Body Mass Index, Adverse Pregnancy Outcomes, and Cardiovascular Disease Risk.

BACKGROUND: Obesity is a well-established risk factor for both adverse pregnancy outcomes (APOs) and cardiovascular disease (CVD). However, it is not known whether APOs are mediators or markers of the obesity-CVD relationship. This study examined the association between body mass index, APOs, and postpartum CVD risk factors. METHODS: The sample included adults from the nuMoM2b (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be) Heart Health Study who were enrolled in their first trimester (6 weeks-13 weeks 6 days gestation) from 8 United States sites. Participants had a follow-up visit at 3.7 years postpartum. APOs, which included hypertensive disorders of pregnancy, preterm birth, small-for-gestational-age birth, and gestational diabetes, were centrally adjudicated. Mediation analyses estimated the association between early pregnancy body mass index and postpartum CVD risk factors (hypertension, hyperlipidemia, and diabetes) and the proportion mediated by each APO adjusted for demographics and baseline health behaviors, psychosocial stressors, and CVD risk factor levels. RESULTS: Among 4216 participants enrolled, mean±SD maternal age was 27±6 years. Early pregnancy prevalence of overweight was 25%, and obesity was 22%. Hypertensive disorders of pregnancy occurred in 15%, preterm birth in 8%, small-for-gestational-age birth in 11%, and gestational diabetes in 4%. Early pregnancy obesity, compared with normal body mass index, was associated with significantly higher incidence of postpartum hypertension (adjusted odds ratio, 1.14 [95% CI, 1.10-1.18]), hyperlipidemia (1.11 [95% CI, 1.08-1.14]), and diabetes (1.03 [95% CI, 1.01-1.04]) even after adjustment for baseline CVD risk factor levels. APOs were associated with higher incidence of postpartum hypertension (1.97 [95% CI, 1.61-2.40]) and hyperlipidemia (1.31 [95% CI, 1.03-1.67]). Hypertensive disorders of pregnancy mediated a small proportion of the association between obesity and incident hypertension (13% [11%-15%]) and did not mediate associations with incident hyperlipidemia or diabetes. There was no significant mediation by preterm birth or small-for-gestational-age birth. CONCLUSIONS: There was heterogeneity across APO subtypes in their association with postpartum CVD risk factors and mediation of the association between early pregnancy obesity and postpartum CVD risk factors. However, only a small or nonsignificant proportion of the association between obesity and CVD risk factors was mediated by any of the APOs, suggesting APOs are a marker of prepregnancy CVD risk and not a predominant cause of postpartum CVD risk.

Reducing Care Overuse in Older Patients Using Professional Norms and Accountability : A Cluster Randomized Controlled Trial.

BACKGROUND: Effective strategies are needed to curtail overuse that may lead to harm. OBJECTIVE: To evaluate the effects of clinician decision support redirecting attention to harms and engaging social and reputational concerns on overuse in older primary care patients. DESIGN: 18-month, single-blind, pragmatic, cluster randomized trial, constrained randomization. (ClinicalTrials.gov: NCT04289753). SETTING: 60 primary care internal medicine, family medicine and geriatrics practices within a health system from 1 September 2020 to 28 February 2022. PARTICIPANTS: 371 primary care clinicians and their older adult patients from participating practices. INTERVENTION: Behavioral science-informed, point-of-care, clinical decision support tools plus brief case-based education addressing the 3 primary clinical outcomes (187 clinicians from 30 clinics) were compared with brief case-based education alone (187 clinicians from 30 clinics). Decision support was designed to increase salience of potential harms, convey social norms, and promote accountability. MEASUREMENTS: Prostate-specific antigen (PSA) testing in men aged 76 years and older without previous prostate cancer, urine testing for nonspecific reasons in women aged 65 years and older, and overtreatment of diabetes with hypoglycemic agents in patients aged 75 years and older and hemoglobin A1c (HbA1c) less than 7%. RESULTS: At randomization, mean clinic annual PSA testing, unspecified urine testing, and diabetes overtreatment rates were 24.9, 23.9, and 16.8 per 100 patients, respectively. After 18 months of intervention, the intervention group had lower adjusted difference-in-differences in annual rates of PSA testing (-8.7 [95% CI, -10.2 to -7.1]), unspecified urine testing (-5.5 [CI, -7.0 to -3.6]), and diabetes overtreatment (-1.4 [CI, -2.9 to -0.03]) compared with education only. Safety measures did not show increased emergency care related to urinary tract infections or hyperglycemia. An HbA1c greater than 9.0% was more common with the intervention among previously overtreated diabetes patients (adjusted difference-in-differences, 0.47 per 100 patients [95% CI, 0.04 to 1.20]). LIMITATION: A single health system limits generalizability; electronic health data limit ability to differentiate between overtesting and underdocumentation. CONCLUSION: Decision support designed to increase clinicians' attention to possible harms, social norms, and reputational concerns reduced unspecified testing compared with offering traditional case-based education alone. Small decreases in diabetes overtreatment may also result in higher rates of uncontrolled diabetes. PRIMARY FUNDING SOURCE: National Institute on Aging.

Social and Psychosocial Determinants of Racial and Ethnic Differences in Cardiovascular Health in the United States Population.

BACKGROUND: Social and psychosocial factors are associated with cardiovascular health (CVH). Our objective was to examine the contributions of individual-level social and psychosocial factors to racial and ethnic differences in population CVH in the NHANES (National Health and Nutrition Examination Surveys) 2011 to 2018, to inform strategies to mitigate CVH inequities. METHODS: In NHANES participants ages ≥20 years, Kitagawa-Blinder-Oaxaca decomposition estimated the statistical contribution of individual-level factors (education, income, food security, marital status, health insurance, place of birth, depression) to racial and ethnic differences in population mean CVH score (range, 0-14, accounting for diet, smoking, physical activity, body mass index, blood pressure, cholesterol, blood glucose) among Hispanic, non-Hispanic Asian, or non-Hispanic Black adults compared with non-Hispanic White adults. RESULTS: Among 16 172 participants (representing 255 million US adults), 24% were Hispanic, 12% non-Hispanic Asian, 23% non-Hispanic Black, and 41% non-Hispanic White. Among men, mean (SE) CVH score was 7.45 (2.3) in Hispanic, 8.71 (2.2) in non-Hispanic Asian, 7.48 (2.4) in non-Hispanic Black, and 7.58 (2.3) in non-Hispanic White adults. In Kitagawa-Blinder-Oaxaca decomposition, education explained the largest component of CVH differences among men (if distribution of education were similar to non-Hispanic White participants, CVH score would be 0.36 [0.04] points higher in Hispanic, 0.24 [0.04] points lower in non-Hispanic Asian, and 0.23 [0.03] points higher in non-Hispanic Black participants; P<0.05). Among women, mean (SE) CVH score was 8.03 (2.4) in Hispanic, 9.34 (2.1) in non-Hispanic Asian, 7.43 (2.3) in non-Hispanic Black, and 8.00 (2.5) in non-Hispanic White adults. Education explained the largest component of CVH difference in non-Hispanic Black women (if distribution of education were similar to non-Hispanic White participants, CVH score would be 0.17 [0.03] points higher in non-Hispanic Black participants; P<0.05). Place of birth (born in the United States versus born outside the United States) explained the largest component of CVH difference in Hispanic and non-Hispanic Asian women (if distribution of place of birth were similar to non-Hispanic White participants, CVH score would be 0.36 [0.07] points lower and 0.49 [0.16] points lower, respectively; P<0.05). CONCLUSIONS: Education and place of birth confer the largest statistical contributions to the racial and ethnic differences in mean CVH score among US adults.

Rural-urban disparities in pregestational and gestational diabetes in pregnancy: Serial, cross-sectional analysis of over 12 million pregnancies.

OBJECTIVE: To compare trends in pregestational (DM) and gestational diabetes (GDM) in pregnancy in rural and urban areas in the USA, because pregnant women living in rural areas face unique challenges that contribute to rural-urban disparities in adverse pregnancy outcomes. DESIGN: Serial, cross-sectional analysis. SETTING: US National Center for Health Statistics (NCHS) Natality Files from 2011 to 2019. POPULATION: A total of 12 401 888 singleton live births to nulliparous women aged 15-44 years. METHODS: We calculated the frequency (95% confidence interval [CI]) per 1000 live births, the mean annual percentage change (APC), and unadjusted and age-adjusted rate ratios (aRR) of DM and GDM in rural compared with urban maternal residence (reference) per the NCHS Urban-Rural Classification Scheme overall, and by delivery year, reported race and ethnicity, and US region (effect measure modification). MAIN OUTCOME MEASURES: The outcomes (modelled separately) were diagnoses of DM and GDM. RESULTS: From 2011 to 2019, there were increases in both the frequency (per 1000 live births; mean APC, 95% CI per year) of DM and GDM in rural areas (DM: 7.6 to 10.4 per 1000 live births; APC 2.8%, 95% CI 2.2%-3.4%; and GDM: 41.4 to 58.7 per 1000 live births; APC 3.1%, 95% CI 2.6%-3.6%) and urban areas (DM: 6.1 to 8.4 per 1000 live births; APC 3.3%, 95% CI 2.2%-4.4%; and GDM: 40.8 to 61.2 per 1000 live births; APC 3.9%, 95% CI 3.3%-4.6%). Individuals living in rural areas were at higher risk of DM (aRR 1.48, 95% CI 1.45%-1.51%) and GDM versus those in urban areas (aRR 1.17, 95% CI 1.16%-1.18%). The increased risk was similar each year for DM (interaction p = 0.8), but widened over time for GDM (interaction p < 0.01). The rural-urban disparity for DM was wider for individuals who identified as Hispanic race/ethnicity and in the South and West (interaction p < 0.01 for all); and for GDM the rural-urban disparity was generally wider for similar factors (i.e. Hispanic race/ethnicity, and in the South; interaction p < 0.05 for all). CONCLUSIONS: The frequency of DM and GDM increased in both rural and urban areas of the USA from 2011 to 2019 among nulliparous pregnant women. Significant rural-urban disparities existed for DM and GDM, and increased over time for GDM. These rural-urban disparities were generally worse among those of Hispanic race/ethnicity and in women who lived in the South. These findings have implications for delivering equitable diabetes care in pregnancy in rural US communities.

Associations of Clinical and Social Risk Factors With Racial Differences in Premature Cardiovascular Disease.

BACKGROUND: Racial differences in cardiovascular disease (CVD) are likely related to differences in clinical and social factors. The relative contributions of these factors to Black-White differences in premature CVD have not been investigated. METHODS: In Black and White adults aged 18 to 30 years at baseline in the CARDIA study (Coronary Artery Risk Development in Young Adults), the associations of clinical, lifestyle, depression, socioeconomic, and neighborhood factors across young adulthood with racial differences in incident premature CVD were evaluated in sex-stratified, multivariable-adjusted Cox proportional hazards models using multiply imputed data assuming missing at random. Percent reduction in the ß estimate (log-hazard ratio [HR]) for race quantified the contribution of each factor group to racial differences in incident CVD. RESULTS: Among 2785 Black and 2327 White participants followed for a median 33.9 years (25th-75th percentile, 33.7-34.0), Black (versus White) adults had a higher risk of incident premature CVD (Black women: HR, 2.44 [95% CI, 1.71-3.49], Black men: HR, 1.59 [1.20-2.10] adjusted for age and center). Racial differences were not statistically significant after full adjustment (Black women: HR, 0.91 [0.55-1.52], Black men: HR 1.02 [0.70-1.49]). In women, the largest magnitude percent reduction in the ß estimate for race occurred with adjustment for clinical (87%), neighborhood (32%), and socioeconomic (23%) factors. In men, the largest magnitude percent reduction in the ß estimate for race occurred with an adjustment for clinical (64%), socioeconomic (50%), and lifestyle (34%) factors. CONCLUSIONS: In CARDIA, the significantly higher risk for premature CVD in Black versus White adults was statistically explained by adjustment for antecedent multilevel factors. The largest contributions to racial differences were from clinical and neighborhood factors in women, and clinical and socioeconomic factors in men.

Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population.

We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.

Association of statin use with outcomes of patients admitted with COVID-19: an analysis of electronic health records using superlearner.

IMPORTANCE: Statin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency. OBJECTIVE: To estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19. DESIGN, SETTING AND PARTICIPANTS: We retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission. EXPOSURE: Antecedent statin use-statin prescription ≥ 30 days prior to COVID-19 admission. MAIN OUTCOME: Composite end point of in-hospital death, intubation, and intensive care unit (ICU) admission. RESULTS: Of 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner. Statin users still had lower rates of the composite outcome (adjusted risk difference: - 3.4%; 95% CI: - 4.6% to - 2.1%), ICU admissions (- 3.3%; - 4.5% to - 2.1%), and intubation (- 1.9%; - 2.8% to - 1.0%) but comparable inpatient deaths (0.6%; - 1.3% to 0.1%). CONCLUSIONS AND RELEVANCE: After controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit.

'Are Routine Post-discharge Diuretics Necessary After Pediatric Cardiac Surgery?'

A prospective, one-armed, safety non-inferiority trial with historical controls was performed at a single-center, quaternary, children's hospital. Inclusion criteria were children aged 3 months-18 years after pediatric cardiac surgery resulting in a two-ventricle repair between 7/2020 and 7/2021. Eligible patients were compared with patients from a 5-year historical period (selected using a database search). The intervention was that "regular risk" patients received no diuretics and pre-specified "high risk" patients received 5 days of twice per day furosemide at discharge. 61 Subjects received the intervention. None were readmitted for pleural effusions, though 1 subject was treated for a symptomatic pleural effusion with outpatient furosemide. The study was halted after an interim analysis demonstrated that 4 subjects were readmitted with pericardial effusion during the study period versus 2 during the historical control (2.9% versus 0.2%, P = 0.003). We found no evidence that limited post-discharge diuretics results in an increase in readmissions for pleural effusions. This conclusion is limited as not enough subjects were enrolled to definitively show that this strategy is not inferior to the historical practice. There was a statistically significant increase in readmissions for pericardial effusions after implementation of this study protocol which can lead to serious complications and requires further study before conclusions can be drawn regarding optimal diuretic regimens.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study.

BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Impact of an Electronic Health Record Pain Medication Prescribing Tool on Opioid Prescriptions for Postoperative Pain in Hand, Orthopedic, Plastic, and Spine Surgery Across a Health Care System.

PURPOSE: We hypothesized that a pain management prescribing tool embedded in the electronic health record system of a multihospital health care system would decrease prescription opioids for postoperative pain by hand, orthopedic, plastic, and spine surgeons. METHODS: A prescribing tool for postoperative pain was designed for hand, orthopedic, plastic, and spine surgeons and implemented into electronic discharge order sets in a 10-hospital health care system. Stakeholders were educated on tool use in person and/or by email on 2 occasions. A dashboard was created to monitor opioid pill quantities and morphine milligram equivalents (MMEs) prescribed. Overall compliance with the suggested opioid amounts was assessed for 20 months after tool implementation. A subgroup of 6 hand surgeons, one of whom was instrumental in designing the tool, were evaluated for MMEs prescribed, opioid refills, patient emergency room visits, and patient readmissions within 30 days after discharge. Comparisons in this subgroup were made from 12 months before to 15 months after tool implementation. RESULTS: The mean system-wide compliance with the suggested opioid pill quantities and MMEs prescribed in all 4 specialties improved by less than 5%. In the subgroup of hand surgeons, 5 of whom championed tool use, prescribed MMEs decreased by 10% during each of the 4 quarters before launching the tool and contracted an additional 26% in the first quarter after tool implementation. Opioid refills held steady at 5%, and there were no emergency room visits or readmissions within 30 days after discharge in this patient subgroup. CONCLUSIONS: The prescribing tool had a negligible impact on system-wide compliance with suggested prescription opioid pill quantities and MMEs. In a small group of surgeons who championed the use of the tool, there was a significant and sustained decline in MMEs prescribed without adversely impacting patient refills, emergency room visits, or readmissions. CLINICAL RELEVANCE: An electronic prescribing tool to assist surgeons in lowering opioid prescription pill quantities and MMEs may have a negligible impact on prescribing behavior in a multihospital health care system.

Toward Causally Interpretable Meta-analysis: Transporting Inferences from Multiple Randomized Trials to a New Target Population.

We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that the assumptions that allow inferences to be transported from all trials in the collection to the same target population have implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis Trial.

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

Background: Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. Objective: To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Design: Population-based prospective studies. Setting: United States. Participants: Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Measurements: Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). Results: At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen. Limitation: No consensus on risk thresholds for screening. Conclusion: The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening. Primary Funding Source: Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Gestational Weight Gain-for-Gestational Age Z-Score Charts Applied across U.S. Populations.

BACKGROUND: Gestational weight gain may be a modifiable contributor to infant health outcomes, but the effect of gestational duration on gestational weight gain has limited the identification of optimal weight gain ranges. Recently developed z-score and percentile charts can be used to classify gestational weight gain independent of gestational duration. However, racial/ethnic variation in gestational weight gain and the possibility that optimal weight gain differs among racial/ethnic groups could affect generalizability of the z-score charts. The objectives of this study were (1) to apply the weight gain z-score charts in two different U.S. populations as an assessment of generalisability and (2) to determine whether race/ethnicity modifies the weight gain range associated with minimal risk of preterm birth. METHODS: The study sample included over 4 million live, singleton births in California (2007-2012) and Pennsylvania (2003-2013). We implemented a noninferiority margin approach in stratified subgroups to determine weight gain ranges for which the adjusted predicted marginal risk of preterm birth (gestation <37 weeks) was within 1 or 2 percentage points of the lowest observed risk. RESULTS: There were minimal differences in the optimal ranges of gestational weight gain between California and Pennsylvania births, and among several racial/ethnic groups in California. The optimal ranges decreased as severity of prepregnancy obesity increased in all groups. CONCLUSIONS: The findings support the use of weight gain z-score charts for studying gestational age-dependent outcomes in diverse U.S. populations and do not support weight gain recommendations tailored to race/ethnicity.

The Role of Maternal Adverse Childhood Experiences and Race in Intergenerational High-Risk Smoking Behaviors.

INTRODUCTION: A history of adversity in childhood is associated with cigarette smoking in adulthood, but there is less evidence for prenatal and next-generation offspring smoking. We investigated the association between maternal history of childhood adversity, pregnancy smoking, and early initiation of smoking in offspring, overall and by maternal race/ethnicity. METHODS: Data on maternal childhood exposure to physical abuse, household alcohol abuse, and household mental illness, prenatal smoking behaviors, and offspring age of smoking initiation were analyzed from the US National Longitudinal Survey of Youth 1979 (NLSY79, n = 2999 mothers) and the NLSY79 Children and Young Adults Survey (NLSYCYA, n = 6596 children). Adjusted risk ratios were estimated using log-linear regression models. We assessed multiplicative interaction by race/ethnicity for all associations and a three-way interaction by maternal exposure to adversity and race/ethnicity for the association between prenatal and child smoking. RESULTS: Maternal exposure to childhood physical abuse was significantly associated with 39% and 20% increased risks of prenatal smoking and child smoking, respectively. Household alcohol abuse was associated with significantly increased risks of 20% for prenatal smoking and 17% for child smoking. The prenatal smoking-child smoking relationship was modified by maternal exposure to household alcohol abuse and race. There were increased risks for Hispanic and white/other mothers as compared to the lowest risk group: black mothers who did not experience childhood household alcohol abuse. CONCLUSIONS: Mothers in this national sample who experienced adversity in childhood are more likely to smoke during pregnancy and their offspring are more likely to initiate smoking before age 18. Findings varied by type of adversity and race/ethnicity. IMPLICATIONS: These findings support the importance of a life-course approach to understanding prenatal and intergenerational smoking, and suggest that maternal early-life history is a potentially important risk factor that could be targeted with screening and interventions to reduce smoking in pregnant women and their children.

Changes in Age Distribution and Maternal Mortality in a Subset of the U.S., 2014-2021.

INTRODUCTION: This study aimed to determine the association between changes in age distribution and maternal mortality rates (MMR) in a subset of the United States between 2014 and 2021. METHODS: A serial cross-sectional analysis of birthing individuals aged 15-44 years from 2014 to 2021 was performed. States that had not adopted the pregnancy checkbox as of 2014 were excluded from the primary analysis. A significant inflection point in MMR was identified in 2019 with the Joinpoint Regression Program, so all analyses were stratified: 2014-2019 and 2019-2021. The Kitagawa decomposition was applied to quantify the contribution from (1) changes in age distribution and (2) changes in age-specific MMR (ASMR) to total MMR. Data analysis occurred between 2022 and 2023. RESULTS: From 2014 to 2021, the mean (standard deviation) age of birthing individuals changed from 28.3 (5.8) to 29.4 (5.7) years. The MMR (95% CI) increased significantly from 16.5 (15.8-18.5) to 18.9 (17.4-20.5) per 100,000 live births from 2014 to 2019 with acceleration in MMR to 31.8 (30.0-33.8) by 2021. The change in maternal age distribution contributed to 36% of the total change in the MMR from 2014 to 2019 and 4% from 2019 to 2021. Age-specific MMR components increased significantly for those aged 25-29 years and 30-34 years from 2014 to 2019. All 5-year age strata except the 15-19 year old group saw increases in age-specific MMR from 2019 to 2021. CONCLUSIONS: MMR increased significantly from 2014 to 2021 with rapid increase after 2019. However, older age of birthing individuals explained only a minority of the increased MMR in both periods. The greatest contribution to MMR arose from increases in age-specific MMR.

Blood pressure outcomes at 18 months in primary care patients prescribed remote physiological monitoring for hypertension: a prospective cohort study.

This pragmatic matched cohort study using EHR data extended the follow up to 18 months for BP outcomes comparing individuals prescribed remote patient monitoring (n = 288) and temporally-matched controls (n = 1152) from six primary care practices. After 18 months, the RPM-prescribed cohort had greater BP control < 140/90 mm Hg (RPM cohort: 71.5%, control cohort: 51.9%, p < 0.001) and lower systolic BP (131.6 versus 136.0 mm Hg, p = 0.004) using office and home measurements. BP control at 18 months assessed by office measurements only was also higher in the RPM group (62.2% versus 51.9%, p = 0.004).

Designing target trials using electronic health records: A case study of second-line disease-modifying anti-rheumatic drugs and cardiovascular disease outcomes in patients with rheumatoid arthritis.

BACKGROUND: Emulation of the "target trial" (TT), a hypothetical pragmatic randomized controlled trial (RCT), using observational data can be used to mitigate issues commonly encountered in comparative effectiveness research (CER) when randomized trials are not logistically, ethically, or financially feasible. However, cardiovascular (CV) health research has been slow to adopt TT emulation. Here, we demonstrate the design and analysis of a TT emulation using electronic health records to study the comparative effectiveness of the addition of a disease-modifying anti-rheumatic drug (DMARD) to a regimen of methotrexate on CV events among rheumatoid arthritis (RA) patients. METHODS: We used data from an electronic medical records-based cohort of RA patients from Northwestern Medicine to emulate the TT. Follow-up began 3 months after initial prescription of MTX (2000-2020) and included all available follow-up through June 30, 2020. Weighted pooled logistic regression was used to estimate differences in CVD risk and survival. Cloning was used to handle immortal time bias and weights to improve baseline and time-varying covariate imbalance. RESULTS: We identified 659 eligible people with RA with average follow-up of 46 months and 31 MACE events. The month 24 adjusted risk difference for MACE comparing initiation vs non-initiation of a DMARD was -1.47% (95% confidence interval [CI]: -4.74, 1.95%), and the marginal hazard ratio (HR) was 0.72 (95% CI: 0.71, 1.23). In analyses subject to immortal time bias, the HR was 0.62 (95% CI: 0.29-1.44). CONCLUSION: In this sample, we did not observe evidence of differences in risk of MACE, a finding that is compatible with previously published meta-analyses of RCTs. Thoughtful application of the TT framework provides opportunities to conduct CER in observational data. Benchmarking results of observational analyses to previously published RCTs can lend credibility to interpretation.

Special Supplemental Nutrition Program for Women, Infants, and Children Enrollment and Adverse Pregnancy Outcomes Among Nulliparous Individuals.

OBJECTIVE: To evaluate the relationship between changes in Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) enrollment during pregnancy from 2016 to 2019 and rates of adverse pregnancy outcomes in U.S. counties in 2019. METHODS: We conducted a serial, cross-sectional ecologic study at the county level using National Center for Health Statistics natality data from 2016 to 2019 of nulliparous individuals eligible for WIC. The exposure was the change in county-level WIC enrollment from 2016 to 2019 (increase [more than 0%] vs no change or decrease [0% or less]). Outcomes were adverse pregnancy outcomes assessed in 2019 and included maternal outcomes (ie, gestational diabetes mellitus [GDM], hypertensive disorders of pregnancy, cesarean delivery, intensive care unit [ICU] admission, and transfusion) and neonatal outcomes (ie, large for gestational age [LGA], small for gestational age [SGA], preterm birth, and neonatal intensive care unit [NICU] admission). RESULTS: Among 1,945,914 deliveries from 3,120 U.S. counties, the age-standardized rate of WIC enrollment decreased from 73.1 (95% CI, 73.0-73.2) per 100 live births in 2016 to 66.1 (95% CI, 66.0-66.2) per 100 live births in 2019, for a mean annual percent change decrease of 3.2% (95% CI, -3.7% to -2.9%) per year. Compared with individuals in counties in which WIC enrollment decreased or did not change, individuals living in counties in which WIC enrollment increased had lower rates of maternal adverse pregnancy outcomes, including GDM (adjusted odds ratio [aOR] 0.71, 95% CI, 0.57-0.89), ICU admission (aOR 0.47, 95% CI, 0.34-0.65), and transfusion (aOR 0.68, 95% CI, 0.53-0.88), and neonatal adverse pregnancy outcomes, including preterm birth (aOR 0.71, 95% CI, 0.56-0.90) and NICU admission (aOR 0.77, 95% CI, 0.60-0.97), but not cesarean delivery, hypertensive disorders of pregnancy, or LGA or SGA birth. CONCLUSION: Increasing WIC enrollment during pregnancy at the county level was associated with a lower risk of adverse pregnancy outcomes. In an era when WIC enrollment has decreased and food and nutrition insecurity has increased, efforts are needed to increase WIC enrollment among eligible individuals in pregnancy.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. OBJECTIVE: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. DESIGN: Retrospective, cohort study. PARTICIPANTS: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. MAIN MEASURES: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. KEY RESULTS: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. CONCLUSIONS: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Social and psychosocial determinants of racial and ethnic differences in cardiovascular health: The MASALA and MESA studies.

Background: Social and psychosocial determinants are associated with cardiovascular health (CVH). Objectives: To quantify the contributions of social and psychosocial factors to racial/ethnic differences in CVH. Methods: In the Multi-Ethnic Study of Atherosclerosis and Mediators of Atherosclerosis in South Asians Living in America cohorts, Kitagawa-Blinder-Oaxaca decomposition quantified the contributions of social and psychosocial factors to differences in mean CVH score (range 0-14) in Black, Chinese, Hispanic, or South Asian compared with White participants. Results: Among 7,978 adults (mean age 61 [SD 10] years, 52 % female), there were 1,892 Black (mean CVH score for decomposition analysis 7.96 [SD 2.1]), 804 Chinese (CVH 9.69 [1.8]), 1,496 Hispanic (CVH 8.00 [2.1]), 1,164 South Asian (CVH 9.16 [2.0]), and 2,622 White (CVH 8.91 [2.1]) participants. The factors that were associated with the largest magnitude of explained differences in mean CVH score were income for Black participants (if mean income in Black participants were equal to White participants, Black participants' mean CVH score would be 0.14 [SE 0.05] points higher); place of birth for Chinese participants (if proportion of US-born and foreign-born individuals among Chinese adults were equivalent to White participants, Chinese participants' mean CVH score would be 0.22 [0.10] points lower); and education for Hispanic and South Asian participants (if educational attainment were equivalent to White participants, Hispanic and South Asian participants' mean CVH score would be 0.55 [0.11] points higher and 0.37 [0.11] points lower, respectively). Conclusions: In these multiethnic US cohorts, social and psychosocial factors were associated with racial/ethnic differences in CVH.