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Small-area methods are being used in spatial epidemiology to understand the effect of location on health and detect areas where the risk of a disease is significantly elevated. Disease mapping models relate the observed number of cases to an expected number of cases per area. Expected numbers are often calculated by internal standardization, which requires both accurate population numbers and disease rates per gender and/or age group. However, confidentiality issues or the absence of high-quality information about the characteristics of a population-at-risk can hamper those calculations. Based on methods in point process analysis for situations without accurate population data, we propose the use of a case-control approach in the context of lattice data, in which an unrelated, spatially unstructured disease is used as a control disease. We correct for the uncertainty in the estimation of the expected values, which arises by using the control-disease's observed number of cases as a representation of a fraction of the total population. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardized expected counts. The simulation study results confirm our findings for different spatial structures. We show that the proposed method can adequately address the problem of inaccurate or unavailable population data in disease mapping analysis. FondsWetenschappelijk Onderzoek, Grant/Award Number: 12S7217N; Stichting Tegen Kanker, Grant/Award Number: 2012-222 Thomas Neyens was funded as a postdoctoral researcher by the Research Foundation Flanders (12S7217N). The data sets used for this paper were provided by the Belgian Cancer Registry in the framework of a research project funded by the Foundation against Cancer, Belgium (project 2012-222).

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. The data used in this manuscript were provided by the Belgian public health institute (Sciensano). This research received funding from the Flemish Government (AI Research Program). Authors Beutels, Faes, and Hens acknowledge funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (No. 101003688). Authors Beutels, Hens, Neyens, and Van Damme acknowledge funding from the Research Foundation Flanders (No. G0G1920N). Authors Faes and Neyens acknowledge funding from the Research Foundation Flanders (No. G0G9820N). Author Neyens acknowledges funding by the Internal Funds KU Leuven (project number 3M190682).

Background The impact of the COVID-19 pandemic on mental health in general practice remains uncertain. Several studies showed an increase in terms of mental health problems during the pandemic. In Belgium, especially during the first waves of the pandemic, access to general practice was limited. Specifically, it is unclear how this impacted not only the registration of mental health problems itself but also the care for patients with an existing mental health problem. Objective This study aimed to know the impact of the COVID-19 pandemic on (1) the incidence of newly registered mental health problems and (2) the provision of care for patients with mental health problems in general practice, both using a pre–COVID-19 baseline. Methods The prepandemic volume of provided care (care provision) for patients with mental health problems was compared to that from 2020-2021 by using INTEGO, a Belgian general practice morbidity registry. Care provision was defined as the total number of new registrations in a patient’s electronic medical record. Regression models evaluated the association of demographic factors and care provision in patients with mental health problems, both before and during the pandemic. Results During the COVID-19 pandemic as compared to before the COVID-19 pandemic, the incidence of registered mental health problems showed a fluctuating course, with a sharp drop in registrations during the first wave. Registrations for depression and anxiety increased, whereas the incidence of registered eating disorders, substance abuse, and personality problems decreased. During the 5 COVID-19 waves, the overall incidence of registered mental health problems dropped during the wave and rose again when measures were relaxed. A relative rise of 8.7% and 40% in volume of provided care, specifically for patients with mental health problems, was seen during the first and second years of the COVID-19 pandemic, respectively. Care provision for patients with mental health problems was higher in older patients, male patients, patients living in center cities (centrumsteden), patients with lower socioeconomic status (SES), native Belgian patients, and patients with acute rather than chronic mental health problems. Compared to prepandemic care provision, a reduction of 10% was observed in people with a low SES. Conclusions This study showed (1) a relative overall increase in the registrations of mental health problems in general practice and (2) an increase in care provision for patients with mental health problems in the first 2 years of the COVID-19 pandemic. Low SES remained a determining factor for more care provision, but care provision dropped significantly in people with mental health problems with a low SES. Our findings suggest that the pandemic in Belgium was also largely a “syndemic,” affecting different layers of the population disproportionately.

Background: A year after the start of the COVID-19 outbreak, the global rollout of vaccines gives us hope of ending the pandemic. Lack of vaccine confidence, however, poses a threat to vaccination campaigns. This study aims at identifying individuals' characteristics that explain vaccine willingness in Flanders (Belgium), while also describing trends over time (July-December 2020). Methods: The analysis included data of 10 survey waves of the Great Corona Survey, a large-scale online survey that was open to the general public and had 17,722-32,219 respondents per wave. Uni-and multivariable general additive models were fitted to associate vaccine willingness with socio-demographic and behavioral variables, while correcting for temporal and geographical variability. Results: We found 84.2% of the respondents willing to be vaccinated, i.e., respondents answering that they were definitely (61.2%) or probably (23.0%) willing to get a COVID-19 vaccine, while 9.8% indicated maybe, 3.9% probably not and 2.2% definitely not. In Flanders, vaccine willingness was highest in July 2020 (90.0%), decreased over the summer period to 80.2% and started to increase again from late September, reaching 85.9% at the end of December 2020. Vaccine willingness was significantly associated with respondents' characteristics: previous survey participation, age, gender, province, educational attainment, household size, financial situation, employment sector, underlying medical conditions, mental well-being, government trust, knowing someone with severe COVID-19 symptoms and compliance with restrictive measures. These variables could explain much, but not all, variation in vaccine willingness. Conclusions: Both the timing and location of data collection influence vaccine willingness results, emphasizing that comparing data from different regions, countries and/or timepoints should be done with caution. To maximize COVID-19 vaccination coverage, vaccination campaigns should focus on (a combination of) subpopulations: aged 31-50, females, low educational attainment, large households, difficult financial situation, low mental well-being and labourers, unemployed and self-employed citizens. (c) 2021 Elsevier Ltd. All rights reserved. The Great Corona Survey is supported by the Research Foundation Flanders (Grant G0G1920N, 2020) and the University of Antwerp Fund. Authors FV, NH and PB acknowledge funding from the European Union’s Horizon 2020 research and innovation programme (Project EpiPose - No. 101003688, 2020). All VAXINFECTIO authors acknowledge funding as part of the Methusalem-Centre of Excellence consortium VAX–IDEA. Support from the Methusalem finance programme of the Flemish Government is gratefully acknowledged. These funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. The authors acknowledge the expert assistance of Linguapolis for linguistical support (for the Great Corona Survey) and Peter De Meyer for distributing news and media items to promote participation in the Great Corona Survey.