New paper from our CAS-project:

The COVID-19 pandemic has become one of the most devastating worldwide crises. The pandemic has heavily affected the most vulnerable groups, including Indigenous communities. Our study aimed to evaluate the attitudes and behaviors relating to care and prevention of COVID-19 in a predominantly Indigenous university population in Mexico.

Our new study highlights significant vaccination disparities between the university population and their parents, although no substantial differences regarding attitudes and prevention of COVID-19 between the Indigenous and non-Indigenous populations were found. Findings suggest that efforts to expand prevention to students’ families and surrounding communities could lead to significant public health gains and should be further investigated. Furthermore, the university setting may improve access to prevention tools against COVID-19.

You can read the paper here:

Attitudes and behaviors of university students during the COVID-19 pandemic in a predominantly Indigenous population in Mexico: a survey study | Discover Social Science and Health (springer.com)

New paper: Predicting COVID-19 exposure risk perception using machine learning

Nan Zou Bakkeli (Consumption Research Norway, SIFO, and affiliated with PANSOC) has just published a new research paper Predicting COVID-19 exposure risk perception using machine learning | SpringerLink in BMC Public Health, funded by PANSOC.

Abstract
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

New paper out: What is the role of isolation in 1918 flu mortality?

In the third paper in our Centre for Advanced Study (CAS) funded project, Social Science Meets Biology: Indigenous People and Severe Influenza Outcomes – CAS, we study the the role of living remotely in ethnic mortality differences during the “Spanish” flu pandemic of 1918-20. You can read more here:

Full article: Age-specific mortality and the role of living remotely: The 1918-20 influenza pandemic in Kautokeino and Karasjok, Norway (tandfonline.com)

New publication

Vibeke Narverud Nyborg has published a chapter in the new book Olhares cruzados sobre a história da saúde da Idade Média à contemporaneidade (Crossed perspectives on the history of health from the Middle Ages to the present day) edited by Alexandra Esteves & Helena da Silva. Her chapter is called “Health policies and fighting epidemic diseases in Scandinavia – different trajectories towards the development of public health and the Nordic welfare model.”

The fight against epidemic diseases contributed to the development of public health. The aim of health policies in Europe as well as in the Scandinavian countries was to secure a healthy population and contribute to the development of a modern state. While there are many similarities in approaches and solutions within the Scandinavian countries through history, there are also differences. This chapter explores these differences and similarities in an early stage of health policies development. A variety of actors and power relations contributed to frame health policies to control and fight epidemic diseases, while at the same time we can find cultural and political similarities contributing to the growth of a common Nordic Welfare model.

New Paper! COVID-19 vaccine hesitancy in eastern Oslo

We are proud to announce that one of our earlier masters’ students just published a paper in BMC Public Health.

Photo: Lara Steinmetz presenting her work in Bergen 2021.

Results show that vaccine hesitancy was low overall (5.8%). Findings indicate that participants with younger ages, lower education, and lower household income, and those born outside of Norway were prone to vaccine hesitancy. Over half of the vaccine hesitant sample cited barriers relating to confidence in the vaccines. Women and participants born in Norway were more likely hesitant due to fear of side effects and there being little experience with the vaccines. Otherwise, complacency barriers such as not feeling that they belonged to a risk group (46.1%), not needing the vaccines (39.1%), and wanting the body to develop natural immunity (29.3%) were frequently selected by participants.

You can read the full paper here: Sociodemographic predictors of and main reasons for COVID-19 vaccine hesitancy in eastern Oslo: a cross-sectional study (springer.com)