Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^

Non-staphylococcal infections of cardiac implantable electronic devices

Background. The number of infections of cardiac implantable electronic devices (CIED) continues to escalate out of proportion to the increase rate of device implantation. Staphylococcal organisms account for 70% to 90% of all CIED infections. However, little is known about non-staphylococcal infections, which have been described only in case reports, small case series or combined in larger studies with staphylococcal CIED infections, thereby diluting their individual impact. ^ Methods. A retrospective review of hospital records of patients admitted with a CIED-related infections were identified within four academic hospitals in Houston, Texas between 2002 and 2009. ^ Results. Of the 504 identified patients with CIED-related infection, 80 (16%) had a non-staphylococcal infection and were the focus of this study. Although the demographics and comorbities of subjects were comparable to other reports, our study illustrates many key points: (a) the microbiologic diversity of non-staphylococcal infections was rather extensive, as it included other Gram-positive bacteria like streptococci and enterococci, a variety of Gram-negative bacteria, atypical bacteria including Nocardia and Mycobacteria, and fungi like Candida and Aspergillus; (b) the duration of CIED insertion prior to non-staphylococcal infection was relatively prolong (mean, 109 ± 27 weeks), of these 44% had their device previously manipulated within a mean of 29.5 ± 6 weeks; (c) non-staphylococcal organisms appear to be less virulent, cause prolonged clinical symptoms prior to admission (mean, 48 ± 12.8 days), and are associated with a lower mortality (4%) than staphylococcal organisms; (d) thirteen patients (16%) presented with CIED-related endocarditis; (e) although not described in prior reports, we identified 3 definite and 2 suspected cases of secondary Gram-negative bacteremia seeding of the CIED; and (f) inappropriate antimicrobial coverage was provided in approximately 50% of patients with non-staphylococcal infections for a mean period of 2.1 days. ^ Conclusions. Non-staphylococcal CIED-related infections are prevalent and diverse with a relatively low virulence and mortality rate. Since non-staphylococcal organisms are capable of secondarily seeding the CIED, a high suspicion for CIED-related infection is warranted in patients with bloodstream infection. Additionally, in patients with suspected CIED infection, adequate Gram positive and -negative antibacterial coverage should be administered until microbiologic data become available.^