Standard Death Certificates Versus Enhanced Surveillance to Identify Heroin Overdose-Related Deaths.

OBJECTIVES: To compare 2 approaches to identifying heroin-related deaths in cases of overdose: standard death certificates and enhanced surveillance. METHODS: We reviewed Maryland death certificates from 2012 to 2015 in cases of overdose to determine specific mentions of heroin. Counts were compared with estimates obtained through an enhanced surveillance approach that included a protocol considering cause of death, toxicology, and scene investigation findings. RESULTS: Death certificates identified 1130 heroin-related deaths. Enhanced surveillance identified 2182 cases, nearly double the number found through the standard approach. The major factors supporting enhanced surveillance in identifying cases were the presence of morphine, either alone or in combination with quinine, and scene investigation information suggesting heroin use. CONCLUSIONS: Death certificates, the primary source of state and national data on overdose deaths, may underestimate the contribution of heroin to drug-related mortality. Enhanced surveillance efforts should be considered to allow a better understanding of the contribution of heroin to the overdose crisis. Public Health Implications. If enhanced surveillance can be incorporated into the death certificate process, national data on overdoses may better reflect the contribution of heroin to the opioid crisis.

Development of Maryland Local Overdose Fatality Review Teams: A Localized, Interdisciplinary Approach to Combat the Growing Problem of Drug Overdose Deaths.

The Maryland Local Overdose Fatality Review Teams (LOFRTs) are multiagency, multidisciplinary teams that critically analyze individual cases of drug overdose in their jurisdictions to identify preventable risk factors and missed opportunities for intervention, and to make policy and programmatic recommendations to prevent future overdose deaths. Three Maryland LOFRTs were first piloted in early 2014, and became established in law in May of the same year. LOFRTs provide unique opportunities for enhanced interagency collaboration and locally driven prevention efforts. This study describes the process of establishing LOFRTs in Maryland. The experiences and information regarding LOFRTs may help counties in other states combat the growing problem of deaths by drug overdose.

Sharing Overdose Data Across State Agencies to Inform Public Health Strategies: A Case Study.

Data sharing and analysis are important components of coordinated and cost-effective public health strategies. However, legal and policy barriers have made data from different agencies difficult to share and analyze for policy development. To address a rise in overdose deaths, Maryland used an innovative and focused approach to bring together data on overdose decedents across multiple agencies. The effort was focused on developing discrete intervention points based on information yielded on decedents' lives, such as vulnerability upon release from incarceration. Key aspects of this approach included gubernatorial leadership, a unified commitment to data sharing across agencies with memoranda of understanding, and designation of a data management team. Preliminary results have yielded valuable insights and have helped inform policy. This process of navigating legal and privacy concerns in data sharing across multiple agencies may be applied to a variety of public health problems challenging health departments across the country.

Intra-individual variability of CO2 breath isotope enrichment compared to blood glucose in the oral glucose tolerance test.

BACKGROUND: Glucose tolerance can be assessed noninvasively using (13)C-labeled glucose added to a standard oral glucose load, by measuring isotope-enriched CO(2) in exhaled air. In addition to the clear advantage of the noninvasive measurements, this approach may be of value in overcoming the high variability in blood glucose determination. METHODS: We compared within-individual variability of breath CO(2) isotope enrichment with that for blood glucose in a 75-g oral glucose tolerance test (OGTT) by adding 150 mg of d-[(13)C]glucose ((13)C 99%) to a standard 75-g glucose load. Measurements of whole blood glucose (by glucose oxidase) and breath isotope enrichment (by isotope ratio mass spectrometry) were made every 30 min for 3 h. Subjects underwent three repeat tests over a 3-week period. Values for variability of breath isotope enrichment at 3 h (∂‰180) and of area under the curve for enrichment to 180 min (AUC180) were compared with variability of the 2-h OGTT blood glucose. RESULTS: Breath test-derived measures exhibited lower within-subject variability than the 2-h OGTT glucose. The coefficient of variation for ∂‰180 was 7.4 ± 3.9% (mean ± SD), for AUC180 was 9.4 ± 6.3%, and for 2-h OGTT blood glucose was 13 ± 7.1% (P = 0.005 comparing ∂‰180 versus 2-h blood glucose; P = 0.061 comparing AUC180 versus 2-h blood glucose; P = 0.03 comparing ∂‰180 versus AUC180). CONCLUSIONS: Breath test-derived measurements of glucose handling had lower within-subject variability versus the standard 2-h blood glucose reading used in clinical practice. These findings support further development of this noninvasive method for evaluating glucose tolerance.

Simple modeling allows prediction of steady-state glucose disposal rate from early data in hyperinsulinemic glucose clamps.

After a constant insulin infusion is initiated, determination of steady-state conditions for glucose infusion rates (GIR) typically requires >or=3 h. The glucose infusion follows a simple time-dependent rise, reaching a plateau at steady state. We hypothesized that nonlinear fitting of abbreviated data sets consisting of only the early portion of the clamp study can provide accurate estimates of steady-state GIR. Data sets from two independent laboratories were used to develop and validate this approach. Accuracy of the predicted steady-state GDR was assessed using regression analysis and Altman-Bland plots, and precision was compared by applying a calibration model. In the development data set (n = 88 glucose clamp studies), fitting the full data set with a simple monoexponential model predicted reference GDR values with good accuracy (difference between the 2 methods -0.37 mg x kg(-1) x min(-1)) and precision [root mean square error (RMSE) = 1.11], validating the modeling procedure. Fitting data from the first 180 or 120 min predicted final GDRs with comparable accuracy but with progressively reduced precision [fitGDR-180 RMSE = 1.27 (P = NS vs. fitGDR-full); fitGDR-120 RMSE = 1.56 (P < 0.001)]. Similar results were obtained with the validation data set (n = 183 glucose clamp studies), confirming the generalizability of this approach. The modeling approach also derives kinetic parameters that are not available from standard approaches to clamp data analysis. We conclude that fitting a monoexponential curve to abbreviated clamp data produces steady-state GDR values that accurately predict the GDR values obtained from the full data sets, albeit with reduced precision. This approach may help reduce the resources required for undertaking clamp studies.