboard revealed an increase in several causes of death including Alzheimer’s Disease and Diabetes, a decrease in Chronic Lower Respiratory Disease deaths, and a slight increase in Influenza deaths. These findings, while at odds with much of the media surrounding COVID-19 mortality, are corroborated by adjacent scientific research.

Countering the potential re-emergence of a deadly infectious disease—Information warfare, identifying strategic threats, launching countermeasures

Eradicated infectious diseases like smallpox can re-emerge through accident or the designs of bioterrorists, and cause heavy casualties. Presently, the populace is largely susceptible as only a small percentage is vaccinated, and their immunity is likely to have waned. And when the disease re-emerges, the susceptible individuals may be manipulated by disinformation on Social Media to refuse vaccines. Thus, a combination of countermeasures consisting of antiviral drugs and vaccines and a range of policies for their application need to be investigated. Opinions regarding whether to receive vaccines evolve over time through social exchanges via networks that overlap with but are not identical to the disease propagation networks. These couple the spread of the biological and information contagion and necessitate a joint investigation of the two. We develop a computationally tractable metapopulation epidemiological model that captures the joint spatio-temporal evolution of an infectious disease (e.g., smallpox, COVID-19) and opinion dynamics. Considering smallpox, the computations based on the model show that opinion dynamics have a substantial impact on the fatality count. Towards understanding how perpetrators are likely to seed the infection, we identify a) the initial distribution of infected individuals that maximize the overall fatality count; and b) which habitation structures are more vulnerable to outbreaks. We assess the relative efficacy of different countermeasures and conclude that a combination of vaccines and drugs minimize the fatalities, and by itself, drugs reduce fatalities more than the vaccine. Accordingly, we assess the impact of increase in the supply of drugs and identify the most effective among a collection of policies for administering of drugs for various parameter combinations. Many of the observed patterns are stable to variations of a diverse set of parameters. Our findings provide a quantitative foundation for various important elements of public health discourse that have largely been conducted qualitatively.

A Decade of Terrorism in the United States and the Emergence of Counter-Terrorism Medicine.

The United States (US) is ranked 22nd on the Global Terrorism Index (2019), a scoring system of terrorist activities. While the global number of deaths from terrorism over the past five years is down, the number of countries affected by terrorism is growing and the health care repercussions remain significant. Counter-Terrorism Medicine (CTM) is rapidly emerging as a necessary sub-specialty, and this study aims to provide the epidemiological context over the past decade supporting this need by detailing the unique injury types responders are likely to encounter and setting the stage for the development of training programs utilizing these data. The Global Terrorism Database (GTD) was searched for all attacks in the US from 2008-2018. Attacks met inclusion criteria if they fulfilled the three terrorism-related criteria as set by the GTD. Ambiguous events were excluded when there was uncertainty as to whether the incident met all of the criteria for inclusion in the GTD. The grey literature was reviewed, and each event was cross-matched with reputable international and national newspaper sources online to confirm or add details regarding weapon type used and, whenever available, details of victim and perpetrator fatalities and injuries. In total, 304 events were recorded during the period of study. Of the 304 events, 117 (38.5%) used incendiary-only weapons, 80 (26.3%) used firearms as their sole weapon, 55 (18.1%) used explosives, bombs, or dynamite (E/B/D), 23 (7.6%) were melee-only, six (2.0%) used vehicles-only, four (1.3%) were chemicals-only, two (0.7%) used sabotage equipment, two (0.7%) were listed as "others," and one (0.3%) used biological weapon. There was no recorded nuclear or radiological weapon use. In addition, 14 (4.6%) events used a mix of weapons. In the decade from 2008 through 2018, terrorist attacks on US soil used weapons with well-understood injury-causing modalities. A total of 217 fatal injuries (FI) and 660 non-fatal injuries (NFI) were sustained as a result of these events during that period.Incendiary weapons were the most commonly chosen methodology, followed by firearms and E/B/D attacks. Firearm events contributed to a disproportionality high fatality count while E/B/D events contributed to a disproportionally high NFI count.

Do Police Body-Worn Cameras Reduce Citizen Fatalities? Results of a Country-Wide Natural Experiment

This study assesses the effects of body-worn cameras (BWCs) on rates of fatalities arising from police-citizen encounters. While existing experimental research has not examined this outcome because it is so rare, the staggered roll-out of BWCs across the nation’s law enforcement agencies provides an opportunity for quasi-experimental analysis. Difference-in-difference (DID) analyses using Poisson models compare changes in U.S. law enforcement agencies’ fatality counts with changes in BWC acquisition. Using a federal law enforcement survey focused on body worn cameras (LEMAS-BWCS) and media-sourced data on fatal encounters from fatalencounters.org (FE), the research examines agencies acquiring BWCs between 2013/14 and 2015/16 and those that did not acquire them up to 2016 and had no plans to do so. It includes a fixed effects annual panel data analysis with data from 2005/06 to 2018/19 and two two-group analyses focusing on a pre-treatment period (2010/11 to 2012/13) and a post-treatment period (2016/17 to 2018/19). The latter includes a propensity score matched comparison. Two out of three DID analyses showed statistically significant negative effects of BWCs on citizen fatalities. The propensity score matched two-group analysis returned a non-significant negative effect. The research finds some evidence for BWC effects on citizen fatalities. However, there are important validity threats to this conclusion. These include the possibility that BWC acquisition serves as a marker for other policy changes focused on BWC-acquiring agencies in the 2013/14 to 2015/16 period and beyond.

Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities.

The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.

Terrorism in China and the Emerging Needs for Counter- Terrorism Medicine Following a Decade of Deaths and Injuries.

China is ranked 42nd on the Global Terrorism Index (2019), a scoring system of terrorist activities. While China has a relatively low terrorism risk, events globally have wide-ranging repercussions for future attacks, putting first responders and emergency health workers at risk. This study aims to provide the epidemiological context for the past decade detailing the unique injury types responders are likely to encounter and to develop training programs utilizing these data. The Global Terrorism Database (GTD) was searched for all attacks in China between the years 2008-2018. Attacks met inclusion criteria if they fulfilled the terrorism-related criteria as set by the GTD's Codebook. Ambiguous events, as defined by the GTD's Codebook, were excluded. English language grey literature was searched to ensure no events meeting these criteria were missed. A focused search of online English language newspaper articles was also performed for any terrorist events between 2008-2018. One-hundred and eight terrorist events occurred in the study time period. Of the 108 incidents, forty-seven (43.5%) involved Explosives/Bombs/Dynamite (E/B/D) only, with an average fatality count of 2.9 and injury count of 7.5 per event. Twenty-seven (25.0%) used bladed or blunt weapons in melees with an average fatality count of 9.7 and an injury count of 8.8 per event. Five (4.6%) involved incendiary weapons with an average fatality count of 2.4 and an injury count of 7.2 per event. Two used only chemical weapons (1.8%) with no recorded deaths and an injury count of 27.0 per event. Two events had unknown weapon types (1.8%) with one recorded death and no injury count. One event used a firearm (0.9%) and led to one death and no injuries. One event used a vehicle (0.9%), which also led to one death and no recorded injuries. Twenty-three attacks used a mix of weapons (21.2%) with an average fatality count of 17.1 and an injury count of 12.0 per event. One-hundred and eight terrorist attacks were recorded between 2008-2018 on Chinese soil using well-understood modalities. This resulted in a total of 809 recorded fatalities with 956 non-fatal injuries. The most commonly chosen methodology was E/B/D, followed by melees and the use of bladed weapons. Three events individually recorded a combined casualty toll of over 100 people.