The bivariate combined model for spatial data analysis

Using a general practice research database to assess the spatio-temporal COVID-19 risk

BACKGROUND

In Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs' morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics.

METHODS

Spatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes.

RESULTS

The two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19.

CONCLUSION

We were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.

Area Deprivation and COVID-19 Incidence and Mortality in Bavaria, Germany: A Bayesian Geographical Analysis

Background

Area deprivation has been shown to be associated with various adverse health outcomes including communicable as well as non-communicable diseases. Our objective was to assess potential associations between area deprivation and COVID-19 standardized incidence and mortality ratios in Bavaria over a period of nearly 2 years. Bavaria is the federal state with the highest infection dynamics in Germany and demographically comparable to several other European countries.

Methods

In this retrospective, observational ecological study, we estimated the strength of associations between area deprivation and standardized COVID-19 incidence and mortality ratios (SIR and SMR) in Bavaria, Germany. We used official SARS-CoV-2 reporting data aggregated in monthly periods between March 1, 2020 and December 31, 2021. Area deprivation was assessed using the quintiles of the 2015 version of the Bavarian Index of Multiple Deprivation (BIMD 2015) at district level, analyzing the overall index as well as its single domains.

Results

Deprived districts showed higher SIR and SMR than less deprived districts. Aggregated over the whole period, the SIR increased by 1.04 (95% confidence interval (95% CI): 1.01 to 1.07, p = 0.002), and the SMR by 1.11 (95% CI: 1.07 to 1.16, p < 0.001) per BIMD quintile. This represents a maximum difference of 41% between districts in the most and least deprived quintiles in the SIR and 110% in the SMR. Looking at individual months revealed clear linear association between the BIMD quintiles and the SIR and SMR in the first, second and last quarter of 2021. In the summers of 2020 and 2021, infection activity was low.

Conclusions

In more deprived areas in Bavaria, Germany, higher incidence and mortality ratios were observed during the COVID-19 pandemic with particularly strong associations during infection waves 3 and 4 in 2020/2021. Only high infection levels reveal the effect of risk factors and socioeconomic inequalities. There may be confounding between the highly deprived areas and border regions in the north and east of Bavaria, making the relationship between area deprivation and infection burden more complex. Vaccination appeared to balance incidence and mortality rates between the most and least deprived districts. Vaccination makes an important contribution to health equality.

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data

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.

[Regional monitoring of infections by means of standardized case fatality rates using the example of SARS-CoV-2 in Bavaria]

BACKGROUND

Maps of the temporal evolution of the regional distribution of a health-related measure enable public health-relevant assessments of health outcomes.

OBJECTIVES

The paper introduces the concept of standardized case fatality rate (sCFR). It describes the ratio of the regional variation in mortality to the regional variation in the documented infection process. The regional sCFR values are presented in maps and the time-varying regional heterogeneity observed in them is interpreted.

MATERIALS AND METHODS

The regional sCFR is the quotient of the regional standardized mortality and case rate. It is estimated using a bivariate model. The sCFR values presented in maps are based on SARS-CoV‑2 reporting data from Bavaria since the beginning of April 2020 until the end of March 2021. Four quarters (Q2/20, Q3/20, Q4/20, and Q1/21) are considered.

RESULTS

In the quarters considered, the naïve CFR values in Bavaria are 5.0%, 0.5%, 2.5%, and 2.8%. In Q2/20, regional sCFR values are irregularly distributed across the state. This heterogeneity weakens in the second wave of the epidemic. In Q1/21, only isolated regions with elevated sCFR (> 1.25) appear in southern Bavaria. Clusters of regions with sCFR > 1.25 form in northern Bavaria, with Oberallgäu being the region with the lowest sCFR (0.39, 95% credibility interval: 0.25-0.55).

CONCLUSIONS

In Bavaria, heterogeneous regional SARS-CoV-2-specific sCFR values are shown to change over time. They estimate the relative risk of dying from or with COVID-19 as a documented case. Strong small-scale variability in sCFR suggests a preference for regional over higher-level measures to manage the incidence of infection.

Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium?

Although COVID-19 has been spreading throughout Belgium since February, 2020, its spatial dynamics in Belgium remain poorly understood, partly due to the limited testing of suspected cases during the epidemic's early phase. We analyse data of COVID-19 symptoms, as self-reported in a weekly online survey, which is open to all Belgian citizens. We predict symptoms' incidence using binomial models for spatially discrete data, and we introduce these as a covariate in the spatial analysis of COVID-19 incidence, as reported by the Belgian government during the days following a survey round. The symptoms' incidence is moderately predictive of the variation in the relative risks based on the confirmed cases; exceedance probability maps of the symptoms' incidence and confirmed cases' relative risks overlap partly. We conclude that this framework can be used to detect COVID-19 clusters of substantial sizes, but it necessitates spatial information on finer scales to locate small clusters.

Disease mapping of zero-excessive mesothelioma data in Flanders

PURPOSE

To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion.

METHODS

The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature.

RESULTS

The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary.

CONCLUSIONS

Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this.