Specialized managed care for children with cystic fibrosis: Resource use patterns and rate design for a high cost Medicaid population

Objective. This research study had two goals: (1) to describe resource consumption patterns for Medi-Cal children with cystic fibrosis, and (2) to explore the feasibility from a rate design perspective of developing specialized managed care plans for such a special needs population.^ Background. Children with special health care needs (CSHN) comprise about 2% of the California Medicaid pediatric population. CSHN have rare but serious health problems, such as cystic fibrosis. Medicaid programs, including Medi-Cal, are enrolling more and more beneficiaries in managed care to control costs. CSHN, however, do not fit the wellness model underlying most managed care plans. Child health advocates believe that both efficiency and quality will suffer if CSHN are removed from regionalized special care centers and scattered among general purpose plans. They believe that CSHN should be "carved out" from enrollment in general plans. One alternative is the Specialized Managed Care Plan, tailored for CSHN.^ Methods. The study population consisted of children under age 21 with CF who were eligible for Medi-Cal and California Children's Services program (CCS) during 1991. Health Care Financing Administration (HCFA) Medicaid Tape-to-Tape data were analyzed as part of a California Children's Hospital Association (CCHA) project.^ Results. Mean Medi-Cal expenditures per month enrolled were $2,302 for 457 CF children, compared to about \$1,270 for all 47,000 CCS special needs children and roughly $60 for almost 2.6 million ``regular needs'' children. For CF children, inpatient care (80\%) and outpatient drugs (9\%) were the major cost drivers, with {\it all\/} outpatient visits comprising only 2\% of expenditures. About one-third of CF children were eligible due to AFDC (Aid to Families with Dependent Children). Age group explained about 17\% of all expenditure variation. Regression analysis was used to select the best capitation rate structure (rate cells by age and eligibility group). Sensitivity analysis estimated moderate financial risk for a statewide plan (360 enrollees), but severe risk for single county implementation due to small numbers of children.^ Conclusions. Study results support the carve out of CSHN due to unique expenditure patterns. The Specialized Managed Care Plan concept appears feasible from a rate design perspective given sufficient enrollees. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.