Alan M. Zaslavsky

BACKGROUND: Violence‐exposed youth rarely receive mental health services, even though exposure increases risk for academic and psychosocial problems. This study examines the association between violence exposure and mental health service contact. The 4 forms of violence exposure were peer, family, sexual, and witnessing.METHODS: Data are from 1534 Boston public high school students who participated in a 2008 self‐report survey of violence exposure and its correlates. Multivariate logistic regressions estimated associations between each form of violence with service contact, then examined whether associations persisted when controlling for suicidality and self‐injurious behaviors.RESULTS: In unadjusted models, violence‐exposed students more often reported service contact than their peers. However, in multivariate models, only exposure to family (odds ratio [OR] = 1.69, 95% confidence interval [CI] = 1.23‐2.31) and sexual violence (OR = 2.34, 95% CI = 1.29‐4.20) were associated with service contact. Associations attenuated when controlling for suicidality and self‐injurious behaviors, indicating they were largely explained by self‐harm. Sexual violence alone remained associated with mental health service contact in fully adjusted models, but only for girls (OR=3.32, 95% CI=1.30‐8.45), suggesting sex‐specific pathways.CONCLUSIONS: Associations between adolescent violence exposure and mental health service contact vary by forms of exposure. Outreach to a broader set of exposed youth may reduce the impact of violence and its consequences for vulnerable students.

End-of-life medical expenses are a significant proportion of all health care expenditures. These costs were studied using costs of services from Medicare claims and cause of death (CoD) from death certificates. In the absence of a unique identifier linking the two datasets, common variables identified unique matches for only 33% of deaths. The remaining cases formed cells with multiple cases (32% in cells with an equal number of cases from each file and 35% in cells with an unequal number). We sampled from the joint posterior distribution of model parameters and the permutations that link cases from the two files within each cell. The linking models included the regression of location of death on CoD and other parameters, and the regression of cost measures with a monotone missing data pattern on CoD and other demographic characteristics. Permutations were sampled by enumerating the exact distribution for small cells and by the Metropolis algorithm for large cells. Sparse matrix data structures enabled efficient calculations despite the large dataset (≈1.7 million cases). The procedure generates m datasets in which the matches between the two files are imputed. The m datasets can be analyzed independently and results can be combined using Rubin’s multiple imputation rules. Our approach can be applied in other file-linking applications. Supplementary materials for this article are available online.

Background: The patient-centered medical home (PCMH) model has gained support, but the impact of this model on the quality and equity of care merits further evaluation. Objective: To determine if PCMHs are associated with improved quality and equity in pediatric primary care. Research Design: Using the 2007/2008 National Survey of Children’s Health, a nationally representative survey of parents/guardians of children (age, 0–17 y), we evaluated the association of PCMHs with 10 quality-of-care measures using multivariable regression models, adjusting for demographic and socioeconomic covariates. For quality indicators that were significantly associated with medical homes, we determined if this association differed by race/ethnicity. Results: Compared with children without medical homes, those with medical homes had significantly better adjusted rates for 6 of 10 quality measures (all P≤0.02), such as obtaining a developmental history [adjusted rates % (SE): 41.7 (1.3) vs. 52.0 (1.1), PP=0.83]. Conclusions: Our results underscore the benefits of the medical home model for children while highlighting areas for improvement, such as narrowing disparities. Our findings also emphasize the key role of patient experience measures in the evaluation of medical homes.

Summary Cancer registry records contain valuable data on provision of adjuvant therapies for cancer patients. Previous studies, however, have shown that these therapies are underreported in registry systems. Hence direct use of the registry data may lead to invalid analysis results. We propose first to impute correct treatment status, borrowing information from an additional source such as medical records data collected in a validation sample, and then to analyze the multiply imputed data, as in Yucel and Zaslavsky (2005, Journal of the American Statistical Association 100, 1123–1132). We extend their models to multiple therapies using multivariate probit models with random effects. Our model takes into account the associations among different therapies in both administration and probability of reporting, as well as the multilevel structure (patients clustered within hospitals) of registry data. We use Gibbs sampling to estimate model parameters and impute treatment status. The proposed methodology is applied to the data from the Quality of Cancer Care project, in which stage II or III colorectal cancer patients were eligible to receive adjuvant chemotherapy and radiation therapy.

Objective. To estimate effects of patient sociodemographic characteristics on differential performance within and between plans in a single market area on the HEDIS® quality of care measures, widely used for purchasing and accreditation decisions in the United States. Design. Using logistic regression, we modeled associations of age, sex, and zip-code-linked sociodemographic characteristics of health plan members with HEDIS measures of screening and preventive services. We calculated the impact of adjusting for these associations on measures of health plan performance. Setting. Twenty-two California health plans provided individual-level HEDIS data and zip codes of residence for up to 2 years. Participants. 110 541 commercially insured health plan members. Main outcome measures. Ten HEDIS quality-of-care measures. Results. Performance on quality measures was negatively associated with percent receiving public assistance in the local area (seven out of 10 measures), percent Black (three measures), and percent Hispanic (four measures), and positively associated with percent college educated (six measures), and percent urban (three measures), controlling for plan, while associations with percent Asian were positive for three measures and negative for one (P < 0.05 for six associations, P < 0.01 for four, P < 0.001 for 17). Associations were consistent across plans and over time. Adjustment for these characteristics changed rates for most plans and measures by Conclusions. Adjustment for socioeconomic case mix has little impact on the measured performance of most plans in California, but substantially affects a few. The impact of case mix on indicators should be considered when making comparisons of health plan quality.