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Asthma is one of the nation's most common and costly chronic conditions, affecting over 38 million Americans at some time in their lives. Managing asthma requires a long term, multifaceted approach, including patient education, behavior changes, asthma trigger avoidance, pharmacological therapy, and frequent medical follow-up. This study provides asthma data available at the state and local level to direct and evaluate interventions undertaken by asthma control programs located in the state health departments. Improved tracking for asthma is critical for planning and evaluating efforts to reduce the health burden from the disease.

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based system of health surveys that collects information on health risk behaviors, preventive health practices, and health care access primarily related to chronic disease and injury. For many states, the BRFSS is the only available source of timely, accurate data on health-related behaviors. BRFSS was established in 1984 by the Centers for Disease Control and Prevention (CDC); currently data are collected monthly in all 50 states, the District of Columbia, Puerto Rico, the United States Virgin Islands, and Guam. More than 350,000 adults are interviewed each year, making the BRFSS the largest telephone health survey in the world. States use BRFSS data to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs. The BRFSS is a cross-sectional telephone survey conducted by state health departments with technical and methodological assistance provided by CDC. States conduct monthly telephone surveillance using a standardized questionnaire to determine the distribution of risk behaviors and health practices among adults. Responses are forwarded to CDC, where the monthly data are aggregated for each state, returned with standard tabulations, and published at the year's end by each state. The BRFSS questionnaire was developed jointly by CDC's Behavioral Surveillance Branch (BSB) and the states. Data derived from the questionnaire provide health departments, public health officials, and policymakers with necessary behavioral information. When combined with mortality and morbidity statistics, these data enable public health officials to establish policies and priorities and to initiate and assess health promotion strategies. Demographic variables include race, age, sex, education level, marital status, employment status, and income level.

The Behavioral Risk Factor Surveillance System (BRFSS) is a system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year.

Purpose. This was a Data Capacity-Building Project, to build a robust comparative effectiveness research infrastructure, agenda, and collaborative partnerships focused on eliminating health disparities. Specifically, a database was built comprised of all Medicaid enrollees and claims in the states that share in common both adverse minority health outcomes and the historical roots of racial health disparities in the South.

Setting and Participants. A 100 percent sample of four years 2004-2007 of Medicaid Analytic Extract (MAX-file) data (plus Medicare-linked claims for dual-eligibles) from fourteen southern states, representing 3.8 to 5.4 million persons each year (one-third of all United States Medicaid enrollees, nearly half [48 percent] of African American and 21 percent of Latino Medicaid enrollees in the United States) was obtained from the Centers for Medicare and Medicaid Services (CMS). This region is the epicenter of the Black-White health disparities epidemic, and has also experienced a recent and rapid influx of Latino immigrants. This project provided support for personnel and infrastructure needed to efficiently organize and analyze these data to support minority investigators. The HBCU-based team had extensive previous experience training health services researchers (especially minority investigators) to use Medicaid claims data for research.

Specific Aims: Using Medicaid Claims Data

1. To build a Medicaid claims dataset (including socieconomic, contextual, and geospatial analytic variables, NDC cross-walk data and therapeutic class codes, as well as certain Medicare data for dual-eligibles) to support projects focused on the intersection between disparities research and comparative effectiveness research in clinically and socially complex patient populations.

2. To create an efficient process for assisting non-Morehouse investigators to develop research protocols, analysis plans, CMS data re-use requests, and analytic files for collaborative research.

3. To train, develop, cultivate, and support emerging minority investigators (especially at Historically Black colleges and universities (HBCUs) and other minority-serving institutions) as independently-funded health services researchers who are increasingly proficient in multivariate analysis of Medicaid and Medicare claims data.

4. Cultivate comparative effectiveness and disparities research collaborations with Georgia Tech experts in mathematics, complexity science, simulation modeling, and interactive computing.

Relevance. Medicaid patients are characterized by clinical and social complexity -- the very characteristics which often exclude them from clinical trials and yet drive health disparities. This Medicaid-based dataset populates studies that help users understand how local area, provider-level, and patient-level differences in treatment (natural experiments in comparative effectiveness) influence clinical and economic outcomes. Variation implies that disparities are not inevitable. The comparative impact of this natural variation can be measured in meaningful outcomes such as emergency department visits, hospital admissions, inpatient bed-days, deaths, and total Medicaid expenditures, as well as community-level disparity rate-ratios. Medicaid data allow users to follow a complex patient (e.g., comorbid diabetes and schizophrenia or COPD and CHF) from treatment to outcomes through every billable service in the health care system (i.e., from doctor's visit to lab tests to prescriptions to emergency room visits or hospital admissions). Morehouse School of Medicine has a unique ability to develop a new cadre of minority investigators to conduct and interpret the results of health services research with a racially sensitive, culturally competent perspective.

Data Overview. The Centers for Medicare and Medicaid Services produces the MAX-files from Medicaid Statistical Information System (MSIS) data submitted by each state, with some data cleaning and validation by CMS sub-contractors before data are released to researchers.

The MAX-file data from CMS were loaded onto encrypted, secure servers at Morehouse School of Medicine. Research analytic files were created for each sub-project, including sickle cell disease, diabetes and schizophrenia; asthma; dementia; and congestive heart failure. For specific sub-projects, contextual variables from census data or area resource file were linked by county FIPS code.

Data Access. The data cannot be made publicly available. Data are stored on Morehouse School of Medicine encrypted servers, and may be used only for projects covered within the aims of the original research protocol and Centers for Medicare and Medicaid Services (CMS)-approved data use agreement. Data sharing is allowed only for research protocols approved under data re-use requests by the CMS privacy board. The CMS process for data re-use requests is described at the ResDAC Web site.

Due to limitations of research staff within the Morehouse National Center for Primary Care, and limitations of the existing CMS data use agreement, only re-use requests consistent with the original aims of the approved research protocol are considered (temporal and geographic variation in racial-ethnic disparities in quality, access and outcomes for Medicaid enrollees in 14 southern states). Specific aims of the current research protocol define the boundaries of what kind of research questions could be answered or sub-projects developed within the existing research protocol and data use agreement (see above "Specific Aims" section). A worksheet for developing an analysis plan for a specific research question is attached. Parties interested in the data should contact George Rust, MD, MPH ([email protected]).

Six SAS program syntax files used for data analysis, however, are available on the ICPSR site.

Aside from data re-use requests, the Morehouse National Center for Primary Care is open to collaborations which address these research aims and are consistent with their health equity research priorities, in which analyses could be performed by the Morehouse National Center for Primary Care research team and papers authored or co-authored by faculty from other minority-serving institutions or affiliated with the Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN).

The purpose of the National Health Interview Survey (NHIS) is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. Implementation of a redesigned NHIS, consisting of a basic module, a periodic module, and a topical module, began in 1997 (See NATIONAL HEALTH INTERVIEW SURVEY, 1997 [ICPSR 2954]).

The 2001 NHIS contains the Household, Family, Person, Sample Adult, Sample Child, Child Immunization, and Injury and Poison Episode data files from the basic module. Each record in the Household-Level File (Part 1) contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit.

The Family-Level File (Part 2) is made up of reconstructed variables from the person-level data of the basic module and includes information on sex, age, race, marital status, Hispanic origin, education, veteran status, family income, family size, major activities, health status, activity limits, and employment status, along with industry and occupation.

As part of the basic module, the Person-Level File (Part 3) provides information on all family members with respect to health status, limitation of daily activities, cognitive impairment, and health conditions. Also included are data on years at current residence, region variables, height, weight, bed days, doctor visits, hospital stays, and health care access and utilization.

A randomly-selected adult in each family was interviewed for the Sample Adult File (Part 4) regarding respiratory conditions, renal conditions, AIDS, joint symptoms, health status, limitation of daily activities, and behaviors such as smoking, alcohol consumption, and physical activity. Also included in this file are variables pertaining to the Healthy People 2010 Objectives.

The Sample Child File (Part 5) provides information from an adult in the household on medical conditions of one child in the household, such as respiratory problems, seizures, allergies, and use of special equipment such as hearing aids, braces, or wheelchairs. Also included are variables regarding child behavior, the use of mental health services, and Attention Deficit Hyperactivity Disorder (ADHD).

The Child Immunization File (Part 6) presents information from shot records and supplies vaccination status, along with the number and dates of shots, and information about the chicken pox vaccine.

Episode-based information regarding injuries and poisonings are found in the Injury and Poison Episode File (Part 7), which examines the cause and date of injury or poisoning, loss of time from work or school, and whether the episode resulted in hospitalization.

Information in the Injury and Poison Verbatim File (Part 8) is comprised of narrative text describing injuries, including type of injury, how the injury occurred, and the body part injured.