Polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches.

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005-2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p'-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58-2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. The mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p'- DDE, PCDD TEQ and some PCB groups with diabetes.

Interpreting borderline BeLPT results.

BACKGROUND: The beryllium lymphocyte proliferation test (BeLPT) identifies persons sensitized to beryllium (BeS) and thus at risk for chronic beryllium disease (CBD). BeLPT test results are abnormal (AB), borderline (BL), or normal (NL). This manuscript addresses the predictive value and interpretation of BL BeLPT results. METHODS: The various three-result combinations that meet or exceed a nominal referral criteria of 1 AB + 1 BL are assessed with probability modeling and compared. RESULTS: At 2% prevalence, the three-result combinations that meet or exceed this referral criteria and associated probabilities of BeS are: (a) 1 AB + 1 BL + 1 NL (72%); (b) 3 BL (91%); (c) 2 AB + 1 NL (95%); (d) 1 AB + 2 BL (99%); (e) 2 AB + 1 BL (100%); and (f) 3 AB (100%). CONCLUSION: These results suggest that BL results are meaningful and that three BL results predict BeS across a broad range of population prevalences. An analysis of longitudinal BeLPT results and clinical findings from an actual surveillance program is warranted to confirm the model's predictions.

Optimizing BeLPT criteria for beryllium sensitization.

BACKGROUND: The beryllium lymphocyte proliferation test (BeLPT) is used to identify persons sensitized to beryllium. ATSDR convened an expert panel of physicians and scientists in April 2006 to discuss this test and to consider what BeLPT test results actually establish beryllium sensitization. The three criteria proposed by panel members were (1)one abnormal result, (2)one abnormal and one borderline result, and (3)two abnormal results. METHODS: Complete algorithms were developed for each of the three proposed criteria. Using single-test outcome probabilities developed by Stange et al. [2004. Am J Ind Med 46:453-462], we calculated and compared the sensitivity, specificity, and positive predictive values (PPVs) for each set of criteria. RESULTS: The overall sensitivity and specificity of the three criteria were similar. When the criteria required confirmation of an abnormal result the PPV was higher--whether the requirement was satisfied by a borderline result, or only by another abnormal result. Confirmation also reduced the likelihood of false positives. The differences between the three criteria decreased as the prevalence of sensitization increased. CONCLUSIONS: A single unconfirmed abnormal is usually insufficient to establish sensitization for an apparently healthy person. When the prevalence of beryllium sensitization in a group is high, however, even a single abnormal BeLPT can be a strong predictor.

The BeLPT: algorithms and implications.

BACKGROUND: The beryllium lymphocyte proliferation test (BeLPT) is used to identify persons with beryllium sensitization. The variability of laboratory results and lack of a "gold standard" have led to questions about the test's performance. Fortunately, a recently published study has credibly estimated standard epidemiologic parameters for the BeLPT. METHODS: Information from this recent study was used to assess the performance of two common algorithms for BeLPT testing. Standard epidemiologic parameters were determined for two common algorithms and then compared. RESULTS: One of the two algorithms was more sensitive than the other (86% vs. 66%). The specificity of both algorithms (99.8% or greater) was high. At an estimated 2% prevalence, the positive predictive value of both algorithms for beryllium sensitization remained high (90% or higher). CONCLUSIONS: A priori characterization of the testing algorithm under consideration can enhance public health decision-making.

A multivariate linear regression model for predicting children's blood lead levels based on soil lead levels: A study at four superfund sites.

For the purpose of examining the association between blood lead levels and household-specific soil lead levels, we used a multivariate linear regression model to find a slope factor relating soil lead levels to blood lead levels. We used previously collected data from the Agency for Toxic Substances and Disease Registry's (ATSDR's) multisite lead and cadmium study. The data included the blood lead measurements (0.5 to 40.2 microg/dL) of 1015 children aged 6-71 months, and corresponding household-specific environmental samples. The environmental samples included lead in soil (18.1-9980 mg/kg), house dust (5.2-71,000 mg/kg), interior paint (0-16.5 mg/cm2), and tap water (0.3-103 microg/L). After adjusting for income, education of the parents, presence of a smoker in the household, sex, and dust lead, and using a double log transformation, we found a slope factor of 0.1388 with a 95% confidence interval of 0.09-0.19 for the dose-response relationship between the natural log of the soil lead level and the natural log of the blood lead level. The predicted blood lead level corresponding to a soil lead level of 500 mg/kg was 5.99 microg/kg with a 95% prediction interval of 2. 08-17.29. Predicted values and their corresponding prediction intervals varied by covariate level. The model shows that increased soil lead level is associated with elevated blood leads in children, but that predictions based on this regression model are subject to high levels of uncertainty and variability.