A systematic review of the animal and human literature on the use of vaccines to prevent dental caries

Streptococcus mutans has been identified as the primary etiological agent of human dental caries. Since its identification, there has been research focused on the development of a vaccine to prevent this disease. Preliminary research has been conducted to test both active and passive vaccines for Streptococcus mutans in animals and humans. Although a vaccine for dental caries caused by Streptococcus mutans would most likely be administered to children, no testing of any type of dental caries vaccines has been conducted on children as of yet. The public health imperative for the development of a vaccine is great. Not only will a vaccine reduce the various consequences, but it would also improve quality of life for many individuals. Among the many possible vaccine antigen candidates, researchers have also been focusing on protein antigens, GTFs, and Gbps as possible candidates for a vaccine. There are also many routes of administration under research, with topical, oral, and intranasal showing a lot of promise. This review will provide an overview on the current state of research, present key factors influencing prevalence of caries, and summarize and discuss the results of animal and human studies on caries vaccines against Streptococcus mutans. The progress and obstacles facing the development of a vaccine to fight dental caries will also be discussed. ^

Excess stroke in Mexican Americans compared with non-Hispanic Whites: the Brain Attack Surveillance in Corpus Christi Project.

Mexican Americans are the largest subgroup of Hispanics, the largest minority population in the United States. Stroke is the leading cause of disability and third leading cause of death. The authors compared stroke incidence among Mexican Americans and non-Hispanic Whites in a population-based study. Stroke cases were ascertained in Nueces County, Texas, utilizing concomitant active and passive surveillance. Cases were validated on the basis of source documentation by board-certified neurologists masked to subjects' ethnicity. From January 2000 to December 2002, 2,350 cerebrovascular events occurred. Of the completed strokes, 53% were in Mexican Americans. The crude cumulative incidence was 168/10,000 in Mexican Americans and 136/10,000 in non-Hispanic Whites. Mexican Americans had a higher cumulative incidence for ischemic stroke (ages 45-59 years: risk ratio = 2.04, 95% confidence interval: 1.55, 2.69; ages 60-74 years: risk ratio = 1.58, 95% confidence interval: 1.31, 1.91; ages >or=75 years: risk ratio = 1.12, 95% confidence interval: 0.94, 1.32). Intracerebral hemorrhage was more common in Mexican Americans (age-adjusted risk ratio = 1.63, 95% confidence interval: 1.24, 2.16). The subarachnoid hemorrhage age-adjusted risk ratio was 1.57 (95% confidence interval: 0.86, 2.89). Mexican Americans experience a substantially greater ischemic stroke and intracerebral hemorrhage incidence compared with non-Hispanic Whites. As the Mexican-American population grows and ages, measures to target this population for stroke prevention are critical.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^