ABSTRACT
There is limited literature on the frequency and distribution of pesticide exposures, specifically with respect to demographic and environmental factors in the United States. The purpose of this exploratory study was to investigate geographic trends and factors associated with unintentional pesticide exposures in children and adolescents in Texas. The study used an ecological design with secondary data. A spatial scan statistic, based on a Poisson regression model, was employed to identify spatial clusters of unintentional pesticide-related poison center exposures. Next, logistic regression models were constructed to identify potential demographic and environmental factors associated with unintentional pesticide-related poison center exposures. There were 59,477 unintentional pesticide-related poison center exposures from 2000 to 2013. The spatial scan statistic found a change in the number of counties in the identified clusters (e.g. , aggregation of counties with higher than expected exposures) for two time periods (2000-2006; 2007-2013). Based on the logistic regression models, factors associated with unintentional pesticide-related poison center exposures were percent black or African American population, year structure built, and percent moved in the past 12 months. In conclusion, this study found certain demographic and environmental factors may be associated with unintentional pesticide-related poison center exposures. Through understanding trends and associated factors, public health professionals can design interventions for populations at higher risk of pesticide exposures. This study also supports the use of spatial methods being utilized to expand upon current analysis of poison center data. Future research should confirm and build upon these findings.
Subject(s)
Adolescent , Child , Humans , Logistic Models , Pesticides , Poisoning , Public Health , Texas , United StatesABSTRACT
There is limited literature on the frequency and distribution of pesticide exposures, specifically with respect to demographic and environmental factors in the United States. The purpose of this exploratory study was to investigate geographic trends and factors associated with unintentional pesticide exposures in children and adolescents in Texas. The study used an ecological design with secondary data. A spatial scan statistic, based on a Poisson regression model, was employed to identify spatial clusters of unintentional pesticide-related poison center exposures. Next, logistic regression models were constructed to identify potential demographic and environmental factors associated with unintentional pesticide-related poison center exposures. There were 59,477 unintentional pesticide-related poison center exposures from 2000 to 2013. The spatial scan statistic found a change in the number of counties in the identified clusters (e.g. , aggregation of counties with higher than expected exposures) for two time periods (2000-2006; 2007-2013). Based on the logistic regression models, factors associated with unintentional pesticide-related poison center exposures were percent black or African American population, year structure built, and percent moved in the past 12 months. In conclusion, this study found certain demographic and environmental factors may be associated with unintentional pesticide-related poison center exposures. Through understanding trends and associated factors, public health professionals can design interventions for populations at higher risk of pesticide exposures. This study also supports the use of spatial methods being utilized to expand upon current analysis of poison center data. Future research should confirm and build upon these findings.
Subject(s)
Adolescent , Child , Humans , Logistic Models , Pesticides , Poisoning , Public Health , Texas , United StatesABSTRACT
The regional incidence rates of out-of-hospital cardiac arrest (OHCA) were traditionally calculated with the residential population as the denominator. The aim of this study was to estimate the true incidence rate of OHCA and to investigate characteristics of regions with overestimated and underestimated OHCA incidence rates. We used the national OHCA database from 2006 to 2010. The nighttime residential and daytime transient populations were investigated from the 2010 Census. The daytime population was calculated by adding the daytime influx of population to, and subtracting the daytime outflow from, the nighttime residential population. Conventional age-standardized incidence rates (CASRs) and daytime corrected age-standardized incidence rates (DASRs) for OHCA per 100,000 person-years were calculated in each county. A total of 97,291 OHCAs were eligible. The age-standardized incidence rates of OHCAs per 100,000 person-years were 34.6 (95% CI: 34.3-35.0) in the daytime and 24.8 (95% CI: 24.5-25.1) in the nighttime among males, and 14.9 (95% CI: 14.7-15.1) in the daytime, and 10.4 (95% CI: 10.2-10.6) in the nighttime among females. The difference between the CASR and DASR ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. Through the Bland-Altman plot analysis, we found the difference between the CASR and DASR increased as the average CASR and DASR increased as well as with the larger daytime transient population. The conventional incidence rate was overestimated in counties with many OHCA cases and in metropolitan cities with large daytime population influx and nighttime outflow, while it was underestimated in residential counties around metropolitan cities.
Subject(s)
Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Age Factors , Geography , Incidence , Out-of-Hospital Cardiac Arrest/epidemiology , Republic of Korea/epidemiology , Seasons , Survival Rate , Time FactorsABSTRACT
OBJECTIVES: Despite increasing availability of global positioning system (GPS), no research has been conducted to analyze GPS data for exposure opportunities associated with time at indoor and outdoor microenvironments. We developed location-based and time-weighted spatial measures that incorporate indoor and outdoor time-location data collected by GPS. METHODS: Time-location data were drawn from 38 female subjects in California who wore a GPS device for seven days. Ambient standard deviational ellipse was determined based on outdoor locations and time duration, while indoor time weighted standard deviational ellipse (SDE) was developed to incorporate indoor and outdoor times and locations data into the ellipse measure. RESULTS: Our findings indicated that there was considerable difference in the sizes of exposure potential measures when indoor time was taken into consideration, and that they were associated with day type (weekday/weekend) and employment status. CONCLUSIONS: This study provides evidence that time-location weighted measure may provide better accuracy in assessing exposure opportunities at different microenvironments. The use of GPS likely improves the geographical details and accuracy of time-location data, and further development of such location-time weighted spatial measure is encouraged.