Faculty Bibliography

INTRODUCTION AND OBJECTIVE/OBJECTIVE:Cigarette smoking is the leading modifiable risk factor for several genitourinary (GU) malignancies. Although smoking cessation after GU cancer diagnosis is a critical component of survivorship, factors related to continued smoking are understudied. METHODS:A cross-sectional analysis was conducted using data from the NHIS (2014-2018). Our primary study outcome was the prevalence and correlates of cigarette smoking among adults with history of smoking-related (kidney or bladder) urologic cancer compared with a nonsmoking-related control (prostate cancer). We used regression analyses to assess the association of having a smoking-related GU cancer history with continued cigarette smoking after diagnosis. Secondary outcomes were yearly smoking trends, quit attempts and reported receipt of smoking cessation counseling. RESULTS:A total of 2,664 respondents reported a history of a GU cancer, representing weighted estimates of 990,820 (smoking-related GU cancer) and 2,616,596 (prostate cancer) adults. Survivors of smoking-related GU cancers had a significantly higher overall prevalence of current cigarette use (14.8% vs 8.6%, p <0.001) and also reported more frequent receipt of counseling (79.8% vs 66.2%, p=0.02) but did not attempt to quit any more often than those with prostate cancer (52.4% vs 47.2%, p=0.44). Time trends demonstrated stable and persistent cigarette use among survivors of all GU cancers. After adjustment for sociodemographic confounders, cancer type was not associated with current cigarette smoking (OR 1.23, 95% CI 0.86-1.77). However, older age and more advanced educational attainment were associated with lower odds of current cigarette smoking while single marital status was associated with higher odds. CONCLUSIONS:In this population-based cross-sectional study of survivors of GU cancers, those with a reported smoking-related GU cancer had a higher prevalence of current cigarette smoking compared to prostate cancer, our nonsmoking related control. Those with smoking-related GU cancers reported more frequent receipt of smoking cessation counseling.

Objective: Although the rapid increase in opioid use disorders (OUD) and concurrent increase in Hepatitis C virus (HCV) in the United States is well-documented, little is known about HCV testing among high-risk populations. We examine patterns of HCV testing across OUD treatment settings for individuals with OUD in New York. Methods: Using 2014 New York Medicaid claims data, we identified OUD diagnosis, OUD treatment (methadone, buprenorphine, naltrexone, other treatment (inpatient or outpatient non-medication-based psychosocial treatment, such as psychotherapy) and no treatment) utilization and HCV-testing status among beneficiaries. We performed multivariable logistic regression to identify factors associated with HCV screening across OUD treatment settings. Results: 79,764 individuals with OUD diagnoses were identified in 2014. The prevalence of HCV screening was 32.4%, 16.2%, 20.6%, 16.8%, and 18.1% for those receiving methadone, buprenorphine, naltrexone, other treatment, and no treatment, respectively. In the adjusted logistic regression, those receiving any OUD treatment had greater odds of being screened, with the highest odds among methadone clients. Conclusions: Engagement in medication for OUD is associated with increased HCV testing. Findings indicate the importance of access to medication-based treatment for OUD and a need to further improve HCV screening rates.

Background: Little is known about how COVID-19-related social distancing has affected illegal drug use. We surveyed electronic dance music (EDM) partygoers-a population known for high levels of drug use-to determine whether their drug use patterns had changed during state-mandated social distancing in New York. Methods: Individuals were recruited online and screened for eligibility throughout April and May 2020. We surveyed 128 eligible adults and queried, retrospectively, whether their drug use behavior had changed during COVID-19-related social distancing. Results: Most participants reporting past-three-month use reported decreased frequency of use during COVID-19-related social distancing. Specifically, 78.6% reduced frequency of use of cocaine, 71.1% reduced frequency of use of ecstasy/MDMA/Molly, and 68.0% reduced frequency of use of LSD. Although some participants reported increased frequency of use of cocaine (7.1%), ecstasy (7.9%), or LSD (12.0%), 35.0% reported increased frequency of cannabis use. Most (66.7%) of those reporting cocaine use reduced the amount used. The majority of those reporting use of cannabis, ecstasy, cocaine, and/or LSD reported that drug cost (80.0-84.0%) and drug quality (84.2-92.0%) did not change during social distancing. Having a college degree was associated with higher odds for decreasing frequency of cannabis use. Older participants (ages ≥23) were at lower odds for decreasing frequency of cocaine use, as were those earning >$500 per week, and participants who attended EDM events biweekly or more often were at higher odds for decreasing frequency of LSD use. Conclusions: Participants in this sample tended to reduce party drug use during COVID-19-related social distancing.

This study explored the divergence in population-level estimates of insufficient sleep (<6 h) by examining the explanatory role of race/ethnicity and contrasting values derived from logistic and Poisson regression modeling techniques. We utilized National Health and Nutrition Examination Survey data to test our hypotheses among 20-85 year-old non-Hispanic Black and non-Hispanic White adults. We estimated the odds ratios using the transformed logistic regression and Poisson regression with robust variance relative risk and 95% confidence intervals (CI) of insufficient sleep. Comparing non-Hispanic White (10176) with non-Hispanic Black (4888) adults (mean age: 50.61 ± 18.03 years, female: 50.8%), we observed that the proportion of insufficient sleepers among non-Hispanic Blacks (19.2-26.1%) was higher than among non-Hispanic Whites (8.9-13.7%) across all age groupings. The converted estimated relative risk ranged from 2.12 (95% CI: 1.59, 2.84) to 2.59 (95% CI: 1.92, 3.50), while the estimated relative risks derived directly from Poisson regression analysis ranged from 1.84 (95% CI: 1.49, 2.26) to 2.12 (95% CI: 1.64, 2.73). All analyses indicated a higher risk of insufficient sleep among non-Hispanic Blacks. However, the estimates derived from logistic regression modeling were considerably higher, suggesting the direct estimates of relative risk ascertained from Poisson regression modeling may be a preferred method for estimating population-level risk of insufficient sleep.

BACKGROUND:Recent research shows an increase in drug and alcohol-related hospitalizations in the USA, especially among older adults. However, no study examines trends in discharges to a skilled nursing facility (SNF) after a drug or alcohol-related hospitalization. Older adults are more likely to need post-hospital care in a SNF after a hospitalization due to an increased presence of chronic diseases and functional limitations. Therefore, the objective of this study was to estimate trends in drug or alcohol-related hospitalizations with discharge to a SNF among adults age 55 and older. METHODS:We analyzed data from New York State's Statewide Planning and Research Cooperative System to calculate the number of cannabis, cocaine, opioid, and alcohol-related hospitalizations in New York City that resulted in discharge to a SNF from 2008 to 2014 among adults age 55 and older. Using New York City population estimates modified from US Census Bureau, we calculated age-specific rates per 100,000 adults. Trend tests were estimated using joinpoint regressions to calculate annual percentage change (APC) with 95% confidence intervals (CI) and stratified by adults age 55-64 and adults age 65 and older. RESULTS:During the study period, among adults age 55-64, there were significant increases in cocaine, cannabis, and opioid-related hospitalizations that resulted in discharge to a SNF. For adults ≥ 65 years, there were sharp increases across all substances with larger increases in opioids (APC of 10.66%) compared to adults 55-64 (APC of 6.49%). For both age groups and among the four substances, alcohol-related hospitalizations were the leading cause of discharge to a SNF. CONCLUSIONS:We found an increase in hospital discharges to SNFs for patients age 55 and older admitted with alcohol or drug-related diagnoses. Post-acute and long-term care settings should prepare to care for an increase in older patients with substance use disorders by integrating a range of harm reduction interventions into their care settings.

BACKGROUND:Results reveal indicators of implementation success and strategies to address challenges to screening for substance use in primary care.

Background/UNASSIGNED:Elevated blood pressure is the leading cause of death worldwide; however, treatment and control rates remain very low. An expanding literature supports the strategy of task redistribution of hypertension care to nurses. Objective/UNASSIGNED:We aimed to evaluate the effect of a nurse-based hypertension management program in Kenya. Methods/UNASSIGNED:We conducted a retrospective data analysis of patients with hypertension who initiated nurse-based hypertension management care between January 1, 2011, and October 31, 2013. The primary outcome measure was change in systolic blood pressure (SBP) over one year, analyzed using piecewise linear mixed-effect models with a cut point at 3 months. The primary comparison of interest was care provided by nurses versus clinical officers. Secondary outcomes were change in diastolic blood pressure (DBP) over one year, and blood pressure control analyzed using a zero-inflated Poisson model. Results/UNASSIGNED:The cohort consisted of 1051 adult patients (mean age 61 years; 65% women). SBP decreased significantly from baseline to three months (nurse-managed patients: slope -4.95 mmHg/month; clinical officer-managed patients: slope -5.28), with no significant difference between groups. DBP also significantly decreased from baseline to three months with no difference between provider groups. Retention in care at 12 months was 42%. Conclusions/UNASSIGNED:Nurse-managed hypertension care can significantly improve blood pressure. However, retention in care remains a challenge. If these results are reproduced in prospective trial settings with improvements in retention in care, this could be an effective strategy for hypertension care worldwide.

The understanding of male factors of infertility has grown exponentially in the past ten years. While clear guidelines for obstructive azoospermia have been developed, management of non-obstructive azoospermia has lagged. Specifically, management of Kallmann Syndrome and central non-obstructive azoospermia has been limited by a lack of understanding of the molecular pathogenesis and investigational trials exploring the best option for management and fertility in these patients. This review aims to summarize our current understanding of the causes of central hypogonadotropic hypogonadism with a focus on genetic etiologies while also discussing options that endocrinologists and urologists can utilize to successfully treat this group of infertile men.

We develop the distribution for the number of hypotheses found to be statistically significant using the rule from Simes (Biometrika 73: 751"“754, 1986) for controlling the family-wise error rate (FWER). We find the distribution of the number of statistically significant p-values under the null hypothesis and show this follows a normal distribution under the alternative. We propose a parametric distribution ΨI(·) to model the marginal distribution of p-values sampled from a mixture of null uniform and non-uniform distributions under different alternative hypotheses. The ΨI distribution is useful when there are many different alternative hypotheses and these are not individually well understood. We fit ΨI to data from three cancer studies and use it to illustrate the distribution of the number of notable hypotheses observed in these examples. We model dependence in sampled p-values using a latent variable. These methods can be combined to illustrate a power analysis in planning a larger study on the basis of a smaller pilot experiment.