Background We sought to determine the association between maternal vitamin D status at ≤26 weeks gestation and the risk of preeclampsia separately by clinical subtype. Methods We conducted a case-cohort study among women enrolled at 12 U.S. sites from 1959 to 1966 in the Collaborative Perinatal Project. In 717 women who later developed preeclampsia (560 mild and 157 severe cases) and in 2986 mothers without preeclampsia, we measured serum 25-hydroxyvitamin D at ≤26 weeks gestation (median 20.9 weeks) over 40 years later using liquid-chromatography-tandem mass spectrometry. Results Half of women in the subcohort had 25(OH)D <50 nmol/L. Maternal 25(OH)D 50–<75 nmol/L was associated with a reduction in the absolute and relative risk of preeclampsia and mild preeclampsia compared with 25(OH)D <30 nmol/L, but the effects were no longer present after adjustment for confounders including race, prepregnancy body mass index, and parity. For severe preeclampsia, 25(OH)D ≥50 nmol/L was associated with a reduction of 3 cases per 1,000 pregnancies (adjusted RD −.003, 95% CI: −.005, .0002) and a 40% reduction in risk (adjusted RR .65, 95% CI .43, .98) compared with 25(OH)D <50 nmol/L. The conclusions were the same after restricting to women with 25(OH)D measured at <22 weeks gestation and after formal sensitivity analyses for unmeasured confounding. Conclusions Maternal vitamin D deficiency may be a risk factor for severe preeclampsia, but it is not associated with preeclampsia overall or its mild subtypes. Contemporary cohorts with large numbers of severe preeclampsia cases are needed to confirm or refute these findings.

Coverage was evaluated by selected community-level characteristics matched to vaccine recipients' county of residence. § § § County-level rankings of social vulnerability from the 2018 CDC Social Vulnerability Index (SVI), which is used to identify community needs during emergencies, were categorized into quartiles based on distribution among all U.S. counties. ¶ ¶ ¶ County-level data on Social Determinants of Health**** obtained from the American Community Survey † † † † were dichotomized based on the median of all U.S. counties. § § § § County-level urbanicity was based on the 2013 National Center for Health Statistics urban-rural classification scheme. ¶ ¶ ¶ ¶ Generalized estimating equation models with binomial regression and an identity link were used to † † † Periods are based on eligibility and other process factors (e.g., phase of vaccine rollout, eligible population, supply, and programs and policy enacted) important in framing the specific needs and constraints at that time. Period 1 represented when most states opened eligibility to health care workers, residents in long-term care facilities, and older adults while there was a highly constrained supply, which overlapped phase 1a, and a portion of phase 1b (https://www.cdc.gov/mmwr/volumes/69/wr/ mm695152e2.htm). Period 2 represented when states were expanding eligibility inconsistently, and supply was becoming more available, which overlapped with phases 1b and 1c. Period 3 represented when all states expanded eligibility to all adults while supply was steady and increased, which overlapped with phases 1c and 2. § § § The following jurisdictions were excluded from all county-level analyses (National Center for Health Statistics urban-rural, SVI, and Social Determinants of Health) due to lack of county-level vaccination data: all counties in Hawaii and eight counties in California for which total population was <20,000. Among all first doses analyzed during December 14, 2020-May 22, 2021, 5.9% were missing county data and were therefore excluded from models. ¶ ¶ ¶ Fifteen elements categorized into four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation) are included in SVI (https:// www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/ SVI2018Documentation-H.pdf ). Overall SVI includes all 15 indicators as a composite measure (https://www.atsdr.cdc.gov/placeandhealth/svi/ fact_sheet/fact_sheet.html). One county in New Mexico was excluded because SVI ranking could not be calculated (https://www.atsdr.cdc.gov/ placeandhealth/svi/index.html). **** Measures of Social Determinants of Health from the American Community Survey: percentage of the total population 1) unemployed, 2) uninsured, 3) that earned an income below the federal poverty level, 4) without a computer (e.g., desktop or laptop computer [excludes mobile phones]), 5) with a computer but without Internet access, and 6) identifying as a racial/ethnic group other than non-Hispanic White (https://healt...

Prepregnancy BMI is a widely used marker of maternal nutritional status that relies on maternal self‐report of prepregnancy weight and height. Pregravid BMI has been associated with adverse health outcomes for the mother and infant, but the impact of BMI misclassification on measures of effect has not been quantified. The authors applied published probabilistic bias analysis methods to quantify the impact of exposure misclassification bias on well‐established associations between self‐reported prepregnancy BMI category and five pregnancy outcomes (small for gestational age (SGA) and large for gestational age (LGA) birth, spontaneous preterm birth (sPTB), gestational diabetes mellitus (GDM), and preeclampsia) derived from a hospital‐based delivery database in Pittsburgh, PA (2003–2005; n = 18,362). The bias analysis method recreates the data that would have been observed had BMI been correctly classified, assuming given classification parameters. The point estimates derived from the bias analysis account for random error as well as systematic error caused by exposure misclassification bias and additional uncertainty contributed by classification errors. In conventional multivariable logistic regression models, underweight women were at increased risk of SGA and sPTB, and reduced risk of LGA, whereas overweight, obese, and severely obese women had elevated risks of LGA, GDM, and preeclampsia compared with normal‐weight women. After applying the probabilistic bias analysis method, adjusted point estimates were attenuated, indicating the conventional estimates were biased away from the null. However, the majority of relations remained readily apparent. This analysis suggests that in this population, associations between self‐reported prepregnancy BMI and pregnancy outcomes are slightly overestimated.

Objective We examined the association between gestational weight gain (GWG) and offspring obesity at age 36 months. Methods Mother-infant dyads (n=609) were followed from a first study visit (mean (standard deviation): 18.8 (2.7) weeks gestation) to 36 months postpartum. Total GWG over the entire pregnancy was defined as excessive or non-excessive according to the 2009 Institute of Medicine guidelines. Four mutually exclusive categories of excessive or non-excessive GWG across early (conception to first study visit) and late (first study visit to delivery) pregnancy defined GWG pattern. Body mass index (BMI) z-scores ≥95th percentile of the 2000 CDC references defined offspring obesity at 36 months. Multivariable log-binomial models adjusted for prepregnancy BMI and breastfeeding were used to estimate the association between GWG and childhood obesity risk. Results Nearly half of the women had total excessive GWG. Of these, 46% gained excessively during both early and late pregnancy while 22% gained excessively early and non-excessively late, and the remaining 32% gained non-excess weight early and excessively later. Thirteen percent of all children were obese at 36 months. Excessive total GWG was associated with more than twice the risk of child obesity [adjusted risk ratio (95% CI): 2.20 (1.35, 3.61)] compared with overall non-excessive GWG. Compared with a pattern of non-excessive GWG in both early and late pregnancy, excessive GWG in both periods was associated with an increased risk of obesity [2.39 (1.13, 5.08)]. Conclusions Excessive GWG is a potentially modifiable factor that may influence obesity development in early childhood.

Background Reducing transmission depends on the percentage of infected partners treated; if many are missed, impact on transmission will be low. Traditional partner services metrics evaluate the number of partners found and treated. We estimated the proportion of partners of syphilis patients not locatable for intervention. Methods We reviewed records of early syphilis cases (primary, secondary, early latent) reported in 2015 to 2017 in 7 jurisdictions (Florida, Louisiana, Michigan, North Carolina, Virginia, New York City, and San Francisco). Among interviewed syphilis patients, we determined the proportion who reported named partners (with locating information), reported unnamed partners (no locating information), and did not report partners. For patients with no reported partners, we estimated their range of unreported partners to be between one and the average number of partners for patients who reported partners. Results Among 29,719 syphilis patients, 23,613 (80%) were interviewed and 18,581 (63%) reported 84,224 sex partners (average, 4.5; 20,853 [25%] named and 63,371 [75%] unnamed). An estimated 11,138 to 54,521 partners were unreported. Thus, 74,509 to 117,892 (of 95,362–138,745) partners were not reached by partner services (78%–85%). Among interviewed patients, 71% reported ≥1 unnamed partner or reported no partners; this proportion was higher for men who reported sex with men (75%) compared with men who reported sex with women only (65%) and women (44%). Conclusions Approximately 80% of sex partners were either unnamed or unreported. Partner services may be less successful at interrupting transmission in networks for men who reported sex with men where a higher proportion of partners are unnamed or unreported.