Using Cancer Registry Data to Prioritize Community Actions

Transcription

1 Using Cancer Registry Data to Prioritize Community Actions Dave Stinchcomb Westat October 9, 2015 Introduction Goals: Share information about types of cancer registry data that are used for cancer control activities Raise awareness of how cancer registry data are used to prioritize cancer control activities Outline: Using registry data for cancer control Data types and prioritization Data sources Examples - prioritizing cancer control activities: Susan G. Komen s Community Profiles Kentucky Cancer Program s logic model for combining data from multiple sources 2 1

2 Using registry data for cancer control Cancer control actions focus on behavioral risk factors Smoking, diet, exercise Screening (breast, cervical, colorectal, prostate, skin, lung) Cancer registry data has two key functions in cancer control Prioritize community actions Measure impact of actions Key data types Incidence Prevalence Late-stage diagnosis 3 Prioritization of cancer control actions Prioritize geographically areas with the most need Ideally by neighborhood or community Most data is available by county Prioritize by population subgroup Racial and ethnic groups Language / culture Unemployed, uninsured 4 2

3 Data sources for cancer control Cancer Control P.L.A.N.E.T Data: State Cancer Profiles Research Synthesis Cancer Control Plans Research-Tested Intervention Programs (RTIPS) Evaluation 5 Data sources for cancer control State Cancer Profiles State and county level data Rate tables, maps, trend analysis, prioritization Data types: Incidence (rates and counts) Mortality (rates and counts) Prevalence (counts and percentages) Screening and risk factor rates Demographics Cancer knowledge 6 Driven by Cancer Registry Data! 3

4 State Cancer Profiles map example 7 State Cancer Profiles rate trend example 8 4

5 State Cancer Profiles table example 9 Interactive Cancer Atlas (InCA)

6 Community Health Status Indicators (CHSI) 11 CHSI cancer-related indicators Cancer incidence rates All cancer sites Colon and rectum cancer Female breast cancer Lung and bronchus cancer Male prostate cancer Overall cancer death rate Health behaviors Adult smoking rates Adult female routine pap tests 12 6

7 CHSI Peer Counties Peer counties identified for all 3,143 U.S. counties 89 groups Based on a cluster analysis of 19 county-level variables: Population size Population growth Population density Population mobility Percent children Percent elderly Sex ratio Percent foreign born Percent H.S. graduates Single parent households Median home value Housing stress Percent owner occupied homes Median household income Receipt of government income Household income Overall poverty Elderly poverty Unemployment Interactive map of Anne Arundel County s Peer Counties: 13 County Health Rankings and Roadmaps

8 Cancer-related indicators 15 Roadmaps to Health 16 8

9 Roadmaps Action Center model 17 Examples - prioritizing cancer control activities Susan G. Komen s Community Profiles Kentucky Cancer Program s logic model for combining data from multiple sources 18 9

10 Example Susan G. Komen Komen community health programs are implemented through a network of Affiliates Affiliate service areas are generally a group of counties Komen Affiliate Network 19 Community Profiles An assessment of: The breast health/cancer needs in the Affiliate service area The resources that exist to meet the needs Purpose: Identify areas of highest need in the Affiliate service area Identify community partners to address needs, barriers and gaps in services Communicate the state of breast health and breast cancer in the target community Make data-driven decisions about how to use Affiliate resources in the best way to make the greatest impact 20 10

11 Components 21 Quantitative data geographic levels Data generated by County Affiliate service area as a whole State as a whole (when different from the Affiliate service area) Comparisons of rates and trends Each county compared to Affiliate statistics Affiliate compared to state statistics Statistically higher, lower, or not statistically different 22 11

12 Quantitative data included Breast cancer incidence and late-stage incidence Number of new cases, age-adjusted rate, and trend Mortality Number of deaths, age-adjusted rate, and trend Screening mammography rates Demographic characteristics (females only) Percent White, Black, API, AIAN Percent Hispanic/Latina, non-hispanic/latina Percent age 40-plus, 50-plus, 65-plus Socioeconomic characteristics (females and males) Education Poverty Unemployment Urban/rural Foreign born Language isolation Medically underserved Uninsured 23 Sample data table 24 ( additional counties ) 12

13 Prioritization Compared county late-stage incidence and death rates and trends with Healthy People 2020 goals: HP 2020 death rate goal: 20.6 deaths per 100,000 HP 2020 late-stage goal: 41.0 cases per 100,000 Estimated the number of years needed to reach the goal Categories for late-stage and death rate goals: Currently meets the HP 2020 target 0 to 6 years to meet the target 7 to 12 years to meet the target 13 years or longer to meet the target 25 Example Anne Arundel County Breast cancer death rates (HP2020 target 20.6): Base rate for : 25.0 Annual percent change: -1.9% Years to achieve target: 11 years Late-stage incidence rates (HP2020 target 41.0): Base rate for : 47.7 Annual percent change: -2.1% Years to achieve target: 8 years 26 13

14 Final prioritization matrix Overall county priority determined by position in this table: 8 years 11 years 27 Anne Arundel County: 11 years to reach death rate target 8 years to reach late-stage target Final priority: How the results are used Target communities are selected for program actions Counties in the highest priority categories Additional information about key population characteristics Screening rates Demographic characteristics Socioeconomic characteristics Outcome of the process: a Mission Action Plan 28 14

15 Example Kentucky Cancer Program Another approach to setting priorities based on multiple indicators Challenges associated with using cancer registry data for cancer control Multiple measures from across the cancer continuum Behavioral risk factors Screening Incidence, late-stage incidence Mortality The screening effect screening can increase incidence Geographic areas with small populations 29 Kentucky Area Development Districts Kentucky has lots of counties, some with very small populations Cancer control uses the 15 Area Development Districts 30 15

16 Logic model combining data Combining data from multiple sources: 31 Breast cancer example 32 16

17 Education and screening rates 33 Screening rates and incidence 34 17

18 Incidence and late-stage diagnosis 35 Prioritization combined ranking 36 18

19 Four identified priority areas 1. Kentucky River 2. Gateway 3. Cumberland Valley 4. Lake Cumberland 37 Assessing cancer control actions In 2001, Kentucky had the highest colorectal cancer incidence rates in the country Kentucky was ranked 49 th in colorectal cancer screening So Kentucky launched a cancer control program aimed at increasing colorectal cancer screening rates Polyp Man being apprehended by two doctors: 38 Tom Reynolds J Natl Cancer Inst 2003;95:575 19

20 Change in screening rates 39 Change in incidence 40 20

21 Change in mortality 41 Colorectal screening program results 42 21

22 Conclusions Cancer registry data are a vital part of the process of prioritizing cancer control actions Incidence rates and trends Late-stage rates and trends Prevalence data (number of cancer survivors) Often combined with an array of additional data to set priorities Cancer registry data are also key for assessing the impact of cancer control actions Identify the programs that are most effective in reducing the burden of cancer in communities 43 Wrap-up Key message: the data you work so hard to collect is IMPORTANT! Case completeness finding every cancer Stage at diagnosis key identifier for cancer control Acknowledgements: Susan G. Komen: Jeremy Patch, Becky Royer, Stephanie Reffey Westat: Serban Negoita, Elaine Flores Kentucky Cancer Program: Tom Tucker 44 22

23 Thank You. References: Patch J, Reffey S, Royer B, Negoita S, Stinchcomb D, Community Profiles: Utilizing Data to Drive Action, NAACCR Annual Conference, Ottawa, Ontario, June 14, Tucker TC, The Cancer Burden in Kentucky, KCC Steering Committee Meeting Louisville, Kentucky, July 30,