Census tract-level social vulnerability and gonorrhea rates in central Ohio: results from the STD Surveillance Network, 2020-2021.

BACKGROUND: Gonorrhea remains a significant burden in central Ohio, particularly in socially disadvantaged communities. This study evaluates gonorrhea case rates and changes from 2020 to 2021, focusing on geographic patterns associated with social vulnerability. METHODS: We analyzed gonorrhea case data from the Columbus STD Surveillance Network (SSuN) for 2020 and 2021. Cases were linked to census tracts and categorized into low (0-0.66) or high (0.67-1.0) vulnerability groups based on the CDC/ATSDR social vulnerability index (SVI). We mapped gonorrhea case rates per 100,000 population, stratified by SVI group, and compared changes using Jenks natural breaks to determine cutpoints. High case rates were defined as ≥605 (2020) and ≥ 532 (2021). A change in case rates was defined as <154 (decrease/no change) or ≥ 154 (increase). Demographic factors were summarized for both years. RESULTS: In 2020 and 2021, nearly half of high SVI tracts had high gonorrhea rates, compared to about 4-7% of low SVI tracts. Median rates in high SVI tracts were approximately five times higher than in low SVI tracts, with rates 546 and 598 per 100,000 in 2020 and 2021, compared to 98 and 104 per 100,000, respectively. Gonorrhea rates increased in 23 (9%) of low SVI tracts and 37 (25%) of high SVI tracts. Demographic characteristics remained consistent between the two years. CONCLUSIONS: Gonorrhea rates increased in central Ohio between 2020 and 2021, with higher burdens in high SVI census tracts. Understanding the dynamics of social vulnerability at the community level is crucial for targeting limited STD resources effectively.

Quality improvement initiative to impact Golden Hour timeliness using a dedicated delivery team.

OBJECTIVE: Golden Hour (GH) care impacts immediate and long-term outcomes for premature infants. We hypothesized that creation of a dedicated delivery team, the Stork Team, would improve delivery of GH care. METHODS: A GH quality improvement initiative was created for infants born at <32 weeks and implemented in July 2018. Data were collected from GH checklists and the electronic medical record. RESULTS: Following Stork Team implementation there was special cause variation noted in the minute of life (MOL) for administration of dextrose containing fluids and antibiotics. Dextrose containing fluid time improved from 111 to 67 MOL, with an increase in the percentage of patients receiving fluids by 60 MOL. Antibiotic administration improved from 180 to 82.5 MOL. GH checklist completion increased from 77% to 98% and time to isolette closure improved from 88 to 62 MOL. CONCLUSION: Implementation of the Stork Team was associated with improvements in timeliness of GH care.

Associations Between Childhood Sexual Abuse, Intimate Partner Violence Trauma Exposure, Mental Health, and Social Gender Affirmation Among Black Transgender Women.

Introduction: The purpose of this study was to examine associations between social gender affirmation and mental health outcomes. The resulting relationships were explored within the context of childhood sexual abuse (CSA) and intimate partner violence (IPV) trauma history. Materials and Methods: A community sample of 138 Black trans women completed structured interviews assessing gender affirmation, mental health, and trauma history. Bivariate associations between gender affirmation scale and mental health measures were assessed using Pearson coefficients. Multiple linear regression models were used to generate adjusted estimates of association. Results: Childhood sexual victimization and IPV were common. Depressive symptoms, anxiety, quality of life, and body image satisfaction were significantly associated with social gender affirmation. Recent IPV dampened this association to not significant. Gender affirmation and anxiety/panic and quality-of-life outcomes did not retain significance in models adjusted for lifetime IPV. CSA did not weaken the association. Conclusion: Incorporating trauma-informed and gender-affirmative frameworks into medical care and evidence-based interventions is a crucial structural step toward protection of the mental health of Black trans women.

Prepregnancy and Gestational Diabetes and Cessation of Breastfeeding <1 Week Postpartum, United States, 2016-2018.

OBJECTIVES: Diabetes may delay milk letdown, and perceiving milk production as insufficient can lead to breastfeeding cessation. We evaluated whether prepregnancy or gestational diabetes is associated with cessation of breastfeeding by 1 week postpartum. METHODS: We analyzed 2016-2018 data from 42 sites in the Pregnancy Risk Assessment Monitoring System, a population-based survey of women with a recent live birth. Participants were surveyed 2-6 months after childbirth. We used logistic regression models to evaluate the relationship between prepregnancy or gestational diabetes only and breastfeeding <1 week postpartum among women who had initiated breastfeeding. RESULTS: Among 82 050 women who initiated breastfeeding, 4.5% reported breastfeeding <1 week postpartum. Overall, 11.7% of women reported any history of diabetes in the 3 months before becoming pregnant; 3.3% reported prepregnancy diabetes, and 8.4% reported gestational diabetes only. In both unadjusted and adjusted models, the prevalence of breastfeeding <1 week postpartum did not differ significantly among women with prepregnancy diabetes or gestational diabetes only compared with women without any history of diabetes. The prevalence of breastfeeding <1 week postpartum was 4.4% among women without any history of diabetes, 5.6% among women with prepregnancy diabetes (adjusted prevalence ratio [aPR] = 1.15; 95% CI, 0.91-1.46), and 4.5% among women with gestational diabetes only (aPR = 1.01; 95% CI, 0.84-1.20). CONCLUSIONS: We found no association between a history of diabetes prepregnancy or gestational diabetes only and breastfeeding <1 week postpartum in a large, population-based survey of postpartum women who initiated breastfeeding. Regardless of their diabetes status, women who want to breastfeed might benefit from interventions that support their ability to continue breastfeeding.

County socioeconomic characteristics and heart transplant outcomes in the United States.

BACKGROUND: Geographic disparities in survival after heart transplantation have received mixed support in prior studies, and specific geographic characteristics that might be responsible for these differences are unclear. We tested for differences in heart transplant outcomes across United States (US) counties after adjustment for individual-level covariates. Our secondary aim was to evaluate whether specific county-level socioeconomic characteristics explained geographic disparities in survival. METHODS: Data on patients aged ≥18 years undergoing a first-time heart transplant between July 2006 and December 2014 were obtained from the United Network for Organ Sharing. Residents of counties represented by <5 patients were excluded. Patient survival (censored in March 2016) was analyzed using multivariable Cox regression. Shared frailty models were used to test for residual differences in overall all-cause mortality across counties after adjusting for recipient and donor characteristics. Measures of county economic disadvantage, inequality, and racial segregation were obtained from US Census data and coded into quintiles. A likelihood ratio test determined whether adjusting for each county measure improved the fit of the Cox model. RESULTS: Multivariable analysis of 10,879 heart transplant recipients found that, adjusting for individual-level characteristics, there remained statistically significant variation in mortality hazard across US counties (P=.004). Adjusting for quintiles of community disadvantage, economic inequality, or racial segregation did not significantly improve model fit (likelihood ratio test P=.092, P=.273, and P=.107, respectively) and did not explain residual differences in patient survival across counties. CONCLUSIONS: Heart transplantation outcomes vary by county, but this difference is not attributable to county-level socioeconomic disadvantage.