Putting Regulatory Data to Work at the Service of Public Health: Utilizing Data Collected Under the Clean Water ActPublished: 02 July 2013 by Springer Nature in Water Quality, Exposure and Health
Under the Clean Water Act, the US Environmental Protection Agency (EPA) collects information from states on intended use and impairment of each water body. We explore the feasibility of using these data, collected for regulatory purposes, for public health analyses. Combining EPA impairment data and stream hydrology information we estimated the percent of stream length impaired for any use, recreational use, or drinking water use per county in the US as exposure variables. For health outcomes we abstracted county-level hospitalization rates of gastrointestinal infections, GI (ICD-9CM 001-009 excluding 008.45) and gastrointestinal symptoms, GS (ICD-9CM 558.9, 787) among US adults aged 65 years and older from the Center for Medicare and Medicaid Services (1991–2004). Linear mixed-effects models were used to assess county-level associations between percent impaired waters and hospitalization rates adjusted for population density, a proxy for person-to-person transmission. Contrary to expectation, both GI and GS were negatively associated with any water impairment in adjusted models (GI: −0.052, 95 % CI: −0.077, −0.028; GS: −0.438, 95 % CI: −0.702, −0.174). GI was also negatively associated with recreational water impairment (−0.079, 95 % CI: −0.123, −0.036 after adjustment). Neither outcome was associated with drinking water impairment. Limited state data were reported to the EPA for specific recreational (27 states) and drinking (13 states) water impairment, thus limiting the power of the study. Though limited, this analysis demonstrates the feasibility of utilizing regulatory data for public health analyses.
Seasonality of Rotavirus in South Asia: A Meta-Analysis Approach Assessing Associations with Temperature, Precipitation,...Published: 31 May 2012 by Public Library of Science (PLoS) in PLOS ONE
Rotavirus infection causes a significant proportion of diarrhea in infants and young children worldwide leading to dehydration, hospitalization, and in some cases death. Rotavirus infection represents a significant burden of disease in developing countries, such as those in South Asia. We conducted a meta-analysis to examine how patterns of rotavirus infection relate to temperature and precipitation in South Asia. Monthly rotavirus data were abstracted from 39 published epidemiological studies and related to monthly aggregated ambient temperature and cumulative precipitation for each study location using linear mixed-effects models. We also considered associations with vegetation index, gathered from remote sensing data. Finally, we assessed whether the relationship varied in tropical climates and humid mid-latitude climates. Overall, as well as in tropical and humid mid-latitude climates, low temperature and precipitation levels are significant predictors of an increased rate of rotaviral diarrhea. A 1°C decrease in monthly ambient temperature and a decrease of 10 mm in precipitation are associated with 1.3% and 0.3% increase above the annual level in rotavirus infections, respectively. When assessing lagged relationships, temperature and precipitation in the previous month remained significant predictors and the association with temperature was stronger in the tropical climate. The same association was seen for vegetation index; a seasonal decline of 0.1 units results in a 3.8% increase in rate of rotavirus. In South Asia the highest rate of rotavirus was seen in the colder, drier months. Meteorological characteristics can be used to better focus and target public health prevention programs.
Waterborne gastrointestinal (GI) illnesses demonstrate seasonal increases associated with water quality and meteorological characteristics. However, few studies have been conducted on the association of hydrological parameters, such as streamflow, and seasonality of GI illnesses. Streamflow is correlated with biological contamination and can be used as proxy for drinking water contamination. We compare seasonal patterns of GI illnesses in the elderly (65 years and older) along the Ohio River for a 14-year period (1991–2004) to seasonal patterns of streamflow. Focusing on six counties in close proximity to the river, we compiled weekly time series of hospitalizations for GI illnesses and streamflow data. Seasonal patterns were explored using Poisson annual harmonic regression with and without adjustment for streamflow. GI illnesses demonstrated significant seasonal patterns with peak timing preceding peak timing of streamflow for all six counties. Seasonal patterns of illness remain consistent after adjusting for streamflow. This study found that the time of peak GI illness precedes the peak of streamflow, suggesting either an indirect relationship or a more direct path whereby pathogens enter water supplies prior to the peak in streamflow. Such findings call for interdisciplinary research to better understand associations among streamflow, pathogen loading, and rates of gastrointestinal illnesses.