Gery P. Guy Jr, PhD

As the population ages, the financial amount spent on cancer care is expected to increase substantially. In this study, we projected cancer-related medical costs by state from 2010 through 2020.

We used pooled Medical Expenditure Panel Survey data for 2004 to 2008 and US Census Bureau population projections to produce state-level estimates of the number of people treated for cancer and the average cost of their treatment, from a health system perspective, by age group (18-44, 45-64, 

65 years) and sex. In the base model, we assumed that the percentage of people in each of the 6 age-by-sex categories who had been treated for cancer would remain constant and that the inflation-adjusted average cancer treatment cost per person would increase at the same rate as Congressional Budget Office projections of overall medical spending.

We projected that state-level cancer-related medical costs would increase by 34% to 115% (median = 72%) and that state-level costs in 2020 would range from $347 million to $28.3 billion in 2010 dollars (median = $3.7 billion).

The number of people treated for cancer and the costs of their cancer-related medical care are projected to increase substantially for each state. Effective prevention and early detection strategies are needed to limit the growing burden of cancer.

We project that the cost of medical care for cancer patients will increase substantially through the year 2020 in all US states.

		Effective prevention and early detection strategies are needed to limit the growing burden of cancer.

		These estimates provide a useful baseline against which to gauge the impact of current and future cancer policies.

		These estimates could be useful for budget allocations for investments in cancer prevention and early detection

Healthcare costs continue to rise nationally and impose greater burdens on state budgets.

Since cancer-related medical care costs constitute a substantial portion of overall US medical care costs,

accurate projections of future cancer-related care costs are critical. Over the past 20 years, the cost of treating cancer has nearly doubled nationally.

 As a result of an aging population and more expensive cancer treatments, the national costs of cancer care are expected to increase significantly in the near future.

 Although previous increases in spending on cancer have occurred despite the decreases in cancer incidence rates and increases in average survival times for patients with many types of cancers,

 researchers have noted many opportunities to further improve cancer detection and treatment while controlling costs.

To take advantage of these opportunities, state-administered insurance providers such as Medicaid and public healthcare providers such as the National Breast and Cervical Cancer Early Detection Program

 need state-level projections of future cancer care costs. Previous projections of cancer prevalence and cancer care costs have focused only on the national level.

This study produces state-level projections of cancer care costs through 2020. While our goal is not to explain differences across states, our projections do reflect projected changes in the distribution of state residents by age and sex during this period. They provide a useful baseline against which to gauge the impact of current and future cancer policies and could be useful for budget allocations for investments in cancer prevention and early detection.

First, we generated estimates of the number of adults who had been treated for cancer and the average cost of their treatment by age group (18-44, 45-64, 

65 years) and sex (male, female). The small number of children with cancer in our data prevented reliable estimates for children. Second, in our base projections, we assumed that the treatment rate for cancer in each of the 6 age-by-sex groups would remain constant and that the inflationadjusted initial average cancer treatment cost per person would increase at the same rate as Congressional Budget Office (CBO) projections of overall medical spending.

Third, we generated state-level projections of the total number of adults who will be treated for cancer and the costs of their treatment by multiplying treated cancer prevalence and average costs by the Census-projected population of each demographic cell. Therefore, the projections reflect expected changes in the distribution of state residents by sex and age group but assume that there will be no policy changes that could affect cancer treatment costs. For example, the projections do not account for possible changes in national healthcare policies mandated by the Affordable Care Act.

Projections of the Annual Number of US Adults Treated for Cancer

To estimate the number of adults in each state who will be treated for cancer, we used cancer prevalence data from the 2004 to 2008 Medical Expenditure Panel Survey (MEPS)

 and the US Census Bureau’s projections of state population counts for 2010 through 2020. The MEPS, a nationally representative survey of the civilian noninstitutionalized population administered by the Agency for Healthcare Research and Quality, provides data on participants’ use of medical services and on the costs of those services. MEPS provides a single, consistent data source to link disease prevalence and expenditures. Medical conditions are identified in the MEPS medical condition files; we restricted our condition indicators to those for which respondents received care within the interview year. Medical conditions were classified using the

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes based on self-reported conditions that were transcribed by professional coders. Cancer was defined using clinical classification codes 11 through 43 and 45, which group 

We estimated logit models for the probability of cancer treatment that controlled for survey year and survey participants’ age, sex, and region of residence (northeast, south, midwest, and west). We used stepwise regressions to identify significant interactions among these variables to be included in the models. The significant interactions in the stepwise regressions represent age-by-sex-by-region categories with enough sample and power to detect differences in cancer treatment rates. We estimated cancer treatment rates (ie, the proportion of the population treated for cancer) for US adults in each age/sex/region group using coefficients from the logit regressions and adjusted these estimates to account for the nursing home care population using data from the 2004 National Nursing Home Survey.

We used the projected state population counts for 2010 through 2020 generated in 2008 by the US Census Bureau on the basis of data from the 2000 Census.

 For each state, we multiplied the predicted percentage of people treated for cancer in each of the 6 age-by-sex categories by the projected number of state residents in the corresponding category for each year from 2010 through 2020. We then aggregated these projections to project the total number of people who will be treated for cancer in each state in each year.

Projections of Direct Medical Care Costs of Cancer

MEPS measures total annual medical spending, including payments by insurers and out-of-pocket spending by patients (copayments, deductibles, and payments for noncovered services). The costs captured by MEPS represent payments (not charges) from the payer to the provider. MEPS spending data are obtained through a combination of self-reports by the respondents and validation of the self-reports from payers.

We projected direct cancer-related medical care costs of cancer in 6 steps. First, we estimated per-person medical costs as a function of cancer by using a 2-part regression model (a logit model to predict the probability of any expenditure and a generalized linear model with a gamma distribution and a log link to estimate total annual medical expenditures for people having any such expenditures). To choose among alternative nonlinear estimators, we used an algorithm recommended by Manning and Mullahy

 and found the generalized linear model was the most appropriate for the data. All regressions included the following variables: age; age

; sex; race/ethnicity; education; family income; other sources of health insurance; year indicators; and indicators for cancer, arthritis, asthma, back problems, congestive heart failure, chronic obstructive pulmonary disease, coronary heart disease, depression, diabetes, dyslipidemia, human immunodeficiency virus/acquired immunodeficiency syndrome, hypertension, injuries, other cardiovascular disease, other mental health/substance abuse, pneumonia, pregnancies, renal failure, skin disorders, and stroke.

Second, we calculated expenditures attributable to cancer by comparing predicted expenditures from the 2-part regression model. To ensure no double counting of expenditures for co-occurring diseases, we used the “complete classification” technique described in an earlier study.

 We treated each disease and combination of diseases as a separate entity and, for each unique combination of diseases, compared predicted expenditures with and without the disease(s) holding all else constant. For example, we treated cancer alone and cancer with hypertension as 2 different “diseases.” We then divided the total expenditures attributable to the combinations of diseases back to the constituent diseases using the parameters from the model to construct shares for each constituent disease within a combination (ie, a share of all cancer with hypertension disease costs that are attributable to cancer). The shares attribute a greater share of the joint expenditures to the disease with the larger coefficient in the main effect.

 We estimated per-person costs attributable to cancer for each age/sex/region category on the basis of coefficients from the national model.

Third, we used the 2004 National Nursing Home Survey and National Health Accounts to adjust our per-person cost estimates to account for nursing home spending. We assumed that average per-person, non-nursing home expenditures attributable to cancer were the same for the nursing home population as for the non-institutionalized population. Fourth, we used confidential MEPS data that identified the most populous 30 states and 9 Census divisions to generate state-specific per-person cost estimates. Sample sizes were not large enough for us to replicate the full analysis for each state. We regressed log (positive) medical expenditures on the variables in the model plus state/census division dummies. The coefficients on the dummies provided measures of the differences in average medical care costs across states that we used to scale the national estimates to make them state-specific.

Fifth, we estimated future costs by inflating dollar values in the MEPS data to the equivalent of 2010 values in accordance with recommendations from the Agency for Healthcare Research and Quality

 and then multiplied the projected perperson cost of cancer for people in each age/sex/state category by the number of people in the corresponding category that we projected will be treated for cancer in 2010 and in 2020. We then added the projections for each category to estimate total annual costs of cancer care.

Finally, we adjusted our cost projections on the basis of CBO assumptions that future healthcare costs not attributable to population growth and aging will increase by an average annual rate of 3.6% between 2010 and 2020.

We generated four 10-year projections of cancer care costs using the following assumptions about future US cancer prevalence rates: 1) no change in cancer incidence or survival rates (base projections), 2) continued trends in cancer incidence rates, 3) continued trends in cancer survival rates, and 4) continued trends in both incidence and survival rates. Incidence trends represent changes due to prevention and risk factor prevalence, and survival trends represent changes in early detection and treatment. We used trends in incidence and survival reported by Mariotto et al.

First, using Census projections for the year 2020, we converted their estimates of the number of cancer survivors for all sites under the 4 modeling assumptions

 to the implied cancer prevalence rates in each model. Second, we calculated the percentage difference in the predicted 2020 prevalence rates between the 3 alternative models and the base model. The differences between the alternative models and the base model hold the 2020 population constant and reflect differences due to the alternative assumptions. Third, we applied the percentage differences in 2020 prevalence rates between each of the 3 alternative assumptions and the base model at the national level to each of our age/sex/state categories in the year 2020. For years 2010 to 2020, we assumed linear growth from the 2010 value to the 2020 value by age, sex, and state.

We also generated cancer medical care cost projections using alternative assumptions of medical cost growth. Our baseline assumption was that per-person costs of cancer grew at the historical rate of growth of overall medical spending, 3.6% per year.

 In the sensitivity analysis, we applied the following growth rates to per-person cancer costs: 0%, 2%, and 5%.

In the base model, projected state-level changes in the number of residents treated for cancer between 2010 and 2020 ranged from −7% in the District of Columbia to 46% in Arizona (median = 17%; data not shown). The states with the largest projected increases in the number of people treated for cancer were Florida (353,000), California (351,000), and Texas (249,000). Projected percentage increases in cancer care costs between 2010 and 2020 ranged from 34% in the District of Columbia to 115% in Arizona (median = 72%) (

). Projected actual increases in costs ranged from $347 million in the District of Columbia to $28.3 billion in California (median, $3.7 billion) (

Our projections of cancer-related medical costs were not sensitive to alternative assumptions about future cancer incidence and survival rates (

). Accounting for trends in cancer incidence rates,

 projected medical costs were 3% lower than in the base model. Accounting for trends in cancer survival rates,

 projected medical costs were 10% higher than in the base model. However, accounting for both of these assumptions simultaneously, projected medical care costs were only 1% higher than in the base model.

Compared with our base model, projections based on the assumption of 0% increase in per capita medical care costs were 34% lower and those based on the assumption of 5% growth were 18% higher. Changes from 2010 to 2020 in projected state-level cancer care costs derived from the 0% cost-growth model, in which we assumed no change in treated cancer prevalence or inflation-adjusted per capita medical care costs, reflect solely the impact of projected changes in state populations and in the age and sex distribution of state residents (

). The combined impact of these 2 factors on projected changes in cancer-related medical costs between 2010 and 2020 ranged from a 12% decrease for the District of Columbia to a 41% increase for Arizona (median = 13% increase).

Articles

State-level estimates of the number of people treated for cancer and the average cost of their treatment by state from 2010 through 2020.

Gery P. Guy Jr, PhD

Articles

State-Level Projections of Cancer-Related Medical Care Costs: 2010 to 2020