Variability in Drug Use Prevalence Across School Districts in the Same Locale in Ohio
ABSTRACT Substance use by adolescents continues to present a problem schools must address. Data from survey research can prove useful in helping schools determine the nature and extent of youth drug use. This study identified variations in drug use prevalence among ninth grade students in different school districts in the same locale in Ohio. Possible explanations for the differences were explored. Students (n = 3,016) from 12 suburban high schools anonymously completed self-report drug use questionnaires. School and community-level data were collected from other sources. Cluster analysis was used to group the school districts. ANOVA revealed statistically significant differences (P < or = 0.001) in levels of drug use by cluster. The cluster with significantly lower levels of drug use consisted of districts with at least one full-time drug abuse prevention coordinator, higher economic levels, and the highest per pupil expenditures. Drug use among youth is influenced, at least in part, by local contextual factors. Local survey data can inform local policy and programs. These findings have practical implications for policymakers, program developers, and school districts in other areas of the country.
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ABSTRACT: The Ohio Substance Abuse MonitoringNetwork (OSAM) is designed to provide accurate, timely, qualitatively-oriented epidemiologic descriptions of substance abuse trends and emerg,ngproblems in the state's major urban and rural areas. Use of qualitative methods in identifying and assessing substance abuse practices in local communities is one of the main assets of OSAM Network. Qualitative methods are sensitive to local contextual variability, flexible enough to capture emergent trends, and can be imple-mented with limited financial resources. This paper describes how qualita-tive epidemiolo^c methods, like those used by the OSAM Network, could be applied to inform substance abuse prevention activities, particularly those directed at adolescents.
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ABSTRACT: Characteristics of the built environment, including availability and type of retail food outlets, vary with area poverty. This affects consumption patterns of area residents and may, in turn, affect both local incidence of obesity and rates of food borne illness. This research utilizes a unique approach to analyze retail food access and food safety risk. Geographic information systems (GIS) were used to plot retail food listings, from two databases, and foodservice critical health code violations (CHV) over poverty in Philadelphia Co., Pennsylvania. Retail listings were purchased from Dun and Bradstreet (D&B) and identified using inspection records from the Philadelphia Health Department (PDPH). Addresses were geocoded to census tracts (N=368). Tracts were classified into quintiles using Census Bureau poverty data. GIS overlay analysis was used to group locations within tracts. To examine degree of retail food access produced by both data sources, Chi-square statistic was utilized to test interaction between poverty and store type. Using either database (D&B, N=4,263; PHD, N=5,847), a significant interaction was found between poverty and the distribution of food markets, indicating that rates of all grocery stores, including corner markets, were highest in high poverty areas. Further analysis revealed that high poverty areas contained both lower percentages of chain markets and supermarkets compared to low poverty areas. Though fast food was more prevalent in high poverty areas versus low, the interaction between poverty and the distribution of fast-food and full service restaurants was only significant using PDPH but not D&B. Significant differences in distances to convenience and grocery stores were similar between datasets. However, D&B failed to show significant differences in travel distance to supermarkets across poverty groups, while lowest poverty groups (highest income) weresignificantly different from other groups using PDPH. Significant differences in distance to fast food and full service restaurants between poverty groups were similar using both datasets. However, the relative literature-established direction of the relationship between poverty and proximity to fast food restaurants was conserved using PDPH but not D&B. To examine distribution of CHV, PDPH inspection records from 2005 to 2008 for all public foodservice locations (N=10,859) were analyzed. Less than half (46.5%) of facilities had an average of zero CHV. The average rate of CHV for all foodservice facilities was 0.765 per inspection. Rates of CHV across poverty groups were significantly greater in the lowest poverty (highest income) group at 0.93 (0.04) compared to other groups. Average days between inspection was also significantly greater in the two lowest poverty (highest income) groups compared to higher poverty groups. These results confirm an association of increased access to chain food markets for low poverty areas and increased access to corner markets/groceries for high poverty areas in Philadelphia. Furthermore, results suggest that data source can affect the assessment of food environments and subsequent interpretation of degree of impact on residents’ health. These results also indicate an association of higher rates of violations and longer periods between inspections with lowest poverty rates. This study demonstrates the use of GIS technology to assess food safety risks and the novel comparison of two data sources to assess community food access.
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ABSTRACT: Small-area analysis in health is essential in uncovering local-level disparities often masked by health estimates for large areas (e.g., cities, counties, states). In this context, 14 health status indicators (HSIs) were examined for six Chicago community areas that reflect the substantial diversity of the city. HSIs were compared over time (from 1989-90 to 1999-2000) and across community areas. Important disparities among these community areas in mortality rates, birth outcomes, and infectious diseases were found. In many cases the disparities were in the expected direction with the richest and predominantly White community area experiencing the lowest rates. However, some surprises did manifest themselves. For example, only the poorest community area experienced a statistically significant decline in the infant mortality rate. Since so much of attention is now being paid to reducing and eliminating these disparities, it is important to examine their existence to better understand how to minimize them.Journal of Medical Systems 09/2004; 28(4):397-411. DOI:10.1023/B:JOMS.0000032854.99522.0d · 2.21 Impact Factor