Pharmacoepidemiologic Studies: An Interrupted-time Series Analysis on Drug Utilization and Evalution of Beneficial or Adverse Drug Effects

Background: Pharmacoepidemiologic research is a valuable tool to enable one to understand medication utilization patterns, beneficial/harmful outcomes of drug therapy, and to evaluate the impact of other interventions on outcomes of drug therapy in “real-world” settings. Objectives: This dissertation aimed to apply pharmacoepidemiologic methods to examine (1) changes in utilization patterns of cholesterol-lowering medications following the release of the guidelines and evidence-based data, (2) the associations between statin use and gait speed decline in older adults, and (3) the associations between aspirin, non-aspirin nonsteroidal anti-inflammatory drugs or acetaminophen and risk of ovarian cancer. Methods: The study samples were from two sources including (1) community-dwelling older adults in the Health, Aging and Body Composition Study, and (2) 902 women with ovarian cancer and 1,802 controls in a population-based case-control study. An interrupted time-series analysis, multivariable generalized estimating equations, and multivariable logistic regression were used to examine our three objectives, respectively. Results: First, the use of cholesterol-lowering medication increased substantially over a decade in older adults, but was not related to a change in level or trend following the release of the evidence-based guidelines. Secondly, statin use had a decreased risk of gait speed decline. Thirdly, risk reductions of ovarian cancer were observed with the use of aspirin or selective COX-2 inhibitors. Conclusion: These findings suggest that further studies are needed to investigate risk-benefit balance of cholesterol-lowering therapy and the potential benefits/barriers of the treatment among adults aged ≥ 80 years. Moreover, further investigations are warranted to confirm the risk-benefit balance of statin use and physical function decline in older adults. Future research on the associations between aspirin use and the risk of ovarian cancer should better characterize accompanying medical conditions, health and lifestyle behaviors, genetic susceptibility, and the overall risk-benefit balance. The public health relevance of these findings is that understanding the utilization patterns of cholesterol-lowering therapy and potential benefits of statins on physical function may prevent cardiovascular disease and disability in older adults. In addition, aspirin or COX-2 inhibitors may be potential agents for the prevention of ovarian cancer, the second leading gynecologic cancer in the US

Trends in the Management of Headache Disorders in US Emergency Departments: Analysis of 2007–2018 National Hospital Ambulatory Medical Care Survey Data

We examined trends in management of headache disorders in United States (US) emergency department (ED) visits. We conducted a cross-sectional study using 2007–2018 National Hospital Ambulatory Medical Care Survey data. We included adult patient visits (≥18 years) with a primary ED discharge diagnosis of headache. We classified headache medications by pharmacological group: opioids, butalbital, ergot alkaloids/triptans, acetaminophen/nonsteroidal anti-inflammatory drugs (NSAIDs), antiemetics, diphenhydramine, corticosteroids, and intravenous fluids. To obtain reliable estimates, we aggregated data into three time periods: 2007–2010, 2011–2014, and 2015–2018. Using multivariable logistic regression, we examined medication, neuroimaging, and outpatient referral trends, separately. Among headache-related ED visits, opioid use decreased from 54.1% in 2007–2010 to 28.3% in 2015–2018 (Ptrend < 0.001). There were statistically significant increasing trends in acetaminophen/NSAIDs, diphenhydramine, and corticosteroids use (all Ptrend < 0.001). Changes in butalbital (6.4%), ergot alkaloid/triptan (4.7%), antiemetic (59.2% in 2015–2018), and neuroimaging (37.3%) use over time were insignificant. Headache-related ED visits with outpatient referral for follow-up increased slightly from 73.3% in 2007–2010 to 79.7% in 2015–2018 (Ptrend = 0.02). Reflecting evidence-based guideline recommendations for headache management, opioid use substantially decreased from 2007 to 2018 among US headache-related ED visits. Future studies are warranted to identify strategies to promote evidence-based treatment for headaches (e.g., sumatriptan, dexamethasone) and appropriate outpatient referral and reduce unnecessary neuroimaging orders in EDs

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

IMPORTANCE Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. OBJECTIVE To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. DESIGN, SETTING, AND PARTICIPANTS A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. EXPOSURES Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. MAIN OUTCOMES AND MEASURES Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. RESULTS Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2%[142 180] of the cohort), medium-risk (18.6%[34 579] of the cohort), and high-risk (5.2%[9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. CONCLUSIONS AND RELEVANCE Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.NIH/National Institute on Drug Abuse [R01DA044985]; Pharmaceutical Research and Manufacturers of America Foundation Research Starter AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Patterns of Cough Medication Prescribing among Patients with Chronic Cough in Florida: 2012–2021

Among patients with chronic cough (CC) in the 2012–2021 statewide OneFlorida Clinical Research Consortium database, we examined trends in cough medication (CM) prescribing prevalence over time in repeated cross-sectional analyses and identified distinct CM utilization trajectories using group-based trajectory modeling (GBTM) in a retrospective cohort study. Among eligible adults (≥18 years) without cancer/benign respiratory tumor diagnoses, we identified CC patients and non-CC patients with any cough-related diagnosis. In the GBTM analysis, we calculated the number of monthly prescriptions for any CMs (excluding gabapentinoids) during the 12 months from the first qualifying cough event to identify distinct utilization trajectories. From 2012 to 2021, benzonatate (9.6% to 26.1%), dextromethorphan (5.2% to 8.6%), and gabapentinoid (5.3% to 14.4%) use increased among CC patients, while opioid antitussive use increased from 2012 to 2015 and decreased thereafter (8.4% in 2012, 14.7% in 2015, 6.7% in 2021; all p < 0.001). Of 15,566 CC patients and 655,250 non-CC patients identified in the GBTM analysis, CC patients had substantial burdens of respiratory/non-respiratory comorbidities and healthcare service and concomitant medication use compared to non-CC patients. Among CC patients, GBTM identified three distinct CM utilization trajectories: (1) no CM use (n = 11,222; 72.1%); (2) declining CM use (n = 4105; 26.4%); and (3) chronic CM use (n = 239; 1.5%). CC patients in Florida had limited CM use with increasing trends in use of benzonatate, dextromethorphan, and gabapentinoids and a decreasing trend in opioid antitussive use. CC patients, particularly with chronic prescription CM use, experienced substantial disease burden

Evaluation of Cough Medication Use Patterns in Ambulatory Care Settings in the United States: 2003–2018

Using 2003–2018 National Ambulatory Medical Care Survey data for office-based visits and 2003–2018 National Hospital Ambulatory Medical Care Survey data for emergency department (ED) visits, we conducted cross-sectional analyses to examine cough medication (CM) use trends in the United States (US) ambulatory care settings. We included adult (≥18 years) patient visits with respiratory-infection-related or non-infection-related cough as reason-for-visit or diagnosis without malignant cancer or benign respiratory tumor diagnoses. Using multivariable logistic regressions, we examined opioid antitussive, benzonatate, dextromethorphan-containing antitussive, and gabapentinoid use trends. From 2003–2005 to 2015–2018, opioid antitussive use decreased in office-based visits (8.8% to 6.4%, Ptrend = 0.03) but remained stable in ED visits (6.3% to 5.9%, Ptrend = 0.99). In both settings, hydrocodone-containing antitussive use declined over 50%. Benzonatate use more than tripled (office-based:1.6% to 4.8%; ED:1.5% to 8.0%; both Ptrend < 0.001). Dextromethorphan-containing antitussive use increased in ED visits (1.8% to 2.6%, Ptrend = 0.003) but stayed unchanged in office-based visits (3.8% to 2.7%; Ptrend = 0.60). Gabapentinoid use doubled in office-based visits (1.1% in 2006–2008 to 2.4% in 2015–2018, Ptrend < 0.001) but was negligible in ED visits. In US office-based and ED ambulatory care settings, hydrocodone-containing antitussive use substantially declined from 2003 to 2018, while benzonatate use more than tripled, and dextromethorphan-containing antitussive and gabapentinoid use remained low (<3%)

The preventive services use self-efficacy (PRESS) scale in older women: development and psychometric properties

Background\ud Preventive services offered to older Americans are currently under-utilized despite considerable evidence regarding their health and economic benefits. Individuals with low self-efficacy in accessing these services need to be identified and provided self-efficacy enhancing interventions. Scales measuring self-efficacy in the management of chronic diseases exist, but do not cover the broad spectrum of preventive services and behaviors that can improve the health of older adults, particularly older women who are vulnerable to poorer health and lesser utilization of preventive services. This study aimed to evaluate the psychometric properties of a new preventive services use self-efficacy scale, by measuring its internal consistency reliability, assessing internal construct validity by exploring factor structure, and examining differences in self-efficacy scores according to participant characteristics.\ud \ud Methods\ud The Preventive Services Use Self-Efficacy (PRESS) Scale was developed by an expert panel at the University of Pittsburgh Center for Aging and Population Health - Prevention Research Center. It was administered to 242 women participating in an ongoing trial and the data were analyzed to assess its psychometric properties. An exploratory factor analysis with a principal axis factoring approach and orthogonal varimax rotation was used to explore the underlying structure of the items in the scale. The internal consistency of the subscales was assessed using Cronbach’s alpha coefficient.\ud \ud Results\ud The exploratory factor analysis defined five self-efficacy factors (self-efficacy for exercise, communication with physicians, self-management of chronic disease, obtaining screening tests, and getting vaccinations regularly) formed by 16 items from the scale. The internal consistency of the subscales ranged from .81 to .94. Participants who accessed a preventive service had higher self-efficacy scores in the corresponding sub-scale than those who did not.\ud \ud Conclusions\ud The 16-item PRESS scale demonstrates preliminary validity and reliability in measuring self-efficacy in the use of preventive services among older women. It can potentially be used to evaluate the impact of interventions designed to improve self-efficacy in the use of preventive services in community-dwelling older women

Prevalence of substance use disorder and psychiatric comorbidity burden among pregnant women with opioid use disorder in a large administrative database, 2009–2014

Objectives Using data from the Healthcare Cost and Utilization Project (HCUP), we estimated prevalence of individual substance use disorders (SUDs) and psychiatric comorbidities among pregnant women with opioid use disorder (OUD) in the New York State from 2009 to 2014. Methods In this cross-sectional study, pregnancy outcome and gestational age at delivery were estimated, and OUD diagnosis during pregnancy or at delivery discharge was identified. Prevalence of SUDs and psychiatric comorbidities were then calculated. Results Among 1,463,302 pregnant women, 8324 (0.57%) were diagnosed with OUD during pregnancy or at delivery. The most frequent SUDs or psychiatric comorbidities among pregnant women with OUD were non-opioid SUD (78.2%), followed by tobacco use disorder (74.9%), generalized anxiety disorder (38.0%), major depressive disorder (36.9%), cannabis use disorder (28.3%) and cocaine use disorder (27.4%). Conclusions Most pregnant women with OUD were diagnosed with at least one non-opioid SUD and tobacco use disorder. Generalized anxiety disorder and major depressive disorder were also common, suggesting that mental health screenings should be prioritized for pregnant women with OUD