Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources.

Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical evidence on how privacy concerns impact participation in studies that collect these novel data sources to supplement surveys. We further discuss potential approaches for safeguarding participants' data privacy during data collection and dissemination that may assuage their concerns. Finally, we conclude with open questions and suggested avenues for future research.

Estimating mortality and disability in Peru before the COVID-19 pandemic: a systematic analysis from the Global Burden of the Disease Study 2019.

Background: Estimating and analyzing trends and patterns of health loss are essential to promote efficient resource allocation and improve Peru's healthcare system performance. Methods: Using estimates from the Global Burden of Disease (GBD), Injuries, and Risk Factors Study (2019), we assessed mortality and disability in Peru from 1990 to 2019. We report demographic and epidemiologic trends in terms of population, life expectancy at birth (LE), mortality, incidence, prevalence, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by the major diseases and risk factors in Peru. Finally, we compared Peru with 16 countries in the Latin American (LA) region. Results: The Peruvian population reached 33.9 million inhabitants (49.9% women) in 2019. From 1990 to 2019, LE at birth increased from 69.2 (95% uncertainty interval 67.8-70.3) to 80.3 (77.2-83.2) years. This increase was driven by the decline in under-5 mortality (-80.7%) and mortality from infectious diseases in older age groups (+60 years old). The number of DALYs in 1990 was 9.2 million (8.5-10.1) and reached 7.5 million (6.1-9.0) in 2019. The proportion of DALYs due to non-communicable diseases (NCDs) increased from 38.2% in 1990 to 67.9% in 2019. The all-ages and age-standardized DALYs rates and YLLs rates decreased, but YLDs rates remained constant. In 2019, the leading causes of DALYs were neonatal disorders, lower respiratory infections (LRIs), ischemic heart disease, road injuries, and low back pain. The leading risk factors associated with DALYs in 2019 were undernutrition, high body mass index, high fasting plasma glucose, and air pollution. Before the COVID-19 pandemic, Peru experienced one of the highest LRIs-DALYs rates in the LA region. Conclusion: In the last three decades, Peru experienced significant improvements in LE and child survival and an increase in the burden of NCDs and associated disability. The Peruvian healthcare system must be redesigned to respond to this epidemiological transition. The new design should aim to reduce premature deaths and maintain healthy longevity, focusing on effective coverage and treatment of NCDs and reducing and managing the related disability.

Establishment survey participation during the COVID-19 pandemic.

Establishment surveys around the globe have measured the impact of the COVID-19 pandemic on establishments' conditions and business practices. At the same time, the consequences of the pandemic, such as closures, hygiene standards, or remote work arrangements, may have also altered patterns of survey participation and introduced nonresponse bias, threatening the quality of establishment survey data. To investigate these issues, this article examines fieldwork outcomes, nonresponse bias, and predictors of survey participation in the IAB-Job Vacancy Survey. As comparisons with previous survey years show, it became more difficult to successfully interview establishments during the COVID-19 pandemic. Using linked administrative data, we show that nonresponse bias was higher in 2020 compared to previous years, even after applying the standard weighting adjustment. However, general patterns of survey participation in 2020 were similar to previous years and COVID-19 related measures were not strong predictors of survey participation in 2020. Further, we provide evidence that nonresponse bias during the pandemic can be reduced by incorporating additional administrative variables into the weighting procedure relative to the standard weighting variables. We conclude this article with a discussion of the findings and implications for survey practitioners. Supplementary Information: The online version contains supplementary material available at 10.1186/s12651-022-00321-8.

Reducing Nonresponse and Data Linkage Consent Bias in Large-Scale Panel Surveys.

Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection. A second source of selection bias in panel studies is the inability to link respondents to supplementary administrative records, either because respondents do not consent to link or the matching algorithm fails to locate their administrative records. Both sources of selection bias can affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods of reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.

How to Catch a Falsifier: Comparison of Statistical Detection Methods for Interviewer Falsification.

Deviant interviewer behavior is a potential hazard of interviewer-administered surveys, with interviewers fabricating entire interviews as the most severe form. Various statistical methods (e.g., cluster analysis) have been proposed to detect falsifiers. These methods often rely on falsification indicators aiming to measure differences between real and falsified data. However, due to a lack of real-world data, empirical evaluations and comparisons of different statistical methods and falsification indicators are scarce. Using a large-scale nationally representative refugee survey in Germany with known fraudulent interviews, this study tests, evaluates, and compares statistical methods for identifying falsified data. We investigate the use of new and existing falsification indicators as well as multivariate detection methods for combining them. Additionally, we introduce a new and easy-to-use multivariate detection method that overcomes practical limitations of previous methods. We find that the vast majority of used falsification indicators successfully measure differences between falsifiers and nonfalsifiers, with the newly proposed falsification indicators outperforming some existing indicators. Furthermore, different multivariate detection methods perform similarly well in detecting the falsifiers.

The reliability of adult self-reported height: The role of interviewers.

Surveys serve as an important source of information on key anthropometric characteristics such as body height or weight in the population. Such data are often obtained by directly asking respondents to report those values. Numerous studies have examined measurement errors in this context by comparing reported to measured values. However, little is known on the role of interviewers on the prevalence of irregularities in anthropometric survey data. In this study, we explore such interviewer effects in two ways. First, we use data from the US National Health and Nutrition Examination Survey and the UK Household Longitudinal Study to evaluate whether differences between reported and measured values are clustered within interviewers. Second, we investigate changes in adult self-reported height over survey waves in two German large-scale panel surveys. Here, we exploit that height should be constant over time for the majority of adult age groups. In both analyses, we use multilevel location-scale models to identify interviewers who enhance reporting errors and interviewers for whom unlikely height changes over waves occur frequently. Our results reveal that interviewers can play a prominent role in differences between reported and measured height values and changes in reported height over survey waves. We further provide an analysis of the consequences of height misreporting on substantive regression coefficients where we especially focus on the role of interviewers who reinforce reporting errors and unlikely height changes.

Differences in beliefs about COVID-19 by gun ownership: a cross-sectional survey of Texas adults.

OBJECTIVES: We investigated the association between gun ownership and perceptions about COVID-19 among Texas adults as the pandemic emerged. We considered perceived likelihood that the pandemic would lead to civil unrest, perceived importance of taking precautions to prevent transmission and perceptions that the threat of COVID-19 has been exaggerated. METHODS: Data were collected from 5 to 12 April 2020, shortly after Texas' stay-at-home declaration. We generated a sample using random digit dial methods for a telephone survey (n=77, response rate=8%) and by randomly selecting adults from an ongoing panel to complete the survey online (n=1120, non-probability sample). We conducted a logistic regression to estimate differences in perceptions by gun ownership. To account for bias associated with use of a non-probability sample, we used Bayesian data integration and ran linear regression models to produce more accurate measures of association. RESULTS: Among the 60% of Texas adults who reported gun ownership, estimates of past 7-day gun purchases, ammunition purchases and gun carrying were 15% (n=78), 20% (n=100) and 24% (n=130), respectively. We found no evidence of an association between gun ownership with perceived importance of taking precautions to prevent transmission or with perceived likelihood of civil unrest. Results from the logistic regression (OR 1.27, 95% CI 0.99 to 1.63) and the linear regression (ß=0.18, 95% CI 0.07 to 0.29) suggest that gun owners may be more likely to believe the threat of COVID-19 was exaggerated. CONCLUSIONS: Compared with those without guns, gun owners may have been inclined to downplay the threat of COVID-19 early in the pandemic.

Nurse effects on measurement error in household biosocial surveys.

BACKGROUND: Biosocial survey data are in high demand, yet little is known about the measurement quality of health measures collected by nurses in respondents' homes. Our objective was to analyze the degree to which nurses influence measurement in anthropometric and physical performance indicators collected from respondents in two nationally-representative UK biosocial surveys. METHODS: The English Longitudinal Survey of Ageing and the UK Household Longitudinal Study - Understanding Society were used to analyze fourteen anthropometric and physical performance measures covering weight, height, pulse, grip strength, and lung capacity. Cross-classified multilevel models were used to estimate "nurse effects" on measurement error. RESULTS: Overall, there is a medium effect of nurses on measurement. Across all measures collected in both studies, nurses explain around 13% of all measurement variation. Variation in specific measures range between approximately 2 and 25%. Grip strength and lung capacity are more heavily influenced by nurses than are height, weight, and pulse. Lastly, nurse characteristics explain only a very small proportion of nurse measurement variation. CONCLUSION: Objective health measures collected by nurses in household biosocial surveys are susceptible to non-trivial amounts of measurement variation. Nurse ID numbers should be regularly included in biosocial data releases to allow researchers to account for this unnecessary source of variation. Further, researchers are advised to conduct sensitivity analyses using control variables that account for nurse variation to confirm whether their substantive findings are influenced by nurse measurement effects.

The Effect of Framing and Placement on Linkage Consent.

Numerous surveys link interview data to administrative records, conditional on respondent consent, in order to explore new and innovative research questions. Optimizing the linkage consent rate is a critical step toward realizing the scientific advantages of record linkage and minimizing the risk of linkage consent bias. Linkage consent rates have been shown to be particularly sensitive to certain design features, such as where the consent question is placed in the questionnaire and how the question is framed. However, the interaction of these design features and their relative contributions to the linkage consent rate have never been jointly studied, raising the practical question of which design feature (or combination of features) should be prioritized from a consent rate perspective. We address this knowledge gap by reporting the results of a placement and framing experiment embedded within separate telephone and Web surveys. We find a significant interaction between placement and framing of the linkage consent question on the consent rate. The effect of placement was larger than the effect of framing in both surveys, and the effect of framing was only evident in the Web survey when the consent question was placed at the end of the questionnaire. Both design features had negligible impact on linkage consent bias for a series of administrative variables available for consenters and non-consenters. We conclude this research note with guidance on the optimal administration of the linkage consent question.

Does Benefit Framing Improve Record Linkage Consent Rates? A Survey Experiment.

Survey researchers are increasingly seeking opportunities to link interview data with administrative records. However, obtaining consent from all survey respondents (or certain subgroups) remains a barrier to performing record linkage in many studies. We experimentally investigated whether emphasizing different benefits of record linkage to respondents in a telephone survey of employee working conditions improves respondents' willingness to consent to linkage of employment administrative records relative to a neutral consent request. We found that emphasizing linkage benefits related to "time savings" yielded a small, albeit statistically significant, improvement in the overall linkage consent rate (86.0) relative to the neutral consent request (83.8 percent). The time savings argument was particularly effective among "busy" respondents. A second benefit argument related to "improved study value" did not yield a statistically significant improvement in the linkage consent rate (84.4 percent) relative to the neutral request. This benefit argument was also ineffective among the subgroup of respondents considered to be most likely to have a self-interest in the study outcomes. The article concludes with a brief discussion of the practical implications of these findings and offers suggestions for possible research extensions.

Biases in multilevel analyses caused by cluster-specific fixed-effects imputation.

When datasets are affected by nonresponse, imputation of the missing values is a viable solution. However, most imputation routines implemented in commonly used statistical software packages do not accommodate multilevel models that are popular in education research and other settings involving clustering of units. A common strategy to take the hierarchical structure of the data into account is to include cluster-specific fixed effects in the imputation model. Still, this ad hoc approach has never been compared analytically to the congenial multilevel imputation in a random slopes setting. In this paper, we evaluate the impact of the cluster-specific fixed-effects imputation model on multilevel inference. We show analytically that the cluster-specific fixed-effects imputation strategy will generally bias inferences obtained from random coefficient models. The bias of random-effects variances and global fixed-effects confidence intervals depends on the cluster size, the relation of within- and between-cluster variance, and the missing data mechanism. We illustrate the negative implications of cluster-specific fixed-effects imputation using simulation studies and an application based on data from the National Educational Panel Study (NEPS) in Germany.

How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?

Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences based on the survey data that effectively negate the resources dedicated to these surveys. In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed journal articles presenting analyses of data from a variety of national health surveys, which suggested that analytic errors may be extremely prevalent in these types of investigations. We first perform a meta-analysis of a stratified random sample of 145 additional research products analyzing survey data from the Scientists and Engineers Statistical Data System (SESTAT), which describes features of the U.S. Science and Engineering workforce, and examine trends in the prevalence of analytic error across the decades used to stratify the sample. We once again find that analytic errors appear to be quite prevalent in these studies. Next, we present several example analyses of real SESTAT data, and demonstrate that a failure to perform these analyses correctly can result in substantially biased estimates with standard errors that do not adequately reflect complex sample design features. Collectively, the results of this investigation suggest that reviewers of this type of research need to pay much closer attention to the analytic methods employed by researchers attempting to publish or present secondary analyses of survey data.

The association among medical home readiness, quality, and care of vulnerable patients.

OBJECTIVES: Despite broad support for the patient-centered medical home (PCMH), the implications of PCMH implementation efforts that require that participants have some degree of PCMH readiness at baseline are unclear. Therefore, we sought to examine the association among PCMH readiness, quality, and the care of vulnerable patients. STUDY DESIGN: We conducted a cross-sectional study of adult visits to a nationally representative sample of US office-based primary care physicians in 2007 and 2008. METHODS: Using National Committee for Quality Assurance criteria, we determined whether or not a visit occurred at a PCMH-ready practice. We used t tests and multiple linear regression to measure the association between PCMH readiness and performance on 9 validated outpatient quality indicators. RESULTS: Among 12,235 visits to general practitioners and 5123 visits to general internists, 73% occurred at practices that were PCMH-ready. Visits by patients with 3 or more chronic medical conditions were more likely to occur at ready practices (P = .001). Visits by patients that were poor or minority were equally likely to occur at ready and unready practices. Performance at ready practices was higher for 3 of 9 quality indicators related to chronic disease management and preventive counseling (P = .031 [beta-blocker or diuretic prescribed for hypertension]; P = .018 [diet counseling]; and P <.001 [exercise counseling]). CONCLUSIONS: Implementation efforts that encourage the enrollment of practices most ready for the PCMH could improve the quality of care for complex patients without exacerbating socioeconomic disparities in access to care.

Identifying diabetics in Medicare claims and survey data: implications for health services research.

BACKGROUND: Diabetes 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. METHODS: Claims 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. RESULTS: The Chronic Condition Warehouse (CCW) algorithm yielded a higher rate of diabetes than respondent self-reports (27.3 vs. 21.2, p < 0.05). 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. CONCLUSIONS: Concordance 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.

Important considerations when analyzing health survey data collected using a complex sample design.

Researchers often use survey data to answer important public health policy questions. Examples of common data sources used in public health research include the National Health and Nutrition Examination Survey, the National Health Interview Survey, the Medical Expenditure Panel Survey, and the National Ambulatory Medical Care Survey. All these surveys employ a complex sample design to recruit participants into the survey. When performing secondary analyses of complex sample survey data, it is necessary to remind ourselves of the key features of these designs that must be taken into account to produce valid statistical estimates.

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.

Availability of In-Office Laboratory Services and Use of Prostate Specific Antigen Testing.

INTRODUCTION: There are concerns that the availability of in-office ancillary services may lower thresholds for evaluation, leading to the overuse of testing without clear benefit. Motivated by this issue, we analyzed nationally representative survey data, and examined for associations between the availability of in-office laboratory services and the use of prostate specific antigen testing. METHODS: Using restricted data from the 2006-2008 NAMCS, we determined the prevalence of physician practices offering on-site laboratory services. We then characterized differences between practices with and without these capabilities as well as among the physicians working in them. Finally, we fitted multivariable logistic regression models to estimate the odds of prostate specific antigen testing given a man's mortality risk and the availability of in-office laboratory services at the practice where he received care. RESULTS: Approximately half of all primary care and urology practices offered in-office laboratory services. Practice characteristics associated with these capabilities included practice size (p <0.001) and breadth of specialization (p = 0.021). Employed physicians were more likely to work in practices with in-office laboratory services than self-employed physicians (p <0.001). On multivariable regression the availability of on-site laboratory services was not associated with the use of prostate specific antigen testing (OR 0.86, 95% CI 0.62-1.20, p = 0.362). In fact, the probability of prostate specific antigen testing among patients with the highest mortality risk was lower if they were seen at a practice with in-office laboratory services. CONCLUSIONS: These findings provide some reassurance that in-office ancillaries do not lead to overuse of prostate specific antigen testing.

Urologists and the patient centered medical home.

PURPOSE: Hopes 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. MATERIALS AND METHODS: For 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. RESULTS: Nearly 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. CONCLUSIONS: Urology 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.

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.