Temporal trends and geographic variations in mortality rates from tobacco and firearms in the United States.

We explored temporal trends and geographic variations in United States of America (US) mortality rates from smoking and firearms from 1999 to 2019. To do so, we used the publicly available Centers for Disease Control and Prevention (CDC) Wide Ranging Online Data for Epidemiologic Research (WONDER) with Multiple Cause of Death files from 1999 to 2019. Using age-specific rates and ArcGIS Pro Advanced software for Optimized Hot Spot Analyses from Esri, we generated maps of statistically significant spatial clusters with 90-99% confidence intervals with the Getis-Ord Gi* statistic for mortality from smoking-related causes and firearms. These data show temporal trends and geographic variations in mortality from smoking and firearms in the US. Smoking and firearm-related mortality from assault and suicide increased throughout the US and clustered in the Southeast. Firearm-related suicide also clustered in the continental West and Alaska. These descriptive data generate many hypotheses which are testable in analytic epidemiologic studies designed a priori to do so. The trends suggest smoking and firearm-related causes pose particular challenges to the Southeast and firearms also to the West and Alaska. These data may aid clinicians and public health authorities to implement evidence-based smoking avoidance and cessation programs as well as address firearm mortality, with particular attention to the areas of highest risks. As has been the case with cigarettes, individual behavior changes as well as societal changes are likely to be needed to achieve decreases in premature mortality.

Different biases in meta-analyses of case-control and cohort studies: an example from genomics and precision medicine.

PURPOSE: Meta-analyses of observational studies reduce the role of chance but also introduce bias because the individual component studies are not randomized. Further, it is plausible that the bias may be different in case-control and cohort studies. We explored these issues in meta-analyses of observational studies of Opioid Use Disorder (OUD). METHODS: From a systematic literature review of 152 published meta-analyses, 11 fulfilled the initial inclusion criteria of observational studies of OUD. Of these, 9 were meta-analyses of case-control studies and 2 were meta-analyses of cohort studies but only 4 (3 case-control and 1 cohort) targeted more than one specific chromosomal location. RESULTS: The meta-analyses of the 3 case-control studies, which included 13 individual studies, identified 12 different single nucleotide polymorphisms on 6 different genes on 5 different chromosomes. None was the same as the gene on Chromosome 15 identified from the meta-analysis of the cohort studies. CONCLUSIONS: These data, from genetic studies, suggest biases are different in meta-analyses of case-control and cohort studies, perhaps due to greater selection bias in case-control studies. These observations have potential importance in the application of meta-analyses to many common and serious diseases, as well as genomics and precision medicine, including OUD.

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

Importance: Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD). Objective: To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. Design, Setting, and Participants: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020. Main Outcomes and Measures: The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system. Results: Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes. Conclusions and Relevance: These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Temporal Trends and Geographic Variations in Mortality Rates from Prescription Opioids: Lessons from Florida and West Virginia.

OBJECTIVES: To explore temporal trends and geographic variations in mortality from prescription opioids from 1999 to 2016. METHODS: Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research Multiple Cause of Death files were used to calculate age-adjusted rates and 95% confidence intervals (CIs) and create spatial cluster maps. RESULTS: From 1999 to 2016, counties in West Virginia experienced the highest overall mortality rates in the United States from prescription opioids. Specifically, from 1999 to 2004, the highest rate in West Virginia of 24.87/100,000 (95% CI 17.84-33.73) was the fourth highest in the United States. From 2005 to 2009, West Virginia experienced the highest rate in the United States, 60.72/100,000 (95% CI 47.33-76.71). From 2010 to 2016, West Virginia also experienced the highest rate in the United States, which was 90.24/100,000 (95% CI 73.11-107.36). As such, overall, West Virginia experienced the highest rates in the United States and the largest increases overall of ~3.6-fold between 1999 and 2004 and 2010 and 2016. From 1999 to 2004, Florida had no "hot spots," but from 2006 to 2010 they did appear, and from 2011 to 2016, they disappeared. CONCLUSIONS: These data show markedly divergent temporal trends and geographic variations in mortality rates from prescription opioids, especially in the southern United States. Specifically, although initial rates were high and continued to increase alarmingly in West Virginia, they increased but then decreased in Florida. These descriptive data generate hypotheses requiring testing in analytic epidemiological studies. Understanding the divergent patterns of prescription opioid-related deaths, especially in West Virginia and Florida, may have important clinical and policy implications.