Impact of killer-immunoglobulin-like receptor and human leukocyte antigen genotypes on the efficacy of immunotherapy in acute myeloid leukemia

Interactions between killer-immunoglobulin-like receptors (KIRs) and their HLA class I ligands are instrumental in natural killer (NK) cell regulation and protect normal tissue from NK cell attack. Human KIR haplotypes comprise genes encoding mainly inhibitory receptors (KIR A) or activating and inhibitory receptors (KIR B). A substantial fraction of humans lack ligands for inhibitory KIRs (iKIRs), that is, a 'missing ligand' genotype. KIR B/x and missing ligand genotypes may thus give rise to potentially autoreactive, unlicensed NK cells. Little is known regarding the impact of such genotypes in untransplanted acute myeloid leukemia (AML). For this study, NK cell phenotypes and KIR/HLA genotypes were determined in 81 AML patients who received immunotherapy with histamine dihydrochloride and low-dose IL-2 for relapse prevention (NCT01347996). We observed that presence of unlicensed NK cells impacted favorably on clinical outcome, in particular among patients harboring functional NK cells reflected by high expression of the natural cytotoxicity receptor (NCR) NKp46. Genotype analyses suggested that the clinical benefit of high NCR expression was restricted to patients with a missing ligand genotype and/or a KIR B/x genotype. These data imply that functional NK cells are significant anti-leukemic effector cells in patients with KIR/HLA genotypes that favor NK cell autoreactivity

Small area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling approach

BACKGROUND: Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. OBJECTIVE: The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. METHODS: We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts\u27 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. RESULTS: Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. CONCLUSIONS: Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy

Opioid overdose deaths and potentially inappropriate opioid prescribing practices (PIP): A spatial epidemiological study

INTRODUCTION: Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots. METHODS: Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering \u3e98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: \u3e /=100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes. RESULTS: More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p \u3c 0.05). Large PIP clusters for \u3e /=100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p \u3c 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p \u3c 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots. CONCLUSIONS: Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses

Overdose risk for veterans receiving opioids from multiple sources

OBJECTIVES: The aim of this study was to evaluate whether veterans in Massachusetts receiving opioids and/or benzodiazepines from both Veterans Health Administration (VHA) and non-VHA pharmacies are at higher risk of adverse events compared with those receiving opioids at VHA pharmacies only. STUDY DESIGN: A cohort study of veterans who filled a prescription for any Schedule II through V substance at a Massachusetts VHA pharmacy. Prescriptions were recorded in the Massachusetts Department of Public Health Chapter 55 data set. METHODS: The study sample included 16,866 veterans residing in Massachusetts, of whom 9238 (54.8%) received controlled substances from VHA pharmacies only and 7628 (45.2%) had filled prescriptions at both VHA and non-VHA pharmacies ( dual care users ) between October 1, 2013, and December 31, 2015. Our primary outcomes were nonfatal opioid overdose, fatal opioid overdose, and all-cause mortality. RESULTS: Compared with VHA-only users, more dual care users resided in rural areas (12.6% vs 10.6%), received high-dose opioid therapy (26.3% vs 7.3%), had concurrent prescriptions of opioids and benzodiazepines (34.8% vs 8.2%), and had opioid use disorder (6.8% vs 1.6%) (P \u3c .0001 for all). In adjusted models, dual care users had higher odds of nonfatal opioid overdose (odds ratio [OR], 1.29; 95% CI, 0.98-1.71) and all-cause mortality (OR, 1.66; 95% CI, 1.43-1.93) compared with VHA-only users. Dual care use was not associated with fatal opioid overdoses. CONCLUSIONS: Among veterans in Massachusetts, receipt of opioids from multiple sources was associated with worse outcomes, specifically nonfatal opioid overdose and mortality. Better information sharing between VHA and non-VHA pharmacies and prescribers has the potential to improve patient safety

Estimated Prevalence of Opioid Use Disorder in Massachusetts, 2011-2015: A Capture-Recapture Analysis

OBJECTIVES: To estimate the annual prevalence of opioid use disorder (OUD) in Massachusetts from 2011 to 2015. METHODS: We performed a multisample stratified capture-recapture analysis to estimate OUD prevalence in Massachusetts. Individuals identified from 6 administrative databases for 2011 to 2012 and 7 databases for 2013 to 2015 were linked at the individual level and included in the analysis. Individuals were stratified by age group, sex, and county of residence. RESULTS: The OUD prevalence in Massachusetts among people aged 11 years or older was 2.72% in 2011 and 2.87% in 2012. Between 2013 and 2015, the prevalence increased from 3.87% to 4.60%. The greatest increase in prevalence was observed among those in the youngest age group (11-25 years), a 76% increase from 2011 to 2012 and a 42% increase from 2013 to 2015. CONCLUSIONS: In Massachusetts, the OUD prevalence was 4.6% among people 11 years or older in 2015. The number of individuals with OUD is likely increasing, particularly among young people

Non-fatal opioid-related overdoses among adolescents in Massachusetts 2012-2014

BACKGROUND: Opioid-related overdoses and deaths among adolescents in the United States continue to increase, but little is known about adolescents who experience opioid-related non-fatal overdose (NFOD). Our objective was to describe (1) the characteristics of adolescents aged 11-17 who experienced NFOD and (2) their receipt of medications for opioid use disorder (MOUD) in the 12 months following NFOD, compared with adults. METHODS: We created a retrospective cohort using six Massachusetts state agency datasets linked at the individual level, with information on 98% of state residents. Individuals entered the cohort if they experienced NFOD between January 1, 2012 and December 31, 2014. We compared adolescents to adults experiencing NFOD, examining individual characteristics and receipt of medications for opioid use disorder (MOUD)-methadone, buprenorphine, or naltrexone. RESULTS: Among 22,506 individuals who experienced NFOD during the study period, 195 (0.9%) were aged 11-17. Fifty-two percent (102/195) of adolescents were female, whereas only 38% of adults were female (P \u3c 0.001). In the year prior to NFOD, 11% (21/195) of adolescents received a prescription opioid, compared to 43% of adults (P \u3c 0.001), and \u3c 5% ( \u3c 10/195) received any MOUD compared to 23% of adults (P \u3c 0.001). In the 12 months after NFOD, only 8% (15/195) of adolescents received MOUD, compared to 29% of adults. CONCLUSION: Among individuals experiencing NFOD, adolescents were more likely to be female and less likely to have been prescribed opioids in the year prior. Few adolescents received MOUD before or after NFOD. Non-fatal overdose is a missed opportunity for starting evidence-based treatment in adolescents