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Do Characteristics of Kentucky Counties Influence Rates of Heavy ATOD Consumption Among Youth? Relationships Between Broad Risk/Protective Factors and ATOD Abuse at the County Level. Overview.

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  1. Do Characteristics of Kentucky Counties Influence Rates of Heavy ATOD Consumption Among Youth?Relationships Between Broad Risk/Protective Factors and ATOD Abuse at the County Level

  2. Overview Step 1: County-level demographic and contextual information were reduced to a more manageable set of underlying factors (n=8). Step 2: The most recent ATOD 30-day abuse data for each county* were also reduced through factor analysis, yielding 5 core ATOD abuse groupings. Step 3: Stepwise multiple regression was then used to determine the relative contribution of county-level demographics (risk and protective factors) to ATOD abuse within each of these five “clusters” of abuse. *Note: Data were derived from a combination of standardized surveys administered within the 2002-2004 time frame, including KIP, PRYDE, & ADAS. A small group of counties had no data (n=5) and these were assigned a synthetic number, based on a composite of similar adjoining counties.

  3. Key Findings • 48 county-level demographic and contextual variables that roughly correspond to risk and protective factors were successfully reduced to eight underlying (“latent”) variables • Using factor scores derived from this analysis, counties were then portrayed along these dimensions, demonstrating considerable variability in their influence across the state (as shown on the maps that follow). • Given that these factors are not distributed evenly across the state, it was important to test their relative influence on ATOD consumption, in order to determine whether problem analysis, site selection, and intervention planning processes should take these factors into account • Comprehensive data on 30-day heavy consumption of ATOD were successfully reduced to five groupings, labeled as: (1) acute (emergent) illegal drugs, (2) alcohol, (3) chronic illegal drugs, (4) tobacco, and (5) inhalants. • When the relationship between county-level risk/ protective factors and the five groupings of heavy ATOD consumption was examined, only a few appeared to be related to abuse. • Measures of SES, school bonding, and family functioning had a strong predictive relationship for tobacco use (cigarettes, smokeless) • Measures of school bonding and family functioning were moderately predictive of chronic illegal drugs • Small to insignificant relationships were found for the remaining three groupings

  4. Implications • The basic implication of these findings appears to be that in the instances of tobacco and (to a lesser extent) chronic illegal drugs, where you live makes a difference in terms of your likelihood to abuse these substances. This may make a difference in defining problems, selecting communities, and planning preventive interventions. • For example, the knowledge that high rate use of tobacco co-varies with SES at the county level (i.e., poorer counties are much more likely to have higher rates of youth smoking) could lead to discussion of why this might be. • Are the sequelae of poverty (e.g., education levels, access to health care) related to the conditions that give rise to smoking? Given that poverty is not “actionable”, how should this fact be taken into account? On the other hand, school bonding and family functioning are also part of the tobacco equation. Taking this into account may have implications for planning prevention efforts in a given community. • In the case of the other substance groupings, the data do not appear to show meaningful relationships with risk and protective factors. • it appears that other factors (i.e., not these broad risk and protective factors) are more likely to account for variability in heavy alcohol use at the county level. • Thus, when examining alcohol, a closer look at issues such as availability, attitudes, and enforcement may be fruitful.

  5. The Bottom Line Available data seem to show that in Kentucky, received wisdom about the contribution and importance of risk and protective factors may not be as salient as some literature would suggest (at least, with the county as the level of focus and analysis). Rather, it may be more important to make decisions and plan programs based more on patterns of ATOD consumption.

  6. Step 1: Reducing County-Level Data for Risk and Protective Factors • Began with 48 Kentucky demographic and context variables from wide variety of sources (see maps for specific items) • Conducted exploratory factor analysis • Principal components with varimax rotation • Eigenvalue set at 1.0, .5 factor loading or above for inclusion within a factor • A substantial amount of shared variance among these variables was demonstrated • These factors are rough expressions of county-level risk and protective factors

  7. Results of Step 1 Analysis • Eight principal factors emerged from the analysis, accounting for 75.3% of the total variance • They were labeled (in order of strength) as: • FACTOR 1: Low SES & low school achievement • FACTOR 2: Family functioning • FACTOR 3: School bonding • FACTOR 4: Birth outcomes • FACTOR 5: Teen pregnancy • FACTOR 6: Safety and crime • FACTOR 7: Pre-natal health care • FACTOR 8: Youth Disengagement • It is important to remember that these factors are really proxies for clusters of inter-related variables (both measured and unmeasured) • These factors are used in subsequent analyses as “predictors” and to demonstrate county-level variability • Initially, counties are portrayed in relation to each factor, showing that county-level variability differs in relation to the factor under consideration. • The maps that follow portray this variability in graphic fashion.

  8. County-Level Risk and Protective Factors Factor 1: Low SES (Income, child poverty, SSI, Medicaid, Food Stamps, AFDC, WIC, KTAP, unemployed) & low school achievement (9th gr. CTBS, Composite ACT) (Factor loadings by county) Poverty and low school achievement appears to be heavily concentrated in the southeastern part of the state.

  9. County-Level Risk and Protective Factors Factor 2: Problematic Family Functioning (Abuse, Neglect, GP as Caregivers, Injuries) (Factor loadings by county) Indicators of family disintegration, however, show a distribution that looks rather different, with far southeastern Kentucky and the urban/suburban centers of the state evidencing higher rates.

  10. County-Level Risk and Protective Factors Factor 3: Poor School Bonding (Drop-out, ADA, Retention) (Factor loadings by county) Problems of school bonding appear to be more concentrated in the central region of the state, with some exceptions in other areas.

  11. County-Level Risk and Protective Factors Factor 4: Positive Birth Outcomes (Gestation, birth weight) (Factor loadings by county) This variable may be more difficult to interpret, but could be related to access to health care and supports. In any case, south central Kentucky seems to evidence the strongest outcomes.

  12. County-Level Risk and Protective Factors Factor 5: Teen Pregnancy (Teen births, teen repeat births) (Factor loadings by county) High rates of teen pregnancy seem to be distributed across the state variably, but it is notable that eastern Kentucky has some of the lower rates.

  13. County-Level Risk and Protective Factors Factor 6: Crime and violence (Crime rate, violent death) (Factor loadings by county) Higher rates of crime and violence appear to cluster around Fayette County in this analysis.

  14. County-Level Risk and Protective Factors Factor 7: Prenatal Health Care (Service accessed, # visits) (Factor loadings by county) South central Kentucky appears to have higher rates of participating in prenatal health care, consistent with the earlier finding related to birth outcomes.

  15. County-Level Risk and Protective Factors Factor 8: Youth Disengagement (Unemployed HS graduates) (Factor loadings by county) Counties with high rates of unemployed HS graduates are distributed across the state. This variable may be most sensitive the presence/absence of industry in a particular county, as well as the economic viability of farming in that area.

  16. Step 2: Data Reduction for County-Level ATOD Youth AbuseExploratory Factor Analysis

  17. Methodology • Reduced data for twelve 30-day ATOD heavy use variables in Grade 10 • Smokeless • Cigarettes • Alcohol • Binge drinking • Drunkenness • Marijuana • Cocaine • Uppers • Inhalants • Ecstasy • Methamphetamine • Oxycontin • Principal components analysis with varimax rotation; Eigenvalue set at 1.0 for inclusion; .5 factor loading or above for inclusion within a factor • Resulted in a high degree of separation with substantial explanatory value (i.e., a great deal of shared variance was accounted for) • Similar to data reduction for demographics, this allows for a smaller number of variables to be used in subsequent analyses. • It also has implications for thinking about individual substances versus patterns of co-occurring abuse (e.g., deciding what substances to prioritize).

  18. Results • Five underlying factors emerged, accounting for 83.0% of the total variance (representing a high degree of shared variance) • They were labeled as: • FACTOR 1: Acute (emergent) illegal drugs (ecstacy, meth, oxycontin) – 22.3% • FACTOR 2: Alcohol (alcohol, binging, drunkenness) – 19.6% • FACTOR 3: Chronic illegal drugs (marijuana, cocaine, uppers) – 17.2% • FACTOR 4: Tobacco ( smokeless, cigarettes) – 13.5% • FACTOR 5: Inhalants – 10.4% • Factor loadings were consistently high within factors, with no variables loading on multiple factors (see next slide) • These can be thought of as useful groupings of abuse patterns; rather than looking at all possible substances as “stand-alone problems” to be prioritized, these groupings may allow for more broad-based thinking about prioritization and community selection.

  19. Factor Loadings for ATOD Abuse Variables from 2004 KIP Survey (Grade 10)* *Note: Includes only loadings of .5 or above

  20. Step 3: Correlating County-Level Risk and Protective Factor Scores with Patterns of ATOD Abuse

  21. Methodology • As part of prior analyses, county-level factor scores were established for each emergent dimension (risk/protective factor, ATOD abuse factor) • Stepwise multiple regressions were computed for each of the criterion variables (factor scores for each the five groups of ATOD abuse) to determine the best weighted linear combination of risk and protective factors that could predict the criterion (e.g., heavy use of tobacco). • Most predictors did not contribute significantly. Adjusted R2s for those that did are shown below:

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