Health Insurance Coverage Mandates: Colorectal Cancer Screening in the Post-ACA Era

2 Virginia Commonwealth University, Department of Health Behavior and Policy, Massey Cancer Center, Office of Health Equity & Disparities Research, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

Levi Ross

3 The University of Alabama, Health Science, 461 Russell Hall, Box 870311, Tuscaloosa, AL 35487

Askar Chukmaitov

1 Virginia Commonwealth University, Department of Health Behavior and Policy, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

Sharla A. Smith

4 University of Kansas School of Medicine-Wichita, Department of Population Health, 1010 N. Kansas Street, Wichita, KS 67214

Michelle L. Odlum

5 Columbia University, School of Nursing, 617 West 168 th Street, Rm 225, New York, NY 10032

Bassam Dahman

1 Virginia Commonwealth University, Department of Health Behavior and Policy, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

Vanessa B. Sheppard

1 Virginia Commonwealth University, Department of Health Behavior and Policy, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

2 Virginia Commonwealth University, Department of Health Behavior and Policy, Massey Cancer Center, Office of Health Equity & Disparities Research, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

1 Virginia Commonwealth University, Department of Health Behavior and Policy, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

2 Virginia Commonwealth University, Department of Health Behavior and Policy, Massey Cancer Center, Office of Health Equity & Disparities Research, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149

4 University of Kansas School of Medicine-Wichita, Department of Population Health, 1010 N. Kansas Street, Wichita, KS 67214

Corresponding author: Michael A. Preston, PhD, MPH, Virginia Commonwealth University, Department of Health Behavior and Policy, Office of Health Equity & Disparities Research, 830 East Main Street, PO Box 980149, Richmond, VA 23298-0149, Telephone: 804-628-3972, Fax: 888-856-8827, ude.ucv@2mnotserp

Associated Data

Abstract

Building a culture of precision public health requires research that includes health delivery model with innovative systems, health policies, and programs that support this vision. Health insurance mandates are effective mechanisms that many state policymakers use to increase the utilization of preventive health services, such as colorectal cancer (CRC) screening. The current study estimated the effects of health insurance mandate variations on CRC screening post Affordable Care Act (ACA) era. The study analyzed secondary data from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Cancer Institute State Cancer Legislative Database (SCLD) from 1997–2014. BRFSS data were merged with SCLD data by state ID. The target population was U.S. adults, age 50 to 74, who lived in states where health insurance was mandated or non-mandated before and after the implementation of ACA. Using a difference-in-differences (DD) approach with a time-series analysis, we evaluated the effects of health insurance mandates on CRC screening status based on U.S. Preventive Services Task Force guidelines. The adjusted average marginal effects from the DD model indicate that health insurance mandates increased the probability of up-to-date screenings vs. non-compliance by 2.8% points, suggesting that an estimated 2.37 million additional age-eligible persons would receive a screening with such health insurance mandates. Compliant participants’ mean age was 65 years and 57% were women (n=32,569). Our findings are robust for various model specifications. Health insurance mandates that lower out-of-pocket expenses constitute an effective approach to increase CRC screenings for the population, as a whole.

Keywords: Health services research, colorectal cancer screening, health policy, health care reform, preventive health services

Introduction

Cancer is a major public health problem in the United States [1, 2]. Colorectal cancer (CRC) is the second most common cancer diagnosed in men and women, and in 2019 CRC was estimated to have caused over 49,190 deaths [3–7]. The American Cancer Society predicts that approximately 104,610 new cases of colon cancer and 43,340 new cases of rectal cancer will be diagnosed in 2020 [2, 8]. Additionally, CRC is the third most common cancer diagnosed in African American men and women [9]. African Americans have a higher incidence of CRC and a lower 5-year survival rate than White and Hispanic counterparts [10, 11]. This disparity may be due, in part, to differences in the utilization of CRC screening or the lack of physician recommendations for CRC screenings in African American populations [12]. Although the national death rate from CRC has declined over the past 20 years, gaps in screening remain [6]. African Americans are less likely to receive a CRC screening or a physician recommendation for a CRC screening compared to their White counterparts [13].

Because of these disparities in CRC screenings, major provisions were offered through health insurance mandates, allowing more Americans access to health insurance coverage; however, patient cost-sharing has been associated with a decrease in preventive service use and known to impact screening preferences [14, 15]. Efforts to improve preventive services utilization, like CRC screenings, led to many state policymakers passing legislation to expand coverage to include CRC screenings. In 2010, the Patient Protection and Affordable Care Act (ACA) was signed by President Barack Obama into law [16]. The primary purpose of the ACA was to reduce the number of uninsured Americans and to decrease overall health care costs. The ACA mechanism includes subsidies, mandates, and tax credits to individuals and employers to increase the overall coverage rate for U.S. citizens [15, 16].

Contributions to Science

With the current administration and the promise to roll back the ACA, it is critical to study health insurance mandates that could be impacted in the event that drastic changes to or elimination of the ACA occurs. The ACA’s health insurance mandates are a key instrument for increasing CRC screening rates, because it reduces the costs and increases coverage rates in the U.S. With the signing of the ACA, “first-dollar coverage” for CRC screenings mandate health plans, for all persons age 50 or older, to pay for preventive services even before the participant has met his/her deductible, and the participant cannot be required to pay any co-payment, co-insurance, or other cost sharing [16–18]. Provisions under the ACA required or mandated new health insurance plans or policies, on or after September 23, 2010, to cover preventive services without requiring patients to pay a co-payment or co-insurance or to meet a deductible. According to the U.S. Preventive Services Task Force (USPSTF), category “A” coverage means that these preventive services are recommended, and that there is a high-degree of certainty that the service’s net benefit is substantial enough to be covered by the insurer practices [14,16]. For CRC screening, these provisions fall under category “A” coverage under the ACA.

Under the ACA, health insurance mandates reduce cost sharing for members who have coverage [15, 19]. Before ACA, the amount of cost sharing varied based on state mandates and the consumer’s eligibility for Medicare or Medicaid [18,19]. Twenty-eight states covered the full range of CRC tests, and 6 states covered some of the CRC screening tests; the remaining states did not provide coverage (except through Medicaid) for CRC tests prior to the ACA.

The objective of our study was to determine the effects of ACA’s health insurance mandates on CRC screening rates over time. Under the ACA, increased access to preventive health services via reduced out-of-pocket expenses should increase overall CRC screening rates. The mandates that reduce out-of-pocket expenses exemplify an effective approach for increasing the number of CRC screenings. Such policies have been used historically to improve health outcomes, and they are currently being used as public health strategies to increase access to preventive health services in an effort to improve the nation’s health [20, 21].

Methods

Research Design

The study used a quasi-experimental research design with time-series statistical methods—a difference-in-difference (DD) model—to examine variations in state mandates from 1997 to 2014 and to estimate the effect of health insurance mandates on preventive health services (without cost sharing) on CRC screening rates [12, 22].

We considered states that implemented mandates as reform states (pre-ACA) and independent of the implementation of ACA [12]. We exploited the variation in pre-existing mandates from 1997 to 2014 since states were not randomly assigned. This approach allowed us to identify state-level effects of mandates on CRC screening. A comparison of pre-mandate and post-mandate states before and after ACA was analyzed. Our analysis showed that the findings are robust for various model specifications.

Study Population and Dataset

The study population was a sample of U.S. adults, age 50 to 74, from the Behavioral Risk Factor Surveillance System (BRFSS) [12]. The BRFSS is an ongoing, state-based, random digit-dialed telephone survey of non-institutionalized U.S. civilians, 18 years and older, that collects information on health risk behaviors, preventive health practices, and health care access in the U.S., the District of Columbia (DC), Guam, Puerto Rico, and the U.S. Virgin Islands [23]. The BRFSS was used to assess state-level estimates of health behaviors and health care use by building a state-year longitudinal dataset from 1997 to 2014 [12]. These data provided information on types of CRC screenings, the date of the latest test performed, insurance status, race/ethnicity, and socioeconomic status (SES) for the years studied. The final analytical sample included a total of 32,569 BRFSS respondents, with 25,098 in mandate states and 7,471 in non-mandate states [12].

We merged the BRFSS dataset with the National Cancer Institute State Cancer Legislative Database (SCLD) by state ID (Federal Information Processing Standard [FIPS] Code) [12]. Using the SCLD we examined which states have passed laws and determined the types of legislation that states have enacted to address CRC screening in each state.

The DD approach has been used most often in health policy analyses to predict what outcomes occur in the absence of a particular policy [24]. The approach allows for causal effect in examining observational data if assumptions are met [24, 25]. The underlying assumption is that the post-policy trend for the treatment group will resemble that of the control group, if not for the policy. These models are unique in that they allow us to observe if CRC screening rates increase because of the mandates or occurred due to existing, unmeasured factors that affect screening outcomes in the population studied.

Measures

Variables of Interest

Our dependent variable of interest is CRC screening [12]. Based on USPSTF guidelines, screening is recommended, starting at age 50 for average-risk individuals, with one of the following strategies: an annual high-sensitivity fecal occult blood test (FOBT), a sigmoidoscopy (SIG) every 5 years combined with FOBT every 3 years, or screening colonoscopy (COL) at intervals of 10 years [26]. We used USPSTF’s guidelines and CDC’s definition of up-to-date to calculate CRC screening compliance [26].

The dependent variable was coded as a dichotomous dummy variable (1,0) for people who have been screened for CRC through a FOBT or a SIG/COL test. These variables were coded for individuals who had a CRC screening test within the recommended time period [12, 26].

Control Measures

The explanatory variables we used included demographic characteristics, a doctor visit, the existence of a personal physician, self-reported health status, insurance status, actual health status, policy mandates, SES, and the date of the most recent CRC screening test. Dummy variables for each year were included to provide a fixed effect for each year, thereby removing secular trends among mandate states that may affect the population studied [12, 22, 24].

Analysis

Multivariate logistic models with time-trend variables were used to estimate the effect of mandates on CRC screenings. Time-trend variables were used to account for general trends in CRC screening compliance that occurred irrespective of the mandates [12]. This analysis examined the dependent variable, measuring whether a person had at least one CRC screening test based on the recommended guidelines from the USPSTF [26]. These tests include screenings using a FOBT and screenings using a SIG/COL test. The model controlled for independent variables that are perceived to influence an individual’s choice to be screened [12]. States were not randomly assigned, which potentially introduces selection bias. To address this problem, we included states’ fixed-effects as dummy variables and a DD approach [12, 24]. The convergence criteria were satisfied in the fixed effects model. Our primary analyses used a fixed effects model to examine the effects of the key predictor (health insurance mandates and the ACA) on CRC screening compliance. The dependent variable was dichotomized as compliant or up-to-date with CRC screening versus non-compliant. In this DD specification, some states had policies in place prior to the ACA, and the analysis compared outcomes between CRC screening in mandate states (has a mandate prior to ACA) and non-mandate states (no CRC mandate prior to ACA), before and after the ACA. We also utilized the SCLD to determine which states had mandates before ACA was implemented [12].

Model Specification 1

DD = ( CRCscreening reform , post-state mandate − CRCscreening reform , pre-state mandate ) − ( CRCscreening non-reform , post-state mandate − CRCscreening non-reform , pre-state mandate )

Pr ( Compliance ist = 1 ) = β 0 + β 1 * REFORM t + β 2 * MANDATE s + β 3 * REFORM t * MANDATE s + X β 4 + λ s + φ t

In the model estimated in this analysis, the DD estimation is described by Compliance_ist, which is the outcome of interest; REFORM_t is an indicator equal to “1” when ACA policies become law and “0” otherwise; MANDATE_s is an indicator equal to “1” if a state had no mandate prior to the ACA policies; and λ_s is a state fixed effect [12]. The coefficient of interest is β_3, which is our DD estimator that measures the effect of health insurance mandates on CRC screenings in states without mandates [12]. The error terms were assumed to be normally distributed, with φ_t as the error term.

A key assumption in the DD estimation is that the underlying trends of both groups are the same. We assumed that, in the absence of the ACA, the trends in CRC screening rates between mandate and non-mandate states would be the same and that no other factors would have affected the outcome that occurred during implementation of these mandates [12]. A violation of this assumption in the DD would produce biased estimates. State-specific linear time trends in the DD specifications were used to address this issue.

Sensitivity Analysis

We conducted several parallel analyses to estimate the robustness of the results with respect to new weighting techniques [12, 25]. First, we conducted the DD analysis using both the new and prior weighting techniques. We estimated the effects of mandates for 2 years prior to 2011 and 2 years after. We treated 2011 as a transition year by adding a 2011 dummy variable in the model and also by removing 2011 from the analysis [23]. Second, we conducted parallel analyses on insured individuals since mandates only affect those with coverage. Third, we conducted parallel analyses for adults age 65 and older to estimate mandate effects on the Medicare-eligible population [15, 27].

Threats to Causal Effect

Our analytical approach considered the number of potential threats to valid causal effect [12, 24]. The BRFSS dataset does not provide us with a true panel dataset. Although we did not have panel data, conducting the analysis in the manner described can be advantageous. True panel data present challenges as well, and these challenges need to be addressed as individuals change over time, which can directly affect whether CRC screenings are up-to-date. Additional threats to consider are changes to population characteristics that can be directly tied to ACA. Because ACA affects new policies, we are unable to determine the status of insurance coverage (i.e., old versus new health plans) at the time of the screening. We discuss other limitations in our discussion.

Results

Over 57% of the 32,569 screening-eligible participants in the BRFSS national survey from 1997 to 2014 were considered up-to-date on CRC screening. Of the participants, 61% were female, 50% were married, while 82% were White. Temporal trends were examined using the combined longitudinal data file. Our findings show that CRC screening increased over time during the study period ( Figure 1 ). We also found an increase over time in the proportion of persons who were considered up-to-date based on recommended guidelines. Table 1 shows the aggregate descriptive statistics for the members of the study population who were considered up-to-date for CRC screenings. Compliant participants’ mean age was 65 years and 57% were women. Overall, these findings demonstrate that health insurance mandates increased screening compliance during this time period.

Colorectal Cancer Screening Over Time

Table 1.

Summary statistics of the study population receiving any colorectal cancer screening.

Abbreviation: FOBT = fecal occult blood test.

The bivariate analysis found a majority of the demographic characteristics influencing screening compliance were highly significant. Compared with non-compliant participants, those who were up-to-date with CRC screenings were White versus non-White (57.5%; 54.8%, p), Hispanic versus non-Hispanic (47.5%; 57.7%, p<0.001), 50 to 74 years old (56.2%, p), married (59.3%, p), and non-smokers (59.3%, p). In addition, compliant participants had visited a doctor within the past 12 months (62.5%, p), had at least one personal physician (59.5%, p), and were covered by some type of health insurance (59.2%, p).

Table 1 provides summary statistics for states before and after mandated coverage. Over time, policy adoption in states that were early adopters (i.e., states with mandates prior to ACA, n=34) of CRC screening laws increased until the ACA was implemented ( Figure 1 ). Over time, CRC screening compliance was similar between mandate and non-mandate states. Our findings show that 53% (n=28) of the CRC screening laws offered strong provisions and addressed health disparities.

CRC screening compliance results from the fixed effects model are reported in Table 2 . Table 2 provides the adjusted average marginal effects from our DD model, which suggested that health insurance mandates increased the probability of individuals being up-to-date relative to being non-compliant by 2.8 percentage points after controlling for all other variables in the model. In a state with a coverage mandate, there was a 12.1 percentage point reduction in CRC screening during the pre-ACA period compared with a 2.8 percentage point increase in CRC screening during the post-ACA period, after controlling for all other variables in the model. Although the difference in these estimates is large and statistically significant in the post-mandate implementation, the effect of health insurance mandates on compliance in non-mandate states suggested no significant difference. This is likely because of the few numbers of years included after implementation of ACA.

Table 2.

Marginal effects of health insurance coverage mandates on colorectal cancer screening.

Variable	Coefficient	SE	95% CI	Marginal Effects	p-value
State with coverage mandate	−0.575	0.5478	(−1.65, 0.49)	−0.1218	***
Post-mandate implementation	0.450	0.3397	(−0.22, 1.12)	0.0955
Insurance mandate effect (difference-in-differences model)	0.134	0.3772	(−0.61, 0.87)	0.0284
Age (50–74)	0.059	0.0604	(−0.06, 0.18)	0.0127
Self-reported health status (Fair/poor)	0.181	0.0555	(0.07, 0.29)	0.0384	***
Covered by health insurance	0.854	0.1068	(0.64, 1.06)	0.1811	***
Did not see doctor because of medical costs	−0.075	0.0832	(−0.24, 0.09)	−0.0159
Smoking status	−0.319	0.0603	(−0.44, −0.20)	−0.0677	***
Race/ethnicity
White/Caucasian	0.144	0.0588	(0.03, 0.26)	0.0305	**
Hispanic	−0.335	0.0764	(−0.48, −0.19)	−0.0711	***
Marital status	0.096	0.0455	(0.01, 0.19)	0.0204	**
Education level
Not a high school graduate	−0.441	0.0690	(−0.58, −0.31)	−0.0935	***
High school graduate	−0.342	0.0497	(−0.44, −0.25)	−0.0726	***
Some college or more (Reference category)
Employment status
Currently employed	−0.621	0.0516	(−0.72, −0.52)	−0.1317	***
Currently unemployed	−0.427	0.1180	(−0.66, −0.19)	−0.0905	***
Homemaker/student/unable to work	−0.352	0.0657	(−0.48, −0.22)	−0.0747	***
Retired (Reference category)
Annual income
	−0.469	0.0780	(−0.62, −0.32)	−0.0994	***
$25,000–$49,999	−0.257	0.0682	(−0.39, −0.12)	−0.0545	***
$50,000–$74,999	−0.182	0.0751	(−0.33, −0.03)	−0.0385	**
>$75,000 (Reference category)
Gender (Male)	0.073	0.0438	(−0.01, 0.16)	0.0155	*
Alcohol consumption (Yes, in past 30 days)	0.149	0.0455	(0.06, 0.24)	0.0316	***
Presence of a personal physician	0.772	0.0896	(0.59, 0.95)	0.1638	***

Findings indicate that CRC screening is impacted by most of the variables based on the magnitude of the predictors in the model. We controlled for all other variables in the model with our analytical strategies. White individuals’ screening compliance were 3.1 percentage points higher than that of non-White. Those who reported that their health was poor had compliance of CRC screening that was 3.8 percentage points higher than that of individuals who reported that their health was excellent or good and statistically significant. Smokers’ screening rates were 6.7 percentage points lower than those of non-smokers.

Sensitivity Analysis

Our sensitivity analysis suggested that our DD models produced similar positive findings using the new and previous weighting techniques (9.6 percentage points, p=0.51), populations age 65 or older (15.3 percentage points, p0.05), and examining insured only (3.6 percentage points, p0.05). Insured were more likely to have no cost barriers (−4.0 percentage points, p0.05) [12].

Discussion

“80% In Every Community” is the latest national initiative by the National Colorectal Cancer Roundtable (NCCRT) to screen for CRC. NCCRT states that achieving 80% would prevent 277,000 new cases and 203,000 deaths by 2030 [28, 29]. There are many barriers that are perceived to assist in the increased incidence of CRC in groups that tend to go unscreened [30, 31]. Our study found that health insurance mandates can assist individuals with overcoming access barriers to the health care system. This study also provided initial insight to the role of physician utilization in moderating CRC screening [21, 31–34]. We used physician utilization for this analysis since physician recommendation is not captured within the BRFSS dataset.

The association between health insurance mandates and CRC screening compliance was explored and we found evidence that mandates had significant impacts on screening compliance for White compared to non-White and Hispanic compared to non-Hispanic groups. Our findings suggest that, as out-of-pocket expenses decreased for preventive health services under health insurance mandates, the overall number of CRC screenings increased among the population studied. Insurance mandates increased the probability of being up-to-date relative to being non-compliant by 2.8 percentage points, suggesting that an estimated 2.37 million additional age-eligible persons would receive a screening with such health insurance mandates. These findings are consistent with evidence that such health policies affect the temporal trend of CRC screenings over time [4, 24, 35].

Future studies should consider early-onset of CRC in young adults [8, 36]. Current recommendations do not consider screening this population until age 45 and mandates do not provide provisions for young adults. Reasons for the increase in CRC incidence in this age group remain unknown [36]. Even with symptoms, many providers may not recommend screening to this group to avoid over screening. Although we do not examine participants under the age of 50, this may be a missed opportunity considering the increase in incidence among this population.

Finally, in light of the coronavirus disease 2019 (COVID-19) pandemic, there are many downstream effects related to CRC screening. In response to COVID-19, many nonurgent medical procedures and surgeries were delayed. The downstream effects of these delays include suspension of colonoscopies for CRC) screening and surveillance, reduction in social support, research and advocacy for cancer patients, and the increase in racial disparities in underserved communities [37, 38]. These effects are often exacerbated in populations that are underserved and we continue to see racial disparities increase due to COVID-19. Future mandates can play an important role in addressing CRC screening in the form of FIT testing during unexpected public health crisis.

Implications

With the introduction of the health policies such as ACA, responsive precision public health systems require strategies to determine what policies, systems, and administrative strategies are most effective in increasing the use of preventive health services and reducing disparities [12, 39, 40]. This study addressed policy influences on CRC screenings among various populations and states with mandates before and after ACA implementation. Health insurance mandates are effective mechanisms with which to increase CRC screenings in the U.S.; however, additional strategies should be explored to reach those who are not prepared to take advantage of such provisions [30]. Underway since 2019, the previous requirement for most U.S. citizens to have health insurance or pay a tax penalty will no longer be assessed. However, with the reduction of out-of-pocket expenses for preventive health services, policymakers should expect an increase in the demand for preventive services related to CRC. Finally, under the current administration’s promise to roll back the ACA, it is important to examine future health policies that may have been affected since 2017.

Limitations

Despite our promising findings, there are several limitations to consider in this study. First, the methodological issues in this analysis are cause for concern because provider access in the form of doctor visits may be influenced by unobserved individual characteristics that also influence screenings rates. This factor is a potential source of bias in our model. Using a fixed effect model is one way of minimizing this concern [41]. A fixed effects estimation strategy was used to implicitly control for all time invariance differences for subjects at the state level. Another limitation is the use of endogenous variables. The selection of states that developed mandates prior to ACA may provide evidence of the differences between states that chose to have such policies in place prior to ACA. To address this concern, states with mandates are used as a partial counterfactual in the estimation model. Selection bias was considered for individuals who chose to have a CRC screening after ACA. Since ACA now removes financial barriers, determining whether such policies increase CRC screening rates is important. Uninsured individuals are used as another test population in the estimation model because they are not affected by such policies.

Although telephone surveys allow researchers to evaluate more participants using fewer resource requirements, conducting a telephone survey presents certain limitations. The findings should be interpreted with caution because the data collected do not come from all states due to reporting deadlines. The states not represented may have different confounding variables that may influence preventive measures and thus increase the number of people who obtain CRC screenings. Another limitation of this study is that the BRFSS telephone survey was originally conducted via landlines, and the population affected most may not have landlines or may have them but not use them because they use their cellular phones as their sole means of communication [42]. In 2011, the BRFSS survey included cellular phone participants, which made determining whether the results were caused by the measurement change or ACA difficult [43]. The inclusion of cell phone data introduced a new technique to develop survey weights [43]. To test whether measurement bias occurred, an additional specification model was conducted that did not include the years after the implementation of the ACA.

Conclusion

Our analysis supports the implementation of health insurance mandates and stronger policies that will increase screenings overall. Policies that reduce the amount of out-of-pocket expenses have historically been used to improve health outcomes and are now being used as a precision public health strategy to increase the use of preventive health services to improve the nation’s health. Lowering out-of-pocket costs under ACA is an effective approach to increase CRC screenings and prevent the number of deaths from CRC.

Supplementary Material

1

Acknowledgements

The work by Dr. M.A. Preston was supported by the NCI Designated Massey Cancer Center with funding from NIH-NCI Cancer Center Support Grant (P30 CA016059) which supported M.A. Preston and V.B. Sheppard; and National Coordinating Center for Public Health Services and Systems Research, funded by the Robert Wood Johnson Foundation, as a Junior Investigator Award (grant number 19868) which supported M.A. Preston. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Special thanks to L. Alexis Hoeferlin, PhD, Scientific Writer at Massey Cancer Center, for her technical review.

This work by Dr. M.A. Preston was supported by the NCI Designated Massey Cancer Center with funding from NIH-NCI Cancer Center Support Grant (P30 CA016059) which supported M.A. Preston and V.B. Sheppard and the National Coordinating Center for Public Health Services and Systems Research, funded by the Robert Wood Johnson Foundation, as a Junior Investigator Award (grant number 19868) which supported M.A. Preston. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

The authors declare no potential conflicts of interest.

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