• Brook T Alemu, Hind A Beydoun, and Olaniyi Olayinka.
• School of Health Sciences, Western Carolina University, Cullowhee, North Carolina, United States.
• Pain Physician. 2022 Jan 1; 25 (1): E95-E103.

BackgroundPregnant women are among the groups most affected by the United States opioid epidemic.ObjectivesTo determine latent classes of maternal comorbidities, examine their relationship to opioid use disorder (OUD), and how they can predict hospital discharge status among hospitalized pregnant women with and without OUD.Study DesignThis is a cross-sectional study.SettingHospitals in North Carolina.MethodsA latent class analysis (LCA) was performed using 929,085 hospital discharge records from the 2000-2014 State Inpatient Databases for North Carolina. We defined OUD status and 24 maternal comorbid conditions based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes and Clinical Classification Software codes, respectively. Discharge status was categorized as home, institution, or died. Binary and multinomial logistic regression models were constructed adjusting for demographic and hospital characteristics.ResultsLCA of maternal comorbid conditions resulted in 591,745 records belonging to Class 1 (birth complications) and 337,340 records belonging to Class 2 (pre-existing and pregnancy-related morbidities). Class 2 records less frequently belonged to patients with OUD than those without OUD, and more frequently to younger, Black/Hispanic/other race or ethnicity, and patients with a higher socioeconomic status who resided in large metropolitan areas. Non-Medicare primary payers were more likely among Class 2 records. Irrespective of OUD status, patients belonging to Class 2 were less likely to be discharged to an institution or be deceased, controlling for confounders.LimitationsAdministrative database; data clustering; misclassification bias; confounding bias; temporality; data-driven approach; generalizability.ConclusionsHospitalized pregnant women may be classified based on comorbid conditions into 2 latent classes ("birth complications" and "pre-existing and pregnancy-related morbidities"), with the former exhibiting greater OUD frequency than the latter. These findings can inform health care needs of populations with a high-risk for OUD.

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