Jeremy Aldworth

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

The Mental Health Surveillance Study (MHSS) is an ongoing initiative by the Substance Abuse and Mental Health Services Administration to develop and implement methods for measuring the prevalence of serious mental illness (SMI) among adults in the USA. The 2008 MHSS used data from clinical interviews administered to a sub‐sample of respondents to calibrate mental health screening scale data from the National Survey on Drug Use and Health (NSDUH) for estimating the prevalence of SMI in the full NSDUH sample. The mental health scales included the K6 screening scale of psychological distress (administered to all respondents) along with two measures of functional impairment (each administered to a random half‐sample of respondents): the World Health Organization Disability Assessment Schedule (WHODAS) and the Sheehan Disability Scale (SDS). The Structured Clinical Interview for DSM‐IV (SCID) was administered to a sub‐sample of 1506 adult NSDUH respondents within 4 weeks of completing the NSDUH interview. Results indicate that while SMI prediction accuracy of the K6 is improved by adding either the WHODAS or the SDS to the prediction equation, the models with the WHODAS are more robust. The results of the calibration study and methods used to derive prevalence estimates of SMI are presented. Copyright

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

The Mental Health Surveillance Study (MHSS) is an ongoing initiative by the Substance Abuse and Mental Health Services Administration (SAMHSA) to monitor the prevalence of serious mental illness (SMI) among adults in the USA. In 2008, the MHSS used data from clinical interviews to calibrate mental health data from the National Survey on Drug Use and Health (NSDUH) for estimating the prevalence of SMI based on the full NSDUH sample. The clinical interview used was the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM‐IV; SCID). NSDUH interviews were administered via audio computer‐assisted self‐interviewing (ACASI) to a nationally representative sample of the population aged 12 years or older. A total of 46 180 NSDUH interviews were completed with adults aged 18 years or older in 2008. The SCID was administered by mental health clinicians to a sub‐sample of 1506 adults via telephone. This paper describes the MHSS calibration study procedures, including information on sample selection, instrumentation, follow‐up, data quality protocols, and management of distressed respondents. Copyright

Estimated drug use based on direct questioning and open‐ended questions: responses in the 2006 National Survey on Drug Use and Health

Substance use surveys may use open‐ended items to supplement questions about specific drugs and obtain more exhaustive information on illicit drug use. However these questions are likely to underestimate the prevalence of use of specific drugs. Little is known about the extent of such underestimation or the groups most prone to under‐reporting. Using data from the 2006 National Survey on Drug Use and Health (NSDUH), a civilian, non‐institutionalized population survey of persons aged 12 or older in the United States, we compared drug use estimates based on open‐ended questions with estimates from a new set of direct questions that occurred later in the interview. For these drugs, estimates of lifetime drug use based on open‐ended questions often were at least seven times lower than those based on direct questions. Among adults identified in direct questions as substance users, lower educational levels were consistently associated with non‐reporting of use in the open‐ended questions. Given NSDUHs large annual sample size (∼67  000 interviews), combining data across future survey years could increase our understanding of characteristics associated with non‐reporting of use in open‐ended questions and allow drug use trends to be extrapolated to survey years in which only open‐ended question data are available. Copyright

Statistical tools for determining appropriate selection of regression models for analysis of environmental fate datasets

BACKGROUND In the NAFTA regulatory community, a currently consistent methodology used to estimate dissipation times for environmental fate data is not applied. RESULTS This work demonstrates through a case study that the inappropriate use of pseudo-first-order regression models can result in inaccurate estimates of soil degradation rates, and it proposes some statistical tools that can be used to identify an appropriate statistical model to fit a particular environmental fate dataset. Diagnostic procedures have been proposed to identify the appropriate scale, and statistical testing procedures have been proposed to select the appropriate model within that scale. CONCLUSION Results from this work demonstrate that, unless the proposed diagnostic and statistical procedures are used, inaccurate estimates of dissipation times may result.

Estimating the prevalence of any impairing childhood mental disorder in the national health interview survey

This study investigates whether the six‐item Strengths and Difficulties Questionnaire SDQ (five symptoms and one impact item) included in the National Health Interview Survey (NHIS) can be used to construct models that accurately estimate the prevalence of any impairing mental disorder among children 4–17 years old as measured by a shortened Child/Adolescent or Preschool Age Psychiatric Assessment (CAPA or PAPA). A subsample of 217 NHIS respondents completed a follow‐up CAPA or PAPA interview. Logistic regression models were developed to model presence of any child mental disorder with impairment (MDI) or with severe impairment (MDSI). Models containing only the SDQ impact item exhibited highly biased prevalence estimates. The best‐performing model included information from both the five symptom SDQ items and the impact item, where absolute bias was reduced and sensitivity and concordance were increased. This study illustrates the importance of using all available information from the six‐item SDQ to accurately estimate the prevalence of any impairing childhood mental disorder from the NHIS. Copyright

Flow-covariate prediction of stream pesticide concentrations

Potential peak functions (e.g., maximum rolling averages over a given duration) of annual pesticide concentrations in the aquatic environment are important exposure parameters (or target quantities) for ecological risk assessments. These target quantities require accurate concentration estimates on nonsampled days in a monitoring program. We examined stream flow as a covariate via universal kriging to improve predictions of maximum m-day (m = 1, 7, 14, 30, 60) rolling averages and the 95th percentiles of atrazine concentration in streams where data were collected every 7 or 14 d. The universal kriging predictions were evaluated against the target quantities calculated directly from the daily (or near daily) measured atrazine concentration at 32 sites (89 site-yr) as part of the Atrazine Ecological Monitoring Program in the US corn belt region (2008-2013) and 4 sites (62 site-yr) in Ohio by the National Center for Water Quality Research (1993-2008). Because stream flow data are strongly skewed to the right, 3 transformations of the flow covariate were considered: log transformation, short-term flow anomaly, and normalized Box-Cox transformation. The normalized Box-Cox transformation resulted in predictions of the target quantities that were comparable to those obtained from log-linear interpolation (i.e., linear interpolation on the log scale) for 7-d sampling. However, the predictions appeared to be negatively affected by variability in regression coefficient estimates across different sample realizations of the concentration time series. Therefore, revised models incorporating seasonal covariates and partially or fully constrained regression parameters were investigated, and they were found to provide much improved predictions in comparison with those from log-linear interpolation for all rolling average measures. Environ Toxicol Chem 2018;37:260-273.

Evaluation of the use of bias factors with water monitoring data

Aquatic exposure assessments using surface water quality monitoring data are often challenged by missing extreme concentrations if sampling frequency is less than daily. A bias factor method has been previously proposed to address this concern for peak concentrations, where a bias factor is a multiplicative quantity to upwardly adjust estimates so that the true value is exceeded 95% of the time. In other words, bias factors are statistically protective adjustments. We evaluate this method using a research data set of 69 near-daily sampled site-years from the Atrazine Ecological Monitoring Program, dividing the data set into 23 reference and 46 validation site-years. Bias factors calculated from the reference data set are used to evaluate the method using the validation set for 1) point estimation, 2) interval estimation, and 3) decision-making. Sampling designs are every 7, 14, 28, and 90 d; and target quantities of assessment interest are the 90th and 95th percentiles and maximum m-day rolling averages (m = 1, 7, 21, 60, 90). We find that bias factors are poor point estimators in comparison with alternative methods. For interval estimation, average coverage is less than nominal, with coverage at individual site-years sometimes very low. Positive correlation of bias factors and target quantities, where present, adversely affects method performance. For decision rules or screening, the method typically shows very low false-negative rates but at the cost of extremely high false-positive rates. Environ Toxicol Chem 2018;37:1864-1876.