Joseph W. Sakshaug

Linking Survey and Administrative Records: Mechanisms of Consent

Survey records are increasingly being linked to administrative databases to enhance the survey data and increase research opportunities for data users. A necessary prerequisite to linking survey and administrative records is obtaining informed consent from respondents. Obtaining consent from all respondents is a difficult challenge and one that faces significant resistance. Consequently, data linkage consent rates vary widely from study to study. Several studies have found significant differences between consenters and nonconsenters on sociodemographic variables, but no study has investigated the underlying mechanisms of consent from a theory-driven perspective. In this study, we describe and test several hypotheses related to respondents’ willingness to consent to an earnings and benefit data linkage request based on mechanisms related to financial uncertainty, privacy concerns, resistance toward the survey interview, level of attentiveness during the interview, the respondents’ preexisting relationship with the administrative data agency, and matching respondents and interviewers on observable characteristics. The results point to several implications for survey practice and suggestions for future research.

Physician Practices and Readiness for Medical Home Reforms: Policy, Pitfalls, and Possibilities

OBJECTIVEnTo determine the proportion of physician practices in the United States that currently meets medical home criteria.nnnDATA SOURCE/STUDY SETTINGn2007 and 2008 National Ambulatory Medical Care Survey.nnnSTUDY DESIGNnWe mapped survey items to the National Committee on Quality Assurances (NCQAs) medical home standards. After awarding points for each passed element, we calculated a practices infrastructure score, dividing its cumulative total by the number of available points. We identified practices that would be recognized as a medical home (Level 1 [25-49 percent], Level 2 [50-74 percent], or Level 3 [infrastructure score ≥75 percent]) and examined characteristics associated with NCQA recognition.nnnRESULTSnForty-six percent (95 percent confidence interval [CI], 42.5-50.2) of all practices lack sufficient medical home infrastructure. While 72.3 percent (95 percent CI, 64.0-80.7 percent) of multi-specialty groups would achieve recognition, only 49.8 percent (95 percent CI, 45.2-54.5 percent) of solo/partnership practices meet NCQA standards. Although better prepared than specialists, 40 percent of primary care practices would not qualify as a medical home under present criteria.nnnCONCLUSIONnAlmost half of all practices fail to meet NCQA standards for medical home recognition.

Readiness of primary care practices for medical home certification

OBJECTIVES: To assess the prevalence of medical home infrastructure among primary care practices for children and identify practice characteristics associated with medical home infrastructure. METHODS: Cross-sectional analysis of restricted data files from 2007 and 2008 of the National Ambulatory Medical Care Survey. We mapped survey items to the 2011 National Committee on Quality Assurance’s Patient-Centered Medical home standards. Points were awarded for each “passed” element based on National Committee for Quality Assurance scoring, and we then calculated the percentage of the total possible points met for each practice. We used multivariate linear regression to assess associations between practice characteristics and the percentage of medical home infrastructure points attained. RESULTS: On average, pediatric practices attained 38% (95% confidence interval 34%–41%) of medical home infrastructure points, and family/general practices attained 36% (95% confidence interval 33%–38%). Practices scored higher on medical home elements related to direct patient care (eg, providing comprehensive health assessments) and lower in areas highly dependent on health information technology (eg, computerized prescriptions, test ordering, laboratory result viewing, or quality of care measurement and reporting). In multivariate analyses, smaller practice size was significantly associated with lower infrastructure scores. Practice ownership, urban versus rural location, and proportion of visits covered by public insurers were not consistently associated with a practice’s infrastructure score. CONCLUSIONS: Medical home programs need effective approaches to support practice transformation in the small practices that provide the vast majority of the primary care for children in the United States.

Synthetic data for small area estimation

Increasingly, researchers are demanding greater access to microdata for small geographic areas to compute estimates that may affect policy decisions at local levels. Statistical agencies are prevented from releasing detailed geographical identifiers in public-use data sets due to privacy and confidentiality concerns. Existing procedures allow researchers access to restricted geographical information through a limited number of Research Data Centers (RDCs), but this method of data access is not convenient for all. An alternative approach is to release fully-synthetic, public-use microdata files that contain enough geographical details to permit small area estimation. We illustrate this method by using a Bayesian Hierarchical model to create synthetic data sets from the posterior predictive distribution. We evaluate the analytic validity of the synthetic data by comparing small area estimates obtained from the synthetic data with estimates obtained from the U.S. American Community Survey.

Identifying diabetics in medicare claims and survey data: Implications for health services research

BackgroundDiabetes health services research often utilizes secondary data sources, including survey self-report and Medicare claims, to identify and study the diabetic population, but disagreement exists between these two data sources. We assessed agreement between the Chronic Condition Warehouse diabetes algorithm for Medicare claims and self-report measures of diabetes. Differences in healthcare utilization outcomes under each diabetes definition were also explored.MethodsClaims data from the Medicare Beneficiary Annual Summary File were linked to survey and blood data collected from the 2006 Health and Retirement Study. A Hemoglobin A1c reading, collected on 2,028 respondents, was used to reconcile discrepancies between the self-report and Medicare claims measures of diabetes. T-tests were used to assess differences in healthcare utilization outcomes for each diabetes measure.ResultsThe Chronic Condition Warehouse (CCW) algorithm yielded a higher rate of diabetes than respondent self-reports (27.3 vs. 21.2, pu2009<u20090.05). A1c levels of discordant claims-based diabetics suggest that these patients are not diabetic, however, they have high rates of healthcare spending and utilization similar to diabetics.ConclusionsConcordance between A1c and self-reports was higher than for A1c and the CCW algorithm. Accuracy of self-reports was superior to the CCW algorithm. False positives in the claims data have similar utilization profiles to diabetics, suggesting minimal bias in some types of claims-based analyses, though researchers should consider sensitivity analysis across definitions for health services research.

Urologists and the patient centered medical home.

PURPOSEnHopes are high that the delivery system reforms embodied in the patient centered medical home will improve the quality of care for patients with chronic diseases. While primary care physicians, given their training, will likely be the locus of care under this model, there are certain conditions for which urologists are well suited to provide the continuous and comprehensive care called for by the patient centered medical home. To assess the feasibility of the urology based patient centered medical home, we analyzed national survey data.nnnMATERIALS AND METHODSnFor our measure of medical home infrastructure, we mapped items from the 2007 and 2008 NAMCS (National Ambulatory Medical Care Survey) to the NCQA (National Committee on Quality Assurance) standards for patient centered medical home recognition. We determined the proportion of urology practices in the United States that would achieve patient centered medical home recognition. Finally, we used NAMCS data to estimate the impact of consolidating genitourinary cancer (ie prostate, bladder, kidney and testis) followup care among the current supply of urologists.nnnRESULTSnNearly three-quarters of urology practices meet NCQA standards for patient centered medical home recognition. At present, primary care physicians spend 9,295 cumulative workweeks providing direct and indirect care to survivors of genitourinary cancers. Off-loading half of this care to urology practices, in the context of the patient centered medical home, would generate an average of 0.73 additional workweeks for each practicing urologist.nnnCONCLUSIONSnUrology practices may possess the capacity needed to direct medical homes for their patients with genitourinary cancers. Successful implementation of this model would likely require a willingness to manage some nonurological conditions.

Nonparametric Generation of Synthetic Data for Small Geographic Areas

Computing and releasing statistics for small geographic areas is a common task for many statistical agencies, but releasing public-use microdata for these areas is much less common due to data confidentiality concerns. Accessing the restricted microdata is usually only possible within a research data center (RDC). This arrangement is inconvenient for many researchers who must travel large distances and, in some cases, pay a sizeable data usage fee to access the nearest RDC. An alternative data dissemination method that has been explored is to release public-use synthetic data. In general, synthetic data consists of imputed values drawn from a predictive model based on the observed data. Data confidentiality is preserved because no actual data values are released. The imputed values are typically drawn from a standard, parametric distribution, but often key variables of interest do not follow strict parametric forms. In this paper, we apply a nonparametric method for generating synthetic data for continuous variables collected from small geographic areas. The method is evaluated using data from the 2005-2007 American Community Survey. The analytic validity of the synthetic data is assessed by comparing parametric (baseline) and nonparametric inferences obtained from the synthetic data with those obtained from the observed data.

Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey

Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.

SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY

Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.

In-Office Imaging Capabilities Among Procedure-Based Specialty Practices

Background. Stark law’s in-office ancillary services exception permits physicians to furnish designated health services in the office, including advanced imaging. Objectives. To determine whether arrangements tailored to fit this loophole spur utilization. Research design. Cross-sectional. Subjects. Procedure-based specialty clinics participating in the National Ambulatory Medical Care Survey. Measures. Using restricted data files (2006-2008), we identified specialty practices with on-site advanced imaging capabilities (ie, computed tomography, magnetic resonance imaging, and/or positron emission tomography). We then characterized these practices and the physicians who worked in them over a variety of factors. Finally, we performed multivariable regression to evaluate the association between imaging use and the availability of in-office imaging. Results. Fourteen percent of practices performed advanced imaging on site. While this proportion remained stable over the study period for most specialties, it rose significantly among orthopedic surgery clinics from 13.6% to 31.3% (P = .023 for the temporal trend). The availability of advanced imaging varied by practice organization and size. For instance, 32.6% of large single-specialty groups provided in-office imaging as compared to only 10.1% of solo/partnership practices. While less than a quarter of specialty visits were made to practices that offered advanced imaging, these locations generated a third of all advanced imaging studies. In fact, 1 in 11 visits (9.0%; 95% confidence interval = 6.8% to 11.6%; P = .030) to them resulted in advanced imaging. Conclusions. The availability of in-office advanced imaging is associated with increased imaging use.