Ecological Regression Research Articles

Association Between COVID-19 and Self-Harm: Nationwide Retrospective Ecological Spatiotemporal Study in Metropolitan France.

The COVID-19 pandemic has not been associated with increases in suicidal behavior at the national, regional, or county level. However, previous studies were not conducted on a finer scale or adjusted for ecological factors. Our objective was to assess the fine-scale spatiotemporal association between self-harm and COVID-19 hospitalizations, while considering ecological factors. Using the French national hospital discharge database, we extracted data on hospitalizations for self-harm of patients older than 10 years (from 2019 to 2021) or for COVID-19 (from 2020 to 2021) in metropolitan France. We first calculated monthly standardized incidence ratios (SIRs) for COVID-19 between March 2020 and December 2021, using a Besag, York, and Mollié spatiotemporal model. Next, we entered the SIRs into an ecological regression in order to test the association between hospital admissions for self-harm and those for COVID-19. Lastly, we adjusted for ecological variables with time lags of 0 to 6 months. Compared with a smoothed SIR of ≤1, smoothed SIRs from 1 to 3, from 3 to 4, and greater than 4 for COVID-19 hospital admissions were associated with a subsequent increase in hospital admissions for self-harm, with a time lag of 2 to 4 months, 4 months, and 6 months, respectively. A high SIR for hospital admissions for COVID-19 was a risk factor for hospital admission for self-harm some months after the epidemic peaks. This finding emphasizes the importance of monitoring and seeking to prevent suicide attempts outside the epidemic peak periods.

Comparing Spatial Ecological Regression Models in Breast Cancer Incidence in Iran

Comparing Spatial Ecological Regression Models in Breast Cancer Incidence in Iran

Роль екологічного аудиту в концепції розвитку збалансованого природокористування: правові аспекти

The article is devoted to the study of the legal aspects of the implementation and effectiveness of the environmental audit in the concept of the development of sustainable nature management as an important mechanism of environmental security. Rapid technological development has led to the globalization of all social processes without exception. It was this factor that influenced the ecological crisis, because the dominant way of using nature turned out to be incompatible with the biosphere, the cyclic cycle of resources, energy and information that ensure the stability of the global ecosystem. This provoked ecological regression and real global omnicide. Solving the environmental problem, in our opinion, boils down to the introduction of effective legal, institutional and specialized mechanisms for ensuring environmental safety, which, in their entirety, will have a positive effect on all components of the ecosystem and help to effectively implement the concept of the development of balanced nature management. And it is the environmental audit that is the effective mechanism that is designed to solve a significant part of the problems on the way to the restoration of the natural environment, by implementing the idea of balanced nature management. The relevance of this issue is that the effectiveness of an environmental audit directly depends on the legal aspects of its implementation, because dynamic changes in social development, the instability of economic factors, the emergence of new social factors, especially in the conditions of martial law, require a quick response and the introduction of the necessary regulators at the legislative level. Having analyzed the composition of environmental legal relations and national legislation, we found gaps in national legislation that require attention from the legislator and, accordingly, harmonization with EU law. Legal approaches to the interpretation of environmental audits in the context of stimulating the rational implementation of balanced environmental management by local self-government bodies were studied.

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.

In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.

Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 – 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.

The ethnic density effect as a contextual influence in ecological disease models: Establishing its quantitative expression.

Research suggests higher neighbourhood ethnic minority density to be associated with lessened chances of ethnic group illness. We focus on the density effect on psychosis, arguing that (at higher ethnic concentrations) it acts as a contextual influence attenuating the compositional influence whereby minority ethnicity is associated with higher psychosis risk. In terms of ecological disease regression, the ethnic density effect will then be apparent in nonlinear impacts of minority concentration. Contextual effects may also be evident in spatially varying regression coefficient models for psychosis. Nonlinearity or heterogeneity may be associated with other contextual processes where geography modifies demography (e.g. deprivation amplification). We illustrate these issues with an analysis of psychosis prevalence in 4835 London neighbourhoods. The data are collected in primary care (during 2019/20) using clinical diagnosis (e.g. based on referrals to specialists or psychosis hospitalisation), and refer to patients currently under care: such care may extend retrospectively over several years. The data offer a complete population perspective in contrast to survey data, which typically offer limited geographic perspectives. We consider impacts on psychosis prevalence of non-white ethnicity, as well as those of deprivation, social fragmentation and urbanicity. We find evidence suggesting nonlinear impacts of non-white ethnicity on psychosis (essentially flat risk above a threshold concentration), but find no evidence for deprivation amplification.

Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States.

Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Spatio-temporal changes in pre-exposure prophylaxis uptake among MSM in mainland France between 2016 and 2021: a Bayesian small area approach with MSM population estimation.

In France, oral pre-exposure prophylaxis (PrEP) for HIV prevention has been publicly available since 2016, mainly targeting at men who have sex with men (MSM). Reliable and robust estimations of the actual PrEP uptake among MSM on a localized level can provide additional insights to identify and better reach marginalized MSM within current HIV prevention service provision. This study used national pharmaco-epidemiology surveillance data and regional MSM population estimations to model the spatio-temporal distribution of PrEP uptake among MSM in France 2016-2021 to identify marginalized MSM at risk for HIV and increase their PrEP uptake. We first applied Bayesian spatial analyses with survey-surveillance-based HIV incidence data as a spatial proxy to estimate the size of (1) regional HIV-negative MSM populations and (2) MSM who could be eligible for PrEP use according to French PrEP guidelines. We then applied Bayesian spatio-temporal ecological regression modelling to estimate the regional prevalence and relative probability of the overall- and new-PrEP uptake from 2016 to 2021 across France. HIV-negative and PrEP-eligible MSM populations vary regionally across France. Île-de-France was estimated to have the highest MSM density compared to other French regions. According to the final spatio-temporal model, the relative probability of overall PrEP uptake was heterogeneous across France but remained stable over time. Urban areas have higher-than-average probabilities of PrEP uptake. The prevalence of PrEP use increased steadily (ranging from 8.8% [95% credible interval 8.5%;9.0%] in Nouvelle-Aquitaine to 38.2% [36.5%;39.9%] in Centre-Val-de-Loire in 2021). Our results show that using Bayesian spatial analysis as a novel methodology to estimate the localized HIV-negative MSM population is feasible and applicable. Spatio-temporal models showed that despite the increasing prevalence of PrEP use in all regions, geographical disparities and inequalities of PrEP uptake continued to exist over time. We identified regions that would benefit from greater tailoring and delivery efforts. Based on our findings, public health policies and HIV prevention strategies could be adjusted to better combat HIV infections and to accelerate ending the HIV epidemic.

MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.

Social determinants of tuberculosis in Nigeria: an ecological approach

Background: Nigeria is 4th among 22 tuberculosis high-burden countries. However, TB is not evenly distributed in the country, presumably due to state-specific risk factors. Objectives: This study maps TB and socioeconomic risk factors in Nigeria. Methods: State-level age/sex standardized tuberculosis notification data was utilized in an ecological design to evaluate the spatial distribution of TB in Nigeria and its social and economic consequences between 2012 and 2015. Negative binomial regression analysis examined the relationship between TB and five state-level covariates: HIV, BCG coverage, GDP per capita, percentage underweight, and percentage treatment success rate. Global and Local Moran’s I test statistics in R were used for spatial analysis. Results: The mean age/sex TB CNR was 54.4/100,000. Non-spatial ecological regression analysis found that TB was greater in states with high HIV, low BCG, low GDP per capita, and low TB death rates. Three states—Nasarawa, Benue, and Taraba—had high TB rates and spatially auto-correlated TB CNRs. Conclusions: TB case notification differed by age and gender. Economically-disadvantaged states exhibited higher TB case notification, HIV prevalence, lower BCG coverage, and lower mortality rates. The study found three TB hotspots. To reduce the national TB notification rate discrepancy, TB policies should incorporate social variables and target high-risk states with specific initiatives.

Sensitivity of river ecological baseflow to climate change in arid areas

River ecological baseflow is key to river ecosystem health and stability and has become particularly important with global climate change aggravation. By considering the Niya River Basin in Xinjiang, based on meteorological data from 1958 to 2021 and hydrological data from 1978 to 2018, the Tennant method was determined to be the best basin ecological baseflow calculation method, the M–K test was used to analyze the abrupt ecological baseflow and climate change characteristics, and the ecological baseflow regression response and sensitivity coefficient models concerning climate change were established. The results showed that 75% of the ecological baseflow in the Niya River Basin ranged from 15 to 31 m3•s−1 in 1978–2018, the average annual temperature increased by 1.6°C at a 0.22°C•(10a)−1 rate, and the annual precipitation increased by 6.3 mm at a 0.98 mm•(10a)−1 rate. The prediction accuracy of the regression model was good, R2 exceeded 0.7, the ecological baseflow response to climate change lagged, and precipitation greatly impacted ecological baseflow. The basin sensitivity coefficient showed a decreasing trend from upstream to downstream, with the annual maximum value in 2010, the minimum value in 1984, the monthly maximum value in April and the monthly minimum value in November. Based on the climate change trend and the social water use of the basin, the ecological baseflow protection targets and measures were proposed according to the season and the hydrological period for actual water resource management and scheduling of the river in this and similar regions.

The Role of Habitat Protection in Maintaining the Diversity of Aquatic Fauna in Rural and Industrial Areas

In Natural Landscape Complexes and Ecological Sites, local environmental protection is used to cover previous industrial activities, fragments of the cultural landscape, and habitats of both vertebrates and invertebrates. In water bodies within the different types of habitat protection, aquatic invertebrate fauna was studied to investigate whether it is a general rule that different forms of protection ensure the diversity of aquatic invertebrates in rural and industrial areas. The research revealed differences between invertebrate assemblages within complexes and between reservoirs. Compared with unprotected reservoirs located in the same area, in the majority of the studied water bodies, either no alien species were found or their relative abundance in assemblages was very low. Significant differences in the density, the number of taxa, the diversity, and the percentage of alien species were observed between different geographical locations. The location of water bodies within the protected area plays an important role in maintaining benthos diversity in industrial areas. These findings are useful for comparison with those of future research to document possible improvements or ongoing ecological regression in the quality of aquatic ecosystems in industrial areas. This study can help guide revisions of protected habitat networks for adequate protection of freshwater biodiversity in industrial areas.

Porcine reproductive and respiratory syndrome prevalence and processing fluids use for diagnosis in United States breeding herds.

Processing fluids have been recently adopted by the U.S. swine industry as a breeding herd PRRS monitoring tool due to their increased representativeness of animals within the herd. Here, we use the Morrison Swine Health Monitoring Project (MSHMP) database, representative of ~50% of the U.S. swine breeding herd, to describe processing fluids submissions for PRRS diagnosis and their relation to PRRS prevalence and time to stability over time between 2009 and 2020. An ecological time series Poisson regression modeling the number of status 1 farms and weekly percentage of processing fluids submissions for PRRS diagnosis was done. Time to stability was calculated for sites that detected a PRRS outbreak within the study period and modeled through a proportional hazards mixed effect survival model using production system as a random-effect factor and epiweek as a panel variable. Processing fluids diagnosis submissions increased starting in 2017. The difference between each year's highest and lowest weekly prevalence averaged 10.9% between 2009 and 2017, whereas it averaged 5.0% in 2018-2020 period. Each year's lowest weekly prevalence ranged from 11.3 to 19.5% in 2009-2017 and from 22.4 to 29.2% in 2018-2020. We also detected an increasing proportion of breeding sites that did not reach stability within 1 year of reporting an outbreak (chi-square for trend p < 0.0001). The total time to stability was not associated with the region of the country in which the site was located, the site's air filtration status, its PRRS status before the outbreak, or the different statuses a site achieved to be classified as stable, when accounting for the production system in the multivariate model. However, a higher proportion of system-wide processing fluids use was associated with increased time to stability. Altogether, the temporal concurrence of processing fluids used for PRRS virus monitoring suggests that the adoption of this sampling strategy may help explain the changes observed in PRRS status 1 prevalence since 2018, although further studies are still needed.

Towards a Sustainable World: Diversity of Freshwater Gastropods in Relation to Environmental Factors—A Case in the Konya Closed Basin, Türkiye

The Konya Closed Basin (KCB) in Türkiye plays a key role in agricultural production and freshwater supply. However, the basin is impacted by humanly derived nitrogenous compounds and toxic metals. Keeping the water quality at a potable level in the basin is compulsory. This study was part of a project yielding monitoring of water quality in KCB in accordance with the Water Framework Directive (2000/60/EC). Eleven stations, except Beyşehir Lake and Mamasın Dam, were sampled for the first time for freshwater molluscs. Community structure indexes and multivariate statistical analyses were applied to determine the microhabitats of gastropods and their responses to environmental changes. The structure and distribution of gastropod assemblages differed depending on total phosphate, total nitrogen, dissolved oxygen, and pH. This study revealed that most of the gastropods in KCB are relatively tolerant to biodegradable pollution. However, there is a strong observed decline in population size requiring intensive future monitoring; measures have to be taken to preserve the remaining populations. Two endemic species need an urgent action plan to protect their habitats: Theodoxus anatolicus of Çeltik Canal and Bithynia pseudemmericia of Beyşehir Lake; a re-assessment of their extinction risk according to the IUCN rules is needed (2022). The results of this study will be useful for comparison with future studies to document potential improvements or continued ecological regression in the quality of aquatic ecosystems in the watershed.

No seasonality in the risk of multiple sclerosis in an equatorial country: A case–control ecological study

Background: Multiple sclerosis risk has been shown to have seasonal variations that are more pronounced in higher latitudes. However, this phenomenon has not been adequately studied near the Equator. Objective: To explore the risk of multiple sclerosis associated with month, season of birth, and sunlight exposure variables in Colombia. Methods: In this case–control study, 668 multiple sclerosis cases were matched to 2672 controls by sex and age. Association of multiple sclerosis with each month/season of birth and sunlight exposure variables was estimated with multilevel mixed-effects logistic regression and ecological regression models, respectively. Seasonality in the births of multiple sclerosis was assessed with a non-parametric seasonality test. Results: We found a higher probability of multiple sclerosis in September (0.25; 95% confidence interval (CI) = 0.21–0.31) and lower in March (0.15; 95% CI = 0.10–0.18), which turned non-significant after a multiple comparisons test. Sunlight exposure variables had no significant effect on the risk of MS, and the tests of seasonality in the births of MS did not show significant results. Conclusion: Our results show no seasonality in the risk of multiple sclerosis near the Equator, supporting the hypothesis that this phenomenon is latitude dependent.

Routine programmatic data show a positive population-level impact of HIV self-testing: the case of Côte d'Ivoire and implications for implementation.

Objectives:We estimate the effects of ATLAS's HIV self-testing (HIVST) kit distribution on conventional HIV testing, diagnoses, and antiretroviral treatment (ART) initiations in Côte d’Ivoire.Design:Ecological study using routinely collected HIV testing services program data.Methods:We used the ATLAS's programmatic data recorded between the third quarter of 2019 and the first quarter of 2021, in addition to data from the President's Emergency Plan for AIDS Relief dashboard. We performed ecological time series regression using linear mixed models. Results are presented per 1000 HIVST kits distributed through ATLAS.Results:We found a negative but nonsignificant effect of the number of ATLAS’ distributed HIVST kits on conventional testing uptake (−190 conventional tests; 95% confidence interval [CI]: −427 to 37). The relationship between the number of HIVST kits and HIV diagnoses was significant and positive (+8 diagnosis; 95% CI: 0 to 15). No effect was observed on ART initiation (−2 ART initiations; 95% CI: −8 to 5).Conclusions:ATLAS’ HIVST kit distribution had a positive impact on HIV diagnoses. Despite the negative signal on conventional testing, even if only 20% of distributed kits are used, HIVST would increase access to testing. The methodology used in this paper offers a promising way to leverage routinely collected programmatic data to estimate the effects of HIVST kit distribution in real-world programs.

Improving Estimates Accuracy of Voter Transitions. Two New Algorithms for Ecological Inference Based on Linear Programming

The estimation of RxC ecological inference contingency tables from aggregate data is one of the most salient and challenging problems in the field of quantitative social sciences, with major solutions proposed from both the ecological regression and the mathematical programming frameworks. In recent decades, there has been a drive to find solutions stemming from the former, with the latter being less active. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, these two new algorithms prove to be quite competitive and more accurate than the current linear programming baseline algorithm. Their accuracy is assessed using a unique dataset with almost 500 elections, where the real transfer matrices are known, and their sensitivity to assumptions and limitations are gauged through an extensive simulation study. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. Interested readers can use these new algorithms easily with the aid of the R package lphom.

The geographical epidemiology of smoking-related premature mortality: A registry-based small-area analysis of the Czech death statistics

ObjectivesSmoking-related mortality varies over different social, environmental, and policy contexts. However, spatial patterns, examined at a small area level, have been seldom considered. Therefore, the study provides a detailed analysis of socio-spatial inequalities in premature mortality related to smoking in the contemporary Czech adult population. Design, settings, and methodsWe conducted a population-based, cross-sectional study to investigate the spatial pattern of the age-adjusted smoking-related mortality across Czechia. The spatial inequalities, as measured at the municipality level, were investigated using geostatistical modeling techniques. The ecological regression of the local mortality risk on socioeconomic composition of municipalities was also conducted. The target population was defined as permanent adult residents of Czechia aged 25–64 years in the period of 2011–2015. ResultsAmong both sexes, a significant spatial gradient in the South-East (lower relative risk) – North-West (higher relative risk) axis was detected. The local mortality risk was significantly related to the level of relative deprivation of the municipalities (a composite index comprised from unemployment rate and level of education): adjusted RR among males (for an increase by 1 SD): 1.21 [95% CI: 1.158–1.256], p < 0.001; adjusted RR among females (for an increase by 1 SD): 1.14 [95% CI: 1.090–1.186], p < 0.001. Mortality among males was approximately twice as high as opposed to females. Regarding the spatial inequalities of the phenomena, however, only rather minor sex-specific patterns were identified. Contrasted to males, mortality among females was unrelated to unemployment rates. ConclusionsConsistent spatial patterns of the premature mortality were identified. The mortality risk was significantly related to socioeconomic composition of the Czech municipalities. The higher the level of local deprivation, the higher the local mortality risk. The results of the study can be found beneficial for planning of both socially and spatially integrated public health policy.

Characteristics of the Third COVID-19 Pandemic Wave with Special Focus on Socioeconomic Inequalities in Morbidity, Mortality and the Uptake of COVID-19 Vaccination in Hungary.

Governments are increasingly looking to vaccination to provide a path out of the COVID-19 pandemic. Hungary offers an example to investigate whether social inequalities compromise what a successful vaccine program can achieve. COVID-19 morbidity, mortality, and vaccination coverage were characterized by calculation of indirectly standardized ratios in the Hungarian population during the third pandemic wave at the level of municipalities, classified into deprivation quintiles. Then, their association with socioeconomic deprivation was assessed using ecological regression. Compared to the national average, people living in the most deprived municipalities had a 15–24% lower relative incidence of confirmed COVID-19 cases, but a 17–37% higher relative mortality and a 38% lower vaccination coverage. At an ecological level, COVID-19 mortality showed a strong positive association with deprivation and an inverse association with vaccination coverage (RRVaccination = 0.86 (0.75–0.98)), but the latter became non-significant after adjustment for deprivation (RRVaccination = 0.95 (0.84–1.09), RRDeprivation = 1.10 (1.07–1.14)). Even what is widely viewed as one of the more successful vaccine roll outs was unable to close the gap in COVID-19 mortality during the third pandemic wave in Hungary. This is likely to be due to the challenges of reaching those living in the most deprived municipalities who experienced the highest mortality rates during the third wave.

Race, ethnicity and mortality in the United States during the first year of the COVID-19 pandemic: an assessment.

The fact that a disproportionate share of the disease burden from COVID-19, including mortality, has been borne by racial and ethnic minority communities is well documented. The purpose of this paper is to reassess the “race/ethnicity effect” in COVID-19 mortality in the United States. Using an ecological regression framework and county-level data, the study aims to make two contributions. First, it estimates the race/ethnicity effect for all major racial/ethnic groups at four important junctures during the first year of the pandemic. In doing so, it seeks to provide the fullest possible picture of the nature and evolution of the race/ethnicity effect. Second, it estimates the race/ethnicity effect net of basic socioeconomic factors (SEF). This helps to identify the likely mechanisms through which the race/ethnicity effect operates. Racial/ethnic composition is flexibly measured in two ways—by percentage contributions to county population and by indicators of group plurality. The ecological regressions revealed a positive association between the size of three racial/ethnic groups—non-Hispanic Blacks, non-Hispanic American Indian and Alaskan Natives (AIAN) and Hispanics—and county mortality, although the association was stronger and more consistent for Blacks and AIANs. Furthermore, accounting for basic SEF had different impacts on the race/ethnicity-mortality association for the three groups. For Hispanics, it was almost fully mediated. For Blacks, it decreased but remained statistically significant [62–6% higher mortality associated with a 1-standard deviation increase in Black share of county population; 2.3–1.1 times higher mortality in the average Black plurality county]. For AIANs, it was largely unaffected or even increased [44–10% higher mortality associated with a 1-standard deviation increase in AIAN share; 6.2–1.8 times higher mortality in AIAN plurality county). For all groups, the race/ethnicity effect generally decreased as the pandemic wore on during the first year.Supplementary InformationThe online version contains supplementary material available at 10.1007/s44155-022-00019-9.