scholarly journals Finding Chances to Intervene Before the Fatal Overdose: Linking ED and Mortality Data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Evan Mobley ◽  
Chelsea Fischer ◽  
Andrew Hunter
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Primary Diagnosis ◽  
Mortality Data ◽  
Opioid Overdose ◽  
Department Of Health ◽  
Icd 10 ◽  
Substance Related Disorders ◽  
Senior Services ◽  
Overdose Deaths

ObjectiveLink emergency department (ED) with death certificate mortality data in order to examine the prior medical history of opioid overdose victims leading up to their death.IntroductionIn 2017, 951 Missouri residents died from an opioid overdose—a record number for the state.1 This continues the trend from 2016, which saw an increase of over 30% in opioid overdose deaths compared to 2015. The Missouri Department of Health and Senior Services (MDHSS) manages several public health surveillance data sources that can be used to inform about the opioid epidemic. Opioid overdose deaths are identified through death certificates which are collected through the vital records system. MDHSS also manages the Patient Abstract System (PAS), which contains ED and inpatient hospitalization data from approximately 132 non-federal Missouri hospitals. PAS contains about 130 variables, which include demographic data, diagnoses codes, procedures codes, and other visit information. Records can have up to 23 diagnosis fields, which are coded using ICD-10-CM (International Classification of Diseases, Clinically Modified). The first diagnosis field is the primary reason for a visit.MethodsLinkage and analysis of the data was performed using SAS Enterprise Guide 6.1. Opioid overdose deaths were identified through ICD-10 analysis looking for drug poisoning underlying cause of death codes and opioid-specific codes found in the multiple cause (contributing cause) of death fields. Table 1, below, summarizes the ICD-10 codes used. Mortality data from the 951 decedents were linked to ED data from 2016 and 2017. Records were linked using multiple passes over the ED records. Records were first linked on social security number. Following this linkage, ED records with no initial match went through a second pass and linked on name and date of birth. Finally, a third pass for records still without a match was conducted using date of birth, census tract, and sex. After these passes, the linkages were reviewed to identify any false positives. The 23 diagnosis fields contained in PAS were analyzed to look for patterns in diagnosis coding. ICD-10-CM codes were too broad so CCS (Clinical Classifications Software) categories were utilized.ResultsIn total, 3,500 ED records were linked to the 951 decedents. After removing false positives, the total number of ED records was 3,357. Approximately 70% (687) of decedents were linked to at least one ED record. One hundred and eighty-eight visits were due to drug overdose (153 opioid overdoses). The most common primary diagnosis CCS categories (category numbers in parentheses) were: substance-related disorders (661), Spondylosis; intervertebral disc disorders; other back problems (205), abdominal pain (251), and other nervous system disorders (95). Collectively, these four categories represented over 20% of all primary diagnoses. Across all 23 diagnosis fields there were similar results. The most common CCS categories were as follows: substance-related disorders (661), other aftercare (257), essential hypertension (98), and mood disorders (657). Pie charts (Fig. 1 and 2) below show proportions of CCS categories across all diagnoses fields and primary diagnosis broken into three major categories: pain/injury, substance abuse/mental health, and other. In order to reduce the impact of CCS categories with small numbers, these graphics represent only CCS categories that made up 1% or more of the total collection of diagnoses codes. Of the 687 decedents that were matched successfully to ED records, 96% had at least one pain/injury or one substance abuse/mental health ICD-CM code in at least one record, and 68% had both.ConclusionsThese findings suggest that many overdose decedents visited the ED in the years prior to death. Many of these visits were not due to an overdose; however, they could be indicative of a problem with opioids (i.e. pain, drug-seeking, substance use-related). ED staff and public health professionals could utilize these opportunities to refer patients to recovery services and recommend they heed caution when using opioids.References1. Missouri Department of Health and Senior Services. (2018). Missouri Resident Overdose Deaths by Opioid Type. Retrieved September 27, 2018 from https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-9.pdf.

Alcohol- and Drug-Use Disorders in Australia: Implications of the National Survey of Mental Health and Wellbeing

2000 ◽  
Vol 34 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Maree Teesson ◽  
Wayne Hall ◽  
Michael Lynskey ◽  
Louisa Degenhardt
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Alcohol Use ◽  
Drug Use ◽  
Alcohol Use Disorders ◽  
Treatment Seeking ◽  
Alcohol And Drug Use ◽  
Health And Wellbeing ◽  
Drug Use Disorders ◽  
Icd 10

Objective: This study reports the prevalence and correlates of ICD-10 alcohol- and drug-use disorders in the National Survey of Mental Health and Wellbeing (NSMHWB) and discusses their implications for treatment. Method: The NSMHWB was a nationally representative household survey of 10 641 Australian adults that assessed participants for symptoms of the most prevalent ICD-10 and DSM-IV mental disorders, including alcohol- and drug-use disorders. Results: In the past 12 months 6.5%% of Australian adults met criteria for an ICD-10 alcohol-use disorder and 2.2%% had another ICD-10 drug-use disorder. Men were at higher risk than women of developing alcohol- and drug-use disorders and the prevalence of both disorders decreased with increasing age. There were high rates of comorbidity between alcohol- and other drug-use disorders and mental disorders and low rates of treatment seeking. Conclusions: Alcohol-use disorders are a major mental health and public health issue in Australia. Drug-use disorders are less common than alcohol-use disorders, but still affect a substantial minority of Australian adults. Treatment seeking among persons with alcohol- and other drug-use disorders is low. A range of public health strategies (including improved specialist treatment services) are needed to reduce the prevalence of these disorders.

Assessing the Impact of Person-level Matching Algorithms to Identify Risk of Fatal Opioid Overdose Across Disparate Datasets: Retrospective Analysis (Preprint)

2020 ◽  
Author(s):  
Lindsey Ferris ◽  
Jonathan P. Weiner ◽  
Brendan Saloner ◽  
Hadi Kharrazi
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
At Risk ◽  
Area Under The Curve ◽  
The United States ◽  
Risk Indicators ◽  
Mortality Data ◽  
Opioid Overdose ◽  
Fatal Overdose ◽  
The Impact

BACKGROUND The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions. A major impediment to using datasets from separate agencies, has been the lack of a cross-organization unique identifier. Although different matching techniques that leverage patient demographic information can be used, the impact of using a particular matching approach is not well understood. OBJECTIVE This study compares the impact of using probabilistic versus deterministic matching algorithms to link disparate datasets together for identifying persons at risk of a fatal overdose. METHODS This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using a probabilistic and two deterministic matching algorithms. Impact of matching was assessed by comparing the prevalence of key risk indicators, the outcome, and performance of a multivariate logistic regression for fatal overdose using the combined datasets. RESULTS The probabilistically matched population had the highest degree of matching within the PDMP data and with arrest and mortality data, resulting in the highest prevalence of high-risk indicators and the outcome. Model performance using area under the curve (AUC) was comparable across the algorithms (probabilistic: 0.847; deterministic-basic: 0.854; deterministic+zip: 0.826), but demonstrated tradeoffs between sensitivity and specificity. CONCLUSIONS The probabilistic algorithm was more successful in linking patients with PDMP data with death and arrest data, resulting in a larger at-risk population. However, deterministic-basic matching may be a suitable option for understanding high-level risk based on the model’s area under the curve (0.854). The clinical use case should be considered when selecting a matching approach, as probabilistic algorithms can be more resource-intensive and costly to maintain compared with deterministic algorithms.

Spatial Epidemiological Analysis and Geographical Information System of Opioid Overdoses in New York State (Preprint)

2020 ◽  
Author(s):  
Anuwat Pengput ◽  
Peter Elkin
Keyword(s):  
Public Health ◽  
New York ◽  
New York State ◽  
Geographical Information ◽  
Opioid Overdose ◽  
Hospital Inpatient ◽  
Opioid Use ◽  
Public Health Services ◽  
Public Health Response ◽  
Overdose Deaths

BACKGROUND Opioid analgesics are pain relievers. There are no better drugs than opioids for treating severe pain, however, opioids are the main drugs associated with overdose deaths. OBJECTIVE The study aimed to identify the distribution and clusters of opioid overdoses across New York. METHODS We used the deidentified hospital inpatient discharges datasets (SPARCS) from 2010 – 2015. ICD 9 and ICD 10 codes were used to identify and retrieve opioid overdose patients. We merged and aggregated SPARCS datasets to a geographic shapefile by all counties in New York. RESULTS More than half of the opioid overdose population (n = 235,178) were male (70%). Most patients were 30 - 49 years old (48.3%). Among patients, white non-Hispanics had the highest opioid overdose. Nearly all counties showed increasing rates of overdoses over six years. The high overdose clusters were identified in Niagara, Orleans, Genesee, Madison, Chenango, Delaware, and Sullivan counties (P < 0.05). The highest overdose rates were identified in the Central and Eastern New York regions. CONCLUSIONS The areas of highest overdose deaths among opioid use disorders were not necessarily the areas with the highest usage rates. This tells us that public health services may be lacking in these communities and this represents an opportunity for the New York Department of Public Health to improve our education and public health response in these communities. Opioid use and overdose rates do not always correlate well. This shows that special attention to counties with high overdose/user rates is warranted. The findings could inform health policy decisions at the county and state levels based on the geographic and demographic patterns to prevent and control opioid crises.

CommunityStat: A Public Health Intervention to Reduce Opioid Overdose Deaths in Burlington, Vermont, 2017–2020

2021 ◽  
pp. 009145092110521
Author(s):  
Brandon del Pozo
Keyword(s):  
Public Health ◽  
New York ◽  
Performance Metrics ◽  
Public Health Intervention ◽  
Police Department ◽  
Opioid Use Disorder ◽  
Health Staff ◽  
Opioid Overdose ◽  
Opioid Use ◽  
Overdose Deaths

From 2017 to early 2020, the US city of Burlington, Vermont led a county-wide effort to reduce opioid overdose deaths by concentrating on the widespread, low-barrier distribution of medications for opioid use disorder. As a small city without a public health staff, the initiative was led out of the police department—with an understanding that it would not be enforcement-oriented—and centered on a local adaptation of CompStat, a management and accountability program developed by the New York City Police Department that has been cited as both yielding improvements in public safety and overemphasizing counterproductive police performance metrics if not carefully directed. The initiative was instrumental to the implementation of several novel interventions: low-threshold buprenorphine prescribing at the city’s syringe service program, induction into buprenorphine-based treatment at the local hospital emergency department, elimination of the regional waiting list for medications for opioid use disorder (MOUD), and the de-facto decriminalization of diverted buprenorphine by the chief of police and county prosecutor. An effort by local legislators resulted in a state law requiring all inmates with opioid use disorder be provided with MOUD as well. By the end of 2018, these interventions were collectively associated with a 50% (17 vs. 34) reduction in the county’s fatal overdose deaths, while deaths increased 20% in the remainder of Vermont. The reduction was sustained through the end of 2019. This article describes the effort undertaken by officials in Burlington to implement these interventions. It provides an example that other municipalities can use to take an evidence-based approach to reducing opioid deaths, provided stakeholders assent to sustained collaboration in the furtherance of a commitment to save lives. In doing so, it highlights that police-led public health interventions are the exception, and addressing the overdose crisis will require reform that shifts away from criminalization as a community’s default framework for substance use.

Health Promotion and Disease Prevention in the Primary Health Care Setting: Setting the Scene

1994 ◽  
Vol 11 (3) ◽  
pp. 129-131 ◽  
Author(s):  
Brian Oldenburg
Keyword(s):  
Public Health ◽  
Mental Health ◽  
United States ◽  
Premature Death ◽  
The United States ◽  
United States Department ◽  
The Past ◽  
Department Of Health ◽  
Related Risk ◽  
National Public Health

Last (1983) defines public health as: the efforts organised by society to protect, promote and restore the public's health. It is the combination of sciences, skills and beliefs that are directed to the maintenance and improvement of the health of all people through collective or social actions. The programs, services and institutions involved emphasise the prevention of disease and the health needs of the population as a whole. Public health activities change with changing technology and values, but the goals remain the same: to reduce the amount of disease, premature death and disability in the population. (p.45)Recommended goals and targets for addressing national public health problems and directed at reducing the amount of death and premature death have been proposed in many countries over the past 10 years, including the United States of America (United States Department of Health and Human Services, 1990), the United Kingdom (Department of Health, 1992), Canada (Ontario Premiers' Council on Health, 1987) and Australia (Nutbeam, Wise, Bauman, Harris, & Leeder, 1993). In Australia for example, over the past 2 years, much attention has been directed at health outcomes related to cardiovascular disease, cancers, accidents and injuries and mental health. All of these reports have emphasised the importance of changing those lifestyle and related risk factors associated with preventable causes of death. Priority lifestyle areas that have been identified include physical inactivity, diet and nutrition, smoking, alcohol and other drug use, safety behaviours, sun protective behaviours, appropriate use of medicines, immunisation, sexuality and reproductive health, oral hygiene, and mental health. Priority populations and appropriate settings for intervening in these areas have also been identified.

Sociodemographic factors, prescription history and opioid overdose deaths: a statewide analysis using linked PDMP and mortality data

2018 ◽  
Vol 190 ◽  
pp. 62-71 ◽  
Author(s):  
Sarah J. Nechuta ◽  
Benjamin D. Tyndall ◽  
Sutapa Mukhopadhyay ◽  
Melissa L. McPheeters
Keyword(s):  
Sociodemographic Factors ◽  
Mortality Data ◽  
Opioid Overdose ◽  
Overdose Deaths

scholarly journals Improving risk factor identification for opioid overdose deaths in Tennessee

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah J. Nechuta ◽  
Jenna Moses ◽  
Molly Golladay ◽  
Adele Lewis ◽  
Julia Goodin ◽  
...  
Keyword(s):  
Risk Factor ◽  
Drug Overdose ◽  
Medical Examiner ◽  
Prescription Opioid ◽  
Opioid Overdose ◽  
Prescription Opioids ◽  
Death Certificates ◽  
Postmortem Toxicology ◽  
Icd 10 ◽  
Overdose Deaths

ObjectiveTo examine specific drugs present based on postmortem toxicology for prescription opioid, heroin, and fentanyl overdoses classified based on ICD-10 coding. To compare drugs identified from postmortem toxicology with those listed on the death certificate for opioid overdoses.IntroductionUsing death certificates alone to identify contributing substances in drug overdose deaths may result in misclassification and underestimation of the burden of illicit and prescription opioids and other drugs in drug-related deaths. To enable timely and targeted prevention in Tennessee (TN), the identification and monitoring of new drugs and trends in use should utilize toxicology and medicolegal death investigation data directly, as recommended by others 1-3. These data can inform mortality outcome definitions for improved surveillance and risk factor identification 4-7. To our knowledge, this is the first analysis to use statewide linked toxicology and death certificate data in TN.MethodsWe identified 615 opioid involved overdose deaths in TN of unintentional (underlying ICD-10 codes: X40-X44) or undetermined (underlying ICD-10 codes: Y10-Y14) intent during June 1st to December 31st 2017. Utilizing the Interim Medical Examiner Database (I-MED), we identified postmortem toxicology reports for 454 cases, which were from one of three national laboratories used by a state Regional Forensic Center. Toxicology data were abstracted and independently verified by two co-authors and linked to the TN death statistical file that included cause of death information (literal text and ICD-10 codes) and demographics. The analysis focuses on cases with an available toxicology report.ResultsWe identified 171 prescription opioid overdoses, 221 fentanyl overdoses, and 113 heroin overdoses. Table 1 displays postmortem toxicology profiles for major drugs/classes. For prescription opioid deaths (excluding fentanyl and heroin), positive toxicology results for prescription opioids were as follows: methadone (11%), buprenorphine (14%), hydrocodone (14%), oxycodone (36%) and oxymorphone (also a metabolite, 47%). Benzodiazepines were present in close to 58% of prescription opioid overdoses; stimulants (cocaine, amphetamines, methamphetamines) in about 25%. For fentanyl and heroin deaths, prescription opioids were detected in about 26% and 34%, respectively; stimulants in about 57.9% and 52.2%, respectively, and benzodiazepines 36-37%. Fentanyl was present on toxicology in about half of heroin overdoses, and 6–monoacetylmorphine in 72.6%.ConclusionsUsing medical examiners’ data, including toxicology data, improves estimation of contributing drugs involved in opioid deaths. This analysis provides jurisdiction-specific data on drugs that can help with monitoring trends and informs risk factor identification. Future work includes adding information on prescribed opioid and benzodiazepines using TN’s Prescription Drug Monitoring Database and evaluating demographic variation in contributing drugs between toxicology and DC data to identify susceptible populations.References1. Slavova S, O'Brien DB, Creppage K, Dao D, Fondario A, Haile E, Hume B, Largo TW, Nguyen C, Sabel JC, Wright D, Council of S, Territorial Epidemiologists Overdose S. Drug Overdose Deaths: Let's Get Specific. Public Health Rep.2. Horon IL, Singal P, Fowler DR, Sharfstein JM. Standard Death Certificates Versus Enhanced Surveillance to Identify Heroin Overdose-Related Deaths. Am J Public Health. 2018;108(6):777-81.3. Mertz KJ, Janssen JK, Williams KE. Underrepresentation of heroin involvement in unintentional drug overdose deaths in Allegheny County, PA. J Forensic Sci. 2014;59(6):1583-5.4. Landen MG, Castle S, Nolte KB, Gonzales M, Escobedo LG, Chatterjee BF, Johnson K, Sewell CM. Methodological issues in the surveillance of poisoning, illicit drug overdose, and heroin overdose deaths in new Mexico. Am J Epidemiol. 2003;157(3):273-8.5. Davis GG, National Association of Medical E, American College of Medical Toxicology Expert Panel on E, Reporting Opioid D. Complete republication: National Association of Medical Examiners position paper: Recommendations for the investigation, diagnosis, and certification of deaths related to opioid drugs. J Med Toxicol. 2014;10(1):100-6.6. Slavova S, Bunn TL, Hargrove SL, Corey T. Linking Death Certificates, Postmortem Toxicology, and Prescription History Data for Better Identification of Populations at Increased Risk for Drug Intoxication Deaths. Pharmaceutical Medicine. 2017;31(3):155-65.7. Hurstak E, Rowe C, Turner C, Behar E, Cabugao R, Lemos NP, Burke C, Coffin P. Using medical examiner case narratives to improve opioid overdose surveillance. Int J Drug Policy. 2018;54:35-42. 

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