Regulation of angiogenesis and matrix remodeling by localized, matrix-mediated antisense gene delivery.

Implantation of biomaterials, such as glucose sensors, leads to the formation of a poorly vascularized collagenous capsule that can lead to implant failure. This process, known as the foreign body reaction (FBR), develops in response to almost all biomaterials and consists of overlapping phases similar to those in wound healing. Implantation of porous biomaterials, such as polyvinyl alcohol sponges, also leads to granuloma formation within the interstices of the sponge prior to encapsulation by the FBR. We asked whether delivery of an antisense cDNA for the potent angiogenesis inhibitor thrombospondin (TSP) 2 would enhance blood vessel formation and alter collagen fibrillogenesis in the sponge granuloma and capsule. Collagen solutions were mixed with plasmid to generate gene-activated matrices (GAMs) and applied to biomaterials that were then implanted subcutaneously. Sustained expression of plasmid-encoded proteins was observed at 2 weeks and a month following implantation. In vivo delivery of plasmids, encoding either sense or antisense TSP2 cDNA, altered blood vessel formation and collagen deposition in TSP2-null and wild-type mice, respectively. Untreated implants, implanted next to GAM-treated implants, did not show exogenous gene expression and did not elicit altered responses, suggesting that gene delivery was limited to implant sites. This method of antisense DNA delivery has the potential to improve the performance and life span of implantable delivery devices and biosensors.

Children's health care use in the Healthy Kids Program.

BACKGROUND: In 1990, the Florida Legislature established the Florida Healthy Kids Corporation to implement the concept of school enrollment-based health insurance coverage for children. The county school districts are used as a grouping mechanism to negotiate health insurance policies. The Florida Healthy Kids Corporation negotiates contracts with health maintenance organizations (HMOs) to assume financial risk and to provide health care services at each program site. In 1994, there were five sites with four different participating HMOs. Assessing quality of care is particularly important when contracting with HMOs because of the perception that financial and utilization review arrangements may restrict the enrollees' access to needed health care. One essential component of health care quality is the extent to which health care services are used in a manner consistent with the expected pattern of use for the population of enrolled children. The purpose of this study is to compare children's health care use across five different Florida Healthy Kids Program sites. Specifically, we compare the enrollees' actual health care use across HMO settings and program sites to the expected health care use based on the enrollees' case-mix. METHODS: Each HMO provided child-specific health care use data including Physician's Current Procedural Terminology codes and International Classification of Diseases, 9th Revision codes. We used the Ambulatory Care Groups (ACGs) software to compare the children's actual health care use to the expected health care use at each site adjusted for case-mix. Several steps were then taken to determine if the children were receiving the anticipated number of health care visits based on their diagnoses. First, we divided the average number of encounters at each site by the group average across all of the sites, without adjusting for the case-mix of the enrollees. We then divided the average number of visits at each site by the expected number of visits based on the case-mix adjustment. A value of 1.00 means that the actual use and the expected use are identical. Values below 1 indicate underuse and values over 1 indicate overuse of health care services. Statistical comparisons of the actual versus expected average health care use across the five sites were performed by deriving the appropriate chi2 statistics. RESULTS: A census of all children (N = 14 688) enrolled in the Florida Healthy Kids Program at each of the sites for 6 months or longer were included in the analysis. The average number of health care encounters across all sites for a 12-month time period was 2.98 +/- 4.6 visits. After adjusting for the case-mix of the enrollees in each site using the ACG software, several of the five sites differed from one in a statistically significant way. However, these statistical assessments must be tempered with assessing the practical magnitude of the observed differences. CONCLUSIONS: The number of public and private efforts to insure children who are not eligible for Medicaid and whose parents cannot purchase private insurance has grown dramatically. These programs are vital for ensuring financial access to care for uninsured children. However, it is essential that such programs are not viewed as merely cost containment efforts. Assessing the degree to which children receive the health care services they need across multiple delivery settings is an essential yet challenging component of quality assurance. Generally, our analysis indicates that children in the Florida Healthy Kids Program are receiving the amount of health care expected based on their health care needs; which is one component of a high-quality health care program.

Comparison of two approaches to analyzing correlated binary data in developmental toxicity studies.

Recently developed statistical methodology is presented for analyzing correlated developmental toxicity data where multiple measures are obtained on individual units such as rat pups. Of particular interest is the assessment of the homogeneity of effects from chemical exposure across different outcomes such as malformation types or non-ossification of forepaw digits. Such contrasts are referred to as within-cluster effects, as they represent differences between observations within a cluster (e.g., pup). Between-cluster effects correspond to comparison of observations from different clusters, e.g., pups exposed to varied doses of chemicals or carcinogens. Two classes of statistical models are available for estimation of these distinct types of effects: (1) cluster-specific models in which regression adjustments are made for cluster effects, and hence all parameters are interpreted as within-cluster effects; and (2) population-averaged models in which no explicit adjustments are made for cluster, and thus all parameters are interpreted as group differences without regard to whether they represent between- or within-cluster effects. Many models in the developmental toxicity literature are population-averaged logistic regression models ignoring variation among clusters which may be significant under strong influences of genetic and environmental factors. Under such conditions, confidence interval-based inference for effect homogeneity will depend upon the class of models. These issues are illustrated with data from two developmental toxicity studies: (1) a National Toxicology Program study of the effects of in utero exposure to di(2ethylhexyl)phthalate among three malformation outcomes in mouse pups; and (2) a study conducted by Hartsfield to investigate the effects of anticonvulsant phenytoin on the risk of non-ossification among digits of the left and right forepaws in rat pups.