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. ^

Geographic variation in the utilization and survival associated with oxaliplatin chemotherapy in patients with stage III colon cancer

Background: Overall objectives of this dissertation are to examine the geographic variation and socio-demographic disparities (by age, race and gender) in the utilization and survival of newly FDA-approved chemotherapy agents (Oxaliplatin-containing regimens) as well as to determine the cost-effectiveness of Oxaliplatin in a large nationwide and population-based cohort of Medicare patients with resected stage-III colon cancer. Methods: A retrospective cohort of 7,654 Medicare patients was identified from the Surveillance, Epidemiology and End Results – Medicare linked database. Multiple logistic regression was performed to examine the relationship between receipt of Oxaliplatin-containing chemotherapy and geographic regions while adjusting for other patient characteristics. Cox proportional hazard model was used to estimate the effect of Oxaliplatin-containing chemotherapy on the survival variation across regions using 2004-2005 data. Propensity score adjustments were also made to control for potential bias related to non-random allocation of the treatment group. We used Kaplan-Meier sample average estimator to calculate the cost of disease after cancer-specific surgery to death, loss-to follow-up or censorship. Results: Only 51% of the stage-III patients received adjuvant chemotherapy within three to six months of colon-cancer specific surgery. Patients in the rural regions were approximately 30% less likely to receive Oxaliplatin chemotherapy than those residing in a big metro region (OR=0.69, p=0.033). The hazard ratio for patients residing in metro region was comparable to those residing in big metro region (HR: 1.05, 95% CI: 0.49-2.28). Patients who received Oxalipaltin chemotherapy were 33% less likely to die than those received 5-FU only chemotherapy (adjusted HR=0.67, 95% CI: 0.41-1.11). KMSA-adjusted mean payments were almost 2.5 times higher in the Oxaliplatin-containing group compared to 5-FU only group ($45,378 versus $17,856). When compared to no chemotherapy group, ICER of 5-FU based regimen was $12,767 per LYG, and ICER of Oxaliplatin-chemotherapy was $60,863 per LYG. Oxaliplatin was found economically dominated by 5-FU only chemotherapy in this study population. Conclusion: Chemotherapy use varies across geographic regions. We also observed considerable survival differences across geographic regions; the difference remained even after adjusting for socio-demographic characteristics. The cost-effectiveness of Oxaliplatin in Medicare patients may be over-estimated in the clinical trials. Our study found 5-FU only chemotherapy cost-effective in adjuvant settings in patients with stage-III colon cancer.^