Hysterectomy-Corrected Rates of Endometrial Cancer among Women of Reproductive Age

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1 Hysterectomy-Corrected Rates of Endometrial Cancer among Women of Reproductive Age Annie Noone NAACCR 2017 Albuquerque, NM

2 Motivation Cancer incidence rates are typically calculated as: Number of new cases / population (at-risk of disease) Underestimate incidence when the population contains cases that are not at-risk of disease Women who have had hysterectomy are no longer at risk of endometrial cancer Many young women in the US undergo hysterectomy 2

3 Motivation: Impact on Estimates Rates Underestimation since denominator is too large Trends Incidence Rate Corrected: increasing prev. Corrected: constant prev. Uncorrected Hysterectomy prevalence is increasing over time, so underestimation of rates is worse as time goes on. Trend estimates are not the same. Hysterectomy prevalence is constant over time, so population is reduced by same amount every year. Trends are parallel and estimates are the same. 3

4 Previous Work: Increase in Incidence Rate by Race Paper Data Ages White Black Hispanic Merrill RM (2001) Sherman ME (2005) Merrill RM (2006) Jamison PM (2013) Siegel RL (2013) Utah SEER SEER SEER NPCR % % 95% 58% % 93% % 90% 61% All 60% 78% 4

5 Objective Estimate incidence rates and trends for endometrial cancer in women under 50 corrected for hysterectomy prevalence by race/ethnicity Correction will be done by removing women who have had hysterectomy (prevalent proportion) from the population-at-risk 5

6 Methods: Incidence Data SEER data Connecticut, Hawaii, Iowa, New Mexico, Utah, Detroit, Atlanta, rural Georgia, Seattle-Puget Sound, San Francisco-Oakland, San Jose- Monterey, Los Angeles Diagnosed 1992 to 2010 Non-Hispanic white, non-hispanic black, Hispanic women Age 20 to 49 Invasive Type 1 tumors with microscopic confirmation Primarily endometroid adenocarcinoma Comprises about 81% of all tumors 6

7 Methods: Incidence Rates and Trends Incidence Rates Ages 20-29, 30-34,, Incidence Trends Estimated using Joinpoint regression from 1992 to 2010 using 3-year intervals Years , ,, Trends summarized by the annual percent change (APC) 7

8 Methods: Hysterectomy Data Behavioral Risk Factor Surveillance System (BRFSS) Cross-sectional telephone survey Adults age 18+ living in households Included data from states that contained a SEER registry California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, Washington Hysterectomy status was assessed every year until 2000 then every other year for all included states Have you had a hysterectomy? A hysterectomy is an operation to remove the uterus (womb). 8

9 Methods: Estimated Hysterectomy Prevalence Prevalence was the proportion of women who reported ever having a hysterectomy All available of BRFSS data were used (e.g estimate uses 1992, 1993, 1994 data estimate uses 2002 data) Estimated by race/ethnicity, 5-year age group, 3-year time interval Collapsed into 3-year intervals to obtain large enough unweighted sample sizes 9

10 Methods: Estimated Hysterectomy Prevalence Within each race and age strata, the estimates changed sharply over time Jumps in hysterectomy prevalence may introduce jumps in the corrected incidence rates Weighted logistic regression was performed to smooth the estimates Variables in model were age, calendar year, and their interaction Separate models for each race/ethnicity 10

11 Methods: Correction For each race, age and year group SEER populations were reduced by the corresponding estimated hysterectomy prevalence 11

12 Results: Hysterectomy prevalence 60% Hysterectomy Prevalence, % 50% 40% 30% 20% 10% 0% NH White NH Black Hispanic 12

13 Results: Age-Adjusted Incidence Rates, Corrected Uncorrected N Rate 95% CI Rate NH White , NH Black , Hispanic ,

14 Results: Trends, NH White NH Black Hispanic Age-Adjusted Incidence Rate Corrected Uncorrected 14

15 Discussion Strengths Population-based Ability to produce estimates for larger race/ethnic groups Limitations Survey data 15

16 Discussion Correction is meaningful even in young women Largest impact for black women Black women have lowest rates Trends attenuated for all race/ethnic groups Rates among black women not increasing after correction 16

17 Acknowledgements Sarah Temkin Elise Kohn Lynne Penberthy Kathy Cronin Lois Dickie Lori Minasian Steve Scoppa Todd Gibson 17

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19 Results: Age-Specific Incidence Rates, NH White NH Black Hispanic Uncorrected Corrected 19