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Throughout this article, a two-phase sampling (TPS) technique is employed to estimate the population mean of the sensitive variable. The current article endeavours to develop a chain ratio type estimator for the estimation of sensitive variable(s) in the presence of non-response and measurement error simultaneously by utilizing ORRT models under a two-phase sampling technique. The significant aspects associated with the suggested estimator characterized by bias and mean squared error have been evaluated. Besides this, the utterance for the minimum mean squared error for the optimal values has also been identified. The supremacy of the proposed estimator has been compared with the modified existing estimators under the TPS scheme by using two sensitive auxiliary variables. To clarify the theoretical findings, a simulation study along with a hypothetical generated population and a real population which is based on abortion rates from Statistical Abstract of the United States: 2011 are also addressed in this study. [ABSTRACT FROM AUTHOR]

For many sample surveys, researchers, policymakers, and other stakeholders are interested in obtaining estimates for various domains, such as for geographic levels, for demographic groups, or a cross‐classification of both. Often, the demand for estimates at a disaggregated level exceeds what the sample size and survey design can support when estimation is done by traditional design‐based estimation methods. Small area estimation involves exploiting relationships among domains and borrowing strength from multiple sources of information to improve inference relative to direct survey methods. This typically involves the use of models whose success depends heavily on the quality and predictive ability of the sources of information used. Possible sources of auxiliary information include administrative records, Censuses, big data such as traffic or cell phone data, or previous vintages of the same survey. One rich source of information is that of other surveys, especially in countries like the United States, where multiple surveys exist that cover related topics. We will provide an introduction to the topic of combining information from multiple surveys in small area estimation using area‐level models, including practical advice and a technical introduction, and illustrating with applications. We will discuss reasons to combine surveys and give an overview of some of the most common types of models. This article is categorized under:Statistical Models > Multivariate Models [ABSTRACT FROM AUTHOR]

Background: Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates obtained by egocentric sampling are vulnerable to information bias due to incomplete respondent knowledge.Methods: We estimated patterns of serosorting and PrEP sorting among MSM in the United States using data from a 2017-2019 egocentric sexual network study. Respondents served as proxies to report the HIV status and PrEP use of recent sexual partners. We contrasted results from a complete-case analysis (unknown HIV and PrEP excluded) versus a bias analysis with respondent-reported data stochastically reclassified to simulate unobserved self-reported data from sexual partners.Results: We found strong evidence of preferential partnering across analytical approaches. The bias analysis showed concordance between sexual partners of HIV diagnosis and PrEP use statuses for MSM with diagnosed HIV (39%; 95% simulation interval: 31, 46), MSM who used PrEP (32%; 21, 37), and MSM who did not use PrEP (83%; 79, 87). The fraction of partners with diagnosed HIV was higher among MSM who used PrEP (11%; 9, 14) compared with MSM who did not use PrEP (4%; 3, 5). Comparatively, across all strata of respondents, the complete-case analysis overestimated the fractions of partners with diagnosed HIV or PrEP use.Conclusions: We found evidence consistent with HIV and PrEP sorting among MSM, which may decrease the population-level effectiveness of PrEP. Bias analyses can improve mixing estimates for parameterization of transmission models. [ABSTRACT FROM AUTHOR]

Reference data obtained by interpreters is a key component of sample-based estimation of area of land cover and land cover change. However, interpreters may disagree when assigning the reference class label for a given sample unit and this inconsistency between interpreters contributes to the overall uncertainty of the estimated area. Interpenetrating subsampling (IPS) offers a practical way to incorporate interpreter variability into an unbiased estimator of the total variance. This method requires partitioning the full sample into g nonoverlapping groups with the sample units in each group then evaluated by a different interpreter and each interpreter determines the reference class data for only one group. The total variance is estimated by the among group variability of the g estimates of area. IPS was applied to estimate the total variance of land cover area estimates for a sample of 300 pixels selected from the Puget Sound region of the Northwest United States. The reference land cover data were obtained by seven interpreters who each labeled all 300 pixels. These data provided a unique opportunity to explore properties of IPS such as variability over different random partitions of the sample into groups and variability over different subsets of interpreters. IPS estimates of total variance were produced for each land cover class for group sizes of g = 2 through g = 6 and all possible combinations of the seven interpreters for each group size. The estimated total variance decreased with increasing number of groups. Incorporating interpreter variance increased the estimated total variance by a factor ranging from 1.08 (agriculture) to 7.06 (grass/shrub) in simple random sampling. The total variance estimates varied substantially over the random partitions of the sample into groups, but this variability decreased as the group size increased. Compared with other total variance estimators, the IPS estimator is simpler to compute and is more cost effective because it does not require repeat interpretations • Total variance incorporates both sampling variance and interpreter variance. • Interpenetrating subsampling (IPS) is a practical way to estimate total variance. • Total variance decreases as the number of interpreters increases. • Typical of variance estimators in general, IPS total variance has large variance. • Stability of IPS total variance estimator is improved by using more interpreters. [ABSTRACT FROM AUTHOR]

In this paper, I study how the contrasting coverage of labour and capital incomes affects inequality estimates. I use national accounts as a benchmark to evaluate the scope of household surveys, for a number of countries, and tax data for the United States. Due to both measurement error and conceptual differences, capital income is always more underestimated. In most countries, the gap grows during the last two decades. Based on accounting identities, I show that inequality estimates are likely affected in level, trend and composition. Surveys thus largely exaggerate the impact of changes in the labour income distribution, while they undermine the capital share and its dynamics. As a reference, in a panel of nineteen countries, households collect half of total capital income, as opposed to corporations; but surveys only capture close to twenty percent of that half, versus seventy percent of total labour income. For any quantile group –e.g. the top 10% or bottom 50% share– a unit increase of its labour income share translates into an increase of nine tenths of a unit in the overall share, for capital income, the effect is only one tenth of a unit. Gaps are narrower but still present in tax data. [ABSTRACT FROM AUTHOR]

Background Developing approaches for the objective assessment of sugars intake in population research is crucial for generating reliable disease risk estimates, and evidence-based dietary guidelines. Twenty-four-hour urinary sucrose and fructose (24uSF) was developed as a predictive biomarker of total sugars intake based on 3 UK feeding studies, yet its performance as a biomarker of total sugars among US participants is unknown. Objectives To investigate the performance of 24uSF as a biomarker of sugars intake among US participants, and to characterize its use. Methods Ninety-eight participants, aged 18–70 y, consumed their usual diet under controlled conditions of a feeding study for 15 d, and collected 8 nonconsecutive 24-h urines measured for sucrose and fructose. Results A linear mixed model regressing log 24uSF biomarker on log total sugars intake along with other covariates explained 56% of the biomarker variance. Total sugars intake was the strongest predictor in the model (Marginal R2 = 0.52; P  <0.0001), followed by sex (P  = 0.0002) and log age (P  = 0.002). The equation was then inverted to solve for total sugars intake, thus generating a calibrated biomarker equation. Calibration of the biomarker produced mean biomarker-based log total sugars of 4.79 (SD = 0.59), which was similar to the observed log 15-d mean total sugars intake of 4.69 (0.35). The correlation between calibrated biomarker and usual total sugars intake was 0.59 for the calibrated biomarker based on a single biomarker measurement, and 0.76 based on 4 biomarker repeats spaced far apart. Conclusions In this controlled feeding study, total sugars intake was the main determinant of 24uSF confirming its utility as a biomarker of total sugars in this population. Next steps will include validation of stability assumptions of the biomarker calibration equation proposed here, which will allow its use as an instrument for dietary validation and measurement error correction in diet-disease association studies. [ABSTRACT FROM AUTHOR]

BACKGROUND Blood pressure (BP) measurement error may lead to under- or overtreatment of hypertension. One common source of error is terminal digit preference, most often a terminal digit of "0." The objective was to evaluate national trends in terminal digit preference in office BP measurements among adults with treated hypertension. METHODS Data were from IQVIA's National Disease and Therapeutic Index, a nationally representative, serial cross-sectional survey of office-based physicians. The analysis included office visits from 2015 to 2019 among adults aged ≥18 years receiving antihypertensive treatment. Annual trends were examined in the percent of systolic and diastolic BP measurements ending in zero by patient sex, age, and race/ethnicity, physician specialty, and first or subsequent hypertension treatment visit. RESULTS From 2015 to 2019, there were ~60 million hypertension treatment visits annually (unweighted N : 5,585–9,085). There was a decrease in the percent of visits with systolic (41.7%–37.7%) or diastolic (42.7%–37.8%) BP recordings ending in zero. Trends were similar by patient characteristics. However, a greater proportion of measurements ended in zero among patients aged ≥80 (vs. 15–59 or 60–79) years, first (vs. subsequent) treatment visits, visits to cardiologists (vs. primary care physicians), and visits with systolic BP ≥140 or diastolic BP ≥90 (vs. <140/90) mm Hg. CONCLUSIONS Despite modest improvement, terminal digit preference remains a common problem in office BP measurement in the United States. Without bias, 10%–20% of measurements are expected to end in zero. Reducing digit preference is a priority for improving BP measurement accuracy and hypertension management. [ABSTRACT FROM AUTHOR]

Introduction: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us)., Methods: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from ∼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us., Results: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors., Discussion: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Self-reported tobacco pack-year history plays a large role in decisions about low-dose CT screening for lung cancer, yet is challenging to measure accurately.Research Question: To what extent does random measurement error in pack-year information impact screening decisions and screening effectiveness?Study Design and Methods: Retrospective cohort study of 10,449 patients with pack-year history documented at least twice between October 2013 and July 2017 across 8 academic Veterans Affairs sites. Outcome measures included (1) observed reliability of pack-year information based on all repeat measures for the study population and (2) each person's statistically "true" pack-year information based on best linear unbiased predictor from a multilevel linear random effects model. To examine how unreliability leads to misclassification of screening eligibility and inaccuracy in estimating lung cancer risk, we simulated pack-year observations for each person, first comparing simulated pack-year and lung cancer risk values with true values, then comparing outcomes when basing screening decisions on unreliable pack-year information vs true information.Results: Reliability of assessing pack-year information in routine practice varied across sites. Thus, we examined the clinical impact of two different levels of reliability, based on the range of intraclass correlation coefficients observed. Using a ≥ 30-pack-year threshold led to a high rate of eligibility misclassifications (48.1% misclassified with higher reliability pack-year information and 60.7% misclassified with lower reliability information). However, using a lung cancer risk threshold leads to fewer misclassifications (47.3%-49.7% misclassified when using lower reliability pack-year information) and maintains screening effectiveness better when using unreliable pack-year information.Interpretation: Random error in real-world pack-year assessments leads to a substantial rate of misclassifying who should be offered CT screening if a ≥ 30-pack-year criterion is used. However, using a lung cancer risk threshold mitigates the impact of unreliable pack-year information. Decision-makers concerned about the impact of unreliable pack-year information should consider using risk-based approaches to CT screening. [ABSTRACT FROM AUTHOR]

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession. [ABSTRACT FROM AUTHOR]

Background: Accurate and reliable methods to assess prevalence of use of and nutrient intakes from dietary supplements (DSs) are critical for research, clinical practice, and public health monitoring. NHANES has been the primary source of DS usage patterns using an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), but little is known regarding DS information obtained from 24-h dietary recalls (24HRs).Methods: The objectives of this analysis were to compare results from 4 different methods for measuring DS use constructed from two data collection instruments (i.e., DSMQ and 24HR) and to determine the most comprehensive method for measuring the prevalence of use and estimating nutrient intakes from DS for selected nutrients. NHANES 2011-2014 data from US adults (aged ≥19 y; n = 11,451) were used to examine the 4 combinations of methods constructed for measuring the prevalence of use of and amount of selected nutrients from DSs (i.e., riboflavin, vitamin D, folate, magnesium, calcium): 1) DSMQ, 2) 24HR day 1, 3) two 24HRs (i.e., mean), and 4) DSMQ or at least one 24HR.Results: Half of US adults reported DS use on the DSMQ (52%) and on two 24HRs (mean of 49%), as compared with a lower prevalence of DS use when using a single 24HR (43%) and a higher (57%) prevalence when combining the DSMQ with at least one 24HR. Mean nutrient intake estimates were highest using 24HR day 1. Mean supplemental calcium from the DSMQ or at least one 24HR was 372 mg/d, but 464 mg/d on the 24HR only. For vitamin D, the estimated intakes per consumption day were higher on the DSMQ (46 μg) and the DSMQ or at least one 24HR (44 μg) than those on the 24HR day 1 (32 μg) or the mean 24HR (31 μg). Fewer products were also classed as a default or reasonable match on the DSMQ than on the 24HR.Conclusions: A higher prevalence of use of DSs is obtained using frequency-based methods, whereas higher amounts of nutrients are reported from a 24HR. The home inventory results in greater accuracy for products reported. Collectively, these findings suggest that combining the DSMQ with at least one 24HR (i.e., DSMQ or at least one 24HR) is the most comprehensive method for assessing the prevalence of and estimating usual intake from DSs in US adults.This trial was registered at clinicaltrials.gov as NCT03400436. [ABSTRACT FROM AUTHOR]

Earlier researches have proposed algorithms to quantify the measurement uncertainty in rating curves and found that the magnitude of the uncertainty can be significant enough to impact hydrologic modeling. Therefore, they suggested frameworks to include measurement uncertainty in the rating curve to make it robust. Despite their efforts, a robust rating curve is often ignored in traditional practices, considering the investment of time and money as well as the resulting benefit from it. In the current research, we are interested in understanding the role of the measurement error variance in real‐time streamflow forecasting. Our objectives are (i) to employ a state‐of‐the‐art statistical forecasting model that can handle measurement uncertainty in daily streamflow and (ii) to understand the trade‐off in forecasting performance when substantial knowledge regarding the measurement uncertainty is provided to the modeler. We apply the Bayesian dynamic hierarchical model (BDHM) on four gauging sites in the United States. Results show that the BDHM performs better than the daily climatology and local linear regression model. Also, the forecast variance changes proportionally with the change in the error variance as an input in the observation equation. Following this, we design a simulation‐based study, which assigns the measurement error in the reported streamflow to obtain multiple realizations of the true streamflow. The inclusion of substantial knowledge about the true error improves the BDHM's performance by lowering the CRPS (continuous rank probability score) values. However, the inclusion increases the forecast variance to bring the true streamflow within the sampling variability of the forecasted streamflow. Overall, an improved trade‐off between the success rate of forecasts and the forecast variance can be achieved by including the measurement error in the BDHM for rivers that witness less dispersed streamflow data. Plain Language Summary: Rating curve uncertainty is one of the major sources of measurement uncertainty in reported streamflow. It occurs when a precalibrated stage‐discharge relationship curve is used to indirectly measure the streamflow volume based on river stage measurement. Considering the cost and time involved to include the uncertainty in reported streamflow, rating curve uncertainty is often ignored in traditional streamflow modeling practices. The current study adopts a state‐of‐the‐art statistical model that can include rating curve uncertainty while issuing daily streamflow forecasts 1 to 3 days in advance. The study found that a proper knowledge of rating curve uncertainty at a gauge site significantly improves the forecasting of true streamflow. Key Points: Bayesian dynamic hierarchical model (BDHM) can include the measurement uncertainty that has a substantial impact on real‐time forecastingBDHM efficiently predicts the reported streamflow 1 to 3 days in advance while compared with the daily climatology and a semiparametric forecasting modelKnowledge of the measurement uncertainty improves the overall performance of BDHM in predicting the true streamflows [ABSTRACT FROM AUTHOR]

Latent class models have become a popular means of summarizing survey questionnaires and other large sets of categorical variables. Often these classes are of primary interest to better understand complex patterns in data. Increasingly, these latent classes are reified into predictors of other outcomes of interests, treating the most likely class as the true class to which an individual belongs even though there is uncertainty in class membership. This uncertainty can be viewed as a form of measurement error in predictors, leading to bias in the estimates of the regression parameters associated with the latent classes. Despite this fact, there is very limited literature treating latent class predictors as measurement error models. Most applications ignore this issue and fit a two-stage model that treats the modal class prediction as truth. Here, we develop two approaches-one likelihood-based, the other Bayesian-to implement a joint model for latent class analysis and outcome prediction. We apply these methods to an analysis of how acculturation behaviors predict depression in South Asian immigrants to the United States. A simulation study gives guidance for when a two-stage model can be safely implemented and when the joint model may be required. [ABSTRACT FROM AUTHOR]

Underrepresented identities have been overlooked in the development of measures assessing eating disorders; therefore, limited normative data exist for these identities. To address this, Burnette et al. sought to provide Eating Disorder Examination‐Questionnaire and Eating Attitudes Test‐26 norms for transgender adults using Amazon's MTurk. However, they were unable to achieve this goal due to what they perceived as high rates of invalid responses. Instead, they provided recommendations for conducting MTurk research. However, little or no evidence supports the validity of several recommendations, partly because their study was not designed to derive or validate recommendations. By their own admission, their strategies failed to address what they identified as the central problem. We express concern about Burnette et al.'s recommendations because (a) the recommendations are built on assumptions about the problem that may not be true; and (b) the recommendations are not provided within the context of limitations of self‐report/online data collection writ large. We detail these concerns and propose that strategies for mitigating inattentive/invalid responding be subjected to validation prior to being recommended to prevent the implementation of procedures that result in the exclusion of the target population, individuals who we historically, and perhaps still, unjustly exclude from research. [ABSTRACT FROM AUTHOR]

Background: Accurate quantification of sodium intake based on self-reported dietary assessments has been a persistent challenge. We aimed to apply machine-learning (ML) algorithms to predict 24-hour urinary sodium excretion from self-reported questionnaire information., Methods and Results: We analyzed 3454 participants from the NHS (Nurses' Health Study), NHS-II (Nurses' Health Study II), and HPFS (Health Professionals Follow-Up Study), with repeated measures of 24-hour urinary sodium excretion over 1 year. We used an ensemble approach to predict averaged 24-hour urinary sodium excretion using 36 characteristics. The TOHP-I (Trial of Hypertension Prevention I) was used for the external validation. The final ML algorithms were applied to 167 920 nonhypertensive adults with 30-year follow-up to estimate confounder-adjusted hazard ratio (HR) of incident hypertension for predicted sodium. Averaged 24-hour urinary sodium excretion was better predicted and calibrated with ML compared with the food frequency questionnaire (Spearman correlation coefficient, 0.51 [95% CI, 0.49-0.54] with ML; 0.19 [95% CI, 0.16-0.23] with the food frequency questionnaire; 0.46 [95% CI, 0.42-0.50] in the TOHP-I). However, the prediction heavily depended on body size, and the prediction of energy-adjusted 24-hour sodium excretion was modestly better using ML. ML-predicted sodium was modestly more strongly associated than food frequency questionnaire-based sodium in the NHS-II (HR comparing Q5 versus Q1, 1.48 [95% CI, 1.40-1.56] with ML; 1.04 [95% CI, 0.99-1.08] with the food frequency questionnaire), but no material differences were observed in the NHS or HPFS., Conclusions: The present ML algorithm improved prediction of participants' absolute 24-hour urinary sodium excretion. The present algorithms may be a generalizable approach for predicting absolute sodium intake but do not substantially reduce the bias stemming from measurement error in disease associations.

Objective: To evaluate the veracity of self-reports of month-level health insurance coverage in the Current Population Survey Annual Social and Economic Supplement (CPS)., Data Sources and Study Setting: The CHIME (Comparing Health Insurance Measurement Error) study used health insurance enrollment records from a large regional Midwest insurer as sample for primary data collection in spring 2015., Study Design: A sample of individuals enrolled in a range of public and private coverage types (including Medicaid and marketplace) was administered the CPS health insurance module, which included questions about month-level coverage, by type, over a 17-18-month time span. Survey data was then matched to enrollment records covering that same time frame, and concordance between the records and self-reports was assessed., Data Collection/extraction Methods: Sample was drawn by the insurer's informatics specialists and Census Bureau interviewers conducted the survey. Following data collection, updated enrollment records were matched to the survey data to produce a person-level file of coverage by type at the month-level., Principal Findings: For 91% of the overall sample, coverage status and type were reported accurately for at least 75% of observed months. Results varied somewhat by stability of coverage. Among those who were continuously covered throughout the 17-18 month observation period (which comprised 64% of the overall sample), that level of reporting accuracy was observed for 94% of the sample; for those who had censored spells (34% of the overall sample), the figure was 87%; and among those with gaps and/or changes according to the records (2% of the overall sample), for 82% of the group at least 75% of months were reported accurately., Conclusions: Findings suggest that reporting accuracy of month-level coverage in the CPS is high and that the survey could become a valuable new data source for studying the dynamics of coverage, including the Medicaid unwinding., (© 2024 Health Research and Educational Trust. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Recent research linking administrative to survey data has laid the groundwork for improvements in survey data products. However, the opportunities have not been fully realized yet. In this article, our main objective is to use administrative-survey linked microdata to demonstrate the potential of data linkage to reduce survey error through model-based blended imputation methods. We use parametric models based on the linked data to create imputed values of Medicaid enrollment and food stamp (SNAP) receipt. This approach to blending data from surveys and administrative data through models is less likely to compromise confidentiality or violate the terms of the data sharing agreements among the agencies than releasing the linked microdata, and we demonstrate that it can yield substantial improvements of estimate accuracy. Using the blended imputation approach reduces root mean squared error (RMSE) of estimates by 81 percent for state-level Medicaid enrollment and by 93 percent for substate area SNAP receipt compared with estimates based on the survey data alone. Given the high level of measurement error associated with these important programs in the United States, data producers should consider blended imputation methods like the ones we describe in this article to create improved estimates for policy research. [ABSTRACT FROM AUTHOR]

Background Understanding measurement error in sodium and potassium intake is essential for assessing population intake and studying associations with health outcomes. Objective The aim of this study was to compare sodium and potassium intake derived from 24-h dietary recall (24HDR) with intake derived from 24-h urinary excretion (24HUE). Design Data were analyzed from 776 nonpregnant, noninstitutionalized US adults aged 20–69 y who completed 1-to-2 24HUE and 24HDR measures in the 2014 NHANES. A total of 1190 urine specimens and 1414 dietary recalls were analyzed. Mean bias was estimated as mean of the differences between individual mean 24HDR and 24HUE measurements. Correlations and attenuation factors were estimated using the Kipnis joint-mixed effects model accounting for within-person day-to-day variability in sodium excretion. The attenuation factor reflects the degree to which true associations between long-term intake (estimated using 24HUEs) and a hypothetical health outcome would be approximated using a single 24HDR: values near 1 indicate close approximation and near 0 indicate bias toward null. Estimates are reported for sodium, potassium, and the sodium: potassium (Na/K) ratio. Model parameters can be used to estimate correlations/attenuation factors when multiple 24HDRs are available. Results Overall, mean bias for sodium was −452 mg (95% CI: −646, −259), for potassium −315 mg (CI: −450, −179), and for the Na/K ratio −0.04 (CI: −0.15, 0.07, NS). Using 1 24HDR, the attenuation factor for sodium was 0.16 (CI: 0.09, 0.21), for potassium 0.25 (CI:0.16, 0.36), and for the Na/K ratio 0.20 (CI: 0.10, 0.25). The correlation for sodium was 0.27 (CI: 0.16, 0.37), for potassium 0.35 (CI: 0.26, 0.55), and for the Na/K ratio 0.27 (CI: 0.13, 0.32). Conclusions Compared with 24HUE, using 24HDR underestimates mean sodium and potassium intake but is unbiased for the Na/K ratio. Additionally, using 24HDR as a measure of exposure in observational studies attenuates the true associations of sodium and potassium intake with health outcomes. [ABSTRACT FROM AUTHOR]

Surveys are a key tool for understanding political behavior, but they are subject to biases that render their estimates about the frequency of socially desirable behaviors inaccurate. For decades the American National Election Study (ANES) has overestimated voter turnout, though the causes of this persistent bias are poorly understood. The face-to-face component of the 2012 ANES produced a turnout estimate at least 13 points higher than the benchmark voting-eligible population turnout rate. We consider three explanations for this overestimate in the survey: nonresponse bias, over-reporting and the possibility that the ANES constitutes an inadvertent mobilization treatment. Analysis of turnout data supplied by voter file vendors allows the three phenomena to be measured for the first time in a single survey. We find that over-reporting is the largest contributor, responsible for six percentage points of the turnout overestimate, while nonresponse bias and mobilization account for an additional 4 and 3 percentage points, respectively. [ABSTRACT FROM AUTHOR]

Surveys are the leading method of gathering data on prevalence and correlates of prescription opioid misuse. As the opioid crisis continues in the United States, reliable data on misuse have become increasingly important as under- and overreporting is common. This perspective discusses six potential barriers to researchers accurately assessing opioid misuse on surveys. These barriers include lack of respondent drug knowledge, researchers not properly defining opioids for respondents, querying overall opioid misuse rather than misuse of specific opioids, varying terminology (e.g., of opioids and misuse), and lack of understanding of medical versus nonmedical use or misuse. Finally, a recent barrier is use of counterfeit or adulterated drugs that contain fentanyl, its analogs, or other new synthetic opioids, which can lead to underreporting of exposure. Survey researchers should consider such barriers when querying opioid misuse. [ABSTRACT FROM AUTHOR]

Does completing a web survey on a smartphone or tablet computer reduce the quality of the data obtained compared to completing the survey on a laptop computer? This is an important question, since a growing proportion of web surveys are done on smartphones and tablets. Several earlier studies have attempted to gauge the effects of the switch from personal computers to mobile devices on data quality. We carried out a field experiment in eight counties around the United States that compared responses obtained by smartphones, tablets, and laptop computers. We examined a range of data quality measures including completion times, rates of missing data, straightlining, and the reliability and validity of scale responses. A unique feature of our study design is that it minimized selection effects; we provided the randomly determined device on which respondents completed the survey after they agreed to take part. As a result, respondents may have been using a device (e.g., a smartphone) for the first time. However, like many of the prior studies examining mobile devices, we find few effects of the type of device on data quality. [ABSTRACT FROM AUTHOR]

Some educators are concerned that students with test scores at top of the test score distribution will negatively affect the value-added estimates of teachers of those students. A conventional wisdom has sprung up suggesting that students with very high test scores have "no room to grow," so value-added estimates for teachers with high-performing students will be depressed even for highly effective teachers. Using empirical data, we show that under normal circumstances, in which few students score at the ceiling, a teacher of high-performing students—even with many students scoring at the ceiling on the pre-test—can have a high value-added estimate. To understand how more extreme ceiling effects can change value-added estimates, we simulate a low ceiling, causing student test achievement data of high-scoring students to become less precise when a single score represents a large range of possible achievement. We find that the problem of test score ceilings for an evaluation system is not that it pushes the value added of every teacher of high-achieving students toward the bottom of the distribution of teachers, but rather shrinks it toward the middle. [ABSTRACT FROM AUTHOR]

Background and Aims To assess the burden of excessive alcohol use, researchers estimate alcohol-attributable fractions (AAFs) routinely. However, under-reporting in survey data can bias these estimates. We present an approach that adjusts for under-reporting in the estimation of AAFs, particularly within subgroups. This framework is a refinement of a previous method conducted by Rehm et al. Methods We use a measurement error model to derive the 'true' alcohol distribution from a 'reported' alcohol distribution. The 'true' distribution leverages per-capita sales data to identify the distribution average and then identifies the shape of the distribution with self-reported survey data. Data are from the National Alcohol Survey (NAS), the National Household Survey on Drug Abuse (NHSDA) and the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). We compared our approach with previous approaches by estimating the AAF of female breast cancer cases. Results Compared with Rehm et al.'s approach, our refinement performs similarly under a gamma assumption. For example, among females aged 18-25 years, the two approaches produce estimates from NHSDA that are within a percentage point. However, relaxing the gamma assumption generally produces more conservative evidence. For example, among females aged 18-25 years, estimates from NHSDA based on the best-fitting distribution are only 19.33% of breast cancer cases, which is a much smaller proportion than the gamma-based estimates of approximately 28%. Conclusions A refinement of Rehm et al.'s approach to adjusting for underreporting in the estimation of alcohol-attributable fractions provides more flexibility. This flexibility can avoid biases associated with failing to account for the underlying differences in alcohol consumption patterns across different study populations. Comparisons of our refinement with Rehm et al.'s approach show that results are similar when a gamma distribution is assumed. However, results are appreciably lower when the best-fitting distribution is chosen versus gamma-based results. [ABSTRACT FROM AUTHOR]

Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment ($T$) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation. We examine how the error in $T$ impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method's performance under matching or IPTW. We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among $10\,284$ Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information. [ABSTRACT FROM AUTHOR]

Background: Public health concerns with regard to both low and high folate status exist in the United States. Recent publications have questioned the utility of self-reported dietary intake data in research and monitoring. Objectives: The purpose of this analysis was to examine the relation between self-reported folate intakes and folate status biomarkers and to evaluate their usefulness for several types of applications. Design: We examined usual dietary intakes of folate by using the National Cancer Institute method to adjust two 24-h dietary recalls (including dietary supplements) for within-person variation and then compared these intakes with serum and red blood cell (RBC) folate among 4878 men and nonpregnant, nonlactating women aged ≥19 y in NHANES 2011-2012, a nationally representative, cross-sectional survey, with respect to consistency across prevalence estimates and rank order comparisons. Results: There was a very low prevalence (<1%) of folate deficiency when serum (<7 nmol/L) and RBC (<305 nmol/L) folate were considered, whereas a higher proportion of the population reported inadequate total dietary folate intakes (6%). Similar patterns of change occurred between intakes and biomarkers of folate status when distributions were examined (i.e., dose response), particularly when diet was expressed in mg. Intakes greater than the Tolerable Upper Intake Level greatly increased the odds of having high serum folate (OR: 17.6; 95% CI: 5.5, 56.0). Conclusions: When assessing folate status in the United States, where fortification and supplement use are common, similar patterns in the distributions of diet and biomarkers suggest that these 2 types of status indicators reflect the same underlying folate status; however, the higher prevalence estimates for inadequate intakes compared with biomarkers suggest, among other factors, a systematic underestimation bias in intake data. Caution is needed in the use of dietary folate data to estimate the prevalence of inadequacy among population groups. The use of dietary data for rank order comparisons or to estimate the potential for dietary excess is likely more reliable. [ABSTRACT FROM AUTHOR]

Background: The Women's Health Initiative (WHI) randomized, controlled Dietary Modification (DM) trial of a low-fat dietary pattern suggested intervention benefits related to breast cancer, coronary heart disease (CHD), and diabetes. Here, we use WHI observational data for further insight into the chronic disease implications of adopting this type of low-fat dietary pattern., Objectives: We aimed to use our earlier work on metabolomics-based biomarkers of carbohydrate and protein to develop a fat intake biomarker by subtraction, to use the resulting biomarker to develop calibration equations that adjusts self-reported fat intake for measurement error, and to study associations of biomarker-calibrated fat intake with chronic disease risk in WHI cohorts. Corresponding studies for specific fatty acids will follow separately., Methods: Prospective disease association results are presented using WHI cohorts of postmenopausal women, aged 50-79 y when enrolled at 40 United States clinical centers. Biomarker equations were developed using an embedded human feeding study (n = 153). Calibration equations were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with cancer, cardiovascular diseases, and diabetes incidence in WHI cohorts (n = 81,954) over an approximate 20-y follow-up period., Results: A biomarker for fat density was developed by subtracting protein, carbohydrate, and alcohol densities from one. A calibration equation was developed for fat density. Hazard ratios (95% confidence intervals) for 20% higher fat density were 1.16 (1.06, 1.27) for breast cancer, 1.13 (1.02, 1.26) for CHD, and 1.19 (1.13, 1.26) for diabetes, in substantial agreement with findings from the DM trial. With control for additional dietary variables, especially fiber, fat density was no longer associated with CHD, with hazard ratio (95% confidence interval) of 1.00 (0.88, 1.13), whereas that for breast cancer was 1.11 (1.00, 1.24)., Conclusions: WHI observational data support prior DM trial findings of low-fat dietary pattern benefits in this population of postmenopausal United States women., Trial Registration Number: This study is registered with clinicaltrials.gov identifier: NCT00000611., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Precise dietary assessment is critical for accurate exposure classification in nutritional research, typically aimed at understanding how diet relates to health. Dietary supplement (DS) use is widespread and represents a considerable source of nutrients. However, few studies have compared the best methods to measure DSs. Our literature review on the relative validity and reproducibility of DS instruments in the United States [e.g., product inventories, questionnaires, and 24-h dietary recalls (24HR)] identified five studies that examined validity ( n = 5) and/or reproducibility ( n = 4). No gold standard reference method exists for validating DS use; thus, each study's investigators chose the reference instrument used to measure validity. Self-administered questionnaires agreed well with 24HR and inventory methods when comparing the prevalence of commonly used DSs. The inventory method captured nutrient amounts more accurately than the other methods. Reproducibility (over 3 months to 2.4 years) of prevalence of use estimates on the questionnaires was acceptable for common DSs. Given the limited body of research on measurement error in DS assessment, only tentative conclusions on these DS instruments can be drawn at present. Further research is critical to advancing knowledge in DS assessment for research and monitoring purposes.

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The corresponding author, Staci Hepler, serves on the editorial board of Spatial and Spatio-temporal Epidemiology. (S.A.H), (Copyright © 2023 Elsevier Ltd. All rights reserved.)

It is common to assume that worker productivity is normally distributed, but this assumption is rarely, if ever, tested. We estimate the distribution of worker productivity, where individual productivity is measured with error, using the productivity of teachers as an example. We employ a nonparametric density estimator that explicitly accounts for measurement error using data from the Tennessee STAR experiment, and longitudinal data from North Carolina and Washington. Statistical tests show that the productivity distribution of teachers is not Gaussian, but the differences from the normal distribution tend to be small. Our findings confirm the existing empirical evidence that the differences in the effects of individual teachers on student achievement are large and the assumption that the differences in the upper and lower tails of the teacher performance distribution are far larger than in the middle of the distribution. Specifically, a 10 percentile point movement for teachers at the top (90th) or bottom (10th) deciles of the distribution is estimated to move student achievement by 8–17 student percentile ranks, as compared to a change of 2–7 student percentile ranks for a 10 percentile change in teacher productivity in the middle of the distribution. [ABSTRACT FROM AUTHOR]

This letter provides an estimate of the extent of error in self-reports of college major. A student's field of study is an important determinant of labour market outcomes and of increasing interest to labour economists, but little is known about the reliability of survey data on college major. A unique dataset from the United States with both transcript and survey data on major field of study suggests that the error rate of self-reported college major is almost 20%. Error rates are higher for relatively small or obscure majors, and are lower for larger majors or majors closely associated with licensed professions (e.g. health care). Although these error rates are not trivial, they are comparable to prior estimates of error in reporting educational attainment. [ABSTRACT FROM AUTHOR]

Sampling and reporting biases in the Medical Expenditure Panel Survey (MEPS) survey could render data on inpatient utilization that are not representative for individuals with severe psychiatric conditions. The authors assessed the representativeness of MEPS data on psychiatric inpatient utilization, by comparing MEPS estimates of total annual psychiatric and nonpsychiatric inpatient admissions and bed days, and mean length of stay, for nonelderly U.S. adults in calendar years 2005 to 2010 (N = 9,288) to estimates from the Nationwide Inpatient Sample (NIS), a nationally representative inpatient care database derived from hospitals' administrative records (N = 21,934,378). Compared with the NIS, the MEPS indicated 34% as many psychiatric admissions and 86% as many nonpsychiatric admissions, while mean psychiatric length of stay was greater in MEPS than in NIS. In MEPS data, underrepresentation of psychiatric inpatient utilization at community hospitals may result in measurement distortions for commonly used statistics on psychiatric inpatient utilization and costs. [ABSTRACT FROM AUTHOR]

Researchers frequently rely on survey responses to determine whether families receive government assistance and to study the effects of government programs, but these responses are often inaccurate. This study investigates misreporting in the child-care subsidy program by comparing survey responses on child-care subsidy receipt with program administrative data in two states. While we find a lower rate of misreporting than is typical for other government assistance programs, over-reporting of benefit receipt is surprisingly common and generates overestimates of program participation. Analyses further suggest that the frequency and systematic nature of misreporting bias estimates of the predictors of program receipt and the effects of the program. These findings illustrate the necessity of assessing the frequency of response errors and understanding their implications in generating valid research results on the effects o f government programs. [ABSTRACT FROM AUTHOR]

This paper examines what is known about error in the U.S. Consumer Expenditure Survey (CE). It reviews likely sources of error in consumer expenditure surveys and describes the various approaches that have been used to evaluate estimates from the CE in the United States and from similar surveys around the world. In the United States, many efforts to assess the quality of the CE have compared CE estimates to estimates from other surveys or from the National Income and Product Accounts (NIPA). In addition, other studies have used data internal to the CE in an attempt to assess the level of error. We review studies that take each of these approaches and assess their strengths and weaknesses. Our review shows that, as with other national and international surveys on household spending, the direction and magnitude of measurement errors in the CE are not well understood. The general impression that the CE expenditure estimates are dominated by underreporting is based largely on comparisons of the CE with NIPA estimates. However, other results suggest that the magnitude and direction of errors in the CE depend on the expenditure category examined, characteristics of the respondent and the survey protocol, and the method used to assess these errors. New research clearly is needed to understand the sources of error in surveys collecting consumer expenditure data. [ABSTRACT FROM AUTHOR]

Background: Previous studies on the impact of measurement error for PM 2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM 2.5 concentration may vary by time and region. We aim to correct the measurement error of PM 2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM 2.5 and mortality in the Medicare Cohort., Methods: The "gold-standard" measurements of PM 2.5 were defined as daily monitoring data. We regressed daily monitoring PM 2.5 on modeled PM 2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM 2.5 was calculated with stratum-specific calibration parameters β 0 (intercept) and β 1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM 2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models., Results: Across 208 strata, the median of β 0 and β 1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM 2.5 data, we estimated that each 10 μg/m 3 increase in PM 2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders., Conclusions: Regression calibration parameters of PM 2.5 varied by time and region. Using error-prone measures of PM 2.5 underestimated the association between PM 2.5 and all-cause mortality. Modern exposure models produce relatively small bias., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Total energy consumption and activity-related energy expenditure (AREE) estimates that have been calibrated using biomarkers to correct for measurement error were simultaneously associated with the risks of cardiovascular disease, cancer, and diabetes among postmenopausal women who were enrolled in the Women's Health Initiative at 40 US clinical centers and followed from 1994 to the present. Calibrated energy consumption was found to be positively related, and AREE inversely related, to the risks of various cardiovascular diseases, cancers, and diabetes. These associations were not evident in most corresponding analyses that did not correct for measurement error. However, an important analytical caveat relates to the role of body mass index (BMI) (weight (kg)/height (m)2). In the calibrated variable analyses, BMI was regarded, along with self-reported data, as a source of information on energy consumption and physical activity, and BMI was otherwise excluded from the disease risk models. This approach cannot be fully justified with available data, and the analyses herein imply a need for improved dietary and physical activity assessment methods and for longitudinal self-reported and biomarker data to test and relax modeling assumptions. Estimated hazard ratios for 20% increases in total energy consumption and AREE, respectively, were as follows: 1.49 (95% confidence interval: 1.18, 1.88) and 0.80 (95% confidence interval: 0.69, 0.92) for total cardiovascular disease; 1.43 (95% confidence interval: 1.17, 1.73) and 0.84 (95% confidence interval: 0.73, 0.96) for total invasive cancer; and 4.17 (95% confidence interval: 2.68, 6.49) and 0.60 (95% confidence interval: 0.44, 0.83) for diabetes. [ABSTRACT FROM PUBLISHER]

Forensic pathologists commonly use computed tomography (CT) images to assist in determining the cause and manner of death as well as for mass disaster operations. Even though the design of the CT machine does not inherently produce distortion, most techniques within anthropology rely on metric variables, thus concern exists regarding the accuracy of CT images reflecting an object's true dimensions. Numerous researchers have attempted to validate the use of CT images, however the comparisons have only been conducted on limited elements and/or comparisons were between measurements taken from a dry element and measurements taken from the 3D-CT image of the same dry element. A full-body CT scan was performed prior to autopsy at the Office of the Chief Medical Examiner for the State of Maryland. Following autopsy, the remains were processed to remove all soft tissues and the skeletal elements were subject to an additional CT scan. Percent differences and Bland-Altman plots were used to assess the accuracy between osteometric variables obtained from the dry skeletal elements and from CT images with and without soft tissues. An additional seven crania were scanned, measured by three observers, and the reliability was evaluated by technical error of measurement (TEM) and relative technical error of measurement (%TEM). Average percent differences between the measurements obtained from the three data sources ranged from 1.4% to 2.9%. Bland-Altman plots illustrated the two sets of measurements were generally within 2 mm for each comparison between data sources. Intra-observer TEM and %TEM for three observers and all craniometric variables ranged between 0.46 mm and 0.77 mm and 0.56% and 1.06%, respectively. The three-way inter-observer TEM and %TEM for craniometric variables was 2.6 mm and 2.26%, respectively. Variables that yielded high error rates were orbital height, orbital breadth, inter-orbital breadth and parietal chord. Overall, minimal differences were found among the data sources and high accuracy was noted between the observers, which prove CT images are an acceptable source to collect osteometric variables. [ABSTRACT FROM AUTHOR]

We used a biomarker of activity-related energy expenditure (AREE) to assess measurement properties of self-reported physical activity and to determine the usefulness of AREE regression calibration equations in the Women's Health Initiative. Biomarker AREE, calculated as the total energy expenditure from doubly labeled water minus the resting energy expenditure from indirect calorimetry, was assessed in 450 Women's Health Initiative participants (2007–2009). Self-reported AREE was obtained from the Arizona Activity Frequency Questionnaire (AAFQ), the 7-Day Physical Activity Recall (PAR), and the Women's Health Initiative Personal Habits Questionnaire (PHQ). Eighty-eight participants repeated the protocol 6 months later. Reporting error, measured as log(self-report AREE) minus log(biomarker AREE), was regressed on participant characteristics for each instrument. Body mass index was associated with underreporting on the AAFQ and PHQ but overreporting on PAR. Blacks and Hispanics underreported physical activity levels on the AAFQ and PAR, respectively. Underreporting decreased with age for the PAR and PHQ. Regressing logbiomarker AREE on logself-reported AREE revealed that self-report alone explained minimal biomarker variance (R2 = 7.6, 4.8, and 3.4 for AAFQ, PAR, and PHQ, respectively). R2 increased to 25.2, 21.5, and 21.8, respectively, when participant characteristics were included. Six-month repeatability data adjusted for temporal biomarker variation, improving R2 to 79.4, 67.8, and 68.7 for AAFQ, PAR, and PHQ, respectively. Calibration equations “recover” substantial variation in average AREE and valuably enhance AREE self-assessment. [ABSTRACT FROM PUBLISHER]

Examinees who initially fail and later repeat an SP-based clinical skills exam typically exhibit large score gains on their second attempt, suggesting the possibility that examinees were not well measured on one of those attempts. This study evaluates score precision for examinees who repeated an SP-based clinical skills test administered as part of the US Medical Licensing Examination sequence. Generalizability theory was used as the basis for computing conditional standard errors of measurement ( SEM) for individual examinees. Conditional SEMs were computed for approximately 60,000 single-take examinees and 5,000 repeat examinees who completed the Step 2 Clinical Skills Examination between 2007 and 2009. The study focused exclusively on ratings of communication and interpersonal skills. Conditional SEMs for single-take and repeat examinees were nearly indistinguishable across most of the score scale. US graduates and IMGs were measured with equal levels of precision at all score levels, as were examinees with differing levels of skill speaking English. There was no evidence that examinees with the largest score changes were measured poorly on either their first or second attempt. The large score increases for repeat examinees on this SP-based exam probably cannot be attributed to unexpectedly large errors of measurement. [ABSTRACT FROM AUTHOR]

With increased attention to administrative data for statistical purposes, analyses of the quality of administrative data and comparisons to survey data are greatly needed. This article presents a methodology for identifying sources of error in administrative and survey data and for identifying sources of differences between administrative and survey estimates. The first part of the methodology is a statistical decomposition of the difference between administrative and survey estimates. The second part investigates the causes of measurement error and the factors associated with differences between what the same respondents report in the survey and the administrative records. I illustrate this methodology using a case study of the monthly employment figures gathered from U.S. businesses. This analysis demonstrates that both administrative data and survey data may contain errors and that reporting procedures are likely to differ between the two types of data. The article also identifies practical ways to assess data quality. [ABSTRACT FROM AUTHOR]

With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1–12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America’s Table Study (1997–1998), the authors found that 4–6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2–4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4–6 administrations of the 24HR should be considered along with administration of an FFQ. [ABSTRACT FROM PUBLISHER]

Constructing spatial density maps of seismic events, such as earthquake hypocentres, is complicated by the fact that events are not located precisely. In this paper, we present a method for estimating density maps from event locations that are measured with error. The estimator is based on the simulation–extrapolation method of estimation and is appropriate for location errors that are either homoscedastic or heteroscedastic. A simulation study shows that the estimator outperforms the standard estimator of density that ignores location errors in the data, even when location errors are spatially dependent. We apply our method to construct an estimated density map of earthquake hypocenters using data from the Alaska earthquake catalogue. [ABSTRACT FROM AUTHOR]

The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature. [ABSTRACT FROM PUBLISHER]

The article presents a study which investigates the evidence for identifying the significance of exposure for U.S. multinationals from 1985-1997. The study uses three models of foreign exchange exposure based on a forward index, an index of unanticipated exchange rate changes, and a regional sales-based index. It notes that based on evidence, a high percentage of firms had significant exchange-rate exposure, and mentions strong results to the choice of model specification.

Abstract: Studies examining the wage effect of overeducation have generated very consistent results. Their findings suggest that, for workers with similar educational attainment, workers who are overeducated for the job suffer from significant wage penalties. However, most studies use cross-sectional data, implicitly assuming that workers are randomly assigned to being overeducated. Using data from the Panel Study of Income Dynamics for the period 1979–2005, this study conducts a panel analysis to account for time-constant individual characteristics. It uses a numerical approach to provide the wage effects in the presence of non-classical measurement error in the educational mismatch variables. The results provide evidence that overeducated status does not cause lower earnings. Instead, the significant wage differential found in previous studies is simply a result of ignoring the non-random assignment of workers to jobs. [Copyright &y& Elsevier]

This paper addresses a key methodological challenge in the modeling of individual poverty dynamics—the influence of measurement error. Taking the US and Britain as case studies and building on recent research that uses latent Markov models to reduce bias, we examine how measurement error can affect a range of important poverty estimates. Our data are taken from the British Household Panel Survey and the US Panel Study of Income Dynamics, for working-aged adults over the period 1993–2003. For both national samples we ask how common vulnerability to poverty was over the period in question, what the entry and exit probabilities were for the group likely to transition into or out of poverty, and how effective redistributive programs were at protecting those most at risk. Crucially, in answering these questions we estimate and remove the effects of error in the measurement of poverty status. Throughout, we compare our results with estimates that do not take this error into account, and assess the implications for understanding poverty dynamics both within and between the two countries. Our modeling strategy extends previous research in several respects, enabling us to make stronger statements about measurement error and individual poverty dynamics. We find that correcting for error affects conclusions in important ways: Poverty is less temporary and risks are less widely dispersed than otherwise assumed, while cross-national differences are more pronounced. [ABSTRACT FROM AUTHOR]

Prior research has shown the prevalence of measurement error in models used to estimate aggregate discretionary accruals. In these models, the incremental information content of the various components of accruals is ignored. Limited prior research and data gathered from firms under Securities and Exchange Commission (SEC) litigation indicate that managers use either one or more than one component of accruals simultaneously, in a consistent way to manipulate bottom-line earnings in a given direction. I propose two measures that capture the consistency between the discretionary components of accruals and test their significance in earnings management (EM) detection in firms that have artificially added accrual manipulation and firms that were targeted by the SEC for accrual manipulation. There is evidence that this information is incrementally useful in detecting EM. This finding paves the way for improvements in the discretionary accruals measure by including consistency information from the components of aggregate accruals. [ABSTRACT FROM AUTHOR]

In the United States, the racial and ethnic statistics published by the National Center for Health Statistics (NCHS) assume that each member of the U.S. population has a race and ethnicity and that if a member is black or white with respect to his risk of one disease, he is the same race with respect to his risk of another. Such an assumption is mistaken. Race and ethnicity are taken by the NCHS to be an intrinsic property of members of a population, when they should be taken to depend on interest. The actual or underlying race or ethnicity of members of a population depends on the risk whose variation within the population we wish to describe or explain. [ABSTRACT FROM AUTHOR]

Ethnicity is a labile construct in the United States, and the combination of complex backgrounds and low saliency contributes to variability in reporting. This is demonstrated in an experiment of the 2004 General Social Survey in which a computer-assisted personal interview-based version of a traditional paper-and-pencil interview-based item on ethnicity produced many more mentions of ethnicities. [ABSTRACT FROM AUTHOR]