Michael T. Abrams
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Michael T. Abrams has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
Carol S. Myers
Mr. Abrams and Dr. Boddie-Willis are affiliated with The Hilltop Institute, University of Maryland, Baltimore County (UMBC), 1000 Hilltop Circle, Sondheim Bldg., Third Floor, Baltimore, MD 21250 ( ). Dr. Myers is with the National Institute on Drug Abuse, Baltimore. Ms. Feldman, Dr. McMahon, and Dr. Kelly are with the Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore. Dr. Park is with the Department of Math and Statistics, UMBC
1 shared publicationCynthia Boddie-Willis
Mr. Abrams and Dr. Boddie-Willis are affiliated with The Hilltop Institute, University of Maryland, Baltimore County (UMBC), 1000 Hilltop Circle, Sondheim Bldg., Third Floor, Baltimore, MD 21250 ( ). Dr. Myers is with the National Institute on Drug Abuse, Baltimore. Ms. Feldman, Dr. McMahon, and Dr. Kelly are with the Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore. Dr. Park is with the Department of Math and Statistics, UMBC
1 shared publication- David Salkever
School of Public Policy and Maryland Institute for Policy Analysis and Research, University of Maryland - Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
1 shared publication Deanna L. Kelly
Mr. Abrams and Dr. Boddie-Willis are affiliated with The Hilltop Institute, University of Maryland, Baltimore County (UMBC), 1000 Hilltop Circle, Sondheim Bldg., Third Floor, Baltimore, MD 21250 ( ). Dr. Myers is with the National Institute on Drug Abuse, Baltimore. Ms. Feldman, Dr. McMahon, and Dr. Kelly are with the Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore. Dr. Park is with the Department of Math and Statistics, UMBC
1 shared publicationDonald Steinwachs
Johns Hopkins Bloomberg School of Public Health
1 shared publication- Elizabeth A. Stuart
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD 21205
1 shared publication
Latest Publications
- Open Access
- 1 December 2013
- Published by Ubiquity Press, Ltd. in eGEMs (Generating Evidence & Methods to improve patient outcomes)
- Vol. 1 (3)
Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions
Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not, in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data, and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program among individuals with serious mental illness.
- 1 December 2013
- Published by Ubiquity Press, Ltd. in eGEMs (Generating Evidence & Methods to improve patient outcomes)
- Vol. 1 (3)
Open AccessEstimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions
Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not, in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data, and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program among individuals with serious mental illness.
- 1 August 2012
- Published by American Psychiatric Association Publishing in Psychiatric Services
- Vol. 63 (8) , 815-822
Cervical Cancer Screening and Acute Care Visits Among Medicaid Enrollees With Mental and Substance Use Disorders
Objective:This study compared rates of cervical cancer screening and acute care (primary or gynecological) visits among women with and without a diagnosis of psychosis, substance use disorder, bipolar disorder or mania, or depression. Methods:Using data about women (N=105,681) enrolled in Maryland's Medicaid program in fiscal year 2005, the authors constructed logistic models with cancer screening and acute care visits as dependent variables and serious mental illness flags as independent variables. Covariates were age, race, geography, Medicaid eligibility category, and sexually transmitted diseases. The logistic model of cervical cancer screening outcomes was repeated with acute care visits as a covariate. Results:Women with psychosis (N=4,747), bipolar disorder or mania (N=3,319), or depression (N=5,014) were significantly (p<.05) more likely than women in a control group without such disorders (N=85,375) to receive cancer screening (adjusted odds ratio (AOR) range=1.46–1.78) and to have associated acu...
- 1 August 2012
- Published by American Psychiatric Association Publishing in Psychiatric Services
- Vol. 63 (8) , 815-822
Cervical Cancer Screening and Acute Care Visits Among Medicaid Enrollees With Mental and Substance Use Disorders
Objective:This study compared rates of cervical cancer screening and acute care (primary or gynecological) visits among women with and without a diagnosis of psychosis, substance use disorder, bipolar disorder or mania, or depression. Methods:Using data about women (N=105,681) enrolled in Maryland's Medicaid program in fiscal year 2005, the authors constructed logistic models with cancer screening and acute care visits as dependent variables and serious mental illness flags as independent variables. Covariates were age, race, geography, Medicaid eligibility category, and sexually transmitted diseases. The logistic model of cervical cancer screening outcomes was repeated with acute care visits as a covariate. Results:Women with psychosis (N=4,747), bipolar disorder or mania (N=3,319), or depression (N=5,014) were significantly (p<.05) more likely than women in a control group without such disorders (N=85,375) to receive cancer screening (adjusted odds ratio (AOR) range=1.46–1.78) and to have associated acu...