Wei-Hsuan Lo-Ciganic

Aspirin, Nonaspirin Nonsteroidal Anti-inflammatory Drug, and Acetaminophen Use and Risk of Invasive Epithelial Ovarian Cancer: A Pooled Analysis in the Ovarian Cancer Association Consortium

BACKGROUND Regular aspirin use is associated with reduced risk of several malignancies. Epidemiologic studies analyzing aspirin, nonaspirin nonsteroidal anti-inflammatory drug (NSAID), and acetaminophen use and ovarian cancer risk have been inconclusive. METHODS We analyzed pooled data from 12 population-based case-control studies of ovarian cancer, including 7776 case patients and 11843 control subjects accrued between 1992 and 2007. Odds ratios (ORs) for associations of medication use with invasive epithelial ovarian cancer were estimated in individual studies using logistic regression and combined using random effects meta-analysis. Associations between frequency, dose, and duration of analgesic use and risk of ovarian cancer were also assessed. All statistical tests were two-sided. RESULTS Aspirin use was associated with a reduced risk of ovarian cancer (OR = 0.91; 95% confidence interval [CI] = 0.84 to 0.99). Results were similar but not statistically significant for nonaspirin NSAIDs, and there was no association with acetaminophen. In seven studies with frequency data, the reduced risk was strongest among daily aspirin users (OR = 0.80; 95% CI = 0.67 to 0.96). In three studies with dose information, the reduced risk was strongest among users of low dose (<100 mg) aspirin (OR = 0.66; 95% CI = 0.53 to 0.83), whereas for nonaspirin NSAIDs, the reduced risk was strongest for high dose (≥500 mg) usage (OR = 0.76; 95% CI = 0.64 to 0.91). CONCLUSIONS Aspirin use was associated with a reduced risk of ovarian cancer, especially among daily users of low-dose aspirin. These findings suggest that the same aspirin regimen proven to protect against cardiovascular events and several cancers could reduce the risk of ovarian cancer 20% to 34% depending on frequency and dose of use.

Genital powder use and risk of ovarian cancer: a pooled analysis of 8,525 cases and 9,859 controls

Genital powder use has been associated with risk of epithelial ovarian cancer in some, but not all, epidemiologic investigations, possibly reflecting the carcinogenic effects of talc particles found in most of these products. Whether risk increases with number of genital powder applications and for all histologic types of ovarian cancer also remains uncertain. Therefore, we estimated the association between self-reported genital powder use and epithelial ovarian cancer risk in eight population-based case–control studies. Individual data from each study were collected and harmonized. Lifetime number of genital powder applications was estimated from duration and frequency of use. Pooled ORs were calculated using conditional logistic regression matched on study and age and adjusted for potential confounders. Subtype-specific risks were estimated according to tumor behavior and histology. 8,525 cases and 9,859 controls were included in the analyses. Genital powder use was associated with a modest increased risk of epithelial ovarian cancer [OR, 1.24; 95% confidence interval (CI), 1.15–1.33] relative to women who never used powder. Risk was elevated for invasive serous (OR, 1.20; 95% CI, 1.09–1.32), endometrioid (OR, 1.22; 95% CI, 1.04–1.43), and clear cell (OR, 1.24; 95% CI, 1.01–1.52) tumors, and for borderline serous tumors (OR, 1.46; 95% CI, 1.24–1.72). Among genital powder users, we observed no significant trend (P = 0.17) in risk with increasing number of lifetime applications (assessed in quartiles). We noted no increase in risk among women who only reported nongenital powder use. In summary, genital powder use is a modifiable exposure associated with small-to-moderate increases in risk of most histologic subtypes of epithelial ovarian cancer. Cancer Prev Res; 6(8); 811–21. ©2013 AACR.

Aspirin, nonaspirin nonsteroidal anti-inflammatory drugs, or acetaminophen and risk of ovarian cancer.

Background: Aspirin, nonaspirin nonsteroidal anti-inflammatory drugs (NA-NSAIDs) and acetaminophen all have biologic effects that might reduce the risk of ovarian cancer. However, epidemiologic data on this question are mixed. Methods: A population-based, case-control study in western Pennsylvania, eastern Ohio, and western New York State included 902 women with incident epithelial ovarian cancer who were diagnosed between February 2003 and November 2008 as well as 1802 matched controls. Regular use (at least 2 tablets per week for 6 months or more) of aspirin, NA-NSAIDs, and acetaminophen before the reference date (9 months before interview date) was assessed by in-person interview. We used logistic regression to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: The OR for aspirin use was 0.81 (95% CI = 0.63–1.03). Decreased risks were found among women who used aspirin continuously (0.71 [0.54–0.94]) or at a low-standardized daily dose (0.72 [0.53–0.97]), who used aspirin for the prevention of cardiovascular disease (0.72 [0.57–0.97]), who used aspirin more recently, or who used selective cyclooxygenase-2 inhibitors (0.60 [0.39–0.94]). No associations were observed among women using nonselective NA-NSAIDs or acetaminophen. Conclusions: Risk reductions of ovarian cancer were observed with use of aspirin or selective cyclooxygenase-2 inhibitors. However, the results should be interpreted with caution due to the inherent study limitations and biases.

Identifying type 1 and type 2 diabetic cases using administrative data: a tree-structured model.

Background: To date, few administrative diabetes mellitus (DM) registries have distinguished type 1 diabetes mellitus (T1DM) from type 2 diabetes mellitus (T2DM). Objective: Using a classification tree model, a prediction rule was developed to distinguish T1DM from T2DM in a large administrative database. Methods: The Medical Archival Retrieval System at the University of Pittsburgh Medical Center included administrative and clinical data from January 1, 2000, through September 30, 2009, for 209,647 DM patients aged ≥18 years. Probable cases (8,173 T1DM and 125,111 T2DM) were identified by applying clinical criteria to administrative data. Nonparametric classification tree models were fit using TIBCO Spotfire S+ 8.1 (TIBCO Software), with model size based on 10-fold cross validation. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of T1DM were estimated. Results: The main predictors that distinguished T1DM from T2DM are age <40 years; International classification of Disease, 9th revision, codes of T1DM or T2DM diagnosis; inpatient oral hypoglycemic agent use; inpatient insulin use; and episode(s) of diabetic ketoacidosis diagnosis. Compared with a complex clinical algorithm, the tree-structured model to predict T1DM had 92.8% sensitivity, 99.3% specificity, 89.5% PPV, and 99.5% NPV. Conclusion: The preliminary predictive rule appears to be promising. Being able to distinguish between DM subtypes in administrative databases will allow large-scale subtype-specific analyses of medical care costs, morbidity, and mortality.

An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.

Background: Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist. Objective: Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data. Research Design: A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use. Subjects: Adult Pennsylvania Medicaid program 2007–2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice. Measures: Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes. Results: A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P<0.001). Overdose was higher among those with probable (1.8%) and possible (0.9%) misuse compared with those without (0.2%, P<0.001). Abuse [adjusted rate ratio (ARR), 1.52; 95% confidence interval (CI), 1.10–2.10), probable misuse (ARR, 1.98; 95% CI, 1.46–2.67), and possible misuse (ARR, 1.76; 95% CI, 1.48–2.09) were associated with significantly more events of opioid medication overdose compared with those without. Conclusions: Claims-based measures can be used by health systems to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment.

Patterns and Quality of Buprenorphine Opioid Agonist Treatment in a Large Medicaid Program.

Objectives:Use of buprenorphine – an effective treatment for opioid use disorders (OUDs) – has increased rapidly in recent years and is often financed by Medicaid. We investigated predictors of buprenorphine treatment, patterns of care, and quality of care in a large state Medicaid program. Methods:Data from Pennsylvania Medicaid from 2007 to 2012 provided information regarding diagnoses, demographic characteristics, enrollment, and use of inpatient and outpatient services, and prescription drugs. We identified adult enrollees using buprenorphine, and examined prevalence of OUD diagnosis and patterns of use (duration and dose) and quality of care (physician visits, receipt of behavioral health counseling, urine drug screens, and other prescription drug use). We use a mixed logistic regression model to examine enrollee characteristics associated with buprenorphine use. Results:The share of enrollees with OUD filling prescriptions for buprenorphine increased from 2985 (9.8%) to 12,691 (25.2%) from 2007 to 2012. Between 26.2 and 32.0% of enrollees using buprenorphine had no diagnosis of OUD, depending on the year. Only 60.1% of enrollees with buprenorphine use received at least one urine drug screen, 41.0% had behavioral health counseling services, and 34.7 and 38.0% had other opioid and benzodiazepine claims, respectively, concomitant with buprenorphine use. Quality of care was lower among those with no OUD diagnosis recorded. The mean daily doses of buprenorphine decreased over time. We found wide variation in likelihood of buprenorphine use among those with OUD based upon age, sex, and race. Conclusions:Increases in buprenorphine treatment in a Medicaid population were observed across time; however, increases varied by age, sex, and rate, and the quality of care received seemed to be generally poor. The quality of the provision of buprenorphine treatment occurring in Medicaid populations should be further explored.

Medication-Assisted Treatment and Opioid Use Before and After Overdose in Pennsylvania Medicaid

Author Affiliations: Division of Cardiology, Zuckerberg San Francisco General Hospital, San Francisco, California (Kazi); Center for Vulnerable Populations, Zuckerberg San Francisco General Hospital, San Francisco, California (Penko, Coxson); Division of General Internal Medicine, Columbia University Medical Center, New York, New York (Moran); Institute for Clinical and Economic Review, Boston, Massachusetts (Ollendorf); Department of Medicine, University of California, San Francisco (Tice, Bibbins-Domingo).

Effectiveness and Safety of Non–vitamin K Antagonist Oral Anticoagulants for Atrial Fibrillation and Venous Thromboembolism: A Systematic Review and Meta-analyses

PURPOSE The findings from the observational studies comparing the effectiveness and safety of non-vitamin K antagonist oral anticoagulants (NOACs) versus vitamin K antagonists (VKAs) for atrial fibrillation (AF) and venous thromboembolism (VTE) are inconsistent. We conducted separate meta-analyses examining the efficacy/effectiveness and safety of NOACs versus VKAs by disease (AF vs VTE), study design (randomized controlled trials [RCTs] vs observational studies), and NOAC (dabigatran, rivaroxaban, apixaban, and edoxaban). METHODS The main data sources included PubMed/MEDLINE, EMBASE, Web of Science, CINAHL, and Scopus from January 1, 2005, to February 15, 2016. We searched for Phase III RCTs and observational studies comparing NOACs versus VKAs. The primary outcomes were stroke/systemic embolism (SE) for AF; recurrent VTE/fatal pulmonary embolism (PE) for VTE; and major bleeding for both conditions. Secondary outcomes included stroke and myocardial infarction (MI) for AF, recurrent deep vein thrombosis (DVT)/PE for VTE, and mortality, intracranial hemorrhage (ICH), and gastrointestinal bleeding for both conditions. Pooled hazard ratios (HRs) were reported by using inverse variance-weighted random effects models. FINDINGS A total of 13 RCTs and 27 observational studies (AF, n = 32; VTE, n = 8) were included. For AF, dabigatran and VKAs were comparable for stroke/SE risk in 1 RCT (HR, 0.77 [95% CI, 0.57-1.03]) and 6 observational studies (HR, 1.03 [95% CI, 0.83-1.27]). Rivaroxaban had a 20% decreased risk of stroke/SE in 3 RCTs (HR, 0.80 [95% CI, 0.67-0.95]) compared with VKA, but the effect was nonsignificant in 3 observational studies (HR, 0.78 [95% CI, 0.59-1.04]). Apixaban decreased stroke/systemic embolism risk (HR, 0.79 [95% CI, 0.66-0.95]) compared with VKA in 1 RCT, but edoxaban was comparable to VKA (HR, 0.99 [95% CI, 0.77-1.28]) in 1 RCT (no observational studies available for apixaban/edoxaban). Dabigatran, apixaban, and edoxaban decreased the risk of hemorrhagic stroke, mortality, major bleeding, and ICH by 10% to 71% compared with VKAs but not rivaroxaban. For VTE, NOACs and VKAs were comparable for recurrent VTE/fatal PE/DVT/PE risk in 7 RCTs and 1 observational study. The 7 RCTs demonstrated a 32% to 69% decreased risk of major bleeding for dabigatran, rivaroxaban, and apixaban compared with VKAs. No difference was shown in 1 rivaroxaban observational study (HR, 0.77 [95% CI, 0.40-1.49]) and 1 edoxaban RCT (HR, 0.84 [95% CI, 0.59-1.20]). Except for dabigatran, the NOACs had a 61% to 86% decreased risk of ICH and gastrointestinal bleeding. IMPLICATIONS Overall, NOACs were comparable or superior to VKAs. Although no observational studies are currently available for apixaban/edoxaban, a few notable inconsistencies exist for dabigatran (ischemic stroke, MI) and rivaroxaban (stroke/SE, major bleeding in VTE) between RCTs and observational studies. Individualizing NOAC/VKA therapy based on benefit/safety profiles and patient characteristics is suggested.

Changes in Cholesterol-Lowering Medications Use Over a Decade in Community-Dwelling Older Adults

BACKGROUND The impact of evidence-based guidelines and controlled trial data on use of cholesterol-lowering medications in older adults is unclear. OBJECTIVE To examine whether utilization patterns of cholesterol-lowering medications in community-dwelling older adults changed following the release of the National Cholesterol Education Program Adult Treatment Panel III guidelines and results from the Prospective Study of Pravastatin in the Elderly at Risk in 2002. METHODS Community-dwelling elderly individuals who were enrolled in the Health, Aging and Body Composition Study in 1997–1998 were followed for up to 11 years. An interrupted time series analysis with multivariable generalized estimating equations (GEEs) was used to examine changes in level and trend in cholesterol-lowering medication use before and after 2002, adjusting for sociodemographics, health-related behaviors, and health status. RESULTS Cholesterol-lowering medication use increased nearly 3-fold from 14.9% in 1997–1998 to 42.6% in 2007–2008, with statins representing the most common class used (87–94%). Multivariable GEE results revealed no significant difference in the level of cholesterol-lowering medication use after 2002 (adjusted OR 0.95; 95% CI 0.89–1.02). Multivariable GEE results revealed that trend changes in the rate of increase in cholesterol-lowering medication declined after 2002 (adjusted ratio of ORs 0.92; 95% CI 0.89–0.95). CONCLUSIONS The use of cholesterol-lowering medication increased substantially over a decade in community-dwelling elderly individuals but was not related to a change in level or trend following the release of the guidelines and evidence-based data.

Using machine learning to examine medication adherence thresholds and risk of hospitalization.

Background:Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes. Objective:To apply machine learning to examine how adherence to oral hypoglycemic medications is associated with avoidance of hospitalizations, and to identify adherence thresholds for optimal discrimination of hospitalization risk. Methods:A retrospective cohort study of 33,130 non–dual-eligible Medicaid enrollees with type 2 diabetes. We randomly selected 90% of the cohort (training sample) to develop the prediction algorithm and used the remaining (testing sample) for validation. We applied random survival forests to identify predictors for hospitalization and fit survival trees to empirically derive adherence thresholds that best discriminate hospitalization risk, using the proportion of days covered (PDC). Outcomes:Time to first all-cause and diabetes-related hospitalization. Results:The training and testing samples had similar characteristics (mean age, 48 y; 67% female; mean PDC=0.65). We identified 8 important predictors of all-cause hospitalizations (rank in order): prior hospitalizations/emergency department visit, number of prescriptions, diabetes complications, insulin use, PDC, number of prescribers, Elixhauser index, and eligibility category. The adherence thresholds most discriminating for risk of all-cause hospitalization varied from 46% to 94% according to patient health and medication complexity. PDC was not predictive of hospitalizations in the healthiest or most complex patient subgroups. Conclusions:Adherence thresholds most discriminating of hospitalization risk were not uniformly 80%. Machine-learning approaches may be valuable to identify appropriate patient-specific adherence thresholds for measuring quality of care and targeting nonadherent patients for intervention.