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Keywords:

  • curable cancers;
  • survival outcomes;
  • Medicaid status;
  • linked databases

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

BACKGROUND:

A study was undertaken to compare survival and 5-year mortality by Medicaid status in adults diagnosed with 8 select cancers.

METHODS:

Linking records from the Ohio Cancer Incidence Surveillance System (OCISS) with Ohio Medicaid enrollment data, the authors identified Medicaid and non-Medicaid patients aged 15 to 54 years and diagnosed with the following incident cancers in the years 1996-2002: cancer of the testis; Hodgkin and non-Hodgkin lymphoma; early stage melanoma, colon, lung, and bladder cancer; and pediatric malignancies (n = 12,703). Medicaid beneficiaries were placed in the pre-diagnosis group if they were enrolled in Medicaid at least 3 months before cancer diagnosis, and in the peri/post-diagnosis group if they enrolled in Medicaid upon or after being diagnosed with cancer. The authors also linked the OCISS with death certificates and data from the US Census. By using Cox and logistic regression analysis, they examined the association between Medicaid status and survival and 5-year mortality, respectively, after adjusting for patient covariates.

RESULTS:

Nearly 11% of the study population were Medicaid beneficiaries. Of those, 45% were classified in the peri/post-diagnosis group. Consistent with higher mortality, findings from the Cox regression model indicated that compared with non-Medicaid, patients in the Medicaid pre-diagnosis and peri/post-diagnosis groups experienced unfavorable survival outcomes (adjusted hazard ratio [AHR], 1.52; 95% confidence interval [CI], 1.27-1.82 and AHR, 2.01; 95% CI, 1.70-2.38, respectively).

CONCLUSIONS:

Medicaid status was associated with unfavorable survival, even after adjusting for confounders. The findings reflect the vulnerability of Medicaid beneficiaries and possible inadequacies in the process of care. Cancer 2012. © 2011 American Cancer Society.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Improvement in cancer survival is not shared equally across subgroups of the population.1, 2 Racial disparities have been well documented.1-7 Although it is generally held that low socioeconomic status is associated with disparate outcomes, evidence from population-based databases is lacking, especially for cancers for which 5-year survival is relatively high.1, 3, 8-16

Medicaid beneficiaries constitute a disadvantaged subgroup of the population. In addition to their socioeconomic vulnerabilities, adult beneficiaries who are on Medicaid for reasons other than maternity care suffer from disabling physical and/or mental ailments.

On the other hand, the Medicaid program may alleviate financial barriers, at least partially, and improve access to health services. In the case of cancer patients, the Medicaid program may serve as a 1) (public) health insurance program when history of enrollment indicates that they had been enrolled in Medicaid for some time before cancer diagnosis; or 2) as a safety net program, as may be the case for individuals enrolling in Medicaid upon or after being diagnosed with cancer. Results from previous studies have shown that cancer patients enrolling in Medicaid around the time of cancer diagnosis are significantly more likely to be diagnosed with advanced stage disease than those enrolled in Medicaid before cancer diagnosis.17-20

In this study, we compare survival outcomes for 8 potentially curable cancers in adults 15 to 54 years of age between Medicaid and non-Medicaid populations, using a unique population-based database developed by linking records from the Ohio Cancer Incidence Surveillance System (OCISS), Ohio Medicaid enrollment files, data from Ohio death certificates, and data from the US Census. To distinguish the role of Medicaid as a health insurance versus a safety net program, as described above, we account for the timing of enrollment in Medicaid in relation to cancer diagnosis. We hypothesize that adjusting for patient attributes, as well as for cancer site and stage, Medicaid beneficiaries affected with these cancers experience shorter survival and higher 5-year mortality than their non-Medicaid counterparts. Furthermore, we hypothesize that, among Medicaid beneficiaries, those enrolled in Medicaid before cancer diagnosis experience more favorable survival outcomes than those enrolling in Medicaid upon or after being diagnosed with cancer. By identifying survival differentials across subgroups of the population with curable cancers, this study will pave the way for future research aimed at identifying specific factors contributing to such disparities, and potentially provide a data-driven foundation for the formulation of interventions to address disparities.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Data Sources

The study uses a population-based database developed by linking records from the OCISS, Ohio Medicaid enrollment files, and death certificate data, as detailed below. Furthermore, by using geocoded data from the OCISS, we appended data on income and education, at the census tract level, from the US Census. This study was approved by the Case Cancer Institutional Review Board (IRB), the IRB at the Ohio Department of Health, which maintains the OCISS, and the Ohio Department of Job and Family Services, which administers the state Medicaid program.

OCISS

Established in 1991, all incident cases of cancer diagnosed in Ohio residents are required to be reported to the OCISS. Exceptions are squamous and basal cell carcinoma of the skin and in situ cancer of the cervix uteri. For our study years, the completeness of the OCISS was reported to be >90%.21

The OCISS record carries patient identifiers, including name, Social Security number (SSN), date of birth, sex, race, county, address of residence, and Zip code, as well as cancer-specific data, including date of diagnosis, anatomical site, stage at diagnosis, and tumor markers. Because of missing values in data elements needed to determine cancer stage according to the American Joint Committee on Cancer, we relied on the Surveillance, Epidemiology, and End Results (SEER) summary stage (ie, in situ, local, regional, distant, unstaged/unknown stage).

Ohio Medicaid enrollment files

These files carry records for each individual enrolled in the Ohio Medicaid program. In addition to patient identifiers, these records also carry enrollment spans, which we used to characterize the individual's enrollment history in Medicaid in relation to cancer diagnosis.

Ohio death certificate files

For nearly every decedent who was a resident of the State of Ohio, the death certificate record carries the individual's identifiers, as well as the date and cause of death.

Data from the US Census

The census tract in which the patient resided at that time he or she was diagnosed with cancer is provided in the OCISS data. By using publicly available data from the US Census, we retrieved the median household income and the proportion of adults with high school diplomas at the census tract level.

Study Population

Our study population included Ohio residents 15 to 54 years of age and diagnosed in the years 1996-2002 with incident cancer in the sites and histology types listed below. Testicular, colon, lung, and bladder cancers were identified using site codes, whereas lymphoma, melanoma, and pediatric malignancies were identified with their International Classification of Diseases for Oncology, Second Edition/Third Edition site or histology type codes, as follows: testicular (C620-C629), age 15 to 50 years, all stages (excluding unstaged/unknown stage cases); lymphoma/Hodgkin lymphoma (9650-9667, 9650-9667), age 15 to 50 years, stages in situ, local, and regional; lymphoma/non-Hodgkin lymphoma (9590-9596, 9670-9729), age 15 to 50 years, stages in situ, local, and regional; colon (C180-C189), age 25 to 54 years, stages in situ, local, and regional; lung (C340-C349), age 30 to 50 years, stages in situ and local; bladder (C670-C679), age 45 to 50 years, stages in situ, local, and regional; pediatric malignancies (8900-8905, 8910, 8912, 8920, 8921, 8960, 8991, 9071, 9180-9187, 9192-9195, 9200, 9260, 9310, 9362, 9363, 9364, 9364, 9365, 9470, 9471, 9473, 9474, 9490, 9500, 9506, 9510-9514, 9522, 9826, 9835), age 20 to 50 years, stages in situ, local, and regional; and melanoma (8720-8790), age 20 to 50 years, stages in situ and local.

Data Linkage

Records for individuals identified through the OCISS were linked with records from each of the Medicaid enrollment files and death certificate files, using the following multistep deterministic algorithm used in previous studies20, 22, 23: step 1: SSN, last name, first name, sex; step 2: SSN, last name, date of birth (month), sex; step 3: SSN, first name, date of birth (month), sex; and step 4: first name, last name, date of birth (month and year), sex. The first and last names were truncated to the first 6 digits.

Of cases who were successfully identified in both the OCISS and Medicaid enrollment files, 83.9% were identified through step 1, and 10.7% were identified through step 4. The remaining 5.4% were identified through steps 2 and 3.

Variables of Interest

Outcome variables

Survival time was defined as the time elapsed between the date of diagnosis and date of death. For cancer-specific survival, the outcome was the time between diagnosis and death from any of the curable cancers. Those who did not die by December 31, 2007 were censored on this date.

Five-year mortality was a binary variable (0 of 1), defined as 1 if the individual died within 5 years of cancer diagnosis, and 0 otherwise. For cancer-specific 5-year mortality, patients were assigned 1 if they died of any of the curable cancers, and 0 otherwise.

Independent variables

Medicaid status, the main independent variable, was ascertained through the linkage of the OCISS and Medicaid enrollment files. Furthermore, using the enrollment spans, we constructed beneficiaries' enrollment history in Medicaid relative to their date of cancer diagnosis. This strategy was aimed at distinguishing beneficiaries using the Medicaid program as a health insurance program from those who resort to Medicaid as a safety net program.24 Thus, the following variables were developed:

Medicaid pre-diagnosis, a binary (0 of 1) variable, was defined as 1 if the individual had been enrolled in Medicaid at least 3 months before cancer diagnosis, and 0 otherwise.

Medicaid peri/post-diagnosis, a binary (0 of 1) variable, was defined as 1 if the individual enrolled in Medicaid in the 3-month window before cancer diagnosis, upon cancer diagnosis, or after being diagnosed with cancer, and 0 if the individual enrolled in Medicaid >3 months before but within the calendar year of cancer diagnosis.

Demographic variables, retrieved from the OCISS, included age, race, sex, and marital status. Patients were grouped in the following age categories: 15 to 29, 30 to 39, 40 to 49, and 50 to 54 years. We further identified our subjects as male or female; African American, Caucasians, or “all others”; and as married, nonmarried, or with unknown marital status.

Cancer stage, based on SEER summary stage, was categorized as in situ, local, and regional or distant stages.

County of residence at the time of diagnosis, as recorded in the OCISS, was categorized as follows: Appalachian, rural, metro, and suburban.

Median household income and educational attainment measures were obtained at the census tract level. Income was grouped in quartiles, based on its distribution across the state. Educational attainment reflected the proportion of adults with a high school diploma. Relying on the distribution of this proportion across the state, we flagged low educational attainment if individuals resided in census tracts in which the proportion of adults with a high school education was <10%.

Data Analysis

In addition to detailed descriptive analysis, proportions were compared across the Medicaid pre-diagnosis, peri/post-diagnosis, and non-Medicaid groups using chi-square tests. Moreover, Kaplan-Meier curves were produced for the entire study population (Fig. 1), as well as for each anatomic cancer site (Fig. 2), to compare the survival curves between the Medicaid pre-diagnosis and peri/post-diagnosis groups and non-Medicaid patients. Multivariate survival and logistic regression models were developed to analyze the association between Medicaid status and the outcomes of interest after adjusting for patient demographics, income, geographic area of residence, and cancer site and stage at diagnosis. SAS version 9.2 (SAS Institute, Cary, NC) was used in all of our analyses.

thumbnail image

Figure 1. Disease-specific survival is shown by Medicaid status for all cancer sites combined using Kaplan-Meier curves, log-rank = 323.2, P < .0001.

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RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Our study population included 11,358 non-Medicaid individuals and 1345 Medicaid beneficiaries diagnosed with the relevant cancers (Table 1). Of Medicaid beneficiaries, 755 (56%) were classified in the pre-diagnosis group.

Table 1. Distribution of the Study Population by Medicaid Status, Demographics, Income, Education, County of Residence, and Anatomic Cancer Site/Type
Variable of InterestNon-Medicaid, No. (% of Total)Medicaid, No. (% of Total)Medicaid, Pre-cancer Diagnosis, No. (% of Total)Medicaid, Peri/Post-cancer Diagnosis, No. (% of Total)
  • a

    P = .56, comparing sex and Medicaid/non-Medicaid. All other comparisons are significant at P < .0001.

Age    
 15-29 years1643 (14.5)307 (22.8)147 (19.5)160 (27.1)
 30-39 years2844 (25.0)340 (25.3)180 (23.8)160 (27.1)
 40-49 years5133 (45.2)530 (39.4)316 (41.9)214 (36.3)
 50-54 years1738 (15.3)168 (12.5)112 (14.8)56 (9.5)
Race    
 African American564 (5.0)241 (17.9)147 (19.5)94 (15.9)
 Caucasian10,183 (89.7)1076 (80.0)593 (78.5)483 (81.9)
 All other611 (5.3)28 (2.1)15 (2.0)13 (2.2)
Sexa    
 Male6427 (56.6)750 (55.8)360 (47.7)390 (66.1)
 Female4931 (43.4)595 (44.2)395 (52.3)200 (33.9)
Median household income    
 Quartile 12002 (17.6)669 (49.7)396 (52.5)273 (46.3)
 Quartile 22765 (24.3)362 (26.9)215 (28.5)147 (24.9)
 Quartile 33101 (27.3)204 (15.2)95 (12.6)109 (18.5)
 Quartile 43490 (30.7)110 (8.2)49 (6.5)61 (10.3)
Education    
 Less than High school2047 (18.0)624 (46.4)369 (48.9)255 (43.2)
 High school and more9311 (82.0)721 (53.6)386 (51.1)335 (56.8)
Marital status    
 Married5746 (50.6)313 (23.3)145 (19.2)168 (28.5)
 Nonmarried2660 (23.4)791 (58.8)468 (62.0)323 (54.8)
 Unknown2952 (26.0)241 (17.9)142 (18.8)99 (16.8)
County of residence    
 Appalachian1357 (12.0)267 (19.9)157 (20.8)110 (18.6)
 Metro6286 (55.3)741 (55.1)423 (56.0)318 (53.9)
 Rural1554 (13.7)156 (11.6)74 (9.8)82 (13.9)
 Suburban2161 (19.0)181 (13.5)101 (13.4)80 (13.6)
Anatomic cancer site    
 Bladder577 (5.1)66 (4.9)39 (5.2)27 (4.6)
 Colon2500 (22.0)329 (24.5)202 (26.8)127 (21.5)
 Hodgkin lymphoma739 (6.5)148 (11.0)64 (8.5)84 (14.2)
 Non-Hodgkin lymphoma967 (8.5)207 (15.4)99 (13.1)108 (18.3)
 Lung418 (3.7)134 (10.0)88 (11.7)46 (7.8)
 Melanoma4415 (38.9)208 (15.5)155 (20.5)53 (9.0)
 Pediatric malignancies118 (1.0)36 (2.7)11 (1.5)25 (4.2)
 Testis1624 (14.3)217 (16.1)97 (12.9)120 (20.3)
Cancer stage    
 In situ1983 (17.5)85 (6.3)72 (9.5)13 (2.2)
 Local6707 (59.1)698 (51.9)441 (58.4)257 (43.6)
 Regional or distant2668 (23.5)562 (41.8)242 (32.1)320 (54.2)
5-year cancer specific death    
 Died of cancer in 5 years953 (8.4)295 (21.9)137 (18.2)158 (26.8)
 Did not die of cancer in 5 years10,405 (91.6)1050 (78.1)618 (81.9)432 (73.2)
Total11,358 (100.0)1345 (100.0)755 (100)590 (100)

Across the comparisons groups, we note significant differences in the distribution of the population by the variables of interest. We note a greater representation of younger individuals, African Americans, and nonmarried patients in the Medicaid than in the non-Medicaid group. In characterizing the income level and educational attainment in our study population, we note that whereas 41.9% of non-Medicaid patients resided in census tracts with median household incomes in the lowest 2 quartiles, 81.0% and 71.2% of patients in the Medicaid pre-diagnosis and peri/post-diagnosis groups did so, respectively. Furthermore, the proportion of patients residing in census tracts with low educational attainment was significantly higher in the Medicaid than in non-Medicaid groups. With respect to geographical distribution, we note a greater proportion of Medicaid patients in Appalachian Ohio (20.8% and 18.6% in Medicaid pre-diagnosis and Medicaid peri/post-diagnosis, respectively); only 12% of non-Medicaid patients resided in the disadvantaged areas. Conversely, a greater proportion of non-Medicaid patients resided in suburban counties.

Comparing the distribution of patients by cancer site across Medicaid and non-Medicaid groups, we note greater proportions of patients with Hodgkin and non-Hodgkin lymphoma, as well as lung cancer, among Medicaid than among non-Medicaid patients. With regard to the distribution by stage, we note the significantly higher proportion of patients presenting with regional or distant stage cancer in the Medicaid than in the non-Medicaid population (41.8% vs 23.5%). We also highlight the differences in stage distribution within the Medicaid population between the pre- and the peri/post-diagnosis groups, with the proportion of patients presenting with advanced stage disease being much higher in the latter than in the former group (54.2% and 32.1%, respectively). Finally, the proportion of patients deceased by the end of the 5-year follow-up period was 8.4% in non-Medicaid patients, compared with 18.2% and 26.8% among Medicaid pre-diagnosis and Medicaid peri/post-diagnosis patients, respectively. All of the above comparisons were significant at P < .0001.

Figure 2 shows that the survival curves differ significantly across the study groups for all but pediatric malignancies, with survival being better in non-Medicaid than in Medicaid patients. However, we note interesting differences across the anatomic cancer sites by whether survival is worse for the Medicaid pre-diagnosis or the Medicaid peri/post-diagnosis groups. In patients diagnosed with Hodgkin lymphoma, colon cancer, melanoma, or testicular cancer, survival was worse for patients in the Medicaid peri/post-diagnosis group than for patients in the Medicaid pre-diagnosis group. Conversely, among bladder cancer patients, we note better survival in the Medicaid peri/post-diagnosis group than among their pre-diagnosis counterparts. In patients with lung cancer or non-Hodgkin lymphoma, survival was almost the same for Medicaid pre-diagnosis and for Medicaid peri/post-diagnosis patients.

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Figure 2. Disease-specific survival is shown by Medicaid status for specific cancer sites: (A) bladder, log-rank = 15.3, P = .0005; (B) colon, log-rank = 47.2, P < .0001; (C) Hodgkin lymphoma, log-rank = 13.1, P = .0014; (D) non-Hodgkin lymphoma, log-rank = 24.2, P < .0001; (E) lung, log-rank = 14.1, P = .0009; (F) melanoma, log-rank = 55.7, P < .0001; (G) pediatric malignancies, log-rank = 3.1, P = .2139; and (H) testicular, log-rank = 138.7, P < .0001.

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The results of the multivariate logistic regression analysis and survival models are presented in Table 2. Adjusting for patient demographics, marital status, census tract income and educational attainment, county of residence, and anatomic cancer site, Medicaid patients were significantly more likely to experience unfavorable survival outcomes. Furthermore, the results were more accentuated for those in the Medicaid peri/post-diagnosis group than for those in the Medicaid pre-diagnosis group. With respect to 5-year mortality, patients in the Medicaid pre-diagnosis and peri/post-diagnosis groups were >1.58× and >2.43× as likely as their non-Medicaid counterparts, respectively, to die within 5 years of diagnosis (adjusted odds ratio [AOR], 1.58; 95% confidence interval [CI], 1.25-1.99 and AOR, 2.43; 95% CI, 1.94-3.04). Similar patterns were observed for hazard ratios.

Table 2. Results From the Multivariate Analysis
Variable of Interest5-Year Disease- specific Mortality, AOR (95% CI)Disease-Specific Mortality Through December 31, 2007, AHR (95% CI)
  • Abbreviations: AOR, adjusted odds ratio; CI, confidence interval; AHR, adjusted hazard ratio; ref, reference.

  • a

    P < .001;

  • b

    .001 ≤ P < .01;

  • c

    .01 ≤ P < .05. All other comparisons are not significant at P < .05.

Medicaid status  
 Non-Medicaid, ref
 Medicaid, before cancer diagnosis1.58 (1.25-1.99)a1.52 (1.27-1.82)a
 Medicaid, peri/post-diagnosis2.43 (1.94-3.04)a2.01 (1.70-2.38)a
Age  
 15-29 years, ref
 30-39 years1.23 (0.94-1.61)1.16 (0.93-1.45)
 40-49 years1.59 (1.23-2.07)a1.50 (1.22-1.86)a
 50-54 years1.53 (1.14-2.05)b1.52 (1.20-1.93)a
Race  
 African American1.01 (0.80-1.26)0.98 (0.82-1.16)
 Caucasian, ref
 All other0.49 (0.31-0.78)b0.55 (0.38-0.79)b
Sex  
 Male1.40 (1.23-1.60)a1.36 (1.22-1.51)a
 Female, ref
Median household income  
 Quartile 1, ref
 Quartile 20.93 (0.77-1.13)1.01 (0.87-1.17)
 Quartile 30.92 (0.74-1.15)0.93 (0.79-1.11)
 Quartile 40.77 (0.61-0.97)c0.80 (0.67-0.97)c
Education  
 Less than high school1.05 (0.88-1.26)1.04 (0.90-1.19)
 High school and more, ref
Marital status  
 Married, ref
 Nonmarried1.24 (1.06-1.44)b1.22 (1.08-1.38)c
 Unknown0.95 (0.79-1.14)0.94 (0.82-1.09)
County of residence  
 Appalachian, ref
 Metro1.18 (0.96-1.45)1.16 (0.99-1.37)
 Rural1.31 (1.02-1.69)c1.33 (1.09-1.62)b
 Suburban1.24 (0.97-1.58)1.18 (0.97-1.43)
Anatomic cancer site  
 Bladder7.79 (5.32-11.42)a7.37 (5.35-10.16)a
 Colon4.57 (3.39-6.16)a4.63 (3.56-6.02)a
 Hodgkin lymphoma1.16 (0.79-1.72)1.41 (1.01-1.98)c
 Non-Hodgkin lymphoma3.96 (2.90-5.39)a4.26 (3.24-5.59)a
 Lung23.52 (16.64-33.23)a19.28 (14.38-25.85)a
 Melanoma2.69 (1.94-3.74)a3.22 (2.43-4.28)a
 Pediatric malignancies12.06 (7.71-18.86)a9.60 (6.70-13.74)a
 Testis, ref
Cancer stage  
 In situ, ref
 Local4.49 (3.07-6.56)a3.89 (2.87-5.29)a
 Regional or distant17.26 (11.58-25.73)a12.77 (9.25-17.63)a

Additional notable differences include, first, that men were more likely than women to experience unfavorable survival outcomes. Second, compared with married patients, survival was worse among patients who were not married. We also note great variations in survival outcomes across anatomic cancer sites. Finally, compared with patients with testicular cancer, those with other cancers were significantly more likely to experience 5-year mortality and increased hazard of death over a median follow-up of 7.6 years.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

This study documents the presence of survival disparities by Medicaid status in patients diagnosed with cancers for which 5-year survival is relatively high when adequate treatment is received. These disparities persist even after adjusting for patient demographics, marital status, county of residence, and census tract level income and educational attainment.

Numerous factors can explain these disparities. First, it is possible that Medicaid beneficiaries are initiating treatment late, and/or receiving inadequate treatment. Our data sources in this study preclude us from determining whether treatment-related factors are associated with the differences in survival outcomes between Medicaid and non-Medicaid patients. Second, poor survival associated with Medicaid may be a reflection of the patients' high level of vulnerability, as adults enrolled in the Medicaid program are likely to be disabled, presenting with psychiatric and/or physical comorbidities. Furthermore, although financial barriers may be somewhat reduced with their enrollment in the Medicaid program, these patients encounter various barriers (eg, transportation, poor psychosocial support) that may hinder receipt of adequate treatment and follow-up care.

To our knowledge, this is the first study to analyze survival outcomes by Medicaid status for these cancers, many of which have been largely absent from the disparities literature. The major strength of our study lies in our use of linked databases consisting of the Ohio Cancer Incidence Surveillance System, Medicaid enrollment files, and death certificate files. In addition, rather than accounting for Medicaid status in a dichotomous (yes/no) fashion, we classified Medicaid beneficiaries into the Medicaid pre-diagnosis and peri/post-diagnosis groups to distinguish those using the Medicaid program as a health insurance program from individuals resorting to Medicaid as a safety net program, respectively. Prior studies using a similar approach have reported more favorable stage outcomes in patients enrolled in Medicaid before cancer diagnosis as compared with those enrolling in Medicaid upon or after being diagnosed with cancer, but only for breast, colorectal, lung, and cervical cancer patients.17-20 In addition, longer length of enrollment in Medicaid has been shown to be associated with a greater likelihood of receiving screening mammography.25 Together, these findings suggest that the benefit of being in the pre-diagnosis group may be associated with a greater ability on the part of the beneficiary to develop a network of providers and an ability to navigate the system. However, this remains to be demonstrated in empirical studies. In this study, adjusting for cancer stage, favorable survival outcomes in the pre-diagnosis group were observed for most (early stage colon cancer and melanoma, Hodgkin lymphoma, and all stages of testicular cancer) but not for all cancers. In patients with non-Hodgkin lymphoma and early stage lung cancer, outcomes for the pre-diagnosis and the peri/post-diagnosis groups were almost the same (Fig. 2D, E). In bladder cancer patients, those in the pre-diagnosis group experienced worse survival than those in the peri/post-diagnosis group (Fig. 2A). To better inform policy, additional studies should elucidate the factors associated with these variations by anatomic cancer site.

Given the following limitations, our findings should be interpreted with caution.

First, our data sources preclude us from determining the adequacy of the treatment received, or whether treatment was received in a delayed fashion. Furthermore, we were unable to determine whether the patients experienced cancer relapse.

Second, our income and education measures are at the census tract, and not the individual level. The availability of individual level data on income and educational attainment would have been desirable; in the absence of such measures, however, census tract level measures have been widely used to obtain a proxy of individuals' socioeconomic status.26-28

Third, we note that because our data are specific to Ohio, our results may not be generalizable to patients elsewhere in the United States.

In closing, our study documented important disparities in survival outcomes for the relevant cancers. These disparities persisted after adjusting for patient demographics, marital status, and county of residence. Future studies should focus on the differentials in the vulnerability of the Medicaid and non-Medicaid populations, and elucidate delays and/or inadequacies in the process of care that could explain unfavorable outcomes among Medicaid beneficiaries.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

We thank Ms. Georgette Haydu of the Ohio Department of Health, which maintains the Ohio Cancer Incidence Surveillance System, and Mr. James Gearheart of the Ohio Department of Job and Family Services, which administers the Ohio Medicaid program, for their careful review of the article.

FUNDING SOURCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

This study was supported by National Cancer Institute (R03 CA136064; S.M.K.). S.M.K. is also supported by the Case Western Reserve University/Cleveland Clinic CTSA Grant Number UL1 RR024989 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. D.R. was supported by the M. Frank Rudy and Margaret Domiter Rudy Chair in Translational Cancer Research.

CONFLICT OF INTEREST DISCLOSURES

The authors made no disclosures.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES