Background and Aim: Digital surveillance, which utilizes data from social media, search engines, and other online platforms, has emerged as an innovative approach for the early detection of infectious disease outbreaks. This scoping review aimed to systematically map and characterize the methodologies, performance metrics, and limitations of digital surveillance tools compared to traditional epidemiological monitoring. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute and PRISMA-SCR guidelines. Scientific databases including PubMed, Scopus, and Web of Science were searched, incorporating both empirical studies and systematic reviews without language restrictions. Key elements analyzed included digital sources, analytical algorithms, accuracy metrics, and validation against official surveillance data. Results: The reviewed studies demonstrate that digital surveillance can provide significant lead times (from days to several weeks) compared to traditional systems. While performance varies by platform and disease, many models showed strong correlations (r > 0.8) with official case data and achieved low predictive errors, particularly for influenza and COVID-19. Google Trends and X (formerly Twitter) emerged as the most frequently used sources, often analyzed using supervised regression, Bayesian models, and ARIMA techniques. Conclusions: While digital surveillance shows strong predictive capabilities, it faces challenges related to data quality and representativeness. Key recommendations include the development of standardized reporting guidelines to improve comparability across studies, the use of statistical techniques like stratification and model weighting to mitigate demographic biases, and leveraging advanced artificial intelligence to differentiate genuine health signals from media-driven noise. These steps are crucial for enhancing the reliability and equity of digital epidemiological monitoring.

Purpose: The purpose of this study was to describe the health-related quality of life (HRQoL) characteristics in a population at risk of developing type 2 diabetes in Barranquilla and Bogotá, Colombia. Methods: A cross-sectional study with 1135 participants older than 30 years-of-age recruited in Bogotá D.C., and Barranquilla by cluster sampling in 2018 to 2019. The Finnish Diabetes Risk Score (FINDRISC) was used to detect participants at risk of developing type 2 diabetes (T2D). HRQoL was assessed using the EQ-5D-3L questionnaire. Unadjusted and adjusted logistic regression models were used to calculate odds ratios (OR) and their corresponding 95% confidence intervals CI). Results: Moderate or extreme problems appeared more frequently in the dimensions of Pain/Discomfort (60.8%) and Anxiety/Depression (30.8%). The mean score of the EQ-VAS was 74.3 (± 17.3), significantly larger in the state of complete health (11111) compared with those with problems in more than one of the quality-of-life dimensions. Being female and living in Bogota D.C., were associated with greater odds of reporting problems in the Pain (OR 1.6; 95% CI 1.2–2.2) and Discomfort dimensions (OR 1.6; 95% CI 1.2–2.0) respectively and Anxiety/Depression (OR 1.9; 95% CI 1.3–2.7), (OR 9.1; 95% CI 6.6–12.4), respectively. Conclusions: As living place and sex were associated with dimensions of Pain/Discomfort and Anxiety/Depression in the HRQoL in people at risk of T2D, greater attention should be paid to these determinants of HRQoL to design and reorient strategies with a territorial and gender perspective to achieve better health outcomes. Plain English summary: Diabetes is one of the four non-communicable diseases with increasing prevalence in the world, which has made it a serious public health problem. In Colombia, in 2019 diabetes affected 8.4% of the Colombian adult population and more than one million Colombian adults of this age group have hidden or undetected diabetes. This disease is not only characterized by increased premature mortality, loss of productivity, and economic impact, but it also involves a deterioration in the quality of life of people with diabetes with their respective families. However, very Little is known about health-related quality of life (HRQoL) in a population at risk or with prediabetes. This study has evaluated the quality of life in patients at risk of diabetes and their behavior with some variables as sociodemographic, lifestyle, history, and established their difference in two territories of the Colombian Caribbean. The results of this study indicate that the HRQoL of people at risk of type 2 diabetes is affected by factors such as gender, city, dysglycemia, medication for hypertension and education level. Therefore, greater attention should be paid to these determinants of HRQL to design and implement strategies that reduce this risk of developing type 2 diabetes, prevent prediabetes and improve the quality of life in prediabetic or diabetic patients.

Purpose: To estimate the health-related quality of life (HRQOL) according to glycemic status, and its relationship with sociodemographic and clinical factors in a population at risk of developing type 2 diabetes (T2D). Methods: Cross-sectional study, using cluster sampling. Data were collected from 1135 participants over 30 years of age, at risk of developing T2D from the PREDICOL project. Participants' glycemic status was defined using an oral glucose tolerance test (OGTT). Participants were divided into normoglycemic subjects (NGT), prediabetes and diabetics do not know they have diabetes (UT2D). HRQOL was assessed using the EQ-5D-3L questionnaire of the EuroQol group. Logistic regression and Tobit models were used to examine factors associated with EQ-5D scores for each glycemic group. Results: The mean age of participants was 55.6 ± 12.1 years, 76.4% were female, and one in four participants had prediabetes or unknown diabetes. Participants reported problems most frequently on the dimensions of Pain/Discomfort and Anxiety/Depression in the different glycemic groups. The mean EQ-5D score in NGT was 0.80 (95% CI 0.79–0.81), in prediabetes, 0.81 (95% CI 0.79–0.83), and in participants with UT2D of 0.79 (95% CI 0.76–0.82), respectively. Female sex, older age, city of residence, lower education, receiving treatment for hypertension, and marital status were significantly associated with lower levels of HRQOL in the Tobit regression analysis. Conclusions: HRQOL of NGT, prediabetes, and UT2D participants was statistically similar. However, factors such as gender, age. and place of residence were found to be significant predictors of HRQOL for each glycemic group.

This study aimed to measure the impact of a community-based lifestyle modification intervention program on the Health-Related Quality of Life (HRQoL) of adults with prediabetes in two Latin American cities. A quasi-experimental study was conducted with participants aged 30 and above in two Colombian cities between 2018 and 2022. The glycemic status of study participants was determined through the administration of an oral glucose tolerance test. Individuals exhibiting impaired glucose tolerance (IGT) were selected for inclusion in the intervention program. Of the 146 individuals identified with IGT, 91 completed the one-year intervention protocol. HRQoL was assessed utilizing the EQ-5D-3L questionnaire, both before and after the intervention. Logistic regression models were used to calculate the odds ratios (OR) and 95 % confidence intervals (CI), while classification models based on machine learning algorithms were utilized to identify factors associated with favorable changes in health-related quality of life (HRQoL). In Bogotá D.C., a significant improvement in HRQoL was documented (pre-intervention: 0.69 ± 0.17; post-intervention: 0.76 ± 0.16), attaining the threshold for clinically meaningful change (0.06). No changes in HRQoL were observed in the study participants. Logistic regression analysis revealed that the improvement in HRQoL was statistically significantly associated with sex (OR 8.75; 95 % CI 1.91-40.03), age (OR 11.61; 95 % CI 1.44-93.44), place of residence (OR 29.31; 95 % CI 5.26-163.54), and weight loss (OR 5.56; 95 % CI 1.15-26.76). According to the XGBoost model, return to normal glycemic status emerged as the most important variable for improvements in HRQoL. Gender, age, place of residence, weight loss, and return to normoglycemic status were identified as significant predictors in lifestyle modification to improve HRQoL among participants at high risk of developing type 2 diabetes.

Introducción: La neutropenia, una disminución del recuento absoluto de neutrófilos circulantes, compromete la respuesta inmunitaria y aumenta la susceptibilidad a infecciones, desde afecciones cutáneas leves hasta complicaciones sistémicas fatales. Este estudio analiza las características clínicas y sociodemográficas, y la prevalencia de neutropenia en pacientes de la Organización Clínica Bonnadona Prevenir entre 2021 y 2022. Métodos: Estudio transversal basado en datos del laboratorio de la Organización Clínica Bonnadona Prevenir. Se incluyeron pacientes con recuento absoluto de neutrófilos < 1,000 células/?L en el periodo 2021-2022. Resultados: Se incluyeron 213 sujetos (edad promedio de 50 ± 19 años), 33.3% hombres y un 66.7% de mujeres. El 70.4% presentó neutropenia moderada y el 29.6% neutropenia severa. La edad, el género, la procedencia y el régimen de afiliación no mostraron asociaciones significativas con el tipo de neutropenia (p > 0.05). El diagnóstico más frecuente fue cáncer de mama (33.3%). Los tratamientos más comunes fueron ciclofosfamida + doxorrubicina seguido de carboplatino + paclitaxel. La hipertensión (HTA) y la hipertensión + diabetes mellitus (DM) fueron las comorbilidades más frecuentes. Conclusión: La neutropenia es una complicación relevante en pacientes con terapias citotóxicas e inmunosupresoras, especialmente en enfermedades hematológicas. Se observó una alta incidencia de neutropenia moderada y severa, mayor en mujeres, posiblemente por la prevalencia del cáncer de mama. Estos hallazgos resaltan la importancia de la vigilancia activa y la necesidad de estrategias de manejo personalizadas para mitigar el riesgo de infecciones graves y mejorar la calidad de vida de los pacientes.

Dengue fever poses an ongoing challenge to global and Colombian public health. Although surveillance microdata are widely available, there remains a gap in converting them into actionable epidemiological intelligence. This study presents a reproducible dataset and analytical resource for transforming routine records into a spatiotemporal framework for territorial risk stratification. To this end, 15 years (2010–2024) of anonymized records from the SIVIGILA in Colombia’s Caribbean region were consolidated, covering 303,801 cases across 197 municipalities. The microdata were aggregated into municipality–year analytical units using seven indicators of magnitude, severity, demographics, and surveillance performance. Subsequently, an unsupervised learning model (K-means) was validated using the Elbow and Silhouette methods. The algorithm consistently identified four heterogeneous epidemiological profiles: high-transmission urban settings, dispersed rural risk municipalities, territories with a pediatric predominance, and clusters of high clinical severity with elevated hospitalization and case fatality rates. In conclusion, this dataset and its methodological framework transform static historical information into an operational tool that facilitates strategic surveillance, the development of interactive dashboards, and the territorial prioritization of evidence-based public health interventions.