A population-based study of blood lead levels in relation to depression in the United States.

PURPOSE: Lead is a known neurotoxicant. Several studies have suggested that occupational exposure to lead may lead to depression, anxiety and other psychiatric illness, but few studies have examined environmental lead exposure and depression. We evaluated the relationship between blood lead levels (BLL) and depression in a sample representative of the US population. METHODS: We analyzed data from 4,159 adults ages ≥20 who participated in the 2005-2006 cycle of the National Health and Nutrition Examination Survey (NHANES). Depression was assessed by the Patient Health Questionnaire-9 (PHQ-9). Relative risks were calculated using Poisson regression, and odds ratios were calculated with ordinal logistic regression using SUDAAN, controlling for pertinent covariates. RESULTS: The risk of depression was only slightly elevated with increasing blood lead levels when lead was modeled as a categorical variable, with adjusted relative risks of 1.16 (95% confidence interval (CI) = 0.99-1.36), 1.20 (CI = 1.07-1.36) and 1.16 (CI = 0.87-1.54) for 0.89-1.40 µg/dL, 1.41-2.17 µg/dL and >2.17 µg/dL, respectively, when compared to 0-0.88 µg/dL using Poisson regression. Similar results were obtained with ordinal logistic regression. Analyses using BLL as a continuous variable did not show a significant relationship with depression. CONCLUSIONS: This cross-sectional study did not provide consistent evidence for an association between environmental lead exposure and depression within the investigated blood lead levels.

Characteristics Associated With U.S. Outpatient Opioid Analgesic Prescribing and Gabapentinoid Co-Prescribing.

INTRODUCTION: A considerable burden of prescription and illicit opioid-related mortality and morbidity in the U.S. is attributable to potentially unnecessary or excessive opioid prescribing, and co-prescribing gabapentinoids may increase risk of harm. Data are needed regarding physician and patient characteristics associated with opioid analgesic and opioid analgesic-gabapentinoid co-prescriptions to elucidate targets for reducing preventable harm. METHODS: Multiple logistic regression was utilized to examine patient and physician predictors of opioid analgesic prescriptions and opioid analgesic-gabapentinoid co-prescriptions in adult noncancer patients using the National Ambulatory Medical Care Survey 2015 public use data set. Potential predictors were selected based on literature review, clinical relevance, and random forest machine learning algorithms. RESULTS: Among the 11.8% (95% CI=9.8%, 13.9%) of medical encounters with an opioid prescription, 16.2% (95% CI=12.6%, 19.8%) had a gabapentinoid co-prescription. Among all gabapentinoid encounters, 40.7% (95% CI=32.6%, 48.7%) had an opioid co-prescription. Predictors of opioid prescription included arthritis (OR=1.87, 95% CI=1.30, 2.69). Predictors of new opioid prescription included physician status as an independent contractor (OR=3.67, 95% CI=1.38, 9.81) or part owner of the practice (OR=3.34, 95% CI=1.74, 6.42). Predictors of opioid-gabapentinoid co-prescription included patient age (peaking at age 55-64 years; OR=35.67, 95% CI=4.32, 294.43). CONCLUSIONS: Predictors of opioid analgesic prescriptions with and without gabapentinoid co-prescriptions were identified. These predictors can help inform and reinforce (e.g., educational) interventions seeking to reduce preventable harm, help identify populations for elucidating opioid-gabapentinoid risk-benefit profiles, and provide a baseline for evaluating subsequent public health measures.