Julie Legler

Comorbidity, additional disease beyond the condition under study that increases a patient’s total burden of illness, is one dimension of health status. For investigators working with observational data obtained from administrative databases, comorbidity assessment may be a useful and important means of accounting for differences in patients’ underlying health status. There are multiple ways of measuring comorbidity. This paper provides an overview of current approaches to and issues in assessing comorbidity using claims data, with a particular focus on established indices and the SEER-Medicare database. In addition, efforts to improve measurement of comorbidity using claims data are described, including augmentation of claims data with medical record, patient self-report, or health services utilization data; incorporation of claims data from sources other than inpatient claims; and exploration of alternative conditions, indices, or ways of grouping conditions. Finally, caveats about claims data and areas for future research in claims-based comorbidity assessment are discussed. Although the use of claims databases such as SEER-Medicare for health services and outcomes research has become increasingly common, investigators must be cognizant of the limitations of comorbidity measures derived from these data sources in capturing and controlling for differences in patient health status. The assessment of comorbidity using claims data is a complex and evolving area of investigation.

Summary We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the significance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.

This study examines mammography-enhancing intervention studies that focus on women in groups with historically lower rates of mammography use than the general population. These groups consist of women who are disproportionately older, poorer, of racial-ethnic minorities, have lower levels of formal education, and live in rural areas. We refer to them as diverse populations. The purpose of this report is to determine which types of mammography-enhancing interventions are most effective for these diverse populations. For this report, United States and international studies with concurrent controls that reported actual receipt of mammograms (usually based on self-report) as an outcome were eligible for inclusion. Intervention effects were measured by differences in intervention and control group screening rates postintervention and were weighted to reflect the certainty of each study's contribution. These effects differed significantly (Q = 218, 34 df), and the variation between studies was best explained by indicators of the use of access-enhancing approaches. Combined intervention effects were estimated for different categories of intervention types using random effects models for subgroups of studies. The strongest combination of approaches used access-enhancing and individual-directed strategies and resulted in an estimated 27% increase in mammography use (95% confidence interval, 9.9-43.9, nine studies). Additionally impressive was the access-enhancing and system-directed combination (20% increase and 95% confidence interval, 8.2-30.6, five studies). Access-enhancing strategies are an important complement to individual- and system-directed interventions for women with historically lower rates of screening.