Chris Delcher

Increases in Fentanyl-Related Overdose Deaths - Florida and Ohio, 2013-2015.

In March and October 2015, the Drug Enforcement Administration (DEA) and CDC issued nationwide alerts identifying fentanyl, particularly illicitly manufactured fentanyl (IMF), as a threat to public health and safety (1,2). IMF is pharmacologically similar to pharmaceutical fentanyl (PF), but is unlawfully produced in clandestine laboratories, obtained via illicit drug markets, and includes fentanyl analogs. Fentanyl is a synthetic opioid 50-100 times more potent than morphine and approved for the management of surgical/postoperative pain, severe chronic pain, and breakthrough cancer pain.* DEAs National Forensic Laboratory Information System (NFLIS) collects drug identification results from drug cases analyzed by federal, state, and local forensic laboratories throughout the United States.(†) In 2014, 80% of fentanyl submissions (i.e., drug products obtained by law enforcement that tested positive for fentanyl) in NFLIS were identified from 10 states, including Florida and Ohio (2), and seven of these 10 states reported sharp increases in fentanyl-related overdose deaths (fentanyl deaths) (3). This report presents findings of increased fentanyl deaths during 2013-2015 from investigations conducted by the University of Florida and the Ohio Department of Public Health, in collaboration with CDC. Analyses examined the association between trends in fentanyl-related law enforcement submissions and fentanyl deaths and describes groups at risk for fentanyl death using medical examiner and coroner reports. The marked increases in fentanyl death in Florida and Ohio during 2013-2015 were closely associated with parallel increases in fentanyl submissions, with the largest impact on persons who use heroin, consistent with reports that IMF is commonly mixed with or sold as heroin (1,4). In Ohio, circumstances associated with fentanyl deaths included a current diagnosed mental health disorder(§) and recent release from an institution such as a jail, rehabilitation facility, or hospital.

Abrupt decline in oxycodone-caused mortality after implementation of Florida's Prescription Drug Monitoring Program.

BACKGROUND In Florida, oxycodone-caused deaths declined substantially in 2012. Multiple important law enforcement, pharmaceutical, policy, and public health actions occurred concurrently, including implementation of a statewide Prescription Drug Monitoring Program (PDMP). The effects of the PDMP on oxycodone-caused mortality in Florida were evaluated. METHODS A time-series, quasi-experimental research design with autoregressive integrated moving average (ARIMA) statistical models, including internal and external covariates. Data included 120 repeated monthly observations. Monthly counts of oxycodone-caused deaths, obtained from the Florida Medical Examiners Commission (MEC) was the outcome variable. Models included market-entry of tamper-resistant oxycodone HC1 controlled release tablets (OxyContin(®)), enforcement crackdowns (Operation Pill Nation), and regulation by FL House Bill 7095, measured by the monthly count of Florida pain management clinics closed. Two approaches were used to test the PDMPs hypothesized effect: (1) a binary indicator variable (0=pre-implementation, 1=post-implementation), and (2) a continuous indicator consisting of the number of PDMP queries by health care providers. RESULTS Oxycodone-caused mortality abruptly declined 25% the month after implementation of Floridas PDMP (p=0.008). The effect remained after integrating other related historical events into the model. Results indicate that for a system-wide increase of one PDMP query per health care provider, oxycodone-caused deaths declined by 0.229 persons per month (p=0.002). CONCLUSIONS This is the first study to demonstrate that the PDMP had a significant effect in reducing oxycodone-caused mortality in Florida. Results have implications for national efforts to address the prescription drug epidemic.

Recreational Cannabis Legalization and Opioid-Related Deaths in Colorado, 2000–2015

Objectives To examine the association between Colorado’s legalization of recreational cannabis use and opioid-related deaths. Methods We used an interrupted time-series design (2000-2015) to compare changes in level and slope of monthly opioid-related deaths before and after Colorado stores began selling recreational cannabis. We also describe the percent change in opioid-related deaths by comparing the unadjusted model-smoothed number of deaths at the end of follow-up with the number of deaths just prior to legalization. Results Colorado’s legalization of recreational cannabis sales and use resulted in a 0.7 deaths per month (b = −0.68; 95% confidence interval = −1.34, −0.03) reduction in opioid-related deaths. This reduction represents a reversal of the upward trend in opioid-related deaths in Colorado. Conclusions Legalization of cannabis in Colorado was associated with short-term reductions in opioid-related deaths. As additional data become available, research should replicate these analyses in other states with legal recreational cannabis.

Driving after drinking among young adults of different race/ethnicities in the United States: unique risk factors in early adolescence?

PURPOSE National guidelines for alcohol screening and brief interventions advise practitioners to consider age, drinking frequency, and context to identify at-risk youth. The purpose of this study was to identify the contextual risk and protective factors in high school-aged adolescents associated with future driving after drinking (Drinking Under the Influence [DUI] at age 21) by race/ethnicity. METHODS Data included 10,271 adolescents (67% white, 12% Hispanic, 16% black, 3.6% Asian; 49% Male) who participated in the National Longitudinal Study of Adolescent Health (Waves I, II, and III) from 1995 to 2001. A lagged panel design and survey logistic regression was used to examine the association between multiple contextual factors (e.g., demographics, parents, peers, social context) during adolescence and self-reported DUI in young adulthood. RESULTS As expected, the likelihood of DUI was higher among whites followed by Hispanics, Asians, and blacks in all models. Perception of easy home access to alcohol increased risk for future DUI for whites (OR: 1.25 CI: 1.04-1.49), Hispanics (OR: 2.02 CI: 1.29-3.16), and Asians (OR: 1.90 CI: 1.13-3.22), but not for black youth. Drinking frequency and prior DUI were not risk factors for Hispanics. Risk-taking attitudes, marijuana use, and religious affiliation were risk factors for whites only. CONCLUSIONS Findings suggest that in addition to screening for drinking behaviors, brief interventions and prevention efforts should assess perceived home access to alcohol and other race-specific factors to reduce alcohol-related injuries and harm.

Attrition from HIV testing to antiretroviral therapy initiation among patients newly diagnosed with HIV in Haiti.

Objective:We report rates and risk factors for attrition in the first cohort of patients followed through all stages from HIV testing to antiretroviral therapy (ART) initiation. Design:Cohort study of all patients diagnosed with HIV between January and June 2009. Methods:We calculated the proportion of patients who completed CD4 cell counts and initiated ART or remained in pre-ART care during 2 years of follow-up and assessed predictors of attrition. Results:Of 1427 patients newly diagnosed with HIV, 680 (48%) either initiated ART or were retained in pre-ART care for the subsequent 2 years. One thousand eighty-three patients (76%) received a CD4 cell count, and 973 (90%) returned for result; 297 (31%) had CD4 cell count <200 cells per microliter, and of these, 256 (86%) initiated ART. Among 429 patients with CD4 >350 cells per microliter, 215 (50%) started ART or were retained in pre-ART care. Active tuberculosis was associated with not only lower odds of attrition before CD4 cell count [odds ratio (OR): 0.08; 95% confidence interval (CI): 0.03 to 0.25] but also higher odds of attrition before ART initiation (OR: 2.46; 95% CI: 1.29 to 4.71). Lower annual income (⩽US

Monitoring health inequities and planning in Virginia: poverty, human immunodeficiency virus, and sexually transmitted infections.

125) was associated with higher odds of attrition before CD4 cell count (OR: 1.65; 95% CI: 1.25 to 2.19) and before ART initiation among those with CD4 cell count >350 cells per microliter (OR: 1.74; 95% CI: 1.20 to 2.52). After tracking patients through a national database, the retention rate increased to only 57%. Conclusions:Fewer than half of patients newly diagnosed with HIV initiate ART or remain in pre-ART care for 2 years in a clinic providing comprehensive services. Additional efforts to improve retention in pre-ART are critically needed.

Association Between Prescription Drug Monitoring Programs and Nonfatal and Fatal Drug Overdoses: A Systematic Review

Monitoring social inequalities in sexual health is important to the effective allocation of resources for human immunodeficiency virus (HIV) and sexually transmitted infection (STI) prevention by state health departments and other outside planning groups. At the Virginia Department of Health, like most US public health agencies, there is a lack of consistent socioeconomic data, such as individual-level poverty measures, collected through routine disease surveillance.1 As a result, state epidemiologists are only able to provide a general description of poverty, such as the percentage of people living in poverty while providing HIV/STI rates for the same administrative area.2–8 This article has 2 objectives. The first is to quantify and compare HIV/STI incidence across 4 stratum of poverty at the census tract level in Virginia using methods developed by the Public Health Disparities Geocoding Project.9 Second, for each poverty strata we examine the distribution of HIV-positive and other high-risk populations, identified as priority concerns for HIV prevention planning in Virginia (“priority populations”). This research can help epidemiologists and outside planning groups, who often lack access to geocoded data guide resource allocation for HIV/STI prevention. The Public Health Disparities Geocoding Project helps facilitate routine monitoring of health inequities in the United States by providing epidemiologists with an established statistical framework for comparing disease rates at the census tract level to a census tract-based measure of poverty.9 The methodology for monitoring socioeconomic inequalities through quantifying the relationship between infection rates and area-based socioeconomic measures (ABSM) was developed and is described by the Public Health Disparities Geocoding Project. Briefly, outcomes were geocoded to the census tract level, tracts were stratified into discrete poverty levels (0%– 4.9%, 5.0%–9.9%, 10.0%–19.99%, and 20%–100% population living below poverty), age-standardized incidence rates were calculated for each stratum of poverty, and 95% confidence intervals based on the distribution were calculated.9 To calculate infection rates at the census tract level, we geocoded clinically diagnosed cases of HIV [regardless of transition to acquired immune deficiency syndrome (AIDS)], chlamydia (CT), gonorrhea (NG), and total early syphilis (TES) between 2000 and 2005 using addresses obtained from Virginia’s HIV and AIDS Reporting System (HARS) and the STD Management Information System (STDMIS). Geocoding was performed with Centrus Geostan.10 Each census tract in Virginia was stratified into 1 of 4 discrete poverty stratum, which are defined by an area-based socioeconomic measure created by the Public Health Disparities Geocoding Project from 2000 census data.11 Each stratum is an aggregation of census tracts based on the percentage of the tract’s total population living below the federal poverty line. Four hundred eighty (31%), 428 (28%), 418 (27%), and 206 (13%) census tracts were assigned to the 0% to 4.9%, 5.0% to 9.9%, 10.0% to 19.9%, and 20.0% to 100% stratum, respectively. Age-standardized incidence rates and incidence rate ratios (IRRs) for each disease were calculated for each stratum of poverty, and 95% confidence intervals based on the distribution were calculated. Gamma intervals are commonly used when the outcomes are directly standardized rates and a small number of cases and a large variability in weights exists.12 Addresses were considered geocodable to the census tract level if they geocoded to a street/house/intersection, the center of a block group/census tract or the center of a zip code, where The authors wish to thank Rene Cabral-Daniels, Jason Carr, Jeanette Gustat, Hongjie Liu and Jeff Stover, for their careful review of the manuscript. This activity was conducted, in part, through the support of the Centers for Disease Control and Prevention (CDC) Cooperative Agreement No. U62-PS000559, “Evaluating Integration of HIV/AIDS Surveillance Data with a Geographic Information System.” Additional support was provided through ongoing collaborative activities associated with the Outcomes Assessment through Systems of Integrated Surveillance (OASIS) workgroup, which was a CDC funded activity from 1998 through 2005. The Health Informatics and Integrated Surveillance Systems staff within the Virginia Department of Health-Division of Disease Prevention are grateful for the impetus OASIS had on our geographic information systems initiatives, our analytical infrastructure and our public health network of colleagues. Correspondence: Carrie Dolan, MPH, 5308 Discovery Park Building, Suite 101, Williamsburg, VA 23188. E-mail: cdolan@wchf.com. Received for publication August 30, 2007, and accepted June 7, 2008. From the Division of Disease Prevention, Virginia Department of Health, Richmond, Virginia Sexually Transmitted Diseases, December 2008, Vol. 35, No. 12, p.981–984 DOI: 10.1097/OLQ.0b013e318182a571 Copyright

Lost to follow-up but perhaps not lost in the health system.

The overuse of prescription opioids during the past 2 decades has evolved into a major public health issue in the United States. Opioid prescribing increased 350% between 1999 and 2015, from 180 to 640 morphine milligram equivalents per capita (1), with parallel increases in nonmedical use (2, 3), neonatal abstinence syndrome (4), and deaths due to both prescription opioid and heroin overdose (5, 6). The age-adjusted rate of prescription opioidrelated deaths rose from 1.0 to 4.4 deaths per 100000 population between 1999 and 2016, whereas heroin-related deaths increased nearly 5-fold since 2010, rising from 1.0 to 4.9 deaths per 100000 population between 2010 and 2016 (7). State prescription drug monitoring programs (PDMPs) have been advanced as a critical tool to better inform clinical care, identify illegal prescribing, and reduce prescription opioidrelated morbidity and mortality (8, 9). By 2017, all 50 states and the District of Columbia had an operational PDMP or passed legislation to operate a PDMP. Although PDMPs in the United States have commonalities in terms of centralized statewide data systems that electronically transmit prescription data, the administrative features of PDMPs have varied substantially among states and over time. Programs operate under different regulatory agencies, collect different types of data, require data to be updated at different intervals, and allow access to different groups of people. Despite this variability in PDMP administrative features, previous studies found implementation of these programs to be associated with reductions in the supply (10), diversion (11), and misuse of prescription opioids (12). As such, PDMPs are increasingly promoted as valuable, user-friendly, accurate, and real-time digital resources for providers and law enforcement alike (13, 14). However, evidence for the effect of PDMPs on drug-induced overdoses remains unclear. The objective of our review was to systematically search and review the literature to assess whether PDMPs are associated with changes in nonfatal or fatal overdoses; to evaluate whether specific administrative features of PDMPs are differentially associated with these outcomes and, if so, which features are most influential; and to investigate any potential unintended consequences associated with PDMPs. Methods Data Sources and Searches We followed a predefined protocol developed in November 2016 (Supplement 1 and structured reporting of the review according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines (15). We searched 5 online databases (MEDLINE, Current Contents Connect [Clarivate Analytics], Science Citation Index [Clarivate Analytics], Social Sciences Citation Index [Clarivate Analytics], and ProQuest Dissertations) for titles and abstracts of articles that examined an association between PDMP implementation and nonfatal or fatal drug overdoses. We did not impose a time or language restriction on searches (that is, queries surveyed the entire history of each online database). We included dissertations and peer-reviewed articles, as well as both published and in-process texts. We also examined references from the selected materials to identify additional articles and searched ClinicalTrials.gov. The search was first conducted in November 2016 and repeated in December 2017. All the resulting study titles and abstracts were exported to Covidence, a Web interface developed by Cochrane to systematize the review process (16). For the search terms and algorithm used in the literature search, see Appendix Table 1. Supplement. Supplementary Material Appendix Table 1. Search Strategy Study Selection All titles and abstracts were independently screened by 1 of 3 investigators (D.S.F., J.P.S., or K.K.G.) for eligibility, and those considered relevant by any investigator advanced to the full-text review. We included observational studies published in English if they estimated the before-and-after change in rates of nonfatal or fatal drug overdoses after a PDMP was implemented within a single U.S. state or in a set of states. No restrictions were placed on sample size or population age. A PDMP was considered implemented when a state operationalized its program and began to collect and distribute data or to make the data available to authorized users. Data Extraction and Quality Assessment Two researchers (J.P.S. and K.K.G.) independently read selected articles. Using a standardized article assessment form, they captured data on the specific policy studied; outcome data sources; study design; and results, including point estimates and CIs or P values. After the data were abstracted independently from each study, the 2 researchers reviewed the data for each article to ensure consistency and resolve differences. Disagreements between the researchers were reconciled by the first author (D.S.F.). Finally, 2 investigators independently assessed risk of bias (ROB) for the overdose outcomes reported in each study by using the Cochrane Risk Of Bias In Non-randomized Studiesof Interventions (ROBINS-I) assessment tool (17). By answering questions provided by ROBINS-I, the investigators assessed ROB within 8 specific bias domains (confounding, selection of participants, classification, deviations from intended interventions, missing data, measurement of outcomes, selection of the reported results, and overall bias), grading each domain as low, moderate, serious, or critical. Disagreements were resolved by consensus. Data Synthesis and Analysis Because of substantial heterogeneity in the policies examined and the analytic methods applied, we did not do a meta-analysis. Instead, we performed a qualitative assessment and synthesis using methods outlined by the Agency for Healthcare Research and Quality (18). We categorized studies into 5 groups: PDMP implementation only, specific administrative features only, both PDMP implementation and specific administrative features, PDMP implementation with other opioid policies, and PDMP robustness. Studies examining only PDMP implementation treated all PDMPs as homogenous programs without considering how their administrative features have varied among states and over time. Studies investigating specific administrative features compared states with a PDMP having a specific feature (such as mandatory registration or use, frequency of reporting, or proactive reporting) with states that either had no PDMP or had a PDMP without the specific feature. Studies of PDMPs implemented with other, associated opioid policies examined the contribution of PDMP features to those policies. Finally, studies examining PDMP robustness presented quantitative ratings of PDMP features according to their potential effectiveness in reducing diversion and overdose. We also examined 3 outcomes: nonfatal overdoses, fatal overdoses, and unintended consequences. The investigators assessed the overall strength of evidence (SOE), considering 5 domains: study limitations (determined by using ROBINS-I), directness (whether evidence linked interventions directly to a key question in the review), consistency (degree to which studies found the same direction of effect estimates), precision (degree of certainty surrounding an effect estimate), and reporting bias (selective publishing or reporting of findings on the basis of favorability of the direction or magnitude of effect estimates). On the basis of grades from the 5 specific domains, we rated the overall SOE for each intervention and outcome as insufficient, low, moderate, or high. Role of the Funding Source The National Institute on Drug Abuse (NIDA) and Bureau of Justice Assistance (BJA) had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Results Figure 1 depicts the literature search and selection process. Seventeen articles met the inclusion criteria; 4 reported nonfatal drug overdoses, and 13 reported fatal drug overdoses. All were published between 2011 and 2018. Three were doctoral dissertations (1921), and 14 were published in peer-reviewed journals (2235). Of note, outcome data from 1 study were extracted from 2 publications (29, 36). Supplement 2 presents the characteristics and Appendix Table 2 the ROB assessments of the studies. Figure 1. Evidence search and selection. PDMP= prescription drug monitoring program. Appendix Table 2. ROB Assessment in Studies That Reported on the Association Between PDMPs and Nonfatal and Fatal Drug Overdoses The Table shows the various PDMP configurations evaluated in the 17 studies. Of these studies, 8 examined PDMP implementation in general (21, 29, 3035), 2 looked at program features alone (23, 24), 5 analyzed both PDMP implementation and program features (19, 20, 22, 27, 28), 1 investigated PDMP implementation with mandated provider review combined with pain clinic laws (25), and 1 assessed PDMP robustness (26). The study that examined robustness generated a score of PDMP administrative strength or robustness by assigning weights to specific administrative features on the basis of extant evidence, or expert judgment if evidence was lacking, regarding the expected effect of the characteristic on prescribing or overdose, then summing the weights for a PDMP in a given state for a particular year (26). Among the 7 studies that examined program features, whether alone (22, 24) or in addition to PDMPs in general (19, 20, 22, 27, 28), mandatory provider use of or registration for the PDMP was the most frequently evaluated administrative feature, with 1 study examining the association with nonfatal overdoses (28), 4 studies investigating the association with fatal overdoses (20, 22, 24, 27), and 1 study looking at the association with both nonfatal and fatal overdoses (23). In addition, 2 studies examined state authorization for providers to access PDMP data (20, 22), 2 focused on proactive repo

Trends in Financial Barriers to Medical Care for Women Veterans, 2003–2004 and 2009–2010

in CD4 positive T-cell count and mortality among HIV-1 infected individuals with virological failure to all three antiretroviral drug classes. Lancet. 2004;364:51–62. 8. Phillips A, Youle M, Lampe F, et al. CD4 cell count changes in individuals with counts above 500 cells/mm3 and viral loads below 50 copies/ml on antiretroviral therapy. AIDS. 2002;16:1073–1075. 9. Clumeck N, Rieger A, Banhegyi D, et al. 96 week results from the MONET trial: a randomized comparison of darunavir/ritonavir with versus without nucleoside analogues, for patients with HIV RNA,50 copies/mL at baseline. J Antimicrob Chemother. 2011;6: 1878–1885. 10. Rockstroh J, Lennox J, DeJesus E, et al. STARTMRK RAL demonstrates durable virologic suppression and superior immunologic response with a favorable metabolic profile through 3 years of treatment: 156-week results from STARTMRK. 18th Conference on Retroviruses and Opportunistic Infections, March, 2011, Boston, MA. Abstract 542. 11. Riddler SA, Haubrich R, DiRienzo AG, et al; AIDS Clinical Trials Group Study A5142 Team. Class-sparing regimens for initial treatment of HIV-1 infection. N Engl J Med. 2008; 358:2095–2106. 12. Sierra-Madero J, Di Perri G, Wood R, et al. Efficacy and safety of maraviroc versus efavirenz, both with zidovudine/lamivudine: 96-week results from the MERIT study. HIV Clin Trials. 2010;11:125–132.

Machine Learning Approaches for Predicting High Utilizers in Health Care

INTRODUCTION Women veterans are a fast-growing segment of the veteran population, yet they face many barriers to medical care. The objective of this study was to examine factors that put women veterans at risk for a financial barrier to medical care. METHODS We conducted repeated cross-sectional analyses of data from the 2003, 2004, 2009, and 2010 Behavioral Risk Factor Surveillance System. We used weighted logistic regression to examine the risk of a financial barrier to medical care as the primary outcome in a multivariate model controlling for factors in health-related domains. RESULTS In 2010, there were an estimated 1,719,750 (11.6%) working-aged veterans who needed to see a doctor in the previous 12 months but could not because of cost. For women, 13.4% faced this financial barrier. Over the study period, facing a financial barrier was consistently associated with insurance coverage, physical and mental distress days, and having children in the home. Other associations emerged in particular years, such as binge drinking in 2010. The trends for women veterans relative to men and for younger women veterans relative to older women veterans show reduction in financial barriers to health care. CONCLUSION The Veterans Health Administration (VHA) should continue efforts to reduce financial and other barriers, especially among the higher risk groups we identified. This will help meet the VHAs objectives of providing comprehensive care to all veterans including women.