Puerto Rico Department of Health

ABSTRACT Adverse cardiometabolic factors and poor mental health in early adulthood are linked to long-term disease risk. We conducted a cross-sectional study of 2,783 adults aged 21–35 years in Puerto Rico to characterize self-reported demographic, health, and behavioral profiles and examine sex and age differences. Findings showed that 40.5% reported ≥6 days of poor mental health and 20.4% ≥6 days of poor physical health in the past month. Most participants had health insurance (90.3%), yet 36.9% reported financial barriers to care, and 32.3% lacked a primary care provider. Preventive care engagement was mixed, with low influenza vaccination (30.4%) but higher HIV/STD testing (70.7%), PAP smear screening (77.9%), and HPV vaccination (54.7%). Over half were overweight or obese (57.2%), 14.7% reported elevated levels of blood sugar or prediabetes, and 10.9% hypertension. Females reported poorer mental health but higher engagement in preventive services, whereas males were more likely to lack insurance or a primary care provider and to report hypertension and tobacco use. Younger adults reported worse mental health and higher loneliness, while older groups showed greater cardiometabolic factors. These findings highlight the need for interventions to improve preventive care, increase awareness of cardiometabolic risk, and enhance mental health among young adults.

Point-of-care, immunochromatographic tests have not reliably detected rabies virus and have not utilized a consistent diagnostic protocol. International diagnostic standards established by the World Organization for Animal Health require strategic and large-scale validation studies prior to widescale use. The United States (US) Centers for Disease Control and Prevention, National Rabies Reference Laboratory undertook a large-scale validation of the BioNote Anigen Rapid Rabies Antigen Test in collaboration with eight US public health laboratories and three international laboratories. Modifications were made to manufacturer instructions to maximize antigen concentration in the test diluent. A total of 1,399 samples underwent paired testing with one of three US gold standard tests, consisting of 31 types of animals and 9 rabies virus variants. Test sensitivity and specificity was 97.1% (CI: 95.2% - 98.3%) and 99.9% (CI: 99.4% - 100.0%). Fourteen samples resulted in false-negative results, primarily impacting dogs that were euthanized early or shown to have a low viral load. Limit of detection studies found that false-negative results often occurred when the sample had a PCR Ct value >23. The BioNote Anigen Test performed well across diverse RVVs found in North America. Sensitivity of the test was slightly lower than, but not statistically inferior to, the minimum 98% value established for the current gold-standard tests. This is the largest systematic evaluation of a rabies point-of-care test that includes diverse RVV and animal type and results suggest that the BioNote Anigen Test with the procedural changes would have broad benefits for rapid diagnosis in animals.

The Puerto Rico Department of Health (PRDH) seeks to identify dengue epidemics as early as possible with high specificity. Development and prospective application of an early warning system for dengue epidemics using routine historical surveillance data. A weekly intercept-only negative binomial regression model was fitted using historical probable and confirmed dengue data. A range of threshold definitions was explored using three model-estimated percentiles of weekly dengue case counts. Dengue is endemic in Puerto Rico with irregular occurrence of large epidemics with substantial impact on health burden and health systems. Probable and confirmed dengue data are routinely collected from all hospitals and private clinics. A total of 86 282 confirmed or probable dengue virus cases were reported from 1 January 1986 to 30 June 2024, with an annual mean of 2212 cases (median: 1533; range: 40-10 356). The model was fitted retrospectively to mimic real-time epidemic detection and assessed based on sensitivity and specificity of epidemic detection. The 75th percentile threshold aligned best with historical epidemic classifications, balancing false alarms and missed detections. This model provides a robust method for defining thresholds, accounting for skewed data, using all historical data and improving on traditional methods like endemic channels. In March 2024, PRDH declared a public health emergency due to an early, out-of-season surge in cases that exceeded the epidemic alert threshold developed in this study. This real-time application highlights the value of these thresholds to support dengue epidemic detection and public health response. Integrating thresholds with other tools and strategies can enhance epidemic preparedness and management.

Estimating the biomass of terrestrial forests generally, and mangrove forests in particular, is an area of considerable interest. Most approaches rely on empirically derived allometric models to predict tree biomass. The single parameter with the strongest predictive ability in most studies is diameter at breast height (DBH); however the use of DBH arose primarily out of convenience, not from an analysis of tree form. While DBH explains a lot of variability in other tree metrics such as height or above ground biomass, its utility in smaller species is uncertain. Here we used measurements from 302 destructively sampled mangrove trees of four species to test which of three bole diameter measurements, basal stem diameter (BSD), diameter at 30 cm (D30), and DBH, is the best predictor of aboveground biomass. D30 had the highest mean coefficient of determination (R2) and lowest mean root mean squared error (RMSE) across all site/species combinations. However, the improvement over DBH was modest, with a mean across all site/species combinations of 1.58 kg RMSE and R2 of 0.948 for D30, compared to 1.63 kg RMSE and R2 of 0.917 for DBH. Nevertheless, D30 may have utility in future studies as it allows for lower size thresholds and has better overall explanatory power than DBH.

Dengue is endemic throughout the tropical areas of the Americas, but little is known about its regional dynamics. We examined seasonal and multiannual dengue trends across the Americas and possible underlying mechanisms using monthly dengue surveillance data from 14 countries. We collected monthly dengue case data from 241 subnational locations in 14 countries, ranging from 1985 to 2018 (6 to 22 years), and used wavelet analysis to isolate seasonal (8 to 16 months) and multiannual (17+ months) patterns. For each location, we assessed seasonal and multiannual dynamics as well as coherence and differences in timing of dengue cycles between 22,578 location pairs. We assessed patterns in coherence over time and space and compared these patterns to variations in temperature, rainfall, and El Niño Southern Oscillation patterns. Strong synchrony in dengue dynamics was identified across the region at the seasonal and multiannual scales. Seasonal dengue dynamics were associated with local climate patterns and dengue dynamics in nearby locations. High synchrony at multiannual scales indicated that large dengue epidemics were shared across the region, with an average temporal lag of only 6 months at distances of up to 10,000 kilometers. Synchrony of these epidemics likely reflects both regional climate variability and human movement. Observed dengue dynamics were not unique to individual countries in the Americas but rather extended across the region reflecting climatic and nonclimatic drivers. These findings can support the development of better early warning tools to support epidemic preparedness and response and underscore the importance of working collaboratively across borders.

The eight U.S. territories and freely associated states (TFAS) have historically faced unique social and structural barriers in the implementation of vaccination programs due to geographic remoteness, a high prevalence of socioeconomic disparities, increasing prevalence of natural disasters, limited vaccine providers and clinics, difficulties with procurement and shipping, and difficulty tracking highly mobile populations. In the months leading up to emergency authorizations for the use of COVID-19 vaccines, the TFAS developed tailored vaccination strategies to ensure that key at-risk populations received timely vaccination, and successfully implemented these strategies during the first six months of the vaccine rollout. Subject matter experts supporting the Centers for Disease Control and Prevention’s COVID-19 Response recognized the unique historical, geographic, social, and cultural dynamics for residents in the TFAS and worked with partners to prevent, detect, and respond to the pandemic in these jurisdictions. As a result of innovative partnerships and vaccine distribution strategies, vaccine equity was improved in the TFAS during the COVID-19 vaccine rollout.

The incidence of dengue is increasing in many countries and at the present time, around half of the world's population is at risk of infection. Dengue is an arthropod-borne virus (arbovirus) that continues to pose an important challenge to public health authorities worldwide. A critical task in managing arboviral outbreaks and epidemics is the selection of laboratory technology for timely and accurate diagnosis. In this study, we assessed the performance of the DYNEX® DS2 Automated Processing System for the diagnosis of dengue vis a vis the manually executed procedure. The levels of agreement were calculated and showed 94%, 96%, and 90% positive percent agreement, negative percent agreement, and overall percent agreement, respectively. In conclusion, both tests displayed comparable performances.

The Energy Storage System Priority Index (ESSPI) was originally presented in the past as a methodology to prioritize load. In this paper, the ESSPI is going to be used as a fast tool for load prioritization in microgrid design. The tool consists of the user inputting the load information based on the classification of the load. The inputs needed are the maximum critical recovery time and the energy consumption of the loads. Loads should be classified into different categories: critical load, essential load, discretionary load, non-essential load, and expendable load. The tool outputs would be the loads arranged based on their priority, the size, and the cost of the microgrid system depending on the user's desired investment for short-, mid-, and long-term investments.