U.S. flag

An official website of the United States government

Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF)

The Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Research Identifiable Files (RIF) are a research-optimized version of T-MSIS data and serve as a data source tailored to meet the broad research needs of the Medicaid and CHIP data user community. These files include data on Medicaid and Children’s Health Insurance Program (CHIP) enrollment, demographics, service utilization and payments.

TAF RIF include annual files that contain demographic and eligibility information for all Medicaid and CHIP beneficiaries, claims files that contain service use and payment records, and annual files containing information on Medicaid and CHIP managed care plans and providers.

The Centers for Medicare & Medicaid Services (CMS) created several resources to support researchers in their use of the TAF RIF, including TAF technical guidance and data quality products.

To obtain access to the files, please see the Research Data Assistance Center (ResDAC) website for information on completing a data use agreement and requesting TAF RIF data. The ResDAC website provides a centralized source of information on CMS datasets.

TAF User Support & Data Quality (DQ) Materials

TAF Technical Guidance

CMS drafted several technical guidance documents to support TAF users, including overviews of the Annual Demographic and Eligibility (DE) File and all four claim types.

TAF DQ Resources Available on DQ Atlas

DQ Atlas is an interactive, web-based tool that helps policymakers, analysts, researchers, and other stakeholders explore the quality and usability of the TAF. The charts, maps, and tables in DQ Atlas show state-level DQ assessments and associated measure values for topics that are pertinent to Medicaid and CHIP.

Additional User Support Materials

Additional user support materials include an Introduction to TAF presentation, a quick guide mapping TAF user support materials to materials that were available for TAF’s predecessor, the Medicaid Analytic eXtract (MAX), and a TAF RIF availability chart. All states were required to begin submitting T-MSIS data by October 2015, but cutover dates vary by state, and the TAF RIF availability chart outlines when each state transitioned from MAX to TAF.

TAF Research Products

To support policy making and program monitoring, CMS is developing research products using the TAF data in several key areas. Click on the topic below for additional products and resources.

Per Capita Expenditures
Substance Use Disorder
Maternal and Infant Health
Physical and Behavioral Health Integration
Enrollment and Service Use Tables

Substance Use Disorder (SUD)

T-MSIS SUD Data Book

The T-MSIS SUD Data Book is congressionally-mandated through the Substance Use–Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities Act (P.L. 115-271) (SUPPORT Act). The SUPPORT Act seeks to address the pressing need for substance use disorder treatment and prevention services, with a focus on opioid use. The SUPPORT Act directs the U.S. Department of Health and Human Services to publish the SUD Data Book no later than October 24, 2019 and to issue an updated version of the SUD Data Book no later than January 1 of each calendar year through 2024.

CMS released the inaugural T-MSIS Substance Use Disorder (SUD) Data Book using preliminary 2017 TAF data on October 24, 2019, the 2018 SUD Data Book on January 19, 2021, the 2019 SUD Data Book on January 21, 2022, and the 2020 SUD Data Book on November 28, 2022. Each SUD Data Book reports the number of Medicaid beneficiaries treated for a SUD and the services they received during the calendar year.

An interactive T-MSIS SUD Data Book data analytics tool for 2017, 2018, 2019 and 2020 is also available. The data and tables in this tool mirror those that have been produced in the SUD Data Book and can be filtered, sorted, and downloaded in multiple formats using a graphical interface. The tool offers enhanced flexibility for users over the static display of information in the SUD Data Book report.

The SUPPORT Act also requires a description of the quality and completeness of the data used in the SUD Data Book. To provide this additional context, CMS produced several stand-alone DQ Briefs for the version of the 2017 data used to create the 2017 SUD Data Book. Information on the quality and completeness of the TAF data used for the 2018, 2019 and 2020 Data Books is available on the DQ Atlas.

For additional information about the quality of the 2017 data used to create the 2017 SUD Data Book, please select the links below:

Identifying Beneficiaries with a SUD

This claims-based algorithm can be used to identify Medicaid and CHIP beneficiaries treated for any of the following 10 SUDs: alcohol; tobacco; opioids; cannabis; hallucinogens; stimulants; inhalants; Sedatives, Hypnotics, Anxiolytics (SHAs); caffeine; and a broad “other or unknown” category. The algorithm also categorizes beneficiaries treated for SUDs in more than one class as having a “polysubstance” diagnosis, which identifies comorbid SUDs.

Per Capita Expenditures

State-level Medicaid per capita expenditures were calculated for inclusion in the Medicaid and Children’s Health Insurance Program Scorecard. This analysis draws on the CMS Office of the Actuary (OACT) methodology for estimating national-level Medicaid per capita spending. The data sources for the analysis are the 2018 and 2019 TAF data and CMS-64 expenditures reported in the Medicaid Budget and Expenditures System (MBES).

Download the methodology document:

This version of the per capita expenditures is a part of the Medicaid and CHIP Scorecard that was released in December 2021. To view the version of the Medicaid and CHIP Scorecard that was published in November 2020, please visit the archived Scorecard page.

Identifying Newborn Beneficiaries with Neonatal Abstinence Syndrome (NAS)

Neonatal abstinence syndrome (NAS), a drug withdrawal syndrome in newborns, can occur following a woman’s use of certain drugs during pregnancy. Although NAS can result from exposure to many different drugs, it refers most commonly to withdrawal symptoms in opioid-exposed newborns. Newborns with NAS develop symptoms, such as tremors, irritability, fast breathing, and difficulty with feeding, within the first few hours or days of life. This claims-based algorithm can be used to identify Medicaid and CHIP infants with NAS.

CMS applied this algorithm to the TAF and developed data tables for calendar years 2017, 2018, and 2019. The table present the rates of NAS per 1,000 newborns whose delivery was covered by Medicaid or the Children’s Health Insurance Program (CHIP) in the year.

Prescription Drug Monitoring Program

The Substance Use-Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities (SUPPORT) Act was signed into law (Pub. L. No. 115-271) on October 24, 2018, as a bipartisan effort to address the nation’s opioid epidemic.

Section 5042(a) of the SUPPORT Act requires all States to establish a qualified prescription drug monitoring program (PDMP). A PDMP ensures that providers have access to information about current and previous opioid prescriptions and other controlled substances at the time of an encounter. However, PDMP access is only the first step in addressing the opioid overdose epidemic. PDMP use must be part of a comprehensive approach that considers potential, unintended consequences.

Beginning in October 2021, all State PDMPs must meet the requirements outlined in the legislation. The SUPPORT Act also authorizes the Centers for Medicare and & Medicaid Services (CMS) to match State investments in their PDMP at 100 percent for approved design, development, and implementation activities, for quarters during fiscal years 2019 and 2020.

CMS has released a report to Congress that discusses state challenges and best practices implementing PDMP requirements under section 5042 of the SUPPORT Act.

Maternal and Infant Health

Identifying Pregnant and Postpartum Medicaid and CHIP Beneficiaries

Medicaid pays for nearly half of all births in the United States. Because pregnancy is a main pathway to Medicaid eligibility, and because income requirements for pregnant women are less strict than income requirements for adults who are not pregnant, pregnancy rates among Medicaid beneficiaries are generally higher than population-level pregnancy rates. Because pregnant woman are a critical subgroup of Medicaid beneficiaries and their identification in many administrative data files, such as the T-MSIS Analytic Files (TAF), is not straightforward, CMS developed a set of specifications and programming code, to help researchers who wish to use administrative data to analyze this population.

Using diagnosis, procedure, or revenue codes related to pregnancy and the postpartum period, this tool and code set can be used to categorize women into four categories:
  1. Ever pregnant refers to female beneficiaries who were pregnant at any time during the analysis period.
  2. Live birth refers to female beneficiaries with a claim for a live birth during the analysis period.
  3. Miscarriage, stillbirth, or termination refers to female beneficiaries with a claim indicating a miscarriage, stillbirth, or termination during the analysis period.
  4. Delivery outcome unknown refers to female beneficiaries with claims indicating that a delivery occurred, but the information in the data was not sufficient to classify the delivery as either a live birth or a miscarriage, stillbirth, or termination.
CMS applied this algorithm to the TAF and developed data tables for calendar years 2017, 2018, and 2019. The tables present the number of Medicaid and CHIP beneficiaries that were pregnant or had a delivery in the analysis period, and pregnancy rates by age group.

Identifying Newborn Beneficiaries with Neonatal Abstinence Syndrome (NAS)

Neonatal abstinence syndrome (NAS), a drug withdrawal syndrome in newborns, can occur following a woman’s use of certain drugs during pregnancy. Although NAS can result from exposure to many different drugs, it refers most commonly to withdrawal symptoms in opioid-exposed newborns. Newborns with NAS develop symptoms, such as tremors, irritability, fast breathing, and difficulty with feeding, within the first few hours or days of life. This claims-based algorithm can be used to identify Medicaid and CHIP infants with NAS.

CMS applied this algorithm to the TAF and developed data tables for calendar years 2017, 2018, and 2019. The table present the rates of NAS per 1,000 newborns whose delivery was covered by Medicaid or the Children’s Health Insurance Program (CHIP) in the year.

Identifying Beneficiaries with Severe Maternal Morbidity (SMM)

Severe maternal morbidity (SMM) is an umbrella term for a number of unexpected labor and delivery outcomes that have significant maternal health consequences. Although there is no standard list of conditions defining SMM that is accepted by the medical and research communities, the Alliance for Innovation on Maternal Health defines SMM on the basis of 21 conditions (such as acute renal failure and shock) and procedures (such as blood transfusion or hysterectomy). This claims-based algorithm can be used to identify Medicaid and CHIP beneficiaries with SMM.

CMS applied this algorithm to the TAF and developed data tables for calendar years 2017, 2018, and 2019 that present: (1) the overall and state-specific incidence rates of SMM in deliveries paid for by Medicaid and CHIP and (2) the overall incidence rate of each of the 21 conditions and procedures that the Alliance for Innovation on Maternal Health includes in its definition of SMM.

Physical and Behavioral Health Integration

Identifying Beneficiaries who would Benefit from Integrated Physical and Behavioral Health Care

Integrating primary care and behavioral health services for beneficiaries with mental health (MH) conditions or substance use disorders (SUDs) is a high priority for Medicaid, which is the largest payer of behavioral health services in the United States. It is common for beneficiaries with a MH condition or a SUD to have at least one co-occurring physical health (PH) condition. As a result, these beneficiaries typically need physical health services in addition to services that address their behavioral health (BH) conditions. However, beneficiaries suffering from BH conditions face many challenges in accessing physical health services because of the stigma associated with mental illness, barriers to navigating appropriate care, a lack of understanding of their conditions, and poor coordination between services. This claims-based algorithm can be used to identify three distinct populations of beneficiaries would benefit from physical and behavioral health care: (1) beneficiaries who received services for any BH condition, (2) beneficiaries who received BH services and services for one of the select co-occurring PH conditions (a subset of Population 1), and (3) beneficiaries prescribed medications for a SUD who did not have a medical claim for a SUD (subset of Population 1).

Enrollment and service use data tables

CMS released two sets of data tables containing enrollment and service use information for Medicaid and Children’s Health Insurance Program (CHIP) beneficiaries. These files are based on analyses that use TAF data. The enrollment data tables include data from calendar years 2016 through 2020. The service use data tables include data from calendar years 2018 through 2020. Both files include information for the 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. These data tables are designed to be used by states or other researchers interested in better understanding patterns of Medicaid and CHIP enrollment and service use. 

Enrollment data tables

Service use data tables