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Abstract Context Women with elevated body mass index are encouraged to lose weight before pregnancy, but no trials have tested the effects of prepregnancy weight loss on risk of developing gestational diabetes. Objective This work aims to determine whether prepregnancy weight loss improved the early metabolic environment as measured by early gestational diabetes diagnosis. Methods This was a secondary analysis of a pragmatic randomized clinical trial conducted between May 2015 and October 2019 in an integrated health system that encouraged first-trimester gestational diabetes screening for high-risk women, including those with obesity. Women aged 18 to 40 years with a body mass index (BMI) greater than or equal to 27 who were planning pregnancy were randomly assigned to a behavioral weight loss intervention or usual care. Clinical care decisions and data collection were blind to condition assignment. We compared rates of diagnosis with gestational diabetes in early pregnancy between the groups using logistic regression. Results Of 326 participants, 168 (89 in the intervention and 79 in usual care) had singleton pregnancies during the study period. At baseline, mean age was 31.3 ± 3.5 years and BMI was 34.8 ± 5.8. Fifty-nine (66%) intervention participants and 57 (72%) usual care participants underwent early screening. Among those, intervention participants were 73% less likely to be diagnosed with gestational diabetes than usual care participants (adjusted odds ratio [aOR], 0.27; 95% CI, 0.09-0.80). There was no difference in diagnosis of gestational diabetes in later pregnancy (aOR, 1.08; 95% CI, 0.41-2.81). Conclusion Participation in a prepregnancy weight loss intervention led to lower rates of gestational diabetes diagnosis in early pregnancy. This suggests positive effects of prepregnancy weight loss on the early metabolic environment, a critical factor in offspring metabolic risk.

We describe a methodology for assessing agreement and reliability among a set of shapes. Motivated by recent studies of the reliability of manually segmented medical images, we focus on shapes composed of rasterized, binary-valued data representing closed geometric regions of interest. The methodology naturally generalizes to N dimensions and other data types, though. We formulate the shape variance, shape correlation and shape intraclass correlation coefficient (ICC) in terms of a simple distance metric, the Manhattan norm, which quantifies the absolute difference between any two shapes. We demonstrate applications of this methodology by working through example shape variance calculations in 1-D, for the analysis of overlapping line segments, and 2-D, for the analysis of overlapping regions. We also report the results of a simulated reliability analysis of manually delineated shape boundaries, and we compare the shape ICC with the more conventional and commonly used area ICC. The proposed shape-sensitive methodology captures all of the variation in the shape measurements, and it provides a more accurate estimate of the measurement reliability than an analysis of only the measured areas.

There are known disparities in U.S. COVID-19 vaccination but there is limited information on national vaccine uptake in a large, racially diverse, all-age population. Here, we describe COVID-19 vaccination coverage in a large U.S. population accessing care in OCHIN (not an acronym), a national network of community-based healthcare organizations. Within OCHIN, we identified patients aged 6 months and older with ≥1 completed clinical encounter since becoming age-eligible for the COVID-19 vaccine between December 13, 2020 and December 31, 2022. Patients' COVID-19 vaccination status was assessed from OCHIN's Epic® electronic health record which includes data from state immunization information systems. Patients were considered vaccinated if they received ≥1 dose of a monovalent vaccine product; coverage was categorized by age groups (6 months-4 years; 5–11 years, 12–15 years, 16+ years). Multivariate analyses assessed factors associated with COVID-19 vaccination across age groups. The cohort included 3.3 million Hispanic (37 %), non-Hispanic (NH) White (31 %), NH Black (15 %), and NH Asian (7 %) patients; 45 % of whom were Medicaid-enrolled, 19 % uninsured, and 53 % with a household income below 100 % of the federal poverty level. The proportion with ≥1 COVID-19 vaccine dose increased with age, from 11.7 % (6 months through 4 years) to 72.3 % (65 years and older). The only factors associated with significantly higher COVID-19 vaccine coverage across age groups were prior receipt of an influenza vaccine and having private insurance. In adjusted modeling, when compared to NH whites, COVID-19 vaccine coverage was significantly higher among Hispanic, NH Asian, and NH multiple-race patients aged ≥5 years and significantly lower among NH Black and NH Native Hawaiian/Other Pacific Islander patients aged 6 months-4 years old. We identified disparities in primary series COVID-19 vaccine coverage by age, race and ethnicity, household income, insurance status, and prior influenza vaccination within this large, diverse population accessing care in community-based healthcare organizations.

Except for spontaneous reporting systems, vaccine safety monitoring generally involves pre-specifying health outcomes and post-vaccination risk windows of concern. Instead, we used tree-based data-mining to look more broadly for possible adverse events after Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccination. Vaccine Safety Datalink enrollees receiving ≥1 dose of COVID-19 vaccine in 2020–2021 were followed for 70 days after Pfizer-BioNTech or Moderna and 56 days after Janssen vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the post-vaccination follow-up period. We used the self-controlled tree-temporal scan statistic and TreeScan software. Monte Carlo simulation was used to estimate p-values; p = 0.01 was the pre-specified cut-off for statistical significance of a cluster. There were 4.1, 2.6, and 0.4 million Pfizer-BioNTech, Moderna, and Janssen vaccinees, respectively. Clusters after Pfizer-BioNTech vaccination included: (1) unspecified adverse effects, (2) common vaccine reactions, such as fever, myalgia, and headache, (3) myocarditis/pericarditis, and (4) less specific cardiac or respiratory symptoms, all with the strongest clusters generally after Dose 2; and (5) COVID-19/viral pneumonia/sepsis/respiratory failure in the first 3 weeks after Dose 1. Moderna results were similar but without a significant myocarditis/pericarditis cluster. Further investigation suggested the fifth signal group was a manifestation of mRNA vaccine effectiveness after the first 3 weeks. Janssen vaccinees had clusters of unspecified or common vaccine reactions, gait/mobility abnormalities, and muscle weakness. The latter two were deemed to have arisen from confounding related to practices at one site. We detected post-vaccination clusters of unspecified adverse effects, common vaccine reactions, and, for the mRNA vaccines, chest pain and palpitations, as well as myocarditis/pericarditis after Pfizer-BioNTech Dose 2. Unique advantages of this data mining are its untargeted nature and its inherent adjustment for the multiplicity of diagnoses and risk intervals scanned.

Children may receive measles-mumps-rubella (MMR) and varicella (VAR) vaccines separately or as measles-mumps-rubella-varicella (MMRV). We examined whether pediatric herpes zoster (HZ) incidence varied by pattern of varicella vaccine administration. In six integrated health systems, we examined HZ incidence among children turning 12 months old during 2003–2008. All received varicella and MMR vaccines on recommended schedules. Cases were identified through 2014 using ICD-9 codes. Incidence was examined by number of varicella vaccine doses and same-day MMR. Among 199,797 children, overall HZ incidence was 18.6/100,000 person-years in the first-dose MMR + VAR group, 17.9/100,000 person-years in the MMRV group, and 7.5/100,000 person-years in the VAR-alone group. HZ incidence was lower following the second dose than before the second dose in all first-dose groups. HZ incidence was not meaningfully different between the MMRV and MMR + VAR first-dose groups. Overall and within first-dose groups, HZ incidence was lower among children receiving two varicella vaccine doses.

•We assessed mortality risk after COVID-19 vaccination using a self-controlled case series study.•Relative incidences of 6 death outcomes with risk intervals of 14 and 28 days were obtained.•Relative incidences of non-COVID-19 and all-cause deaths for vaccinated individuals were below 1.•Relative incidences of four cardiac-related death outcomes for vaccinated individuals were below 1. Although previous studies found no-increased mortality risk after COVID-19 vaccination, residual confounding bias might have impacted the findings. Using a modified self-controlled case series (SCCS) design, we assessed the risk of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes after primary series COVID-19 vaccination. We analyzed all deaths between December 14, 2020, and August 11, 2021, among individuals from eight Vaccine Safety Datalink sites. Demographic characteristics of deaths in recipients of COVID-19 vaccines and unvaccinated individuals were reported. We conducted SCCS analyses by vaccine type and death outcomes and reported relative incidences (RI). The observation period for death spanned from the dates of emergency use authorization to the end of the study period (August 11, 2021) without censoring the observation period upon death. We pre-specified a primary risk interval of 28-day and a secondary risk interval of 14-day after each vaccination dose. Adjusting for seasonality in mortality analyses is crucial because death rates vary over time. Deaths among unvaccinated individuals were included in SCCS analyses to account for seasonality by incorporating calendar month in the models. For Pfizer-BioNTech (BNT162b2), RIs of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes were below 1 and 95 % confidence intervals (CIs) excluded 1 across both doses and both risk intervals. For Moderna (mRNA-1273), RI point estimates of all outcomes were below 1, although the 95 % CIs of two RI estimates included 1: cardiac-related (RI = 0.78, 95 % CI, 0.58–1.04) and non-COVID-19 cardiac-related mortality (RI = 0.80, 95 % CI, 0.60–1.08) 14 days after the second dose in individuals without pre-existing cancer and heart disease. For Janssen (Ad26.COV2.S), RIs of four cardiac-related death outcomes ranged from 0.94 to 0.98 for the 14-day risk interval, and 0.68 to 0.72 for the 28-day risk interval and 95 % CIs included 1. Using a modified SCCS design and adjusting for temporal trends, no-increased risk was found for non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes among recipients of the three COVID-19 vaccines used in the US.

Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code “tree” and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1–3, p = 0.0001–0.0007), influenza (Days 35–56, p = 0.0001), cough (Days 44–55, p = 0.0002), and COVID-19 (Days 52–56, p = 0.0004). For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the “tree.”

The Centers for Disease Control and Prevention’s Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study’s objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10–15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1–10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.

Infection with hepatitis A virus (HAV) during pregnancy, although uncommon, is associated with gestational complications and pre-term labor. Hepatitis A vaccine (HepA) is recommended for anyone at increased risk for contracting hepatitis A, including women at risk who are also pregnant. Limited data are available on the safety of maternal HepA vaccination. Assess the frequency of maternal HepA receipt and evaluate the potential association between maternal vaccination and pre-specified maternal and infant safety outcomes. A retrospective cohort of pregnancies in the Vaccine Safety Datalink (VSD) resulting in live births from 2004 through 2015 was included. Pregnancies with HepA exposure were compared to those with other vaccine exposures, and to those with no vaccine exposures. Risk factors for contracting hepatitis A were identified up to one-year prior to or during the pregnancy using ICD-9 codes. Maternal and fetal adverse events were evaluated according to maternal HepA exposure status. Adjusted odds ratio (OR) were used to describe the association. Among 666,233 pregnancies in the study period, HepA was administered at a rate of 1.7 per 1000 (n = 1140), most commonly within the first six weeks of pregnancy. Less than 3% of those exposed to HepA during pregnancy had an ICD-confirmed risk factor. There were no significant associations between HepA exposure during pregnancy and gestational hypertension, gestational diabetes, pre-eclampsia/eclampsia, cesarean delivery, pre-term delivery, and low birthweight. There was a statistically significant association between HepA exposure during pregnancy and small-for-gestational age (SGA) infants (aOR 1.32, [95% CI 1.09, 1.60], p = 0.004). The rate of maternal HepA vaccination was low and rarely due to documented risk factors for vaccination. HepA vaccination during pregnancy was not associated with an increased risk for a range of adverse events examined among pregnancies resulting in live births, but an identified association between maternal HepA and SGA infant outcomes, while likely due to unmeasured confounding, warrants further exploration.

Objective Diabetes Prevention Programs (DPP) are effective at reducing diabetes incidence via clinically significant weight loss. Co‐morbid mental health condition(s) may reduce the effect of DPP administered in‐person and telephonically but this has not been assessed for digital DPP. This report examines the moderating effect of mental health diagnosis on weight change among individuals who enrolled in digital DPP (enrollees) at 12 and 24 months. Methods Secondary analysis of prospective, electronic health record data from a study of digital DPP among adults (N = 3904) aged 65–75 with prediabetes (HbA1c 5.7%–6.4%) and obesity (BMI ≥30 kg/m2). Results Mental health diagnosis only moderated the effect of digital DPP on weight change during the first 7 months (p = 0.003) and the effect attenuated at 12 and 24 months. Results were unchanged after adjusting for psychotropic medication use. Among those without a mental health diagnosis, digital DPP enrollees lost more weight than non‐enrollees: −4.17 kg (95% CI, −5.22 to −3.13) at 12 months and −1.88 kg (95% CI, −3.00 to −0.76) at 24 months, whereas among individuals with a mental health diagnosis, there was no difference in weight loss between enrollees and non‐enrollees at 12 and 24 months (−1.25 kg [95% CI, −2.77 to 0.26] and 0.02 kg [95% CI, −1.69–1.73], respectively). Conclusions Digital DPP appears less effective for weight loss among individuals with a mental health condition, similar to prior findings for in‐person and telephonic modalities. Findings suggest a need for tailoring DPP to address mental health conditions.