Linfocitosis monoclonal de células B en familiares de pacientes colombianos con síndromes linfoproliferativos crónicos B / B-cell lymphocytosis in relatives of Colombian patients with chronic B-cell lymphoproliferative disorders

Introducción. La linfocitosis monoclonal de células B, generalmente, precede la leucemia linfocítica crónica y afecta alrededor del 12 % de la población adulta sana. Esta frecuencia se incrementa en familiares de pacientes con síndromes linfoproliferativos crónicos de células B. Objetivo. Determinar la frecuencia de linfocitosis monoclonal B en familiares de pacientes con síndromes linfoproliferativos crónicos B, sus características inmunofenotípicas y citogenéticas, posible relación con agentes infecciosos, y seguimiento a corto plazo de población colombiana. Materiales y métodos. Se estudiaron 50 adultos sanos con antecedentes familiares de síndromes linfoproliferativos crónicos de célula B, empleando citometría de flujo multiparamétrica, pruebas citogenéticas y serológicas, encuesta de hábitos de vida y seguimiento a dos años. Resultados. La frecuencia encontrada de linfocitosis monoclonal B fue del 8 %, con predominio del sexo femenino y edad avanzada, incrementándose al 12,5 % en individuos con antecedentes familiares de leucemia linfocítica crónica. Tres de cuatro individuos presentaron inmunofenotipo de tipo leucemia linfocítica crónica, todas con bajo recuento. A su vez, en estos individuos se observa de manera significativa un mayor número de células/µl en subpoblaciones linfocitarias T, junto con mayor predisposición a la enfermedad. Las poblaciones clonales descritas aumentan a lo largo del tiempo de manera no significativa. Conclusiones. La frecuencia y comportamiento de la linfocitosis monoclonal de célula B en pacientes con antecedentes familiares de síndromes linfoproliferativos crónicos B es similar a lo encontrado en estudios relacionados,lo que sugiere que no existe afectación degenes de mayor relevancia que puedan desencadenar una proliferación clonal descontrolada, pero que generan desregulación inmunológica que podría indicar un mayor riesgo de infección grave en estos individuos.

Introduction. Monoclonal B-cell lymphocytosis generally precedes chronic lymphocytic leukemia, affecting about 12% of the healthy adult population. This frequency increases in relatives of patients with chronic B-cell lymphoproliferative disorders. Objective. To determine the frequency of monoclonal B-cell lymphocytosis in relatives of patients with chronic B-cell lymphoproliferative disorders, their immunophenotypic/cytogenetic characteristics, a possible relationship with infectious agents, and short-term follow-up in the Colombian population. Materials and methods. Fifty healthy adults with a family history of chronic B-cell lymphoproliferative disorders were studied using multiparametric flow cytometry, cytogenetic/serological testing, lifestyle survey, and 2-year follow-up. Results. The frequency of monoclonal B-cell lymphocytosis found was 8%, with a predominance of female gender and advanced age, increasing to 12.5% for individuals with a family history of chronic lymphocytic leukemia. Three out of four individuals presented chronic lymphocytic leukemia-type immunophenotype, all with low counts. In turn, a significantly higher number of cells/µl is observed in these individuals in T lymphocyte subpopulations, together with a greater predisposition to the disease. The described clonal populations increase over time in a non-significant manner. Conclusions. The frequency and behavior of monoclonal B-cell lymphocytosis in patients with family history of chronic B-cell lymphoproliferative disorders are like those found in related studies, which suggests that there is no involvement of more relevant genes that can trigger uncontrolled clonal proliferation, but that generates immunological deregulation that could justify a greater risk of serious infection in these individuals.

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015-2016.

We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015-December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia.

Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009-December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference.

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia.

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015⁻2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.

Relative risk estimation of dengue disease at small spatial scale.

BACKGROUND: Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. METHODS: We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1-20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008-2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen's Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation. RESULTS: We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair. CONCLUSION: The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.

Bayesian dynamic modeling of time series of dengue disease case counts.

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.