Background: Simulation-based techniques using three-dimensional models are gaining popularity in neurosurgical training. Most pre-existing models are expensive, so we felt a need to develop a real-life model using 3D printing technology to train in endoscopic third ventriculostomy. Methods: The brain model was made using a 3D-printed resin mold from patient-specific MRI data. The mold was filled with silicone Ecoflex™ 00-10 and mixed with Silc Pig^® pigment additives to replicate the color and consistency of brain tissue. The dura mater was made from quick-drying silicone paste admixed with gray dye. The blood vessels were made from a silicone 3D-printed mold based on magnetic resonance imaging. Liquid containing paprika oleoresin dye was used to simulate blood and was pumped through the vessels to simulate pulsatile motion. Results: Seven residents and eight senior neurosurgeons were recruited to test our model. The participants reported that the size and anatomy of the elements were very similar to real structures. The model was helpful for training neuroendoscopic 3D perception and navigation. Conclusions: We developed an endoscopic third ventriculostomy training model using 3D printing technology that provides anatomical precision and a realistic simulation. We hope our model can provide an indispensable tool for young neurosurgeons to gain operative experience without exposing patients to risk.

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.

Background: Maternal and child health have shown important advances in the world in recent years. However, national averages indicators hide large inequalities in access and quality of care in population subgroups. We explore wealth-related inequalities affecting health coverage and interventions in reproductive, maternal, newborn, and child health in Latin America and the Caribbean. Methods: We analyzed representative national surveys from 15 countries conducted between 2001 and 2016. We estimated maternal-child health coverage gaps using the Composite Coverage Index – a weighted average of interventions that include family planning, maternal and newborn care, immunizations, and treatment of sick children. We measured absolute and relative inequality to assess gaps by wealth quintile. Pearson’s correlation coefficient was used to test the association between the coverage gap and population attributable risk. Results: The Composite Coverage Index showed patterns of inequality favoring the wealthiest subgroups. In eight countries the national coverage was higher than the global median (78.4%; 95% CI: 73.1–83.6) and increased significantly as inequality decreased (Pearson r = 0.9; p < 0.01). Conclusions: There are substantial inequalities between socioeconomic groups. Reducing inequalities will improve coverage indicators for women and children. Additional health policies, programs, and practices are required to promote equity.

Objective. To compare inequalities in full infant vaccination coverage at two different time points between 1992 and 2016 in Latin American and Caribbean countries. Methods. Analysis is based on recent available data from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Reproductive Health Surveys conducted in 18 countries between 1992 and 2016. Full immunization data from children 12–23 months of age were disaggregated by wealth quintile. Absolute and relative inequalities between the richest and the poorest quintile were measured. Differences were measured for 14 countries with data available for two time points. Significance was determined using 95% confidence intervals. Results. The overall median full immunization coverage was 69.9%. Approximately one-third of the countries have a high-income inequality gap, with a median difference of 5.6 percentage points in 8 of 18 countries. Bolivia, Colombia, El Salvador, and Peru have achieved the greatest progress in improving coverage among the poorest quintiles of their population in recent years. Conclusion. Full immunization coverage in the countries in the study shows higher-income inequality gaps that are not seen by observing national coverage only, but these differences appear to be reduced over time. Actions monitoring immunization coverage based on income inequalities should be considered for inclusion in the assessment of public health policies to appropriately reduce the gaps in immunization for infants in the lowest-income quintile.

As the COVID-19 pandemic progresses, millions of infants are unprotected against immune-preventable diseases due to interruptions in vaccination services. The direct effects of the pandemic, as well as the non-pharmacological interventions for its containment, mitigation and suppression adopted by many countries, have affected their vaccination programs. We conducted an ecological study analyzing the performance of the vaccination program in the Dominican Republic before (2019) and during the COVID-19 pandemic (2020). We compared annual public coverage data, analyzed trends and changes in coverage, dropout rate, and number of partially and unvaccinated infants by geographic area and COVID-19 incidence rate. Compared to baseline, coverage for all vaccines decreased by 10.4 (SD, 3.6) percent; among these, coverage for the third dose of the pentavalent vaccine decreased from 90.1% in 2019 to 81.1% in 2020. The number of partially vaccinated (n = 34,185) and unvaccinated (n = 5,593) infants increased 66% and 376%, respectively. The slight increase in the annual dropout rate (1.1%) was directly proportional to the number of COVID-19 cases per month. We found a significant association between the annual absolute change of Penta3 and the subnational Human Development Index. The pandemic significantly weakened the performance of the routine vaccination program. Interventions are needed to recover and maintain lost vaccination coverage, reducing the risk of outbreaks of preventable diseases, especially in those provinces with less human development.

Dengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand drivers of dengue transmission. Such tools are critical for providing timely information to assist healthcare authorities in preparing human, material, and medical resources for outbreaks. Here, we investigate associations between meteorological variables and dengue transmission in the Dominican Republic in 2019, the year in which the country’s largest outbreak to date ocurred. We apply generalized linear mixed modelling with gamma family and log link to model the weekly dengue incidence rate. Because correlations in lags between climate variables and dengue cases exhibited different behaviour among provinces, a backward-type selection method was executed to find a final model with lags in the explanatory variables. We find that in the best models, meteorological conditions such as temperature and rainfall have an impact with a delay of 2–5 weeks in the development of an outbreak, ensuring breeding conditions for mosquitoes.