A comprehensive literature review of hepatitis A, B, and C; HIV; syphilis; gonorrhea; chlamydia; and tuberculosis among pregnant people with OUD. A standardized infrastructure and process for identifying, classifying, and analyzing infectious disease-associated exposures and outcomes among pregnant persons with OUD and their children. This may include cleaning and transformation of laboratory, medications, and administrative data. Routine updates and progress reports to inform enhancements in infectious disease testing, treatment, and prevention among pregnant persons with OUD and their children, including: Clinical site-based reports to inform clinical decision-making and the local epidemiology of infectious diseases among pregnant people with OUD Reports published online that provide overall prevalence of the pathogens of interest during pregnancy among pregnant people with OUD in MAT-LINK, stratified by key sociodemographic characteristics where applicable, including race/ethnicity, pregnant person age, metropolitan status, SDOH (including homelessness and incarceration), substance used, and route of substance use (including injection drug use) Analysis of specific infectious disease- related outcomes among pregnant persons with OUD and their children, including the association with relevant covariates such as mental health, social determinants of health, and infection-specific analyses such as the following: The prevalence of and factors associated with syphilis during pregnancy to identify and make recommendations for additional opportunities for the early diagnosis, treatment, and prevention of syphilis in pregnancy and congenital syphilis Any perinatal HIV transmission to identify the missed opportunity for prevention In collaboration with CDC staff and lead author, present findings regarding infectious disease transmission, testing, and treatment and related maternal and child health outcomes for pregnant people with OUD to inform critical gaps in the literature regarding infections in this population to internal and external audiences Exploration of advanced analytic techniques such as machine learning and natural language processing for categorizing unstructured infectious disease-related data and predicting adverse outcomes, which can be shared online through sources such as GitHub and applied to other diagnostic code datasets Qualifications:

Advanced degree (master’s or higher) in public health, biostatistics, epidemiology, or related field At least 2 years of experience analyzing large and complex datasets using SAS, Stata, or R Ability to independently clean data and perform complex statistical analyses including survey methodology and multivariable modeling Ability to develop standard operating procedures and provide training on their use to public health scientists and clinical personnel Ability to work independently and efficiently in a team environment with support of supervisor and senior staff Excellent English writing abilities; proficiency with Microsoft Word, Excel, and PowerPoint; willingness to learn new software, including the preparation of analytic reports geared toward a public health and clinical audience, as well as peer-reviewed publications Excellent organizational and time management skills Experience with electronic health records data preferred Experience or interest in maternal and child health, substance use, and/or infectious diseases preferred Experience with advance analytics (e.g. machine learning) and software tools such as PowerBI preferred