State-Level Projections of Cancer-Related Medical Care Costs: 2010 to 2020 | Page 2
Published Online: September 20, 2012
Justin G. Trogdon, PhD; Florence K. L. Tangka, PhD; Donatus U. Ekwueme, PhD; Gery P. Guy Jr, PhD; Isaac Nwaise, PhD; and Diane Orenstein, PhD
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 Mullahy18 and found the generalized linear model was the most appropriate for the data. All regressions included the following variables: age; age2; 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.19 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.19 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 Quality20 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 2020.12,13
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.6
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 assumptions6(Table 3) 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.13 In the sensitivity analysis, we applied the following growth rates to per-person cancer costs: 0%, 2%, and 5%.6
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%) (Table 1). Projected actual increases in costs ranged from $347 million in the District of Columbia to $28.3 billion in California (median, $3.7 billion) (Figure 1).
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