Xibei Liu

Effects of the 2009 Medical Cannabinoid Legalization Policy on Hospital Use for Cannabinoid Dependency and Persistent Vomiting

BACKGROUND & AIMS: In 2009, the U.S. Department of Justice issued a memo stating that it would not prosecute users and sellers who complied with the state laws allowing for medical use of marijuana. There are growing concerns about legalization of marijuana use and its related public health effects. We performed an interrupted time series analysis to evaluate these effects. METHODS: We collected a representative sample of hospital discharge data from the Healthcare Cost and Utilization Project, from January 1993 to December 2014. We divided the data in to 3 groups: the prelegalization period (1993–2008), the legalization period (2009), and the postlegalization period (2010–2014). The disease variables were International Classification of Disease‐Ninth Revision‐Clinical Modification 304.30 cannabinoid dependency unspecified (CDU), 536.2 persistent vomiting, and an aggregate of CDU and persistent vomiting. We performed interrupted time series and Poisson‐Gamma regression analysis to calculate each year’s incidence rate of unspecified and persistent vomiting and CDU per 100,000 hospital discharges. CDU, persistent vomiting, and aggregate of CDU and persistent vomiting were modeled separately to estimate average incidence rate ratio and 95% confidence interval for each study phase. RESULTS: We observed an increasing trend of CDU or an aggregate of CDU and persistent vomiting during the prelegalization period. The legalization of marijuana significantly increased the incidence rate during the legalization period (by 17.9%) and the yearly average increase in rate by 6% after policy implementation, compared to the prelegalization period. The increase in rate of persistent vomiting after policy implementation increased significantly (by about 8%), although there were no significant trends in increase prior to or during marijuana legalization in 2009. CONCLUSIONS: In an interrupted time series analysis of before, during, and after medical marijuana legalization, we estimated levels and rate changes in CDU and persistent vomiting. We found persistent increases in rates of CDU and persistent vomiting during and after legalization of marijuana.

Alternative Time Series Analysis and Potential Effect of Center for Medicare & Medicaid Services’ Hospital Readmission Reduction Program

Alternative Time Series Analysis and Potential Effect of Center for Medicare & Medicaid Services’ Hospital Readmission Reduction Program

Early Readmission Risk Identification for Hospitalized Older Adults With Decompensated Heart Failure

The current study evaluated risk factors of early hospital readmission in geriatric patients with acute heart failure (HF) and analyzed 2,279 consecutively hospitalized older adults with decompensated HF from November 2013 to October 2014 across 15 U.S. hospitals. The eTracker-HF was designed to make risk factors known to treating clinicians in electronic health records. Multilevel multivariate logistic regression was applied to examine the association between risk factors and all-cause and HF 30-day readmission rates. All-cause and HF 30-day readmission rates were 22.3% and 9.8%, respectively. Old age, non-White ethnicity, delirium, physical impairment, ejection fraction <40%, advanced chronic kidney disease, and previous myocardial infarction were associated with all-cause and HF readmission. Home health care use was inversely associated with early readmission. In addition to demographic and cardiovascular risk factors, geriatric syndromes were associated with early readmission. Discharge to home health care may reduce early readmission in these patients. [Res Gerontol Nurs. 2018; 11(4):190-197.].

Societal perspectives analysis for evaluating direct‐acting antiviral affordability while awaiting liver transplant

We read with interest the study by Ahmed et al. identifying the cost-effectiveness of all direct-acting antivirals (DAAs) for patients with hepatitis C virus (HCV) awaiting liver transplant (LT) in the United States. A few things should be considered before applying the findings of Ahmed et al. to clinical practice. There are possible selection biases in the base case scenario, for example, the ASTRAL-4 cohort, which may not reflect the full spectrum of the US population. The ASTRAL-4 study included a predominantly white population (90%) compared to patients on the actual United Network of Organ Sharing waitlist (white 70%) in year 2012. According to Beckman et al., <1% of inmates with HCV were treated with DAAs. This inequality in DAAs for HCV treatment is not limited to the United States and is a common dilemma in other developed countries even under universal health care systems such as those of the United Kingdom and Korea. Kieslich et al. identified the limitations of payers’ perspectives that were applied by Ahmed et al., which might not fully reflect vulnerable populations as stated above. According to the United Network of Organ Sharing database in year 2016, approximately 20% of LT waitlist cases resulted in death or removal from the waitlist. The proportion of DAA users among HCV patients awaiting LT is largely unknown. Uncertainty remains regarding the Trump administration’s actions on DAA coverage for patients with HCV waiting LT. In these contexts, Kieslich et al. suggested that both the patient and the public should be proactively engaged to promote DAA coverage for HCV treatment as an urgent agenda for the media and policymakers. Simultaneously, newer costeffectiveness analyses of DAA coverage should be tested again, ideally by two reference case perspectives—the societal and payers’ perspectives—and should be put forward to identify the most affordable ways to cover DAAs for US patients with HCV awaiting LT.

Cost-Effective but Unaffordable and Unequal Hepatitis C Treatment in the US

To the Editor: We read with great interest the study by Ooka et al. (1), profiling Medicaid reimbursement for oral direct antiviral agents for the treatment of chronic hepatitis C. The finding suggests that considerable heterogeneity in the criteria for approving direct antiviral agents (DAAs) as chronic hepatitis C treatment still exists in different states. The authors (1) suggested a combination of patient and provider advocacy and further strengthening of cost-effectiveness data using the payers’ perspectives in order to narrow heterogeneity among state programs and expansion of DAAs coverage by public and private payers.

Clinical implications and quality of evidence in metaanalysis about effects of palliative care in critically ill patients in the intensive care unit

Martins and colleagues (2016) analyzed the effect of hospital palliative care on hospital length of stay (LOS) and in-hospital mortality among seriously ill patients in an intensive care unit (ICU). Their results revealed a significant decrease in both hospital LOS and in-hospital mortality after hospital palliative care in the ICU. These findings may be explained by increased home care or nursing home referrals in critically ill Medicare patients, which results in more deaths outside of the hospital (Teno et al., 2013; Yoo et al., 2013). Further, the recent increase in completion of advance directives among the elderly might have increased the number of deaths outside the hospital (Silveira et al., 2014; Yoo et al., 2013). Two recent national Medicare studies revealed that earlier hospice enrollment can reduce hospital LOS (Zuckerman et al., 2016) and save on costs (Kelley et al., 2013) until death. In spite of these data, a recent Hartford Foundation report showed that less than 20% of primary care physicians discuss advance directives with patients and bill this discussion to Medicare (The John A. Hartford Foundation, 2016). In lieu of a lack of end-of-life discussions in the primary care setting, hospital palliative care is still the initial discussion point on palliative care for seriously ill patients. Reduction of in-hospital mortality and LOS in the ICU by palliative care might influence earlier hospice referral and lead to a lessening of public healthcare cost burdens. There are a few challenges involved in disseminating hospital palliative care to critically ill patients in the ICU. In their assessment of statewide hospitalbased palliative care structures and services, Gibbs et al. (2015) noted widespread variation in hospitalbased palliative care initiation time and delivery type. Midlevel practitioners or training physicians participated in the initial history or setting up of family meetings up to 80% of the time. Curtis et al. (2013) illustrated the challenges involved in trainees acquiring communication skills, and Carson et al. (2016) showed that communication can be counterproductive if the information is not delivered meticulously, or without having built a relationship with patients and their family members. Future studies are urgently required in order to standardize hospital palliative care service and improve communication skills training. In the Martins et al. (2016) study, the methods section did not discuss the quality of evidence for each outcome. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system (see Table 1) could be employed to evaluate the quality of evidence for each outcome (Guyatt et al., 2008). The GRADE system is a new and comprehensive tool for assessment of the quality of evidence of metaanalyses (high, moderate, low, and very low). Because only one study (Cheung et al., 2010) was a randomized controlled study, it was removed from the GRADE system. The metaanalysis from observational studies begins at a low quality of evidence. In our metaanalysis, serious publication bias and extensive heterogeneity significantly lowered the quality of evidence, revealing the need to gather high-quality data on the effects of hospital palliative care on length-of-stay and in-hospital mortality (Table 1). Address correspondence and reprint requests: to Ji Won Yoo, Department of Internal Medicine, University of Nevada School of Medicine, 1701 West Charleston Boulevard #230, Las Vegas, Nevada 89102. E-Mail: jwyoo@med.unr.edu. Palliative and Supportive Care (2017), 15, 513–515. # Cambridge University Press, 2017 1478-9515/17 doi:10.1017/S1478951516001036

Necessity of more sensitivity analyses and interpretations from public health perspectives

We read with interest the study by Rush et al.(1) identifying increased utilization of palliative care in hospitalized patients with end-stage liver disease (ESLD) in the United States. A few considerations should be made, including additional sensitivity analyses and interpretation of these findings from a public health perspective. First, the number of hospitalizations and palliative care consultations were analyzed instead of the number of patients. Analysis of these numbers could over-represent patients or hospitals with frequent palliative care utilization groups. This concern can be appropriately adjusted by using the hierarchical modeling analysis.(2) Because Rush et al. used the SAS v9.4 statistical software, PROC GLIMMIX procedure can fit continuous, binary, or count outcomes to adjust data at multiple levels (patient and hospital).(2) If the researchers used multilevel adjustment methods, it is essential that they clarify this step. Second, the researchers did not fully report the number of missing data points. Identifying the proportion of missing data might be necessary given that this study used the International Classification of Diseases, Ninth Edition code V66.7 (palliative care consultation). Rush et al.(1) cited the validity of using V66.7 code from a study using a small number of patients (100) with a different diagnosis (ischemic stroke) from their analysis (ESLD).(3) However, when evaluating more than 100,000 admissions and a variety of diagnostic subgroups, Hua et al.(4) found that the sensitivity for the V66.7 code was 61.11% in chronic liver disease. Third, Rush et al.(1) collapsed insurance covariate into yes versus no. However, patients under private insurance, when compared to those with Medicaid, are quite heterogenous in annual income and disease severity. Vasveebye Sonoo, M.D.1* Jay V. Kumar, M.D.1* Xibei Liu, M.D.2 Johnson Ukken, M.S. 3 Mary Froehlich, M.A.3 Ji Won Yoo, M.D. 1 1 Department of Internal Medicine University of Nevada Las Vegas School of Medicine, Las Vegas, NV 2 Department of Medicine University of Arizona College of Medicine, Tucson, AZ 3 University of Nevada Reno School of Medicine Reno, NV