Journal Article

Essential Indicators Identifying Chronic Inorganic Mercury Intoxication: Pooled Analysis across Multiple Cross-Sectional Studies

Berger, Ursula, Bose-O’Reilly, Stephan, Doering, Stefan

2016

Overview

The continuous exposure to inorganic mercury vapour in artisanal small-scale gold mining (ASGM) areas leads to chronic health problems. It is therefore essential to have a quick, but reliable risk assessing tool to diagnose chronic inorganic mercury intoxication. This study re-evaluates the state-of-the-art toolkit to diagnose chronic inorganic mercury intoxication by analysing data from multiple pooled cross-sectional studies. The primary research question aims to reduce the currently used set of indicators without affecting essentially the capability to diagnose chronic inorganic mercury intoxication. In addition, a sensitivity analysis is performed on established biomonitoring exposure limits for mercury in blood, hair, urine and urine adjusted by creatinine, where the biomonitoring exposure limits are compared to thresholds most associated with chronic inorganic mercury intoxication in artisanal small-scale gold mining. Health data from miners and community members in Indonesia, Tanzania and Zimbabwe were obtained as part of the Global Mercury Project and pooled into one dataset together with their biomarkers mercury in urine, blood and hair. The individual prognostic impact of the indicators on the diagnosis of mercury intoxication is quantified using logistic regression models. The selection is performed by a stepwise forward/backward selection. Different models are compared based on the Bayesian information criterion (BIC) and Cohen`s kappa is used to evaluate the level of agreement between the diagnosis of mercury intoxication based on the currently used set of indicators and the result based on our reduced set of indicators. The sensitivity analysis of biomarker exposure limits of mercury is based on a sequence of chi square tests. The variable selection in logistic regression reduced the number of medical indicators from thirteen to ten in addition to the biomarkers. The estimated level of agreement using ten of thirteen medical indicators and all four biomarkers to diagnose chronic inorganic mercury intoxication yields a Cohen`s Kappa of 0.87. While in an additional stepwise selection the biomarker blood was not selected, the level of agreement based on ten medical indicators and only the three biomarkers urine, urine/creatinine and hair reduced Cohen`s Kappa to 0.46. The optimal cut-point for the biomarkers blood, hair, urine and urine/creatinine were estimated at 11. 6 μg/l, 3.84 μg/g, 24.4 μg/l and 4.26 μg/g, respectively. The results show that a reduction down to only ten indicators still allows a reliable diagnosis of chronic inorganic mercury intoxication. This reduction of indicators will simplify health assessments in artisanal small-scale gold mining areas.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adolescent / Adult / Aged / Analysis / Bayes Theorem / Bayesian analysis / Bioindicators / Biological monitoring / Biology and Life Sciences / Biomarkers / Biometrics / Biomonitoring / Blood / Blood Chemical Analysis / Chi-square test / Child / Chronic illnesses / Computer and Information Sciences / Contamination / Creatinine / Cross-Sectional Studies / Data mining / Data processing / Demographic aspects / Diagnosis / Disease / Environmental health / Environmental Monitoring / Epidemiology / Exposure / Exposure limits / Female / Gold / Gold - adverse effects / Gold mines & mining / Gold mining / Hair / Hair - chemistry / Hazardous materials / Hazardous substances / Health aspects / Health problems / Health sciences / Health services / Humans / Indicators / Information science / Intoxication / Logistic Models / Male / Mathematical models / Medical wastes / Medicine and Health Sciences / Mercury / Mercury (metal) / Mercury poisoning / Mercury Poisoning - diagnosis / Mercury Poisoning - etiology / Mercury project / Mercury surface / Middle Aged / Miners / Mining / Occupational health / Physical Sciences / Public health / Reduction / Regression analysis / Regression models / Risk assessment / Risk Factors / Sensitivity analysis / Social Sciences / Studies / Urine / Urine - chemistry / Young Adult