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1.  The Impact of Gaps in Health Insurance Coverage on Immunization Status for Young Children 
Health Services Research  2008;43(5 Pt 1):1619-1636.
To examine the impact of full-year versus intermittent public and private health insurance coverage on the immunization status of children aged 19–35 months.
Data Source
2001 State and Local Area Integrated Telephone Survey's National Survey of Children with Special Health Care Needs (NS-CSHCN) and the 2000–2002 National Immunization Survey (NIS).
Study Design
Linked health insurance data from 2001 NS-CSHCN with verified immunization status from the 2000–2002 NIS for a nationally representative sample of 8,861 nonspecial health care needs children. Estimated adjusted rates of up-to-date (UTD) immunization status using multivariate logistic regressions for seven recommended immunizations and three series.
Principal Findings
Children with public full-year coverage were significantly more likely to be UTD for two series of recommended vaccines, (4:3:1:3) and (4:3:1:3:3), compared with children with private full-year coverage. For three out of 10 immunizations and series tested, children with private part-year coverage were significantly less likely to be UTD than children with private full-year coverage.
Our findings raise concerns about access to needed immunizations for children with gaps in private health insurance coverage and challenge the prevailing belief that private health insurance represents the gold standard with regard to UTD status for young children.
PMCID: PMC2653891  PMID: 18522671
Immunization; vaccine; health care access
2.  Medicaid Undercount and Bias to Estimates of Uninsurance: New Estimates and Existing Evidence 
Health Services Research  2008;43(3):901-914.
To examine whether known Medicaid enrollees misreport their health insurance coverage in surveys and the extent to which misreports of lack of coverage bias estimates of uninsurance.
Data Source
Primary survey data from the Medicaid Undercount Experiment.
Study Design
Analyze new data from surveys of Medicaid enrollees in California, Florida, and Pennsylvania and summarize existing research examining bias in coverage estimates due to misreports among Medicaid enrollees.
Data Collection Method
Subjects were randomly drawn from Medicaid administrative records and were surveyed by telephone.
Principal Findings and Conclusions
Cumulative evidence shows that a small percentage of Medicaid enrollees mistakenly report being uninsured, resulting in modest upward bias in estimates of uninsurance. A somewhat larger percentage of enrollees report having some other type of coverage than no coverage, biasing Medicaid enrollment estimates downward but not biasing estimates of uninsurance significantly upward. Implications for policy makers' confidence in survey estimates of coverage are discussed.
PMCID: PMC2442249  PMID: 18546545
Validation study; health insurance coverage; survey and administrative data; Medicaid undercount
3.  Variation in Quality of Diabetes Care at the Levels of Patient, Physician, and Clinic 
Preventing Chronic Disease  2007;5(1):A15.
We studied variance in glycated hemoglobin (HbA1c) values among adults with diabetes to identify variation in quality of diabetes care at the levels of patient, physician, and clinic, and to identify which levels contribute the most to variation and which variables at each level are related to quality of diabetes care.
Study subjects were 120 primary care physicians and their 2589 eligible adult patients with diabetes seen at 18 clinics. The dependent variable was HbA1c values recorded in clinical databases. Multivariate hierarchical models were used to partition variation in HbA1c values across the levels of patient, physician, or clinic and to identify significant predictors of HbA1c at each level.
More than 95% of variance in HbA1c values was attributable to the patient level. Much less variance was seen at the physician and clinic level. Inclusion of patient and physician covariates did not substantially change this pattern of results. Intensification of pharmacotherapy (t = −7.40, P < .01) and  patient age (t = 2.10, P < .05) were related to favorable change in HbA1c. Physician age, physician specialty, number of diabetes patients per physician, patient comorbidity, and clinic assignment did not predict change in HbA1c value. The overall model with covariates explained 11.8% of change in HbA1c value over time.
These data suggest that most variance in HbA1c values is attributable to patient factors, although physicians play a major role in some patient factors (e.g., intensification of medication). These findings may lead to more effective care-improvement strategies and accountability measures.
PMCID: PMC2248776  PMID: 18082004
4.  Hospital Size, Uncertainty, and Pay-for-Performance 
Health Care Financing Review  2007;29(1):45-57.
We construct statistical models to assess whether hospital size will impact the ability to identify “true” hospital ranks in pay-for-performance (P4P) programs. We use Bayesian hierarchical models to estimate the uncertainty associated with the ranking of hospitals by their raw composite score values for three medical conditions: acute myocardial infarction (AMI), heart failure (HF), and community acquired pneumonia (PN). The results indicate a dramatic inverse relationship between the size of the hospital and its expected range of ranking positions for its true or stabilized mean rank. The smallest hospitals among the augmented dataset would likely experience five to seven times more uncertainty concerning their true ranks.
PMCID: PMC4195008  PMID: 18624079
5.  Modeling the costs of case management in long-term care 
Health Care Financing Review  1991;13(1):73-81.
A conceptual approach to developing models for analyzing cost is applied to case management in long-term care. This conceptual approach uses four dimensions to classify case management programs. The application results in identifying five case management cost models. Empirical measures of case management costs and a set of determinants of the within-model variation in these costs are suggested for each model. This article discusses several policy relevant hypotheses that could be addressed by the empirical implementation of these cost models.
PMCID: PMC4193232  PMID: 10114936

Results 1-6 (6)