using cancer registry data to prioritize community actions
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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
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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
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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
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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 http://cancercontrolplanet.cancer.gov/
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 http://statecancerprofiles.cancer.gov/
Driven by Cancer Registry Data!
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State Cancer Profiles – map example
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State Cancer Profiles – rate trend example
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State Cancer Profiles – table example
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Interactive Cancer Atlas (InCA)
10 https://nccd.cdc.gov/DCPC_INCA/
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Community Health Status Indicators (CHSI)
11 http://wwwn.cdc.gov/communityhealth
CHSI cancer-related indicators
Cancer incidence rates– All cancer sites
– Colon and rectum cancer
– Female breast cancer
– Lung and bronchus cancer
– Male prostate cancer
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Overall cancer death rate
Health behaviors– Adult smoking rates
– Adult female routine pap tests
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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:
Interactive map of Anne Arundel County’s Peer Counties:
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Population sizePopulation growthPopulation densityPopulation mobilityPercent childrenPercent elderly
Sex ratioPercent foreign bornPercent H.S. graduatesSingle parent householdsMedian home valueHousing stress
Percent owner‐occupied homesMedian household incomeReceipt of government incomeHousehold incomeOverall povertyElderly povertyUnemployment
County Health Rankings and Roadmaps
14 http://www.countyhealthrankings.org/
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Cancer-related indicators
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Roadmaps to Health
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Roadmaps – Action Center model
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Examples - prioritizing cancer control activities
Susan G. Komen’s Community Profiles
Kentucky Cancer Program’s logic model for combining data from multiple sources
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Example – Susan G. Komen
Komen community health programs are implemented through a network of Affiliates
Affiliate service areas are generally a group of counties
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Komen Affiliate Network
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
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Components
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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
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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
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– Urban/rural– Foreign born– Language isolation
– Medically underserved– Uninsured
Sample data table
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(… additional counties …)
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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
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Example – Anne Arundel County
Breast cancer death rates (HP2020 target 20.6):– Base rate for 2006-2010: 25.0
– Annual percent change: -1.9%
– Years to achieve target: 11 years
Late-stage incidence rates (HP2020 target 41.0):– Base rate for 2006-2010: 47.7
– Annual percent change: -2.1%
– Years to achieve target: 8 years
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Final prioritization matrix
Overall county priority determined by position in this table:
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Anne Arundel County: 11 years to reach death rate target8 years to reach late-stage target
Final priority:
11 years
8 years
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
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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
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Kentucky Area Development Districts
Kentucky has lots of counties, some with very small populations
Cancer control uses the 15 Area Development Districts
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Logic model – combining data
Combining data from multiple sources:
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Breast cancer example
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Education and screening rates
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Screening rates and incidence
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Incidence and late-stage diagnosis
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Prioritization – combined ranking
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Four identified priority areas
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1. Kentucky River2. Gateway3. Cumberland Valley4. Lake Cumberland
Assessing cancer control actions
In 2001, – Kentucky had the highest colorectal cancer incidence rates
in the country
– Kentucky was ranked 49th in colorectal cancer screening
So Kentucky launched a cancer control program aimed at increasing colorectal cancer screening rates
38 Tom Reynolds J Natl Cancer Inst 2003;95:575
Polyp Man being apprehended by two doctors:
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Change in screening rates
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Change in incidence
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Change in mortality
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Colorectal screening program results
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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
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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
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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, 2014. • Tucker TC, “The Cancer Burden in Kentucky,” KCC Steering Committee Meeting‐ Louisville, Kentucky,
July 30, 2012