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Original Article |

The Effect of Hospital Safety-Net Burden Status on Short-term Outcomes and Cost of Care After Head and Neck Cancer Surgery FREE

Dane J. Genther, MD; Christine G. Gourin, MD, MPH
[+] Author Affiliations

Author Affiliations: Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University, Baltimore, Maryland.


Arch Otolaryngol Head Neck Surg. 2012;138(11):1015-1022. doi:10.1001/jamaoto.2013.611.
Text Size: A A A
Published online

Objective To determine the association between safety-net hospital care and short-term outcomes after head and neck cancer surgery.

Design Cross-sectional analysis. Safety-net burden was calculated as the percentage of patients with head and neck cancer with Medicaid or no insurance.

Setting Nationwide Inpatient Sample database.

Patients Adults who underwent an ablative procedure for a malignant oral cavity, laryngeal, hypopharyngeal, or oropharyngeal neoplasm in 2001 through 2008.

Main Outcome Measures Associations between hospital safety-net burden and short-term morality, medical and surgical complications, length of hospitalization, and costs.

Results Overall, 123 662 patients underwent surgery in 2001 through 2008. Patients treated at high–safety-net burden hospitals were significantly more likely to be admitted urgently or emergently (odds ratio [OR], 1.54; 95% CI, 1.06-2.25 [P = .03]), undergo major surgical procedures (OR, 1.24; 95% CI, 1.09-1.39 [P = .001]), have advanced comorbidity (OR, 1.35; 95% CI, 1.06-1.72 [P = .02]), and be black (OR, 1.70; 95% CI, 1.29-2.23 [P < .001]), but less likely to be elderly (OR, 0.66; 95% CI, 0.53-0.82 [P < .001]). High safety-net burden hospitals were significantly more likely to be teaching hospitals (OR, 2.04; 95% CI, 1.26-3.29 [P = .004]) and less likely to be located in the West (OR, 0.18; 95% CI, 0.07-0.44 [P < .001]). Safety-net burden was not associated with in-hospital mortality, acute medical complications, surgical complications, or hospital-related costs after controlling for all other variables including hospital volume status, but was associated with a mean increase in length of hospitalization of 24 hours (P < .001).

Conclusions These data suggest that safety-net hospitals provide valuable specialty care to a vulnerable population without an increase in complications or costs. Health care reform must address the economic challenges that threaten the viability of these institutions at the same time that demand for their services increases.

Safety-net hospitals provide a disproportionate amount of care to those who are uninsured or underinsured, including Medicaid beneficiaries and other vulnerable populations, compared with the average hospital. In their 2000 report “ America's Health Care Safety-Net: Intact but Endangered,” the Institute of Medicine defined a safety-net hospital as one that is characterized by both an explicit mission to offer patients access to services regardless of their ability to pay and a patient base that includes a substantial share of uninsured and underinsured patients.1 These hospitals are often located in underserved areas defined by a high prevalence of minorities and poor individuals, and these vulnerable populations are likely to be cared for at such hospitals.2 In fact, a significant proportion of members of these communities receive their primary care at these institutions because they do not have access, with regard to proximity or insurance status, to private physicians in the community.3 However, it should be noted that these hospitals care for not only uninsured and underinsured patients, but also for a considerable number of patients with commercial insurance plans.2

Many published reports have commented on the nature and quality of care at safety-net institutions, and these reports almost always demonstrate significantly decreased quality of care and worse outcomes.48 To our knowledge, there have been no studies examining the effect of safety-net hospital status on head and neck cancer surgical care, and low socioeconomic status has been associated with a significantly higher incidence of head and neck cancer.9 We sought to determine the effect of safety-net hospital status on short-term outcomes and cost of care in patients undergoing ablative procedures for head and neck cancer.

A cross-sectional analysis of patients with a diagnosis of oral cavity, laryngeal, hypopharyngeal, or oropharyngeal cancer was performed using discharge data from the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality. The NIS is the largest all-payer inpatient care database in the United States, containing data from approximately 8 million hospital stays each year from a stratified sample of 20% of nonfederal US hospitals from participating states.10 The NIS database provides information regarding the index hospital admission and includes patient demographic data, primary and secondary diagnoses, primary and secondary procedures, hospital characteristics, and inpatient and discharge mortality rates. The International Classification of Disease, Ninth Revision (ICD-9) codes were used to identify adult patients (age, ≥18 years) who underwent an ablative procedure for a malignant oral cavity, laryngeal, hypopharyngeal, or oropharyngeal neoplasm for the years 2001 through 2008 (Table 1). Patients with oropharyngeal cancer undergoing biopsy were included if neck dissection was the index admission procedure and no other ablative procedure was recorded. Reconstructive procedures were obtained from codes for pedicled or free-flap reconstruction (Table 1). Prior irradiation was obtained from the codes for previous exposure to therapeutic or other ionizing radiation (ICD-9 code V15.3).

Table Graphic Jump LocationTable 1. International Classification of Diseases, Ninth Revision (ICD-9) Diagnosis and Procedure Codes for Included Cases

Comorbidity was graded using the Romano adaptation of the Charlson comorbidity index,1113 excluding ICD-9 codes for the index cancer diagnosis from the solid tumor category. Cancer staging information is not available in the NIS, and as a result, ICD-9 codes for metastases were excluded because these have not been shown to be a reliable surrogate for disease stage.14 Codes for specific comorbid illnesses were used to create categories for acute medical and surgical complications (Table 2). Acute medical complications were derived from codes for acute cardiac events, acute pulmonary edema or failure, acute renal failure, acute hepatic failure, acute cerebrovascular events, sepsis, pneumonia, and urinary tract infection assigned at the time of hospital discharge; surgical complications were derived from codes for complications directly resulting from surgical procedures assigned at the time of hospital discharge.

Table Graphic Jump LocationTable 2. International Classification of Diseases, Ninth Revision (ICD-9) Diagnosis Codes for Comorbid Conditions

Safety-net burden was defined as the percentage of head and neck cancer surgical patients per hospital with Medicaid or uninsured payer status. Hospital safety-net burden was stratified by tertiles as low (0%-8.4%), medium (8.5%-18.9%), and high (≥19%). The mean annual number of head and neck cancer surgery cases performed per year of surgical activity was obtained by calculating the mean number of cases performed each year for each individual hospital, for the years in which that hospital performed at least 1 head and neck cancer surgery. Mean annual hospital volume was stratified by tertiles, which resulted in cutoff values for annual case volume of 12 or less, 13 to 60, and more than 60 cases per year, which were used to classify hospitals as low, intermediate, and high volume, respectively.

Hospital safety-net burden, extent of surgery, in-hospital death, postoperative complications, length of hospitalization, and costs were examined as dependent variables. Procedures were categorized by severity as minor (excision or destruction of lesion, tonsillectomy, and partial glossectomy, with or without neck dissection, and neck dissection alone when performed as the index admission procedure) and major (partial or total laryngectomy, esophagectomy, total glossectomy, pharyngectomy, mandibulectomy, and maxillectomy, with or without neck dissection). Independent variables included were age, sex, race, payer source (commercial or health maintenance organization, Medicare, Medicaid, self-pay, or other), comorbidity, nature of admission (emergent or urgent or other), hospital ownership/control, hospital bed size, hospital location (rural or urban), geographic region, hospital teaching status, and hospital volume. American Joint Commission on Cancer tumor stage, tumor grade, histological subtype, and outcome after discharge were not available from the NIS database.

Hospital-related charges for each index admission were converted to the organizational cost of providing care using cost to charge ratios for individual hospitals. Cost to charge ratios were calculated using information from the detailed reports by hospitals to the Centers for Medicare and Medicaid Services, providing an estimate of the all-payer inpatient cost to charge ratio by hospital.15 This ratio was multiplied by each patient's charge to obtain the cost per admission.16 All costs were adjusted for inflation based on US Bureau of Labor Statistics indices, with results converted to 2012 US$.17 To obtain national cost estimates, all discharges were reweighted to account for cases for which cost estimates were missing.15

Data were analyzed using Stata 12 (StataCorp). Associations between variables were analyzed using cross-tabulations, multivariate logistic regression, and multinomial logistic regression modeling. Data were weighted and modified hospital and discharge weights to correct for changes in sampling over time were applied. Variance estimation was performed using procedures for survey data analysis with replacement. Strata with 1 sampling unit were centered at the population mean. Variables with missing data for more than 10% of the population were coded with a dummy variable to represent the missing data in regression analysis. The primary clinical end points were evaluated using multiple logistic regression analysis. Generalized linear regression modeling with a log link was used to analyze costs and length of stay because these variables were not normally distributed. This protocol was reviewed and approved by the institutional review board of Johns Hopkins Medical Institutions.

There were 123 662 cases in 2001 through 2008 (Table 3). The majority of patients were male and white, with a mean age of 62 years (range, 18-104 years). High–safety-net burden hospitals comprised 31% of all hospitals, whereas low safety-net burden hospitals accounted for 55% of all hospitals. Patients treated at high–safety-net burden hospitals were significantly more likely to have laryngeal primary tumors, be younger than 65 years, be admitted urgently or emergently, and were more likely to reside in the South and less likely to reside in the West. Prior radiation was recorded in 4% of cases and did not differ significantly by safety-net burden (P = .70).

Multiple logistic regression analysis of variables associated with high–safety-net burden hospitals are given in Table 4. After controlling for all other variables, high–safety-net burden was significantly associated with an increased likelihood of urgent or emergent admission, laryngeal primary site disease, major surgical procedures, advanced comorbidity, black race, and teaching hospital status. High–safety-net burden status was associated with a decreased odds of age 65 years or older and residence in the West, while residence in the South was not statistically significant. There was no significant interaction between safety-net status and hospital volume.

Table Graphic Jump LocationTable 4. Multivariate Logistic Regression Analysis of Variables Associated With High Safety-Net Burden Hospital Care

Multiple logistic regression analysis of independent variables associated with the risk of in-hospital death and postoperative complications are given in Table 5. After controlling for the effects of all variables, the only independently significant factors associated with the risk of in-hospital death, acute medical complications, and postoperative surgical complications were urgent or emergent admission, advanced comorbidity, major surgical procedures, and pedicled or free-flap reconstruction, while age 65 years or older was significantly associated with an increased risk of death and acute medical complications. Safety-net burden status and hospital volume status were not associated with acute morbidity or mortality.

Table Graphic Jump LocationTable 5. Multivariate Logistic Regression Analysis of Variables Associated With Risk of In-Hospital Death and Postoperative Complications

Multivariate generalized linear regression analyses of independent variables predictive of length of hospital stay and hospital-related costs are given in Table 6, with mean values representing the change in the value of the intercept mean. Urgent or emergent admission, age 65 years or older, hypopharyngeal primary site disease, major surgical procedures, pedicled or free-flap reconstruction, comorbidity, and black or Hispanic race were significantly associated with greater length of hospitalization, while urgent or emergent admission, major surgical procedures, pedicled or free-flap reconstruction, comorbidity, and black or Hispanic race were significantly associated with increased hospital costs. High safety-net burden hospitals were associated with an increase in length of stay but were not associated with an increase in costs of care after controlling for all other variables.

Table Graphic Jump LocationTable 6. Generalized Linear Regression Analysis of Length of Stay and Hospital Costs

Many studies have documented worse outcomes for patients treated in safety-net hospitals. Disparities that have been documented include delays in diagnosis, delays in treatment, increased postsurgical complications, and increased mortality.5,18,19 Most reports have focused on areas of high-technology services, cardiac disease, high prevalence cancers such as breast and lung cancer, and conditions requiring specialist referral.2 To our knowledge, the effect of hospital safety-net status on head and neck cancer care has not been previously studied, despite an increased incidence of head and neck cancer among individuals of lower socioeconomic status.9 We undertook the present study to examine the effects of safety-net hospital status on outcomes and cost of care after head and neck cancer surgery. Contrary to currently available literature on outcomes related to treatment at safety-net hospitals in various fields of medicine and surgery, analysis of the NIS data did not demonstrate an association between safety-net status and medical or surgical complications, mortality, or cost of care after head and neck cancer surgery.

The finding of equivalence of outcomes and cost of care in head and neck cancer surgery regardless of safety-net status is encouraging. Safety-net hospitals provide valuable services to a variety of patients and assume a majority of the burden of care for those vulnerable populations who are often underinsured or uninsured. Many of these patients seek care at safety-net hospitals out of necessity, either because of proximity or because there is nowhere else they can seek care without adequate insurance coverage, and vulnerable populations may choose to live in an area served by a safety-net hospital if they lack other access to care. For these same reasons, many uninsured or underinsured patients receive primary care services at safety-net hospitals.3 However, not all patients treated at these hospitals are underinsured or uninsured. Patient who have the means to seek care elsewhere but choose to seek care at safety-net hospitals often do so because they offer a variety of services that other institutions do not, including language assistance, social work, insurance enrollment assistance, transportation, and other services. Many of these institutions offer dental, vision, and mental health services as well.3,20 Safety-net hospitals are often vital to the communities they serve; however, they are at risk because of decreased funding in the setting of an increase in the amount of uncompensated care.3

Because of the high proportion of uncompensated care, these hospitals often struggle financially and frequently rely on subsidies from either state or federal governments.21 Funding for safety-net hospitals has not kept pace with the amount of uncompensated care they provide, and, subsequently, financial strain has become more significant in the setting of recently increased health care costs.2 More than 50% of public hospitals lose money on Medicaid patients in a given year.8 These institutions are at great risk for being forced to limit services or shut down, which would result in the loss of health care access for the patients served by that area. Mobley et al3 demonstrated that closure of safety-net hospitals significantly decreases access to care for uninsured and underinsured patients. Further decreases in funding may lead to a significant decrease in or even collapse of a sector of the safety net, and it would be difficult for other health care providers to fill in the gaps.

Our data suggest that the safety net is currently inadequate to fill certain gaps, such as adequate provision of primary care and cancer screening. Patients treated at high–safety-net burden hospitals in the present study were more often admitted for surgery urgently or emergently, suggesting that their cancer progressed to the point of significant symptomatology requiring emergent attention. This is not an ideal scenario for patients with head and neck cancer who require surgical treatment and suggests the possibility that access to primary care at safety-net hospitals may be inadequate.

Furthermore, in the present study, patients treated for head and neck cancer at high–safety-net burden hospitals more frequently underwent major surgical procedures. While the NIS database does not contain data on the stage of cancer at diagnosis, the finding that patients treated at high–safety-net burden hospitals more frequently underwent major surgical procedures suggests that these patients present more often with advanced-stage cancer. Halpern et al22 retrospectively analyzed data from 3.7 million patients in the United States treated at 12 high-volume cancer centers and found that patients dependent on public insurance programs or without insurance presented with advanced-stage cancer (stage III or IV) significantly more often than patients with commercial insurance, up to 3 times more often depending on the type of cancer. A number of other studies have found that individuals who are underinsured or uninsured more often present with advanced-stage cancer.19,23,24 The advanced stage at presentation of patients without adequate insurance has been proposed to be secondary to decreased participation in and access to screening programs and decreased likelihood of seeing a physician when symptoms arise because of lack of access.5 These findings suggest that safety-net hospitals, given their mission to provide medical care to all patients regardless of insurance status, are vital to improving screening rates and decreasing the proportion of patients who present with advanced-stage cancer.

There are several limitations to the use of hospital discharge data that may influence our findings. The NIS database provides no follow-up data beyond the index admission and is limited to a 30-day postoperative window and contains no information on stage of disease, grade, subtype, or survival. Thus, a meaningful analysis of long-term outcomes is not possible from the available data. The NIS database does not contain information regarding readmission, previous surgical procedures, or prior chemotherapy, which could potentially affect results with regard to the extent of surgery, length of hospital stay, or perioperative morbidity. Because the NIS is an inpatient database, patients who undergo diagnostic procedures only will not be captured, nor will patients treated nonoperatively in this database. There may be differences in the type of patient or disease that are not adequately captured. While comorbidity scores were used for risk classification, the ability to adequately control for case mix is limited when discharge diagnoses from administrative databases are used. Postoperative complications may not be apparent at the time of discharge, and as a result the incidence of complications may be underreported. Another potential limitation is that the cost analysis was based on hospital-related charges, adjusted for institutional expense to revenue ratios and did not include physician-related costs, since these data are not contained in the NIS database.

Despite these limitations, our data suggest that safety-net hospitals provide head and neck cancer surgical care to a vulnerable population, without an increase in short-term mortality, morbidity, or costs, despite the fact that hospitals with a high safety-net burden were more likely to care for patients who are black, admitted urgently, have advanced comorbidity, and require more extensive surgery. This finding is of particular importance, given the increased incidence of head and neck cancer and comorbidity among disadvantaged populations. The safety-net system plays a vital role in the care of these patients, and therefore, preservation of this safety-net must be a health care priority. Health care reform must address the economic challenges that threaten the viability of these institutions at the same time that demand for their services increases.

Correspondence: Christine G. Gourin, MD, MPH, Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins Outpatient Center, 601 N Caroline St, Ste 6260, Baltimore, MD 21287 (cgourin1@jhmi.edu).

Submitted for Publication: June 26, 2012; final revision received August 2, 2012; accepted August 28, 2012.

Author Contributions: Dr Gourin 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: Genther and Gourin. Acquisition of data: Gourin. Analysis and interpretation of data: Genther and Gourin. Drafting of the manuscript: Genther and Gourin. Critical revision of the manuscript for important intellectual content: Genther and Gourin. Statistical analysis: Gourin. Administrative, technical, and material support: Gourin. Study supervision: Gourin.

Conflict of Interest Disclosures: None reported.

Previous Presentation: This study was presented as a poster at the Eighth International Conference on Head and Neck Cancer; July 22, 2012; Toronto, Ontario, Canada.

This article was corrected for errors on December 10, 2012.

Lewin ME, Altman S.Committee on the Changing Market, Managed Care, and the Future viability of Safety-Net Providers; Institute of Medicine.  America's Health Care Safety-Net: Intact but Endangered. Washington, DC: National Academy Press; 2000
Bazzoli GJ, Lee W, Hsieh HM, Mobley LR. The effects of safety net hospital closures and conversions on patient travel distance to hospital services.  Health Serv Res. 2012;47(1, pt 1):129-150
PubMed   |  Link to Article
Mobley L, Kuo TM, Bazzoli GJ. Erosion in the healthcare safety net: impacts on different population groups.  Open Health Serv Policy J. 2011;4:1-14
PubMed   |  Link to Article
Virgo KS, Little AG, Fedewa SA, Chen AY, Flanders WD, Ward EM. Safety-net burden hospitals and likelihood of curative-intent surgery for non-small cell lung cancer.  J Am Coll Surg. 2011;213(5):633-643
PubMed   |  Link to Article
Bradley CJ, Neumark D, Shickle LM, Farrell N. Differences in breast cancer diagnosis and treatment: experiences of insured and uninsured women in a safety-net setting.  Inquiry. 2008;45(3):323-339
PubMed   |  Link to Article
Bradley CJ, Dahman B, Shickle LM, Lee W. Surgery wait times and specialty services for insured and uninsured breast cancer patients: does hospital safety net status matter?  Health Serv Res. 2012;47(2):677-697
PubMed   |  Link to Article
McHugh M, Kang R, Hasnain-Wynia R. Understanding the safety net: inpatient quality of care varies based on how one defines safety-net hospitals.  Med Care Res Rev. 2009;66(5):590-605
PubMed   |  Link to Article
Shields AE. Trends in private insurance, Medicaid/State Children's Health Insurance Program, and the health-care safety net: implications for asthma disparities.  Chest. 2007;132(5):(suppl)  818S-830S
PubMed   |  Link to Article
Johnson S, McDonald JT, Corsten M, Rourke R. Socio-economic status and head and neck cancer incidence in Canada: a case-control study.  Oral Oncol. 2010;46(3):200-203
PubMed   |  Link to Article
Healthcare Cost and Utilization Project.  Overview of the Nationwide Inpatient Sample. http://www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed June 25, 2012
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis. 1987;40(5):373-383
PubMed   |  Link to Article
Liu JH, Zingmond DS, McGory ML,  et al.  Disparities in the utilization of high-volume hospitals for complex surgery.  JAMA. 2006;296(16):1973-1980
PubMed   |  Link to Article
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.  J Clin Epidemiol. 1993;46(10):1075-1079, 1081-1090
PubMed   |  Link to Article
Neighbors CJ, Rogers ML, Shenassa ED, Sciamanna CN, Clark MA, Novak SP. Ethnic/racial disparities in hospital procedure volume for lung resection for lung cancer.  Med Care. 2007;45(7):655-663
PubMed   |  Link to Article
Healthcare Cost and Utilization Project.  Cost-to-charge ratio files. http://www.hcup-us.ahrq.gov/db/state/costtocharge.jsp. Accessed June 25, 2012
Newhouse RP, Mills ME, Johantgen M, Pronovost PJ. Is there a relationship between service integration and differentiation and patient outcomes?  Int J Integr Care. 2003;3:e15
PubMed
US Department of Labor, Bureau of Labor Statistics.  Consumer price index inflation calculator. http://www.bls.gov/bls/inflation.htm. Accessed June 25, 2012
Ross JS, Cha SS, Epstein AJ,  et al.  Quality of care for acute myocardial infarction at urban safety-net hospitals.  Health Aff (Millwood). 2007;26(1):238-248
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Disparities in cancer diagnosis and survival.  Cancer. 2001;91(1):178-188
PubMed   |  Link to Article
Ku L, Jones E, Shin P, Byrne FR, Long SK. Safety-net providers after health care reform: lessons from Massachusetts.  Arch Intern Med. 2011;171(15):1379-1384
PubMed   |  Link to Article
Zwanziger J, Khan N, Bamezai A. The relationship between safety net activities and hospital financial performance.  BMC Health Serv Res. 2010;10:15
PubMed   |  Link to Article
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
Ward EM, Fedewa SA, Cokkinides V, Virgo K. The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the national cancer database.  Cancer J. 2010;16(6):614-621
PubMed   |  Link to Article
Roetzheim RG, Pal N, Tennant C,  et al.  Effects of health insurance and race on early detection of cancer.  J Natl Cancer Inst. 1999;91(16):1409-1415
PubMed   |  Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. International Classification of Diseases, Ninth Revision (ICD-9) Diagnosis and Procedure Codes for Included Cases
Table Graphic Jump LocationTable 2. International Classification of Diseases, Ninth Revision (ICD-9) Diagnosis Codes for Comorbid Conditions
Table Graphic Jump LocationTable 4. Multivariate Logistic Regression Analysis of Variables Associated With High Safety-Net Burden Hospital Care
Table Graphic Jump LocationTable 5. Multivariate Logistic Regression Analysis of Variables Associated With Risk of In-Hospital Death and Postoperative Complications
Table Graphic Jump LocationTable 6. Generalized Linear Regression Analysis of Length of Stay and Hospital Costs

References

Lewin ME, Altman S.Committee on the Changing Market, Managed Care, and the Future viability of Safety-Net Providers; Institute of Medicine.  America's Health Care Safety-Net: Intact but Endangered. Washington, DC: National Academy Press; 2000
Bazzoli GJ, Lee W, Hsieh HM, Mobley LR. The effects of safety net hospital closures and conversions on patient travel distance to hospital services.  Health Serv Res. 2012;47(1, pt 1):129-150
PubMed   |  Link to Article
Mobley L, Kuo TM, Bazzoli GJ. Erosion in the healthcare safety net: impacts on different population groups.  Open Health Serv Policy J. 2011;4:1-14
PubMed   |  Link to Article
Virgo KS, Little AG, Fedewa SA, Chen AY, Flanders WD, Ward EM. Safety-net burden hospitals and likelihood of curative-intent surgery for non-small cell lung cancer.  J Am Coll Surg. 2011;213(5):633-643
PubMed   |  Link to Article
Bradley CJ, Neumark D, Shickle LM, Farrell N. Differences in breast cancer diagnosis and treatment: experiences of insured and uninsured women in a safety-net setting.  Inquiry. 2008;45(3):323-339
PubMed   |  Link to Article
Bradley CJ, Dahman B, Shickle LM, Lee W. Surgery wait times and specialty services for insured and uninsured breast cancer patients: does hospital safety net status matter?  Health Serv Res. 2012;47(2):677-697
PubMed   |  Link to Article
McHugh M, Kang R, Hasnain-Wynia R. Understanding the safety net: inpatient quality of care varies based on how one defines safety-net hospitals.  Med Care Res Rev. 2009;66(5):590-605
PubMed   |  Link to Article
Shields AE. Trends in private insurance, Medicaid/State Children's Health Insurance Program, and the health-care safety net: implications for asthma disparities.  Chest. 2007;132(5):(suppl)  818S-830S
PubMed   |  Link to Article
Johnson S, McDonald JT, Corsten M, Rourke R. Socio-economic status and head and neck cancer incidence in Canada: a case-control study.  Oral Oncol. 2010;46(3):200-203
PubMed   |  Link to Article
Healthcare Cost and Utilization Project.  Overview of the Nationwide Inpatient Sample. http://www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed June 25, 2012
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis. 1987;40(5):373-383
PubMed   |  Link to Article
Liu JH, Zingmond DS, McGory ML,  et al.  Disparities in the utilization of high-volume hospitals for complex surgery.  JAMA. 2006;296(16):1973-1980
PubMed   |  Link to Article
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.  J Clin Epidemiol. 1993;46(10):1075-1079, 1081-1090
PubMed   |  Link to Article
Neighbors CJ, Rogers ML, Shenassa ED, Sciamanna CN, Clark MA, Novak SP. Ethnic/racial disparities in hospital procedure volume for lung resection for lung cancer.  Med Care. 2007;45(7):655-663
PubMed   |  Link to Article
Healthcare Cost and Utilization Project.  Cost-to-charge ratio files. http://www.hcup-us.ahrq.gov/db/state/costtocharge.jsp. Accessed June 25, 2012
Newhouse RP, Mills ME, Johantgen M, Pronovost PJ. Is there a relationship between service integration and differentiation and patient outcomes?  Int J Integr Care. 2003;3:e15
PubMed
US Department of Labor, Bureau of Labor Statistics.  Consumer price index inflation calculator. http://www.bls.gov/bls/inflation.htm. Accessed June 25, 2012
Ross JS, Cha SS, Epstein AJ,  et al.  Quality of care for acute myocardial infarction at urban safety-net hospitals.  Health Aff (Millwood). 2007;26(1):238-248
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Disparities in cancer diagnosis and survival.  Cancer. 2001;91(1):178-188
PubMed   |  Link to Article
Ku L, Jones E, Shin P, Byrne FR, Long SK. Safety-net providers after health care reform: lessons from Massachusetts.  Arch Intern Med. 2011;171(15):1379-1384
PubMed   |  Link to Article
Zwanziger J, Khan N, Bamezai A. The relationship between safety net activities and hospital financial performance.  BMC Health Serv Res. 2010;10:15
PubMed   |  Link to Article
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
Ward EM, Fedewa SA, Cokkinides V, Virgo K. The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the national cancer database.  Cancer J. 2010;16(6):614-621
PubMed   |  Link to Article
Roetzheim RG, Pal N, Tennant C,  et al.  Effects of health insurance and race on early detection of cancer.  J Natl Cancer Inst. 1999;91(16):1409-1415
PubMed   |  Link to Article

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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
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