Using data to plan cancer prevention and screening activities

Health care reform is moving in the direction of a value-based delivery system, which strives to improve outcomes in a cost-effective manner. This movement is affecting all disciplines of medicine, including cancer care.

Although the emphasis is on improving quality of care for the individual cancer patient, cancer outcomes can also be improved through effective prevention and screening programs designed to meet the specific needs of a community. The Commission on Cancer (CoC) accreditation standards support both the modern concept of cancer care for the individual and address the need to reduce the cancer burden within the community through effective prevention and screening activities.

In a recent study, American Cancer Society investigators concluded that 42 percent of cancer cases and nearly half of cancer mortalities are attributable to modifiable factors, most commonly cigarette smoking, excess body weight, and alcohol intake.1 These data demonstrate a great opportunity to reduce the financial burden of caring for cancer patients and improve the health of the community.

The National Cancer Institute (NCI) has produced reports in recent years that have documented progress in reducing cancer incidence and mortality; however, NCI data recognize groups within the population that bear a disproportionate cancer burden characterized by higher incidence rates, as well as more advanced stages of disease with attendant higher mortality rates.2 The prevalence of behavioral risk factors associated with incidence and the rates of adherence to important cancer screening programs are inversely related to the level of the socioeconomic status and certain demographic characteristics of the community, underscoring the importance of studying these factors when planning cancer control programs.3

To conduct effective cancer control programs, it is imperative that a variety of data sources be tapped to thoroughly study a community and identify the groups with high prevalence rates of behavioral risk factors and poor compliance with regular screening. Sources of such data are listed in Table 1.

Table 1. Sources of data on cancer patient characteristics

Data source Description
Individual hospital cancer registry Number of cases by site and patient characteristics
State Cancer Profiles County-level incidence and mortality rates and behavioral risk factors
Behavioral Risk Factor Surveillance System Prevalence of behavioral risk factors
State cancer registries County- or major metropolitan-level data, including incidence rates and stage of disease profiles
Centers for Disease Control and Prevention Incidence data and several demographic variables
U.S. Census American Community Survey Annual estimates for demographic characteristics by county
NCDB datalinks section
(NCDB: Hospital Comparison Benchmark Reports)
Analysis of stage of disease by several demographic variables

Prevention studies

The purpose of a prevention activity is to reduce or eliminate a behavior associated with an increased risk of developing cancer. An effective prevention activity begins with a study of cancer incidence rates and the prevalence of the associated behavioral risk factors within groups in the community. This information allows a cancer program to target patients who would best benefit from prevention activity.

A study to determine community need for prevention includes the following process:

  • Initial evaluation using the cancer registry within the facility to determine the top cancer sites diagnosed and treated at the facility.
  • The sites having related behavioral risk factors are selected for further study to determine the incidence rates using one of the data sources listed in Table 1.
  • The incidence and mortality rates can be determined at the county level using the State Cancer Profiles. This database has a reporting tool that allows a series of studies for a specific cancer site incorporating a number of variables.
  • Incidence rates are reviewed to determine the site most in need of a prevention activity.

The prevalence of behavioral risk factors is often inversely related to the socioeconomic status of individuals in the community.4 A study of the population demographics and socioeconomic status of a community is important to estimate the size of the population at risk, plan an effective means of connecting with this group, and anticipate barriers that would limit participation or render an activity less effective.

The information gathered from these two arms of the study is combined into a cohesive plan to effectively deal with the behavioral risk factors contributing to the high incidence rate for the selected cancer site. To be effective, the prevention activity must be done for a site with high incidence rates and conducted in a manner that can bring about a measurable risk-reduction behavioral change.

Prevention study example

The top cancer sites are lung, breast, prostate, colorectal, and leukemia for this example CoC-accredited cancer program. In this community, lung cancer has a high incidence and mortality rate. A major behavioral risk factor for lung cancer is smoking.1 Socioeconomic factors influence smoking prevalence and should be considered when planning a tobacco cessation program (see Tables 2A–D).5

Prevention Study Example

For the community where this example of a CoC-accredited cancer program community is located, lung cancer has a high incidence rate. Groups with low household income and educational attainment have a high prevalence of smoking (see Figures 1A and 1B). A smoking cessation program should be directed toward these groups. To enhance effectiveness and encourage participation, financial assistance should be provided.

Prevention Study Example

Screening

Effective screening activities improve cancer outcomes by discovering more cancers at an early, more treatable stage. While screening has become a routine aspect of modern health care resulting in improving survival rates, some groups within the community experience low screening rates and increased mortality.6 This activity begins with a study to determine the cancer sites in which there are large numbers of late-stage disease and the demographic and socioeconomic characteristics of that community group. This information enables a program to focus time and resources on a particular group so that the disparity can be eliminated.

Stage at presentation and the attendant higher mortality rates are strongly influenced by socioeconomic factors, especially low educational attainment, poverty, lack of insurance, and other documented access barriers.2 The American College of Surgeons National Cancer Database (NCDB) Benchmark Tool is an accessible resource that provides an opportunity for comparative analysis and contains variables with which to evaluate a community’s need for a screening program.

Screening study example 1

In this community, a list of the top cancer sites is compiled and the NCDB Benchmark Tool is used to compare stage distribution profiles. A consistent pattern of late-stage lung cancer, defined as stage 3 and 4, is found (see Figure 2A). These data can then be analyzed by demographic and socioeconomic variables to identify specific groups within the community that would benefit from a screening program. The target audience can be further defined by household income and insurance status.

Table 3 demonstrates that 69 percent of lung cancer patients have annual incomes of less than $44,000. These numbers provide a better perspective on the size of a problem and the plans needed to address a financial barrier to lung cancer screening.

Analysis of the variables of household income and insurance status reveal several common characteristics among lung cancer patients. The highest levels of late-stage disease are among individuals without insurance (see Figure 2B). Further analysis of household income indicates that high levels of late-stage disease can be found for individuals within this socioeconomic variable.

These findings indicate that an effective screening program for lung cancer needs to address the financial and insurance barriers that may exist. Because most individuals with lung cancer earn less than $44,000, the screening program will need to establish a process to ensure that participation is not cost-prohibitive.

ncdb_fig2a_table3_fig2b

 

Screening study example 2: Discover late-stage disease

It is also important to study sites that have a favorable stage profile. In this example, the facility stage profile for breast cancer is similar to the national data with high percentages of early-stage disease, defined as stage 0 and 1 (see Figure 3A). Even though the community demonstrates a profile with high percentages of early-stage disease, some groups may experience disparities. Individuals with low socioeconomic status frequently present with later-stage disease and do not adhere to regular screening programs.6 To identify these groups, the stage of disease profile by demographic and socioeconomic variables needs to be assessed.

When two variable studies are done to determine the effect of demographic and socioeconomic factors on stage presentation, a group is identified with a higher percentage of late-stage disease. This group is characterized by higher rates of poverty and lack of insurance (see Figures 3B and 3C). Because poverty is a key factor, the program will need to provide reduced cost or free screening for breast cancer to promote and facilitate participation.

Screening Study Example 2

Conclusion

CoC programs are uniquely positioned to make significant improvements in reducing disparities in their community. They have access to data sources that can be used to help determine specific needs for prevention and screening and the resources needed to conduct them. While progress has been made in reducing the burden of cancer, continuing opportunities for improvement can only be ascertained with further study.

Author’s note

For this article, the NCDB Benchmark Tool’s data (Figures 2A, 2B, 3A, 3B, and 3C, and Table 3) were randomly pulled from NCDB data submissions to use as an example; thus this data does not reflect nor identify any individual hospital.

Acknowledgment

Statistical support for this article was provided by Amanda Browner, MS, Statistician, NCDB.


References

  1. Islami F, Goding Sauer A, Miller KD, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin. 2018;68(1):31-54.
  2. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in U.S. cancer mortality: Part I—all cancers and lung cancer and part II—colorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. February 14, 2012 [Epub ahead of print].
  3. Ward E, Jamal A, Cokkinides V, et al. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin. 2004;54(2):78-93.
  4. Clegg LX, Reichman ME, Miller BA, et al. Impact of socioeconomic status on cancer incidence and stage at diagnosis: Selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes Control. 2009;20(4):417-435.
  5. Centers for Disease Control and Prevention. Cigarette smoking and tobacco use among people of low socioeconomic status. Available at: www.cdc.gov/tobacco/disparities/low-ses/index.htm. Accessed February 14, 2018.
  6. Amina A, Jones BL, Yen N, et al. Disparities in disease presentation in the four screenable cancers according to health insurance status. Public Health. 2016;138:50-56.

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