Impact of Age, Race and Socio-economic Status on Temporal Trends in Late-Stage Prostate Cancer Diagnosis in Florida

Abstract

Individual-level data from the Florida Cancer Data System (1981-2007) were analysed to explore temporal trends of prostate cancer late-stage diagnosis, and how they vary based on race, income and age. Annual census-tract rates were computed for two races (white and black) and two age categories (40-65, >65) before being aggregated according to census tract median household incomes. Joinpoint regression and a new disparity statistic were applied to model temporal trends and detect potential racial and socio-economic differences. Multi-dimensional scaling was used as an innovative way to visualize similarities among temporal trends in a 2-D space. Analysis of time-series indicated that late-stage diagnosis was generally more prevalent among blacks, for age category 40-64 compared to older patients covered by Medicare, and among classes of lower socio-economic status. Joinpoint regression also showed that the rate of decline in late-stage diagnosis was similar among older patients. For younger patients, the decline occurred at a faster pace for blacks with rates becoming similar to whites in the late 90s, in particular for higher incomes. Both races displayed distinct spatial patterns with higher rates of late-stage diagnosis in the Florida Panhandle for whites whereas high rates clustered in South-eastern Florida for blacks.

Keywords: PSA screening; census tracts; disparities; joinpoint regression; socio-economic status.

Fig. 1

Joinpoint regression model fitted to time series of proportion of late-stage prostate cancer cases for white and black males for two age categories: (A) 40 to 64 yr. old, and (B) 65 yr. and over. For example, the segmented regression model (solid line) for black males 40 to 64 yr. old includes two joinpoints (τ) that correspond to years of statistically significant changes in rate trend: 1989 and 2000. The estimate and 95% confidence intervals (CI) of the annual percent change (APC) are listed for each segment, as well as the average annual percent change (AAPC) computed for the entire time period. The hypothesis of parallelism of the two joinpoint regression models was rejected at α=0.05 for the youngest age bracket.

Fig. 2

Census-tract level map of five categories of median household incomes that were used to explore the impact of socio-economic status on the proportions of late-stage prostate cancer cases diagnosed.

Fig. 3

Census-tract proportions of late-stage prostate cancer cases that were diagnosed for white males over four time periods and two age categories: [40–64] (left column) and [65,120] (right column). These rates were smoothed using binomial kriging.

Fig. 4

Census-tract proportions of late-stage prostate cancer cases that were diagnosed for black males over four time periods and two age categories: [40–64] (left column) and [65,120] (right column). These rates were smoothed using binomial kriging.

Fig. 5

Time-averaged impact of the number K of neighbors on results of binomial kriging for both white and black males. The vertical axis represents the relative change in estimated rates when three more neighbors are included in kriging.

Fig. 6

Joinpoint regression models fitted to time series of proportion of late-stage diagnosis computed for two age categories ([40–64], [65,120]), two race groups (white and black males), and five classes of increasing median household income (Income 1 → Income 5). The black dashed curve corresponds to the state wide trend model.

Fig. 7

Results of multi-dimensional scaling applied to annual proportion of late-stage prostate cancer in Florida between 1981 and 2007. The 20 time series (2 races × 2 age groups × 5 income levels) were projected in a 2D space based on their dissimilarity quantified using an Euclidian distance metric: time series close to each other in 2D share similar temporal trends; the first letter denotes race (W,B), then age category (40–64, 65+), then income level (I1 – I5). Ellipses are drawn for visualization and have no statistical meaning.

Fig. 8

Results of multi-dimensional scaling applied to annual proportion of late-stage prostate cancer in Florida between 1981 and 2007. The 20 time series (2 races × 2 age groups × 5 income levels) were projected in a 2D space based on their dissimilarity quantified using an APC-based distance metric: time series close to each other in 2D share similar temporal trends; the first letter denotes race (W,B), then age category (40–64, 65+), then income level (I1 – I5). Ellipses are drawn for visualization and have no statistical meaning.

Fig. 9

Joinpoint regression models fitted to time series of proportion of late-stage diagnosis computed for white (dashed lines) and black males (solid lines) over age 64. Temporal trends were fitted for five classes of increasing median household income (Income 1 → Income 5).