Separating potential source exposure from background exposure in subsistence populations in developing countries.
Risk assessment methods of developed countries have prescribed exposure assumptions for calculating health risks that are generally inappropriate for developing countries because of population, cultural, and social differences. For example, populations in developing countries are often subsistence users of natural resources with a more outdoor-oriented lifestyle. Assessments should thus measure specific dietary intake rates and contact rates with environmental media. Chemical analyses of food, environmental media, and any biomarkers of exposure should include a carefully matched reference population to distinguish between exposures due to naturally occurring metals in more mineralized areas and potential anthropogenic sources. Without a reference group, one might predict excess risk associated with the external source, even though exposure is due to background levels. For example, subsistence populations often have a simple diet with high ingestion rates of a few food types (e.g. 200 g/day wet weight of fish; 500 g/day of rice). These foods can be naturally elevated in arsenic (fish and rice) and mercury (fish). Conservative risk assessments that extrapolate toxicity from high to low doses can predict elevated risks for these naturally occurring elements (e.g. greater than 1 in 10,000 cancer risk for arsenic). Whether the calculated risks are actually indicative of harm to subsistence populations should be considered in light of the beneficial properties of the diet and the lack of alternative food choices.
Tsuji
Joyce S
JS
Exponent, 15375 SE 30th Place, Suite 250, Bellevue, WA 98007, USA. tsujij@exponent.com
Robinson
Sue
S
D000818:Animals; D001151:Arsenic / Q000009:adverse effects / Q000032:analysis; D001210:Asia, Southeastern / Q000453:epidemiology; D003625:Data Collection; D003906:Developing Countries / Q000706:statistics & numerical data*; D004032:Diet; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D005399:Fishes; D005504:Food Analysis; D006801:Humans; D008628:Mercury / Q000009:adverse effects / Q000032:analysis; D008670:Metals / Q000009:adverse effects; D009369:Neoplasms / Q000139:chemically induced / Q000453:epidemiology; D018570:Risk Assessment; D014481:United States / Q000453:epidemiology
D008670:Metals; D008628:Mercury; D001151:Arsenic