Association of race and health insurance in treatment disparities of colon cancer: A retrospective analysis utilizing a national population database in the United States.

BACKGROUND: Both health insurance status and race independently impact colon cancer (CC) care delivery and outcomes. The relative importance of these factors in explaining racial and insurance disparities is less clear, however. This study aimed to determine the association and interaction of race and insurance with CC treatment disparities. STUDY
SETTING: Retrospective cohort review of a prospective hospital-based database. METHODS AND
FINDINGS: In this cross-sectional study, patients diagnosed with stage I to III CC in the United States were identified from the National Cancer Database (NCDB; 2006 to 2016). Multivariable regression with generalized estimating equations (GEEs) were performed to evaluate the association of insurance and race/ethnicity with odds of receipt of surgery (stage I to III) and adjuvant chemotherapy (stage III), with an additional 2-way interaction term to evaluate for effect modification. Confounders included sex, age, median income, rurality, comorbidity, and nodes and margin status for the model for chemotherapy. Of 353,998 patients included, 73.8% (n = 261,349) were non-Hispanic White (NHW) and 11.7% (n = 41,511) were non-Hispanic Black (NHB). NHB patients were less likely to undergo resection [odds ratio (OR) 0.66, 95% confidence interval [CI] 0.61 to 0.72, p < 0.001] or to receive adjuvant chemotherapy [OR 0.83, 95% CI 0.78 to 0.87, p < 0.001] compared to NHW patients. NHB patients with private or Medicare insurance were less likely to undergo resection [OR 0.76, 95% CI 0.63 to 0.91, p = 0.004 (private insurance); OR 0.59, 95% CI 0.53 to 0.66, p < 0.001 (Medicare)] and to receive adjuvant chemotherapy [0.77, 95% CI 0.68 to 0.87, p < 0.001 (private insurance); OR 0.86, 95% CI 0.80 to 0.91, p < 0.001 (Medicare)] compared to similarly insured NHW patients. Although Hispanic patients with private and Medicare insurance were also less likely to undergo surgical resection, this was not the case with adjuvant chemotherapy. This study is mainly limited by the retrospective nature and by the variables provided in the dataset; granular details such as continuity or disruption of insurance coverage or specific chemotherapy agents or dosing cannot be assessed within NCDB.
CONCLUSIONS: This study suggests that racial disparities in receipt of treatment for CC persist even among patients with similar health insurance coverage and that different disparities exist for different racial/ethnic groups. Changes in health policy must therefore recognize that provision of insurance alone may not eliminate cancer treatment racial disparities.

Introduction

Over 100,000 new cases of colon cancer (CC) will be diagnosed in 2021, with the highest incidence among non-Hispanic Black (NHB) patients [1]. Overall, patients of NHB and Hispanic race/ethnicity have a higher incidence of CC, are diagnosed with more advanced disease, and experience worse overall survival compared to patients of non-Hispanic White (NHW) race [1]. It has been estimated that the increase in CC mortality among Black patients may be secondary to more advanced or later stage disease at presentation [2]. This is likely also strongly influenced by social determinants of health (SDOH), which can include but are not limited to education level, employment, income level or poverty, and housing or homelessness [2]. Interventions focused on eliminating racial disparities in screening rates by overcoming some of these barriers have shown improvement in, and in some cases, even elimination of regional racial disparities in cancer outcomes [3].

Passage of the Affordable Care Act (ACA) in 2010 aimed to reduce disparities in insurance coverage with the goal of improving overall access to healthcare, including preventative care [4]. Following implementation of the ACA, health insurance coverage, screening rates, and the frequency of physician visits increased for patients of NHB and Hispanic race/ethnicity [5,6]. However, despite these improvements, minority patients still face delays in cancer treatment and are less likely to receive appropriate therapy [7-9]. It has been proposed that disparities in care may be related to environmental, lifestyle, cultural, socioeconomic, behavioral, and biologic factors as well as access to quality healthcare [10]. Ultimately, however, the intersection between racial disparities in treatment and insurance status remains poorly understood.

The primary aim of this study was to evaluate this intersection between race/ethnicity and insurance, specifically to determine whether racial/ethnic disparities in the receipt of CC treatment potentially differ among patients with the same insurance coverage.

Methods

Data source

The National Cancer Database (NCDB), sponsored by the American College of Surgeons and American Cancer Society, gathers data from more than 1,500 Commission on Cancer (CoC)-accredited facilities in the US. CoC cancer registrars are trained and certified to code data according to rigorously established protocols. The NCDB includes data on more than 70% of newly diagnosed cancer cases nationwide and is felt to be representative of national practice patterns in cancer care [11]. This study was reviewed by the Institutional Review Board of the Brody School of Medicine at East Carolina University and determined to be exempt. Results are reported per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [12].

Study population

Patients aged 18 years or older with a new diagnosis of stage I, II, or III adenocarcinoma of the colon, as defined by the American Joint Commission on Cancer (AJCC), between 2006 and 2016 were identified from the NCDB. Stage was defined according to the sixth and seventh edition of the AJCC Cancer Staging ManuaI [13,14]. Patients with a prior cancer diagnosis were excluded. Patients were then divided into cohorts by race/ethnicity for comparison. Patient race and ethnicity were determined from predefined NCDB data based on assignment by a CoC registrar according to fixed categories, specifically NHW, NHB, Hispanic, and Other.

Variables and outcomes

Clinical and demographic variables were selected a priori from the available data provided in the NCDB participant user file. These included age, race, ethnicity, sex, primary payor, median household income, educational attainment (number of adults in the patient’s ZIP code who did not graduate from high school), rural/urban residence, distance traveled for care, and Charlson/Deyo comorbidity index. Cancer-specific variables included primary tumor location, histologic grade, and analytic stage based on the AJCC classification sixth and seventh edition. Primary tumor location was categorized as left (splenic flexure, descending, or sigmoid), right (cecum, ascending, hepatic flexure, or transverse), or overlapping/not otherwise specified. The design and analysis plan for the study is shown in the Supporting information (S1 Table). The primary outcomes of interest were (1) receipt of surgical resection; and (2) receipt of adjuvant chemotherapy in the subgroup of eligible patients with resected stage III CC, stratified by race/ethnicity and insurance.

Statistical analysis

Continuous variables are described by the number of nonmissing observations, mean, standard deviation, median, and 25th and 75th percentiles. Categorical variables are described overall and by cohort with the number of patients and percentage for each category. Missing data were considered as a separate category.

Outcomes of receipt of surgery and receipt of chemotherapy were stratified by race/ethnicity and insurance and presented as unadjusted percentages. Comparisons were made using chi-squared, 1-way ANOVA, and Kruskal–Wallis tests as appropriate. To adjust for confounding and estimate the association of outcome to covariates, data were fit using multivariable binary logistic regression models. Generalized estimating equations (GEEs) approach was used to accommodate facility clustering assuming an exchangeable working correlation structure. Two GEE models were fit to the data: a main effects model with additive terms for race and insurance status adjusted for additional covariates and a joint effects model with a 2-way interaction term for race and insurance also adjusted for additional covariates. These included age, race, sex, insurance status, income level, education, rurality, comorbidity, distance traveled for care, and tumor stage. For the analysis of receipt of adjuvant chemotherapy outcome, the GEE models also included surgical margins status and the number of lymph nodes resected. Parameter estimates were tested using the Z score. The standard errors, confidence intervals (CIs), Z scores, and p-values are based on empirical standard error estimates. The joint effects model was used to evaluate the effect of race on outcome within levels of insurance status. Adjusted odds ratios (ORs) and 95% CIs are provided as measures of strength of association and precision, respectively. The joint effect of race and insurance status on outcomes was tested using the generalized score chi-squared on 12 degrees of freedom. A 2-sided p-value <0.05 was considered statistically significant. Missing/unknown data were excluded in the multivariable analyses. Analyses were performed with SAS statistical software (version 9.4, SAS Institute, Cary, North Carolina, US).

Results

Demographics

Of the 908,503 patients with CC identified in the 2006 to 2016 NCDB participant user file, 353,998 patients met inclusion criteria (). The subgroup of patients with stage III disease assessed for receipt of adjuvant chemotherapy totaled 129,341 patients. Demographic data by racial cohorts are demonstrated in There were some small differences in regard to mean age at diagnosis and sex across groups. Clinical characteristics were also somewhat different among the cohorts, including Charlson/Deyo comorbidity index as well as primary tumor location (right-sided tumor; 61.1% NHW versus 59.8% NHB, p < 0.001). AJCC stage distribution also varied among the cohorts, with approximately 28.1% stage I, 35.2% stage II, and 36.2% stage III among NHW and 27.5% stage I, 32.7% stage II, and 39.8% stage III among NHB (p < 0.001).

Socioeconomic differences were also observed between cohorts. Compared to NHW patients, more NHB patients were uninsured (6.2% versus 2.1%, p < 0.001) or Medicaid insured (9.5% versus 3.0%, p < 0.001). Similarly, more Hispanic patients were uninsured (8.9% versus 2.1%, p < 0.001) or Medicaid insured (12.3% versus 3.0%, p < 0.001) compared to NHW patients. More NHB patients compared to NHW patients resided in a region with lower median income (45.1% versus 15.1% with median income

Receipt of therapy

Among the entire cohort, 347,206 patients (98.08%) underwent surgery, with a mean time to treatment of 16.3 days (SD 28.4) (). Patients across all racial/ethnic cohorts had similar rates of surgery; however, NHB patients had slightly longer time to surgery compared to NHW patients (18.1 versus 15.9 days, p < 0.001). Of the subgroup of patients with stage III CC who underwent definitive resection, only 68.4% (N = 88,489) received adjuvant chemotherapy, at a mean of 52 days from resection to start of treatment. When evaluated by race/ethnic group, 67.6% of patients of NHW race received adjuvant chemotherapy compared to 70.9% of patients of NHB race (p < 0.001). NHB patients had a slightly longer time from surgery to the start of chemotherapy compared to NHW patients (50.1 versus 56.0 days, p < 0.001).

On unadjusted univariate regression analyses, race/ethnic groups were less likely to receive surgery compared to patients of NHW race but were more likely to receive adjuvant chemotherapy compared to patients of NHW race (). All other insurance categories were associated with lower likelihood of receipt of resection or chemotherapy compared to the private insurance category.

Multivariable logistic regression: Main effects

NHB and Hispanic race/ethnicity were independently associated with decreased odds of undergoing surgical resection compared to NHW race [OR 0.66, 95% CI 0.61 to 0.72 (NHB); OR 0.76, 95% CI 0.67 to 0.85 (Hispanic)] (). Other factors independently associated with decreased odds of resection included Medicaid insurance (OR 0.54, 95% CI 0.47 to 0.62) and higher Charlson/Deyo comorbidity index (OR 0.73, 95% CI 0.65 to 0.81, score of 3 or more versus 0). Compared to private insurance, patients with Medicare insurance had higher odds of undergoing surgical resection (OR 1.19, 95% CI 1.11 to 1.28) ().

Adjusted odds of receiving surgery or chemotherapy by insurance and race/ethnicity.

Data points represent OR, and bars represent 95% CI. Regression model also included the following covariates: age, sex, income, Charlson/Deyo comorbidity index, stage, grade, and rurality. For the chemotherapy group, margin status and number of nodes resected were also included. CI, confidence interval; NHB, non-Hispanic Black; NHW, non-Hispanic White; OR, odds ratio.

In regard to receipt of adjuvant therapy in resected patients, NHB patients had a significantly decreased likelihood of receiving adjuvant chemotherapy [OR 0.83, 95% CI 0.78 to 0.87], but Hispanic patients actually had a higher likelihood of receiving adjuvant therapy [OR 1.20, 95% CI 1.09 to 1.33]. Compared to patients with private insurance, patients with Medicaid or no insurance also had a significantly decreased likelihood of receiving adjuvant chemotherapy compared to those with private insurance [OR 0.55, 95% CI 0.50 to 0.61(Medicaid), OR 46, 95% CI 0.41 to 0.53 (no insurance)], but those with Medicare did not (OR 1.02, 95% CI 0.98 to 1.08). ().

Multivariable logistic regression: Joint effects

NHB and Hispanic patients with Medicare insurance had lower odds of receiving surgery compared to NHW patients with Medicare insurance [OR 0.59, 95% CI 0.53 to 0.66 (NHB); OR 0.71, 95% CI 0.61 to 0.84 (Hispanic)] (). Similar findings were also observed among NHB and Hispanic patients with private insurance compared to NHW patients with private insurance [OR 0.76, 95% CI 0.63 to 0.91 (NHB); OR 0.72, 95% CI 0.56 to 0.92 (Hispanic)]. The odds of receiving adjuvant chemotherapy was also lower for NHB compared to NHW among patients with Medicaid (OR 0.81, 95% CI 0.66 to 0.98), Medicare (OR 0.86, 95% CI 0.80 to 0.91), private insurance (OR 0.77, 95% CI 0.68 to 0.87), and other government insurance (OR 0.59, 95% CI 0.35 to 1.00). () Hispanic patients actually had a higher odds of receiving adjuvant chemotherapy compared to NHW patients in both the Medicare (OR 1.33, 95% CI 1.17 to 1.52) and Medicaid (OR 1.38, 95% CI 1.02 to 1.87) cohorts and were similar to NHW in the other insurance groups ().

Discussion

Despite recent advancements in CC screening, diagnosis, and treatment, patients of NHB and Hispanic race/ethnicity continue to experience worse long-term outcomes. In this large, national study of over 300,000 patients with stage I, II, or III CC diagnosed at CoC hospitals, NHB and Hispanic patients had lower odds of undergoing curative-intent resection, and NHB had lower odds of receiving adjuvant chemotherapy, even in the setting of equivalent health insurance. Importantly, NHB patients had higher rates of no insurance or Medicaid insurance, lower median household income, and more often resided in a ZIP code with less educational attainment. Even after adjusting for these socioeconomic differences, NHB had lower odds of undergoing resection or receiving adjuvant chemotherapy. Further, these differences persisted when comparing racial cohorts with the same health insurance status, suggesting that adequate insurance coverage is not associated with mitigated racial disparities in cancer care delivery.

Across all stages of diagnosis, Black patients are less likely to receive treatment for colorectal cancer [15]. Prior studies of the Surveillance Epidemiology and End Results (SEER) registry have demonstrated that Black patients have lower odds of undergoing surgery for colorectal cancer [15-17]. Disparities in receipt of adjuvant chemotherapy for colorectal cancer and receipt of radiation for rectal cancer for Black and Hispanic patients have also been described based on SEER data [7,15-17]. A recently published study of California state registry data from 2000 to 2012 found that Black patients with metastatic colorectal cancer were less likely to receive chemotherapy or to undergo hepatic metastectomy [18]. A recent study of patients with gastrointestinal cancers (including colorectal cancer) identified in the 2004 to 2015 NCDB found that a disparity in the receipt of surgery had significant influence on survival disparity for Black compared to White patients [9]. In addition, Black patients are less likely to enroll in clinical trials and are less likely to discuss or consider trial enrollment [19,20]. Black patients are also less likely to receive posttreatment surveillance testing [21]. The aggregate disparity in receipt of care for Black patients appears to correlate with the ultimate disparity in survival outcomes for these same patients [10,21,22].

It is also well established that minority race/ethnicity patients are more frequently underinsured. Nationally, Black and Hispanic patients have lower rates of private insurance and concurrently higher rates of public or no insurance compared to White patients [23]. Uninsured rates are particularly high among rural residents of racial/ethnic minority and correlate with self-reported poor health [24]. Inadequate insurance not only limits receipt of care but may also even impact the potential therapeutic benefit of experimental therapy in the context of clinical trials. Pooled data from clinical trials found that patients with Medicaid insurance or with no insurance received less benefit from experimental therapy in the context of a clinical trial when compared to patients with Medicare or private insurance [25]. Not surprisingly, those with Medicaid or no insurance included higher percentages of minority race/ethnicity.

Insurance coverage disparities in general are associated with inadequate CC care and survival and represent a key contributing factor to outcome disparities for patients of minority race and ethnicity [10,26]. A 2016 study on the Massachusetts health insurance reform from 2006 identified improved colorectal cancer resection rates in the state compared to 3 control states without similar health insurance reforms [27]. Although an association with racial treatment disparities was not specifically examined, these findings, along with other studies investigating the impact of the ACA, indicate that insurance coverage plays an important role in the observed treatment and survival disparities in colorectal cancer [28,29].

However, insurance is not the only factor. Evidence indicates that disparities in long-term outcomes experienced by Black patients are multilevel in etiology and may include limited access to screening, mistrust of physicians, socioeconomic barriers including financial limitations, and receipt of quality care [3,30]. This study sought specifically to investigate the intersection of race/ethnicity and insurance with cancer treatment disparities. To our knowledge, the only prior similar analysis on cancer treatment utilized the SEER dataset from 1990 to 2010 and found that Black patients had lower odds of receipt of adjuvant chemotherapy regardless of insurance status [7]. However, there are significant limitations in the assessment of chemotherapy use within the SEER dataset. A 2016 study on disparities in minimally invasive surgery (MIS) approach for colorectal surgery did find persistent Black disparities after stratification by private versus public insurance; however, indications for surgery included benign colorectal and diverticular disease. [31]. While other studies have attempted to adjust for either insurance or race/ethnicity as a covariate, this intersection of insurance and race/ethnicity on cancer treatment disparities has not been directly explored. In this analysis, NHB and Hispanic patients had a persistently lower odds of surgical resection. Interestingly, however, although NHB who underwent resection had lower odds of receiving adjuvant chemotherapy, Hispanic patients did not even in the setting of equivalent health insurance status. This was a surprising finding and suggests that independent factors may play a role in explaining disparities among different races as well as different treatment regimens even among underrepresented and underprivileged minorities.

The use of data obtained from the NCDB merits consideration of several limitations [32]. First, continuity or disruption of insurance coverage cannot be assessed within NCDB; therefore, the association between outcomes and interrupted coverage or disruption of preexisting coverage remains unknown. Second, specific details on chemotherapy agents or dosing are not available to assess for standard of care treatment. Third, although the NCDB is based on rigorous comprehensive data collection, the dataset lacks information regarding specific SDOH, thereby limiting a more comprehensive analysis of other social factors likely to affect healthcare access. In addition, the available socioeconomic variables are based on median values from the ZIP code of residence and are not specific to the individual patient. Furthermore, many potentially confounding factors that may help explain findings in this study are not collected within the NCDB. Fifth, reasons for why a specific treatment was not readily available within the dataset. Finally, the NCBD is not inclusive of all cancer care facilities, hence the data presented may not be generalizable to non CoC-accredited facilities.

Conclusions

Patients with Medicaid insurance coverage or lack of insurance and patients of minority race/ethnicity, especially NHB, are less likely to undergo surgical resection or receive adjuvant chemotherapy. Black and Hispanic patients with equivalent insurance coverage still experience lower odds of surgical resection, and Black patients still experience lower odds of receipt of adjuvant chemotherapy. Changes in health policy must recognize that provision of insurance alone is not associated with improved disparities in cancer care among minority populations and that different minority populations may have different challenges precluding receipt of the standard of care. Comprehensive study of other SDOH such as poverty, literacy, and rurality of residence, as well as policy change addressing these factors, is needed to ensure equity in cancer patient care for patients of all races.

Prospective analysis plan.

(DOCX)

Click here for additional data file.

STROBE Checklist. STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.

10 May 2021

Dear Dr Parikh,

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22 Jun 2021

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Comments from the reviewers:

Reviewer #1: This study aims to determine the interaction of race and insurance with CC treatment disparities.

Comments:

The authors have appropriately provided the STROBE checklist in the supplementary material.

"The National Cancer Database (NCDB) sponsored by the American College of Surgeons and American Cancer Society gathers data from more than 1500 Commission on Cancer (CoC)-accredited facilities in the United States."

Did the authors consider accounting for clustering by health care facility within the analysis?

"The primary outcomes of interest were: 1) receipt of surgical resection and 2) receipt 124 of adjuvant chemotherapy in the subgroup of eligible patients with resected stage III CC, 125 stratified by race and insurance."

Can the authors please comment on whether it is possible to attain information on the offer of treatment to patients, and to therefore model patient uptake rates accounting for this?

"Continuous variables are described by the number of non-missing observations, mean, standard deviation, median, and 25th and 75th percentiles. Categorical variables are described overall and by cohort with the number of patients and percentage for each category. Missing data was considered as a separate category".

The authors have used valid statistical descriptors for the data types in hand.

"To adjust for confounding and estimate the association of outcome to covariates, data was fit using multivariable binary logistic regression models. Two models were fit to the data: a main effects model with additive terms for race and insurance status adjusted for additional covariates and a joint effects model with a two way interaction term for race and insurance also adjusted for additional covariates. These included: age, race, sex, insurance status, income level, education, rurality, comorbidity, distance traveled for care, and tumor grade. The joint effects model was used to evaluate the effect of race on outcome within levels of insurance status. Adjusted odds ratios (OR) and 95% confidence intervals (CI) are provided as measures of strength of association and precision, respectively. The joint effect of race and insurance status on outcomes was tested using Wald's Chi-square on 12 degrees of freedom."

With reference to my earlier comment regarding possible clustering in the data by health care facility, did the authors consider a modelling approach (such as multilevel modelling, for example) that takes this into account?

The authors have done well to include covariates in an attempt to adjust for potential confounding in the models. Can they further comment on whether a measure of deprivation is available for inclusion?

Tables 1 and 2: Did the authors consider statistically testing for differences between racial cohorts and insurance groups, and presenting these results within the Tables and in support of statements in the Results text?

Can the authors please comment on and discuss how "When evaluated by race, 67.6% of patients of NHW race received adjuvant chemotherapy compared to 70.9% of patients of NHB race" (Table 2) matches up with the reduced odds seen in Tables 3 and 4?

Reviewer #2: Thank you for the opportunity to review this paper about the intersection of race and health insurance in the treatment of colon cancer. This paper makes an important contribution to the literature by examining whether racial disparities persist after stratifying by health insurance coverage. Below are suggestions for improving the paper.

* Throughout the paper, the authors use the term "non-White." This term centers whiteness and likely categorizes patients in a way they would not self-identify. Furthermore, using this term may imply to some readers that patients of all races other than white were grouped together in analyses, which does not appear to be the case for this study. Please consider being more precise about the racial and ethnic groups of patients (e.g., non-Hispanic Black and Hispanic) when describing study results.

* In the methods, please clarify the approach used to address missing data.

* In the limitations, it is also important to note that some variables (e.g., educational attainment) were derived using data from the patient's zip code and may not accurately represent the patient's actual education level.

* In the discussion, the authors note that, "Third, although the NCDB is based on rigorous comprehensive data collection, the dataset lacks information regarding specific social determinants of health, thereby limiting a more comprehensive analysis of other social factors likely to affect health care access." This is an important point that warrants more discussion in the paper (e.g., in the introduction and conclusion). It may be helpful to provide key examples of prior evidence about social factors and determinants of health (e.g., racism in health care) that likely influence these outcomes. It is important to be specific about these factors so that readers will not make inaccurate assumptions about potential causes of racial disparities in cancer outcomes.

* In the conclusion, please clarify what is meant by "inadequate" insurance coverage.

* Please revise the following sentence as there appears to be a typo: Uninsured rates are particularly high among rural residents of non-White racial minority and correlate with high rates of self-reported poor health.

Reviewer #3: Dear authors,

This is a well-written manuscript assessing the associations between race/ethnicity, insurance status and colon cancer outcomes. The study's main findings are than non-Hispanic Black patients are less likely to undergo resection for colon cancer or to receive chemotherapy in comparison to non-Hispanic white patients. The study's strongest strength is the use of a large, nationally-representative dataset. Additionally, the manuscript is well organized and written clearly enough to be accessible to health professionals that do not specialize in colon cancer research. However, the study has a number of weaknesses, including the lack of data on important confounders, such as individual-level SES and information about the patients' health systems. Also, the study contained limited granularity in the number of racial and ethnic groups examined.

Importantly, this manuscript is unlikely to directly or substantially affect public health policy. The racial disparity experienced by non-Hispanic Blacks in health care access and colorectal cancer screening, diagnosis and treatment has been well-documented in the literature. The results of this study do not provide a substantial advance over existing knowledge in colorectal cancer outcomes research.

Specific suggestions for the manuscript are provided below:

1. Abstract - the second sentence refers to "these disparities", however disparities have not yet been defined in the text. Please rephrase to define the disparities of interest for the manuscript.

2. General - please be consistent in the use of colon vs colorectal cancer

3. General - please use ethnicity (not race) when referring to comparisons to Hispanic patients

4. Introduction - please update 1st paragraph with data from 2021, and there are racial groups that have lower CC rates than non-Hispanic whites (e.g. API), please revise.

5. Introduction - 2nd paragraph: it is not useful to describe all non-white Americans as a monolith. Please be more specific about the disparities being addressed.

6. Methods - does the dataset include indication for why patients did not receive surgery/chemotherapy?

7. Methods/Discussion - educational attainment measured at the zip code-level was included as a covariate. Please discuss how this variable may be associated with the outcomes of the study.

Any attachments provided with reviews can be seen via the following link:

[LINK]

2 Aug 2021

Submitted filename: Response to Reviewers .docx

Click here for additional data file.

16 Sep 2021

Dear Dr. Parikh,

Thank you very much for re-submitting your manuscript "Association of race and health insurance in treatment disparities of colon cancer: A retrospective analysis utilizing a population database." (PMEDICINE-D-21-02061R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

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[LINK]

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Requests from Editors:

1) Please include the study setting/ country in the title.

2) Please include the study country in the abstract’s “Method and Findings” section.

3) Please integrate the author summary with the main text. This should follow the abstract.

4) In the main text and tables, please provide both Odds Ratios (OR) and adjusted ORs for unadjusted and adjusted analyses.

5) Line #161 “… some…” instead of “…som…”

6) References - Please ensure that journal name abbreviations consistently match those found in the National Center for Biotechnology Information (NCBI) databases, and are appropriately formatted and capitalized. https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references.

Comments from Reviewers:

Reviewer #1: The authors have satisfactorily responded to each comment in turn, amending the analytical approach and presenting statistical tests accordingly.

Reviewer #2: Thank you for the opportunity to review the revised version of this manuscript. The authors have addressed my previous concerns and suggestions. This paper will make a useful contribution to the field, especially now that the authors revised their analytic approach and clarified a number of points throughout the paper.

Any attachments provided with reviews can be seen via the following link:

[LINK]

22 Sep 2021

Submitted filename: PLOS Med Response to Reviewers 9-22.docx

Click here for additional data file.

8 Oct 2021

Dear Dr Parikh,

On behalf of my colleagues and the Academic Editor, Dr. Margaret Kruk, I am pleased to inform you that we have agreed to publish your manuscript "Association of race and health insurance in treatment disparities of colon cancer: A retrospective analysis utilizing a national population database in the United States." (PMEDICINE-D-21-02061R3) in PLOS Medicine.

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Table 1

Cohort demographics by race/ethnicity, 2006 to 2016.

	Racial/ethnic group
	NHW n = 261,349	NHB n = 41,511	Hispanic n = 18,835	Other n = 32,303	Overall n = 353,998	p-Value
Age at diagnosis (years) listed as mean (SD)	69.58 (12.53)	64.46 (12.04)	64.95 (12.58)	67.82 (12.68)	68.57 (12.63)	p < 0.001
Insurance
Uninsured	5,503 (2.1%)	2,588 (6.2%)	1,682 (8.9%)	1,100 (3.4%)	10,873 (3.1%)	p < 0.001
Medicaid	7,822 (3.0%)	3,953 (9.5%)	2,322 (12.3%)	2,082 (6.5%)	16,179 (4.6%)
Medicare	158,536 (60.7%)	19,410 (46.8%)	8,028 (42.6%)	16,600 (51.4%)	202,574 (57.2%)
Private	87,311 (33.4%)	15,107 (36.4%)	6,681 (35.5%)	12,189 (37.7%)	121,288 (34.3%)
Other government	2,177 (0.8%)	453 (1.1%)	122 (0.7%)	332 (1.0%)	3,084 (0.9%)
Sex						p < 0.001
Female	135,924 (52.0%)	23,212 (55.9%)	9,251 (49.1%)	16,957 (54.5%)	185,344 (52.4%)
Male	125,425 (48.0%)	18,299 (44.1%)	9,584 (50.9%)	15,346 (47.5%)	168,654 (47.6%)
Median household income						p < 0.001
Less than US$40,227	39,330 (15.1%)	18,699 (45.1%)	5,110 (27.1%)	5,235 (16.2%)	68,374 (19.3%)
US$40,228 to US$50,353	59,548 (22.8%)	8,359 (20.1%)	4,361 (23.2%)	6,542 (20.3%)	78,810 (22.3%)
US$50,354 to US$63,332	63,282 (24.2%)	6,326 (15.2%)	4,412 (23.4%)	7,418 (23.0%)	81,438 (23.0%)
US$63,333+	95,441 (36.5%)	7,445 (17.9%)	4,740 (25.2%)	12,747 (39.5%)	120,373 (34.0%)
Not available	3,748 (1.4%)	682 (1.6%)	212 (1.1%)	361 (1.1%)	5,003 (1.4%)
% did not graduate from HS						p < 0.001
Less than 6.3%	68,457 (26.2%)	3,376 (8.1%)	1,727 (9.2%)	819 (25.4%)	81,753 (23.1%)
6.3% to 10.8%	78,959 (30.2%)	7,459 (18.0%)	2,935 (15.6%)	9,103 (28.2%)	98,456 (27.8%)
10.9% to 17.5%	68,057 (26.0%)	13,081 (31.5%)	3,896 (20.7%)	8,091 (25.1%)	93,125 (26.3%)
17.6% or more	42,683 (16.3%)	16,982 (40.9%)	10,082 (53.5%)	6,607 (20.5%)	76,354 (421.6%)
Not available	3,193 (1.2%)	613 (1.5%)	195 (1.0%)	309 (1.0%)	4,310 (1.2%)
Rurality						p < 0.001
Metro	210,691 (80.6%)	36,998 (89.1%)	17,654 (93.7%)	27,341 (84.6%)	292,684 (82.7%)
Urban	38,396 (14.7%)	3,369 (8.1%)	784 (4.2%)	3,610 (11.2%)	46,159 (13.0%)
Rural	5,445 (2.1%)	454 (1.1%)	48 (0.3%)	679 (2.1%)	6,626 (1.9%)
Not available	6,817 (2.6%)	690 (1.7%)	349 (1.9%)	673 (2.1%)	8,529 (2.4%)
Distance traveled for care						p < 0.001
Mean (SD)	23.25 (93.92)	14.68 (60.03)	16.87 (75.84)	21.99 (110.68)	21.79 (91.47)
25th to 75th	3.90 to 18.90	3.00 to 12.20	3.10 to 11.90	3.50 to 15.40	3.70 to 17.40
Median	8.30	6.20	6.20	7.20	7.80
Charlson/Deyo comorbidity index						p < 0.001
0	175,197 (67.0%)	27,342 (65.9%)	12,857 (68.3%)	22,504 (69.7%)	237,900 (67.2%)
1	58,370 (22.3%)	9,819 (23.7%)	4,357 (23.1%)	6,870 (21.3%)	79,416 (22.4%)
2	18,498 (7.1%)	2,802 (6.8%)	1,042 (5.5%)	2,010 (6.2%)	24,352 (6.9%)
3 or more	9,284 (3.6%)	1,548 (3.7%)	579 (3.1%)	919 (2.8%)	12,330 (3.5%)
Facility type						p < 0.001
Community	35,265 (13.5%)	3,899 (9.4%)	1,926 (10.2%)	3,879 (12.0%)	44,969 (12.7%)
Comprehensive	127,134 (48.7%)	15,706 (37.8%)	7,960 (42.3%)	13,881 (43.0%)	164,681 (46.5%)
Academic	61,016 (23.4%)	15,318 (36.9%)	6,078 (32.3%)	9,671 (29.9%)	92,083 (26.0%)
Integrated network	37,934 (14.5%)	6,588 (15.9%)	2,871 (15.2%)	4,872 (15.1%)	52,265 (14.8%)
Primary site						p < 0.001
Right	159,760 (61.1%)	24,822 (59.8%)	10,354 (55.0%)	17,689 (54.8%)	212,625 (60.1%)
Left	93,789 (35.9%)	15,198 (36.6%)	7,892 (41.9%)	13,532 (41.9%)	130,411 (36.8%)
Overlapping/NOS	7,800 (3.0%)	1,491 (3.6%)	589 (3.1%)	1,082 (3.4%)	10,962 (3.1%)
Grade						p < 0.001
1	27,892 (10.7%)	4,694 (11.3%)	2,037 (10.8%)	3,428 (10.6%)	38,051 (10.7%)
2	171,698 (65.7%)	28,747 (69.2%)	12,583 (66.8%)	21,720 (67.2%)	234,748 (66.3%)
3	42,506 (16.3%)	5,009 (12.1%)	2,822 (15.0%)	4,953 (15.3%)	55,290 (15.6%)
4	7,074 (2.7%)	662 (1.6%)	394 (2.1%)	568 (1.8%)	8,698 (2.5%)
Not available	12,179 (4.7%)	2,399 (5.8%)	999 (5.3%)	1,634 (5.1%)	17,211 (4.9%)
AJCC stage						p < 0.001
I	73,420 (28.1%)	11,410 (27.5%)	4,675 (24.8%)	8,861 (27.5%)	98,366 (27.8%)
II	93,215 (35.7%)	13,567 (32.7%)	6,514 (34.6%)	11,221 (34.7%)	124,517 (35.2%)
III	94,714 (36.2%)	16,534 (39.8%)	7,646 (40.6%)	12,221 (37.8%)	131,115 (37.0%)

AJCC, American Joint Committee on Cancer; HS, high school; NHB, non-Hispanic Black; NHW, non-Hispanic White; NOS, not otherwise specified; SD, standard deviation.

Table 2

Receipt of treatment by insurance and race/ethnicity.

Cohort		Surgery				Chemotherapy
		No n (%)	Yes n (%)	Treatment started, days from Dx [mean (SD)]	p-Value	No n (%)	Yes n (%)	Treatment started, days from surgery [mean (SD)]	p-Value
OVERALL		6,792 (1.9%)	347,206 (98.1%)	16.3 (28.4)	p < 0.001	40,852 (31.6%)	88,489 (68.4%)	51.2 (34.6)	p < 0.001
Primary payor	Uninsured	258 (2.4%)	10,615 (97.6%)	13.9 (31.6)	p < 0.001	1,090 (22.6%)	3,727 (77.4%)	59.0 (41.2)	p < 0.001
	Medicaid	392 (2.4%)	15,787 (97.6%)	18.2 (45.8)	p < 0.001	1,664 (24.3%)	5,180 (75.7%)	48.1 (31.6)	p < 0.001
	Medicare	4,594 (2.3%)	197,980 (97.7%)	16.4 (27.4)	p < 0.001	30,161 (43.5%)	39,127 (56.5%)	58.0 (37.7)	p < 0.001
	Private	1,463 (1.2%)	119,825 (98.8%)	15.9 (26.6)	p < 0.001	7,636 (16.2%)	39,585 (83.8%)	52.8 (36.0)	p < 0.001
	Other government	85 (2.8%)	2,999 (97.2%)	17.1 (32.5)	p < 0.001	301 (25.7%)	870 (74.3%)	51.5 (28.2)	p < 0.001
Race/ethnicity	NHW	4,697 (1.8%)	256,652 (98.2%)	15.9 (26.7)	p < 0.001	30,267 (32.4%)	63,250 (67.6%)	50.1 (33.5)	p < 0.001
	NHB	1,030 (2.5%)	40,481 (97.5%)	18.1 (37.5)	p < 0.001	4,735 (29.1%)	11,543 (70.9%)	56.0 (38.6)	p < 0.001
	Hispanic	424 (2.3%)	18,411 (97.7%)	18.0 (31.3)	p < 0.001	2,146 (28.5%)	5,376 (71.5%)	54.5 (36.1)	p < 0.001
	Other	641 (2.0%)	31,662 (98.0%)	16.1 (26.6)	p < 0.001	3,704 (30.8%)	8,320 (69.2%)	51.3 (34.7)	p < 0.001

Dx, diagnosis; NHB, non-Hispanic Black; NHW, non-Hispanic White; SD, standard deviation.

Table 3

Unadjusted odds of undergoing surgical resection or receiving adjuvant chemotherapy.

Surgical resection, stage I to III (N = 353,998)
Factor	OR	95% CI	p-Value
Insurance status
Private	Ref	—	—
Medicare	0.42	0.51 to 0.57	<0.001
Other government	0.54	0.34 to 0.51	<0.001
Medicaid	0.51	0.46 to 0.56	<0.001
Uninsured	0.55	0.49 to 0.63	<0.001
Race/ethnicity
NHW	Ref	—	—
NHB	0.69	0.65 to 0.73	<0.001
Hispanic	0.78	0.71 to 0.85	<0.001
Other	0.91	0.82 to 1.02	0.10
Age	0.96	0.96 to 0.96	<0.001
Income
Less than US$40,227	Ref	—	—
US$40,227 to US$50,353	1.14	1.07 to 1.22	<0.001
US$50,353 to US$63,332	1.21	1.13 to 1.28	<0.001
US$63,333+	1.28	1.21 to 1.36	<0.001
Sex
Male	Ref	—	—
Female	0.95	0.91 to 0.99	0.18
Rurality
Metro	Ref	—	—
Urban	1.04	0.97 to 1.10	0.26
Rural	1.40	1.16 to 1.67	<0.001
Charlson/Deyo comorbidity index
0	Ref	—	—
1	1.07	1.02 to 1.13	0.01
2	0.78	0.73 to 0.85	<0.001
3 or more	0.58	0.53 to 0.64	<0.001
Stage
1	Ref	—	—
2	2.81	2.67 to 2.95	<0.001
3	3.83	3.62 to 4.05	<0.001
Adjuvant chemotherapy, stage III (N = 129,341)
Insurance status
Private	Ref	—	—
Medicare	0.24	0.24 to 0.25	<0.001
Other government	0.56	0.50 to 0.64	<0.001
Medicaid	0.62	0.59 to 0.66	<0.001
Uninsured	0.71	0.66 to 0.76	<0.001
Race/ethnicity
NHW	Ref	—	—
NHB	1.20	1.16 to 1.24	<0.001
Hispanic	1.42	1.35 to 1.50	<0.001
Other	1.38	1.31 to 1.46	<0.001
Age	0.92	0.92 to 0.92	<0.001
Income
Less than US$40,227	Ref	—	—
US$40,227 to US$50,353	1.06	1.03 to 1.10	<0.001
US$50,353 to US$63,332	1.08	1.03 to 1.10	<0.001
US$63,333+	1.13	1.09 to 1.16	<0.001
Sex
Male	Ref	—	—
Female	0.83	0.0.81 to 0.85	<0.001
Rurality
Metro	Ref	—	—
Urban	1.06	1.03 to 1.10	<0.001
Rural	1.04	0.96 to 1.12	0.35
Charlson/Deyo comorbidity index
0	Ref	—	—
1	0.68	0.67 to 0.70	<0.001
2	0.46	0.44 to 0.48	<0.001
3 or more	0.33	0.31 to 0.35	<0.001
Margin positive
Negative	Ref	—	—
Positive	0.77	0.74 to 0.80	<0.001
Number of lymph nodes resected
≤12	Ref	—	—
≥12	1.52	1.48 to 1.56	<0.001

CI, confidence interval; NHB, non-Hispanic Black; NHW, non-Hispanic White; OR, odds ratio; Ref, reference.

Table 4

Adjusted odds of undergoing surgical resection or receiving adjuvant chemotherapy.

Surgical resection, stage I to III (N = 353,998)
Factor	OR	95% CI	p-Value
Insurance status
Private	Ref	—	—
Medicare	1.19	1.11 to 1.28	<0.001
Other government	0.55	0.44 to 0.70	<0.001
Medicaid	0.54	0.47 to 0.62	<0.001
Uninsured	0.43	0.37 to 0.51	<0.001
Race/ethnicity
NHW	Ref	—	—
NHB	0.66	0.61 to 0.72	<0.001
Hispanic	0.76	0.67 to 0.85	<0.001
Other	0.87	0.79 to 0.97	0.002
Age	0.94	0.94 to 0.95	<0.001
Income
Less than US$40,227	Ref	—	—
US$40,227 to US$50,353	1.05	0.96 to 1.14	0.29
US$50,353 to US$63,332	1.09	1.00 to 1.19	0.059
US$63,333+	1.22	1.11 to 1.34	<0.001
Sex
Male	Ref	—	—
Female	1.04	0.98 to 1.09	0.18
Rurality
Metro	Ref	—	—
Urban	1.01	0.93 to 1.10	0.79
Rural	1.53	1.23 to 1.90	<0.001
Charlson/Deyo comorbidity index
0	Ref	—	—
1	1.19	1.11 to 1.26	<0.001
2	0.95	0.87 to 1.04	0.27
3 or more	0.73	0.65 to 0.81	<0.001
Stage
1	Ref	—	—
2	2.91	2.69 to 3.15	<0.001
3	3.88	3.58 to 4.21	<0.001
Adjuvant chemotherapy, stage III (N = 129,341)
Insurance status
Private	Ref	—	—
Medicare	1.02	0.98 to 1.08	0.26
Other government	0.83	0.67 to 1.03	0.084
Medicaid	0.55	0.50 to 0.61	<0.001
Uninsured	0.46	0.41 to 0.53	<0.001
Race/ethnicity
NHW	Ref	—	—
NHB	0.83	0.78 to 0.87	<0.001
Hispanic	1.20	1.09 to 1.33	<0.001
Other	0.97	0.91 to 1.04	0.43
Age	0.90	0.90 to 0.91	<0.001
Income
Less than US$40,227	Ref	—	—
US$40,227 to US$50,353	1.07	1.01 to 1.12	0.015
US$50,353 to US$63,332	1.17	1.11 to 1.23	<0.001
US$63,333+	1.22	1.16 to 1.29	<0.001
Sex
Male	Ref	—	—
Female	1.01	0.98 to 1.04	0.39
Rurality
Metro	Ref	—	—
Urban	0.97	0.92 to 1.03	0.35
Rural	0.94	0.84 to 1.04	0.24
Charlson/Deyo comorbidity index
0	Ref	—	—
1	0.85	0.82 to 0.88	<0.001
2	0.64	0.60 to 0.67	<0.001
3 or more	0.47	0.44 to 0.51	<0.001
Margin positive
Negative	Ref	—	—
Positive	0.77	0.72 to 0.81	<0.001
Number of lymph nodes resected
≤12	Ref	—	—
≥12	1.28	1.22 to 1.34	<0.001

CI, confidence interval; NHB, non-Hispanic Black; NHW, non-Hispanic White; OR, odds ratio; Ref, reference.

Table 5

Effect modification of insurance on race/ethnicity and surgical resection.

Insurance	Race/ethnicity	Surgical resection
		OR	95% CI	p-Value	E-Value
Uninsured	NHW	Ref
	NHB	0.91	0.64 to 1.28	0.58	1.28
	Hispanic	0.95	0.6 to 1.50	0.81	1.2
Medicaid	NHW	Ref
	NHB	0.94	0.73 to 1.20	0.60	1.22
	Hispanic	1.27	0.91 to 1.77	0.15	1.51
Medicare	NHW	Ref
	NHB	0.59	0.53 to 0.66	<0.001	1.92
	Hispanic	0.71	0.61 to 0.84	<0.001	1.65
Private	NHW	Ref
	NHB	0.76	0.63 to 0.91	0.004	1.55
	Hispanic	0.72	0.56 to 0.92	0.009	1.64
Other government	NHW	Ref
	NHB	0.98	0.49 to 1.95	0.95	1.2
	Hispanic	0.44	0.16 to 1.21	0.11	2.39

CI, confidence interval; NHB, non-Hispanic Black; NHW, non-Hispanic White; OR, odds ratio; Ref, reference.

Table 6

Effect modification of insurance on race/ethnicity and adjuvant chemotherapy.

Insurance	Race/ethnicity	Adjuvant chemotherapy
		OR	95% CI	p-Value	E-value
Uninsured	NHW	Ref
	NHB	0.96	0.72 to 1.29	0.81	1.15
	Hispanic	1.07	0.76 to 1.50	0.70	1.22
Medicaid	NHW	Ref
	NHB	0.81	0.66 to 0.98	0.031	1.47
	Hispanic	1.38	1.02 to 1.87	0.035	1.63
Medicare	NHW	Ref
	NHB	0.86	0.80 to 0.91	<0.001	1.39
	Hispanic	1.33	1.17 to 1.52	<0.001	1.58
Private	NHW	Ref
	NHB	0.77	0.68 to 0.87	<0.001	1.54
	Hispanic	0.96	0.81 to 1.13	0.64	1.16
Other government	NHW	Ref
	NHB	0.59	0.35 to 1.00	0.05	1.92
	Hispanic	0.96	0.81 to 1.13	0.64	1.16

CI, confidence interval; NHB, non-Hispanic Black; NHW, non-Hispanic White; OR, odds ratio; Ref, reference.