An in-depth examination of requirements for disclosure risk assessment.

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

Establishing a clinical informatics umbilical cord: lessons learned in launching infrastructure to support dyadic mother/infant primary care.

The Multimodal Maternal Infant Perinatal Outpatient Delivery System (MOMI PODS) was developed to facilitate the pregnancy to postpartum primary care transition, particularly for individuals at risk for severe maternal morbidity, via a unique multidisciplinary model of mother/infant dyadic primary care. Specialized clinical informatics platforms are critical to ensuring the feasibility and scalability of MOMI PODS and a smooth perinatal transition into longitudinal postpartum primary care. In this manuscript, we describe the MOMI PODS transition and management clinical informatics platforms developed to facilitate MOMI PODS referrals, scheduling, evidence-based multidisciplinary care, and program evaluation. We discuss opportunities and lessons learned associated with our applied methods, as advances in clinical informatics have considerable potential to enhance the quality and evaluation of innovative maternal health programs like MOMI PODS.

A Prediction Model for Severe Maternal Morbidity and Mortality After Delivery Hospitalization.

OBJECTIVE: To develop a risk stratification model for severe maternal morbidity (SMM) or mortality after the delivery hospitalization based on information available at the time of hospital discharge. METHODS: This population-based cohort study included all pregnancies among Ohio residents with Medicaid insurance from 2012 to 2017. Pregnant individuals were identified using linked live birth and fetal death records and Medicaid claims data. Inclusion was restricted to those with continuous postpartum Medicaid enrollment and delivery at 20 or more weeks of gestation. The primary outcome of the study was SMM or mortality after the delivery hospitalization and was assessed up to 42 days postpartum and up to 1 year postpartum separately. Variables considered for the model included patient-, obstetric health care professional-, and system-level data available in vital records or Medicaid claims data. Parsimonious models were created with logistic regression and were internally validated. Receiver operating characteristic curves were used to evaluate model performance, and model calibration was assessed. RESULTS: There were 343,842 pregnant individuals who met inclusion criteria with continuous Medicaid enrollment through 42 days postpartum and 287,513 with continuous enrollment through 1 year. After delivery hospitalization discharge, the incidence of SMM or mortality was 140.5 per 10,000 pregnancies through 42 days of delivery and 330.7 per 10,000 pregnancies through 1 year postpartum. The final model predicting SMM or mortality through 42 days postpartum included maternal prepregnancy body mass index, age, gestational age at delivery, mode of delivery, chorioamnionitis, and maternal diagnosis of cardiac disease, preeclampsia or gestational hypertension, or a mental health condition. Similar variables were included in the model predicting SMM or mortality through 365 days with chronic hypertension, pregestational diabetes, and illicit substance use added and chorioamnionitis removed. Both models demonstrated moderate prediction (area under the curve [AUC] 0.77, 95% CI 0.76-0.78 for 42-day model; AUC 0.72, 95% CI 0.71-0.73 for the 1-year model) and good calibration. CONCLUSION: A prediction model for SMM or mortality up to 1 year postpartum was created and internally validated with information available to health care professionals at the time of hospital discharge. The utility of this model for patient counseling and strategies to optimize postpartum care for high-risk individuals will require further evaluation.

Association of Prepregnancy Body Mass Index With Risk of Severe Maternal Morbidity and Mortality Among Medicaid Beneficiaries.

Importance: The 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. Objective: To 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 from March 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 (≥40.0). Main Outcomes and Measures: The 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 occurred in 5.3% of pregnancies (n = 18 398). Overweight (aRR, 1.07; 95% CI, 1.03-1.11) and obesity (class 1: aRR, 1.19 [95% CI, 1.14-1.24]; class 2: aRR, 1.37 [95% CI, 1.30-1.44]; class 3: aRR, 1.71 [95% CI, 1.63-1.80]) were associated with an elevated risk of SMM and/or mortality during pregnancy to 1 year post partum compared with healthy BMI. Similar findings were observed when the follow-up period was shortened to 42 days post partum or the delivery hospitalization. Hypertension mediated 65.1% (95% CI, 64.6%-65.6%) of the association between obesity and the primary outcome. Conclusions and Relevance: Results of this study showed that maternal prepregnancy obesity was associated with an elevated risk of SMM and/or mortality. Hypertensive disorders appeared to mediate this association, suggesting that improved prevention and management of hypertensive disorders in pregnancy may reduce morbidity and mortality in individuals with obesity.

Examining differences in retention on medication for opioid use disorder: An analysis of Ohio Medicaid data.

BACKGROUND: Medications for opioid use disorder (MOUDs), including methadone, buprenorphine and naltrexone, are associated with lower death rates and improved quality of life for people in recovery from opioid use disorder (OUD). Less is known about each MOUD modality's association with treatment retention and the contribution of behavioral health therapy (BHT). The objectives of the current study were to estimate the association between MOUD type and treatment retention and determine whether BHT was associated with length of time retained. METHODS: We investigated the time from initiation to discontinuation from MOUD by medication type and exposure to BHT using statewide Medicaid Claims data (N = 81,752). We estimated covariate adjusted hazard ratios (AHR) using a Cox proportional hazards model. RESULTS: Compared to methadone, buprenorphine was associated with a higher risk of discontinuation at the time of initiation (AHR = 2.41, 95% CI = 2.28-2.55), however that difference decreased over one year of maintained retention (AHR = 1.44, 95% CI = 1.37-1.50). Compared to methadone and buprenorphine, naltrexone was associated with a higher risk of discontinuation at the time of initiation (naltrexone vs. methadone AHR = 2.49, 95% CI = 2.30-2.65; naltrexone vs. buprenorphine AHR 1.03, 95% CI = 1.00-1.07), and that relative risk increased over the course of one year of retention (naltrexone vs. methadone AHR = 3.85, 95% CI = 3.63-4.09; naltrexone vs. buprenorphine AHR = 2.67, 95% CI = 2.54-2.81). In general, independent of MOUD type, exposure to BHT during MOUD treatment was associated with a lower risk of discontinuation (AHR = 0.94, 95% CI = 0.92-0.96). However, BHT during the treatment episode was not associated with retention in the adolescent/young adult and pregnant women subpopulations. DISCUSSION: From the standpoint of early success, methadone was associated with the lowest risk of treatment discontinuation. While buprenorphine and naltrexone were associated with similar risks at the beginning of treatment, the relative discontinuation risk for buprenorphine was less than half that of naltrexone at one year of retention. In general, BHT with MOUD was associated with a lower risk of treatment discontinuation.

A pooling strategy to effectively use genotype data in quantitative traits genome-wide association studies.

The goal of quantitative traits genome-wide association studies is to identify associations between a phenotypic variable, such as a vitamin level and genetic variants, often single-nucleotide polymorphisms. When funding limits the number of assays that can be performed to measure the level of the phenotypic variable, a subgroup of subjects is often randomly selected from the genotype database and the level of the phenotypic variable is then measured for each subject. Because only a proportion of the genotype data can be used, such a simple random sampling method may suffer from substantial loss of efficiency, especially when the number of assays is relative small and the frequency of the less common variant (minor allele frequency) is low. We propose a pooling strategy in which subjects in a randomly selected reference subgroup are aligned with randomly selected subjects from the remaining study subjects to form independent pools; blood samples from subjects in each pool are mixed; and the level of the phenotypic variable is measured for each pool. We demonstrate that the proposed pooling approach produces considerable gains in efficiency over the simple random sampling method for inference concerning the phenotype-genotype association, resulting in higher precision and power. The methods are illustrated using genotypic and phenotypic data from the Trinity Students Study, a quantitative genome-wide association study.