To explore the association of pre-exposure prophylaxis (PrEP) use with condomless sex and sexually transmitted infections (STIs) among gay, bisexual, and other men who have sex with men (MSM) in Washington, DC. Multivariate analysis of cross-sectional weighted survey data from 2014, 2017, and 2023 among MSM. Methods: This analysis used venue-based, time-space sampling to survey MSM about HIV-related behavioral risk factors, HIV testing, and use of prevention services. Chi-square tests and logistic regression were conducted to calculate adjusted prevalence ratios (aPR) and ascertain the association between the use of PrEP with condomless sex and having a bacterial STI in the past 12 months 774 MSM were surveyed during the three cycles in 2014, 2017, and 2023. The proportion of MSM reporting PrEP use, having a STI in the past 12 months, and condomless sex in the past 12 months increased over time from 2014 to 2023. MSM that used PrEP were more likely to report condomless sex (aPR = 1.33, 95
BackgroundChronic diseases remain a major contributor to morbidity and mortality worldwide. Understanding the sociodemographic and behavioral factors associated with these conditions, as well as potential effect modification across population subgroups, is essential for developing targeted prevention strategies.MethodsWe conducted a cross-sectional analysis of nationally representative survey data to examine the associations between sociodemographic and behavioral factors and hypertension, diabetes, and cardiovascular disease (CVD). Survey-weighted multivariable logistic regression models were fitted for each outcome. Restricted cubic splines were used to assess the non-linearity of age. An age × gender interaction term was evaluated, and predicted probabilities were estimated to visualize effect modification. The issue of missing data was addressed using multiple imputation (five imputations). Model discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.ResultsAdvancing age was strongly associated with higher odds of all three conditions. Significant age × gender interactions were observed for diabetes and CVD, indicating steeper age-related increases in risk among males compared with females. In contrast, age-related increases in hypertension were similar across genders. Sociodemographic and behavioral factors such as income and BMI were independently associated with cardiometabolic outcomes. Model discrimination was good for hypertension (AUC = 0.80), diabetes (AUC = 0.77), and CVD (AUC = 0.83), with adequate calibration across risk deciles. Sensitivity analyses yielded consistent findings.ConclusionsCardiometabolic risk is strongly associated with age and sociodemographic and behavioral factors, with important gender differences in age-related trajectories for diabetes and CVD. These findings underscore the importance of accounting for interaction effects in epidemiologic analyses and support age- and gender-tailored prevention strategies at the population level.
BACKGROUND:Gaps in public health data include jurisdiction specific data systems that are used to measure progress on key regional and national indicators. The ATra Black Box (Box) is an electronic privacy-assuring system developed by Georgetown University which allows for the secure and streamlined exchange and analysis of sensitive data. An enhancement was added to the Box to calculate HIV Care Continuum measures in the Ryan White Part A DC Eligible Metropolitan Area (DC EMA). SETTING:The DC EMA includes DC, southern Maryland, northern Virginia, and 2 counties in West Virginia. METHODS:Georgetown implemented new functionality in the Box to create a DC EMA wide report of deduplicated care continuum data. SAS codes used the new Box functionality to select persons living in the DC EMA counties and produce the HIV Care Continuum for the DC EMA for calendar year 2024, including stratifications by race, sex, age, and transmission category, and place of care receipt. RESULTS:51,033 duplicated and 39,047 deduplicated persons were identified as alive and residing in the DC EMA in 2024. Overall, 68.4% of these persons received HIV care and 61.6% achieved viral suppression as of December 31, 2024. CONCLUSION:This analysis provides deduplicated estimates of persons living with HIV in the DC EMA and key indicators used to measure progress on ending HIV in the United States. The jurisdictions in the DC EMA are able to more accurately monitor engagement in care and viral suppression and more effectively guide public health efforts and resources.
Stimulant use among men who have sex with men (MSM) can contribute to HIV risk and care challenges. Monitoring and responding to local trends are critical for Ending the HIV Epidemic (EHE) initiatives. We assessed stimulant use patterns in Baltimore, Philadelphia, and Washington, DC from 2008 to 2023 using National HIV Behavioral Surveillance data. We collected cross-sectional data in each city using venue-based sampling in 2008, 2011, 2014, 2017, and 2023. We estimated average predicted probabilities of methamphetamine, powder cocaine, and crack cocaine use, and evaluated differences in prevalence of stimulant use overall and by race/ethnicity. In DC, stimulant use was stable or declined. Methamphetamine use remained stable overall (8
Understanding the impact of neighborhood-level factors on stroke prevalence is crucial for addressing existing disparities. However, there is a distinct lack of ecological studies at the census tract level that investigate the social determinants of health (SDOH) influencing stroke prevalence within the U.S. Health and Human Services Region 3 (HHS Region 3: Delaware, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia). This study adopted a multivariate modeling approach to investigate the association between the 13 indicators of the Health Opportunity Index (HOI) and stroke prevalence at the census tract level in HHS Region 3 using four HOI indicator profiles and to highlight the specific SDOHs that are most associated with stroke prevalence. The four HOI indicator profiles include: (a) neighborhood and built environment profile, (b) social and community context profile, (c) resource profile, and (d) economic profile. The methodological approach was quantitative, using secondary data. The sample size was 8021 census tracts. The HOI was estimated for each census tract in the study area. Ordinary least squares regression (OLS) analysis and spatial lag model (SLM) were run to examine whether the 13 indicators of the HOI (categorized into four profiles) reliably predict stroke prevalence and to determine the most appropriate model that best identifies the strongest predictors of stroke prevalence. The results show that affordability, education, spatial segregation, and income inequality indicators were the strongest predictors of stroke prevalence in HHS Region 3. This granular research identifies the neighborhood-level SDOH most strongly linked to stroke prevalence, which can be leveraged to guide the development of targeted public health programs, quality improvement initiatives, resource allocation, and policy creation to combat stroke-related morbidity and mortality across census tracts in HHS Region 3. For example, the built environment, encompassing factors like employment access, affordable housing, and walkability, profoundly influences stroke prevalence and provides urban planners with practical insights for developing healthier, more equitable communities, such as creating neighborhood parks to encourage physical activity, a key factor in stroke prevention. This study also provides neighborhood organizations with the evidence needed to pursue grant funding and raise awareness about the socio-structural influences on stroke outcomes in their respective neighborhoods. Lastly, the insights generated from our study can facilitate collaborative decision-making processes with communities in HHS Region 3 regarding the prioritization of neighborhood-level SDOH for targeted public health interventions. This prioritization should focus on addressing predictors of stroke prevalence that are congruent with the community’s established priorities, thereby maximizing cost savings.
