Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies

Rapid Identification of Suspected Drug Overdose Deaths by Death Investigators, New Jersey, 2020

OBJECTIVE

While the number of overdoses in the United States continues to increase, lags in data availability have undermined efforts to monitor, respond to, and prevent drug overdose deaths. We examined the performance of a single-item mandatory radio button implemented into a statewide medical examiner database to identify suspected drug overdose deaths in near-real time.

MATERIALS AND METHODS

The New Jersey Office of the Chief State Medical Examiner operates a statewide mandated case management data system to document deaths that fall under the jurisdiction of a medical examiner office. In 2018, the New Jersey Office of the Chief State Medical Examiner implemented a radio button into the case management data system that requires investigators to report whether a death is a suspected drug overdose death. We examined the performance of this tool by comparing confirmed drug overdose deaths in New Jersey during 2020 with suspected drug overdose deaths identified by investigators using the radio button. To measure performance, we calculated sensitivity, specificity, positive predictive value, negative predictive value, and false-positive and false-negative error rates.

RESULTS

During 2020, New Jersey medical examiners investigated 26 527 deaths: 2952 were confirmed by the state medical examiner as a drug overdose death and 3050 were identified by investigators using the radio button as a suspected drug overdose death. Sensitivity was calculated as 96.1% (2837/2952), specificity as 99.1% (23 362/23 575), positive predictive value as 93.0% (2837/3050), negative predictive value as 99.5% (23 362/23 477), false-positive error rate as 7.0% (213/3050), and false-negative error rate as 3.9% (115/2952).

PRACTICE IMPLICATIONS

Implementation of a radio button into death investigation databases provides a simple and accurate method for identifying and tracking drug overdose deaths in near-real time.

Toxicology Test Results for Public Health Surveillance of the Opioid Epidemic: Retrospective Analysis

Background

Addressing the opioid epidemic requires timely insights into population-level factors, such as trends in prevalence of legal and illegal substances, overdoses, and deaths.

Objective

This study aimed to examine whether toxicology test results of living individuals from a variety of sources could be useful in surveilling the opioid epidemic.

Methods

A retrospective analysis standardized, merged, and linked toxicology results from 24 laboratories in Marion County, Indiana, United States, from September 1, 2018, to August 31, 2019. The data set consisted of 33,787 Marion County residents and their 746,681 results. We related the data to general Marion County demographics and compared alerts generated by toxicology results to opioid overdose-related emergency department visits. Nineteen domain experts helped prototype analytical visualizations. Main outcome measures included test positivity in the county and by ZIP code; selected demographics of individuals with toxicology results; and correlation of toxicology results with opioid overdose-related emergency department visits.

Results

Four percent of Marion County residents had at least 1 toxicology result. Test positivity rates ranged from 3% to 19% across ZIP codes. Males were underrepresented in the data set. Age distribution resembled that of Marion County. Alerts for opioid toxicology results were not correlated with opioid overdose-related emergency department visits.

Conclusions

Analyzing toxicology results at scale was impeded by varying data formats, completeness, and representativeness; changes in data feeds; and patient matching difficulties. In this study, toxicology results did not predict spikes in opioid overdoses. Larger, more rigorous and well-controlled studies are needed to assess the utility of toxicology tests in predicting opioid overdose spikes.

Spatiotemporal Analysis Exploring the Effect of Law Enforcement Drug Market Disruptions on Overdose, Indianapolis, Indiana, 2020-2021

Objectives. To test the hypothesis that law enforcement efforts to disrupt local drug markets by seizing opioids or stimulants are associated with increased spatiotemporal clustering of overdose events in the surrounding geographic area. Methods. We performed a retrospective (January 1, 2020 to December 31, 2021), population-based cohort study using administrative data from Marion County, Indiana. We compared frequency and characteristics of drug (i.e., opioids and stimulants) seizures with changes in fatal overdose, emergency medical services nonfatal overdose calls for service, and naloxone administration in the geographic area and time following the seizures. Results. Within 7, 14, and 21 days, opioid-related law enforcement drug seizures were significantly associated with increased spatiotemporal clustering of overdoses within radii of 100, 250, and 500 meters. For example, the observed number of fatal overdoses was two-fold higher than expected under the null distribution within 7 days and 500 meters following opioid-related seizures. To a lesser extent, stimulant-related drug seizures were associated with increased spatiotemporal clustering overdose. Conclusions. Supply-side enforcement interventions and drug policies should be further explored to determine whether they exacerbate an ongoing overdose epidemic and negatively affect the nation's life expectancy. (Am J Public Health. 2023;113(7):750-758. https://doi.org/10.2105/AJPH.2023.307291).

Evaluation of an Emergency Department Visit Data Mental Health Dashboard

CONTEXT

Local health departments (LHDs) need timely county-level and subcounty-level data to monitor health-related trends, identify health disparities, and inform areas of highest need for interventions as part of their ongoing assessment responsibilities; yet, many health departments rely on secondary data that are not timely and cannot provide subcounty insights.

OBJECTIVE

We developed and evaluated a mental health dashboard in Tableau for an LHD audience featuring statewide syndromic surveillance emergency department (ED) data in North Carolina from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

DESIGN

We developed a dashboard that provides counts, crude rates, and ED visit percentages at statewide and county levels, as well as breakdowns by zip code, sex, age group, race, ethnicity, and insurance coverage for 5 mental health conditions. We evaluated the dashboards through semistructured interviews and a Web-based survey that included the standardized usability questions from the System Usability Scale.

PARTICIPANTS

Convenience sample of LHD public health epidemiologists, health educators, evaluators, and public health informaticians.

RESULTS

Six semistructured interview participants successfully navigated the dashboard but identified usability issues when asked to compare county-level trends displayed in different outputs (eg, tables vs graphs). Thirty respondents answered all questions on the System Usability Scale for the dashboard, which received an above average score of 86.

CONCLUSIONS

The dashboards scored well on the System Usability Scale, but more research is needed to identify best practices in disseminating multiyear syndromic surveillance ED visit data on mental health conditions to LHDs.

Deep Neural Networks for Fine-Grained Surveillance of Overdose Mortality

Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, Tenth Revision (ICD-10), codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on death certificate, the free-text cause-of-death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on lookup tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep-learning named-entity recognition model was developed, utilizing data from the Kentucky Drug Overdose Fatality Surveillance System (2014-2019), which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance lookup tables, enhancing the surveillance of drug overdose deaths.

Development and Validation of a Syndrome Definition for Suspected Nonfatal Unintentional/Undetermined Intent Stimulant-Involved Overdoses

OBJECTIVES

To monitor stimulant-involved overdose (SOD) trends, the Centers for Disease Control and Prevention (CDC) developed and evaluated the validity of a syndromic surveillance definition for suspected nonfatal, unintentional/undetermined intent stimulant-involved overdose (UUSOD).

METHODS

We analyzed all emergency department (ED) visits in CDC's surveillance system that met the UUSOD syndrome definition (January 2018-December 2019). We classified visits as true positive, possible, or not UUSODs after reviewing diagnosis codes and chief complaints. We first assessed whether visits were acute SODs, subsequently classifying acute SODs by intent. The percentage of true-positive UUSODs did not include intentional or possibly intentional visits. We considered all visits with UUSOD diagnosis codes to be acute SODs and reviewed them for intent. We manually reviewed and double-coded a 10% random sample of visits without UUSOD diagnosis codes using decision rules based on signs and symptoms. The overall percentage of true-positive UUSODs was a weighted average of the percentage of true-positive UUSODs based on diagnosis codes and the percentage of true-positive UUSODs determined by manually reviewing visits without codes.

RESULTS

During 2018-2019, 40 045 ED visits met the syndrome definition for UUSOD. Approximately half (n = 18 793; 46.9%) of 40 045 visits had UUSOD diagnosis codes, indicating acute SOD; of these, 98.6% (n = 18 534) were true-positive UUSODs. Of 2125 manually reviewed visits without UUSOD diagnosis codes, 32.6% (n = 693) were true-positive UUSODs, 54.2% (n = 1151) were possible UUSODs, and 13.2% (n = 281) were not UUSODs. Overall, 63.6% of visits were true-positive UUSODs, 29.3% were possible UUSODs, and 7.1% were not UUSODs.

PRACTICE IMPLICATIONS

CDC's UUSOD definition may assist in surveillance efforts with further refinement to capture data on SOD clusters and trends.

Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial

BACKGROUND AND AIMS

In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level.

DESIGN

Randomized, population-based, community intervention trial.

SETTING

Rhode Island, USA.

PARTICIPANTS

All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes.

INTERVENTION

Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions.

MEASUREMENTS

The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities.

COMMENTS

The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

A Mixed-Methods Comparison of a National and State Opioid Overdose Surveillance Definition

OBJECTIVES

We assessed the differences between the first version of the Centers for Disease Control and Prevention (CDC) opioid surveillance definition for suspected nonfatal opioid overdoses (hereinafter, CDC definition) and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) surveillance definition to determine whether the North Carolina definition should include additional International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and/or chief complaint keywords.

METHODS

Two independent reviewers retrospectively reviewed data on North Carolina emergency department (ED) visits generated by components of the CDC definition not included in the NC DETECT definition from January 1 through July 31, 2018. Clinical reviewers identified false positives as any ED visit in which available evidence supported an alternative explanation for patient presentation deemed more likely than an opioid overdose. After individual assessment, reviewers reconciled disagreements.

RESULTS

We identified 2296 ED visits under the CDC definition that were not identified under the NC DETECT definition during the study period. False-positive rates ranged from 2.6% to 41.4% for codes and keywords uniquely identifying ≥10 ED visits. Based on uniquely identifying ≥10 ED visits and a false-positive rate ≤10.0%, 4 of 16 ICD-10-CM codes evaluated were identified for NC DETECT definition inclusion. Only 2 of 25 keywords evaluated, "OD" and "overdose," met inclusion criteria to be considered a meaningful addition to the NC DETECT definition.

PRACTICE IMPLICATIONS

Quantitative and qualitative trends in coding and keyword use identified in this analysis may prove helpful for future evaluations of surveillance definitions.

Development and Validation of a Syndrome Definition to Identify Suspected Nonfatal Heroin-Involved Overdoses Treated in Emergency Departments

CONTEXT

The Centers for Disease Control and Prevention (CDC) works closely with states and local jurisdictions that are leveraging data from syndromic surveillance systems to identify meaningful changes in overdose trends. CDC developed a suspected nonfatal heroin overdose syndrome definition for use with emergency department (ED) data to help monitor trends at the national, state, and local levels.

OBJECTIVE

This study assesses the percentage of true-positive unintentional and undetermined intent heroin-involved overdose (UUHOD) captured by this definition.

DESIGN/SETTING

CDC applied the UUHOD definition to ED data available in CDC's National Syndromic Surveillance Program (NSSP). Data were analyzed from 18 states that shared access to their syndromic data in NSSP with the CDC overdose morbidity team. Data were analyzed using queries and manual reviews to identify heroin overdose diagnosis codes and text describing chief complaint reasons for ED visits.

MEASURES

The percentage of true-positive UUHOD was calculated as the number of true-positives divided by the number of total visits captured by the syndrome definition.

RESULTS

In total, 99 617 heroin overdose visits were identified by the syndrome definition. Among 95 323 visits identified as acute heroin-involved overdoses, based on reviews of chief complaint text and diagnosis codes, 967 (1.0%) were classified as possible intentional drug overdoses. Among all 99 617 visits, 94 356 (94.7%) were classified as true-positive UUHOD; 2226 (2.2%) and 3035 (3.0%) were classified as "no" and "maybe" UUHOD, respectively.

CONCLUSION

Analysis of the CDC heroin overdose syndrome definition determined that nearly all visits were captured accurately for patients presenting to the ED for a suspected acute UUHOD. This definition will continue to be valuable for ongoing heroin overdose surveillance and epidemiologic analysis of heroin overdose patterns. CDC will evaluate possible definition refinements as new products and terms for heroin overdose emerge.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BACKGROUND

The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

METHODS

We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

RESULTS

Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

CONCLUSIONS

Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Emergency department visits among people with cancer: Frequency, symptoms, and characteristics

OBJECTIVE

People with cancer are increasingly more likely to visit an emergency department for acute care than the general population. They often have long wait times and more exposure to infection and receive treatment from staff less experienced with cancer-related problems. Our objective was to examine emergency department (ED) visits among people with cancer to understand how often and why they seek care.

METHODS

We conducted a retrospective study of ED visits using the National Syndromic Surveillance Program BioSense Platform. Cancer reported during an ED visit was identified using International Classification of Diseases, Tenth Revision codes for any cancer type, including bladder, breast, cervical, colorectal, kidney, liver, lung, ovary, pancreas, prostate, or uterine cancers. Symptoms prompting the visit were identified for people with cancer who visited EDs in the United States from June 2017 to May 2018 in ≈4500 facilities, including 3000 EDs in 46 states and the District of Columbia (66% of all ED visits during a 1-year period).

RESULTS

Of 97 million ED visits examined, 710,297 (0.8%) were among people with cancer. Percentages were higher among women (50.1%) than men (49.5%) and among adults aged ≥65 years (53.6%) than among those ≤64 years (45.7%). The most common presenting symptoms were pain (19.1%); gastrointestinal (13.8%), respiratory (11.5%), and neurologic (5.3%) complaints; fever (4.9%); injury (4.1%); and bleeding (2.4%). Symptom prevalence differed significantly by cancer type.

CONCLUSIONS

The Centers for Medicare & Medicaid Services encourages efforts to reduce acute care visits among people with cancer. We characterized almost 70% of ED visits among this population.

Accuracy of Medical Examiner's Assessment for Near-Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017-February 2018

OBJECTIVES

Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near-real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring.

METHODS

We analyzed data on deaths in the King County (Washington) Medical Examiner's Office (KCMEO) jurisdiction that occurred during March 1, 2017-February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs' classification by using the final death certificate as the gold standard.

RESULTS

KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event.

CONCLUSIONS

King County MEs' probable overdose classification provides a near-real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic.

Emergency department syndromic surveillance systems: a systematic review

BACKGROUND

Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.

METHODS

We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases.

RESULTS

In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).

CONCLUSIONS

EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.

PROSPERO NUMBER

CRD42017069150 .

Epidemiological study of opioid use disorder in French emergency departments, 2010-2018 from OSCOUR database

OBJECTIVES

Opioid consumption in France has remained stable over the last 15 years, with much lower levels than in the USA. However, few data are available on patients who consume opioids and their use of the health system. Emergency department (ED) data has never been used as a source to investigate opioid use disorder (OUD) in France.

DESIGN/SETTINGS/PARTICIPANTS

We used the OSCOUR national surveillance network, collecting daily ED data from 93% of French ED, to select and describe visits and hospitalisations after an OUD-related ED visit between 2010 and 2018 using International Classification of Diseases, version 10 (ICD10) codes. We described the population of interest and used binomial negative regressions to identify factors significantly associated with OUD such as gender, age, administrative region, year of admission and ICD10 codes. We also analysed the related diagnoses.

PRIMARY OUTCOME MEASURE

Trend in ED visits for an OUD-related ED visit.

RESULTS

We recorded 34 362 OUD-related visits out of 97 892 863 ED visits (36.1/100 000 visits). OUD-related visits decreased from 39.2/100 000 visits in 2010 to 32.9/100 000 visits in 2018, resulting in an average yearly decrease of 2.1% (95% CI 1.5% to 2.7%) after multivariate analysis. We recorded 15 966 OUD-related hospitalisations out of 20 359 574 hospitalisations after ED visits (78.4/100 000 hospitalisations) with an increase from 74.0/100 000 hospitalisations in 2010 to 81.4/100 000 hospitalisations in 2018. The analysis of related diagnoses demonstrated mostly polydrug abuse in this population.

CONCLUSIONS

While the proportion of OUD visits decreased in the time frame, the hospitalisation proportion increased. The implementation of a nationwide surveillance system for OUD in France using ED visits would provide prompt detection of changes over time.

Identifying Incidents of Public Health Significance Using the National Poison Data System, 2013-2018

Data System. The American Association of Poison Control Centers (AAPCC) and the Centers for Disease Control and Prevention (CDC) jointly monitor the National Poison Data System (NPDS) for incidents of public health significance (IPHSs).Data Collection/Processing. NPDS is the data repository for US poison centers, which together cover all 50 states, the District of Columbia, and multiple territories. Information from calls to poison centers is uploaded to NPDS in near real time and continuously monitored for specific exposures and anomalies relative to historic data.Data Analysis/Dissemination. AAPCC and CDC toxicologists analyze NPDS-generated anomalies for evidence of public health significance. Presumptive results are confirmed with the receiving poison center to correctly identify IPHSs. Once verified, CDC notifies the state public health department.Implications. During 2013 to 2018, 3.7% of all NPDS-generated anomalies represented IPHSs. NPDS surveillance findings may be the first alert to state epidemiologists of IPHSs. Data are used locally and nationally to enhance situational awareness during a suspected or known public health threat. NPDS improves CDC's national surveillance capacity by identifying early markers of IPHSs.

Strengths and weaknesses of existing data sources to support research to address the opioids crisis

Better opioid prescribing practices, promoting effective opioid use disorder treatment, improving naloxone access, and enhancing public health surveillance are strategies central to reducing opioid-related morbidity and mortality. Successfully advancing and evaluating these strategies requires leveraging and linking existing secondary data sources. We conducted a scoping study in Fall 2017 at RAND, including a literature search (updated in December 2018) complemented by semi-structured interviews with policymakers and researchers, to identify data sources and linking strategies commonly used in opioid studies, describe data source strengths and limitations, and highlight opportunities to use data to address high-priority public health research questions. We identified 306 articles, published between 2005 and 2018, that conducted secondary analyses of existing data to examine one or more public health strategies. Multiple secondary data sources, available at national, state, and local levels, support such research, with substantial breadth in data availability, data contents, and the data's ability to support multi-level analyses over time. Interviewees identified opportunities to expand existing capabilities through systematic enhancements, including greater support to states for creating and facilitating data use, as well as key data challenges, such as data availability lags and difficulties matching individual-level data over time or across datasets. Multiple secondary data sources exist that can be used to examine the impact of public health approaches to addressing the opioid crisis. Greater data access, improved usability for research purposes, and data element standardization can enhance their value, as can improved data availability timeliness and better data comparability across jurisdictions.

A latent class analysis of drug and substance use patterns among patients treated in emergency departments for suspected drug overdose

INTRODUCTION

Polysubstance use and misuse can increase risks for nonfatal and fatal drug overdose. To categorize drugs used in combination in nonfatal overdoses, we analyzed data from emergency department (ED) overdose-related visits in 18 states funded by CDC's Enhanced State Opioid Overdose Surveillance (ESOOS) program.

METHODS

From 2017 to 2018, 120,706 ED visits included at least one hospital discharge code indicating acute drug poisoning for opioids, stimulants, hallucinogens, cannabis, anti-depressants, sedatives, alcohol, benzodiazepines, or other psychotropic drugs. Latent class analyses were conducted to determine the groupings of drug combinations in overdose visits.

RESULTS

Latent class analyses indicated a model of 5 classes - mostly heroin overdose (42.5% of visits); mostly non-heroin opioid overdose/use (27.3%); opioid, polysubstance (11.0%); female, younger (<25 years), other non-opioid drugs (10.5%); female, older (>55 years), benzodiazepine (8.0%). Findings indicated that heroin continues to be a large burden to EDs, yet EDs are also seeing overdose survivors with polydrug toxicity.

CONCLUSIONS

Medication-assisted treatment could be initiated in the emergency department following overdose for patients with opioid use disorder, and post-overdose protocols, such as naloxone provision and linkage to treatment and harm reduction services, have the potential to prevent future overdose for those at risk.

Outreach to people who survive opioid overdose: Linkage and retention in treatment

Cognitive motivation theories contend that individuals have greater readiness for behavioral change during critical periods or life events, and a non-fatal overdose could represent such an event. The objective of this study was to examine if the use of a specialized mobile response team (assertive outreach) could help identify, engage, and retain people who have survived an overdose into a comprehensive treatment program. We developed an intervention, consisting of mobile outreach followed by medication and behavioral treatment, in Houston Texas between April and December 2018. Our primary outcome variables were the level of willingness to engage in treatment, and percent who retained in treatment after 30 and 90 day endpoints. We screened 103 individuals for eligibility, and 34 (33%) elected to engage in the treatment program, while two-thirds chose not to engage in treatment, primarily due to low readiness levels. The average age was 38.2 ± 12 years, 56% were male, 79% had no health insurance, and the majority (77%) reported being homeless or in temporary housing. There were 30 (88%) participants still active in the treatment program after 30 days, and 19 (56%) after 90 days. Given the high rates of relapse using conventional models, which wait for patients to present to treatment, our preliminary results suggest that assertive outreach could be a promising strategy to motivate people to enter and remain in long-term treatment.

Injury Burden in the United States: Accurate, Reliable, and Timely Surveillance Using Electronic Health Care Data

Current injury surveillance systems in the United States, including the National Electronic Injury Surveillance System (NEISS), are unable to draw reliable subnational and subannual incidence estimates.Compared with the International Classification of Diseases (ICD), the clinical ontology system currently used widely in health care, NEISS's coding structure lacks specificity and consistency. In parallel, the quality of ICD codes depends on accurate and complete documentation by health care providers and skillful translation into ICD codes in electronic health care data. Additionally, there is no national mandate to collect external cause of injury data.Electronic health care data, such as health records and claims, with updated codes and uniform adherence to recommendations for coding external cause of injury, have the potential to be used for a more robust and timely surveillance of injury to accurately and reliably reflect the injury burden in the United States.

Suspected Heroin Overdoses in US Emergency Departments, 2017-2018

OBJECTIVES

To describe changes in suspected heroin overdose emergency department (ED) visits. Methods. We analyzed quarterly and yearly changes in heroin overdoses during 2017-2018 by using data from 23 states and jurisdictions (including the District of Columbia) funded by the Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance program. The analyses included the Pearson χ ² test to detect significant changes. Results. Both sexes, all age groups, and some states exhibited increases from quarter 1 (Q1) 2017 to Q2 2017 and significant decreases in both quarters from Q3 2017 to Q1 2018 in heroin overdose ED visits. Overall, there was a significant yearly decline of 21.5% in heroin overdose ED visits. Three states had significant yearly increases (Illinois, Indiana, and Utah), and 9 states (Kentucky, Maryland, Massachusetts, New Hampshire, Ohio, Pennsylvania, Rhode Island, West Virginia, and Wisconsin) and the District of Columbia had significant decreases. Conclusions. We identified decreases in heroin overdose ED visits from 2017 through 2018, but these declines were not consistent among states. Even with the possibility of a stabilization or slowing of this epidemic, it is important that the field of public health and its partners implement strategies to prevent overdoses and target emerging hot spots.

Drug overdose in the ED: a record linkage study examining emergency department ICD-10 coding practices in a cohort of people who inject drugs

Background

Drug overdose is a leading cause of mortality and morbidity amongst people who inject drugs (PWID). Drug overdose surveillance typically relies on the International Classification of Diseases (ICD-10) coding system, however its real world utilisation and the implications for surveillance have not been well characterised. This study examines the patterns of ICD-10 coding pertaining to drug overdoses within emergency departments for a cohort of known PWID.

Methods

Cohort data from 688 PWID was linked to statewide emergency department administrative data between January 2008 and June 2013. ICD-10 diagnostic codes pertaining to poisonings by drugs, medicaments and biological substances (T-codes T36-T50) as well as mental and behavioural disorders due to psychoactive substance use (F-codes F10-F19) were examined.

Results

There were 449 unique ED presentations with T or F code mentions contributed by 168 individuals. Nearly half of the T and F codes used were non-specific and did not identify either a drug class (n = 160, 36%) or clinical reaction (n = 46, 10%) and 8% represented withdrawal states. T and F codes could therefore be used to reasonably infer an illicit drug overdose in only 42% (n = 188) of cases. Majority of presentations with T or F overdose codes recorded only one diagnostic code per encounter (83%) and representing multiple-drug overdose (F19.- = 18%) or unidentified substances (T50.9 = 17%) using a single, broad diagnostic code was common.

Conclusions

Reliance on diagnoses alone when examining ED data will likely significantly underestimate incidence of specific drug overdose due to frequent use of non-specific ICD-10 codes and the use of single diagnostic codes to represent polysubstance overdose. Measures to improve coding specificity should be considered and further work is needed to determine the best way to use ED data in overdose surveillance.

The Impact of Law on Syndromic Disease Surveillance Implementation

CONTEXT

Legal environments influence how health information technologies are implemented in public health practice settings. Syndromic disease surveillance (SyS) is a relatively new approach to surveillance that depends heavily on health information technologies to achieve rapid awareness of disease trends. Evidence suggests that legal concerns have impeded the optimization of SyS.

OBJECTIVES

To (1) understand the legal environments in which SyS is implemented, (2) determine the perceived legal basis for SyS, and (3) identify perceived legal barriers and facilitators to SyS implementation.

DESIGN

Multisite case study in which 35 key informant interviews and 5 focus groups were conducted with 75 SyS stakeholders. Interviews and focus groups were audio recorded, transcribed, and analyzed by 3 coders using thematic content analysis. Legal documents were reviewed.

SETTING

Seven jurisdictions (5 states, 1 county, and 1 city) that were purposively selected on the basis of SyS capacity and legal environment.

PARTICIPANTS

Health department directors, SyS system administrators, legal counsel, and hospital personnel.

RESULTS

Federal (eg, HIPAA) and state (eg, notifiable disease reporting) laws that authorize traditional public health surveillance were perceived as providing a legal basis for SyS. Financial incentives for hospitals to satisfy Meaningful Use regulations have eased concerns about the legality of SyS and increased the number of hospitals reporting SyS data. Legal issues were perceived as barriers to BioSense 2.0 (the federal SyS program) participation but were surmountable.

CONCLUSION

Major legal reforms are not needed to promote more widespread use of SyS. The current legal environment is perceived by health department and hospital officials as providing a firm basis for SyS practice. This is a shift from how law was perceived when SyS adoption began and has policy implications because it indicates that major legal reforms are not needed to promote more widespread use of the technology. Beyond SyS, our study suggests that federal monetary incentives can ameliorate legal concerns regarding novel health information technologies.

Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses - United States, July 2016-September 2017

Introduction

From 2015 to 2016, opioid overdose deaths increased 27.7%, indicating a worsening of the opioid overdose epidemic and highlighting the importance of rapid data collection, analysis, and dissemination.

Methods

Emergency department (ED) syndromic and hospital billing data on opioid-involved overdoses during July 2016–September 2017 were examined. Temporal trends in opioid overdoses from 52 jurisdictions in 45 states were analyzed at the regional level and by demographic characteristics. To assess trends based on urban development, data from 16 states were analyzed by state and urbanization level.

Results

From July 2016 through September 2017, a total of 142,557 ED visits (15.7 per 10,000 visits) from 52 jurisdictions in 45 states were suspected opioid-involved overdoses. This rate increased on average by 5.6% per quarter. Rates increased across demographic groups and all five U.S. regions, with largest increases in the Southwest, Midwest, and West (approximately 7%–11% per quarter). In 16 states, 119,198 ED visits (26.7 per 10,000 visits) were suspected opioid-involved overdoses. Ten states (Delaware, Illinois, Indiana, Maine, Missouri, Nevada, North Carolina, Ohio, Pennsylvania, and Wisconsin) experienced significant quarterly rate increases from third quarter 2016 to third quarter 2017, and in one state (Kentucky), rates decreased significantly. The highest rate increases occurred in large central metropolitan areas.

Conclusions and Implications for Public Health Practice

With continued increases in opioid overdoses, availability of timely data are important to inform actions taken by EDs and public health practitioners. Increases in opioid overdoses varied by region and urbanization level, indicating a need for localized responses. Educating ED physicians and staff members about appropriate services for immediate care and treatment and implementing a post-overdose protocol that includes naloxone provision and linking persons into treatment could assist EDs with preventing overdose.

Epidemiologic Characteristics of Injured School-age Patients Transported via Emergency Medical Services in Korea

BACKGROUND

The purpose of this study was to identify the characteristics of injuries of school-aged children transported via emergency medical services (EMS) that occurred in schools by comparing with injuries that occurred outside of school.

METHODS

Data from the 119 EMS from 2012 to 2014 were analyzed. School and non-school injuries were analyzed in children 6 to 17 years of age. The epidemiologic characteristics were assessed according to school-age groups; low-grade primary (6-8 years), high-grade primary (9-13 years), middle (13-15 years) and high (15-17 years) school. Gender-stratified multivariable logistic regression analysis was conducted to estimate the risks of school injury in each age group.

RESULTS

During the study period, a total of 167,104 children with injury were transported via 119 ambulances. Of these injuries, 13.3% occurred at schools. Boys accounted for 76.9% of school injuries and middle school children accounted for a significantly greater proportion (39.6%) of school injuries (P < 0.001). The most frequent mechanisms of injury at school were falls (43.8%). The peak times for school injury occurrence were lunch time (13:00-13:59) in all age groups. Multivariate regression identified the risky age groups as high-grade primary (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.09-1.20) and middle school-aged boys (OR, 1.82; 95% CI, 1.74-1.90) and middle school-aged girls (OR, 1.30; 95% CI, 1.21-1.40).

CONCLUSION

Notable epidemiologic differences exist between in- and out-of-school injuries. The age groups at risk for school injuries differ by gender.

A statewide evaluation of seven strategies to reduce opioid overdose in North Carolina

BACKGROUND

In response to increasing opioid overdoses, US prevention efforts have focused on prescriber education and supply, demand and harm reduction strategies. Limited evidence informs which interventions are effective. We evaluated Project Lazarus, a centralised statewide intervention designed to prevent opioid overdose.

METHODS

Observational intervention study of seven strategies. 74 of 100 North Carolina counties implemented the intervention. Dichotomous variables were constructed for each strategy by county-month. Exposure data were: process logs, surveys, addiction treatment interviews, prescription drug monitoring data. Outcomes were: unintentional and undetermined opioid overdose deaths, overdose-related emergency department (ED) visits. Interrupted time-series Poisson regression was used to estimate rates during preintervention (2009-2012) and intervention periods (2013-2014). Adjusted IRR controlled for prescriptions, county health status and time trends. Time-lagged regression models considered delayed impact (0-6 months).

RESULTS

In adjusted immediate-impact models, provider education was associated with lower overdose mortality (IRR 0.91; 95% CI 0.81 to 1.02) but little change in overdose-related ED visits. Policies to limit ED opioid dispensing were associated with lower mortality (IRR 0.97; 95% CI 0.87 to 1.07), but higher ED visits (IRR 1.06; 95% CI 1.01 to 1.12). Expansions of medication-assisted treatment (MAT) were associated with increased mortality (IRR 1.22; 95% CI 1.08 to 1.37) but lower ED visits in time-lagged models.

CONCLUSIONS

Provider education related to pain management and addiction treatment, and ED policies limiting opioid dispensing showed modest immediate reductions in mortality. MAT expansions showed beneficial effects in reducing ED-related overdose visits in time-lagged models, despite an unexpected adverse association with mortality.

Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications

INTRODUCTION

Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time.

MATERIALS AND METHODS

We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients' chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions.

RESULTS

Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood.

PRACTICE IMPLICATIONS

Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

OBJECTIVES

Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem.

METHODS

We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire's syndromic surveillance ED data system by querying for (1) chief complaint text related to the words "fentanyl," "heroin," "opiate," and "opioid" and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes.

RESULTS

Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being "pain" (n = 3335, 18%), "overdose" (n = 1555, 8%), "suicidal" (n = 816, 4%), "drug" (n = 803, 4%), and "detox" (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015.

CONCLUSIONS

Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire's ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016

INTRODUCTION

The use of syndromic surveillance has expanded from its initial purpose of bioterrorism detection. We present 6 use cases from New York City that demonstrate the value of syndromic surveillance for public health response and decision making across a broad range of health outcomes: synthetic cannabinoid drug use, heat-related illness, suspected meningococcal disease, medical needs after severe weather, asthma exacerbation after a building collapse, and Ebola-like illness in travelers returning from West Africa.

MATERIALS AND METHODS

The New York City syndromic surveillance system receives data on patient visits from all emergency departments (EDs) in the city. The data are used to assign syndrome categories based on the chief complaint and discharge diagnosis, and analytic methods are used to monitor geographic and temporal trends and detect clusters.

RESULTS

For all 6 use cases, syndromic surveillance using ED data provided actionable information. Syndromic surveillance helped detect a rise in synthetic cannabinoid-related ED visits, prompting a public health investigation and action. Surveillance of heat-related illness indicated increasing health effects of severe weather and led to more urgent public health messaging. Surveillance of meningitis-related ED visits helped identify unreported cases of culture-negative meningococcal disease. Syndromic surveillance also proved useful for assessing a surge of methadone-related ED visits after Superstorm Sandy, provided reassurance of no localized increases in asthma after a building collapse, and augmented traditional disease reporting during the West African Ebola outbreak.

PRACTICE IMPLICATIONS

Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

Validation of a Syndromic Case Definition for Detecting Emergency Department Visits Potentially Related to Marijuana

OBJECTIVES

Reliable methods are needed to monitor the public health impact of changing laws and perceptions about marijuana. Structured and free-text emergency department (ED) visit data offer an opportunity to monitor the impact of these changes in near-real time. Our objectives were to (1) generate and validate a syndromic case definition for ED visits potentially related to marijuana and (2) describe a method for doing so that was less resource intensive than traditional methods.

METHODS

We developed a syndromic case definition for ED visits potentially related to marijuana, applied it to BioSense 2.0 data from 15 hospitals in the Denver, Colorado, metropolitan area for the period September through October 2015, and manually reviewed each case to determine true positives and false positives. We used the number of visits identified by and the positive predictive value (PPV) for each search term and field to refine the definition for the second round of validation on data from February through March 2016.

RESULTS

Of 126 646 ED visits during the first period, terms in 524 ED visit records matched ≥1 search term in the initial case definition (PPV, 92.7%). Of 140 932 ED visits during the second period, terms in 698 ED visit records matched ≥1 search term in the revised case definition (PPV, 95.7%). After another revision, the final case definition contained 6 keywords for marijuana or derivatives and 5 diagnosis codes for cannabis use, abuse, dependence, poisoning, and lung disease.

CONCLUSIONS

Our syndromic case definition and validation method for ED visits potentially related to marijuana could be used by other public health jurisdictions to monitor local trends and for other emerging concerns.