Zachary Butzin-Dozier

All Publications

• IL-6 Receptor Antagonists and Severe Post-COVID-19 Outcomes: An Emulated Target Trial. medRxiv : the preprint server for health sciences Butzin-Dozier, Z., Kumar, M., Ji, Y., Wang, L. C., Jerrod Anzalone, A., Hurwitz, E., Patel, R. C., Wong, R., Bramante, C., Sines, B. 2026

  Abstract

  Interleukin-6 (IL-6) is a cytokine that plays a key role in systemic hyperinflammation and may mediate the relationship between acute COVID-19 and severe long-term outcomes such as Long COVID or death. IL-6 modulating drugs may reduce patients' risk of severe post-COVID-19 outcomes.We conducted an emulated target trial in a retrospective cohort of patients with moderate-to-severe rheumatoid arthritis who were prescribed IL-6 receptor antagonists (sarilumab or tocilizumab, pooled treatment) or other biologic agents (anakinra or baricitinib, pooled comparator) in 2022. We compared the 12-month cumulative incidence of mortality and Long COVID (diagnosed and probable) between groups using Super Learner and targeted maximum likelihood estimation, adjusting for covariates of interest.In our cohort of 3,553 patients, we found that prescription of IL-6 receptor antagonists was associated with a lower 12-month cumulative mortality (adjusted relative risk (aRR) 0.40, 95% CI 0.27, 0.59), diagnosed Long COVID aRR 0.42, 95% CI 0.23, 0.78), and probable Long COVID (aRR 0.71, 95% CI 0.61, 0.83), compared to prescription of other biologic agents, among rheumatoid arthritis patients.IL-6 receptor antagonists may prevent the incidence of severe post-COVID-19 outcomes, such as Long COVID or mortality. This supports the hypothesis that IL-6 may be a mechanistic biomarker of COVID-19 sequelae and that acute COVID-19 severity may mediate this relationship.

  View details for DOI 10.64898/2026.02.27.26347274

  View details for PubMedID 41822694

  View details for PubMedCentralID PMC12976917

• HIV Status and COVID-19 Treatment Disparities in the US National Clinical Cohort Collaborative OPEN FORUM INFECTIOUS DISEASES Essam Nkodo, E., Maheria, P., Hurwitz, E., Anzalone, A., Li, D., Islam, J. Y., Sun, J., Varley, C. D., Butzin-Dozier, Z., Safo, S. E., Kirksey, K., Hassan, S. A., Camacho-Rivera, M., Patel, R. C., Fadul, N. 2026; 13 (1): ofaf731

  Abstract

  While disparities in COVID-19 therapeutic access have been documented, the effect of HIV status on treatment access and how it intersects with other sociodemographic factors has not been well explored. Using data from the National Clinical Cohort Collaborative (N3C), we investigated disparities in COVID-19 therapeutic prescription among persons with HIV and without HIV.This was a retrospective cohort study of patients' data from January 2020 to November 2024. The study included 7 806 412 patients with a COVID-19 diagnosis, of whom 45 508 (0.58%) were persons with HIV. We employed logistic and linear regression models to assess associations between therapeutic receipt and patient characteristics.Persons with HIV had significantly higher adjusted odds of receiving COVID-19 therapeutics compared to persons without HIV (remdesivir, aOR 1.26 [95% CI: 1.20, 1.33]; nirmatrelvir/ritonavir, aOR 2.86 [95% CI: 2.77, 2.95]). Despite this, significant racial/ethnic inequities were observed. American Indian or Alaskan Native persons with HIV (estimated coefficient 0.997) and Hispanic/Latinx persons with HIV (estimated coefficient 0.992) had a lower estimated prevalence of remdesivir receipt compared to White Non-Hispanic individuals. For nirmatrelvir/ritonavir, Black/African American individuals (persons with HIV, estimated coefficient 0.947; persons without HIV, estimated coefficient 0.943), American Indian or Alaskan Native persons with HIV (estimated coefficient 0.996), and Hispanic/Latinx individuals (estimated coefficient 0.992) showed a lower estimated prevalence of receipt compared to their White counterparts.Persons with HIV demonstrated higher odds of receiving COVID-19 therapeutics than persons without HIV. However, persistent racial and ethnic inequities in treatment uptake were evident.

  View details for DOI 10.1093/ofid/ofaf731

  View details for Web of Science ID 001661609100001

  View details for PubMedID 41550697

  View details for PubMedCentralID PMC12805825

• The Effect of COVID-19 on Incident Diabetes in Pediatric Patients: Findings From the National COVID-19 Cohort Collaborative (N3C). Pediatric diabetes Wong, R., Wiggen, T., Hall, M. A., Johnson, S. G., Huling, J. D., Turner, L. E., Wilkins, K. J., Yeh, H. C., Stürmer, T., Bramante, C. T., Butzin-Dozier, Z., Buse, J. B., Reusch, J. 2025; 2025: 3545727

  Abstract

  Studies showing increased diabetes incidence in pediatric patients after COVID-19 are from data early in the pandemic, and some studies found conflicting results. Our objective was to evaluate trends in pediatric diabetes incidence and whether COVID-19 was associated with increased risk across viral variant periods.We conducted a retrospective cohort study using National COVID-19 Cohort Collaborative data to evaluate incident diabetes risk among COVID-19-positive pediatric patients compared to COVID-19-negative patients or controls with acute respiratory illness. Cohorts were weighted on demographics, data site, and body mass index percentile. The primary outcome was the cumulative incidence ratio (CIR) of incident diabetes for each viral variant era.There was no difference in the risk of incident diabetes in pediatric patients after COVID-19 compared to patients in COVID-19 negative or ARI control groups during any of the viral variant periods (e.g., ancestral period CIR 1.03, 95% CI 0.65-1.41). The predominant subtype of incident diabetes was T2D. Incidence rates over time followed a U-shaped curve, with the highest incidence in the ancestral variant period.COVID-19 was not associated with an increased risk of diabetes in pediatric patients. Incidence rates were highest early in the pandemic, and mirrored patterns of pandemic-era healthcare utilization. The predominance of incident T2D subtype is concerning for the adverse effects of pandemic-related lifestyle changes among pediatric patients.

  View details for DOI 10.1155/pedi/3545727

  View details for PubMedID 41409341

  View details for PubMedCentralID PMC12707300

• Associations Between Micronutrient Status, Hormones, and Immune Status During Pregnancy and Child Growth in Rural Bangladesh: A Prospective Cohort Study. Current developments in nutrition Chen, B., Mertens, A. N., Lin, C. H., Tan, S. T., Jamshed, F., Figueroa, D., Hemlock, C., Butzin-Dozier, Z., Fernald, L. C., Stewart, C. P., Hubbard, A. E., Rahman, M. Z., Ali, S., Arnold, B. F., Dhabhar, F. S., Granger, D. A., Rahman, M., Luby, S. P., Colford, J. M., Lin, A. 2025; 9 (12): 107596

  Abstract

  Poor growth in early childhood is associated with increased mortality, impaired cognitive development, and reduced adult economic productivity, which may result in higher risks of social immobility and intergenerational poverty.We aimed to evaluate whether maternal hormones, immune status, and micronutrient status during all trimesters of pregnancy were associated with child growth outcomes in the first two years after birth.This observational study used data collected from the WASH Benefits trial in rural Bangladesh to examine associations between maternal hormones [plasma cortisol, estriol], immune status [C-reactive protein, α-1-acid glycoprotein (AGP), cytokine sum score], and micronutrient status [vitamin D (25-hydroxy-D [25(OH)D]), ferritin, soluble transferrin receptor, retinol binding protein (RBP)] during pregnancy and subsequent measures of child growth. Length-for-age z-score (LAZ), weight-for-length z-score (WLZ), and insulin-like growth factor 1 (IGF-1) at 3, 14, and 28 mo were measured as the primary outcomes. All outcomes were adjusted for confounding variables, and the P values were adjusted using the Benjamini-Hochberg procedure. We used generalized additive models, adjusted for covariates, and reported the mean difference in outcomes between the 25th and 75th percentiles of the exposure distribution.In the adjusted models of this study (n = 636), at 3 mo of age, maternal AGP and RBP were positively associated with infant WLZ. By 14 mo, higher maternal estriol was linked with higher LAZ, and RBP remained positively associated with WLZ. At 28 mo, maternal estriol showed a negative association with IGF-1, and a higher cytokine sum score was negatively associated with WLZ.These findings suggest the possible pathways through which maternal biomarkers influence early childhood growth, highlighting the intrauterine environment's critical role in shaping developmental outcomes.The parent trial was registered at clinicaltrials.gov (NCT01590095).

  View details for DOI 10.1016/j.cdnut.2025.107596

  View details for PubMedID 41467215

  View details for PubMedCentralID PMC12744257

• Identifying People Living With or Those at Risk for HIV in a Nationally Sampled Electronic Health Record Repository Called the National Clinical Cohort Collaborative: Computational Phenotyping Study. JMIR medical informatics Hurwitz, E., Varley, C. D., Anzolone, A. J., Madhira, V., Olex, A. L., Sun, J., Vaidya, D., Fadul, N., Islam, J. Y., Jackson, L. E., Wilkins, K. J., Butzin-Dozier, Z., Li, D., Safo, S. E., McMurry, J. A., Maheria, P., Williams, T., Hassan, S. A., Haendel, M. A., Patel, R. C. 2025; 13: e68143

  Abstract

  Electronic health records (EHRs) provide valuable insights to address clinical and epidemiological research concerning HIV, including the disproportionate impact of the COVID-19 pandemic on people living with HIV. To identify this population, most studies using EHR or claims databases start with diagnostic codes, which can result in misclassification without further refinement using drug or laboratory data. Furthermore, given that antiretrovirals now have indications for both HIV and COVID-19 (ie, ritonavir in nirmatrelvir/ritonavir), new phenotyping methods are needed to better capture people living with HIV. Therefore, we created a generalizable and innovative method to robustly identify people living with HIV, preexposure prophylaxis (PrEP) users, postexposure prophylaxis (PEP) users, and people not living with HIV using granular clinical data after the emergence of COVID-19.The primary aim of this study was to use computational phenotyping in EHR data to identify people living with HIV (cohort 1), PrEP users (cohort 2), PEP users (cohort 3), or "none of the above" (people not living with HIV; cohort 4) and describe COVID-19-related characteristics among these cohorts.We used diagnostic and laboratory measurements and drug concepts in the National Clinical Cohort Collaborative to create a computational phenotype for the 4 cohorts with confidence levels. For robustness, we conducted a randomly sampled, blinded clinician annotation to assess precision. We calculated the distribution of demographics, comorbidities, and COVID-19 variables among the 4 cohorts.We identified 132,664 people living with HIV with a high level of confidence, 36,088 PrEP users, 4120 PEP users, and 20,639,675 people not living with HIV. Most people living with HIV were identified by a combination of medical conditions, laboratory measurements, and drug exposures (74,809/132,664, 56.4%), followed by laboratory measurements and drug exposures (15,241/132,664, 11.5%) and then by medical conditions and drug exposures (14,595/132,664, 11%). A higher proportion of people living with HIV experienced COVID-19-related hospitalization (4650,132,664, 3.5%) or mortality (828/132,664, 0.6%) and all-cause mortality (2083/132,664, 1.6%) compared to other cohorts.Using an extensive phenotyping algorithm leveraging granular data in an EHR repository, we have identified people living with HIV, people not living with HIV, PrEP users, and PEP users. Our findings offer transferable lessons to optimize future EHR phenotyping for these cohorts.

  View details for DOI 10.2196/68143

  View details for PubMedID 40644699

  View details for PubMedCentralID PMC12299939

• Causal Inference via Electronic Health Records in the National Clinical Cohort Collaborative: Challenges and Solutions in Long COVID Research. medRxiv : the preprint server for health sciences Butzin-Dozier, Z., Ji, Y., Wang, L. C., Anzalone, A. J., Hurwitz, E., Patel, R. C., van der Laan, M. J., Colford, J. M., Hubbard, A. E. 2025

  Abstract

  Observational analyses of electronic health record (EHR) data using databases such as the National Clinical Cohort Collaborative include unique challenges for researchers seeking causal inferences, particularly when evaluating subjectively-defined outcomes like Long COVID. We explore several challenges and describe potential solutions. 1. Lack of true negatives: Many diagnoses and conditions either have a positive indicator or a missing status, requiring investigators to carefully consider which patients are likely negative for this condition. 2. Differential monitoring: EHR data include nonrandom missingness driven by patients engaging with the healthcare system at different rates, which is often related to both the exposure and outcome of interest. 3. Bias: EHR data sources face many biases, but are particularly vulnerable to informative missingness, differential monitoring, and model misspecification. 4. Large sample size: High precision (i.e., narrow confidence intervals) paired with potential bias leads to a high risk of incorrectly rejecting the null hypothesis. 5. Defining index time: It is important that investigators deliberately define index time (i.e., t 0 , baseline) to ensure that they only adjust for baseline confounders and do not adjust for (or condition on) factors that are affected by the exposure of interest (i.e., colliders or mediators). 6. Parameter selection: Investigators should only select parameters that are supported by the data distribution. This manuscript provides an overview of these challenges and solutions, using both simulated data and real-world data, with the outcome of Long COVID as the running example.

  View details for DOI 10.1101/2025.06.06.25329168

  View details for PubMedID 40502605

  View details for PubMedCentralID PMC12155030

• Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study. JMIR formative research Hurwitz, E., Meltzer-Brody, S., Butzin-Dozier, Z., Patel, R. C., Elhadad, N., Haendel, M. A. 2025; 9: e67585

  Abstract

  Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. 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 (eg, Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect.We aimed to suggest that wear time of a wearable device may provide additional information about perinatal mental health to facilitate screening and early detection of PPD. We proposed that wear time of a wearable device may also be valuable for managing other mental health disorders.Using the All of Us Research Program dataset, we identified females who experienced childbirth with and without PPD using computational phenotyping. We compared the percentage of days and number of hours per day females with and without PPD wore Fitbit devices during prepregnancy, pregnancy, postpartum, and PPD periods, determined by electronic health records. Comparisons between females with and without PPD were conducted using linear regression models. We also assessed the correlation between Fitbit wear time consistency (measured as the maximum number of consecutive days the Fitbit was worn) during prepregnancy and PPD periods in females with and without PPD using the Pearson correlation. All analyses were run with Bonferroni correction.Our findings showed a strong trend, although nonsignificant after multiple testing correction, that females in the PPD cohort wore their Fitbits more than those in non-PPD cohort during the postpartum (PPD cohort: mean 69.9%, 95% CI 42.7%-97%; non-PPD cohort: mean 50%, 95% CI 25.5%-74.4%; P=.02) and PPD periods (PPD cohort: mean 66.6%, 95% CI 37.9%-95.3%; non-PPD cohort: mean 46.4%, 95% CI 20.5%-72.2%; P=.02). We found no difference in the number of hours per day females in the PPD and non-PPD cohorts wore their Fitbit during any period of pregnancy. Finally, there was no relationship between the consistency of Fitbit wear time during prepregnancy and PPD periods (r=-0.05, 95% CI -0.46 to 0.38; P=.84); however, there was a trend, though nonsignificant, in Fitbit wear time consistency among females without PPD (r=0.25, 95% CI -0.02 to 0.49; P=.07).We hypothesize that increased Fitbit wear time among females with PPD may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that wear time patterns of a wearable device combined with digital biomarkers such as sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns and facilitating timely screenings, which may have implications for other mental health disorders.

  View details for DOI 10.2196/67585

  View details for PubMedID 40409746

  View details for PubMedCentralID PMC12144471

• COVID-19 Vaccination Timing, Relative to Acute COVID-19, and Subsequent Risk of Long COVID. medRxiv : the preprint server for health sciences Butzin-Dozier, Z., Ji, Y., Wang, L. C., Anzalone, A. J., Coyle, J., Phillips, R. V., Patel, R. C., Sun, J., Hurwitz, E., Deshpande, S., Shi, J. S., Mertens, A., van der Laan, M. J., Colford, J. M., Hubbard, A. E. 2025

  Abstract

  Long COVID is a debilitating condition that impacts millions of Americans, but patients and clinicians have little information on how to prevent this disorder. Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID. There is limited evidence regarding the optimal timing of COVID-19 vaccination (i.e., vaccination schedule) to minimize the risk of Long COVID.We applied Longitudinal Targeted Maximum Likelihood Estimation to electronic health record (EHR) data from a retrospective cohort of patients vaccinated against COVID-19 between December 2021 and September 2022. We evaluated the association between binary COVID-19 vaccination status (two or more doses vs. zero doses) and 12-month Long COVID risk among patients diagnosed with acute COVID-19 between December 2021 and September 2022. In addition, we compared the 12-month cumulative risk of Long COVID (ICD-10 code U09.9) among patients diagnosed with acute COVID-19 one to three months after vaccination, three to five months after vaccination, or five to seven months after vaccination while adjusting for relevant high-dimensional baseline and time-dependent covariates.We analyzed EHR data from a retrospective cohort of 1,558,018 patients. In our binary cohort (n = 519,980), we found that vaccinated patients had a lower risk of Long COVID than unvaccinated patients (adjusted marginal risk ratio 0.84 (0.81, 0.88)). In our longitudinal cohort (n = 1,085,291), we did not find a significant difference in Long COVID risk comparing patients who were diagnosed with acute COVID-19 one to three months after vaccination versus patients who were diagnosed with COVID-19 three to five months (adjusted marginal risk ratio 0.93 (95% CI 0.62, 1.41) or 5 to 7 months (adjusted marginal risk ratio 1.06 (95% CI 0.72, 1.56)) after vaccination.We found that COVID-19 vaccination before SARS-CoV-2 infection was protective against Long COVID, and we did not find that this protection significantly waned within 7 months after vaccination. These findings suggest that COVID-19 vaccination protects against Long COVID.

  View details for DOI 10.1101/2025.04.22.25326224

  View details for PubMedID 40313290

  View details for PubMedCentralID PMC12045423

• Maternal experience of intimate partner violence, maternal depression, and parental stress are not associated with child telomere length in Bangladesh. Scientific reports Figueroa, D., Al Mamun, M. M., Jung, D. K., Li, G., Tan, S. T., Jamshed, F., Butzin-Dozier, Z., Mertens, A. N., Lin, J., Pitchik, H. O., Parvin, K., Silvera, A., Fernald, L. C., Arnold, B. F., Ali, S., Shoab, A. K., Famida, S. L., Akther, S., Rahman, M. Z., Hossen, M. S., Mutsuddi, P., Rahman, M., Unicomb, L., Kariger, P., Stewart, C. P., Hubbard, A. E., Benjamin-Chung, J., Dhabhar, F. S., Luby, S. P., Colford, J. M., Naved, R. T., Lin, A. 2025; 15 (1): 8499

  Abstract

  Shorter telomere length (TL) is associated with an increased risk for developing chronic or age-related diseases in adults. The process of telomere shortening is accelerated in response to stress and is well characterized in adult populations from high-income countries. Prior studies suggest the relationship between stress, shorter TL, and disease risk initiates in early life. Nested within the WASH Benefits Bangladesh trial, we examined associations between parental stressors, including maternal exposure to intimate partner violence (IPV), maternal depressive symptoms, and parental perceived stress, and child TL in rural Bangladesh. We measured whole blood relative TL in 660 children at median age 14 months and 702 children at median age 28 months. We estimated mean differences between the 25th and 75th percentile or absence and presence of each exposure using generalized additive models. IPV during pregnancy was associated with more TL attrition between 14 and 28 months (- 0.32 (95% CI - 0.64, - 0.01), p-value 0.05). This association was not significant after correction for multiple comparisons. Other parental psychosocial stressors were not associated with child TL outcomes at 14 or 28 months of age in rural Bangladesh. Telomere biology during early-life development may vary across settings.

  View details for DOI 10.1038/s41598-025-90505-2

  View details for PubMedID 40075126

  View details for PubMedCentralID 8885817

• Treatment heterogeneity of water, sanitation, hygiene, and nutrition interventions on child growth by environmental enteric dysfunction and pathogen status for young children in Bangladesh. PLoS neglected tropical diseases Butzin-Dozier, Z., Ji, Y., Coyle, J., Malenica, I., Rogawski McQuade, E. T., Grembi, J. A., Platts-Mills, J. A., Houpt, E. R., Graham, J. P., Ali, S., Rahman, M. Z., Alauddin, M., Famida, S. L., Akther, S., Hossen, M. S., Mutsuddi, P., Shoab, A. K., Rahman, M., Islam, M. O., Miah, R., Taniuchi, M., Liu, J., Alauddin, S. T., Stewart, C. P., Luby, S. P., Colford, J. M., Hubbard, A. E., Mertens, A. N., Lin, A. 2025; 19 (2): e0012881

  Abstract

  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.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. We analyzed treatment (N+WSH, WSH, N, or control) randomly assigned in-utero, child pathogen and EED data at 14 months of age, and child HAZ at 28 months of age. We estimated the difference in mean child height for age Z-score (HAZ) under the treatment rule and the difference in stratified treatment effect (treatment effect difference) comparing children with high versus low pathogen/biomarker status while controlling for baseline covariates.We analyzed data from 1,522 children who had a median HAZ of -1.56. We found that fecal myeloperoxidase (N+WSH treatment effect difference 0.0007 HAZ, WSH treatment effect difference 0.1032 HAZ, N treatment effect difference 0.0037 HAZ) and Campylobacter infection (N+WSH treatment effect difference 0.0011 HAZ, WSH difference 0.0119 HAZ, N difference 0.0255 HAZ) were associated with greater effect of all interventions on anthropometry. In other words, children with high myeloperoxidase or Campylobacter infection experienced a greater impact of the interventions on anthropometry. We found that a treatment rule that assigned the N+WSH (HAZ difference 0.23, 95% CI (0.05, 0.41)) and WSH (HAZ difference 0.17, 95% CI (0.04, 0.30)) interventions based on EED biomarkers and pathogens increased predicted child growth compared to the randomly allocated intervention.These findings indicate that EED biomarkers and pathogen status, particularly Campylobacter and myeloperoxidase (a measure of gut inflammation), may be related to the impact of N+WSH, WSH, and N interventions on child linear growth.

  View details for DOI 10.1371/journal.pntd.0012881

  View details for PubMedID 39965021

• Association of glycemic control with Long COVID in patients with type 2 diabetes: findings from the National COVID Cohort Collaborative (N3C). BMJ open diabetes research & care Soff, S., Yoo, Y. J., Bramante, C., Reusch, J. E., Huling, J. D., Hall, M. A., Brannock, D., Sturmer, T., Butzin-Dozier, Z., Wong, R., Moffitt, R. 2025; 13 (1)

  Abstract

  Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear.Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D).We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control.Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes.Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.

  View details for DOI 10.1136/bmjdrc-2024-004536

  View details for PubMedID 39904520

  View details for PubMedCentralID PMC11795369

• Water, sanitation, handwashing, and nutritional interventions can reduce child antibiotic use: evidence from Bangladesh and Kenya. Nature communications Ercumen, A., Mertens, A. N., Butzin-Dozier, Z., Jung, D. K., Ali, S., Achando, B. S., Rao, G., Hemlock, C., Pickering, A. J., Stewart, C. P., Tan, S. T., Grembi, J. A., Benjamin-Chung, J., Wolfe, M., Ho, G. G., Rahman, M. Z., Arnold, C. D., Dentz, H. N., Njenga, S. M., Meerkerk, T., Chen, B., Nadimpalli, M., Islam, M. A., Hubbard, A. E., Null, C., Unicomb, L., Rahman, M., Colford, J. M., Luby, S. P., Arnold, B. F., Lin, A. 2025; 16 (1): 556

  Abstract

  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. Over 50% of children used antibiotics at least once in the 90 days preceding data collection. In Bangladesh, the prevalence of antibiotic use was 10-14% lower in groups receiving WSH (prevalence ratio [PR] = 0.90 (0.82-0.99)), nutrition (PR = 0.86 (0.78-0.94)), and nutrition+WSH (PR = 0.86 (0.79-0.93)) interventions. The prevalence of using antibiotics multiple times was 26-35% lower in intervention arms. Reductions were largest when the birth cohort was younger. In Kenya, interventions did not affect antibiotic use. In this work, we show that improving WSH and nutrition can reduce antibiotic use. Studies should assess whether such reductions translate to reduced antimicrobial resistance.

  View details for DOI 10.1038/s41467-024-55801-x

  View details for PubMedID 39788996

  View details for PubMedCentralID 3811232

• SSRI use during acute COVID-19 and risk of long COVID among patients with depression. BMC medicine Butzin-Dozier, Z., Ji, Y., Deshpande, S., Hurwitz, E., Anzalone, A. J., Coyle, J., Shi, J., Mertens, A., van der Laan, M. J., Colford, J. M., Patel, R. C., Hubbard, A. E. 2024; 22 (1): 445

  Abstract

  Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus particles in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication.In an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before acute COVID-19 and not ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using nonparametric targeted maximum likelihood estimation to aggressively adjust for high-dimensional covariates.We analyzed a sample (n = 302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, 0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription 1 to 4 months before acute COVID-19 with no prior history of SSRI use) to nonusers (adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)).These findings suggest that SSRI use during acute COVID-19 may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.

  View details for DOI 10.1186/s12916-024-03655-x

  View details for PubMedID 39380062

  View details for PubMedCentralID PMC11462648

• Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative. EBioMedicine Bergquist, T., Loomba, J., Pfaff, E., Xia, F., Zhao, Z., Zhu, Y., Mitchell, E., Bhattacharya, B., Shetty, G., Munia, T., Delong, G., Tariq, A., Butzin-Dozier, Z., Ji, Y., Li, H., Coyle, J., Shi, S., Philips, R. V., Mertens, A., Pirracchio, R., van der Laan, M., Colford, J. M., Hubbard, A., Gao, J., Chen, G., Velingker, N., Li, Z., Wu, Y., Stein, A., Huang, J., Dai, Z., Long, Q., Naik, M., Holmes, J., Mowery, D., Wong, E., Parekh, R., Getzen, E., Hightower, J., Blase, J. 2024; 108: 105333

  Abstract

  While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID.A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.).Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events.As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions.Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

  View details for DOI 10.1016/j.ebiom.2024.105333

  View details for PubMedID 39321500

  View details for PubMedCentralID PMC11462169

• Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study. JMIR public health and surveillance Butzin-Dozier, Z., Ji, Y., Li, H., Coyle, J., Shi, J., Phillips, R. V., Mertens, A. N., Pirracchio, R., van der Laan, M. J., Patel, R. C., Colford, J. M., Hubbard, A. E. 2024; 10: e53322

  Abstract

  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. However, current knowledge regarding PASC risk factors is limited.Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States.We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites.We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis.The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment.RR2-10.1101/2023.07.27.23293272.

  View details for DOI 10.2196/53322

  View details for PubMedID 39146534

  View details for PubMedCentralID PMC11364083

• Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study. JMIR mHealth and uHealth Hurwitz, E., Butzin-Dozier, Z., Master, H., O'Neil, S. T., Walden, A., Holko, M., Patel, R. C., Haendel, M. A. 2024; 12: e54622

  Abstract

  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. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition.The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD.Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score.Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection.This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

  View details for DOI 10.2196/54622

  View details for PubMedID 38696234

  View details for PubMedCentralID PMC11099816

• Stress biomarkers and child development in young children in Bangladesh. Psychoneuroendocrinology Butzin-Dozier, Z., Mertens, A. N., Tan, S. T., Granger, D. A., Pitchik, H. O., Il'yasova, D., Tofail, F., Rahman, M. Z., Spasojevic, I., Shalev, I., Ali, S., Karim, M. R., Shahriar, S., Famida, S. L., Shuman, G., Shoab, A. K., Akther, S., Hossen, M. S., Mutsuddi, P., Rahman, M., Unicomb, L., Das, K. K., Yan, L., Meyer, A., Stewart, C. P., Hubbard, A. E., Naved, R. T., Parvin, K., Mamun, M. M., Luby, S. P., Colford, J. M., Fernald, L. C., Lin, A. 2024; 164: 107023

  Abstract

  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.We assessed physiologic measures of stress in the first two years of life using measures of the hypothalamic-pituitary-adrenal (HPA) axis (salivary cortisol and glucocorticoid receptor gene methylation), the sympathetic-adrenal-medullary (SAM) system (salivary alpha-amylase, heart rate, and blood pressure), and oxidative status (F2-isoprostanes). We assessed child development in the first two years of life with the MacArthur-Bates Communicative Development Inventories (CDI), the WHO gross motor milestones, and the Extended Ages and Stages Questionnaire (EASQ). We compared development outcomes of children at the 75th and 25th percentiles of stress biomarker distributions while adjusting for potential confounders using generalized additive models, which are statistical models where the outcome is predicted by a potentially non-linear function of predictor variables.We analyzed data from 684 children (49% female) at both 14 and 28 months of age; we included an additional 765 children at 28 months of age. We detected a significant relationship between HPA axis activity and child development, where increased HPA axis activity was associated with poor development outcomes. Specifically, we found that cortisol reactivity (coefficient -0.15, 95% CI (-0.29, -0.01)) and post-stressor levels (coefficient -0.12, 95% CI (-0.24, -0.01)) were associated with CDI comprehension score, post-stressor cortisol was associated with combined EASQ score (coefficient -0.22, 95% CI (-0.41, -0.04), and overall glucocorticoid receptor methylation was associated with CDI expression score (coefficient -0.09, 95% CI (-0.17, -0.01)). We did not detect a significant relationship between SAM activity or oxidative status and child development.Our observations reveal associations between the physiological evidence of stress in the HPA axis with developmental status in early childhood. These findings add to the existing evidence exploring the developmental consequences of early life stress.

  View details for DOI 10.1016/j.psyneuen.2024.107023

  View details for PubMedID 38522372

• Stress Biomarkers and Child Development in Young Children in Bangladesh. medRxiv : the preprint server for health sciences Butzin-Dozier, Z., Mertens, A. N., Tan, S. T., Granger, D. A., Pitchik, H. O., Il'yasova, D., Tofail, F., Rahman, M. Z., Spasojevic, I., Shalev, I., Ali, S., Karim, M. R., Shahriar, S., Famida, S. L., Shuman, G., Shoab, A. K., Akther, S., Hossen, M. S., Mutsuddi, P., Rahman, M., Unicomb, L., Das, K. K., Yan, L., Meyer, A., Stewart, C. P., Hubbard, A., Tabassum Naved, R., Parvin, K., Mamun, M. M., Luby, S. P., Colford, J. M., Fernald, L. C., Lin, A. 2023

  Abstract

  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.We assessed physiologic measures of stress in the first two years of life using measures of the hypothalamic-pituitary-adrenal (HPA) axis (salivary cortisol and glucocorticoid receptor gene methylation), the sympathetic-adrenal-medullary (SAM) system (salivary alpha-amylase, heart rate, and blood pressure), and oxidative status (F2-isoprostanes). We assessed child development in the first two years of life with the MacArthur-Bates Communicative Development Inventories (CDI), the WHO gross motor milestones, and the Extended Ages and Stages Questionnaire (EASQ). We compared development outcomes of children at the 75th and 25th percentiles of stress biomarker distributions while adjusting for potential confounders (hereafter referred to as contrasts) using generalized additive models, which are statistical models where the outcome is predicted by a potentially non-linear function of predictor variables.We analyzed data from 684 children (49% female) at both 14 and 28 months of age; we included an additional 765 children at 28 months of age. We observed 135 primary contrasts of the differences in child development outcomes at the 75th and 25th percentiles of stress biomarkers, where we detected significant relationships in 5 out of 30 contrasts (17%) of HPA axis activity, 1 out of 30 contrasts (3%) of SAM activity, and 3 out of 75 contrasts (4%) of oxidative status. These findings revealed that measures of HPA axis activity were associated with poor development outcomes. We did not find consistent evidence that markers of SAM system activity or oxidative status were associated with developmental status.Our observations reveal associations between the physiological evidence of stress in the HPA axis with developmental status in early childhood. These findings add to the existing evidence exploring the developmental consequences of early life stress.

  View details for DOI 10.1101/2023.09.12.23295429

  View details for PubMedID 37745503

  View details for PubMedCentralID PMC10516093

• A Review of the Ring Trial Design for Evaluating Ring Interventions for Infectious Diseases. Epidemiologic reviews Butzin-Dozier, Z., Athni, T. S., Benjamin-Chung, J. 2022

  Abstract

  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. They can be used to evaluate ring interventions, which are delivered to individuals in proximity to or contact with index cases. Here we review ring trials, compare them to other trial designs for evaluating ring interventions, and describe strengths and weaknesses of each design. We conducted a systematic review to identify trials and trial protocols evaluating ring interventions. Of 849 articles and 322 protocols screened, we identified 26 ring trials, 15 cluster-randomized trials, five trials that randomized households or individuals within rings, and one individually randomized trial. The most common interventions were post-exposure prophylaxis (n = 23) and focal mass drug administration and screening and treatment (n = 7). Ring trials require robust surveillance systems and contact tracing for directly transmitted diseases. For rare diseases with strong spatiotemporal clustering, they may have higher efficiency and internal validity than cluster-randomized designs in part because they ensure that no clusters are excluded from analysis due to zero cluster incidence. Though further research is needed to compare them to other types of trials, ring trials hold promise as a design that can increase trial speed and efficiency while reducing bias.

  View details for DOI 10.1093/epirev/mxac003

  View details for PubMedID 35593400

• Antibiotic use by backyard food animal producers in Ecuador: a qualitative study. BMC public health Waters, W. F., Baca, M., Graham, J. P., Butzin-Dozier, Z., Vinueza, L. 2022; 22 (1): 685

  Abstract

  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. But rural households also engage in small-scale production, often operating literally in backyards. While some small-scale producers use veterinary antibiotics, many do not. This paper examines knowledge, attitudes, beliefs, and agricultural practices (KAP) that represent an alternative to dependence on antibiotics.Qualitative field research was based on four focus group discussions (FGDs) with non-indigenous backyard food animal producers in four communities near Quito, Ecuador and two FGDs with veterinarians. FGDs were supplemented by structured observations and key informant interviews. They were recorded with digital audio devices and transcriptions were analyzed independently by two researchers using a three-stage coding procedure. Open coding identifies underlying concepts, while axial coding develops categories and properties, and selective coding integrates the information in order to identify the key dimensions of the collective qualitative data.Backyard food animal producers in the Ecuadorian highlands generally do not use antibiotics while rearing small batches of animals and poultry for predominantly non-commercial household consumption. Instead, they rely on low cost traditional veterinary remedies. These practices are informed by their Andean history of agriculture and a belief system whereby physical activity is a holistic lifestyle through which people maintain their health by participating in the physical and spiritual environment.Backyard food animal producers in the Ecuadorian highlands implement complex strategies based on both economic calculations and sociocultural underpinnings that shape perceptions, attitudes, and practices. They use traditional veterinary remedies in lieu of antibiotics in most cases because limited production of food animals in small spaces contributes to a predictable household food supply, while at the same time conforming to traditional concepts of human and environmental health.

  View details for DOI 10.1186/s12889-022-13073-4

  View details for PubMedID 35395759

  View details for PubMedCentralID PMC8991794

• EFFECTS OF DRINKING WATER, SANITATION, HANDWASHING, AND NUTRITIONAL INTERVENTIONS ON IMMUNE STATUS IN YOUNG CHILDREN: A CLUSTER-RANDOMIZED CONTROLLED TRIAL IN RURAL BANGLADESH Lin, A., Mertens, A. N., Tan, S., Rahman, M., Hester, L., Kim, L., Arnold, B. F., Karim, M., Shahriar, S., Ali, S., Shoab, A. K., Hossen, M., Mutsuddi, P., Famida, S. L., Akther, S., Rahman, M., Unicomb, L., Butzin-Dozier, Z., Hemlock, C., Hubbard, A. E., Stewart, C. P., Fernald, L. C., Colford, J. M., Luby, S. P., Dhabhar, F. S. AMER SOC TROP MED & HYGIENE. 2021: 287

  View details for Web of Science ID 000778105602586

• Assessing Upstream Determinants of Antibiotic Use in Small-Scale Food Animal Production through a Simulated Client Method. Antibiotics (Basel, Switzerland) Butzin-Dozier, Z., Waters, W. F., Baca, M., Vinueza, R. L., Saraiva-Garcia, C., Graham, J. 2020; 10 (1)

  Abstract

  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 (n = 117) in Ecuador. Eighty percent of households with food animals owned chickens and 78% of those with chickens owned fewer than 10 birds. Among the households with small-scale food animals, 21% reported giving antibiotics to their food animals within the last six months. Simulated client visits indicated that veterinary sales agents frequently recommended inappropriate antibiotic use, as 66% of sales agents recommended growth promoting antibiotics, and 48% of sales agents recommended an antibiotic that was an inappropriate class for disease treatment. In contrast, few sales agents (3%) were willing to sell colistin, an antibiotic banned for veterinary use in Ecuador as of January 2020, which supports the effectiveness of government regulation in antibiotic stewardship. The cumulative evidence provided by this study indicates that veterinary sales agents play an active role in promoting indiscriminate and inappropriate use of antibiotics in small-scale food animal production.

  View details for DOI 10.3390/antibiotics10010002

  View details for PubMedID 33374513

  View details for PubMedCentralID PMC7822171

• Cortisol production patterns in young children living with birth parents vs children placed in foster care following involvement of Child Protective Services. Archives of pediatrics & adolescent medicine Bernard, K., Butzin-Dozier, Z., Rittenhouse, J., Dozier, M. 2010; 164 (5): 438-43

  Abstract

  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).Between-subject comparison of cortisol patterns among 2 groups of children.Children referred from the child welfare system.Three hundred thirty-nine children aged 2.9 to 31.4 months who were living with birth parents (n = 155) or placed in foster care (n = 184) following CPS involvement as well as 96 unmatched children from low-risk environments. Main Exposures Involvement by CPS and foster care. Main Outcome Measure Salivary cortisol samples obtained at waking and bedtime for children on 2 days.Child Protective Services-involved children who continued to live with birth parents and CPS-involved children placed in foster care differed in cortisol production, with children living with their birth parents showing flatter slopes in waking to bedtime values.Continuing to live with birth parents following involvement of CPS is associated with greater perturbation to the diurnal pattern of cortisol production than living with foster parents. Foster care may have a regulating influence on children's cortisol among children who have experienced maltreatment.

  View details for DOI 10.1001/archpediatrics.2010.54

  View details for PubMedID 20439794

  View details for PubMedCentralID PMC3213033