Background and Purpose: Colorectal cancer (CRC) is the third most common cancer, the second leading cause of cancer death, with lower survival rates at later stages. Adherence to CRC screening can prevent the development of cancerous polyps and reduce incidence. Area-level characteristics, such as access to healthcare and deprivation, can create barriers to timely screening, increasing the risk of developing CRC. The degree to which area-level characteristics versus individual-level characteristics are responsible for CRC outcomes, including incidence and stage at diagnosis, are not well-understood. Specifically, deficits in the use of spatial statistical techniques has led to a lack of clarity in the current literature. This study aimed to overcome these deficiencies by identifying and utilizing the optimal measurement for area-level access to healthcare and deprivation, employing robust spatiotemporal and multilevel analytic methods to assess their effects on CRC incidence and late-stage diagnosis in Pennsylvania (PA) at the block group-level from 2008 to 2017. The results of this research will more accurately map areas of high predicted CRC relative risk for targeted public health interventions to reduce the burden of CRC over time. The following three study aims were used to address the research problem: Aim 1: Identify the best predictive measure of access to healthcare for estimating CRC incidence risk at the block group-level in PA from 2008 to 2017. Q1: What is the best measure of access to care for estimating risk of CRC incidence? H1.1: The most comprehensive measurement, Multi-Modal 2SFCA, is optimal for predicting CRC incidence compared to unidimensional distance, availability, and other 2SFCA measures. H1.2: Weighting access to healthcare measures for individual insurance coverage improves predictive performance of CRC incidence. Aim 2: Ascertain the relative risk from area-level deprivation on CRC incidence at the block group-level in PA from 2008 to 2017.Q2: How does area-level deprivation affect CRC incidence? H2.1: WQS will demonstrate the relative importance of an extensive array of SES variables for CRC incidence. H2.2: Higher deprivation will be positively associated with risk of CRC incidence.
Aim 3: Determine the individual-level likelihood of being diagnosed with late-stage CRC based on place of residence across PA from 2008 to 2017.Q3: How does place of residence affect the likelihood of developing late-stage CRC incidence after adjusting for individual-level characteristics and covariates? H3.1: PA residents living in areas of worse deprivation and low access to care have a higher likelihood of being diagnosed with late-stage CRC.
H3.2: The likelihood of late-stage CRC varies significantly by individual characteristics.
Methods: This research used ecologic and cross-sectional study designs to perform secondary data analysis of the cancer registry and publicly available data. The geographic units were block groups in PA (N = 9,740), accessed from the US Census Bureau. The sample included screening age-eligible PA residents, 45-75 years, diagnosed with a primary incident case of CRC from 2008 to 2017 (N=34,250), identified via the PA Cancer Registry. Out-of-state residents at diagnosis and high-risk individuals were excluded. Nine block groups were uninhabitable with no population and thus excluded. Primary exposure variables (i.e., area-level access to healthcare and deprivation) were calculated using the PA Cancer Registry, a provider database, the US Census Bureau’s polygon and network shapefiles, and American Community Survey. Ecologic covariates (see below) were derived from the American Community Survey, the Behavioral Risk Factor Surveillance System, and the USDA’s Rural-Urban Commuting Areas. The PA Cancer Registry provided individual data for patient demographics, tumor characteristics, and insurance coverage. Exploratory spatial, temporal, and spatiotemporal analyses of the CRC data were performed before Aims 1 to 3. Aim 1: CRC cases were aggregated by block group to represent a count of CRC incidence. Area-level access to healthcare measures was calculated using providers’ addresses, population-weighted block group centroids, and road/rail networks (i.e., driving, walking, and public transit). Measures included great-circle distance, driving distance to the nearest provider by miles/time, physician-to-population ratio, enhanced two-step floating catchment area (2SFCA), variable 2SFCA, and multi-modal 2SFCA. Four 15-minute catchment sizes were tested (range = 15-60-minutes). A weighted version of each 2SFCA measure for insurance coverage was calculated. Predictive performance was assessed with model fit statistics from 29 hierarchical Bayesian spatiotemporal Poisson regression models. All models included CRC screening adherence, rurality, age, race, education level, unemployment, and poverty level. Aim 2: CRC cases were aggregated by block group to represent a count of CRC incidence. Area-level deprivation indicators (n=39) were calculated from the American Community Survey’s five-year pooled estimates for demographic, social, economic, and housing characteristics and represented at the census tract or block group-level. Weighted Quantile Sum regression generated an area-level deprivation index, weighting each indicator by its relative relationship with CRC incidence. A hierarchical Bayesian spatiotemporal Poisson regression with conditional autoregressive priors and a first-order autoregressive time series process was used to estimate the relative risk of CRC. The ecologic covariates included in the model were area-level access to healthcare from Aim 1, CRC screening adherence, rurality, age, and sex.
Aim 3: Three binary outcome variables represented localized vs. regional, distant, and regional and distant CRC at diagnosis. Aim 1 and 2’s area-level access to healthcare and deprivation measurements were used for this study’s primary exposure variables. The data was split into three time periods (2008-2009, 2010-2013, and 2014-2017) to analyze CRCS coverage mandates from the Affordable Care Act for private insurers in 2010 and Medicare in 2014. Using binomial distributed outcomes, three two-level generalized linear mixed models using hierarchical Bayesian methods with conditional autoregressive priors were run for each time period. Results: There were 34,250 eligible incident cases with 0-6 cases per block group (N=9,731) each year and an average of 3.5 cases per block group for the pooled study period. From 2008 to 2017, the pooled CRC incidence rate was 7.45 cases per 1,000 for 45 to 75 year olds in PA. Scan statistics found the highest CRC burden was in Philadelphia (northeast, west, and south), Pittsburgh, and rural areas in southwest PA (e.g., Westmoreland County and Fayette County) and northcentral PA (e.g., Lycoming County, Clinton County, and Centre County). In PA, yearly crude CRC rates decreased slightly over the ten years (0.80 to 0.72, Δ =-.08), though not empirically tested. Aim 1: The best fitting model used the Multi-Modal 2SFCA, which included aggregated physician-to-population ratios within 45-minutes from the provider facility for population-weighted block group centroids via driving, walking, and public transit of the same distance. Access was generally worst in rural areas and best in urban/suburban areas. Block groups with access one standard deviation above the state median had 27% decreased CRC risk. Weighting for insurance coverage improved a measure’s predictive ability for shorter travel times (i.e., 15-minutes and 30-minutes). Aim 2: Of a 39 indicator deprivation index, nine were statistically significant and three were related to SES (i.e., median household income, the percent of the block group without a high school degree, or living in a house without heating). However, the most important significant indicators belonged to geography and income domains, collectively representing 71% of the relative influence of the index. The area-level deprivation index was significant and positively associated with CRC incidence at the block group-level in PA from 2008 to 2017 (RR: 1.33, 95% CI: 1.32–1.34).
Aim 3: After accounting for individual age, race, and insurance coverage, the relationship between area-level access to healthcare and deprivation and late-stage CRC became non-significant. While no area-level effects were significant, several individual-level features had consistent significant findings across outcomes and time periods. At the individual-level, having government insurance and being uninsured had significant positive relationships for all outomes and time periods. Age, and race had significant inverse relationships with late-stage CRC diagnosis. Conclusions: In summary, this study addressed the limitations of previous research by employing innovative measurement techniques, such as the Multi-Modal 2SFCA and Weighted Quantile Sum regression, and rigorous spatiotemporal methods to assess the impact of area-level access to healthcare and deprivation on CRC incidence and late-stage diagnosis. The findings highlight the importance of considering walking and public transit access to healthcare in relation to CRC incidence. Additionally, the study demonstrated the effectiveness of the WQS method in calculating an accurate area-level deprivation index, which enhanced the prediction of CRC incidence and identified high-risk areas for targeted interventions. However, individual-level characteristics, particularly insurance coverage, were found to be more influential in predicting the stage at which CRC was diagnosed than area-level effects. Regardless, using inferences and similar methods from this dissertation improves disease mapping and resource allocation for CRCS outreach, supports evidence for policy, and helps guide the development of tailored public health interventions to ultimately reduce the burden of CRC. / Epidemiology
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/8945 |
| Date | 08 1900 |
| Creators | Snead, Ryan, 0000-0003-2876-7003 |
| Contributors | Jones, Resa M., Taylor Wilson, Robin, Henry, Kevin A. |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 300 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/8909, Theses and Dissertations |
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