Alcohol-impaired driving: average quantity consumed and frequency of drinking do matter.

OBJECTIVE: The objective of this article is to estimate and validate a logistic model of alcohol-impaired driving using previously ignored alcohol consumption behaviors, other risky behaviors, and demographic characteristics as independent variables. METHODS: The determinants of impaired driving are estimated using the US Centers for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance System (BRFSS) surveys. Variables used in a logistic model to explain alcohol-impaired driving are not only standard sociodemographic variables and bingeing but also frequency of drinking and average quantity consumed, as well as other risky behaviors. We use interactions to understand how being female and being young affect impaired driving. Having estimated our model using the 1997 survey, we validated our model using the BRFSS data for 1999. RESULTS: Drinking 9 or more times in the past month doubled the odds of impaired driving. The greater average consumption of alcohol per session, the greater the odds of driving impaired, especially for persons in the highest quartile of alcohol consumed. Bingeing has the greatest effect on impaired driving. Seat belt use is the one risky behavior found to be related to such driving. Sociodemographic effects are consistent with earlier research. Being young (18-30) interacts with two of the alcohol consumption variables and being a woman interacts with always wearing a seat belt. Our model was robust in the validation analysis. CONCLUSIONS: All 3 dimensions of drinking behavior are important determinants of alcohol-impaired driving, including frequency and average quantity consumed. Including these factors in regressions improves the estimates of the effects of all variables.

Can the punishment fit the crime when suspects confess child sexual abuse?

OBJECTIVE: To determine significant predictors of severity of sentencing of sex offenders of minors in a jurisdiction which obtains many confessions. METHOD: Data were abstracted from 323 criminal court case records of sexually abused minors over 11 years in a county which places a high priority on sexual abuse prosecution. The sample used in this analysis consisted of 218 men who received a sentence for a sex offense. Multiple regression analysis was used to examine what factors predicted severity of sentences. Specifically this study explores whether, in a county which is very successful in obtaining confessions to sexual abuse of children, the severity of sentence is explained by the seriousness of the crime (more severe type of sexual abuse, younger age of the victim, prior conviction of a sex offense, and abusing more than one child), or by factors unrelated to the seriousness of the offense, offender confession and/or having a court-appointed attorney. RESULTS: Factors that predicted the severity of the sentence were the seriousness of the sex crime, prior conviction of a sex crime, and young age of the victim. CONCLUSIONS: This community, with a high confession rate and a high conviction rate, imposed sentences that were consistent with the crimes, with more severe sentences for more serious crimes. Convenience factors, such as the fact the offender confessed, and systemic factors, such as having a court-appointed attorney, did not result in more severe sentences.

Solving problems of disclosure risk while retaining key analytic uses of publicly released microdata.

MEASURES USED TO PROTECT SUBJECTS in publicly distributed microdata files often have a significant negative impact on key analytic uses of the data. For example, it may be important to analyze subpopulations within a data file such as racial minorities, yet these subjects may present the greatest disclosure risk because their records tend to stand out or be unique. Files or records that are linkable create another type of disclosure risk-common elements between two files can be used to link files with sensitive data to externally available files that disclose identity. Examples of disclosure limitation methods used to address these types of issues include blanking out data, coarsening response categories, or withholding data altogether. However, the very detail that creates the greatest risk also provides insight into differences that are of greatest interest to analysts. Restricted-use agreements that provide unaltered versions of the data may not be available, or only selectively so. The public-use version of the data is very important because it is likely to be the only one to which most researchers, policy analysts, teaching faculty, and students will ever have access. Hence, it is the version from which much of the utility of the data is extracted and often it effectively becomes the historical record of the data collection. This underscores the importance that the disclosure review c ommittee s trikes a g ood b alance b etween protection and u tility. In this paper we d escrib e our disclosure review committee's (DRC) analysis and resulting data protection plans for two national studies and one administrative data system. Three distinct disclosure limitation methods were employed, taking key uses of the data into consideration, to protect respondents while still providing statistically accurate and highly useful public-use data. The techniques include data swapping, microaggregation, and suppression of detailed geographic data. We describe the characteristics of the data sets that led to the selection of these methods, provide measures of the statistical impact, and give details of their implementations so that others may also utilize them. We briefly discuss the composition of our DRC, highlighting what we believe to be the important disciplines and experience represented by the group.