BACKGROUND: Previous research has shown an association between psychological distress and overdose death among specific populations. However, few studies have examined this relationship in a large US population-based cohort. METHODS: Data from the 2010-2018 NHIS were linked to mortality data from the National Death Index through 2019. Psychological distress was measured using the Kessler 6 scale. Drug overdose deaths were examined, and deaths from all other causes were included as a comparison group. Cox proportional hazards regression was used to estimate mortality risk by psychological distress level. RESULTS: The study population included 272,561 adults. Adjusting for demographic covariates and using no psychological distress as the reference, distress level was positively associated with the risk of overdose death: low (HR = 1.8, 95 % CI = 1.1-2.8), moderate (HR = 4.1, 95 % CI = 2.5-6.7), high (HR = 10.3, 95 % CI = 6.5-16.1). A similar pattern was observed for deaths from all other causes: low (HR = 1.2, 95 % CI = 1.1-1.2), moderate (HR = 1.9, 95 % CI = 1.7-2.0), high (HR = 2.6, 95 % CI = 2.4-2.8). LIMITATIONS: Limited substance use information prevented adjustment for this potentially important covariate. DISCUSSION: Adults with psychological distress were at greater risk of drug overdose death, relative to those without psychological distress. Adults with psychological distress were also at increased risk of death due to other causes, though the association was not as strong.

Starting with a high impact paper in Science in 2018, the team at the University of Pittsburgh lead by Hawre Jalal has documented intriguing observations about the factors undergirding the trajectory of the drug overdose epidemic in the United States (Jalal et al., 2018). Their work demonstrates that the epidemic of unintentional drug overdose deaths has followed an exponential growth curve that spans several major drug types as the primary drivers of the epidemic over the decades since 1979. Of note, in July 2021, the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention (CDC) released provisional mortality data indicating an approximately 30 percent increase in the number of overdoses in the U.S. during the 12-months ending December 2020 compared to the same time period for 2019 (Hedegaard et al., 2020; Ahmad et al., 2021). While the 2020 data include deaths determined as from any cause (i.e. unintentional, intentional and undetermined), these overall increases suggest that the exponential growth in overdose deaths identified by Jalal and colleagues continues (see Fig. 1). Additionally, analyses by Jalal and colleagues demonstrate shifting patterns of overdose death by birth cohort and provide insight into the deviation from this curve seen in 2018 when overdose deaths dropped slightly (Jalal & Burke, 2021; Jalal et al., 2020a). Their work includes both intriguing overall observations about the epidemic as well as contributions to analytic methods and data visualization techniques that have implications for epidemiology and public health practice (Jalal & Burke, 2020b). While other commentaries may focus on the methods used by Jalal and the strengths of the evidence supporting the work, in this commentary, we review the major implications of their findings. In particular, we describe some of the potential next steps in public health research and practice that could be informed by this important line of research.

Injury surveillance, the ongoing, systematic collection, analysis, interpretation and dissemination of injury data, provides critical information to support public health efforts to reduce injury-related morbidity, mortality and disability.1 2 For the past several decades, state and local health departments and national agencies in the USA have relied on the use of hospital discharge and emergency department (ED) data coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to conduct injury surveillance.3 Surveillance case definitions and analyses have been based on ICD-9-CM codes. However, a US mandate to code using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)4 5 has resulted in a need to update injury surveillance case definitions and analysis guidance based on ICD-10-CM.6–9

We commend Dr. McClellan for his recent article “Disparities in opioid related mortality between United States counties from 2000 to 2014” (McClellan, 2019), but have concerns about data limitations relevant to all research using National Vital Statistics System (NVSS) mortality data. | | Given the worsening overdose crisis in the U.S., policymakers and researchers are eager for information to guide overdose prevention efforts. Examining how and where county-level opioid-involved overdose deaths have changed is important. Indeed, others have examined county-level opioid overdose deaths to help explicate the impacts of various policies and have populated dashboards based on these data (Langabeer et al., 2019; U.S. Department of Agriculture and NORC at the University of Chicago, 2019).

INTRODUCTION: Since 1999, overdose deaths involving opioids have substantially increased. In 2016, 42,249 opioid-related deaths occurred-a 27.7% increase from the previous year (Hedegaard et al., 2017). As the nation's public health agency, the Centers for Disease Control and Prevention (CDC) has been actively involved in efforts to prevent opioid misuse, opioid use disorder, and opioid overdose since 2014. One of CDC's three principal opioid overdose prevention programs, the Prevention for States (PfS) program, began funding 16 state partners in August 2015 and then expanded to fund a total of 29 states in March 2016. The PfS program aims to prevent opioid morbidity and mortality by implementing evidence-based strategies such as enhancing and maximizing prescription drug monitoring programs (PDMPs) and implementing community or health systems interventions. METHODS: In this article, we will describe the origins of the PfS program, provide an overview of program strategies, and locate PfS strategies in the larger landscape of nation-wide opioid overdose prevention efforts advanced by other partners and stakeholders. To describe the implementation of PfS, we offer an iterative model of using information to inform strategy selection, implementation, and evaluation. This model is a product of our observations of program implementation over time and has emerged, post hoc, as a helpful framework for organizing our insights and reflections on the work. RESULTS: For each step of the model, we provide examples of how CDC has supported funded state partners in these efforts. Lastly, we describe innovative facets of the program and implications for both ongoing and future programs. Practical applications: Opioid overdose morbidity and mortality continues to increase across the United States. Adoption of the strategies and the program implementation paradigm described in this article when implementing prevention activities could improve the ability of public health programs to reverse this trend.

OBJECTIVES: Both the National Vital Statistics System (NVSS) and the Fatality Analysis Reporting System (FARS) can be used to examine motor vehicle crash (MVC) deaths. These 2 data systems operate independently, using different methods to collect and code information about the type of vehicle (e.g., car, truck, bus) and road user (e.g., occupant, motorcyclist, pedestrian) involved in an MVC. A substantial proportion of MVC deaths in NVSS are coded as "unspecified" road user, which reduces the utility of the NVSS data for describing burden and identifying prevention measures. This study aimed to describe characteristics of unspecified road user deaths in NVSS to further our understanding of how these groups may be similar to occupant road user deaths. METHODS: Using data from 1999 to 2015, we compared NVSS and FARS MVC death counts by road user type, overall and by age group, gender, and year. In addition, we examined factors associated with the categorization of an MVC death as unspecified road user such as state of residence of decedent, type of medical death investigation system, and place of death. RESULTS: The number of MVC occupant deaths in NVSS was smaller than that in FARS in each year and the number of unspecified road user deaths in NVSS was greater than that in FARS. The sum of the number of occupant and unspecified road user deaths in NVSS, however, was approximately equal to the number of FARS occupant deaths. Age group and gender distributions were roughly equivalent for NVSS and FARS occupants and NVSS unspecified road users. Within NVSS, the number of MVC deaths listed as unspecified road user varied across states and over time. Other categories of road users (motorcyclists, pedal cyclists, and pedestrians) were consistent when comparing NVSS and FARS. CONCLUSIONS: Our findings suggest that the unspecified road user MVC deaths in NVSS look similar to those of MVC occupants according to selected characteristics. Additional study is needed to identify documentation and reporting challenges in individual states and over time and to identify opportunities for improvement in the coding of road user type in NVSS.

In this issue of AJPH, Lowder et al. (p. 1682) report on their analysis of local vital statistics data and retrospective use of postmortem toxicology results to assess opioid overdose mortality in Marion County, Indiana. The authors found that information on the specific drugs involved in the death was not provided on the death certificate for more than half (58%) of the unintentional overdose deaths. They reviewed postmortem toxicology findings for deaths that did not have drug information available in the vital statistics data, and they used generic thresholds to infer whether the drugs that were detected were likely to have been involved in the death. Using this approach, they concluded that 86% of the drug overdose deaths in their county involved an opioid, more than double the proportion identified using vital statistics data alone (34%).

INTRODUCTION: Understanding the geographic patterns of suicide can help inform targeted prevention efforts. Although state-level variation in age-adjusted suicide rates has been well documented, trends at the county-level have been largely unexplored. This study uses small area estimation to produce stable county-level estimates of suicide rates to examine geographic, temporal, and urban-rural patterns in suicide from 2005 to 2015. METHODS: Using National Vital Statistics Underlying Cause of Death Files (2005-2015), hierarchical Bayesian models were used to estimate suicide rates for 3,140 counties. Model-based suicide rate estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences. Analyses were conducted in 2016-2017. RESULTS: Posterior predicted mean county-level suicide rates increased by >10% from 2005 to 2015 for 99% of counties in the U.S., with 87% of counties showing increases of >20%. Counties with the highest model-based suicide rates were consistently located across the western and northwestern U.S., with the exception of southern California and parts of Washington. Compared with more urban counties, more rural counties had the highest estimated suicide rates from 2005 to 2015, and also the largest increases over time. CONCLUSIONS: Mapping county-level suicide rates provides greater granularity in describing geographic patterns of suicide and contributes to a better understanding of changes in suicide rates over time. Findings may inform more targeted prevention efforts as well as future research on community-level risk and protective factors related to suicide mortality.

Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.

BACKGROUND: Describing the characteristics and patterns of suicidal behavior is an essential component in developing successful prevention efforts. The Data and Surveillance Task Force (DSTF) of the National Action Alliance for Suicide Prevention was charged with making recommendations for improving national data systems for public health surveillance of suicide-related problems, including suicidal thoughts, suicide attempts, and deaths due to suicide. PURPOSE: Data from the national systems can be used to draw attention to the magnitude of the problem and are useful for establishing national health priorities. National data can also be used to examine differences in rates across groups (e.g., sex, racial/ethnic, and age groups) and geographic regions, and are useful in identifying patterns in the mechanism of suicide, including those that rarely occur. METHODS: Using evaluation criteria from the CDC, WHO, and the U.S.A.-based Safe States Alliance, the DSTF reviewed 28 national data systems for feasibility of use in the surveillance of suicidal behavior, including deaths, nonfatal attempts, and suicidal thoughts. The review criteria included attributes such as the aspects of the suicide-related spectrum (e.g., thoughts, attempts, deaths) covered by the system; how the data are collected (e.g., census, sample, survey, administrative data files, self-report, reporting by care providers); and the strengths and limitations of the survey or data system. RESULTS: The DSTF identified common strengths and challenges among the data systems based on the underlying data source (e.g., death records, healthcare provider records, population-based surveys, health insurance claims). From these findings, the DSTF proposed several recommendations for improving existing data systems, such as using standard language and definitions, adding new variables to existing surveys, expanding the geographic scope of surveys to include areas where data are not currently collected, oversampling of underrepresented groups, and improving the completeness and quality of information on death certificates. CONCLUSIONS: Some of the DSTF recommendations are potentially achievable in the short term (<1-3 years) within existing data systems, whereas others involve more extensive changes and will require longer-term efforts (4-10 years). Implementing these recommendations would assist in the development of a national coordinated program of fatal and nonfatal suicide surveillance to facilitate evidence-based action to reduce the incidence of suicide and suicidal behavior in all populations.