About

Dr. Christina A. Porucznik is a Professor in the Division of Public Health and the Vice Dean of Faculty for the Spencer Fox Eccles School of Medicine. She is a doctorally-trained epidemiologist who has worked in nonprofit, government, academic, and corporate settings with scientists, engineers, and policy makers. She received her Master of Science in Public Health and PhD in Epidemiology from the University of North Carolina at Chapel Hill School of Public Health. Her research interests include appropriately timing measurement of exposures, with a focus on environmental health, studying endocrine disruptors and reproductive endpoints such as fertility, pregnancy outcomes, and breastfeeding. She also concentrates on prescription medications, primarily opioids, and the impact of policy changes on drug dispensing and adverse events. Dr. Porucznik served as an Epidemic Intelligence Service officer for the CDC at the Utah Department of Health before joining the faculty in 2005.

Research topics

• Medicine
• Internal medicine
• Demography
• Pediatrics
• Gerontology
• Geography
• Environmental health
• Computer Science
• Intensive care medicine
• Clinical psychology
• Family medicine
• Immunology
• Bioinformatics
• Statistics
• Genetics
• Virology
• Physiology
• Surgery
• Biology
• Obstetrics
• Cartography
• Physical therapy
• Dentistry
• Pathology

Selected publications

• The Utah Children’s Project: Design, Enrollment and Measures for a Child and Family Cohort

  J. Willard Marriott Library · 2026-03-27

  otherOpen access1st authorCorresponding
  PublisherDOI

• Prenatal over-the-counter acetaminophen use and birth outcomes in the ECHO cohort

  American Journal of Epidemiology · 2026-05-20

  articleOpen access

  Acetaminophen is among the most common over-the-counter medications used during pregnancy. Given inconsistent findings from both experimental and epidemiological studies on associations between use and adverse health outcomes, further research is warranted. To address this, our objective was to assess the relationship between prenatal acetaminophen use and birth outcomes. We studied 8957 mother-infant pairs from 36 pediatric study sites participating in the Environmental influences on Child Health Outcomes (ECHO) program. After imputation and inverse probability weighting, we used regression models to examine the relationship between acetaminophen during pregnancy and the following outcomes: (1) preterm birth, (2) birthweight, (3) small-for-gestational age (SGA), and (4) large-for-gestational-age (LGA). Approximately 59% of mothers reported using acetaminophen at any point during their pregnancy (n = 5257). After adjustment for relevant covariates, prenatal acetaminophen use was associated with lower odds of LGA (adjusted odds ratio (aOR): 0.87; 95% CI: 0.79, 0.96). Prenatal acetaminophen use was not associated with preterm birth (aOR: 0.99; 95% CI: 0.86, 1.14), birthweight (aβ: -7.52 g; 95% CI: -27.80, 12.77) or SGA (aOR: 1.02; 95% CI: 0.88, 1.18). Based on these findings, future research should test for dose-response, trimester-specific exposures, and factors affecting individual responses.

  PublisherDOI

• Early-Life Factors and Body Mass Index Trajectories Among Children in the ECHO Cohort

  JAMA Network Open · 2025-05-22 · 9 citations

  articleOpen access

  Importance: Identifying atypical body mass index (BMI) trajectories in children and understanding associated, modifiable early-life factors may help prevent childhood obesity. Objective: To characterize multiphase BMI trajectories in children and identify associated modifiable early-life factors. Design, Setting, and Participants: This cohort study included longitudinal data obtained from January 1997 to June 2024, from the Environmental influences on Child Health Outcomes (ECHO) cohort, which included children aged 1 to 9 years with 4 or more weight and height assessments. Analyses were conducted from January to June 2024. Exposures: Prenatal exposure to substances and stress (smoking, alcohol, depression, anxiety), maternal characteristics (prepregnancy BMI, gestational weight gain), child characteristics (preterm birth, birth weight, breastfeeding), and demographic covariates. Main Outcomes and Measures: BMI (calculated as weight in kilograms divided by length in meters squared for children aged 1 and 2 years and as weight in kilograms divided by height in meters squared for children older than 2 years) obtained using medical records, staff measurements, caregiver reports, or remote study measures. The analysis was conducted using a multiphase latent growth mixture model. Results: This study included 9483 children (4925 boys [51.9%]). Two distinct 2-phase BMI patterns were identified: typical and atypical. The typical group (n = 8477 [89.4%]) showed linear decreases in BMI (b2, -0.23 [95% CI, -0.24 to -0.22]), with the lowest BMI at age 6 years (95% CI, 5.94-6.11), followed by linear increases from 6 to 9 years (slope difference [b4 - b2], 0.81 [95% CI, 0.76-0.86]; mean BMI at 9 years: 17.33). The atypical group (n = 1006 [10.6%]) showed a stable BMI from ages 1 to 3.5 years (b6, 0.06 [95% CI, -0.04 to 0.15]), followed by rapid linear increases from ages 3.5 to 9 years (slope difference [b8 - b6], 1.44 [95% CI, 1.34-1.55]). At age 9 years, this group reached a mean BMI (26.2) that exceeded the 99th percentile. Prenatal smoking, high prepregnancy BMI, high gestational weight gain, and high birth weight were key risk factors for the atypical trajectory. Conclusions and Relevance: In this cohort study of children in the ECHO cohort, analyses identified children on the path to obesity as early as age 3.5 years. Modifiable factors could be targeted for early prevention and intervention programs aimed at reducing childhood obesity.

  PublisherOA PDFDOI

• Ambient particulate air pollution is associated with higher risk of microvascular complications and diabetic ketoacidosis among persons with type 1 diabetes mellitus in an observational survival study

  Diabetes Research and Clinical Practice · 2025-11-01

  articleOpen access

  <h2>Abstract</h2><h3>Aims</h3> The burden of Type 1 Diabetes Mellitus (DM, T1D) is growing and represents a major health care cost. This study investigated the relationship between ambient particulate matter (PM_2.5) exposure and T1D complications. The research was motivated by the potential for air pollution's known inflammatory effects to exacerbate T1D's microvascular harms (i.e., damaged peripheral tissues from poor glucose control). <h3>Methods</h3> In a cohort of 12, 925 Utah participants, competing-risk Cox proportional hazards regression analyzed the increased risk of PM_2.5 exposure and diabetic ketoacidosis (DKA), along with kidney, ophthalmic, and neurological complications. <h3>Results</h3> An interquartile range increase in one-year PM_2.5 exposure was associated with increased risk of DKA by 28.8 % (95 % CI: 14.3, 45.2), ophthalmic complications by 33.5 % (95 % CI: 10.0, 62.0), and neurological complications by 35.1 % (95 % CI: 14.3, 59.6). No statistically significant effect was found for diabetic kidney complications. Acute exposures of 30, 60, and 90 days was also associated with increased risk of diabetic ketoacidosis, although less than one-year exposure. <h3>Conclusions</h3> We hypothesize these effects stem from PM_2.5-induced oxidative stress and systemic inflammation, which may exacerbate metabolic disruptions already present in hyperglycemic individuals. Clients and providers may want to consider environmental factors, like air pollution, in T1D management.

  PublisherDOI

• Associations among PM_2.5, corticotropin releasing hormone, estriol, and progesterone in pregnant persons in Puerto Rico

  Environmental Research Communications · 2025-03-01

  articleOpen access

  Abstract Background. Exposure to PM 2.5 is associated with adverse birth outcomes and early development. Pregnancy is typically characterized by the production of several important hormones that impact aspects of maternal and fetal physiology, including progesterone, estriol, and corticotropin releasing hormone (CRH). No previous studies have examined PM associations in pregnant persons for CRH and estriol. Methods. We used linear mixed effects models to investigate associations between PM 2.5 and pregnancy hormones in 1,041 pregnant persons ages 18–41 living in Puerto Rico between 2011 and 2020. Individual 3–, 7–, and 30-day moving average exposures were assigned from EPA data sources. Hormone levels were analyzed in blood collected at study visits at 16–20 and 20–24 weeks of gestation. Models were adjusted for demographics, socioeconomic status, and health behaviors. Results. Mean participant exposures for 3−, 7−, and 30-day PM 2.5 were 8.0 ± 5.9, 8.2 ± 5.3, and 8.1 ± 4.4 μg m −3 . In base models, increased PM 2.5 exposure was associated with lower levels of progesterone, CRH, and estriol. In adjusted models, 10 μg m −3 increase in PM 2.5 was associated with 11.2% (95% CI: 17.6, 4.3; p = 0.003) and 14.9% (95% CI: 23.4, 5.4; p = 0.004) lower CRH for 7-day and 30-day exposures. In cross-sectional models, the inverse CRH association was driven by the 20–24 week gestation period with a 12.4% reduction (95% CI: 21.8, 1.9; p = 0.022) for 7-day and 17.5% reduction (95% CI: 29.7, 3.0; p = 0.020) for 30-day exposure. Other investigated associations were null. Conclusions. In pregnant persons in Puerto Rico, we observed that elevated PM 2.5 exposures were significantly associated with decrements in CRH, but not in other pregnancy-associated hormones. CRH may be an important pathway through which prenatal PM 2.5 impacts normal pregnancy.

  PublisherDOI

• Estimating Impact of SARS-CoV-2 Infection on Health-Related Quality of Life among Persons Aged 8 years and Older, August 2020-July 2022

  medRxiv · 2025-12-11

  preprintOpen access

  Abstract Background Severe COVID-19 results in substantial economic burden and impacts quality of life. Assessing how non-hospitalized COVID-19 impacts health utilities during acute infection and long term is important to estimate the full economic impact of SARS-CoV-2 infection. Methods We analyzed EQ-5D-3L survey data from SARS-CoV-2 infected adults (aged ≥16 years) and children (aged 8-15 years) from three community and household cohorts in the United States (2020-2022). EQ-5D-3L scores were analyzed at three time points after symptom onset or first positive SARS-CoV-2 test result and converted to health utilities on a scale of 0-1 (1=perfect health). Among adults, regression models were used to compare differences in health utility by demographic/clinical characteristics. Results Among 538 SARS-CoV-2 non-hospitalized asymptomatic/symptomatic infections from 575 adults with EQ-5D-3L surveys, mean utilities were near 1 throughout the observation period. During 0-14 days, vaccinated participants had higher health utilities (Beta:0.57, 95% CI:0.07,1.07). Seeking medical care and having gastrointestinal symptoms (vs. none), were associated with lower health utilities (Beta, 95% CI:-0.96, −1.60, −0.31; and −0.76, −1.30, −0.21 respectively). During 15-30 days, unemployment was associated with lower health utility (Beta:-0.64, 95% CI:-1.15,-0.14). During 31-90 days, underlying conditions were associated with lower health utilities (Beta:-0.32, 95% CI:-0.54, −0.09). Results for children were similar to adults. Conclusion Non-hospitalized COVID-19 may have minimal overall impact on quality of life; however, health utilities differed by vaccination status, presence of gastrointestinal symptoms, employment status, and presence of underlying conditions. Vaccination may play an important role in minimizing illness impact from SARS-CoV-2 infection. Key points Severe COVID-19 illness causes substantial economic burden and impacts on quality of life; however, the incidence of non-hospitalized COVID-19 is far greater. Assessing how non-hospitalized COVID-19 impacts health during acute infection and long term is important to understand the full impact of SARS-CoV-2 infection. This study utilizes the EQ-5D-3L, a standardized generic preference-based instrument used in population health studies, to estimate health utilities at multiple time points following SARS-CoV-2 infection. The study also examines demographic/medical characteristics that are associated with health utility over time. While mean health utilities were high for all infection periods regardless of age, health utility was lower at varying time points post-infection among adults who sought medical care, reported gastrointestinal symptoms, were unemployed, and with underlying conditions. Vaccinated adults (vs. unvaccinated) had higher health utility and were less likely to report reduced health. Findings can be used as inputs for economic evaluation and assessing impact of interventions for non-hospitalized SARS-CoV-2 illness, such as vaccination.

  PublisherOA PDFDOI

• Associations between neighborhood-level gun violence and child general health status: An ECHO cohort analysis

  Injury · 2025-09-08

  articleOpen access
  PublisherOA PDFDOI

• Pregnancies, intentions, and fertility behaviors during use of the Creighton Model FertilityCare System after initial intention to avoid pregnancy: Results from the Creighton Model effectiveness, intentions, behaviors assessment study

  PLoS ONE · 2025-07-29

  articleOpen accessSenior authorCorresponding

  BACKGROUND: Knowledge of the fertile and infertile phases of the menstrual cycle can be applied to conceive or to avoid pregnancy. Fertility intentions and sexual behaviors during the fertile time may influence whether and when pregnancy occurs. The Creighton Model FertilityCare System (CrMS) is a specific system of fertility appreciation used to conceive or to avoid pregnancy. The objective of this paper is to report intentions, behaviors, and pregnancy rates during use of the CrMS among couples who initially intended to avoid pregnancy. DATA AND METHODS: We analyzed a prospective cohort study conducted in 17 CrMS centers across the USA and Canada, following 296 couples for up to one year after onset of initial use of the CrMS to avoid pregnancy. Baseline data included demographics, motivations, and pregnancy intentions for each partner. Couples contributed 2894 menstrual cycles, most of which had data collected (by questionnaires and daily diary) on cycle-specific pregnancy intentions, days of potential fertility, and fertility behaviors. Pregnancies were prospectively actively ascertained. RESULTS: We found a high concordance (91%) in cycle pregnancy intentions between partners. However, 44% of cycles with strong intentions to avoid pregnancy included intercourse on potentially fertile days or days of undetermined fertility status. Across all sensitivity scenarios, cumulative 13-cycle pregnancy rates with cycle intention to conceive ranged from 88.0% to 89.8%, and cumulative 13-cycle pregnancy rates with cycle intention to avoid ranged from 29.1% to 35.3%. In multivariate analysis, baseline motivations and intentions for pregnancy within 2 years were strongly correlated with the likelihood of pregnancy, more so than cycle intentions. CONCLUSION: The findings suggest that in some populations using natural family planning, baseline motivations and intentions may be more strongly related to pregnancy rates than cycle intentions. Our findings also highlight essential elements for evaluating correct use, including complete recording of intercourse and its timing.

  PublisherDOI

• Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration

  Aerosol and Air Quality Research · 2025-04-01 · 4 citations

  articleOpen access

  Abstract Background Low-cost sensors (LCS) are widely used for air quality monitoring, but their accuracy depends on proper calibration. This study compares linear regression (LR) and machine learning (ML) techniques, particularly random forest (RF), to determine optimal calibration strategies. Objectives This study aims to compare the effectiveness of LR and RF models in calibrating the Plantower PMS 3003 sensor under different environmental conditions. It also explores ways to streamline calibration efforts while maintaining accuracy. Methods Sensor data were collected in a controlled laboratory setting, with measurements compared against a reference monitor. LR and RF models were developed to calibrate the sensor, and their performance was evaluated based on RMSE, R 2 , and bias. Additionally, the study examined whether using fewer sensors for training could still produce reliable calibration models. Results Both LR and RF models demonstrated strong calibration performance. LR models were effective for low to moderate PM2.5 concentrations and required fewer computational resources, making them suitable for large-scale monitoring with limited resources. RF models captured nonlinear relationships, showing superior accuracy at high PM concentrations and in conditions with high relative humidity. The findings suggest that LR models trained on smaller datasets can achieve practical accuracy, reducing the need for extensive individual sensor calibration. Conclusions The selection of a calibration model should be guided by study-specific requirements, including environmental conditions and resource availability. LR models are recommended for large-scale studies with constrained resources, while RF models may offer advantages in high-exposure environments due to their ability to model complex interactions. This study is the first to explore reducing sensor calibration efforts while maintaining accuracy, highlighting the potential for optimized strategies in resource-limited settings. Future research should validate these findings in real-world deployments to further refine calibration models for LCS applications. Graphical Abstract

  PublisherOA PDFDOI

• Assessing the impact of real-world environmental factors on low-cost PM2.5 monitor performance by comparing calibration before and after deployment

  The Science of The Total Environment · 2025-07-22 · 1 citations

  article
  PublisherDOI

Recent grants

• Peri-Conceptional Biomonitoring

  NIH · $2.1M · 2011–2017

• Utah Children's Project

  NIH · $9.4M · 2016–2025

• Utah Children's Project

  NIH · $15.0M · 2016–2030

Frequent coauthors

• Joseph B. Stanford

  Utah Department of Health

  136 shared

• Melissa S. Stockwell

  New York Hospital Queens

  58 shared

• Fatimah S. Dawood

  Centers for Disease Control and Prevention

  56 shared

• Vic Veguilla

  56 shared

• Ashton Dixon

  Centers for Disease Control and Prevention

  52 shared

• Jazmin Duque

  52 shared

• Karen C. Schliep

  36 shared

• Mia Hashibe

  Huntsman Cancer Institute

  34 shared

Education

• Ph.D., Epidemiology

  University of Utah

  2005