Annals of Epidemiology 25 (2015) 613e619

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Original article

Emergency department utilization and subsequent prescription drug overdose death Joanne E. Brady PhD a, b, c, *, Charles J. DiMaggio PhD d, Katherine M. Keyes PhD b, c, John J. Doyle DrPH b, Lynne D. Richardson MD e, Guohua Li MD, DrPH a, b, c a

Department of Anesthesiology, College of Physicians and Surgeons, Columbia University, New York, NY Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY Center for Injury Epidemiology and Prevention, Columbia University Medical Center, New York, NY d Department of Surgery, New York University School of Medicine, New York, NY e Department of Emergency Medicine, Mount Sinai School of Medicine, New York, NY b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 October 2014 Accepted 26 March 2015 Available online 2 April 2015

Purpose: Prescription drug overdose (PDO) deaths are a critical public health problem in the United States. This study aims to assess the association between emergency department (ED) utilization patterns in a cohort of ED patients and the risk of subsequent unintentional PDO mortality. Methods: Using data from the New York Statewide Planning and Research Cooperative System for 2006 e2010, a nested case-control design was used to examine the relationship between ED utilization patterns in New York State residents of age 18e64 years and subsequent PDO death. Results: The study sample consisted of 2732 case patients who died of PDO and 2732 control ED patients who were selected through incidence density sampling. With adjustment for demographic characteristics, and diagnoses of pain, substance abuse, and psychiatric disorders, the estimated odds ratios of PDO death relative to one ED visit or less in the previous year were 4.90 (95% confidence interval [CI]: 4.50 e5.34) for those with two ED visits, 16.61 (95% CI: 14.72e18.75) for those with three ED visits, and 48.24 (95% CI: 43.23e53.83) for those with four ED visits or more. Conclusions: Frequency of ED visits is strongly associated with the risk of subsequent PDO death. Intervention programs targeting frequent ED users are warranted to reduce PDO mortality. Ó 2015 Elsevier Inc. All rights reserved.

Keywords: Drug overdose Emergency service, hospital Prescription drugs Prevention and control

Introduction Increases in drug overdose death in the United States are a critical public health problem. Nearly 60% of drug overdose deaths involved prescription drugs, and in 2011, 1.4 million emergency department (ED) visits involved nonmedical use of prescription drugs [1,2]. Frequent ED utilization has been shown to be a marker for substance usednarcotic use, especially [3,4], and is associated with nonmedical opioid use, drug diversion, and poorly controlled pain [3e5]. Therefore, frequent ED utilization may be a marker for increased risk for prescription drug overdose (PDO), and this point of clinician contact may serve as a setting to launch preventive intervention efforts [6e15]. Preventive interventions targeted at ED patients at high risk for PDO death, may include advice such as not mixing opioids with sedatives or alcohol [16], or targeted patient education initiatives [17]. Furthermore, identifying high risk populations can help medical * Corresponding author. Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, 5th Fl, New York, NY 10032. Tel.: þ1-212-305-9518. E-mail address: [email protected] (J.E. Brady). http://dx.doi.org/10.1016/j.annepidem.2015.03.018 1047-2797/Ó 2015 Elsevier Inc. All rights reserved.

practitioners decide whom to target with screening, intervention (e.g., take-home naloxone), and treatment program referrals [18e24]. Despite the importance of the ED as a key clinical entry point for patients at high risk of PDO, the relationship between ED utilization patterns and subsequent drug overdose death is understudied. In the present study, we used a nested case-control design to examine the relationship between ED utilization in a cohort of ED patients and the risk of subsequent unintentional PDO death. Characterizing risk markers for fatal PDO available in administrative databases can be useful for identifying individuals at increased likelihood of PDO death. The goal of the analysis was to determine whether increased ED utilization in a cohort of ED patients was associated with subsequent unintentional PDO death. Materials and methods Data sources Data for this study came from the New York Statewide Planning and Research Cooperative System (SPARCS) for the years

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2006e2010. SPARCS is a data reporting system that collects ED visit data from nonfederal hospitals in New York State (NYS) [25]. SPARCS contains patient-level data including patient characteristics, discharge diagnoses, procedures received, and charges for ED visits [25]. To ensure data quality, SPARCS visit data are examined for proper formatting upon submission format [26]. Data completeness and accuracy are assessed for each facility. Data are reviewed monthly and compared with other benchmarks from Department of Health data [26,27]. To ascertain dates of death, and underlying and contributing causes of death for patients who visited the ED during the study period, SPARCS ED data were linked to NYS and New York City vital statistics records by a SPARCS analyst. Data were linked based on the patient’s date of birth, residential address, first two characters of the patient’s first name, the first and last two characters of the patient’s last name, and the last four digits of the patient’s social security number. PDO mortality within one year of the most recent ED visit within the study period 2006e2010 was evaluated. The study protocol was reviewed and approved by the Institutional Review Board of Columbia University Medical Center (IRB-AAAK6304 New York, NY). ED utilization ED utilization (1, 2, 3, and 4 visits) in last year of follow-up was calculated with respect to date of death for cases. The choice of these time frames was based on research by the Agency for Healthcare Research and Quality, which proposed two ED visits or more in 3 days, three ED visits or more in 90 days, and four ED visits or more in a year as metrics to track suboptimal quality of care [28]. Model evaluation for which ED utilization time frame best fit the data was based on Akaike information criterion statistics [29]. Time-varying 365-day ED utilization was operationalized using three separate indicator variables: two, three, or four or more ED visits in a 365-day time window. When calculating these ED utilization measures, admissions for patients who were transferred on the day of discharge to other acute care hospitals, including patients admitted to hospital specialty units, inpatient rehabilitation facilities, skilled nursing facilities, and long-term care hospitals, were collapsed and treated as one continuous admission. If a patient died at the ED, this visit was not counted. Same-day ED revisits were included in the analyses.

deaths (X40eX44, Y10eY14), where there was also an indication that a prescription drug was a contributing cause of death (T40.2, T40.3, T40.4 [opioid analgesics], T40.6 [Other and unspecified narcotics], T42.2 [Succinimides and oxazolidinediones], T42.3 [Barbiturates], T42.4 [Benzodiazepines], T43.0 [Tricyclic and tetracyclic antidepressants], T43.1 [Monoamine-oxidase-inhibitor antidepressants], T43.2 [other and unspecified antidepressants], T50.9 [other and unspecified drugs]), were evaluated as the primary outcome of interest [43,44]. Study design A nested case-control study design was used. ED patients who subsequently died of PDO during the follow-up were considered cases. Using incidence density sampling, one control was randomly selected from the same base population from which the cases arose and was matched to the corresponding case on follow-up time [45]. The first visit date in the study period was considered the entry visit. The end of follow-up for cases was the date when PDO death occurred. For controls, the end of follow-up time corresponded to elapsed time from study entry to the time when death occurred for the matched case. To be eligible to be a control the individual must be alive when the case occurred. All case patients were able to be matched with control patients. Incidence density sampling was used because ED utilization is time-varying. Time-dependent covariates may be accurately assessed in nested case-control study by using incidence density-based sampling to create risk sets [45]. The cohort from which the cases arose included patients who were NYS residents, were 18e64 years of age, and who visited the ED in NYS from 2006e2010 (Fig. 1). Patients with any discharge diagnoses indicating palliative care, cancer, metastatic carcinoma, or sickle cell anemia at any ED visit were excluded from the cohort because they may have different ED utilization patterns and may receive higher doses of opioids than other patients. Patients without a previous ED visit who died during the index ED visit were excluded from the cohort because there was no pattern of prior ED visits to predict the study outcome. Of the remaining patients who visited the ED, all individuals who subsequently died of a PDO death were used as cases. Statistical analysis

Other risk markers Other risk markers that were evaluated included age, sex, and race (white, black, Asian/Pacific Islander/Native American, and other). To help improve model fit, a discharge against medical advice (AMA) at the initial ED visit and several other diagnoses were examined as potential risk markers for drug overdose death given that discharges AMA are associated with drug abuse [30]. Previous studies have noted a rise in nontraumatic dental condition-related ED visits that may result in an opioid analgesic prescription (Online Appendix A) [31e33]. Studies of drug overdose have noted that substance use disorders, mental health disorders, and chronic pain conditions are associated with drug overdose [34e42]. Accordingly, these conditions were evaluated as potential risk markers of PDO death. In accordance with the State Personal Privacy Protection Law, individual characteristics involving less than six patients may not be reported. Outcome measurements Classification codes from the International Classification of Diseases and Related Health Problems, 10th Revision were used to classify PDO deaths. Unintentional and undetermined intent overdose

To better understand the attributes of the individuals included in this study, the frequency distribution of patient characteristics at the initial ED visit from 2006e2010 who subsequently died of a PDO was compared with those of patients who did not. Differences between groups were compared using c2 tests. A P-value of .05 or less was considered statistically significant. Baseline characteristics were examined in unadjusted models to estimate the odds ratio (OR) and 95% confidence interval (CI) for PDO death. Conditional logistic regression was used to estimate ORs [46,47]. Because this study is based on the nested case-control design, the measure of association is the OR. With incidence density sampling, the OR approximates the incidence rate ratio [48]. To prevent overadjustment in multivariable models, demographic characteristics (age, sex, and composite independent variables of diagnoses at study entry) were created and included in the final multivariable model. Four binary composite independent variables of diagnoses at entry were created, including presence or absence of the following: (1) Diagnosis of alcohol abuse, alcohol drug seeking, drug dependence, opioid abuse, other drug abuse, nonfatal opioid overdose, or sedative anxiolytic abuse at the initial ED visit; (2) Diagnosis of anxiety disorder, depression, episodic mood disorder, major depressive disorder, personality disorder, post-traumatic

J.E. Brady et al. / Annals of Epidemiology 25 (2015) 613e619

New York State Residents, ages 18 -64 years with ED visits in 2006-2010: • Patients = 7,651,963

Patients =7,651,461 • Prescription drug overdose= 3,156

Patients =5,464 Prescription drug overdose = 2,732

615

Total patients removed: • Patients with illogical death date (> 1 day before an admission date) = 502 o Drug-related death= 17

Total patients removed = 7,645,997 Reason for removal*: • Died during only ED visit = 13,042 • Exclusion diagnosis* =49,229 o palliative care (n=162) o cancer (n=40,820) o metastatic carcinoma (n=14,501) o sickle cell anemia (n= 6,169) *Total does not add total patients removed because patient may have may be removed for multiple reasons

Randomly selected patients matched on follow-up time=2,732 Patients who were not a case or randomly selected=7,583,726

Fig. 1. Flowchart of ED data study population, New York SPARCS, New York State, 2006e2010.

stress disorder, psychosis or agitation, or schizophrenia at their initial ED visit; (3) Diagnosis of back pain, fibromyalgia, or musculoskeletal pain at their initial ED visit; (4) Diagnosis of general symptoms, soft tissue disorders, nontraumatic dental conditions, or vertebrae disorders at their initial ED visit. The multivariable models included the following independent variables: time independent age at study entry, sex, race, AMA status, composite independent variables of diagnoses at study entry, and time-varying ED utilization. Analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC).

higher risk of PDO death (aOR 1.38, 95% CI 1.28e1.63). A discharge AMA at the initial visit was also associated with subsequent PDO death (aOR: 1.38, 95% CI: 1.16e1.58). At the end of follow-up, about 50% of the cases and 15% of the controls had two or more visits in the prior year. Relative to one or less visit in the previous year, the aORs of PDO death were 4.90 (95% CI: 4.50e5.34) for those with two visits, 16.61 (95% CI: 14.72e18.75) for those with three visits, and 48.24 (95% CI: 43.23e53.83) for those with four or more visits (Table 3).

Discussion Results A total of 2732 PDO deaths were recorded during the follow-up and were used as cases in this study (Fig. 1). Characteristics of cases and controls at study entry showed that patients who subsequently died of a PDO were disproportionately 35e54 years of age (59.1% vs. 42.2%, c2 ¼ 170.5, P < .0001), male (65.2% vs. 44.2%, c2 ¼ 243.3, P < .0001), and identified as white race (74.4% vs. 51.6%, c2 ¼ 319.3, P < .0001) compared with patients that did not die of PDO (Table 1). Additionally, patients who subsequently died of a PDO were more likely to be discharged AMA at the initial visit (3.9% vs. 2.1%, c2 ¼ 15.2, P < .0001, Table 1) than those that did not die. In unadjusted models, patients who were discharged AMA at the initial ED visit were more likely to subsequently die of PDO (Table 2) than patients were not discharged AMA. Having a psychiatric disorder diagnosis, or any substance abuse, or dependence diagnosis was also associated with increased risk of PDO death (Table 2). Multivariable models adjusted for age, race, and sex show that drug, or alcohol abuse, or dependence diagnoses at an initial ED visit were associated with five times greater risk of PDO death (adjusted odds ratio [aOR]: 5.40, 95% CI: 4.84e6.03, Table 3). Having a psychiatric disorder diagnosis at the initial ED visit was associated with a three times higher risk of subsequent PDO death compared with patients without a psychiatric disorder diagnoses at the initial ED visit (aOR: 3.71, 95% CI: 3.23e4.14). Having a pain-related diagnosis at the initial ED visit was associated with a 73% higher risk of PDO death (aOR: 1.88, 95% CI: 1.60e1.86). Other diagnoses, including general symptoms, soft tissue, nontraumatic dental, or vertebrae disorders at an initial ED visit were associated with 40%

This study is among one of the first studies to evaluate the relationship between patterns of ED utilization and PDO death, as opposed to the more well-defined literature on “doctor shopping,” the practice of visiting multiple health care providers to obtain controlled substances [49e53]. ED utilization patterns are thought to be related to poorly controlled pain and in some instances substance use [3,54,55]. This study indicates that ED utilization patterns are also important predictors of PDO death. ED visits are common among people who abuse drugs and alcohol [6,56e58]. An alcohol intervention trial of college students found that 30% of men and 27% women visited the ED at least once in the 2 years of follow-up [6]. Another study of adolescents using ED at the University of Michigan Medical Center found that 35% of past-year self-reported nonmedical prescription opioid users had an ED visit in the prior year, compared with 25% of respondents not reporting past-year nonmedical prescription opioid users [58]. Compared with individuals not reporting past-year nonmedical sedative use, self-reported past-year nonmedical sedative users were more likely to have had an ED visit in the past year (25% vs. 40%) [58]. Additionally, this investigation demonstrates that patients who were discharged AMA were more likely to die of PDO death than patients who were not discharged AMA; previous literature has shown that leaving AMA is associated with substance use [30]. Although it is generally believed that drug-seeking patients have increased ED utilization and that patients discharged AMA are more likely to abuse drugs and alcohol, examination of ED utilization patterns with respect to overdose is scant [30,54,55].

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Table 1 ED utilization and demographic and clinical characteristics by case-control status, SPARCS emergency department data, New York State, 2006e2010 Characteristics*

ED visits in the last year of follow-up 1 2 3 4 Demographics Agey (y) 18e24 25e34 35e44 45e54 55e64 Sexy Female Male Racey White Black or African American Native American/Asian/Pacific Islander Other or unknown Clinical characteristics Discharged AMAy Yes No Psychiatric diagnosis Anxiety disordery Yes No Depressiony Yes No Episodic mood disordery Yes No Major depressive disorder Yes No Personality disordery Yes No Psychosis or agitationy Yes No Schizophreniay Yes No Substance use diagnosis Alcohol abusey Yes No Alcohol drug seekingy Yes No Drug dependencey Yes No Opioid abusey Yes No Other drug abusey Yes No Tobacco use disordery Yes No Pain-related diagnosis Arthritis Yes No

Cases

Controls

No.

%

No.

%

1388 464 278 602

50.8 17.0 10.2 22.0

2312 244 81 95

84.6 8.9 3.0 3.5

351 541 796 818 226

12.9 19.8 29.1 29.9 8.3

592 655 596 558 331

21.7 24.0 21.8 20.4 12.1

951 1781

34.8 65.2

1525 1207

55.8 44.2

2033 313 26 360

74.4 11.5 1.0 13.2

1410 679 76 567

51.6 24.9 2.8 20.8

106 2626

3.9 96.1

57 2675

2.1 97.9

125 2607

4.6 95.4

51 2681

1.9 98.1

125 2607

4.6 95.4

35 2697

1.3 98.7

100 2632

3.7 96.3

25 2707

0.9 99.1

18 2714

0.7 99.3

11 2721

0.4 99.6

107 2625

3.9 96.1

26 2706

1.0 99.1

95 2637

3.5 96.5

12 2720

0.4 99.6

42 2690

1.5 98.5

7 2725

0.3 99.7

215 2517

7.9 92.1

68 2664

2.5 97.5

486 2246

17.8 82.2

97 2635

3.6 96.4

125 2607

4.6 95.4

13 2719

0.5 99.5

48 2684

1.8 98.2

7 2725

0.3 99.7

137 2595

5.0 95.0

12 2720

0.4 99.6

104 2628

3.8 96.2

69 2663

2.5 97.5

99 2633

3.6 96.4

88 2644

3.2 96.8

(continued)

Table 1 ( continued) Characteristics*

Back painy Yes No Fibromyalgia Yes No Headaches Yes No Joint disorder Yes No Musculoskeletal pain Yes No Neuropathy Yes No Visceral painy Yes No Other conditions Abdominal symptomsy Yes No General symptomsy Yes No Nontraumatic dental Yes No Respiratory symptomsy Yes No Soft tissue disorder Yes No

Cases

Controls

No.

%

No.

%

244 2488

8.9 91.1

134 2598

4.9 95.1

30 2702

1.1 98.9

26 2706

1.0 99.1

73 2659

2.7 97.3

92 2640

3.4 96.6

78 2654

2.9 97.1

69 2663

2.5 97.5

306 2426

11.2 88.8

301 2431

11.0 89.0

8 2724

0.3 99.7

10 2722

0.4 99.6

24 2708

0.9 99.1

66 2666

2.4 97.6

132 2600

4.8 95.2

170 2562

6.2 93.8

189 2543

6.9 93.1

146 2586

5.3 94.7

65 2667

2.4 97.6

65 2667

2.4 97.6

135 2597

4.9 95.1

204 2528

7.5 92.5

94 2638

3.4 96.6

79 2653

2.9 97.1

* Data for prior nonfatal opioid overdose, sedative abuse, post-traumatic stress disorder, central sleep apnea, vertebrae disorders not shown because cell counts less than 6. y P < 0.05.

This study confirms prior findings that patients who are male, ages 35e54 years, and are identified as white race are at increased risk for PDO compared with females, other age groups, and races [34,39,40,43,52,59e63], and it mirrors previous research on the association of clinical risk markers for overdose, such as psychiatric comorbidities, drug and alcohol abuse, previous ED visits for opioid overdose and chronic pain conditions [34e36,39,40,64]. Of note, at study entry, chronic pain and other conditions, such as visceral pain and headaches, which previous studies have found to be frequently occurring in patients who overdose [36,55], were more frequently noted for patients who did not subsequently die of PDO. ED visits may serve as an opportunity to launch a preventive intervention, as to date findings suggest that prescription drug monitoring programs have had limited or mixed effects on reducing drug overdose deaths and opioid or other controlled substance prescribing [65e69]. Risk assessment and stratification can help identify patients most vulnerable to overdose who are using ED services [40,62]. Based on all 2006e2010 SPARCS ED data, about 40% of patients who subsequently die of overdose, who were 18e64 years, and NYS residents had multiple ED encounters (data not shown). About 19% of people who died of PDO did not visit the ED during the study period (data not shown). These repeated visits represent opportunities to intervene, such as referral to substance abuse treatment, or access to rescue treatments,

J.E. Brady et al. / Annals of Epidemiology 25 (2015) 613e619 Table 2 Unadjusted ORs and 95% CIs for risk of PDO death by ED utilization and demographic and clinical characteristics from nested case-control analysis, SPARCS ED data, New York State, 2006e2010 Characteristics ED visits in the past year 1 2 3 4 Demographics Age (y) 18e24 25e34 35e44 45e54 55e64 Sex Female Male Race White Black or African American Native American/Asian/Pacific Islander Other or unknown Clinical characteristics Discharged AMA Psychiatric diagnoses Anxiety disorder Depression Episodic mood disorder Major depressive disorder Personality disorder Post-traumatic stress disorder Psychosis or agitation Schizophrenia Substance use diagnoses Alcohol abuse Alcohol drug seeking Drug dependence Opioid abuse Other drug abuse Prior nonfatal opioid overdose Sedative anxiolytic abuse Tobacco use disorder Pain-related diagnoses Arthritis Back pain Fibromyalgia Headaches Joint disorder Musculoskeletal pain Neuropathy Visceral pain Other conditions Abdominal symptoms Central sleep apnea General symptoms Nontraumatic dental Respiratory symptoms Soft tissue disorder Vertebrae disorders

OR (95% CI)

3.2 (2.7e3.7) 5.7 (4.4e7.4) 10.6 (8.4e13.2)

0.63 1.00 2.26 1.74 0.69

(0.60e0.67) (2.15e2.37) (1.65e1.83) (0.65e0.74)

1.00 2.01 (1.94e2.08) 1.00 0.29 (0.27e0.30) 0.27 (0.23e0.31) 0.47 (0.45e0.50)

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Table 3 Adjusted ORs and 95% CIs for risk of PDO death by ED utilization, demographic and clinical characteristics, SPARCS ED data, New York State, 2006e2010 Characteristic ED visits in the past year 2 3 4 Age, y 18e24 35e44 45e54 55e64 Race Black or African American Native American/Asian/Pacific Islander Other or Unknown Sex Male Discharged AMA at initial ED visit Drug or alcohol abuse, or dependence diagnosis Mental health diagnosis Pain-related diagnosis Other diagnosis

OR (95% CI) 4.90 (4.50e5.34) 16.61 (14.72e18.75) 48.24 (43.23e53.83) 0.49 1.86 1.74 0.82

(0.44e0.54) (1.71e2.01) (1.60e1.90) (0.74e0.92)

0.27 (0.25e0.29) 0.27 (0.21e0.34) 0.42 (0.39e0.45) 2.35 1.38 5.40 3.71 1.73 1.38

(2.22e2.50) (1.16e1.63) (4.84e6.03) (3.23e4.14) (1.60e1.86) (1.26e1.50)

2.89 (2.74e3.04)

Limitations 4.16 2.82 4.27 1.60 4.76 2.91 7.25 5.18 3.23 5.03 8.63 13.71 6.81 13.40 8.57 1.46

(3.72e4.64) (2.54e3.14) (3.70e4.91) (1.26e2.04) (2.8e8.04) (1.81e4.67) (5.99e8.78) (4.07e6.59) (2.96e3.51) (4.68e5.39) (7.16e10.41) (10.13e18.55) (5.86e7.93) (7.93e22.64) (5.02e14.61) (1.33e1.61)

1.02 2.20 1.34 0.98 1.09 1.15 0.64 0.39

(0.94e1.12) (2.06e2.35) (1.10e1.62) (0.89e1.07) (0.99e1.22) (1.09e1.21) (0.48e0.86) (0.33e0.45)

0.89 0.18 1.09 1.13 0.72 1.38 2.22

(0.83e0.95) (0.05e0.60) (1.02e1.17) (1.02e1.26) (0.67e0.77) (1.25e1.51) (1.78e2.78)

including take-home naloxone for reversing opioid overdose. A Canadian study found that by monitoring frequent ED users, it was possible to reduce ED visits and improve patient care by using medical and social interventions, such as denying narcotic and benzodiazepine prescriptions, restricting patients to using one pharmacy, providing supportive counseling, and linking patients with community mental health services [70]. Although there is debate regarding the appropriateness of interventions in already overburdened EDs, reports show that individuals with four ED visits or more in a year make up 28% of all ED visits [28], suggesting interventions targeted at high ED utilizers adequate care outside the ED may help alleviate strain on EDs.

This study has several notable limitations. First, this study examines whether ED utilization patterns can predict overdose death. Preventive interventions launched in the ED will not directly affect drug user populations who do not use ED services, and only 40% of all overdose victims 18e64 years of age in NYS had multiple ED visits. If patients who died at their only ED visit had been included, effect estimates would have been slightly attenuated. Second, increased health care utilization is a marker of illness and sicker patients who are more likely to receive more pharmacologic treatments and are at increased risk for all-cause mortality. Third, this investigation is based on currently coded diagnoses and not necessarily conditions present in individuals; some conditions may not be recorded in administrative data. It is likely that only the most severe comorbidities of patients are recorded, and that the findings of this study may not be generalizable to patients with less severe disease. Fourth, this study does not evaluate provider prescribing or its appropriateness. Because SPARCS does not collect prescription drug data, it is not possible to determine if patients were prescribed prescription opioids or other drugs at their ED visit. Similarly, other important characteristics cannot be accounted for such as, drugs taken, drug dosages, the setting in which drugs were taken or the mode of drug administration (e.g., oral or intravenous), factors associated with opioid adverse events, and overdose death [39,60,62,71e73]. Fifth, there have been conflicting studies regarding the accuracy of drug overdose death certification and coding [74e77]. If drug overdose deaths are not ascertained and indiscriminately not recorded on death certificates, this study may underestimate effect estimates. Additionally, if a death to a NYS resident occurred out of state, the death would not be captured in this analysis. Conclusions ED utilization patterns are a strong predictor of subsequent overdose death. Understanding the timing of overdose death in relation to ED utilization is essential to recognizing which patients to target with overdose prevention interventions. Identifying markers for increased risk is paramount for optimizing the effectiveness of preventive intervention efforts, such as providing highrisk patients, their friends and families with take-home naloxone, or drug treatment referral. Future studies should focus on

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identifying other health encounter patterns and alternative approaches to aid vulnerable individuals who do not use ED services. Acknowledgment This work was supported in part by the National Center for Injury Prevention and Control of the Centers for Disease Control and Prevention (Grant 1 R49 CE002096) and the National Institutes of Health (NIAAA: K01 AA021511, Keyes). The contents of the manuscript are solely the responsibility of the authors and do not necessarily reflect the official views of the funding agency. The authors thank Barbara Lang and Kristin Wong for their editorial and administrative assistance. Data for this manuscript were obtained from the New York Statewide Planning and Research Cooperative System (SPARCS), New York State Department of Health and the New York Department of Vital Statistics, New York State Department of Health. The information contained herein was derived from data provided in part by the Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene. References [1] Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, United States, 2010. JAMA 2013;309(7):657e9. [2] Substance Abuse and Mental Health Services Administration (SAMHSA). Highlights of the 2011 Drug Abuse Warning Network (DAWN) findings on drug-related emergency department visits. Rockville, MD: DSHS Health and Recovery Services Administration and Research and Data Analysis Division. 2013. [3] Thompson JCP, Dowler D, Mancuso D. Overview of new DSHS drug and alcohol programs, PRR program, and ER initiativedwhat physicians, hospitals & communities need to know. 2006. Available from: casat.unr.edu/docs/ AddictionsInstitute_7_06_2006_DD.ppt. Accessed December 8, 2013. [4] Mancuso D, Nordlund D, Felver B. Frequent emergency room visits signal substance abuse and mental illness. In: Services WSDoSH. Olympia, WA: Research & Data Analysis Division; 2004. [5] Wisniewski AM, Purdy CH, Blondell RD. The epidemiologic association between opioid prescribing, non-medical use, and emergency department visits. J Addict Dis 2008;27(1):1e11. [6] Mundt MP, Zakletskaia LI. Prevention for college students who suffer alcoholinduced blackouts could deter high-cost emergency department visits. Health Aff (Millwood) 2012;31(4):863e70. [7] Edelman EJ, Dinh A, Radulescu R, Lurie B, D’Onofrio G, Tetrault JM, et al. Combining rapid HIV testing and a brief alcohol intervention in young unhealthy drinkers in the emergency department: a pilot study. Am J Drug Alcohol Abuse 2012;38(6):539e43. [8] Burke PJ, O’Sullivan J, Vaughan BL. Adolescent substance use: brief interventions by emergency care providers. Pediatr Emerg Care 2005;21(11): 770e6. [9] Walton MA, Goldstein AL, Chermack ST, McCammon RJ, Cunningham RM, Barry KL, et al. Brief alcohol intervention in the emergency department: moderators of effectiveness. J Stud Alcohol Drugs 2008;69(4):550e60. [10] Bernstein SL, Bijur P, Cooperman N, Jearld S, Arnsten JH, Moadel A, et al. Efficacy of an emergency department-based multicomponent intervention for smokers with substance use disorders. J Subst Abuse Treat 2013;44(1): 139e42. [11] Murphy MK, Bijur PE, Rosenbloom D, Bernstein SL, Gallagher EJ. Feasibility of a computer-assisted alcohol SBIRT program in an urban emergency department: patient and research staff perspectives. Addict Sci Clin Pract 2013;8(1):2. [12] Babor TF, Kadden RM. Screening and interventions for alcohol and drug problems in medical settings: what works? J Trauma 2005;59(3 Suppl):S80e7. discussion S94eS100. [13] Woolard R, Baird J, Longabaugh R, Nirenberg T, Lee CS, Mello MJ, et al. Project reduce: reducing alcohol and marijuana misuse: effects of a brief intervention in the emergency department. Addict Behav 2013;38(3):1732e9. [14] Wu LT, Swartz MS, Wu Z, Mannelli P, Yang C, Blazer DG. Alcohol and drug use disorders among adults in emergency department settings in the United States. Ann Emerg Med 2012;60(2):172e180.e5. [15] D’Onofrio G, Fiellin DA, Pantalon MV, Chawarski MC, Owens PH, Degutis LC, et al. A brief intervention reduces hazardous and harmful drinking in emergency department patients. Ann Emerg Med 2012;60(2):181e92. [16] Gudin JA, Mogali S, Jones JD, Comer SD. Risks, management, and monitoring of combination opioid, benzodiazepines, and/or alcohol use. Postgrad Med 2013;125(4):115e30. [17] Gugelmann H, Shofer FS, Meisel ZF, Perrone J. Multidisciplinary intervention decreases the use of opioid medication discharge packs from 2 urban EDs. Am J Emerg Med 2013;31(9):1343e8.

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Appendix A ICD codes for risk marker and outcome definitions ICD-10 codes for outcome definitions Definition 1: PDO (unintentional or of undetermined intent) X40eX44 Y10eY14 Exclusion diagnoses V66 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,160, 161, 162, 163, 164, 165, 170, 171, 172, 174, 175, 176,179,180, 181, 182, 183, 184, 185, 186, 187, 188, 189,190, 191, 192, 193, 194, 195, 200, 201, 202, 203, 204, 205, 206, 207, 208 282.6, 282.41 196, 197, 198, 199.0, 199.1 Other risk markers Psychiatric disorder 300 327.21, 327.23, 327.20, 327.29, 780.53, 780.57 298, 311, 309.0, 309.1 296 296.2, 296.3 296, 301 309.81 292, 293.0, 293.8, 294, 295, 297.1, 297.3, 297.8, 297.9, 299 295 Substance Abuse 304 305.0 305.5 305.4 305.7 305.8 305.2, 305.3, 305.6, 305.9 305.1 303, 304, 305.0, 305.2, 305.3, 305.4, 305.5, 305.6, 305.7, 305.8, 305.9, 965.0, V11.3 V11.3, 291, 303.0, 303.9, 305.0, 357.5, 425.5, 535.3, 571.0, 571.1, 571.3, 790.3, 980.0 965.00, 965.01, 965.02, 965.09 Other conditions 789 780 521, 522, 523, 525.3, 525.9, 873.63 786 729 722 Chronic pain 719 307.81, 339.00, 339.01, 339.02, 339.03, 339.04, 339.05, 339.09, 339.10, 339.11, 339.12, 339.20, 339.21, 339.22, 339.3, 339.41, 339.42, 339.43, 339.44, 339.81, 339.82, 339.83, 339.84, 339.85, 339.89, 346.0, 346.00, 346.01, 346.02, 346.03, 346.1, 346.10, 346.11, 346.12, 346.13, 346.2, 346.20, 346.21, 346.22, 346.23, 346.30, 346.31, 346.32, 346.33, 346.40, 346.41, 346.42, 346.43, 346.50, 3465.1, 346.52, 346.53, 346.70, 346.71, 346.72, 346.73, 346.8, 346.80, 346.81, 346.82, 346.83, 346.9, 3469.0, 3469.1, 346.92, 346.93, 784.0 720.1, 720.2, 720.81, 720.89, 720.9, 721.0, 721.1, 721.2, 721.3, 721.41, 721.42, 721.5, 721.6, 721.7, 721.8, 721.90, 721.91, 722.0, 722.10, 722.11, 722.2, 722.30, 722.31, 722.32, 722.39, 722.4, 722.51, 722.52, 722.6, 722.70, 722.71, 722.72, 722.73, 72.280, 722.81, 722.82, 722.83, 722.90, 722.91, 722.92, 722.93, 723.0, 723.1, 723.2, 723.3, 72.35, 723.6, 723.7, 723.9, 724.00, 724.01, 724.02, 724.03, 724.09, 724.1, 724.2, 724.3, 724.4, 724.5, 724.6, 724.70, 724.71, 724.79, 724.8, 724.9, 756.10, 756.11, 756.12, 756.13, 756.14, 756.15, 756.16, 756.17, 756.19 550.00, 550.01, 550.02, 550.03, 550.10, 550.11, 550.12, 550.13, 551.00, 551.01, 551.02, 551.03, 551.1, 551.20, 551.21, 551.29, 552.00, 552.01, 552.02, 552.03, 552.1, 552.20, 552.21, 552.29, 560.81, 562.01, 562.03, 562.11, 562.13, 567.2, 567.21, 567.22, 567.31, 567.38, 567.8, 567.82, 567.89, 567.9, 569.5, 574.00, 574.01, 574.10, 574.11, 574.20, 574.21, 574.30, 574.31, 574.40, 574.41, 574.50, 574.51, 574.60, 574.61, 574.70, 574.71, 574.80, 574.81, 574.90, 574.91, 575.0, 575.1, 575.10, 575.11, 575.12, 577.0, 577.1, 592.0, 592.1, 592.9, 594.0, 594.1, 594.2, 594.8, 594.9, 596.0, 788.0, 789.0, 789.02, 789.60, 789.61, 789.62, 789.63, 789.64, 789.65, 789.66, 789.67, 789.69 053.13, 072.72, 337.0, 337.1, 353.0, 353.1, 353.2, 353.3, 353.4, 353.5, 353.6, 353.8, 353.9, 354.0, 354.1, 354.2, 354.3, 354.4, 354.5, 354.8, 354.9, 355.0, 355.1, 355.2, 355.3, 355.4, 355.5, 355.6, 355.7, 355.71, 355.79, 355.8, 355.9, 356.0, 356.1, 356.2, 356.3, 356.4, 356.8, 356.9, 357.0, 357.1, 357.2, 357.4, 357.5, 357.6, 357.7, 357.8, 357.81, 357.82, 357.89, 357.9, 377.33, 377.34, 377.41

Unintentional poisoning due to drugs Poisoning of undetermined intent with contributing causes Palliative care Cancer

Sickle cell anemia Metastatic carcinoma

Anxiety disorder Central sleep apnea Depression Episodic mood disorder [1] Major depressive disorder Personality disorder Post-traumatic stress disorder Psychoses agitation [35] Schizophrenia Drug dependence Alcohol abuse Opioid abuse Sedative anxiolytic abuse Amphetamine abuse Antidepressant abuse Other drug abuse Tobacco use disorder Alcohol drug seeking Alcohol abuse [35] Prior opioid overdose Abdominal symptoms [1] General symptoms that is, alteration of consciousness, and so forth [1] Nontraumatic dental conditions Respiratory syndrome [1] Soft tissue disorderdfasciitis or limb pain [1] Vertebrae disorders [1] Joint disorder [1] Headaches [44]

Back pain [44]

Visceral pain [44]

Neuropathy [44]

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J.E. Brady et al. / Annals of Epidemiology 25 (2015) 613e619 ( continued) ICD-10 codes for outcome definitions 729.1 725, 726.0, 726.10, 726.11, 726.12, 726.19, 726.2, 726.30, 726.31, 726.32, 726.33, 726.39, 726.4, 726.5, 726.60, 726.61, 726.62, 726.63, 726.64, 726.65, 726.69, 726.71, 726.72, 726.90, 727.00, 727.03, 727.04, 727.05, 727.06, 727.09, 727.2, 727.3, 727.49, 727.50, 727.51, 727.60, 727.61, 727.62, 727.63, 727.64, 727.65, 727.66, 727.67, 727.68, 727.69, 727.89, 727.9, 728.11, 728.12, 728.81, 728.83, 728.86, 728.89, 728.9, 729.0, 729.1, 72.94, 729.5, 729.71, 729.72, 729.73, 729.79, 729.89, 729.9, 729.91, 729.92, 781.99, 830.0, 830.1, 831.00, 831.01, 831.02, 831.03, 831.04, 831.09, 831.10, 831.11, 831.12, 831.13, 831.14, 831.19, 832.00, 832.01, 832.02, 832.03, 832.04, 832.09, 832.10, 832.11, 832.12, 832.13, 832.14, 832.19, 832.2, 833.00, 833.01, 833.02, 833.03, 833.04, 833.05, 833.09, 833.10, 833.11, 833.12, 83.313, 833.14, 833.15, 833.19, 834.00, 834.01, 834.02, 834.10, 834.11, 834.12, 835.00, 835.01, 835.02, 835.03, 835.10, 835.11, 835.12, 835.13, 836.0, 836.1, 836.2, 836.3, 836.4, 836.50, 836.51, 836.52, 836.53, 836.54, 836.59, 836.60, 836.61, 836.62, 836.63, 836.64, 836.69, 837.0, 837.1, 838.00, 838.01, 838.02, 838.03, 838.04, 838.05, 838.06, 838.09, 838.10, 838.11, 838.12, 838.13, 838.14, 838.15, 838.16, 838.19, 839.00, 839.01, 839.02, 839.03, 839.04, 839.05, 839.06, 839.07, 839.08, 839.10, 839.11, 839.12, 839.13, 839.14, 839.15, 839.16, 839.17, 839.18, 839.20, 839.21, 839.30, 839.31, 839.40, 839.41, 839.42, 839.49, 839.50, 839.51, 839.52, 839.59, 839.61, 839.69, 839.71, 839.79, 83.98, 838.9, 840.0, 840.1, 840.2, 840.3, 840.4, 840.5, 840.6, 840.7, 840.8, 840.9, 841.0, 841.1, 841.2, 841.3, 841.8, 841.9, 842.00, 842.01, 842.02, 842.09, 842.10, 842.11, 842.12, 842.13, 842.19, 843.0, 843.1, 843.8, 843.9, 844.0, 844.1, 844.2, 844.3, 844.8, 844.9, 845.00, 845.01, 845.02, 845.03, 845.09, 845.10, 845.11, 845.12, 845.13, 845.19, 846.0, 846.1, 846.2, 846.3, 846.8, 846.9, 847.0, 847.1, 847.2, 847.3, 847.4, 847.9, 848.0, 848.1, 848.2, 848.3, 848.40, 848.41, 848.42, 848.49, 848.5, 848.8, 848.9, 905.6, 905.7, V43.60, V43.61, V43.62, V436.3, V436.4, V436.5, V436.6, V43.69, V43.7, V48.3, V49.60, V49.61, V49.62, V49.63, V49.64, V49.65, V49.66, V49.67, V49.70, V49.71, V49.72, V49.73, V49.74, V49.75, V49.76, V49.77 710.0, 710.1, 710.2, 710.3, 710.4, 710.8, 710.9, 711.00, 711.01, 711.02, 711.03, 711.04, 711.05, 711.06, 711.07, 711.08, 711.09, 711.10, 711.11, 711.12, 711.13, 711.14, 711.15, 711.16, 711.17, 711.18, 711.19, 711.20, 71.121, 711.22, 711.23, 711.24, 711.25, 711.26, 711.27, 711.28, 711.29, 711.30, 711.31, 711.32, 711.33, 711.34, 711.35, 711.36, 711.37, 711.38, 711.39, 711.40, 711.41, 711.42, 711.43, 711.44, 711.45, 711.46, 711.47, 711.48, 711.49, 711.50, 711.51, 711.52, 711.53, 711.54, 711.55, 711.56, 711.57, 711.58, 711.59, 711.60, 711.61, 711.62, 711.63, 711.64, 711.65, 711.66, 711.67, 711.68, 711.69, 711.70, 711.71, 711.72, 711.73, 711.74, 711.75, 711.76, 711.77, 711.78, 711.79, 711.80, 711.81, 711.82, 711.83, 711.84, 711.85, 711.86, 71.187, 711.88, 711.89, 711.90, 711.91, 711.92, 711.93, 711.94, 711.95, 7119.6, 711.97, 711.98, 711.99, 713.0, 713.1, 713.2, 713.3, 713.4, 713.5, 713.6, 713.7, 713.8, 714.0, 714.1, 714.2, 714.30, 714.31, 714.32, 714.33, 714.4, 714.81, 714.89, 714.9, 715.00, 715.04, 715.09, 715.10, 715.11, 715.12, 715.13, 715.14, 715.15, 715.16, 715.17, 715.18, 715.20, 715.21, 715.22, 715.23, 715.24, 715.25, 715.26, 715.27, 715.28, 715.30, 715.31, 715.32, 715.33, 715.34, 715.35, 71.536, 715.37, 715.38, 715.80, 715.89, 715.90, 715.91, 715.92, 715.93, 715.94, 715.95, 715.96, 715.97, 715.98, 716.00, 716.01, 716.02, 716.03, 716.04, 716.05, 716.06, 716.07, 716.08, 716.09, 716.10, 716.11, 716.12, 716.13, 716.14, 716.15, 716.16, 716.17, 716.18, 716.19, 716.20, 716.21, 716.22, 716.23, 716.24, 716.25, 716.26, 716.27, 716.28, 716.29, 716.30, 716.31, 716.32, 716.33, 716.34, 716.35, 716.36, 716.37, 716.38, 716.39, 716.40, 71.641, 716.42, 716.43, 716.44, 716.45, 716.46, 716.47, 716.48, 716.49, 716.50, 716.51, 716.52, 716.53, 716.54, 716.55, 716.56, 716.57, 716.58, 716.59, 716.60, 716.61, 716.62, 716.63, 716.64, 716.65, 716.66, 716.67, 716.68, 716.80, 716.81, 716.82, 716.83, 716.84, 716.85, 716.86, 716.87, 716.88, 716.89, 716.90, 716.91, 716.92, 716.93, 716.94, 716.95, 716.96, 716.97, 716.98, 716.99, 717.0, 717.1, 717.2, 717.3, 717.40, 717.41, 717.42, 717.43, 717.49, 717.5, 717.6, 717.7, 717.81, 717.82, 717.83, 717.84, 717.85, 717.89, 717.9, 718.00, 718.01, 718.02, 718.03, 718.04, 718.05, 718.07, 718.08, 718.09, 718.10, 718.11, 718.12, 718.13, 718.14, 718.15, 718.17, 718.18, 718.19, 718.20, 718.21, 718.22, 718.23, 718.24, 718.25, 718.26, 718.27, 718.28, 718.29, 718.30, 71.831, 718.32, 718.33, 718.34, 718.35, 718.36, 718.37, 718.38, 718.39, 718.50, 718.51, 718.52, 718.53, 718.54, 718.55, 718.56, 718.57, 718.58, 718.59, 718.60, 718.65, 718.70, 718.71, 718.72, 718.73, 7187.4, 718.75, 718.76, 718.77, 718.78, 718.79, 718.80, 718.81, 718.82, 718.83, 718.84, 718.85, 718.86, 718.87, 718.88, 718.89, 718.90, 718.91, 718.92, 7189.3, 718.94, 718.95, 718.97, 718.98, 718.99, 719.00, 719.01, 719.02, 719.03, 719.04, 719.05, 719.06, 719.07, 719.08, 719.09, 719.10, 719.11, 719.12, 719.13, 719.14, 719.15, 719.16, 719.17, 719.18, 719.19, 719.20, 719.21, 719.22, 719.23, 719.24, 719.25, 719.26, 7192.7, 719.28, 719.29, 719.30, 719.31, 719.32, 719.33, 719.34, 719.35, 719.36, 719.37, 719.38, 719.39, 719.40, 719.41, 719.42, 719.43, 719.44, 719.45, 719.46, 719.47, 719.48, 719.49, 719.50, 719.51, 719.52, 719.53, 719.54, 719.55, 719.56, 719.57, 719.58, 719.59, 719.60, 719.61, 719.62, 719.63, 719.64, 719.65, 719.66, 719.67, 719.68, 719.69, 719.70, 719.75, 719.76, 719.77, 719.78, 719.79, 719.80, 719.81, 719.82, 719.83, 719.84, 719.85, 719.86, 719.87, 719.88, 719.89, 719.90, 719.91, 719.92, 719.93, 719.94, 719.95, 719.96, 719.97, 719.98, 719.99, 720.0, V13.4 ICD ¼ International Classification of Diseases.

Fibromyalgia [44] Musculoskeletal pain [44]

Arthritis [44]

619.e2