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 RELATIONSHIP BETWEEN OBESITY AND PSYCHOLOGICAL GENERAL WELL-BEING IN UNITED STATES WOMEN

While clinical studies have shown a negative relationship between obesity and mental health in women, population studies have not shown a consistent association. However, many of these studies can be criticized regarding fatness level criteria, lack of control variables, and validity of the psychological variables.^ The purpose of this research was to elucidate the relationship between fatness level and mental health in United States women using data from the First National Health and Nutrition Examination Survey (NHANES I), which was conducted on a national probability sample from 1971 to 1974. Mental health was measured by the General Well-Being Schedule (GWB), and fatness level was determined by the sum of the triceps and subscapular skinfolds. Women were categorized as lean (15th percentile or less), normal (16th to 84th percentiles), or obese (85th percentile or greater).^ A conceptual framework was developed which identified the variables of age, race, marital status, socioeconomic status (education), employment status, number of births, physical health, weight history, and perception of body image as important to the fatness level-GWB relationship. Multiple regression analyses were performed separately for whites and blacks with GWB as the response variable, and fatness level, age, education, employment status, number of births, marital status, and health perception as predictor variables. In addition, 2- and 3-way interaction terms for leanness, obesity and age were included as predictor variables. Variables related to weight history and perception of body image were not collected in NHANES I, and thus were not included in this study.^ The results indicated that obesity was a statistically significant predictor of lower GWB in white women even when the other predictor variables were controlled. The full regression model identified the young, more educated, obese female as a subgroup with lower GWB, especially in blacks. These findings were not consistent with the previous non-clinical studies which found that obesity was associated with better mental health. The social stigma of being obese and the preoccupation of women with being lean may have contributed to the lower GWB in these women. ^

GENERAL WELL-BEING AND USE OF SOCIAL SUPPORT

Research interest on well-being and social support has focused largely on social factors as related to attaining and maintaining well-being, self-perceptions of well-being and to a lesser extent the relationship of current level of self-perceived well-being to use of formal or informal sources of social support. This study analyzed responses to the General Well-Being Schedule of 6,913 subjects (25-74 years) interviewed during the National Health and Nutrition Examination Survey (1971-1975). The purpose of this analysis was to relate the level of GWBS scores to the use of social support, both informal (family and friends) and formal (community professionals).^ Study questions addressed were whether well-being level was related to selection of a specific social support resource and/or rate of use of resources and whether gender differences were apparent in level of well-being and social support use. Because age, sex, race, socioeconomic status (income and education) and marital status may confound the relation between level of GWB and type of social support chosen, the association between these variables with GWB and use of social support were considered. For analysis, test scores were grouped into four categories and for detailed analysis, two categories: low (0-70) and high (71-110). Cross tabulations and percentages were computed and the chi-square test of significance was used.^ Although 16 to 25 percent of the sample population reported low well-being, less than 10 percent used formal resources to discuss emotional, mental or behavior problems. Medical resources, mostly physicians, were the most used formal social supports. Informal social support was important for all well-being levels where 65-77% of each category reported using this resource.^ While well-being level does not appear to serve as a screener/selector of type of formal social support used, it is related to rates of use. Females reported slightly lower well-being than males, and except in the lowest well-being group, had higher rates of social support use. Findings support the conclusion that perceived well-being is related to use of social support such that the lower the well-being, the greater tendency to use formal and/or informal social support. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

An analysis of the RightCare policy at Ben Taub General Hospital, Houston, Texas

Introduction. This study is a two-part evaluation of the RightCare policy, a policy implemented to reduce crowding at the Emergency Center (EC) at Ben Taub General Hospital in Houston, Harris County, Texas. This research includes an evaluation of the policy's impact on specific hospital measures, along with a description of the policy's demise from the point of view of hospital staff. Objective. The purpose of this study is two-fold: (1) To determine whether RightCare policy affected the level of crowding in the Emergency Center and (2) to identify the conditions that may have led to the policy's demise. Methods. For the policy impact portion of this research, hospital measures were collected from existing databases. Analysis included a pre-post comparative design in which the 12 months preceding the policy's implementation were compared with the 12 months following the policy's implementation. For the policy perception portion, employees were surveyed using an on-line questionnaire. Results. The results of the study are mixed. Some measures improved, including time spent on ambulance diversion and the proportion of those who left without being seen, while others did not, such as return visits and total length of stay. Employees generally supported the policy, but expressed concerns over insufficient training and funding. Conclusion. The RightCare policy was a good initial attempt to improve crowded conditions in the EC. The study showed that a clearer policy design, improved training, adequate staffing levels, and better communication would improve operational outcomes in the future.^

The effect of chronic arthritis and hypertension on general well-being

The purpose of the investigation is to elucidate the effect of chronic symptomatic and asymptomatic disease, i.e. arthritis and hypertension, on general well-being, and to quantify the contribution that the duration of the disorder makes to this effect. The data is drawn from the National Health and Nutrition Examination Survey (NHANES I). The results show that arthritics do not have a significant effect on GWB scores while hypertension has a significantly negative impact on GWB. The most striking difference is seen between those without knowledge of hypertension and those recently diagnosed. This difference is attributed to the labeling effect on changes in perceived wellness. ^