title: Spatial modeling of households' knowledge about arsenic pollution in Bangladesh.
abstract: Arsenic in drinking water is an important public health issue in Bangladesh, which is affected by households' knowledge about arsenic threats from their drinking water. In this study, spatial statistical models were used to investigate the determinants and spatial dependence of households' knowledge about arsenic risk. The binary join matrix/binary contiguity matrix and inverse distance spatial weight matrix techniques are used to capture spatial dependence in the data. This analysis extends the spatial model by allowing spatial dependence to vary across divisions and regions. A positive spatial correlation was found in households' knowledge across neighboring districts at district, divisional and regional levels, but the strength of this spatial correlation varies considerably by spatial weight. Literacy rate, daily wage rate of agricultural labor, arsenic status, and percentage of red mark tube well usage in districts were found to contribute positively and significantly to households' knowledge. These findings have policy implications both at regional and national levels in mitigating the present arsenic crisis and to ensure arsenic-free water in Bangladesh.
authors.affiliation: Institute of Economic Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8603, Japan. mrs_bd@yahoo.com
authors.forename: M Mizanur Rahman
authors.initials: MM
authors.lastname: Sarker
mesh_terms: D001151:Arsenic / Q000032:analysis*; D001459:Bangladesh; D016000:Cluster Analysis; D060766:Drinking Water / Q000737:chemistry* / Q000592:standards; D005191:Family Characteristics; D007722:Health Knowledge, Attitudes, Practice; D006306:Health Surveys; D006801:Humans; D015233:Models, Statistical; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D060766:Drinking Water; D014874:Water Pollutants, Chemical; D001151:Arsenic