Aims
To determine the utility of community wide drug testing with wastewater samples as a population measure of community drug use and to to test the hypothesis that the association with urbanicity would vary for three different stimulant drugs of abuse.
Design and participants
Single day samples were obtained from a convenience sample of 96 municipalities representing 65% of the population of the State of Oregon.
Measurements
Chemical analysis of 24 hour composite influent samples for benzoylecgonine (BZE, a cocaine metabolite), methamphetamine, and 3,4-methylenedioxymethamphetamine (MDMA). The distribution of community index drug loads accounting for total wastewater flow (i.e. dilution) and population are reported.
Findings
The distribution of wastewater derived drug index loads were found to correspond with expected epidemiological drug patterns. Index loads of BZE were significantly higher in urban areas and below detection in many rural areas. Conversely, methamphetamine was present in all municipalities with no significant differences in index loads by urbanicity. MDMA was at quantifiable levels in less than half of the communities, with a significant trend towards higher index loads in more urban areas.
Conclusion
This demonstration provides the first evidence of the utility of wastewater derived community drug loads for spatial analyses. Such data have the potential to dramatically improve measurement of the true level and distribution of a range of drugs. Drug index load data provide information for all people in a community and are potentially applicable to a much larger proportion of the total population than existing measures.
This study examined whether locally felt weather had a measurable effect on the amount of walking occurring in a given locale, by examining the observed walking rate in relation to air temperature, sunlight, and precipitation. Web-based cameras in nine cities were used to collect 6,255 observations over 7 months. Walking volumes and levels of precipitation and sunlight were captured by visual inspection; air temperature was obtained from local meteorological stations. A quasi-Poisson regression model to test the relationship between counts of pedestrians and weather conditions revealed that all three weather variables had significant associations with fluctuations in volumes of pedestrians, when controlling for city and elapsed time. A 5°C increase in temperature was associated with a 14% increase in pedestrians. A shift from snow to dry conditions was associated with an increase of 23%, and a 5% increase in sunlit area was associated with a 2% increase.
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