Breast Cancer

Breast cancer is the second leading cause of cancer death among women. Each year, approximately 200,000 women in the US are diagnosed with breast cancer, and 40,000 will die of the disease [15]. Treatment can involve a difficult regimen of surgery, radiation therapy, chemotherapy, and reconstructive surgery for those women undergoing mastectomy. Even women who experience five-year disease-free survival have reduced quality of life during relatively extensive periods of treatment [16].

The probability of cure or extended remission of breast cancer depends on the stage of disease at diagnosis [17]. Mammography is part of a recommended set of breast cancer screening procedures (along with clinical breast exam and breast self-examination) whose main objective is diagnosis at the earliest possible stage of disease. Some variability exists among national guidelines for mammography frequency, but most guidelines recommend annual mammography for women over 50 [18]. The main benefit of increased mammography rates is in shifting the distribution of stage at diagnosis, and therefore the subsequent survival probabilities, among those women diagnosed with breast cancer [19]. The National Committee for Quality Assurance (NCQA) Health Employer Data and Information Set (HEDIS) for managed care plans includes a measure of mammography use, which is defined as the percentage of women between 50 and 65 who have been continuously enrolled in the plan for two years and have had at least one mammogram in the past two years [20].

There is some controversy about the extent to which there are meaningful racial/ethnic disparities in mammography rates. Recent national surveys have suggested that black, Hispanic, and white women all receive mammograms at approximately the same rate [21]. However, analyses of Medicare and other claims data sets typically show a disparity of 7-15 percentage points in mammography rates, with black and Hispanic women less likely to receive mammograms than non-Hispanic white women [22, 23]. These disparities have been found within some individual managed care plans, suggesting that at least some number of plans will find disparities in mammography rates when analyzing their own HEDIS data [24].

Eliminating a disparity in mammography rates would involve raising rates in black or Hispanic women to the level currently found for white women. For a typical health plan with relatively high current mammography screening rates, that might involve increasing the mammography rate for minority women from 70% to 80%. The question we addressed in this simulation was: What would be the benefits to an employer in terms of attendance and productivity at work, and medical care costs, for an employed population of African-American women if a typical disparity of 10% in mammography rates were eliminated?

Mandelblatt et al [25] used a simulation modeling approach to estimate the costs and benefits of improving breast cancer outcomes among African-American women through either improved screening or improved treatments after diagnosis. Their results showed a relatively modest benefit for improved screening in a hypothetical cohort of 40-year-old African-American women. We used a similar modeling approach, but the analyses were focused on the 50- to 65-year-old age group that is the defined denominator population for the HEDIS breast cancer screening measures, and the annual mammography patterns being modeled were selected to match those typically found among members of managed care plans. We used published data specific to African-American women on mammography rates, distribution of stage at diagnosis, survival by stage, and days worked to estimate the benefits of eliminating disparities in mammography rates between African-American and white women.

Asthma

Asthma is a common chronic disease affecting 10-20 million Americans in 2003 [26, 27]. (Variation in estimates depends on whether the specific measure is ever having had asthma, having had an asthma attack in the past 12 months, or report of currently having asthma.) Asthma is more prevalent in African-Americans and Hispanics than in non-Hispanic whites, so the burden of morbidity and mortality related to asthma occurs disproportionately in those groups [28]. Deaths from asthma are rare [29], but morbidity in the form of acute exacerbations and related days lost from work or school is common [30]. Data from national surveys and other studies suggest that members of minority groups experience more asthma-related lost work and school days than whites, even among those with comparable asthma severity levels [31, 32].

National guidelines since 1991 recommend use of inhaled preventive medications, including inhaled corticosteroids (ICS), as the preferred method of long-term management for patients with persistent asthma [33]. (Patients with only mild, intermittent asthma may be appropriately managed with acute, "rescue" medications as needed.) Based on these guidelines, NCQA has included a measure of Appropriate Medications for Asthma in its HEDIS measures since 2000 [34]. The measure is defined as the proportion of health plan members with persistent asthma who have been prescribed at least one of the approved preventive medications in the past year. The most recent average rate reported by the NCQA for commercial managed care plans publicly reporting their HEDIS data was approximately 70% in 2008, and the 90^th percentile rate for such plans was approximately 80% [35].

Data from a number of studies suggest that African-American children and adults are less likely to receive prescriptions for preventive medications (and use them) than their white counterparts [36, 37]. Since the medications are effective in all racial/ethnic groups and the national guidelines do not make different recommendations for different groups, disparities in use of recommended medications are presumed to be one of the key underlying reasons for disparities in morbidity and mortality [38, 39]. Improving rates of appropriate medication use in minority patients should result in a reduction in disparities in measures like lost work or school days, emergency room (ER) visit rates, hospital admission rates, or deaths [40].

Our goal in this project was to model the effects of improvements in appropriate medication rates for children, adolescents, and adults, on lost school or work days and on direct medical care costs [41, 42]. To link data on disparities in children's use of asthma medications to an employer business case, we made the assumption that an employed adult would have to take one day off work for every day in which a child had an asthma exacerbation requiring a physician office visit, an ER visit, or a hospital admission [43]. The model specifically focused on the effects of reducing or eliminating disparities in the HEDIS Use of Appropriate Medication for People with Asthma measure [44]. The modeling exercise asked the question of how many lost school or work days would be averted by increasing medication use rates in minority populations to those observed in non-Hispanic white populations. These benefits were estimated over time periods ranging from one to five years.

Sensitivity Analysis

Sensitivity analyses were conducted to determine how the life expectancy, QALYs, employment rates, working hours missed plus turnover costs and medical care costs change when the distribution of stage at diagnosis in the simulation model moves from the most "pessimistic" one (i.e., least benefit due to mammography) to the most "optimistic" one (i.e., greatest effect of mammography on stage). In addition to the base model described, four more versions of the model with different combinations of distribution of cancer stage from Table 1 were defined and used for sensitivity analysis. Based on the different combinations, models are considered as moving from the most pessimistic to the most optimistic models. The definitions of all five models are summarized in the Appendix.

The base model did not include the costs or effects of false-positive mammograms, but one recent review suggested that over 10% of mammograms will be read as suspicious for cancer and then followed up with biopsy or other tests, even though cancers are not found in the vast majority of these [49]. Two additional versions of the model were created - one with an explicit cost added for false positive tests requiring additional followup, and another with those costs included and a slight increase in the subsequent probability of mammography after a false positive test [50].

Other input parameters were not included in sensitivity analyses either because they had one widely accepted value (e.g., annual cancer incidence rate) or because they could be set arbitrarily to fit any specific new application of the model (e.g., increase in mammography rate; hourly rate for employees). The relationship between mammography and stage distribution at diagnosis seemed to be the relationship with greatest variation in published values and greatest potential impact on model outputs.

Asthma Basic Model Structure

The basic modeling approach was a Markov model simulating the likelihood of various events like acute exacerbations, ER visits or hospital admissions, lost school or work days, or deaths as a function of taking or not taking appropriate medications. The model (illustrated in Figure 3) was structured to capture the range of possible events during a month. Hypothetical patients in the model began each month either taking or not taking appropriate medications, and then experienced events during the month with probabilities that depended on whether or not they were taking medications. At the end of each month, patients "cycle back" and began a new month either taking or not taking appropriate medications. The model was run for 12, 24, 36, 48, or 60 monthly cycles to simulate experience over time periods from one to five years.

The model is similar in structure to that developed by Paltiel et al [51], but does not include forced expiratory volume (FEV1) as a core concept, because we were unable to find adequate data for that approach specific to individual racial/ethnic minority groups. When possible, parameters from the Paltiel model were used in our model to try to maximize comparability across the two modeling approaches.

The structure of the model is illustrated in Figures 3, 4, and 5. Figure 3 shows the first set of branches, or starting states. Figure 4 shows the next set of state transition options, with associated monthly probabilities in the situation where a child is not using appropriate medications. The same transition options exist in the model for children using appropriate medications, but with different probabilities. Figure 5 illustrates the complete set of transition states, for a child not using appropriate medications and experiencing an exacerbation requiring an ER or urgent care visit during the month. Similar sets of branches follow from other "exacerbation" branches, but with different probabilities.

The primary input parameters for the model are summarized in Table 2. They included: probability of use of appropriate controller medications in a month, probability of exacerbations in a month given use or non-use of controller medications, probability of ER or urgent care visit or hospitalization given exacerbation, number of days missed from work given exacerbations of varying severity, medical care costs associated with treatment of exacerbations of varying severity, costs of controller medication [52], and utilities associated with specific states of exacerbations and freedom from exacerbations. The models were run for both adults and children; for children, days missed from school was used instead of days missed from work, but in analyzing days missed from work to calculate a business case from the employer perspective, it was assumed that an employed parent would miss a day of work for every day a child with asthma was out of school because of an exacerbation.

	Base Model Parameter	Range Used in Sensitivity Analysis	Source(s)
State Transition Probabilities
Probability of Asthma Medication Use	80%	70% (90%)	[37, 95, 96, 99]
Probability of Exacerbation not requiring ER visit or Admission	With Medication = 0.27 W/O Medication = 0.53	Same as 80%	[32, 95, 98]
Probability of Exacerbation Requiring ER Visit, not Admission	With Medication = 0.0382 W/O Medication = 0.0756	Same as 80%	[95, 98, 102–104]
Probability of Exacerbation Requiring Admission	With Medication = 0.0035 W/O Medication = 0.0069	Same as 80%	[49, 101]
Probability of Medication use Following Exacerbation	With Medication - no ER visit or Admission = 0.96 - ER visit, no Admission = 0.99 - Admission = 0.99	With Medication - no ER visit or Admission = 0.97 (0.99) - ER visit, no Admission = 0.99 (0.99) - Admission = 0.99 (0.99)	Authors' values, constrained by requirement that overall medication use be constant over entire simulation
	W/O Medication - no ER visit or Admission = 0.25 - ER visit, no Admission = 0.30 - Admission = 0.30	W/O Medication - no ER visit or Admission = 0.15 (0.4) - ER visit, no Admission = 0.25 (0.45) - Admission = 0.25 (0.45)
Probability of Medication Use Following Month of No Exacerbation	With Medication = 0.96 W/O Medication = 0.01	With Medication = 0.95 (0.965) W/O Medication = 0.01	Authors' values, constrained by requirement that overall medication use be constant over entire simulation
Probability of Death from Other Causes	0.0001	Same as 80%
State Values
Utility of Month without Exacerbation	With Medication = 0.0748 W/O Medication = 0.0756		[49]
Utility of Exacerbation not requiring ER visit or Admission	With Medication = 0.0706 W/O Medication = 0.0712		[49]
Utility of Exacerbation requiring ER visit, no Admission	With Medication = 0.0698 W/O Medication = 0.0704		[49]
Utility of Exacerbation requiring Admission	With Medication = 0.0649 W/O Medication = 0.0654		[49]
Days Missed per month from Work or School from Exacerbation not requiring ER visit or Admission	3 days		[95, 98]
Days Missed from Work or School from Exacerbation requiring ER visit, no Admission	4 days		[95, 98]
Days Missed from Work or School from Exacerbation requiring Admission	7 days		[33, 98]
Cost of Doctor Office Visit for Exacerbation	$155.44		[105], authors' calculation
Cost of ER Visit	$1,080.00		[105], authors' calculation
Cost of Admission	$13,512.00		[105], authors' calculation
Cost of Monthly Asthma Medications	$112.00		[50]
Hourly Wage for Time Missed from Work	$21.31		Department of Labor, May 2007 [46]

Patients are not consistent from month to month or year to year in their use of medications. Although a rate of adherence may be stable over time for a large population, individuals can alternate between periods of use and non-use, and the Markov model structure allows for this factor to be included. We presumed that medication use would be generally stable from month to month, but that some individuals taking medication and experiencing no exacerbations in a given month would not use medications in the following month (in various versions of the model we used proportions of 5-10%). Even some individuals experiencing ER visits or hospitalizations may not use medications in the following month (perhaps believing them to be ineffective); we estimated this proportion as 1%, with 99% of those experiencing ER visits or hospitalizations continuing on medications.

Among those not using medications in a given month, we presumed that 1% of those not experiencing exacerbations would begin to use medications in the following month, that 15% of those experiencing an exacerbation would begin to use medications in the following month, and that 25% of those requiring an ER visit or hospitalization would begin using medications in the following month.

In the absence of published data on these transitions among minority group patients with asthma, some of the transition probabilities are somewhat arbitrary. They were adjusted to produce the effect of gradually rising use of medication for the group as a whole over time. This trend of gradual increase is consistent with national data on improvement in the HEDIS asthma medication measure over time [9].

Life Expectancy

Quality-Adjusted Life Years

Table 3 also shows the effect of a 10% increase in HEDIS mammography rate on the five-year cumulative QALYs for an African-American woman in the 50-65 age cohort. As in the case of life expectancy, the gain is relatively modest - approximately 2 QALYs gained per 1,000 women in the cohort over five years. Improving mammography rates for African-American women from 70% to 80% could be expected to add 24 quality-adjusted life years in a population of 10,000 women aged 50-64. The QALY gains are greater than the life expectancy gains because some of the women alive at the end of five years after having had cancer would have avoided the treatments associated with late-stage cancer at diagnosis by having more frequent mammography.

Costs Associated with Absenteeism and Workforce Turnover

In the scenario with a 70% HEDIS mammography rate among African-American women aged 50-65, an employer could expect to incur approximately $178 of cost per woman in the cohort related to absenteeism and employer turnover following breast cancer diagnosis and treatment in that cohort. When the mammography rate rises to 80% (disparity in mammography eliminated), the expected cost related to absenteeism and turnover dropped to approximately $166 (savings of $12 per woman in cohort) because of less absenteeism and turnover among women with less advanced stage of cancer at diagnosis. Therefore, in a cohort of 100 women, the projected savings would be $1,200 over five years; $12,000 in a cohort of 1,000 women, etc.

Direct Medical Care Costs

Similarly, an employer in the 70% HEDIS mammography scenario could expect to incur approximately $1,123 in medical care costs per employed African-American woman aged 50-65 related to new breast cancer diagnoses; that estimate became $1,086 as the HEDIS mammography rate rose to 80%. Again, the main reason for the effect is lower medical care cost associated with less advanced stage at diagnosis. The projected saving with a 10% increase in mammography rate is $38 per woman in an employed cohort over a five-year period.

The Employer "Business Case" - Combination of Absenteeism, Turnover, and Medical Care Costs

The combined savings per employed African-American woman aged 50-65 associated with a 10% increase in HEDIS mammography rate, from all three areas combined, was $50. For an employer with 100 African-American women in that age group, then, the projected savings associated with eliminating a 10% disparity in mammography rates would be $5,000. A quality improvement or health education initiative designed to enhance mammography screening, and that did in fact raise mammography rates by 10%, would have to cost $5,000 or less to be cost-saving.

Sensitivity Analysis

Several alternative versions of the model were run with varying assumptions about stage distributions with or without mammography. In the alternative models, 13-16 lives per 10,000 women would be saved as a result of increasing mammography rates in African-American women by 10%. The small effects of variations of the model reflect the low incidence of cancer in any one- or two-year period, and that the shift in stage at diagnosis as a result of mammography is relatively modest, even using the most optimistic data on stage distributions in the literature.

The results of these sensitivity analyses are shown in Table 3. The pattern observed across all major output parameters in the model is similar. As expected, a more optimistic estimate of the effect of mammography on stage distribution is associated with a larger effect on QALYs, cumulative employment, and costs to employers for time missed from work. The size of the differences across different versions of the model is small, suggesting that the model results are robust across all reasonable estimates of the effect of mammography on stage at diagnosis from the published literature.

Adding an explicit cost for false positive tests had a somewhat more significant effect on model outputs, although in all cases the direct medical care costs were still slightly lower in the 80% mammography scenario than in the 70% scenario. The cost saving was reduced from $50 to $18 in the model that included just the direct cost of false positives ($1,248 vs. $1,230), and to $17 in the model that included both costs of false positives and a slight increase in likelihood of mammography in subsequent cycles after a false positive.

Life Expectancy

Probabilities of death were extremely low in all versions of the model, and did not vary significantly with improvements in medication use rates from 80% to 90%.

Quality-Adjusted Life Years

Costs Associated with Absenteeism

From the employer perspective, there is a gain of over one full work day per year per African-American employee with asthma by eliminating a disparity of 10% in use of appropriate medications. There was also a gain of over one school day per year per African-American child or adolescent with asthma by eliminating a disparity of 10% in use of appropriate medications.

Direct Medical Care Costs

Eliminating a disparity of 10% in asthma controller medication use would produce a savings of $793.72 per patient in terms of reduction in costs associated with ER and urgent care visits and hospitalizations over a five-year period. However, when the additional controller medication costs associated with that 10% increase are included in the calculation, the overall savings in direct medical care costs associated with that 10% increase drop to $86.50 per person over the five-year period, or $17.30 per employee with asthma (or parent of child with asthma) per year.

State Probabilities

Values for Health States

Rates of Use of Appropriate Medications

Several published studies have shown racial/ethnic disparities in the use of IHC or other preventive medications (e.g., cromolyn) for asthma [36, 37, 95]. The relationship between some of these findings and the HEDIS measure of appropriate asthma medications is complicated because the HEDIS measure only requires a single prescription to be filled during a year, and some of the studies use regular ICS use as the key measure of medication use. Rates of regular ICS use will inevitably be lower than rates of having received at least one prescription for preventive medications.

The NCQA State of Health Care report for 2008 shows a median rate of appropriate medication use according to the HEDIS measure definition of approximately 92% across all three age groups for commercial enrollees and 87% for Medicaid [35]. There is considerable variability in these rates from plan to plan [96]. These rates are for all plan members and are not stratified by race/ethnicity.

Studies comparing medication use across racial/ethnic groups report odds ratios of appropriate medication use between .36 and .65 when comparing African-American adults to a non-Hispanic white reference group [97, 98]. The study by Ortega et al [36] found medication use rates for African-American children and Hispanic children at approximately half of the rate for non-Hispanic white children; smaller disparities were reported for children and adolescents in a Medicaid sample by Lieu et al [95]. A more recent study by Smith et al [99] found a 12% absolute disparity between black and white children in a measure of underuse of controlled medications.

The baseline scenario for the asthma model, then, starts with a rate of appropriate medication use for minority adults and children of 80% and posits a rate of 90% for non-Hispanic white children and adults. Elimination of the disparity involves raising the rate of medication use in minority adults and children to 90%.

Acute Exacerbations

Although there can be some asthma-related symptoms and related lung function deficits on "normal" days, most problems of lost work or school or need for medical care come as a result of acute exacerbations caused by some environmental trigger. Some exacerbations are severe enough to require an ER visit or hospital admission.

Monthly probabilities of exacerbations leading to at least one day off work or school were estimated from studies using patient surveys to inquire about either days off work or school or "physically unhealthy days" in a given time period. In the three most directly relevant studies, the monthly probabilities of at least one day of work or school missed due to acute exacerbations were .30 [95], .50 [98], and .37 [32]. We chose to use a probability in the middle of this range: .40. These studies included patients with all levels of medication use. To be consistent with the Sin et al report of a 50% decrease in acute exacerbations through use of inhaled corticosteroids [100, 101], we used a monthly probability of acute exacerbations of .27 for those taking medications and .53 for those not taking medications.

Five recent studies included data on monthly probabilities of ER or urgent care visits, with a range of probabilities from .01 to .06 [49, 95, 98, 102–104]. These studies include patients of all severity levels and degrees of compliance with medications. The ER visit rates for patients with persistent asthma only (HEDIS denominator population) would be slightly higher. The probability of an ER visit is higher for African-American or Hispanic children and adults, with one study in a managed care setting showing ER visit rates twice as high in African-American patients as in non-Hispanic white patients.

A recent review of clinical trials of inhaled corticosteroids and other preventive medications suggested that use of these medications cut the rate of acute exacerbations in half [101]. It is not clear exactly how to model the effect of just barely meeting the HEDIS requirement of one filled prescription in a year, but our "base case" model presumes that most patients with asthma in a HEDIS denominator population who receive a prescription are taking medications as regularly as patients in the studies reviewed. In sensitivity analyses, we model the effects of a smaller benefit related to poorer levels of adherence.

To maximize comparability of our results to those of Paltiel et al [49], we chose to use rates from their model, combining the probability of ER and urgent care use. We multiplied those rates by two to estimate the probability of ER/urgent care visits for African-American or Hispanic adults and children. The monthly probabilities in the base model are .038 for patients using medications and .076 for patients not using medications.

Monthly probabilities of hospital admission were also derived from rates used by Paltiel et al [49], with adjustment for the finding in several published studies of higher hospitalization rates for African-American or Hispanic patients with asthma. Monthly probabilities in those studies ranged from .003 to .010. The ratio of rates for patients using vs. not using medications were also adjusted from those used by Paltiel et al [49] to match the recent summary by Sin et al [101] showing a decrease in hospitalization rates of approximately 50% in patients using inhaled corticosteroids. Our base model monthly probabilities for hospital admission were .0035 for patients using medications and .0069 for patients not using medications.

Days Missed from Work or School

For adults, we used 21.7 as the estimate of the number of days per month potentially available for attendance at work (2080 hours as a full-time work year = 260 days per year = 21.67 days per month). For children and adolescents, we used 15 as the estimate of the number of days per month potentially available for attendance at school (180 school days in a year = 15 days per month). The 180 school days are actually not evenly distributed across all 12 months of the year, so the effects of exacerbations are clearly more important on lost school days between September and June than they are in the summer. However, we did not have any data that would allow us to model seasonal variation in rates of exacerbation by race/ethnicity, so for purposes of creating a base model we chose to model days off school as if those days were distributed over an entire year. The model will therefore underestimate the effect of exacerbations on lost school days for 9-10 months of the year and overestimate the effect for 2-3 months, but the average impact over the entire year should be reasonably accurate.

The base model presumes that an average of four days of work or school are lost per month among those patients having acute exacerbations [32, 95, 98]. It presumes an additional day per month lost for those patients requiring an ER or urgent care visit for an exacerbation. It presumes an additional three days lost for those patients requiring hospital admission for an exacerbation. The number of days lost is not presumed to vary for members of different racial/ethnic groups. There are studies showing more "physically unhealthy days" or days lost from work or school for African-American or Hispanic patients with asthma [6], but we are presuming that this is due to the higher probability of exacerbations rather than a greater number of days lost from work or school given an exacerbation.

QALYs

We used utility values from the Paltiel et al [49] model to estimate the decrement in health status produced by acute exacerbations, ER visits, and hospitalizations. We also used their estimate of a .01 decrement in utility related to the use of medications. The utility value assigned to a "normal" day without medications in the model is .92; the value is .91 for a "normal" day with medications. The utility value for a day with an exacerbation leading to being off from work or school is .72. (We did not subtract the .01 for medication use on these days, on the argument that the "disutility" of an acute exacerbation would dominate any minor decrement in health status due to side effects of medications on those days.) We subtracted an additional .05 on days in which an exacerbation required an ER visit, and an additional .10 for days hospitalized. Utilities on ER days, then, were .67 and on hospitalized days, .62. In sensitivity analyses, the disutility of an ER visit or hospitalization was increased to .10 and .20, respectively. The utility values for a month in various branches of the model were derived by multiplying the utilities for each type of day by the number of days per month estimated for each of those states (see previous section).

For patients in the model who died in a given month, a utility of .92 or .91 (depending on medication use) was assigned for 15 days and 0 for the remainder.

Deaths

We presumed that asthma-related deaths would only occur among those patients experiencing exacerbations requiring an ER visit or a hospital admission. The probability of deaths in these groups was estimated at .0002 for patients requiring ER visits and .0014 for patients requiring hospitalization. Given the relatively low probability of ER visits or admissions, the model's estimates for mortality in the entire denominator population are reasonably consistent with those of Suissa et al [29] and Paltiel et al [49]. The probability of death from causes other than asthma was estimated at .0001 per month.

Transition Probabilities

Patients are not consistent from month to month or year to year in their use of medications. Although a rate of adherence may be stable over time for a large population, individuals can alternate between periods of use and non-use, and the Markov model structure allows for this factor to be included. We presumed that medication use would be generally stable from month to month, but that some individuals taking medication and experiencing no exacerbations in a given month would not use medications in the following month (in various versions of the model we used proportions of 5-10%). Even some individuals experiencing ER visits or hospitalizations may not use medications in the following month (perhaps believing them to be ineffective); we estimated this proportion as 1%, with 99% of those experiencing ER visits or hospitalizations continuing on medications.

Among those not using medications in a given month, we presumed that 1% of those not experiencing exacerbations would begin to use medications in the following month, that 6% of those experiencing an exacerbation would begin to use medications in the following month, and that 25% of those requiring an ER visit or hospitalization would begin using medications in the following month.

In the absence of published data on these transitions among minority group patients with asthma, some of the transition probabilities are somewhat arbitrary. They were adjusted, though, to produce the effect of gradually rising use of medication for the group as a whole over time. This trend of gradual increase is consistent with national data on improvement in the HEDIS asthma medication measure over time [96].