The Environmental Exposures in Lebanese Infants (EELI) birth cohort: an investigation into the Developmental Origins of Health and Diseases (DOHaD).

The EELI Study is a longitudinal birth cohort launched in 2021 in Lebanon to examine the long-term impact of environmental exposures on the health of prospective Lebanese mothers and infants and disease outcomes. This article delineates the adopted study design and protocols, current progress, and contextual considerations for the planning and launching of a birth cohort in a resource-limited setting. A sample of n = 135 pregnant women expecting to give birth at the Hôtel-Dieu de France University Hospital has been recruited since the study launch. Over 500 variables have been recorded for each participant, and over 1000 biological specimens have been processed and stored in a biobank for further analysis. The EELI study establishes methodological and logistic basis to explore the concept of the exposome and its implementation and to establish a toolkit of the SOPs and questionnaires that can be employed by the other countries in the Eastern Mediterranean region.

Prospective correlational time-series analysis of the influence of weather and air pollution on joint pain in chronic rheumatic diseases.

OBJECTIVES: The primary objective was to evaluate the association between weather variables and joint pain in patients with chronic rheumatic diseases (CRD: rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA)). A secondary objective was to study the impact of air pollution indicators on CRD pain. METHOD: The study is prospective, correlational, with time-series analysis. Patients with CRD, living in a predefined catchment area, filled their level of pain daily using a 0-10 numerical scale (NS), for 1 year. Weather (temperature, relative humidity (H), atmospheric pressure (P)) and air pollution indicators (particulate matters (PM10, PM2.5), nitrogen dioxide (NO2), and ozone (O3)) were recorded daily using monitoring systems positioned in the same area. Association between pain and weather and air pollution indicators was studied using Pearson's correlation. Time-series analysis methodology was applied to determine the temporal relationship between pain and indicators. RESULTS: The study included 94 patients, 82% reported they were weather-sensitive. Pain variation was similar across diseases over a year. Pain was associated negatively with temperature, H, and O3, and positively with P and NO2. However, the strength of correlation was moderate; temperature explained 22% of pain variance. A drop of 10°C in temperature corresponded to an increase of 0.5 points in pain NS. Also, there was a significant interaction among environmental factors. In time-series analysis, temperature and NO2 remained independently associated with pain. CONCLUSIONS: The perception of joint pain in patients with CRD was correlated with weather and air pollution. The strength of association was moderate and independent of underlying disease. Key Points •Weather variation was moderately correlated with joint pain in chronic rheumatic diseases, with an inverse association with temperature, humidity, and O3. • Air pollution indicators, mainly nitrogen dioxide and ozone, were correlated with joint pain; particulate matters were also correlated but to a lesser extent. • The influence of these environmental factors was independent of the type of rheumatic disease, thus raising the hypothesis of their impact on pain perception mechanisms.

Establishing a sorting protocol for healthcare databases.

BACKGROUND: Health information records in many countries, especially developing countries, are still paper based. Compared to electronic systems, paper-based systems are disadvantageous in terms of data storage and data extraction. Given the importance of health records for epidemiological studies, guidelines for effective data cleaning and sorting are essential. They are, however, largely absent from the literature. The following paper discusses the process by which an algorithm was developed for the cleaning and sorting of a database generated from emergency department records in Lebanon. DESIGN AND METHODS: Demographic and health related information were extracted from the emergency department records of three hospitals in Beirut. Appropriate categories were selected for data categorization. For health information, disease categories and codes were selected according to the International Classification of Disease 10th Edition. RESULTS: A total of 16,537 entries were collected. Demographic information was categorized into groups for future epidemiological studies. Analysis of the health information led to the creation of a sorting algorithm which was then used to categorize and code the health data. Several counts were then performed to represent and visualize the data numerically and graphically. CONCLUSIONS: The article describes the current state of health information records in Lebanon and the associated disadvantages of a paper-based system in terms of storage and data extraction. Furthermore, the article describes the algorithm by which health information was sorted and categorized to allow for future data analysis using paper records.