Enforcement Data Research Articles

The impact of buprenorphine prescriber data on geospatial access to treatment in HEALing Communities Study communities, 2022.

The location of buprenorphine treatment providers in the United States is pivotal to the understanding of regional factors associated with prescription and uptake. We evaluated how distinct data sources of treatment providers and their associated locations contribute to the differences observed when measuring buprenorphine accessibility. We compared buprenorphine treatment provider data from the Drug Enforcement Administration (DEA) and data from the behavioral health treatment locator from the Substance Abuse and Mental Health Services Administration (SAMHSA) for July 2022. Both DEA and SAMHSA data, while similar in spirit, vary substantially in how and why each data set is collected. DEA registration was required by law, while SAMHSA data was an opt-in registry of provider-submitted details. Analyzing the underlying semantics of the data is important for understanding the contextual factors driving observable differences in analytical outputs. We measured accessibility using enhanced two-step floating catchment area (E2SFCA) analysis in three states participating in the HEALing Communities Study (Kentucky, Ohio, Massachusetts). Within communities, we compared decile rankings of accessibility per census tract using each data source. We linked prescribing data from Kentucky's prescription drug monitoring program (PDMP) to measure accessibility using providers prescribing buprenorphine. We explored the significance of localized rank exchanges using neighbor set local indicators of mobility association (LIMA). The number and rate of providers per capita differed substantially at the community level between data sources in the three states. These differences were less impactful in the spatial context of buprenorphine accessibility, which required both supply and demand in regions smaller than our intervention communities. Shifts did occur when measuring the intercommunity decile ranking of accessibility of census tracts, but LIMA results indicated that these rank exchanges were not significant. When analyzing accessibility within a community using E2SFCA analyses, either DEA or SAMHSA data sources are acceptable; linkage to Kentucky's PDMP demonstrated that SAMHSA provider data is equally suitable to PDMP data for research studies involving spatial relationships with providers while being both significantly easier to obtain and less sensitive. When analyzing treatment provider rates per capita, results may vary substantially across these different data sources. Therefore, context must be considered when choosing an appropriate data source to use.

Naloxone administration and survival in overdoses involving opioids and stimulants: An analysis of law enforcement data from 63 Pennsylvania counties

BackgroundIn consideration of rising opioid-stimulant deaths in the United States, this study explored rates of naloxone administration and survival in suspected opioid overdoses with, versus without, stimulants co-involved. MethodsThe study analyzed 26,635 suspected opioid-involved overdoses recorded by law enforcement/first-responders in the Pennsylvania Overdose Information Network in 63 Pennsylvania counties, January 2018–July 2024. All measures, including suspected drug involvement, were based on first-responder assessment/report. Relative frequencies and chi-square tests were first used to compare suspected opioid overdoses with, versus without, stimulants (cocaine or methamphetamine) co-involved. Next, mediation analyses tested naloxone administration as a mediator in the association between stimulant co-involvement (in opioid overdoses) and survival. ResultsNaloxone was reportedly administered in 72.2 % of the suspected opioid-no-cocaine overdoses, compared to 55.1 % of the opioid-cocaine-involved overdoses, and 72.1 % of the opioid-no-methamphetamine overdoses vs. 52.4 % of the opioid-methamphetamine-involved overdoses. With respect to survival rates, 18.0 % of the suspected opioid-no-cocaine overdoses ended in death, compared to 41.3 % of the opioid-cocaine overdoses; 18.1 % of the opioid-no-methamphetamine overdoses ended in death, versus 42.9 % of the opioid-methamphetamine overdoses. In mediation analyses (adjusted for demographics, county, year, and other drug co-involvement), naloxone administration mediated 38.7 % (95 % Confidence Interval [CI], 31.3–46.0 %) of the association between suspected cocaine co-involvement and survival and 39.2 % (95 % CI, 31.3–47.1 %) of the association between suspected methamphetamine co-involvement and survival. ConclusionsAmong suspected opioid overdoses recorded in the Pennsylvania Overdose Information Network, stimulant co-involvement was associated with lower naloxone administration and higher fatality, with naloxone partially mediating the association between stimulant co-involvement and death.

Naloxone Administration and Survival in Overdoses Involving Opioids and Stimulants: An Analysis of Law Enforcement Data from 63 Pennsylvania Counties.

In consideration of rising opioid-stimulant deaths in the United States, this study explored rates of naloxone administration and survival in suspected opioid overdoses with, versus without, stimulants co-involved. The study analyzed 26,635 suspected opioid-involved overdoses recorded by law enforcement/first-responders in the Pennsylvania Overdose Information Network in 63 Pennsylvania counties, January 2018-July 2024. All measures, including suspected drug involvement, were based on first-responder assessment/report. Relative frequencies and chi-square tests were first used to compare suspected opioid overdoses with, versus without, stimulants (cocaine or methamphetamine) co-involved. Next, mediation analyses tested naloxone administration as a mediator in the association between stimulant co-involvement (in opioid overdoses) and survival. Naloxone was reportedly administered in 72.2% of the suspected opioid-no-cocaine overdoses, compared to 55.1% of the opioid-cocaine-involved overdoses, and 72.1% of the opioid-no-methamphetamine overdoses vs. 52.4% of the opioid-methamphetamine-involved overdoses. With respect to survival rates, 18.0% of the suspected opioid-no-cocaine overdoses ended in death, compared to 41.3% of the opioid-cocaine overdoses; 18.1% of the opioid-no-methamphetamine overdoses ended in death, versus 42.9% of the opioid-methamphetamine overdoses. In mediation analyses (adjusted for demographics, county, year, and other drug co-involvement), naloxone administration mediated 38.7% (95% Confidence Interval [CI], 31.3%-46.0%) of the association between suspected cocaine co-involvement and survival and 39.2% (95% CI, 31.3%-47.1%) of the association between suspected methamphetamine co-involvement and survival. Among suspected opioid overdoses recorded in the Pennsylvania Overdose Information Network, stimulant co-involvement was associated with lower naloxone administration and higher fatality, with naloxone partially mediating the association between stimulant co-involvement and death.

Revolutionizing Justice: Strategic Approaches to AI in Pakistan’s Courts

The implementation of artificial intelligence (AI) in the judicial system of Pakistan deals encouraging solutions to the challenges in order to inefficiency, case backlogs and access to justice. The proposed Smart Court System (SCS) would modernize judicial procedures, including digital case management, e-filing, and the auto-generation of court orders. Key components of the module include AI-Assisted Workflow and Decision Support, which offers predictive case analysis, automated document drafting, and resource optimization across jurisdictions. AI-based sentiment analysis also enhanced fairness by detecting biases in court transcripts. Additionally, the inclusion of Online Dispute Resolution (ODR) mechanisms, supported by AI-driven bargaining tools, are expanded access to justice for geographically distant and differently-abled individuals, further enhancing judicial inclusivity. AI-Powered Legal Research revolutionized the speed and accuracy of case law retrieval and document drafting, while advanced encryption and anomaly detection systems will ensure the integrity and security of court data. Moreover, Judicial Efficiency Tools integrated with AI improve case management through predictive modeling, performance metric tracking, and resource allocation, reducing court backlogs and streamlining criminal and civil proceedings. Public engagement tools, including AI chatbots and virtual court systems, will increase accessibility and participation for all citizens, promoting transparency and accountability within the judicial framework. Through integration and interoperability, the AI module will connect various judicial platforms and law enforcement agencies, fostering collaboration and data sharing. Advanced Analytical Tools will allow the judicial system to employ predictive modeling and trend detection, providing insights for strategic case planning and judicial reform.

Case C-80/23: A Quest for Clarity. The Law Enforcement Directive 2016/680, a Bulgarian Court and Case C-205/21

Abstract This contribution examines the legal intricacies surrounding law enforcement data collection practices in Bulgaria within the framework of the Law Enforcement Directive 2016/680 (LED) and relevant Court of Justice of the European Union (CJEU) rulings, particularly cases C-205/21 and C-80/23. The analysis underscores challenges in interpreting the concept of “strict necessity” and ensuring compliance with its provisions, in particular, Art. 10. Key findings reveal ambiguities in the LED’s application, particularly concerning judicial review and the scope of data collection. The subsequent Case C-80/23 further seeks clarification on the strict necessity standard and the scope of judicial review in the collection of biometric and genetic data. The outcome of both cases has broader implications for EU member states, highlighting the need for legislative alignment and underscoring the complexities and challenges in balancing effective law enforcement with the protection of fundamental rights.

The impact of regulatory workplace safety inspections on workers' compensation claim rates.

Studies on the impact of workplace safety inspections on work injuries have found mixed effectiveness. Most studies are from the United States, examining Occupational Health and Safety Administration (OSHA) regulatory inspections in manufacturing firms with more than 10 employees. This study examines whether regulatory inspections in Alberta, Canada, result in reductions in workers' compensation claims rates for inspected firms relative to comparable non-inspected firms. Firm and claim-level data from the Workers' Compensation Board of Alberta were linked with regulatory enforcement data from the Government of Alberta for construction, manufacturing, and transportation firms with at least one full-time employee for 37 consecutive months. A matched difference-in-differences study design was used to estimate changes in lost-time claim rates for work-related injuries and musculoskeletal diseases of inspected and comparable non-inspected firms between the year pre-inspection and 2 years, post-inspection, controlling for firm-level characteristics. Inspections were not effective in reducing firm-level claim rates, with the exception of transportation firms with more than one inspection experiencing a 28% decrease in their claim rate in the second year post-inspection, relative to the change in non-inspected firms. In construction, inspected firms experienced a 12% increase in their claim rate in the first year post-inspection. No effect was observed in the manufacturing sector. Regulatory workplace safety inspections in Alberta generally do not result in greater reductions in firm-level claim rates in the construction, manufacturing, and transportation sectors. Inspections alone may not be sufficient to induce compliance or hazard management changes that lead to reductions in firm-level injuries.

Blockchain-enabled transparent traffic enforcement for sustainable road safety in cities

With the progress of the times, cars have become an important means of transportation in our lives. However, with new cars of all kinds, road safety issues still cannot be effectively improved. While countries have responded by enforcing traffic laws and using electronic technology to enhance enforcement, challenges still need to be addressed, including blurred images and misjudgments by police agencies, and even exploiting loopholes in traffic enforcement frameworks to evade consequences through bribery or connections. exacerbating an accident and leaving victims and families in the lurch of the aftermath. To solve this problem, we proposed a data traceability law enforcement system based on blockchain and InterPlanetary File System (IPFS). The system ensures rapid traceability and protects law enforcement data from external attacks. The goal of leveraging blockchain’s decentralized nature and smart contracts is to instill accountability and fairness in road safety measures, mitigate the effects of corruption, and pave the way for a safer and more just transportation environment. Our experimental results show that under our stress test of 50 transactions per second, the throughput can be as high as 300 and the maximum delay can reach 2.01s, which is enough to experimentally prove that our system is feasible. Our approach is designed to solve the data corruption problem caused by the centralized server being paralyzed. It applies to all technology enforcement, with high flexibility and scalability for all participants to join or set privileges. we also provide an automated traffic law enforcement system in a smart city. To make the data storage more secure and fair, and not easily damaged by malicious people or tampered with by intentional attackers. This brings a breakthrough to the country’s program security.

Law enforcement fentanyl seizures and overdose mortality in US counties, 2013–2020

BackgroundThe spread of illicitly manufactured fentanyl is driving steep increases in US overdose deaths. Fentanyl seizures are correlated with state-level opioid-related mortality; however, more granular seizure surveillance information has the potential to better inform overdose prevention and harm reduction efforts. MethodsUsing data on fentanyl pill and powder seizures from High Intensity Drug Trafficking Areas (HIDTA), we tested associations between seizure prevalence and overdose mortality, from 2013 to 2020. The primary exposure—seizure burden—was constructed by identifying counties having high (above the median) prevalence of pill, powder, or combined pill/powder seizure burden per 100,000 population. Poisson models accounted for county demographic, law enforcement and time trends. ResultsDuring the timeframe, there were 13,842 fentanyl seizures in 606 US counties. In adjusted models, counties with a high burden of pill or powder fentanyl seizures, or both (combined pills/powder) exhibited higher total overdose mortality than non-high burden counties (pills adjusted prevalence ratio [aPR]: 1.10 [95 % confidence interval [CI]: 1.08, 1.12]; powder aPR 1.12 [CI: 1.11, 1.13]; combined pills/powder aPR: 1.27 [CI: 1.25, 1.29]). A similar pattern of associations with fentanyl seizure burden was noted for overdose deaths involving synthetic opioids (pills [aPR]: 0.99 [CI: 0.96, 1.02]; powder aPR 1.29 [CI: 1.27, 1.30]; combined pills/powder aPR 1.55 [CI: 1.52, 1.58]). ConclusionsLaw enforcement data on fentanyl seizures predicts drug overdose mortality at the county-level. Integrating these data with more traditional epidemiologic surveillance approaches has the potential to inform community overdose response efforts.

Gender differences in homicides. A comparative analysis of 106 fatalities in forensic autopsy data.

While most homicides worldwide are committed against men, women and girls are disproportionately affected by domestic violence and its fatal consequences. The Istanbul Convention mandates the prevention of gender-based violence, particularly against women. This study analyzes the characteristics of male and female victims of homicides to provide a comprehensive understanding of the different situations and ways in which men and women are killed. Files of 106 forensic autopsies of homicide victims aged 14 years and older (51.9% males, 48.1% females) from 2012 to 2019 were reviewed. Demographic data, previous history with a focus on the perpetrator-victim relationship, substance influence, type of violence and law enforcement data were recorded. A gender-separated statistical analysis was carried out. Male victims were killed most often outside their own home (63.6%) by acquaintances or friends (54.5%). They were frequently under the influence of substances (56.4%). The perpetrators suffered from addictive diseases in 49.1%, and 52.7% had prior convictions. In both groups, stab/cut injuries were the most common causes of death, but stab/cut and gunshot injuries occurred more frequently in male victims. Female victims were killed most often in their own homes (76.5%) by intimate partners (62.7%). Compared to male victims, violent asphyxiation and blunt force trauma were more common causes of death. Furthermore, there was a higher frequency of multiple injuries (33.3%). The prevalence of a guilty verdict of the perpetrators was nearly equal in both groups. Gender-specific aspects should be considered when investigating homicides and establishing prevention and intervention strategies for interpersonal violence. Protective concepts are easier to establish in the public sphere than in private homes, which is why homicides against women require special attention in society, law enforcement, and legislation. Significant gender differences were found in the circumstances and the perpetrator-victim relationships of homicides. Gender-specific aspects should be taken into account when investigating homicides and developing prevention and intervention strategies. Homicides against women require special attention in society, law enforcement and legislation.

Navigating Hurdles: A Review of the Obstacles Facing the Development of the Pandemic Treaty.

The emergence of the COVID-19 pandemic has served as a call for enhanced global cooperation and a more robust pandemic preparedness and response framework. As a result of this pressing demand, dialogues were initiated to establish a pandemic treaty designed to foster a synchronized global strategy for addressing forthcoming health emergencies. In this review, we discussed the main obstacles to this treaty. Among several challenges facing the pandemic treaty, we highlighted (1) global cooperation and political will, (2) equity in access to resources and treatments, (3) sustainable financing, (4) compliance and enforcement mechanisms, (5) sovereignty concerns, and (6) data sharing and transparency. Navigating the hurdles facing the development of the pandemic treaty requires concerted efforts, diplomatic finesse, and a shared commitment to global solidarity. Addressing challenges in global cooperation, equitable access, transparency, compliance, financing, and sovereignty is essential for forging a comprehensive and effective framework for pandemic preparedness and response on the global stage.

Evolution of the Merger Guidelines: Is This Fox Too Clever by Half?

The 2023 Merger Guidelines make some notable improvements over the 2010 Horizontal Merger Guidelines. They give greater emphasis to the idea that predicting the competitive effects of a proposed merger is inherently difficult and that to block a merger the government need only show a risk that the merger may substantially lessen competition – not that it will do so. They also give greater emphasis to dynamic competition and innovation – especially with regard to acquisitions of potential entrants – and they add useful material on multi-sided platforms. However, the treatment of market definition in the 2023 Merger Guidelines may weaken horizontal merger enforcement by demoting the role of the “hypothetical monopolist test,” which is used to define markets for the purpose of measuring market shares, and by removing extensive material from prior guidelines that explained why market shares measured in narrower markets tend to be more informative than market shares measured in broader markets. The 2023 Merger Guidelines lower the market concentration thresholds that trigger a presumption by the antitrust enforcement agencies that a merger may substantially lessen competition, but the enforcement data suggest that change will have little effect in practice. The 2023 Merger Guidelines also may lead to less effective deterrence of harmful mergers because they are not well targeted at the mergers that are most likely to substantially lessen competition. One cannot prioritize everything.

The Painful and Chilling Effects of Legal Violence: Immigration Enforcement and Racialized Legal Status Inequities in Worker Well-Being

A wave of restrictive immigration policies implemented over the past several decades dramatically increased immigrant detentions and deportations in the United States (U.S.), with important consequences for a host of immigrant outcomes. Still, questions remain as to how temporal and geographic variation in immigration enforcement within and across the U.S. shaped racialized legal status inequities in health and well-being, particularly among those employed in precarious occupations. To fill this gap, we interrogated the links between changes in county-level immigration enforcement and racialized legal status inequalities in musculoskeletal pain and social welfare benefits utilization among U.S. agricultural workers over nearly two decades (2002–2018). We merged data from three sources [(1) restricted-access, geocoded data from the National Agricultural Workers Survey (NAWS) (n = 37,619); (2) county-level immigration enforcement data from the Transactional Records Access Clearinghouse (TRAC); and (3) population data from the Census and American Community Survey (ACS)] and estimated linear probability models with year, month, and state fixed effects. We show that, in counties with high enforcement rates, workers—especially undocumented workers—were at increased risk of musculoskeletal pain, including pain that was severe. Heightened enforcement was also associated with declines in needs-based benefits utilization, especially among documented and U.S.-citizen non-White workers and undocumented White and non-White workers. Together, these findings highlight how changes in sociopolitical and legal contexts can shift and maintain racialized legal status hierarchies, with especially important consequences for the well-being of vulnerable workers.

First records of the sicklefin (Mobula tarapacana), bentfin (Mobula thurstoni), and spinetail (Mobula mobular) devil rays in the Chagos Archipelago.

Recent encounters with sicklefin (Mobula tarapacana) and bentfin (Mobula thurstoni) devil rays in the Chagos Archipelago provide the first confirmed observations of live specimens of these species in this region. Examination of illegal fishing photo archives collected during enforcement revealed these endangered species, and spinetail devil rays (Mobula mobular), are being caught within the archipelago's vast no-take marine protected area. Future cooperation between authorities and mobulid ray experts is crucial to improve the availability and accuracy of enforcement data and improve management of illegal fishing and mobulid ray conservation activities.

Impact of Fraud on Elderly Residents in Washington State

Fraud offenses are a pervasive problem for individuals throughout society, especially for elderly victims. This paper explores the impact of fraud on elderly victims in Washington state both by the number of victims and financial losses. A solution to this problem will assist law enforcement agencies, financial institutions, elder advocacy groups, and the public in understanding the impact and types of fraud utilized successfully against elderly victims. By exploring existing data sources of fraud victims, this study attempts to break down the fraud offenses to understand what types of fraud are being committed and what further research is necessary to fully understand and combat fraud offenses with elderly victims. Understanding the impact of elder fraud is difficult due to the low reporting rates, non-standardized definitions across jurisdictions, and rapidly evolving techniques used by offenders. Prior studies have been published analyzing the national impact of fraud offenses using self-reporting data collection from victims, such as the FBI’s Elder Fraud Report from the Internet Crime Complaint Center. These reports rely on victims to self-report to the agency in question and analyze trends at the national level. This study attempts to use local law enforcement data specific to Washington state to determine the number of victims and their financial losses. Overall, Washington’s elderly residents are one of the highest impacted demographics, making up 18.7% of fraud victims in 2022, with $44.8 million in losses. False pretenses, swindles, and confidence games continue to be the predominant fraud offense type in 2022 for elderly victims in Washington. While these data detail the scope of elderly fraud in Washington, further analysis and research are needed to determine why elderly victims continue to be targets of fraud and why false pretenses, swindles, and confidence games are the most successful types.

The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.

Harassment and obstruction of observers in U.S. fisheries

Fishery observers play a crucial role in the management and conservation of fish stocks, but the treatment they receive aboard fishing vessels can affect their ability to perform their duties. Using law enforcement data from the Northeast and Alaska regions, home to the most important commercial fisheries in the United States, we explore the extent of observer harassment, assault, interference, and obstruction (OHAIO). We find that 16% of 10,346 fishery violations reported in the Northeast and Alaska regions from 2014-2018 are observer-related, and over 80% of those involve OHAIO. We trace how OHAIO incidents are reported and processed and propose steps to mitigate the issue.

Urban wireless traffic evolution: The role of new devices and the effect of policy

The emergence of new wireless technologies, such as the Internet of Things, allows digitalizing new and diverse urban activities. Thus, wireless traffic grows in volume and complexity, making prediction, investment planning, and regulation increasingly difficult. This article characterizes urban wireless traffic evolution, supporting operators to drive mobile network evolution and policymakers to increase national and local competitiveness. We propose a holistic method that widens previous research scope, including new devices and the effect of policy from multiple government levels. We provide an analytical formulation that combines existing complementary methods on traffic evolution research and diverse data sources. Results for a centric area of Helsinki during 2020–2030 indicate that daily volumes increase, albeit a surprisingly large part of the traffic continues to be generated by smartphones. Machine traffic gains importance, driven by surveillance video cameras and connected cars. While camera traffic is sensitive to law enforcement policies and data regulation, car traffic is less affected by transport electrification policy. High-priority traffic remains small, even under encouraging autonomous vehicle policies. Based on peak hour results, we suggest that 5G small cells might be needed around 2025, albeit the utilization of novel radio technology and additional mid-band spectrum could delay this need until 2029. We argue that mobile network operators inevitably need to cooperate in constructing a single, shared small cell network to mitigate the high deployment costs of massively deploying small cells. We also provide guidance to local and national policymakers for IoT-enabled competitive gains via the mitigation of five bottlenecks. For example, local monopolies for mmWave connectivity should be facilitated on space-limited urban furniture or risk an eventual capacity crunch, slowing down digitalization.

Reflexive thematic analysis of job-related problems associated with pharmacist suicide, 2003-2019.

Few qualitative studies have focused on suicide and specific job-related problems associated with suicide in pharmacists. Greater knowledge of specific work-related stressors amongst pharmacists could inform suicide prevention strategies. To identify job-related stressors associated with pharmacist suicides and compare stressors to those previously reported in physicians and nurses. Free-text law enforcement and medical examiner data for pharmacist suicides were extracted from the National Violent Death Registry (NVDRS) for 2003-2019. Reflexive thematic analysis was deployed via a deductive approach utilizing codes and themes found in previous research on nurse and physician suicides. New codes were also identified through inductive coding. A total of 291,872 suicides occurred between 2003 and 2019, of which 392 were pharmacist deaths. Of these, 62 pharmacist suicides were coded with job-related problems. Almost all deductive themes/codes extracted from nurses and physicians were present in pharmacists. Common codes found in the pharmacist dataset that were also found previously in physicians and nurses were: history of mental health, substance use disorder, hopelessness impending or proceeding job loss, and access to lethal weapons and/or drugs. Novel codes were added through inductive content analysis. Codes novel to pharmacists were: verbalized suicidal ideation (SI) or intent, diversion for the purpose of suicide, and the fear of job loss. Disciplinary action at the institutional level was associated with the fear of job loss and cited as the event triggering suicide completion. Pharmacists have similar job-related stressors associated with suicide as physicians and nurses. Evaluating the process of disciplinary action is warranted. Future research is indicated to evaluate causal relationship between work-related stressors and mental health outcomes leading to suicide in pharmacists.

Web scraping with excel

Web scraping is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where doesisn’t provided in readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry Web scraping with Excel is the process of automatically extracting data from websites and importing it into Excel for analysis and reporting purposes. Excel's built-in web scraping tool, Power Query, allows users to connect to a website, select the data they want to extract, and transform the data into a usable format. However, it's important to be aware of the potential legal and ethical issues surrounding web scraping, and to use discretion when extracting data from websites KEYWORDS Scraping, Excel, Data, structure.

Taxi drivers’ traffic violations detection using random forest algorithm: A case study in China

Objective To effectively explore the impacts of several key factors on taxi drivers’ traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries. Methods 43,458 electronic enforcement data about taxi drivers’ traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers’ traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework. Results Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers’ traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers’ traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively. Conclusions Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management.