Online Program

Joint Modeling of Spatial-Longitudinal Binary and Count Data Using Multilevel and Multiresolution Random Effects for Large Data Sets

Tuesday, November 3, 2015 : 9:30 a.m. - 9:50 a.m.

Rajib Paul, PhD, Department of Statistics/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Amy B. Curtis, PhD, MPH, Interdisciplinary Health Sciences PhD Program/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Kathleen Baker, PhD, Department of Geography/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Monica Kwasnik, MA, Michigan Department of Community Health and Western Michigan University, Kalamazoo, MI
Background: Medicaid adults have a high prevalence of diabetes and those with diabetes are recommended to receive low-density lipoprotein cholesterol (LDL-C) and hemoglobin A1c testing to reduce related short-term hospitalizations. It is important to identify those with low use of these preventive services as well as experiencing frequent hospitalizations for use in targeting future interventions. We developed a class of spatial-longitudinal generalized linear models with multilevel fixed and random effects for large data sets to examine individual and county-level predictors.

Methods: Michigan Medicaid (2011-13) data were analyzed to identify individual (race, gender, insurance plans) and county level factors (obesity rates, high school graduate percentage) associated with whether a person received recommended 1) LDC-C or 2) A1c testing as well as 3) count data on diabetes short-term complication-related admissions. Binary response variables were modeled using binomial distributions and zero-inflated negative binomial distribution was used for count data due to excess zeros and overdispersion. A multiresolution spatial covariance was developed to capture long range and small range spatial dependences. Temporal dependencies were imposed through first order autoregressive processes and within-subject dependencies were also incorporated. For model fitting and inferences, a data-augmentation Markov Chain Monte Carlo (MCMC) algorithm was developed.

Results: Females with diabetes were more likely to receive the recommended LDL-C (Bayesian p-value 0.0005) and those who get their LDL-C screening regularly were less likely to be hospitalized (Bayesian p-value 0.0121).

Conclusion: Some Medicaid plans had higher LDL-C screening rates. LDL-C was associated with decreased number of short-term hospitalizations.

Learning Areas:

Biostatistics, economics
Chronic disease management and prevention
Public health administration or related administration
Public health or related education

Learning Objectives:
Formulate spatial-longitudinal generalized linear models for multivariate response variables with varying supports. Describe multilevel Bayesian statistical model for assessing associations between health service utilization and individual and county level demographic factors.

Keyword(s): Diabetes, Medicaid

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have a PhD in statistics and have expertise in bayesian and spatial statistics, with applications to health and climate. I also serve as the statistical lead on the research included in this abstract.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.