2014 CCN-II Cancer Health Disparities Pilot Funding Program: Inequalities in surgical menopause may contribute to North Carolina breast cancer

Transcription

1 2014 CCN-II Cancer Health Disparities Pilot Funding Program: Inequalities in surgical menopause may contribute to North Carolina breast cancer disparities

2 Breast Cancer rate - Black Breast Cancer rate - White future Years

3 Conceptual diagram of relationships between surgical menopause and breast cancer incidence BMI Race Mammography Childbearing/ Breastfeedin g Surgical Menopause Benign Indication, e.g., fibroids + - E only HRT - P + E HRT?? Breast Cancer ER+ or ER-

4 Oophorectomy trends? Black Black Black t t t White White White 4

5 Surgical Menopause - Black Surgical Menopause - White future Years

6 Surgical Menopause - Black Surgical Menopause - White Breast Cancer rate - Black Breast Cancer rate - White future Years

7 GAPS GAP 1: No population-representative studies have described racial differences in rates of inpatient premenopausal oophorectomy. GAP 2: No studies of declining oophorectomy trends have accounted for outpatient surgeries. GAP 3: No studies have investigated how racial differences in rates of premenopausal bilateral oophorectomy contribute to racial differences in breast cancer incidence. 7

8 CCN-II Pilot Aims GAP 1: No population-representative studies have described racial differences in rates of inpatient premenopausal oophorectomy. Aim 1. How large were racial disparities in inpatient bilateral oophorectomy before age 40 years among Black and White North Carolina women between 1989 and 2012? GAP 2: No studies of declining oophorectomy trends have accounted for outpatient surgeries. Aim 2. Accounting for migration of inpatient surgeries to outpatient ambulatory settings, how large were racial disparities in bilateral oophorectomy before age 40 years among Black and White North Carolina women between 1997 and 2012? GAP 3: No studies have investigated how racial differences in rates of premenopausal bilateral oophorectomy contribute to racial differences in breast cancer incidence. Aim 3. Do North Carolina counties with lower race-specific rates of bilateral oophorectomy before age 40 years experience higher rates of race-specific breast cancer? We will also contrast CCN IItargeted counties with non-targeted counties. 8

9 Timeline from CCN-II proposal 9

10 Data! NC Hospital Discharge Data & NC Ambulatory Surgery Visit Data Healthcare Cost and Utilization Project (HCUP): State partnerships with AHRQ NC: since 1996, hospitals report to 1 data processor (currently Truven Health Analytics) NC: Sheps Center maintains and distributes data for use in research and state health planning Perfect data! Universe of claims No self-report: CPT & ICD-9 procedure codes Long-term trends: 1989-present inpatient; 1997-present outpatient Data on race and hispanicity Diagnostic codes to separate out cancer-related from benign conditions Absolute rates: residential information so we can link with census Hospital information potential to further research But no data are perfect Restrictions: No efforts to identify individuals! No linkage! Pick 2: County, zip code, hospital 10

11 Data! NC Hospital Discharge Data & NC Ambulatory Surgery Visit Data Healthcare Cost and Utilization Project (HCUP): State partnerships with AHRQ NC: since 1996, hospitals report to 1 data processor (currently Truven Health Analytics) NC: Sheps Center maintains and distributes data for use in research and state health planning Perfect data! Universe of claims No self-report: CPT & ICD-9 procedure codes Long-term trends: 1989-present inpatient; 1997-present outpatient Data on race and hispanicity Diagnostic codes to separate out cancer-related from benign conditions Absolute rates: residential information so we can link with census Hospital information potential to further research But no data are perfect Restrictions: No efforts to identify individuals! No linkage! Pick 2: County, zip code, hospital 11

12 Timeline from CCN-II proposal Sheps responds to initial data request 12

13 Problems and Solutions Problem 1: race data unreliable 2011 Solution: change aims Aim 1: Assess the magnitude of racial disparities in inpatient and outpatient bilateral oophorectomy among Black and White North Carolina women between 2011 and Aim 2: Evaluate county-level differences in the rates of bilateral oophorectomy among North Carolina women between 1997 and 2012 after accounting for migration of inpatient surgeries to outpatient ambulatory settings. Aim 3: Determine whether North Carolina counties with lower rates of bilateral oophorectomy (both outpatient and inpatient) experience higher rates of breast cancer. We will run 5 sets of models, one assuming different lags between surgery rates and breast cancer rates, e.g., 5-year lag, 20-year lag. 13

14 Problems and Solutions Problem 2: You are requesting a massive amount of data going back many years Not one dataset 40 datasets! Each dataset has different distribution of variable values and distinct lables For years prior to 1995 there is little to no documentation as it was collected under a completely different entity. I have been working with this data for about 10 years and have never handled a request for all of our inpatient data, or for any data prior to 95 so there are some unknowns. Solution new timeline Get data in 3 chunks: ; , Focus on new Aim 1: racial disparities in recent years 14

15 Timeline from CCN-II proposal Sheps responds to initial data request Revised data request New aims Longer timeline Baby Louie 15

16 Timeline from CCN-II proposal Data cleaning finds problem with IDs; requested revised data New revised data: data 16

17 Timeline from CCN-II proposal Baby Simon! Data cleaning finds problem with IDs; requested revised data New revised data: data 17

18 Problems and Solutions Problem 3: Procedure codes aren t perfect Vague CPT oophorectomy codes Validation study! Both CPT and ICD-9 procedure codes in 2011» Annie Green Howard biostatistician» Kemi hunting down medical coders» Still ongoing Solution Start with a hysterectomy paper Racial differences in age-standardized hysterectomy rates in inpatient and outpatient settings 18

19 Timeline from CCN-II proposal Analyses for Paper 1: contemporary hysterectomy trends Additional funding from CCN-II! 19

20 2011 age-standardized inpatient hysterectomy rates 20

21 2013 age-standardized inpatient hysterectomy rates 21

22 2011 age-standardized outpatient hysterectomy rates 22

23 2013 age-standardized outpatient hysterectomy rates 23

24 2011 age-standardized overall hysterectomy rates 24

25 2013 age-standardized overall hysterectomy rates 25

26 Next steps Paper 1: Hysterectomy, first to document extent of hysterectomy s migration to outpatient settings Paper 2: Premenopausal bilateral oophorectomy, Preliminary evidence of outpatient surgery Collaborations? Trends in surgery for cancer treatment Validation data for healthcare system-specific work 26

27 Acknowledgements Wendy Brewster, Mariah Cheng, Annie Green Howard, Funding CCN-II Cancer Health Disparities Pilot Funding Application: Grant Award Number (U54CA153602) Dr. Robinson was supported by the National Cancer Institute (K01-CA172717). We are grateful to the Carolina Population Center (R24 HD050924) for general support. Rebecca Tippett UNC PhD students Nathan DeBono & Danielle Gartner Bill Carpenter & Anissa Vines Anne-Marie Meyer UNC CORPS 27

28 HCUP is relatively decentralized State File Type Total Cost California* Inpatient California* Ambulatory Florida Inpatient ,255 Florida Ambulatory ,300 Maryland Inpatient Maryland Ambulatory New Jersey Inpatient ,300 New Jersey Ambulatory ,615 New York* Inpatient ,960 New York* Ambulatory ,670 North Carolina* Inpatient ,490 North Carolina* Ambulatory ,490 South Carolina Inpatient ,115 South Carolina Ambulatory ,598 Wisconsin Inpatient ,340 Wisconsin Ambulatory ,760 *Prices for Not-For-Profit Affiliation **AHRQ Grantee? Availability of HCUP databases: Total State Cost 560 5,555 1,365 3,915 13,630 14,980 23,713 29,100 Total cost 92,818 92,818 Purchase Availability: 28

29 Early gynecologic surgery and breast cancer risk among Black and White women in the U.S. South Whitney R. Robinson 1,2, C. K. Tse 1, Andrew F. Olshan 1, Melissa A. Troester 1 1 Epidemiology Department, UNC Gillings School of Global Public Health, 2 Carolina Population Center Acknowledgements: UNC doctoral students Marc Emerson, MPH, and Nathaniel debono Funding: This research was funded in part by the University Cancer Research Fund of North Carolina and the National Cancer Institute Specialized Program of Research Excellence (SPORE) in Breast Cancer (NIH/NCI P50-CA58223). Dr. Robinson was supported by the National Cancer Institute (K01-CA172717). Dr. Troester was supported by the following grants from the National Institutes of Health: U01 CA179715, U01 ES019472, P50 CA We are grateful to the Carolina Population Center (R24 HD050924) for general support.

30 Black-White differences in breast cancer subtypes & mortality Age-specific incidence rates by breast cancer phenotype in black and white women, SEER* Racial differences in mortality Paradoxical differences in incidence (only makes sense w subtypes) 30

31 Hysterectomy vs Oophorectomy When doing a hysterectomy, oophorectomy is generally elective Clinical indications range from strongly indicated all the way to completely elective Pros: prevent ovarian cancer Cons: Oophorectomy may be associated with lower life expectancy; side effects treated by exogenous hormones (cite parker, Nurses Health) 31

32 Surgical Menopause - Black Surgical Menopause - White Breast Cancer rate - Black Breast Cancer rate - White future Years

33 Breast Cancer rate - Black Breast Cancer rate - White future Years