Rapid Increase in Seroprevalence of Borrelia burgdorferi Antibodies among Dogs, Northwestern North Carolina, USA, 2017-20211.

We evaluated spatial-temporal risk for Lyme disease in northwestern North Carolina, USA, by using individual-level canine Borrelia burgdorferi seroprevalence data collected during 2017-2021 at routine veterinary screenings for tickborne diseases. Seroprevalence in dogs increased from 2.2% (47/2,130) in 2017 to 11.2% (339/3,033) in 2021. The percentage of incident seropositivity increased from 2.1% (45/2,130) in 2017 to 7.6% (231/3,033) in 2021. Exploratory geographic analyses found canine seroprevalence shifted from clustered (2017, Moran's I = 0.30) to dispersed (2021, Moran's I = -0.20). Elevation, slope, aspect, and forest land cover density were associated with canine seroprevalence within various household buffer regions in 2017. Slope was associated with seroprevalence at the household level in 2021. Results support the use of individual-level canine seroprevalence data for monitoring human risk for Lyme disease. Establishing sentinel veterinary clinics within Lyme disease-emergent communities might promote prevention and control efforts and provide opportunities for educational and behavioral interventions.

The geography of COVID-19 vaccine completion by age in North Carolina, U.S.

BACKGROUND: Geographic variation in COVID-19 vaccination can create areas at higher risk of infection, complications, and death, exacerbating health inequalities. This ecological study examined geographic patterns of COVID-19 vaccine completion, using age and sociodemographic characteristics as possible explanatory mechanisms. METHODS AND FINDINGS: Using 2020-2022 data from the North Carolina COVID-19 Vaccination Management System and U.S. Census Bureau American Community Survey, at the Zip code-level, we evaluated completion of the primary COVID-19 vaccine series across age groups. We examined geographic clustering of age-specific completion by Zip code and evaluated similarity of the age-specific geographic patterns. Using unadjusted and adjusted spatial autoregressive models, we examined associations between sociodemographic characteristics and age-specific vaccine completion. COVID-19 vaccine completion was moderately geographically clustered in younger groups, with lower clustering in older groups. Urban areas had clusters of higher vaccine completion. Younger and middle-aged groups were the most similar in completion geographically, while the oldest group was most dissimilar to other age groups. Higher income was associated with higher completion in adjusted models across all age groups, while a higher percent of Black residents was associated with higher completion for some groups. CONCLUSIONS: COVID-19 vaccination completion is more variable among younger age groups in North Carolina, and it is higher in urban areas with higher income. Higher completion in areas with more Black residents may reflect the success of racial equity efforts in the state. The findings show a need to reach younger populations and lower income areas that were not prioritized during early vaccination distribution.

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda.

BACKGROUND: Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey. METHODS: The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model's ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated. RESULTS: Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier. CONCLUSIONS: These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.