Opioid-Related Hospitalization and Its Association With Chronic Diseases

Findings From the National Inpatient Sample, 2011-2015

Janani Rajbhandari-Thapa, PhD; Donglan Zhang, PhD; Heather M Padilla, PhD; Sae Rom Chung, MS

Disclosures

Prev Chronic Dis. 2019;16(11):e157 

In This Article

Methods

In this cross-sectional study, we analyzed data on patients aged 18 years or older from the National Inpatient Sample (NIS) from January 1, 2011, to September 30, 2015. Claims occurring from October through December of 2015 were not included because of the transition from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to the 10th revision (ICD-10-CM) coding system on October 1, 2015. We included arthritis, spinal disease, asthma, liver disease, stroke, cancer, and obesity because these diseases are most likely to be prescribed opioids in the internal and family medicine specialties. Family medicine (21.8%) and internal medicine (17.6%) reportedly write the most opioid prescriptions, followed by physical medicine/rehabilitation, anesthesiology/pain, hematology/oncology, and neurology.[4–6] Relationships between asthma and opioid abuse and dependence[7] and between chronic liver disease and opioid use[8] have been established, as has a relationship between stroke and opioid abuse among young stroke patients.[9] Cancer patients rely on opioid medications for relief from cancer pain.[10] Obesity was included because of its significant association with chronic pain.[11] Using the NIS sample, we identified 3,239,136 opioid-related hospitalization cases for from January 1, 2011, to September 30, 2015.

We combined opioid dependence and unspecified use, adverse effects of opioids, opioid abuse, and opioid poisoning to create a single variable, opioid-related hospitalization (specific ICD-9-CM codes are available in Appendices A and Appendices B. The data were weighted by using sampling weights provided by the NIS Healthcare Cost and Utilization Project.[12] For creating national estimates for the prevalence of opioid-related hospitalizations and all analyses, we implemented Stata's trend weight (TRENDWT) for 2011 and discharge weight (DISCWT) from 2012 through 2015 (StataCorp LLC). Because of the redesign of NIS data in 2012, we used TRENDWT for 2011, which allowed the national estimates for trend analysis in 2011 to be consistent with data from 2012–2015. To use trend weight, we merged the trend weight file (www.hcup-us.ahrq.gov/db/nation/nis/trendwghts.jsp) into the original NIS 2011 data. We analyzed the trends in opioid-related hospitalizations by type of chronic disease for 2011–2015 and used the Pearson χ 2 test to test for significance. Significance was set at P < .05.

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