Publications by authors named "Michael W Robbins"

Policymakers often require information on programs' long-term impacts that is not available when decisions are made. For example, while rigorous evidence from the Oregon Health Insurance Experiment (OHIE) shows that having health insurance influences short-term health and financial measures, the impact on long-term outcomes, such as mortality, will not be known for many years following the program's implementation. We demonstrate how data fusion methods may be used address the problem of missing final outcomes and predict long-run impacts of interventions before the requisite data are available.

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Joint Imputation of General Data.

J Surv Stat Methodol

February 2024

High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense's Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data.

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Objectives: CMS measures and reports hospital performance to drive quality improvement (QI), but information on actions that hospitals have taken in response to quality measurement is lacking. We aimed to develop national estimates of QI actions undertaken by hospitals and to explore their relationship to performance on CMS quality measures.

Study Design: Nationally representative cross-sectional survey of acute care hospitals in 2016 (n = 1313 respondents; 64% response rate).

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Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods.

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Background: The Centers for Medicare & Medicaid Services (CMS) Home Health Quality Reporting Program (HHQRP) uses performance measurement to spur improvements in home health agencies' (HHAs') quality of care. We examined quality improvement (QI) activities HHAs reported making to improve on HHQRP quality measures, and whether reported QI activities were associated with better measure performance.

Methods: We used responses (N = 1052) from a Web- and mail-based survey of a stratified random sample of HHAs included in CMS Home Health Compare in October 2019.

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Objectives: The Centers for Medicare & Medicaid Services (CMS) Nursing Home Quality Initiative aims to improve quality through performance measurement. We describe quality improvement (QI) changes that skilled nursing facilities (SNFs) reported making in response to CMS performance measurements and whether reported QI changes were associated with better performance on CMS performance measures.

Design: Nationally representative survey.

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The fundamental difficulty of establishing causal relationships between an exposure and an outcome in observational data involves disentangling causality from confounding factors. This problem underlies much of neighborhoods research, which abounds with studies that consider associations between neighborhood characteristics and health outcomes in longitudinal data. Such analyses are confounded by selection issues; individuals with above average health outcomes (or associated characteristics) may self-select into advantaged neighborhoods.

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We examine a range of options for recruiting teachers into a nationally representative survey panel. Recruitment strategies considered include a telephone-based approach and the use of promised incentives and pre-incentives of varying amounts and forms. Using a randomized experiment, we evaluate the effectiveness of five separate recruitment strategies and conduct a cost-benefit analysis.

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