Socio-demographic and regional differences in unmet healthcare needs among migrants in Europe.

BACKGROUND: Different barriers that hinder migrants' access to healthcare may have detrimental effect on health but also contribute to health inequalities. Given the lack of evidence on unmet healthcare needs among European migrant population, the study aimed to analyse the demographic, socio-economic and health-related patterning of unmet healthcare needs among migrants in Europe. METHODS: European Health Interview Survey data from 2013-2015 covering 26 countries was used to analyse associations of individual-level factors and unmet healthcare needs among migrants (n = 12,817). Prevalences and 95% confidence intervals for unmet healthcare needs were presented for geographical regions and countries. Associations between unmet healthcare needs and demographic, socio-economic, and health indicators were analysed using Poisson regression models. RESULTS: The overall prevalence of unmet healthcare needs among migrants was 27.8% (95% CI 27.1-28.6) but the estimate varied substantially across geographical regions in Europe. Unmet healthcare needs due to cost or access were patterned by various demographic, socio-economic, and health-related indicators but higher prevalence of UHN were universally found for women, those with the lowest income, and poor health. CONCLUSIONS: While the high level of unmet healthcare needs illustrate migrants' vulnerability to health risks, the regional variations in the prevalence estimates and individual-level predictors highlight the variations in national policies regarding migration and healthcare legislations and differences in welfare-systems across Europe in general.

Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis.

BACKGROUND: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. METHODS: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. FINDINGS: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. INTERPRETATION: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. FUNDING: This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Perceived Stress During the First Wave of COVID-19 Outbreak: Results From Nationwide Cross-Sectional Study in Estonia.

Objective: To study the population-level mental health responses during the first wave of coronavirus disease 2019 (COVID-19) outbreak in Estonia and analyze its socio-demographic, behavioral, and health-related variations among general population. Methods: This study used nationally representative data on 4,606 individuals, aged 18-79 years from a rapid-response cross-sectional survey conducted in April 2020. Point prevalence and mutually adjusted prevalence rate ratios for perceived stress from log-binomial regression analysis were presented for socio-demographic, behavioral, and health-related variables. Results: This study found that 52.2% of population aged 18-79 reported elevated stress levels in relation to COVID-19 outbreak. Higher levels of perceived stress were found in women, in younger age groups, in Estonians, and in those with higher self-perceived infection risk, presence of respiratory symptoms, and less than optimal health, according to self-reports. Conclusion: Although, the potential long-term health effects of the current crisis are yet unknown, the alarmingly high stress levels among people indicate that the COVID-19 pandemic might have had a widespread effect on people's mental health.