Explaining regional differences in mortality during the first wave of Covid-19 in Italy

Urban-rural disparities in COVID-19 hospitalisations and mortality: A population-based study on national surveillance data from Germany and Italy

PURPOSE

Recent literature has highlighted the overlapping contribution of demographic characteristics and spatial factors to urban-rural disparities in SARS-CoV-2 transmission and outcomes. Yet the interplay between individual characteristics, hospitalisation, and spatial factors for urban-rural disparities in COVID-19 mortality have received limited attention.

METHODS

To fill this gap, we use national surveillance data collected by the European Centre for Disease Prevention and Control and we fit a generalized linear model to estimate the association between COVID-19 mortality and the individuals' age, sex, hospitalisation status, population density, share of the population over the age of 60, and pandemic wave across urban, intermediate and rural territories.

FINDINGS

We find that in what type of territory individuals live (urban-intermediate-rural) accounts for a significant difference in their probability of dying given SARS-COV-2 infection. Hospitalisation has a large and positive effect on the probability of dying given SARS-CoV-2 infection, but with a gradient across urban, intermediate and rural territories. For those living in rural areas, the risk of dying is lower than in urban areas but only if hospitalisation was not needed; while for those who were hospitalised in rural areas the risk of dying was higher than in urban areas.

CONCLUSIONS

Together with individuals' demographic characteristics (notably age), hospitalisation has the largest effect on urban-rural disparities in COVID-19 mortality net of other individual and regional characteristics, including population density and the share of the population over 60.

Spatial Variation in Excess Mortality Across Europe: A Cross-Sectional Study of 561 Regions in 21 Countries

OBJECTIVE

To measure the burden of the COVID-19 pandemic in 2020 at the subnational level by estimating excess mortality, defined as the increase in all-cause mortality relative to an expected baseline mortality level.

METHODS

Statistical and demographic analyses of regional all-cause mortality data provided by the vital statistics systems of 21 European countries for 561 regions in Central and Western Europe. Life expectancy losses at ages 0 and 60 for males and females were estimated.

RESULTS

We found evidence of a loss in life expectancy in 391 regions, whilst only three regions exhibit notable gains in life expectancy in 2020. For 12 regions, losses of life expectancy amounted to more than 2 years and three regions showed losses greater than 3 years. We highlight geographical clusters of high mortality in Northern Italy, Spain and Poland, whilst clusters of low mortality were found in Western France, Germany/Denmark and Norway/Sweden.

CONCLUSIONS

Regional differences of loss of life expectancy are impressive, ranging from a loss of more than 4 years to a gain of 8 months. These findings provide a strong rationale for regional analysis, as national estimates hide significant regional disparities.

The trade-off between health system resiliency and efficiency: evidence from COVID-19 in European regions

The objective of this paper was to investigate the existence of a trade-off between health system resilience and the economic efficiency of the health system, using data for 173 regions in the European Union and the European Free Trade Association countries. Data Envelopment Analysis was used to measure the efficiency of regional health systems before the COVID-19 pandemic. Then, a spatial econometrics model was used to estimate whether this measure of efficiency, adjusted for several covariates, has a significant impact on regional health system resilience during the COVID-19 pandemic, measured by the number of COVID-19 deaths per hundred thousand inhabitants. The results show that COVID-19 death rates were significantly higher in regions with higher population density, higher share of employment in industry, and higher share of women in the population. Results also show that regions with higher values of the health system efficiency index in 2017 had significantly higher rates of COVID-19 deaths in 2020 and 2021, suggesting the existence of a trade-off between health system efficiency and health system resilience during the COVID-19 pandemic.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Effects of the COVID-19 pandemic on life expectancy and premature mortality in the German federal states in 2020 and 2021

The mortality impact of COVID-19 has mainly been studied at the national level. However, looking at the aggregate impact of the pandemic at the country level masks heterogeneity at the subnational level. Subnational assessments are essential for the formulation of public health policies. This is especially important for federal countries with decentralised healthcare systems, such as Germany. Therefore, we assess geographical variation in the mortality impact of COVID-19 for the 16 German federal states in 2020 and 2021 and the sex differences therein. For this purpose, we adopted an ecological study design, using population-level mortality data by federal state, age, and sex, for 2005-2021 obtained from the German Federal Statistical Office. We quantified the impact of the pandemic using the excess mortality approach. We estimated period life expectancy losses (LE losses), excess premature mortality, and excess deaths by comparing their observed with their expected values. The expected mortality was based on projected age-specific mortality rates using the Lee-Carter methodology. Saxony was the most affected region in 2020 (LE loss 0.77 years, 95% CI 0.74;0.79) while Saarland was the least affected (-0.04, -0.09;0.003). In 2021, the regions with the highest losses were Thuringia (1.58, 1.54;1.62) and Saxony (1.57, 1.53;1.6) and the lowest in Schleswig-Holstein (0.13, 0.07;0.18). Furthermore, in 2021, eastern regions experienced higher LE losses (mean: 1.13, range: 0.85 years) than western territories (mean: 0.5, range: 0.72 years). The regional variation increased between 2020 and 2021, and was higher among males than among females, particularly in 2021. We observed an unequal distribution of the mortality impact of COVID-19 at the subnational level in Germany, particularly in 2021 among the male population. The observed differences between federal states might be partially explained by the heterogeneous spread of the virus in 2020 and by differences in the population's propensity to follow preventive guidelines.

Variation in mortality burden of the COVID-19 pandemic across federal states in Germany

BACKGROUND

Intra-annual excess mortality is the most reliable measure of losses of lives due to short-term risk factors. The objectives of our study are (i) to estimate excess mortality across German states in the course of the coronavirus disease 2019 (COVID-19) pandemic years 2020 and 2021 and (ii) to identify possible regional-level determinants of spatial inequality in pandemic-related excess mortality.

METHODS

We use weekly mortality data series for the calculation of weekly death rates, standardized by age for each federal state of Germany. We estimate the expected level of mortality as state-specific mortality trends and excess mortality in 2020 and 2021. We explore ecological statistical relationships between excess mortality, COVID-19 morbidity, and selected regional socioeconomic indicators using fixed-effects regression models.

RESULTS

Our study shows that during the first pandemic year, there was South-to-North gradient in excess mortality in Germany, with excess mortality being higher in the South. Over the course of the second pandemic year 2021, this gradient changed to become an East-to-West gradient, with excess mortality being higher in the East. The results of the study show stronger effects of COVID-19 morbidity on excess mortality in East Germany. State-level indicators reflecting economic activity, employment, and capacity of intensive care units show significant correlations with excess mortality across the states.

CONCLUSIONS

The results show pronounced state-level differences in the magnitude of excess mortality during the COVID-19 pandemic in Germany. Economic activity, employment and capacity of intensive care units were the most important state-level characteristics associated with the observed spatial variations in excess mortality.

Case fatality rate considering the lag time from the onset of COVID-19 infection to related death from 2020 to 2022 in Japan

Importance

On an ecological scale, the lag time between coronavirus disease 2019 (COVID-19) infection and related fatality has varied between epidemic waves and prefectures in Japan. The variability in lag time across areas of Japan during the seven distinct waves can help derive a more appropriate estimation of the weekly confirmed case fatality rate (CFR) of COVID-19.

Objective

To estimate the 7-day moving average CFR across area block levels in Japan from February 2020 to July 2022 using the lag time between COVID-19 infection and related fatality.

Main outcomes and measures

The 7-day moving average CFR of COVID-19 for area blocks in Japan considering the lag time between infection and death (total and subgroup analysis of elderly).

Results

Lag time was found to vary substantially among prefectures in Japan from the first wave to the seventh wave of the COVID-19 epidemic. The estimated 7-day moving average CFR based on the lag time reflects the Japanese COVID-19 pandemic and related policy interventions (e.g. vaccination of elderly people) rather than other standard CFR estimations.

Conclusions and relevance

The variation in estimated lag time across prefectures in Japan for different epidemic waves indicates that it is inadequate to use the clinical results of the period from the start of infection to death for evaluation of the ecological scale of the CFR. Moreover, the lag time between infection and related fatality was found to be either shorter or longer than the clinically reported period. This revealed that preliminary reports of CFR may be overestimated or underestimated, even if they consider the lag based on clinical reports.

Clinical Characteristics of Hospitalized Patients With COVID-19: A Retrospective Cohort Study in Pakistan

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic of severe upper respiratory tract infections known as COVID-19. This single-center study aimed to investigate the demographics, comorbidities, symptoms, and disease severity of COVID-19 patients in Pakistan.

METHODS

A retrospective descriptive study was conducted at the Pakistan Kidney and Liver Institute and Research Center from April 2020 to August 2020. A total of 430 PCR-positive COVID-19 patients were categorized into symptomatic and asymptomatic groups. The symptomatic group was further classified into severe and non-severe subgroups. Patients' demographics, comorbid conditions, presenting symptoms, laboratory parameters, and clinical outcomes were assessed in these two subgroups. Statistical tests were applied to determine significant differences.

RESULTS

A total of 430 patients with COVID-19 presented in our center, of whom 334 (78%) were symptomatic and included in the study. Severe disease was observed in 83 (24.8%) symptomatic patients, with a male predominance (75.9%) and higher mean age (61.7 ± 13.2). Travel to high-risk destinations (p < 0.002) and close contact with COVID-19 patients (p < 0.001) were significant risk factors. Major comorbid conditions included diabetes mellitus (30.5%) and hypertension (39.8%). Frequent symptoms included fever (71.8%), cough (68.8%), dyspnea (53.8%), and myalgias (35.9%). Higher C-reactive protein (median = 12.76 vs. 1.45, p = 0.001), ferritin (median = 996.70 vs. 628, p = 0.004), and D-dimers (median = 1121 vs. 439.50, p = 0.009) were noted in severe vs non-severe disease. Lymphopenia was more prevalent in severe vs. non-severe disease (83.1% vs. 14.3% p-value = 0.033). More deaths (28.9%) and ICU admissions (53%) with a prolonged hospital stay (median = 25 days, IQR = 16.0-31.0) were noted in the severe group.

CONCLUSION

This retrospective study provides insights into the clinical characteristics and outcomes of COVID-19 patients. Age, male gender, comorbidities, and specific symptoms were associated with disease severity. Inflammatory markers, including D-dimers, ferritin, and CRP, were elevated in severe cases. These findings contribute to a better understanding of COVID-19 and may aid in clinical management and decision-making for patients affected by the disease.

Intergenerational Ties and COVID-19 Contagion: A Study on European Adults Aged 50 Years and Older Using SHARE Data

OBJECTIVES

Intergenerational coresidence and interdependence among family members are salient forms of family support. However, they can also likely increase social and physical contact and thus potential coronavirus disease 2019 (COVID-19) transmission, especially among older adults. This study makes an original contribution to the literature by investigating which individual and household characteristics are associated with the risk of COVID-19 contagion among community-dwelling adults aged 50 years or older living in 27 European countries. We accounted for multiple indicators of intergenerational relationships and conducted a gendered analysis.

METHODS

The data came from the Survey of Health, Ageing and Retirement in Europe (SHARE), including 2 waves of the SHARE Corona Survey. Using linear probability models, the risk of experiencing COVID-19 outcomes was predicted by different family structures and intergenerational relationship indicators.

RESULTS

While intergenerational coresidence was not associated with the risk of COVID-19, a higher frequency of face-to-face contact with adult children was associated with a lower risk of COVID-19 among mothers. This result stresses the importance of social support from adult children during the COVID-19 pandemic. However, we also showed that grandparents who took care of grandchildren were at a higher risk of COVID-19. Additionally, childless individuals had a lower risk of COVID-19 during the second wave of the pandemic.

DISCUSSION

This study highlights the importance of intergenerational relationships in pandemic studies and underscores the need to examine how intergenerational ties might be a source of social support. Implications for policy interventions are discussed in the final section.

Deaths during the first year of the COVID-19 pandemic: insights from regional patterns in Germany and Poland

BACKGROUND

Given the nature of the spread of SARS-CoV-2, strong regional patterns in the fatal consequences of the COVID-19 pandemic related to local characteristics such as population and health care infrastructures were to be expected. In this paper we conduct a detailed examination of the spatial correlation of deaths in the first year of the pandemic in two neighbouring countries - Germany and Poland, which, among high income countries, seem particularly different in terms of the death toll associated with the COVID-19 pandemic. The analysis aims to yield evidence that spatial patterns of mortality can provide important clues as to the reasons behind significant differences in the consequences of the COVID-19 pandemic in these two countries.

METHODS

Based on official health and population statistics on the level of counties, we explore the spatial nature of mortality in 2020 in the two countries - which, as we show, reflects important contextual differences. We investigate three different measures of deaths: the officially recorded COVID-19 deaths, the total values of excessive deaths and the difference between the two. We link them to important pre-pandemic regional characteristics such as population, health care and economic conditions in multivariate spatial autoregressive models. From the point of view of pandemic related fatalities we stress the distinction between direct and indirect consequences of COVID-19, separating the latter further into two types, the spatial nature of which is likely to differ.

RESULTS

The COVID-19 pandemic led to much more excess deaths in Poland than in Germany. Detailed spatial analysis of deaths at the regional level shows a consistent pattern of deaths officially registered as related to COVID-19. For excess deaths, however, we find strong spatial correlation in Germany but little such evidence in Poland.

CONCLUSIONS

In contrast to Germany, for Poland we do not observe the expected spatial pattern of total excess deaths and the excess deaths over and above the official COVID-19 deaths. This difference cannot be explained by pre-pandemic regional factors such as economic and population structures or by healthcare infrastructure. The findings point to the need for alternative explanations related to the Polish policy reaction to the pandemic and failures in the areas of healthcare and public health, which resulted in a massive loss of life.

Statistical modelling of COVID-19 data: Putting generalized additive models to work

Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data.

Mass gathering events and the spread of infectious diseases: Evidence from the early growth phase of COVID-19

This paper studies the impact on reported coronavirus 2019 (COVID-19) cases and deaths in Spain resulting from large mass gatherings that occurred from March 6 to March 8, 2020. To study these outcomes, the geographic differences in the planned pre-pandemic major events that took place on these dates were exploited, which is a quasi-random source of variation for identification purposes. We collected daily and detailed information about the number of attendees at football (soccer) and basketball matches in addition to individuals participating in the Women's Day marches across Spain, which we merged with daily data on reported COVID-19 cases and deaths at the provincial level. Our results reveal evidence of non-negligible COVID-19 cases related to the differences in the percentage of attendees at these major events from March 6 to March 8. In a typical province, approximately 31% of the average daily reported COVID-19 cases per 100,000 inhabitants between mid-March and early April 2020 can be explained by the participation rate in those major events. A back-of-the-envelope calculation suggests that this implies almost five million euros (169,000 euros/day) of additional economic cost in the health system of a typical province with one million inhabitants in the period under consideration. Several mechanisms behind the spread of COVID-19 are also examined.

Heterogeneity in subnational mortality in the context of the COVID-19 pandemic: the case of Belgian districts in 2020

BACKGROUND

The COVID-19 pandemic has led to major shocks in mortality trends in many countries. Yet few studies have evaluated the heterogeneity of the mortality shocks at the sub-national level, rigorously accounting for the different sources of uncertainty.

METHODS

Using death registration data from Belgium, we first assess change in the heterogeneity of districts' standardized mortality ratios in 2020, when compared to previous years. We then measure the shock effect of the pandemic using district-level values of life expectancy, comparing districts' observed and projected life expectancy, accounting for all sources of uncertainty (stemming from life-table construction at district level and from projection methods at country and district levels). Bayesian modelling makes it easy to combine the different sources of uncertainty in the assessment of the shock. This is of particular interest at a finer geographical scale characterized by high stochastic variation in annual death counts.

RESULTS

The heterogeneity in the impact of the pandemic on all-cause mortality across districts is substantial: while some districts barely show any impact, the Bruxelles-Capitale and Mons districts experienced a decrease in life expectancy at birth of 2.24 (95% CI:1.33-3.05) and 2.10 (95% CI:0.86-3.30) years, respectively. The year 2020 was associated with an increase in the heterogeneity of mortality levels at a subnational scale in comparison to past years, measured in terms of both standardized mortality ratios and life expectancies at birth. Decisions on uncertainty thresholds have a large bearing on the interpretation of the results.

CONCLUSION

Developing sub-national mortality estimates taking careful account of uncertainty is key to identifying which areas have been disproportionately affected.