Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstructed records can differ from their confidential source on even a single value for these five variables. Using only published data, an attacker can verify that all records in 70% of all census blocks (97 million people) are perfectly reconstructed. The tabular publications in Summary File 1 thus have prohibited disclosure risk similar to the unreleased confidential microdata. Reidentification studies confirm that an attacker can, within blocks with perfect reconstruction accuracy, correctly infer the actual census response on race and ethnicity for 3.4 million vulnerable population uniques (persons with nonmodal characteristics) with 95% accuracy, the same precision as the confidential data achieve and far greater than statistical baselines. The flaw in the 2010 Census framework was the assumption that aggregation prevented accurate microdata reconstruction, justifying weaker disclosure limitation methods than were applied to 2010 Census public microdata. The framework used for 2020 Census publications defends against attacks that are based on reconstruction, as we also demonstrate here. Finally, we show that alternatives to the 2020 Census Disclosure Avoidance System with similar accuracy (enhanced swapping) also fail to protect confidentiality, and those that partially defend against reconstruction attacks (incomplete suppression implementations) destroy the primary statutory use case: data for redistricting all legislatures in the country in compliance with the 1965 Voting Rights Act.
IMPORTANCEThe association between body mass index (BMI, which is calculated as weight in kilograms divided by height in meters squared) and severe maternal morbidity (SMM) and/or mortality is uncertain, judging from the current evidence. OBJECTIVETo examine the association between prepregnancy BMI and SMM and/or mortality through 1 year post partum and to identify both the direct and indirect implications of maternal obesity for SMM and/or mortality by examining hypertensive disorders and pregestational diabetes as potential mediators. DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study was conducted fromMarch to October 2021 using the vital records and linked Medicaid claims data in the state of Ohio from January 1, 2012, through December 31, 2017. The cohort comprised pregnant Medicaid beneficiaries who delivered at 20 weeks' gestation or later and had prepregnancy BMI information.EXPOSURES The primary exposure was maternal prepregnancy BMI, which was categorized as follows: underweight (<18.5), healthy weight (18.5-24.9), overweight (25.0-29.9), class 1 obesity (30.0-34.9), class 2 obesity (35.0-39.9), and class 3 obesity (Ն40.0). MAIN OUTCOMES AND MEASURESThe primary outcome was a composite of SMM (defined using Centers for Disease Control and Prevention criteria) and/or maternal mortality between 20 weeks' gestation and 1 year post partum. Additional periods were assessed, including 20 weeks' gestation through delivery hospitalization and 20 weeks' gestation through 42 days post partum. Generalized estimating equation models were used to estimate adjusted relative risks (aRRs) for the primary outcome according to BMI category. Maternal hypertensive diseases and pregestational diabetes were assessed as potential meditators. RESULTS In a cohort of 347 497 pregnancies among 276 691 Medicaid beneficiaries (median [IQR] maternal age at delivery, 25 [21-29] years; 210 470 non-Hispanic White individuals [60.6%]), the prevalence of maternal obesity was 30.5% (n = 106 031). Composite SMM and/or mortality outcome
In observational studies, propensity score methods are popular for estimating causal effects. With completely observed data, this approach is valid under several assumptions; however, in practice data are often missing which can have a substantial impact on the estimation. Current remedies to deal with missing covariates in propensity score methods generally fall into two categories. Some authors propose to account for the missing data patterns in propensity score estimation. Others propose to first impute the missing data, then utilize conventional propensity score adjustment methods. Both approaches assume that the data are missing at random (MAR), and there is little discussion regarding the impact on treatment effect estimation if covariates are missing not at random (MNAR). In this paper, we first examine the implication of the MAR assumption under the potential outcome framework. We then propose a sensitivity analysis method for assessing the impact of a MNAR covariate on treatment effect estimation with a matching estimator, with varying magnitudes of unmeasured confounding effect due to the missing covariate. Our method takes full advantage of the information contained in the partially missing covariate by matching on the observed portion and identifying a bounding distribution for the missing portion. It can be interpreted similarly as Rosenbaum's sensitivity analysis, and the results are robust since we make few parametric assumptions. We illustrate the application of the method using the 2012 Ohio Medicaid Assessment Survey (OMAS) to investigate the effect of health insurance on health outcomes, where an important covariate, household income, is partially missing.Keywords: Propensity score; Matching; Not Missing At Random; Sensitivity Analysis This report is released to inform interested parties of (ongoing) research and to encourage discussion (of work in progress.) The views expressed are those of the authors and not necessarily those of the U.S. Census Bureau.
The notion of respondent contact burden in sample surveys is defined, and a multi-stage process to develop policies for curtailing nonresponse follow-up is described with the goal of reducing this burden on prospective survey respondents. The method depends on contact history paradata containing information about contact attempts both for respondents and for sampled nonrespondents. By analysis of past data, policies to stop case follow-up based on control variables measured in paradata can be developed by calculating propensities to respond for paradata-defined subgroups of sampled cases. Competing policies can be assessed by comparing outcomes (lost interviews, numbers of contacts, patterns of reluctant participation, or refusal to participate) as if these stopping policies had been followed in past data. Finally, embedded survey experiments may be used to assess contact-burden reduction policies when these are implemented in the field. The multi-stage method described here abstracts the stages followed in a series of research studies aimed at reducing contact burden in the Computer Assisted Telephone Interview (CATI) and Computer Assisted Personal Interview (CAPI) modes of the American Community Survey (ACS), which culminated in implementation of policy changes in the ACS.
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