Impact of the COVID-19 Pandemic on Functions of Nursing Professionals in the Care of Opioid Use Disorder: Systematic Review.

BACKGROUND: Nursing professionals are vitally involved in the cascade of care for opioid use disorders (OUDs). The global spread of COVID-19 has had complex effects on public health aspects of major diseases, including OUDs. There are limited data on the major ways in which the COVID-19 pandemic has affected the functions of nursing professionals in the care of OUDs. METHOD: This systematic review followed Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines and examined published data for trends in OUD care during the first 2 years of the COVID-19 pandemic, focusing on nursing functions. The National Library of Medicine PubMed database and the EMBASE database were examined for peer-reviewed studies with primary data published between January 1, 2020, and December 31, 2021. REVIEW FINDINGS AND CONCLUSIONS: Rapid changes were observed in numerous aspects of OUDs during the early pandemic stage, as well as its care by nursing and other health professionals. These changes include increased overdoses (primarily from synthetic opioids such as fentanyl) and emergency department visits. These trends varied considerably across U.S. jurisdictions, underscoring the importance of region-specific examinations for public health policy and intervention. Out of necessity, healthcare systems and nursing professionals adapted to the challenges of OUD care in the pandemic. These adaptations included increases in telehealth services, increases in take-home doses of methadone or buprenorphine/naloxone, and expansion of layperson training in the use of naloxone for overdose reversal. It is likely that some of these adaptations will result in long-term changes in standards of care practices for OUDs by nursing professionals.

Has the United States Reached a Plateau in Overdoses Caused by Synthetic Opioids After the Onset of the COVID-19 Pandemic? Examination of Centers for Disease Control and Prevention Data to November 2021.

Background: Overdoses caused by synthetic mu-opioid receptor (MOR) agonists such as fentanyl are causing increasing mortality in the United States. The COVID-19 pandemic continues to have complex effects on public health, including opioid use disorders (OUD). It is unclear whether recent increases in mortality caused by synthetic opioids have reached a plateau (i.e., a stable period), after the onset of the COVID-19 pandemic. Method: This study examined provisional overdose mortality data from the Centers for Disease Control and Prevention, for synthetic opioids excluding methadone (code T40.4; monthly data available from 39 States, plus New York City and Washington DC), for June 2019-November 2021. Data were first examined as crude mortality rates. The presence of a maximum plateau was analyzed for the last 4 months of available data. For authorities in which a plateau in mortality was detected, sigmoidal Boltzmann equations were used to model parameters of this phenomenon (e.g., level of the plateau). Results: At the end of the study period, all but one authority (New Hampshire) reported increases in mortality rates for synthetic opioids, compared to the baseline month of June 2019 (range: 111-745% of baseline). A plateau was observed over the last 4 months of the study period (Aug 2021-Nov 2021) in 29 of the authorities. Ten other authorities had not reached a stable plateau at the end of the study period. For the authorities where a plateau was detected, a sigmoidal Boltzmann model revealed a fitted maximum of 262% rise in mortality over the study period, from the baseline month. The midpoint in the rise in mortality was fitted in September 2020. After separation of data into census regions, the highest plateau was observed in the West region, followed by South, Midwest, and Northeast (fitted plateau values were 409, 262, 204, and 149% of baseline, respectively). Discussion: There were increases in overdose mortality due to synthetic opioids across most states, ranging considerably in magnitude. A plateau in overdose mortality was detected at the end of the study period in most of these authorities. The reasons for these plateaus should be explored, in order to develop optimized public health interventions.

Global and regional spending on dementia care from 2000-2019 and expected future health spending scenarios from 2020-2050: An economic modelling exercise.

Background: The global burden of dementia is increasing. As diagnosis and treatment rates increase and populations grow and age, additional diagnosed cases will present a challenge to healthcare systems globally. Even modelled estimates of the current and future healthcare spending attributable to dementia are valuable for decision makers and advocates to prepare for growing demand. Methods: We modelled healthcare spending attributable to dementia from 2000 to 2019 and expected estimated future spending from 2020 to 2050 under multiple scenarios. Data were from the Global Burden of Diseases 2019 study and from two systematic literature reviews. We used meta-regression to estimate the fraction of dementia spending that is attributable to dementia for those receiving nursing home-based care and for those receiving community-based care. We used spatiotemporal Gaussian process regression to account for data missingness and model diagnosis and treatment rates, nursing home-based care and community-based care rates, and unit costs for the many countries without their own underlying estimates. Projections of future spending estimate a baseline scenario from 2020 to 2050 based on ongoing growth. Alternative scenarios assessed faster growth rates for dementia diagnosis and treatment rates, nursing home-based care, and healthcare costs. All spending is reported in 2019 United States dollars or 2019 purchasing-power parity-adjusted dollars. Findings: Based on observed and modelled inputs, we estimated that global spending on dementia increased by 4.5% (95% uncertainty interval: 3.4-5.4%) annually from 2000 to 2019, reaching $263 billion (95% uncertainty interval [UI] $199- $333) attributable to dementia in 2019. We estimated total healthcare spending on patients with dementia was $594 billion (95% UI $457-$843). Under the baseline scenario, we estimated that attributable dementia spending will reach $1.6 trillion (95% UI $0.9-$2.6) by 2050. We project it will represent 11% (95% UI 6-18%) of all expected health spending, although it could be as high as 17% (95% UI 10-26%) under alternative scenarios. Interpretation: Health systems will experience increases in the burden of dementia in the near future. These modelled direct cost estimates, built from a relatively small set of data and linear time trends, highlight the magnitude of health system resources expected to be used to provide care and ensure sufficient and adequate resources for aging populations and their caretakers. More data are needed to corroborate these important trends.

Bidirectional influence of heroin and cocaine escalation in persons with dual opioid and cocaine dependence diagnoses.

Persons with dual severe opioid and cocaine use disorders are at risk of considerable morbidity, and the bidirectional relationship of escalation of mu-opioid agonists and cocaine use is not well understood. The aim of this study was to examine the bidirectional relationship between escalation of heroin and cocaine use in volunteers dually diagnosed with opioid and cocaine dependence (OD + CD). Volunteers from New York with OD + CD (total n = 295; male = 182, female = 113; age ≥ 18 years) were interviewed with the Structured Clinical Interview for the DSM-IV Axis I Disorders and Kreek-McHugh-Schluger-Kellogg scales for dimensional measures of drug exposure, which also collect ages of 1st use and onset of heaviest use. Time of escalation was defined as age of onset of heaviest use minus age of 1st use in whole years. Times of escalation of heroin and cocaine were positively correlated in both men (Spearman r = .34, 95% confidence interval [CI: .17, .48], p < .0001) and women (Spearman r = .51, [.27, .50], p < .0001) volunteers. After we adjusted for demographic variables, a Cox regression showed that time of cocaine escalation was a predictor of time of heroin escalation (hazard ratio [HR] = 0.97, 95% CI [0.95, 0.99], p = .003). Another Cox regression showed that this relationship is bidirectional, because time of heroin escalation was also a predictor of time of cocaine escalation (HR = 0.98, [0.96-0.99], p = .016). In these adjusted models, gender was not a significant predictor of time of escalation of either heroin or cocaine. Therefore, escalation did not differ robustly by gender when adjusting for demographics and other major variables. Overall, rapid escalation of cocaine use was a predictor of rapid escalation of heroin use, and vice versa, in persons with dual severe opioid and cocaine use disorders. These findings suggest a shared vulnerability to rapid escalation of these 2 drugs in persons with dual severe opioid and cocaine use disorders. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Estimating health care delivery system value for each US state and testing key associations.

OBJECTIVE: To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value. DATA SOURCES: Annual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts. STUDY DESIGN: Using non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991-2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: US state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01). CONCLUSIONS: Substantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.

Healthcare spending in U.S. emergency departments by health condition, 2006-2016.

BACKGROUND: Healthcare spending in the emergency department (ED) setting has received intense focus from policymakers in the United States (U.S.). Relatively few studies have systematically evaluated ED spending over time or disaggregated ED spending by policy-relevant groups, including health condition, age, sex, and payer to inform these discussions. This study's objective is to estimate ED spending trends in the U.S. from 2006 to 2016, by age, sex, payer, and across 154 health conditions and assess ED spending per visit over time. METHODS AND FINDINGS: This observational study utilized the National Emergency Department Sample, a nationally representative sample of hospital-based ED visits in the U.S. to measure healthcare spending for ED care. All spending estimates were adjusted for inflation and presented in 2016 U.S. Dollars. Overall ED spending was $79.2 billion (CI, $79.2 billion-$79.2 billion) in 2006 and grew to $136.6 billion (CI, $136.6 billion-$136.6 billion) in 2016, representing a population-adjusted annualized rate of change of 4.4% (CI, 4.4%-4.5%) as compared to total healthcare spending (1.4% [CI, 1.4%-1.4%]) during that same ten-year period. The percentage of U.S. health spending attributable to the ED has increased from 3.9% (CI, 3.9%-3.9%) in 2006 to 5.0% (CI, 5.0%-5.0%) in 2016. Nearly equal parts of ED spending in 2016 was paid by private payers (49.3% [CI, 49.3%-49.3%]) and public payers (46.9% [CI, 46.9%-46.9%]), with the remainder attributable to out-of-pocket spending (3.9% [CI, 3.9%-3.9%]). In terms of key groups, the majority of ED spending was allocated among females (versus males) and treat-and-release patients (versus those hospitalized); those between age 20-44 accounted for a plurality of ED spending. Road injuries, falls, and urinary diseases witnessed the highest levels of ED spending, accounting for 14.1% (CI, 13.1%-15.1%) of total ED spending in 2016. ED spending per visit also increased over time from $660.0 (CI, $655.1-$665.2) in 2006 to $943.2 (CI, $934.3-$951.6) in 2016, or at an annualized rate of 3.4% (CI, 3.3%-3.4%). CONCLUSIONS: Though ED spending accounts for a relatively small portion of total health system spending in the U.S., ED spending is sizable and growing. Understanding which diseases are driving this spending is helpful for informing value-based reforms that can impact overall health care costs.

US Health Care Spending by Race and Ethnicity, 2002-2016.

Importance: Measuring health care spending by race and ethnicity is important for understanding patterns in utilization and treatment. Objective: To estimate, identify, and account for differences in health care spending by race and ethnicity from 2002 through 2016 in the US. Design, Setting, and Participants: This exploratory study included data from 7.3 million health system visits, admissions, or prescriptions captured in the Medical Expenditure Panel Survey (2002-2016) and the Medicare Current Beneficiary Survey (2002-2012), which were combined with the insured population and notified case estimates from the National Health Interview Survey (2002; 2016) and health care spending estimates from the Disease Expenditure project (1996-2016). Exposure: Six mutually exclusive self-reported race and ethnicity groups. Main Outcomes and Measures: Total and age-standardized health care spending per person by race and ethnicity for each year from 2002 through 2016 by type of care. Health care spending per notified case by race and ethnicity for key diseases in 2016. Differences in health care spending across race and ethnicity groups were decomposed into differences in utilization rate vs differences in price and intensity of care. Results: In 2016, an estimated $2.4 trillion (95% uncertainty interval [UI], $2.4 trillion-$2.4 trillion) was spent on health care across the 6 types of care included in this study. The estimated age-standardized total health care spending per person in 2016 was $7649 (95% UI, $6129-$8814) for American Indian and Alaska Native (non-Hispanic) individuals; $4692 (95% UI, $4068-$5202) for Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals; $7361 (95% UI, $6917-$7797) for Black (non-Hispanic) individuals; $6025 (95% UI, $5703-$6373) for Hispanic individuals; $9276 (95% UI, $8066-$10 601) for individuals categorized as multiple races (non-Hispanic); and $8141 (95% UI, $8038-$8258) for White (non-Hispanic) individuals, who accounted for an estimated 72% (95% UI, 71%-73%) of health care spending. After adjusting for population size and age, White individuals received an estimated 15% (95% UI, 13%-17%; P < .001) more spending on ambulatory care than the all-population mean. Black (non-Hispanic) individuals received an estimated 26% (95% UI, 19%-32%; P < .001) less spending than the all-population mean on ambulatory care but received 19% (95% UI, 3%-32%; P = .02) more on inpatient and 12% (95% UI, 4%-24%; P = .04) more on emergency department care. Hispanic individuals received an estimated 33% (95% UI, 26%-37%; P < .001) less spending per person on ambulatory care than the all-population mean. Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals received less spending than the all-population mean on all types of care except dental (all P < .001), while American Indian and Alaska Native (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 90% more; 95% UI, 11%-165%; P = .04), and multiple-race (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 40% more; 95% UI, 19%-63%; P = .006). All 18 of the statistically significant race and ethnicity spending differences by type of care corresponded with differences in utilization. These differences persisted when controlling for underlying disease burden. Conclusions and Relevance: In the US from 2002 through 2016, health care spending varied by race and ethnicity across different types of care even after adjusting for age and health conditions. Further research is needed to determine current health care spending by race and ethnicity, including spending related to the COVID-19 pandemic.

Age of onset of heaviest use of cannabis or alcohol in persons with severe opioid or cocaine use disorders.

BACKGROUND: Persons with severe opioid or cocaine use disorders are particularly vulnerable to morbidity and mortality. Heaviest use of mu-opioid receptor agonists and cocaine typically commences in early adulthood and is preceded by substantial adolescent exposure to cannabis and/or alcohol. Little information exists on the age trajectories of exposure to cannabis or alcohol in persons diagnosed with severe opioid or cocaine use disorders, compared to persons diagnosed with other substance use disorders (unrelated to opioids or cocaine). METHOD: This observational study had n = 854 volunteers (male = 581, female = 273; ≥18 years of age at the time of interview) and examined the ages of onset of heaviest use of cannabis and alcohol in persons diagnosed by DSM-IV criteria with opioid dependence (OD), both opioid and cocaine dependence (OD + CD) and cocaine dependence (CD). These age trajectory measures were compared to persons with other substance use disorders (primarily cannabis and alcohol use disorders, termed "Any Other Diagnoses"). RESULTS: Unadjusted survival analyses showed persons diagnosed with either OD + CD or CD had earlier onset of heaviest use of cannabis (mean ages of 16.2 and 17.8, respectively) compared to the "Any Other Diagnoses" reference group (mean age = 19.5). A multivariate logistic regression showed that later onset of heaviest use of cannabis was associated with lower odds of being in the OD + CD or CD groups, when compared to the reference group. CONCLUSIONS: Persons diagnosed with severe cocaine use disorders or dual opioid and cocaine use disorders exhibit a pattern of heavy and especially early adolescent exposure to cannabis, compared to persons with other substance use disorders.

Assessing Catastrophic Health Expenditures Among Uninsured People Who Seek Care in US Hospital-Based Emergency Departments.

Importance: Uninsured people uniquely rely on the emergency department (ED) for care as they are less likely to have access to lower-cost alternatives. Prior work has demonstrated that most uninsured patients are at risk of catastrophic health expenditure (CHE) after being hospitalized for life-saving care. The risk of CHE for a single treat-and-release ED visit that does not result in a hospitalization among uninsured patient encounters is currently unknown. Objective: To estimate the overall national risk of CHE among uninsured patients after a single treat-and-release ED visit from 2006 through 2017, and to characterize this risk across key traits. Design Setting and Population: This cross-sectional study is based on a nationally representative sample of hospital-based ED visits between 2006 and 2017 in the United States (US) from the Nationwide Emergency Department Sample (NEDS). It examined outpatient ED visits among uninsured patients. Main Outcomes and Measures: Risk of CHE for ED care defined as an ED charge that exceeds 40% of one's estimated annual post-subsistence income. Results: From 2006 to 2017, there were 41.7 million NEDS encounters that met inclusion criteria for this analysis, equating to a nationally weighted estimate of 184.6 million uninsured treat-and-release ED encounters over this period. The median ED charge for a single treat-and-release encounter grew from $842 in 2006 to $2033 by 2017. Approximately 1 in 5 uninsured patients (18% [95% CI, 18.0%-18.0%]) were at risk of CHE for a single treat-and-release ED visit over the study period. This estimated CHE risk increased from 13.6% (95% CI, 13.6%-13.6%) in 2006 to 22.6% (95% CI, 22.6%-22.7%) in 2017. Those living in the lowest income quartile faced a disproportionate share of this risk, with nearly 1 in 3 (28.5% [95% CI, 28.5%-28.6%]) facing CHE risk in 2017. In 2017, an estimated 3.2 million patient encounters nationally were at risk of CHE after a single treat-and-release ED visit. Conclusions and Relevance: This cross-sectional analysis from 2006 to 2017 of 184.6 million uninsured treat-and-release visits found that 1 in 5 uninsured patient encounters are at risk for CHE. This risk has grown over time. Future policies designed to improve access for unscheduled care must consider the unique role of the ED as the de facto safety net and ensure that uninsured patients are not at undue risk of financial harm for seeking care.

Health-care spending attributable to modifiable risk factors in the USA: an economic attribution analysis.

BACKGROUND: There is a robust understanding of how specific behavioural, metabolic, and environmental risk factors increase the risk of health burden. However, there is less understanding of how these risks individually and jointly affect health-care spending. The objective of this study was to quantify health-care spending attributable to modifiable risk factors in the USA for 2016. METHODS: We extracted estimates of US health-care spending by condition, age, and sex from the Institute for Health Metrics and Evaluation's Disease Expenditure Study 2016 and merged these estimates with population attributable fraction estimates for 84 modifiable risk factors from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 to produce estimates of spending by condition attributable to these risk factors. Because not all spending can be linked to health burden, we adjusted attributable spending estimates downwards, proportional to the association between health burden and health-care spending across time and age for each aggregate health condition. We propagated underlying uncertainty from the original data sources by randomly pairing the draws from the two studies and completing our analysis 1000 times independently. FINDINGS: In 2016, US health-care spending attributable to modifiable risk factors was US$730·4 billion (95% uncertainty interval [UI] 694·6-768·5), corresponding to 27·0% (95% UI 25·7-28·4) of total health-care spending. Attributable spending was largely due to five risk factors: high body-mass index ($238·5 billion, 178·2-291·6), high systolic blood pressure ($179·9 billion, 164·5-196·0), high fasting plasma glucose ($171·9 billion, 154·8-191·9), dietary risks ($143·6 billion, 130·3-156·1), and tobacco smoke ($130·0 billion, 116·8-143·5). Spending attributable to risk factor varied by age and sex, with the fraction of attributable spending largest for those aged 65 years and older (45·5%, 44·2-46·8). INTERPRETATION: This study shows high spending on health care attributable to modifiable risk factors and highlights the need for preventing and controlling risk exposure. These attributable spending estimates can contribute to informed development and implementation of programmes to reduce risk exposure, their health burden, and health-care cost. FUNDING: Vitality Institute.

US Health Care Spending by Payer and Health Condition, 1996-2016.

Importance: US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. Objective: To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Design and Setting: Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Exposures: Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. Main Outcomes and Measures: National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Results: Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Conclusions and Relevance: Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.

Are trait impulsivity and exposure to cannabis or alcohol associated with the age of trajectory of cocaine use? A gender-specific dimensional analysis in humans with cocaine dependence diagnosis.

Cocaine use disorders (CUD) cause major morbidity and optimized prevention efforts are critical. It is unclear if trait impulsivity and exposure to cannabis or alcohol are associated with age trajectory of cocaine use (e.g., age of onset of heaviest use, or time of escalation), or with vulnerability to develop a CUD. This is an observational study with volunteers (≥ 18 years old), from a metropolitan area. The sample (n = 1,010) included: n = 360 normal volunteers, n = 438 with cocaine dependence (CD) diagnoses, and n = 212 with other addictive diseases. Trait impulsivity was examined with BIS-11 scores. Maximal self-exposure to cannabis, alcohol, and cocaine were characterized dimensionally with Kreek-McHugh-Schluger-Kellogg (KMSK) scales. Time of escalation was defined as the interval between age of first use and age of onset of heaviest use. Onset of maximal use of cannabis (median age = 17) and alcohol (median age = 21) preceded that of cocaine (median age = 27), in volunteers with CD. Multivariate Cox regressions in volunteers with CD show that increasing self-exposure to cannabis was a predictor of earlier onset of heaviest use of cocaine. Also, more rapid time of escalation of alcohol was a predictor of more rapid time of escalation of cocaine. A multiple logistic regression shows that increasing self-exposure to cannabis or alcohol was a positive predictor of odds of CD diagnosis. Trait impulsivity and gender were not significant predictors in these multivariate analyses. This study shows that aspects of adolescent exposure to nonmedical cannabis and alcohol are predictors of early onset of CUD, and may be potentially targeted for prevention efforts. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Escalation of drug use in persons dually diagnosed with opioid and cocaine dependence: Gender comparison and dimensional predictors.

BACKGROUND: Persons dually diagnosed with opioid and cocaine dependence (OD + CD) present a clinical challenge and are at risk of morbidity and mortality. The time of escalation of heroin and cocaine exposure in persons with OD + CD remain understudied, and the influence of gender and other variables have not been examined. This observational study focused on the time of escalation of heroin and cocaine in volunteers with OD + CD, examining gender and exposure to other drugs (e.g., cannabis or alcohol) as predictors. Ages of first use and of onset of heaviest use of each drug were collected (in whole years). Time of escalation was defined as the interval between age of first use and onset of heaviest use. VOLUNTEERS: sequentially ascertained adult volunteers recruited from the New York Metropolitan area, of which n = 297 were diagnosed with OD + CD. METHODS: Instruments administered were the SCID-I diagnostic interview (DSM-IV criteria), BIS-11 impulsiveness scale, and KMSK scales, dimensional measures of maximal exposure to specific drugs. RESULTS: In volunteers with OD + CD, ages of onset of heaviest use of cannabis (median age = 15) and alcohol (median age = 19) were in adolescence or emerging adulthood and preceded those for heroin and cocaine (median ages = 26 and 25, respectively). Maximal levels of cannabis and alcohol exposure were high, in volunteers with OD + CD. In adjusted Cox regressions, gender was not a significant predictor of time of heroin or cocaine escalation. However, more rapid time of alcohol escalation was a predictor of more rapid time of escalation of both heroin and cocaine, in volunteers with OD + CD.

On the feasibility of small communities wellhead RO treatment for nitrate removal and salinity reduction.

The feasibility of wellhead water treatment in small communities for nitrate removal and salinity reduction via a flexible high recovery RO system was evaluated through analysis of treatment options, laboratory and onsite field tests. In small remote communities that rely on septic systems for residential wastewater treatment, discharge of the RO residual stream (containing nitrate) to the community septic tank is shown to be a feasible option. It is demonstrated that RO treatment with a system that employs partial concentrate recycle, integrated with a pressure intensifier, enabled the use of a relatively low-pressure feed pump while allowing high recovery operation. The approach of integrating RO treatment into existing community small water systems is demonstrated to be suitable for providing effective nitrate removal and salinity reduction over wide range of nitrate and salinity levels, while meeting community water demand and regulatory water quality requirements.

Non-medical Cannabis Self-Exposure as a Dimensional Predictor of Opioid Dependence Diagnosis: A Propensity Score Matched Analysis.

Background: The impact of increasing non-medical cannabis use on vulnerability to develop opioid use disorders has received considerable attention, with contrasting findings. A dimensional analysis of self-exposure to cannabis and other drugs, in individuals with and without opioid dependence (OD) diagnoses, may clarify this issue. Objective: To examine the age of onset of maximal self-exposure to cannabis, alcohol, cocaine, and heroin, in volunteers diagnosed with OD, using a rapidly administered instrument (the KMSK scales). To then determine whether maximal self-exposure to cannabis, alcohol, and cocaine is a dimensional predictor of odds of OD diagnoses. Methods: This outpatient observational study examined maximal self-exposure to these drugs, in volunteers diagnosed with DSM-IV OD or other drug diagnoses, and normal volunteers. In order to focus more directly on opioid dependence diagnosis as the outcome, volunteers who had cocaine dependence diagnoses were excluded. Male and female adults of diverse ethnicity were consecutively ascertained from the community, and from local drug treatment programs, in 2002-2013 (n = 574, of whom n = 94 had OD diagnoses). The age of onset of maximal self-exposure of these drugs was examined. After propensity score matching for age at ascertainment, gender, and ethnicity, a multiple logistic regression examined how increasing self-exposure to non-medical cannabis, alcohol and cocaine affected odds of OD diagnoses. Results: Volunteers with OD diagnoses had the onset of heaviest use of cannabis in the approximate transition between adolescence and adulthood (mean age = 18.9 years), and onset of heaviest use of alcohol soon thereafter (mean age = 20.1 years). Onset of heaviest use of heroin and cocaine was detected later in the lifespan (mean ages = 24.7 and 25.3 years, respectively). After propensity score matching for demographic variables, we found that the maximal self-exposure to cannabis and cocaine, but not to alcohol, was greater in volunteers with OD diagnoses, than in those without this diagnosis. Also, a multiple logistic regression detected that increasing self-exposure to cannabis and cocaine, but not alcohol, was a positive predictor of OD diagnosis. Conclusions/Importance: Increasing self-exposure to non-medical cannabis, as measured with a rapid dimensional instrument, was a predictor of greater odds of opioid dependence diagnosis, in propensity score-matched samples.

Re-evaluation of the KMSK scales, rapid dimensional measures of self-exposure to specific drugs: Gender-specific features.

BACKGROUND: The Kreek-McHugh-Schluger-Kellogg (KMSK) scales provide a rapid assessment of maximal self-exposure to specific drugs and can be used as a dimensional instrument. This study provides a re-evaluation of the KMSK scales for cannabis, alcohol, cocaine, and heroin in a relatively large multi-ethnic cohort, and also the first systematic comparison of gender-specific profiles of drug exposure with this scale. METHODS: This was an observational study of n = 1,133 consecutively ascertained adult volunteers. The main instruments used were the SCID-I interview (DSM-IV criteria) and KMSK scales for cannabis, alcohol, cocaine, and heroin. RESULTS: Participants were 852 volunteers (297 female) with specific DSM-IV abuse or dependence diagnoses, and 281 volunteers without any drug diagnoses (154 female). Receiver operating characteristic (ROC) curves were calculated for concurrent validity of KMSK scores with the respective DSM-IV dependence diagnoses. The areas under the ROC curves for men and women combined were 99.5% for heroin, 97% for cocaine, 93% for alcohol, and 85% for cannabis. Newly determined optimal KMSK "cutpoint" scores were identical for men and women for cocaine and heroin dependence diagnoses, but were higher in men than in women, for cannabis and alcohol dependence diagnoses. CONCLUSIONS: This study confirms the scales' effectiveness in performing rapid dimensional analyses for cannabis, alcohol, cocaine, and heroin exposure, in a cohort larger than previously reported, with "cutpoints" changed from initial determinations, based on this larger sample. The KMSK scales also detected gender differences in self-exposure to alcohol and cannabis that are associated with the respective dependence diagnoses.