Relationship between payment problems and health: A nation-wide register study in Norway.

Previous studies have found a solid correlation between payment problems and health, and a large body of literature has recognised the impact of debt burden on ill health. However, few have looked at the reversed causality-the impact of health on over-indebtedness and payment problems. In this article, we investigate whether or not a person with mental and physical health challenges is more likely to experience debt enforcement, and we take a step further to explore the role of health status on receiving debt settlement for those with severe payment problems. The article uses register data from Statistics Norway, the Norwegian Patient Registry and the Mortgages Registry from 2009 to 2018. When using conditional logistic models and fixed-effects Poisson regressions with a one-year lagged effect, we find that mental disorders significantly contribute to individuals' financial strains, while physical health does not play a substantial role. When integrating the models with dynamic health effects, all health indicators turned out to have substantial impacts, indicating an extended delayed physical health effect on financial outcomes. Poor health leads to increased payment problems, yet individuals facing health challenges have a lower likelihood of receiving necessary assistance in debt settlement. These findings emphasise the need for tailored services to address the financial challenges of debtors with diverse health conditions.

Predicting COVID-19 exposure risk perception using machine learning.

BACKGROUND: Self-perceived exposure risk determines the likelihood of COVID-19 preventive measure compliance to a large extent and is among the most important predictors of mental health problems. Therefore, there is a need to systematically identify important predictors of such risks. This study aims to provide insight into forecasting and understanding risk perceptions and help to adjust interventions that target various social groups in different pandemic phases. METHODS: This study was based on survey data collected from 5001 Norwegians in 2020 and 2021. Interpretable machine learning algorithms were used to predict perceived exposure risks. To detect the most important predictors, the models with best performance were chosen based on predictive errors and explained variances. Shapley additive values were used to examine individual heterogeneities, interpret feature impact and check interactions between the key predictors. RESULTS: Gradient boosting machine exhibited the best model performance in this study (2020: RMSE=.93, MAE=.74, RSQ=.22; 2021: RMSE=.99, MAE=.77, RSQ=.12). The most influential predictors of perceived exposure risk were compliance with interventions, work-life conflict, age and gender. In 2020, work and occupation played a dominant role in predicting perceived risks whereas, in 2021, living and behavioural factors were among the most important predictors. Findings show large individual heterogeneities in feature importance based on people's sociodemographic backgrounds, work and living situations. CONCLUSION: The findings provide insight into forecasting risk groups and contribute to the early detection of vulnerable people during the pandemic. This is useful for policymakers and stakeholders in developing timely interventions targeting different social groups. Future policies and interventions should be adapted to the needs of people with various life situations.

Socioeconomic status and disparities in COVID-19 vaccine uptake in Eastern Oslo, Norway.

Objective: The objective of this study was to assess whether socioeconomic status still remain a barrier to COVID-19 vaccination in eastern Oslo, Norway. Study design: A cross-section study. Methods: We conducted a web-based survey among the residents of six eastern parishes in Oslo, Norway. Text (SMS) messages were sent to 59978 potential participants. 5447 surveys were completed for a response rate of 9.1%. After removing participants who had not been offered the COVID-19 vaccine, we ended up with a valid sample of 4000. Results: We find a significant association between education and the likelihood of taking the COVID-19 vaccine in bivariate logistic regression. Further, we find a significant higher likelihood of taking the vaccine in the above-low-income group compared to the low-income group. However, when we add control variables to the regression, the significant results concerning both income and education are eliminated. In further analysis, we found that age worked as a moderator between socioeconomic status and vaccine uptake: In the youngest age group (18-29), we found a significant higher likelihood of taking the vaccine in the above-low-income group compared to the low-income group, and in the higher education group compared to the primary education group. Conclusion: Socioeconomic status remains a barrier to COVID-19 vaccination in the eastern parishes of Oslo, Norway. Indicating that Norwegians of lower socioeconomic status still disproportionately face barriers such as transportation, language, flexible work hours, and paid sick time. However, our analysis shows that this association is only found in the age group 18-29.

Identifying obesity and COVID-19 overlapping risk-factors: Protocol for a systematic review and meta-analysis.

This systematic review main goal is to identify the common risk factors of obesity and COVID-19 overall, and highlight the ones related to urban settings specifically, using a syndemic framework. COVID-19 highlighted the interaction between infectious diseases and non-communicable diseases. We hypothesise that obesity and COVID-19 share determinants, thus our main goal is to identify the overlapping risk factors and their magnitude of association with both health outcomes. Literature search was conducted in Medline and Embase, Cochrane Library and Epistemonikos, Web of Science and Scopus, ASSIA and SocINDEX and, Google Scholar, in June 2021. Covidence will be used to conduct the title and abstract, and full-text screening, considering the following inclusion criteria: (a) study addresses both health outcomes, (b) full-length articles, (c) study focuses on humans and (d) studies in English. The exclusion criteria will be: (a) study addresses one of the outcomes in combination with other pathologies, (b) not full-length article, (c) study focuses on animals, (d) study not written in English, (e) study focuses on treatment (pharmacological or other), testing (prognostic) or specific patients with other pathologies and (f) study focussed in clinical and/or physiological mechanisms associated with obesity and/or COVID-19. The included studies will be assessed for quality using the Effective Public Health Practice Project for quantitative studies and the tool described by Hawker for qualitative studies. Qualitative results will be assessed using thematic analysis methods to synthesise findings and presented in summary tables. Quantitative results - meta-analysis - will be analysed and presented using Q test and Funnel Plot.

Health, work, and contributing factors on life satisfaction: A study in Norway before and during the COVID-19 pandemic.

BACKGROUND: The COVID-19 outbreak has posed considerable challenges for people's health, work situations and life satisfaction. This article reports on a study of the relationship between self-reported health and life satisfaction before and during the COVID-19 pandemic in Norway, and examines the role of work in explaining the health-life satisfaction relationship. METHOD: The study was based on survey data collected from 3185 Norwegian employees in 2019 and 3002 employees in 2020. Propensity score matching techniques were used to assess the mediating effects of work situations and income loss on the health-life satisfaction relationship. Skew-t regression models were further applied to estimate changes in life satisfaction before and during the pandemic, as well as to explore different underlying mechanisms for the health-life satisfaction association. RESULTS: The study found a negative association between ill health and life satisfaction. Compared to the healthy population, people with poor health were more likely to experience worsened work situations. A negative work situation is further associated with lower life satisfaction, and the pandemic aggravated life satisfaction for those who had worsened work situations. When exploring central contributing factors for life satisfaction, we found that health-related risks and work-life balance played predominant roles in predicting life satisfaction before the pandemic, while different types of household structure were among the most important predictors of life satisfaction during the pandemic. CONCLUSION: A reduction in life satisfaction is explained by ill health, but different underlying mechanisms facilitated people's life satisfaction before and during the pandemic. While work situation and health risks were important predictors for life satisfaction in 2019, worries about more unstable work situations and less access to family support accentuated worsened life satisfaction in 2020. The findings suggest the necessity of labour market interventions that address the security and maintenance of proper and predictable work situations, especially in these more uncertain times.

Health and economic scarcity: Measuring scarcity through consumption, income and home ownership indicators in Norway.

It is widely recognised that income alone may not accurately reflect people's economic circumstances. In recent years, there has been increasing focus on multidimensional measures of economic scarcity. This study employs the newest survey data from Consumption Research Norway to explore the relationship between economic scarcity and self-reported health (SRH) in Norway. It defines economic scarcity by identifying disadvantaged social groups in terms of consumption, income and wealth/homeownership. Using propensity score matching, we compare health outcomes for economically disadvantaged and advantaged social groups - finding that consumption measures of scarcity are significantly associated with health, while there is no significant relationship between health and homeownership. When using matching estimators, health scores differ significantly between people with higher and lower incomes, but the associations are weakened when other socioeconomic variables are controlled for. This study applies empirical evidence from Norway to the existing health literature and contributes to a relatively new analytical approach by incorporating consumption into the prediction of health outcomes.

Income inequality and health in China: A panel data analysis.

During the last decades, the level of income inequality in China has increased dramatically. Despite rapid economic growth and improved living conditions, the health performance in China has dropped compared to the period before the economic reform. The "Wilkinson hypothesis" suggests that increased income inequality in a society is correlated to worse health performance. China is a particular interesting case due to the rapid socioeconomic change in the country. This study uses the China Health and Nutrition Survey (CHNS) to address the question of whether income inequality has an impact on individuals' risks of having health problems in China. Unlike previous studies with health measures such as self-reported health or mortality rate, our study uses physical functions to measure individual health. By analysing panel data using county/city-level dummies and year fixed-effects, we found that income inequality does not have a significant impact on individuals' risks of having health problems. This result is robust when changing between different indicators for income inequality.