North Carolina Department of Health and Human Services

Climate change will continue to increase the frequency and intensity of flood events in North Carolina for the foreseeable future. The extreme flooding in Western North Carolina caused by Tropical Storm Helene in September of 2024 is a recent and devastating example of this trend. Communities of color and low-income populations are more likely to reside in flood-prone areas due to structural factors, including residential racial segregation and economic inequality. As such, the adverse health and financial consequences of flood exposure overburden historically marginalized communities, which may have a more limited adaptive capacity to anticipate, respond to, and recover from flood events. Exposure to severe flooding further exacerbates chronic health conditions by impeding access to vital healthcare infrastructure and services. This study examines the spatial patterning of coastal and inland flood risk, neighborhood-level structural determinants (i.e., racial and economic inequality), and flood-sensitive health conditions in North Carolina using bivariate local indicators of spatial autocorrelation (LISA) statistics. High-high clusters capture areas where neighborhoods with high racial or economic inequality surround elevated flood risks. These clusters are distinguished by select sociodemographic characteristics and concentrated in the eastern coastal and western mountainous regions of North Carolina. Cluster locations are priority areas for targeted resource allocation and interventions that strengthen the adaptive capacity of these communities in the context of climate change.

BACKGROUND:While recent national increases in unknown duration or late syphilis (UDLS) have outpaced diagnoses of earlier stages of syphilis, the causes and contributors remain unknown. We evaluated changes in the proportion of UDLS cases with high nontreponemal test titers in one jurisdiction to identify if UDLS cases, which often have lower titers compared with early syphilis cases, might increasingly be early syphilitic infections. METHODS:We identified UDLS cases with high titers (defined as rapid plasma reagin titers ≥1:32) reported to North Carolina during the years 2015 and 2023. We evaluated changes in case counts and in the proportion of UDLS cases with high titers overall, by demographics, clinical factors, and incarceration history. RESULTS:When comparing years 2015 and 2023, UDLS case counts increased (628 vs 2498). The proportion of cases with high titers remained relatively stable between both years (51.3% vs 50.2%), but increased among women (29.1% vs 46.1%), women pregnant at the time of syphilis diagnosis (30.4% vs 43.2%), men who have sex with women only (45.0% vs 57.4%), and people who were recently incarcerated (41.7% vs 52.9%). The proportion of cases with high titers decreased among men who have sex with men (66.4% vs 58.1%), and men with human immunodeficiency virus (68.0% vs 55.4%). CONCLUSIONS:The proportion of cases with high titers increased among women, men who have sex with women only, and people who were recently incarcerated, suggesting that an increasing proportion of UDLS cases in these populations might be early syphilitic infections. Future initiatives could identify methods of improving case staging to facilitate syphilis prevention and control.

BACKGROUND:Injection drug use (IDU) remains a major mode of HIV transmission in the United States, with rising use putting more people who inject drugs (PWID) at risk for HIV. Understanding HIV transmission clusters in this population can inform public health outreach strategies. We examined factors associated with linkage to HIV clusters among PWID in North Carolina (NC). SETTING:NC maintains a statewide molecular HIV surveillance system, enabling identification of transmission clusters across diverse urban and rural communities. METHODS:We analyzed data from people ≥13 years old, diagnosed with HIV, residing in NC, with ≥1 HIV sequence collected between April 2010 and March 2023. Genetic clusters were defined as ≥2 sequences with a pairwise genetic distance <1.5%. Poisson regression was used to estimate prevalence ratios for factors associated with cluster linkage. A logistic generalized additive model was then used to analyze the trend in cluster linkage among PWID over time. RESULTS:Among 21,245 people with a sequence, 1,526 clusters were identified; 284 (19%) included ≥1 PWID (size range 2-218). Cluster linkage was associated with non-Hispanic White race/ethnicity, recent sequence collection, and factors associated with early HIV infection (high CD4 T-cell count, high viral load, and acute infection). The proportion of PWID linked to a cluster increased by year of HIV sequence collection. CONCLUSION:Early HIV testing and care are important for reducing transmission among PWID. Increased linkage of PWID to clusters likely reflects expansion of sequencing, which supports use of molecular clustering to identify groups needing additional outreach.

Infectious-disease forecasts increasingly inform public-health preparedness, yet it is unclear whether hybrid mechanistic-statistical models gain more accuracy from added biological complexity or from assimilating richer data, a choice that affects how forecasting programs invest limited resources. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4 -28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, we find that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity.

IntroductionAccess to reliable internet is recognized as a social determinant of health, yet many migrant and seasonal farmworkers in North Carolina (NC) face barriers to connectivity due to rural housing locations and limited infrastructure. In response to these challenges, the NC Farmworker Health Program implemented a pilot program that installed ruggedized internet routers in employer-provided farmworker housing where other internet options were not available to improve digital inclusion and telehealth access.MethodsThis qualitative study evaluated the program four years after implementation through interviews with Hispanic farmworkers (n=9) and farm owners (n=5), internet performance tests, and site observations at participating NC farms in 2024.ResultsFindings showed that internet access fostered communication with family among Hispanic farmworkers. However, there were significant programmatic challenges, including weak or limited signal range, poor internet speed, lack of privacy, minimal cybersecurity, and low awareness of telehealth services. Farm owners supported the program but expressed concerns about sustaining it without continued funding due to performance limitations and costs.ConclusionThese findings highlight the importance of sustainable, high-quality internet in rural farmworker housing and suggest a need for expanded support, improved performance, and greater education around tools such as telehealth to address inequities that agricultural communities face.