Anna Booman

All Publications

• Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population. BMC pregnancy and childbirth Booman, A., Vesco, K. K., Springer, R., Dinh, D., Liu, S., Lyon-Scott, K., Marino, M., O'Malley, J., Palma, A., Schmidt, T., Snowden, J. M., Stratton, K., Tran, S., Boone-Heinonen, J. 2025; 25 (1): 35

  Abstract

  BACKGROUND: Understanding the risks and effects of gestational weight gain (GWG) is a prominent area of perinatal research but approaches for quantifying GWG are evolving and remain underdeveloped, especially in clinical settings for underserved demographic subgroups. To fill this gap, we demonstrated and compared six GWG metrics across pre-pregnancy BMI classifications: total GWG, trimester-specific linear rate of GWG, adherence to total and trimester-specific recommendations, area under the curve, and GWG for gestational age z-scores.METHODS: We used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups.RESULTS: Total GWG was lower in individuals with higher pre-pregnancy BMI; yet more temporally resolved analyses revealed differences in trimester-specific weight change. Differences included common first trimester weight loss in people with pre-pregnancy class II or III obesity and substantial first trimester weight gain in people with pre-pregnancy underweight, with the greatest pre-pregnancy BMI-related variation in GWG occurring in the second trimester. These differences are reflected to varying degrees in the AUC and GWG z-score metrics.CONCLUSIONS: Our findings inform development of GWG guidelines within BMI categories, especially in obesity subclasses and underweight, and selection, refinement, and application of GWG metrics in future research. GWG metrics differ to varying degrees across BMI categories in a population consisting of several underserved subgroups: pregnant people of color, with larger body sizes, or with lower incomes. Stronger evidence on safe levels of first trimester weight loss and obesity class-specific recommendations is needed.

  View details for DOI 10.1186/s12884-025-07139-5

  View details for PubMedID 39825224

• Insurance coverage and discontinuity during pregnancy: Frequency and associations documented in the PROMISE cohort. Health services research Booman, A., Stratton, K., Vesco, K. K., O'Malley, J., Schmidt, T., Boone-Heinonen, J., Snowden, J. M. 2024; 59 (2): e14265

  Abstract

  To describe insurance patterns and discontinuity during pregnancy, which may affect the experiences of the pregnant person: their timely access to care, continuity of care, and health outcomes.Data are from the PROMISE study, which utilizes data from community-based health care organizations (CHCOs) (e.g., federally qualified health centers that serve patients regardless of insurance status or ability to pay) in the United States from 2005 to 2021.This descriptive study was a cohort utilizing longitudinal electronic health record data.Insurance type at each encounter was recorded in the clinical database and coded as Private, Public, and Uninsured. Pregnant people were categorized into one of several insurance patterns. We analyzed the frequency and timing of insurance changes and care utilization within each group.Continuous public insurance was the most common insurance pattern (69.2%), followed by uninsured/public discontinuity (11.8%), with 6.4% experiencing uninsurance throughout the entirety of pregnancy. Insurance discontinuity was experienced by 16.6% of pregnant people; a majority of these reflect people transitioning to public insurance. Those with continuous public insurance had the highest frequency of inadequate prenatal care (19.5%), while those with all three types of insurance during pregnancy had the highest percentage of intensive prenatal care (16.5%). The majority (71.7%-81.2%) of those with a discontinuous pattern experienced a single insurance change.Insurance discontinuity and uninsurance are common within our population of pregnant people seeking care at CHCOs. Our findings suggest that insurance status should be regarded as a dynamic rather than a static characteristic during pregnancy and should be measured accordingly. Future research is needed to assess the drivers of perinatal insurance discontinuity and if and how these discontinuities may affect health care access, utilization, and birth outcomes.

  View details for DOI 10.1111/1475-6773.14265

  View details for PubMedID 38123135

  View details for PubMedCentralID PMC10915475

• Pregnancy health in a multi-state U.S. population of systemically underserved patients and their children: PROMISE cohort design and baseline characteristics. BMC public health Boone-Heinonen, J., Lyon-Scott, K., Springer, R., Schmidt, T., Vesco, K. K., Booman, A., Dinh, D., Fortmann, S. P., Foster, B. A., Hauschildt, J., Liu, S., O'Malley, J., Palma, A., Snowden, J. M., Stratton, K., Tran, S. 2024; 24 (1): 886

  Abstract

  Gestational weight gain (GWG) is a routinely monitored aspect of pregnancy health, yet critical gaps remain about optimal GWG in pregnant people from socially marginalized groups, or with pre-pregnancy body mass index (BMI) in the lower or upper extremes. The PROMISE study aims to determine overall and trimester-specific GWG associated with the lowest risk of adverse birth outcomes and detrimental infant and child growth in these underrepresented subgroups. This paper presents methods used to construct the PROMISE cohort using electronic health record data from a network of community-based healthcare organizations and characterize the cohort with respect to baseline characteristics, longitudinal data availability, and GWG.We developed an algorithm to identify and date pregnancies based on outpatient clinical data for patients 15 years or older. The cohort included pregnancies delivered in 2005-2020 with gestational age between 20 weeks, 0 days and 42 weeks, 6 days; and with known height and adequate weight measures needed to examine GWG patterns. We linked offspring data from birth records and clinical records. We defined study variables with attention to timing relative to pregnancy and clinical data collection processes. Descriptive analyses characterize the sociodemographic, baseline, and longitudinal data characteristics of the cohort, overall and within BMI categories.The cohort includes 77,599 pregnancies: 53% had incomes below the federal poverty level, 82% had public insurance, and the largest race and ethnicity groups were Hispanic (56%), non-Hispanic White (23%) and non-Hispanic Black (12%). Pre-pregnancy BMI groups included 2% underweight, 34% normal weight, 31% overweight, and 19%, 8%, and 5% Class I, II, and III obesity. Longitudinal data enable the calculation of trimester-specific GWG; e.g., a median of 2, 4, and 6 valid weight measures were available in the first, second, and third trimesters, respectively. Weekly rate of GWG was 0.00, 0.46, and 0.51 kg per week in the first, second, and third trimesters; differences in GWG between BMI groups were greatest in the second trimester.The PROMISE cohort enables characterization of GWG patterns and estimation of effects on child growth in underrepresented subgroups, ultimately improving the representativeness of GWG evidence and corresponding guidelines.

  View details for DOI 10.1186/s12889-024-18257-8

  View details for PubMedID 38519895

  View details for PubMedCentralID PMC10960496

• Causal and Associational Language in Observational Health Research: A Systematic Evaluation. American journal of epidemiology Haber, N. A., Wieten, S. E., Rohrer, J. M., Arah, O. A., Tennant, P. W., Stuart, E. A., Murray, E. J., Pilleron, S., Lam, S. T., Riederer, E., Howcutt, S. J., Simmons, A. E., Leyrat, C., Schoenegger, P., Booman, A., Dufour, M. K., O'Donoghue, A. L., Baglini, R., Do, S., Takashima, M. D., Evans, T. R., Rodriguez-Molina, D., Alsalti, T. M., Dunleavy, D. J., Meyerowitz-Katz, G., Antonietti, A., Calvache, J. A., Kelson, M. J., Salvia, M. G., Parra, C. O., Khalatbari-Soltani, S., McLinden, T., Chatton, A., Seiler, J., Steriu, A., Alshihayb, T. S., Twardowski, S. E., Dabravolskaj, J., Au, E., Hoopsick, R. A., Suresh, S., Judd, N., Peña, S., Axfors, C., Khan, P., Rivera Aguirre, A. E., Odo, N. U., Schmid, I., Fox, M. P. 2022

  Abstract

  We estimated the degree to which language used in the high profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched and screened for 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, three reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as None (no causal implication) in 13.8%, Weak 34.2%, Moderate 33.2%, and Strong 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

  View details for DOI 10.1093/aje/kwac137

  View details for PubMedID 35925053