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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.

DavidStinchcomb@westat.com

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