Category: Publications

Nan Zou Bakkeli at PANSOC and Consumption Reserch Norway has just published a new paper in the journal “Social Science Computer Review”. You can read it here: Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic Factors Matter? – Nan Zou Bakkeli, 2022 (sagepub.com)

The COVID-19 pandemic has posed considerable challenges to people’s mental health, and the prevalence of anxiety and depression increased substantially during the pandemic. Early detection of potential depression is crucial for timely preventive interventions; therefore, there is a need for depression prediction.

This study was based on survey data collected from 5001 Norwegians (3001 in 2020 and 2000 in 2021). Machine learning models were used to predict depression risk and to select models with the best performance for each pandemic phase. Probability thresholds were chosen based on cost-sensitive analysis, and measures such as accuracy (ACC) and the area under the receiver operating curve (AUC) were used to evaluate the models’ performance.

The study found that decision tree models and regularised regressions had the best performance in both 2020 and 2021. For the 2020 predictions, the highest accuracies were obtained using gradient boosting machines (ACC = 0.72, AUC = 0.74) and random forest algorithm (ACC = 0.71, AUC = 0.75). For the 2021 predictions, the random forest (ACC = 0.76, AUC = 0.78) and elastic net regularisation (ACC = 0.76, AUC = 0.78) exhibited the best performances. Highly ranked predictors of depression that remained stable over time were self-perceived exposure risks, income, compliance with nonpharmaceutical interventions, frequency of being outdoors, contact with family and friends and work–life conflict. While epidemiological factors (having COVID symptoms or having close contact with the infected) influenced the level of psychological distress to a larger extent in the relatively early stage of pandemic, the importance of socioeconomic factors (gender, age, household type and employment status) increased substantially in the later stage.Conclusion: Machine learning models consisting of demographic, socioeconomic, behavioural and epidemiological features can be used for fast ‘first-hand’ screening to diagnose mental health problems. The models may be helpful for stakeholders and healthcare providers to provide early diagnosis and intervention, as well as to provide insight into forecasting which social groups are more vulnerable to mental illness in which social settings.

As an OsloMet Centre of Research Excellence, we at PANSOC are so proud to have co-authored an original research article with Swiss colleagues in “Annals of Internal Medicine”. This highly prestigious journal has an impact factor of 25.4 and is considered one of top 5 in medicine together with JAMA, New England Journal of Medicine, The Lancet and The BMJ. You can read the paper here:

Historically High Excess Mortality During the COVID-19 Pandemic in Switzerland, Sweden, and Spain | Annals of Internal Medicine (acpjournals.org)

The new paper is published in Scandinavian Journal of Public Health, see here: https://journals.sagepub.com/doi/full/10.1177/14034948211060635

Abstract

Aims:

This study aimed to estimate the size of the risk group for severe influenza and to describe the social patterning of the influenza risk group in Norway, defined as everyone ⩾65 years of age and individuals of any age with certain chronic conditions (medical risk group).

Methods:

Study data came from a nationally representative survey among 10,923 individuals aged 16–79 years. The medical risk group was defined as individuals reporting one or more relevant chronic conditions. The associations between educational attainment, employment status, age and risk of belonging to the medical risk group were studied with logistic regression.

Results:

Nearly a fifth (19.0%) of respondents reported at least one chronic condition, while 29.4% belonged to the influenza risk group due to either age or chronic conditions. Being older, having a low educational level (comparing compulsory education to higher education, odds ratio (OR)=1.4, 95% confidence interval (CI) 1.2–1.8 among women, and OR=1.3, 95% CI 1.1–1.7 among men) and a weaker connection to working life (comparing disability pension to working full-time, OR=6.8, 95% CI 5.3–8.7 among women, and OR=6.5, 95% CI 4.9–8.5 among men) was associated with a higher risk of belonging to the medical risk group for severe influenza.

Conclusions:

This study indicates that the prevalence of medical risk factors for severe influenza is disproportionally distributed across the socio-economic spectrum in Norway. These results should influence both public funding decisions regarding influenza vaccination and communication strategies towards the public and health professionals.

In this invited paper for the 75 years of Population Studies diamond anniversary special issue, Svenn-Erik Mamelund and Jessica Dimka discuss the mechanisms (differential exposure, susceptibility, and consequences) underlying the mortality and morbidity disparities by socio-economic status and race/ethnicity in the 1918 flu and COVID-19 pandemics, emphasizing the tendency of pandemics to inflate pre-existing health disparities through these means. The authors use both historical and contemporary data and they make the case for thinking about the reduction of health disparities as an important pandemic preparedness strategy. Read full paper here:

Full article: Not the great equalizers: Covid-19, 1918–20 influenza, and the need for a paradigm shift in pandemic preparedness (tandfonline.com)

The the second special issue on the social, economic and health-related consequences of COVID-19 in Scandinavian Journal of Public Health now published! We at PANSOC are proud to have contributed with 2 articles. The new issue is available here: https://journals.sagepub.com/toc/sjpc/49/7Scandinavian Journal of Public Health – Volume 49, Number 7, Nov 01, 2021 (sagepub.com)

This study by Mari Holm Ingelsrud https://www.tandfonline.com/doi/full/10.1080/10301763.2021.1979449 investigates how work-related consequences of COVID-19 in Norway during the first wave varied between workers in different employment arrangements. The generalised linear model (GLM) regressions estimate the relative risk of being directed to work from home, temporarily laid off, having reduced working time and income loss in a representative sample of 3002 workers. The models compare temporarily employed and self-employed workers with permanently employed workers and workers in voluntary and involuntary part-time positions with full-time workers. Results indicate that the self-employed had a higher likelihood of experiencing reduced working time and income loss. Temporary employment did not entail a higher likelihood of any measured outcomes. Part-time workers had a higher chance of income loss and a lower chance of being directed to work from home than full-time workers. Results also indicate that employees in part-time positions had a higher likelihood of having reduced working hours. The findings are discussed with perspectives on flexibility, risk and how standard jobs form regulation and welfare policy. Despite the government’s efforts to increase the safety nets for new groups of workers, our results indicate that the coverage was not wide enough. Thus, illustrating the individual economic risk inherent in non-standard employment relationships.

The association between socioeconomic status and pandemic influenza: Systematic review and meta-analysis (plos.org)

Background: The objective of this study was to document whether and to what extent there is an association between socioeconomic status (SES) and disease outcomes in the last five influenza pandemics.

Methods/principle findings: The review included studies published in English, Danish, Norwegian and Swedish. Records were identified through systematic literature searches in six databases. We summarized results narratively and through meta-analytic strategies. Only studies for the 1918 and 2009 pandemics were identified. Of 14 studies on the 2009 pandemic including data on both medical and social risk factors, after controlling for medical risk factors 8 demonstrated independent impact of SES. In the random effect analysis of 46 estimates from 35 studies we found a pooled mean odds ratio of 1.4 (95% CI: 1.2–1.7, p < 0.001), comparing the lowest to the highest SES, but with substantial effect heterogeneity across studies,–reflecting differences in outcome measures and definitions of case and control samples. Analyses by pandemic period (1918 or 2009) and by level of SES measure (individual or ecological) indicated no differences along these dimensions. Studies using healthy controls tended to document that low SES was associated with worse influenza outcome, and studies using infected controls find low SES associated with more severe outcomes. A few studies compared severe outcomes (ICU or death) to hospital admissions but these did not find significant SES associations in any direction. Studies with more unusual comparisons (e.g., pandemic vs seasonal influenza, seasonal influenza vs other patient groups) reported no or negative non-significant associations.

Conclusions/significance: We found that SES was significantly associated with pandemic influenza outcomes with people of lower SES having the highest disease burden in both 1918 and 2009. To prepare for future pandemics, we must consider social vulnerability. The protocol for this study has been registered in PROSPERO (ref. no 87922) and has been published Mamelund et al. (2019).

We at PANSOC have been co-authoring a preprint that might help answer that question using 100 years of data from three countries including Sweden, Switzerland and Spain. We looked at age adjusted monthly estimates of excess mortality to show that in 2020 these countries recorded highest monthly excess and all-cause mortality levels driven by an infectious disease since the 1918 pandemic.

The preprint can be downloaded here 37204759 (medrxiv.org)