Laura Jelliffe-Pawlowski

Faculty

Jelliffe-Pawlowski Headsot

Laura Jelliffe-Pawlowski

MS PhD

1 212 998 9020

433 First Ave
New York, NY 10010
United States

Laura Jelliffe-Pawlowski, PhD, MS, is a Professor. Prof. Jelliffe-Pawlowski’s research interests focus on understanding and addressing the drivers and consequences of adverse pregnancy outcomes with a special emphasis on preterm birth and associated racial/ethnic and socioeconomic inequities. Her work is highly transdisciplinary and looks at the interplay of biomolecular, social, and policy factors in observed patterns and outcomes. Her teaching and mentorship activities reflect this transdisciplinary approach with an emphasis on motivating the translation of research findings into action.

 

Prof. Jelliffe-Pawlowski leads a number of statewide, national, and international research efforts funded by the National Institutes of Health, the Bill and Melinda Gates Foundation, the March of Dimes, the State of California, and other entities. These includes, notably, the “Healthy Outcomes of Pregnancy for Everyone (HOPE)” consortium and study which focuses on understanding the experience of pregnant people and their infants pre- and post-COVID 19 pandemic. HOPE examines how biomolecular, social, and community factors affect the well-being and outcomes of mothers and infants and includes enrollment during pregnancy with outcome follow-up to 18-months after birth. Other ongoing projects include, for example, the NIH funded “Prediction Of Maturity, Morbidity, and Mortality in PreTerm Infants (PROMPT)”, study which focuses on examining the metabolic profiles of newborns with early preterm birth and associated outcomes, the “Transforming Health and Reducing PerInatal Anxiety through Virtual Engagement (THRIVE)”, randomized control trial (RCT), funded by the State of California which examines whether digital cognitive behavior therapy delivered by mobile app can assist in reducing anxiety symptoms in pregnant people and also examines participant acceptability of the application. Ongoing efforts also include leading the “California Prediction of Poor Outcomes of Pregnancy (CPPOP)” cohort study which focuses on investigating multi-omic drivers of preterm birth. The study interrogates biomolecular signals associated with preterm birth and includes full genome sequencing and mid-pregnancy biomolecular signaling related to metabolic, immune, stress, and placental function in hundreds of pregnancies with and without preterm birth. 

 

Prior to her joining NYU Meyers, Prof. Jelliffe-Pawlowski was a Professor of Epidemiology & Biostatistics, Chief of the Division of Lifecourse Epidemiology, a Professor in the Institute of Global Health Sciences, and Director of Discovery and Precision Health for the UCSF California Preterm Birth Initiative in the University of California San Francisco (UCSF) School of Medicine. She has a lifetime appointment as an Emeritus Professor of Epidemiology & Biostatistics in the UCSF School of Medicine and continues to work closely with the new Center for Birth Equity at UCSF. Prior to her appointment at UCSF, she was a leader at the Genetic Disease Screening Program within the California Department of Public Health. 

 

Prof. Jelliffe-Pawlowski efforts have been highlighted in numerous academic and lay articles including in the New York Times, in WIRED Magazine, in the Atlantic, on CNN, and on MSNBC. In 2023, she was recognized by Forbes Magazine as one of the top 50 over 50 Innovators in the United States. She is also a Phase I and Phase II Bill and Melinda Gates Foundation Grand Challenges awardee for her work in the United States and Uganda which focused on the development and validation of newborn metabolic profile as a novel measure of gestational age in infants.

BA, Psychology, University of California Los Angeles
MS, Child Development, University of California Davis
PhD, Human Development, University of California Davis

Preterm Birth

Forbes 50 over 50 awardee in Innovation (2023)
Delegate, African Academy of Sciences (2016)
Governor Brown Appointee for the California Department of Public Health, Interagency Coordinating Council on Early Intervention
Awardee, Bill and Melinda Bates Foundation, Gates Grand Challenges Phase I and II

Developing a resiliency model for survival without major morbidity in preterm infants

Steurer, M. A., Ryckman, K. K., Baer, R. J., Costello, J., Oltman, S. P., McCulloch, C. E., Jelliffe-Pawlowski, L. L., & Rogers, E. E. (2023). Journal of Perinatology, 43(4), 452-457. 10.1038/s41372-022-01521-3
Abstract
Abstract
Objective: Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning. Study design: Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center. Results: Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882–0.908) for survival and 0.867 (95% CI 0.857–0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741–0.893 and 0.804, CI 0.770–0.837, respectively). Conclusion: Our successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.

Estimating the effect of timing of earned income tax credit refunds on perinatal outcomes: a quasi-experimental study of California births

Karasek, D., Batra, A., Baer, R. J., Butcher, B. D., Feuer, S., Fuchs, J. D., Kuppermann, M., Gomez, A. M., Prather, A. A., Pantell, M., Rogers, E., Snowden, J. M., Torres, J., Rand, L., Jelliffe-Pawlowski, L., & Hamad, R. (2023). BMC Public Health, 23(1). 10.1186/s12889-023-16920-0
Abstract
Abstract
Background: The largest poverty alleviation program in the US is the earned income tax credit (EITC), providing $60 billion to over 25 million families annually. While research has shown positive impacts of EITC receipt in pregnancy, there is little evidence on whether the timing of receipt may lead to differences in pregnancy outcomes. We used a quasi-experimental difference-in-differences design, taking advantage of EITC tax disbursement each spring to examine whether trimester of receipt was associated with perinatal outcomes. Methods: We conducted a difference-in-differences analysis of California linked birth certificate and hospital discharge records. The sample was drawn from the linked CA birth certificate and discharge records from 2007–2012 (N = 2,740,707). To predict eligibility, we created a probabilistic algorithm in the Panel Study of Income Dynamics and applied it to the CA data. Primary outcome measures included preterm birth, small-for-gestational age (SGA), gestational diabetes, and gestational hypertension/preeclampsia. Results: Eligibility for EITC receipt during the third trimester was associated with a lower risk of preterm birth compared with preconception. Eligibility for receipt in the preconception period resulted in improved gestational hypertension and SGA. Conclusion: This analysis offers a novel method to impute EITC eligibility using a probabilistic algorithm in a data set with richer sociodemographic information relative to the clinical and administrative data sets from which outcomes are drawn. These results could be used to determine the optimal intervention time point for future income supplementation policies. Future work should examine frequent income supplementation such as the minimum wage or basic income programs.

Predicting the risk of 7-day readmission in late preterm infants in California: A population-based cohort study

Amsalu, R., Oltman, S. P., Medvedev, M. M., Baer, R. J., Rogers, E. E., Shiboski, S. C., & Jelliffe-Pawlowski, L. (2023). Health Science Reports, 6(1). 10.1002/hsr2.994
Abstract
Abstract
Background and aims: The American Academy of Pediatrics describes late preterm infants, born at 34 to 36 completed weeks' gestation, as at-risk for rehospitalization and severe morbidity as compared to term infants. While there are prediction models that focus on specific morbidities, there is limited research on risk prediction for early readmission in late preterm infants. The aim of this study is to derive and validate a model to predict 7-day readmission. Methods: This is a population-based retrospective cohort study of liveborn infants in California between January 2007 to December 2011. Birth certificates, maintained by California Vital Statistics, were linked to a hospital discharge, emergency department, and ambulatory surgery records maintained by the California Office of Statewide Health Planning and Development. Random forest and logistic regression were used to identify maternal and infant variables of importance, test for association, and develop and validate a predictive model. The predictive model was evaluated for discrimination and calibration. Results: We restricted the sample to healthy late preterm infants (n = 122,014), of which 4.1% were readmitted to hospital within 7-day after birth discharge. The random forest model with 24 variables had better predictive ability than the 8 variable logistic model with c-statistic of 0.644 (95% confidence interval 0.629, 0.659) in the validation data set and Brier score of 0.0408. The eight predictors of importance length of stay, delivery method, parity, gestational age, birthweight, race/ethnicity, phototherapy at birth hospitalization, and pre-existing or gestational diabetes were used to drive individual risk scores. The risk stratification had the ability to identify an estimated 19% of infants at greatest risk of readmission. Conclusions: Our 7-day readmission predictive model had moderate performance in differentiating at risk late preterm infants. Future studies might benefit from inclusion of more variables and focus on hospital practices that minimize risk.

Racial/ethnic disparities in the risk of preterm birth among women with systemic lupus erythematosus or rheumatoid arthritis

Strouse, J., Sabih, L., Bandoli, G., Baer, R., Jelliffe-Pawlowski, L., Chambers, C., Ryckman, K., & Singh, N. (2023). Clinical Rheumatology, 42(9), 2437-2444. 10.1007/s10067-023-06606-8
Abstract
Abstract
Objective: In a large multi-racial/ethnic cohort of women, we examined racial/ethnic disparities in preterm birth (PTB) risk stratified by autoimmune rheumatic disease (ARD) type, which included systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Methods: Birth records linked to hospital discharge data of singleton births in California from 2007 to 2012 were leveraged for a retrospective cohort study including women with SLE or RA. The relative risk of PTB (< 37 versus ≥ 37 weeks’ gestation) was compared among different racial/ethnic groups (Asian, Hispanic, Non-Hispanic (NH) Black, and NH White) and stratified by ARD type. Results were adjusted for relevant covariates using Poisson regression. Results: We identified 2874 women with SLE and 2309 women with RA. NH Black, Hispanic, and Asian women with SLE were 1.3 to 1.5 times more likely to have PTB compared to NH White women. NH Black women with RA were 2.0 to 2.4 times more likely to have PTB compared to Asian, Hispanic, or NH White women. The NH Black-NH White and NH Black-Hispanic disparity in PTB risk was significantly higher in women with RA compared to SLE or the general population. Conclusion: Our findings highlight the racial/ethnic disparities for risk of PTB among women with SLE or RA and highlight that several of the disparities are higher for women with RA compared to those with SLE or the general population. These data may provide important public health information for addressing racial/ethnic disparities in the risk of preterm birth, particularly among women with RA.Key Points• There is an unmet need for studies that evaluate racial/ethnic disparities in birth outcomes specifically in women with RA or SLE.• This is one of the first studies describing racial/ethnic disparities in PTB risk for women with RA, and to draw conclusions regarding Asian women in the USA with rheumatic diseases and PTB.• These data provide important public health information for addressing racial/ethnic disparities in the risk of preterm birth among women with autoimmune rheumatic diseases.

Structural racism is associated with adverse postnatal outcomes among Black preterm infants

Karvonen, K. L., McKenzie-Sampson, S., Baer, R. J., Jelliffe-Pawlowski, L., Rogers, E. E., Pantell, M. S., & Chambers, B. D. (2023). Pediatric Research, 94(1), 371-377. 10.1038/s41390-022-02445-6
Abstract
Abstract
Background: Structural racism contributes to racial disparities in adverse perinatal outcomes. We sought to determine if structural racism is associated with adverse outcomes among Black preterm infants postnatally. Methods: Observational cohort study of 13,321 Black birthing people who delivered preterm (gestational age 22–36 weeks) in California in 2011–2017 using a statewide birth cohort database and the American Community Survey. Racial and income segregation was quantified by the Index of Concentration at the Extremes (ICE) scores. Multivariable generalized estimating equations regression models were fit to test the association between ICE scores and adverse postnatal outcomes: frequent acute care visits, readmissions, and pre- and post-discharge death, adjusting for infant and birthing person characteristics and social factors. Results: Black birthing people who delivered preterm in the least privileged ICE tertiles were more likely to have infants who experienced frequent acute care visits (crude risk ratio [cRR] 1.3 95% CI 1.2–1.4), readmissions (cRR 1.1 95% CI 1.0–1.2), and post-discharge death (cRR 1.9 95% CI 1.2–3.1) in their first year compared to those in the privileged tertile. Results did not differ significantly after adjusting for infant or birthing person characteristics. Conclusion: Structural racism contributes to adverse outcomes for Black preterm infants after hospital discharge. Impact statement: Structural racism, measured by racial and income segregation, was associated with adverse postnatal outcomes among Black preterm infants including frequent acute care visits, rehospitalizations, and death after hospital discharge.This study extends our understanding of the impact of structural racism on the health of Black preterm infants beyond the perinatal period and provides reinforcement to the concept of structural racism contributing to racial disparities in poor postnatal outcomes for preterm infants.Identifying structural racism as a primary cause of racial disparities in the postnatal period is necessary to prioritize and implement appropriate structural interventions to improve outcomes.

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

Lewis, K. A., Chang, L., Cheung, J., Aouizerat, B. E., Jelliffe-Pawlowski, L. L., McLemore, M. R., Piening, B., Rand, L., Ryckman, K. K., & Flowers, E. (2023). Frontiers in Endocrinology, 13. 10.3389/fendo.2022.971354
Abstract
Abstract
Purpose: Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM. Design: The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Methods: PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM. Results: Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person. Conclusions: The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

The validity of hospital diagnostic and procedure codes reflecting morbidity in preterm neonates born &lt;32 weeks gestation

Ryckman, K. K., Holdefer, P. J., Sileo, E., Carlson, C., Weathers, N., Jasper, E. A., Cho, H., Oltman, S. P., Dagle, J. M., Jelliffe-Pawlowski, L. L., & Rogers, E. E. (2023). Journal of Perinatology, 43(11), 1374-1378. 10.1038/s41372-023-01685-6
Abstract
Abstract
Objective: To determine the validity of diagnostic hospital billing codes for complications of prematurity in neonates <32 weeks gestation. Study Design: Retrospective cohort data from discharge summaries and clinical notes (n = 160) were reviewed by trained, blinded abstractors for the presence of intraventricular hemorrhage (IVH) grades 3 or 4, periventricular leukomalacia (PVL), necrotizing enterocolitis (NEC), stage 3 or higher, retinopathy of prematurity (ROP), and surgery for NEC or ROP. Data were compared to diagnostic billing codes from the neonatal electronic health record. Results: IVH, PVL, ROP and ROP surgery had strong positive predictive values (PPV > 75%) and excellent negative predictive values (NPV > 95%). The PPVs for NEC (66.7%) and NEC surgery (37.1%) were low. Conclusion: Diagnostic hospital billing codes were observed to be a valid metric to evaluate preterm neonatal morbidities and surgeries except in the instance of more ambiguous diagnoses such as NEC and NEC surgery.

Adverse Maternal Fetal Environment Partially Mediates Disparate Outcomes in Non-White Neonates with Major Congenital Heart Disease

Santana, S., Peyvandi, S., Costello, J. M., Baer, R. J., Collins, J. W., Branche, T., Jelliffe-Pawlowski, L. L., & Steurer, M. A. (2022). Journal of Pediatrics, 251, 82-88.e1. 10.1016/j.jpeds.2022.06.036
Abstract
Abstract
Objective: To determine whether differential exposure to an adverse maternal fetal environment partially explains disparate outcomes in infants with major congenital heart disease (CHD). Study design: Retrospective cohort study utilizing a population-based administrative California database (2011-2017). Primary exposure: Race/ethnicity. Primary mediator: Adverse maternal fetal environment (evidence of maternal metabolic syndrome and/or maternal placental syndrome). Outcomes: Composite of 1-year mortality or severe morbidity and days alive out of hospital in the first year of life (DAOOH). Mediation analyses determined the percent contributions of mediators on pathways between race/ethnicity and outcomes after adjusting for CHD severity. Results: Included were 2747 non-Hispanic White infants (reference group), 5244 Hispanic, and 625 non-Hispanic Black infants. Hispanic and non-Hispanic Black infants had a higher risk for composite outcome (crude OR: 1.18; crude OR: 1.25, respectively) and fewer DAOOH (−6 & −12 days, respectively). Compared with the reference group, Hispanic infants had higher maternal metabolic syndrome exposure (43% vs 28%, OR: 1.89), and non-Hispanic Black infants had higher maternal metabolic syndrome (44% vs 28%; OR: 1.97) and maternal placental syndrome exposure (18% vs 12%; OR, 1.66). Both maternal metabolic syndrome exposure (OR: 1.21) and maternal placental syndrome exposure (OR: 1.56) were related to composite outcome and fewer DAOOH (−25 & −16 days, respectively). Adverse maternal fetal environment explained 25% of the disparate relationship between non-Hispanic Black race and composite outcome and 18% of the disparate relationship between Hispanic ethnicity and composite outcome. Adverse maternal fetal environment explained 16% (non-Hispanic Black race) and 21% (Hispanic ethnicity) of the association with DAOOH. Conclusions: Increased exposure to adverse maternal fetal environment contributes to racial and ethnic disparities in major CHD outcomes.

Association of Alcohol Use Diagnostic Codes in Pregnancy and Offspring Conotruncal and Endocardial Cushion Heart Defects

Harvey, D. C., Baer, R. J., Bandoli, G., Chambers, C. D., Jelliffe-Pawlowski, L. L., & Ram Kumar, S. (2022). Journal of the American Heart Association, 11(2). 10.1161/JAHA.121.022175
Abstract
Abstract
BACKGROUND: The pathogenesis of congenital heart disease (CHD) remains largely unknown, with only a small percentage explained solely by genetic causes. Modifiable environmental risk factors, such as alcohol, are suggested to play an important role in CHD pathogenesis. We sought to evaluate the association between prenatal alcohol exposure and CHD to gain insight into which components of cardiac development may be most vulnerable to the teratogenic effects of alcohol. METHODS AND RESULTS: This was a retrospective analysis of hospital discharge records from the California Office of Statewide Health Planning and Development and linked birth certificate records restricted to singleton, live-born infants from 2005 to 2017. Of the 5 820 961 births included, 16 953 had an alcohol-related International Classification of Diseases, Ninth and Tenth Revisions (ICD-9; ICD-10) code during pregnancy. Log linear regression was used to calculate risk ratios (RR) for CHD among individuals with an alcohol-related ICD-9 and ICD10 code during pregnancy versus those without. Three models were cre-ated: (1) unadjusted, (2) adjusted for maternal demographic factors, and (3) adjusted for maternal demographic factors and comorbidities. Maternal alcohol-related code was associated with an increased risk for CHD in all models (RR, 1.33 to 1.84); conotruncal (RR, 1.62 to 2.11) and endocardial cushion (RR, 2.71 to 3.59) defects were individually associated with elevated risk in all models. CONCLUSIONS: Alcohol-related diagnostic codes in pregnancy were associated with an increased risk of an offspring with a CHD, with a particular risk for endocardial cushion and conotruncal defects. The mechanistic basis for this phenotypic enrich-ment requires further investigation.

Development of a risk prediction score for acute postpartum care utilization

Wen, T., Baer, R. J., Oltman, S., Sobhani, N. C., Venkatesh, K. K., Friedman, A. M., & Jelliffe-Pawlowski, L. L. (2022). Journal of Maternal-Fetal and Neonatal Medicine, 35(26), 10506-10513. 10.1080/14767058.2022.2131387
Abstract
Abstract
Background: Acute postpartum care utilization and readmissions are increasing in the United States and contribute significantly to maternal morbidity, mortality, and healthcare costs. Currently, there are limited data on the prediction of patients who will require acute postpartum care utilization. Objective: To develop and validate a risk prediction model for acute postpartum care utilization. Study design: A retrospective cohort study of delivery hospitalizations with a linked birth certificate and discharge records in California from 2011 to 2015 was divided into a training and testing set for analysis and validation. Predictive models for acute postpartum care utilization using demographic, comorbidity, obstetrical complication, and other factors were developed using a backward stepwise logistic regression on training data. A risk score for acute postpartum care utilization was developed using beta coefficients from the factors remaining in the final multivariable model. Risk scores were validated using the testing dataset. Results: The final sample included 2,045,988 delivery hospitalizations with an acute postpartum care utilization rate of 7.6% in both training and testing cohorts. Twenty-two risk factors were identified for the final multivariable model, including several that were associated with two or more increased odds of acute care utilization (public insurance, postpartum hemorrhage, extremes of maternal age). The mean risk score was 2.45, conferring a 15 times higher risk of acute postpartum care utilization compared to those with a risk score <1 (RR 15.4, 95% CI: 11.0, 21.7). Demographics and test performance characteristics were comparably similar in predictive capability in both models (0.67 in both the training and testing cohorts). Conclusion: Risk factors that are identifiable before discharge can be used to create a cumulative risk score to stratify patients at the lowest and highest risk of acute postpartum care utilization with satisfactory accuracy. External validation and the addition of other granular clinical variables are necessary to validate the feasibility of use.