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Original Article | ONLINE FIRST

Treatment Patterns and Survival Among Low-Income Medicaid Patients With Head and Neck Cancer FREE

Sujha Subramanian, PhD; Amy Chen, MD
[+] Author Affiliations

Author Affiliations: RTI International, Waltham, Massachusetts (Dr Subramanian); and Department of Otolaryngology Head and Neck Surgery, Emory University School of Medicine, Atlanta, Georgia (Dr Chen).


JAMA Otolaryngol Head Neck Surg. 2013;139(5):489-495. doi:10.1001/jamaoto.2013.2549.
Text Size: A A A
Published online

Importance Medicaid beneficiaries by definition are low income but they are not necessarily a homogeneous group. No study has assessed differences and disparities among Medicaid beneficiaries with head and neck cancers.

Objective To examine predictors of treatment receipt and mortality among Medicaid patients with head and neck cancer.

Design Retrospective cohort study using Medicaid claims linked with cancer registry data for 2 states, California and Georgia, for the years 2002 through 2006.

Setting Inpatient and ambulatory care.

Participants Medicaid beneficiaries aged 18 to 64 years diagnosed as having head and neck cancer (N = 1308) were included. Descriptive statistics and multivariate regression models analyzed the likelihood of treatment receipt and survival.

Main Outcomes and Measures Receipt of treatment and 12- and 24-month mortality.

Results Fewer than one-third of Medicaid patients with cancer received a diagnosis at an early stage. Overall, black patients were less likely to get surgical treatment and more likely to die than white patients, even after controlling for demographics, stage at diagnosis, and tumor site. Older age and disability status also increased 12-month mortality. Patients in California, who were alive for at least 12 months, have approximately half the odds of dying within 24 months compared with those in Georgia.

Conclusions and Relevance Concrete steps should be taken to address the significant racial disparities observed in head and neck cancer outcomes among Medicaid beneficiaries. Further research is needed to explore the state-level policies and attributes to examine the startling differences in mortality among the state Medicaid programs analyzed in this study. Pooled comparisons of Medicaid beneficiaries with individuals covered by other types of insurance could mask important disparities among Medicaid beneficiaries, which need to be acknowledged and addressed to improve outcomes for these low-income patients with head and neck cancer.

Cancer of the head and neck remains a significant cause of cancer deaths in the United States.1 An estimated 40 250 new cases of cancer of the head and neck are expected in 2012, with an estimated 7850 deaths.2 Studies have consistently provided evidence that individuals with more disadvantaged socioeconomic status have a higher probability of developing head and neck cancers.3,4 The risk factors traditionally associated with oral cancers, which comprise a large segment of head and neck cancers, are smoking and alcohol use, but in recent years cancers of the oropharynx have been associated with the presence of the human papillomavirus (HPV).5,6 Cancers of the oropharynx are currently increasing in incidence unlike other major cancer sites such as the lung and colorectal area, which are declining.2 These cancers, when identified at early stages, can be successfully treated using a multidisciplinary approach, which could involve surgery, radiation, and chemotherapy.6

The Medicaid program is the nation's single largest source of health insurance for low-income nonelderly individuals and plays a critical role in providing coverage for these individuals when diagnosed as having cancer.7,8 Medicaid enrollees include a large number of individuals with disabilities, and those diagnosed as having human immunodeficiency virus or AIDS. In fiscal year 2009, there were approximately 62 million Medicaid enrollees, and federal and state expenditures totaled more than $350 billion.9 When health care reform is implemented, Medicaid coverage will be further expanded, with states receiving federal funding to increase their Medicaid enrollment to cover low-income individuals earning up to 133% of the poverty level.

Prior studies have reported that Medicaid enrollees are more likely to be diagnosed as having late-stage cancers compared with those enrolled in private health plans.10,11 In addition, low-income men and women have significantly lower overall survival than those at higher socioeconomic status, even after controlling for stage of diagnosis.1214 Therefore, those enrolled in Medicaid experience worse outcomes during the treatment and follow-up phase.

To our knowledge, no study to date has assessed whether there are differences and disparities among the Medicaid beneficiaries themselves. Medicaid beneficiaries are not necessarily a homogeneous group, although they are all low-income. Unlike the Medicare program, Medicaid is administered by states and there can be variation in program policies related to eligibility, coverage, and health care provider reimbursement that can lead to differences in cancer treatment and outcomes. Studies that have reported results pooling all Medicaid beneficiaries together may mask variation within those covered by the Medicaid programs. No prelinked data, such as the Surveillance, Epidemiology, and End Results–Medicare linked database, exist for Medicaid beneficiaries, and this makes it challenging to perform analysis on a large cohort of Medicaid patients with cancer, who received a head and neck cancer diagnosis, because compiling individualized, linked Medicaid claims and cancer registry data poses a variety of challenges.15 To perform this study, we have successfully linked Medicaid claims data with cancer registry data for 2 state Medicaid programs, California and Georgia. The findings from this analysis will provide valuable insights into understanding the potential disparities in head and neck cancer treatment and outcomes related to individual characteristics of Medicaid beneficiaries and state policies that have an impact on eligibility criteria and continuity of enrollment.

DATA SOURCES AND STUDY POPULATION

We analyzed patient-level Medicaid administrative data from Georgia and California (standardized files from Centers of Medicaid & Medicare Services) linked with cancer registry data for the years from 2002 through 2006. These states were selected because they provide relatively large numbers of patients with head and neck cancer, have good-quality cancer registry and Medicaid claims data, have a racially diverse population, and have differences in Medicaid policy related to eligibility and coverage. All patients, excluding those identified via death certificates or autopsies, were selected from the cancer registry files for linkage with the Medicaid eligibility files using social security numbers, date of birth, and sex. Our analytic cohort was restricted to those 18 to 64 years at the time of cancer diagnosis and those who were not dually enrolled in both Medicare and Medicaid because Medicare is the primary insurer for these individuals. This study was approved by RTI Internationals' institutional review board.

DEMOGRAPHICS, MEDICAID ELIGIBILITY, AND CANCER-RELATED VARIABLES

These variables were created using data from the Medicaid and cancer registry databases as appropriate. Age at diagnosis was created in 3-year groupings based on date of diagnosis reported in the cancer registry. Beneficiaries were categorized as white, black, other (includes races/ethnicities such as Hispanics, Asians) and unknown race based on the information on race and ethnicity provided in the Medicaid eligibility file (when missing or unknown we supplemented using race information reported in the registry). We also created a variable to identify disabled beneficiaries. The following 4 categories of Medicaid eligibility at the time of diagnosis were created: cash (eligibility based on receipt of cash assistance); medically needy (those who have high medical bills and spend down to qualify); poverty (meets low income threshold); or other (includes expansion programs). The medically needy and expansion programs are not mandated eligibility groups, and states have discretion over what population they choose to cover. For example, the medically needy program must cover pregnant women and children up to age 18 years, but there are several other groups that are optional including parents or caretakers of dependent children and blind or disabled individuals. Therefore, the size of a medically needy program differs by state based on which categories they choose to cover and the proportion among those diagnosed as having head and neck cancers can also vary (depends on the underlying population because a younger cohort will have fewer cancers). Using the cancer staging information provided in the registry, we identified the stage at diagnosis as in situ, local, regional, distant, or unknown. Finally, we also created categories for tumor sites to control for potential differences between the sites.

Medicaid beneficiaries can experience significant interruptions in coverage, and this “churn” in coverage can result in beneficiaries losing enrollment permanently or experiencing gaps that may last several months.16,17 Individuals in Medicaid are subject to repetitive eligibility verification (sometimes as often as every 3 months along with income reported monthly) and changes in employment status, and income related to either the individual with cancer or their family can alter their eligibility for Medicaid. In addition, even when a beneficiary remains eligible, cumbersome paperwork requirements can lead to loss of coverage. The breaks in coverage experienced by Medicaid beneficiaries can be particularly detrimental to individuals who are in the process of undergoing intensive treatment for chronic diseases such as cancer.1820 The few studies that have specifically analyzed Medicaid enrollment patterns after cancer diagnosis have shown that a significant proportion of Medicaid beneficiaries disenroll in the few months after diagnosis.21,22 Because this could have a significant impact on treatment and outcomes, we stratified our cohort by continuity of enrollment in Medicaid. Individuals with gaps in coverage of 2 months or more during the 12-month period from diagnosis were classified as not continuously enrolled. The 12-month period was selected to reflect the period when patients receive their first course of treatment. We excluded those who died during the 12-month follow-up period because our data do not allow us to distinguish between intended palliative and curative treatments, and these patients may be in their terminal phase and may not be engaged in active curative treatment.

OUTCOME MEASURES

We created a dichotomous variable to indicate whether a patient with cancer was alive or dead. In addition, we also created a 24-month mortality indicator to assess factors associated with mortality for those who survived at least 12 months from diagnosis. The mortality variables were created based on data reported in the cancer registry data and supplemented with additional data from the Medicaid eligibility files. We also assessed receipt of treatment as an outcome measure, specifically whether surgery, radiation, chemotherapy was received by the patient. We focus on the first course of treatment, which can include multiple modalities. The treatment measures were constructed from the cancer registry data because Medicaid claims do not contain complete information for those who are enrolled in managed care or for those who disenroll during the follow-up period. We calculated rates for each treatment variable, excluding those who were unable to undergo the treatment because of their medical condition or who refused the treatment recommended. This was done to ensure that the proportion of patients not receiving treatment was appropriately identified.

STATISTICAL ANALYSIS

We report a series of descriptive statistics to identify potential differences between the study cohorts for each state separately. These include demographics, cancer and tumor variables, and unadjusted outcomes. We also provide details on Medicaid enrollment patterns, including number of months continuously enrolled and reenrollment among those who disenroll.

We performed logistic regressions to identify factors that have an impact on both mortality and receipt of cancer treatment. The independent variables in the logistic regressions were 12-month mortality, 24-month mortality, and receipt of treatment (surgery, chemotherapy and radiation). The 12-month mortality regression includes all patients, whereas the 24-month and treatment regressions include only those who were alive for at least 12 months to assess the impact of continuity of enrollment. All regressions were run pooling together patients from both states, and a state program identifier was included to test for differences between Georgia and California. All previously described variables were included as dependent variables. A dichotomous variable to identify those continuously enrolled was included as an independent variable in the treatment regressions. We report the odds ratio, 95% confidence intervals, and significance at the 95% level for all variables. All programming and statistical analysis were performed using the SAS software (SAS Institute Inc).

Table 1 presents the patient characteristics stratified by state and by 12-month survival. In both California and Georgia, approximately 30% died within a year of diagnosis. Being older, male, black, and disabled and having more advanced stage cancer were associated with a higher mortality rate. In California, 90.7% of those alive for 12 months or more were continuously enrolled compared with 71.0% in Georgia. Among those alive for 12 months or more, those continuously enrolled were older and more likely to have qualified on the basis of cash eligibility than those not continuously enrolled in both states. In addition, in California, those who were disabled were more likely to be continuously enrolled. In Georgia, there was no difference based on disability status, but overall, Georgia had a higher proportion of disabled enrollees than California.

Table Graphic Jump LocationTable 1. Distribution of Selected Characteristics Among Medicaid Patients With Head and Neck Cancer

Table 2 presents the Medicaid enrollment patterns for those who survived at least 12 months but were not continuously enrolled in Medicaid during those 12 months. In both California (54.7%) and Georgia (63.4%), most of these patients were not continuously enrolled in Medicaid for the first 6 months after cancer diagnosis. On average, they were continuously enrolled for the first 5.8 months in California and the first 5.1 months in Georgia. In both states, more than half of those who lost Medicaid coverage were reenrolled during the 12-month period after diagnosis. Overall, in the 12 months after cancer diagnosis, the mean total number of months enrolled among those without continuous coverage was 6.8 months in California and 6.5 months in Georgia. There were few differences in the unadjusted outcomes between those continuously and not continuously enrolled in each of the 2 states (Table 3). According to information provided in the cancer registry, 5.1% to 8.5% of patients across the groups did not receive any treatment. More than half received multiple treatment modalities. Unadjusted 24-month all-cause mortality did not differ significantly between the groups in each state, though those without continuous enrollment did have slightly higher rates. However, the mortality rate in Georgia (33.1%) was 10% higher than in California (22.2%), even for those continuously enrolled.

Table Graphic Jump LocationTable 2. Medicaid Enrollment Patterns of Patients Not Continuously Enrolled in Medicaid for the 12 Months After Cancer Diagnosisa
Table Graphic Jump LocationTable 3. Unadjusted First-Course Treatment and All-Cause Mortality for Medicaid Patients With Head and Neck Cancer Surviving at Least 12 Months After Diagnosisa

Table 4 presents the correlations between mortality and patient, treatment, and Medicaid coverage characteristics. Among all patients, being older, black, disabled, impoverished, and having more advanced cancer were associated with greater 12-month mortality. Among those alive for 12 months or more, the key factors affecting receipt of treatment were stage at diagnosis and site of tumor. The one exception was receipt of surgery; black patients were less likely to receive surgery (odds ratio [OR], 0.63) compared with white patients after controlling for demographics, disability status, Medicaid eligibility, stage at diagnosis, tumor site, state, and continuity of enrollment. No statistically significant differences were present in first course treatment receipt owing to lack of continuous Medicaid enrollment.

Table Graphic Jump LocationTable 4. Factors Associated With Treatment Receipt and Mortality Among Medicaid Patients With Head and Neck Cancer

A few key variables were significant in the 24-month mortality regression among those alive for at least 12 months. Black patients compared with white patients and those diagnosed at a distance and regional stages compared with local stage were more likely to die within 24 months. Finally, Medicaid enrollees in California (odds ratio, 0.58) were less likely to die within 24 months than those enrolled in Georgia.

In this study, we analyzed Medicaid claims linked with cancer registry data to identify the factors that affect receipt of treatment and mortality among patients with head and neck cancer. A substantial proportion of these patients, almost a third, die within 12 months of diagnosis. Less than one-third of the patients are diagnosed at an early stage, when cancer is localized and has not spread, and treatments have a higher probability of success. Previous studies have shown that Medicaid beneficiaries receive a diagnosis at later stages in their cancer progression compared with individuals enrolled by private health plans.10,11 Overall, lower socioeconomic status seems to increase the incidence of these cancers, and this increase could be due to a combination of factors including lack of awareness of these cancers and barriers to obtaining care for screening or early detection of these cancers.3 Therefore, it is essential that steps are taken to educate this population on the symptoms of these cancers and emphasize the importance of dental checkups to improve early detection of head and neck cancer in this population.

The study findings indicate that black patients are more likely to die, both at the 12- and 24-month follow-up, than white patients, even after controlling for stage at diagnosis and other factors. We also found that black patients were less likely to undergo surgery for these cancers. A recent study using data from the Surveillance, Epidemiology, and End Results Program,23 which contains population-based data on patients with oropharyngeal cancer in selected geographic areas, also identified similar racial disparities in undergoing surgery and found that the magnitude of the racial disparity was attenuated with increasing age and rural location. The authors found slightly lower relative odds of death for black patients (odds ratio, 1.2) than we found (odds ratio, 1.6). Our study involved low-income individuals enrolled in Medicaid, and it is possible that the racial discrepancies seen in these individuals are indeed more attenuated. Potential reasons for the lower surgical rate among black patients could include lower acceptance of surgical interventions, lack of regular source of care, and poor communication between patient and health care provider.24,25 Further research is urgently required to explore the reasons for lower rates of surgical intervention and higher mortality rates among black patients. Interventions, such as patient navigation, improved physician-patient communication, and improved access to cancer facilities may help reduce the disparities seen in the receipt of surgical interventions.

In addition, disabled Medicaid beneficiaries were almost twice as likely to die within 12 months of cancer diagnosis compared with those without disability. Similarly, those eligible for Medicaid due to poverty (individuals who meet certain low-income thresholds) were more likely to die with a year of diagnosis compared with individuals who qualified because they were receiving cash assistance. These individuals are among the most vulnerable of the Medicaid beneficiaries, and the findings highlight the disparity in outcomes experienced among the Medicaid beneficiaries themselves. It is possible that there are other factors apart from those we controlled for in the multivariate analysis that could explain the differences in the outcomes, but these results raise caution against considering the Medicaid population as a homogeneous group. Therefore, pooled comparisons of Medicaid beneficiaries with individuals covered by other types of insurance could mask important disparities within the Medicaid program.

Continuity of enrollment over a 12-month period did not significantly affect the treatment measures assessed in this study. Although on average, Medicaid beneficiaries with cancer were enrolled for less than 6 months from the time of diagnosis, more than half of those who lost coverage reenrolled in Medicaid within the 12-month period from diagnosis. It is important to note that in our analysis we captured receipt of treatment, but we cannot confirm whether the course of recommended treatment was successfully completed. This would be especially true for radiation and chemotherapy. In addition, cancer registry data are generally considered to be high quality for inpatient services such as surgery and generally less complete for services such as chemotherapy, which is usually provided in the outpatient setting or oncologist's office.26

There was no difference in 12-month mortality among the patients in Georgia and California, but those who survived at least 12 months in California had approximately half the odds of dying within the subsequent 24 months compared with those in Georgia after controlling for several key factors including stage at diagnosis. This difference between the 2 states is startling, and further research is needed to understand the reasons for this large variation in cancer mortality. In this study, we report that approximately 9% of Medicaid patients were not continuously enrolled in California compared with almost 32% in Georgia. This variation, which is likely due to differences in Medicaid program policies in the 2 states, may at least to some extent explain the differences in the long-term outcomes.

A potential limitation of our study is that this analysis is not based on random assignment of states, and only 2 states were included. We used information available in the Medicaid claims and cancer registry data to control for potential variation between patients and across the states in our multivariate analysis, but we may not have been able to control for all differences. Second, although we analyzed all available patients with head and neck cancer in the 2 selected states for a 5-year period, the samples after stratifications were small for some groups. Third, we restricted our population to those continuously enrolled for at least a12-month period after cancer diagnosis to ensure that adequate uninterrupted follow-up period was available to assess continuity of enrollment. We assessed initial treatment within this timeframe but assessed mortality at 24 months. We did not have enrollment details to capture potential differences among the patients after the initial 12-month period.

The Medicaid program fulfills a critical need by providing insurance coverage for low-income patients diagnosed as having cancer, and the importance of Medicaid coverage will increase with the implementation of health reform. The findings from this study indicate differences in head and neck cancer outcomes based on both individual, specifically race, and state-level factors. The marked difference in survival between California and Georgia Medicaid recipients suggests that features of the Medicaid program at a state level must be carefully considered to optimize access, treatment, and quality of care. Further research, using data from a larger number of states, is needed to test and explore the findings from this study on continuity of enrollment and racial disparities. We will submit a grant proposal to perform a multifaceted assessment, using Medicaid claims, cancer registry data, and patient survey from several states, to investigate the reasons for the racial disparities identified in the present analysis. This future study will help identify targeted interventions that can be systematically evaluated and then implemented to reduce these racial disparities.

Submitted for Publication: September 23, 2012; final revision received December 18, 2012; accepted January 31, 2013.

Published Online: April 18, 2013. doi:10.1001/jamaoto.2013.2549

Correspondence: Sujha Subramanian, PhD, Senior Health Economist, RTI International, 1440 Main St, Ste 310, Waltham, MA 02451-1623 (ssubramanian@rti.org).

Author Contributions: Dr Subramanian had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Both authors. Acquisition of data: Subramanian. Analysis and interpretation of data: Both authors. Drafting of the manuscript: Both authors. Critical revision of the manuscript for important intellectual content: Chen. Statistical analysis: Subramanian. Obtained funding: Subramanian. Administrative, technical, and material support: Both authors.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by grant R21 CA165093-02 from the National Institute of Dental and Craniofacial Research.

Howlader N, Noone AM, Krapcho M,  et al.  SEER Cancer Statistics Review, 1975-2009 (Vintage 2009 Populations). Bethesda, MD: National Cancer Institute; based on November 2011 SEER data submission, posted to the SEER website, April 2012.http://seer.cancer.gov/csr/1975_2009_pops09. Accessed July 25, 2012
American Cancer Society.  Cancer Facts & Figures 2012. Atlanta, GA: American Cancer Society; 2012
Johnson S, McDonald JT, Corsten MJ. Socioeconomic factors in head and neck cancer.  J Otolaryngol Head Neck Surg. 2008;37(4):597-601
PubMed
Conway DI, Petticrew M, Marlborough H, Berthiller J, Hashibe M, Macpherson LM. Socioeconomic inequalities and oral cancer risk: a systematic review and meta-analysis of case-control studies.  Int J Cancer. 2008;122(12):2811-2819
PubMed   |  Link to Article
Shah JP, ed, Johnson NW, ed, Batsakis J, edOral Cancer. London, England: Martin Dunitz; 2003
National Comprehensive Cancer Network.  Guidelines for the management of head and neck cancers. http://www.nccn.org/clinical.asp. Accessed October 30, 2012
French C, True S, McIntyre R, Sciulli M, Maloy KA. State implementation of the Breast and Cervical Cancer Prevention and Treatment Act of 2000: a collaborative effort among government agencies.  Public Health Rep. 2004;119(3):279-285
PubMed   |  Link to Article
Subramanian S, Trogdon J, Ekwueme DU, Gardner JG, Whitmire JT, Rao C. Cost of breast cancer treatment in Medicaid: implications for state programs providing coverage for low-income women.  Med Care. 2011;49(1):89-95
PubMed   |  Link to Article
The Henry J. Kaiser Family Foundation-statehealthfacts.org.  Total Medicaid spending, FY2010. http://www.statehealthfacts.org/comparemaptable.jsp?ind=177&cat=4. Accessed May 4, 2012
Halpern MT, Ward EM, Pavluck AL, Schrag NM, Bian J, Chen AY. Association of insurance status and ethnicity with cancer stage at diagnosis for 12 cancer sites: a retrospective analysis.  Lancet Oncol. 2008;9(3):222-231
PubMed   |  Link to Article
Chen AY, Schrag NM, Halpern M, Stewart A, Ward EM. Health insurance and stage at diagnosis of laryngeal cancer: does insurance type predict stage at diagnosis?  Arch Otolaryngol Head Neck Surg. 2007;133(8):784-790
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Race, socioeconomic status, and breast cancer treatment and survival.  J Natl Cancer Inst. 2002;94(7):490-496
PubMed   |  Link to Article
Du XL, Fang S, Coker AL,  et al.  Racial disparity and socioeconomic status in association with survival in older men with local/regional stage prostate carcinoma: findings from a large community-based cohort.  Cancer. 2006;106(6):1276-1285
PubMed   |  Link to Article
Byers TE, Wolf HJ, Bauer KR,  et al; Patterns of Care Study Group.  The impact of socioeconomic status on survival after cancer in the United States: findings from the National Program of Cancer Registries Patterns of Care Study.  Cancer. 2008;113(3):582-591
PubMed   |  Link to Article
Subramanian S. Why we need to create standardized Medicaid administrative data linked with cancer registry databases.  J Registry Manag. 2009;36(1):5-6
PubMed
Ku L, MacTaggart P, Pervez F, Rosenbaum S. Improving Medicaid's Continuity of Coverage and Quality of Care. Washington, DC: The Association of Community Affiliated Plans; 2009
Hall AG, Harman JS, Zhang J. Lapses in Medicaid coverage: impact on cost and utilization among individuals with diabetes enrolled in Medicaid.  Med Care. 2008;46(12):1219-1225
PubMed   |  Link to Article
Christakis DA, Mell L, Koepsell TD, Zimmerman FJ, Connell FA. Association of lower continuity of care with greater risk of emergency department use and hospitalization in children.  Pediatrics. 2001;107(3):524-529
PubMed   |  Link to Article
Short PF, Graefe DR. Battery-powered health insurance? stability in coverage of the uninsured.  Health Aff (Millwood). 2003;22(6):244-255
PubMed   |  Link to Article
Gill JM, Mainous AG III, Nsereko M. The effect of continuity of care on emergency department use.  Arch Fam Med. 2000;9(4):333-338
PubMed   |  Link to Article
Chien LN, Adams EK. The effect of the Breast and Cervical Cancer Prevention and Treatment Act on Medicaid disenrollment.  Womens Health Issues. 2010;20(4):266-271
PubMed   |  Link to Article
Ramsey SD, Zeliadt SB, Richardson LC,  et al.  Disenrollment from Medicaid after recent cancer diagnosis.  Med Care. 2008;46(1):49-57
PubMed   |  Link to Article
Weng Y, Korte JE. Racial disparities in being recommended to surgery for oral and oropharyngeal cancer in the United States.  Community Dent Oral Epidemiol. 2012;40(1):80-88
PubMed   |  Link to Article
McCann J, Artinian V, Duhaime L, Lewis JW Jr, Kvale PA, DiGiovine B. Evaluation of the causes for racial disparity in surgical treatment of early stage lung cancer.  Chest. 2005;128(5):3440-3446
PubMed   |  Link to Article
Cykert S, Dilworth-Anderson P, Monroe MH,  et al.  Factors associated with decisions to undergo surgery among patients with newly diagnosed early-stage lung cancer.  JAMA. 2010;303(23):2368-2376
PubMed   |  Link to Article
Malin JL, Kahn KL, Adams J, Kwan L, Laouri M, Ganz PA. Validity of cancer registry data for measuring the quality of breast cancer care.  J Natl Cancer Inst. 2002;94(11):835-844
PubMed   |  Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. Distribution of Selected Characteristics Among Medicaid Patients With Head and Neck Cancer
Table Graphic Jump LocationTable 2. Medicaid Enrollment Patterns of Patients Not Continuously Enrolled in Medicaid for the 12 Months After Cancer Diagnosisa
Table Graphic Jump LocationTable 3. Unadjusted First-Course Treatment and All-Cause Mortality for Medicaid Patients With Head and Neck Cancer Surviving at Least 12 Months After Diagnosisa
Table Graphic Jump LocationTable 4. Factors Associated With Treatment Receipt and Mortality Among Medicaid Patients With Head and Neck Cancer

References

Howlader N, Noone AM, Krapcho M,  et al.  SEER Cancer Statistics Review, 1975-2009 (Vintage 2009 Populations). Bethesda, MD: National Cancer Institute; based on November 2011 SEER data submission, posted to the SEER website, April 2012.http://seer.cancer.gov/csr/1975_2009_pops09. Accessed July 25, 2012
American Cancer Society.  Cancer Facts & Figures 2012. Atlanta, GA: American Cancer Society; 2012
Johnson S, McDonald JT, Corsten MJ. Socioeconomic factors in head and neck cancer.  J Otolaryngol Head Neck Surg. 2008;37(4):597-601
PubMed
Conway DI, Petticrew M, Marlborough H, Berthiller J, Hashibe M, Macpherson LM. Socioeconomic inequalities and oral cancer risk: a systematic review and meta-analysis of case-control studies.  Int J Cancer. 2008;122(12):2811-2819
PubMed   |  Link to Article
Shah JP, ed, Johnson NW, ed, Batsakis J, edOral Cancer. London, England: Martin Dunitz; 2003
National Comprehensive Cancer Network.  Guidelines for the management of head and neck cancers. http://www.nccn.org/clinical.asp. Accessed October 30, 2012
French C, True S, McIntyre R, Sciulli M, Maloy KA. State implementation of the Breast and Cervical Cancer Prevention and Treatment Act of 2000: a collaborative effort among government agencies.  Public Health Rep. 2004;119(3):279-285
PubMed   |  Link to Article
Subramanian S, Trogdon J, Ekwueme DU, Gardner JG, Whitmire JT, Rao C. Cost of breast cancer treatment in Medicaid: implications for state programs providing coverage for low-income women.  Med Care. 2011;49(1):89-95
PubMed   |  Link to Article
The Henry J. Kaiser Family Foundation-statehealthfacts.org.  Total Medicaid spending, FY2010. http://www.statehealthfacts.org/comparemaptable.jsp?ind=177&cat=4. Accessed May 4, 2012
Halpern MT, Ward EM, Pavluck AL, Schrag NM, Bian J, Chen AY. Association of insurance status and ethnicity with cancer stage at diagnosis for 12 cancer sites: a retrospective analysis.  Lancet Oncol. 2008;9(3):222-231
PubMed   |  Link to Article
Chen AY, Schrag NM, Halpern M, Stewart A, Ward EM. Health insurance and stage at diagnosis of laryngeal cancer: does insurance type predict stage at diagnosis?  Arch Otolaryngol Head Neck Surg. 2007;133(8):784-790
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Race, socioeconomic status, and breast cancer treatment and survival.  J Natl Cancer Inst. 2002;94(7):490-496
PubMed   |  Link to Article
Du XL, Fang S, Coker AL,  et al.  Racial disparity and socioeconomic status in association with survival in older men with local/regional stage prostate carcinoma: findings from a large community-based cohort.  Cancer. 2006;106(6):1276-1285
PubMed   |  Link to Article
Byers TE, Wolf HJ, Bauer KR,  et al; Patterns of Care Study Group.  The impact of socioeconomic status on survival after cancer in the United States: findings from the National Program of Cancer Registries Patterns of Care Study.  Cancer. 2008;113(3):582-591
PubMed   |  Link to Article
Subramanian S. Why we need to create standardized Medicaid administrative data linked with cancer registry databases.  J Registry Manag. 2009;36(1):5-6
PubMed
Ku L, MacTaggart P, Pervez F, Rosenbaum S. Improving Medicaid's Continuity of Coverage and Quality of Care. Washington, DC: The Association of Community Affiliated Plans; 2009
Hall AG, Harman JS, Zhang J. Lapses in Medicaid coverage: impact on cost and utilization among individuals with diabetes enrolled in Medicaid.  Med Care. 2008;46(12):1219-1225
PubMed   |  Link to Article
Christakis DA, Mell L, Koepsell TD, Zimmerman FJ, Connell FA. Association of lower continuity of care with greater risk of emergency department use and hospitalization in children.  Pediatrics. 2001;107(3):524-529
PubMed   |  Link to Article
Short PF, Graefe DR. Battery-powered health insurance? stability in coverage of the uninsured.  Health Aff (Millwood). 2003;22(6):244-255
PubMed   |  Link to Article
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