scholarly journals The National Survey on Drug Use and Health Mental Health Surveillance Study: calibration analysis

2010 ◽  
Vol 19 (S1) ◽  
pp. 61-87 ◽  
Author(s):  
Jeremy Aldworth ◽  
Lisa J. Colpe ◽  
Joseph C. Gfroerer ◽  
Scott P. Novak ◽  
James R. Chromy ◽  
...  
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
National Survey ◽  
Health Surveillance ◽  
Surveillance Study

scholarly journals The National Survey on Drug Use and Health Mental Health Surveillance Study: calibration study design and field procedures

2010 ◽  
Vol 19 (S1) ◽  
pp. 36-48 ◽  
Author(s):  
Lisa J. Colpe ◽  
Peggy R. Barker ◽  
Rhonda S. Karg ◽  
Kathy R. Batts ◽  
Katherine B. Morton ◽  
...  
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
Study Design ◽  
National Survey ◽  
Health Surveillance ◽  
Surveillance Study ◽  
Calibration Study

scholarly journals Co-occurrence across time and space of drug- and cannabinoid- exposure and adverse mental health outcomes in the National Survey of Drug Use and Health: combined geotemporospatial and causal inference analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Albert Stuart Reece ◽  
Gary Kenneth Hulse
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Drug Use ◽  
Health Outcomes ◽  
Causal Inference ◽  
Serious Mental Illness ◽  
National Survey ◽  
Cannabis Use ◽  
Mental Health Outcomes ◽  
Major Depressive

Abstract Background: Whilst many studies have linked increased drug and cannabis exposure to adverse mental health (MH) outcomes their effects on whole populations and geotemporospatial relationships are not well understood. Methods Ecological cohort study of National Survey of Drug Use and Health (NSDUH) geographically-linked substate-shapefiles 2010–2012 and 2014–2016 supplemented by five-year US American Community Survey. Drugs: cigarettes, alcohol abuse, last-month cannabis use and last-year cocaine use. MH: any mental illness, major depressive illness, serious mental illness and suicidal thinking. Data analysis: two-stage, geotemporospatial, robust generalized linear regression and causal inference methods in R. Results 410,138 NSDUH respondents. Average response rate 76.7%. When drug and sociodemographic variables were combined in geospatial models significant terms including tobacco, alcohol, cannabis exposure and various ethnicities remained in final models for all four major mental health outcomes. Interactive terms including cannabis were related to any mental illness (β-estimate = 1.97 (95%C.I. 1.56–2.37), P <  2.2 × 10− 16), major depressive episode (β-estimate = 2.03 (1.54–2.52), P = 3.6 × 10− 16), serious mental illness (SMI, β-estimate = 2.04 (1.48–2.60), P = 1.0 × 10− 12), suicidal ideation (β-estimate = 1.99 (1.52–2.47), P <  2.2 × 10− 16) and in each case cannabis alone was significantly associated (from β-estimate = − 3.43 (− 4.46 − −2.42), P = 3.4 × 10− 11) with adverse MH outcomes on complex interactive regression surfaces. Geospatial modelling showed a monotonic upward trajectory of SMI which doubled (3.62 to 7.06%) as cannabis use increased. Extrapolated to whole populations cannabis decriminalization (4.26%, (4.18, 4.34%)), Prevalence Ratio (PR) = 1.035(1.034–1.036), attributable fraction in the exposed (AFE) = 3.28%(3.18–3.37%), P < 10− 300) and legalization (4.75% (4.65, 4.84%), PR = 1.155 (1.153–1.158), AFE = 12.91% (12.72–13.10%), P < 10− 300) were associated with increased SMI vs. illegal status (4.26, (4.18–4.33%)). Conclusions Data show all four indices of mental ill-health track cannabis exposure across space and time and are robust to multivariable adjustment for ethnicity, socioeconomics and other drug use. MH deteriorated with cannabis legalization. Cannabis use-MH data are consistent with causal relationships in the forward direction and include dose-response and temporal-sequential relationships. Together with similar international reports and numerous mechanistic studies preventative action to reduce cannabis use is indicated.

scholarly journals Mental Health Service Use in Adolescence: Findings From the National Survey on Drug Use and Health

2016 ◽  
Vol 67 (7) ◽  
pp. 787-789 ◽  
Author(s):  
Heather Ringeisen ◽  
Shari Miller ◽  
Breda Munoz ◽  
Harley Rohloff ◽  
Sarra L. Hedden ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Drug Use ◽  
Health Service ◽  
National Survey ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use

scholarly journals Predictive Model of Multiple Emergency Department Visits Among Adults: Analysis of the Data from the National Survey of Drug Use and Health (NSDUH)

2020 ◽  
Author(s):  
Georgiy Bobashev ◽  
Lauren Warren ◽  
Li-Tzy Wu
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
National Survey ◽  
Predictive Ability ◽  
Health Indicators ◽  
Emergency Department Visits ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Ed Visits ◽  
Fair Poor

Abstract Objective. In this methodological paper, we use a novel, predictive approach to examine how demographics, substance use, mental and other health indicators predict multiple visits (≥3) to emergency departments (ED) within a year. Methods. State-of-the-art predictive methods were used to evaluate predictive ability and factors predicting multiple visits to ED within a year and to identify factors that influenced the strength of the prediction. The analysis used public-use datasets from the 2015-2018 National Surveys on Drug Use and Health (NSDUH), which used the same questionnaire on the variables of interest. Analysis focused on adults aged ≥18 years. Several predictive models (regressions, trees, and random forests) were validated and compared on independent datasets. Results. Predictive ability on a test set for multiple ED visits (≥3 times within a year) measured as the area under the receiver operating characteristic (ROC) was 0.79, which is good for a national survey. Models revealed consistency in predictive factors across the 4 survey years. The most influential variables for predicting ≥3 ED visits per year were fair/poor self-rated health, having a lower income, asthma, heart condition/disease, having chronic obstructive pulmonary disease (COPD), African-American race, female sex, having diabetes, being restless/fidgety, and being of younger age (18-25). Conclusions. The findings reveal the need to address behavioral and mental health contributors to ED visits and reinforce the importance of developing integrated care models in primary care settings to improve mental health for medically vulnerable patients. Presented modeling approach can be broadly applied to national and other large surveys.

scholarly journals Correlates of Mental Health Service Use Among Young Adults With Mental Illness: Results From the National Survey on Drug Use and Health

2016 ◽  
Vol 67 (6) ◽  
pp. 642-649 ◽  
Author(s):  
Shari Miller ◽  
Heather Ringeisen ◽  
Breda Munoz ◽  
Sarra L. Hedden ◽  
Lisa J. Colpe ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Young Adults ◽  
Mental Health Service ◽  
Drug Use ◽  
Health Service ◽  
National Survey ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use

The association between psychosis and problematical drug use among Australian adults: findings from the National Survey of Mental Health and Well-Being

2001 ◽  
Vol 31 (4) ◽  
pp. 659-668 ◽  
Author(s):  
LOUISA DEGENHARDT ◽  
WAYNE HALL
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Drug Use ◽  
National Survey ◽  
Well Being ◽  
Psychotic Symptoms ◽  
Opiate Abuse ◽  
Drug Use Disorders ◽  
Logistic Regressions ◽  
Health And Well Being

Background. The present paper aimed to: (a) provide Australian estimates of the population-level association between psychotic ‘caseness’ and substance use; (b) examine liability to problematical substance use according to ‘caseness’ via the conditional prevalence (prevalence among users); and (c) examine associations between problematical substance use and the number of psychotic symptoms using ordinal logistic regression.Method. Data were from the National Survey of Mental Health and Well-Being (NSMHWB), a stratified multi-stage probability sample of Australian adults, using a subset of persons under the age of 50 years (N = 6722). A screener assessed the presence of characteristic psychotic symptoms. Associations between ‘case’ status and DSM-IV alcohol, cannabis and other drug use disorders were examined. Ordinal logistic regressions predicting psychosis scores were carried out, including demographic, mental health and drug use variables.Results. Ninety-nine persons (1·2 %) screened positively for psychosis. Regular tobacco, alcohol and cannabis use were much more common among persons screening positively, as were alcohol, cannabis and other drug use disorders. Among alcohol and cannabis users, psychosis ‘cases’ were much more likely to be dependent. Ordinal logistic regressions revealed that regular tobacco use, cannabis and alcohol dependence, and opiate abuse were predictors of psychosis scores.Conclusions. The mental health risks of problematical substance use need to be disseminated to persons at risk of, or suffering from, psychotic illness, and to heavy substance users. Work is needed to develop effective treatment approaches for problematical substance use among persons with psychosis.

scholarly journals Predictive model of multiple emergency department visits among adults: analysis of the data from the National Survey of Drug Use and Health (NSDUH)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Georgiy Bobashev ◽  
Lauren Warren ◽  
Li-Tzy Wu
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
National Survey ◽  
Predictive Ability ◽  
Health Indicators ◽  
Emergency Department Visits ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Ed Visits ◽  
Fair Poor

Abstract Background In this methodological paper, we use a novel, predictive approach to examine how demographics, substance use, mental and other health indicators predict multiple visits (≥3) to emergency departments (ED) within a year. Methods State-of-the-art predictive methods were used to evaluate predictive ability and factors predicting multiple visits to ED within a year and to identify factors that influenced the strength of the prediction. The analysis used public-use datasets from the 2015–2018 National Surveys on Drug Use and Health (NSDUH), which used the same questionnaire on the variables of interest. Analysis focused on adults aged ≥18 years. Several predictive models (regressions, trees, and random forests) were validated and compared on independent datasets. Results Predictive ability on a test set for multiple ED visits (≥3 times within a year) measured as the area under the receiver operating characteristic (ROC) reached 0.8, which is good for a national survey. Models revealed consistency in predictive factors across the 4 survey years. The most influential variables for predicting ≥3 ED visits per year were fair/poor self-rated health, being nervous or restless/fidgety, having a lower income, asthma, heart condition/disease, having chronic obstructive pulmonary disease (COPD), nicotine dependence, African-American race, female sex, having diabetes, and being of younger age (18–20). Conclusions The findings reveal the need to address behavioral and mental health contributors to ED visits and reinforce the importance of developing integrated care models in primary care settings to improve mental health for medically vulnerable patients. The presented modeling approach can be broadly applied to national and other large surveys.

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