Opioid Overdose Deaths in the City and County of San Francisco: Prevalence, Distribution, and Disparities.
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ABSTRACT:
SUBMITTER: Visconti AJ
PROVIDER: S-EPMC4524842 | biostudies-other | 2015 Aug
REPOSITORIES: biostudies-other
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Project description:Objectives. To contrast the geographic distribution of fentanyl-involved and non-fentanyl-involved fatal overdoses between 2014 and 2018 in Cook County, Illinois.Methods. We conducted a spatial analysis using locations of fentanyl-involved fatal overdoses (n?=?1433) compared with nonfentanyl opioid and polydrug fatal overdoses (n?=?1838) collected through the Cook County Medical Examiner's Office from 2014 to 2018. We also used logistic regression to test significant individual- and neighborhood-level covariates.Results. Fentanyl overdoses geographically clustered more than nonfentanyl overdoses, and this difference was statistically significant. One area in particular showed significantly elevated risk for fentanyl overdoses (P?<?.05) located in 2 specific neighborhoods of Chicago. The odds of a fentanyl-involved overdose were significantly increased for men, Blacks, Latinos/as, and younger individuals. Neighborhood deprivation score was the only significant neighborhood-level predictor (odds ratio?=?1.11; 95% confidence interval?=?1.07, 1.17).Conclusions. Fentanyl-involved fatal overdoses follow a distinct geographic distribution associated with resource deprivation in neighborhoods where they occur. This suggests an evolving bifurcated drug market, with drug markets in resource-deprived neighborhoods disproportionately likely to include fentanyl.
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Project description:Background: This study uses a novel geographic approach to summarize the distribution of breast cancer in San Francisco and aims to identify the neighborhoods and racial/ethnic groups that are disproportionately affected by this disease.Methods: Nine geographic groupings were newly defined on the basis of racial/ethnic composition and neighborhood socioeconomic status. Distribution of breast cancer cases from the Greater Bay Area Cancer Registry in these zones were examined. Multivariable logistic regression models were used to determine neighborhood associations with stage IIB+ breast cancer at diagnosis. Cox proportional hazards regression was used to estimate the hazard ratios for all-cause and breast cancer-specific mortality.Results: A total of 5,595 invasive primary breast cancers were diagnosed between January 1, 2006 and December 31, 2015. We found neighborhood and racial/ethnic differences in stage of diagnosis, molecular subtype, survival, and mortality. Patients in the Southeast (Bayview/Hunter's Point) and Northeast (Downtown, Civic Center, Chinatown, Nob Hill, Western Addition) areas were more likely to have stage IIB+ breast cancer at diagnosis, and those in the East (North Beach, Financial District, South of Market, Mission Bay, Potrero Hill) and Southeast were more likely to be diagnosed with triple-negative breast cancers (TNBC). Compared with other racial/ethnic groups, Blacks/African Americans (B/AA) experienced the greatest disparities in breast cancer-related outcomes across geographic areas.Conclusions: San Francisco neighborhoods with lower socioeconomic status and larger minority populations experience worse breast cancer outcomes.Impact: Our findings, which reveal breast cancer disparities at sub-county geographic levels, have implications for population-level health interventions.
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Project description:BackgroundUS rates of overdose deaths involving stimulants (e.g., cocaine or methamphetamine) have increased, but little is known about non-fatal stimulant overdoses, particularly among vulnerable populations. We characterized rates of non-fatal stimulant overdose identified outside of health care settings among women at high risk.MethodsHomeless and unstably housed women in San Francisco, California using stimulants were administered questionnaires on drug use and outcomes (stimulant overdose, health care utilization) monthly for six months. Based on pilot interviews, stimulant overdose during follow-up was defined as acute toxicity from stimulant use ("over-amping") resulting in "feeling sick, really scared, or like one's life may be in danger". Poisson regression estimated unadjusted incidence rate ratio (IRR) comparing participant characteristics.ResultsWe included 160 women (41% Black, 26% White, 15% Latina, median age 54 years) using crack cocaine (81%), methamphetamine (48%), and powdered cocaine (36%). Participants reported 67 non-fatal stimulant overdoses over 685 person-months of observation, a rate of 117.4 per 100 person-years (95% CI 85.8-160.5). Rates were higher among participants who were Latina vs. White (IRR 4.18 [1.60-10.94]), used methamphetamine (IRR 1.80 [0.96-3.38]), or used any stimulant daily/almost daily (IRR 2.63 [1.41-4.91]). Among women reporting stimulant overdose, 4% received emergency and 3% inpatient care for overdose of any drug.ConclusionsWomen in this setting, particularly those who used stimulants frequently or used methamphetamine, experienced high non-fatal stimulant overdose and rarely received health care for these events. Efforts should be made to increase awareness and reduce harms of stimulant toxicity in vulnerable populations.
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Project description:IntroductionPrescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids.MethodsFrom December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination.ResultsPredictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77).ConclusionsA model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
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Project description:Overdose Education and Naloxone Distribution (OEND) is an effective public health intervention to reduce opioid overdose fatalities (McDonald and Strang, Addiction 111:1177-1187, 2016). However, we know little about OEND implementation outcomes (i.e., indicators of implementation success), specifically the fidelity of training delivery, and how these may relate to intervention outcomes (i.e., indicators of the success or effectiveness of an intervention), such as overdose knowledge and attitudes. This study evaluated 16 OEND trainings conducted at different Opioid Overdose Prevention Programs in New York City. Trainees (N = 75) completed the Opioid Overdose Knowledge and Attitude Scales before and after training (intervention outcomes). Implementation outcomes were fidelity (competence and adherence of the trainer, N = 10; modified Fidelity Checklist) and acceptability of OEND (Acceptability of Intervention Measure), assessed from multiple perspectives (trainees, trainers, and an independent observer). Trainees' overdose knowledge, t(71) = - 8.12, p < 0.001, 95% CI [- 6.54, - 3.96], and attitudes, t(65) = - 6.85, p < 0.001, 95% CI [- 0.59, - 0.33], improved significantly from pre- to post-training. Stepwise multiple regression models indicated that adherence of the trainer rated from the observer perspective added significantly to the prediction of changes in overdose knowledge, F(1, 67) = 9.81, p = 0.003, and explained 13% of the variance in outcome. However, fidelity measures from the perspective of trainees or trainers and acceptability of OEND were not associated with changes in trainees' overdose knowledge or attitudes. OEND implementation outcomes and their relationship with intervention outcomes differed depending on the role of the fidelity rater in relation to the intervention. Specifically, our findings indicate that fidelity should be measured from an independent perspective (i.e., an individual who is experienced with fidelity rating but not directly involved in the intervention).Supplementary informationThe online version contains supplementary material available at 10.1007/s43477-021-00021-4.
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Project description:IntroductionOpioid 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.MethodsSixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: ≥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.ResultsMore 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 < 0.05). Large PIP clusters for ≥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 < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 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.ConclusionsOverdose 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.
| S-EPMC6685426 | biostudies-literature
Project description:BACKGROUND:As several regulatory and environmental changes have occurred in North America, trends in overdose deaths were examined in the United States (US), Ontario and British Columbia (BC), including changes in consumption levels of prescription opioids (PO) and overdose deaths, changes in correlations between consumption levels of PO and overdose deaths and modeled differences between observed and predicted overdose deaths if no changes had occurred. METHODS:Consumption levels of PO included defined daily doses for statistical purposes per million inhabitants per day for the US and Canada (2001-2015). Overdose deaths included opioid overdose deaths for the US (2001-2017) and Ontario (2003-2017) and illicit drug overdose deaths for BC (2001-2017). The analytic techniques included structural break point analyses, Pearson product-moment correlations and multivariate Gaussian state space modeling. RESULTS:Consumption levels of PO changed in the US in 2010 and in Canada in 2012. Overdose deaths changed in the US in 2014 and in Ontario and BC in 2015. Prior to the observed changes in consumption levels of PO, there were positive correlations between consumption levels of PO and overdose deaths in the US (r?=?0.99, p?<?0.001) and Ontario (r?=?0.92, p?=?0.003). After the observed changes in consumption levels of PO, there was a negative correlation between consumption levels of PO and overdose deaths in the US (r?=?-?0.99, p?=?0.002). Observed overdose deaths exceeded predicted overdose deaths by 5.7 (95% Confidence Interval [CI]: 4.8-6.6), 3.5 (95% CI: 3.2-3.8) and 21.8 (95% CI: 18.6-24.9) deaths per 100,000 people in the US, Ontario and BC, respectively in 2017. These excess deaths corresponded to 37.7% (95% CI: 31.9-43.6), 39.2% (95% CI: 36.3-42.1) and 72.2% (95% CI: 61.8-82.6) of observed overdose deaths in the US, Ontario and BC, respectively in 2017. CONCLUSIONS:The opioid crisis has evolved in North America, as a sizeable proportion of overdose deaths are now attributable to the several regulatory and environmental changes. These findings necessitate substance use policies to be conceptualized more broadly as well as the continued expansion of harm reduction services and types of pharmacotherapy interventions.
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