National Trends and Factors Associated With Inpatient Mortality in Adult Patients With Opioid Overdose

Brittany N. Burton, MHS, MAS; Timothy C. Lin, MAS; Engy T. Said, MD; Rodney A. Gabriel, MD, MAS


Anesth Analg. 2019;128(1):152-160. 

In This Article


In this retrospective analysis of NIS data, we examined trends and the impact of perioperative factors on inpatient mortality (ie, primary outcome) and explored unadjusted differences among opioid overdose cohorts (ie, secondary outcomes). We estimated a mortality rate of 2.6% in patients admitted for opioid overdose from 2010 to 2014. Small cohort studies have published findings related to opioid overdose; however, most are descriptive and only evaluate trends in outcomes.[13–16] To our knowledge, there are limited studies using a large national database to identify factors associated with inpatient mortality. Given the public health and economical burden of opioid misuse, recognizing the differences in vulnerable populations and risk factors of opioid overdose has important implications for targeted and effective intervention and prevention.

The sales of prescription opioids quadrupled from 1999 to 2014, and the National Institute of Drug Abuse estimated a 167% increase in opioid-related death from 2002 to 2015.[17] Of patients prescribed opioids, 8%–9% develop an opioid misuse disorder and 4%–6% progressed to heroin abuse.[17] From this, further understanding demographic factors and comorbidities associated with fatal opioid overdose serves to direct primary, secondary, and tertiary prevention strategies and proactive intervention. Knowledge of these factors associated with mortality will help physicians to not only target the vulnerable patients but also educate and counsel them on substance abuse in the setting of their comorbidities. Furthermore, identifying vulnerable populations will allow health care providers and public health officials to develop screening tools aimed at detecting opioid misuse, which may help to reduce the incidence of opioid misuse. Appropriately allocating resources to manage underlying risk factors may help to decrease the morbidity and mortality, and such allocation will serve to reduce the negative impact of the opioid epidemic.

We first sought to describe the demographics of patients admitted for either IOD or POD. IOD was more commonly found in younger, male, Caucasian patients, and those with Medicaid, whereas POD patients were more likely to be older, Caucasian, female, and have Medicare. In a previous 12-year study, POD patients were more likely to be female, Caucasian, and have Medicare when compared to heroin opioid overdose patients.[7] We found that patients admitted for overdose had a median number of 12 diagnoses with smoking, hypertension, depression, and benzodiazepine poisoning being most common. These demographics and comorbidities may play a role in risk stratification; thus, early recognition is important for preventative strategies or interventions during hospitalization.

Here, we showed that IOD admissions increased by 23% per year, whereas POD admissions decreased by 19% per year. Unick and Ciccarone[6] similarly demonstrated that during 2012–2014, the rate of POD hospitalization declined, whereas the rate of heroin overdose hospitalizations steadily increased. We also found that IOD admissions are more prevalent in the Northeast and Midwest regions of the United States, while POD admissions are most common in the West followed by the South. These geographical patterns have been similarly described.[6] The drop in the opioid prescribing rate due to stringent prescription drug monitoring programs coupled with an increasing demand of more potent opioids may have contributed to the increase in IOD admissions.[18–22]

While we only observed an increase in IOD admissions from 2010 to 2014, inpatient mortality increased among both cohorts. IOD admissions had a 2 times increased odds for inpatient death compared to POD. This may be explained partly by either an increasing prevalence of illicit opioid use or widespread use of more potent formulations of illicit opioids. The development and widespread misuse of illicit fentanyl made it 30–50 times more potent than heroin and led to its significant contribution in opioid overdose–related deaths.[23] The National Forensic Laboratory Information System suggests that direct use or heroin "laced" with nonpharmaceutical fentanyl contributes to the rising prevalence of IOD.[24]

Identifying factors associated with mortality is an important next step for risk-stratifying opioid users. We found that several baseline characteristics were associated with inpatient mortality, including age, ethnicity (African Americans were protective of mortality), and presence of solid tumor malignancy. Recent studies show a surge in opioid-related death rates among African Americans.[16,25] Further work is needed to evaluate the impact of the opioid-related morbidity in racial/ethnic minorities. The explanation of why solid tumor malignancy is associated with increased death in this patient population likely related to the underlying malignancy, comorbidity burden, or previous opioid abuse/misuse history. Other medical factors such as sepsis, hypotension, and inpatient interventions were also associated with mortality. These comorbidities either represent the severity of opioid overdose (ie, metabolic acidosis or acute kidney injury) or a direct result of the disease process itself (ie, sepsis).

Once patients are admitted for an opioid overdose, strategic interventions should be in place at respective institutions to prevent further morbidity and mortality. Active enrollment in drug and alcohol rehabilitation and therapy conferred a significant protective effect against inpatient mortality. This supports a previous finding that opioid replacement therapy was associated with a reduction in heroin overdose deaths.[26] While treatment may not fully prevent overdose events, it plays a significant role in decreasing mortality. These findings underscore the importance of assessing a patient's readiness to engage in rehabilitation and arranging the necessary support that patients may require on discharge. In a previous study, inpatient consultation by an addiction consult team reduced addiction severity as well as increased number of days of self-reported abstinence after discharge in adults at high risk for alcohol or drug use disorder.[27] Likewise, a patient engaged in rehabilitation or therapy may have greater social support or access to other resources, which may positively impact prognosis as well.[28,29]

There are important limitations in our analysis. By analyzing only hospitalizations, we exclude cases of opioid overdose that result in death before admission or cases of overdose that were managed in the emergency department without need for admission; this introduces significant bias in our analysis. Because the NIS is an administrative inpatient database, we are unable to determine whether IOD occurred after prescription opioid misuse. Moreover, other important clinical data are not available, such as the severity and degree of chronic pain, cause of death, dose, type, and route of administration of opioid, length of opioid misuse/abuse, time to event, or events that transpire after discharge. NIS does not capture cause-specific mortality; therefore, it is unclear if mortality is secondary to opioid overdose or other inpatient comorbidities. NIS does not allow us to determine the time of diagnosis of comorbidities. There is debate on the optimal model building strategy. Here, we have used backward model selection with P values. This method, however, has several limitations which include the following: (1) multiple testing problem (ie, the probability of observing significance by chance alone) that leads to an elevated type 1 error rate, (2) CIs falsely narrow, and (3) using P values to exclude potential confounding covariates.[30,31] Strengths of our study include a large sample size, allowing us to examine a relatively rare outcome (ie, mortality), using data over a span of years, and the use of a nationally representative sample.