Maternal comorbidity index and severe maternal morbidity among medicaid covered pregnant women in a US Southern rural state

Abstract

Background The rates of SMM have been steadily increasing in Arkansas, a southern rural state, which has the 5th highest maternal death rate among the US states. The aims of the study were to test the functionality of the Bateman index in association to SMM, in clustering the risks of pregnancies to SMM, and to study the predictability of SMM using the Bateman index. Study design From the ANGELS database, 72,183 pregnancies covered by Medicaid in Arkansas between 2013 and 2016 were included in this study. The expanded CDC ICD-9/ICD-10 criteria were used to identify SMM. The Bateman comorbidity index was applied in quantifying the comorbidity burden for a pregnancy. Multivariable logistic regressions, KMeans method, and five widely used predictive models were applied respectively for each of the study aims. Results SMM prevalence remained persistently high among Arkansas women covered by Medicaid (195 per 10,000 deliveries) during the study period. Using the Bateman comorbidity index score, the study population was divided into four groups, with a monotonically increasing odds of SMM from a lower score group to a higher score group. The association between the index score and the occurrence of SMM is confirmed with statistical significance: relative to Bateman score falling in 0–1, adjusted Odds Ratios and 95% CIs are: 2.1 (1.78, 2.46) for score in 2–5; 5.08 (3.81, 6.79) for score in 6–9; and 8.53 (4.57, 15.92) for score ≥10. Noticeably, more than one-third of SMM cases were detected from the studied pregnancies that did not have any of the comorbid conditions identified. In the prediction analyses, we observed minimal predictability of SMM using the comorbidity index: the calculated c-statistics ranged between 62% and 67%; the Precision-Recall AUC values are <7% for internal validation and <9% for external validation procedures. Conclusions The comorbidity index can be used in quantifying the risk of SMM and can help cluster the study population into risk tiers of SMM, especially in rural states where there are disproportionately higher rates of SMM; however, the predictive value of the comorbidity index for SMM is inappreciable.