A.A. Ali

Multilevel Factors Associated With Overall Mortality for Men Diagnosed With Prostate Cancer in Florida

To identify individual and contextual factors contributing to overall mortality among men diagnosed with prostate cancer in Florida, a random sample of patients (between October 1, 2001, and December 31, 2007) was taken from the Florida Cancer Data System. Patient’s demographic and clinical information were obtained from the Florida Cancer Data System. Comorbidity was computed following the Elixhauser Index method. Census-tract-level socioeconomic status and farm house presence were extracted from Census 2000 and linked to patient data. The ratio of urologists and radiation oncologists to prostate cancer cases at the county level was computed. Multilevel logistic regression was conducted to identify significance of individuals and contextual factors in relation to overall mortality. A total of 18,042 patients were identified, among whom 2,363 died. No racial difference was found in our study. Being older at diagnosis, unmarried, current smoker, uninsured, diagnosed at late stage, with undifferentiated, poorly differentiated, or unknown tumor grade were significantly associated with higher odds of overall mortality. Living in a low-income area was significantly associated with higher odds of mortality (p = .0404). After adjusting for age, stage, and tumor grade, patients who received hormonal, combination of radiation with hormone therapy, and no definitive treatment had higher odds of mortality compared with those who underwent surgery only. A large number of comorbidities were associated with higher odds of mortality. Although disease-specific mortality was not examined, our findings suggest the importance of careful considerations of patient sociodemographic characteristics and their coexisting conditions in treatment decision making, which in turn affects mortality.

Parameterization of a disease progression simulation model for sequentially treated metastatic human epidermal growth factor receptor 2 positive breast cancer patients

Abstract Background The objective of this study is twofold: 1) to propose a simulation model for HER2+ metastatic breast cancer (mBC) which could further be used to assess the overall cost-effectiveness of the treatment sequences that would maximize survival of patients, and 2) to estimate transitional probabilities between treatment lines required to parameterize the simulation model, in the absence of individual patient data (IPD). Methods Individual patient data (IPD) were reconstructed for treatment lines composing four treatment sequences. Parametric models were tested to select the model that best fits the IPD. The transitional probability equations, used for disease progression modeling, were obtained by substituting the parameters of the general equation for transitional probabilities by the parameters estimated from fitted distributions. Results The log-logistic model best fitted the reconstructed data for progression-free and overall survival curves for each line of treatment. The shapes and scales of the log-logistic models were used to develop the transitional probability equations for the HER2+ mBC simulation model. Key limitations: The estimation of the transitional probabilities depends heavily on the accuracy of the IPD reconstruction. Nonetheless, analytical and graphical tests can be performed to check the face validity of the reconstructed data. Additionally, sensitivity analyses can be conducted to test the impact of uncertainty surrounding the estimated parameters defining equations for transitional probabilities. Conclusion The results of this study can be used as input in model-based economic evaluations of sequential therapy for HER2+ mBC.

Factors associated with overall survival prostate cancer in Florida: a multilevel analysis.

Objective. To investigate individual and contextual factors contributing to overall prostate cancer (PCa) survival in Florida. Methods. A random sample of 6,457 PCa cases diagnosed between 10/1/2001 and 12/31/2007 was extracted from Florida Cancer Data System. Comorbidity was computed following Elixhauser Index. Survival probability curve was generated using Kaplan-Meier method. The Wei, Lin, and Weissfel model was used for the multivariate analysis. Results. Older age at diagnosis was associated with shorter time to death. Current smokers had a higher hazard rate than non-current smokers. Higher hazard of overall mortality was associated with being diagnosed with advanced stage compared with localized stage and having poorly-differentiated tumor compared with well-moderately differentiated tumor. No definitive treatment, radiation alone, and hormone alone were significantly associated with elevated hazard rate compared with surgery. Fifteen comorbidities were significantly associated with shorter time-to-death. Conclusions. Effective control of comorbidity in PCa patients should help improve life expectancy and lead to prolonged survival.

Impact of Comorbidities on Prostate Cancer Stage at Diagnosis in Florida

To examine the association of major types of comorbidity with late-stage prostate cancer, a random sample of 11,083 men diagnosed with prostate cancer during 2002-2007 was taken from the Florida Cancer Data System. Individual-level covariates included demographics, primary insurance payer, and comorbidity following the Elixhauser Index. Socioeconomic variables were extracted from Census 2000 data and merged to the individual level data. Provider-to-case ratio at county level was alsocomputed. Multilevel logistic regression was used to assess associations between these factors and late-stage diagnosis of prostate cancer. Higher odds of late-stage diagnosis was significantly related to presence of comorbidities, being unmarried, current smoker, uninsured, and diagnosed in not-for-profit hospitals. The study reported that the presence of certain comorbidities, specifically 10 out of the 45, was associated with late-stage prostate cancer diagnosis. Eight out of 10 significant comorbid conditions were associated with greater risk of being diagnosed at late-stage prostate cancer. On the other hand, men who had chronic pulmonary disease, and solid tumor without metastasis, were less likely to be diagnosed with late-stage prostate cancer. Late-stage diagnosis was associated with comorbidity, which is often associated with increased health care utilization. The association of comorbidity with late-stage prostate cancer diagnosis suggests that individuals with significant comorbidity should be offered routine screening for prostate cancer rather than focusing only on managing symptomatic health problems.

Factors Associated with Time-to-Treatment of Prostate Cancer in Florida

Prostate cancer is the most commonly diagnosed cancer after skin cancer and the second leading cause of cancer death for American men, behind only lung cancer.1 The American cancer society estimates that there will be about 238,590 new cases of prostate cancer, and 29,720 men will die from the disease in 2013.1 Prostate cancer mortality rate is declining in developed countries, however it is not clear whether this is due to the increasing use of screening procedures based on prostate-specific antigen (PSA) blood test, improved treatment,2 or combination of these and/or other factors.3 In spite of declining prostate cancer mortality, striking racial disparities in prostate cancer outcomes exist in the U.S. Compared with Caucasian men, African American are more likely to be diagnosed at advanced stage disease and die from prostate cancer in the U.S.1,4 Studies have shown differences in prostate cancer treatment among patients with various races/ethnicities or socioeconomic backgrounds.5–8 Existing literature suggests that African American men have not been receiving optimal treatment for prostate cancer and have been experiencing delays in treatment.9–11 Such differences in treatment must be understood better and explained as they might be one cause of the racial disparities in prostate cancer mortality observed in the U.S. This information is critical for the development of appropriate policy and intervention strategies to eliminate long-term racial/ethnic disparities. Several factors have been suggested to influence time-to-treatment in cancer patients, including socioeconomic status and other demographic characteristics.12–15 In breast cancer, for instance, studies have shown that time-to-treatment may be linked to socioeconomic status as well as race. The same studies suggested that delays of greater than three months after an initial diagnosis may decrease breast cancer survival by 12%.13–16 Some studies found that men with prostate cancer experience longer wait time for diagnosis and treatment than those observed for other cancers.17–19 Studies in both the urologic and medical literature have paid increasing attention to the question of overdiagnosis in prostate cancer. As evidence, a study reported overdiagnosis rates of 15% among White men and 37% among Black men.20 The association between wait time and prognosis of prostate cancer is inconclusive.21–26 Even though we recognize the current literature’s debate on whether or not prostate cancer has been over-treated, the intent of this project was not to make a clinical judgment about this matter. This study intended to investigate factors contributing to time-to-treatment and examine whether there was a difference in wait time or treatment rate between African American and Caucasian men in Florida.

Comparison of health utility weights among elderly patients receiving breast-conserving surgery plus hormonal therapy with or without radiotherapy

Abstract Background: The selection of the most appropriate treatment combinations requires the balancing of benefits and harms of these treatment options as well as the patients’ preferences for the resulting outcomes. Objective: This research aimed at estimating and comparing the utility weights between elderly women with early stage hormone receptor positive (HR+) breast cancer receiving a combination of radiotherapy and hormonal therapy after breast conserving surgery (BCS) and those receiving a combination of BCS and hormonal therapy. Methods: The Surveillance, Epidemiology, and End Results (SEER) linked with Medicare Health Outcomes Survey (MHOS) was used as the data source. Health utility weights were derived from the VR-12 health-related quality of life instrument using a mapping algorithm. Descriptive statistics of the sample were provided. Two sample t-tests were performed to determine potential differences in mean health utility weights between the two groups after propensity score matching. Results: The average age at diagnosis was 72 vs. 76 years for the treated and the untreated groups, respectively. The results showed an inverse relationship between the receipt of radiotherapy and age. Patients who received radiotherapy had, on average, a higher health utility weight (0.70; SD = 0.123) compared with those who did not receive radiotherapy (0.676; SD = 0.130). Only treated patients who had more than two comorbid conditions had significantly higher health utility weights compared with patients who were not treated. Conclusions: The mean health utility weights estimated for the radiotherapy and no radiotherapy groups can be used to inform a comparative cost-effectiveness analysis of the treatment options. However, the results of this study may not be generalizable to those who are outside a managed care plan because MHOS data is collected on managed care beneficiaries.

Improving Health Care Decision Making in the USA Through Comparative Effectiveness Research: The Role of Economic Evaluation

Comparative effectiveness research (CER) has received growing attention in the USA, and elsewhere, in recent years. CER aims to produce the best evidence to empower clinicians and other health-care providers to make rational decisions regarding what treatment is most effective at the individual and population level. However, unlike many other countries, the evidence generated by CER in the USA has traditionally been limited to the effectiveness, benefits and harms of health-care interventions, with cost being omitted from the analysis. The inclusion of economic evaluation as part of CER remains a debate. Based on other countries’ experience, the inclusion of economic evaluation into CER would allow decision makers to make trade-off assessments between the benefits and opportunity costs associated with all the possible treatment options before making a decision. However, bridging economic evaluation and CER is not without pitfalls. This paper discusses the role of economic evaluation in improving health-care decision making in the USA through CER and proposes the establishment of an independent institution in each US state to generate the necessary data and make drug coverage recommendations.

Value Assessment Frameworks in the United States: A Call for Patient Engagement

In our current environment of sustained increases in healthcare costs, evidence-based frameworks serve to inform healthcare decision-making in healthcare systems worldwide and to help control healthcare expenditures through the reduction of clinical variation. In the United States (US), decision-making regarding the evaluation of healthcare technologies was originally informed by health technology assessment (HTA) as evidenced by the creation of the Office of Technology Assessment (OTA) in 1972 [1]. Since the 1990s, healthcare expenditures in the US, as a percentage of gross domestic product (GDP) have dramatically risen compared to peer Organisation for Economic Co-operation and Development (OECD) countries, with healthcare outcomes that lag significantly behind other peer countries that spend much less. Consequently, the US has started transitioning from a volume-based healthcare system to one that focused more on “value” [2]. But what does value mean in a healthcare context? There is a lack of agreement on the meaning of value given the differing perspectives that exist (e.g., those of researchers, payers, clinicians, and patients). With rationality and asymmetric information assumptions, economists typically define value as an opportunity cost, meaning what the consumer is willing to forgo to obtain specific goods or services [3]. Value assessment frameworks have diverse applications in a healthcare setting. Some are used primarily to make coverage and reimbursement decisions, while others are used to help inform treatment decisions. As an example, from a payer perspective, the value of a pharmaceutical product indicated to treat a chronic condition relates to its ability to either avoid or reduce the number of emergency room visits and hospitalizations, thus resulting in cost savings. A clinician would value the clinical attributes of a medication (safety, efficacy, toxicity), which usually would not explicitly take into account other attributes less directly related to the clinical effectiveness of a particular medication, such as, route of treatment administration (e.g., oral, subcutaneous injection, intravenous, etc.) or patient out-of-pocket costs. Over the last decade, we have witnessed the emergence of several value assessment frameworks that are used by different professional organizations in the US. Figure 1 shows historical events preceding the rise of these value assessment frameworks. Professional organizations involved in the development of value assessment frameworks include the American Society of Clinical Oncology (ASCO) [5], the American Heart Association (AHA)/the American College of Cardiology (ACC) [6], the Institute for Clinical and Economic Review (ICER) [7], the Memorial Sloan Kettering Cancer Center (MSKCC) [8], and the National Comprehensive Cancer Network (NCCN) [9]. The primary purpose of these value assessment frameworks is to inform different stakeholders and to realign healthcare decision-making, based on the most robust clinical evidence, with health economic considerations such as reimbursement and coverage decisions. Understanding the potential impact that these frameworks may have on informing healthcare decision-making and their * Vakaramoko Diaby v.diaby@cop.ufl.edu

Prostate Cancer Overall Survival: Multilevel Analysis of A Population-Based Cancer Registry Data.

 It has been estimated that there will be 233,000 new cases and 29,480 deaths from prostate cancer (PCa) in the United States in 2014.1 In 2014, the State of Florida ranks  Second behind California in PCa incidence (16,590 estimated new cases).1  Second behind California for mortality (2,170 estimated deaths) from PCa.1  Few studies have investigated the contribution of individual-level and contextual factors to PCa survival.  The objective is to identify individual and contextual factors contributing to overall survival among prostate cancer patients in Florida. Results (Cont.) 1Florida A & M University, Tallahassee, FL; 2BioMedware, Ann Arbor, MI; 3Indiana University-Purdue University Indianapolis, Indianapolis, IN; 4Moffitt Cancer Center, Tampa, FL; 5Florida Department of Health, Tallahassee, FL.

Abstract 4131: Individual and contextual factors associated with overall prostate cancer survival in Florida

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Background: Few studies have looked at the independent contribution that individual-level and contextual factors make to prostate cancer survival. The objective of this study was to identify individual and contextual factors contributing to overall prostate cancer survival in Florida. Methods: A random sample of 6453 cases diagnosed with prostate cancer between 10/1/2001 and 12/31/2007 in the Florida Cancer Data System provided data on: demographics, type of health insurance at diagnosis, tumor stage, treatment and all-cause death. Census-tract level socioeconomic status & farm house presence were extracted from Census 2000 and linked to patient data. Comorbidity was computed following Elixhauser Index. Estimated survival probability curve was generated using the Kaplan-Meier estimator. Wei, Lin and Weissfeld (WLW) survival model was adopted for the multivariate analysis. Times from prostate cancer diagnosis to overall mortality were evaluated for patients who died during study period. The observation times were censored at June 30, 2012 for patients who were still alive at end of study. Hazard ratios, confidence intervals, and p-values were calculated. Results: The average age at diagnosis was 66.55 years with 12.16% men being diagnosed with advanced stage. The range of observation period was 5 to 3925 days, where 1100 patients (17.05%) died during this period. Among patients who died, 50% died within approximately 3 years after diagnosis. Older diagnosis age was associated with shorter time-to-death. Overall death rate for African American patients was 14.3% higher than that of Caucasian patients, although this relationship was not significant (p = 0.2305). Patients with no insurance had a 66.7% higher mortality rate than that of patients holding private insurance (p = 0.0351). Mortality rate for current smokers was 62.4% higher than that of non-current smokers (p < 0.0001). Higher hazard of overall mortality was also associated with being diagnosed with advanced stage compared to localized stage (HR = 1.89, p < 0.0001) and having undifferentiated or unknown tumor compared to well-moderately differentiated tumor (HR = 1.36, p = 0.0172). Having poorly differentiated tumor was related to higher death rate immediately after diagnosis, but this disadvantageous effect gradually vanished over time. Fourteen comorbidity conditions were significantly associated with shorter time-to-death. Conclusions: Older age and late stage at diagnosis, being black, having no insurance, being a smoker, having poorly-differentiated, undifferentiated or unknown tumor, and comorbidity status were prognostic indicators for shorter survival. Effective control of comorbidity in prostate cancer patients should help improve life expectancy and lead to prolonged survival. Further research is needed to understand mechanisms in which individual and contextual factors impact prostate cancer survival. Citation Format: Hong Xiao, Fei Tan, Georges Adunlin, Askal Ali, Pierre Goovaerts, Youjie Huang, Clement K. Gwede. Individual and contextual factors associated with overall prostate cancer survival in Florida. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4131. doi:10.1158/1538-7445.AM2014-4131