This work seeks to document the latest available Medicaid and CHIP data sources and to describe an updated Medicaid and CHIP data methodology for use in producing the U.S. Census Bureau’s Small Area Health Insurance Estimates (SAHIE). SAHIE represent the only source of single-year health insurance coverage estimates for all counties in the United States; they are model-based estimates that enhance the American Community Survey (ACS) estimates by combining them with timely and informative administrative records data. During 2013 and 2014, many states opted to expand their Medicaid eligibility criteria under the Patient Protection and Affordable Care Act (ACA). With this policy change at hand, the historical one- or two-year Medicaid data lag in the SAHIE models seemed a bit long and potentially limiting. In response, the SAHIE program has developed methods to reduce the Medicaid time lag by updating its detailed Medicaid tallies (by age, sex, county, basis of eligibility) from the Medicaid Statistical Information System (MSIS) with up-to-date Medicaid growth rates based on Centers for Medicare and Medicaid Services (CMS) data and Kaiser Family Foundation (KFF) data. These new methods also utilize year-to-year growth rates from aggregated IRS 1040 data and ACS 1-year estimates in order to update the Medicaid tallies’ county-level and demographic detail. In this work, we lay out conceptual differences between various Medicaid and Children’s Health Insurance Program (CHIP) data sources, citing key assumptions and filters, and considering criteria for usage in modeling. We propose an approach for combining the lagged Medicaid MSIS data with other more timely data, and we study the differences between the resulting Medicaid predictions and the lagged MSIS Medicaid data. Finally, we compare summary results from modeling SAHIE using the proposed Medicaid data methods relative to using the prior Medicaid data methods.