Model-based analyses to compare health and economic outcomes of cancer control: inclusion of disparities

Abstract

Background: Disease simulation models of the health and economic consequences of different prevention and treatment strategies can guide policy decisions about cancer control. However, models that also consider health disparities can identify strategies that improve both population health and its equitable distribution.

Methods: We devised a typology of cancer disparities that considers types of inequalities among black, white, and Hispanic populations across different cancers and characteristics important for near-term policy discussions. We illustrated the typology in the specific example of cervical cancer using an existing disease simulation model calibrated to clinical, epidemiological, and cost data for the United States. We calculated average reduction in cancer incidence overall and for black, white, and Hispanic women under five different prevention strategies (Strategies A1, A2, A3, B, and C) and estimated average costs and life expectancy per woman, and the cost-effectiveness ratio for each strategy.

Results: Strategies that may provide greater aggregate health benefit than existing options may also exacerbate disparities. Combining human papillomavirus vaccination (Strategy A2) with current cervical cancer screening patterns (Strategy A1) resulted in an average reduction of 69% in cancer incidence overall but a 71.6% reduction for white women, 68.3% for black women, and 63.9% for Hispanic women. Other strategies targeting risk-based screening to racial and ethnic minorities reduced disparities among racial subgroups and resulted in more equitable distribution of benefits among subgroups (reduction in cervical cancer incidence, white vs. Hispanic women, 69.7% vs. 70.1%). Strategies that employ targeted risk-based screening and new screening algorithms, with or without vaccination (Strategies B and C), provide excellent value. The most effective strategy (Strategy C) had a cost-effectiveness ratio of $28,200 per year of life saved when compared with the same strategy without vaccination.

Conclusions: We identify screening strategies for cervical cancer that provide greater aggregate health benefit than existing options, offer excellent cost-effectiveness, and have the biggest positive impact in worst-off groups. The typology proposed here may also be useful in research and policy decisions when trade-offs between fairness and cost-effectiveness are unavoidable.

Figure 1

Framework for typology of inequalities and disparities. Top row: An individual’s initial cancer risk represents biological and genetic factors and environmental exposures throughout life, from the time he or she may acquire risk factors (some more modifiable than others) and through a period during which access to cancer screening may modify risk. Following a cancer diagnosis, an individual’s mortality rate and risk of recurrence (ie, long-term survival) are modified by the access, quality, and ongoing availability of health care. For each cancer (Figures 2–4), those inequalities we would consider disparities are highlighted in yellow. Left column: Selected social determinants (upper section) and factors representing health-care access (lower section). Although social determinants themselves influence and are associated with the likelihood of health-care accessibility and quality, they may also affect cancer incidence, mortality, and long-term survival through exposure to environmental risks and other risk factors. Third column: Some lifestyle factors that may be influenced by socioeconomic determinants would be considered disparities rather than differences attributable to fully informed choices. Right three columns: For disparities of ethical importance, these columns indicate whether the social determinants and health access indicators are generally included in disease-specific cancer models. A priority (1–3, far right column) for near-term policy is assigned depending on whether the influential factors contributing to the inequality are modifiable or tractable. In general, we assume that access and care factors are more tractable than social determinants and assign a policy priority of 1. Modifiable social determinants such as poverty and poor neighborhoods are the least tractable and assigned a 3. Those factors and interventions assessed as between these two in tractability are assigned a 2. For each cancer (Figures 2–4), characteristics for which we have identified an opportunity to narrow a true disparity with an effective intervention and tractable policy option are highlighted in pink.

Figure 2

Framework for typology of inequalities and disparities for cervical cancer. Top row: A woman’s initial risk of cervical cancer represents biological and genetic factors and environmental exposures throughout her life, from the time she may acquire risk factors (some more modifiable than others) and through a period during which access to cancer screening may modify risk. Following a cervical cancer diagnosis, a woman’s mortality rate and risk of recurrence (ie, long-term survival) are modified by the access, quality, and ongoing availability of health care. Those inequalities we would consider disparities in cervical cancer are highlighted in yellow. Left column: Selected social determinants (upper section) and factors representing health-care access (lower section). Although social determinants themselves influence and are associated with the likelihood of health-care accessibility and quality, they may also affect cancer incidence, mortality, and long-term survival through exposure to environmental risks and other risk factors. Third column: Some lifestyle factors that may be influenced by socioeconomic determinants would be considered disparities rather than differences attributable to fully informed choices. Right three columns: For disparities of ethical importance, these columns indicate whether the social determinants and health access indicators are generally included in disease-specific cancer models. A priority (1–3, far right column) for near-term policy is assigned depending on whether the influential factors contributing to the inequality are modifiable or tractable. In general, we assume that access and care factors are more tractable than social determinants and assign a policy priority of 1. Modifiable social determinants such as poverty and poor neighborhoods are the least tractable and assigned a 3. Those factors and interventions assessed as between these two in tractability are assigned a 2. For cervical cancer, characteristics for which we have identified an opportunity to narrow a true disparity with an effective intervention and tractable policy option are highlighted in pink. HPV = human papillomavirus.

Figure 3

Framework for typology of inequalities and disparities for breast cancer. Top row: A woman’s initial risk of breast cancer represents biological and genetic factors and environmental exposures throughout her life, from the time she may acquire risk factors (some more modifiable than others) and through a period during which access to cancer screening may modify risk. Following a breast cancer diagnosis, a woman’s mortality rate and risk of recurrence (ie, long-term survival) are modified by the access, quality, and ongoing availability of health care. Those inequalities we would consider disparities in breast cancer are highlighted in yellow. Left column: Selected social determinants (upper section) and factors representing health-care access (lower section). Although social determinants themselves influence and are associated with the likelihood of health-care accessibility and quality, they may also affect cancer incidence, mortality, and long-term survival through exposure to environmental risks and other risk factors. Third column Some lifestyle factors that may be influenced by socioeconomic determinants would be considered disparities rather than differences attributable to fully informed choices. Right three columns: For disparities of ethical importance, these columns indicate whether the social determinants and health access indicators are generally included in disease-specific cancer models. A priority (1–3, far right column) for near-term policy is assigned depending on whether the influential factors contributing to the inequality are modifiable or tractable. In general, we assume that access and care factors are more tractable than social determinants and assign a policy priority of 1. Modifiable social determinants such as poverty and poor neighborhoods are the least tractable and assigned a 3. Those factors and interventions assessed as between these two in tractability are assigned a 2. For breast cancer, characteristics for which we have identified an opportunity to narrow a true disparity with an effective intervention and tractable policy option are highlighted in pink. HRT = hormone replacement therapy.

Figure 4

Framework for typology of inequalities and disparities for colorectal cancer. Top row: An individual’s initial cancer risk represents biological and genetic factors and environmental exposures throughout life, from the time he or she may acquire risk factors (some more modifiable than others) and through a period during which access to cancer screening may modify risk. Following a cancer diagnosis, an individual’s mortality rate and risk of recurrence (ie, long-term survival) are modified by the access, quality, and ongoing availability of health care. Those inequalities we would consider disparities in colorectal cancer are highlighted in yellow. Left column: Selected social determinants (upper section) and factors representing health-care access (lower section). Although social determinants themselves influence and are associated with the likelihood of health-care accessibility and quality, they may also affect cancer incidence, mortality, and long-term survival through exposure to environmental risks and other risk factors. Third column: Some lifestyle factors that may be influenced by socioeconomic determinants would be considered disparities rather than differences attributable to fully informed choices. Right three columns: For disparities of ethical importance, these columns indicate whether the social determinants and health access indicators are generally included in disease-specific cancer models. A priority (1–3, far right column) for near-term policy is assigned depending on whether the influential factors contributing to the inequality are modifiable or tractable. In general, we assume that access and care factors are more tractable than social determinants and assign a policy priority of 1. Modifiable social determinants such as poverty and poor neighborhoods are the least tractable and assigned a 3. Those factors and interventions assessed as between these two in tractability are assigned a 2. For colorectal cancer, characteristics for which we have identified an opportunity to narrow a true disparity with an effective intervention and tractable policy option are highlighted in pink. FOBT = fecal occult blood test.

Figure 5

Reduction in lifetime risk of cervical cancer according to different strategies. A) Overall average US cervical cancer reduction and distribution of outcomes by racial and ethnic subgroups. The average cervical cancer reduction with the addition of adolescent vaccination to cervical cancer screening is depicted by the distance between the lower and upper dashed blue lines. Disparities are widest for Hispanic, followed by black women. Without altering race-specific screening patterns, and only adding human papillomavirus (HPV) vaccination, disparities persist despite the overall gain in clinical benefits for the average population, and for each individual subgroup. B) Reduction in the lifetime risk of cervical cancer with alternative strategies. Results are shown for current screening patterns (green-shaded area) and two new strategies, Strategy B and Strategy C. Strategy B (blue-shaded area) used the new HPV screening algorithm in women older than 30 years, but the frequency of screening was every 2–3 years for all racial subgroups, in contrast to the variation in screening frequency among subgroups, which ranged from every year to more than every 5 years; subsequent screening protocols were based on risk and a woman’s screening history. Strategy C (yellow-shaded area) assumed that women would also have access to HPV vaccination as adolescents. Although all subgroups experience substantial clinical benefits, the magnitude of cancer risk reduction is greatest in the worst-off populations, and disparities are dramatically reduced. Cost saving refers to the difference in lifetime costs between this strategy and the next best nondominated strategy (average per woman lifetime savings of approximately $300). Strategies that are more costly and less effective than an alternative strategy are considered strongly dominated. Those that are less costly and less cost-effective are considered weakly dominated.