Environmental health outcomes and exposure risks among at-risk communities living in the Upper Olifants River Catchment, South Africa

Abstract

Potential exposure to water and air pollution and associated health impacts of three low-income communities in the Upper Olifants River Catchment, South Africa, was investigated through a cross-sectional epidemiological study comprising a household survey. Water samples were collected and analysed for microbial indicators and pathogens. Ambient air-monitoring included some of the criteria pollutants, as well as mercury and manganese. Associations between environmental exposure and health outcomes were analysed by means of logistic regression. Despite poor water and air quality episodes, the communities' self-perceived health was good with relatively low prevalence of reported health outcomes. Hygiene practices with respect to water collection and storage were often poor, and most likely contributed to the regularly contaminated water storage containers. Community proximity to the polluted stream was associated with increased prevalence in adverse health outcomes. This paper reports on preliminary results and additional multivariate analyses are necessary to further understand study results.

Diarrhoeal Disease in Relation to Possible Household Risk Factors in South African Villages

“…The self-report questionnaire was adapted from two existing questionnaires [15,16] and was conducted in a face-to-face interview (between trained fieldworker and primary household caregiver). We extracted data pertaining to demographics, water collection, storage, use and household diarrhoea prevalence (see Table 1 and Table 2) from the full questionnaire (including captured additional environmental risk factors such as air pollution; full questionnaire provided in Supplementary Materials).…”

Human Enteric Pathogens in Eight Rivers Used as Rural Household Drinking Water Sources in the Northern Region of South Africa

“…Table 1 lists the various activities that were observed taking place around the river sites during this study. Many of these activities are considered to be risk factors and they could lead to the contamination of river water, such as agricultural activities, washing laundry, car washing, littering, dumping of animal blood, and domestic sewage disposal [4,5,43,44]. Table 2 summarises the physical and the microbial indicator data for the winter and summer seasons.…”

Spatial statistical modelling of urban particulate matter

“…Further work will include investigating the applicability of a multivariate spatiotemporal cokriging method developed by Liu and Koike (2007) for non-collocated network configurations and irregular absences of data. Interest (Matooane et al, 2011) c Estimates from a cross-sectional public health survey in three communities at risk of exposure to poor air and water quality in the Nkangala district municipality (John et al, 2014) will be on an extension that accommodates the presence of covariates. This has been explored in terms of an expectation-maximization algorithm for a linear coregionalization model with dynamic components to calibrate aerosol optical depth measurements from a satellite with PM 10 measurements from a network of ground monitoring stations (Fassò and Finazzi, 2011).…”