Prevalence of DSM-IV Substance Abuse and Dependence Disorders among Prison Inmates

The study examined the 30-day and lifetime prevalence of DSM-IV alcohol and drug disorders among state prison inmates. A sample of 400 inmates consecutively admitted to a state prison reception center were assessed for alcohol and drug disorders using the !Structured Clinical Interview for DSM-IV (SCID-IV). Test-retest reliabilities were calculated for the SCID-IV. Lifetime substance abuse or dependence disorders were detected among 74% of inmates, including over half who were dependent on alcohol or drugs. For the 30 days prior to incarceration, over half of the sample were diagnosed as having substance abuse or dependence disorders, including 46% who were dependent on alcohol or drugs. Black inmates were significantly less likely to be diagnosed as alcohol dependent than whites or Hispanics The high rates of substance use disorders are consistent with previous findings from other studies conducted in correctional settings and reflect the need to expand treatment capacity in prisons

New Energy Finance

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Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015.

Detection of clusters of Legionnaires' disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires' disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires' disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires' disease epidemiology would improve cluster detection and decrease time to public health action