Publications by authors named "Zachary Butzin-Dozier"

Antibiotics can trigger antimicrobial resistance and microbiome alterations. Reducing pathogen exposure and undernutrition can reduce infections and antibiotic use. We assess effects of water, sanitation, handwashing (WSH) and nutrition interventions on caregiver-reported antibiotic use in Bangladesh and Kenya, longitudinally measured at three timepoints among birth cohorts (ages 3-28 months) in a cluster-randomized trial.

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Understanding treatment effects on health-related outcomes using real-world data requires defining a causal parameter and imposing relevant identification assumptions to translate it into a statistical estimand. Semiparametric methods, like the targeted maximum likelihood estimator (TMLE), have been developed to construct asymptotically linear estimators of these parameters. To further establish the asymptotic efficiency of these estimators, two conditions must be met: 1) the relevant components of the data likelihood must fall within a Donsker class, and 2) the estimates of nuisance parameters must converge to their true values at a rate faster than .

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Postpartum depression (PPD) is a mood disorder affecting one in seven women after childbirth that is often under-screened and under-detected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (e.

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Article Synopsis
  • Long COVID is a condition that can cause many symptoms after someone has COVID-19 and can make people feel really sick.
  • Some scientists think that parts of the COVID-19 virus might stop the body from making serotonin, a chemical that helps regulate mood, and this could lead to Long COVID symptoms.
  • A study found that people who took a type of medicine called SSRIs, which help boost serotonin levels, had a lower chance of developing Long COVID compared to those who didn't take these medicines.
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  • Patients recovering from COVID-19 often experience lingering symptoms known as Long COVID, which can manifest weeks or months after their initial infection, but the prevalence of this condition is not well understood.
  • To address this, a collaborative initiative called the Long COVID Computational Challenge (L3C) was launched to develop effective risk prediction tools for identifying individuals at risk of Long COVID using extensive healthcare data from over 75 institutions in the U.S.
  • The challenge resulted in 74 teams creating 35 predictive models, with the top models achieving high accuracy scores, demonstrating the potential for machine learning to enhance the identification of patients at risk for Long COVID.
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Background: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts.

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Background: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging.

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Background: Water, sanitation, hygiene (WSH), nutrition (N), and combined (N+WSH) interventions are often implemented by global health organizations, but WSH interventions may insufficiently reduce pathogen exposure, and nutrition interventions may be modified by environmental enteric dysfunction (EED), a condition of increased intestinal permeability and inflammation. This study investigated the heterogeneity of these treatments' effects based on individual pathogen and EED biomarker status with respect to child linear growth.

Methods: We applied cross-validated targeted maximum likelihood estimation and super learner ensemble machine learning to assess the conditional treatment effects in subgroups defined by biomarker and pathogen status.

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Background: Hundreds of millions of children in low- and middle-income countries are exposed to chronic stressors, such as poverty, poor sanitation and hygiene, and sub-optimal nutrition. These stressors can have physiological consequences for children and may ultimately have detrimental effects on child development. This study explores associations between biological measures of chronic stress in early life and developmental outcomes in a large cohort of young children living in rural Bangladesh.

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Article Synopsis
  • Postpartum depression (PPD) affects 1 in 7 women and is often missed due to reliance on in-person visits for detection.
  • A study explored using digital biomarkers from consumer wearables to identify PPD, showing that machine learning models can effectively distinguish between different stages related to pregnancy and postpartum mental health.
  • The key finding was that calories burned from the basal metabolic rate were the most predictive of PPD, with individualized machine learning models outperforming traditional methods, suggesting a new, effective approach for early detection of the condition.
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Background: Hundreds of millions of children in low- and middle-income countries are exposed to chronic stressors, such as poverty, poor sanitation and hygiene, and sub-optimal nutrition. These stressors can have physiological consequences for children and may ultimately have detrimental effects on child development. This study explores associations between biological measures of chronic stress in early life and developmental outcomes in a large cohort of young children living in rural Bangladesh.

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In trials of infectious disease interventions, rare outcomes and unpredictable spatiotemporal variation can introduce bias, reduce statistical power, and prevent conclusive inferences. Spillover effects can complicate inference if individual randomization is used to gain efficiency. Ring trials are a type of cluster-randomized trial that may increase efficiency and minimize bias, particularly in emergency and elimination settings with strong clustering of infection.

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Background: Antibiotics are increasingly used throughout the world in food animal production for controlling and preventing disease and for promoting growth. But this trend also has the potential for promoting antibiotic resistance, which represents a threat to human, animal, and environmental health. The use of antibiotics and the potential effects of antibiotic dependence has often been associated with large-scale food animal production.

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Small-scale food animal production has been celebrated as a means of economic mobility and improved food security but the use of veterinary antibiotics among these producers may be contributing to the spread of antibiotic resistance in animals and humans. In order to improve antibiotic stewardship in this sector, it is critical to identify the drivers of producers' antibiotic use. This study assessed the determinants of antibiotic use in small-scale food animal production through simulated client visits to veterinary supply stores and surveys with households that owned food animals ( = 117) in Ecuador.

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Objective: To examine differences in waking to bedtime cortisol production between children who remained with birth parents vs children placed in foster care following involvement of Child Protective Services (CPS).

Design: Between-subject comparison of cortisol patterns among 2 groups of children.

Setting: Children referred from the child welfare system.

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