The effects of population density on the spread of disease

The objective of this study is to identify the relationship between population density and the initial stages of the spread of disease in a local population. This study proposes to concentrate on the question of how population density affects the distribution of the susceptible individuals in a local population and thus affects the spread of the disease, measles. Population density is measured by the average of the number of contacts with susceptible individuals by each individual in the population during a fixed-length time period. The term “contact with susceptible individuals” means sufficient contact between two people for the disease to pass from an infectious person to a susceptible person. The fixed-length time period is taken to be the average length of time an infected person is infectious without symptoms of the disease. For this study of measles, the time period will be seven days. ^ While much attention has been given to modeling the entire epidemic process of measles, attempts have not been made to study the characteristics of contact rates required to initiate an epidemic. This study explores the relationship between population density, given a specific herd immunity rate in the population, and initial rate of the spread of the disease by considering the underlying distribution of contacts with susceptibles by the individuals in the population. ^ This study does not seek to model an entire measles epidemic, but to model the above stated relationship for the local population within which the first infective person is introduced. This study describes the mathematical relationship between population density parameters and contact distribution parameters. ^ The results are displayed in graphs that show the effects of different population densities on the spread of disease. The results support the idea that the number of new infectives is strongly related to the distribution of susceptible contacts. The results also show large differences in the epidemic measures between populations with densities equal to four versus three. ^

The local safety net, health insurance, preventable hospitalizations and costs: Harris County (Houston), Texas

The aim of this study was to examine the association between determinants of access to healthcare and preventable hospitalizations, based on Davidson et al.'s framework for evaluating the effects of individual and community determinants on access to healthcare. The study population consisted of the low income, non-elderly, hospitalized adults residing in Harris County, Texas in 2004. The objectives of this study were to examine the proportion of the variance in preventable hospitalizations at the ZIP-code level, to analyze the association between the proximity to the nearest safety net clinic and preventable hospitalizations, to examine how the safety net capacity relates to preventable hospitalizations, to compare the relative strength of the associations of health insurance and the proximity to the nearest safety net clinic with preventable hospitalizations, and to estimate and compare the costs of preventable hospitalizations in Harris County with the average cost in the literature. The data were collected from Texas Health Care Information Collection (2004), Census 2000, and Project Safety Net (2004). A total of 61,841 eligible individuals were included in the final data analysis. A random-intercept multi-level model was constructed with two different levels of data: the individual level and the ZIP-code level. The results of this study suggest that ZIP-code characteristics explain about two percent of the variance in preventable hospitalizations and safety net capacity was marginally significantly associated with preventable hospitalizations (p= 0.062). Proximity to the nearest safety net clinic was not related to preventable hospitalizations; however, health insurance was significantly associated with a decreased risk of preventable hospitalization. The average direct cost was $6,466 per preventable hospitalization, which is significantly different from reports in the literature. ^

Smoke free restaurant ordinance, compliance with the regulation to reduce exposure to environmental tobacco smoke while dining at local restaurants in San Antonio, Texas

This study is a secondary analysis of a survey developed by Dr. Jimmy Perkins and administered by San Antonio/Bexar County Metropolitan Health District. The survey was developed subsequent to the implementation of the city smoking ordinance effective January 1, 2004. The survey had a multi-purpose plan to establish the number of restaurants having smoke free status prior to and following the ordinance, determine compliance as it relates to a necessary smoking section and proper signage, and expose the rationale for restaurants to become smoke free. The data resulting from the survey was presented to the San Antonio/Bexar County Metropolitan Health District. The summary presented the types of establishments surveyed, smoking status of the establishment, reasons for the establishment becoming smoke free, compliance with smoking sections, compliance with signage requirements, awareness of ordinance, and chain status of the establishment. ^ The results of this study display the relationships among the variables previously mentioned. The following relationships have been examined and the outcomes have determined whether each is significant. After careful analysis, knowledge translates into compliance with signage regulations, which then translate into ordinance compliance. Size does matter as it relates to an establishment's number of employees and seating capacity. The smaller the establishment the more likely the establishment is to have become smoke free before the ordinance went into effect. Restaurants, rather than fast food establishments most commonly cited their reason for becoming smoke free was to comply with the ordinance and only ten percent of restaurants gave policy as the main reason for becoming smoke free. ^ This study is important for public health because the negative health effects of environmental tobacco smoke (ETS) are still an overwhelming problem in the United States (3). ETS is a Known Human Group A Carcinogen (5). The Environmental Protection Agency (EPA) has estimated that around 3,000 non-smoking Americans die every year from lung cancer caused by ETS (6). This information illustrates the importance of providing smoke free establishments, especially to non-smoking patrons. ^

The effects of DNA cytosine methylation on H -ras promoter structure and function

The effect of DNA cytosine methylation on H-ras promoter activity was assessed using a transient expression system employing the plasmid H-rasCAT (NaeI H-ras promoter linked to the chloramphenicol acetyltransferase (CAT) gene). This 551 bp promoter is 80% GC rich, enriched with 168 CpG dinucleotides, and contains six functional GC box elements which represent major DNA methylation target sites. Prokaryotic methyltransferases HhaI (CGm$\sp5$CG) and HpaII (Cm$\sp5$CGG) alone or in combination with a human placental methyltransferase (HP MTase) were used to introduce methyl groups at different CpG sites within the promoter. To test for functional promoter activity, the methylated plasmids were introduced into CV-1 cells and CAT activity assessed 48 h post-transfection. Methylation at specific HhaI and HpaII sites reduced CAT expression by 70%, whereas more extensive methylation at generalized CpG sites with HP MTase inactivated the promoter $>$95%. The inhibition of H-ras promoter activity was not attributable to methylation-induced differences in DNA uptake or stability in the cell, topological form of the plasmid, or methylation effects in nonpromoter regions. We also observed that DNA cytosine methylation of a 360 bp promoter fragment by HP MTase induced a local change in DNA conformation. Using three independent methodologies (nitrocellulose filter binding assays, gel mobility shifts, and Southwestern blots), we determined that this change in promoter conformation affected the interaction of nuclear proteins with cis-regulatory sequences residing in the promoter region. The results provide evidence to suggest that DNA methylation may regulate gene expression by inducing changes in local promoter conformation which in turn alters the interactions between DNA and protein factors required for transcription. The results provide supportive evidence for the hypothesis of Cedar and Riggs, who postulated that DNA methylation may regulate gene expression by altering the binding affinities of proteins for DNA. ^