Longitudinal changes in co-involved drugs, comorbidities, and demographics of methamphetamine-related deaths in Los Angeles County.

INTRODUCTION: We conducted a population-based observational study of all medical examiner cases in Los Angeles County from January 2012 through June 2021 in which methamphetamine was listed as a cause of or contributing factor to death (n = 6125). We aimed to characterize demographics, comorbidities, and co-involved substances in methamphetamine-related deaths longitudinally in Los Angeles County, California. METHODS: We used detailed death record data to manually classify fatalities by involvement of each organ system, opioids, alcohol, cocaine, other drugs or medications, and external/traumatic causes. Primary outcomes included: the number of methamphetamine-involved deaths, demographics of decedents, percentage of methamphetamine deaths also involving other drugs, and percentage of methamphetamine deaths involving different organ systems. We performed Mann Kendall tests of trends to identify statistically significant longitudinal changes. RESULTS: During the study period, the percentage of methamphetamine-related deaths involving opioids significantly increased from 16 % in 2012 to 54 % in 2021 (p < 0.001). Concurrently, the percentage involving cardiovascular causes significantly decreased from 47 % to 26 % (p < 0.05). Methamphetamine-related deaths in LAC increasingly affected people experiencing homelessness, for whom the percentage tripled from 13 % in 2012 to 35 % in 2021. The share of decedents under 40 years old increased from 33 % to 41 %. The percentage of Black or African American decedents increased over five-fold from 3 % to 17 %. CONCLUSIONS: Methamphetamine-related deaths involving opioids more than tripled in Los Angeles County from 2012 to 2021, reflecting the drug supply's shift to illicit fentanyl. More than a quarter involved cardiovascular causes. These findings have implications for treatment and prevention, including scaling up contingency management, distributing naloxone to people who primarily use stimulants, and including cardiovascular care alongside these interventions directly targeted to reduce harms of methamphetamine use.

Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths.

Importance: Overdose is one of the leading causes of death in the US; however, surveillance data lag considerably from medical examiner determination of the death to reporting in national surveillance reports. Objective: To automate the classification of deaths related to substances in medical examiner data using natural language processing (NLP) and machine learning (ML). Design, Setting, and Participants: Diagnostic study comparing different natural language processing and machine learning algorithms to identify substances related to overdose in 10 health jurisdictions in the US from January 1, 2020, to December 31, 2020. Unstructured text from 35 433 medical examiner and coroners' death records was examined. Exposures: Text from each case was manually classified to a substance that was related to the death. Three feature representation methods were used and compared: text frequency-inverse document frequency (TF-IDF), global vectors for word representations (GloVe), and concept unique identifier (CUI) embeddings. Several ML algorithms were trained and best models were selected based on F-scores. The best models were tested on a hold-out test set and results were reported with 95% CIs. Main Outcomes and Measures: Text data from death certificates were classified as any opioid, fentanyl, alcohol, cocaine, methamphetamine, heroin, prescription opioid, and an aggregate of other substances. Diagnostic metrics and 95% CIs were calculated for each combination of feature extraction method and machine learning classifier. Results: Of 35 433 death records analyzed (decedent median age, 58 years [IQR, 41-72 years]; 24 449 [69%] were male), the most common substances related to deaths included any opioid (5739 [16%]), fentanyl (4758 [13%]), alcohol (2866 [8%]), cocaine (2247 [6%]), methamphetamine (1876 [5%]), heroin (1613 [5%]), prescription opioids (1197 [3%]), and any benzodiazepine (1076 [3%]). The CUI embeddings had similar or better diagnostic metrics compared with word embeddings and TF-IDF for all substances except alcohol. ML classifiers had perfect or near perfect performance in classifying deaths related to any opioids, heroin, fentanyl, prescription opioids, methamphetamine, cocaine, and alcohol. Classification of benzodiazepines was suboptimal using all 3 feature extraction methods. Conclusions and Relevance: In this diagnostic study, NLP/ML algorithms demonstrated excellent diagnostic performance at classifying substances related to overdoses. These algorithms should be integrated into workflows to decrease the lag time in reporting overdose surveillance data.

Elevated Mortality Among People Experiencing Homelessness With COVID-19.

We reviewed publicly available data from major US health jurisdictions to compare severe acute respiratory syndrome coronavirus 2 case fatality rates in people experiencing homelessness with the general population. The case fatality rate among people experiencing homelessness was 1.3 times (95% CI, 1.1-1.5) that of the general population, suggesting that people experiencing homelessness should be prioritized for vaccination.

Predictors of Medical Malpractice Outcomes After Spine Surgery: A Comprehensive Analysis From 2010 to 2019.

STUDY DESIGN: Retrospective review of spine surgery malpractice cases. OBJECTIVES: The aim was to compare medical malpractice outcomes among different types of spine surgery and identify predictors of litigation outcomes. SUMMARY OF BACKGROUND DATA: Spine surgery is highly litigious in the United States with data suggesting favorable outcomes for defendant surgeons. However, factor specific data and explanations for plaintiff verdicts are lacking. METHODS: Westlaw legal database was queried for spine surgery malpractice outcomes from 2010 to 2019. Clinical data, reasons for litigation, and legal outcomes were tabulated. Statistical analysis was performed to identify factors associated with litigation outcomes. RESULTS: A total of 257 cases were identified for inclusion. There were 98 noninstrumented and 148 instrumented cases; 110 single-level and 99 multilevel; 83 decompressions, 95 decompression and fusions, and 47 fusion only. In all, 182 (71%) resulted in a defendant verdict, 44 (17%) plaintiff verdict, and 31 (12%) settlement. Plaintiff verdicts resulted in payouts of $2.03 million, while settlements resulted in $1.11 million (P=0.34). Common reasons for litigation were intraoperative error, hardware complication, and improper postoperative management. Cases were more likely to result for the plaintiff if postoperative cauda equina syndrome (55% vs. 26%, P<0.01), a surgical site infection (46% vs. 27%, P=0.03), or other catastrophic injury (40% vs. 26%, P=0.03) occurred. Higher monetary awards were associated with multi versus single-level (median: $2.61 vs. $0.92 million, P=0.01), improper postoperative management cited (median: $2.29 vs. $1.12 million, P=0.04), and permanent neurological deficits ($2.29 vs. $0.78 million, P<0.01). Plaintiff payouts were more likely if defendant specialty was neurosurgery versus orthopedic surgery (33% vs. 18%, P=0.01). CONCLUSIONS: Spine surgery is a litigious field with multiple factors associated with outcomes. Efforts to reduce intraoperative errors and complications may improve patient care and decrease the risk of litigation.