RAND Corporation

About: RAND Corporation is a nonprofit organization based out in Santa Monica, California, United States. It is known for research contribution in the topics: Health care & Population. The organization has 9602 authors who have published 18570 publications receiving 744658 citations.

Treatment of comorbid alcohol use disorders and depression with cognitive-behavioural therapy and motivational interviewing: A meta-analysis

Abstract: Background and Aims To review published studies on the effectiveness of combining cognitive-behavioural therapy (CBT) and motivational interviewing (MI) to treat comorbid clinical and subclinical alcohol use disorder (AUD) and major depression (MDD) and estimate the effect of this compared with usual care. Methods We conducted systematic literature searches in PubMed, PsycINFO and Embase up to June 2013 and identified additional studies through cross-references in included studies and systematic reviews. Twelve studies comprising 1721 patients met our inclusion criteria. The studies had sufficient statistical power to detect small effect sizes. Results CBT/MI proved effective for treating subclinical and clinical AUD and MDD compared with controls, with small overall effect sizes at post-treatment [g = 0.17, confidence interval (CI) = 0.07–0.28, P < 0.001 for decrease of alcohol consumption and g = 0.27, CI: 0.13–0.41, P < 0.001 for decrease of symptoms of depression, respectively]. Subgroup analyses revealed no significant differences for both AUD and MDD. However, digital interventions showed a higher effect size for depression than face-to-face interventions (g = 0.73 and g = 0.23, respectively, P = 0.030). Conclusions Combined cognitive-behavioural therapy and motivational interviewing for clinical or subclinical depressive and alcohol use disorders has a small but clinically significant effect in treatment outcomes compared with treatment as usual.

Gender Wage Disparities among the Highly Educated

Abstract: In the U.S. college-educated women earn approximately 30 percent less than their non-Hispanic white male counterparts. We conduct an empirical examination of this wage disparity for four groups of women-non-Hispanic white, black, Hispanic, and Asian-using the National Survey of College Graduates, a large data set that provides unusually detailed information on higher-level education. Nonparametric matching analysis indicates that among men and women who speak English at home, between 44 and 73 percent of the gender wage gaps are accounted for by such pre-market factors as highest degree and major. When we restrict attention further to women who have "high labor force attachment" (i.e., work experience that is similar to male comparables) we account for 54 to 99 percent of gender wage gaps. Our nonparametric approach differs from familiar regression-based decompositions, so for the sake of comparison we conduct parametric analyses as well. Inferences drawn from these latter decompositions can be quite misleading.

Inverse Probability Tilting for Moment Condition Models with Missing Data

Abstract: We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favorably with existing IPW estimators, including augmented inverse probability weighting (AIPW) estimators, in terms of efficiency, robustness, and higher order bias. We illustrate our method with a study of the relationship between early Black-White differences in cognitive achievement and subsequent differences in adult earnings. In our dataset the early childhood achievement measure, the main regressor of interest, is missing for many units. bustness, (Augmented) Inverse Probability Weighting (IPW), HigherOrder Comparisons, Black-White Gap, Causal Inference, Average Treatment Effect (ATE)

Body Mass Index, Neighborhood Fast Food and Restaurant Concentration, and Car Ownership

Abstract: Eating away from home and particularly fast food consumption have been shown to contribute to weight gain. Increased geographic access to fast food outlets and other restaurants may contribute to higher levels of obesity, especially in individuals who rely largely on the local environment for their food purchases. We examined whether fast food and restaurant concentrations are associated with body mass index and whether car ownership might moderate this association. We linked the 2000 US Census data and information on locations of fast food and other restaurants with the Los Angeles Family and Neighborhood Study database, which consists of 2,156 adults sampled from 63 neighborhoods in Los Angeles County. Multilevel modeling was used to estimate associations between body mass index (BMI), fast food and restaurant concentration, and car ownership after adjustment for individual-level factors and socioeconomic characteristics of residential neighborhoods. A high concentration of local restaurants is associated with BMI. Car owners have higher BMIs than non-car owners; however, individuals who do not own cars and reside in areas with a high concentration of fast food outlets have higher BMIs than non-car owners who live in areas with no fast food outlets, approximately 12 lb more (p = 0.02) for an individual with a height of 5 ft. 5 in. Higher restaurant density is associated with higher BMI among local residents. The local fast food environment has a stronger association with BMI for local residents who do not have access to cars.