This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Jones, L. E. Articles by Doebbeling, C. C. Search for Related Content PubMed PubMed Citation Articles by Jones, L. E. Articles by Doebbeling, C. C. Social Bookmarking                            What's this?

Beyond the Traditional Prognostic Indicators: The Impact of Primary Care Utilization on Cancer Survival

Laura E. Jones, Caroline Carney Doebbeling

From the Roudebush Veterans Affairs Medical Center, Center of Excellence on Implementing Evidence-Based Practice; Department of Internal Medicine, Indiana University School of Medicine; and Regenstrief Institute, Indianapolis, IN

Address reprint requests to Caroline Carney Doebbeling, MD, MSc, 410 West 10th St, Suite 2000, Indianapolis, IN 46202; e-mail: caroline.carneydoebbeling{at}fssa.in.gov

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
Purpose To our knowledge to date, the effect of primary care utilization on health outcomes in cancer patients has not been described. The objective of this study was to investigate the impact of primary care utilization within 6 months of cancer diagnosis on survival in patients with lung cancer.

Patients and Methods We used electronic medical record data (1997 to 2005) to identify male veterans with incident lung cancers (N = 323). Primary care utilization was assessed in the 6 months after cancer diagnosis. Patients were observed from cancer diagnosis to death or to last date of health care utilization (ie, censoring date). Univariate and multivariate Cox proportional hazards models tested whether primary care utilization was associated with improved survival. Multivariate analyses adjusted for demographic and clinical characteristics.

Results During an average follow-up of 16.6 months, 259 patients died. In multivariate analysis, the risk of death was 36% (hazard ratio [HR], 0.64; 95% CI, 0.45 to 0.90), 56% (HR, 0.44; 95% CI, 0.29 to 0.65), and 57% (HR, 0.43; 05% CI, 0.29 to 0.64) lower for patients who had one, two, or at least three primary care visits, respectively, in the first 6 months after cancer diagnosis as compared with those without primary care utilization. The median survival duration (P < .0001, log-rank test) was 3.68, 7.52, 13.88, and 13.75 months for patients with no, one, two, or at least three primary care visits, respectively.

Conclusion Primary care utilization in the early phase of cancer treatment has a marked effect that results in a reduced mortality risk in patients with incident lung cancer. Additional research is required to determine how and why primary care utilization is an important prognostic indicator of prolonged survival in patients with lung cancer.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
Lung cancer is widely recognized as an increasing public health burden because of its high incidence and mortality rates,¹ economic consequences,² and disparities in care and outcomes.^3-5 The American Cancer Society estimates that more than 213,000 people in the United States will be diagnosed with lung cancer in 2007.¹ More than 160,000 people will die from lung cancer in 2007; lung cancer represents the leading cause of cancer mortality and the second leading reason of all-cause mortality in the United States.¹

Recent advances in diagnostic and therapeutic technologies for lung cancer have contributed to an increase in survival in recent years, but patient prognosis remains poor. Only 42% of patients with lung cancer diagnosed during 1996 to 2003 survived at least 1 year,⁶ and only 16% survived 5 years or more.¹ Although the cancer stage is the most widely used and commonly accepted predictor of survival, several other factors (eg, race,⁷ histology,⁸ cancer treatment modality,^9,10 medical comorbidity^9,11-14) that influence survival have also been described. The impact of primary care utilization, a proxy for receipt of noncancer medical services, on survival in patients with lung cancer has not been investigated. Research that focuses on the patterns of primary care utilization in patients with newly diagnosed cancer is sparse.^15-17 With regard to newly diagnosed lung cancer, only 45% of patients report having seen a primary care provider in the first 6 months after diagnosis (active treatment phase).¹⁵

We speculate that primary care utilization during active treatment may be an important determinant of survival because of the comprehensiveness of care that is received in primary care settings. For example, the ongoing management of a chronic and pervasive medical comorbidity (eg, hypertension) and receipt of preventive care services (eg, influenza vaccination) impact survival duration, and these services generally occur in the primary care setting.^18-22 Continuity of care in the primary care setting may also result in improved cancer- and non–cancer-related treatment compliance. However, the converse relationship may also be true. Health care utilization typically increases at the end of life²³; thus, cancer patients with increased primary care utilization may have more severe disease, which would directly influence survival duration. It is important, therefore, to assess the effect of early primary care utilization on risk of mortality, rather than only in the setting of end-stage disease.

There are no published data about the impact of primary care utilization on health outcomes and survival in the face of cancer. We hypothesized that primary care utilization would be an independent prognostic indicator of prolonged survival. To test this hypothesis, we undertook a pilot study in a cohort of patients with lung cancer to investigate the prognostic impact of primary care utilization on survival. Lung cancer was chosen because the impact of early primary care utilization on survival may be more noticeable given the low, 1-year lung cancer survival rate.⁶

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
Study Data
Linked administrative and clinical data, which include all ambulatory care encounters abstracted from the facility's electronic medical record (EMR), from a Midwestern Veterans Health Administration (VHA) facility, were obtained from the facility's data warehouse. Data were available from January 1, 1997, to April 30, 2005. Study data included demographics, service utilization dates, clinic stop codes, and diagnostic codes. The VHA does not maintain a cancer registry that is available for research purposes or that can be linked to centralized VHA databases.

Study Population
Patients diagnosed with a primary lung cancer (International Classification of Disease code [ICD]-9 162.xx) from January 26, 1998, to April 30, 2004, were identified. The cancer diagnosis date was the first date that any ICD-9 primary cancer code was recorded.

Inclusion/Exclusion Criteria
Analyses were restricted to patients who were established primary care patients before the cancer diagnosis to exclude patients who may have primarily used non-VHA care for primary care services. Established primary care patients²⁴ were those who utilized primary care in both the 13- to 24-month period and in the 12-month period before cancer diagnosis.²⁴ Thus, January 26, 1998, was the earliest date that permitted a patient to meet the criteria as an established primary care patient. Women were excluded, because few women (n = 23) were diagnosed with lung cancer. Additionally, patients were required to have at least two ICD-9 cancer codes. Although previous VHA studies^25-28 have required only a single ICD-9 code to identify cancer cases, we believed greater specificity was required, particularly given the unavailability of cancer registry data. Patients with a date of death that occurred on the same date as the cancer diagnosis were also excluded, given that the cancer was likely diagnosed only during autopsy.

Primary Care Utilization
The independent variable was the number of primary care visits (none, one, two, or three) in the first 6 months after lung cancer diagnosis. Primary care visits were identified in the EMR via clinic stop codes (ie, 301, 319, 323, 348, 350, 531) Only one visit per day was counted.

Mortality
The primary outcome was all-cause mortality. Death information was collected from a variety of sources inside and outside the VHA, including family members applying for death benefits, VHA facilities, the VA National Cemetery Administration, and the Social Security Administration. VHA death records were more than 95% complete, which compares favorably to records of the National Death Index, the currently accepted gold standard for mortality determination.^29,30 Survival duration was calculated from cancer diagnosis to the earliest of either death or censoring, which occurred on the last date of health care utilization if death had not occurred before April 30, 2005 (last date of data availability).

Covariates
Demographic characteristics included age, race, marital status, and service-connected disability percentage, which determines access and priority for VHA clinic appointments and drug copayments. Medical comorbidity in the 12-month period before cancer diagnosis was based on a count of conditions included in the Elixhauser Comorbidity Index (excluding cancer and psychiatric conditions) and other conditions prevalent in veterans by using the Klabunde methodology.^31-33 Complicated and uncomplicated diabetes were combined into a single category. Documented tobacco use was based on presence of ICD-9 diagnostic codes. As has been used in other administrative database studies,^2,25,34-36 the extent of disease (ie, local, metastatic) was assessed by the presence of metastatic disease, which was defined as the spread of a tumor beyond regional lymphatics. Metastatic disease was indicated if ICD-9 codes for metastases existed or if sites other than lung cancer were recorded in the EMR.

Statistical Analysis
Demographic and clinical characteristics were compared using ² analyses for categorical variables and analysis of variance for continuous variables. The effect of primary care utilization in the first 6 months after cancer diagnosis on survival was analyzed using univariate and multivariate Cox proportional hazards models. Multivariate models adjusted for the previously described covariates. Data are presented as hazard ratios (HRs) that have two-sided 95% CIs. The log-log survival plots were used to examine the proportional hazards assumption, which was met in all models. Survival curves were calculated using the Kaplan-Meier method and were compared by log-rank statistics.

All analyses were conducted using SAS 9.1 (SAS Institute Inc, Cary, NC). Two-tailed tests were used to determine statistical significance, and was set at .05. The institutional review boards at the VHA facility and at the affiliated university approved this study.

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
The analytic sample included 323 male veterans with a primary lung cancer. On average, primary care was utilized 1.88 times (standard deviation [SD], 1.97; range, 0 to 11) during the first 6 months after cancer diagnosis. Eighty-one patients (25%) did not utilize primary care in the first 6 months after cancer diagnosis. In comparison, 92 (28%), 71 (22%), and 79 (24%) patients had one, two, or at least primary care visits, respectively.

In general, patients were aged (mean age, 69.1 years; SD, 8.4 years), were white (82%), were married (55%), were without service-connected disability (69%), were documented smokers (60%), and had localized disease (61%). Nearly half of patients (44%) had three or more medical comorbidities (mean, 2.6; SD, 2.0). Age, marital status, service-connected disability, smoking status, and extent of disease were not associated (P > .05) with degree of primary care utilization. However, race and number of medical comorbidities differed according to the number of primary care visits. Patients with three or more primary care visits were significantly more likely to be nonwhite (P = .0486) and to have increased medical comorbidity (P < .0001; Table 1).

View larger version (16K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Probability of survival in veterans with no, one, two, or at least three primary care visits within 6 months of lung cancer diagnosis (P < .0001, log-rank test).

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

In these analyses, the risk of mortality was reduced by 36% to 57% for patients who utilized primary care at least once in the first 6 months after cancer diagnosis. Patients who utilized primary care had a median survival duration that was 3 to 10 months longer than those who did not utilize primary care. With few exceptions, the degree of primary care utilization was not associated with other important determinants of survival, including extent of disease, age, and smoking status. Because the survival analysis controlled for known confounders, our results are not likely attributable to underlying differences in demographic and clinical characteristics. The propensity for primary care utilization after cancer diagnosis also cannot be attributed to differences in utilization before cancer diagnosis, because all patients were established primary care utilizers before lung cancer diagnosis. Patients averaged more than five primary care visits in the 12-month period before cancer diagnosis, and > 50% had at least four primary care visits during that time period. Thus, given the similarities between groups, other immeasurable factors must be responsible for determining primary care utilization after lung cancer diagnosis, which is associated with increased survival.

We speculate that the reasons are multifactorial and include integrated care and information, improved performance status, modulating of the course of underlying acute and chronic conditions, promotion of increased adherence to therapies, and delivery of preventive medical care.

The observed positive influence of primary care utilization on survival among established primary care patients may be a function of receiving care in the VHA. Veterans who are well connected to the VHA health care system receive centrally located care in the nation's largest and most integrated health care system. The VHA's EMR provides a complete picture of patients' current and former health care problems and needs, and it is available to all VHA providers, including primary care providers, oncologists, pharmacists, nurses, and others.

As demonstrated in this study and in others, patients with lung cancer have a high degree of medical comorbidity; more than 83% have at least one chronic condition, most commonly hypertension or chronic obstructive pulmonary disease.^9,15,16,37 Comorbidity may jeopardize survival,^{9,11-14,38,39} may increase the risk of postsurgical complications,^40,41 and is an important determinant of cancer treatment selection.^9,42 Despite the potential detrimental effect of comorbidity on survival, ongoing management of chronic, pervasive comorbidity may prolong survival, particularly based on evidence^12,43 that a large percentage (15% to 57%, depending on the stage of disease) of patients with lung cancer die from other competing causes. For example, a study²² of hypertensive women with lung cancer found that those with untreated hypertension had an increased risk of death but that mortality was not increased among patients with lung cancer with treated hypertension. Management of comorbidity before surgery may also lessen the risk of postoperative complications.⁴⁴ Therefore, we speculate that management of acute and chronic underlying conditions may positively affect a patient's performance status, thereby enhancing cancer treatment options that are known to influence survival.⁹

In addition, ongoing management of comorbidity in the primary care setting may promote patient treatment compliance and thereby reduce the risk of cancer-related therapy interruptions that may jeopardize survival.^45,46 Primary care utilization during active cancer treatment may result in prolonged survival, because primary care providers may be more knowledgeable of the presence and treatment history of preexisting comorbidities, may be more familiar in the management of common acute primary care conditions (eg, sinusitis), and may be a central point of contact for management of some cancer-related treatment complications (eg, pain, nausea) that may reduce adherence and impede survival.⁴⁷

Lastly, patients with cancer who utilize primary care may have prolonged survival because they may be more likely to receive preventive care services. For example, an influenza vaccination in patients with colorectal cancer reduced mortality by 14% and resulted in fewer cancer-related treatment interruptions.¹⁸ Moreover, because a large percentage of patients with lung cancer (> 20%) continue to smoke after cancer diagnosis,^48-50 smoking cessation counseling at the time of diagnosis and during cancer treatment is important, given the negative impact of smoking on cancer treatment, prognosis, and survival.^19,51,52 Primary care providers are another part of the health care team that can address smoking cessation.

Although thought provoking, the results of this study should be evaluated with caution. The VHA has not released national cancer registry information to researchers, which prevented us from corroborating lung cancer diagnoses, dates of diagnoses, treatments (eg, chemotherapy), types of lung cancer (eg, small-cell), and stages of disease. However, the methodology we employed to identify incident cases and extent of disease in this study was similar to that used in other cancer studies.^2,25,34-36 We also note that 40% of patients in this study had metastatic disease, which was similar to the proportion of late-stage lung cancers nationwide,^1,53 that the risk of death was substantially higher among patients with metastatic disease (as would be expected), and that the proportion of patients surviving at least 1 year in this study (48%, data not shown) was similar to rates observed nationwide. All of these findings lend credibility and validity to our methodology.⁶ It is possible that misclassification occurred and that some prevalent cases were included in the analyses. However, to help reduce misclassification, all patients had at least two ICD-9 cancer codes, and the first code occurred more than 1 year after the first health care encounter, which helped eliminate prevalent cases. More than 50% of patients had at least thirteen ICD-9 cancer codes, and patients were observed, on average, for at least 3.5 years before the first recorded ICD-9 cancer code (data not shown). Primary care utilization did not include information from non-VHA facilities. However, less than one-third of veterans with cancer report dual use of VHA and non-VHA primary care services, and the majority report that they receive most of their primary care in the VHA.⁵⁴ Previous studies have noted racial disparities in lung cancer survival rates.⁷ Our findings did not indicate that survival was influenced by race, perhaps because of the study setting, which was a VHA facility with integrated and equal access that provides care at little to no cost for eligible veterans. Finally, these results should not be extrapolated to non-VHA cancer populations because of differences in the structure of VHA and non-VHA health care systems and in the demographics of the veteran population. Results also may not be generalizable to people with other types of cancer. However, the findings may be generalizable to veterans with lung cancers at other VHA facilities because of demographic and clinical similarities.⁵³

Despite these limitations, this is the first study, to our knowledge, that provides information about the impact of primary care utilization during acute cancer treatment on the risk of mortality in the lung cancer population. The use of EMR data provides population-based, long-term, unbiased follow-up data. Although this is an exploratory study, the strength of the findings, the rigorous case selection, appropriate statistical analyses, and excellent data quality make these findings worthy of further study.

Our results are intriguing and require additional confirmation in both VHA and non-VHA facilities and in populations with other types of cancer. If corroborated, the implications are striking. These data suggest the need to determine how and why primary care utilization is such an influential prognostic determinant of survival. Further, on confirmation of these results, the need to reengineer integrated systems of health care to ensure that oncology and primary care work collaboratively will be heightened. These results may also stress the importance of developing recommendations about the role of primary care providers in the delivery of noncancer care management (eg, treatment of hypertension) during active cancer treatment. The Institute of Medicine has released recommendations about the role of primary care providers in the delivery of survivorship care after active cancer treatment has ended but has not defined primary care provider roles during the active phase of cancer treatment, despite having recommendations about the care of noncancer comorbidities.⁵⁵ We believe that the results of this study should drive future research to identify how primary care utilization influences patient survival.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
The author(s) indicated no potential conflicts of interest.

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
Conception and design: Laura E. Jones, Caroline Carney Doebbeling

Provision of study materials or patients: Laura E. Jones, Caroline Carney Doebbeling

Collection and assembly of data: Laura E. Jones, Caroline Carney Doebbeling

Data analysis and interpretation: Laura E. Jones, Caroline Carney Doebbeling

Manuscript writing: Laura E. Jones, Caroline Carney Doebbeling

Final approval of manuscript: Laura E. Jones, Caroline Carney Doebbeling

	NOTES

 
The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS REFERENCES

 
1. Jemal A, Siegel R, Ward E, et al: Cancer statistics, 2007. CA Cancer J Clin 57:43-66, 2007[Abstract/Free Full Text]

2. Kutikova L, Bowman L, Chang S, et al: The economic burden of lung cancer and the associated costs of treatment failure in the United States. Lung Cancer 50:143-154, 2005[CrossRef][Medline]

3. Wang SJ, Fuller CD, Thomas CR Jr: Ethnic disparities in conditional survival of patients with non–small-cell lung cancer. J Thorac Oncol 2:180-190, 2007[CrossRef][Medline]

4. Jazieh AR, Kyasa MJ, Sethuraman G, et al: Disparities in surgical resection of early-stage non–small-cell lung cancer. J Thorac Cardiovasc Surg 123:1173-1176, 2002[Abstract/Free Full Text]

5. Bach PB, Cramer LD, Warren JL, et al: Racial differences in the treatment of early-stage lung cancer. N Engl J Med 341:1198-1205, 1999[Abstract/Free Full Text]

6. National Cancer Institute: Cancer Query Systems: SEER Survival Statistics. Bethesda, MD, National Cancer Institute, 2007

7. Clegg LX, Li FP, Hankey BF, et al: Cancer survival among US whites and minorities: A SEER (Surveillance, Epidemiology, and End Results) Program population-based study. Arch Intern Med 162:1985-1993, 2002[Abstract/Free Full Text]

8. Skuladottir H, Olsen JH: Conditional survival of patients with the four major histologic subgroups of lung cancer in Denmark. J Clin Oncol 21:3035-3040, 2003[Abstract/Free Full Text]

9. Tammemagi CM, Neslund-Dudas C, Simoff M, et al: In patients with lung cancer, age, race-ethnicity, gender and smoking predict adverse comorbidity, which in turn predicts treatment and survival. J Clin Epidemiol 57:597-609, 2004[CrossRef][Medline]

10. Janssen-Heijnen ML, Lemmens VE, van den Borne BE, et al: Negligible influence of comorbidity on prognosis of patients with small cell lung cancer: A population-based study in the Netherlands. Crit Rev Oncol Hematol 62:172-178, 2007[CrossRef][Medline]

11. Tammemagi CM, Neslund-Dudas C, Simoff M, et al: Impact of comorbidity on lung cancer survival. Int J Cancer 103:792-802, 2003[CrossRef][Medline]

12. Battafarano RJ, Piccirillo JF, Meyers BF, et al: Impact of comorbidity on survival after surgical resection in patients with stage I non–small-cell lung cancer. J Thorac Cardiovasc Surg 123:280-287, 2002[Abstract/Free Full Text]

13. Gonlugur TE, Gonlugur U: Comorbidity as a prognostic factor in small-cell lung cancer. Tumori 92:423-428, 2006[Medline]

14. Birim O, Kappetein AP, Bogers AJ: Charlson comorbidity index as a predictor of long-term outcome after surgery for non–small-cell lung cancer. Eur J Cardiothorac Surg 28:759-762, 2005[Abstract/Free Full Text]

15. Kurtz ME, Kurtz JC, Given CW, et al: Predictors of use of health care services among elderly patients with lung cancer: The first year after diagnosis. Support Care Cancer 14:243-250, 2006[CrossRef][Medline]

16. Ko C, Chaudhry S: The need for a multidisciplinary approach to cancer care. J Surg Res 105:53-57, 2002[CrossRef][Medline]

17. Kurtz ME, Kurtz JC, Given CW, et al: Utilization of services among elderly cancer patients: Relationship to age, symptoms, physical functioning, comorbidity, and survival status. Ethn Dis 15:S17-22, 2005[Medline]

18. Earle CC: Influenza vaccination in elderly patients with advanced colorectal cancer. J Clin Oncol 21:1161-1166, 2003[Abstract/Free Full Text]

19. Tammemagi CM, Neslund-Dudas C, Simoff M, et al: Smoking and lung cancer survival: The role of comorbidity and treatment. Chest 125:27-37, 2004[CrossRef][Medline]

20. Grossman E, Messerli FH, Boyko V, et al: Is there an association between hypertension and cancer mortality? Am J Med 112:479-486, 2002[CrossRef][Medline]

21. Lee SY, Kim MT, Jee SH, et al: Does hypertension increase mortality risk from lung cancer? A prospective cohort study on smoking, hypertension and lung cancer risk among Korean men. J Hypertens 20:617-622, 2002[CrossRef][Medline]

22. Peeters PH, van Noord PA, Hoes AW, et al: Hypertension, antihypertensive drugs, and mortality from cancer among women. J Hypertens 16:941-947, 1998[CrossRef][Medline]

23. Mukamel DB, Bajorska A, Temkin-Greener H: Health care services utilization at the end of life in a managed care program integrating acute and long-term care. Med Care 40:1136-1148, 2002[CrossRef][Medline]

24. Veterans Health Administration.FY2004 VHA Performance Measurement System Technical Manual. Washington, DC, Veterans Health Administration, 2004

25. Rabeneck L, Davila JA, Thompson M, et al: Outcomes in elderly patients after surgery for colorectal cancer in the veterans affairs health care system. Aliment Pharmacol Ther 20:1115-1124, 2004[CrossRef][Medline]

26. Rabeneck L, Souchek J, El-Serag HB: Survival of colorectal cancer patients hospitalized in the Veterans Affairs Health Care System. Am J Gastroenterol 98:1186-1192, 2003[CrossRef][Medline]

27. Freeman VL, Durazo-Arvizu R, Arozullah AM, et al: Determinants of mortality after a diagnosis of prostate cancer in Veterans Affairs and private sector health care systems. Am J Public Health 93:1706-1712, 2003[Abstract/Free Full Text]

28. Dominitz JA, Maynard C, Billingsley KG, et al: Race, treatment, and survival of veterans with cancer of the distal esophagus and gastric cardia. Med Care 40:I14-26, 2002 (suppl)[Medline]

29. Sohn MW, Arnold N, Maynard C, et al: Accuracy and completeness of mortality data in the Department of Veterans Affairs. Popul Health Metr 4:2, 2006[CrossRef][Medline]

30. Page WF, Mahan CM, Kang HK: Vital status ascertainment through the files of the Department of Veterans Affairs and the Social Security Administration. Ann Epidemiol 6:102-109, 1996[CrossRef][Medline]

31. Elixhauser A, Steiner C, Harris DR, et al: Comorbidity measures for use with administrative data. Med Care 36:8-27, 1998[CrossRef][Medline]

32. Selim AJ, Fincke G, Ren XS, et al: Comorbidity assessments based on patient report: Results from the Veterans Health Study. J Ambul Care Manage 27:281-295, 2004[Medline]

33. Klabunde CN, Potosky AL, Legler JM, et al: Development of a comorbidity index using physician claims data. J Clin Epidemiol 53:1258-1267, 2000[CrossRef][Medline]

34. Ball JK, Elixhauser A: Treatment differences between blacks and whites with colorectal cancer. Med Care 34:970-984, 1996[CrossRef][Medline]

35. Davila JA, Rabeneck L, Berger DH, et al: Postoperative 30-day mortality after surgical resection for colorectal cancer in veterans: Changes in the right direction. Dig Dis Sci 50:1722-1728, 2005[CrossRef][Medline]

36. Paramore LC, Thomas SK, Knopf KB, et al: Estimating costs of care for patients with newly diagnosed metastatic colorectal cancer. Clin Colorectal Cancer 6:52-58, 2006[Medline]

37. Ogle KS, Swanson GM, Woods N, et al: Cancer and comorbidity: Redefining chronic diseases. Cancer 88:653-663, 2000[CrossRef][Medline]

38. Firat S, Bousamra M, Gore E, et al: Comorbidity and KPS are independent prognostic factors in stage I non–small-cell lung cancer. Int J Radiat Oncol Biol Phys 52:1047-1057, 2002[CrossRef][Medline]

39. Firat S, Byhardt RW, Gore E: Comorbidity and Karnofksy performance score are independent prognostic factors in stage III non–small-cell lung cancer: An institutional analysis of patients treated on four RTOG studies—Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys 54:357-364, 2002[CrossRef][Medline]

40. Birim O, Kappetein AP, Goorden T, et al: Proper treatment selection may improve survival in patients with clinical early-stage non–small-cell lung cancer. Ann Thorac Surg 80:1021-1026, 2005[Abstract/Free Full Text]

41. Brouchet L, Bauvin E, Marcheix B, et al: Impact of induction treatment on postoperative complications in the treatment of non–small-cell lung cancer. J Thorac Oncol 2:626-631, 2007[Medline]

42. de Rijke JM, Schouten LJ, ten Velde GP, et al: Influence of age, comorbidity, and performance status on the choice of treatment for patients with non-small cell lung cancer: Results of a population-based study. Lung Cancer 46:233-245, 2004[CrossRef][Medline]

43. Klabunde CN, Legler JM, Warren JL, et al: A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and patients with lung cancer. Ann Epidemiol 17:584-590, 2007[CrossRef][Medline]

44. Pavia R, Spinelli F, Monaco M, et al: Lung cancer and cardiovascular diseases: Occurrence, comorbidity and surgical timing. J Cardiovasc Surg (Torino) 48:227-231, 2007[Medline]

45. Chen M, Jiang GL, Fu XL, et al: The impact of overall treatment time on outcomes in radiation therapy for non–small-cell lung cancer. Lung Cancer 28:11-19, 2000[CrossRef][Medline]

46. Jeremic B, Shibamoto Y, Milicic B, et al: Impact of treatment interruptions due to toxicity on outcome of patients with early stage (I/II) non–small-cell lung cancer (NSCLC) treated with hyperfractionated radiation therapy alone. Lung Cancer 40:317-323, 2003[Medline]

47. Efficace F, Bottomley A, Smit EF, et al: Is a patient's self-reported health-related quality of life a prognostic factor for survival in non-small-cell patients with lung cancer? A multivariate analysis of prognostic factors of EORTC study 08975. Ann Oncol 17:1698-1704, 2006[Abstract/Free Full Text]

48. Yang P, Allen MS, Aubry MC, et al: Clinical features of 5,628 primary patients with lung cancer: Experience at Mayo Clinic from 1997 to 2003. Chest 128:452-462, 2005[CrossRef][Medline]

49. Conron M, Phuah S, Steinfort D, et al: Analysis of multidisciplinary lung cancer practice. Intern Med J 37:18-25, 2007[CrossRef][Medline]

50. Baser S, Shannon VR, Eapen GA, et al: Smoking cessation after diagnosis of lung cancer is associated with a beneficial effect on performance status. Chest 130:1784-1790, 2006[CrossRef][Medline]

51. Videtic GM, Stitt LW, Dar AR, et al: Continued cigarette smoking by patients receiving concurrent chemoradiotherapy for limited-stage small-cell lung cancer is associated with decreased survival. J Clin Oncol 21:1544-1549, 2003[Abstract/Free Full Text]

52. Fox JL, Rosenzweig KE, Ostroff JS: The effect of smoking status on survival after radiation therapy for non–small-cell lung cancer. Lung Cancer 44:287-293, 2004[CrossRef][Medline]

53. United States Department of Veterans Affairs: Search VACCR. http://www1.va.gov/cancer/page.cfm?pg=17

54. Borowsky SJ, Cowper DC: Dual use of VA and non-VA primary care. J Gen Intern Med 14:274-280, 1999[CrossRef][Medline]

55. Hewitt M, Simone JV Ensuring Quality Cancer Care. Washington, DC, National Academy Press, 1999

Submitted July 19, 2007; accepted September 20, 2007.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Facebook    Reddit    Technorati    Twitter    What's this?

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Jones, L. E. Articles by Doebbeling, C. C. Search for Related Content PubMed PubMed Citation Articles by Jones, L. E. Articles by Doebbeling, C. C. Social Bookmarking                            What's this?