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Plan and Operations of the National Health and Nutrition Examination Survey, August 2021-August 2023. 2021 年 8 月至 2023 年 8 月全国健康与营养状况调查的计划和运作。
Ana L Terry, Michele M Chiappa, Juliana McAllister, David A Woodwell, Jessica E Graber

The continuous National Health and Nutrition Examination Survey began data collection in 1999 and proceeded without interruption until operations were suspended in March 2020 in response to the COVID-19 pandemic. Once the Division of Health and Nutrition Examination Surveys was able to determine and resume safe field operations, the next survey cycle was conducted between August 2021 and August 2023. This report describes the survey content, procedures, and methodologies implemented in the August 2021-August 2023 National Health and Nutrition Examination Survey cycle.

连续性的全国健康与营养状况调查于 1999 年开始收集数据,一直持续到 2020 年 3 月因 COVID-19 大流行而暂停。一旦健康与营养检查调查司能够确定并恢复安全的现场操作,下一个调查周期将在 2021 年 8 月至 2023 年 8 月期间进行。本报告介绍了 2021 年 8 月至 2023 年 8 月全国健康与营养状况调查周期中实施的调查内容、程序和方法。
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引用次数: 0
Developing Sampling Weights for Statistical Analysis of Parent-Child Pair Data From the National Health Interview Survey. 为对全国健康访谈调查中的亲子配对数据进行统计分析开发抽样权重。
Guangyu Zhang, Yulei He, Van Parsons, Chris Moriarity, Stephen J Blumberg, Benjamin Zablotsky, Aaron Maitland, Matthew D Bramlett, Jonaki Bose

The National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics since 1957, is the principal source of information on the health of the U.S. civilian noninstitutionalized population. NHIS selects one adult (Sample Adult) and, when applicable, one child (Sample Child) randomly within a family (through 2018) or a household (2019 and forward). Sampling weights for the separate analysis of data from Sample Adults and Sample Children are provided annually by the National Center for Health Statistics. A growing interest in analysis of parent-child pair data using NHIS has been observed, which necessitated the development of appropriate analytic weights. Objective This report explains how dyad weights were created such that data users can analyze NHIS data from both Sample Children and their mothers or fathers, respectively. Methods Using data from the 2019 NHIS, adult-child pair-level sampling weights were developed by combining each pair's conditional selection probability with their household-level sampling weight. The calculated pair weights were then adjusted for pair-level nonresponse, and large sampling weights were trimmed at the 99th percentile of the derived sampling weights. Examples of analyzing parent-child pair data by means of domain estimation methods (that is, statistical analysis for subpopulations or subgroups) are included in this report. Conclusions The National Center for Health Statistics has created dyad or pair weights that can be used for studies using parent-child pairs in NHIS. This method could potentially be adapted to other surveys with similar sampling design and statistical needs.

美国国家卫生统计中心(National Center for Health Statistics)自 1957 年起开展的全国健康访谈调查(National Health Interview Survey,NHIS)是有关美国非住院平民健康状况的主要信息来源。NHIS 在一个家庭(至 2018 年)或一个住户(2019 年及以后)中随机抽取一名成人(成人样本),并在适用情况下抽取一名儿童(儿童样本)。国家卫生统计中心每年都会提供用于分别分析成人样本和儿童样本数据的抽样权重。人们对使用 NHIS 分析亲子配对数据的兴趣日益浓厚,因此有必要制定适当的分析权重。本报告解释了如何创建配对权重,以便数据用户能够分析样本儿童及其母亲或父亲的 NHIS 数据。方法 利用 2019 年 NHIS 的数据,通过将每对样本的条件选择概率与其家庭层面的抽样权重相结合,建立成人-儿童配对层面的抽样权重。然后对计算出的配对权重进行配对级非响应调整,并在得出的抽样权重的第 99 个百分位数处对大抽样权重进行修剪。本报告中包含了通过领域估计方法(即针对亚群或分组的统计分析)分析亲子配对数据的示例。结论 美国国家卫生统计中心已建立了配对权重,可用于使用 NHIS 中的亲子配对数据进行研究。这种方法有可能适用于具有类似抽样设计和统计需求的其他调查。
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引用次数: 0
Assessing Laboratory Method Validations for Informing Inference Across Survey Cycles in the National Health and Nutrition Examination Survey. 评估实验室方法验证,为全国健康与营养调查中跨调查周期的推论提供依据。
Kevin Chuang, Jennifer Rammon, Hee-Choon Shin, Te-Ching Chen

Background and objectives Laboratory tests conducted on survey respondents' biological specimens are a major component of the National Health and Nutrition Examination Survey. The National Center for Health Statistics' Division of Health and Nutrition Examination Surveys performs internal analytic method validation studies whenever laboratories undergo instrumental or methodological changes, or when contract laboratories change. These studies assess agreement between methods to evaluate how methodological changes could affect data inference or compromise consistency of measurements across survey cycles. When systematic differences between methods are observed, adjustment equations are released with the data documentation for analysts planning to combine survey cycles or conduct a trend analysis. Adjustment equations help ensure that observed differences from methodological changes are not misinterpreted as population changes. This report assesses the reliability of statistical methods used by the Division of Health and Nutrition Examination Surveys when conducting method validation studies to address concerns that adjustment equations are being overproduced (recommended too frequently). Methods Public-use 2017-2018 National Health and Nutrition Examination Survey laboratory data were used to simulate "new" measurements for 120 analytic method validation studies. Blinded studies were analyzed to determine the final adjustment recommendation for each study using difference plots, descriptive statistics, t-tests, and Deming regressions. Final recommendations were compared with simulated difference types to assess how often spurious results were observed. Concordance estimates (concordance, misclassification, sensitivity, specificity, and positive and negative predictive values) informed assessments. Results Adjustment equations were appropriately recommended for 75.0% of the studies, over-recommended for 5.8%, under-recommended for 15.8%, and recommended with an inappropriate technique for 3.3%. Across simulated difference types, sensitivity ranged from 65.9% to 84.4% and specificity from 74.7% to 97.5%. Conclusions Findings from this report suggest that the current methodology used by the Division of Health and Nutrition Examination Surveys performs moderately well. Based on these data and analyses, underadjustment was more prevalent than overadjustment, suggesting that the current methodology is conservative.

背景和目标 对调查对象的生物标本进行实验室检测是全国健康与营养状况调查的主要组成部分。每当实验室在仪器或方法上发生变化,或者合同实验室发生变化时,国家卫生统计中心的健康与营养检查调查处都会进行内部分析方法验证研究。这些研究对各种方法之间的一致性进行评估,以评价方法的变化会如何影响数据推断或损害各调查周期测量的一致性。当观察到方法之间存在系统性差异时,将随数据文档发布调整方程,供计划合并调查周期或进行趋势分析的分析人员使用。调整方程有助于确保观察到的方法变化差异不会被误解为人口变化。本报告评估了健康与营养检查调查司在进行方法验证研究时所使用的统计方法的可靠性,以解决人们对调整方程制作过多(推荐频率过高)的担忧。方法 在 120 项分析方法验证研究中,使用 2017-2018 年国家健康与营养检查调查实验室公共使用数据来模拟 "新 "测量。使用差异图、描述性统计、t 检验和戴明回归对盲法研究进行分析,以确定每项研究的最终调整建议。将最终建议与模拟差异类型进行比较,以评估观察到虚假结果的频率。一致性估计值(一致性、误分类、灵敏度、特异性以及阳性和阴性预测值)为评估提供了依据。结果 75.0%的研究推荐了适当的调整方程,5.8%的研究推荐过度,15.8%的研究推荐不足,3.3%的研究推荐了不适当的技术。在所有模拟差异类型中,灵敏度从 65.9% 到 84.4% 不等,特异性从 74.7% 到 97.5%。结论 本报告的研究结果表明,健康与营养状况调查部目前使用的方法效果一般。根据这些数据和分析,调整不足比调整过度更为普遍,这表明目前的方法是保守的。
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引用次数: 0
Validation of the Enhanced Opioid Identification and Co-occurring Disorders Algorithms. 强化阿片类药物识别和共存障碍算法的验证。
Amy M Brown, Donielle G White, Nikki B Adams, Rihem Rihem PharmD, Salah Shaikh, Lello Guluma

Objectives This report documents the results of a validation study conducted to assess the reliability of two algorithms applied to the 2016 National Hospital Care Survey. One algorithm identifies opioid-involved and opioid overdose hospital encounters, and the other identifies encounters with patients that have substance use disorders and selected mental health issues. These algorithms use both medical codes and natural language processing to identify encounters. Methods To validate the algorithms, medical record abstraction was performed on a stratified sample of 900 hospital encounters from the 2016 National Hospital Care Survey. The abstractors recorded their determinations of opioid involvement, opioid overdose, substance use disorder, and mental health issues on a standard form. Abstractors' determinations were compared with algorithm output to assess the overall performance using F-score and Matthews correlation coefficient. The latter provided a secondary measure of performance. The 2016 National Hospital Care Survey data are unweighted and not nationally representative. Results Overall algorithm performance varied by topic and by metric. The opioid-involvement algorithm achieved the highest performance, performing well with an F-score of 0.95, followed by the substance use disorder algorithm (F-score of 0.79), the mental health issues algorithm (F-score of 0.68), and the opioid overdose algorithm (F-score of 0.48). Assessment by Matthews correlation coefficient indicated an overall poorer level of performance, ranging from a high of 0.57 for the mental health issues algorithm to a low of 0.33 for the opioid-involvement algorithm. The causes of false positives and false negatives likewise varied, including both overly broad code and keyword inclusions as well as incompleteness of data submitted to the National Hospital Care Survey. Conclusion The validation study illustrates which aspects of the developed algorithms performed well and which aspects should be altered or discarded in future iterations. It further emphasizes the importance of data completeness, therefore laying the groundwork for improvements to future survey analyses.

目的 本报告记录了一项验证研究的结果,该研究旨在评估应用于 2016 年全国医院护理调查的两种算法的可靠性。一种算法可识别涉及阿片类药物和阿片类药物过量的医院就诊情况,另一种算法可识别患有药物使用障碍和特定精神健康问题的患者的就诊情况。这些算法使用医疗代码和自然语言处理来识别就诊情况。方法 为了验证这些算法,我们对 2016 年全国医院护理调查中的 900 个医院就诊病例进行了分层抽样。摘要员在标准表格上记录了他们对阿片类药物参与、阿片类药物过量、药物使用障碍和精神健康问题的判断。摘要员的判断结果与算法输出结果进行比较,使用 F 分数和马修斯相关系数评估总体性能。后者是衡量性能的次要指标。2016 年全国医院护理调查数据未经加权,不具有全国代表性。结果 算法的总体性能因主题和指标而异。阿片类药物介入算法的性能最高,F 值为 0.95,表现良好,其次是药物使用障碍算法(F 值为 0.79)、心理健康问题算法(F 值为 0.68)和阿片类药物过量算法(F 值为 0.48)。通过马修斯相关系数进行的评估表明,总体性能水平较差,精神健康问题算法的最高值为 0.57,而阿片类药物过量算法的最低值为 0.33。造成假阳性和假阴性的原因同样各不相同,包括代码和关键词包含范围过广,以及提交给全国医院护理调查的数据不完整。结论 验证研究说明了所开发算法的哪些方面表现良好,哪些方面应在今后的迭代中进行修改或摒弃。它进一步强调了数据完整性的重要性,从而为改进未来的调查分析奠定了基础。
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引用次数: 0
National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3. 国家卫生统计中心2019年研究与发展调查,RANDS 3。
Li-Yen R Hu, Paul Scanlon, Kristen Miller, Yulei He, Katherine E Irimata, Guangyu Zhang, Kristen Cibelli Hibben

Objective This report on the third round of the Research and Development Survey (RANDS 3) provides a general description of RANDS 3 and presents percentage estimates of selected demographic and health-related variables from the overall sample and by one set of experimental groups embedded in the survey. Statistical tests comparing estimates for the two randomized groups were conducted to evaluate the randomization. Methods NORC at the University of Chicago conducted RANDS 3 for the National Center of Health Statistics in 2019 using its AmeriSpeak Panel in web-only mode. To assess question-response patterns, probe questions and four sets of experiments were embedded in RANDS 3, with panelists randomized into two groups for each set of experiments. Participants in each group received questions with differences in wording, question-andresponse formats, or question order. Results Of the 4,255 people sampled, 2,646 completed RANDS 3 for a completion rate of 62.2% and a weighted cumulative response rate of 18.1%. Iterative raking was performed using demographic and selected health condition variables to calibrate the RANDS 3 sample to 2019 National Health Interview Survey (NHIS) estimates. As a result, the overall demographic distribution and percentages of asthma, diabetes, hypertension, and high cholesterol for the calibrated RANDS 3 sample aligned with the percentages estimated from the 2019 NHIS. The distributions of demographic and healthrelated variables were comparable between the two randomized groups examined except for ever-diagnosed hypertension. Conclusion As part of a research series using probability-based survey panels, RANDS 3 included health-related questions with a focus on disability and opioids. Because RANDS is an ongoing research platform, a variety of persistent and emergent research questions relating to survey methodology will continue to be examined in current and future rounds of RANDS.

目的第三轮研究与发展调查(RANDS 3)的报告提供了对RANDS 3的一般描述,并提出了从总体样本和调查中的一组实验组中选择的人口统计学和健康相关变量的百分比估计。对两个随机化组的估计值进行了比较统计测试,以评估随机化。方法芝加哥大学的NORC在2019年使用其AmeriSpeak Panel在纯网络模式下为国家卫生统计中心进行了RANDS 3。为了评估问题-反应模式,在RANDS 3中嵌入了探究问题和四组实验,每组实验的小组成员随机分为两组。每组参与者收到的问题在措辞、问答形式或问题顺序上存在差异。结果在4255名样本中,2646人完成了RANDS 3,完成率为62.2%,加权累积有效率为18.1%。使用人口统计学和选定的健康状况变量进行迭代耙取,以将RANDS 3样本校准为2019年国家健康访谈调查(NHIS)的估计值。因此,校准后的RANDS 3样本的哮喘、糖尿病、高血压和高胆固醇的总体人口分布和百分比与2019年NHIS估计的百分比一致。人口统计学和健康相关变量的分布在两个随机对照组之间具有可比性,但从未诊断出高血压除外。结论作为使用基于概率的调查小组的一系列研究的一部分,RANDS 3纳入了与健康相关的问题,重点是残疾和阿片类药物。由于兰德公司是一个正在进行的研究平台,与调查方法有关的各种持续和突发的研究问题将在当前和未来几轮兰德公司中继续进行审查。
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引用次数: 0
National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3. 国家卫生统计中心2019年研究与发展调查,RANDS 3。
Q3 Medicine Pub Date : 2023-09-01 DOI: 10.15620/cdc:130273
Li-Yen R. Hu, P. Scanlon, Kristen Miller, Yulei He, Katherine E. Irimata, Guangyu Zhang, Kristen Cibelli Hibben
Objective This report on the third round of the Research and Development Survey (RANDS 3) provides a general description of RANDS 3 and presents percentage estimates of selected demographic and health-related variables from the overall sample and by one set of experimental groups embedded in the survey. Statistical tests comparing estimates for the two randomized groups were conducted to evaluate the randomization. Methods NORC at the University of Chicago conducted RANDS 3 for the National Center of Health Statistics in 2019 using its AmeriSpeak Panel in web-only mode. To assess question-response patterns, probe questions and four sets of experiments were embedded in RANDS 3, with panelists randomized into two groups for each set of experiments. Participants in each group received questions with differences in wording, question-andresponse formats, or question order. Results Of the 4,255 people sampled, 2,646 completed RANDS 3 for a completion rate of 62.2% and a weighted cumulative response rate of 18.1%. Iterative raking was performed using demographic and selected health condition variables to calibrate the RANDS 3 sample to 2019 National Health Interview Survey (NHIS) estimates. As a result, the overall demographic distribution and percentages of asthma, diabetes, hypertension, and high cholesterol for the calibrated RANDS 3 sample aligned with the percentages estimated from the 2019 NHIS. The distributions of demographic and healthrelated variables were comparable between the two randomized groups examined except for ever-diagnosed hypertension. Conclusion As part of a research series using probability-based survey panels, RANDS 3 included health-related questions with a focus on disability and opioids. Because RANDS is an ongoing research platform, a variety of persistent and emergent research questions relating to survey methodology will continue to be examined in current and future rounds of RANDS.
目的第三轮研究与发展调查(RANDS 3)的报告提供了对RANDS 3的一般描述,并提出了从总体样本和调查中的一组实验组中选择的人口统计学和健康相关变量的百分比估计。对两个随机化组的估计值进行了比较统计测试,以评估随机化。方法芝加哥大学的NORC在2019年使用其AmeriSpeak Panel在纯网络模式下为国家卫生统计中心进行了RANDS 3。为了评估问题-反应模式,在RANDS 3中嵌入了探究问题和四组实验,每组实验的小组成员随机分为两组。每组参与者收到的问题在措辞、问答形式或问题顺序上存在差异。结果在4255名样本中,2646人完成了RANDS 3,完成率为62.2%,加权累积有效率为18.1%。使用人口统计学和选定的健康状况变量进行迭代耙取,以将RANDS 3样本校准为2019年国家健康访谈调查(NHIS)的估计值。因此,校准后的RANDS 3样本的哮喘、糖尿病、高血压和高胆固醇的总体人口分布和百分比与2019年NHIS估计的百分比一致。人口统计学和健康相关变量的分布在两个随机对照组之间具有可比性,但从未诊断出高血压除外。结论作为使用基于概率的调查小组的一系列研究的一部分,RANDS 3纳入了与健康相关的问题,重点是残疾和阿片类药物。由于兰德公司是一个正在进行的研究平台,与调查方法有关的各种持续和突发的研究问题将在当前和未来几轮兰德公司中继续进行审查。
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引用次数: 1
The 2021 Physician Pain Management Questionnaire Pilot Study. 2021年医师疼痛管理问卷试点研究。
Doreen M Gidali, Brian W Ward

This report outlines the methodology, development, and fielding of the 2021 Physician Pain Management Questionnaire (PPMQ) pilot study. The study was conducted by the National Center for Health Statistics and was designed to test the feasibility of a large, nationally representative survey assessing physician awareness and use of established guidelines for prescribing opioids to manage pain.

本报告概述了2021年医师疼痛管理问卷(PPMQ)试点研究的方法、开发和应用。这项研究由国家卫生统计中心进行,旨在测试一项具有全国代表性的大型调查的可行性,该调查评估医生对阿片类药物处方管理疼痛的认识和使用情况。
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引用次数: 0
Sampling Procedures for the Collection of Electronic Health Record Data From Federally Qualified Health Centers, 2021-2022 National Ambulatory Medical Care Survey. 2021-2022年全国门诊医疗调查中联邦合格医疗中心电子健康记录数据收集的抽样程序。
Sonja N Williams, Joy Ukaigwe, Brian W Ward, Titilayo Okeyode, Iris M Shimizu

As part of modernization efforts, in 2021 the National Ambulatory Medical Care Survey (NAMCS) began collecting electronic health records (EHRs) for ambulatory care visits in its Health Center (HC) Component. As a result, the National Center for Health Statistics (NCHS)needed to adjust the approaches used in the sampling design for the HC Component. This report provides details on these changes to the 2021-2022 NAMCS.

作为现代化工作的一部分,2021年,国家门诊医疗调查(NAMCS)开始在其健康中心(HC)部分收集门诊就诊的电子健康记录(EHRs)。因此,国家卫生统计中心(NCHS)需要调整HC组成部分抽样设计中使用的方法。本报告详细介绍了2021-2022年NAMCS的这些变化。
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引用次数: 0
Calibration Weighting Methods for the National Center for Health Statistics Research and Development Survey. 国家卫生统计研究与发展调查中心校正加权方法。
Q3 Medicine Pub Date : 2023-03-01 DOI: 10.15620/cdc:123463
Katherine E. Irimata, Yulei He, V. Parsons, Hee-Choon Shin, Guangyu Zhang
Objectives The Research and Development Survey (RANDS) is a series of web-based, commercial panel surveys that have been conducted by the National Center for Health Statistics (NCHS) since 2015. RANDS was designed for methodological research purposes,including supplementing NCHS' evaluation of surveys and questionnaires to detect measurement error, and exploring methods to integrate data from commercial survey panels with high-quality data collections to improve survey estimation. The latter goal of improving survey estimation is in response to limitations of web surveys, including coverage and nonresponse bias. To address the potential bias in estimates from RANDS,NCHS has investigated various calibration weighting methods to adjust the RANDS panel weights using one of NCHS' national household surveys, the National Health Interview Survey. This report describes calibration weighting methods and the approaches used to calibrate weights in web-based panel surveys at NCHS.
研究与发展调查(rand)是自2015年以来由国家卫生统计中心(NCHS)进行的一系列基于网络的商业小组调查。rand的设计是为了方法学研究的目的,包括补充NCHS对调查和问卷的评估,以发现测量误差,并探索将商业调查面板数据与高质量数据收集相结合的方法,以改进调查估计。改进调查估计的后一个目标是回应网络调查的局限性,包括覆盖面和非回应偏差。为了解决rand估计的潜在偏差,NCHS研究了各种校准加权方法,以调整rand面板权重,使用NCHS的全国家庭调查之一,即全国健康访谈调查。本报告描述了校准加权方法和用于校准NCHS基于网络的小组调查中的权重的方法。
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引用次数: 0
Evaluation of the National Center for Health Statistics Data Presentation Standards for Rates From Vital Statistics and Sample Surveys. 评估国家卫生统计中心关于生命统计和抽样调查比率的数据呈现标准。
Q3 Medicine Pub Date : 2023-03-01 DOI: 10.15620/cdc:123462
M. Talih, Katherine E. Irimata, Guangyu Zhang, J. Parker
For the CIs used in the Standards for rates from vital statistics and complex health surveys, this report evaluates coverage probability, relative width, and the resulting percentage of rates flagged as statistically unreliable when compared with previously used standards. Additionally, the report assesses the impact of design effects and the denominator's sampling variability, when applicable.
对于生命统计和复杂健康调查费率标准中使用的CI,与以前使用的标准相比,本报告评估覆盖概率、相对宽度以及标记为统计不可靠的费率的百分比。此外,该报告评估了设计效应的影响和分母的抽样可变性(如适用)。
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引用次数: 0
期刊
Vital and health statistics. Ser. 1: Programs and collection procedures
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