Major Determinants of Well-Being in Patients With Axial Spondyloarthritis: 2 Year Follow-Up.

OBJECTIVES: Bath Ankylosing Spondylitis Patient Global Score (BAS-G) is a uni-dimensional scale that enables patients to evaluate the effects of their illness on their health. The aim of this study was to determine the impact of disease related outcomes on the BAS-G scores in patients with axSpA. METHODS: A total of 309 patients (56.6% of whom were male, mean age 44 ± 11) were included in the study. Socio-demographic characteristics (age, sex and education level) and clinical characteristics such as disease activity (BASDAI and CRP), spinal mobility (BASMI), functional status (BASFI), radiographic structural damage (mSASS, mNY, and BASRI-hip), and health related quality of life (SF-36 and ASQoL) of the patients were recorded at baseline. In addition, BASDAI total and each item score, BASFI, BAS-G, and CRP levels were collected at 6, 12, and 24 months. RESULTS: Female patients had significantly higher BAS-G scores (p = 0.037). Baseline BASDAI total score (p < 0.001) and all BASDAI item scores (p < 0.001 for each item), BASFI total score (p < 0.001), ASQoL total score (p < 0.001), and SF-36 PCS sum-score (p < 0.001) were moderately/highly correlated with BAS-G. Multivariate analysis revealed that back pain (BASDAI Q2) (p < 0.001) and the severity of morning stiffness (BASDAI Q5) (p < 0.001) were the main determinants of BAS-G in patients with axSpA. In 2-year follow-up, BASDAI Q1, BASDAI Q5, and BASFI scores were independent determinants of BAS-G in patients with axSpA. CONCLUSION: According to the results of the present study, patients with axSpA mainly rely on morning stiffness and back pain to determine their global health status. Moreover, fatigue, severity of morning stiffness and function are the determinants of BAS-G during follow-up.

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources.

OBJECTIVE: To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries. DESIGN: Epidemiological study. SETTING: Country-based data from publicly available online databases of international organisations. PARTICIPANTS: The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia). PRIMARY AND SECONDARY OUTCOME MEASURES: The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19. RESULTS: In the model for the COVID-19 cases (R2=0.45), obesity (ß=0.460), hypertension (ß=0.214), sunshine (ß=-0.157) and transparency (ß=0.147); whereas in the model for COVID-19 deaths (R2=0.41), obesity (ß=0.279), hypertension (ß=0.285), alcohol consumption (ß=0.173) and urbanisation (ß=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index. CONCLUSIONS: This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04486508).