Citation

Derrington TM, Bernstein J, Belanoff C, Cabral HJ, Babakhanlou-Chase H, Diop H, Evans SR, Kotelchuck M. Matern. Child Health J. 2015; 19(10): 2168-2178.

Copyright

(Copyright © 2015, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10995-015-1730-1

PMID

unavailable

Abstract

Substance use disorder (SUD) in women of reproductive age is associated with adverse health consequences for both women and their offspring. US states need a feasible population-based, case-identification tool to generate better approximations of SUD prevalence, treatment use, and treatment outcomes among women. This article presents the development of the Explicit Mention Substance Abuse Need for Treatment in Women (EMSANT-W), a gender-tailored tool based upon existing International Classification of Diseases, 9th Edition, Clinical Modification diagnostic code-based groupers that can be applied to hospital administrative data. Gender-tailoring entailed the addition of codes related to infants, pregnancy, and prescription drug abuse, as well as the creation of inclusion/exclusion rules based on other conditions present in the diagnostic record. Among 1,728,027 women and associated infants who accessed hospital care from January 1, 2002 to December 31, 2008 in Massachusetts, EMSANT-W identified 103,059 women with probable SUD. EMSANT-W identified 4,116 women who were not identified by the widely used Clinical Classifications Software for Mental Health and Substance Abuse (CCS-MHSA) and did not capture 853 women identified by CCS-MHSA. Content and approach innovations in EMSANT-W address potential limitations of the Clinical Classifications Software, and create a methodologically sound, gender-tailored and feasible population-based tool for identifying women of reproductive age in need of further evaluation for SUD treatment. Rapid changes in health care service infrastructure, delivery systems and policies require tools such as the EMSANT-W that provide more precise identification methods for sub-populations and can serve as the foundation for analyses of treatment use and outcomes.

Language: en