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AimsCurrently, there is no data available assessing the association between body mass index (BMI) and periodontitis among women living with HIV (WLWH). This study aims to investigate this association among WLWH and women at risk for HIV (WRH) in the United States.Methods and resultsData from 351 WLWH and 52 WRH participants from the Women's Interagency HIV Study having pocket depths and clinical periodontal attachment loss assessments in 2003-2004 were included. Multinomial logistic regression analyses in the full sample assessed the relationship between BMI (underweight/normal, overweight, or obese) and periodontitis by severity (mild, moderate, severe), adjusting for study sites, age, education, annual household income, smoking, alcohol consumption, and diabetes. Overall, 75.2% women (76.0% WLWH; 69.0% WRH) had periodontitis. Moreover, 75.0% obese and 75.3% overweight women were affected by periodontitis. In the full sample, adjusted odds ratio (aOR) of having mild, moderate, and severe periodontitis in obese women were: 1.14 (95% confidence interval [CI]: 0.51-2.52), 1.02 (95% CI: 0.46-2.29), and 0.24 (95% CI: 0.06-1.07), respectively, and in overweight women: 0.70 (95% CI: 0.31-1.58), 0.85 (95% CI: 0.38-1.90), and 0.31 (95% CI: 0.08-1.15), respectively.ConclusionsEven with high prevalence of periodontitis among women with or without HIV infection in this cohort, this study does not provide evidence of an association between BMI and periodontitis.

BackgroundData on the prevalence and correlates of restless legs syndrome (RLS) in people with HIV are limited. This study sought to determine the prevalence of RLS, associated clinical correlates, and characterize sleep-related differences in men with and without HIV.MethodsSleep-related data were collected in men who have sex with men participating in the Multicenter AIDS Cohort Study (MACS). Demographic, health behaviors, HIV status, comorbidities, and serological data were obtained from the MACS visit coinciding with sleep assessments. Participants completed questionnaires, home polysomnography, and wrist actigraphy. RLS status was determined with the Cambridge-Hopkins RLS questionnaire. RLS prevalence was compared in men with and without HIV. Multinomial logistic regression was used to examine correlates of RLS among all participants and men with HIV alone. Sleep-related differences were examined in men with and without HIV by RLS status.ResultsThe sample consisted of 942 men (56% HIV+; mean age 57 years; 69% white). The prevalence of definite RLS was comparable in men with and without HIV (9.1% vs 8.7%). In multinomial regression, HIV status was not associated with RLS prevalence. However, white race, anemia, depression, and antidepressant use were each independently associated with RLS. HIV disease duration was also associated with RLS. Men with HIV and RLS reported poorer sleep quality, greater sleepiness, and had worse objective sleep efficiency/fragmentation than men without HIV/RLS.ConclusionsThe prevalence of RLS in men with and without HIV was similar. Screening for RLS may be considered among people with HIV with insomnia and with long-standing disease.

Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a new mediation methodology for zero‐inflated count outcomes using the marginalized zero‐inflated Poisson (MZIP) model and the counterfactual approach to mediation. This novel work gives population‐average mediation effects whose variance can be estimated rapidly via delta method. This methodology is extended to cases with exposure‐mediator interactions. We apply this novel methodology to explore if diabetes diagnosis can explain BMI differences in healthcare utilization and test model performance via simulations comparing the proposed MZIP method to existing zero‐inflated and Poisson methods. We find that our proposed method minimizes bias and computation time compared to alternative approaches while allowing for straight‐forward interpretations. [ABSTRACT FROM AUTHOR]

This study examined the role of partnership status (married, unmarried-partnered, and unpartnered) on retention in care among newly diagnosed (2013–2017), cisgender heterosexual people with HIV in Birmingham, Alabama (n = 152). This study evaluated all scheduled HIV primary care provider visits for two years following diagnosis date. A kept-visit measure was calculated such that, if an individual attended ≥1 visit in each of the four 6-month intervals, they were considered to have high visit constancy. A missed-visit measure was categorized as ≥1 no-show vs. 0 no-show for first and second year after diagnosis. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression models. Models were adjusted for confounding sociodemographic and clinical characteristics. The study population was 76% Black, 57% male, median age of 37 years. Overall, 65% had high visit consistency and 34.5% had ≥1 no-show in both years. Compared to unpartnered, married individuals had higher visit constancy [AOR (95% CI): 2.88 (1.02, 8.16)]; no differences were observed among unmarried-partnered individuals. No differences in having ≥1 no-show among partnership status groups were observed for either year. These findings suggest potential success of interventions involving a social confidant in optimizing retention in care among newly diagnosed, heterosexual PWH. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 causes a wide spectrum of clinical manifestations and significant mortality. Studies investigating underlying immune characteristics are needed to understand disease pathogenesis and inform vaccine design. In this study, we examined immune cell subsets in hospitalized and nonhospitalized individuals. In hospitalized patients, many adaptive and innate ^immune cells were decreased in frequency compared with those of healthy and convalescent individuals, with the exception of an increase in B lymphocytes. Our findings show increased frequencies of T cell activation markers (CD69, OX40, HLA- DR, and CD154) in hospitalized patients, with other T cell activation/exhaustion markers (PD-L1 and TIGIT) remaining elevated in hospitalized and nonhospitalized individuals. B cells had a similar pattern of activation/exhaustion, with increased frequency of CD69 and CD95 during hospitalization followed by an increase in PD1 frequencies in nonhospitalized individuals. Interestingly, many of these changes were found to increase over time in nonhospitalized longitudinal samples, suggesting a prolonged period of immune dysregulation after SARS-CoV-2 infection. Changes in T cell activation/exhaustion in nonhospitalized patients were found to positively correlate with age. Severely infected individuals had increased expression of activation and exhaustion markers. These data suggest a prolonged period of immune dysregulation after SARS-CoV-2 infection, highlighting the need for additional studies investigating immune dysregulation in convalescent individuals., Introduction Since the first reports in December 2019, coronavirus disease 2019 (COVID-19), caused by the novel virus SARS-CoV-2, has spread worldwide and caused enormous public health and economic impacts (1, [...]

Background In the United States (US), 44% of people with human immunodeficiency virus (PWH) live in the Southeastern census region; many PWH remain undiagnosed. Novel strategies to inform testing outreach in rural states with dispersed HIV epidemics are needed. Methods Alabama state public health HIV testing surveillance data from 2013 to 2017 were used to estimate time from infection to HIV diagnosis using CD4 T-cell depletion modeling, mapped to county. Diagnostic HIV tests performed during 2013–2021 by commercial testing entities were used to estimate HIV tests per 100 000 adults (aged 15–65 years), mapped to client ZIP Code Tabulation Area (ZCTA). We then defined testing "cold spots": those with <10% adults tested plus either (1) within or bordering 1 of the 13 counties with HIV prevalence >400 cases per 100 000 population or (2) within a county with average time to diagnosis greater than the state average to inform testing outreach. Results Time to HIV diagnosis was a median of 3.7 (interquartile range [IQR], 0–9.2) years across Alabama, with a range of 0.06–12.25 years. Approximately 63% of counties (n = 42) had a longer time to diagnosis compared to national US estimates. Six hundred forty-three ZCTAs tested 17.3% (IQR, 10.3%–25.0%) of the adult population from 2013 to 2017. To prioritize areas for testing outreach, we generated maps to describe 47 areas of HIV-testing cold spots at the ZCTA level. Conclusions Combining public health surveillance with commercial testing data provides a more nuanced understanding of HIV testing gaps in a state with a rural HIV epidemic and identifies areas to prioritize for testing outreach. [ABSTRACT FROM AUTHOR]

Background: U.S. women who have been incarcerated report high rates of sexual risk behavior and sexually transmitted infections (STIs). Materials and Methods: We estimated the effect of incarceration on the time to first incident STI in a multicenter cohort of U.S. women with or at risk for HIV. We used marginal structural models to compare time to first self-reported gonorrhea, chlamydia, or trichomonas infection for nonincarcerated women and incarcerated women. Covariates included demographic factors, HIV status, sex exchange, drug/alcohol use, and prior incarceration. Results: Three thousand hundred twenty-four women contributed a median of 4 at-risk years and experienced 213 first incident STI events. The crude incidence of STIs was 3.7 per 100 person-years for incarcerated women and 1.9 per 100 person-years for nonincarcerated women. The weighted hazard ratio for incident STIs was 4.05 (95% confidence interval: 1.61–10.19). Conclusion: Women with or at risk for HIV in the United States who have recently experienced incarceration may be at increased STI risk. [ABSTRACT FROM AUTHOR]