• Association between History of Chronic Lung Disease and Non-Small Cell Lung Carcinoma in Maryland

      Gardner, Lisa Dawn Marie; Amr, Sania; 0000-0003-3340-2818 (2016)
      Although cigarette smoking is the primary risk factor for non-small cell lung cancer (NSCLC), 25% of cases are not due to smoking or other established risk factors. Chronic lung diseases (chronic bronchitis, emphysema, and asthma) are major sources of inflammation in lung tissue, and a history of these diseases may increase one's risk of NSCLC, especially among never smokers. We used data from the Maryland Lung Cancer Study to investigate whether a history of chronic lung disease is associated with NSCLC risk, and if use of aspirin and/or other non-steroidal anti-inflammatory drugs (NSAIDs) decreases such risk, independently of smoking status. In the present case-control study, 1,660 NSCLC cases and 1,959 population controls were interviewed using a standardized questionnaire. Logistic regression estimated adjusted odds ratios (OR) for having NSCLC by: 1) history and 2) mean duration of chronic lung disease; and 3) regular use, 4) mean frequency, and 5) mean duration of aspirin and/or other NSAIDs. A history of chronic lung disease was statistically significantly associated with having NSCLC (OR = 1.87, 95% confidence interval (CI) 1.54-2.28). When stratified by smoking status, a history of chronic lung disease significantly increased the odds of having NSCLC in never (OR = 1.99, 95% CI 1.19-3.34), former (OR = 1.68, 95% CI 1.29-2.20), and current smokers (OR = 2.40, 95% CI 1.62-3.57), compared to those without chronic lung disease. Regular aspirin use was significantly associated with a 36% decreased risk of NSCLC, compared to non-regular use (OR = 0.64, 95% CI 0.52-0.79), but this association remained significant only in former (OR = 0.62, 95% CI 0.47-0.82) and current smokers (OR = 0.55, 95% CI 0.37-0.81). Regular use of other NSAIDs was associated with a statistically significant increase in the risk of having NSCLC compared to non-regular use (OR = 1.55, 95% CI 1.08-2.22); this association remained significant in former smokers (OR = 1.89, 95% CI 1.12-3.21). This study provides support for: 1) chronic inflammation as a potential contributing factor to NSCLC risk, regardless of smoking status, sex, and race; and 2) regular use of aspirin as a protective factor in former and current smokers.
    • Influence of Comorbid Depression on Mortality among SSDI-eligible Medicare Beneficiaries with Chronic Obstructive Pulmonary Disease

      Qian, Jingjing; Simoni-Wastila, Linda (2012)
      Background: Chronic obstructive pulmonary disease (COPD) is a condition with high mortality and morbidity. Comorbid depression can place COPD patients at increased risk of adverse outcomes. Although both COPD and depression are associated with significant morbidity, to date few studies addressing COPD-related outcomes have included individuals who receive Social Security Disability Insurance (SSDI). Objectives: To examine the influence of comorbid depression on mortality among a nationally-representative sample of Medicare beneficiaries suffering from COPD by SSDI-eligibility status. Methods: This retrospective cohort study used a 5% random sample of the 2006-2008 Chronic Condition Warehouse administrative data. The study cohort included 93,019 Medicare beneficiaries diagnosed with COPD who lived through 2006 and were continuously enrolled in Medicare Parts A, B, and D. Two-year (2007-2008) all-cause mortality was the study outcome. Comorbid depression was measured in 2006-2008. SSDI-eligibility was defined using the original reason for Medicare entitlement. Multivariable generalized estimating equations models estimated the association between SSDI-eligibility and depression, as well as the modification effect of SSDI-eligibility on their relationship. Survival analyses using extended Cox proportional hazards models further estimated risk of death from depression and antidepressant treatment among beneficiaries aged 65 and older (n=75,699) by SSDI-eligibility. Results: About two-fifths (39.4%) of beneficiaries with COPD had a depression diagnosis in 2006-2008; of those, 79.5% received antidepressant treatment. SSDI-eligibility was not only associated with a 12% (95%CI=10%,15%) higher likelihood of depression but also modified factors in regard to depression diagnosis and receipt of antidepressant treatment. COPD beneficiaries with a baseline depression diagnosis had a higher risk of death (HR=1.13; 95%CI=1.09, 1.18) in non-SSDI-eligible beneficiaries. Those who received antidepressant treatment had reduced risk of death, with greater benefits on mortality in SSDI-eligible than non-SSDI-eligible beneficiaries. Conclusions: This study provides the first evidence suggesting that SSDI-eligibility is not only associated with higher likelihood of having a depression diagnosis, but also is a significant effect modifier of the relationship between antidepressant treatment and mortality in Medicare beneficiaries with COPD. Findings demonstrate the benefits of antidepressant treatment on mortality in both SSDI-eligible and non-SSDI-eligible beneficiaries. In practice, clinicians should consider timely antidepressant treatment to improve outcomes for this population.
    • Medical Costs of Alpha-1 Antitrypsin Deficiency-associated Chronic Obstructive Pulmonary Disease in the United States

      Sieluk, Jan; Mullins, C. Daniel; 0000-0002-1833-0273 (2018)
      Objectives: The objective of this study was to isolate the healthcare resource utilization and economic burden attributable to the presence of a genetic factor among Chronic Obstructive Pulmonary Disease (COPD) patients with and without Alpha-1 Antitrypsin Deficiency (AATD), twelve months before and after their initial COPD diagnosis. Methods: Retrospective analysis of OptumLabs® Data Warehouse claims (OLDW; 2000 – 2017). The OLDW is a comprehensive, longitudinal real-world data asset with de-identified lives across claims and clinical information. AATD-associated COPD cases were matched with up to 10 unique non-AATD-associated COPD controls. Healthcare resource use and costs were assigned into the following categories: office (OV), outpatient (OP), and emergency room visits (ER), inpatients stays (IP), prescription drugs (RX), and other services (OTH). A generalized linear model was used to estimate total pre- and post-index (initial COPD diagnosis) costs from a third-party payer’s perspective (2018 USD) controlling for age, gender, race/ethnicity, census region, augmentation therapy use, oxygen use, insurance type, year of COPD diagnosis, and Charlson Comorbidity Score. Healthcare resource utilization was estimated using a negative binomial regression. Results: The study population consisted of 8,881 patients (953 cases matched with 7,928 controls). The AATD-associated COPD cohort had higher expenditures and use of OV and OTH services, as well as OV, OP, ER, RX, and OTH before and after the index date, respectively. Adjusted total cost ratios for AATD-associated COPD patients as compared to controls were 2.036 [95% CI: 1.601 – 2.590] and 1.976 [95% CI: 1.550 – 2.517] while the incremental cost difference totaled $6,861 [95% CI: $3,025 - $10,698] an $5,772 [95% CI: $1,940 - $9,604] per patient before and after the index date, respectively. Conclusions: Twelve months before and after their initial COPD diagnosis, patients with AATD incur higher healthcare utilization costs that are double the cost of similar patients without AATD. This study also suggests that increased costs of AATD-associated COPD are not solely attributable to augmentation therapy use. Future studies should further explore the relationship between augmentation therapy, healthcare resource use, and other AATD-associated COPD expenditures.
    • Use of Machine Learning To Predict COPD Treatments and Exacerbations in Medicare Older Adults: A Comparison of Multiple Approaches

      Le, Tham Thi; Simoni-Wastila, Linda (2021)
      Background: Multiple comorbidities, suboptimal adherence to maintenance medications (MMs), and exacerbations remain clinically important problems among older adults with chronic obstructive pulmonary disease (COPD). To better understand comorbidity profiles and to facilitate risk-based strategies for disease management, this dissertation quantified the prevalence and newly diagnosed rates of comorbidities, and validated predictive models of COPD medication non-adherence and exacerbations in the older Medicare population. Methods: Comorbidities were quantified in COPD beneficiaries and compared with matched non-COPD individuals using multivariable logistic regression. In a cohort of COPD beneficiaries with prevalent and new MM use, logistic and LASSO regressions were used to cross-validate the prediction of one-year non-adherence to MMs using different sets of predictors. A time-varying design was applied to assess improvement in predicting COPD exacerbations of the super learner versus component approaches (logistic regression, elastic net regression, random forest, gradient boosting, and neural network). Results: COPD beneficiaries had significantly increased odds of 40 measured comorbidities relative to matched non-COPD controls. The best-performing models in predicting MM non-adherence were those including initial MM adherence as a predictor, with validated Area Under the ROC Curves (AUC: 0.871-0.881). In predicting COPD exacerbations there were time-varying estimates of predictive accuracy and associations between predictors and the exacerbation outcome. Super learner performed slightly better (AUC: 0.650-0.761) than individual machine learning methods. Conclusions: Comorbidity burden is substantial and increases over time among Medicare older adults with COPD. Generated models achieved good and average discrimination in predicting COPD medication non-adherence and exacerbations, respectively. COPD hospitalization, oxygen supplementation, COPD treatment adherence, and numbers of inpatient visits were the most important predictors of COPD medication non-adherence and exacerbations. Super learner demonstrates a slight improvement compared to component methods, suggesting potential usability in augmenting prediction. Validated models with good discrimination can be adopted using friendly tools to optimizing resources for risk-based management and interventions of COPD.