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      Opioid overdose rates and implementation of overdose education and nasal naloxone distribution in Massachusetts: interrupted time series analysis

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          Abstract

          Objective To evaluate the impact of state supported overdose education and nasal naloxone distribution (OEND) programs on rates of opioid related death from overdose and acute care utilization in Massachusetts.

          Design Interrupted time series analysis of opioid related overdose death and acute care utilization rates from 2002 to 2009 comparing community-year strata with high and low rates of OEND implementation to those with no implementation.

          Setting 19 Massachusetts communities (geographically distinct cities and towns) with at least five fatal opioid overdoses in each of the years 2004 to 2006.

          Participants OEND was implemented among opioid users at risk for overdose, social service agency staff, family, and friends of opioid users.

          Intervention OEND programs equipped people at risk for overdose and bystanders with nasal naloxone rescue kits and trained them how to prevent, recognize, and respond to an overdose by engaging emergency medical services, providing rescue breathing, and delivering naloxone.

          Main outcome measures Adjusted rate ratios for annual deaths related to opioid overdose and utilization of acute care hospitals.

          Results Among these communities, OEND programs trained 2912 potential bystanders who reported 327 rescues. Both community-year strata with 1-100 enrollments per 100 000 population (adjusted rate ratio 0.73, 95% confidence interval 0.57 to 0.91) and community-year strata with greater than 100 enrollments per 100 000 population (0.54, 0.39 to 0.76) had significantly reduced adjusted rate ratios compared with communities with no implementation. Differences in rates of acute care hospital utilization were not significant.

          Conclusions Opioid overdose death rates were reduced in communities where OEND was implemented. This study provides observational evidence that by training potential bystanders to prevent, recognize, and respond to opioid overdoses, OEND is an effective intervention.

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          Most cited references24

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          Akaike's information criterion in generalized estimating equations.

          W. Pan (2001)
          Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
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            Measuring diagnoses: ICD code accuracy.

            To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Main error sources along the "patient trajectory" include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the "paper trail" include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
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              Patterns of abuse among unintentional pharmaceutical overdose fatalities.

              Aron Hall (2008)
              Use and abuse of prescription narcotic analgesics have increased dramatically in the United States since 1990. The effect of this pharmacoepidemic has been most pronounced in rural states, including West Virginia, which experienced the nation's largest increase in drug overdose mortality rates during 1999-2004. To evaluate the risk characteristics of persons dying of unintentional pharmaceutical overdose in West Virginia, the types of drugs involved, and the role of drug abuse in the deaths. Population-based, observational study using data from medical examiner, prescription drug monitoring program, and opiate treatment program records. The study population was all state residents who died of unintentional pharmaceutical overdoses in West Virginia in 2006. Rates and rate ratios for selected demographic variables. Prevalence of specific drugs among decedents and proportion that had been prescribed to decedents. Associations between demographics and substance abuse indicators and evidence of pharmaceutical diversion, defined as a death involving a prescription drug without a documented prescription and having received prescriptions for controlled substances from 5 or more clinicians during the year prior to death (ie, doctor shopping). Of 295 decedents, 198 (67.1%) were men and 271 (91.9%) were aged 18 through 54 years. Pharmaceutical diversion was associated with 186 (63.1%) deaths, while 63 (21.4%) were accompanied by evidence of doctor shopping. Prevalence of diversion was greatest among decedents aged 18 through 24 years and decreased across each successive age group. Having prescriptions for a controlled substance from 5 or more clinicians in the year prior to death was more common among women (30 [30.9%]) and decedents aged 35 through 44 years (23 [30.7%]) compared with men (33 [16.7%]) and other age groups (40 [18.2%]). Substance abuse indicators were identified in 279 decedents (94.6%), with nonmedical routes of exposure and illicit contributory drugs particularly prevalent among drug diverters. Multiple contributory substances were implicated in 234 deaths (79.3%). Opioid analgesics were taken by 275 decedents (93.2%), of whom only 122 (44.4%) had ever been prescribed these drugs. The majority of overdose deaths in West Virginia in 2006 were associated with nonmedical use and diversion of pharmaceuticals, primarily opioid analgesics.
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                Author and article information

                Contributors
                Role: assistant professor of medicine, medical director of Massachusetts opioid overdose prevention pilot
                Role: research assistant professor
                Role: epidemiologist
                Role: statistical manager
                Role: public health researcher
                Role: program manager
                Role: assistant director of planning and development
                Role: director, design and analysis core
                Journal
                BMJ
                BMJ
                bmj
                BMJ : British Medical Journal
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2013
                31 January 2013
                : 346
                : f174
                Affiliations
                [1 ]Clinical Addiction Research Education Unit, Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA
                [2 ]Department of Community Health Sciences, Boston University School of Public Health, USA
                [3 ]Massachusetts Department of Public Health, USA
                [4 ]Data Coordinating Center, Boston University School of Public Health, USA
                [5 ]Design and Analysis Core, Clinical Research Center, Children’s Hospital Boston, USA
                [6 ]Department of Biostatistics, Boston University School of Public Health, USA
                Author notes
                Correspondence to: A Y Walley Boston Medical Center, Section of General Internal Medicine, 801 Massachusetts Avenue, 2nd Floor, Boston, MA 02118, USA awalley@ 123456bu.edu
                Article
                wala007070
                10.1136/bmj.f174
                4688551
                23372174
                54be5904-1ace-4e2a-b58f-b7480e8d7455
                © Walley et al 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 31 December 2012
                Categories
                Research
                1779

                Medicine
                Medicine

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